Natural variation in biomarkers indicating mastitis in healthy cows

Natural variation in biomarkers indicating mastitis in healthy cows

Maria Åkerstedt1,2*, Linda Forsbäck1, Torben Larsen3 and Kerstin Svennersten-Sjaunja1

1Department of Animal Nutrition and Management, Kungsängen Research Centre, Swedish University of Agricultural Sciences,SE-753 23 Uppsala, Sweden2Department of Food Science, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden3Department of Animal Health and Bioscience, Faculty of Agricultural Sciences, Aarhus University, DK-8830 Tjele, Denmark

Received 25 June 2010; accepted for publication 18 October 2010; first published online 7 December 2010

Dairy herds are expanding and, with increasing numbers of animals in each herd, there is a need forautomatic recording of indicators in milk in order to detect mastitis, inflammation of the udder.A number of biomarkers for mastitis have been suggested over the years. Mastitis usually occurs inone of the four udder quarters and since it is now possible to milk each udder quarter separately inautomatedmilking systems, it is important to evaluate the normal variation in the biomarkers at udderquarter level. This study evaluated the normal variations between milkings for some biomarkersin clinically healthy cows, determined by repeated somatic cell count and bacteriological analysis.The biomarkers studied were serum amyloid A (SAA), haptoglobin (Hp), lactate dehydrogenase(LDH), N-acetyl-β-D-glucosaminidase (NAGase) and alkaline phosphatase (AP), parameters that havebeen suggested as markers for mastitis. Ten cows were monitored on 42 consecutive milkingoccasions through collection of udder quarter milk samples and representative cow compositemilk samples, giving a total of 2100 individual milk samples. Each cow had its individual profile forthe concentrations and variations in the parameters analysed. Although there was relatively largevariation between cows for the biomarkers analysed, the variation between milkings in clinicallyhealthy quarters within cows was often below 10%. The biomarker with the lowest variation in thisstudy was LDH. The results suggest that comparing quarters within an individual cow can identifydeviations from the natural variations betweenmilkings. This could be a valuable tool instead of, or incombination with, a cut-off value for each parameter in order to detect changes in the milk indicatingmastitis.

Keywords: Biomarkers, mastitis, milk, cow.

Commercial dairies require milk of the highest possiblecomposition and hygiene standards to meet consumerdemand for safe and high-quality products. The productioncapacity of dairy cows has increased tremendously with highdemands to maintain cow health and welfare, since onlyhealthy cows will produce milk of high quality. Despitemany control programmes and much research, mastitis(inflammation of the udder) is still the most common diseaseof dairy cows and is one of the main reasons for impairedmilk quality (Bradley, 2002; Le Roux et al. 2003).

During recent years the number of dairy herds has de-creased, while the number of cows per herd has increased.Consequently, there is less time for the farmer to manageeach animal and more automated milking systems have

been installed in dairy farms. There is an increased need fortools that can help the farmer in management decisions, forexample through frequent analyses of the milk. It would beuseful if different biomarkers in the milk could be used todetect changes related to different conditions, e.g. udderhealth disturbances. It is common for only one udder quarterto be affected by mastitis and the condition may go unde-tected owing to the dilution effect from the other threequarters on cow composite samples, especially in cases ofsubclinical mastitis (Berglund et al. 2007; Forsbäck et al.2009). Today in-line recording is possible on udder quarterlevel in automated milking systems and on cow compositemilk level in milking parlours.Somatic cell count (SCC) has been used as the gold

standard for decades to diagnose subclinical mastitis and isalso an important parameter in dairy cooperative qualityprogrammes, greatly affecting the price to the producer.However, SCC does not always correlate with infection of*For correspondence: [email protected]

Journal of Dairy Research (2011) 78 88–96. © Proprietors of Journal of Dairy Research 2010doi:10.1017/S0022029910000786

88

the udder (Schepers et al. 1997, Lakic et al. 2009) and it maybe affected by other factors, e.g. lactation number, stage oflactation, milk production level, stress, season and breed(Harmon, 1994; Schepers et al. 1997). Various cut-off valuesfor SCC in mastitis diagnostics have been discussed, but thesuggested level differs between studies since different con-ventions are used in different countries and different breedsare involved, as well as various types of milk samples(Mattila et al. 1986; Holdaway et al. 1996). New indicatorsfor inflammation are needed for more sensitive and specificidentification of infected animals (Pyörälä, 2003; Leitneret al. 2006).

