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Dairy Consumption, Systolic Blood Pressure and Risk of
Hypertension: a Mendelian Randomization from 32 Studies
with 197,332 Participants
Journal: BMJ
Manuscript ID BMJ.2016.035062
Article Type: Research
BMJ Journal: BMJ
Date Submitted by the Author: 19-Aug-2016
Complete List of Authors: Ding, Ming; Harvard School of Public Health, Qi, Lu; Harvard T.H. Chan School of Public Health,
Keywords: dairy, blood pressure, hypertension, Mendelian randomization
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Dairy Consumption, Systolic Blood Pressure and Risk of Hypertension: a
Mendelian Randomization from 32 Studies with 197,332 Participants
Ming Ding 1*, Tao Huang
1, 2, 3, 4*, Helle K.M. Bergholdt
5, 6, 7*, Alexis C. Frazier-Wood
8,
Stella Aslibekyan 9, Kari E. North
10, 11, Trudy Voortman
12, Mariaelisa Graff
10, Caren E
Smith 13
, Chao-Qiang Lai 13
, Anette Varbo 7, 14
, Rozenn N Lemaitre 15
, Ester A.L. de
Jonge 12, 16
, Frédéric Fumeron 17, 18, 19, 20
, Dolores Corella
21, 22, Carol A. Wang
23, Anne
Tjønneland 24
, Kim Overvad 25, 26
, Thorkild IA Sørensen 27, 28
, Mary F Feitosa 29
, Mary K
Wojczynski 29
, Mika Kähönen 30, 31
, Shafqat Ahmad 1, 32
, Frida Renström 32, 33
, Bruce M
Psaty 15, 20, 34, 35
, David S Siscovick 36
, Inês Barroso 37, 38, 39
, Ingegerd Johansson 40
, Dena
Hernandez 41
, Luigi Ferrucci 42
, Stefania Bandinelli 43
, Allan Linneberg 44, 45
, Camilla
Helene Sandholt 27
, Oluf Pedersen 27, 46
, Torben Hansen 27, 47
, Christina-Alexandra Schulz 48
, Emily Sonestedt 48
, Marju Orho-Melander 48
, Tzu-An Chen 8, Jerome I. Rotter
49,
Mathew A. Allison 50
, Stephen S. Rich 51
, Jose V. Sorlí 21, 22
, Oscar Coltell 22, 52
, Craig E.
Pennell 23
, Peter Eastwood 53
, Albert Hofman 12, 54
, Andre G. Uitterlinden 16
, M.Carola
Zillikens 16
, Frank J.A. van Rooij 12
, Audrey Y. Chu 55
, Lynda M. Rose 55
, Paul M Ridker 55, 56
, Jorma Viikari 57, 58
, Olli Raitakari 59, 60
, Terho Lehtimäki 61, 62
, Vera Mikkilä 60, 63
,
Walter C. Willett 1, 54, 64
, Yujie Wang 10
, Katherine L Tucker 65
, Jose M Ordovas 13, 66, 67
,
Tuomas O. Kilpeläinen 27
, Michael A Province 29
, Paul W. Franks 1, 32, 68
, Donna K Arnett 9, Toshiko Tanaka
42, Ulla Toft
44, Ulrika Ericson
48, Oscar H. Franco
12, CHARGE
consortium, Dariush Mozaffarian 69
, Frank B. Hu 1, 54, 64
, Daniel I. Chasman 55, 70, 71
,
Børge G Nordestgaard 7, 14, 72
*, Christina Ellervik 73, 74
*, and Lu Qi 1, 4
*
*Contribute equally to this work
1.Department of Nutrition, Harvard School of Public Health, Boston, MA 02115, USA.
2.Epidemiology Domain, Saw Swee Hock School of Public Health, National University
of Singapore, 117549, Singapore
3.Department of Medicine, Yong Loo Lin School of Medicine, National University of
Singapore, 117549, Singapore
4.Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane
University, New Orleans, LA 70112, USA
5.Department of Clinical Biochemistry, Naestved Hospital, Denmark.
