bmjopen.bmj.com€¦ · For peer review only Item Item number STROBE Guideline Extension for Genetic (STREGA) Association Studies Descriptive data Both patient groups (with or without

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For peer review only

Clinical and Genetic Associations of Renal Function and

Diabetic Kidney Disease in the United Arab Emirates

Journal: BMJ Open

Manuscript ID bmjopen-2017-020759

Article Type: Research

Date Submitted by the Author: 26-Nov-2017

Complete List of Authors: Osman, Wael; Khalifa University of Science Technology and Research, Biotecnology Center Jelinek, H. F.; Macquarie University Faculty of Medicine and Health Sciences, Clinical Medicine Tay, Guan ; Khalifa University of Science Technology and Research, Biomedical Engineering Khandoker , Ahsan ; Khalifa University of Science Technology and Research, Biomedical Engineering

Khalaf, Kinda; Khalifa University of Science Technology and Research, Biomedical Engineering AlMahmeed , Wael; Sheikh Khalifa Medical City, Institute of Cardiac Science Hassan, Mohamed; Sheikh Khalifa Medical City, Medical Institute ALsafar, Habiba; Khalifa University of Science Technology and Research, Biomedical Engineering

<b>Primary Subject Heading</b>:

Diabetes and endocrinology

Secondary Subject Heading: Genetics and genomics, Global health

Keywords: Diabetes, UAE, ARAB, Renal

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the 1

United Arab Emirates 2

Wael M. Osman 1, Herbert F. Jelinek

2, 3, Guan K. Tay

1, 4, 5, Ahsan H. Khandoker

6, Kinda 3

Khalaf 6, Wael Almahmeed

7,8, Mohamed H. Hassan

9 & Habiba S. Alsafar

1, 6,* 4

1 Center of Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates. 5

2 School of Community Health, Charles Sturt University, Albury, Australia. 6

3 Clinical Medicine, Macquarie University, Sydney, Australia. 7

4 School of Health and Medical Sciences, Edith Cowan University, Australia. 8

5 School of Psychiatry and Clinical Neurosciences, University of Western Australia, Australia. 9

6 Department of Biomedical Engineering, Khalifa University, United Arab Emirates. 10

7 Institute of Cardiac Science, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates. 11

8Heart and Vascular Institute, Cleveland Clinic, Abu Dhabi, United Arab Emirates. 12

9 Medical Institute, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates. 13

Number of words: 298 in the abstract, 3143 in the main text 14

Number of tables: 5; embedded in the main text. No supplementary data. 15

Number of references: 44 16

Key words: type 2 diabetes, United Arab Emirates, diabetic kidney disease, genetics, 17

SHROOM3 gene. 18

* Corresponding author 19

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Dr. Habiba S. Al Safar 20

Director of Biotechnology Center, Assistant Professor 21

Khalifa University 22

PO BOX 127788 Abu Dhabi, UAE 23

Phone: +971 (0) 2 401 8109, Fax: +971 (0)2 447 2442 24

E-mail: [email protected] 25

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Abstract 38

Objectives: Within the Emirati population, risk factors and genetic predisposition of diabetic 39

kidney disease (DKD) have not been investigated. The aim of this report was to determine 40

possible clinical and laboratory risk factors and investigate the reported genetic loci linked to 41

DKD. 42

Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2 43

diabetes mellitus (T2DM) were recruited with and without DKD and their clinical and laboratory 44

data obtained. Following adjustments for possible confounders, a logistic regression model was 45

used to test the associations of 63 SNPs in 43 genetic loci with DKD (145 patients and 265 46

controls). Linear regression models, adjusted for age and gender, were used to test the genetic 47

associations of five renal function traits, including 83 SNPs with albumin-to-creatinine ratio, 92 48

SNPs with Vitamin D (25-OH Cholecalciferol), 288 SNPs with estimated glomerular filtration 49

rate (eGFR), 363 SNPs with serum creatinine, and 73 SNPs with blood urea. 50

Results: Patients with DKD compared to those without DKD were mostly males (~52% versus 51

38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and 52

dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 53

years), which in an additive manner was the single factor that significantly contributed to the 54

development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the 55

associations of the genetic loci with different renal function traits found that the most notable 56

associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD 57

(Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 58

with serum creatinine and eGFR levels. 59

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Conclusions: Strong associations were found between several genetic loci and risk markers for 60

diabetic kidney disease, which may influence kidney function traits and DKD in a population of 61

Arab ancestry. 62

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Strengths and limitations of this study 79

• This is the first study to investigate clinical and genetic risk factors of diabetic kidney disease 80

(DKD) in a population of Arab descendants from the United Arab Emirates (UAE), where 81

prevalence for type 2 diabetes is 23%. 82

• The mean age of development of DKD is 67 years, and diabetes duration is the single factor 83

that significantly contributed to the development of DKD (mean duration is 16 years). 84

• Patients who develop DKD have significant rates of hypertension and dyslipidemia. 85

• Some genetic loci reported in other populations also influence different renal function traits 86

and DKD in this population of Arab ethnicity, such as SHROOM3 gene with levels of serum 87

creatinine, eGFR, and DKD ; CASR, GC, and CYP2R1 genes with vitamin D levels; as well 88

as WDR72 gene with serum creatinine and eGFR levels. 89

• A larger population sample across the Middle East is required for sufficient statistical power 90

to discover novel clinical and genetic predisposition for DKD in the region. 91

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Introduction 102

Diabetic kidney disease (DKD), considered as the most common cause of nephropathy in many 103

populations, is a public health challenge worldwide. Globally, the prevalence of chronic kidney 104

disease (CKD) among adults in the general population was reported to be around 10%, with the 105

United States showing a figure around 3.3% for DKD in 2008 1 2

. In addition to increasing the 106

risk of cardiovascular morbidity and mortality 3 4, DKD is reported to be the single strongest 107

predictor of mortality in patients with diabetes 5 with five year survival in the range of 30%

6. 108

DKD often progresses to end-stage renal disease (ESRD). Patients with ESRD have a 20% 109

annual mortality rate, which is higher than the rate for many solid cancers 7

. Overall, the risk of 110

DKD in T2DM is approximately 2% per year 8. The current trend suggests that the prevalence of 111

DKD will continue to increase, constituting a significant socioeconomic burdens on global 112

healthcare systems and leading to increased morbidity and mortality 2. A thorough literature 113

search shows that there remains a knowledge gap related to the understanding of risk factors and 114

pathophysiological mechanisms associated with DKD, especially in the Middle East. Since 115

ESRD can only be treated with highly invasive and costly procedures, such as dialysis or kidney 116

transplantation, a better knowledge of genetic, clinical, and epidemiological factors associated 117

with DKD is required to allow earlier and more effective treatment outcomes. 118

In clinical practice, renal function is assessed using a number of tests that are reported to have 119

high heritability rates 9, indicating that genetic factors contribute significantly to inter-individual 120

variance in kidney function, and hence, to the susceptibility to CKD and related conditions. 121

So far, several genetic loci have been linked to DKD, chronic kidney disease (CKD), and renal 122

function traits in adults 10-21

and children 22

. However, in comparison to other diseases, including 123

T2DM and other cardiometabolic disorders, studies of kidney disease and kidney function traits 124

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are insufficient. A recent report by Pierre-Jean Saulnier et al. suggested that three serum 125

biomarkers (midregional-proadrenomedullin: MR-proADM, soluble tumor necrosis factor 126

receptor 1: sTNFR1, and N-terminal prohormone brain natriuretic peptide: NT-proBNP) can 127

improve the risk prediction of the loss of renal function in patients with T2DM in addition to the 128

established risk factors for DKD such as age, sex, diabetes duration, HbA1c, blood pressure, 129

baseline eGFR, and albumin-to-creatinine ratio 23

. However, the issues of whether the levels of 130

these markers are affected by genetic variations, and whether the encoding genes contribute to 131

DKD development and progress need further investigation. 132

The United Arab Emirates (UAE) is among the countries with the highest prevalence rates of 133

T2DM, obesity and cardiovascular conditions 24 25

. Al-Safar and colleagues recently reported that 134

approximately 80% of T2DM patients within UAE present with at least one complication 135

associated with T2DM, including kidney disease (approximately 6%) 26

. Furthermore, there is 136

increasing evidence suggesting that the genome structure of individuals of Arabic descent is 137

different from individuals from other populations 27

. Despite the high prevalence rate of DKD in 138

the UAE, to our best knowledge, there have been no investigations of the genetic associations of 139

chronic kidney conditions and kidney functions in this population, particularly associated with 140

T2DM. Therefore, the current work aimed to investigate the clinical and laboratory variables 141

linked to DKD in a T2DM Emirati population, and to investigate the associations of the reported 142

genetic loci linked to different renal function tests, in CKD and DKD. 143

144

Materials and Methods 145

Study type and subjects 146

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This report describes a prospective cross-sectional study of Emirati patients from the city of Abu 147

Dhabi. The demographic information and clinical data for the participants are presented in 148

Tables 1 and 2. Four hundred and ninety patients with T2DM were included in the study, with 149

145 diagnosed with DKD. The participants were recruited from Sheikh Khalifa Medical City 150

(SKMC) and Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE. All subjects were 151

UAE born and with Arabic descent. 152

Clinical variables and laboratory data 153

Various clinical and laboratory measures were assessed and collected during the hospital visits. 154

Blood pressure was taken on two different occasions. Hypertension was defined as systolic blood 155

pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking 156

antihypertensive medications. Dyslipidemia was reported in the clinical records of participants or 157

diagnosed as previously indicated 28

. The presence of T2DM was confirmed by a qualified 158

physician based on the criteria outlined by the World Health Organization (WHO) consultation 159

group report 29

. Trained nurses measured the height and weight of each participant using a 160

calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the 161

weight in kilograms divided by the square of height of each subject (kg/m2). 162

Definition of the DKD 163

DKD was defined as either decreased levels of eGFR <60 ml/min/1.73 m2 with or without renal 164

damage over a period of at least three months 30

, or based on an albumin-to-creatinine ratio ≥30 165

mg/g, or proteinuria > 500 mg over a 24 hour period in the setting of T2DM and/or 166

abnormalities as assessed by imaging or histology 31

. Accordingly, 145 TD2M patients were 167

identified with DKD, while 265 were disease free (Tables 1 and 2). The remaining 80 patients 168

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could not be classified with or without DKD at the time of the study, and were excluded from 169

subsequent analyses. 170

Selection of SNPs 171

The SNPs tested for each trait are summarized in Table 1. These SNPs were selected from a 172

recent Genome Wide Association Study (GWAS) that was intended to determine the genetic 173

associations of T2DM in the UAE population, establishing the Emirates Family Registry for 174

T2DM 24

. The GWAS was performed for the 490 samples with T2DM, and 450 healthy controls 175

on the Infinium Omni5ExomeHuman chip (Illumina Inc., San Diego, USA). To select SNPs that 176

are associated with kidney function traits included in the study (blood urea, serum creatinine, 177

eGFR values, albumin-to-creatinine ratio, and vitamin D levels) as well as DKD, various search 178

engines and data bases including PubMed, Google Scholar, the GWAS catalog 179

(https://www.ebi.ac.uk/gwas/home), the Phenolyzer database (http://phenolyzer.wglab.org/), the 180

infinome genome interpretation platform (https://www.infino.me/), and the GWAS Central 181

database (http://www.gwascentral.org/) were consulted. 182

Our search strategy consisted of identifying reported SNPs that cleared the GWAS significance-183

level and were found in our GWAS data, and if necessary selecting possible proxy SNPs (r2 and 184

D’ > 0.8) if the original signal SNPs were missing from the GWAS data. Proxy SNPs were 185

selected using the SNAP database for SNP Annotation and Proxy Search 186

(http://archive.broadinstitute.org/mpg/snap/ldsearch.php). 187

All SNPs located within and flanking genes that have been reported with CKD and DKD were 188

included. In total, 43 genetic loci were identified as linked to CKD, DKD, or a decline in renal 189

functions. Specifically, the gene regions are ACACB, ACE, ACTN4, ADIPOQ, ADM, AFF3, 190

AGTR1, APOL1, CARS-CNDP1, CNDP2, CPS1, CPVL, CPVL, CHN2, CYBA-ELMO1, ENPP1, 191

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ERBB4, FABP2, FRMD3, GLUT1, IRS2, MYO16, LIMK2, MCTP2, MYO16, MYH9, NCALD, 192

NCK1, NOS3, NPHS1, NPHS2, NPPB, PLCE1, PPARγ2, PVT1, RAGE, RGMA, RPS12, SFI1, 193

SHROOM3, TMEM22, TNFRSF1A, and TRPC6. 194

Statistical analyses 195

Continuous variables were presented as the means ± standard deviations. Vitamin D and eGFR 196

levels were normally distributed. However, urea levels, creatinine levels, and albumin-to-197

creatinine ratio data were converted to normal distributions using natural log transformation. The 198

associations of trait values or their natural logs (if transformed) were tested with SNPs using 199

linear regression models, which included age and gender as covariates using PLINK software 200

version 1.07 (http://zzz.bwh.harvard.edu/plink/). The same software was used for counting allele 201

frequencies and testing the quality control (QC) variables, where any SNPs with minor allele 202

frequency (MAF) <0.05, >5% missing genotype rate, or those that failed the Hardy-Weinberg 203

equilibrium (HWE) test at the 0.001, were excluded. Associations with P < 0.05 were reported, 204

pointing to the replication of previously-reported associations. 205

All statistical analyses for the clinical and laboratory variables were performed using Stata 206

software version 14 (StataCorp LLC, Texas, USA). For continuous data, statistical differences 207

were assessed using two-sided t-tests, while the Pearson chi-square test was used for percentage 208

data. A P-value < 0.05 was considered as significant. PLINK software was also used for testing 209

the associations between the SNPs and DKD using a case-control logistic regression model, 210

which included age, gender, hypertension status, T2DM duration, and eGFR levels as covariates 211

(Table 1). The results are presented as P-values (Padjusted < 0.05) and odds ratios with the 212

corresponding 95% confidence intervals. The same approach was adopted to test the associations 213

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between the possible risk factors and the development of the DKD (Table 3). However, the 214

logistic model in this case was validated by the Hosmer–Lemeshow goodness of fit test (P=0.11) 215

to allow for the inclusion of several covariates. 216

Ethical considerations 217

Each patient agreed to take part in this study and gave signed consent after a brief session to 218

explain aims and methods. The study was approved by the Institutional Ethics Committee of 219

both hospitals (REC-04062014 and R292, respectively) and conforms to the ethical principles 220

outlined in the Helsinki declaration. 221

222

Results 223

Baseline data of kidney function associated traits and SNPs selection 224

Of the four hundred nighty patients with T2DM that were recruited for this study one hundred 225

and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 226

for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum 227

creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications 228

relating to the diagnosis of kidney disease (145 patients and 265 controls). The possible 229

confounding factors which may affect the genetic associations were included for each trait 230

analysis and summarized in Table 1. 231

232

233

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Table 1: Baseline data of tested traits and tested SNPs 234

Trait Mean ± SD

N of subjects

(Male/Female)

N of SNPs

reported

N of SNPs

tested

Covariates

ACR (mg/mmol) 47.8 ± 173.4 115 (56/59) 331 83 Age, Gender

Vitamin D (ng/ml) 63.7 ± 27.8 328 (146/182) 442 92 Age, Gender

eGFR (ml/min/1.73m2) 81.5 ± 28.5 395 (172/223) 1478 288 Age, Gender

Serum Creatinine (µmol/l) 91.4 ± 83.9 474 (246/228) 1792 363 Age, Gender

Blood Urea (mmol/l) 6.4 ± 5.4 450 (263/214) 446 73 Age, Gender

Diabetic Kidney Disease (DKD)

Cases: 145

Controls: 265

288 63

Age, Gender,

Hypertension, T2DM

duration, eGFR levels

Abbreviations: N: number; SD: standard deviation; SNP: single nucleotide polymorphism, ACR: albumin-to-creatinine ratio; T2DM: type 2 235

diabetes mellitus; eGFR: estimated glomerular filtration rate.236

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Clinical and laboratory characteristics of patients with and without DKD 237

Expectedly, both patient groups (with or without kidney disease) had poor glycemic control with 238

blood glucose levels above 8 mmol/L. A comparison of patients with DKD to those with no 239

DKD indicated that the majority were males (~52% versus 38% for controls), older (67 versus 240

~59 years), had significant rates of comorbidities such as hypertension and dyslipidemia, and had 241

a longer T2DM duration (16 versus 10 years). A clear decline in all renal function indices was 242

also observed in patients with DKD. However, DKD patients tended to have lower LDL-243

cholesterol results compared to those without DKD, which suggests these patients received better 244

medical care or at least received intensive statin medication (Table 2). 245

246

247

248

249

250

251

252

253

254

255

256

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Table 2: Demographic, clinical, and laboratory characteristics of T2DM patients with or without 257

kidney disease 258

Type of variables Variable DKD No DKD P 1

Demographic variables

Gender: Female 2 70 (48.3%) 165 (62.3%) 0.006

Age (years) 67.0 ± 10.4 58.6 ± 10.6 < 0.0001

Clinical variables

Clinical hypertension 2 140 (96.6%) 197 (74.3 %) < 0.0001

Dyslipidemia 2 138 (95.2%) 241 (90.9%) < 0.0001

Smoking history 2 47 (32.4%) 64 (24.2%) 0.07

Diabetes duration (years) 16.0 ± 9.2 10.1 ± 7.3 < 0.0001

BMI (kg/m2) 31.3 ± 6.0 32.5 ± 6.3 0.07

Laboratory variables

Glycemic indices HbA1c (%) 7.7 ± 1.5 7.8 ± 1.7 0.63

Fasting plasma glucose

(mmol/l) 8.3 ± 3.1 8.9 ± 3.8 0.43

Random blood glucose

(mmol/l) 9.1 ± 3.9 9.5 ± 4.3 0.34

Lipids profile Total-cholesterol (mmol/l) 3.7 ± 1.0 4.0 ± 1.1 0.003

Triglyceride (mmol/l) 1.5 ± 0.8 1.6 ± 0.8 0.25

HDL-cholesterol (mmol/l) 1.2 ± 0.6 1.2 ± 0.5 0.45

LDL-cholesterol (mmol/l) 1.9 ± 0.8 2.1 ± 0.9 0.01

Renal function

indices

Albumin:Creatinine Ratio

(mg/mmol) 132.9 ± 289.4 7.8 ± 16.3 0.01

Vitamin D (nmol/l) 62.9 ± 28.4 65.3 ± 26.8 0.48

eGFR (ml/min/1.73m2) 57.5 ± 27.7 96.1 ± 17.3 < 0.0001

Creatinine (µmol/l) 142.5 ± 134.8 65.9 ± 17.5 < 0.0001

Urea (mmol/l) 9.7 ± 8.3 4.7 ± 1.5 < 0.0001

1 P-value for continuous data, calculated using two-sided t-test, and for percentage data using Pearson chi-259

square test. 260

2 Proportional data shown as number of positive outcome and its percentage. All remaining continuous 261

data shown as mean ± standard deviation. 262

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Risk factors for developing DKD in UAE patients 263

Table 3 shows that T2DM duration was the single factor that significantly contributed to the 264

development of DKD. Increased risk for DKD was significantly associated with increasing 265

duration of T2DM, cumulatively, for 20 years of duration. At a T2DM duration ≥ 20 years, the 266

risk stabilized at 3.12 times higher than patients with duration ≤ 5 years (P=0.02, 95% CI=1.21-267

