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Lipids and Cardiovascular/Metabolic Health

Circulating inflammatory and atherogenic biomarkers are not increased following single meals of dairy foods

Abstract

Background/Objectives:

Inflammation characterizes obesity and is nutritionally modifiable. The hypothesis of this study is that full-fat dairy foods influence circulating inflammatory and atherogenic biomarkers according to fermentation status.

Subjects/Methods:

Thirteen overweight subjects participated in five test meals. Single breakfasts containing control low-fat milk or 45 g fat from butter, cream, yoghurt or cheese were tested over 3 weeks. Plasmas obtained 3 and 6 h were later analyzed for inflammatory markers interleukin (IL)-6, IL-1β, tumor necrosis factor-α and high-sensitive C-reactive protein, and atherogenesis-related markers monocyte chemoattractant protein-1, macrophage inflammatory protein-1α, intercellular adhesion molecule-1 and vascular cell adhesion molecule-1. A 4-week study in 12 subjects compared the effects on these biomarkers of diets containing 50 g dairy fat daily as either butter, cream and ice cream (non-fermented) or cheese plus yoghurt (fermented) dairy foods.

Results:

In single-meal study, one outlier subject showed marked increments in biomarkers, hence the following results apply to 12. Within group analysis includes significant falls at 3 h in four inflammatory markers after cream, butter and low fat, and three atherogenesis-related biomarkers after cream. Changes were few after cheese and yoghurt. By 6 h, most values returned to baseline. However, between group analysis showed no differences between the five meals. The 4-week study showed no significant differences in fasting biomarker concentrations between non-fermented and fermented dairy diets.

Conclusions:

Single high-fat meals containing sequentially four different full-fat dairy foods did not increase eight circulating biomarkers related to inflammation or atherogenesis. Among subjects, significant falls occurred at 3 h in inflammatory biomarkers after cream and butter but were not specific for full-fat dairy foods. We could not confirm the reported increments in inflammation after fat meals.

Introduction

Atherosclerosis and metabolic disorders that increase the risk of thrombo-vascular disease including metabolic syndrome and diabetes are characterized by inflammatory and oxidative processes (Libby et al., 2002; Pischon et al., 2008). The inflammatory lesions occur at multiple sites including macrophages in arterial wall, endothelium lining the arteries and in adipose tissue. This gives rise to the appearance in the circulation of inflammatory cytokines and chemokines and markers of endothelial dysfunction among a large number of biomarkers that are considered indicators of heightened inflammation and atherogenesis (Blake et al., 2005).

Eating certain foods may be associated with a rise in the circulating concentrations of some of these molecules. It is uncertain whether repeated exposure to increased circulating inflammatory molecules may be detrimental. Fat and glucose have most often been reported to increase the levels of one or more biomarkers (Basu et al., 2006; Giugliano et al., 2006), including tumor necrosis factor-α (TNF-α), interleukin (IL)-6, endothelial adhesion molecules ICAM-1 (intercellular adhesion molecule-1) and VCAM-1 (vascular cell adhesion molecule-1), the monocyte chemoattractant protein-1 (MCP-1), the macrophage inflammatory protein-1α and the downstream marker of inflammation high-sensitive C-reactive protein (hsCRP) (Blake et al., 2005). The expression of markers of inflammation increases in circulating monocytes following fatty meals (Aljada et al., 2004).

The fat-rich test meals have generally included several sources of fat including meat fat, eggs and dairy fats (Esposito et al., 2003; Blackburn et al., 2006; van Wijk et al., 2006; Manning et al., 2008; Depurkar et al., 2010; El-Khoury et al., 2010). Individual dairy fats have not been specifically tested, which may be important given the findings in a large prospective study, suggesting a cardioprotective effect from consuming fermented but not non-fermented dairy foods (Warrensjo et al., 2010). Cheese intake had been previously found not to be associated with cardiovascular disease in observation studies (Nestel, 2008).

We have, therefore, tested the hypothesis that individual dairy foods may differ in their effects on inflammatory cytokines. This has been explored following single meals rich in one of the four dairy foods (butter, cheese, cream or yoghurt) and in longer 4-week periods during which either fermented or non-fermented dairy foods predominated.

Subjects and methods

Dietary interventions

Twenty-one subjects aged 44–69 years who were overweight or obese, with body mass index=26–40 kg/m2, participated in the study; 13 in the 1-day studies and 12 in the longer 12-week study, where four subjects participated in both studies albeit at least 9 months apart (Table 1). Exclusion criteria included treatment for dyslipidemia, unstable anti-hypertensive medication, hepatic or renal impairment, thyroid dysfunction, diabetes, smokers or drinkers of more than two standard glasses of alcoholic beverage daily. Anti-inflammatory drugs were prohibited although paracetamol was allowed.