6.Department of Clinical Pharmacology, Copenhagen University Hospital Bispebjerg
Frederiksberg, Copenhagen, Denmark
7.Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
8.USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, 1100
Bates Street, Houston, TX 77071, USA
9. Department of Epidemiology, University of Alabama at Birmingham, Birmingham,
AL 35205, USA
10.Department of Epidemiology, University of North Carolina, Chapel Hill, NC, 27514,
USA
11.Carolina Center for Genome, Sciences University of North Carolina, Chapel Hill, NC,
27514, USA
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12.Department of Epidemiology Erasmus MC, University Medical Center, Rotterdam,
the Netherlands
13.Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University
Boston, MA 02111, USA
14.Department of Clinical Biochemistry and the Copenhagen General Population Study,
Herlev and Gentofte Hospital, Copenhagen University Hospital, Denmark
15.Department of Medicine, University of Washington, WA 98101, USA
16.Department of Internal Medicine Erasmus MC, University Medical Center, Rotterdam,
the Netherlands
17.INSERM UMR_S 1138, Centre de Recherche des Cordeliers, F-75006, Paris, France.
18.Univ Paris Diderot Sorbonne Paris Cité, UMR_S 1138, Centre de Recherche des
Cordeliers, F-75006, Paris, France
19.Sorbonne Universités UPMC Univ Paris 06, UMR_S 1138, Centre de Recherche des
Cordeliers, F-75006, Paris, France
20.Group Health Research Institute, Group Health Cooperative, Seattle, WA
21.Department of Preventive Medicine and Public Health, University of Valencia, 46010-
Valencia, Spain
22.CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III,
Madrid Spain
23. School of Women's and Infants' Health, University of Western Australia, Australia
24.Danish Cancer Society Research Center, Copenhagen 2100, Denmark
25.Department of Public Health Section for Epidemiology, Aarhus University, DK-8000
Aarhus C, Denmark
26.Aalborg University Hospital, DK-9000 Aalborg, Denmark
27. The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of
Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen,
DK-2100 Copenhagen, Denmark
28.Institute of Preventive Medicine Bispebjerg and Frederiksberg Hospitals, The Capital
Region, Copenhagen 2000, Denmark
29.Department of Genetics Washington University School of Medicine, Saint Louis, MO
63108, USA
30.Department of Clinical Physiology, Tampere University Hospital, Finland
31.Department of Clinical Physiology, University of Tampere School of Medicine
32.Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund
University, 20502 Malmö, Sweden
33.Department of Biobank Research, Umeå University, 90187 Umeå, Sweden
34.Department of Epidemiology, University of Washington, WA 98101, USA
35.Department of Health Sciences, University of Washington, WA 98101, USA
36.New York Academy of Medicine, New York, NY 10029, USA
37.NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science,
Addenbrooke's Hospital, Cambridge CB2 0QQ, United Kingdom
38.University of Cambridge, Metabolic Research Laboratories Institute of Metabolic
Science, Addenbrooke's Hospital, Cambridge CB2 0QQ United Kingdom
39.Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton,
Cambridge, CB10 1SA United Kingdom
40.Department of Biobank Research, Umeå University, 90187 Umeå, Sweden
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41.Laboratory of Neurogenetics National Institute on Aging, Bethesda MD 20892
42.Translational Gerontology Branch, Baltimore, MD, 21225
43.Geriatric Unit Azienda Sanitaria Firenze (ASF), Florence, Italy
44.Research Centre for Prevention and Health, the Capital Region of Denmark,
Copenhagen, Denmark
45.Department of Clinical Experimental Research Rigshospitalet, Glostrup, Denmark
46.Faculty of Health Sciences, University of Aarhus, Aarhus, Denmark
47.Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
48.Department of Clinical Sciences in Malmö, Lund University, Sweden
49.Institute for Translational Genomics and Population Sciences, Los Angeles
Biomedical Research Institute and Department of Pediatrics at Harbor-UCLA Medical
Center, 1124 W. Carson St, Torrance, CA 90502, USA
50.Division of Preventive Medicine, Department of Family Medicine and Public Health,
University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
51.Center for Public Health Genomics, 3232 West Complex, University of Virginia,
Charlottesville, VA, USA
52.Department of Computer Languages and Systems, University Jaume I Castellon,
Spain
53. Centre for Sleep Science, School of Anatomy Physiology and Human Biology,
University of Western Australia, Australia
54.Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115,
