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10/10/2018

Major Revision of manuscript ID BMJ.2018.045372

Dear Doctor Cook,

Thank you for giving us the opportunity to resubmit a new version of our manuscript entitled

"The role of diet in the prevention of type 2 diabetes: An umbrella review of meta-analyses of

prospective studies” with the ID BMJ.2018.045372.

We would like to thank the experts for their constructive comments and suggestions. Please

find below the point-by-point response to the comments of the reviewers. We have

incorporated their comments and suggestions that were raised. Please also find the new

version of our manuscript. All changes to the originally submitted version have been

highlighted in yellow color. In this letter, the changes made refer to the marked version of the

manuscript. In addition, we have uploaded a clear version of the manuscript.

We hope that the revised version is suitable for publication in BMJ.

Sincerely,

Manuela Neuenschwander and

Sabrina Schlesinger

Dr. Sophie Cook UK research editor, BMJ scook@bmj.com

Manuela Neuenschwander Sabrina Schlesinger (PhD) Institute for Biometrics and Epidemiology German Diabetes Center (DDZ) Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf Auf´m Hennekamp 65 40225 Düsseldorf, Germany Tel.: +49-(0)-211-33-82-415 manuela.neuenschwander@ddz.uni-duesseldorf.de

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Detailed comments from the meeting:

*Our statistician had many concerns about this paper and has provided a detailed report

below.

Response: We thank Professor Riley for his constructive feedback. We apologize for the

misunderstanding about the level of data pooling. To clarify, we did not summarize multiple

meta-analyses. For each exposure we chose the one meta-analysis that included the largest

numbers of studies and study participants, which was usually the most recent one. Our

umbrella review was conducted as it has been done in the past (e.g. Poole BMJ 2017, PMID:

29167102; Kalliala BMJ 2017, PMID: 29074629; Kyrgiou BMJ 2017, PMID: 28246088;

Tsilidis BMJ 2015, PMID: 25555821). Umbrella reviews systematically search, organise, and

evaluate existing evidence from previous meta-analyses on exposures and health outcomes.

For clarification, we have revised the contents of description of the methods, including the

study design. We addressed the further comments according to Professor Riley‘s feedback.

The detailed information are shown in the responses to Professor Riley‘s comments.

*Such a big umbrella review makes it hard to get to grips with what it actually means given

that it covers such broad issues and becomes quite detached from the primary studies. This

has the potential to lose the thread of what has gone on in the original studies. For example

what variables have been considered?

Response: We believe it is a strength of our umbrella review that it provides such a

comprehensive overview of any existing evidence regarding dietary factors and incidence of

T2D, especially since there is such a large body of research available. By giving such a

broad overview and by evaluating the quality of evidence, internal consistencies or

inconsistencies can be examined and relevant research directions can be identified. Since

we included only meta-analyses of prospective studies we are aware that information of

primary studies, like confounding factors, cannot be ignored. However, to consider this

comment, we went back to all primary studies (n=277) and checked the methods of

adjustment and the confounders which were included in the primary studies, and included

more detailed information about this. Please see our point-by-point answers to comment 4 by

Doctor Merino and to comments 2, 3 and 4 by Professor Riley for detailed information on

changes made.

*The editors thought it would be hard to replicate what the authors had done to get to this

point and the methods section lacks clarity.

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Response: We thank the editors for bringing this lack of clarity to our attention. We added

more information to the methods section and clarified questions raised about the methods.

Please see our point-by-point answers to comments 3a and 3b by Doctor Merino and

comments 6, 7, 8 and 16 by Professor Riley for more details on changes made.

*The abstract contains a small selection of results, but how were these chosen from the

many findings?

Response: In order to systematically summarize the most important findings and to keep the

abstract short and clear, we particularly emphasized the findings with high quality of

evidence (see comment 2 by Professor Hu).

*We noted that some of these findings are confirmatory and struggled to appreciate what is

new here, this could be better explained for the general reader.

Response: An umbrella review is a useful tool to summarize evidence of published

systematic reviews and meta-analyses (Ioannidis J.P.A, CMAJ 2009). Our umbrella review

includes a wide spectrum of exposures as it has not been done in the past. What we added

to this existing evidence is the evaluation of the methodological quality and quality of

evidence of the meta-analyses. Therefore, internal consistencies and inconsistencies were

uncovered and relevant research directions could be identified. We added this explanation to

our discussion.

*The editors had concerns about the inclusion of both RCTS and observational studies and

feel the pitfalls of this approach needs to be better considered.

Response: We thank the editors for raising this point. According to this comment we now

focus only on observational studies and excluded RCTs (see comment 1, Reviewer 2,

Professor Hu).

Reviewer 1, Doctor Jordi Merino

Comment 1: There are significant inconsistencies between original data reported in included

meta-analysis and extracted data (Supplementary Tables 3,4,5,6). For example, Imamura F,

et al. BMJ 2015 conducted a systematic review and meta-analysis to examine the

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prospective associations between consumption of sugar sweetened beverages, artificially

sweetened beverages, and fruit juice with T2D. The original study included data extracted

from 17 cohorts (38 253 cases/10 126 754 person years). Higher consumption of sugar

sweetened beverages was associated with a greater incidence of T2D, by 18% per one

serving/day (95%CI: 9- 28, I2 for heterogeneity=89%). Supplementary Table 4 shows that 14

studies were included and the RR per one s/d of sugar sweetened beverages was 1.28

(95%CI 1.12-1.46). Similar inconsistences have been detected in Lee & Park, et al. 2017

Nutrients or Saneei, et al. Public Health Nutrition 2017 (or 2016).

Response: Thank you for raising our attention to this. We checked the extracted data

accordingly and would like to explain the inconsistencies. As for Imamura et al. (BMJ 2015),

the model without adjustment for adiposity and within-person variation included 17 cohorts

and yielded a summary relative risk of 1.18 (1.09 to 1.28). Since we always extracted the

maximally adjusted relative risk, we extracted the data from the model which adjusted for

adiposity and within person variation, which yielded a summary relative risk (95% CI) of 1.28

(1.12 to 1.46) in the published meta-analysis. For this model 14 cohorts with RR and their

95%-CI were available for our recalculation, which resulted in a summary relative risk (95%

CI) of 1.26 (1.11 to 1.43). The small difference in the risk estimates explained by rounding

differences in the recalculations.

As for Lee & Park (Nutrients 2017) the reported summary relative risk (95% CI) of 0.73 (0.61,

0.87) included results from cross-sectional and prospective cohort studies. Since we only

included meta-analyses of prospective cohort studies, we extracted the summary relative risk

(95% CI) of a subgroup analysis (Table 1), which only included prospective cohort studies

(RR (95% CI): 0.64 (0.57, 0.74)).

As for Saneei, et al. (Public Health Nutrition 2017), the number of included studies and the

effect estimate differs slightly between the publication and our umbrella review, because they

counted the same cohort twice (men and women), which we first combined using fixed effect

methods, as described in the methods section.

Comment 2: Literature search was conducted in Medline and Web of Science. Examining

other sources of information such as Embase or Cochrane Database of Systematic Reviews

would be relevant for this umbrella review. In addition, it seems that relevant systematic

reviews and meta-analysis have not been included in this manuscript and are not listed as

excluded studies. Hu EA, et al (BMJ 2012). White rice consumption and risk of type 2

diabetes: meta-analysis and systematic review.

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Response: We thank Dr. Merino for this comment. We conducted an additional search on

Embase up to August 17th 2018. 3631 publications were identified in this search, including

three new relevant published meta-analyses: one on potatoes (Schwingshackl et al,

European Journal of Nutrition, 2018), one on polyphenols (Rienks et al, American Journal of

Clinical Nutrition, 2018) and one on protein and types of protein (Zhao et al, European

Journal of Nutrition, 2017), which replaced the previously included meta-analyses on this

exposure. The flow chart (Supplementary Figure 1) and the list of excluded studies

(Supplementary Table 2) have been adapted accordingly. Changes in the results section

have been highlighted throughout the manuscript. In addition, the reviewer is correct. We

missed to list the meta-analysis by Hu EA, et al (BMJ 2012). We apologize for the mistake

and thank you for raising our attention to this. We now added the reference to the list of

excluded studies (Supplementary Table 2).

Page 5, line 123ff: The systematic literature search was conducted in Pubmed, Web of

Science and Embase until August 2018 for meta-analyses of observational studies

investigating the association between diet and T2D, using a predefined search strategy

(Supplementary Table 1).

Page 10, line 255f: Of the 11’413 publications initially identified, we finally selected 53

published meta-analyses including 153 SHRs (Supplementary Figure 1).

Page 10, line 260ff: We found meta-analyses on the following exposures: [S], potatoes and

types of potatoes44, [S] total protein and types of protein59, [S] polyphenols and subgroups

of polyphenols73 [S].

Comment 3a: A better description of exposure variables in the methods section would be

desirable.

