Torben Larsen
email: torben.larsen@agrsci.dk

Mette Krogh Larsen

Nic C Friggens

Enzymatic and fluorometric determination of triacylglycerols in cow milk and other opaque matrices

Keywords:

The fat content of milk is an important indication of quality, both economically and physiologically. Existing analytical methods of milk fat are based on physical determination, i.e. gravimetric determination or near infrared spectroscopy (IR). Triacylglycerols (TAG) constitute up to 98% of total fat in milk. The present study describes a new method for determination of triacylglycerols in milk. The present method is a chemical, i.e. enzymatic-fluorometric assessment of glycerol, the content of TAG is consequently given in mol per litre. The method demonstrates a good accuracy and precision and the correlation with standard IR methods shows a fine association between the different assessment methods (n = 228; r = 0.905). Fatty acid profiles of sixty individual milk samples indicate that parameters co-linearly connected to specific fatty acids may explain a considerable amount of the variation between the methods. Furthermore, discrepancies between the present method and the established IR method may partly be due to the fact that IR spectroscopy is calibrated individually against gravimetric methods not taking fatty acid profiles and thereby molar weight of TAG into account.

Introduction

The lipid content of cow's milk varies with such factors as diet, breed, stage of lactation, number of lactations and season of the year. In general, however, the fat content lies within 30-120 g l À1 [START_REF] Davies | The composition of milk[END_REF]. The main lipid class (up to 98%) consists of triacylglycerols (TAG), one glycerol molecule covalently connected to three fatty acids, and this is accompanied by small amounts of diacylglycerols and monoacylglycerols, cholesterol, non-esterified fatty acids and phospholipids. In addition to the main lipid classes, small amounts of hydrocarbons, carotenoids, sterols, a-tocopherol and various glycerol ethers have been found in milk [START_REF] Christie | Composition and structure of milk lipids[END_REF][START_REF] Kuzdzal-Savoie | Nonglyceride constituents of the lipid fraction of milk[END_REF]. Some 95% or more of the total lipids in milk are present in the milk fat globule fraction, and 95% or more of the lipids in the globule are TAG. Lipids in the milk serum are present primarily in a heterogeneous fraction of membranes and fractions hereof; phospholipids and TAG comprise most of the lipids in milk serum [START_REF] Macgibbon | Composition and structure of bovine milk lipids[END_REF].

Milk fat globules range from 0.2 to 15 lm in diameter, with a volume-weighted diameter of about 4 lm [START_REF] Walstra | Physical Chemistry of milk fat globules[END_REF]. Natural milk fat globules are surrounded by a complex, biological membrane, the milk fat globule membrane (MFGM). The native MFGM consists of a complex mixture of proteins, phospholipids, glycoproteins, triacylglycerols, cholesterol, enzymes and other minor components [START_REF] Mather | A review and proposed nomenclature for major proteins of the milk-fat globule membrane[END_REF][START_REF] Mcpherson | Reviews of the progress of dairy science: The bovine milk fat globule membrane -It's formation, composition, structure and behaviour in milk and dairy products[END_REF][START_REF] Patton | The milk fat globule membrane[END_REF]. The structure of the MFGM is multilayered, where polar molecules on the outer surface provide the water solubility and hence prevent aggregation and coalescence of milk fat [START_REF] Danthine | Progress in milk fat globule membrane research: A review[END_REF][START_REF] Evers | The milkfat globule membrane -Compositional and structural changes post secretion by the mammary secretory cell[END_REF][START_REF] Lopez | Lipid rafts in the bovine milk fat globule membrane revealed by the lateral segregation of phospholipids and heterogeneous distribution of glycoproteins[END_REF][START_REF] Rombaut | Properties, analysis and purification of milk polar lipids[END_REF]).

Cow's milk lipids in particular contain a much wider range of fatty acids than has been reported for the lipids from any other tissue [START_REF] Davies | The composition of milk[END_REF], [START_REF] Patton | Lipid metabolism and membrane functions of the mammary gland[END_REF] list 437 different fatty acids, characterised from cow's milk. The known acids include all the normal saturated components from C 2 to C 28 , branch-chain acids from C 11 to C 26 , cis-and trans monoenoic and polyenoic acids and various isomers of these, and keto and hydroxyl fatty acids. However, the quantitative majority of fat is made up by only 12-15 fatty acids incorporated in TAG. The frequency of common fatty acids (C 4 to C 18 ) incorporated in cow's milk is variable due to stage of lactation and diet. Numerous studies have demonstrated that cow ration may influence the mammary cell synthesis of fatty acids (de novo synthesis of short and medium length chains) versus the fatty acids mobilised from the peripheral adipose tissues (medium to long chain acids). However, in broad terms, on a weight percentage basis, C 14:0 , C 16:0 , C 18:0 , and C 18:1 fatty acids constitute in the vicinity of 80% of the milk fat, whilst e.g. C 4 (butyric acid) only accounts for approximately 3-4%. Conversely, on a molar basis, the short chain fatty acids make up a considerable number of molecules incorporated in milk fat due to their lower molecular weight, i.e. C 4 to C 10 represent 22% of fatty acids [START_REF] Parodi | Positional distribution of fatty acids in triglycerides from milk of several species of mammals[END_REF]. The final composition of the individual acids in the TAG molecule will determine the molar weight of the molecule. Theoretically, single TAG in the milk will exist, having molar weights between 400 and 1000 Da, however, the molar weight of composite milk often ranges between 680 and 800.

The positional distribution of fatty acids on the glycerol molecule seem to vary, often depending upon the specific acid. Butyric acid and hexanoic acid seems preferentially to be linked to the sn-3 position whilst C 16:0 (palmitate) preferentially is linked to sn-1 and sn-2 [START_REF] Christie | [END_REF][START_REF] Parodi | Positional distribution of fatty acids in triglycerides from milk of several species of mammals[END_REF]. The specific distribution of fatty acids in the triacyl molecule may very well be of importance in the enzymatic lipolysis.

The fat content of milk is an important indication of quality, both economically and physiologically. In the dairy industry, the milk fat is most often determined using rapid mid-infra-red methods, i.e. spectrometric methods (IDF, 2000). These methods, however, regularly have to be calibrated against reference methods, which is more labour intensive and costly. Several methods have been developed, where the essential step is the extraction of milk fat with nonpolar solvents and subsequent gravimetric determination of fat.

Here, the Röse-Gottlieb method has most likely obtained the greatest attention (IDF, 1996). The ''Gerber Method", or Acid-Butyrometric method [START_REF] Gerber | Neuer butyrometer[END_REF] is one of the traditionally used methods, because it is simple, inexpensive and suitable for a relatively high number of samples per day. Nevertheless, the determination cannot be automated, involves strong acids, and requires relatively experienced technicians. Numerous other methods have been suggested to measure the milk fat content or to harmonise, automate or simplify the reference methods to the quick methods used in the dairy business. Common to all fat determinations is that they are based on physical methods and they intend to determine the total fat content rather than the TAG content although the TAG constitutes more than 97% of total milk fat.

The present paper describes a chemical method for determination of TAG in milk or other non-transparent matrices. The access to the milk TAG, mainly within the MFGM, is mediated by moderate heat treatment, detergents and the addition of phospholipase C (EC 3.1.4.3). TAG molecules are hydrolysed by lipoprotein lipase (EC 3.1.1.34) and lipase (EC 3.1.1.3) to free fatty acids and glycerol. The glycerol unit is subsequently oxidised by glycerol dehydrogenase (EC 1.1.1.6) to dihydroxy acetone, and the cofactor NAD + is concomitantly reduced to NADH + H + . NADH + H + equivalently reduces the non-fluorescent resazurin to resorufin mediated by the enzyme diaphorase (EC 1.6.99._). The developed resorufin is terminally estimated by its fluorescense compared with a standard curve. The present TAG analysis determines the molar concentration of TAG.

Materials and methods

Milk samples

Milk samples were collected at random from daily milking on the local research farm which was housing both Danish Fresian-Holstein and Jersey breeds. Fresh milk was brought to the laboratory every morning. Milk samples were heated in a water bath at 75 °C for 10 min before analyses.

Reagents

Diluent: A 0.2% Triton X-100 solution containing 12 mM bile acids (FLUKA 48305).

Reagent 1: lipoprotein lipase (EC 3.1.1.34; Toyobo Enzymes); lipase (EC 3.1.1.3; FLUKA 62285); phospholipase C (EC 3.1.4.3; Sig-ma P7633) in Pipes buffer, pH 7.40, 60 mM. The enzyme activities were 9.5; 6.1; and 0.23 U/ml, respectively.

Reagent 2: Na,K-carbonate buffer, 100 mM, pH 10.3 with glycerol dehydrogenase (EC 1.1.1.6; Toyobo Enzymes), 10.4 U/ml; (NH 4 ) 2 SO 4 , 28 mM; b-NAD + (MW 717.5), 6.3 mM.

Reagent 3: phosphate buffer, 100 mM, pH 7.4 containing resazurin (Sigma R-2127) 2.55 mM; and diaphorase enzyme (EC 1.6.99._, Toyoba Enzymes) 9 U/ml.

Analytical procedure

Dosage and dilution of sample and addition of reagents were performed in a robotic system (Biomek Ò 2000; Beckman Coulter).

(a) Milk samples (standards and control samples) distributed in a 96-well micro plate, were initially diluted 1 + 9 with diluent.

Fifty micro litre reagent 1 was pipetted into the wells; thirty micro litre (diluted) milk was pipetted on top.