Table 1 Characteristics of the participating subjects

Single-meal study

Five meals containing different full-fat dairy foods (four meals) and one low-fat milk meal were tested in randomized order (using random tables) over 2–3 weeks with several days between consecutive test days. Thirteen subjects fasted overnight before consuming breakfast between 0800 and 0900 hours. Freshly prepared meals comprised 45 g fat (7 g for the low-fat control) and 100 g carbohydrate (130 g for the low-fat meal) (Table 2a). Food constituents include 110 g matured plain cheddar cheese in the cheese study, 115 ml thickened cream in the cream study, 50 g butter in the butter study, 600 ml plain yoghurt in the yoghurt study and 400 ml reduced fat milk in the low-fat study. Common foods included one to three slices of wholemeal bread and 200–400 ml fruit juice, depending on the amounts needed to balance the carbohydrate intake. A sachet of warmed ‘quick oats’ in the cream study improved the palatability of the large volume of cream. Calcium intakes were standardized by adding supplemental calcium with the low-fat meal; sodium intakes were not significantly different between meals by adding a sodium-rich spread, vegemite (Kraft, Sydney, NSW, Australia) in meals other than cheese (Table 2a). Polyjoule (Nutricia Australia Pty Ltd, Sydney, NSW, Australia) was used to balance energy or Sustagen (Mead Johnson & Company, Sydney, NSW, Australia) to provide energy and protein for the low-fat meal. The focus on energy and total dairy fat led to less protein in the butter and cream meals (see Discussion). Since the cream meal was served on warmed oatmeal (Uncle Tobys, Nestle, Sydney, NSW, Australia), three additional studies were carried out on six of the subjects to exclude an effect from oatmeal: cream plus oatmeal, cream alone with wholemeal bread and oatmeal alone resembling the low-fat meal in terms of energy, fat and carbohydrate. In all, 30 ml blood was obtained before and 3 and 6 h after completion of the meal. This included sufficient to assay the eight biomarkers, full blood count, glucose and main plasma lipids. Subjects refrained from exercise and remained seated for much of the time.

Table 2a Composition of meals tested as single mealsa,b

4-Week interventions

Twelve subjects participated, including eight whose entry criteria on average did not differ significantly from that of the first group of 13 with the exception of high-density lipoprotein (HDL) cholesterol that was higher among the eight supplementary subjects (P<0.05). This study was commenced 9 months after the single-meal studies. The design tested effects on biomarkers between diets enriched with either non-fermented or fermented dairy foods. Each period was allocated randomly lasting 4 weeks preceded by 2-week run-in and separated by 2-week washout periods (12 weeks total), containing all four dairy foods that contributed 45 g fat. The aim was to minimize metabolic perturbations between test diets focusing on comparison of non-fermented versus fermented dairy foods. Rotating diets ensured attractive choices of foods although the range of food choices was limited by providing a lengthy list of foods to be avoided to minimize variability across diets in a free-living population. Diets were carefully monitored at each visit and analyzed by a dietitian from 3-day records during all four periods to ensure compliance. Subjects weighed themselves daily and made minor adjustments from ‘free foods’. The macronutrient intakes were calculated from the 3-day diaries using the Australian Composition of Foods data (Table 2b); calculated consumption accorded closely with the planned diets. In general, the daily menus comprised four serves cereal, four serves vegetable/salad, two serves fruit, one to two serves of a protein-rich food (meat, poultry, fish, pulse or egg) plus the dairy foods that were being tested. The low-fat periods allowed two servings of dairy (200 g low-fat yoghurt plus low-fat milk). Of 72 g fat eaten during the test diets, 50 g fat was from dairy foods, derived from butter plus cream and full-fat ice cream (non-fermented) and mature cheddar cheese plus full-cream yoghurt (fermented). Carbohydrate intake derived almost equally from starches and simpler mono- and disaccharides, partly reflecting the lactose content. Two glasses of alcohol were allowed daily but intake averaged less. Cream and yoghurt that were provided fresh and the other dairy foods (cheese, butter, ice cream) were provided in conveniently packaged amounts donated in bulk by dairy companies. Body weights changed minimally over the 12-week period. Blood pressures measured by automated means did not change significantly.

Table 2b Composition of average intakes during each longer-term phasea

Blood samples were obtained before and after each intervention period including run-in and washout. Measurements of analytes were those made in the single-meal study.