USA
55.Division of Preventive Medicine, Brigham and Women's Hospital and Harvard
Medical School, Boston, MA 02215 USA
56.Division of Cardiovascular Medicine, Brigham and Women's Hospital and Harvard
Medical Schoo, Boston, MA 02115 USA
57.Division of Medicine, Turku University Hospital, Finland
58.Department of Medicine, University of Turku, Finland
59.Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital,
Finland
60.Research Centre of Applied and Preventive Cardiovascular Medicine, University of
Turku, Finland
61.Department of Clinical Chemistry Fimlab Laboratories, Tampere University Hospital,
Finland
62.Department of Clinical Chemistry, University of Tampere School of Medicine,
Finland
63.Department of Food and Environmental Sciences, University of Helsinki, Finland
64.Channing Division of Network Medicine, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
65.Clinical Laboratory & Nutritional Sciences Center for Population Health & Health
Disparities, Center for Gerontology Research & Partnerships, University of
Massachusetts, Lowell, MA, USA
66.Department of Epidemiology and Population Genetics, Centro Nacional Investigación
Cardiovasculares (CNIC), Madrid, Spain
67.Instituto Madrileño de Estudios Avanzados en Alimentación Madrid, Spain
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68.Department of Public Health and Clinical Medicine Section for Medicine, Umeå
University, 90187 Umeå, Sweden
69.Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA 02111,
USA
70.Division of Genetics, Brigham and Women's Hospital and Harvard Medical School,
Boston MA 02115, USA
71.Broad Institute of MIT and Harvard, Cambridge MA 02142, USA
72.The Copenhagen City Heart Study, Frederiksberg Hospital, Copenhagen University
Hospital, Denmark
73.Department of Research, Nykoebing, Falster Hospital, Nykoebing Falster, Denmark
74.Department of Laboratory Medicine, Boston Children’s Hospital, Boston, MA, USA
Correspondence and requests for reprint:
Dr. Lu Qi.
Department of Epidemiology,
School of Public Health and Tropical Medicine,
Tulane University,
1440 Canal St, New Orleans, LA 70112
Telephone: 504-988-3549;
Email: [email protected]; [email protected]
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ABSTRACT
Background: Dairy intake has been inversely related to systolic blood pressure (SBP)
and risk of hypertension in observational studies. However, whether such associations are
causal remains inconclusive.
Method: We conducted a Mendelian randomization study using SNP rs4988235 related
to lactase persistence as an instrumental variable (IV). We collected data from 22 studies
with 171,213 participants and additionally included 10 published prospective studies with
26,119 participants into the observational analysis. The IV estimation was conducted
using the ratio of coefficients approach. We further summarized eight published clinical
trials (RCTs) on dairy consumption with SBP.
Results: Per T allele increase in LCT-13910 rs4988235 was associated with higher dairy
consumption (0.08 serving/day; 95% CI: 0.06, 0.11), and was associated with higher SBP
(0.17 mmHg; 95% CI: 0.01, 0.33) but not risk of hypertension (OR = 1.00; 95% CI: 0.98,
1.02). Using LCT-13910 rs4988235 as IV, we found that genetically determined dairy
consumption was associated with higher SBP (β = 2.13 mmHg per serving/day; 95% CI:
0.02, 4.23) but not risk of hypertension (OR = 1.00; 95% CI: 0.78, 1.28). Moreover, our
meta-analysis of the published RCTs showed that higher dairy intake has no significant
effect on change of SBP over 1 to 12 month interventions (comparing intervention to
control groups: β = -0.21 mmHg; 95% CI: -0.98, 0.57). In observational analysis, each
serving/day increase of dairy consumption was associated with -0.11 mmHg (95% CI: -
0.20, -0.02) lower SBP but not risk of hypertension (OR = 0.98; 95% CI: 0.97, 1.00).
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Conclusion: The weak inverse association between dairy intake and SBP in
observational studies was not supported by our comprehensive IV analysis and
systematic review of existing RCTs.
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INTRODUCTION
Elevated blood pressure (BP) is an important risk factor of cardiovascular disease (CVD)
and has been the top single contributor to the global burden of morbidity and mortality,
leading to 9.4 million deaths each year 1. In clinical trials, lowering blood pressure has
been demonstrated to be an effective strategy to reduce CVD incidence 2, with each
5mmHg reduction in BP associated with 20 % lower risk of coronary heart disease and
29 % lower risk of stroke 3.