The plate was sealed/closed with a lid and incubated for 50 min at 40 °C (incubation 1). (b) Eighty micro litre reagent 2 was added to the wells, the plate was incubated for another 20 min at 40 °C (incubation 2). (c) One hundred and twenty micro litre reagent 3 was added to the wells, the plate was incubated further for 50 min at 40 °C (incubation 3). (d) The reaction was measured as development of fluorescence (emission at 590 nm) after excitation at 544 nm light compared with a standard curve on the same plate. Diluted milk (30 ll) sample was mixed with 50 ll enzyme solution (1), containing lipoprotein lipase, lipase and phospholipase C in PIPES buffer, pH 7.4. pH 7.4 in the medium is an acceptable compromise for the optimal conditions of the three enzymes. Incubation at 40 °C is correspondingly an effective temperature, not challenging the temperature stability of the enzymes. The phospholipase C is expected to hydrolyse phospholipids (in the milk fat globule membrane and in milk serum) on the glycerol side of the P-lipid, thereby producing diacylglycerols and free ''P-side chains". The lipoprotein lipase and the lipase enzymes are further expected to liberate the fatty acids from the glycerol backbone of TAG and diacylglycerols (hydrolyse TAG). The products are free glycerol and free fatty acids.

Step b. (enzymatic oxidation of glycerol and development of NADH + H + )

Eighty micro litre of reagent 2 was pipetted on top of the ''up till now" reaction medium. The glycerol dehydrogenase enzyme in the reagent has a pH optimum between 9 and 11. The 100 mM carbonate buffer, pH 10.3, following the enzyme will force pH in the alkaline direction (calculated to pH 9.7). The oxidative degradation is stimulated by NH 4 + and K + which are provided by the K-phosphate and carbonate buffers used and ammonium sulphate in reagent 2 (both approximately 25 mM in the reaction medium). NAD + is concomitantly reduced to NADH + H + when glycerol is oxidised to dihydroxyacetone.

Step c. (reduction of resazurin to resorufin by NADH + H + and diaphorase)

The addition of 120 ll of reagent 3 brings pH down to approximately 8.0 to 8.2 which is more in accordance with the optimum of the used diaphorase enzyme. Diaphorase mediates the reduction of resazurin to resorufin via NADH + H + whereby NAD + is regener-ated. Resazurin is only very marginally fluorescent whilst resorufin is highly fluorescent. The amount of resorufin produced in the process was measured by fluorometry (emission at 590 nm) after excitation at 544 nm light and compared with a standard curve. The fluorometric measurement was ''gained" on the highest standard (12.6 mM), meaning that the measurement of emission was calibrated on this light intensity.

Standards

Fresh, raw milk was centrifuged at 1000g at 4 °C for 10 min. Milk fat was aspirated from the milk. Centrifugation and aspiration of fat was repeated, this time at 1200g. The fat free milk was heat treated at 75 °C for 30 min. After cooling, Bronopol Ò (2-bromo-2nitropropan-1,3 diol) was added to a final concentration of 200 mg/l.

Standards were based on glycerol and fat free milk. A 14 mM glycerol stock solution was prepared in the diluent, and further diluted with diluent to standards of 0, 0.35, 2.8, 4.2, 5.6, 7.0, 9.8, and 12.6 mM. The fat free milk content was constant, i.e. 10% of the standards, right as milk made up 10% of the sample, due to dilution with water.

Every 96-well micro plate used for milk samples contained 2 Â 8 standards as well.

Validation of assay

Linearity of the assay was determined by mixing milk samples with a relatively low fat content together with milk samples with a relatively high fat content.

The precision of the assay was estimated by five separate series each at one week-intervals. Each series consisted of two (plates) Â 24 milk samples that were analysed in three replicates on two subsequent days (i.e. each series numbered 288 single analyses).

Accuracy of the assay was assessed on 230 randomly chosen milk samples collected directly from our dairy barns, and analysed over five different days. The milking system is robotic, where recordings are automatically connected to the milk ID. The samples were analysed according to the present method and by the near IR technology (MilkoScan 4000, Foss Electric A/S, Hillerød, Denmark).

Sixty samples were selected equally amongst the two breeds and the 230 samples, i.e. 2 Â 10 samples with low, 2 Â 10 with middle and 2 Â 10 with high values of the residuals from the regression TAG, mM vs. fat percentage including breed (see below). The samples were analysed for individual fatty acids after hydrolysis and the molar weight of the average TAG was calculated.

Analysis of fatty acids: Cream was separated from skim milk after centrifugation at 1700g at 4 °C for 15 min. The cream was centrifuged at 13,000g at 20 °C for 10 min, followed by heating at 60 °C for 10 min and centrifugation at 13,000g at 20 °C for 10 min, [START_REF] Feng | Technical note: A rapid lipid separation method for determining fatty acid composition of milk[END_REF][START_REF] Murphy | Effect on cow performance and milk-fat composition of including full fat soybeans and rapeseeds in the concentrate mixture for lactating dairy-cows[END_REF]. Fatty acid methyl esters were prepared by mixing 10 mg of milk fat with 1 ml pentane and 10 ll 2.5 M sodium methylate in methanol. Samples were mixed on a whirl mixer for 1 min and centrifuged, and the pentane supernatant was used for gas chromatography (GC) analysis [START_REF] Badings | Glass-capillary gas-chromatography of fatty-acid methyl-esters -A study of conditions for the quantitative-analysis of shortchain and long-chain fatty-acids in lipids[END_REF]. Samples were analysed with and without the addition of tridecanoin as internal standard. Fatty acids (FA) were identified, and content as g/100 g FA was calculated based on the use of external standards.

Gas chromatographic conditions: HP6890 GC-system (Hewlett Packard Co., Palo Alto, CA, USA) with a flame-ionisation detector and a Supelco SI 2560 column (100 m  0.25 mm  0.20 lm, Supelco, Bellafonte, PA, USA). The inlet temperature was 275 °C with a split ratio of 40:1, and the carrier gas helium with a constant flow of 1.5 ml min À1 . The starting temperature of 140 °C was held for 5 min and increased by 4 °C per minute to an end temperature of 240 °C. The detector temperature was 300 °C.

Calculation of average molar weight: The molar content of each FA was calculated by dividing the content as g/100 g FA by the molar weight of the corresponding FA TAG. The sum of these values was the total molar content of TAG per 100 g and the average molar weight was the reciprocal value multiplied by 100.

Results

Table 1 summarises the results of the linearity experiment. The assay showed good linearity, the measured fat content of mixed samples revealed slightly higher levels than calculated, on average mixtures measured were 6-9% higher than calculated.

The results of the precision determination, i.e. descriptive statistics, intra plate precision and inter day precision are given in Table 2. The chosen milk samples demonstrated a wide range of fat content, i.e. from 27 mM to 109 mM with a median value of 53 mM. The intra plate precision was on average a little below 5% (CV) and the between day precision on average a little above 5%.

Table 3 shows the descriptive statistics of the 230 milk samples, where 173 samples originated from Friesian-Holstein cattle and 57 samples from Danish Jersey. Further, 166 samples were from 1st parity cows, 46 samples from 2nd parity cows, 14 samples from 3rd parity cows and 4 samples from 4th parity cows. The single regression TAG, mM vs. fat percentage (measured by IR spectrometry) explained 82% (R 2 ) of the variation in the material (Fig. 1). Including breed (and the interaction breed  fat percentage) increased the R 2 of the model to 85.3%. Adding in days from calving did not improve the model further. Of the other constituents measured, milk yield and protein content were those that most improved the model although the increase in R 2 was only from 85.3 to 86.6% and they did not account for the breed effect.

The calculated molar weight of the triglyceride-fat amongst the sixty samples selected for FA determination (MW mean 711.6, range 687-743) was not a significant explanatory variable in the regression of TAG, mM on fat percentage, breed and the interaction between these. The FA profile of the sixty milk samples was used as a basis for a grouping of FA (fractional amount). C 4 to C 10, C 11 to C 15, C 16 to C 18, and C 20 to C 24 acids were grouped and tested as predictors of the variation in the residuals from the TAG vs. fat percentage regression together with or without molar weight included. These groups did not account for any variation at all. The effects of relative distribution of the individual FA on the residuals between fat percentage and molar content of TAG, were then investigated. None of the individual FA convincingly explained the residual variation between the two determinations. However, a regression model containing five FA (C 18 , C 18:3N6 , C 21 , C 22:1N9 , and C 20:5N3 ) explained 50.1% of the variation in the residuals (R 2 = 50.1%). Principal component analysis (PCA) confirmed this influence of individual FA. In a PCA including all individual FA, the proportion of the variation accounted for by the first 4 principal components was 59%.

Discussion

The aim of this study was to develop a chemical method for quantification of milk TAG. It can be seen by the strong correlation with milk fat measured by IR spectrometry that this aim was achieved. The present method for quantification of milk TAG is, to our knowledge, the only chemical method described. Fat in milk is a delicate object for chemical determination for at least two reasons: the fat is protected in a chemically resistant and very hydrophobic compartment, i.e. the milk fat globules are protected by the Author's personal copy MFGM and, secondly, milk is extremely difficult to distribute in smaller quantities in a representative way, milk tends to fractionate easily to a fat fraction and a water phase, just as milk fat and protein adhere to surfaces of various materials.

