AJCN. First published ahead of print May 19, 2010 as doi: 10.3945/ajcn.2009.29054.
Biomarkers of milk fat and the risk of myocardial infarction in men and
women: a prospective, matched case-control study1–3
Eva Warensjo¨, Jan-Ha˚kan Jansson, Tommy Cederholm, Kurt Boman, Mats Eliasson, Go¨ran Hallmans, Ingegerd Johansson,
and Per Sjo¨gren
ABSTRACT
Background: High intakes of saturated fat have been associated
with cardiovascular disease, and milk fat is rich in saturated fat.
Objective: The objective of this study was to investigate the association between the serum milk fat biomarkers pentadecanoic acid
(15:0), heptadecanoic acid (17:0), and their sum (15:0+17:0) and
a first myocardial infarction (MI).
Design: The study design was a prospective case-control study
nested within a large population-based cohort in Sweden. Included
in the study were 444 cases (307 men) and 556 controls (308 men)
matched on sex, age, date of examination, and geographic region.
Clinical, anthropometric, biomarker fatty acid, physical activity, and
dietary data were collected. The odds of a first MI were investigated
by using conditional logistic regression.
Results: In women, proportions of milk fat biomarkers in plasma
phospholipids were significantly higher (P , 0.05) in controls than
in cases and were, in general, negatively, albeit weakly, correlated
with risk factors for metabolic syndrome. The crude standardized
odds ratios of becoming an MI case were 0.74 (95% CI: 0.58, 0.94)
in women and 0.91 (95% CI: 0.77, 1.1) in men. After multivariable
adjustment for confounders, the inverse association remained in
both sexes and was significant in women. In agreement with biomarker data, quartiles of reported intake of cheese (men and
women) and fermented milk products (men) were inversely related
to a first MI (P for trend , 0.05 for all).
Conclusions: Milk fat biomarkers were associated with a lower risk
of developing a first MI, especially in women. This was partly
confirmed in analysis of fermented milk and cheese intake. Components of metabolic syndrome were observed as potential intermediates for the risk relations.
Am J Clin Nutr doi: 10.
3945/ajcn.2009.29054.
INTRODUCTION
Dairy products are high in saturated fat, and high intakes have
been associated with cardiovascular diseases (CVDs) (1). It has
been estimated that dairy products (excluding butter) contribute
to 24% of the saturated fat intake in the US diet (2). Paradoxically, consumption of dairy products has been suggested to be
associated with lower cholesterol concentrations (3) and to
ameliorate characteristics of metabolic syndrome (4), which has
an effect on cardiovascular complications. The worldwide escalation of the metabolic syndrome and its components, such as
obesity, dyslipidemia, and hypertension (5), constitutes a threat to
public health, especially in relation to cardiovascular endpoints
such as myocardial infarction (MI). In the CARDIA (Coronary
Artery Risk Development in Young Adults) study, frequent
consumption of dairy products was associated with a 70% decreased risk of developing metabolic syndrome over 10 y (6), but
a higher dairy intake could not be linked to lower body weight or
advantageous levels of other components of metabolic syndrome
(except for a slightly lower blood pressure) in the Hoorn Study
(7). A blood pressure–lowering effect from dairy consumption
was established (2, 4, 8, 9), but its relation to obesity and dyslipidemia remains controversial (9).
Dairy products contribute energy, high-quality protein, and
important micronutrients to our diet (10). Until acceptance of the
diet-heart hypothesis, full-fat dairy products were part of
a healthy diet. Today, a diet low in saturated fat, including
avoidance of full-fat milk, remains central nutritional advice for
lowering plasma cholesterol and optimizing cardiovascular
health (11).
The absence of clear evidence linking dairy consumption to
cardiovascular events may be explained by the content of healthpromoting components in dairy products (2, 10), but the exact
mechanisms remain uncertain. To overcome the bias associated
with dietary assessment, especially for fatty food, biomarkers
may be used. Dairy fat contains the ruminant-specific fatty acids
pentadecanoic acid (15:0) and heptadecanoic acid (17:0), and the
presence of these fatty acids in serum lipids can be used as
objective biomarkers of milk fat intake (12–14). This was recently validated in the survey population from which the present
1
From the Department of Public Health and Caring Sciences, Clinical
Nutrition and Metabolism, Uppsala University, Uppsala, Sweden (EW, TC,
and PS); the Department of Surgical Sciences, Orthopedics, and Uppsala
Clinical Research Center, Uppsala, Sweden (EW); the Department of Public
Health and Clinical Medicine, Umea˚ University, Umea˚, Sweden (J-HJ, KB,
ME, GH); the Department of Medicine, Skelleftea˚ Hospital, Skelleftea˚,
Sweden (J-HJ); Sunderby Hospital, Lulea˚, Sweden (ME); and the Department of Odontology, Umea˚ University, Umea˚, Sweden (IJ).
2
Supported by grants from the National Dairy Council/Dairy Management Inc, the Joint Committee of the Northern Sweden Health Care Region,
the Va¨sterbotten County Council, the Swedish Research Council, and the
Swedish Council for Working Life and Social Research.
3
Address correspondence to E Warensjo¨, Department of Public Health
and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University,
Uppsala Science Park, SE-75185 Uppsala, Sweden. E-mail: eva.warensjo@
pubcare.uu.se.
Received December 12, 2009. Accepted for publication April 8, 2010.
doi: 10.3945/ajcn.2009.29054.