Maintaining a healthful diet is critical for the prevention of hypertension 4, however,
whether dairy products should be incorporated into a healthful diet is controversial. In
epidemiological studies, the association of dairy consumption with blood pressure has
been inconsistent. Several observational studies have reported inverse associations of
dairy consumption with systolic blood pressure (SBP) and risk of hypertension 5-7
,
however, such associations were not observed in other studies 8-10
. Two meta-analyses of
prospective cohort studies consistently indicated that dairy consumption was associated
with lower SBP and lower risk of hypertension 11 12
. However, due to the observational
nature of the studies included, the reported associations may not indicate causality.
In recent years, Mendelian randomization analysis has been widely used to assess
potential causal estimates of various risk factors with health outcomes. This approach has
the advantage over traditional observational studies in minimizing confounding by using
genetic markers as instrumental variables (IV) of environmental risk factors. A SNP
rs4988235 upstream from the lactase persistence gene (LCT-13910) has been consistently
related to dairy intake across multiple populations 13 14
, representing a strong IV for
analyzing the causal relation between dairy intake and disease risk.
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In this study, using data collected from 32 studies with 197,332 participants, we
performed an IV analysis to examine the possible causal estimate of dairy consumption
on SBP and risk of hypertension. In addition, we conducted a meta-analysis to summarize
results of eight randomized clinical trials (RCTs) assessing dairy intake intervention on
changes of SBP.
METHODS
Study design and population
We used an IV approach to examine associations of dairy consumption with SBP and risk
of hypertension. We collected data from 22 observational studies with 171,213
participants within the CHARGE (Cohorts for Heart and Aging Research in Genomic
Epidemiology) consortium. All participants provided written informed consent, and all
participating studies received approval from local research ethics committees.
Description of all the studies in the analysis is in the appendix (supplemental pages 1 -
5).
To provide comprehensive evidence on associations of dairy intake with SBP and
risk of hypertension, we conducted a systematic review of previously published cohort
studies and RCTs. In the appendix, we describe the procedure of the systematic review
(supplemental pages 6), and show the flow chart of study selection (Supplemental
Figure 1).
Patients' involvement
No patient was involved in our study.
Dairy consumption
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Dairy product was measured by questionnaire, and included ‘skim/low fat milk’, ‘whole
milk’, ‘ice cream’, ‘yogurt’, ‘cottage/ricotta cheese’, ‘cream cheese’, ‘other cheese’, and
‘cream’. Total dairy consumption was calculated as sum of all dairy categories. The
detailed description of dairy consumption measurement of included studies is shown in
Supplemental table 1.
Outcome measures
Given that SBP is superior to diastolic blood pressure (DBP) as a major risk factor of
CVD, SBP was used as the main outcome in our analysis. The measurement of SBP is
presented in Supplemental table 1. For participants taking antihypertensive medication,
we added 15 mmHg to SBP to adjust for treatment effect. Hypertension was defined as
SBP of 140 mmHg or higher or current use of antihypertensive medication.
Genotype rs4988235
Genotyping platforms, genotype frequencies, Hardy Weinberg equilibrium P values, and
call rates for lactase persistence SNP rs4988235 are shown in Supplemental table 1. The
SNP rs4988235 was not genotyped or imputed in two studies, in which proxy SNPs
(rs309137: r2 = 0.77; rs1446585: r
2 = 1.00) were used instead.
Statistical analysis
Statistical analyses were initially conducted within each included study in accordance
with a standard analysis plan. We used additive models to examine associations of dairy
consumption with SBP and risk of hypertension adjusting for baseline age, sex, ethnicity,
and region. We examined associations of dairy consumption with SBP and risk of
hypertension using linear/logistic models adjusting for baseline age, body mass index
(BMI), BP, smoking status, physical activity, total energy intake, alcohol consumption,
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sex, ethnicity, region, and years of follow-up. After results were obtained from each
study within CHARGE, we combined the collected results with results extracted from
published cohort studies using random-effects models. Statistical heterogeneity across
studies was assessed by Cochrane Q test, with P < 0.1 indicating significant between-
study heterogeneity. In addition, we calculated the I2 statistic to evaluate the percentage
of heterogeneity that was due to between-study variation 15
.
We used instrumental variable (IV) ratio method to estimate the possible causal
relationship of dairy consumption with SBP and risk of hypertension. The variance of the
IV ratio was estimated using first-order Taylor expansion 16
.