Milk fat is fairly well protected against chemical and physical aggression. Freshly drawn milk contains only small amounts of free FA, i.e. lipolysis is not advanced. Further, if the milking and storage procedures are appropriate, the milk can be kept for several days with little further development of free FA. It is well recognised, that natural milk contains indigenous lipases (or lipoprotein lipase) that under optimal conditions would be able to hydrolyse the innate content of TAG in hours. The majority of lipoprotein lipase activity seems to be associated with casein micelles in the milk serum [START_REF] Hohe | Milk lipoprotein lipase distribution in the major fractions bovine milk[END_REF]. The non-occurrence of spontaneous lipolysis has been ascribed to at least two factors: natural inhibition of the native lipoprotein lipase and physical constraints due to the MFGM. The inhibition of lipoprotein lipase may further be affected by indigenous components in the skim milk fraction or attached to proteins of the MFGM [START_REF] Deeth | Factors governing the susceptibility of milk to spontaneous lipolysis[END_REF][START_REF] Sundheim | Hydrolysis of bovine milk fat globules by lipoprotein lipase: Inhibition by proteins extracted from milk fat globule membrane[END_REF]. Inhibition of lipoprotein lipase may also be accomplished by free FA (end product inhibition) or even by blockage of enzymes involved in the process of reforming TAG [START_REF] Olivecrona | Lipase[END_REF]. The MFGM is generally considered to be an effective protection against enzymatic action, mainly oxidation and lipolysis by lipoprotein lipases [START_REF] Deeth | Lipolytic enzymes and hydrolytic rancidity in milk and milk products[END_REF][START_REF] Olivecrona | Lipase[END_REF][START_REF] Walstra | Dairy Chemistry and Physics[END_REF] as long as it is not disrupted by extreme temperatures, mechanical stress or strong chemical agents.

The vast majority of TAG is situated in the internal part of globules which are enveloped by the MFGM the latter being resistant to lipoprotein lipases and lipases. Some sort of disruption of the MFGM is necessary if a chemical determination of TAG is intended. Various forms of physical stress, e.g. pumping, agitation and homogenisation, and termal treatment, like freezing, refrigeration, and heating have been shown to damage the membrane to varying extent. Heat treatment, especially, leads to significant compositional changes in the MFGM, i.e. loss of original membrane components, such as TAG, phospholipids, lipoproteins and proteins (Houlihan, Goddard, Kitchen, & Masters, 1992;[START_REF] Houlihan | Interaction between the bovine milk fat globule membrane and skim milk components on heating whole milk[END_REF] because the fat globules coalesce and the surface is reduced. In addition, a well known effect of heating is that milk serum proteins will interact with the MFGM [START_REF] Kim | Heat induced interactions between the proteins of milk fat globule membrane and skim milk[END_REF][START_REF] Lee | Chemical changes in bovine milk fat globule membrane caused by heat treatment and homogenization of whole milk[END_REF].

Chemical disruption of the MFGM is another way to get access to the TAG content of the globule. Most analyses of MFGM reveal that it consists mainly of protein and lipids (approx. 90%). The TAG content seems variable, presumably due to difficulties in separating from the core content; however, the phospholipid content seems to be less variable, being about 26 to 31% of MFGM fat (Keenan & [START_REF] Keenan | Origin of milk fat globules and the nature of the milk fat globule membrane[END_REF] and phospholipids furthermore seem to be an essential matrix of other MFGM components [START_REF] Lopez | Lipid rafts in the bovine milk fat globule membrane revealed by the lateral segregation of phospholipids and heterogeneous distribution of glycoproteins[END_REF]. Several studies have described that milk fat is more exposed to lipolysis by lipoprotein lipases and lipases when micro organisms producing phospholipase C (EC 3.1.4.3) are present or isolated phospholipase C is added to milk (reviewed by [START_REF] Deeth | Lipolytic enzymes and hydrolytic rancidity in milk and milk products[END_REF]. The MFGM phospholipids are primarily phosphatidyl choline, phosphatidyl ethanolamine, and sphingomyelin, all obvious substrates for phospholipase C. Phospholipases apparently disrupt the integrity of the MFGM and expose the membrane fat and the core fat to degradation [START_REF] Chrisope | Combined action of lipase and microbial phospholipase C on a model fat globule emulsion and raw milk[END_REF]. Lipases and lipoprotein lipases obviously have different affinity for specific TAG, presumably due to the composition of the TAG, i.e. specific fatty acids (size, number of double or triple bonds) and the mutual placing on the glycerol molecule. The present use of both a lipase and a lipoprotein lipase was intended so as to counter this enzyme-substrate preference.

The regression of individual FA on the residuals between the molar content of TAG and fat percentage revealed that FA do play an important role in the deviation between methods. It is not likely that especially C 18:3N6 , C 21 , C 22:1N9 , and C 20:5N3 fatty acids are decisive per se; together they constitute 0.09% of all fatty acids, but these FA indicate the existence of important components co-linearily linked to the acids under consideration. This is not obvious in the present material due to a limited number of samples and analytical constraints.

The present chemical determination of TAG gives the quantity on a molar basis (mM). We acknowledge that it is a bit surprising that the molar weight of selected milk samples alone or with the grouping of FA did not improve the regression between the two methods of determination. However, the IR-spectrometric determination of fat (as any other determination) reports the result in weight percentage because it is calibrated against gravimetric determinations. Consequently, if the molar weight of a certain fat is low, the number of moles will be higher than a similar amount of fat where the molar weight is high. Jersey cows are known to produce milk containing TAGs with lower molar weight, because the fractional part of short-chain FA is higher, and the fractional part of longer chain-length FA is lower than milk from Friesian-Holstein [START_REF] Beaulieu | Differential effects of high fat diets on fatty acid composition in milk of Jersey and Holstein cows[END_REF][START_REF] Stull | Fatty acid composition of milk. II Some differences in common dairy breeds[END_REF][START_REF] White | Comparison of fatty acid content of milk from Jersey and Holstein cows consuming pasture or a total mixed ration[END_REF]. A part of the disparity between IR-spectrometric and the present enzymatic-fluorometric measurement may be founded on the fact that IR instruments are individually calibrated against gravimetric methods (e.g. Röse Gottlieb, etc.) not taking molar weight nor breed of the milk producing cows into consideration.

The present chemical quantification of milk TAG will in no way be an alternative to or competitive to the widely used IR technology used in practical dairy business. The present method is evidently a laboratory technique, more time and personnel consuming, using sophisticated chemicals and gear not suitable for exposure to practical farm conditions or large-scale, fast and inexpensive analyses. On the other hand, using the outlined methodology makes it possible to perform hundreds of analyses per day, you only need very small quanta of milk, and the present method gives additional information (the molar content) compared to other methods. The present method may substitute for the Gerber method or the Röse-Gottlieb method, usually used for calibration of fast, commercial methods like the IR. Furthermore, the present method, may find application in animal science (feeding, breeding, and physiology) and dairy science where the additional information is estimated.

2. 4 .

 4 Chronological chemistry of the analysis 2.4.1. Step a. (enzymatic degradation of phospholipids and TAG)

Fig. 1 .

 1 Fig. 1. Milk from Danish Jersey () and Friesian-Holstein (+) was sampled at random on 5 days during 2 months. The enzymatic-fluorometric TAG analysis was compared with the fat percentage measured by IR-spectrometry (fat percentage). The regression equation was: TAG = 15.7 Â fat percentage -10.1 (R 2 = 0.822; r = 0.906; n = 228; p < 0.001).

Table 1

 1 Linearity. Two groups of each sixteen samples (I and II) were selected and analysed together with mixtures of the samples (1 + 1, between groups), all in three replicates.

		Mesurements (mM)
		Mean, I (range)	Mean, II (range)	Mean (a), mix (range)	Theoretical mean (b), mix (range)	Deviation, a/b (range)
	Plate 1, n = 8	33.0 (21.9-40.2)	43.1 (24.7-53.9)	40.2 (27.5-47.3)	38.0 (26.8-45.3)	1.06 (1.02-1.14)
	Plate 2, n = 8	37.6 (20.7-44.9)	55.3 (36.8-67.2)	50.9 (31.9-59.3)	46.5 (28.7-56.1)	1.09 (1.04-1.18)

Table 2

 2 Intra plate and inter plate variation. Variation of determination was estimated on milk samples collected directly from the dairy barns (random sampling). Intra plate and inter plate precision were assessed in five series each spaced by one week. Each series contained 2 (plates)  24 milk samples  3 replicates analysed on two consecutive days, i.e. in total 240 different milk samples in 2  3 replicates (= 1440 analyses).

	TAG content, mM	Series 1	Series 2	Series 3	Series 4	Series 5
	Minimum	32.1	30.3	30.2	37.2	27.7
	0.10 Quantile	39.4	41.1	40.6	42.2	34.2
	0.20 Quantile	43.0	45.0	43.4	44.5	38.3
	0.50 Quantile	51.0	57.6	56.4	55.2	45.1
	0.80 Quantile	61.2	87.0	70.6	76.2	53.5
	0.90 Quantile	67.4	95.5	90.2	83.4	59.0
	Maximum	93.7	117.8	108.6	101.8	70.7
	Intra plate precision,	4.2 (3.5)	4.2 (3.6)	4.2 (3.6)	5.3 (4.7)	6.5 (6.0)
	CV%; N = 96; n = 3 mean (median)
	Between day precision,	4.2 (3.7)	4.4 (3.7)	4.3 (3.3)	7.1 (4.4)	5.5 (4.5)
	CV% N = 144; n = 2 mean (median)

Table 3

 3 Descriptive statistics of selected samples used for IR spectrometric fat analysis as well as chemical TAG analyses.

	Variable	n	Mean	Median	Minimum	Maximum
	DFC	230	170	149	2.8	502
	Milk yield, kg	230	9.82	9.36	1.75	22.46
	Cells  1000 (ml À1 )	228	123.3	55.0	7.0	4211
	Protein (%)	228	3.59	3.52	2.74	5.20
	Urea (mM)	228	4.08	4.04	1.26	6.25
	Lactose (%)	228	4.95	4.96	4.55	5.34
	Fat (%)	228	4.32	4.13	2.29	12.59
	TAG (mM)	230	57.2	52.5	27.7	116.4
	DFC = days from calving; TAG = triacylglycerols.

