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Nutrients 2015,7, 7749-7763; doi:10.3390/nu7095363 OPEN ACCESS
nutrients
ISSN 2072-6643
www.mdpi.com/journal/nutrients
Article
Milk Consumption and Mortality from All Causes,
Cardiovascular Disease, and Cancer: A Systematic Review and
Meta-Analysis
Susanna C. Larsson 1, *, Alessio Crippa 1, 2, Nicola Orsini 1 ,2 , Alicja Wolk 1and
Karl Michaëlsson 3
1Unit of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet,
SE-171 77 Stockholm, Sweden; E-Mails: alessio.crippa@ki.se (A.C.); nicola.orsini@ki.se (N.O.);
alicja.wolk@ki.se (A.W.)
2Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet,
SE-171 77 Stockholm, Sweden
3Department of Surgical Sciences, Uppsala University, SE-751 85 Uppsala, Sweden;
E-Mail: karl.michaelsson@surgsci.uu.se
*Author to whom correspondence should be addressed; E-Mail: susanna.larsson@ki.se;
Tel.: +46-8-52486059.
Received: 9 July 2015 / Accepted: 27 August 2015 / Published: 11 September 2015
Abstract: Results from epidemiological studies of milk consumption and mortality are
inconsistent. We conducted a systematic review and meta-analysis of prospective studies
assessing the association of non-fermented and fermented milk consumption with mortality
from all causes, cardiovascular disease, and cancer. PubMed was searched until August
2015. A two-stage, random-effects, dose-response meta-analysis was used to combine
study-specific results. Heterogeneity among studies was assessed with the I2statistic.
During follow-up periods ranging from 4.1 to 25 years, 70,743 deaths occurred among
367,505 participants. The range of non-fermented and fermented milk consumption and
the shape of the associations between milk consumption and mortality differed considerably
between studies. There was substantial heterogeneity among studies of non-fermented
milk consumption in relation to mortality from all causes (12 studies; I2= 94%),
cardiovascular disease (five studies; I2= 93%), and cancer (four studies; I2= 75%) as well
as among studies of fermented milk consumption and all-cause mortality (seven studies;
I2= 88%). Thus, estimating pooled hazard ratios was not appropriate. Heterogeneity among
studies was observed in most subgroups defined by sex, country, and study quality. In
Nutrients 2015,77750
conclusion, we observed no consistent association between milk consumption and all-cause
or cause-specific mortality.
Keywords: cancer; cardiovascular disease; meta-analysis; milk; mortality
1. Introduction
Milk is a widely consumed dairy product. Being rich in protein, saturated fat (whole milk), lactose,
calcium, and other essential nutrients, milk consumption may influence the risk of disease and mortality.
Evidence indicates that milk consumption may be associated with an increased risk of prostate cancer [1]
but with a reduced risk of colorectal cancer [2]. Milk consumption has been inconsistently associated
with cardiovascular disease [3–5] and type 2 diabetes [6,7], and does not appear to reduce the risk of
hip fractures [8]. Whether milk consumption is related to all-cause mortality remains unclear. We
therefore conducted a systematic review and meta-analysis to evaluate any potential association between
non-fermented milk consumption and mortality from all causes, overall cardiovascular disease, and
overall cancer. In addition, we assessed whether consumption of fermented milk, which might have
antioxidant and anti-inflammatory effects [9,10], is associated with all-cause mortality.
2. Experimental Section
2.1. Literature Search
We followed standard criteria for performing and reporting of meta-analyses of observational
studies [11]. Studies were identified by a systematic review of the literature until August 2015 by
using the electronic PubMed database. No restrictions were imposed. We used the search terms:
(dairy OR milk OR yogurt) AND (mortality or death) AND (cohort OR prospective). In addition, we
manually searched the reference lists of recent reviews and other retrieved publications to search for
further articles.
2.2. Study Selection
We included prospective studies that provided hazard ratios (HRs) with 95% confidence intervals (CI)
for at least three categories (including the reference group) of milk consumption in relation to mortality
from all causes, overall cardiovascular disease, or overall cancer, We omitted studies that only reported
results for total milk products or combined non-fermented and fermented milk because non-fermented
and fermented milk may have different associations with mortality.