8.02). Although the levels of serum creatinine indicated a trend for disease development 268

(P=0.03), this was not significant as the odds ratio showed no increase risk, being 1.03 (Table 3). 269

Table 3: Risk factors for development of kidney disease in patients with T2DM from UAE 270

Covariate OR 95% CI SE Z P-value

Age 0.99 0.96 - 1.04 0.020 -0.25 0.80

Gender 0.90 0.34 - 2.36 0.443 -0.21 0.83

BMI 1.02 0.97 – 1.08 0.028 0.69 0.49

Hypertension 2.00 0.60 - 6.74 1.241 1.12 0.26

HbA1c 0.86 0.69 – 1.07 0.098 -1.33 0.19

Cholesterol 0.59 0.19 – 1.76 0.329 -0.95 0.34

Triglyceride 1.09 0.62 – 1.94 0.319 0.30 0.76

HDL-cholesterol 1.25 0.64 – 2.45 0.429 0.65 0.52

LDL-cholesterol 1.38 0.42 – 4.57 0.842 0.52 0.60

Smoking history 0.80 0.34 – 1.87 0.346 -0.52 0.61

Creatinine 1.03 1.00 – 1.06 0.016 2.14 0.03

Urea 1.13 0.92 – 1.38 0.119 1.13 0.26

eGFR 0.97 0.94 – 1.00 0.016 -1.69 0.09

Diabetes duration*

5-10 years 0.75 0.26 – 2.16 0.405 -0.54 0.59

11-15 years 1.49 0.56 – 3.91 0.733 0.80 0.42

16-20 years 3.35 1.22 – 9.23 1.732 2.34 0.02

> 20 years 3.12 1.21 – 8.02 1.503 2.36 0.02

*Diabetes duration reference is duration ≤ 5 years 271

Abbreviations: BMI: body mass index, eGFR: estimated glomerular filtration rate. 272

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Genetic associations of renal function associated traits in UAE participants 273

The results of genetic associations for each tested renal function trait are shown in Table 4. 274

For blood urea, the best observed association was with rs11868441 in BCAS3 (effect size: -0.038 275

log per allele A, P=0.014), followed by multiple SNPs in the RSPO3 gene. 276

For serum creatinine, two SNPs (rs6999484 and rs1705699) in the intergenic region between 277

STC1 and ADAM28, showed the best associations with similar effect sizes (0.03 log per one copy 278

of the corresponding minor allele), followed by the SNP rs2828785, which is located in the non-279

gene area on chromosome 21. In addition, multiple genetic areas were also indicated, although 280

with less significant associations. 281

eGFR levels were significantly associated with SNPs within the MED1 gene, rs2168785 with 282

effect size -4.299 per allele G and rs12452509 with effect size of -4.232 per allele G and P=0.015 283

and 0.017. In addition, the SNPs in two genetic regions, WDR72 and SHROOM3, with 284

associations with serum creatinine levels, were also associated with eGFR levels, indicating a 285

strong link to renal function. 286

Vitamin D levels were associated with three genetic regions including rs1801725 in CARS 287

(effect size: -6.923 per allele A, P=0.0078), rs1155563 in GC (effect size: -6.951, P=0.0081), and 288

two SNPs in CYP2R1 with effect sizes of approximately -5.0 and P-values near 0.01. 289

One SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 290

(effect size: -0.323 log per allele A, P=0.027) was strongly associated with the albumin-to-291

creatinine ratio. 292

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This data showed that renal function traits, are linked to several loci in the UAE population, and 293

that some loci (e.g. WDR27 and SHROOM3) are linked to more than one trait. 294

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Table 4: Results of genetic association analyses of different renal function indices 295

SNP Chr: BP Gene A1/A2 1 MAF_% Beta

2 SE P

Blood Urea

rs11868441 17: 59239221 BCAS3 A/G 30.1 -0.038 0.015 0.014

rs1892172 6: 127476516 RSPO3 T/C 46.9 0.031 0.014 0.028

rs4644087 6: 127481154 RSPO3 C/A 46.8 0.030 0.014 0.030

rs4382293 6: 127475433 RSPO3 G/A 47.0 0.030 0.014 0.031

rs2489629 6: 127476717 RSPO3 G/A 44.9 -0.029 0.014 0.039

Serum Creatinine

rs6999484 8: 23728271 STC1-ADAM28 A/G 23.8 0.033 0.011 0.0030

rs1705699 8: 23781453 STC1-ADAM28 G/A 24.2 0.031 0.011 0.0048

rs2828785 21: 25437505 - A/G 19.9 -0.030 0.011 0.0082

rs11227279 11: 65495211 KRT8P26-AP5B1 A/G 28.4 -0.024 0.010 0.019

rs7785065 7: 32915204 KBTBD2 A/C 43.5 0.022 0.010 0.022

rs4859682 4: 77410318 SHROOM3 A/C 25.7 0.021 0.010 0.034

rs1031755 15: 53951435 WDR72 C/A 11.7 -0.029 0.014 0.038

rs7740534 6: 25077179 - C/A 9.6 -0.032 0.015 0.042

Estimated Glomerular Filtration Rate

rs2168785 17: 37407135 MED1 G/A 28.2 -4.299 1.754 0.015

rs12452509 17: 37574722 MED1 G/A 28.1 -4.232 1.757 0.017

rs4776168 15: 53936907 WDR72 G/A 10.8 5.828 2.699 0.031

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rs10518733 15: 53940307 WDR72 C/A 10.8 5.828 2.699 0.031

rs7541937 1: 35341982 DLGAP3 C/A 44.0 3.590 1.693 0.035

rs10032549 4: 77398015 SHROOM3 G/A 32.2 -3.655 1.750 0.037

rs2484639 1: 243462367 SDCCAG8 A/G 43.5 3.378 1.656 0.042

Vitamin D (25-OH Cholecalciferol)

rs1801725 3: 122003757 CASR A/C 22.6 -6.923 2.584 0.0078

rs1155563 4: 72643488 GC G/A 18.9 -6.951 2.608 0.0081

rs12794714 11: 14913575 CYP2R1 A/G 41.5 -5.110 2.099 0.015

rs10500804 11: 14910273 CYP2R1 C/A 42.2 -5.004 2.060 0.016


rs4528660 18: 3043516 LPIN2-MYOM1 G/A 17.8 -0.323 0.144 0.027

1 A1/A2: minor to major alleles. 296

2 Beta: regression coefficient for interaction in the linear regression model, calculated based on A1 (the minor allele). 297

Abbreviations: BP: base pair position; Chr: chromosome; MAF: minor allele frequency; SE: standard error; SNP: single nucleotide polymorphism.298

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Genetic associations of DKD in patients from UAE 299

Sixty-three SNPs in 43 genetic loci that have previously been linked to chronic or diabetic 300

kidney disease were included in our genetic analysis of DKD (145 case versus 265 controls). A 301

logistic regression model including five possible covariates, which may affect the development 302

of kidney disease, was applied (Table 1). As shown in Table 5, the unadjusted analysis indicates 303

two associations in CPS1 (rs7422339, P=0.019) and SHROOM3 (rs4859682, P=0.024). 304

Following the adjustment for possible covariates, only the SHROOM3 rs4859682 remained 305

significant (P=0.04, OR=1.46). This result confirms that SHROOM3 is a string risk locus for 306

DKD, considering the similar associations with serum creatinine and eGFR levels. 307

308

309

310

311

312

313

314

315

316

317

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Table 5: Association between SNPs linked to CKD and DKD patients from UAE. 318

SNP Chr: BP Gene 1 A1/A2

2 MAF Punadjusted Padjusted OR (95% CI)

Case (N=145) Control (N=265)

rs7422339 2: 211540507 CPS1 A/C 35.8% 27.9% 0.019 0.20 1.25 (0.89 – 1.75)

rs4859682 4: 77410318 SHROOM3 A/C 29.0% 21.9% 0.024 0.04 1.46 (1.01 - 2.10)

1 CPS1: Carbamoyl-Phosphate Synthase 1; SHROOM3: Shroom Family Member 3. 319

2 A1/A2: minor to major alleles. 320

Abbreviations: BP: base pair position; Chr: chromosome; CI: confidence intervals; MAF: minor allele frequency; N: number; OR: odds ratio; 321

SNP: single nucleotide polymorphism. 322

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Discussion 323

A combination of environmental and clinical factors in genetically predisposed individuals have 324

been suggested to be involved in DKD such as persistent hyperglycemia, arterial hypertension 325

and/or dyslipidemia 32

. In fact, familial aggregation of nephropathy in T2DM has been reported 326

in several populations 33

.Therefore, understanding the interactions between genetic, clinical and 327

traditional kidney disease risk factors can provide insight into new treatment strategies for 328

improving DKD and reducing the likelihood of developing ESRD. In this study, we investigated 329

whether genetic markers correspond to DKD and renal function traits that were reported in 330

different populations are similar or different in the Arab population. 331

The current Emirati population sample indicated that most T2DM patients who developed DKD 332

were males, older by about 10 years than those that did not develop the disease, had more 333

frequent comorbidities specifically hypertension, and showed a marked decline in their renal 334

function profiles. However, even T2DM patients who did not develop DKD had higher rates of 335

comorbities and had poor diabetic control, in agreement with our previous results 26

. We also 336

found that the duration of diabetes was the single factor that significantly contributed to the 337

development of kidney disease, regardless any other factor. However, this risk of the DKD 338

development becomes significant when the duration of T2DM reaches the 15 year mark. This is 339

in concordance with previous reports, which suggest that T2DM patients who do not develop 340

signs of kidney disease by 15 years duration of diabetes onset seem to be protected, most likely 341

due to genetic factors 34

. 342

The most notable finding of this study was the association of SHROOM3 (Shroom Family 343

Member 3) with serum creatinine, eGFR, and DKD. The minor allele for the SNP rs4859682 (A) 344

was observed to increase the serum creatinine (0.021 log increase per one copy), and also to 345

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increase the risk for DKD (OR=1.46). SHROOM3 was first reported to be associated with eGFR 346

levels in DKD patients 10

, then with serum creatinine 35

and serum magnesium levels 36

. This 347

association was further replicated in different ethnicities 16 37

. The SHROOM3 gene product is 348

expressed in the human kidney and is reported to play an important role in epithelial cell shape 349

regulation 38

, as well as the maintenance of the glomerular filtration barrier integrity 39

. 350

Furthermore, genetic variants (such as the intronic variant rs17319721), were found to contribute 351

to kidney allograft injury and fibrosis development through a mechanism involving TGF-β1 352

signaling 40

. Although the variant rs17319721 was not found in our dataset, it is highly linked to 353

the SNP rs4859682 (r2=0.85, D’=1), which was reported in this study to increase the risk for 354

DKD and affect the levels of serum creatinine. Overall, this suggests that SHROOM3 may be 355

considered as a multi-ethnic risk gene for DKD and its related kidney function traits in various 356

populations, including the UAE. 357

Similarly, the two loci; WDR72 (WD Repeat Domain 72, associated with eGFR and serum 358

creatinine levels), and BCAS3 (Breast Carcinoma-Amplified Sequence 3, associated with blood 359

urea levels), are also well-known trans-ethnic renal function traits loci 41

. In addition, RSPO3 (R-360

Spondin 3) was previously reported to be associated with blood urea nitrogen concentration, in 361

line with the results of the current study 37

. The association of rs4528660 near MYOM1 362

(Myomesin 1) with the albumin-to-creatinine ratio was also in alignment with previous work, 363

which linked this locus to albuminuria in patients with diabetes 42

. The analyses performed also 364

replicated the associations of CASR (Calcium-sensing receptor), GC (Group-specific 365

component), and CYP2R1 (Cytochrome P450 Family 2 Subfamily R Member 1) with levels of 366

vitamin D 43

. These genes encode proteins which are involved in vitamin D function including 367

activation by hydroxylation (CYP2R1), transportation (GC), and serum calcium level sensoring 368

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(CASR) 43

. These genes have also been reported to be associated with calcium-vitamin D 369

biology and pathology, such as serum calcium levels, familial hypocalciuric hypercalcemia, 370

tertiary hyperparathyroidism, and vitamin D deficiency presenting as Rickets (see OMIM entries: 371

CARS: 601199; CYP2R1: 608713; and GC: 139200). Furthermore, these genes have recently 372

been shown to influence outcome of Vitamin D3 supplementation, which in the Arab context is 373

an important finding of our study 44

. 374

In summary, this work presents the first study to investigate the clinical and genetic factors 375

influencing kidney function traits and DKD in a population of Arab ancestry. The results 376

demonstrate that the duration of T2DM is the single most important risk factor for DKD 377

development in patients with T2DM from UAE. Our study highlights that several genetic loci, 378

which have been previously linked to renal function associated traits, are shared between diverse 379

ethnic groups. As such we have replicated previous findings of the association of SHROOM3 380

with DKD. It is worthwhile to note that a larger population sample across the Middle East is 381

required to confirm the extent of the shared genetic predisposition reported in the current study. 382

Considering the high prevalence of T2DM in this population and the recent evidence of genomic 383

structure variations among different ethnic groups, more genetic-driven population studies are 384

warranted towards effective genetically guided personalized medicine. 385

386

Acknowledgments: We acknowledge the patients who volunteered to make this study possible. 387

388

Author contribution: HSA obtained the fund for this study. WMO, HSA and HFJ have 389

designed the study. WMO analyzed the data and prepared the manuscript. HFJ, GKT, AHK, and 390

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KK reviewed/edited the manuscript and contributed to the discussion and reviewed/edited the 391

manuscript. WA and MHH provided assistance in patients’ recruitment and clinical data 392

collection. 393

394

Funding: This study was supported by research incentive funds from Khalifa University Internal 395

Research Fund Level 2 granted to Dr. Habiba Al Safar. 396

397

Conflict of interests: None declared 398

399

Patient consent: Obtained. 400

401

Ethics approval: The Institutional Ethics Committee of Mafraq and Shaikh Khalifa Medical 402

Centre hospitals (REC-04062014 and R292, respectively). 403

404

Provenance and peer review: Not commissioned; externally peer reviewed. 405

406

Data sharing statement: No additional data are available. 407

408

References 409

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522

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For peer review onlyClinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the United Arab Emirates: A

Cross-sectional Study

Journal: BMJ Open

Manuscript ID bmjopen-2017-020759.R1


Date Submitted by the Author: 07-Jul-2018

Complete List of Authors: Osman, Wael; Khalifa University of Science Technology and Research, Biotecnology Center Jelinek, H. F.; Macquarie University Faculty of Medicine and Health Sciences, Clinical MedicineTay, Guan ; Khalifa University of Science Technology and Research, Biomedical Engineering Khandoker , Ahsan ; Khalifa University of Science Technology and Research, Biomedical Engineering Khalaf, Kinda; Khalifa University of Science Technology and Research, Biomedical Engineering AlMahmeed , Wael; Sheikh Khalifa Medical City, Institute of Cardiac ScienceHassan, Mohamed; Sheikh Khalifa Medical City, Medical InstituteALsafar, Habiba; Khalifa University of Science Technology and Research, Biomedical Engineering

<b>Primary Subject Heading</b>: Diabetes and endocrinology






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United Arab Emirates: A Cross-sectional Study 2


2, 3, Guan K. Tay

1, 4, 5, Ahsan H. Khandoker

6, Kinda 3




1, 6,* 4














SHROOM3 gene. 18


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2


Director of Biotechnology Center, Associate Professor 21



Phone: +971 (0) 2 401 8109, Fax: +971 (0)2 447 2442 24


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3

Abstract 38


kidney disease (DKD) have not been investigated. The aim of this report was to determine 40

possible clinical, laboratory, and reported genetic loci which are risk factors of DKD. 41


diabetes mellitus (T2DM) were recruited with and without DKD and their clinical and laboratory 43

data obtained. Following adjustments for possible confounders, a logistic regression model was 44

used to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci 45

with DKD (145 patients and 265 controls). Linear regression models, adjusted for age and 46

gender, were used to test the genetic associations of five renal function traits, including 83 SNPs 47

with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH Cholecalciferol), 288 SNPs 48

with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine, and 73 SNPs 49

with blood urea. 50

Results: Patients with DKD compared to those without DKD were mostly males (~52% versus 51

38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and 52

dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 53



associations of the genetic loci with different renal function traits found that the most notable 56




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Conclusions: Strong associations were found between several genetic loci and risk markers for 60

diabetic kidney disease, which may influence kidney function traits and DKD in a population of 61

Arab ancestry. 62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

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• This is a cross-sectional study to determine the clinical, laboratory, and genetic associations 80

of the diabetic kidney disease (DKD) and renal function traits in a sample of patients with 81

type 2 diabetes mellitus (T2DM) from the United Arab Emirates. 82

• For the DKD associations, analyses were performed using logistic regression models, and 83

for renal function traits’ associations, linear regressions were performed; all included 84

adjustment for possible covariates. 85

• Genetic association studies were performed using the candidate-gene approach; in which we 86

selected single nucleotide polymorphisms (SNPs) which reported with different tested traits 87

and being exist in database of the Emirates Family Registry for type 2 diabetes mellitus 88

(T2DM). 89

• A limitation of this study is that analyses carried in this study did not include treatment 90

modalities because most of patients had multiple conditions and they used several treatments; 91

which made the models largely unstable and difficult to interpret. 92

• Current analyses of the DKD genetic association had a statistical power ~ 57%; necessitating 93

a larger population sample across the Middle East for sufficient statistical power to discover 94

novel clinical and genetic predisposition for DKD in the region. 95

96

97

98

99

100

101

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Introduction 102

Diabetic kidney disease (DKD), considered as the most common cause of nephropathy in many 103

populations, is a public health challenge worldwide. Globally, the prevalence of chronic kidney 104

disease (CKD) among adults in the general population was reported to be around 10%, with the 105

United States showing a figure around 3.3% for DKD in 2008 1 2

. In Arab world, the prevalence 106

of DKD is widely variable (10.8% to 61.2%) depending on the study design, source of study 107

population, sample selection, race, age, sex structure of the study population, diagnostic criteria 108

among other factors 3. Meta-analysis showed that DKD is the leading cause of the end-stage 109

renal disease (ESRD) in the Gulf Cooperation Council (GCC) with a prevalence of ~ 17% 4. In 110

addition to increasing the risk of cardiovascular morbidity and mortality 5 6, DKD is reported to 111

be the single strongest predictor of mortality in patients with diabetes 7 with five year survival in 112

the range of 30% 8. DKD often progresses to end-stage renal disease (ESRD). Patients with 113

ESRD have a 20% annual mortality rate, which is higher than the rate for many solid cancers 9

. 114

Overall, the risk of DKD in T2DM is approximately 2% per year 10

. The current trend suggests 115

that the prevalence of DKD will continue to increase, constituting a significant socioeconomic 116

burdens on global healthcare systems and leading to increased morbidity and mortality 2. A 117

thorough literature search shows that there remains a knowledge gap related to the understanding 118

of risk factors and pathophysiological mechanisms associated with DKD, especially in the 119

Middle East. Since ESRD can only be treated with highly invasive and costly procedures, such 120

as dialysis or kidney transplantation, a better knowledge of genetic, clinical, and epidemiological 121

factors associated with DKD is required to allow earlier and more effective treatment outcomes. 122

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high heritability rates 11

, indicating that genetic factors contribute significantly to inter-individual 124

variance in kidney function, and hence, to the susceptibility to CKD and related conditions. 125