For many years there has been an interest in using differentenzymes in milk as biomarkers for mastitis. Lactate dehydro-genase (LDH), which is a cytoplasmic enzyme, has beensuggested as a biomarker for udder health disturbances(Chagunda et al. 2006b; Hiss et al. 2007) and statisticalmodels for mastitis detection have been developed usingLDH (Chagunda et al. 2006a). The enzyme is now beingused as a mastitis indicator in commercial herd manage-ment, and it is possible to analyse LDH in-line in cowcomposite milk samples. Furthermore, there are a numberof publications concerning N-acetyl-β-D-glucosaminidase(NAGase) as a marker for mastitis (Mattila et al. 1986;Holdaway et al. 1996; Nielsen et al. 2005). NAGase is anintracellular enzymewhich is mainly released from the poly-morphonuclear leucocytes during phagocytosis and celllysis. The connection between milk alkaline phosphatase(AP) and mastitis has been recognized for decades (Bogin &Ziv, 1973; Babaei et al. 2007). However, in practice only afew studies on this enzyme have been published, although ithas recently been suggested as a biomarker for humanmastitis (Rasmussen et al. 2008).

The major bovine acute phase proteins, serum amyloid A(SAA) and haptoglobin (Hp), have been suggested asbiomarkers for mastitis (Eckersall et al. 2001; O’Mahonyet al. 2006; Hiss et al. 2007) but recently also as markers formilk quality (Åkerstedt et al. 2008). Hp and SAA arehepatically produced, but there is also local production inthe udder (McDonald et al. 2001; Hiss et al. 2004). Both Hpand SAA have antibacterial functions; Hp through bindingfree haem groups (Eaton et al. 1982) and SAA throughattaching to Gram-negative bacteria and thereby facilitatingphagocytosis (Larson et al. 2005), and consequently they areinteresting as early biomarkers for mastitis.

There is a lack of knowledge concerning the variationsbetween milkings in those parameters that could be usedas indicators for mastitis in clinically healthy udderquarters. It is important to gain information about the naturalvariations in clinically healthy animals in order to detectdeviations outside these normal variations. Such informationis needed to evaluate whether the biomarker is a possiblecandidate for routine analyses of the milk for mastitisindication, and if so how often samples must be collected,since a biomarker with high variation between milkings hasto be sampled more often than a biomarker with low naturalvariation.

The aim of this study was to investigate the level andvariations between milkings in the biomarkers SAA, Hp,LDH, NAGase and AP at udder quarter level in ten clinicallyhealthy cows during 42 consecutive milkings.

Materials and Methods

Animals

Ten cows of the Swedish Red breed were included in thestudy. They belonged to the Swedish University of Agricul-tural Sciences dairy farm, Kungsängen Research Farm,Uppsala. The study was approved by the Uppsala EthicalCommittee.The cows were considered clinically healthy, i.e. had

no clinical signs of disease or abnormalities in the udder ormilk. The criterion for inclusion in the study was a cow com-posite SCC <100000 cells/ml and bacteriologically negativesamples in all four udder quarters before the trial started.Udder health status was determined by SCC analysis and re-peated bacteriological sampling on udder quarter level. Thecow composite SCC ranged from 9000 to 72 000 cells/ml(mean±SD 33 000±21000 cells/ml). Despite a low cowcomposite SCC (53000 cells/ml) before the trial started andbacteriologically negative samples in all four udder quarters,one of the cows (cow A) appeared to be subclinicallyinfected in one udder quarter with Enterobacter cloacae. Thequarter was tested bacteriologically positive for the first timeon day 3 of the study period and continued to be infectedthroughout the whole study period.The cows were tethered in a tie stall, fed according to

Swedish recommendations (Spörndly, 2003) and weremilked twice a day. The study was performed during threeconsecutive weeks in the spring (April–May). Mean±SD

for lactation number, lactation week and daily milk yieldwere 2±2 lactations, 27±9 weeks and 21·6±1·7 kg milk,respectively.