Response: Thank you for this comment. We added a more detailed description of the

exposure variables to the methods section.

Page 6, line 131ff: Studies were included if they met the following criteria: (1) Meta-analysis

of observational prospective cohort studies in adult populations with multivariable adjusted

summary risk estimates, (2) considering the incidence of T2D as outcome, (3) investigating

the association of different dietary factors assessed by established dietary assessment

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instruments such as food frequency questionnaires, diet history, 24h dietary recalls, and

dietary records with risk incidence for T2D. Eligible dietary factors included:

• Dietary behaviours/diet quality indices, including dietary patterns as combinations of

nutrients, foods and beverages. Examples are breakfast skipping for dietary behaviours,

glycemic index (GI), glycemic load (GL) or potential renal acid load (PRAL) for dietary

quality indices, Healthy Eating Index (HEI), Dietary Approaches to Stop Hypertension

(DASH), Mediterranean Diet or vegetarian diet for a priori dietary patterns, and the

application of principal component analysis, factor analysis or reduced rank regression for

exploratory-derived dietary patterns.

• Food groups, foods and beverages, including dairy products, eggs, meat, fish, fats (e.g.

butter) and oils, potatoes, whole-grain, grains, cereals rice, legumes, nuts, vegetables,

fruit, tea, coffee, sugar-sweetened and alcoholic beverages.

• Macronutrients (carbohydrates, fats, protein), micronutrients (vitamins, minerals), fibre

and polyphenols.

Comment 3b: I am wondering whether it makes sense to report estimated effect sizes for

exposures that only include data from one single meta-analysis. To my understanding,

umbrella meta-analyses are useful for aggregating findings from several meta-analyses that

address the same question. Regarding to this, if the exposure of interest is overall dietary

pattern quality, it would make more sense to combine results from different meta-analysis

despite these meta-analyses used different methods to define dietary quality (i.e., HEI, AHEI,

MedDiet, DASH).

Response: We thank Dr Merino for raising these questions. Compared to systematic reviews

and meta-analyses that examine one exposure (or one outcome), umbrella reviews are

useful tools to provide an overview of multiple systematic reviews and/or meta-analyses on

several exposures (or several outcomes) (Ioannidis J.P.A, CMAJ, 2009). For most of our

exposures more than one meta-analysis was identified. However, more recent meta-

analyses usually include the same studies with an update of one or two additional primary

studies. To avoid the inclusion of duplicate reports, we chose the meta-analysis providing the

largest number of primary studies and/or the largest number of cases as it has been done in

previously published umbrella reviews (Tsilidis et al, BMJ, 2014; Kyrgiou et al, BMJ 2017;

Poole et al, BMJ 2017). For clarification we added a sentence to the methods section.

In addition, the dietary pattern scores were generated by the inclusion of similar, but also

different components of diet. For example, a component of the Mediterranean diet is a high

intake of monounsaturated fatty acids (mainly through the consumption of olive oil), while

other dietary patterns rather focus on the intake of saturated fatty acids and total fat (HEI,

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HEI-2005), the intake of polyunsaturated fatty acids (AHEI-2010), or the ratio between

polyunsaturated fatty acids and saturated fatty acids (HEI-2010, AHEI) (Koloverou et al,

Metabolism Clinical and Experimental, 2014; Schwingshackl et al, Journal of the Academy of

Nutrition and Dietetics, 2015). Another example is red and processed meat, which is only

included in the DASH-, AHEI and AHEI-2010-score, while the other scores include meat as a

total (Schwingshackl et al, Journal of the Academy of Nutrition and Dietetics, 2015).

We fully agree with Doctor Merino that an overall dietary pattern quality that combines

different aspects of the single patterns would be of high interest regarding incidence of T2D.

Unfortunately, we do not believe that it is meaningful to combine these findings on the level

of an umbrella review. However, this could be an objective for future primary studies. We

thus added this point to our discussion.

Methods: Page 6, line 153ff: If more than one published meta-analysis on the same

association was identified, we chose only one meta-analysis for each exposure to avoid the

inclusion of duplicate studies. In that case, we included the one with the largest number of

primary studies. [S]

Discussion: Page 26, line 700ff: To account for the full spectrum of the association between

diet and T2D, future studies could investigate a dietary score, including all important aspects

of a healthy diet that have been identified to play a role in the risk of T2D. This approach

might be more predictive of T2D risk than the investigation of single foods and nutrients146.

Comment 3c: The number of meta-analysis included for each exposure would be very

informative as well as the proportion of heterogeneity explained by study size.

Response: Since we included one meta-analysis per exposure, we provide information on

the number of included primary studies in the meta-analysis for each exposure.

For clarification we changed the titles for the respective columns in Figures 1-4 and

Supplemental Table 3 from „number of studies“ to „number of primary studies“.

Heterogeneity was assessed using tau2 and 95%-prediction intervals and are shown in

Supplemental Table 3 and described in the results section.

Page 9, line 237ff: However, I2 is dependent on the study size (it increases with increasing

study size). Therefore, we additionally calculated τ2, which is independent of study size and

describes between study-variability of the risk estimates20. In addition, we used the two-

sigma rule (θ�± 2τ) to calculate the interval where 95% of the primary HRs lie within to

further evaluate the dispersion around the SHR. Finally, we calculated 95%-prediction

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intervals (95%-PIs) which also account for heterogeneity and show the range in which the

effect estimates of future studies will lie with 95% certainty20.

Comment 4: Are included studies restricted to European descent individuals? In addition,

average age and sex of included studies should be presented to investigate whether

combined estimates are modified by demographic characteristics.

Response: Thank you for raising our attention to this lack of clarity. The included studies are

not restricted to European descent individuals. We agree, that age and sex are important

confounding factors to be considered. However, more than 90% of the studies adjusted for

these factors. This information is now provided in more detail in the description of the

included meta-analyses. However, it was beyond the scope of this umbrella review to

conduct subgroup analyses. We added a statement to the limitations section.

Results: Page 11, line 294f: All published meta-analyses included primary studies from the

US, Europe and Asia/Australia.

Results: Page 12, line 297ff: Almost all of the primary studies (90%) adjusted for age and

sex, 87% for smoking, 86% for BMI and physical activity, respectively, 67% for total energy

intake, 65% for alcohol intake, 60% for other dietary factors or cardiovascular risk factors

(e.g. Hypertension), respectively, and 52% for family history of diabetes.

Discussion: Page 28, line 748ff: Third, we did not explore subgroup analysis (e.g. by sex,

geographic locations, adjustment factors like BMI) or sensitivity analysis (e.g. exclusion of

studies at high risk of bias).

Comment 5: Given the relevance of alcohol intake on health outcomes, I would suggest to

provide evidence on the association between alcohol intake and T2D risk.

Response: We thank Dr. Merino for this suggestion. We have added information on the

available evidence on alcohol intake and T2D (meta-analysis for total alcohol intake: Li et al,

American Journal of Clinical Nutrition, 2016 and meta-analysis for wine, beer and spirits:

Huang et al, Journal of Diabetes Investigation, 2017). The information are shown in Figure 3

and Supplemental Table 3.

Page 10, line 260ff: We found meta-analyses on the following exposures: [S], as well as

total alcohol75, wine, beer and spirits76.

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Comment 6: Patient involvement statement is missing.

Response: We added a patient involvement statement.

Page 32, line 875f: Patient involvement statement: not required, since no individual patient

data was used in this study

Reviewer 2, Professor Frank Hu

Comment 1: The authors mixed data from prospective cohort studies and RCTs. Because

the evaluation of study quality and data interpretation are different between the two types of

studies. it would be helpful to conduct separate reviews for them. Of note, several RCTs

included in the review analyzed T2D as a secondary outcome rather than a primary outcome.

Response: We thank Professor Hu for raising this important issue. We agree and excluded

RCTs from our review. Consequently, the exposures niacin and selenium were excluded.

Comment 2: In the abstract, the authors emphasized the findings for whole grains, cereal

fiber, red meat, processed meats, and SSBs. However, other findings such as healthy eating

patterns and coffee consumption are also very robust and the quality of the evidence is

similar.

Response: Thank you for this comment. The evidence for the association between a healthy

dietary pattern and coffee consumption with incidence of T2D was graded as moderate. This

was also the case for 31 other associations (s. Table 1). In order to systematically

summarize the most important findings and to keep the abstract short and clear, we

particularly emphasized the findings with high quality of evidence.

Comment 3: The data on fruit juices and T2D risk are confusing. There are several items on

fruit juices (100% fruit juices, fruit juices, and fruit juices with added sugars). It is unclear

how these items were differentiated in the original studies.

Response: We thank Professor Hu for making this point. We added a description of these

items in the footnote of Figure 2b.