So far, several genetic loci have been linked to DKD, chronic kidney disease (CKD), and renal 126


and children 24



are insufficient. In spite of efforts to describe novel biomarkers for DKD, no tested candidates 129

outperform albumin. A recent report by Pierre-Jean Saulnier et al. suggested that three serum 130

biomarkers (midregional-proadrenomedullin: MR-proADM, soluble tumor necrosis factor 131


improve the risk prediction of the loss of renal function in patients with T2DM in addition to the 133















the UAE, to our best knowledge, there have been no investigations of the genetic associations of 144

chronic kidney conditions and kidney functions in this population, particularly associated with 145

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T2DM. Therefore, the current work aimed to investigate the clinical and laboratory variables 146

linked to DKD in a T2DM Emirati population, and to investigate the associations of the reported 147

genetic loci linked to different renal function tests, in CKD and DKD. 148

149



This report describes a cross-sectional study of Emirati patients from the city of Abu Dhabi. The 152

demographic information and clinical data for the participants are presented in Tables 1 and 2. 153

Four hundred and ninety patients with T2DM were included in the study, with 145 diagnosed 154

with DKD. The participants were recruited from Sheikh Khalifa Medical City (SKMC) and 155

Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE. All subjects were UAE born and 156

with Arabic descent. 157

Patient and Public Involvement 158

Patients and public were not involved in setting the research question, results analyses or results 159

writing. However the study was designed because T2DM is a major health problem in the UAE 160

and has a growing public interest. 161




pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking 165




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group report 31


calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the 170

weight in kilograms divided by the square of height of each subject (kg/m2). 171

Definition of the DKD 172

DKD definition was according to Ref #2. Briefly, this was defined as either decreased levels of 173

eGFR <60 ml/min/1.73 m2 with or without renal damage over a period of at least three months 174

32, or based on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria > 500 mg over a 24 hour 175

period in the setting of T2DM and/or abnormalities as assessed by imaging or histology 33

. 176

eGFR was calculated according to CKD-EPI Creatinine Equation 34

.Accordingly, 145 TD2M 177

patients were identified with DKD, while 265 were disease free (Tables 1 and 2). The remaining 178

80 patients could not be classified with or without DKD at the time of the study, and were 179

excluded from subsequent analyses. 180


Genetic studies in this study follow the candidate gene approach. The SNPs tested for each trait 182

are summarized in Table 1. These SNPs were selected from a recent Genome Wide Association 183

Study (GWAS) that was intended to determine the genetic associations of T2DM in the UAE 184

population, establishing the Emirates Family Registry for T2DM 26

. The GWAS was performed 185

for the 490 samples with T2DM, and 450 healthy controls on the Infinium Omni5ExomeHuman 186

chip (Illumina Inc., San Diego, USA). To select SNPs that are associated with kidney function 187

traits included in the study (blood urea, serum creatinine, eGFR values, albumin-to-creatinine 188

ratio, and vitamin D levels) as well as DKD, various search engines and data bases including 189

PubMed, Google Scholar, the GWAS catalog (https://www.ebi.ac.uk/gwas/home), the 190

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Phenolyzer database (http://phenolyzer.wglab.org/), the infinome genome interpretation platform 191

(https://www.infino.me/), and the GWAS Central database (http://www.gwascentral.org/) were 192

consulted. 193


level and were found in our GWAS data. If the original signal SNP was missing from the GWAS 195

data, we searched for a possible proxy SNP utilizing the concept of linkage disequilibrium (LD), 196

which indicates a non-random association of alleles at different genetic loci in a given population 197

and their tendency to be inherited as a block (mathematical values r2 and D’ > 0.8 indicates high 198

LD). Proxy SNPs were selected using the SNAP database for SNP Annotation and Proxy Search 199




functions. Specifically, the gene regions are ACACB, ACE, ACTN4, ADIPOQ, ADM, AFF3, 203

AGTR1, APOL1, CARS-CNDP1, CNDP2, CPS1, CPVL, CPVL, CHN2, CYBA-ELMO1, ENPP1, 204

ERBB4, FABP2, FRMD3, GLUT1, IRS2, MYO16, LIMK2, MCTP2, MYO16, MYH9, NCALD, 205

NCK1, NOS3, NPHS1, NPHS2, NPPB, PLCE1, PPARγ2, PVT1, RAGE, RGMA, RPS12, SFI1, 206



Continuous variables were presented as the means ± standard deviations or lower / median and 209

upper quartiles where the distributions are highly skewed. Vitamin D and eGFR levels were 210

normally distributed. However, urea levels, creatinine levels, and albumin-to-creatinine ratio data 211

were converted to normal distributions using natural log transformation. The associations of trait 212

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values or their natural logs (if transformed) were tested with SNPs using linear regression 213

models, which included age and gender as covariates using PLINK software version 1.07 214

(http://zzz.bwh.harvard.edu/plink/). The same software was used for counting allele frequencies 215

and testing the quality control (QC) variables, where any SNPs with minor allele frequency 216

(MAF) <0.05, >5% missing genotype rate, or those that failed the Hardy-Weinberg equilibrium 217

(HWE) test at the 0.001, were excluded. Hardy-Weinberg equilibrium is an important QC test for 218

the genetic association studies assumes that allele and genotype frequencies can be estimated. If 219

the frequencies of the measured genotypes significantly differ from the HWE assumptions, it can 220

indicate genotyping errors among other possible factors, such as ethnic diversity and high level 221

of consanguinity in the population; therefore those SNPs were excluded from further analyses. 222

Association with P < 0.05 were reported, pointing to the replication of previously-reported 223

associations. 224



were assessed using two-sided t-tests for normally distributed data or Wilcoxon rank-sum 227

(Mann-Whitney) test for highly skewed data. The Pearson chi-square test was used for 228

percentage data or Fishers Exact test when expected frequencies are less than 5. A P-value < 229

0.05 was considered as significant. PLINK software was also used for testing the associations 230

between the SNPs and DKD using a case-control logistic regression model, which included age, 231

gender, hypertension status, T2DM duration, and eGFR levels as covariates (Table 1). The 232

results are presented as P-values (Padjusted < 0.05) and odds ratios with the corresponding 95% 233

confidence intervals. The same approach (patients with DKD vs. patients without DKD) was 234

adopted to test the associations between the possible risk factors and the development of the 235

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DKD (Table 3). However, the logistic model which includes all the associated factors in Table 4 236

was validated by the Hosmer–Lemeshow goodness of fit test (P=0.11) to allow for the inclusion 237

of several covariates. Analyses between SNPs and renal function traits were conducted in PLINK 238

using linear regression models that included age and gender as covariates. Results were 239

presented as beta (regression coefficient for the linear regression model, calculated based on the 240

minor allele), standard errors, and P-values. 241

As this is a replication study, we reported here all P-values < 0.05, suggesting possible 242

replications. However, using a Bonferroni correction for multiple testing, P-values with 243

statistically significant associations are like the following: < 0.00079 for the DKD associations, < 244

0.0006 for the ACR, < 0.0005 for the Vitamin D, < 0.00017 for the eGFR, < 0.00019 for the 245

creatinine, and 0.00068 for the urea associations. 246

Statistical power considerations: 247

Assuming the current sample size (145 cases vs. 265 controls for DKD), prevalence of DKD 248

~20% (depending on references # 3 and #4), genotype risk (odds ratio) = 1.5, significance level 249

of 0.0008 (following correction of multiple testing using Bonferroni correction), disease allele 250

frequency = 0.5, and multiplicative model, the current study has a power ~ 57%. Calculation are 251

made using the Genetic Association Study (GAS) Power Calculator 252

(http://csg.sph.umich.edu//abecasis/cats/gas_power_calculator/index.html ), which was 253

developed by the Department of Biostatistics Center for Statistical Genetics, University of 254

Michigan School of Public Health. 255


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both hospitals (REC-04062014 and R292, respectively) and conforms to the ethical principles 259




263

Results 264


Of the four hundred nighty patients with T2DM that were recruited for this study one hundred 266

and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 267

for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum 268

creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications 269

relating to the diagnosis of kidney disease (145 patients and 265 controls) according to the 270

diagnostic criteria [Reference #2] or patients’ medical records. The possible confounding factors 271

which may affect the genetic associations were included for each trait analysis and summarized 272

in Table 1. 273

274

275

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Trait

Mean ± SD or

quartiles

N of subjects

(Male/Female)

N of SNPs

reported **

N of SNPs

tested **

Covariates

ACR (mg/mmol) 1.05 / 3.9/ 12.7 * 115 (56/59) 331 83 Age, Gender



Serum Creatinine (µmol/l) 60 / 74/ 95.8 * 474 (246/228) 1792 363 Age, Gender



Cases: 145

Controls: 265

288 63

Age, Gender,

Hypertension, T2DM


* For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25

th/ 50

th/ 75

th quartiles and not as mean ± standard 277

deviation because their distributions are skewed. 278

** Reported: means the total number of SNPs found in the search in the literature, tested: means the actual number of SNPs found from the 279

reported SNPs and used for the analyses in this study for each corresponding trait. 280



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Expectedly, both patient groups (with or without kidney disease) had poor glycemic control with 284

blood glucose levels above 8 mmol/L. A comparison of patients with DKD to those without 285

DKD indicated that the majority of those with DKD were males (~52% versus 38% for controls), 286

older (67 versus ~59 years), had significant rates of comorbidities such as hypertension and 287

dyslipidemia, and had a longer T2DM duration (16 versus 10 years). A clear decline in renal 288

function indices was also observed in patients with DKD vs. those who don’t have DKD 289

indicated by the significantly higher rates of ACR, urea, and creatinine; and significantly lower 290

eGFR (Table 2). However, DKD patients tended to have lower LDL-cholesterol results 291

compared to those without DKD, which suggests these patients received better medical care or at 292

least received intensive statin medication (Table 2). 293

294

295

296

297

298

299

300

301

302

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kidney disease 304

Type of variables Variable DKD No DKD P *


Gender: Female **

70 (48.3%) 165 (62.3%) 0.006

Age (years) 67.0 ± 10.4 58.6 ± 10.6 < 0.0001

Clinical variables

Clinical hypertension **

96.6% 74.3 % < 0.0001

Dyslipidemia **

95.2% 90.9% 0.17

Smoking history **

32.4% 24.2% 0.07


BMI (kg/m2) 31.3 ± 6.0 32.5 ± 6.3 0.078




(mmol/l) 8.3 ± 3.1 8.9 ± 3.8 0.43


(mmol/l) 9.1 ± 3.9 9.5 ± 4.3 0.34





Renal function

indices


(mg/mmol) ***

3.3 / 10.1/ 69.6 0.8/ 2.2/ 9.0 0.0001

Vitamin D (nmol/l) 62.9 ± 28.4 65.3 ± 26.8 0.48

eGFR (ml/min/1.73m2) 57.5 ± 27.7 96.1 ± 17.3 < 0.0001

Creatinine (µmol/l) ***

82/ 111/ 146 55/ 64/ 76 < 0.0001

Urea (mmol/l) 9.7 ± 8.3 4.7 ± 1.5 < 0.0001

* P-value for continuous data, calculated using two-sided t-test except for Albumin: Creatinine Ratio and 305

Creatinine where it is for Wilcoxon rank-sum (Mann-Whitney) test. P-value for percentage data 306

calculated using Pearson chi-square test with except of hypertension and Dyslipidemia where Fisher 307

Exact test was used. 308

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** Proportional data shown as number of positive outcome and its percentage. All remaining continuous 309

data shown as mean ± standard deviation except of Albumin: Creatinine Ratio and Creatinine. 310

*** For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25

th/ 50

th/ 311

75th quartiles and not as mean ± standard deviation because their distributions are highly skewed. 312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

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Associated factors for developing DKD in UAE patients 329





8.02). Although the levels of serum creatinine indicated a trend for disease development 334

(P=0.03), this was not significant as the odds ratio showed no increase risk, being 1.03 (Table 3). 335

Table 3: Risk factors for development of kidney disease in patients with T2DM from UAE 336

Covariate OR 95% CI P-value

Age 0.99 0.96 - 1.04 0.80

Gender 0.90 0.34 - 2.36 0.83

BMI 1.02 0.97 – 1.08 0.49

Hypertension 2.00 0.60 - 6.74 0.26

HbA1c 0.86 0.69 – 1.07 0.19

Cholesterol 0.59 0.19 – 1.76 0.34

Triglyceride 1.09 0.62 – 1.94 0.76

HDL-cholesterol 1.25 0.64 – 2.45 0.52

LDL-cholesterol 1.38 0.42 – 4.57 0.60

Smoking history 0.80 0.34 – 1.87 0.61

Creatinine 1.03 1.00 – 1.06 0.03

Urea 1.13 0.92 – 1.38 0.26

eGFR 0.97 0.94 – 1.00 0.09

Diabetes duration*

5-10 years 0.75 0.26 – 2.16 0.59

11-15 years 1.49 0.56 – 3.91 0.42

16-20 years 3.35 1.22 – 9.23 0.02

> 20 years 3.12 1.21 – 8.02 0.02



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The results of genetic associations for each tested renal function trait are shown in Table 4. In 340

summary, no association passed the Bonferroni correction for multiple testing. However, we 341

report here the most suggestive associations which point to replications of previous reports. 342





of the corresponding minor allele), followed by the SNP rs2828785, which is located in the non-347

gene area on chromosome 21. In addition, multiple genetic areas were also indicated, although 348

with less significant associations. 349


effect size -4.299 per allele G and rs12452509 with effect size of -4.232 per allele G and P=0.015 351

and 0.017. In addition, the SNPs in two genetic regions, WDR72 and SHROOM3, with 352

associations with serum creatinine levels, were also associated with eGFR levels, indicating a 353

strong link to renal function. 354



two SNPs in CYP2R1 with effect sizes of approximately -5.0 and P-values near 0.01. 357




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SNP Chr: BP Gene A1/A2 * MAF_% Beta

** SE P

Blood Urea

rs11868441 17: 59239221 BCAS3 A/G 30.1 -0.038 0.015 0.014

rs1892172 6: 127476516 RSPO3 T/C 46.9 0.031 0.014 0.028

rs4644087 6: 127481154 RSPO3 C/A 46.8 0.030 0.014 0.03

rs4382293 6: 127475433 RSPO3 G/A 47.0 0.030 0.014 0.03

rs2489629 6: 127476717 RSPO3 G/A 44.9 -0.029 0.014 0.039

Serum Creatinine

rs6999484 8: 23728271 STC1-ADAM28 A/G 23.8 0.033 0.011 0.003

rs1705699 8: 23781453 STC1-ADAM28 G/A 24.2 0.031 0.011 0.005

rs2828785 21: 25437505 - A/G 19.9 -0.030 0.011 0.008

rs11227279 11: 65495211 KRT8P26-AP5B1 A/G 28.4 -0.024 0.010 0.019

rs7785065 7: 32915204 KBTBD2 A/C 43.5 0.022 0.010 0.022

rs4859682 4: 77410318 SHROOM3 A/C 25.7 0.021 0.010 0.034

rs1031755 15: 53951435 WDR72 C/A 11.7 -0.029 0.014 0.038

rs7740534 6: 25077179 - C/A 9.6 -0.032 0.015 0.042


rs2168785 17: 37407135 MED1 G/A 28.2 -4.299 1.754 0.015

rs12452509 17: 37574722 MED1 G/A 28.1 -4.232 1.757 0.017

rs4776168 15: 53936907 WDR72 G/A 10.8 5.828 2.699 0.031

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rs10518733 15: 53940307 WDR72 C/A 10.8 5.828 2.699 0.031

rs7541937 1: 35341982 DLGAP3 C/A 44.0 3.590 1.693 0.035

rs10032549 4: 77398015 SHROOM3 G/A 32.2 -3.655 1.750 0.037

rs2484639 1: 243462367 SDCCAG8 A/G 43.5 3.378 1.656 0.042


rs1801725 3: 122003757 CASR A/C 22.6 -6.923 2.584 0.008

rs1155563 4: 72643488 GC G/A 18.9 -6.951 2.608 0.008

rs12794714 11: 14913575 CYP2R1 A/G 41.5 -5.110 2.099 0.015

rs10500804 11: 14910273 CYP2R1 C/A 42.2 -5.004 2.060 0.016


rs4528660 18: 3043516 LPIN2-MYOM1 G/A 17.8 -0.323 0.144 0.027

* A1/A2: minor to major alleles. 364

** Beta: regression coefficient for the linear regression model, calculated based on A1 (the minor allele). 365


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kidney disease were included in our genetic analysis of DKD (145 case versus 265 controls). A 369

logistic regression model including five possible covariates, which may affect the development 370

of kidney disease, was applied (Table 1). As shown in Table 5, the unadjusted analysis indicates 371

two associations in CPS1 (rs7422339, P=0.019) and SHROOM3 (rs4859682, P=0.024). 372

Following the adjustment for possible covariates, only the SHROOM3 rs4859682 remained 373

significant (P=0.04, OR=1.46). This result confirms that SHROOM3 is a string risk locus for 374

DKD, considering the similar associations with serum creatinine and eGFR levels. 375

376

377

378

379

380

381

382

383

384

385

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SNP Chr: BP Gene * A1/A2

** MAF Punadjusted Padjusted OR (95% CI)

Case (N=145) Control (N=265)

rs7422339 2: 211540507 CPS1 A/C 35.8% 27.9% 0.019 0.20 1.25 (0.89 – 1.75)

rs4859682 4: 77410318 SHROOM3 A/C 29.0% 21.9% 0.024 0.04 1.46 (1.01 - 2.10)

* CPS1: Carbamoyl-Phosphate Synthase 1; SHROOM3: Shroom Family Member 3. 387

** A1/A2: minor to major alleles. 388

Abbreviations: BP: base pair position; Chr: chromosome; CI: confidence intervals; MAF: minor allele frequency; N: number; OR: odds ratio; 389

SNP: single nucleotide polymorphism. 390

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Discussion 391


been suggested to be involved in DKD such as persistent hyperglycemia, arterial hypertension 393


. In fact, familial aggregation of nephropathy in T2DM has been reported 394

in several populations 36

.Therefore, understanding the interactions between genetic, clinical and 395

traditional kidney disease risk factors can provide insight into new treatment strategies for 396

improving DKD and reducing the likelihood of developing ESRD. In this study, we investigated 397

whether genetic markers correspond to DKD and renal function traits that were reported in 398

different populations are similar or different in the Arab population. 399





comorbities and had poor diabetic control, in agreement with our previous results 28

. We also 404


development of kidney disease, regardless any other factor. However, this risk of the DKD 406

development becomes significant when the duration of T2DM reaches the 15 year mark. This is 407

in concordance with previous reports, which suggest that T2DM patients who do not develop 408

signs of kidney disease by 15 years duration of diabetes onset seem to be protected, most likely 409

due to genetic factors 37

. 410




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. This 415




regulation 41


. Defective 418

Shroom3 protein leads to decreased actin organisation and affects mechanical characteristics and 419

integrity of the glomerular podocyte resulting in thinning of the glomerular filtration membrane 420

42. Additionally, Shroom3 heterozygous (Shroom3

Gt/+) mice showed developmental irregularities 421

that manifested as adult-onset glomerulosclerosis and proteinuria 43

. Furthermore, genetic 422

variants (such as the intronic variant rs17319721), were found to contribute to kidney allograft 423

injury and fibrosis development through a mechanism involving TGF-β1 signaling 44