Milking equipment and sampling procedure

Udder quarter milk samples and cow composite milksamples were collected during every milking for a total of42 consecutive milking occasions. Cows were milked with aspecially designed milking machine, constructed and pro-vided by DeLaval International (14721 Tumba, Sweden)with monovac, pulsation ratio 70/30 and system vacuum42 kPa. This milking machine collects the total milk volumefrom each udder quarter in a separate bucket for the wholemilking. After milking was completed, samples from eachindividual udder quarter were collected, and then all milkfrom the four buckets was commingled and a representativecow composite milk sample was taken. After SCC analysis,the milk samples were frozen and stored at �70 °C untilanalysis of SAA, Hp, LDH, NAGase and AP.Bacteriological sampling was performed from all udder

quarters at the end of each morning milking. Before samplecollection, the teats were wiped with an udder towel, the first

Biomarkers for mastitis in milk 89

jets of milk were discarded and the teat ends were dis-infected with 70% alcohol and allowed to dry. Within anhour after milking was completed, each quarter milk samplewas cultured on 5% blood agar with 0·05% aesculin andincubated for 48 h. When growth was observed, the agarplate was sent to the mastitis laboratory, National VeterinaryInstitute, Uppsala, Sweden, and evaluated accordingto quality assurance protocol SS-EN ISO/IEC 17025. SCCwas measured by an electronic fluorescence-based cell-counting technique (Fossomatic 5000, Foss, 3400 Hillerød,Denmark).

Assay of serum amyloid A and haptoglobin

SAA was determined using a commercial ELISA kit(Mast ID RANGE Milk Amyloid A Assay, cat TP-807,Tridelta Development Ltd, Wicklow, Ireland). Milk sampleswere initially diluted 1:50, and samples with an opticaldensity above the range of the standard curve were furtherdiluted and re-analysed. Optical density was read onan automatic plate reader (Model ELx 800; Bio-Tek Inc,San Diego CA, USA) at 450 nm with a reference at 630 nm.The limit of detection (LOD) of the ELISA was 0·1mg/laccording to the manufacturer.

Hp was analysed by a previously described optical bio-sensor assay (Åkerstedt et al. 2006) with somemodifications.The sensor surface was immobilized using a solution of20 mg/l instead of 500mg/l Hp in 0·01 M-acetate buffer, andthe activation of the surface during immobilization wasreduced from7min to 3min. The concentration of the regen-eration solution, sodium dodecyl sulphate, was increasedfrom 2mM to 3mM. In addition, an extra reconditioning stepwas added after the ordinary regeneration step, in which50mM-glycine pH 9·5 was injected over the sensor surfacefor 30 s. Bovine Hp (Life diagnostics, 19380 West ChesterPA, USA) was used for immobilization and standards, andthe LOD was 1·0 mg/l.

Assay of lactate dehydrogenase,N-acetyl-β-D-glucosaminidase and alkaline phosphatase

NAGase (EC 3.2.1.30) activity, was determined by a kinetic,fluorometric assay, in principle according to Kitchen et al.(1978) and Schüttel (1999). The intra assay precision was 5·0and 3·7 (CV%), respectively, and the inter assay precisionwas 6·3 and 5·4 (CV%) for low (1·11 U/l) and high (5·56 U/l)controls, respectively. LDH (EC 1.1.1.27) activity, wasanalysed according to Larsen (2005), by a fluorometric,kinetic method. The intra assay and inter assay precision was6·6 and 4·2 (CV%) and 9·7 and 10·1 (CV%) for low and highcontrols, respectively. AP (EC 3.1.3.1) activities were deter-mined by kinetic, fluorometric detection, using 4-methyl-umbelliferone phosphate (41504-0010, Acros Organics,2440 Geel, Belgium) as substrate (Fernley & Walker,1969). The activity was determined at pH 10·0 (120mM-diethanolamine buffer). Excitation wavelength was 355 nm,emission was detected at 460 nm. The intra assay precision

was 3·9 (low) and 5·2 (high) (CV%), respectively, and interassay precision was 11·2 (low) and 7·7 (high) (CV%) forcontrol samples, respectively.