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Figure 2b, Footnote: **Total fruit juice = fruit juices with added sugar and without added

sugar; 100% fruit juice = fruit juice without added sugar; sugar-sweetened fruit juice = fruit

juice with added sugar; fruit juice, not specified = type of fruit juice (with or without added

sugar) was not specified in these studies

Comment 4: The authors indicated that there was no significant association between nut

consumption and risk of T2D. However, previous meta-analyses on this topic examined this

association with and without adjustment for BMI. The association was significant before BMI

adjustment. One interpretation is that part of the association between nut consumption and

risk of T2D was mediated through body weight. It is important for the authors to capture

nuances of these associations.

Response: In our umbrella review, we included maximally adjusted SHRs for the association

between nut intake and T2D. All included primary studies adjusted for BMI. To capture the

nuances of this association, we added a statement to our manuscript in which we discussed

different results of two meta-analyses regarding nut consumption and incidence of T2D. In

addition, we have added a statement to the limitation section, that we did not explore

differences in subgroups.

Page 20, line 536ff: [S] This is mostly in accordance with our findings with the exception of

the beneficial association of nuts and the harmful association of unprocessed red meat with

incidence of T2D. Disagreements could be explained by the inclusion of different primary

studies. While Micha et al included a meta-analysis with both, RCTs and observational

studies, our report only focused on observational studies90. In addition, the meta-analysis of

Micha et al. missed one primary study, that reported an increased incidence of T2D for

higher intake of nut intake and T2D91, which resulted in a decreased but not statistically

significant summary estimate in our report.

Page 28, line 748ff: Third, we did not explore subgroup analysis (e.g. by sex, geographic

locations, adjustment factors like BMI) or sensitivity analysis (e.g. exclusion of studies at high

risk of bias). [S] Additionally, BMI has been shown to be an influencing factor in the

association between nut intake and incidence of T2D, with an inverse association before and

a null association after adjustment for BMI. In subgroup analysis a reduction in incidence of

T2D was observed for participants with BMI ≥25 kg/m2, no association was shown for

participants with BMI <25 kg/m2 149. However, this was beyond the scope of this umbrella

review, and future reports could explore these more in detail.

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Comment 5: In terms of omega-6 fatty acids, the authors should focus on linoleic acid, which

is the main type of omega-6. A recent pooled analysis found a robust inverse association

between LA biomarker and incident T2D (Lancet Diabetes Endocrinol. 2017 Dec;5(12):965-

974. doi: 10.1016/S2213-8587(17)30307-8. Epub 2017 Oct 12). This study should be

included.

Response: Thank you for this reference. In our umbrella review, we included only exposures

on dietary intakes rather than biomarkers, and thus, this report did not meet the inclusion

criteria. We added this to our limitations.

Page 29, line 773ff: Sixth, it was beyond the scope of this umbrella review to include

exposures of biomarkers. However, the measurement of certain exposures, e.g. fatty acids,

may lead to bias60 and more specific information on long-term intake may be obtained from

biomarkers151 152, and thus may add to current evidence.

Comment 6: Regarding fish/long-chain omega-3 fatty acids, previous meta-analyses have

found ethnic/racial/geographic differences. Studies conducted in Asian populations tend to

find an inverse association, whereas studies in North America tends to find a slight positive

association. Therefore, the overall association would be null when studies from Asian and

North American cohorts were combined.

Response: We thank Professor Hu for raising this important issue. While it is a valid point, it

is beyond the scope of an umbrella review to conduct subgroup analyses or report subgroup

results. However, we added this to our limitations section.

Page 28, line 748ff: Third, we did not explore subgroup analysis (e.g. by sex, geographic

locations, adjustment factors like BMI) or sensitivity analysis (e.g. exclusion of studies at high

risk of bias). For example, for total omega-3 fatty acids differences between US,

Australian/Asian and European have been shown, with an increased incidence of T2D in US

populations, no association for European countries and an inverse association in Asian

populations148. [S] However, this was beyond the scope of this umbrella review, and future

reports could explore these more in detail.

Comment 7: This review found a positive association between consumption of artificially

sweetened beverages and risk of T2D. This association needs to be interpreted with caution

because of the reverse causation problem. Typically the association was attenuated after

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adjustment for baseline BMI and metabolic diseases. The authors should include meta-

analyses results with and without adjustment for these variables.

Response: Thank you for making this point. In our umbrella review we show the maximally

adjusted summary risk estimate for each exposure. In their meta-analysis, Imamura et al

(BMJ, 2015) made an effort to account for the reverse causation problem by adjusting for

adiposity and within person variation. The adjustment for adiposity and within person

variation did not alter the results significantly for artificially sweetened beverages (SRR (95%

CI) without adjustment for adiposity and within person variation: 1.25 (1.18 to 1.33) vs. SRR

(95 %) with adjustment for adiposity and within person variation: 1.29 (1.08 to 1.54)).

However, residual confounding cannot be ruled out. We added a statement to our

discussion. Furthermore, as mentioned in the response to comment 6, we added to our

limitations section that it is beyond the scope of this umbrella review to report results on

subgroups (e.g. adjusting and not-adjusting for obesity).

Page 21, line 560ff: [S] Nevertheless, residual confounding cannot be ruled out, perhaps

particularly in the analysis of artificially sweetened beverages where obese persons may

have switched from sugar-sweetened beverages to artificially sweetened beverages to lose

weight. This might explain the association observed before adjustment for BMI and the

attenuation of the association with BMI adjustment56.

Page 28, line 748ff: Third, we did not explore subgroup analysis (e.g. by sex, geographic

locations, adjustment factors like BMI) or sensitivity analysis (e.g. exclusion of studies at high

risk of bias).

Reviewer 3, Professor Rob M. van Dam

General comments:

Comment 1: The Nutrigrade assessment of the quality of evidence for different dietary

factors is of key importance for the conclusions of the paper, but scoring for different dietary

factors for different components of Nutrigrade is not shown. This makes the methodology

difficult to replicate; for example why coffee consumption receives a lower score than red

meat consumption cannot readily be assessed based on the presented data. More

information on this scoring is essential for the transparency of this review.

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Response: Thank you for making this important point. To increase transparency we added a

table showing the scoring for the different components of NutriGrade for each exposure (see

Supplementary Table 5).

Comment 2: Earlier systematic reviews on dietary factors and diabetes risk have been

published last year (Schwingshackl L, Hoffmann G, Lampousi AM, Knüppel S, Iqbal K,

Schwedhelm C, Bechthold A, Schlesinger S, Boeing H. Food groups and risk of type 2

diabetes mellitus: a systematic review and meta-analysis of prospective studies. Eur J

Epidemiol. 2017 May;32(5):363-375. Micha R, Shulkin ML, Peñalvo JL, Khatibzadeh S,

Singh GM, Rao M, Fahimi S, Powles J, Mozaffarian D. Etiologic effects and optimal intakes

of foods and nutrients for risk of cardiovascular diseases and diabetes: Systematic reviews

and meta-analyses from the Nutrition and Chronic Diseases Expert Group (NutriCoDE).

PLoS One. 2017 Apr 27;12(4):e0175149). The authors cite these papers, but should provide

some more discussion on the existing evidence and what this study adds to the Introduction

section. The current paper does consider a wider range of dietary factors that these previous

efforts.

Response: We thank Professor van Dam for this comment. We added some more discussion

of these papers to the introduction and the discussion.

Introduction: Page 4, line 95ff: Recent reports summarized evidence for selected dietary

factors regarding prevention of T2D9-11. Strong evidence was observed for a decreased

incidence of T2D for higher consumption of whole grains10 11 and a higher adherence to a

healthy dietary pattern10 as well as an increased incidence of T2D for higher intake of total

red meat11, processed meat10 11 and sugar sweetened beverages (SSB)10 11. Micha et al

summarized findings with probable or convincing evidence and found a higher incidence of

T2D for low intake of whole grain, yogurt, nuts/seeds and dietary fibre as well as for high

consumption of unprocessed red meat, processed meat, foods with a high glycemic load

(GL) and SSB9. However, none of these studies focuses on any existing evidence between

dietary factors (including a wide range of dietary factors such as dietary behaviours/diet

quality indices, food groups, foods and beverages, alcoholic beverages as well as macro-

and micronutrients) and incidence of T2D.

Discussion: Page 20, line 533ff: Micha et al. evaluated the evidence between dietary factors

and T2D. They found probable or convincing evidence for an association between low

consumption of nuts, whole grain and dietary fibre, as well as high consumption of

unprocessed red meat, processed red meat and foods with high glycaemic load and the

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incidence of T2D9. This is mostly in accordance with our findings with the exception of the

beneficial association of nuts and the harmful association of unprocessed red meat with

incidence of T2D. [S]

Comment 3: The main review does not include evidence from trials of dietary factors with

measures of glucose homeostasis as an outcome. This is an important source of evidence,

but it is understood this is difficult to include in the main review. However, the authors should

provide a more balance overview of the evidence from trials of intermediary outcomes in the

Discussion section. Currently, the authors do discuss this for the foods with high quality

evidence, but do not highlight any trials that do not support effects. For eg whole grains there

are several trials that do not show any impact on markers of glucose homeostasis.