. Although 424

the variant rs17319721 was not found in our dataset, it is highly linked to the SNP rs4859682 425

(r2=0.85, D’=1), which was reported in this study to increase the risk for DKD and affect the 426

levels of serum creatinine. Overall, this suggests that SHROOM3 may be considered as a multi-427

ethnic risk gene for DKD and its related kidney function traits in various populations, including 428

the UAE. 429




. WDR72 in particular 432

was well studied in association with kidney function traits and pathologies. For instance, WDR72 433

was reported to be associated with creatinine production or secretion 46

, and suggested to be 434

associated with signal transduction, cell cycle regulation, and vesicular trafficking that affects 435

already mentioned podocyte activity, reduced eGFR and a progression of CKD 47

.In addition, 436

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RSPO3 (R-Spondin 3) was previously reported to be associated with blood urea nitrogen 437

concentration, in line with the results of the current study 40

. The association of rs4528660 near 438

MYOM1 (Myomesin 1) with the albumin-to-creatinine ratio was also in alignment with previous 439

work, which linked this locus to albuminuria in patients with diabetes 48

. The analyses performed 440

also replicated the associations of CASR (Calcium-sensing receptor), GC (vitamin D-binding 441

protein (VDBP) is also known as group-specific component (GC)- globulin), and CYP2R1 442

(Cytochrome P450 Family 2 Subfamily R Member 1) with levels of vitamin D 49

. These genes 443

encode proteins which are involved in vitamin D function including activation by hydroxylation 444

(CYP2R1), transportation (GC), and serum calcium level sensoring (CASR) 49

. The genes have 445

also been reported to be associated with calcium-vitamin D biology and pathology, such as 446

serum calcium levels, familial hypocalciuric hypercalcemia, tertiary hyperparathyroidism, and 447

vitamin D deficiency presenting as Rickets (see OMIM entries: CARS: 601199; CYP2R1: 448

608713; and GC: 139200). For instance, CASR protein is expressed in the kidney amongst other 449

tissue and regulates ion metabolism including calcium and magnesium. Mutations in CASR lead 450

to abnormalities in the regulation of parathyroid gland and renal function causing hypercalcemia 451

and increased blood pressure that in turn may affect kidney function 50

. Similarly, GC proteins 452

binds actin and acts as an actin scavenger (as such it may play a role in podocyte integrity) as 453

well as a binding site for Vit D. GC is the precursor to Gc-macrophage activating factor 454

(GcMAF), a macrophage activator and suggests together with Vit D that GC has an important 455

role in immune function and the pathogenesis of CKD 51

. For CYP2R1, it is reported that vitamin 456

D is hydroxylated at the C25 position by specific hydroxylase coded by the CYP2R1 gene to 25-457

hydroxyvitamin D (25(OH) D), which is the main circulating form of vitamin D. Low Vit D 458

observed in CKD due to reduced CYP2R1 production by the liver or due to a mutation in the 459

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gene, disturbs calcium balance and leads to hyperparathyroidism. Conversely the loss of renal 460

protection brought about by Vitamin D and an increase in the renin-angiotensin pathway leads to 461

hypertension that further advances kidney disease 52

. Furthermore, these genes have recently 462



. 464




development in patients with T2DM from UAE. Our study highlights that several genetic loci, 468



with DKD. The logistic analyses carried in this study did not included treatment modalities 471

because most of patients have multiple conditions and their usage of several treatments, which 472

make the logistic largely unstable and difficult to interpret. It is worthwhile to note that a larger 473

population sample across the Middle East is required to confirm the extent of the shared genetic 474

predisposition reported in the current study. Considering the high prevalence of T2DM in this 475

population and the recent evidence of genomic structure variations among different ethnic 476

groups, more genetic-driven population studies are warranted towards effective genetically 477

guided personalized medicine. 478

479


We also acknowledge patients’ care advisers for their roles in this study. 481

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482

Author contribution: HSA obtained the fund for this study. WMO, HSA and HFJ have 483

designed the study. WMO analyzed the data and prepared the manuscript. HFJ, GKT, AHK, and 484

KK reviewed/edited the manuscript and contributed to the discussion and reviewed/edited the 485

manuscript. WA and MHH provided assistance in patients’ recruitment and clinical data 486

collection. 487

488


490


492



495


497


499

References 500

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3. Aldukhayel A. Prevalence of diabetic nephropathy among Type 2 diabetic patients in some of 505

the Arab countries. International Journal of Health Sciences 2017;11(1):1. 506

4. Hassanien AA, Al-Shaikh F, Vamos EP, et al. Epidemiology of end-stage renal disease in the 507

countries of the Gulf Cooperation Council: a systematic review. JRSM Short Reports 508

2012;3(6):1-21. 509



participant data. The Lancet Diabetes & Endocrinology 2015;3(7):514-25. 512



2013;382(9889):339-52. 515



2004;141(2):95-101. 518

8. collinsAJ F. United States Renal Data System 2011 Annual Data Report: Atlas of chronic 519

kidney disease& end-stage renal disease in the United States. American Journal of 520

Kidney Diseases 2012;59 (1Suppl1) :A7. 521



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10. Gross JL, De Azevedo MJ, Silveiro SP, et al. Diabetic nephropathy: diagnosis, prevention, 524

and treatment. Diabetes Care 2005;28(1):164-76. 525










Genetics 2010;6(7):e1001039. 535











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PLoS One 2014;9(2):e88273. 555




2016;31(2):262-69. 559









2012;102(5):909-14. 568

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33. Andrassy KM. Comments on'KDIGO 2012 clinical practice guideline for the evaluation and 582


34. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration 584

rate. Annals of Internal Medicine 2009;150(9):604-12. 585



2003;47(3):207-19. 588



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2008;135(8):1493-502. 603



43. Khalili H, Sull A, Sarin S, et al. Developmental origins for kidney disease due to Shroom3 606

deficiency. Journal of the American Society of Nephrology 2016:ASN. 2015060621. 607



2015;125(1):208. 610



2016;99(3):636-46. 613

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46. O'seaghdha CM, Fox CS. Genome-wide association studies of chronic kidney disease: what 614

have we learned? Nature Reviews Nephrology 2012;8(2):89. 615

47. Good MC, Zalatan JG, Lim WA. Scaffold proteins: hubs for controlling the flow of cellular 616

information. Science 2011;332(6030):680-86. 617





50. Riccardi D, Brown EM. Physiology and pathophysiology of the calcium-sensing receptor in 622

the kidney. American Journal of Physiology-Renal Physiology 2009;298(3):F485-F99. 623

51. Thyer L, Ward E, Smith R, et al. A novel role for a major component of the vitamin D axis: 624

vitamin D binding protein-derived macrophage activating factor induces human breast 625

cancer cell apoptosis through stimulation of macrophages. Nutrients 2013;5(7):2577-89. 626

52. Jean G, Souberbielle JC, Chazot C. Vitamin D in chronic kidney disease and dialysis 627

patients. Nutrients 2017;9(4):328. 628





633

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STREGA reporting recommendations, extended from STROBE Statement

Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the United Arab Emirates: A Cross-sectional Study

By Wael Osman et al.

Item Item

number

STROBE Guideline

Extension for Genetic Association Studies

(STREGA)

Title and Abstract

1

(a) Indicate the study’s design with a commonly used term in the title or the abstract.


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Objectives: Within the Emirati population, risk factors and genetic predisposition of diabetic kidney disease (DKD) have not been investigated. The aim of this report was to determine possible clinical, laboratory, and reported genetic loci which are risk factors of DKD. Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2 diabetes mellitus (T2DM) were recruited with and without DKD and their clinical and laboratory data obtained. Following adjustments for possible confounders, a logistic regression model was used to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci with DKD (145 patients and 265 controls). Linear regression models, adjusted for age and gender, were used to test the genetic associations of five renal function traits, including 83 SNPs with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH Cholecalciferol), 288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine, and 73 SNPs with blood urea. Results: Patients with DKD compared to those without DKD were mostly males (~52% versus 38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels. Conclusions: Strong associations were found between several genetic loci and risk markers for diabetic kidney disease, which may influence kidney function traits and DKD in a population of Arab ancestry.

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(STREGA)

Introduction

Background rationale

2 Explain the scientific background and rationale for the investigation being reported.

Explain the scientific background and rationale for the investigation being reported.

Objectives 3 State specific objectives, including any pre-specified hypotheses.

The aim of this report was to determine possible clinical, laboratory, and reported genetic loci which are risk factors of diabetic kidney disease in UAE population.

State if the study is the first report of a genetic association, a replication effort, or both.

This is a replication study for the genetic association part

Methods

Study design 4 Present key elements of study design early in the paper.

This report describes a cross-sectional study of Emirati patients from the city of Abu Dhabi.

Setting 5 Describe the setting, locations and relevant dates, including periods of recruitment, exposure, follow-up, and data collection.

The participants were recruited from Sheikh Khalifa Medical City (SKMC)

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(STREGA)

and Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE.

Participants 6 (a) Cohort study – Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up.

Case-control study – Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls.

Cross-sectional study – Give the eligibility criteria, and the sources and methods of selection of participants.

All subjects were UAE born and with Arabic descent.

Give information on the criteria and methods for selection of subsets of participants from a larger study, when relevant.

145 diagnosed with DKD and 265 did not.

(b) Cohort study – For matched studies, give matching criteria and number of exposed and unexposed.

Case-control study – For matched studies, give matching criteria and the number of controls per case.


Variables 7 (a) Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable.

Various clinical and laboratory measures were assessed and collected

(b) Clearly define genetic exposures (genetic variants) using a widely-used nomenclature system. Identify

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(STREGA)

during the hospital visits. Blood pressure was taken on two different occasions. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking antihypertensive medications. Dyslipidemia was reported in the clinical records of participants. The presence of T2DM was confirmed by a qualified physician based on the criteria outlined by the World Health Organization (WHO) consultation group report. DKD definition was according to PMID: 21693741. Briefly, this was defined as either decreased levels of eGFR <60 ml/min/1.73 m2 with or without renal damage over a period of at least three months 32, or based on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria > 500 mg over a 24 hour period in the setting of T2DM and/or abnormalities as assessed by imaging or histology. eGFR was calculated according to CKD-EPI Creatinine Equation.

To select SNPs that are associated with kidney function traits included in the study (blood urea, serum creatinine, eGFR values, albumin-to-creatinine ratio, and vitamin D levels) as well as DKD, various search engines and data bases including PubMed, Google Scholar, the GWAS catalogue (https://www.ebi.ac.uk/gwas/home ), the Phenolyzer database (http://phenolyzer.wglab.org/ ), the infinome genome interpretation platform (https://www.infino.me/ ), and the GWAS Central database (http://www.gwascentral.org/ ) were consulted.

variables likely to be associated with population stratification (confounding by ethnic origin).

Genetic studies in this study follow the candidate gene approach. The SNPs tested for each trait are summarized in Table 1. These SNPs were selected from a recent Genome Wide Association Study (GWAS) that was intended to determine the genetic associations of T2DM in the UAE population, establishing the Emirates Family Registry for T2DM (Habiba Alsafar et al. 2012).

Data sources measurement

8*

(a) For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group.

(b) Describe laboratory methods, including source and storage of DNA, genotyping methods and platforms

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Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or when patients were taking antihypertensive medications. Dyslipidemia was reported in the clinical records of participants. The presence of T2DM was confirmed by a qualified physician based on the criteria outlined by the World Health Organization (WHO) consultation group report. DKD definition was according to PMID: 21693741. Briefly, this was defined as either decreased levels of eGFR <60 ml/min/1.73 m2 with or without renal damage over a period of at least three months 32, or based on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria > 500 mg over a 24 hour period in the setting of T2DM and/or abnormalities as assessed by imaging or histology. eGFR was calculated according to CKD-EPI Creatinine Equation. Trained nurses measured the height and weight of each participant using a calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the weight in kilograms divided by the square of height of each subject (kg/m2).

(including the allele calling algorithm used, and its version), error rates and call rates. State the laboratory/centre where genotyping was done. Describe comparability of laboratory methods if there is more than one group. Specify whether genotypes were assigned using all of the data from the study simultaneously or in smaller batches.

The original Genome Wide Association Study (GWAS) from which we selected the tested SNPs was reported to establishing the Emirates Family Registry for T2DM (Habiba Alsafar et al. 2012).

Bias 9 (a) Describe any efforts to address potential sources of bias.

We applied filters for genotyping like H-W testing, allele frequencies

(b) For quantitative outcome variables, specify if any investigation of

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and genotype call rates filters. We also excluded data which is missed > 20% of participants from the analyses, and used covariates adjustment for each analysis.

potential bias resulting from pharmacotherapy was undertaken. If relevant, describe the nature and magnitude of the potential bias, and explain what approach was used to deal with this.

Not applicable

Study size 10 Explain how the study size was arrived at.

No prior calculations conducted

Quantitative variables

11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen, and why.

Trained nurses measured the height and weight of each participant using a calibrated wall-mounted stadiometer and a weigh scale, respectively. BMI was calculated as the weight in kilograms divided by the square of height of each subject (kg/m2). Others quantitative variables like age or laboratory variables were collected without handling.

If applicable, describe how effects of treatment were dealt with.

For genetic associations with quantitative variables in this study, data was normalized using log transformation.

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Statistical methods

12

(a) Describe all statistical methods, including those used to control for confounding.

For continuous data, statistical differences were assessed using two-sided t-tests for normally distributed data or Wilcoxon rank-sum (Mann-Whitney) test for highly skewed data. The Pearson chi-square test was used for percentage data or Fishers Exact test when expected frequencies are less than 5. A P-value < 0.05 was considered as significant. PLINK software was also used for testing the associations between the SNPs and DKD using a case-control logistic regression model, which included age, gender, hypertension status, T2DM duration, and eGFR levels as covariates. The results are presented as P-values (Padjusted < 0.05) and odds ratios with the corresponding 95% confidence intervals. The same approach (patients with DKD vs. patients without DKD) was adopted to test the associations between the possible risk factors and the development of the DKD. However, the logistic model which includes all the associated factors in Table 4 was validated by the Hosmer–Lemeshow goodness of fit test to allow for the inclusion of several covariates. Analyses between SNPs and renal function traits were conducted in PLINK using linear regression models that included age and gender as covariates. Results were presented as beta (regression coefficient for the linear regression model, calculated based on the minor allele), standard errors, and P-values.

State software version used and options (or settings) chosen.

PLINK software version 1.07

Stata software version 14 (StataCorp LLC, Texas, USA)

(b) Describe any methods used to examine subgroups and interactions.

No methods used to examine subgroups and interactions.

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(STREGA)

(c) Explain how missing data were addressed.

Any missing data in > 20% of participants were excluded to avoid bias.

(d) Cohort study – If applicable, explain how loss to follow-up was addressed.

Case-control study – If applicable, explain how matching of cases and controls was addressed.

Cross-sectional study – If applicable, describe analytical methods taking account of sampling strategy.

No specific sampling strategy.

(e) Describe any sensitivity analyses.

No sensitivity analyses.

(f) State whether Hardy-Weinberg equilibrium was considered and, if so, how.

SNPs failed the Hardy-Weinberg equilibrium (HWE) test at the 0.001 were excluded.

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(g) Describe any methods used for inferring genotypes or haplotypes.

No methods used for inferring genotypes or haplotypes.

(h) Describe any methods used to assess or address population stratification.

The original GWAS by Habiba Alsafar et. Al 2012 was addressed the issue of the population stratification. This current study is not a GWAS, so we did not address this issue again.

(i) Describe any methods used to address multiple comparisons or to control risk of false positive findings.

We mentioned that the current

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study is a replication study, therefore we reported here all P-values < 0.05, suggesting possible replications. However, using a Bonferroni correction for multiple testing, P-values with statistically significant associations are like the following: < 0.00079 for the DKD associations, < 0.0006 for the ACR, < 0.0005 for the Vitamin D, < 0.00017 for the eGFR, < 0.00019 for the creatinine, and 0.00068 for the urea associations.

(j) Describe any methods used to address and correct for relatedness among subjects.

The original GWAS by Habiba Alsafar et. Al 2012 was addressed the issue of the relatedness among subjects. This current study is not a GWAS, so we did not address this issue again.

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Results

Participants

13* (a) Report the numbers of individuals at each stage of the study – e.g., numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed.

Of the four hundred nighty patients with T2DM that were recruited for this study one hundred and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications relating to the diagnosis of kidney disease (145 patients and 265 controls)

Report numbers of individuals in whom genotyping was attempted and numbers of individuals in whom genotyping was successful.

In all of participants genotyping was successful.

(b) Give reasons for non-participation at each stage.

Non-participation were excluded. This is a one stage study

(c) Consider use of a flow diagram.

No flow diagram

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(STREGA)

Descriptive data

14*

(a) Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders.

Both patient groups (with or without kidney disease) had poor glycemic control with blood glucose levels above 8 mmol/L. A comparison of patients with DKD to those without DKD indicated that the majority of those with DKD were males (~52% versus 38% for controls), older (67 versus ~59 years), had significant rates of comorbidities such as hypertension and dyslipidemia, and had a longer T2DM duration (16 versus 10 years). A clear decline in renal function indices was also observed in patients with DKD vs. those who don’t have DKD indicated by the significantly higher rates of ACR, urea, and creatinine; and significantly lower eGFR. However, DKD patients tended to have lower LDL-cholesterol results compared to those without DKD, which suggests these patients received better medical care or at least received intensive statin medication. T2DM duration was the single factor that significantly contributed to the development of DKD. Increased risk for DKD was significantly associated with increasing duration of T2DM, cumulatively, for 20 years of duration. At a T2DM duration ≥ 20 years, the risk stabilized at 3.12 times higher than patients with duration ≤ 5 years (P=0.02, 95% CI=1.21-8.02). Although the levels of serum creatinine indicated a trend for disease development (P=0.03), this was not significant as the odds ratio showed no increase risk, being 1.03. We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.

Consider giving information by genotype.

- Rs11868441 in BCAS3 followed by multiple SNPs in the RSPO3 gene with urea.

- Two SNPs (rs6999484 and rs1705699) in the intergenic region between STC1 and ADAM28 with creatinine.

- SNPs within the MED1 gene (rs2168785 and rs12452509), and SNPs in two genetic regions, WDR72 and SHROOM3 with eGFR.

- Rs1801725 in CARS, rs1155563 in GC, and two SNPs in CYP2R1 with vitamin D levels.

- SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 with albumin-to-creatinine ratio.

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(STREGA)

(b) Indicate the number of participants with missing data for each variable of interest.

Of the four hundred nighty patients with T2DM that were recruited for this study one hundred and fifteen patients were tested for genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood urea levels. Among the patients, 410 had clear classifications relating to the diagnosis of kidney disease (145 patients and 265 controls).

(c) Cohort study – Summarize follow-up time, e.g. average and total amount.

The study was not a cohort study.

Outcome data 15 * Cohort study-Report numbers of outcome events or summary measures over time.

Report outcomes (phenotypes) for each genotype category over time

Case-control study – Report numbers in each exposure category, or summary measures of exposure.

Report numbers in each genotype category

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Item Item

number

STROBE Guideline


(STREGA)

Cross-sectional study – Report numbers of outcome events or summary measures.

There is no exposure-outcome measurements.

Report outcomes (phenotypes) for each genotype category

Not applicable

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence intervals). Make clear which confounders were adjusted for and why they were included.