Am J Clin Nutr doi: 10.3945/ajcn.2009.29054. Printed in USA. Ó 2010 American Society for Nutrition
Copyright (C) 2010 by the American Society for Nutrition
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WARENSJO
study sample was nested (15). Shorter-term changes in the
composition of dietary fatty acid intake are reflected in serum
lipids a few weeks after intake (16). Previous studies have
generated conflicting results for the relations between milk fat
biomarkers and heart disease (17–19). Against this background,
we aimed to investigate the association between serum milk fat
biomarkers 15:0, 17:0, and 15:0+17:0 and a first MI and risk
factors associated with metabolic syndrome in a large, prospective, nested case-control study. In addition, the reported
intake of selected milk products was investigated in association
with a first MI.
SUBJECTS AND METHODS
Study population and study design
The present study is a prospective case-control study nested
within the Northern Sweden Health and Disease Study (NSHDS;
Figure 1). The NSHDS is made up of the Va¨sterbotten Intervention Program (VIP), the World Health Organization
Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) Study, and the local Mammography Screening
Project (MSP). All participants in the study participated in one
of these health surveys between 1987 and 1999.
The VIP and MONICA studies are both community-based
programs that monitor risk factors for CVD. The VIP started in
1985, and all men and women aged 30, 40, 50, and 60 y and living
in county of Va¨sterbotten were asked to participate in a health
survey at their local primary health care center (20). The 3 health
surveys included from the northern Swedish part of the MONICA
study were conducted in 1990, 1994, and 1999 (21). The health
surveys in VIP and MONICA were similar to each other and
included a medical examination and the completion of a questionnaire regarding cardiovascular risk factors, educational level,
physical activity, medications, tobacco use, and dietary intake.
Participants were also asked to donate a blood sample to the
Northern Sweden Medical Biobank for future research. Overall
participation rates were 59% in VIP and exceeded 75% in the
MONICA screenings. To increase the number of women in the
present study, data from the MSP were added. In the MSP, which
started in 1995, all women in Va¨sterbotten aged 50–70 y are
invited to a mammography screening every 2 or 3 y. The women
are asked to donate a blood sample to the Northern Sweden
Medical Biobank (22). The participation rate in the MSP was
85% in the screening phase, and 57% of these participants donated a blood sample. Information on dietary intake was not
available for MSP participants. By 31 December 1999, ’73,000
unique subjects had been included in the NSHDS from these 3
subcohorts (Figure 1). Of the present study population, 73.5%
originated from the VIP, 7.5% from the MONICA study, and
19% from the MSP.
Consecutive cases of MI occurring from 1987 to 31 December
1999 were identified through the Northern Sweden MONICA
incidence registry (23). Criteria for a first MI and participation in
FIGURE 1. Recruitment procedures are shown in the flowchart. Consecutive cases of myocardial infarction (MI) that occurred between 1987 and 31
December 1999 were identified through the Northern Sweden MONICA (Monitoring of Trends and Determinants in Cardiovascular Disease) incidence
registry. If previous MI, stroke, or malignant disease could not be excluded, cases and controls were excluded. Subjects without a plasma sample at the biobank
were also excluded. In the present study, 375 cases and 434 controls were from the Va¨sterbotten Intervention Program (VIP) and MONICA studies and 69
cases and 122 controls were from the Mammography Screening Project (MSP). 1One control for men, and 2 controls for women. These were matched to cases
for sex, age (62 y), date of health survey (64 mo), and geographic region.
BIOMARKERS OF MILK FAT AND MYOCARDIAL INFARCTION
the surveys before the MI were fulfilled by 696 cases (Figure 1).
Controls, 2 women and 1 man, were randomly selected from the
same subcohort by the following matching criteria: sex, age (62
y), date of health survey (64 mo), and geographic region. If
a previous MI, stroke, or malignant disease could not be excluded, cases and controls were excluded. Subjects lacking
plasma samples for fatty acid analyses were also excluded. The
final study population was made up of 375 cases and 434 controls from the VIP and MONICA studies and 69 cases and 122
controls from the MSP (Figure 1). The study was approved by
the Regional Ethics Committee of Umea˚ University (Umea˚,
Sweden), and data handling was approved by the National
Computer Data Inspection Board (Stockholm, Sweden). All
participants gave informed consent.
Survey information
Participants in the VIP and MONICA subcohorts were asked to
fill out a questionnaire, including information on socioeconomic
status, medical history, physical activity, and tobacco use. Participants in the MSP were asked only about smoking habits.
Levels of leisure-time and occupational physical activity were
graded 1 to 5 (with 5 being the highest category) and used as
a categorical covariate. Smokers were defined as current daily
smokers and nonsmokers (including former smokers and occasional smokers). Educational level was defined as whether university degree was attained or not, and diabetes status (yes/no)
was on the basis of self-report.