Laboratory methods

Whole blood was collected into 9 ml EDTA vacutainers (Interpath Services, Heidleberg West, VIC, Australia) and plasma stored at −80 °C with 100 μM butylhydroxytoluene. Cytokines were quantified with Milliplex MAP kits (Applied Biosystems, Melbourne, VIC, Australia). IL-6, IL-1β and TNF-α were tested using ‘High-Sensitivity’ kits, while soluble ICAM-1 (sICAM-1), soluble VCAM (sVCAM-1), adiponectin, MCP-1α and macrophage inflammatory protein-1 were determined using human cytokine/chemokine kits (Millipore, St Charles, MO, USA). Interassay coefficients of variation (%CV) were calculated for each cytokine from 10 separate assays and ranged between 4.5 and 14%. High-sensitivity CRP was determined on a COBAS Integra 400 plus blood chemistry analyzer (C-Reactive Protein Latex (CRP)); Roche Diagnostics Australia, Castle Hill, NSW, Australia).

Plasma samples were analyzed for glucose, total cholesterol, HDL, low-density lipoprotein (LDL) and triglycerides (COBAS Integra 400 plus blood chemistry analyzer; Roche Diagnostics Australia). The study was approved by the Alfred Hospital Human Ethics Committee, and informed signed consent was obtained from all the subjects.

Statistical methods

Variables were tested for normal distribution by the Kolmogonov–Smirnov test. Those with normal distribution are expressed as mean (s.d.), while non-normally distributed variables are expressed as median (interquartile range). Within group differences were analyzed by either paired samples t-test or the Wilcoxon signed rank test for normally and non-normally distributed variables, respectively. Differences between groups were analyzed either by repeated measures analysis of variance or by Friedman's two-way analysis of variance by ranks for normally and non-normally distributed variables, respectively.

Our ability to demonstrate significant changes for most cytokines under some conditions indicates that the study was adequately powered with 12 subjects. Further, based on the data for sICAM-1, we observed a normal distribution for the changes between 0 and 3 h with a s.d. of 14; with 12 participants, this would provide statistical power of 97% for a difference in the mean response of 20 at the 0.05 level.

Results

The 21 subjects (13 single-meal volunteers and 8 additional subjects for the longer-term study) were overweight to obese (average body mass index=30.4±3.9 kg/m2) and four participants were hypertensive (>140 mm Hg systolic on most days of the study). Other parameters that characterize the metabolic syndrome, plasma glucose, triglyceride and HDL concentrations were on average normal. Five subjects were hypercholesterolemic (6.0–6.7 mmol/l), five had abnormally low HDL cholesterol levels (0.80–0.88 mmol/l), two were hypertriglyceridemic at 3.4 and 2.1 mmol/l and four were hyperglycemic when studied (5.6–6.3 mmol/l). Individual variations in these parameters across the study period were small and body weights fluctuated minimally.

Single-meal studies

Lipid and glucose responses

Plasma triglyceride levels rose significantly with each of the fat containing diets and at 3 h were +22, +20, +40, +48% following butter, cheese, cream and yoghurt, respectively. There were small but statistically significant falls in LDL cholesterol in the first 3 h with all meals (−6 to −9% across the meals). Falls in HDL cholesterol were significant after butter (−3%), cream (−6%) and yoghurt (−7%). Plasma glucose fell significantly at 3 h following the butter (−10%) and cheese (−23%) meals.

Biomarkers: within group changes

In one subject, a healthy overweight woman, several biomarkers were initially among the highest and rose progressively over the 6 h after all meals including the low-fat breakfast. Results have therefore been analyzed with her inclusion as well as exclusion. Table 3 shows the baseline data for the remaining 12 subjects and the changes between 0 and 3 h after the meals expressed as percentage change and level of significance. Three of the four inflammatory markers (IL-6, IL-1β and TNF-α) tended to fall on average after 3 h especially after the butter, cream and low-fat meals, but there were only minor effects from eating cheese or yoghurt, the two fermented dairy foods. Falls in hsCRP were significant after butter and cream. Falls in the atherogenesis and endothelium-related biomarkers fell mostly after the cream meal. By 6 h, those biomarkers that had fallen most initially tended to return to baseline values.