For testing the associations between the SNPs and DKD using a case-control logistic regression model, which included age, gender, hypertension status, T2DM duration, and eGFR levels as covariates. Analyses between SNPs and renal function traits were conducted using linear regression models that included age and gender as covariates.

(b) Report category boundaries when continuous variables were categorized.

Continuous variables were not categorized.

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period.

We did not translating estimates of relative risk into absolute risk for a meaningful time period.

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Item Item

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STROBE Guideline


(STREGA)

(d) Report results of any adjustments for multiple comparisons.

We discussed the Bonferroni correction for multiple testing in the study.

Other analyses 17 (a) Report other analyses done – e.g., analyses of subgroups and interactions, and sensitivity analyses.

No analyses for subgroups, interactions, or sensitivity analyses.

(b) If numerous genetic exposures (genetic variants) were examined, summarize results from all analyses undertaken.

Rs11868441 in BCAS3 followed by multiple SNPs in the RSPO3 gene with urea.

- Two SNPs (rs6999484 and rs1705699) in the intergenic region between STC1 and ADAM28 with

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Item Item

number

STROBE Guideline


(STREGA)

creatinine.




- SHROOM3 rs4859682 remained significant (P=0.04, OR=1.46) with DKD.

(c) If detailed results are available elsewhere, state how they can be accessed.

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STROBE Guideline


(STREGA)

Not applicable

Discussion

Key results 18 Summarize key results with reference to study objectives. Patients with DKD compared to those without DKD were mostly males (~52% versus 38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias. A limitation of this study is that analyses carried in this study did not included treatment modalities because most of patients had multiple conditions and they used several treatments; which make the models largely unstable and difficult to interpret. Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for

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STROBE Guideline


(STREGA)

sufficient statistical power to discover novel clinical and genetic predisposition for DKD in the region.

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence. Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for sufficient statistical power to discover novel clinical and genetic predisposition for DKD in the region.

Generalizability 21 Discuss the generalizability (external validity) of the study results. Some of the loci which reported previously in other populations did not replicated because of the statistical power issue. In addition, due to lack of results in this topic in the other populations, we cannot confirm the results of this study will be replicated

Other Information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based. This study was supported by research incentive funds from Khalifa University Internal Research Fund Level 2 granted to Dr. Habiba Al Safar. No role of funders taken in this study.

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Journal: BMJ Open



Date Submitted by the Author: 16-Sep-2018








jopen.bmj.com

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United Arab Emirates: A Cross-sectional Study 2


2, 3, Guan K. Tay

1, 4, 5, 6, Ahsan H. Khandoker

6, Kinda 3




1, 6,* 4














SHROOM3 gene. 18


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Director of Biotechnology Center, Associate Professor 21



Phone: +971 (0) 2 401 8109, Fax: +971 (0)2 447 2442 24


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Abstract 38


kidney disease (DKD) have not been investigated. The aim of this research was to determine 40

possible clinical, laboratory, and reported genetic loci which are risk factors of DKD. 41


diabetes mellitus (T2DM) were recruited with and without DKD and clinical and laboratory data 43

obtained. Following adjustments for possible confounders, a logistic regression model was used 44

to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci with 45

DKD (145 patients with DKD and 265 without DKD). Linear regression models, adjusted for 46

age and gender, were used to test the genetic associations of five renal function traits, including 47

83 SNPs with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH Cholecalciferol), 48

288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine, and 49

73 SNPs with blood urea. 50

Results: Patients with DKD compared to those without DKD were mostly males (52% versus 51

38% for controls), older (67 versus 59 years), and had significant rates of hypertension and 52

dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus 10 53



associations of the genetic loci with different renal function traits and found that the most notable 56




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Conclusions: Associations were found between several genetic loci and risk markers for DKD, 60

which may influence kidney function traits and DKD in a population of Arab ancestry. 61

62

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• This cross-sectional study to determine the clinical, laboratory, and genetic associations of 80

diabetic kidney disease (DKD) and renal function traits in a sample of patients with type 2 81

diabetes mellitus (T2DM), was the first in a population of Arab ancestry from the United 82

Arab Emirates (UAE). 83

• The study designed led to the identification of DKD associated SNPs and risk markers in the 84

UAE population. 85

• A limitation of this study was that the analyses carried out did not include treatment 86

modalities due to patients with multiple conditions and treatments, which made the models 87

largely unstable and difficult to interpret. 88

• Current analyses of the DKD genetic association had a statistical power of ~ 57%; suggesting 89

that a larger population sample across the Middle East is required to discover with better 90

statistical power novel clinical and genetic predisposition for DKD in the region. 91

92

93

94

95

96

97

98

99

100

101

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Introduction 102

Diabetic kidney disease (DKD) is considered the most common cause of nephropathy in many 103

populations and is a public health challenge worldwide. Globally, the prevalence of chronic 104

kidney disease (CKD) among adults in the general population was reported to be around 10% 1. 105

Thomas et al. reported that about 20% of adults with type 2 diabetes mellitus (T2DM) will have 106

DKD depending on the eGFR measurements (<60 ml/min/1.73 m2), and a diagnosis between 107

30% and 50% depending on the urinary albumin excretion levels. The considerable variation is 108

due to settings, geographical area, and ethnicity 2. Overall, the risk of DKD in T2DM is 109

approximately 2% per year 3. In the Arab world, the prevalence of DKD is highly variable 110

(10.8% to 61.2%) depending on the study design, source of study population, sample selection, 111

race, age, sex structure of the study population, and diagnostic criteria among other factors 4. 112

Meta-analysis has shown that DKD is the leading cause of end-stage renal disease (ESRD) in the 113

Gulf Cooperation Council (GCC) with a prevalence of ~ 17% 5. Patients with ESRD have a 20% 114

annual mortality rate, which is higher than the rate for many solid cancers 6

. In addition to 115

increasing the risk of cardiovascular morbidity and mortality 7 8, DKD is reported to be the single 116

strongest predictor of mortality in patients with diabetes 9 with a five year survival in the range 117

of 30% 10

. The current trend suggests that the prevalence of DKD will continue to increase, 118

constituting a significant socioeconomic burdens on global healthcare systems and leading to 119

increased morbidity and mortality 11

. A thorough literature search has shown that there remains a 120

knowledge gap related to the understanding of risk factors and pathophysiological mechanisms 121

associated with DKD, especially in the Middle East. Since ESRD can only be treated with highly 122

invasive and costly procedures, such as dialysis or kidney transplantation, a better knowledge of 123

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genetic, clinical, and epidemiological factors associated with DKD is required to allow earlier 124

and more effective treatment outcomes. 125


high heritability rates 12

, indicating that genetic factors contribute significantly to inter-individual 127

variance in kidney function, and hence, to the susceptibility to CKD and related conditions. So 128

far, several genetic loci have been linked to DKD, chronic kidney disease (CKD), and renal 129


and children 25



are insufficient and inconclusive. In spite of efforts to describe novel biomarkers for DKD, no 132

tested candidates outperform albumin. A recent report by Saulnier et al. suggested that three 133

serum biomarkers (midregional-proadrenomedullin: MR-proADM, soluble tumor necrosis factor 134


improve risk prediction of the loss of renal function in patients with T2DM in addition to the 136















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the UAE, there have been no investigations of the genetic associations of chronic kidney 147

conditions and kidney functions in this population, particularly associated with T2DM. 148

Therefore, the current work aimed to investigate the clinical and laboratory variables linked to 149

DKD in a T2DM Emirati population, and to investigate the associations of the reported genetic 150

loci linked to different renal function tests, in CKD and DKD. 151

152



This report describes a cross-sectional study of Emirati patients from the city of Abu Dhabi. The 155

demographic information and clinical data for the participants are presented in Tables 1 and 2. 156

Four hundred and ninety (n=490) patients with T2DM were included in the study, with 145 157

diagnosed with DKD. The participants were recruited from Sheikh Khalifa Medical City 158

(SKMC) and Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE. All subjects were 159

UAE born and of Arabian descent. 160

Patient and Public Involvement 161

The study was designed because T2DM is a major health problem in the UAE and has a growing 162

public interest. However, patients and public were not involved in setting of the research 163

question, analyses, interpretation or writing of the results. 164




pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or if patients were taking 168

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group report 32


calibrated wall-mounted stadiometer and a weight scale, respectively. Body mass index (BMI) 173

was calculated as the weight in kilograms divided by the square of height (kg/m2). 174

Diabetic Kidney Disease 175

DKD was defined as either decreased levels of estimated glomerular filtration rate (eGFR) <60 176

ml/min/1.73 m2 with or without renal damage over a period of at least three months

33, or based 177

on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria >500 mg over a 24 hour period in the 178

setting of T2DM and/or abnormalities as assessed by imaging or histology 34

. eGFR was 179

calculated according to CKD-EPI Creatinine Equation 35

.Accordingly, 145 TD2M patients were 180

identified with DKD, while 265 were disease free (Tables 1 and 2). The remaining 80 patients 181

could not be classified with or without DKD at the time of the study, and were excluded from 182

subsequent analyses. 183


Genetic studies in this study followed the candidate gene approach 36

. The SNPs tested for each 185

trait are summarized in Table 1. These SNPs were selected from a recent Genome Wide 186

Association Study (GWAS) that was intended to determine the genetic associations of T2DM in 187

the UAE population and establishing the Emirates Family Registry for T2DM 27

. The GWAS 188

was performed for the 490 samples with T2DM, and 450 healthy controls on the Infinium 189

Omni5ExomeHuman chip (Illumina Inc., San Diego, USA). To select SNPs that are associated 190

with kidney function traits included in the study (blood urea, serum creatinine, eGFR values, 191

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albumin-to-creatinine ratio, and vitamin D levels) as well as DKD, various search engines and 192

data bases including PubMed, Google Scholar, the GWAS catalog 193

(https://www.ebi.ac.uk/gwas/home), the Phenolyzer database (http://phenolyzer.wglab.org/), the 194

infinome genome interpretation platform (https://www.infino.me/), and the GWAS Central 195

database (http://www.gwascentral.org/) were consulted. 196


level and were found in our GWAS data. If the original signal SNP was missing from the GWAS 198

data, we searched for a possible proxy SNP utilizing the concept of linkage disequilibrium (LD), 199

which indicates a non-random association of alleles at different genetic loci in a given population 200

and their tendency to be inherited as a block (mathematical values r2 and D’ > 0.8 indicates high 201

LD). Proxy SNPs were selected using the SNAP database for SNP Annotation and Proxy Search 202




functions. Specifically, the gene loci are ACACB, ACE, ACTN4, ADIPOQ, ADM, AFF3, AGTR1, 206

APOL1, CARS-CNDP1, CNDP2, CPS1, CPVL, CPVL, CHN2, CYBA-ELMO1, ENPP1, ERBB4, 207

FABP2, FRMD3, GLUT1, IRS2, MYO16, LIMK2, MCTP2, MYO16, MYH9, NCALD, NCK1, 208

NOS3, NPHS1, NPHS2, NPPB, PLCE1, PPARγ2, PVT1, RAGE, RGMA, RPS12, SFI1, 209



Continuous variables were presented as mean ± standard deviation or lower / median and upper 212

quartiles where the distributions are highly skewed. Vitamin D and eGFR levels were normally 213

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distributed. However, urea levels, creatinine levels, and albumin-to-creatinine ratio data were 214

converted to normal distributions using natural log transformation. The associations of trait 215

values or their natural logs (if transformed) were tested with SNPs using linear regression 216

models, which included age and gender as covariates using PLINK software version 1.07 217

(http://zzz.bwh.harvard.edu/plink/). The same software was used for counting allele frequencies 218

and testing the quality control (QC) variables, where any SNPs with minor allele frequency 219

(MAF) <0.05, and >5% missing genotype rate, or those that failed the Hardy-Weinberg 220

equilibrium (HWE) test at the 0.001, were excluded. Hardy-Weinberg equilibrium is an 221

important QC test for genetic association studies and assumes that allele and genotype 222

frequencies can be estimated. If the frequencies of the measured genotypes significantly differ 223

from the HWE assumptions, it can indicate genotyping errors among other possible factors, such 224

as ethnic diversity and high level of consanguinity in the population; therefore those SNPs were 225

excluded from further analyses. Association with P < 0.05 were reported, pointing to the 226

replication of previously-reported associations. 227



were assessed using two-sided t-tests for normally distributed data or the Wilcoxon rank-sum 230

(Mann-Whitney) test for highly skewed data. The Pearson chi-square test was used for 231

percentage data or Fishers Exact test when expected frequencies were less than 5. A P-value < 232

0.05 was considered as significant. PLINK software was also used for testing the associations 233

between the SNPs and DKD using a case-control logistic regression model, which included age, 234

gender, hypertension status, T2DM duration, and eGFR levels as covariates (Table 1). The 235

results are presented as P-values (Padjusted < 0.05) and odds ratios with the corresponding 95% 236

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confidence intervals. The same approach (patients with DKD vs. patients without DKD) was 237

adopted to test the associations between the possible risk factors and the development of the 238

DKD (Table 3). However, the logistic model, which included all the associated factors listed in 239

Table 4 was validated by the Hosmer–Lemeshow goodness of fit test (P=0.11) to allow for the 240

inclusion of several covariates. Analyses between SNPs and renal function traits were conducted 241

in PLINK using linear regression models that included age and gender as covariates. Results 242

were presented as beta (regression coefficient for the linear regression model, calculated based 243

on the minor allele), standard errors, and P-values. 244

As this was a replication study, we reported all P-values < 0.05, suggesting possible replications. 245

However, using a Bonferroni correction for multiple testing, some model P-values with 246

statistically significant associations included P < 0.00079 for the DKD associations, < 0.0006 for 247

the ACR, < 0.0005 for the Vitamin D, < 0.00017 for the eGFR, < 0.00019 for the creatinine, and 248

P< 0.00068 for associations with urea. 249

Statistical power considerations: 250

Taking the current sample size (145 with DKD vs. 265 without DKD), prevalence of DKD ~20% 251

(depending on reference # 2), genotype risk (odds ratio) = 1.5, significance level of 0.0008 252

(following correction of multiple testing using Bonferroni correction), disease allele frequency = 253

0.5, and multiplicative model, the current study had a power ~ 57%. Calculations were made 254

using the Genetic Association Study (GAS) Power Calculator 255

(http://csg.sph.umich.edu//abecasis/cats/gas_power_calculator/index.html ). 256


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both hospitals (REC-04062014 and R292, respectively) and conformed to the ethical principles 260




264

Results 265


Of the 490 patients with T2DM that were recruited for this study, 115 patients were tested for 267

genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as 268

25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood 269

urea levels. Among the patients, 410 had clear classifications relating to the diagnosis of kidney 270

disease (145 patients with DKD and 265 without DKD) according to the diagnostic criteria 271

[References #33 and #34] or patient medical records. The possible covariates which may affect 272

the genetic associations were included for each trait analysis and summarized in Table 1. 273

274

275

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Trait

Mean ± SD or

quartiles

N of subjects

(Male/Female)

N of SNPs

reported **

N of SNPs

tested **

Covariates







with: 145

without: 265

288 63

Age, Gender,

Hypertension, T2DM


* For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25

th/ 50

th/ 75

th quartiles and not as mean ± standard 277

deviation because their distributions are skewed. 278

** Reported: means the total number of SNPs found in the search in the literature, tested: means the actual number of SNPs found from the 279

reported SNPs and used for the analyses in this study for each corresponding trait. 280



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15


Both patient groups (with or without kidney disease) had poor glycemic control with blood 284

glucose levels above 8 mmol/L. A comparison of patients with DKD to those without DKD 285

indicated that the majority of those with DKD were males (52% versus 38% for controls), older 286

(67 versus 59 years), had significant rates of comorbidities such as hypertension and 287

dyslipidemia, and had a longer T2DM duration (16 versus 10 years). A clear decline in renal 288

function indices was also observed in patients with DKD compared to those who did not have 289

DKD, which was indicated by the significantly higher rates of ACR, urea, and creatinine, and 290

significantly lower eGFR (Table 2). However, DKD patients tended to have lower LDL-291

cholesterol results compared to those without DKD, which suggested that these patients received 292

were more likely to have received intensive statin therapy (Table 2). 293

294

295

296

297

298

299

300

301

302

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kidney disease 304



Gender: Female **

70 (48.3%) 165 (62.3%) 0.006

Age (years) 67.0 ± 10.4 58.6 ± 10.6 < 0.0001

Clinical variables

Clinical hypertension **

96.6% 74.3 % < 0.0001

Dyslipidemia **

95.2% 90.9% 0.17

Smoking history **

32.4% 24.2% 0.07


BMI (kg/m2) 31.3 ± 6.0 32.5 ± 6.3 0.078




(mmol/l) 8.3 ± 3.1 8.9 ± 3.8 0.43


(mmol/l) 9.1 ± 3.9 9.5 ± 4.3 0.34





Renal function

indices


(mg/mmol) ***

3.3 / 10.1/ 69.6 0.8/ 2.2/ 9.0 0.0001

Vitamin D (nmol/l) 62.9 ± 28.4 65.3 ± 26.8 0.48

eGFR (ml/min/1.73m2) 57.5 ± 27.7 96.1 ± 17.3 < 0.0001

Creatinine (µmol/l) ***

82/ 111/ 146 55/ 64/ 76 < 0.0001

Urea (mmol/l) 9.7 ± 8.3 4.7 ± 1.5 < 0.0001

* P-value for continuous data, calculated using two-sided t-test except for Albumin: Creatinine Ratio and 305

Creatinine where it is for Wilcoxon rank-sum (Mann-Whitney) test. P-value for percentage data 306

calculated using Pearson chi-square test with except of hypertension and Dyslipidemia where Fisher 307

Exact test was used. 308

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** Proportional data shown as number of positive outcome and its percentage. All remaining continuous 309

data shown as mean ± standard deviation except of Albumin: Creatinine Ratio and Creatinine. 310

*** For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25

th/ 50

th/ 311

75th quartiles and not as mean ± standard deviation because their distributions are highly skewed. 312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

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Factors associated with developing DKD in UAE patients with T2DM 331





8.02). Although the levels of serum creatinine indicated a significant difference between no 336

DKD and DKD patients, (P=0.03), the odds ratio of 1.03 showed no increased risk (Table 3). 337

Table 3: Risk factors for development of kidney disease in patients with T2DM from the UAE 338


Age 0.99 0.96 - 1.04 0.80

Gender 0.90 0.34 - 2.36 0.83

BMI 1.02 0.97 – 1.08 0.49

Hypertension 2.00 0.60 - 6.74 0.26

HbA1c 0.86 0.69 – 1.07 0.19

Cholesterol 0.59 0.19 – 1.76 0.34

Triglyceride 1.09 0.62 – 1.94 0.76

HDL-cholesterol 1.25 0.64 – 2.45 0.52

LDL-cholesterol 1.38 0.42 – 4.57 0.60

Smoking history 0.80 0.34 – 1.87 0.61

Creatinine 1.03 1.00 – 1.06 0.03

Urea 1.13 0.92 – 1.38 0.26

eGFR 0.97 0.94 – 1.00 0.09

Diabetes duration*

5-10 years 0.75 0.26 – 2.16 0.59

11-15 years 1.49 0.56 – 3.91 0.42

16-20 years 3.35 1.22 – 9.23 0.02

> 20 years 3.12 1.21 – 8.02 0.02



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The results of genetic associations for each tested renal function trait are shown in Table 4. In 342

summary, no association passed the Bonferroni correction for multiple testing. However, we 343

report here the most suggestive associations which point to replications of previous reports. 344





of the corresponding minor allele), followed by SNP rs2828785 (P=0.008, effect size = -0.030 349

log per one copy of allele A), which is located in the non-coding gene area on chromosome 21. 350