Nutrients 2015,77751
2.3. Data Extraction and Quality Assessment
From each publication, we extracted the first author’s last name, year of publication, name of the
cohort, country, sex, age range of the study population, sample size, number of deaths, duration of
follow-up, variables adjusted for in the statistical analysis, and HRs with 95% CIs for each category of
milk consumption. We extracted the HRs from the most fully adjusted model, except when adjustments
were made for major components of milk, such as dietary calcium. Data were extracted separately
for women and men if sex-specific results were provided. Study quality was assessed using the
Newcastle-Ottawa Scale [12]. The score ranged from 0–9 stars (9 representing the highest quality).
2.4. Statistical Analysis
A two-stage, random-effects, dose-response meta-analysis [13,14] was conducted to assess potential
nonlinear associations between milk consumption and mortality. This was done by modeling milk
consumption by using restricted cubic splines with three knots at fixed percentiles [14]. First, a
restricted cubic spline model with two spline transformations was fitted, taking into account the
correlation within each set of published relative risks [13,14]. Second, the two regression coefficients
and the variance/covariance matrices estimated for each study were combined using a multivariate
random-effects meta-analysis [15]. An overall p-value was computed by testing that the two regression
coefficients were equal to zero. We calculated a p-value for nonlinearity by testing that the coefficient of
the second spline was equal to zero [16]. The dose-response meta-analysis method requires that (1) risk
estimates with CIs are available for at least three exposure categories (including the reference group);
(2) the number of cases and participants (or person-time) for each category are known (to be able to
estimate variance/covariance matrices); and (3) the mean or median milk consumption for each exposure
category is reported in the article or can be estimated.
Heterogeneity among studies was evaluated using the I2statistics [17]. Low, moderate-to-high, and
substantial heterogeneity was defined by I2-values of <25%, 25%–75%, and >75%, respectively. To
investigate the influence of single studies on the overall results, we conducted a sensitivity analysis in
which one study at a time was removed and the rest analyzed. Potential sources of heterogeneity due
to sex, country, and study quality were assessed using stratified analysis. The statistical analyses were
conducted using the dosresmeta [18] and metaphor [19] packages in R (R Foundation for Statistical
Computing, Vienna, Austria) [20]. p-values < 0.05 were considered statistically significant.
3. Results
3.1. Literature Search
We identified 12 prospective studies [21–31] (one article presented results from two separate
cohort studies [30]) that reported HRs of mortality from all causes (n= 12), cardiovascular disease
(n= 5), or cancer (n= 4) in relation to non-fermented milk consumption (Figure 1). Six of those
studies (five articles) also provided results on fermented milk (yogurt and/or soured milk) [25–28,30]
consumption. We identified an additional study on consumption of yogurt in relation to mortality [32].
Nutrients 2015,77752
Nutrients 2015, 7 4
Figure 1. Flow diagram of literature search and study selection. Studies excluded based on
title and abstract included experimental studies in animals and in vitro, review articles, and
other studies unrelated to milk consumption and mortality. * One article reported results
from two separate cohorts and one article reported results for fermented milk only.
3.2. Study Characteristics
Characteristics of the included studies on non-fermented milk consumption in relation to all-cause
mortality are shown in Table 1. Four studies were conducted in the UK or Scotland, two in Sweden, two
in the US, and one each in the Netherlands, Japan, and Australia. One study included cohorts from
10 European countries. Combined, these 12 studies included 70,743 deaths among 367,505 participants.
All studies controlled for age and sex (if applicable). Most studies also adjusted for smoking (n = 11),
body mass index (n = 10), alcohol consumption or drinking status (n = 9), total energy intake (n = 8),
physical activity (n = 7), and markers of socioeconomic status (n = 7). Few studies adjusted for other
food items or a healthy eating pattern (n = 5). Information on milk consumption was obtained through
self-report in all studies. Table S1 presents the scores assigned to each study and Figure S1 lists details
of how the criteria of study quality were applied.