Response: Thank you for this comment. We added trials on intermediary outcomes to our

discussion.

Page 21, line 573ff: A recent meta-analysis of randomized controlled trials (RCTs) showed

acute beneficial effects for an intervention with increased whole grain consumption compared

to control meals, including mainly white wheat bread, on postprandial glucose and insulin

response106, which reduces pancreas exhaustion107 108. However, in medium and long-term

RCTs intervention of increased whole grain consumption had no effect on fasting glucose,

fasting insulin and insulin resistance compared to the control diet. Nevertheless, when RCTs

with people at higher risk for T2D were excluded, fasting glucose was lower in the

intervention group compared to the control group106.

Page 23, line 617ff: However, beverages high in fructose or isomaltulose, which is a slowly

absorbable disaccharide used in sports drinks, have a lower GI132. An RCT compared two

intervention groups consuming 20% of their energy requirement in form of beverages

sweetened with isomaltulose (low GI) and maltodextrin (high GI). The insulin response was

lower and insulin sensitivity better preserved in the group consuming beverages with low GI

compared to the group consuming beverages with high GI132. However, fructose that may be

contained in these beverages increases hepatic lipogenesis and insulin resistance133.

Additionally, an RCT comparing interventions of 4 servings/d of SSB, fructose-sweetened

and aspartame-sweetened beverages for eight days, ad lipitum energy intake was

significantly increased in the SSB and fructose-sweetened beverages group compared to the

aspartame-sweetened beverages group, with no difference between the first two groups.

However, since all groups received the same standard diet, which they consumed ad lipitum,

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the excess calories in the SSB and fructose-sweetened beverages possibly contributed to

the increased calorie intake in those groups134.

Comment 4: The internal inconsistency of findings may warrant some more discussion for

example for fresh red meat and total red meat. It is relevant for the public to know if they

should be avoiding all red meat or only processed meat. Currently, total red meat receives a

high evidence rating but fresh red meat does not which raises questions for actual

recommended eating practices. Similarly, if caffeinated and decaffeinated coffee show

similar associations with diabetes risk how can the evidence for caffeine be just as strong?

Some discussion on the integration of key results would be useful.

Response: We thank Professor van Dam for raising this very important point. Accordingly,

we included this issue in our discussion.

Page 22, line 590ff: High quality of evidence was also observed for the positive association

between red meat, processed meat and bacon and incidence of T2D. In a recent pooled

analysis of fourteen studies, the consumption of processed meat and unprocessed red meat

was associated with higher fasting glucose and fasting insulin levels110, and some other

studies99 111 112, but not all110, have reported similar results as well as associations with CRP,

ferritin, HbA1C and GGT99 112. In some of these studies associations were attenuated when

adjusted for BMI99 111 112, which is consistent with the much stronger associations reported

between unprocessed and processed red meat intake and T2D in analyses unadjusted for

BMI than when adjusted for BMI39 113 114. Given that both unprocessed and processed red

meat has been associated with weight gain over time109 115 it is possible that increased weight

gain may be an important mechanism by which meat intake increases incidence of T2D.

Although the association with unprocessed red meat was not significant in the meta-analysis

from 201336 , this finding needs to be interpreted with caution as additional cohort studies

have since been published116-121 and most of the larger of these cohorts found an increased

risk also with unprocessed red meat116-119. Processed meat contains high amounts of

sodium, that may cause microvascular dysfunction and increase incidence of T2D122-124, as

well as nitrates, nitrites and their by-products, such as peroxynitrite, which seems to play a

role in the pathogenesis of T2D125.

Page 25, line 678ff: In terms of internal consistency, we observed, that related exposures

showed the same direction of the association with incidence of T2D. For example, a healthy

dietary pattern (characterized, amongst others, by a high intake of whole grain products and

low intake of red and processed meat), high consumption of whole-grain products, fibre and

16

magnesium were all associated with a reduced incidence of T2D. In accordance, an

unhealthy dietary pattern, high consumption of red meat, as well as processed meat (e.g.

bacon), animal protein and heme iron were related to an increased incidence of T2D.

However, as explained above, the role of unprocessed red meat regarding incidence of T2D

needs further investigation. Moreover, the results on caffeinated and decaffeinated coffee

and caffeine warrant further discussion. Both caffeinated and decaffeinated coffee were

associated with a decreased incidence of T2D, suggesting that caffeine does not play a

major role in the health effect of coffee. Nevertheless, caffeine was also observed to

decrease T2D incidence. All associations were graded as moderate quality of evidence.

While caffeine is discussed to have beneficial properties, e.g. increase insulin sensitivity145,

the results are hard to interpret because of the strong correlation with coffee consumption55.

Therefore, caffeine might act as a marker for coffee intake, which contains several beneficial

compounds, e.g. chlorogenic acid and antioxidants, that contribute to the reduction of T2D

incidence55. Since decaffeinated coffee showed a similar association with incidence of T2D

as caffeinated coffee, it seems plausible that these other bioactive compounds in coffee

mainly contribute to the reduction of T2D incidence with coffee consumption.

Comment 5: In the 'what does this study add section' the authors conclude that the effects of

diet on type 2 diabetes risk are moderate. It should be noted, however, that the Predimed

trial showed a 50% reduction in risk with only dietary changes. Whether effects of diet as a

whole are moderate or stronger may depend on the contrast of intakes that is examined in

studies and how many different aspects of the diet are targeted in combination.

Response: Thank you for this comment. To be more careful, we changed the statement in

this section. We also included the results from the Predimed trial in the discussion.

Page 31, line 832f: There is existing evidence that dietary factors play a role in the

development and prevention of T2D.

Page 26, line 698ff: In general, diet is a complex combination of foods and nutrients that act

synergistically146. In this umbrella review, dietary patterns were all associated with incidence

of T2D, but the quality of evidence for dietary patterns was moderate. To account for the full

spectrum of the association between diet and T2D, future studies could investigate a dietary

score, including all important aspects of a healthy diet that have been identified to play a role

in the risk of T2D. This approach might be more predictive of T2D risk than the investigation

of single foods and nutrients146. For example, a strong reduced risk of T2D (reduction by

17

52%; 95% CI: 14%, 73%) was identified for the adherence to the Mediterranean diet

(supplemented with either extra virgin olive oil or mixed nuts) compared to a control diet

(advice to reduce only dietary fat) in the PREDIMED trial147. Thus, to give accurate

recommendations regarding diabetes prevention, it is important to identify the optimal diet(s).

Comment 6: The authors should be careful about the similarity in units of exposure when

they are comparing the strength of associations for different dietary factors. For example, the

units for hamburger vs bacon intake are rather different.

Response: We thank Professor van Dam for raising our attention to this lack of clarity. We

think it might add to the confusion, that we’ve given the range of summary hazard ratios in

the result section, since this might look like a direct comparison of the strengths of

associations. We therefore removed these from the text.

Specific comments

Comment 7: Abstract results: the authors present the percentage of dietary factors that were

directly, inversely, or not associated with T2D. The authors should make clear that these are

not inconsistent results, but are different results for different aspects of the diet.

Response: Thank you for pointing this out. We adapted the sentence accordingly.

Page 2, line 46ff: Of these meta-analyses of different dietary factors, 31% reported a

decreased incidence, 16% an increased risk incidence and 53% found no association

between diet and risk incidence of T2D.

Comment 8: Avoid terms such as ‘increased risk’ or ‘decreased risk’ as you are focusing only

on observational studied comparing risk for different groups of individuals rather that

changes in risk in individuals.

Response: Thank you for making this point. We changed „increased risk“ and „decreased

risk“ to „increased incidence“ and „decreased incidence“ throughout the manuscript.

Comment 9: The authors should explicitly discuss what level of evidence they believe is

sufficient to support dietary recommendations and discuss their findings for different dietary

factors accordingly. I am surprised they state that their findings support recommendations for

a Low glycemic index diet, whereas their evidence classification for this is ‘Low’.

18

Response: This is an interesting point. The statement regarding low GI was corrected. In

order to systematically discuss the most important findings, we particularly focused on the

findings with high quality of evidence, as we did in our abstract. We specified this in the

discussion section.

Page 19, line 496ff: Though intake of foods with a high GI were associated with an increased

risk incidence of T2D in our umbrella review, quality of evidence was only low and further

studies should investigate if this recommendation can be supported.

Page 21, line 565f: In the next section, further potential mechanisms for the observed

associations with high quality of evidence will be discussed.

Comment 10: The authors state or imply in the discussion section that whole grains are low

in glycemic index (GI) and sugar sweetened beverages and sucrose are high in GI. Please

note that whole grains can both be high or Low in glycemic index so this does not reflect the

same dimension of the diet. Similarly, sucrose or high fructose corn syrup have an

intermediate GI as the fructose component has a Low GI.