In addition, multiple genetic areas were also indicated, although with less significant 351

associations. 352


effect size of -4.299 per allele G and rs12452509 with effect size of -4.232 per allele G and 354

P=0.015 and 0.017 respectively. In addition, the SNPs in two genetic regions, WDR72 and 355

SHROOM3, that are associated with serum creatinine levels, were also associated with eGFR 356

levels, indicating a strong link to renal function. 357



two SNPs, rs12794714 and rs10500804, in CYP2R1 with effect sizes of approximately -5.0 and 360

P-values of 0.015 and 0.016, respectively. 361

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SNP Chr: BP Gene A1/A2 * MAF_% Beta

** SE P

Blood Urea

rs11868441 17: 59239221 BCAS3 A/G 30.1 -0.038 0.015 0.014

rs1892172 6: 127476516 RSPO3 T/C 46.9 0.031 0.014 0.028

rs4644087 6: 127481154 RSPO3 C/A 46.8 0.030 0.014 0.03

rs4382293 6: 127475433 RSPO3 G/A 47.0 0.030 0.014 0.03

rs2489629 6: 127476717 RSPO3 G/A 44.9 -0.029 0.014 0.039

Serum Creatinine

rs6999484 8: 23728271 STC1-ADAM28 A/G 23.8 0.033 0.011 0.003

rs1705699 8: 23781453 STC1-ADAM28 G/A 24.2 0.031 0.011 0.005

rs2828785 21: 25437505 - A/G 19.9 -0.030 0.011 0.008

rs11227279 11: 65495211 KRT8P26-AP5B1 A/G 28.4 -0.024 0.010 0.019

rs7785065 7: 32915204 KBTBD2 A/C 43.5 0.022 0.010 0.022

rs4859682 4: 77410318 SHROOM3 A/C 25.7 0.021 0.010 0.034

rs1031755 15: 53951435 WDR72 C/A 11.7 -0.029 0.014 0.038

rs7740534 6: 25077179 - C/A 9.6 -0.032 0.015 0.042


rs2168785 17: 37407135 MED1 G/A 28.2 -4.299 1.754 0.015

rs12452509 17: 37574722 MED1 G/A 28.1 -4.232 1.757 0.017

rs4776168 15: 53936907 WDR72 G/A 10.8 5.828 2.699 0.031

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rs10518733 15: 53940307 WDR72 C/A 10.8 5.828 2.699 0.031

rs7541937 1: 35341982 DLGAP3 C/A 44.0 3.590 1.693 0.035

rs10032549 4: 77398015 SHROOM3 G/A 32.2 -3.655 1.750 0.037

rs2484639 1: 243462367 SDCCAG8 A/G 43.5 3.378 1.656 0.042


rs1801725 3: 122003757 CASR A/C 22.6 -6.923 2.584 0.008

rs1155563 4: 72643488 GC G/A 18.9 -6.951 2.608 0.008

rs12794714 11: 14913575 CYP2R1 A/G 41.5 -5.110 2.099 0.015

rs10500804 11: 14910273 CYP2R1 C/A 42.2 -5.004 2.060 0.016


rs4528660 18: 3043516 LPIN2-MYOM1 G/A 17.8 -0.323 0.144 0.027

* A1/A2: minor to major alleles. 368

** Beta: regression coefficient for the linear regression model, calculated based on A1 (the minor allele). 369


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kidney disease were included in our genetic analysis of DKD (145 patients with DKD versus 265 373

without DKD). A logistic regression model including five possible covariates, which may affect 374

the development of kidney disease, was applied (Table 1). As shown in Table 5, the unadjusted 375

analysis indicates two associations in CPS1 (rs7422339, P=0.019) and SHROOM3 (rs4859682, 376

P=0.024). Following the adjustment for possible covariates, only the SHROOM3 rs4859682 377

remained significant (P=0.04, OR=1.46). This result confirms that SHROOM3 is a string risk 378

locus for DKD, considering the similar associations with serum creatinine and eGFR levels. 379

380

381

382

383

384

385

386

387

388

389

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SNP Chr: BP Gene * A1/A2

** MAF Punadjusted Padjusted OR (95% CI)

DKD (N=145) No DKD (N=265)

rs7422339 2: 211540507 CPS1 A/C 35.8% 27.9% 0.019 0.20 1.25 (0.89 – 1.75)

rs4859682 4: 77410318 SHROOM3 A/C 29.0% 21.9% 0.024 0.04 1.46 (1.01 - 2.10)

* CPS1: Carbamoyl-Phosphate Synthase 1; SHROOM3: Shroom Family Member 3. 391

** A1/A2: minor to major alleles. 392

Abbreviations: BP: base pair position; Chr: chromosome; CI: confidence intervals; DKD: diabetic kidney disease, MAF: minor allele frequency; 393

N: number; OR: odds ratio; SNP: single nucleotide polymorphism. 394

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Discussion 395


been suggested to be involved in DKD including persistent hyperglycemia, arterial hypertension 397


. In addition, familial aggregation of nephropathy in T2DM has been 398

reported in several populations 38

. Therefore, understanding the interactions between genetic, 399

clinical and traditional kidney disease risk factors can provide insight into new treatment 400

strategies for improving DKD and reducing the likelihood of developing ESRD. In this study, we 401

investigated whether genetic markers that correspond to DKD and renal function traits and 402

reported in different populations are similar or different in the Arab population. 403





comorbidities and had poor diabetic control, in agreement with our previous results 29

. We also 408


development of kidney disease. The risk of developing DKD became significant when the 410

duration of T2DM reached the 15 year mark. This is in concordance with previous reports, which 411

suggested that T2DM patients who do not develop signs of kidney disease by 15 years duration 412

of diabetes seem to be protected, most likely due to genetic factors 39

. 413





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. This 418




regulation 43


. Defective 421

Shroom3 protein leads to decreased actin organisation and affects mechanical characteristics and 422

integrity of the glomerular podocyte resulting in thinning of the glomerular filtration membrane 423

44. Additionally, Shroom3 heterozygous (Shroom3

Gt/+) mice showed developmental irregularities 424

that manifested as adult-onset glomerulosclerosis and proteinuria 45

. Furthermore, genetic 425

variants (such as the intronic variant rs17319721), were found to contribute to kidney allograft 426

injury and fibrosis development through a mechanism involving TGF-β1 signaling 46

. Although 427

the variant rs17319721 was not found in our dataset, it is highly linked to the SNP rs4859682 428

(r2=0.85, D’=1), which was reported in this study to increase the risk for DKD and affect the 429

levels of serum creatinine. Overall, this suggests that SHROOM3 may be considered as a multi-430

ethnic risk gene for DKD and its related kidney function traits in various populations, including 431

the Arabs who inhabit the UAE. 432




. WDR72 in particular 435

has been well studied in association with kidney function traits and pathologies. For instance, 436

WDR72 was reported to be associated with creatinine production or secretion 48

, and suggested to 437

be associated with signal transduction, cell cycle regulation, and vesicular trafficking that affects 438

podocyte activity, reduced eGFR and progression of CKD 49

. In addition, RSPO3 (R-Spondin 3) 439

was previously reported to be associated with blood urea nitrogen concentration, in line with the 440

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results of the current study 42

. The association of rs4528660 near MYOM1 (Myomesin 1) with the 441

albumin-to-creatinine ratio is also in agreement with previous work, which linked this locus to 442

albuminuria in patients with diabetes 50

. The current analyses performed also replicated the 443

associations of CASR (Calcium-sensing receptor), GC (vitamin D-binding protein (VDBP) is 444

also known as group-specific component (GC)- globulin), and CYP2R1 (Cytochrome P450 445

Family 2 Subfamily R Member 1) with levels of vitamin D 51

. These genes encode proteins 446

which are involved in vitamin D function including activation by hydroxylation (CYP2R1), 447

transportation (GC), and serum calcium level sensoring (CASR) 51

. These genes have also been 448

reported to be associated with calcium-vitamin D physiology and pathology, such as serum 449

calcium levels, familial hypocalciuric hypercalcemia, tertiary hyperparathyroidism, and vitamin 450

D deficiency presenting as Rickets (see OMIM entries: CARS: 601199; CYP2R1: 608713; and 451

GC: 139200). For instance, CASR protein is expressed in the kidney amongst other tissue and 452

regulates ion metabolism including calcium and magnesium. Mutations in CASR lead to 453

abnormalities in the regulation of parathyroid gland and renal function causing hypercalcemia 454

and increased blood pressure that in turn may affect kidney function 52

. Similarly, GC proteins 455

bind actin and act as actin scavengers (as such GC may play a role in podocyte integrity) as well 456

as a binding site for Vitamin D. The GC protein is the precursor to the Gc-macrophage 457

activating factor (GcMAF), a macrophage activator and suggests together with Vitamin D that 458

GC has an important role in immune function and the pathogenesis of CKD 53

. Vitamin D is 459

hydroxylated at the C25 position by specific hydroxylase coded by the CYP2R1 gene to 25-460

hydroxyvitamin D (25(OH) D), which is the main circulating form of vitamin D. Low Vitamin D 461

observed in CKD due to reduced CYP2R1 production by the liver or due to a mutation in the 462

gene, disturbs calcium balance and leads to hyperparathyroidism. Conversely the loss of renal 463

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protection brought about by Vitamin D and an increase in the renin-angiotensin pathway leads to 464

hypertension that further advances kidney disease 54

. Furthermore, these genes have recently 465



. 467




development in patients with T2DM in the UAE. Our study highlights that several genetic loci, 471



with DKD. The logistic analyses carried in this study did not include treatment modalities 474

because most of the patients had multiple conditions and underwent multiple treatments, which 475

makes logistic regression largely unstable and difficult to interpret. A larger population sample 476

across the Middle East is now being considered to confirm the extent of the shared genetic 477

predisposition reported in the current study. Considering the high prevalence of T2DM in this 478

population and the recent evidence of genomic structure variations among different ethnic 479

groups, more genetic-driven population studies are warranted towards effective genetically 480

guided personalized medicine. 481

482


We also acknowledge patient care advisers for their roles in this study. 484

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Author contribution: HSA obtained the funding for this study. WMO, HSA and HFJ designed 485

the study. WMO analyzed the data and prepared the manuscript. HFJ, GKT, AHK, and KK 486

reviewed/edited the manuscript and contributed to the discussion and reviewed/edited the 487

manuscript. WA and MHH provided assistance in patient recruitment and clinical data 488

collection. 489







496

References 497



2. Thomas MC, Brownlee M, Susztak K, et al. Diabetic kidney disease. Nature Reviews Disease 500

Primers 2015;1:15018. 501

3. Gross JL, De Azevedo MJ, Silveiro SP, et al. Diabetic nephropathy: diagnosis, prevention, and 502

treatment. Diabetes Care 2005;28(1):164-76. 503

4. Aldukhayel A. Prevalence of diabetic nephropathy among Type 2 diabetic patients in some of 504

the Arab countries. International Journal of Health Sciences 2017;11(1):1. 505

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5. Hassanien AA, Al-Shaikh F, Vamos EP, et al. Epidemiology of end-stage renal disease in the 506

countries of the Gulf Cooperation Council: a systematic review. JRSM Short Reports 507

2012;3(6):1-21. 508





participant data. The Lancet Diabetes & Endocrinology 2015;3(7):514-25. 513



2013;382(9889):339-52. 516



2004;141(2):95-101. 519

10. Collins AJ, Foley RN, Chavers B, et al. United States Renal Data System 2011 Annual Data 520

Report: Atlas of chronic kidney disease & end-stage renal disease in the United States. 521

American Journal of Kidney Diseases 2012;59(1Suppl1):A7. 522






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Genetics 2010;6(7):e1001039. 534
















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PLoS One 2014;9(2):e88273. 554




2016;31(2):262-69. 558









2012;102(5):909-14. 567






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34. Andrassy KM. Comments on 'KDIGO 2012 clinical practice guideline for the evaluation and 581


35. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration 583

rate. Annals of Internal Medicine 2009;150(9):604-12. 584

36. Kwon JM, Goate AM. The candidate gene approach. Alcohol Research and Health 585

2000;24(3):164-68. 586



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2008;135(8):1493-502. 604



45. Khalili H, Sull A, Sarin S, et al. Developmental origins for kidney disease due to Shroom3 607

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2015;125(1):208. 611



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48. O'seaghdha CM, Fox CS. Genome-wide association studies of chronic kidney disease: what 615

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49. Good MC, Zalatan JG, Lim WA. Scaffold proteins: hubs for controlling the flow of cellular 617

information. Science 2011;332(6030):680-86. 618





52. Riccardi D, Brown EM. Physiology and pathophysiology of the calcium-sensing receptor in 623

the kidney. American Journal of Physiology-Renal Physiology 2009;298(3):F485-F99. 624

53. Thyer L, Ward E, Smith R, et al. A novel role for a major component of the vitamin D axis: 625

vitamin D binding protein-derived macrophage activating factor induces human breast 626

cancer cell apoptosis through stimulation of macrophages. Nutrients 2013;5(7):2577-89. 627

54. Jean G, Souberbielle JC, Chazot C. Vitamin D in chronic kidney disease and dialysis 628

patients. Nutrients 2017;9(4):328. 629





634

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Item Item

number

STROBE Guideline


(STREGA)

Title and Abstract

1

(a) Indicate the study’s design with a commonly used term in the title or the abstract.


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Objectives: Within the Emirati population, risk factors and genetic predisposition of diabetic kidney disease (DKD) have not been investigated. The aim of this research was to determine possible clinical, laboratory, and reported genetic loci which are risk factors of DKD. Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2 diabetes mellitus (T2DM) were recruited with and without DKD and clinical and laboratory data obtained. Following adjustments for possible confounders, a logistic regression model was used to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci with DKD (145 patients with DKD and 265 without DKD). Linear regression models, adjusted for age and gender, were used to test the genetic associations of five renal function traits, including 83 SNPs with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH Cholecalciferol), 288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine, and 73 SNPs with blood urea. Results: Patients with DKD compared to those without DKD were mostly males (52% versus 38% for controls), older (67 versus 59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus 10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits and found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels. Conclusions: Associations were found between several genetic loci and risk markers for DKD, which may influence kidney function traits and DKD in a population of Arab ancestry.

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Introduction








Methods





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Participants 6 (a) Cohort study – Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up.












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8*

(a) For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group.


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Not applicable








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Statistical methods

12








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Participants

13* (a) Report the numbers of individuals at each stage of the study – e.g., numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed.







No flow diagram

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Outcome data 15 * Cohort study-Report numbers of outcome events or summary measures over time.




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creatinine.






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Discussion

Key results 18 Summarize key results with reference to study objectives. Patients with DKD compared to those without DKD were mostly males (~52% versus 38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias. A limitation of this study is that analyses carried in this study did not included treatment modalities because most of patients had multiple conditions and they used several treatments; which make the models largely unstable and difficult to interpret. Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for

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Item Item

number

STROBE Guideline


(STREGA)


Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence. Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for sufficient statistical power to discover novel clinical and genetic predisposition for DKD in the region.

Generalizability 21 Discuss the generalizability (external validity) of the study results. Some of the loci which reported previously in other populations did not replicated because of the statistical power issue. In addition, due to lack of results in this topic in the other populations, we cannot confirm the results of this study will be replicated

Other Information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based. This study was supported by research incentive funds from Khalifa University Internal Research Fund Level 2 granted to Dr. Habiba Al Safar. No role of funders taken in this study.

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Date Submitted by the Author: 18-Oct-2018








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1 Clinical and Genetic Associations of Renal Function and Diabetic Kidney Disease in the

2 United Arab Emirates: A Cross-sectional Study

3 Wael M. Osman 1, Herbert F. Jelinek 2, 3, Guan K. Tay 1, 4, 5, 6, Ahsan H. Khandoker 6, Kinda

4 Khalaf 6, Wael Almahmeed 7,8, Mohamed H. Hassan 9 & Habiba S. Alsafar 1, 6,*

5 1 Center of Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates.

6 2 School of Community Health, Charles Sturt University, Albury, Australia.

7 3 Clinical Medicine, Macquarie University, Sydney, Australia.

8 4 School of Health and Medical Sciences, Edith Cowan University, Australia.

9 5 School of Psychiatry and Clinical Neurosciences, University of Western Australia, Australia.

10 6 Department of Biomedical Engineering, Khalifa University, United Arab Emirates.

11 7 Institute of Cardiac Science, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates.

12 8Heart and Vascular Institute, Cleveland Clinic, Abu Dhabi, United Arab Emirates.

13 9 Medical Institute, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates.

14 Number of words: 300 in the abstract, 4028 in the main text

15 Number of tables: 5; embedded in the main text. No supplementary data.

16 Number of references: 55

17 Key words: type 2 diabetes, United Arab Emirates, diabetic kidney disease, genetics, SHROOM3

18 gene.

19 * Corresponding author

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20 Dr. Habiba S. Al Safar

21 Director of Biotechnology Center, Associate Professor

22 Khalifa University

23 PO BOX 127788 Abu Dhabi, UAE

24 Phone: +971 (0) 2 401 8109, Fax: +971 (0)2 447 2442

25 E-mail: [email protected]

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38 Abstract

39 Objectives: Within the Emirati population, risk factors and genetic predisposition to diabetic

40 kidney disease (DKD) have not yet been investigated. The aim of this research was to determine

41 potential clinical, laboratory, and reported genetic loci as risk factors for DKD.

42 Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2

43 diabetes mellitus (T2DM) were recruited with and without DKD, and clinical and laboratory data

44 were obtained. Following adjustments for possible confounders, a logistic regression model was

45 developed to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci

46 with DKD (145 patients with DKD and 265 without DKD). Linear regression models, adjusted for

47 age and gender, were then used to study the genetic associations of five renal function traits,

48 including 83 SNPs with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH

49 Cholecalciferol), 288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum

50 creatinine, and 73 SNPs with blood urea.

51 Results: Patients with DKD, as compared to those without the disease, were mostly males (52%

52 versus 38% for controls), older (67 versus 59 years), and had significant rates of hypertension and

53 dyslipidemia. Furthermore, patients with DKD had T2DM for a longer duration of time (16 versus

54 10 years), which in an additive manner was the single factor that significantly contributed to the

55 development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). Among the replicated associations

56 of the genetic loci with different renal function traits, the most notable included SHROOM3 with

57 levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1

58 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.

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59 Conclusions: Associations were found between several genetic loci and risk markers for DKD,

60 which may influence kidney function traits and DKD in a population of Arab ancestry.

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78 Strengths and limitations of this study

79 This cross-sectional study to determine the clinical, laboratory, and genetic associations of

80 diabetic kidney disease (DKD) and renal function traits in a sample of patients with type 2

81 diabetes mellitus (T2DM) was the first of its kind in a population of Arab ancestry from the

82 United Arab Emirates (UAE).

83 A limitation inherent to this study was that the analyses carried out did not include treatment

84 modalities due to patients having multiple conditions and treatments, which made the models

85 largely unstable and difficult to interpret.

86 Current analyses of the DKD genetic association had a statistical power of ~ 57%; suggesting

87 that a larger population sample across the Middle East is required to discover novel clinical

88 and genetic predisposition to DKD in the region with better statistical power.