Information on habitual diet over the past year was obtained by
a semiquantitative food-frequency questionnaire (FFQ). An 84item FFQ was originally used but was replaced by a shorter
version (64–66 items). Frequency alternatives, meal-size illustrations, and selected sections of the FFQ were left intact,
whereas some questions were deleted and a few questions were
merged. The food frequencies were reported as never, several
times per year, 1–3 times/mo, once a week, 2–3 times/wk, 4–6
times/wk, once a day, 2–3 times/d, and 4 times/d. Intakes per
day for different food groups were calculated as previously reported (15) and used as covariates in the present study. The
numbers of participants with dietary information varies in the
present study because dietary assessment was not included in
the MSP (n = 191). Furthermore, dietary data were not available
for participants who completed the FFQ before it was optically
readable, and FFQs missing 10% answers were excluded. The
reported intake of milk products and fatty acids 15:0+17:0 was
transformed into grams per day and controlled for age and sex
portion sizes as previously described (15). The FFQ has been
shown to be a valid tool in estimating estimate the intake of milk
and dairy-specific fatty acids 15:0 and 17:0 relative to repeated
24-h diet recalls and fatty acid profiles in erythrocyte membranes (24). Dietary records were available for 75% of men and
80% of women (after excluding MSP participants) in the present
study population.
Blood pressure was recorded either in a supine or recumbent
position after a 5-min rest. Hypertension was defined as a systolic
blood pressure (SBP) 140 mm Hg or a diastolic blood pressure
(DBP) 90 mm Hg or by participant self-report of receiving
antihypertensive treatment 14 d before the health survey. In
the VIP and MONICA cohorts, blood samples were obtained in
the morning and after 4 h of fasting. In the MSP, blood sam-
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ples were collected for analyses of serum fatty acids and apolipoproteins throughout the day. In the VIP and MONICA
cohorts, 96.4% had fasted for 4 h and 77.1% for 8 h before
blood samples were taken. In the MSP, the corresponding percentages were 83.1% and 66.0%, respectively (22). Cholesterol
and triglycerides were measured by using a Reflotron bench top
analyzer or an enzymatic method (both from Boehringer Mannheim GmbH Diagnostica, Mannheim, Germany). The Reflotron has been validated for cholesterol measurement The mean
difference between the Reflotron bench top analyzer and an
enzymatic method (CHOD-PAP; Boehringer Mannheim) was
0.04 mmol/L, and the CV was 0.90 (25). Plasma glucose
(after 4 h of fasting and 2 h after an oral glucose challenge)
was analyzed with the Reflotron bench top analyzer. Body mass
index (BMI) was calculated as weight (kg)/height (m2). Apolipoproteins A-I (apo A-I) and B (apo B) were analyzed with
reagents from Dako (Glostrup, Denmark) and calibrated (·0947)
on a Hitachi 911 multianalyzer (Roche Diagnostics GmbH,
Mannheim, Germany). The ratio of apolipoproteins (apo-ratio)
was calculated as apo B:apo A-I.
Milk fat biomarkers
Relative amounts of the dairy-specific fatty acids 15:0 and 17:0
in serum phospholipids were analyzed by gas-liquid chromatography (GLC) as described in detail elsewhere (26). Briefly, the
serum phospholipid fraction was extracted in chloroform and
then separated by thin-layer chromatography. Fatty acids were
transmethylated and separated by GLC on a capillary column.
The analyses were carried out on a GC 5890 equipped with
a 7671A autoinjector, a 3392A integrator (all from HewlettPackard, Avondale, PA), and a 25-m Nordion fused silica column
NS-351 (HNU Systems Inc, Helsinki, Finland), with helium as
the carrier gas. Fatty acids were identified by comparing each
peak’s retention time with those of methyl ester standards (GLC68A; Nu Check Prep, Elysian, MN). The CVs for the analyses
were ,10% for all fatty acids.
Statistical analyses
Statistical analysis was carried out by using the statistical
software STATA, version 10 (STATA Corp, College Station, TX).
P , 0.05 was considered significant. Normality of the data was
checked with Shapiro-Wilk’s test. The following variables were
log-transformed before analysis: BMI; concentrations of glucose,
triacylglycerol, and 17:0; and food groups. Continuous data are
presented as means 6 SDs or medians and interquartile ranges depending on distribution. Categorical data are presented as numbers of
individuals and percentages. Missing data were not included. Differences between groups were assessed with independent t,
Wilcoxon’s rank-sum, or chi-square tests when appropriate.
Correlations between 15:0, 17:0, and 15:0+17:0 and risk factors
related to metabolic syndrome (SBP and DBP, fasting plasma
glucose, triacylglycerols, apo-ratio, total cholesterol, and BMI) were
investigated with Spearman rank correlations. Partial correlations
were used to adjust for BMI in combination with smoking.
The association between milk fat biomarkers and a first MI was
investigated with conditional logistic regression analysis. The
milk fat biomarker variables 15:0, 17:0, and their sum (15:0
+17:0) were treated in 2 different ways in the analyses. In the
¨ ET AL
WARENSJO
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continuous models the biomarker fatty acids were standardized
(mean = 0, SD = 1), and in the threshold model the participants
were classified into quartiles on the basis of plasma fatty acid
distributions. It was a priori decided that the conditional logistic
regression analysis would be performed on sex strata, and results
are presented as odds ratios (ORs) and 95% CIs. Multivariable
models included the following covariates—model 1: leisure-time
and occupational physical activity and BMI; model 2: smoking,
intake of fruit and vegetables, educational level, and physical
activity; and model 3: all variables from model 2 as well as SBP,
BMI, apo-ratio, and prevalence of diabetes. Separate adjustments
for individual food groups (servings/d of fruit, vegetables, alcohol, fish, and meat) were also carried out.
In post hoc analysis, the participants were stratified according
to quartiles of their reported intake of cheese, fermented milk
products, total milk products (excluding butter), and fatty acids
15:0+17:0, respectively, and analyzed in association with a first
MI. Multivariable adjustment for leisure-time and occupational
physical activity and BMI (model 1) and cardiovascular risk
factors [apo-ratio, smoking, and SBP (model 2)] were carried out.