T. Larsen et al. / Food Chemistry 125 (2011) 1110-1115

Acknowledgements

Carsten Berthelsen is kindly acknowledged for excellent technical assistance and skilled analytical work. This study, which was part of the Biosens project, was funded by the Danish Ministry of Food, Agriculture and Fisheries and the Danish Cattle Association.

Author's personal copy


Justyna Gorzecka
email: justyna.gorzecka@agrsci.dk

Henrik Callesen

Kurt M Pedersen

Nicolas C Friggens

The relationship between postpartum vaginal discharge symptoms and progesterone profile characteristics in lactating dairy cows in Denmark

In this paper, the effect of clinical symptoms of uterine inflammation on progesterone profile characteristics was quantified in dairy cows. A continuous scale based on visual observation of vaginal discharge (the previously developed D-index) was used to describe the clinical symptoms. Progesterone profiles in milk were used to describe the ovarian cycles, and to determine the distinguishing features of these profiles, a multivariate statistical procedure (principal component analysis) was performed.

Significant negative effects of the D-index were seen during the first and second postpartum ovarian cycles. The D-index had a significant effect on the shape of progesterone profiles and the length of the ovarian cycles but it only accounted for a small proportion of the variation in these ovarian cycle features. The D-index was not a significant risk factor for the length of postpartum anovulatory period in the present study.

Introduction

The effects of bacterial infection in the uterus are much wider reaching than simply provoking a uterine inflammation. Bacterial endotoxin and immune mediators can disturb hormonal interactions, both systemically at the level of hypothalamus and pituitary gland, and also locally with effects on follicular selection, growth and function [START_REF] Sheldon | Influence of uterine bacterial contamination after parturition on ovarian dominant follicle selection and follicle growth and function in cattle[END_REF]. Uterine inflammation can also be associated with prolonged postpartum anestrus or ovarian cysts, as well as abnormalities in progesterone profiles [START_REF] Mateus | Influence of Puerperal Uterine Infection on Uterine Involution and Postpartum Ovarian Activity in Dairy Cows[END_REF][START_REF] Opsomer | Risk factors for post partum ovarian dysfunction in high producing dairy cows in Belgium: A field study[END_REF][START_REF] Peter | Effects of intrauterine infection on the function of the corpora lutea formed after first postpartum ovulations in dairy cows[END_REF][START_REF] Williams | The relationship between uterine pathogen growth density and ovarian function in the postpartum dairy cow[END_REF]. However, studies of the relationship be-tween progesterone profiles and the clinical findings of uterine inflammation have revealed differing results ranging from strong to no relationship [2,3,6 -9].

A possible reason for these differing reports relates to the different methods used to characterize uterine inflammation, especially under field conditions. Under such conditions, the most useful, simple and noninvasive method for estimating uterine inflammation is to evaluate the vaginal discharge [10 -12]. Vaginal discharge has been reported to accurately reflect uterine bacterial infection and immune response [START_REF] Williams | Clinical evaluation of postpartum vaginal mucus reflects uterine bacterial infection and the immune response in cattle[END_REF], and presence of purulent vaginal discharge was associated with the presence of pathogenic bacteria in the uterus [START_REF] Huszenicza | Uterine Bacteriology, Resumption of Cyclic Ovarian Activity and Fertility in Postpartum Cows kept in Large-Scale Dairy Herds[END_REF][START_REF] Williams | Clinical evaluation of postpartum vaginal mucus reflects uterine bacterial infection and the immune response in cattle[END_REF][START_REF] Dohmen | The relationship between bacteriological and clinical findings in cows with subacute/chronic endometritis[END_REF]. However, most vaginal discharge scoring systems can only distinguish severe from less severe cases, and are usually limited to one category of infec-tion. As such, they are not well suited to finding quantitative relationships to progesterone profile characteristics. Recently, by using a multivariate analysis of vaginal discharge features, we have created a more finely graded index for vaginal discharge symptoms (the D-index) that is universal in the sense that it can be applied to any type of uterine infection at any time from one to six weeks postpartum in dairy cows [START_REF] Gorzecka | A universal index of uterine discharge symptoms from calving to six weeks postpartum[END_REF].

Over the last decade, milk progesterone profiles have become increasingly common not only in research herds but also, with the advent of automated in-line monitoring systems, in commercial dairy herds [START_REF] Friggens | Improved detection of reproduction status in dairy cows using milk progesterone[END_REF][START_REF] Ostergaard | Technical and economic effects of an inline progesterone indicator in a dairy herd estimated by stochastic simulation[END_REF]. Such monitoring systems are expected to provide reliable information on dairy cow fertility, so effects of uterine inflammation on ovarian cycle features should be included. Thus, the aim of the present study was to quantify the relationship between vaginal discharge symptoms, evaluated using the new D-index, and progesterone profile characteristics in milk.

Materials and methods

The data for this study were collected from 2002 to 2009 in one dairy herd at the Danish Cattle Research Center in Foulum, Denmark. This herd included three breeds (Danish Holstein, Danish Jersey, Danish Red) until 2008, when the Danish Red cows were excluded. The cows were kept in a loose housing system and fed ad libitum with a total mixed ration containing 50 percent roughage (approximately fifty/fifty corn silage and grass silage) and 50 percent standard dairy concentrate. The cows were milked by VMS (Voluntary Milking System, DeLaval, Tumba, Sweden) and the average milk yield per year was 9640 kg ECM (energy-corrected milk yield).

In this study two separate data sets were used: one data set with uterine inflammation measurements and milk progesterone measurements collected during the present trial, and a second data set with only milk progesterone measurements collected from previous studies and used here to characterize the profiles.

Vaginal discharge symptoms 2.1.1. Animals

The data were collected from January to December 2008 from a total of 75 cows in their first to fifth lactation: 48 Danish Holstein kept in two groups and 27 Danish Jersey kept in one group.

Clinical examination

The cows were examined once weekly by the same person, from week one until week six after calving. At each examination rectal temperature was measured and vaginal discharge was collected by hand with a rectal glove and lubricant, after cleaning the vulva with paper towels. Each sample of collected discharge was scored separately for five features, according to the system of Gorzecka et al [START_REF] Gorzecka | A universal index of uterine discharge symptoms from calving to six weeks postpartum[END_REF] (Table 1): proportion of purulent material, amount, consistency, smell and color (content of blood).

Index of vaginal discharge symptoms (D-index)

After scoring (Table 1), the D-index value was calculated for each sample of discharge as described in detail by Gorzecka et al [START_REF] Gorzecka | A universal index of uterine discharge symptoms from calving to six weeks postpartum[END_REF]. Briefly, the D-index was derived from a multivariate analysis (principal component analysis) of vaginal discharge features and rectal temperature. The D-index reflects the severity of vaginal discharge symptoms and ranges from zero to ten. It can be used under field conditions without any adjustment from one to six weeks post calving, for any type of vaginal discharge and without defining the type of infection or differentiating between infection and contamination.

The D-index was calculated by summing the Dcoefficients for each discharge feature score, rectal temperature and an offset (Table 2). Rectal temperature, as a non-class variable, needed to be first multiplied by the D-coefficient. For example, a cow * Score amount ϭ 0 was given when there was no discharge present in the vagina, in these cases the other features could not be evaluated and were all given score 0.

recorded as: Ͼ handful vaginal discharge, Ͼ 50% purulent material, semi-viscous consistency, smellyes, pink color and temperature ϭ 39.5 °C will have a D-index value ϭ 9.89 (see Table 3).

Progesterone profiles 2.2.1. Animals

The data collection was done from 2002 to 2009, and the resulting data represents 785 lactations from 554 cows in their first to fifth lactation: 240 Danish Holstein, 178 Danish Red and 136 Danish Jersey.

Milk collection and progesterone analysis

Milk progesterone measurements were made on proportional whole milk samples collected automatically from the cows during their milking in a robotic milking system with free traffic (mean no. of visits/day ϭ 2.4). One progesterone measure was made daily during the first 120 d from calving, while it was made every second day for the remainder of lactation. The actual average intervals between progesterone samples before and after 120 d from calving were 1.4 and 2.4 d, respectively.

Progesterone was analyzed using the Ridgeway ELISA-kit (Ridgeway Science Ltd, Gloucestershire, UK). Milk samples were pipetted, diluted and distributed using a Biomek 2000 © (Laboratory Automation workstation, Beckman Coulter, Fullerton, CA, USA). Milk samples (25 L, diluted 1:2 with water) were handled according to the manufacturer's instructions, however, incubation with substrate-buffer was increased to 1 h 30 min. Plates were read using a spectrophotometer/fluorometer at 575 nm (Fluostar© BMG Labtechnologies, Offenburg, Germany). Analyses were performed in 96-well plates; two sets of seven standards (0 -30 ng/mL), locally made using milk from an ovariectomized cow and ethanolic progesterone solutions, and two sets of two control samples were used for every analysis and plate. For the low and high controls, the intra-assay precision (CV %) was 14.9 and 1.4, respectively; the inter-assay precision (CV %) was 32.7 and 20.1, respectively; and the average inaccuracy (bias) was Ϯ 0.82 and 0.60 ng/mL, respectively.

The time series of milk progesterone measurements were examined for gaps longer than 14 d with no progesterone measurements. All such gaps were caused by technical reasons and all data following such gaps were excluded. Further, the entire lactation was excluded if there were insufficient data to identify the end of the postpartum anovulatory period (i.e., the time series did not start at calving and there were less than five measurements before the cow was detected to have elevated progesterone and resumed ovarian cycles).