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101 Introduction

102 This overall dramatic worldwide increase in the number of people with diabetes has had a major

103 impact on the increasing incidence and prevalence of diabetic kidney disease (DKD) as one of the

104 most frequent complications of both types of diabetes. Globally, the prevalence of chronic kidney

105 disease (CKD) among adults in the general population is reported to be around 10% 1. On the other

106 hand, 20% of adults with type 2 diabetes mellitus (T2DM) are expected to develop DKD based on

107 eGFR measurements (<60 ml/min/1.73 m2), while the number reaches 30% to 50% based on the

108 urinary albumin excretion levels1. This considerable variation is due to variances in settings,

109 geographical area, and ethnicity 2. Overall, the risk of DKD in T2DM is approximately 2% per

110 year 3. In the Arab world, the prevalence of DKD is also highly variable (10.8% to 61.2%)

111 depending on the study design, population, sample selection, race, age, sex, as well as diagnostic

112 criteria among other factors 4. Meta-analysis has shown that DKD is the leading cause of end-stage

113 renal disease (ESRD) in the Gulf Cooperation Council (GCC) with a prevalence of ~ 17% 5.

114 Patients with ESRD have a 20% annual mortality rate, which is higher than the rate for many solid

115 cancers 6. In addition to increasing the risk of cardiovascular morbidity and mortality 7 8, DKD is

116 reported to be the single strongest predictor of mortality in patients with diabetes 9 , with a five

117 year survival in the range of 30% 10.

118 The current trend suggests that the prevalence of DKD will continue to increase worldwide,

119 leading to increased morbidity and mortality and imposing significant socioeconomic burdens on

120 global healthcare systems11. A thorough literature search reveals that there remains a wide

121 knowledge gap related to the understanding of risk factors and pathophysiological mechanisms

122 associated with DKD, especially in the GCC and Middle East. Since ESRD can only be treated

123 with highly invasive and costly procedures, such as dialysis or kidney transplantation, better

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124 knowledge of genetic, clinical, and epidemiological factors associated with DKD is required to

125 allow for timely and more effective treatment options.

126 In clinical practice, renal function is assessed using a number of tests that are reported to have high

127 heritability rates 12. This indicates that genetic factors contribute significantly to inter-individual

128 variance in kidney function, and hence, to the susceptibility to CKD and related conditions. Thus

129 far, several genetic loci have been linked to DKD, chronic kidney disease (CKD), and renal

130 function traits in adults 13-24 as well as children 25. However, in comparison to other diseases,

131 including T2DM and other cardiometabolic disorders, studies of kidney disease and kidney

132 function traits are largely insufficient and inconclusive. In spite of efforts to describe novel

133 biomarkers for DKD, currently no tested candidates outperform albumin. A recent report by

134 Saulnier et al. suggests that three serum biomarkers (midregional-proadrenomedullin: MR-

135 proADM, soluble tumor necrosis factor receptor 1: sTNFR1, and N-terminal prohormone brain

136 natriuretic peptide: NT-proBNP) can improve risk prediction of the loss of renal function in

137 patients with T2DM, in addition to the established risk factors for DKD such as age, sex, diabetes

138 duration, HbA1c, blood pressure, baseline eGFR, and albumin-to-creatinine ratio 26. However,

139 issues such as whether the levels of these markers are affected by genetic variations, and whether

140 the encoding genes contribute to DKD development and progress need further investigation.

141 The United Arab Emirates (UAE) is among the countries with the highest prevalence rates of

142 T2DM, obesity and cardiovascular disease 27 28. Al-Safar and colleagues have recently reported

143 that approximately 80% of T2DM patients within UAE present with at least one complication

144 associated with T2DM, including kidney disease (approximately 6%) 29. Furthermore, there is

145 increasing evidence suggesting that the genome structure of individuals of Arabic descent is

146 different from individuals from other populations 30. Despite the high prevalence rate of DKD in

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147 the UAE, there have been no investigations up to date of the genetic associations of chronic kidney

148 conditions and kidney functions, particularly as associated with T2DM. Therefore, the current

149 work aimed to investigate the clinical and laboratory variables linked to DKD in a T2DM Emirati

150 population, and to study the associations of the reported genetic loci linked to different renal

151 function tests in CKD and DKD.

152

153 Materials and Methods

154 Study type and subjects

155 This work describes a cross-sectional study of Emirati patients from the city of Abu Dhabi. The

156 demographic information and clinical data for the participants are presented in Tables 1 and 2.

157 Four hundred and ninety (n=490) patients with T2DM were included in the study, with 145

158 diagnosed with DKD. The participants were recruited from Sheikh Khalifa Medical City (SKMC)

159 and Mafraq Hospital, major tertiary hospitals in Abu Dhabi, UAE. All subjects were UAE born

160 and of Arabian descent.

161 Patient and Public Involvement

162 The study was designed because T2DM, along with its multi organ complications, is a major health

163 challenge in the UAE with increasingly growing public interest. However, patients and the public

164 at large were not involved in defining the research questions, analyses, interpretation or

165 dissemination of the results.

166

167

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168 Clinical variables and laboratory data

169 Various clinical and laboratory measures were collected/assessed during the hospital visits. Blood

170 pressure was taken on two different occasions. Hypertension was defined as systolic blood

171 pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or if the patients were taking any

172 antihypertensive medications. Dyslipidemia was either reported based on clinical records of the

173 participants or diagnosed as previously indicated 31. The presence of T2DM was confirmed by a

174 qualified physician based on criteria outlined by the World Health Organization (WHO) 32. Trained

175 nurses measured the height and weight of each participant using a calibrated wall-mounted

176 stadiometer and a weight scale, respectively. Body mass index (BMI) was calculated as the weight

177 in kilograms divided by the square of the height in meters (kg/m2).

178 Diabetic Kidney Disease

179 DKD was defined as either decreased levels of estimated glomerular filtration rate ((eGFR) <60

180 ml/min/1.73 m2) with or without renal damage over a period of at least three months 33, or based

181 on an albumin-to-creatinine ratio ≥30 mg/g, or proteinuria >500 mg over a 24 hour period in the

182 setting of T2DM and/or abnormalities, as assessed by imaging or histology 34. eGFR was

183 calculated according to CKD-EPI Creatinine Equation 35. Accordingly, 145 TD2M patients were

184 identified with DKD, while 265 were identified as disease free (Tables 1 and 2). The remaining

185 80 patients could not be classified with or without DKD at the time of the study, and were excluded

186 from subsequent analyses.

187 Selection of SNPs

188 Genetic investigation in this study followed the candidate gene approach 36. The SNPs tested for

189 each trait are summarized in Table 1. These SNPs were selected from a recent Genome Wide

190 Association Study (GWAS) that was intended to determine the genetic associations of T2DM in

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191 the UAE population towards establishing the Emirates Family Registry for T2DM 27. The GWAS

192 was performed for 490 samples with T2DM, and 450 healthy controls using the Infinium

193 Omni5ExomeHuman chip (Illumina Inc., San Diego, USA). To select the SNPs associated with

194 the kidney function traits included in the study (blood urea, serum creatinine, eGFR values,

195 albumin-to-creatinine ratio, and vitamin D levels) as well as DKD, various search engines and data

196 bases including PubMed, Google Scholar, the GWAS catalog (https://www.ebi.ac.uk/gwas/home),

197 the Phenolyzer database (http://phenolyzer.wglab.org/), the infinome genome interpretation

198 platform (https://www.infino.me/), and the GWAS Central database

199 (http://www.gwascentral.org/) were consulted.

200 Our search strategy consisted of identifying reported SNPs that cleared the GWAS significance-

201 level and were found in our GWAS data. If the original signal SNP was missing from the GWAS

202 data, we searched for a possible proxy SNP utilizing the concept of linkage disequilibrium (LD).

203 This typically indicates a non-random association of alleles at different genetic loci in a given

204 population and their tendency to be inherited as a block (mathematical values r2 and D’ > 0.8

205 indicates high LD). Proxy SNPs were selected using the SNAP database for SNP Annotation and

206 Proxy Search (http://archive.broadinstitute.org/mpg/snap/ldsearch.php).

207 All SNPs located within and flanking genes that have been previously reported in association with

208 CKD and DKD were included. In total, 43 genetic loci were identified as linked to CKD, DKD, or

209 a decline in renal function. Specifically, the gene loci comprised: ACACB, ACE, ACTN4, ADIPOQ,

210 ADM, AFF3, AGTR1, APOL1, CARS-CNDP1, CNDP2, CPS1, CPVL, CPVL, CHN2, CYBA-

211 ELMO1, ENPP1, ERBB4, FABP2, FRMD3, GLUT1, IRS2, MYO16, LIMK2, MCTP2, MYO16,

212 MYH9, NCALD, NCK1, NOS3, NPHS1, NPHS2, NPPB, PLCE1, PPAR2, PVT1, RAGE, RGMA,

213 RPS12, SFI1, SHROOM3, TMEM22, TNFRSF1A, and TRPC6.

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214 Statistical modeling and analyses

215 Continuous variables were presented as mean ± standard deviation or lower / median and upper

216 quartiles where the distributions were highly skewed. Vitamin D and eGFR levels were normally

217 distributed. However, urea levels, creatinine levels, and albumin-to-creatinine ratio data were

218 converted to normal distributions using natural log transformation. The associations of trait values

219 or their natural logs (if transformed) were tested with SNPs using linear regression models, which

220 included age and gender as covariates using PLINK software version 1.07

221 (http://zzz.bwh.harvard.edu/plink/). The same software was used for counting allele frequencies

222 and testing the quality control (QC) variables. Any SNPs with minor allele frequency (MAF)

223 0.05, and 5% missing genotype rate, or those that failed the Hardy-Weinberg equilibrium

224 (HWE) test at the 0.001, were excluded. Hardy-Weinberg equilibrium is considered as an

225 important QC test for genetic association studies and assumes that allele and genotype frequencies

226 can be estimated. If the frequencies of the measured genotypes significantly differed from the

227 HWE assumptions, genotyping errors among other possible factors, such as ethnic diversity and

228 high levels of consanguinity in the population, are indicated leading to excluding the SNPs from

229 further analyses. Association with P < 0.05 were reported, indicating the replication of previously-

230 reported associations.

231 Statistical analyses for all clinical and laboratory variables were performed using Stata software

232 version 14 (StataCorp LLC, Texas, USA). For continuous data, statistical differences were

233 assessed using two-sided t-tests for normally distributed data or the Wilcoxon rank-sum (Mann-

234 Whitney) test for highly skewed data. The Pearson chi-square test was used for percentage data as

235 well as the Fishers Exact test when the expected frequencies were less than 5. A P-value < 0.05

236 was considered as significant. PLINK software was also used for testing the associations between

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237 the SNPs and DKD using a case-control logistic regression model, which included age, gender,

238 hypertension status, T2DM duration, and eGFR levels as covariates (Table 1). The results are

239 presented as P-values (Padjusted < 0.05) and odds ratios with corresponding 95% confidence

240 intervals. The same approach (patients with DKD vs. patients without DKD) was adopted to test

241 the associations between the possible risk factors and the development of the DKD (Table 3).

242 However, the logistic model, which included all the associated factors listed in Table 4 was

243 validated by the Hosmer–Lemeshow goodness of fit test (P=0.11) to allow for the inclusion of

244 several covariates. Analyses between SNPs and renal function traits were conducted in PLINK

245 using linear regression models that included age and gender as covariates. The results were

246 presented as beta coefficients (regression coefficients for the linear regression model, calculated

247 based on the minor allele), standard errors, and P-values.

248 As this was a replication study, we reported all P-values < 0.05, suggesting possible replications.

249 However, using a Bonferroni correction for multiple testing, the P-values of some of the models

250 with statistically significant associations included P < 0.00079 for the DKD associations, < 0.0006

251 for the ACR, < 0.0005 for the Vitamin D, < 0.00017 for the eGFR, < 0.00019 for the creatinine,

252 and P< 0.00068 for associations with urea.

253 Statistical power considerations:

254 The current study had a power ~ 57%. This is based on the current sample size (145 with DKD vs.

255 265 without DKD), prevalence of DKD ~20% (depending on reference # 2), genotype risk (odds

256 ratio) = 1.5, significance level of 0.0008 (following the correction of multiple testing using the

257 Bonferroni correction), disease allele frequency = 0.5, and multiplicative model. Calculations were

258 verified using the Genetic Association Study (GAS) Power Calculator

259 (http://csg.sph.umich.edu//abecasis/cats/gas_power_calculator/index.html ).

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260 Ethical considerations

261 Each patient agreed to take part in this study and provided an informed signed consent after a brief

262 session to explain the aims and methods. The study was approved by the Institutional Ethics

263 Committees of both local hospitals (REC-04062014 and R292, respectively) and conformed to the

264 ethical principles outlined in the Helsinki declaration.

265

266 Results

267 Baseline data of kidney function associated traits and SNPs selection

268 Of the 490 patients with T2DM that were recruited for this study, 115 patients were tested for

269 genetic associations with the albumin-to-creatinine ratio, 328 for vitamin D levels (measured as

270 25-OH Cholecalciferol), 395 for eGFR levels, 474 for serum creatinine levels, and 450 for blood

271 urea levels. Among the 490 patients, 410 had clear classifications for the diagnosis of kidney

272 disease (145 patients with DKD and 265 without DKD) according to the adopted diagnostic criteria

273 [References #33 and #34] and/or patient medical records. The possible covariates that may affect

274 the genetic associations for trait analyses are summarized in Table 1.

275

276

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277 Table 1: Baseline data of tested traits and tested SNPs

TraitMean ± SD or

quartiles

N of subjects

(Male/Female)

N of SNPs

reported **

N of SNPs

tested **Covariates






Diabetic Kidney Disease (DKD)with: 145

without: 265288 63

Age, Gender,

Hypertension, T2DM


278 * For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25th/ 50th/ 75th quartiles and not as mean ± standard 279 deviation because their distributions are skewed.

280 ** Reported: means the total number of SNPs found in the search in the literature, tested: means the actual number of SNPs found from the 281 reported SNPs and used for the analyses in this study for each corresponding trait.

282 Abbreviations: N: number; SD: standard deviation; SNP: single nucleotide polymorphism, ACR: albumin-to-creatinine ratio; T2DM: type 2 283 diabetes mellitus; eGFR: estimated glomerular filtration rate.

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284 Clinical and laboratory characteristics of patients with and without DKD

285 Both patient groups (with or without kidney disease) had poor glycemic control with blood glucose

286 levels above 8 mmol/L. A comparison of patients with DKD to those without DKD indicated that

287 the majority of DKD patients were males (52% versus 38% for controls), older in age (67 versus

288 59 years), had significant rates of comorbidities, such as hypertension and dyslipidemia, and had

289 a longer T2DM duration (16 versus 10 years). A clear decline in renal function indices was also

290 observed in patients with DKD compared to those without DKD, as indicated by the significantly

291 higher rates of ACR, urea, and creatinine, and significantly lower eGFR (Table 2). However, DKD

292 patients tended to have lower LDL-cholesterol results as compared to those without DKD, which

293 suggests that these patients were more likely to have received intensive statin therapy (Table 2).

294

295

296

297

298

299

300

301

302

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303 Table 2: Demographic, clinical, and laboratory characteristics of T2DM patients with or without

304 kidney disease



Gender: Female ** 70 (48.3%) 165 (62.3%) 0.006

Age (years) 67.0 ± 10.4 58.6 ± 10.6 < 0.0001

Clinical variables

Clinical hypertension ** 96.6% 74.3 % < 0.0001

Dyslipidemia ** 95.2% 90.9% 0.17

Smoking history ** 32.4% 24.2% 0.07


BMI (kg/m2) 31.3 ± 6.0 32.5 ± 6.3 0.078




(mmol/l)8.3 ± 3.1 8.9 ± 3.8 0.43


(mmol/l)9.1 ± 3.9 9.5 ± 4.3 0.34





Renal function

indices


(mg/mmol) ***3.3 / 10.1/ 69.6 0.8/ 2.2/ 9.0 0.0001

Vitamin D (nmol/l) 62.9 ± 28.4 65.3 ± 26.8 0.48

eGFR (ml/min/1.73m2) 57.5 ± 27.7 96.1 ± 17.3 < 0.0001

Creatinine (µmol/l) *** 82/ 111/ 146 55/ 64/ 76 < 0.0001

Urea (mmol/l) 9.7 ± 8.3 4.7 ± 1.5 < 0.0001

305 * P-value for continuous data, calculated using two-sided t-test except for Albumin: Creatinine Ratio and 306 Creatinine where it is for Wilcoxon rank-sum (Mann-Whitney) test. P-value for percentage data calculated 307 using Pearson chi-square test with except of hypertension and Dyslipidemia where Fisher Exact test was 308 used.

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309 ** Proportional data shown as number of positive outcome and its percentage. All remaining continuous 310 data shown as mean ± standard deviation except of Albumin: Creatinine Ratio and Creatinine.

311 *** For the Albumin: Creatinine Ratio and Creatinine analyses, summary statistics indicated by 25th/ 50th/ 312 75th quartiles and not as mean ± standard deviation because their distributions are highly skewed.

313

314

315

316

317

318

319

320

321

322

323

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331 Factors associated with developing DKD in Emirati patients with T2DM

332 Table 3 shows that T2DM duration was the single factor that significantly contributed to the

333 development of DKD. Increased risk for DKD was significantly associated with the increasing

334 duration of T2DM, cumulatively, up to 20 years of duration. At a T2DM duration ≥ 20 years, the

335 DKD risk stabilized at approximately 3.12 times higher as compared to patients with duration ≤ 5

336 years (P=0.02, 95% CI=1.21-8.02). Although the levels of serum creatinine indicated a significant

337 difference between no DKD and DKD patients, (P=0.03), the odds ratio of 1.03 showed no

338 increased risk (Table 3).

339 Table 3: Risk factors for the development of kidney disease in patients with T2DM from the UAE


Age 0.99 0.96 - 1.04 0.80

Gender 0.90 0.34 - 2.36 0.83

BMI 1.02 0.97 – 1.08 0.49

Hypertension 2.00 0.60 - 6.74 0.26

HbA1c 0.86 0.69 – 1.07 0.19

Cholesterol 0.59 0.19 – 1.76 0.34

Triglyceride 1.09 0.62 – 1.94 0.76

HDL-cholesterol 1.25 0.64 – 2.45 0.52

LDL-cholesterol 1.38 0.42 – 4.57 0.60

Smoking history 0.80 0.34 – 1.87 0.61

Creatinine 1.03 1.00 – 1.06 0.03

Urea 1.13 0.92 – 1.38 0.26

eGFR 0.97 0.94 – 1.00 0.09

Diabetes duration*

5-10 years 0.75 0.26 – 2.16 0.59

11-15 years 1.49 0.56 – 3.91 0.42

16-20 years 3.35 1.22 – 9.23 0.02

> 20 years 3.12 1.21 – 8.02 0.02

340 *Diabetes duration reference is duration ≤ 5 years

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341 Abbreviations: BMI: body mass index, eGFR: estimated glomerular filtration rate.

342 Genetic associations of renal function associated traits in Emirati patients

343 The results of genetic associations for each tested renal function trait are shown in Table 4. In

344 summary, no association passed the Bonferroni correction for multiple testing. However, we report

345 here the most suggestive associations which point to replications of previous reports.