Statistical analyses

Unadjusted mean, SD and coefficient of variation (CV) forSAA, Hp, LDH, NAGase and AP in udder quarter milk werecalculated with the GLM and VARCOMP procedure in SAS9.1 (SAS Institute, 2004) using the following model:

Yijklm ¼ mþ ai þ ðabÞij þ ðabgÞijk þ ðadÞil þ eijklm

where:Yijklm = is the ijklmth observation of each traitμ = general meanαi = the effect of the ith cow (1, 2,..,10) if ten cows or(1, 2,.., 9) if nine cows(αβ)ij = the effect of the jth udder quarter within cow(1, 2, 3, 4)(αβγ)ijk = the effect of the kth milking within cow and udderquarter (1, 2)(αδ)il = the effect of the lth week within cow (1, 2, 3)eijklm = random error, normal distributed

Unadjusted mean, SD and CV for SAA, Hp, LDH, NAGaseand AP in cow composite milk were calculated with theGLM and VARCOMP procedure in SAS using the followingmodel:

Yijkl ¼ mþ ai þ ðabÞij þ ðagÞik þ eijkl

where:Yijkl = is the ijklth observation of each traitμ = general meanαi = the effect of the ith cow (1, 2,..,10) if ten cows or(1, 2,.., 9) if nine cows(αβ)ij = the effect of the jth milking within cow (1, 2)(αγ)ik = the effect of the kth week within cow (1, 2, 3)eijkl = random error, normal distributed

Unadjusted mean and CV for each udder quarter ofevery cow were calculated with the GLM and VARCOMPprocedure in SAS procedure using the following model:

Yijk ¼ mþ ai þ bj þ eijk

where:Yijk = is the ijkth observation of each traitμ = general meanαi = the effect of the ith milking (1, 2)βj = the effect of the jth week (1, 2, 3)eijk = random error, normal distributed

CV for random error, designated as variations betweenmilkings, was calculated from the mean of each parameterand the SD of the random error.

Results

Nine of the ten cows included in the study werebacteriologically negative in all udder quarters throughout

90 M Åkerstedt and others

the study period. One cow was bacteriologically positivein one quarter. The average SCC for the nine healthy cowswas 41400±55800 cells/ml on udder quarter level and38100±31600 cells/ml on cow composite level. When thesubclinically infected cow was included, the average SCCfor the ten cows was 198900±1455800 cells/ml on udderquarter level and 219700±914700 cells/ml on cow com-posite level. Table 1 presents the mean±SD and variationsbetween milkings for each parameter analysed in udderquarter milk samples and representative cow composite milksamples for the nine clinically healthy cows and all tencows, including the subclinically infected cow (cow A).

The mean values and the variations between milkings forSAA, LDH and NAGase increased when the subclinicallyinfected cow A was included, both when comparingquarters and cow composite milk samples in comparisonwith the nine clinically healthy cows. The correspondingdifference in mean values or variations between milkingswas not observed for APwhen the subclinically infected cowwas included. Hp is not presented in Table 1 since Hp was<LOD for all samples except for the subclinically infectedudder quarter in cow A. The lowest variation was observed

for LDH among the nine clinically healthy cows on quarteras well as cow composite level.The mean values for each udder quarter within individual

cows were calculated and the minimum and maximummean values for each cow are presented in Table 2. Cow Awas subclinically infected in one quarter, while the othercows (B–J) tested bacteriologically negative in all quartersthroughout the study period. The mean value for SAA variedbetween <LOD and 5·5mg/l in the clinically healthyquarters, while the subclinically infected quarter (cow A)had a mean value of 336·4±459·4 mg/l. The subclinicallyinfected cowA had three quarters with SAA values >LODand one quarter with values <LOD. Five cows (C, D, F, Gand J) had SAAvalues >LOD in all four quarters. The remain-ing cows (B, E, H and I) had one quarter with SAA values>LOD and the remaining three quarters had values <LOD.Hp only had detectable levels, >LOD, for the subclini-

cally infected quarter (cowA), where Hp ranged from <LODto 35mg/l, mean value of 10·5±9·2 mg/l. The mean valuefor LDH varied between 1·1 and 3·2 U/l in the clinicallyhealthy udder quarters, while the subclinically infectedquarter (cow A) had a mean value of 45·0±58·9 U/l.