The rationale for the choice of dairy products to be analyzed was
that reported intake of cheese and fermented milk products was
statistically different between cases and controls. Total milk
products and 15:0+17:0 were chosen for comparative reasons.
Note that food data were only available in a subset of the study
population for reasons stated earlier.
RESULTS
Baseline characteristics of study participants
The study population in the present study was composed of
307 male and 137 female cases and 308 male and 248 female
controls. The average time between the baseline investigation and
the MI event was 3.9 6 2.4 y in men and 3.1 6 2.2 y in women.
Baseline characteristics of the study participants by sex and
case-control status are presented in Table 1. Compared with
controls, cases (both men and women) had significantly higher
apo-ratio, triacylglycerol concentration, SBP, and DBP; more
often had a lower level of education; and were more frequently
smokers. Furthermore, male cases, compared with their controls,
had a higher BMI, higher total cholesterol concentrations, and
higher prevalence of diabetes. The proportion of milk biomarkers 15:0 and 17:0 and their sum was significantly higher
(P , 0.05) in female controls than in female cases (Table 1).
Both male and female cases reported numerically lower median intakes (g/d) of cheese, fermented milk, total dairy products,
and fatty acids 15:0+17:0 compared with controls. Male, but not
female, cases had lower intakes of ice cream and cream (Table 2).
A significant difference was shown only for cheese intake in women
(P , 0.001) and fermented milk products in men (P , 0.05).
Cheese intake was borderline significantly different in men (P =
0.07). The reported frequency of butter did not differ between cases
and controls in both sexes and was ’1.5 servings/d (data not shown).
TABLE 1
Baseline characteristics of the study population by sex and case-control status1
Men
Characteristics
Age (y)
BMI (kg/m2)
Total cholesterol (mmol/L)
Apo A-I (mg/L)
Apo B (mg/L)
Apo-ratio
Fasting glucose (mmol/L)
2
H Glucose (mmol/L)
Triacylglycerol (mmol/L)
SBP (mm Hg)
DBP (mm Hg)
Hypertension (%)
Smoking status (%)5
Current
Nonsmoker6
Diabetes (%)7
Educational level8 (%)
15:0 (% of total phospholipids)
17:0 (% of total phospholipids)
15:0+17:0 (% of total phospholipids)
Women
Cases
n
Controls
n
P2
Cases
n
Controls
n
P2
50 (49–60)3
26.7 (24.6–28.7)
6.5 6 1.24
1351 6 211
1304 6 281
0.99 6 0.25
5.3 (4.9–5.9)
6.2 (5.3–7.4)
1.7 (1.3–2.4)
139 6 17
88 6 10
33
307
296
294
307
307
307
271
243
222
294
294
307
307
60 (53–64)
25.5 (23.4–28.0)
6.0 6 1.2
1415 6 204
1170 6 259
0.84 6 0.23
5.3 (4.9–5.8)
6.1 (5.2–7.7)
1.3 (0.97–1.8)
134 6 16
85 6 8.7
21
308
297
293
308
308
308
269
259
210
293
293
308
308
0.9
0.001
0.001
0.001
0.008
0.001
0.15
0.36
0.001
0.001
0.001
0.001
0.001
50 (50–60)
26.0 (23.3–29.4)
6.7 6 1.2
1521 6 229
1290 6 263
0.87 6 0.22
5.2 (4.9–5.7)
7.2 (6.3–8.5)
1.6 (1.3–2.2)
145 6 22
89 6 8.8
51
137
66
66
137
137
137
57
54
53
64
64
68
137
60 (53–63)
25.7 (23.2–28.0)
6.4 6 1.4
1593 6 265
1183 6 292
0.76 6 0.22
5.3 (4.8–6.0)
6.8 (5.9–7.7)
1.2 (0.88–1.8)
134 6 18
84 6 7.6
24
248
122
122
248
248
248
107
104
93
121
121
126
248
0.9
0.7
0.14
0.008
0.001
0.001
0.43
0.06
0.003
0.001
0.001
0.001
0.004
248
126
248
248
248
0.36
0.01
0.05
0.04
0.03
38
57
8.8
7.6
0.18 6 0.04
0.38 (0.33–0.41)
0.56 6 0.09
307
305
307
307
307
20
77
4.2
15
0.19 6 0.04
0.38 (0.34–0.41)
0.57 6 0.13
308
303
308
308
308
0.021
0.005
0.36
0.19
0.29
19
27
7
9.1
0.177 6 0.04
0.36 (0.31–0.39)
0.53 6 0.09
137
66
137
137
137
11
39
8
24.6
0.184 6 0.04
0.37 (0.33–0.41)
0.56 6 1.0
1
Apo A-I, apolipoprotein A-I; Apo B, apolipoprotein B; Apo-ratio, apolipoprotein B:apolipoprotein A-I; SBP, systolic blood pressure; DBP, diastolic
blood pressure.
2
Independent t, Wilcoxon’s rank-sum, or chi-square tests were used to assess P values for differences between cases and controls.
3
Median; interquartile range in parentheses (all such values).
4
Mean 6 SD (all such values).
5
Percentages do not add up to 100 because of lack of information on the remainder of the subjects.
6
Included those who reported that they had never smoked, used to smoke, or were occasional smokers.
7
Information on diabetes was missing for a total 200 subjects.
8
Defined as whether or not a university degree was attained.