Table 3 Plasma concentrations of the eight inflammatory and atherogenesis-related biomarkers at 0 h and changes at 3 h in 12 subjects: within group differencesa

When the 13th subject's results are included, the overall findings differed with the levels of significance for some of the differences attenuated (data not shown). Specifically, the significant falls after butter and cream remained for two inflammatory markers (IL-6 and TNF-α) and only the fall in MCP-1 after cream remained significant among the atherogenesis markers. Yoghurt and cheese did not elicit significant falls with the exception for macrophage inflammatory protein-1. Thus, butter and cream alone elicited a number of significant falls in biomarker concentrations 3 h after the meals when the findings in all 13 subjects were analyzed for within group differences. Importantly, none of the eight biomarkers rose significantly on average across 3 and 6 h whether or not the 13th subject was included. Further, the changes in biomarker levels were unrelated to changes in glucose or any of the lipid concentrations.

Biomarkers: between group analysis

There were no significant differences between the five groups for any of the biomarkers (data not shown).

Supplementary study with oats

Because the cream meal that induced substantial biomarker falls included oatmeal, six subjects participated in a comparison of three meals, cream alone, cream+oats (as in the main study) and oatmeal with skim milk. The 3-h findings resembled those in the main study with modest average falls in the three biomarkers tested: IL-6, IL-1β and TNF-α with the cream-only meal (data not shown). A possible confounding effect of oats was therefore excluded.

4-Week study

Since our hypothesis that biomarker responses would differ after fermented and non-fermented dairy foods was not sustained after single meals, we carried out a longer-term comparison between the two fermented (yoghurt plus cheese) versus the non-fermented dairy foods (butter, cream and full-cream ice cream). However, as shown in Table 4, the average biomarker concentrations in fasted plasmas did not differ significantly neither between the two test periods nor from the washout period during which all dairy products contributed a fat content only 10 g less than that in the test diets. It should be noted that the variations in biomarker concentrations across the four periods totaling 12 weeks was small. There were no differences across the four periods in average plasma glucose, triglyceride, total cholesterol, LDL cholesterol and HDL cholesterol concentrations (data not shown).

Table 4 Plasma concentrations of the eight inflammatory and atherogenesis-related biomarkers following 4 weeks on each of two test diets (fermented dairy and unfermented dairy) and the mixed dairy fat run-in and washout periods

Discussion

The major finding has been an absence of significant increments in any of the eight biomarkers following four different dairy food based breakfasts of similar fat content. Of additional interest was the significant falls in several biomarkers especially 3 h after the butter and cream based meals (Table 3). The second finding was largely nullified when the 13th subject's data are included; she was the only person to show persistent and large rises in most biomarkers with all meals including the low-fat meal. Although our cohort of 12 subjects is relatively small, each subject was tested after four dairy based meals providing a database of 48 studies without including the low-fat period. Further, six subjects were retested with three supplementary meals and all three inflammatory markers tested showed an initial falling trend at 3 h.

The 4-week comparisons of meals rich in the two non-fermented versus the two fermented dairy products failed to show differences in the concentrations of any of the eight biomarkers tested in the fasting state (Table 4). Our finding may resemble the results from a reported longer 6-month intervention with three to five daily portions of dairy foods that doubled the intake of milk, cheese and yoghurt but failed to affect any of the inflammatory markers IL-6, CRP, E-selectin or TNF-α (Wennersberg et al., 2009). Nevertheless, our data do not shed light on a recent Swedish report of a lower association between incident myocardial infarction and consumption of fermented dairy foods than when non-fermented dairy foods were eaten (Warrensjo et al., 2010).

We cannot explain the falls in biomarker concentrations at 3 h. They were unrelated to hemodilution (concentrations of albumin in plasma did not change significantly). Nor could the findings be explained through correlations with plasma glucose, plasma lipid or plasma insulin changes all of which were non-significant. Our findings are therefore at variance with reported increments after fat meals. However, in most of the studies, that will be reviewed briefly, the nature of the fats varied and generally contained non-dairy as well as dairy foods, and further, fewer biomarkers were tested of which mostly only one or two were found to increase.

Aljada et al. (2004) reported that high-fat meals based on egg and sausages led to increments after 3 h in mRNA for matrix metallopeptidase-9, TLR2 and TLR4 within circulating monocytes. However, plasma sICAM-1 did not change and CRP rose only marginally. In another study, a meal rich in cream increased the expression of IL-1β and TNF-α in mononuclear cells (Depurkar et al., 2010). Van Wijk et al. (2006) tested 50 g fat cream meals in diabetic patients with no effect on either CRP or MCP-1 levels but a delayed rise in IL-6 at 4 h. High-fat meals based on peanut butter, cream, eggs and cheese lowered TNF-α after 4 h but raised IL-6 while CRP concentration was unchanged (Blackburn et al., 2006). IL-18 but not IL-8, ILs linked to inflammation rose at 4 h (but not at 2 h) after high-fat meals of eggs and sausage (Esposito et al., 2003). Finally, Manning et al. (2008) tested cream and two vegetable oils as well as meals of potato or bran reporting little difference between cream, canola oil, olive oil or potato on IL-6 levels that fell initially and then rose after 6 h; TNF-α fell significantly at 4 and 6 h after the meals. Thus, most of the reported changes, both rises and falls in biomarkers occurred between 2 and 6 h after the meals, spanning our measurements at 3 and 6 h.