Response: Thank you, this is a very good point. We deleted the comparison for whole grain

and sucrose (page 22, line 587ff) and specified examples for sugar sweetened beverages.

Page 23, line 615ff: SSB, such as sugar-containing lemonades, can have high GI131, which is

related to an increase in blood sugar levels and associated with increased incidence of

T2D107 108. However, beverages high in fructose or isomaltulose, which is a slowly absorbable

disaccharide used in sports drinks, have a lower GI132. An RCT compared two intervention

groups consuming 20% of their energy requirement in form of beverages sweetened with

isomaltulose (low GI) and maltodextrin (high GI). The insulin response was lower and insulin

sensitivity better preserved in the group consuming beverages with low GI compared to the

group consuming beverages with high GI132.

Comment 11: The authors mention the fact that high quality evidence evidence was

observed for only 5% of examined dietary factors as a study limitation. Why would that be a

limitation of this study?

Response: Thank you for making this point. We eliminated this point from the limitations

section.

19

Reviewer 4, Doctor Patricia Metcalf

Comments:

Comment 1: Some of the information presented is useful, but there is too much information

which results in the manuscript being too overwhelming and losing the interest of the reader.

The manuscript needs to be shortened if it is to retain the interest of the reader. Perhaps the

manuscript should just focus on the high quality of evidence nutrients/foods.

This is an extremely long article comprising 30 pages before the Tables and 141 pages with

Figures, Tables and Supplementary material. Table 1 is 11 pages long. Most of the

paragraphs are very long. Because of the length of the manuscript, it becomes tedious.

Response: We thank Doctor Metcalf for making this point. We excluded Supplementary

Tables 3-20, which shortened the supplement by 53 pages. Supplementary Tables 3-6

contained extracted data (e.g. study characteristics and results) of the meta-analyses

included in our umbrella review. Supplementary Tables 8-20 included the same information

for all identified meta-analyses regarding dietary factors and incidence of type 2 diabetes,

including duplicate meta-analyses on the same topic. To exclude duplicate data and to avoid

confusion regarding extracted and recalculated results, we excluded these tables. We further

moved Table 1 from the manuscript to the supplement (now Supplementary Table 3), which

shortened the manuscript by 11 pages. This table provides information on the characteristics

of the included meta-analyses and results of the recalculation.

Comment 2: There is quite a lot of repetition of words in the results section. The manuscript

would be enhanced by concentrating on a briefer list of foods/nutrients, rather than including

them all. For example, breast feeding is probably not necessary, nor are most of the

micronutrients. The Supplementary Figures could be plotted with more Figures on a page to

shorten the manuscript.

Response: Thank you for this comment. We believe it is a strength of our umbrella review

that it provides such a comprehensive overview of any existing evidence regarding dietary

factors and incidence of T2D, especially since there is such a large body of research

available. By giving such a broad overview and by evaluating the quality of evidence, internal

consistencies or inconsistencies can be examined and relevant research directions can be

identified.

However, we made an effort to shorten the manuscript, as described in comment 1.

Additionally, instead of showing results from both high vs low and dose-response analyses in

20

the results and (Supplementary) Tables and Figures, we excluded the high vs low analysis

where a dose-response analysis was available. According to Doctor Metcalf’s suggestion, we

excluded the meta-analysis on ‘being breastfed‘ and incidence of type 2 diabetes. In addition,

since the editor and some of the reviewers suggested to exclude meta-analyses of RCTs, the

exposures selenium and niacin have been excluded from our report. Finally, to shorten the

manuscript, we plotted more Supplementary Figures on one page.

Page 10, line 256ff: These 153 SHRs correspond to one meta-analysis per exposure. If a

high vs low as well as a dose-response analysis was available for one exposure, we present

the dose-response analysis.

Comment 3: Most of the associations are relatively small. Where summary relative risks are

calculated for an increase in serving size, these serving sizes seem to be rather large,

emphasizing that the original relative risks were very small.

Response: Thank you for raising this point. Since it was beyond the scope of this umbrella

review to conduct our own dose-response meta-analyses, we resumed the doses from the

published meta-analyses. They were not standardized and the doses have to be considered

when interpreting the results. We added this point to our limitations section.

Page 27, line 722ff: In this context, it also has to be noted that we resumed the doses

defined in the published meta-analyses. Therefore, they are not standardized and the doses

have to be considered when interpreting the results. For example, the serving sizes defined

for total sugars, sucrose and fructose were rather large and the summary risk estimate might

be smaller when choosing a smaller serving size.

Comment 4: Some sentences are difficult to understand as they do not conform to traditional

English rules. There are some grammatical errors. Sentences should not begin with a

numerical number. The number written as a word is acceptable. 'Vegetable' not 'Vegatable'

on page 41.

Response: We thank you for raising our attention to this and apologize. We re-read the

manuscript carefully and corrected errors wherever we found them. We changed number in

the beginning of sentences to written words and corrected typeos wherever we found them.

Reviewer 5, Doctor Ulrika Ericson

21

Comments:

Comment 1: A problem might be that all criteria included in Nutrigrade contributes 0-1 points,

except the bias/study quality/ study limitations, although they may be more or less important.

This could be brought up in the discussion.

Response: We thank Doctor Ericson for raising this point, which we added to our limitations.

Page 29, line 790ff: Eight, in the NutriGrade tool all criteria contribute to the overall score

with one point, except for bias/study quality/study limitations as well as effect size which

contribute with two points. Therefore they receive more weight. However, bias/study

quality/study limitations includes several aspects, such as assessment of exposure and

outcome and confounding, which may justify a higher weight.

Comment 2: Page 21, line 38-43. In the conclusion, it is stated that future studies should

focus on less frequently investigated dietary exposures for which there is low quality

evidence for associations. However, I mean that it is important to focus on factors for which

there are plausible biological hypothesis and maybe not on single food items such as

sherbet, despite the low quality evidence. In line with this, I suggest that the conclusion is

somewhat changed in order to highlight that some exposures are more important to examine

than others.

Response: Thank you for this comment. We agree that this is an important distinction and

that our conclusions in that matter need to be more specific. We therefore adapted our

conclusions accordingly.

Page 30, line 809ff: Moreover, future studies should focus on exposures, which are

biologically likely to be associated with incidence of T2D, but for which quality of evidence is

still low. Additionally, since recommendations are based on foods and food groups, future

studies should focus on answering open questions in terms of internal inconsistencies, such

as the role of unprocessed meat and processed red meat in the harmful association of total

meat and red meat with incidence of T2D. In that context, more research is also needed on

specific foods for which evidence is still low, such as types of rice (white rice, brown rice),

types of fish (oily or lean fish) or types of fat (e.g. olive oil).

Comment 3: The importance of dietary data of high validity in future studies should be

stressed, because some true associations may not have been observed as some exposures

may be more difficult to measure than others, e.g. due to issues related to misreporting.

22

Response: We thank Doctor Ericson for making this important point. We stressed this issue

in our conclusions.

Page 30, line 807ff: It is important to attain dietary data with high validity by improving dietary

measurement methods and by assessing and accounting for changes in dietary behaviour

over time

Comment 4: Figure numbers are missing for Figures 1-3b

Response: We added the Figure numbers.

Comment 5: Page 14, Line 42: Replace in Table 4 by in Table 2?

Response: Thank you. We corrected the Table number.

Comment 6: It would be valuable to identify some specific dietary exposes that would be

especially important to examine, instead of only mentioning that less frequenltly examined

factors or factors for which the evidence is graded as low should be examined.

Response: We agree that our conclusions need to be more specific. Please see comment 2

for changes made in the manuscript.

Comment 7: Table 2: “Evidence” could be replaced by “Quality of evidence”

Response: We adapted the title of the column in Table 2 accordingly.

Reviewer 6, Professor Richard Riley

Comment 1: I must admit that I am circumspect of many nutritional epidemiology studies, as

often it is hard to identify exactly the type of food/diet under review and exactly if/how this is

related to outcome risk. I think similar concerns arise in this overview, as by taking an

umbrella overview of all diet studies in this field, the scale is very broad and it is hard to

identify specific implications for what diet is beneficial. For example, in the abstract the main

conclusions relate to food/drink such as sugary sweetened beverages, processed meat, and

red meat. But these are very broad groups – e.g. what specific sweetened beverages are we

talking about? What specific processed meat? E.g. in their discussion they say: “In

23

accordance, an unhealthy dietary pattern, high consumption of red meat, especially

processed meat (e.g. bacon, hamburgers or hot dogs), animal protein and heme iron were

related to an increased risk of T2D.” – but is it bacon or a hot dog I should be avoiding?