Table 1. Mean values±SD and variations between milkings (CV%) for the biomarkers analysed: serum amyloid A (SAA), lactatedehydrogenase (LDH), N-acetyl-β-D-glucosaminidase (NAGase) and alkaline phosphatase (AP) in representative quarter and cow compositemilk samples for the nine clinically healthy cows and also for all ten cows, including one subclinically infected cow

Parameter

9 cows 10 cows

Udder quarter(n=1512)

Cow composite(n=378)

Udder quarter(n=1680)

Cow composite(n=420)

SAA, mg/l Mean±SD 0·8±1·5 0·5±1·0 9·2±89·7 2·2±8·6CV,% 50 32 761 283

LDH, U/l Mean±SD 2·4±0·7 2·4±0·7 3·4±11·4 3·0±3·2CV,% 8 11 270 77

NAGase, U/l Mean±SD 2·6±1·7 2·7±1·8 3·1±5·9 3·1±2·5CV,% 22 17 149 50

AP, U/l Mean±SD 569±238 611±265 575±239 615±264CV,% 22 22 24 25

Table 2. Mean values of somatic cell count (SCC), serum amyloid A (SAA), haptoglobin (Hp), lactate dehydrogenase (LDH), N-acetyl-β-D-glucosaminidase (NAGase) and alkaline phosphatase (AP) for individual quarters within cows, presented as minimum and maximum valuesfor the four udder quarters. Cow A was subclinically infected in one quarter

Cow

Parameter

SCC × 10�3 cells/ml SAA, mg/l Hp, mg/l LDH, U/l NAGase, U/l AP, U/l

A 24–6300 0·1–336·4 <LOD–10·5† 1·6–45·0 1·4–25·0 552–807B 15–89 <LOD–0·3 <LOD 2·3–3·2 1·1–1·7 321–368C 75–101 2·4–5·1 <LOD 2·8–3·0 4·3–6·1 374–541D 5–6 0·2–0·2 <LOD 2·9–3·2 0·8–1·1 524–579E 52–100 <LOD–0·2 <LOD 2·2–2·7 1·8–2·4 284–327F 6–13 0·3–1·3 <LOD 1·7–1·9 1·9–2·5 522–625G 12–265 0·4–5·5 <LOD 1·1–3·0 1·8–3·3 384–670H 9–42 <LOD–0·2 <LOD 2·0–2·5 1·1–1·9 718–943I 48–65 <LOD–0·1 <LOD 2·7–3·0 4·0–5·7 710–927J 11–12 0·3–0·9 <LOD 1·7–1·9 2·6–3·5 685–786

†LOD = limit of detection


AP values ranged between 284 and 943 U/l, irrespective ofudder health status, while the level of NAGase was between0·8 and 6·1 U/l in the healthy udder quarters, but a meanvalue of 25·0±28·9 U/l in the subclinically infected quarter.

Table 3 presents the variations between milkings for theparameters analysed for individual cows. For SAA, thevariations between milkings are presented for the cows withvalues >LOD in three udder quarters (cow A) or all fourudder quarters (C, D, F, G and J). The variation betweenmilkings was 12–35% for SAA in the healthy udder quarters,while in the subclinically infected udder quarter it was113%. It was only the subclinically infected udder quarter incow A that had values >LOD for Hp, and therefore only onefigure for the variation between milkings is presented. ForLDH, the variation between milkings ranged from 7 to 33%in the healthy udder quarters, while the variation in thesubclinically infected udder quarter was 128%. For AP thevariation was 11–57% for all udder quarters, irrespective ofudder heath status, and the variation for NAGasewas 7–40%in healthy udder quarters, while the subclinically infectedudder quarter had a variation of 113%. In addition, the dif-ference within each cow is presented in Table 3 as the ratiobetween maximum and minimum value for the variation,e.g. cow A, SAA (113/30=3·8).