Figure 1. Flow diagram of literature search and study selection. Studies excluded based
on title and abstract included experimental studies in animals and in vitro, review articles,
and other studies unrelated to milk consumption and mortality. * One article reported results
from two separate cohorts and one article reported results for fermented milk only.
3.2. Study Characteristics
Characteristics of the included studies on non-fermented milk consumption in relation to all-cause
mortality are shown in Table 1. Four studies were conducted in the UK or Scotland, two in Sweden, two
in the US, and one each in the Netherlands, Japan, and Australia. One study included cohorts from 10
European countries. Combined, these 12 studies included 70,743 deaths among 367,505 participants.
All studies controlled for age and sex (if applicable). Most studies also adjusted for smoking (n= 11),
body mass index (n= 10), alcohol consumption or drinking status (n= 9), total energy intake (n= 8),
physical activity (n= 7), and markers of socioeconomic status (n= 7). Few studies adjusted for other
food items or a healthy eating pattern (n= 5). Information on milk consumption was obtained through
self-report in all studies. Table S1 presents the scores assigned to each study and Figure S1 lists details
of how the criteria of study quality were applied.
Nutrients 2015,77753
Table 1. Characteristics of studies included in meta-analysis of milk consumption and all-cause mortality.
First Author,
Year Cohort Name Country No. of
Deaths
Sex (No. of
Participants)
Age
Range,
Years
Duration
of
Follow-up,
Years
Milk Intake
Categories HR (95% CI) Adjustments
Mann,
1997 [21]NA UK 392 Women and
men (10,802) 16–79 13.3
<280 mL/day a1.00 (ref.)
Age, sex, smoking, and social class
280 mL/day 0.70 (0.55–0.88)
>280 mL/day 0.87 (0.68–1.13)
Ness, 2001 [22]Collaborative
Study Scotland 2350 Men (5,765) 35–64 25
<190 mL/day a1.00 (ref.) Age, education, social class, father’s social class,
smoking, BMI, diastolic blood pressure,
cholesterol, adjusted FEV1, deprivation category,
siblings, car user, angina, ECG ischemia,
bronchitis, and alcohol intake
190–750
mL/day 0.90 (0.83–0.97)
ě760 mL/day 0.81 (0.61–1.09)
Elwood,
2004 [23]
Caerphilly
Cohort Study UK 811 Men (2512) 45–59 20–24
0 1.00 (ref.) Age, social class, smoking, BMI, systolic blood
pressure, prior vascular disease, intake of fat,
alcohol, and total energy
<280 mL/day a0.99 (0.73–1.34)
280–570
mL/day 0.98 (0.72–1.35)
>570 mL/day 1.20 (0.80–1.80)
Paganini-Hill,
2007 [24]
Leisure World
Cohort Study US 11,396 Women and
men (13,624) 44–101 23
0 glasses/day 1.00 (ref.) Age, sex, smoking, BMI, exercise, histories of
hypertension, angina, heart attack, stroke,
diabetes, rheumatoid arthritis, and cancer,
alcohol intake
<1 glasses/day 0.95 (0.90–1.00)
1 glasses/day 1.01 (0.96–1.06)
ě2 glasses/day 1.04 (0.98–1.10)
Bonthuis,
2010 [25]NA Australia 177 Women and
men (1529) 25–78 14.4
<198 g/day 1.00 (ref.) Age, sex, school leaving age, smoking, BMI,
physical activity level, dietary supplement use,
beta-carotene treatment during trial, presence of
any medical condition, and alcohol and total
energy intake
198–328 g/day 0.85 (0.54–1.33)
ě329 g/day 0.93 (0.59–1.48)
Nutrients 2015,77754
Table 1. Cont.