The reviewed investigations demonstrate the variability in the inflammatory responses to single fatty meals. Some of the variability may have related to the subjects studied, some of whom were healthy while others were obese or diabetic, or to the type of fat in the meals. Our study had the strength of studying 13 subjects with a randomly sequenced set of four separate dairy foods on four inflammatory and four atherogenesis-related biomarkers. With the exception of one subject, the spread of results was narrow and the supplementary studies that retested the cream plus oatmeal breakfast replicated qualitatively the results of the first trial. Thus, the findings appear robust at least to the extent that none of the dairy foods providing about 45 g fat significantly raised on average any of the eight biomarkers tested. In fact, the between group changes (each group representing one dairy food) were not significantly different among the four full-fat dairy foods and the low-fat meal that contained skim milk.

The surprising falls in the concentrations of a number of biomarkers, especially after butter and cream were consumed, remains unexplained. We excluded the possibility that increments in insulin concentration after these mixed meals had suppressed the levels of the biomarkers by finding no correlation between the changes in plasma insulin concentration and any biomarkers (data not shown). The protein intake with the butter and the cream meals was substantially less than with the cheese and yoghurt meals (Tables 2a and 2b) that may be relevant since Mortensen et al. (2009) reported that different sources of protein added to a high-fat meal elicited different triglyceridemic and glycemic responses. Our priority was to minimize the differences in dairy fat and in energy content. However, since this study was completed, Papageorgiou et al. (2011) reported findings that resembled the overall conclusion of significant falls in sICAM-1 and in TNF-α 3 h after consuming several vegetable oils.

In the absence of a low-fat control period, interpretation of the 4-week study is limited to the absence of significant differences in biomarkers between the fermented and non-fermented dairy periods. However, the recent report from the ATTICA, Greece study (A health and nutrition survey in the Greek city of ATTICA; Panagiotakos et al., 2010) of lower circulating inflammatory markers among high consumers of dairy foods suggests a potential cardioprotective effect of such foods. The present study did not demonstrate significant differences in any of the lipid parameters between the fermented and non-fermented dairy food periods. While this may appear to be at odds with the previous demonstration by ourselves and by others of lower levels of plasma LDL cholesterol with consumption of cheese than with butter (Nestel, 2008), the amounts of fat in cheese and in butter in those studies (40 g dairy fat from cheese or from butter) substantially exceeded that eaten in the present study (45 g total dairy fat from two sources during each period only about half being derived from butter or from cheese).

Conclusions

Our study failed to show an increase in circulating inflammatory or postulated atherogenic biomarkers after high-fat breakfasts. In fact, at least cream and butter led to significant reductions in several biomarkers at 3 h but not at 6 h, whereas the fermented dairy fats cheese and yoghurt failed to affect the majority of the eight markers that were tested. However, the low-fat control meal that contained skim milk also led to falls in several cytokines. The hypothesis that longer-term consumption of fermented dairy foods and non-fermented dairy foods would result in different cytokine responses in the fasting state was also not substantiated. Hence, the only clear conclusion is that, contrary to several other reports, single high-fat meals comprising several individual full-fat dairy foods did not raise the circulating concentrations of four inflammatory and four postulated atherogenic biomarkers. This does not exclude that high-fat dairy foods may stimulate the expression of such molecules within cells.

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Acknowledgements

The following food companies contributed to a grant-in-aid through the Dairy Health and Nutrition Consortium and provided bulk quantities of butter, cheese and ice cream: Tatura Milk Industries and Bega Cheese, National Foods, Fonterra Australia, Parmalat Australia, Geoffrey Gardiner Foundation, Murray-Goulburn Co-operative, Warrnambool Cheese and Butter, Dairy Innovation Australia.

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Correspondence to P J Nestel.

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Nestel, P., Pally, S., MacIntosh, G. et al. Circulating inflammatory and atherogenic biomarkers are not increased following single meals of dairy foods. Eur J Clin Nutr 66, 25–31 (2012). https://doi.org/10.1038/ejcn.2011.134

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Keywords

  • inflammatory biomarkers
  • dairy foods
  • overweight subjects

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