Figure 1 (a–d) shows typical profiles for the parametersanalysed in cow J, with all four udder quarters included. Thisillustrates the observed variations between milkings for42 consecutive milkings. In Fig. 2 (a–e) data for cow A(subclinically infected in one quarter) are presented in thesame way. Owing to the magnitude of the differences invalues between healthy udder quarters and the infectedquarter, SAA, LDH, NAGase and SCC values are presentedon a secondary y-axis for the infected udder quarter.

Discussion

The normal variations in some biomarkers, mainly evaluatedasmarkers for mastitis, were evaluated inmilk from clinically

healthy udder quarters. Since udder quarters are milked in-dividually in automated milking systems, it is important toinvestigate results obtained from each quarter separately, es-pecially when evaluating udder health disturbances. Usingcow composite milk samples may increase the risk that aninfected quarter will be masked owing to dilution effectsfrom the healthy quarters (Berglund et al. 2007; Forsbäcket al. 2009). The results presented here indicate that com-paring the individual quarters within the cow can be anappropriate way to detect deviations from the normalvariations between milkings.Among the biomarkers analysed, LDH showed the lowest

variation for udder quarter level comparisons within cows(8%), but also when comparing all quarters and cow com-posite milk samples for the nine clinically healthy cows(11%) (Table 1). When plotting the raw data it was oftenobserved that the patterns in LDH were similar for the fourudder quarters, as illustrated in Fig. 1b. The correspondingpattern for LDH was also apparent in the three healthyquarters in the subclinically infected cow, illustrated inFig. 2b. LDH showed a low variation in mean values whencomparing quarters between cows (Table 2), as well as lowvariations between milkings (Table 3), when the subclini-cally infected cow was excluded.Relatively high variations between milkings were ob-

served for SAA in quarter and cow composite milk samplesfor the nine clinically healthy cows (Table 1), but also whencomparing udder quarters within each cow (Table 3). Onepossible explanation might be that SAA is an apolipoprotein(Westermark et al. 1986) and thus possibly associated withthe fat phase in the milk. Milk fat can vary between 1 and10% during milking, while the residual milk may contain asmuch as 20% fat (Harding, 1995). To investigate whetherSAA was associated with the fat phase, a number of milksamples were analysed as whole milk and defatted milk. Theresults showed a clear decrease in SAA concentration whenthe samples were defatted, indicating that a large proportionof the SAA in milk was associated with the fat (M Åkerstedt,unpublished data). Since fat content varies during milking,

Table 3. Variations between milkings (%) in serum amyloid A (SAA), haptoglobin (Hp), lactate dehydrogenase (LDH), N-acetyl-β-D-glucosaminidase (NAGase) and alkaline phosphatase (AP). Results are presented as minimum and maximum values for the udder quarterswithin each cow. Differences are presented in brackets as the ratio between maximum and minimum value

Cow SAA Hp LDH NAGase AP

A 30–113† (3·8) 87‡ 21–128 (6·1) 8–113 (14·1) 16–50 (3·1)B ND ND 15–21 (1·4) 7–18 (2·6) 13–31 (2·4)C 12–17 (1·4) ND 7–10 (1·4) 9–13 (1·4) 12–18 (1·5)D 12–20 (1·7) ND 14–15 (1·1) 10–19 (1·9) 18–21 (1·2)E ND ND 9–14 (1·6) 13–21 (1·6) 14–27 (1·9)F 24–28 (1·2) ND 8–12 (1·5) 14–20 (1·4) 13–17 (1·3)G 21–35 (1·7) ND 12–33 (2·8) 16–40 (2·5) 13–57 (4·4)H ND ND 9–12 (1·3) 12–15 (1·3) 11–21 (1·9)I ND ND 10–11 (1·1) 16–25 (1·6) 15–25 (1·7)J 23–35 (1·5) ND 7–10 (1·4) 14–17 (1·2) 20–30 (1·5)