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BIOMARKERS OF MILK FAT AND MYOCARDIAL INFARCTION
TABLE 2
Median intake of milk products and milk fatty acids, as reported in a semiquantitative food-frequency questionnaire at baseline, in a subgroup of the study
population by sex and case-control status
Men
Intake
Total milk products1
Cream
Cheese, 17% and 28% fat
Fermented milk
Total milk5
Ice cream
15:0+17:0
Cases
470
1.4
14.7
78.4
200
5.1
23.0
n
g/d
(255–602)2
(1.2–3.2)
(6.8–23.8)
(31–202)
(139–481)
(5.1–5.8)
(13.6–36.8)
220
237
221
237
236
237
238
Women
Controls
484
2.5
19
109
196
5.4
24.4
n
g/d
(244–672)
(1.2–3.2)
(7.8–27.2)
(33–288)4
(74–481)
(5.1–9.0)
(15–33.8)
215
233
217
233
231
217
234
294
2.0
9.7
95.2
160
4.4
13.6
Cases
n
g/d
(209–447)
(1.8–3.1)
(4.7–16.6)
(35.6–172)
(112–320)
(2.0–5.0)
(9.3–23.19
55
55
52
55
55
52
57
Controls
308
2.0
14.1
107
160
4.4
15.2
n
g/d
(231–454)
(1.8–3.5)
(10.2–27)3
(36–220)
(112–269)
(2.0–5.2)
(9.1–23.9)
91
97
91
97
97
91
101
1
Included cream, cheese, fermented products, milk, and ice cream (excluding butter).
Median; interquartile range in parentheses (all such values).
3,4
Significantly different from cases (Wilcoxon’s rank-sum test): 3P , 0.001, 4P , 0.05.
5
Included skim (0.5%), low-fat (1.5%), and full-fat (3%) milk.
2
women, adjustment for physical activity in combination with
BMI (model 1) did not affect the OR of a first MI for 15:0 and
was not significant. For 17:0 and 15:0+17:0, the same adjustment strengthened the ORs, but the models were only significant
in women. Adjustment for possible confounders (model 2)
lowered the OR in both sexes, except for 15:0 in men. The
models with 17:0 and 15:0+17:0 were significant in women. In
model 3, the ORs remained virtually unchanged compared with
the crude models and were nonsignificant in both sexes (Table
4). For sensitivity analysis, the crude analyses were constrained
to individuals with no missing data for any of the variables
used in models 1, 2, and 3, and this rendered similar inverse
relations, although the ORs in some cases were lower and the
95% CIs were wider because of fewer observations (data not
shown).
Further multivariable analyses were restricted to the biomarker
sum (15:0+17:0), because this variable captures the effect of both
15:0 and 17:0 and aligns with our previous study (19). Adjustments for single food groups other than dairy products (ie, intake
of fruit, vegetables, alcohol, fish, and meat) strengthened the OR
in women (OR: 0.53–0.59) and was significant. In men, the ORs
Correlations between milk fat biomarkers and risk
markers for metabolic syndrome
The fatty acid 15:0 was significantly and inversely correlated with
BMI, triacylglycerol, and total cholesterol, whereas 17:0 and 15:0
+17:0 in general were negatively associated with all risk markers
associated with metabolic syndrome. Most correlations were weak
(Table 3). Most significant correlations remained, although attenuated, after adjustment for BMI in combination with smoking. The
correlation between milk fat biomarkers and apo-ratio became
significant, although weakly, after adjustment for BMI and smoking. Apo A-I and apo B were not associated with the biomarkers
and were not included in Table 3. Correlations were similar when
men and women were considered separately.
Probability of a first MI by plasma concentrations of fatty
acids 15:0, 17:0, and 15:0+17:0
Crude conditional logistic regression (Table 4) revealed
a significant inverse association between 15:0, 17:0, and 15:0
+17:0 and a first MI in women. The tendency was similar in men
(ORs ’0.90) but did not reach significance. In both men and
TABLE 3
Spearman rank correlations (r) between milk fat biomarkers (15:0, 17:0, and 15:0+17:0) and risk markers associated with metabolic syndrome in the study
population1
Unadjusted
15:0
BMI
Triacylglycerols
Fasting glucose
SBP
DBP
Apo-ratio
Total cholesterol
1
17:0
20.08
20.183
20.05
20.05
20.06
20.06
20.104
2
20.14
20.223
20.163
20.114
20.163
0.05
0.008
3
Adjusted for BMI and smoking
15:0+17:0
n
15:0
17:0
15:0+17:0
20.135
20.233
20.133
20.104
20.133
0.005
20.04
781
578
738
772
772
1000
776
20.173
20.04
20.06
20.06
20.082
20.124
20.114
20.082
20.104
20.124
0.092
0.009
20.1643
20.0852
20.092
20.124
20.082
20.04
3
SBP, systolic blood pressure; DBP, diastolic blood pressure; Apo-ratio, apolipoprotein B:apolipoprotein A-I. Spearman rank correlation test was used to
estimate r and P values.
2
P , 0.05.
3
P , 0.01.
4
P , 0.001.