I find that focussing recommendations on a broad class is difficult to interpret. Of course, this

is a consequence of the authors summarising the existing evidence – so I am not criticising

the authors themselves, as they can only summarise what is reported. But I do worry about

the translation of the findings for the BMJ reader, and that the press may pick up on some

broad (non-specific) message.

Response: We thank Professor Riley for making this important point. We agree that it is very

important to give precise recommendations. Therefore we believe it is a strength of our

umbrella review that it provides such a comprehensive overview of the evidence regarding

dietary factors, including specific foods and subgroups, and incidence of type 2 diabetes. We

identified evidence on food subgroups for example for total dairy (e.g. low-fat, high-fat dairy

products), total grains (whole grain, refined grain), rice (white rice, brown rice), total meat

(red meat, processed meat, processed red meat, unprocessed red meat), fish (e.g. lean fish,

oily fish), total vegetables (green leafy vegetables, cruciferous vegetables, yellow

vegetables), total fruit (berries, citrus fruits, apples and pears), coffee (caffeinated,

decaffeinated, caffeine), total fruit juice (fruit juices with and without added sugar) and total

alcohol (beer, wine, spirits), as well as for specific foods for example milk (total, high-fat and

low-fat), yogurt, cheese, whole grain bread, whole grain cereals, bacon, hamburgers and hot

dogs. Sugar-sweetened beverages include sugar-sweetened carbonated lemonades and

fruit-flavoured carbonated sugar soft drinks. However, for specific drinks no evidence was

available. By giving such a broad overview and by evaluating the quality of evidence, internal

consistencies or inconsistencies can be examined and relevant research directions can be

identified. In our abstract we emphasize results with high quality of evidence. For specific

foods and food subgroups, however, the quality of evidence is low or very low and more

research is needed for specific recommendations to be made. We added this to our

discussion and conclusion.

Discussion: Page 22, line 590ff: High quality of evidence was also observed for the positive

association between red meat, processed meat and bacon and incidence of T2D. In a recent

pooled analysis of fourteen studies, the consumption of processed meat and unprocessed

red meat was associated with higher fasting glucose and fasting insulin levels110, and some

other studies99 111 112, but not all110, have reported similar results as well as associations with

CRP, ferritin, HbA1C and GGT99 112. In some of these studies associations were attenuated

24

when adjusted for BMI99 111 112, which is consistent with the much stronger associations

reported between unprocessed and processed red meat intake and T2D in analyses

unadjusted for BMI than when adjusted for BMI39 113 114. Given that both unprocessed and

processed red meat has been associated with weight gain over time109 115 it is possible that

increased weight gain may be an important mechanism by which meat intake increases

incidence of T2D. Although the association with unprocessed red meat was not significant in

the meta-analysis from 201336 , this finding needs to be interpreted with caution as additional

cohort studies have since been published116-121 and most of the larger of these cohorts found

an increased risk also with unprocessed red meat116-119. Processed meat contains high

amounts of sodium, that may cause microvascular dysfunction and increase incidence of

T2D122-124, as well as nitrates, nitrites and their by-products, such as peroxynitrite, which

seems to play a role in the pathogenesis of T2D125.

Discussion: Page 25, line 678ff: In terms of internal consistency, we observed, that related

exposures showed the same direction of the association with incidence of T2D. For example,

a healthy dietary pattern (characterized, amongst others, by a high intake of whole grain

products and low intake of red and processed meat), high consumption of whole-grain

products, fibre and magnesium were all associated with a reduced incidence of T2D. In

accordance, an unhealthy dietary pattern, high consumption of red meat, as well as

processed meat (e.g. bacon), animal protein and heme iron were related to an increased

incidence of T2D. However, as explained above, the role of unprocessed red meat regarding

incidence of T2D needs further investigation. Moreover, the results on caffeinated and

decaffeinated coffee and caffeine warrant further discussion. Both caffeinated and

decaffeinated coffee were associated with a decreased incidence of T2D, suggesting that

caffeine does not play a major role in the health effect of coffee. Nevertheless, caffeine was

also observed to decrease T2D incidence. All associations were graded as moderate quality

of evidence. While caffeine is discussed to have beneficial properties, e.g. increase insulin

sensitivity145, the results are hard to interpret because of the strong correlation with coffee

consumption55. Therefore, caffeine might act as a marker for coffee intake, which contains

several beneficial compounds, e.g. chlorogenic acid and antioxidants, that contribute to the

reduction of T2D incidence55. Since decaffeinated coffee showed a similar association with

incidence of T2D as caffeinated coffee, it seems plausible that these other bioactive

compounds in coffee mainly contribute to the reduction of T2D incidence with coffee

consumption.

Conclusion: Page 30, line 811ff: Additionally, since recommendations are based on foods

and food groups, future studies should focus on answering open questions in terms of

25

internal inconsistencies, such as the role of unprocessed meat and processed red meat in

the harmful association of total meat and red meat with incidence of T2D. In that context,

more research is also needed on specific foods for which evidence is still low, such as types

of rice (white rice, brown rice), types of fish (oily or lean fish) or types of fat (e.g. olive oil).

Comment 2: Another reason for concern is the difficulty in adjusting for confounders, as the

findings are all based on primary studies that were observational. As the focus in the review

is at the broad umbrella review level, I do find it quite detached from the original primary

studies. In particular, what adjustment factors were used in each primary study? Were they

adequate? What methods were used to adjust for confounding in primary studies and were

they suitable? Is a linear dose response relationship truly justified? Indeed, was this even

checked in the original studies, let alone at this umbrella review stage? These are just some

examples of why I find the review rather detached from the original primary studies, and thus

it is hard to ascertain whether the findings are meaningful.

Response: Professor Riley is correct. The primary studies, which were included in the meta-

analyses and thus, in our umbrella review had all a prospective observational study design.

To account for the influence of potential confounding regarding the association between

dietary factors and incidence of T2D, we excluded primary studies showing only crude

estimates. Almost all of the primary studies (90%) adjusted for age and sex. Further

important potential confounders were considered in most of the studies: 87% adjusted for

smoking status, 86% for BMI and physical activity. Two thirds of the studies also adjusted for

further dietary factors, including total energy intake (67%), alcohol intake (65%), or other

dietary factors (60%). Half of the studies (52%) adjusted for family history of diabetes. The

corresponding risk ratios with their 95% CI were calculated by using multivariable Cox

proportional hazard regression models in 80% of the studies and multivariable logistic

regression model in the remaining 20%. We added more detailed information about the

adjustment factors and methods. In addition, our limitation section includes a statement

about residual confounding.

Since it was beyond the scope of this umbrella review to conduct our own dose-response

meta-analyses, we recalculated them if the dose-response estimate for each primary study

was presented separately. If this information was missing, we could not recalculate the dose-

response meta-analysis, but extracted the SHRs from the published meta-analysis.

Information on linearity of the dose-response relations were available for 72% of the dose-

response analyses. For one third of these dose-response relations there was indication for

non-linearity (potential renal acid load (PRAL), yogurt, ice cream, chocolate, processed meat,

26

olive oil, whole grain, total grains, whole grain bread, whole grain cereals, wheat bran, brown

rice, total fruit, apples and pear, total vegetables, cereal fibre, fruit fibre, vegetable fibre,

magnesium and anthocyanins). To derive recommendations, further investigation is needed

to set optimal cut-points. We added this to our results and limitations.

Methods: Page 9, line 224ff: If the published meta-analysis included a primary study only

reporting crude estimates, this study was excluded from our re-analysis.

Results: Page 12, line 295ff: All included primary studies conducted multivariable adjustment

using regression models (80% Cox proportional hazard regression model, 20% multivariable

logistic regression). Almost all of the primary studies (90%) adjusted for age and sex, 87%

for smoking, 86% for BMI and physical activity, respectively, 67% for total energy intake,

65% for alcohol intake, 60% for other dietary factors or cardiovascular risk factors (e.g.

Hypertension), respectively, and 52% for family history of diabetes.

Discussion: Page 27, line 738ff: Nevertheless, the most important confounders were

adjusted for in most of the primary studies (90% for age and sex, 87% for smoking, 86% for

BMI and physical activity, respectively. However, residual confounding cannot be completely

ruled out. For example, only half of the studies (52%) adjusted for family history of diabetes,

which should be included in the adjustment model of future studies.

Results: Page 12, line 303ff: Information on linearity of the dose-response relations were

available for 72% of the dose-response analyses. For one third of these dose-response

relations there was indication for non-linearity (PRAL, yogurt, ice cream, chocolate,

processed meat, olive oil, whole grain, total grains, whole grain bread, whole grain cereals,

wheat bran, brown rice, total fruit, apples and pear, total vegetables, cereal fibre, fruit fibre,

vegetable fibre, magnesium and anthocyanins).

Discussion: Page 27, line 727ff: Additionally, information on linearity of the dose-response

relations were available for 72% of the dose-response analyses. For one third of these dose-

response relations there was indication for non-linearity. To derive recommendations, further

investigation is needed to set optimal cut-points.