We further conducted stratified analysis on the causal estimates of dairy intake
with SBP and risk of hypertension by frequency of CC alleles, ethnicity, country, study
design, and measurement of SBP. We used meta-regression to evaluate effect
modification by each study-level characteristics. In sensitivity analyses, we repeated our
analyses using dominant (CC vs. CT/TT) and recessive models (CC/CT vs. TT). All
meta-analyses were conducted at Harvard T.H. Chan School of Public Health using Stata
version 11.2 (STATA Corp, College Station, Texas)
RESULTS
We included 22 studies with 171,213 participants from the CHARGE consortium, and
baseline characteristics of the studies are shown in Table 1. Of the 22 studies, 9 were
conducted in the U.S. and 12 were conducted in European countries. Participants were
Caucasian in 18 studies. Dairy intake and SBP were measured prospectively in most of
the studies. The frequency of CC alleles varied across studies, with the lowest frequency
in European countries.
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By conducting a systematic review, we additionally identified 10 published cohort
studies with 26,119 participants (4-9, 23-26) and eight clinical trials with 735 participants.
The RCTs examined the effect of dairy intake on SBP with 1-12 months intervention 17-24
.
Of the 10 cohort studies, seven studies assessed SBP as the outcome 5-10 25 26
, and five
studies used hypertension as the outcome 5 25-28
. The characteristics of previous
publications were shown in Supplemental Tables 2, 3.
In the analysis including 171,213 participants in the CHARGE consortium, per T
allele increase in LCT-13910 rs4988235 was associated with higher dairy consumption
(0.08 serving/day; 95% CI: 0.06, 0.11). However, significant heterogeneity was found
across studies (P < 0.001) (Figure 1). Per T allele increase in LCT-13910 rs4988235 was
associated with higher SBP (0.17 mmHg; 95% CI: 0.01, 0.33) but not risk of
hypertension (OR = 1.00; 95% CI: 0.98, 1.02) (Figure 2). Using LCT-13910 rs4988235
as IV, we found that genetically determined dairy consumption was associated with
higher SBP (β = 2.13 mmHg per serving/day; 95% CI: 0.02, 4.23) but not risk of
hypertension (OR = 1.00; 95% CI: 0.78, 1.28).
In observational analysis combining studies within CHARGE consortium and
published observational studies, each serving/day increase in dairy consumption was
associated with lower SBP (β = -0.11 mmHg; 95% CI: -0.20, -0.02) but was not
associated with lower risk of hypertension (OR = 0.98; 95% CI: 0.97, 1.00) (Figures 3a,
3b). In addition, dairy intake did not show significant effect on changes of SBP over 1-12
months of interventions (comparing intervention to control group: β = -0.21 mmHg; 95%
CI: -0.98, 0.57) (Figure 3c). No publication bias of included RCTs was found.
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In stratified analysis, genetically determined dairy consumption was associated
with higher SBP in studies conducted in Caucasians (β = 2.38 mmHg per serving/day; 95%
CI: 0.19, 4.56) and in studies with clinical measurement of SBP (β = 2.75 mmHg per
serving/day; 95% CI: 0.09, 5.41) (Table 2). No significant effect modification on the
causal estimations was found by the stratification variables.
In sensitivity analyses, we examined the associations of dairy consumption with
SBP and risk of hypertension by modeling the LCT-13910 genotype in recessive and
dominant inheritance manner. Using recessive or dominant models, SNP rs4988235 was
not associated with SBP or risk of hypertension (Supplemental Figures 2, 3), and
genetically determined dairy consumption was not associated with SBP or risk of
hypertension (Supplemental Table 4).
DISCUSSION
In this study, using Mendelian randomization analysis in 32 studies with 197,332
participants, we examined the potential causal effect of dairy consumption on SBP and
risk of hypertension. Using the LCT-13910 variant affecting lactase persistence as the
instrumental variable, our study showed that genetically determined dairy intake slightly
increased SBP and did not effect risk of hypertension. Furthermore, a meta-analysis of
the results from published RCTs showed that dairy consumption had no effect on changes
of SBP in response to 1 to 12 months of intervention.