¨ ET AL
WARENSJO
6 of 9
TABLE 4
Crude and multivariable standardized odds ratios (ORs) and 95% CIs for a first myocardial infarction1
Men
Fatty acid and model
15:0
Crude
Model 1
Model 2
Model 3
17:0
Crude
Model 1
Model 2
Model 3
15:0+17:0
Crude
Model 1
Model 2
Model 3
Women
n
OR
95% CI
n
OR
612
380
320
306
0.92
0.87
0.94
1.02
0.78,
0.69,
0.72,
0.75,
612
380
320
306
0.92
0.83
0.84
0.88
612
380
320
306
0.91
0.83
0.86
0.93
95% CI
1.10
1.10
1.24
1.41
385
122
99
91
0.78
0.82
0.56
0.88
0.61,
0.52,
0.30,
0.43,
0.99
1.30
1.04
1.82
0.78,
0.67,
0.65,
0.65,
1.10
1.04
1.08
1.19
385
122
99
91
0.74
0.50
0.45
0.68
0.58,
0.29,
0.21,
0.29,
0.95
0.86
0.98
1.59
0.77,
0.66,
0.67,
0.68,
1.10
1.04
1.11
1.26
385
122
99
91
0.74
0.57
0.47
0.74
0.58,
0.34,
0.23,
0.33,
0.94
0.95
0.95
1.66
1
Cases and controls were matched for sex, age, date of health survey, and geographic region. Dietary intake data were obtained in a subgroup of the study
population on the basis of information from a semiquantitative food-frequency questionnaire and converted to servings per day. ORs and 95% CIs were
calculated by using conditional logistic regression. Model 1was adjusted by leisure-time physical activity, occupational physical activity and BMI; model 2
was adjusted by smoking habit, reported intake of fruits and vegetables, leisure-time physical activity, occupational physical activity, and educational level;
model 3 was adjusted as in model 2 and additionally adjusted for apo-ratio (apolipoprotein B:apolipoprotein A-I), systolic blood pressure, BMI, and
prevalence of diabetes.
remained (OR: 0.86–0.90) and were nonsignificant (data not
shown). The odds of a first MI were investigated when study
subjects were grouped according to quartiles of plasma concentrations for 15:0+17:0 (separately for men and women). The
results did not follow a strict dose-response trend but did align
with the results from the continuous models. Thus, the ORs in
women in quartiles I, II, III, and IV were 1.0, 0.88 (95% CI: 0.48,
1.59), 0.80 (95% CI: 0.45, 1.41), and 0.50 (95% CI: 0.25, 1.0),
respectively (P for trend = 0.07). The corresponding values in
men were 1.0, 0.63 (95% CI: 0.40, 0.98), 0.76 (95% CI: 0.47,
1.23), and 0.84 (95% CI: 0.52, 1.36) (P for trend = 0.61).
Probability of a first MI in quartile groups of milk product
intakes
The OR to have a first MI followed a descending trend in
quartile groups on the basis of the distribution of cheese intake in
both sexes and fermented milk intake in men (Table 5). In men,
increasing reported cheese and fermented milk intake was inversely associated with an MI (P for trend = 0.025 and 0.01,
respectively). In women, only cheese intake was inversely related to a first MI (P for trend = 0.005). The significant trends for
both men and women were, however, lost after multivariable
adjustment, possibly with the exception of cheese intake in
women. Intakes of total milk products and 15:0+17:0 were not
related to a first MI in this study. Multivariable adjustment for
physical activity in combination with BMI (model 1) as well as
cardiovascular risk factors (model 2) still yielded an OR ,1.0,
but the significance was lost in men. In women, the effects did
not follow a consistent pattern and should be interpreted carefully due to limited power.
DISCUSSION
In the present prospective case-control study, biomarkers of
milk fat were inversely related to a first MI in Swedish women but
not in Swedish men. Each SD increase of the proportion of the
biomarker sum (15:0+17:0) was associated with a 26% risk
reduction of a first MI in women. After adjustment for possible
confounders (smoking habits, reported fruit and vegetable intake,
leisure-time physical activity, occupational physical activity, and
educational level), the relations remained and ORs were lowered
(except for 15:0 in men) and were significant in women. Adding
possible mediators (apo-ratio, SBP, diabetes prevalence, and
BMI) to the model removed any relation. In agreement with the
biomarker data, we found an inverse association between MI and
reported cheese (in both men and women) and fermented milk
(only in men) intake. Moreover, milk fat biomarkers were
negatively related, although weakly, to risk factors associated
with metabolic syndrome, providing a potential causal link between estimated milk fat intake and reduced heart disease risk.
Central obesity is considered a main driver in metabolic syndrome (27), and that many of these correlations remained after
adjustment for BMI may indicate that these associations are
related to central obesity rather than to being overweight per se.
In addition, the relation between dairy food intake and weight
management remains inconclusive (28).