346 For blood urea, the best observed association was with rs11868441 in BCAS3 (effect size: -0.038

347 log per allele A, P=0.014), followed by multiple SNPs in the RSPO3 gene.

348 For serum creatinine, two SNPs (rs6999484 and rs1705699) in the intergenic region between

349 STC1 and ADAM28, showed the best associations with similar effect sizes (0.03 log per one copy

350 of the corresponding minor allele), followed by SNP rs2828785 (P=0.008, effect size = -0.030 log

351 per one copy of allele A), which is located in the non-coding gene area on chromosome 21. In

352 addition, multiple genetic areas were also indicated, although with less significant associations.

353 eGFR levels were significantly associated with SNPs within the MED1 gene, rs2168785 with

354 effect size of -4.299 per allele G and rs12452509 with effect size of -4.232 per allele G and P=0.015

355 and 0.017, respectively. In addition, the SNPs in two genetic regions, WDR72 and SHROOM3,

356 that are associated with serum creatinine levels, were also associated with eGFR levels, indicating

357 a strong link to renal function.

358 Vitamin D levels were associated with three genetic regions including rs1801725 in CARS (effect

359 size: -6.923 per allele A, P=0.0078), rs1155563 in GC (effect size: -6.951, P=0.0081), and two

360 SNPs, rs12794714 and rs10500804, in CYP2R1 with effect sizes of approximately -5.0 and P-

361 values of 0.015 and 0.016, respectively.

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362 One SNP, rs4528660, which is located in the intergenic region between LPIN2 and MYOM1 (effect

363 size: -0.323 log per allele A, P=0.027) was strongly associated with the albumin-to-creatinine

364 ratio.

365 This data demonstrated that renal function traits, are linked to several loci in the UAE population,

366 and that some loci (e.g. WDR27 and SHROOM3) are linked to more than one trait.

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367 Table 4: Results of genetic association analyses of different renal function indices

SNP Chr: BP Gene A1/A2 * MAF_% Beta ** SE P

Blood Urea

rs11868441 17: 59239221 BCAS3 A/G 30.1 -0.038 0.015 0.014

rs1892172 6: 127476516 RSPO3 T/C 46.9 0.031 0.014 0.028

rs4644087 6: 127481154 RSPO3 C/A 46.8 0.030 0.014 0.03

rs4382293 6: 127475433 RSPO3 G/A 47.0 0.030 0.014 0.03

rs2489629 6: 127476717 RSPO3 G/A 44.9 -0.029 0.014 0.039

Serum Creatinine

rs6999484 8: 23728271 STC1-ADAM28 A/G 23.8 0.033 0.011 0.003

rs1705699 8: 23781453 STC1-ADAM28 G/A 24.2 0.031 0.011 0.005

rs2828785 21: 25437505 - A/G 19.9 -0.030 0.011 0.008

rs11227279 11: 65495211 KRT8P26-AP5B1 A/G 28.4 -0.024 0.010 0.019

rs7785065 7: 32915204 KBTBD2 A/C 43.5 0.022 0.010 0.022

rs4859682 4: 77410318 SHROOM3 A/C 25.7 0.021 0.010 0.034

rs1031755 15: 53951435 WDR72 C/A 11.7 -0.029 0.014 0.038

rs7740534 6: 25077179 - C/A 9.6 -0.032 0.015 0.042


rs2168785 17: 37407135 MED1 G/A 28.2 -4.299 1.754 0.015

rs12452509 17: 37574722 MED1 G/A 28.1 -4.232 1.757 0.017

rs4776168 15: 53936907 WDR72 G/A 10.8 5.828 2.699 0.031

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rs10518733 15: 53940307 WDR72 C/A 10.8 5.828 2.699 0.031

rs7541937 1: 35341982 DLGAP3 C/A 44.0 3.590 1.693 0.035

rs10032549 4: 77398015 SHROOM3 G/A 32.2 -3.655 1.750 0.037

rs2484639 1: 243462367 SDCCAG8 A/G 43.5 3.378 1.656 0.042


rs1801725 3: 122003757 CASR A/C 22.6 -6.923 2.584 0.008

rs1155563 4: 72643488 GC G/A 18.9 -6.951 2.608 0.008

rs12794714 11: 14913575 CYP2R1 A/G 41.5 -5.110 2.099 0.015

rs10500804 11: 14910273 CYP2R1 C/A 42.2 -5.004 2.060 0.016


rs4528660 18: 3043516 LPIN2-MYOM1 G/A 17.8 -0.323 0.144 0.027

368 * A1/A2: minor to major alleles.

369 ** Beta: regression coefficient for the linear regression model, calculated based on A1 (the minor allele).

370 Abbreviations: BP: base pair position; Chr: chromosome; MAF: minor allele frequency; SE: standard error; SNP: single nucleotide polymorphism.

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371 Genetic associations of DKD in Emirati patients

372 Sixty-three SNPs in 43 genetic loci, which have previously been linked to chronic or diabetic

373 kidney disease, were included in our genetic analyses of DKD (145 patients with DKD versus 265

374 without DKD). A logistic regression model including five possible covariates that may affect the

375 development of kidney disease was applied (Table 1). As depicted in Table 5, the unadjusted

376 analyses indicates two associations in CPS1 (rs7422339, P=0.019) and SHROOM3 (rs4859682,

377 P=0.024), respectively. Following the adjustment for possible covariates, only the SHROOM3

378 rs4859682 remained significant (P=0.04, OR=1.46). This confirms that SHROOM3 is a string risk

379 locus for DKD, considering similar associations with serum creatinine and eGFR levels.

380

381

382

383

384

385

386

387

388

389

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390 Table 5: Association between SNPs linked to CKD and DKD patients from the UAE.

SNP Chr: BP Gene * A1/A2 ** MAF Punadjusted Padjusted OR (95% CI)

DKD (N=145) No DKD (N=265)

rs7422339 2: 211540507 CPS1 A/C 35.8% 27.9% 0.019 0.20 1.25 (0.89 – 1.75)

rs4859682 4: 77410318 SHROOM3 A/C 29.0% 21.9% 0.024 0.04 1.46 (1.01 - 2.10)

391 * CPS1: Carbamoyl-Phosphate Synthase 1; SHROOM3: Shroom Family Member 3.

392 ** A1/A2: minor to major alleles.

393 Abbreviations: BP: base pair position; Chr: chromosome; CI: confidence intervals; DKD: diabetic kidney disease, MAF: minor allele frequency; 394 N: number; OR: odds ratio; SNP: single nucleotide polymorphism.

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395 Discussion

396 A combination of environmental and clinical factors in genetically predisposed individuals have

397 been suggested to be involved in DKD, including persistent hyperglycemia, arterial hypertension

398 and/or dyslipidemia 37. In addition, familial aggregation of nephropathy in T2DM has been

399 reported in several populations 38. Therefore, understanding the complex multifactorial

400 interactions between genetic, clinical and traditional kidney disease risk factors can provide insight

401 into novel drugs and treatment strategies for DKD towards reducing the likelihood of developing

402 ESRD. In this study, we investigated whether the genetic markers that correspond to DKD and

403 renal function traits reported for different populations are similar to the Arab population.

404 The current Emirati population sample indicated that most T2DM patients who developed DKD

405 were males, about 10 years older, had more frequent comorbidities, specifically hypertension, and

406 showed a marked decline in their renal function profiles, as compared to those who did not develop

407 the disease. However, T2DM patients who did not develop DKD still had higher rates of

408 comorbidities and poor diabetic control, in agreement with our previous results 29. We also found

409 that the duration of diabetes was the single factor that significantly contributed to the development

410 of kidney disease. Specifically, the risk of developing DKD became significant when the duration

411 of T2DM reached the 15 year mark. This is in alignment with previous reports, which suggest that

412 T2DM patients who do not develop signs of kidney disease by 15 years duration of diabetes seem

413 to be protected, most likely due to genetic factors 39.

414 The most notable finding of this study was the association of SHROOM3 (Shroom Family Member

415 3) with serum creatinine, eGFR, and DKD. The minor allele for the SNP rs4859682 (A) was

416 observed to increase the serum creatinine (0.021 log increase per one copy), and also to increase

417 the risk for DKD (OR=1.46). SHROOM3 was first reported to be associated with eGFR levels in

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418 DKD patients 13, then with serum creatinine 40 and serum magnesium levels 41. This association

419 was further replicated in different ethnicities 19 42. The SHROOM3 gene product is expressed in

420 the human kidney and is reported to play an important role in epithelial cell shape regulation 43, as

421 well as the maintenance of the glomerular filtration barrier integrity 44. Defective Shroom3 protein

422 leads to decreased actin organisation and affects the mechanical characteristics and integrity of the

423 glomerular podocyte resulting in thinning of the glomerular filtration membrane 44.

424 Additionally, Shroom3 heterozygous (Shroom3Gt/+) mice showed developmental irregularities that

425 manifested as adult-onset glomerulosclerosis and proteinuria 45. Furthermore, genetic variants

426 (such as the intronic variant rs17319721), were found to contribute to kidney allograft injury and

427 the development of fibrosis through a mechanism involving TGF-β1 signaling 46. Although the

428 variant rs17319721 was not found in our dataset, it is highly linked to the SNP rs4859682 (r2=0.85,

429 D’=1), which was reported in this study to increase the risk for DKD and affect the levels of serum

430 creatinine. Overall, this suggests that SHROOM3 may be considered as a multi-ethnic risk gene

431 for DKD and associated kidney function traits in various populations, including the Arabs who

432 inhabit the UAE.

433 Similarly, the two loci; WDR72 (WD Repeat Domain 72, associated with eGFR and serum

434 creatinine levels), and BCAS3 (Breast Carcinoma-Amplified Sequence 3, associated with blood

435 urea levels), are also well-known trans-ethnic renal function traits loci 47. WDR72, in particular,

436 has been well studied in association with kidney function traits and pathologies. For instance,

437 WDR72 has been reported to be associated with creatinine production or secretion 48, as well as

438 signal transduction, cell cycle regulation, and vesicular trafficking that affects podocyte activity,

439 reduced eGFR and progression of CKD 49. In addition, RSPO3 (R-Spondin 3) has been previously

440 reported to be associated with blood urea nitrogen concentration, in line with the results of the

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441 current study 42. The association of rs4528660 near MYOM1 (Myomesin 1) with the albumin-to-

442 creatinine ratio is also in agreement with previous work, which link this locus to albuminuria in

443 patients with diabetes 50. The current analyses also replicated the associations of CASR (Calcium-

444 sensing receptor), GC (vitamin D-binding protein (VDBP), also known as group-specific

445 component (GC)- globulin), and CYP2R1 (Cytochrome P450 Family 2 Subfamily R Member 1)

446 with levels of vitamin D 51. These genes encode proteins which are involved in vitamin D function,

447 including activation by hydroxylation (CYP2R1), transportation (GC), and serum calcium level

448 sensoring (CASR) 51. They have also been associated with calcium-vitamin D physiology and

449 pathology, such as serum calcium levels, familial hypocalciuric hypercalcemia, tertiary

450 hyperparathyroidism, and vitamin D deficiency presenting as Rickets (see OMIM entries: CARS:

451 601199; CYP2R1: 608713; and GC: 139200). For instance, CASR protein is expressed in the

452 kidney amongst other tissue and regulates ion metabolism including calcium and magnesium.

453 Mutations in CASR lead to abnormalities in the regulation of the parathyroid gland and renal

454 function causing hypercalcemia and increased blood pressure, which in turn may affect kidney

455 function 52. Similarly, GC proteins bind actin and work as actin scavengers (as such GC may play

456 a role in podocyte integrity), as well as a binding site for Vitamin D. The GC protein is the

457 precursor to the Gc-macrophage activating factor (GcMAF), a macrophage activator and suggests,

458 together with Vitamin D, that GC has an important role in the immune function and pathogenesis

459 of CKD 53. Vitamin D is hydroxylated at the C25 position by specific hydroxylase coded by the

460 CYP2R1 gene to 25-hydroxyvitamin D (25(OH) D), which is the main circulating form of vitamin

461 D. The low levels of Vitamin D observed in CKD, due to reduced CYP2R1 production by the liver

462 or due to a mutation in the gene, disturb calcium balance and lead to hyperparathyroidism.

463 Conversely, the loss of renal protection caused by Vitamin D and the increase in the renin-

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464 angiotensin pathway leads to hypertension that further advances kidney disease 54. Furthermore,

465 these genes have recently been shown to influence the outcome of Vitamin D3 supplementation,

466 which in the Arab context is an important finding of our study 55.

467 In summary, this work presents the first study to investigate the clinical and genetic factors

468 influencing kidney function traits and DKD in a population of Arab ancestry. The results

469 demonstrate that the duration of T2DM is the single most important risk factor for DKD

470 development in patients with T2DM in the UAE. Our study highlights that several genetic loci,

471 which have been previously linked to renal function associated traits, are shared between diverse

472 ethnic groups. As such, we have replicated previous findings of the association of SHROOM3 with

473 DKD. The logistic analyses performed here did not include treatment modalities since most of the

474 patients had multiple conditions and underwent multiple treatments, which makes logistic

475 regression largely unstable and difficult to interpret. A larger population sample across the Middle

476 East is now being considered to confirm the extent of the shared genetic predisposition reported in

477 the current study. Considering the high prevalence of T2DM in this population and the recent

478 evidence of genomic structure variations among different ethnic groups, more genetic-driven

479 population studies are warranted towards effective genetically guided personalized medicine.

480

481 Acknowledgments: We acknowledge the patients who volunteered to make this study possible.

482 We also acknowledge patient care advisers for their roles in this study.

483 Author contribution: HSA obtained the funding for this study. WMO, HSA and HFJ designed

484 the study. WMO analyzed the data and prepared the manuscript. HFJ, GKT, AHK, and KK

485 provided critical revision the manuscript, contributed to writing the discussion, and writing the

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486 revision of the manuscript. WA and MHH did the patient recruitment process and provided

487 acquisition clinical data collection. All authors gave final approval of the version to be published.

488 Conflict of interests: None declared

489 Patient consent: Obtained.

490 Ethics approval: The Institutional Ethics Committee of Mafraq and Shaikh Khalifa Medical

491 Centre hospitals (REC-04062014 and R292, respectively).

492 Funding: This study was supported by research incentive funds from Khalifa University Internal

493 Research Fund Level 2 granted to Dr. Habiba Al Safar.

494 Provenance and peer review: Not commissioned; externally peer reviewed.

495 Data sharing statement: No additional data are available.

496

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595 European isolates reveals an association of COL22A1, SYT1, and GABRR2 with serum

596 creatinine level. BMC Medical Genetics 2010;11(1):41.

597 41. Meyer TE, Verwoert GC, Hwang S-J, et al. Genome-wide association studies of serum

598 magnesium, potassium, and sodium concentrations identify six Loci influencing serum

599 magnesium levels. PLoS Genetics 2010;6(8):e1001045.

600 42. Okada Y, Sim X, Go MJ, et al. Meta-analysis identifies multiple loci associated with kidney

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606 glomerular filtration barrier integrity. Genome Research 2015;25(1):57-65.

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609 46. Menon MC, Chuang PY, Li Z, et al. Intronic locus determines SHROOM3 expression and

610 potentiates renal allograft fibrosis. The Journal of Clinical Investigation 2015;125(1):208.

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612 Genes Linked to Salt Sensitivity. The American Journal of Human Genetics

613 2016;99(3):636-46.

614 48. O'seaghdha CM, Fox CS. Genome-wide association studies of chronic kidney disease: what

615 have we learned? Nature Reviews Nephrology 2012;8(2):89.

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617 information. Science 2011;332(6030):680-86.

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619 associated with albuminuria in diabetes. Diabetes 2015:db151313.

620 51. Wang TJ, Zhang F, Richards JB, et al. Common genetic determinants of vitamin D

621 insufficiency: a genome-wide association study. The Lancet 2010;376(9736):180-88.

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Item Item number

STROBE Guideline Extension for Genetic Association Studies

(STREGA)

Title and Abstract

1 (a) Indicate the study’s design with a commonly used term in the title or the abstract.


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(STREGA)

Objectives: Within the Emirati population, risk factors and genetic predisposition of diabetic kidney disease (DKD) have not been investigated. The aim of this research was to determine possible clinical, laboratory, and reported genetic loci which are risk factors of DKD. Research Design and Methods: Four hundred and ninety unrelated Emirati nationals with type 2 diabetes mellitus (T2DM) were recruited with and without DKD and clinical and laboratory data obtained. Following adjustments for possible confounders, a logistic regression model was used to test the associations of 63 single nucleotide polymorphisms (SNPs) in 43 genetic loci with DKD (145 patients with DKD and 265 without DKD). Linear regression models, adjusted for age and gender, were used to test the genetic associations of five renal function traits, including 83 SNPs with albumin-to-creatinine ratio, 92 SNPs with Vitamin D (25-OH Cholecalciferol), 288 SNPs with estimated glomerular filtration rate (eGFR), 363 SNPs with serum creatinine, and 73 SNPs with blood urea. Results: Patients with DKD compared to those without DKD were mostly males (52% versus 38% for controls), older (67 versus 59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus 10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits and found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels. Conclusions: Associations were found between several genetic loci and risk markers for DKD, which may influence kidney function traits and DKD in a population of Arab ancestry.

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Introduction








Methods





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(a) Cohort study – Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up.






Participants 6







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8* (a) For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group.


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Not applicable








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Statistical methods

12



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Results

(a) Report the numbers of individuals at each stage of the study – e.g., numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed.






Participants

13*


No flow diagram

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Descriptive data

14* (a) Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders.








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Cohort study-Report numbers of outcome events or summary measures over time.


Outcome data 15 *



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Not applicable

(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence intervals). Make clear which confounders were adjusted for and why they were included.




Main results 16



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creatinine.






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Discussion

Key results 18 Summarize key results with reference to study objectives.Patients with DKD compared to those without DKD were mostly males (~52% versus 38% for controls), older (67 versus ~59 years), and had significant rates of hypertension and dyslipidemia. Furthermore, patients with DKD had longer duration of T2DM (16 versus ~10 years), which in an additive manner was the single factor that significantly contributed to the development of DKD (P=0.02, OR=3.12, 95% CI=1.21-8.02). We replicated some of the associations of the genetic loci with different renal function traits found that the most notable associations included SHROOM3 with levels of serum creatinine, eGFR, and DKD (Padjusted=0.04, OR=1.46); CASR, GC, and CYP2R1 with vitamin D levels; as well as WDR72 with serum creatinine and eGFR levels.

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias.A limitation of this study is that analyses carried in this study did not included treatment modalities because most of patients had multiple conditions and they used several treatments; which make the models largely unstable and difficult to interpret.Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for

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Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence.Current analyses of the DKD genetic association had a statistical power ~ 60%; necessitating a larger population sample across the Middle East for sufficient statistical power to discover novel clinical and genetic predisposition for DKD in the region.

Generalizability 21 Discuss the generalizability (external validity) of the study results.Some of the loci which reported previously in other populations did not replicated because of the statistical power issue. In addition, due to lack of results in this topic in the other populations, we cannot confirm the results of this study will be replicated

Other Information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based.This study was supported by research incentive funds from Khalifa University Internal Research Fund Level 2 granted to Dr. Habiba Al Safar. No role of funders taken in this study.

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Documents

bmjopen.bmj.com€¦ · For peer review only Item Item number STROBE Guideline Extension for Genetic (STREGA) Association Studies Descriptive data Both patient groups (with or without