Dairy products are widely consumed in the U.S. and European countries, and the
association between dairy intake and blood pressure has been examined in several cross-
sectional 29-31
and prospective cohort studies (4-9, 23-26). An inverse association between
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dairy intake and SBP was found in all of the three cross-sectional studies. The results
from cohort studies have been summarized in two meta-analyses. One meta-analysis
involving approximately 45,000 subjects and 11,500 cases of elevated blood pressure
showed that dairy products were associated with lower risks of elevated pressure 11
. In
line with this, another meta-analysis, which included 9 cohort studies with a sample size
of 57,256 and a total of 15,367 incident hypertension cases, found an inverse association
between dairy foods and risk of hypertension 12
.
In addition to considering dairy products as one food group, dairy products have
been categorized into high-fat dairy including whole milk, cream, and cream cheese and
low-fat dairy including skim milk and yogurt. Although in observational studies an
inverse association between overall dairy intake and risk of hypertension/elevated BP
was found, the associations of high-fat and low-fat dairy with blood pressure were
inconsistent. In the two meta-analyses aforementioned, the observed inverse association
was mainly due to consumption of low-fat dairy products 11 12
. Furthermore, one meta-
analysis summarized 14 RCTs involving 702 participants and found that probiotic
fermented milk including yogurt resulted in a significant reduction in blood pressure
comparing to placebo 32
. Regarding nutrients in dairy products, clinical trials showed that
milk-derived tripeptides and peptides have hypotensive effects in prehypertensive and
hypertensive subjects 33 34
.
In our study, we found that dairy consumption did not decrease SBP or risk of
hypertension using IV estimation. Moreover, the meta-analyzed results of RCTs showed
that dairy intake had no effect on changes of SBP. There could be several reasons that the
reported associations between dairy intake and blood pressure from observational studies
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were inconsistent with our IV results. First, higher low-fat dairy intake was more likely to
be associated with a healthy diet and lifestyle 35
. Therefore, the observed inverse
association of particularly low-fat dairy intake with SBP might be due to confounding of
intake of other food items and a healthy lifestyle. Second, even if yogurt and specific
nutrients in dairy such as milk peptides have antihypertensive effects, specific dairy
products such as yogurt only compose a small fraction of total dairy products. Therefore,
as shown in our IV results, dairy intake could still increase SBP when using overall dairy
products consumed by general population as main exposure. The saturated fat and D-
galactase with relatively high content in high-fat dairy products might be responsible for
the non-favorable association of dairy intake with blood pressure 36 37
. Third, due to the
methodology limitation that the instrumental variable is assumed to be associated with
the outcome only through the exposure under study 38
, we could not separate the effect of
individual dairy product in our study to further explain the inconsistency between
observational and instrumental results.
Given heterogeneity of the association between SNP rs4988235 and dairy intake
across studies, we further conducted stratified analysis by CC frequency, ethnicity, and
country. SNP rs4988235 consistently associated with higher dairy intake across
subgroups, demonstrating the robustness of our instrumental variable. We further found
that the positive effect of dairy intake on SBP was mainly from Caucasian living in
European countries. The reason might be that dairy consumption is high in Europe,
particularly in northern European countries 13
. The dose response relationship of dairy
intake on SBP is worth further investigation.
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Our study has several strengths. First, our study is a large IV analysis on the
causality of dairy intake on blood pressure and hypertension. The large sample size
provided us enough power to estimate the causal effect of dairy intake on blood pressure.
Second, the lactase persistence SNP is a well-established variant associated with dairy
intake with solid biological basis, and therefore a highly valid IV. Even though the SNP
might not be associated with Swiss cheese and mozzarella cheese which contain trivial
amounts of lactase, those products only composited a very low proportion of total dairy
products in the whole population. Therefore, the effect of those products on the validity
of the instrumental variable might be minimal. Third, most of the studies included were
genetically homogeneous, and we performed analysis within each study first. Therefore,
the effect of population stratification on the instrumental results within each study might
be minimal. Moreover, no significant effect modification by country, ethnicity, and CC
frequency of SNP rs4988235was found in stratified analysis. Fourth, we summarized
published clinical trials on dairy consumption with SBP, which provided further
supportive evidence to the IV results.
Our study has several limitations. First, SBP was self-reported in several studies,
resulting in measurement errors. This might be the reason that dairy consumption had no
effect on self-reported SBP in subgroup analysis. Second, dairy consumption was self-
reported by questionnaire, and might be affected by measurement errors. If measurement
errors were random, the observed associations would be biased to null. However, the IV
estimates results would not be biased, although the confidence interval might be larger.