First Author,
Year Cohort Name Country No. of
Deaths
Sex (No. of
Participants)
Age
Range,
Years
Duration
of
Follow-up,
Years
Milk Intake
Categories HR (95% CI) Adjustments
Goldbohm,
2011 [26]
Netherlands
Cohort Study Netherlands
5478 in
women:
10,658 in men
Women
(62,573) and
men (58,279)
55–69 10
Women Women
Age, education, smoking, BMI, non-occupational
and occupational physical activity, multivitamin
use, intake of fruits and vegetables,
monounsaturated fat, polyunsaturated fat, alcohol,
and total energy
Q1: 0 g/day c1.00 (ref.)
Q2: 21 g/day 0.96 (0.87–1.05)
Q3: 52 g/day 0.96 (0.88–1.04)
Q4: 107 g/day 0.94 (0.86–1.04)
Q5: 238 g/day 1.00 (0.91–1.09)
Men Men
Q1: 0 g/day c1.00 (ref.)
Q2: 34 g/day 0.99 (0.93–1.05)
Q3: 90 g/day 1.00 (0.94–1.08)
Q4: 156 g/day 1.01 (0.94–1.08)
Q5: 342 g/day 1.02 (0.95–1.09)
Soedamah-Muthu,
2013 [27]
Whitehall II
prospective
cohort study
UK 237 Women and
men (4526) 56 b11.7
147 g/day 1.00 (ref.) Age, sex, ethnicity, employment grade, smoking,
BMI, physical activity, family history of
CHD/hypertension, fruit and vegetables, bread,
meat, fish, coffee, tea, alcohol, and total
energy intake
294 g/day 0.98 (0.72–1.34)
441 g/day (median) 0.89 (0.64–1.25)
Dik, 2014 [28]
European
Prospective
Investigation
into Cancer
and Nutrition
10
European
countries
d
1525 Women and
men (3859)e64.2 b4.1
<24 g/day 1.00 (ref.)
Age, sex, center, smoking, pre-diagnostic BMI,
tumor sub-site (colon and rectum), disease stage,
differentiation grade, and total energy intake
24–147 g/day 1.05 (0.90–1.23)
48–293 g/day 1.04 (0.89–1.22)
>293 g/day 1.21 (1.03–1.43)
Yang,
2014 [29]
Cancer
Prevention
Study II
Nutrition
Cohort
US 949 Women and
men (2284) e64 b17
Q1 f1.00 (ref.)
Age, sex, tumor stage, folate and total
energy intake
Q2 1.01 (0.84–1.23)
Q3 0.99 (0.82–1.19)
Q4 0.95 (0.79–1.15)
Nutrients 2015,77755
Table 1. Cont.
First Author,
Year Cohort Name Country No. of
Deaths
Sex (No. of
Participants)
Age
Range,
Years
Duration
of
Follow-up,
Years
Milk Intake
Categories HR (95% CI) Adjustments
Michaëlsson,
2014 [30]
Swedish
Mammography
Cohort
Sweden 15,541 Women
(61,433) 39–74 20.1
<200 g/day 1.00 (ref.) Age, education, living alone, smoking status,
BMI, height, physical activity, cortisone use, use
of estrogen replacement therapy, nulliparity,
Charlson’s comorbidity index, calcium and
vitamin D supplementation, healthy dietary
pattern, alcohol and total energy intake
200–399 g/day 1.21 (1.16–1.25)
400–599 g/day 1.60 (1.53–1.68)
ě600 g/day 1.93 (1.80–2.06)
Michaëlsson,
2014 [30]
Cohort of
Swedish Men Sweden 10,112 Men (45,339) 45–79 11.2
<200 g/day 1.00 (ref.) Age, education, living alone, smoking status,
BMI, height, physical activity, cortisone use,
Charlson’s comorbidity index, calcium and
vitamin D supplementation, healthy dietary
pattern, alcohol and total energy intake
200–399 g/day 0.99 (0.94–1.05)
400–599 g/day 1.05 (1.00–1.11)
ě600 g/day 1.10 (1.03–1.17)
Wang,
2015 [31]
Japan
Collaborative
Cohort Study
Japan
9572 in
women;
12,203 in men
Women
(55,341);
Men (39,639)
40–79 19
Women Women
Age, education, smoking status, drinking status,
BMI, physical activity, sleeping duration,
participation in health check-ups, history of
hypertension, diabetes, and liver disease,
green-leafy vegetable intake
Never 1.00 (ref.)