However, it was not possible to delineate the exact mechanism
behind the relations or exclude a more beneficial lifestyle pattern
in milk fat consumers. Many factors in milk fat and factors
associated with milk fat may have the potential to account for
beneficial effects. This study corroborates the results from our
previous smaller case-control study (19) and the results from
a Norwegian study (17). On the contrary, the present study
contradicts the results from the Nurses’ Health Study (18). The
7 of 9
BIOMARKERS OF MILK FAT AND MYOCARDIAL INFARCTION
TABLE 5
Odds ratios (ORs) and 95% CIs for a first myocardial infarction in quartiles of reported milk product intake and by sex strata1
Men
Cheese
Crude
Model 1
Model 2
Fermented dairy products
Crude
Model 1
Model 2
Total milk products
Crude
Model 1
Model 2
15:0+17:0
Crude
Model 1
Model 2
Women
Cheese
Crude
Model 1
Model 2
Fermented dairy products
Crude
Model 1
Model 2
Total milk products
Crude
Model 1
Model 2
15:0+17:0
Crude
Model 1
Model 2
Quartile I
Quartile II
Quartile III
Quartile IV
g/d
g/d
g/d
g/d
,7.2 [114]
1.0
1.0
1.0
.33 [124]
1.0
1.0
1.0
,236 [101]
1.0
1.0
1.0
,12.2 [94]
1.0
1.0
1.0
7.2–14.7 [95]
0.62 (0.35, 1.08)
0.65 (0.32, 1.31)
0.72 (0.38, 1.39)
33–101 [116]
1.36 (0.82, 2.28)
1.27 (0.68, 2.4)
1.21 (0.67, 2.2)
236–405 [92]
1.48 (0.85, 2.6)
2.34 (1.13, 4.8)
2.13 (1.09, 4.2)
12.2–21.4 [11]
1.10 (0.60, 1.88)
1.34 (0.62, 2.9)
1.11 (0.57, 2.17)
14.8–23.8 [114]
0.58 (0.34, 0.98)
0.60 (0.32, 1.32)
0.57 (0.30, 1.06)
101.1–219 [107]
1.11 (0.66, 1.87)
1.20 (0.61, 2.34)
1.47 (0.80, 2.7)
405.1–586 [118]
1.20 (0.71, 2.0)
1.42 (0.71, 2.86)
1.38 (0.73, 2.6)
21.5–31.7 [123]
0.65 (0.37, 1.14)
0.66 (0.32, 1.36)
0.70 (0.37, 1.35)
.23.8 [115]
0.52 (0.29, 0.93)
0.69 (0.34, 1.39)
0.60 (0.30, 1.20)
.219 [123]
0.49 (0.28, 0.84)
0.58 (0.29, 1.14)
0.49 (0.26, 0.93)
.586 [124]
0.93 (0.54, 1.6)
0.99 (0.49, 2.0)
1.36 (0.71, 2.6)
.31.7 [144]
0.95 (0.55, 1.63)
0.73 (0.34, 1.56)
0.93 (0.49, 1.77)
,6.7 [41]
1.0
1.0
1.0
,30.8 [32]
1.0
1.0
1.0
,239 [44]
1.0
1.0
1.0
,12.2 [64]
1.0
1.0
1.0
6.7–14.6 [45]
0.26 (0.09, 0.75)
0.29 (0.08, 1.07)
0.46 (0.12, 1.69)
30.8–97.2 [47]
1.38 (0.54, 3.55)
1.43 (0.34, 5.99)
1.21 (0.39, 3.8)
239–401 [52]
0.64 (0.27, 1.53)
1.33 (0.40, 4.42)
0.26 (0.07, 1.02)
12.2–21.1 [46]
0.95 (0.44, 2.1)
0.58 (0.18, 1.93)
1.21 (0.48, 3.07)
14.7–20.3 [27]
0.42 (0.12, 1.44)
0.15 (0.02, 1.01)
0.57 (0.12, 2.8)
97.3–220 [42]
1.09 (0.37, 3.28)
2.33 (0.42, 12.89)
0.77 (0.22, 2.7)
401.1–582 [27]
0.59 (0.20, 1.72)
0.91 (0.24, 3.4)
0.27 (0.07, 1.06)
21.2–31.4 [35]
0.72 (0.30, 1.75)
0.88 (0.26, 2.9)
0.62 (0.20, 1.95)
.20.3 [30]
0.12 (0.03, 0.46)
0.12 (0.02, 0.68)
0.38 (0.07, 2.2)
.220 [32]
0.68 (0.19, 2.34)
0.67 (0.09, 5.4)
0.34 (0.97, 1.64)
.582 [20]
0.75 (0.23, 2.50)
1.8 (0.33, 9.8)
0.45 (0.09, 2.32)
.31.4 [13]
0.98 (0.28, 3.74)
1.22 (0.20, 7.3)
0.89 (0.18, 4.5)
P for trend
0.025
0.10
0.31
0.010
0.12
0.17
0.66
0.57
0.68
0.54
0.53
0.15
0.005
0.015
0.36
0.43
0.19
0.82
0.86
0.14
0.71
0.65
0.59
0.99
1
ORs and 95% CIs were calculated by conditional logistic regression; n in brackets. Cases and controls were matched for sex, age, date of health survey,
and geographic region. Model 1 was adjusted for leisure-time, occupational physical activity, and BMI; model 2 was adjusted for cardiovascular risk factors
(ratio of apolipoprotein B to apolipoprotein A-I, smoking, and systolic blood pressure).
differences in results between the Scandinavian compared with
US studies may reflect the context in which dairy food is consumed. In the United States, cheese and milk are common in
takeaway meals such as cheeseburgers and shakes, whereas dairy
intake in Scandinavia may be related to a more healthy food
pattern and a higher socioeconomic status.
Coronary artery disease remains the single largest cause of
death in Western populations, including Sweden. Mortality from
coronary heart disease has declined since the 1980s, including in
the Northern Sweden MONICA cohort (29). This can be attributed to lifestyle changes as well as new medical and surgical
treatments. Mortality decreased .50% in Sweden between 1986
and 2002, and it was estimated that 40% could be explained by
a decrease in cholesterol concentrations in the population (30).
On the other hand, prevalence of metabolic syndrome has increased in recent decades (27).