Third, dairy consumption and SBP were examined cross-sectionally in several studies
and it might result in reverse causation even if using IV analysis. However, no significant
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effect modification by study design was found in stratified analysis, indicating that
reverse causation caused by study design might be minimal.
In conclusion, the weak association between dairy intake and SBP in observational
studies was not supported by our comprehensive IV analysis and systemic review of
existing RCTs.
FUNDING
The CGPS is was funded by The Danish Council for Independent Research; Medical
Sciences(FSS); Herlev Hospital, Copenhagen University Hospital; Copenhagen County
Foundation; and Chief Physician Johan Boserup and Lise Boserup’s Fund, Denmark.
The GESUS was funded by the Region Zealand Foundation, Naestved Hospital
Foundation. Edith and Henrik Henriksens Memorial Scholarship, Johan and Lise Boserup
Foundation, TrygFonden, Johannes Fog’s Foundation, Region Zealand, Naestved
Hospital, The National Board of Health, and the Local Government Denmark Foundation.
The WGHS is supported by HL043851 and HL080467 from the National Heart, Lung,
and Blood Institute and CA047988 from the National Cancer Institute, the Donald W.
Reynolds Foundation and the Fondation Leducq, with collaborative scientific support and
funding for genotyping provided by Amgen. The NHS and the HPFS was supported by
grants UM1 CA186107, P01 CA87969, R01 CA49449, R01 HL034594, R01 HL088521,
UM1 CA167552, R01 HL35464, HL126024, HL034594, DK100383, DK091718,
HL071981, HL073168, CA87969, CA49449, CA055075, HL34594, HL088521,
U01HG004399, DK080140, P30DK46200, U01CA137088, U54CA155626, DK58845,
DK098311, U01HG004728, EY015473, CA134958, DK70756 and DK46200 from the
National Institutes of Health, with additional support for genotyping from Merck
Research Laboratories, North Wales, PA. LQ is a recipient of the American Heart
Association Scientist Development Award (0730094N). LRP is supported by the Arthur
Ashley Williams Foundation and a Harvard Ophthalmology Scholar Award (Harvard
Medical School) from the Harvard Glaucoma Center of Excellence. ATC is a Damon
Runyon Cancer Foundation Clinical Investigator. The funding sources had no role in the
design or conduct of the study; collection, management, analysis, and interpretation of
the data; or preparation, review, or approval of the manuscript. ARIC is carried out as a
collaborative study supported by National Heart, Lung, and Blood Institute contracts
(HHSN268201100005C, HHSN268201100006C, HHSN268201100007C,
HHSN268201100008C, HHSN268201100009C, HHSN268201100010C,
HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and
R01HL086694; National Human Genome Research Institute contract U01HG004402;
and National Institutes of Health contract HHSN268200625226C. The authors thank the
staff and participants of the ARIC study for their important contributions. Infrastructure
was partly supported by Grant Number UL1RR025005, a component of the National
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Institutes of Health and NIH Roadmap for Medical Research. The Inter99 study was
funded by the Danish Research Councils, Health Foundation, Danish Centre for
Evaluation and Health Technology Assessment, Copenhagen County, Danish Heart
Foundation, Ministry of Health and Prevention, Association of Danish Pharmacies,
Augustinus Foundation, Novo Nordisk, Velux Foundation, Becket Foundation, and Ib
Henriksens Foundation. The D.E.S.I.R. study has been supported by INSERM contracts
with CNAMTS, Lilly, Novartis Pharma and Sanofi-Aventis; by INSERM (Réseaux en
Santé Publique, Interactions entre les déterminants de la santé, Cohortes Santé TGIR
2008), the Association Diabète Risque Vasculaire, the Fédération Française de
Cardiologie, La Fondation de France, ALFEDIAM, CNIEL, ONIVINS, Société
Francophone du Diabète, Ardix Medical, Bayer Diagnostics, Becton Dickinson,
Cardionics, Merck Santé, Novo Nordisk, Pierre Fabre, Roche, Topcon. The funding
sources had no role in the design or conduct of the study; collection, management,
analysis, and interpretation of the data; or preparation, review, or approval of the
manuscript. The D.E.S.I.R. Study Group: INSERM CESP U1018: B Balkau, P
Ducimetière, E Eschwège; INSERM U367: F. Alhenc-Gelas; CHU d’Angers: A Girault;
Bichat Hospital: F Fumeron, M Marre, R Roussel; CHU de Rennes: F Bonnet; CNRS
UMR8090, Lille: S Cauchi P Froguel; Centres d’Examens de Santé: Alençon, Angers,