1–2
times/month 1.00 (0.91–1.05)
1–2 times/week 0.98 (0.91–1.05)
3–4 times/week 0.91 (0.85–0.98)
Almost daily 0.96 (0.91–1.01)
Men Men
Never 1.00 (ref.)
1–2
times/month 0.92 (0.86–0.99)
1–2 times/week 0.91 (0.85–0.96)
3–4 times/week 0.89 (0.84–0.96)
Almost daily 0.93 (0.89–0.98)
Abbreviations: BMI, body mass index; CHD, coronary heart disease; CI, confidence interval; ECG, electrocardiogram; FEV1, forced expiratory volume in the first
second. HR, hazard ratio; NA, not available; Q, quartile or quintile. aAmount was expressed in pints (1 pint = 568 mL). bMean age. cMedian intake in each tertile.
dIncluding Denmark, France, Germany, Greece, Italy, Netherlands, Norway, Spain, Sweden, and UK. eColorectal cancer patients. fQuartiles for women were 0,
0.1–5.0, 5.1–10.0, and ě10.1 serving/week; quartiles for men were 0, 0.1–5.6, 5.7–10.4, and ě10.5 serving/week. One serving was assumed to equal 200 mL.
Nutrients 2015,77756
3.3. Non-Fermented Milk
The range of non-fermented milk consumption and the shape of the association between milk
consumption and all-cause mortality differed between studies (Figure 2). Due to substantial
heterogeneity among studies (I2= 94%), estimating a pooled HR was not appropriate. In a sensitivity
analysis, in which one study at the time was removed and the rest analyzed to assess the influence of
single studies on the overall results, we found that the Swedish Mammography Cohort [30] contributed
most to the heterogeneity. After excluding this study, the heterogeneity was reduced (I2= 58%).
Heterogeneity among studies was observed in most subgroups defined by sex (women: I2= 93.9%;
men I2= 70%; both: I2= 47%), country (UK/Scotland: I2= 44%; Sweden: I2= 99%; rest of Europe:
I2= 19%; US: I2= 40%), and study quality (Newcastle-Ottawa Scale < 7: I2= 45%; ě7: I2= 97%).
Nutrients 2015, 7 8
3.3. Non-Fermented Milk
The range of non-fermented milk consumption and the shape of the association between milk
consumption and all-cause mortality differed between studies (Figure 2). Due to substantial
heterogeneity among studies (I
2
= 94%), estimating a pooled HR was not appropriate. In a sensitivity
analysis, in which one study at the time was removed and the rest analyzed to assess the influence of
single studies on the overall results, we found that the Swedish Mammography Cohort [30] contributed
most to the heterogeneity. After excluding this study, the heterogeneity was reduced (I
2
= 58%).
Heterogeneity among studies was observed in most subgroups defined by sex (women: I
2
= 93.9%;
men I
2
= 70%; both: I
2
= 47%), country (UK/Scotland: I
2
= 44%; Sweden: I
2
= 99%; rest of Europe: I
2
= 19%; US: I
2
= 40%), and study quality (Newcastle-Ottawa Scale < 7: I
2
= 45%; ≥ 7: I
2
= 97%).
Figure 2. Dose-response association between non-fermented milk consumption and
all-cause mortality in individual studies. The hazard ratios are plotted on a log scale.
Figure 2. Dose-response association between non-fermented milk consumption and
all-cause mortality in individual studies. The hazard ratios are plotted on a log scale.
The dose-response associations of milk consumption with cardiovascular disease and cancer mortality
in individual studies are shown in Figures 3and 4respectively. There was substantial heterogeneity
among studies of cardiovascular disease (I2= 93%) and cancer (I2= 75%) mortality.