Extraction of progesterone profiles and their features

In order to obtain features of the progesterone profiles (e.g., duration of the luteal phase and rate of decline in progesterone at the end of the luteal phase) in a consistent manner, a computerized extraction procedure was used. The progesterone profile features were extracted by fitting a spline curve to the progesterone data and then using the first and second derivatives of the spline function (hereafter referred to as velocity and acceleration, respectively) to identify the peaks and troughs of the progesterone profile, and the points with greatest rates of decrease or increase in the profile. Smoothing was carried out using the functional data analysis approach of Ramsay and Silverman [START_REF] Ramsay | Functional Data Analysis[END_REF] im- plemented in R [START_REF]R: A Language and Environment for Statistical Computing[END_REF]. The smoothing spline was a fifth order polynomial B-spline with knots placed at each data point and a roughness penalty imposed on the second derivative (see [START_REF] Hansen | The influence of breed and parity on milk yield, and milk yield acceleration curves[END_REF]). At the peaks and the troughs of the smoothed profiles, velocity, i.e., the rate of change in progesterone, is zero and when the velocity is at a local maximum or minimum, the acceleration is zero. In a first step, all points were identified where velocity ϭ 0 and velocity had exceeded a lower or upper velocity threshold of Ϯ 1 ng/mL/d in the interval since the preceding zero velocity was identified. The velocity threshold was imposed to filter out zero velocity values associated with trivial undulations in the profile. Likewise, points of zero acceleration were identified using an acceleration threshold of Ϯ 0.35 ng/mL/d/d.

In a second step, to extract progesterone profile features, the following cyclicity marks were set on each smoothed profile, based on the following six definitions: 1) Luteal phase peaks (LutProgPeak) were defined as a point of zero velocity crossing from positive to negative velocity values and a smoothed progesterone value of Ͼ 8 ng/mL. When more than 1 such point occurred in a luteal phase (the interval between two estrus marks), the last occurrence was chosen as the luteal phase peak as this marks the end of the luteal phase. The threshold of Ͼ8 ng/mL was determined from the data of confirmed pregnant cows, as progesterone never fell below 8 ng/mL during the luteal phase of pregnancy in these animals. 2) Estrus marks were defined as a point of zero velocity with velocity crossing from negative to positive values and a smoothed progesterone value of Ͻ 8 ng/mL. 3) The end of the postpartum anovulatory period was defined as the first point on the smoothed profile Ͼ 3 ng/mL immediately prior to the first two consecutive progesterone measurements Ͼ3 ng/mL [START_REF] Lamming | The use of milk progesterone profiles to characterise components of subfertility in milked dairy cows[END_REF]. 4) The pre-estrus maximum rate of decline in progesterone (Down Rate) was defined as the velocity of the smoothed profile at the point of zero acceleration crossing from negative to positive acceleration in the period from luteal peak to estrus mark. If there was more than one such point, then the point with the minimum (i.e., most negative) velocity was chosen. 5) The post-estrus maximum rate of increase in progesterone (Up Rate) was defined as the velocity of the smoothed profile at the point of zero ac-celeration crossing from positive to negative acceleration in the period from an estrus mark to a luteal peak. If there was more than one such point, then the point with the maximum (i.e., most positive) velocity was chosen. In the last period (usually pregnancy) after the luteal peak mark, acceleration marks were excluded. 6) The length of the ovarian cycle was calculated as the number of days between two subsequent estrus marks.

Two further features, "Longer" and "Shorter", were created for the each ovarian cycle as the absolute value of the negative and positive deviation from the median cycle length, respectively. The median cycle length (based on progesterone profiles) was calculated to be 23 d (25% ϭ 20 600, 75% ϭ 26 400) from the 2046 ovarian cycles (cycle number first to ninth) available in the data set. For the 737 first postpartum ovarian cycles, the median was 22.5 d, but 23 d was used as the median length of the cycle in all analyses. For each ovarian cycle shorter than 23 d, a value of zero was attributed to the feature "Longer". Similarly, for the cycles longer than 23 d, a value of zero was attributed to the feature "Shorter".

Statistical analysis 2.3.1. Principal component analysis (PCA) of progesterone profile features

In order to describe the relationships between the different progesterone profile features, and through this to describe the different progesterone profiles, PCA was used as this deals appropriately with correlations between variables. The following progesterone profile features were analyzed using LatentiX 2.00 (Latent5, Copenhagen, Denmark, www.latentix. com): LutProgPeak, DownRate, UpRate, Longer and Shorter. The PCA model was based on 1827 ovarian cycles, which had available data concerning all mentioned features. Before creating the PCA model, the data were auto-scaled to mean 0 and SD 1. Using these scaled data the loading coefficients for each variable and for each principal component were calculated. Loading coefficients, i.e., describing the relationship between all variables, were used to calculate the scores, i.e., the position of all observations in the PCA model [START_REF] Esbensen | Multivariate Data Analysis-in practise[END_REF].

The relationship between D-index and progesterone profile characteristics

The relationship between the D-index and progesterone profile characteristics for the postpartum ovar-ian cycles were estimated by including the "D-index" variable in the PCA model described above and by regressing individual progesterone-based ovarian cycle features on the D-index. For this analysis, 75 first and 50 second ovarian cycles were available. Because of the short-term action of uterine inflammation on ovarian cycle features [START_REF] Sheldon | Influence of uterine bacterial contamination after parturition on ovarian dominant follicle selection and follicle growth and function in cattle[END_REF], the last D-index value before the onset of the particular ovarian cycle was used for this analysis (last D-index). Addition to the regressions of earlier values of the D-index did not alter the relationships found, and no significant effect of earlier values of the D-index was observed. This "D-index history" had no predictive value in this study.

Principal component analysis of progesterone profile features and D-index.

In order to investigate the relationship between progesteronebased ovarian cycle features and last D-index, the variable "D-index" was added to the existing PCA model of progesterone-based ovarian cycle features. The resulting PCA model was based on 94 ovarian cycles that had available values of last D-index. These 94 ovarian cycles came from 75 cows that were clinically examined for uterine inflammation and thus had calculated values of D-index.

Regression. The effect of D-index on individual progesterone-based ovarian cycle features was

examined by multiple linear regression with stepwise reduction of non-significant variables, using Minitab 9 software (www.minitab.com). Three descriptors of the progesterone-based ovarian cycle features were examined: the first principal component (PC#1) that described the shape of the progesterone profile, and the variables "Longer" and "Shorter" that described the length of the ovarian cycle. In these analyses, the variables "Longer" than and "Shorter" than typical in this study ovarian cycle length (median ϭ 23 d) were used rather than PC#2 because there are good a priori reasons for not assuming that the factors affecting longer and shorter cycles are the same. Given that the length parameters comprising PC#2 were found to be independent of the shape parameters comprising PC#1 this presented no methodological difficulties.

To account for possible breed effects, D-index value was adjusted for breed (i.e., residuals values of last D-index from a one-way analysis of variance with breed as the only explanatory factor were used). Other possible factors affecting progesterone-based ovarian cycle features were also included in the regressions: breed, parity, milk yield (at the first day of ovarian cycling) and body condition score (BCS). Milk yield and BCS were adjusted for breed as described above (correlations between breed, milk yield, parity, D-index and BCS are shown in Table 4). Available data for BCS did not allow the calculation of the rate of change of body condition and therefore the last measurement before the onset of the ovarian cycles was used.

BCS was scored manually every two weeks based on the system described by Kristensen [START_REF] Kristensen | Method for estimation of body condition of dairy cows[END_REF] using a one to five point scale with 0.25 point intervals, where one is very thin and five is obese.

Regressions were done for three groups of ovarian cycles: all ovarian cycles, first postpartum ovarian cycles and second postpartum ovarian cycles.

The length of postpartum anovulatory period

The relationship between the length of postpartum anovulatory period and possible risk factors was estimated using the regression model in Minitab 9 software. Predictors for the length of postpartum anovulatory period were: breed, parity, average milk yield from calving to the first ovarian cycle, BCS at calving and maximum D-index during the time from calving to the first ovarian cycle. Variables were adjusted for the effect of breed by using the residuals for maximum D-index, average milk yield, BCS at calving from one way analysis of variance with breed as the only explanatory factor.

Results

PCA model of progesterone profile features during ovarian cycles

General description of relationship between progesterone features

PCA was used to characterize the relationships between different features of the progesterone profile during ovarian cycles, and to identify the most influential feature combinations. The results of this analysis are shown in Table 5. In Table 5 it can be seen that the principal components first and second (PC#1, PC#2) account for 73.4 percent of the total variance in progesterone profile features (respectively 47.4 and 26.0 percent). The numerically highest values of the loading coefficients indicate the features that have the greatest influence in the PCs. For PC#1 these features are the ones that describe the shape of the progesterone profiles during the ovarian cycle, since LutProgPeak, Down-Rate and UpRate have the highest values. This can also be seen in Figure 1, where the progesterone features most strongly influencing PC#1 are those furthest from zero on the horizontal axis of the loadings plot, i.e., LutProgPeak, UpRate, and DownRate. These loadings indicate that less negative values of DownRate (i.e., a shallow downward slope), low progesterone peaks and less positive values of UpRate (i.e., a shallow upward slope) are characteristic of observations on the left side of the scores plot in Figure 1. Steeper downward slopes (more negative DownRate), high progesterone peaks and steeper upward slopes (more positive UpRate) are characteristic of observations on the right side of the scores plot in Figure 1.