Comment 3: In regards to the adjustment factors, the authors say: “Almost all of the primary

studies adjusted at least for age and sex, with the exception of four primary studies which

reported crude estimates.” – surely these 4 studies should be removed? Further, “ 80% of

the primary studies conducted a multivariate adjustment (e.g. for total energy, body mass

27

index, smoking status and physical activity).” – yes, but were the adjustment factors

adequate? It would perhaps have been clearer had the authors pre-specified a set of

adjustment factors that were considered essential (minimum required), in order to have some

credence that the adjusted results were only prone to small residual confounding.

Response: We thank Professor Riley for raising this point. To account for the influence of

potential confounders regarding the association between dietary factors and incidence of

T2D, we excluded primary studies showing only crude estimates. In the investigation of

dietary factors and T2D, the most important confounders include age, sex, other lifestyle

factors (such as smoking, physical activity), overweight, total energy, other dietary factors,

alcohol intake, and family history of diabetes. We checked all primary studies (n=277) which

confounders were included in their statistical analysis and added this in more detail to our

results and discussion section. Moreover, the level of adjustment was also considered in the

assessment of the quality of the evidence by using NutriGrade. The first item focuses on

(amongst others) the inclusion of potential confounders (Item 1: Risk of bias/ study quality/

study limitations).

Methods: Page 9, line 224ff: If the published meta-analysis included a primary study only

reporting crude estimates, this study was excluded from our re-analysis.

Results: Page 12, line 301ff: Three primary studies only reported crude estimates and where

therefore excluded from the meta-analyses on milk81, total coffee81 and total alcohol82 83. This

did not affect the results.

Results: Page 12, line 297ff: Almost all of the primary studies (90%) adjusted for age and

sex, 87% for smoking, 86% for BMI and physical activity, respectively, 67% for total energy

intake, 65% for alcohol intake, 60% for other dietary factors or cardiovascular risk factors

(e.g. Hypertension), respectively, and 52% for family history of diabetes.

Discussion: Page 27, line 738ff: Nevertheless, the most important confounders were

adjusted for in most of the primary studies (90% for age and sex, 87% for smoking, 86% for

BMI and physical activity, respectively. However, residual confounding cannot be completely

ruled out. For example, only half of the studies (52%) adjusted for family history of diabetes,

which should be included in the adjustment model of future studies.

Comment 4: Related point: in the discussion it says “It is likely that individuals with unhealthy

dietary behaviours, such as low intake of whole grains and fibre, as well as higher intake of

28

red and processed meat, have an unhealthier lifestyle per se, such as higher rates of obesity,

smoking and physical inactivity83-85. Most of the included studies adjusted for these factors,

and associations persisted” – the word ‘most’ is not reassuring to me, but moreover the

question remains as to whether the adjustment of these factors (when done) was actually

adequate. Was a regression approach used without backwards/forwards selection of

adjustment variables? Or perhaps a propensity score analysis was done – but was it done

well? Etc.

Response: According to this comment, we went back to all primary studies (n=277), and

checked the methods of adjustment and the confounders which were included in the primary

studies. As described above (comment 3), we predefined a set of important confounders and

checked all primary studies which confounders were included in their statistical analysis. In

addition, 80% of these studies used multivariable Cox proportional hazard regression models

and the remaining 20% multivariable logistic regression model. No further approach has

been applied.

We added this information to the methods section and the mentioned part in the discussion.

Methods: Page 7, line 168ff: For each primary study included in the published meta-analysis

we extracted [S] as well as the adjustment factors included in the model to check if relevant

confounders were accounted for. Based on the literature, the most important potential

confounders in the investigation between dietary factors and incidence of T2D include age,

sex, smoking, physical activity, overweight, other dietary factors, including total energy

intake, alcohol intake, and family history of diabetes.

Discussion: Page 21, line 557ff: However, 87% of the included primary studies adjusted for

smoking and 86% for BMI and physical activity, respectively, in multivariable regression

models and the associations persisted. Nevertheless, residual confounding cannot be ruled

out, [S].

Comment 5: Multivariate adjustment should say multivariable adjustment

Response: We apologize for the mistake and adapted it in the manuscript.

Comment 6: I find the data extraction description confusing in the methods. E.g. “If the RR

estimates from primary studies of a dose-response meta-analysis were not reported in the

published meta-analysis, we did not recalculate the meta-analysis, but extracted the SRR

from the published meta-analysis. If we could not identify a RR estimate from a primary study

29

of a high vs. low meta-analysis in the published meta-analysis or the primary study itself, we

excluded that particular primary study from our meta-analysis.” – please re-write this in

clearer language for the BMJ reader to follow.

Response: Thank you for this comment. We re-wrote the sentences to be clearer.

Page 9, line 226ff: We recalculated dose-response meta-analyses if the dose-response

estimate for each primary study was presented separately. If this information was missing,

we could not recalculate the dose-response meta-analysis, but extracted the SHRs from the

published meta-analysis.

Comment 7: I might be wrong, but it appears to me that the authors are pooling meta-

analysis results. Why not actually take the original primary study results, and pool these in a

single meta-analysis? I do not see why pooling the original meta-analysis results is more

helpful. Please can they justify this. Also, does this not then make the heterogeneity a

between-meta-analysis heterogeneity? Rather than a between-study heterogeneity? If so,

this is hard to interpret.

Response: We apologize for the misunderstanding about the level of data pooling. We did

not pool meta-analysis results. For each exposure we chose the one meta-analysis that

included the largest numbers of studies and study participants, which was usually the most

recent one. Our umbrella review was conducted as it has been done in the past (e.g. Poole

BMJ 2017, PMID: 29167102; Kalliala BMJ 2017, PMID: 29074629; Kyrgiou BMJ 2017,

PMID: 28246088; Tsilidis BMJ 2015, PMID: 25555821). We recalculated the existing meta-

analyses to make sure that the calculations were done by the same random effects model,

and to receive further information for the evaluation of the quality of evidence, including tau²,

prediction intervals, I², publication bias etc. For clarification, we have revised the contents of

description of the methods, including the study design.

Introduction: Page 4, line 91ff: Umbrella reviews are very useful tools in research that provide

a comprehensive overview of evidence of published systematic reviews and meta-analyses

on a specific topic. They are helpful to elucidate the strength of evidence and the precision of

the estimates and evaluate risk of bias of the published reports8.

Methods: Page 6, line 153ff: If more than one published meta-analysis on the same

association was identified, we chose only one meta-analysis for each exposure to avoid the

inclusion of duplicate studies.

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Methods: Page 9, line 211ff: For each exposure the meta-analysis was recalculated using

the maximally adjusted hazard ratios of the primary studies included in the published meta-

analyses. To assure that all summary hazard ratios (SHRs) were calculated by using a

random effects model and to receive further information for the evaluation of the quality of

evidence, including tau² (τ2), 95%-prediction intervals (95%-PIs), I² and publication bias, we

recalculated the SRRs SHRs and their corresponding 95%-CIs by using the random effects

model by DerSimonian and Laird, which takes into account both within and between study

heterogeneity18.

Comment 8: It also appears that the meta-analysis results bare eing pooled ignoring the

uncertainty in heterogeneity estimates (within a meta-analysis and across meta-analyses).

This would be easier to address if pooling all the study-specific results in one go. See

references such as Cornell et al. and the use of methods such as the hartung Knapp method

for widening confidence intervals Cornell JE, Mulrow CD, Localio R, et al. Random-effects

meta-analysis of inconsistent effects: a time for change. Ann Intern Med 2014;160(4):267-70.

Hartung J, Knapp G. A refined method for the meta-analysis of controlled clinical trials with

binary outcome. Stat Med 2001;20(24):3875-89.

Response: Thank you for raising this very interesting point. As stated earlier, we pooled

findings from primary studies and not estimates from different meta-analysis. We decided to

apply random effects model by DerSimonian and Laird, which takes into account both within

and between study heterogeneity. Since, this method has been used in previous meta-

analyses, we chose this approach, to ensure comparability with the published meta-

analyses.

Page 9, line 213ff: To assure that all summary hazard ratios (SHRs) were calculated by

using a random effects model and to receive further information for the evaluation of the

quality of evidence, including tau² (τ2), 95%-prediction intervals (95%-PIs), I² and publication

bias, we recalculated the SHRs and their corresponding 95%-CIs by using the random

effects model by DerSimonian and Laird, which takes into account both within and between

study heterogeneity18. Since, this method has been used in previous meta-analyses, we

chose this approach, to ensure comparability with the published meta-analyses.

Comment 9: Heterogeneity should not be measured by I2, and it is wrong to use values of I2

to define low, moderate or high heterogeneity. Better to report estimate of the heterogeneity

itself (tau-squared) and, possible, prediction intervals to disseminate the heterogeneity.