Nutrients 2015,77757
Nutrients 2015, 7 9
The dose-response associations of milk consumption with cardiovascular disease and cancer
mortality in individual studies are shown in Figures 3 and 4, respectively. There was substantial
heterogeneity among studies of cardiovascular disease (I
2
= 93%) and cancer (I
2
= 75%) mortality.
Figure 3. Dose-response association between non-fermented milk consumption and
cardiovascular disease mortality in individual studies. The hazard ratios are plotted on a
log scale.
Figure 3. Dose-response association between non-fermented milk consumption and
cardiovascular disease mortality in individual studies. The hazard ratios are plotted on a
log scale.
Nutrients 2015, 7 10
Figure 4. Dose-response association between non-fermented milk consumption and cancer
mortality in individual studies. The hazard ratios are plotted on a log scale.
3.4. Fermented Milk
The HRs of all-cause mortality by levels of fermented milk consumption are presented in Table S2.
Most studies indicated a U-shaped association between fermented milk consumption and all-cause
mortality (Figure S2). The range of fermented milk consumption differed among studies and there was
substantial heterogeneity among studies (I
2
= 88%).
4. Discussion
This systematic review and meta-analysis found substantial heterogeneity among studies of
non-fermented and fermented milk consumption and mortality from all causes, cardiovascular disease,
Figure 4. Cont.
Nutrients 2015,77758
Nutrients 2015, 7 10
Figure 4. Dose-response association between non-fermented milk consumption and cancer
mortality in individual studies. The hazard ratios are plotted on a log scale.
3.4. Fermented Milk
The HRs of all-cause mortality by levels of fermented milk consumption are presented in Table S2.
Most studies indicated a U-shaped association between fermented milk consumption and all-cause
mortality (Figure S2). The range of fermented milk consumption differed among studies and there was
substantial heterogeneity among studies (I
2
= 88%).
4. Discussion
This systematic review and meta-analysis found substantial heterogeneity among studies of
non-fermented and fermented milk consumption and mortality from all causes, cardiovascular disease,
Figure 4. Dose-response association between non-fermented milk consumption and cancer
mortality in individual studies. The hazard ratios are plotted on a log scale.
3.4. Fermented Milk
The HRs of all-cause mortality by levels of fermented milk consumption are presented in Table
S2. Most studies indicated a U-shaped association between fermented milk consumption and all-cause
mortality (Figure S2). The range of fermented milk consumption differed among studies and there was
substantial heterogeneity among studies (I2= 88%).
4. Discussion
This systematic review and meta-analysis found substantial heterogeneity among studies of
non-fermented and fermented milk consumption and mortality from all causes, cardiovascular disease,
and cancer. Due to the large variation in the range of milk consumption across populations and the
considerable heterogeneity, it was not appropriate to pool the results.
Among the 12 studies of non-fermented milk consumption, Michaëlsson et al. [30] observed
statistically significant positive associations of non-fermented milk consumption with all-cause and
cardiovascular disease mortality in cohorts of Swedish women and men. In the same study [30],
non-fermented milk consumption was also positively associated with cancer mortality in the female
cohort (but the association was weaker than for all-cause and cardiovascular disease mortality) but not
in the male cohort. Likewise, Dik et al. [28] observed a positive association between high consumption
of non-fermented milk and all-cause mortality in a pooled analysis of cohort studies from 10 European
countries. In contrast, Mann et al. [21] and Ness et al. [22] found some indication of an inverse relation
between non-fermented milk consumption and all-cause mortality. The study by Mann et al. [21] did
not control for potential confounders such as body mass index, physical activity, alcohol consumption,
and diet. Wang et al. [31] observed a U-shaped association of non-fermented milk consumption
with all-cause and cardiovascular disease mortality in a population of Japanese adults with very low
milk consumption. The other six studies reported no significant relation between non-fermented milk
consumption and all-cause [23–27,29] or cardiovascular disease [25] mortality. A potential explanation
for the inconsistent findings may be related to the different range of milk consumption in different
populations. Milk consumption was high and the range of consumption was large in the studies by
Michaëlsson et al. [30]. Although there was a wide range of milk consumption also in the studies
by Ness et al. [22] and Elwood et al. [23], those studies had limited power to detect a statistically
Nutrients 2015,77759
significant association because of a small number of deaths and participants in the highest exposure
category. Furthermore, it was not totally clear if the reported milk consumption in those two studies
included non-fermented milk only or also fermented milk.