In contrast, PC#2 is mostly influenced by the length of the ovarian cycle, with Longer and Shorter having the highest values and being furthest from zero on the vertical axis on a "Up Rate" is the maximum rate of increase in progesterone post-estrus, "LutProgPeak" is luteal phase peak of progesterone, "Down Rate" is the maximum rate of decline in progesterone pre-estrus, "Longer" is the length the cycle longer than median cycle length, "Shorter" is the length the cycle shorter than median cycle length. ovarian cycles) have in PC#2 a value ϭ 0. As the first two principal components accounted for the majority of the total variance, these were thus used in the subsequent analysis. PC#3-PC#5 accounted for too small a percent of total variance, and they largely represent the noise part of the model [START_REF] Esbensen | Multivariate Data Analysis-in practise[END_REF].

Effect of ovarian cycle number

To study the relationship between progesterone features and ovarian cycle number (Ov.Cycle.Nb), this variable was added to the PCA model (Fig. 2). Adding this variable did not adversely affect the relationship between the other variables, which strongly suggests that the associations between progesterone profile features during ovarian cycles are unaffected by cycle number. Indeed, coding the scores in Figure 1 to distinguish first ovarian cycles (coded as zero in the PCA) from second to ninth ovarian cycle number (coded as one) showed that both first and greater ovarian cycle numbers occurred throughout the whole space of the model (data not shown). Because the loading for Ov.Cycle.Nb. is positive for PC#1 and PC#2 this indicates that the proportion of high, steep and longer ovarian cycles increases with ovarian cycle number higher than one and that the proportion of low, flat and shorter ovarian cycles increases with first ovarian cycle. However, because it is placed close to zero both in PC#1 and in PC#2, it does not have any meaningful influence on the shape of progesterone profiles and length of the cycle.

The relationship between D-index and progesterone profile features 3.3.1. PCA model of progesterone features and last D-index

For exploratory purposes, the D-index was added to the PCA model, as shown in Figure 3. In this model, the two most influential components (PC#1 and PC#2) together accounted for 70.1 percent of a total variance. Figure 4 presents the scores plot (i.e., the PC values calculated for each observation) shaded according to the value of last D-index. Low values of last D-index (healthy) were correlated with high and steep profiles and with ovarian cycles of typical length in this study (median cycle length ϭ 23 d). High values of last D-index were correlated with low and flat ovarian cycles, and prolonged or shortened cycle duration (Fig. 3). The effect of last D-index on the progesterone profile features is relatively small, because the "D-index" variable is placed close to zero in PC#1 and PC#2.

Regression of progesterone profile features and last D-index

For the purpose of quantifying the effect of D-index and other risk factors on the distinguishing features of progesterone profiles during ovarian cycles (PC#1, lon- ger, shorter) stepwise linear regression was carried out. The results of these regressions analyses are presented in Tables 678.

The regression on PC#1 values (describing the shape of the profile) of breed, parity, adjusted last D-index, adjusted last milk yield and adjusted BCS, resulted -after stepwise removal of non-significant terms -in an equation with two significant predictors: adjusted last D-index and adjusted last milk yield. PC#1 values diminish with increasing last D-index and last milk yield, which means that the progesterone profile shape becomes flatter during the following ovarian cycle when the D-index increases. The coefficients for these factors were significant for both first postpartum ovarian cycles and all ovarian cycles (first ϩ second), but not for second ovarian cycles (Table 6).

The regression on "Longer" (indicating the prolongation of cycle length) resulted in equations with three predictors: last D-index, breed and parity. The length of the cycle extends with an increase in last D-index, parity, and in Holstein cows (breed ϭ 0). The parity effect was not significant for all groups and the effect of last D-index was not significant in the group of second ovarian cycles (Table 7).

The stepwise regression on "Shorter" (indicating the shortening of cycle length) resulted in the equations with three predictors: last D-index, breed and parity.

Table 6

The regression coefficients of the relationship PC#1 ϭ a ϩ b*lastD-index ϩ c*lastMilkYield for three groups of estrous cycles (all, only 1 st and only 2 nd ). Last D-index is the latest value of the vaginal discharge score (the D-index) before the ovarian cycle in question. Last milk yield is the week average milk yield (kg/d) at the corresponding time. Last D-index and last milk yield are both adjusted for breed effects. The significance values (P) for each coefficient, the R 2 , and the residual standard deviation of the regressions are also presented. The length of the cycle shortens with an increase in last D-index, with parity one and in Jersey cows (Table 8).

PC1

The relationship between the length of postpartum anovulatory period, D-index and other risk factors

The regression model indicated that postpartum anovulatory period was longer in Holstein than in Jersey cows and the length of anovulatory period extended with: parity number greater than one, increasing average milk yield, maximum D-index, and lower BCS at calving. However, only average milk yield was a significant factor for the length of the postpartum anovulatory period in this study.

Discussion

The aim of this study was to quantify the relationship between clinical symptoms of uterine inflammation and progesterone profile characteristics of the ovarian cycle. To quantify severity of inflammation symptoms we used a continuous scale based on visual observation of vaginal discharge, the D-index, developed and described by Gorzecka et al [START_REF] Gorzecka | A universal index of uterine discharge symptoms from calving to six weeks postpartum[END_REF]. Williams et al [START_REF] Williams | Clinical evaluation of postpartum vaginal mucus reflects uterine bacterial infection and the immune response in cattle[END_REF] have shown that vaginal discharge accurately reflects uterine bacterial infection and immune response. With regard to ovarian cycle features we chose to examine the aspects that are reflected in progesterone profiles because they provide objective measurements throughout the ovarian cycle. However, from the literature it is not clear which aspects of the progesterone profile during ovarian cycles are likely to be of importance. Therefore rather than make a priori assumptions we used a multivariate statistical procedure, PCA, to determine the distinguishing features of these progesterone profiles, i.e., which measurements are correlated and which measurements account for the variation in the shape of progesterone profiles and length of the cycles. Such an approach has not been previously reported.

PCA model of progesterone profile features during ovarian cycles

The major cause of variation, PC#1, were features describing the shape of the progesterone profile (a combination of peak progesterone concentration and the upward and downward slopes at the beginning and end of the luteal phase) that contrast low-progesterone and flat profiles (indicating low corpus luteum function) with high-progesterone and steep profiles (indicating elevated corpus luteum function). Importantly, the shape parameters were largely independent of cycle length parameters.

Abnormal luteal function, associated with reduced progesterone concentration after insemination [24 -26],

Table 7 The coefficients of relationship between last D-index, breed, parity and the variable "Longer" in three groups of ovarian cycles (Longer ϭ a ϩ b*lastD-ind ϩ c*breed ϩ d*parity). Last D-index was adjusted for breed effects. The breed effect represents the difference for Jersey relative to Holstein. The parity effect represents the difference of multiparous relative to primaparous. and also before insemination [START_REF] Erb | Profiles of reproductive hormones associated with fertile and nonfertile inseminations of dairy cows[END_REF][START_REF] Friggens | Where did she come from? Where is she going? -The potential of on-farm fertility profiles[END_REF][START_REF] Rosenberg | Seasonal variations in postpartum plasma progesterone levels and conception in primiparous and multiparous dairy cows[END_REF], has previously been correlated with reduced fertility. It has also been shown that pregnancy is more likely to be maintained in cows with high luteal phase progesterone [START_REF] Hommeida | Luteal function and conception in lactating cows and some factors influencing luteal function after first insemination[END_REF]. These findings suggests that this combination of progesterone profile features, discriminating low and flat progesterone profiles (reduced quality) from high and steep profiles (improved quality), describes one valuable aspect of ovarian cycle characteristics.

Longer

The second major axis of variation in progesterone profile features, PC#2, related to the cycle length parameters, Longer and Shorter, independently of the shape parameters. It has been shown that both shortened [START_REF] Inskeep | Factors that affect fertility during estrous cycles with short phases in postpartum cows[END_REF][START_REF] Schonkypl | Prostaglandininduced shortening of the luteal phase in cattle affects progesterone receptors, uterine secretory function and pregnancy rate[END_REF] and prolonged luteal phases [START_REF] Mccoy | Milk progesterone profiles and their relationship with fertility, production and disease in dairy cows in Northern Ireland[END_REF][START_REF] Lamming | The use of milk progesterone profiles to characterise components of subfertility in milked dairy cows[END_REF][START_REF] Royal | Declining fertility in dairy cattle: changes in traditional and endocrine parameters of fertility[END_REF][START_REF] Shrestha | Effects of abnormal ovarian cycles during pre-service period postpartum on subsequent reproductive performance of high-producing Holstein cows[END_REF] are associated with poorer reproductive performance, suggesting that progesterone derived cycle durations represent another valuable aspect of ovarian cycle characteristics. Different mechanisms are responsible for these two abnormalities in cycle length, such as premature release or lack of release of PGF2␣ [START_REF] Garverick | Mechanisms associated with corpus luteum lifespan in animals having normal or subnormal luteal function[END_REF][START_REF] Niswender | Mechanisms Controlling the Function and Life Span of the Corpus Luteum[END_REF] and late embryonic mortality [START_REF] Sreenan | The extent and timing of embryonic mortality in cattle[END_REF].

The PCA model revealed that this clustering of ovarian cycle features was the same regardless of ovarian cycle number (from first to fifth ovarian cycle postpartum). However, there was an indication that the proportion of low, flat and shorter ovarian cycles was greater with first ovarian cycle and the proportion of high, steep and longer ovarian cycles increases with ovarian cycle number greater than one. Accordingly, we looked at first and second ovarian cycles separately.