Dairy fat is high in saturated fatty acids (.60%), and it is
known that several saturated fatty acids raise cholesterol concentrations (31). In the Nurses’ Health Study, a higher ratio of
high- to low-fat dairy product consumption was significantly
associated with heart disease over 14 y of follow-up (32).
However, in the Hoorn Study, low-fat dairy consumption was
positively related and high-fat dairy consumption was inversely
related to metabolic syndrome risk factors (7). The inverse associations between milk fat biomarkers and a first MI in the
present study contradict the traditional diet-heart hypothesis (33)
but are not entirely unexpected. The evidence from prospective
cohort studies investigating intake of milk products and CVD
risk and risk factors are inconsistent (reviewed in references 9
and 34). The inconsistent findings in prospective cohort studies
may be due to differences in study populations, including ethnicity and general dietary patterns, sample size, how dietary
intake was measured, and selection of covariates included in the
analyses.
Dairy products contain [apart from the cholesterol-elevating
longer-chained saturated fatty acids (12:0, 14:0, and 16:0)]
calcium, phosphorus, potassium, magnesium, vitamin D, shorterchained fatty acids, stearic acid (18:0), whey and casein proteins, and other potentially bioactive compounds that may promote beneficial effects (35). Dairy products also elevate HDL
8 of 9
¨ ET AL
WARENSJO
cholesterol, which is associated with a reduced risk of CVD (9).
Furthermore, individuals consume milk products and not individual nutrients, and the food matrix may deliver effects beyond
the sum of its nutrients (36). In addition, dairy products are
consumed several times a day, which may potentiate both favorable and detrimental effects. In agreement with the results of
the present study, 15:0 and 17:0 measured in plasma phospholipids were associated with a less atherogenic LDL profile in
a cross-sectional study (37), and 15:0 measured in cholesteryl
esters was inversely associated with the cholesterol concentration
in adolescents (38).
The differences between men and women observed in the
present study were similar to a previous study investigating the
relation between milk fat biomarkers and stroke (39). Differences
in background diets and metabolism between sexes may have
played a role. In the present study, women had a better metabolic
profile at baseline, which possibly reflects a healthier food
pattern. However, adjustment for food groups (fruit, vegetables,
alcohol, fish, and meat) actually strengthened the observed ORs,
which minimizes the possibility that overall healthy food habits
confounded the present associations. Although we did not adjust
for energy intake in the present study, we did adjust for physical
activity in combination with BMI as a proxy for energy intake,
and this did not affect the odds in men but strengthened the odds
in women (17:0 and 15:0+17:0). Adjustment for energy intake
confirmed the stronger relation in women and removed the relation in men (OR: 0.99; data not shown).
Compared with cases, controls reported generally higher
intakes of different milk products. This suggests a potential
causal link between low intake of milk products and heart disease. Relative to cases, female controls reported significantly
higher cheese consumption, and male cases reported a significantly higher intake of fermented milk. It has been suggested that
cheese intake may increase LDL concentrations to a lesser extent
than butter (40–42). Cheese intake was inversely related to MI in
a Norwegian study (43); however, in a Costa Rican study, higher
cheese consumption was associated with increased risk of MI,
whereas low-fat milk was not (44). Moreover, it has been suggested that fermented milk (yogurt and kefir) may act as
a nutraceutical with cholesterol-lowering potential (45).
The strengths of the study include the large study sample; the
population-based prospective design, and careful ascertainment
of MI cases. Other strengths were our characterization of dairy
intake both with the biomarker fatty acids and with dietary data.
Furthermore, the prospective design of the study limits the potential recall and selection biases, which are known limitations of
a case-control study. The limitations include the observational
nature of a case-control study, and that information on diet was
only available for a subsample. Another limitation was that the
fatty acids were only measured at one time point. However,
a previous study reported that the fatty acid composition in serum
lipids remained fairly stable over 20 y (46). This implies that
a single measurement may be adequate to capture dietary fat
quality over a longer period of time. It is also possible that
participants changed food habits before study entry, but it is
unlikely that this influenced the results.
In conclusion, data from this prospective case-control study
suggest a protective association between high proportions of
plasma milk fat biomarkers on the risk of developing a first MI in
women. Fermented dairy products (especially cheese) were as-
sociated with a decreased risk in both sexes, but this finding
should be interpreted cautiously because the significance was lost
after adjustment. The ORs were lower in women than in men, and
our data further indicate components of metabolic syndrome as
modifiable intermediates for the observed risk relations. The
exact mechanism behind these associations cannot be deduced from
the present study, but the range of bioactive components present in
the food matrix of milk products as well as associated lifestyle
factors may all have contributed to the observed associations.
We thank Lars Berglund and Karin Jensevik at Uppsala Clinical Research
Center for statistical advice.
The authors’ responsibilities were as follows—EW and J-HJ: conceptualized the study; EW: performed the statistical analyses; EW and PS: drafted the
manuscript; J-HJ: was the principal investigator of the myocardial infarction
sample in northern Sweden; KB: represented the VIP study; ME: was the principal investigator of the Northern Sweden MONICA study; GH: was principal
investigator of the Northern Sweden Medical Research Bank; IJ: was the principal investigator of the NSHDS dietary database; and EW, J-HJ, TC, KB,
ME, IJ, and PS: participated in the interpretation of data and in finalizing
the manuscript. EW previously received compensation for speaking engagements from the Swedish Dairy Association and the International Dairy Federation. None of the authors had any financial or personal conflicts of interests.
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