In addition to the different range of milk consumption, the proportion of different types of milk (e.g.,
whole milk, reduced-fat and fat-free milk, organic milk, and lactose-free milk) consumed is likely to
vary and could contribute to the disparate findings. Moreover, the composition of milk may differ.
For example, the proportion of conjugated linoleic acid in milk fat depends on what the cows are
fed [33]. Studies with animal models have shown that the predominant conjugated linoleic acid isomer
(cis-9,trans-11) has anti-carcinogenic and anti-atherogenic activities [33].
The confounders controlled for in the included studies differed, and this may also, in part, explain
the inconsistent results. Whereas most studies adjusted for major risk factors for mortality (e.g., age,
sex, smoking, body mass index, physical activity, and alcohol consumption), few studies controlled
for other foods [26,27,31] or a healthy food pattern [30]. Potential dietary confounders include fruits
and vegetables [34], red meat and processed meat [35], and coffee [36] which have been associated
with all-cause mortality. Most studies had a long follow-up (usually between 10 and 25 years) and,
with the exception of the study in women by Michaëlsson et al. [30], did not update information on
milk consumption during follow-up. This along with the use of a dietary questionnaire to assess milk
consumption would most likely have resulted in some misclassification and attenuated HRs. In fact, in
the study of Swedish women by Michaëlsson et al. [30], the HRs were attenuated when only a single
exposure assessment was applied and were similar to the HRs obtained in the Swedish male cohort,
which was based on a single assessment of diet.
Two previous meta-analyses have examined the association between total milk consumption
(non-fermented and fermented milk/yogurt combined) and all-cause mortality. One of the meta-analyses
included eight prospective studies and showed a HR of all-cause mortality of 0.99 (95% CI 0.95–1.03)
per 200-g/day increment of total milk consumption [37], but a meta-regression analytical approach to
detect non-linear patterns in risk was not applied. In the other meta-analysis, based on five prospective
studies, the HR of all-cause mortality was 1.01 (95% CI 0.92–1.11) for the highest versus lowest category
of total milk consumption [38]. Five of the studies included in one or both of those meta-analyses were
excluded from the current meta-analysis because results were only presented for total milk products [39],
for milk and yogurt combined [40], or for comparisons of types of milk (skimmed and semi-skimmed
versus whole milk) [41], results were unpublished [42], or odds ratios without CIs were reported [43].
Among the excluded studies, Fortes et al. [40] observed an inverse association between combined milk
and yogurt consumption and all-cause mortality (HR = 0.38; 95% CI, 0.14–1.01, for ě3 times/week
versus <1 time/week) in a cohort of 162 Italians, whereas Knoops et al. [39] reported a positive
association between total milk products and all-cause mortality (HR = 1.10; 95% CI, 1.00–1.21, for
consumption above versus below the median) in a cohort of 3117 elderly adults from 10 European
countries. No association was observed in the other three studies [41–43].
Nutrients 2015,77760
5. Conclusions
In summary, we observed no consistent association between non-fermented or fermented milk
consumption and mortality. Further large prospective studies assessing the relation between milk
consumption and mortality are warranted.
Acknowledgments
This research was supported by a Young Scholars Award Grant from the Strategic Research Area in
Epidemiology at Karolinska Institutet.
Author Contributions
Susanna Larsson designed the research, handled funding, interpreted the data, and drafted the
manuscript. Alessio Crippa performed the statistical analysis and made critical revision of the
manuscript. Nicola Orsini handled funding, contributed to the statistical analysis, and made critical
revision of the manuscript. Alicja Wolk made critical revision of the manuscript. Karl Michaëlsson
interpreted the data and made critical revision of the manuscript. All authors read and approved the
final manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
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