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Rucker G, Schwarzer G, Carpenter JR, et al. Undue reliance on I(2) in assessing

heterogeneity may mislead. BMC Med Res Methodol 2008;8:79.

Response: We thank Professor Riley for making this point. We do not define heterogeneity

by categorization of I² anymore. We additionally calculated tau2 and 95% prediction intervals

as recommended. In addition, after discussion with our director of the institute (Professor

Oliver Kuß, Biometrician), we decided to include the dispersion around the SHR by

calculating the interval where 95% of the primary HRs lie within (two-sigma rule: θ�± 2τ) to

provide further information about heterogeneity. We have added the related information in

the methods and the results section and in Supplementary Table 3.

Methods: Page 9, line 237ff: Heterogeneity was evaluated by using the I2 statistics. The I2

value ranges from 0% to 100% and represents the percentage of the total variation across

studies that can be explained by heterogeneity19. However, I2 is dependent on the study size

(it increases with increasing study size). Therefore, we additionally calculated τ2, which is

independent of the study size and describes between study-variability of the risk estimate20.

In addition, we used the two-sigma rule (θ�± 2τ) to calculate the interval where 95% of the

primary HRs lie within to further evaluate the dispersion around the SHR. Finally, we

calculated 95%-prediction intervals (95%-PIs) which further account for heterogeneity and

show the range in which the effect estimates of future studies will lie with 95% certainty21.

Results: Page 15, line 401ff: I², τ 2, dispersion around the SHRs and 95%-PIs are reported in

Supplementary Table 3. For 23% and 29% of the meta-analyses τ2 and the 95%- PIs could

not be recalculated. As for the 95-% PIs, only 5% of the meta-analyses excluded the null-

value, namely the high vs low analyses of healthy dietary pattern, unhealthy dietary pattern

and breakfast skipping and the dose-response analyses of apples and pears, total coffee,

artificially sweetened beverages, light, moderate and high wine intake and magnesium. That

indicates that it is expected that findings in future studies on these exposure will point to the

same directions. However, for the majority of the findings, it is likely that the estimate

obtained in a future primary study might result in a null finding.

Comment 10: Funnel plot asymmetry does not imply publication bias; a better word is

small=study effects, which indeed may be due to pub bias, but might also be due to other

things.

Response: Thank you for this remark. We changed the terms accordingly.

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Page 16, line 432f: When we explored the funnel plots (Supplementary Figures 2-19), there

was indication for small study effects for [S]

Comment 11: The authors report relative risks. But are the studies really reporting hazard

ratios? And if not, then what are the time-points of interest for diabetes onset, as the RRs are

time-specific measures. This is a critical issue, because I do not see justification for why

relative risks are useful in this context and not hazard ratios. ? If they are hazard ratios, then

really are these constant over time? Was this checked in the original studies? Another

example, perhaps, of being too detached from original studies.

Response: Professor Riley is absolutely right, most of the primary studies (80%) reported on

hazard ratios using the cox proportional hazard regression model. The remaining 20%

calculated relative risk by applying logistic regression models. All of the studies included had

a prospective study design, and participants were free of type 2 diabetes at baseline.

According to this comment, we changed the term summary relative risk (SRR) to summary

hazard ratio (SHR) in the manuscript.

Comment 12: What does this mean: “For most of the associations, there was no indication

for presence of publication bias according to Egger’s test (p≥0.10), with the exception of

chocolate, whole grain, wheat germ, rice, white rice, soy products, legumes, hot dogs, animal

protein, monounsaturated fatty acids, total carbohydrates, total fibre, vitamin D, total iron in

high vs. low meta-analyses, as well as total dairy, low-fat milk, coffee and cereal fibre from

dose-response meta-analyses (Table 1).” – the authors imply no publication bias, and then

list many areas where there may be. I find this confusing.

Response: Thank you for raising our attention to this lack of clarity. We adapted the sentence

accordingly.

Page 16, line 424ff: There was indication for presence of publication bias according to

Egger’s test (p≥0.10) for [S] rice, [S].

Comment 13: Is it justified to mix cohort and case control studies? Moreover, it seems that

‘cross-sectional’ studies are also included. But surely we need a design with a time-to-event

outcome, to at least have reassurance that the diet recording was made at a point before the

onset of diabetes. More explanation is needed in these matters.

33

Response: We apologize that it does not become clear from our manuscript. We only

included prospective cohort studies in the meta-analyses.

Page 6, line 131f: Studies were included if they met the following criteria: (1) Meta-analysis of

observational prospective cohort studies [S]

Page 9, line 222ff: If the published meta-analysis included retrospective case-control studies

or cross-sectional studies as well as prospective cohort studies, we only included results

from the prospective cohort studies in our meta-analysis.

Comment 14: “the quality of evidence by applying the NutriGrade scoring system, which

comprises different sources of bias (including funding), study design, heterogeneity between

studies, the effect size and its precision.” – I do not see why the effect size and its precision

should be used to define quality. A more precise estimate does not imply higher quality.

Indeed a good quality study should be defined independent to any effect size estimate and

any magnitude of precision. Yes, bias may impact these things, but the actual decision about

quality should be based on the information about the factors that cause it.

Response: Thank you for this comment. The quality of evidence provides information about

the level of confidence that can be put in a summary effect estimate, which was calculated by

meta-analysis. According to quality of evidence assessment tools like GRADE and

NutriGrade, both effect size and precision are indicators for certainty or uncertainty of such a

result. The magnitude of the effect is included under the general assumption that very large

effects are less likely driven by confounding (Guyatt et al, Grade guidelines 9, J Clin

Epidemiol, 2011). In GRADE, a large effect (RR >2 or <0.5) in observational studies scores

one more point and a very large effect (RR >5 or <0.2) two more points (GRADE handbook,

2013). Since such very large effects are unlikely in nutritional research, the scoring

procedure was adapted in NutriGrade (Schwingshackl et al, Advances of Nutrition, 2016).

Imprecision is considered an important factor when evaluating the quality of evidence, since

wide confidence intervals are usually seen in small studies and raise uncertainty about the

findings (GRADE handbook, 2013). On the other hand, precise estimates raise confidence in

a result (Guyatt et al, Grade guidelines 6, J Clin Epidemiol, 2011).

Comment 15: In regards to evaluating quality, I also found it confusing that the overall quality

assessment is made at the meta-analysis level (i.e. each meta-analysis included in the

umbrella review), and not at the study-specific level. If the authors rather pool the original

34

studies, rather than the meta-analysis results, should the quality assessment be made at the

study-specific level? They could even remove primary studies that were at high risk of bias,

which would otherwise still be included in the meta-analysis feeding into the umbrella review.

Response: We thank Professor Riley for raising this point. An umbrella review provides an

overview of meta-analyses, thus giving an overview of meta-evidence. Therefore, it is of

interest to evaluate the quality of this meta-evidence especially regarding their possible use

as basis for public health recommendations. This evaluation provides information on the level

of confidence that can be put into the summary risk estimates and if they are robust or likely

to change with future research. We agree that the quality of evidence of a meta-analysis

depends on the quality of primary studies. However, this aspect is included in NutriGrade

(Item 1: Risk of bias/ study quality/ study limitations) and therefore accounted for in the

overall quality of evidence provided in this umbrella review.

It was beyond the scope of this umbrella review to conduct or report subgroup and sensitivity

analyses. We added this to our limitations.

Page 8, line 189ff: The quality of evidence was evaluated by using a modified version of

NutriGrade17 (modifications are described in Supplementary Table 5). It is a numerical

scoring system (max. 10 points), which includes eight items: Risk of bias/ study quality/ study

limitations (mean of all primary studies included in the published meta-analysis) (0-2 points)

[S].

Page 28, line 748ff: Third, we did not explore subgroup analysis (e.g. by sex, geographic

locations, adjustment factors like BMI) or sensitivity analysis (e.g. exclusion of studies at high

risk of bias).

Comment 16: Is publication bias examined at the study-level or the meta-analysis level. That

is, are multiple primary study-specific estimates plotted on the funnel, or the multiple meta-

analysis results per diet type presented on the funnel plot. Again, I find it hard to ascertain

the level of the pooling. I think it is the primary study level.

Response: We thank the reviewer for this comment and apologize for the lack of clarity.

Publication bias was assessed at study-level. Therefore study-specific estimates are plotted

in the funnel.

As described in comment 7 the methods section is now clearer described regarding this

point. Additionally, we adapted the description of Supplementary Figures 2-20 accordingly.

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Page 10, line 249ff: Publication bias and small study effects were assessed for each meta-

analysis by using graphical and statistical tests, namely the funnel plot and Egger’s test22 23.

Therefore, the primary studies from the meta-analyses included in our umbrella review, were

plotted.

For example Supplementary Page 28: Supplementary Figure 4: Funnel plots for the

association between A) eggs (dose-response) and incidence of type 2 diabetes. For each

meta-analysis the study-specific estimates were plotted in the funnel.