†Only values for three quarters are presented because one quarter was below the limit of detection (LOD)‡Only one value is presented because the subclinically infected quarter was the only quarter with values above LODND=Not determined since values were below LOD for the assay


representative milk samples were collected in this study,which meant that all milk was collected from each quarterduring the entire milking and a sample was taken after gentlymixing the milk. The collection of milk samples in this studywas performed in collaboration with another projectevaluating the normal variation in different milk com-ponents, e.g. fat, protein, lactose and SCC. It was foundthat despite representative milk samples being collected, fatwas the parameter with the highest variation (Forsbäck et al.2010). Consequently, biomarkers associated with the fatphase of the milk, e.g. SAA, will probably result in highervariations between milkings and thus in practice samplingmust occur more frequently.

AP was a biomarker with large differences in variationsbetween milkings when comparing the nine clinicallyhealthy cows as a group. Notably, AP was the only bio-marker analysed in this study where no effect on variationsin quarter and cow composite milk samples was observedwhen the subclinically infected quarter was included. Itis interesting to note the great effect on mean valuesand variations between milkings observed for SAA, LDHand NAGase when milk from the cow with one sub-clinically infected quarter was commingled with milk fromthe nine clinically healthy cows. Evaluating fields ofapplication for the different biomarkers included in thisstudy was outside the scope of this paper. The results con-cerning the effect on mean values and variations betweenmilkings when one infected quarter is included must beviewed with caution, since they could have been differentdepending on invading pathogen, severity of inflammation,etc.

In addition to cowAwith its subclinically infected quarter,cowG had high SCC and the variations between quarters forthis cow differed substantially for SAA, LDH, NAGase andAP compared with the other healthy cows. It is possible thatthis cow had a subclinical inflammation, but no infectioncould be observed despite daily bacteriological sampling.Conventional bacteriological sampling has been criticizedfor not being sufficiently sensitive and for requiring morethan one bacteriological sampling to determine whetherthe quarter is infected (Pyörälä & Pyörälä, 1997). It is there-fore possible that cow G was subclinically infected but theconcentration of bacteria was too low to be detected withthe method used.In the present study, SAA was generally below 1mg/l in

the clinically healthy quarters, except for cows C and G. Thisis in agreement with earlier studies, which suggested cut-offvalues between 0·8 and 1·4mg/l to indicate an inflammatoryresponse in quarters and in cow composite milk samples(Grönlund et al. 2003; Jacobsen et al. 2005; O´Mahony et al.2006). A probable explanation for the relatively high concen-tration of SAA in cow C could be lactation number, as thiscow was in lactation seven. There are no earlier studiesreporting changes in APP levels in healthy cows withincreasing lactation number. It is thus important to point outthat the variation between milkings when comparing udderquarters of cow C was one of lowest for all parametersanalysed. For this reason, variation between milkings couldbe a more reliable way to detect deviation from normalvalues compared with a cut-off value. As mentioned earlier,the high SAA value in cow G could be due to subclinicalinflammation.

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Fig. 1a–d. A typical example of profiles for some biomarkers analysed in cow J, for which all udder quarters tested bacteriologically negativeduring the 42 consecutive milkings (x-axis). The biomarkers analysed were: (a) serum amyloid A (SAA), (b) lactate dehydrogenase (LDH),(c) N-acetyl-β-D-glucosaminidase (NAGase) and (d) alkaline phosphatase (AP).