Relationship between D-index and progesterone profile features during ovarian cycles

There was a significant relationship between D-index and progesterone profile features during ovarian cycles that was consistent with the hypothesis that uterine inflammation adversely affects ovarian cycle features. The PCA model of progesterone profile features together with the D-index revealed the general, biological pattern of the relationship (Fig. 3), which was confirmed and quantified by the regression analyses (Tables 6,7, and 8). Low values of last D-index, i.e., from healthier cows, were correlated with a high and steep shape of the profile and with cycles of typical duration in this study (median ϭ 23 d). High values of last D-index, i.e., from less healthy cows, were correlated with low and flat ovarian cycles and prolonged or shortened duration of the ovarian cycles.

However, although the D-index had a significant effect on the shape of progesterone profiles and the length of the cycles, it only accounted for a small proportion of the variation in these progesterone-based ovarian cycle features (the R 2 of the regressions are low and when the D-index is included in the PCA, this variable is placed close to zero in both PC#1 and PC#2). This suggests that one should not overestimate the practical effect of clinical findings of uterine inflammation on ovarian activity postpartum, which is also suggested by McCoy [START_REF] Mccoy | Milk progesterone profiles and their relationship with fertility, production and disease in dairy cows in Northern Ireland[END_REF] who found no significant relationship between vaginal discharge and abnormal progesterone profiles. Further, Shrestha at al [START_REF] Shrestha | Resumption of postpartum ovarian cyclicity in high-producing Holstein cows[END_REF] observed abnormal cervico-vaginal discharge in 20 percent of cows with normal resumption of ovarian activity, and abnormal discharge was detected in 47 percent (8/17) of cows with a normal progesterone profile [START_REF] Taylor | Metabolic profiles and progesterone cycles in first lactation dairy cows[END_REF]. In a trial investigating relationship between uterine infection involution and postpartum ovarian activity, Mateus et al [START_REF] Mateus | Influence of Puerperal Uterine Infection on Uterine Involution and Postpartum Ovarian Activity in Dairy Cows[END_REF] found that ovulation took place in the presence of a heavily contaminated uterus (determined by bacteriology and clinical signs of infection) in 45 percent of the medium endometritis and in 38 percent of the severe endometritis cows (see also [START_REF] Huszenicza | Uterine Bacteriology, Resumption of Cyclic Ovarian Activity and Fertility in Postpartum Cows kept in Large-Scale Dairy Herds[END_REF]). These studies indicate that uterine inflammation symptoms do not preclude ovarian activity although as we have shown, clinical signs of uterine inflammation negatively affect progesterone-based ovarian cycle features.

Although it only accounted for a small proportion of the variation (R 2 in Table 7 and Table 8), we were able to quantify the effect of vaginal discharge symptoms on progesterone profile characteristics by means of the D-index. Thus, an increase by one unit of D-index was associated with an increase in average cycle length of the prolonged cycles of 0.3 d, and a decrease in the average length of shortened cycles of 0.2 d. The negative effects of D-index were consistently significant. Because the D-index applies throughout week one to six post calving, for any type of vaginal discharge, without the need to define the type of infection or differentiate between infection and contamination [START_REF] Gorzecka | A universal index of uterine discharge symptoms from calving to six weeks postpartum[END_REF], this result can be attributed a certain generality. The result of the present study is in agreement with the literature indicating that uterine bacterial inflammation affects ovarian cycle features via bacterial endotoxin and immune mediators, which disturb follicular selection, growth and function and hormonal interactions on the level of hypothalamus and pituitary. Observations in several species indicate that endotoxin or intermediary cytokines, such as IL-1 or TNF␣, can inhibit the pulsatile and surge modes of GnRH secretion, for instance in ewes [START_REF] Battaglia | Endotoxin Inhibits the Reproductive Neuroendocrine Axis While Stimulating Adrenal Steroids: A Simultaneous View from Hypophyseal Portal and Peripheral Blood[END_REF], goats [START_REF] Takeuchi | Lipopolysaccharide-Induced Suppression of the Hypothalamic Gonadotropin-Releasing Hormone Pulse Generator in Ovariectomized Goats[END_REF] or rats [START_REF] Rivest | The chronic intracerebroventricular infusion of interleukin-1 beta alters the activity of the hypothalamic-pituitary-gonadal axis of cycling rats. I. Effect on LHRH and gonadotropin biosynthesis and secretion[END_REF]. It has also been shown that endotoxin inhibits pituitary responsiveness to GnRH in ewes [START_REF] Williams | Endotoxin Inhibits Pituitary Responsiveness to Gonadotropin-Releasing Hormone[END_REF]. In cattle, endotoxin may suppress luteinizing hormone (LH) surges [START_REF] Peter | Suppression of preovulatory luteinizing hormone surges in heifers after intrauterine infusions of Escherichia coli endotoxin[END_REF], interrupt the preovulatory rise in estradiol and delay LH surge and subsequent ovulation [START_REF] Lavon | Exposure to endotoxin during estrus alters the timing of ovulation and hormonal concentrations in cows[END_REF][START_REF] Suzuki | Endotoxin induces delayed ovulation following endocrine aberration during the proestrous phase in Holstein heifers[END_REF], and infections with gram negative bacteria producing endotoxin, such as Escherichia Coli, suppress estradiol production by granulosa cells in the follicle [START_REF] Herath | Ovarian follicular cells have innate immune capabilities that modulate their endocrine function[END_REF]. In the study of Williams et al [START_REF] Williams | The relationship between uterine pathogen growth density and ovarian function in the postpartum dairy cow[END_REF] cows with high pathogen growth density at Day 7 postpartum had a smaller diameter of the first postpartum dominant follicle and lower plasma estradiol concentrations.

Moreover, bacterial endotoxin modulates prostaglandin secretion by the endometrial cells [START_REF] Miller | Arcanobacter pyogenes pyolysin stimulates bovine endometrial cell prostaglandin secretion[END_REF]. Changes in the relative balance of PGF2␣ and PGE2␣ following bacterial infection may decide the fate of the corpus luteum, with luteolysis if PGF2␣ dominates and persistence if PGE2␣ dominates [START_REF] Kaneko | The roles of PGF2␣ and PGE2 in regression of the corpus luteum after intrauterine infusion of Arcanobacterium pyogenes in cows[END_REF]. These mechanisms lead to a shortened [START_REF] Peter | Effects of intrauterine infection on the function of the corpora lutea formed after first postpartum ovulations in dairy cows[END_REF] or prolonged luteal phases, which are the most frequent cyclic abnormalities in the postpartum period [START_REF] Opsomer | Risk factors for post partum ovarian dysfunction in high producing dairy cows in Belgium: A field study[END_REF][START_REF] Shrestha | Resumption of postpartum ovarian cyclicity in high-producing Holstein cows[END_REF][START_REF] Taylor | Metabolic profiles and progesterone cycles in first lactation dairy cows[END_REF].

It is known that uterine inflammation is not the only cause of poorer features of the ovarian cycles, as, e.g., milk yield and negative energy balance can be also responsible [START_REF] Hommeida | Luteal function and conception in lactating cows and some factors influencing luteal function after first insemination[END_REF][START_REF] Vanholder | Interactions between energy balance and ovarian activity in high yielding dairy cows early postpartum:a review[END_REF]. In the present study, milk yield was a significant factor affecting the shape of the progesterone profile during ovarian cycles. Higher milk yield was also associated with a longer postpartum anovulatory period. In addition, we included BCS in our regression models as an indicator of the cow's energy status. However, this factor did not have any significant effect as an explanatory variable for variation in ovarian cycle features. We also found a significant relationship between the breed and the duration of the cycle. Holstein cows were more likely to have either prolonged or shortened cycles, compared with Jersey cows. This may be explained by lower average milk yield [START_REF] Lamming | The use of milk progesterone profiles to characterise components of subfertility in milked dairy cows[END_REF] and less negative energy balance [START_REF] Friggens | Breed and Parity Effects on Energy Balance Profiles Through Lactation: Evidence of Genetically Driven Body Energy Change[END_REF] of the Jersey cows in this study. They also had on average lower D-index value, indicating enhanced uterine health than Holstein cows.

The duration of postpartum anovulatory period

The regression model indicated that postpartum anovulatory period was significantly longer in Holstein than in Jersey cows and was shorter for cows in first parity. In addition, even after adjustment for breed differences, the duration of the postpartum anovulatory period was increased by an increase in average milk yield. However, there was no significant effect of maximum D-index or of BCS at calving on the duration of the postpartum anovulatory period. The effect of milk yield is in agreement with the literature [START_REF] Hommeida | Luteal function and conception in lactating cows and some factors influencing luteal function after first insemination[END_REF][START_REF] Harrison | Effect of lactational level on reactivation of ovarian function, and interval from parturition to first visual oestrus and conception in highproducing Holstein cows[END_REF], and it has been suggested that the unfavorable association between milk yield and the duration of postpartum anovulatory period may be an effect of increased negative energy balance in cows with higher milk production [START_REF] Lucy | Influence of diet composition, dry-matter intake, milk production and energy balance on time of postpartum ovulation and fertility in dairy cows[END_REF].

To examine possible effects of uterine inflammation on postpartum anovulatory period we used the maximum value of D-index before the first ovulation. However, the regression coefficient for maximum D-index was not significant. We also found that constant high D-index values preceding the first ovarian cycle did not prevent ovulation (data not shown). This indicates that uterine inflammation was not a significant risk factor for the duration of postpartum anovulatory period in the examined herd and uterine inflammation was more likely to affect the quality of progesterone-based ovarian cycle features rather than to prevent cyclicity. This finding is supported by some previous studies, where authors suggest that uterine inflammation may be more responsible for prolonged luteal phase whilst negative energy balance might be more responsible for delayed resumption of ovarian activity [START_REF] Mccoy | Milk progesterone profiles and their relationship with fertility, production and disease in dairy cows in Northern Ireland[END_REF][START_REF] Shrestha | Resumption of postpartum ovarian cyclicity in high-producing Holstein cows[END_REF][START_REF] Taylor | Metabolic profiles and progesterone cycles in first lactation dairy cows[END_REF].