Only the subclinically infected quarter of cow A hadvalues >LOD for Hp and therefore variations between milk-ings and mean values for clinically healthy quarters couldnot be calculated in this study. Hiss et al. (2007) suggestedthat a cut-off value for Hp of 2·2–2·7 mg/l could be used todiscriminate between subclinically infected and healthyquarters. Grönlund et al. (2003) suggested a value >4·3mg/lin quarter milk samples to detect acute chronic subclinicalmastitis, while another study by Grönlund et al. (2005) re-commended 0·3 mg/l to indicate an abnormal udder quarter.Since LOD for the method used in the present study was1·0 mg/l, this might indicate that healthy quarters shouldhave values below 1·0 mg/l. The greater differences forsuggested cut-off values concerning Hp might be explainedby different analytical methods used in different studies. Inprevious studies it has been demonstrated that SAA is presentmore often and in higher concentrations than Hp at quarter

and cow level in clinically healthy cows (Grönlund et al.2003; Åkerstedt et al. 2007; Åkerstedt et al. 2008), a findingconfirmed by the present study. In the study by Åkerstedtet al. (2007), 91% of the udder quarter samples included hadHp values below 1·0mg/l.There is a lack of published data concerning the normal

variation in the biomarkers included in this study and nostudies have investigated the variation at udder quarter level.In a study by Chagunda et al. (2006b) the variation inNAGase and LDH was determined in cow composite milkfrom healthy and clinically mastitic cows. The variation inLDH in healthy cows was 49%, while in clinically mastiticcows it varied by 58%. For NAGase the correspondingfigures were 56% and 54%, i.e. the converse of LDH. Thesevalues are substantially higher than those obtained in thepresent study, but it should be borne in mind that weused a statistical model which took milking and week into

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1 4 7 10 13 16 19 22 25 28 31 34 37 40Milking no

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U/l)

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/l)

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a b

c d

e

Fig. 2a–e. An example of profiles for the biomarkers analysed in cow A, for which one udder quarter tested bacteriologically positive duringthe 42 consecutive milkings (x-axis), while the other three udder quarters tested negative. The biomarkers analysed were: (a) serum amyloid A(SAA), (b) lactate dehydrogenase (LDH), (c) N-acetyl-β-D-glucosaminidase (NAGase), (d) alkaline phosphatase (AP) and (e) haptoglobin (Hp).Owing to large differences in magnitude between values for healthy quarters and the infected udder quarter, the values for the infected udderquarter are presented on the secondary y-axis for SAA, LDH andNAGase. Only the subclinically infected udder quarter had values above thelimit of detection for Hp, therefore only one quarter is illustrated.


consideration and that we also studied the individualquarters. In the study by Chagunda et al. (2006b) no statis-tical model was used for these calculations, and sampling ofcow composite samples would therefore have been affectedby the dilution effect from the other udder quarters.

Cut-off values have been discussed for different bio-markers but owing to large individual variations betweenanimals and many animals in the grey area, it could bedifficult to set a cut-off value (Holdaway et al. 1996; Mattilaet al. 1986). Therefore, in agreement with Chagunda et al.(2006b), we suggest the variation over time as an alternativeway to detect changes in the milk. Mattila et al. (1986)recommended comparing all quarters within an udder withthe lowest udder quarter value. Another problem with usinga cut-off value to discriminate between healthy and infectedudder quarters could be the use of methods based ondifferent principles to determine a particular parameter, asthis might give variation in the end results. Whatever themethod used, several measurements over time could be agood approach as well as knowledge about the normal vari-ation in the biomarker in clinically healthy udder quarters.

Regardless of how the biomarkers analysed in the presentpaper are used today and can be used in the future, there is aneed for fast, robust and reliable methods for in-linedetection of different biomarkers. Since dairy herd size isincreasing and it is important to optimize the animal welfaresituation from several perspectives, it is essential to continueevaluating the potential of different biomarkers in in-linerecording.

Our results show that variations over time could be used asan alternative or in combinationwith a cut-off value to detectchanges in the biomarkers analysed, regardless of their fieldof application. LDH was the biomarker with the lowestvariation in this study. The mean values and variationsbetween cowswere rather wide for the biomarkers included,but comparisons of the quarters within each cow could be areliable way to detect changes, since each cow had its ownindividual profile.

The authors wish to thank veterinarian Marie Boman andtechnician Lotta Wall at the Department of Food Science,Swedish University of Agricultural Sciences, for assistance duringmilk sampling, Lars-Ove Sjaunja and Dietrich von Rosen forstatistical assistance and DeLaval International for providing themilking machines.

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