The finding that BCS at calving had no effect on postpartum anovulatory period may seem surprising since BCS at calving is often correlated with negative energy balance (NEB) [START_REF] Broster | Body score of dairy cows[END_REF] and NEB affects postpartum anovulatory period [START_REF] Butler | Energy balance relationships with follicular development, ovulation and fertility in postpartum dairy cows[END_REF]. However, it is increasingly accepted that there is a normal, natural NEB in early lactation [START_REF] Coffey | Genetic evaluation of dairy bulls for energy balance traits using random regression[END_REF][START_REF] Friggens | Body lipid reserves and the reproductive cycle: towards a better understanding[END_REF]. In most mammals, independent of feeding level, there is an increase in body fatness during pregnancy and a decrease in body fatness during early lactation. Thus, a certain degree of NEB in early lactation cannot be prevented and it is supposed that this natural level of energy mobilization does not affect health and reproduction. It Table 9 The coefficients of relationship between the length of post-partum anovulatory period and breed, parity, average milk yield, maximum Dindex and BCS at calving (Y ϭ a ϩ b*breed ϩ c*parity ϩ d*average milk yield ϩ e*maximum D-index ϩ f*BCS). Maximum D-index was adjusted for breed effects. The breed effect represents the difference for Jersey relative to Holstein. The parity effect represents the difference of multiparous relative to primaparous. may well have been the case that this natural level was not exceeded in this study [START_REF] Friggens | Breed and Parity Effects on Energy Balance Profiles Through Lactation: Evidence of Genetically Driven Body Energy Change[END_REF].

Conclusions

Increasing severity of symptoms of uterine inflammation, as reflected by the D-index, was associated with abnormalities in all ovarian cycles examined. D-index had a significant negative effect on the shape of the progesterone profiles during ovarian cycles. It also significantly increased the frequency of prolonged and shortened ovarian cycles. Although the D-index had a significant effect on the shape and the length of progesterone profiles, it accounted for only a small proportion of the variance, suggesting that one should not overestimate the practical effect of clinical findings of uterine inflammation on ovarian activity in postpartum cows.

The D-index was not a significant risk factor for the length of postpartum anovulatory period in this herd, and was more likely to decrease the quality of progesterone-based ovarian cycle features than to prevent cyclicity. Table 9.

Figure 1 .

 1 Negative scores for PC#2 indicate cycles longer than 23 d; the more negative the values, the longer cycles. Similarly, positive values of PC#2 indicate cycles shorter than 23 d; the more positive the values, the shorter the cycle (Figure 1). In this study, within the 1827 ovarian cycles, there was a high positive correlation between the length of the cycle and the length of the luteal phase (0.927, P Ͻ 0.0001). It is striking that the ovarian cycle length features (Shorter, Longer) are close to zero on PC#1 and are thus largely uncorrelated with the shape features, which are close to zero on the vertical axis describing PC#2. Ovarian cycles, with the length 23 d (median cycle length of all

Fig. 1 .

 1 Fig. 1. PCA model of progesterone profile features. The scores plot (upper panel) presents the position of the observed scores for all ovarian cycles (i.e; the data from which the loadings were derived). The loadings plot (lower panel) presents how the variables are correlated with each other. PC#1 describes a shape of the profile, contrasting low-progesterone and flat profiles (on the left side) with high-progesterone and steep profiles (on the right side). PC#2 describes the length of the ovarian cycle, contrasting longer cycles with shorter cycles.

Fig. 3 .

 3 Fig. 3. The loadings plot for the PCA model of progesterone profile features after adding the variable "D-index". Low values of last D-index were correlated with high and steep profiles with typical in this study cycle length (median ϭ 23 days). High values of last D-index were correlated with low and flat profiles, and prolonged or shortened cycle length. Note that due to the rotational ambiguity of PCA, the sign of the loadings is reversed.

Fig. 2 .

 2 Fig. 2. The loadings plot for the PCA model of progesterone profile features after including the variable "Ov.Cycle.Nb." Adding "Ov.Cycle.Nb." did not impair the relationships between the other variables. The proportion of high, steep and longer ovarian cycles increases with ovarian cycle number greater than one and the proportion of low, flat and shorter ovarian cycles increases with first ovarian cycle. Note that due to the rotational ambiguity of PCA, the sign of the loadings is reversed.

Fig. 4 .

 4 Fig. 4. PCA-scores of progesterone profile features shaded according to the value of D-index. The shading scale is given below the main plot. Low values of last D-index were clearly correlated with high and steep profiles with typical length of the cycle. High values of last D-index were correlated with low and flat profiles, and prolonged or shortened length of the cycle.

Table 1

 1 Definitions of scores of vaginal discharge features used in this study. The system used is that of Gorzecka et al[START_REF] Gorzecka | A universal index of uterine discharge symptoms from calving to six weeks postpartum[END_REF].

	Discharge features

Table 3

 3

	An example of calculation of D-index value for a given set of
	vaginal discharge scores.
	Parameter		D-coefficient
	Purulent material	Ͼ50%	1.07
	Amount	Ͼ1 handful	0.82
	Consistency	Semi-viscous	1.15
	Smell	Yes	1.14
	Color	Pink	1.06
	Rectal temperature	39.5	39.5*0.28 ϭ 11.06
	Offset		Ϫ6.41
			D-index ϭ 9.89

Table 2

 2 Coefficients used for calculating the value of D-index according to scores for discharge features and rectal temperature. The derivation of these coefficients is described in Gorzecka et al[START_REF] Gorzecka | A universal index of uterine discharge symptoms from calving to six weeks postpartum[END_REF].

	Parameter		Coeff.
	Rectal temperature	Value	0.28
	Smell	Yes	1.14
	Color	no blood	Ϫ1.02
		pink	1.06
		brownish	0.74
	Consistency	watery	0.35
		semi-visc	1.15
		mucus-like	Ϫ1.22
	Amount	Ͻ1 handful	Ϫ0.96
		1 handful	0.32
		Ͼ1 handful	0.82
	Purulent material	No	Ϫ1.06
		Flecks	Ϫ0.30
		50%	0.23
		Ͼ50%	1.07
	offset		Ϫ6.41

Table 4

 4 The correlations between breed, parity, last D-index and milk yield (at the first day of ovarian cycling).

		Breed	Parity	Last D-index	Milk yield
	Last D-index	Ϫ0.191	Ϫ0.021	-	-
		P ϭ 0.033	P ϭ 0.812
	Milk yield	Ϫ0.503	Ϫ0.026	Ϫ0.108	-
		P ϭ 0.000	P ϭ 0.784	P ϭ 0.258
	BCS	Ϫ0.074	Ϫ0.239	0.083	Ϫ0.060
		P ϭ 0.424	P ϭ 0.008	P ϭ 0.369	P ϭ 0.544

Table 5

 5 The relationships between progesterone profile characteristics expressed in terms of loading coefficients in a principal component analysis of 2046 estrous cycles. The cumulative percent of explained variance is presented for each successive principal component (PC#1 to PC#5) with those having the highest percent explained to the left.

	Loadings a	PC#1	PC#2	PC#3	PC#4	PC#5
	UpRate	0.474582	0.345376	Ϫ0.35179	0.679354	0.26497
	LutProgPeak	0.615835	Ϫ0.06731	0.037355	Ϫ0.07212	Ϫ0.78078
	DownRate	Ϫ0.56307	Ϫ0.11284	0.018163	0.652722	Ϫ0.49381
	Longer	0.019794	Ϫ0.69537	Ϫ0.71527	Ϫ0.04857	0.045822
	Shorter	Ϫ0.27943	0.616373	Ϫ0.60242	Ϫ0.32384	Ϫ0.27244
	Cumulative % explained variance	47.4117	73.4462	87.8417	96.9926	100

Table 8

 8 The coefficients of relationship between last D-index, breed, parity and the variable "shorter" in three groups of estruses (shorter ϭ a ϩ b*lastD-ind ϩ c*breed ϩ d*parity). Last D-index was adjusted for breed effects. The breed effect represents the difference for Jersey relative to Holstein. The parity effect represents the difference of multiparous relative to primaparous.

		a	B	c	d	R 2	Residual SD
	All ovarian cycles	2.52	0.333	Ϫ2.30	1.19	14.6%	3.907
		P ϭ 0.000	P ϭ 0.004	P ϭ 0.003	P ϭ 0.111
	1 st ovarian cycles	2.62	0.458	Ϫ2.56	1.24	16.4%	4.595
		P ϭ 0.003	P ϭ 0.009	P ϭ 0.027	P ϭ 0.258
	2 nd ovarian cycles	1.38	Ϫ0.133	Ϫ1.25	0.632	12.5%	2.089
		P ϭ 0.008	P ϭ 0.296	P ϭ 0.072	P ϭ 0.366

J.Gorzecka et al. / Theriogenology 75 (2011) 1016 -1028

Acknowledgments

The paper is a part of a PhD project funded by the Danish Research Council, Aarhus University, the Faculty of Agricultural Sciences and Lattec I/S.

The authors would like to thank the Danish Cattle Research Center in Foulum, the milk laboratory at the Faculty of Agricultural Sciences, Aarhus University, and the reviewers of this manuscript.