Blois, Caen, Chartres, Chateauroux, Cholet, Le Mans, Orléans, Tours; Institute de
Recherche Médecine Générale: J Cogneau; General practitioners of the region; Institute
inter-Regional pour la Santé: C Born, E Caces, N Copin, JG Moreau, O Lantieri, F
Rakotozafy, J Tichet, S Vol. The Rotterdam Study is funded by Erasmus Medical Center
and Erasmus University, Rotterdam, Netherlands Organization for the Health Research
and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE),
the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and
Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The
authors are grateful to the study participants, the staff from the Rotterdam Study and the
participating general practitioners and pharmacists. The generation and management of
GWAS genotype data for the Rotterdam Study is supported by the Netherlands
Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-
012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-
015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for
Scientific Research (NWO) project nr. 050-060-810. The MDCS was initiated and
planned in collaboration with the International Agency for Research on Cancer, the
Swedish Cancer Society, and Swedish Medical Research Council and the Faculty of
Medicine Lund University, Sweden. The study is also funded by Region Skåne, City of
Malmö, Påhlsson Foundation and the Swedish Heart and Lung Foundation.
The GLACIER Study was funded by project grants from the Swedish Heart-Lung
Foundation, the Swedish Diabetes Association, the Påhlsson’s Foundation, Region Skåne,
the Swedish Research Council, the Umeå Medical Research Foundation, Novo Nordisk,
and The Heart Foundation of Northern Sweden (all to PWF). The authors also
acknowledge the funding agencies supporting the Northern Sweden Diet Database and
the Västerbotten Intervention Project, including the Swedish Research Council.
The MESA study was supported by contracts HHSN268201500003I, N01-HC-
95159,N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-
95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-
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95169 from the National Heart, Lung, and Blood Institute, and by grants UL1-TR-
000040 and UL1-TR-001079 from NCRR. The FamHS was supported by grants
DK089256 and HL117078 from the National Insitutes of Health. Infrastructure for the
CHARGE Consortium is supported in part by the National Heart, Lung, and Blood
Institute grant R01HL105756. This CHS research was supported by NHLBI contracts
HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079,
N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI
grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, and
R01HL120393 with additional contribution from the National Institute of Neurological
Disorders and Stroke (NINDS). Additional support was provided through R01AG023629
from the National Institute on Aging (NIA). A full list of principal CHS investigators and
institutions can be found at CHS-NHLBI.org.The provision of genotyping data was
supported in part by the National Center for Advancing Translational Sciences, CTSI
grant UL1TR000124, and the National Institute of Diabetes and Digestive and Kidney
Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California
Diabetes Endocrinology Research Center. The content is solely the responsibility of the
authors and does not necessarily represent the official views of the National Institutes of
Health. The YFS has been financially supported by the Academy of Finland: grants
286284 (T.L.), 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi),
and 41071 (Skidi); the Social Insurance Institution of Finland; Kuopio, Tampere and
Turku University Hospital Medical Funds (grant X51001 for T.L.); Juho Vainio
Foundation; Paavo Nurmi Foundation; Finnish Foundation of Cardiovascular Research
(T.L.); Finnish Cultural Foundation; Tampere Tuberculosis Foundation (T.L.); Emil
Aaltonen Foundation (T.L.); and Yrjö Jahnsson Foundation (T.L.). The expert technical
assistance in the statistical analyses by Ville Aalto, Irina Lisinen and Mika Helminen is
gratefully acknowledged. The DCH and the DIOGENES cohorts were a part of the
research program of the UNIK: Food, Fitness & Pharma for Health and Disease (see
www.foodfitnesspharma.ku.dk). The UNIK project was supported by the Danish
Ministry of Science, Technology and Innovation. Tuomas O. Kilpeläinen was supported
by the Danish Council for Independent Research (DFF – 1333-00124 and Sapere Aude
program grant DFF – 1331-00730B). The PREDIMED-VALENCIA study was supported
by the Spanish Ministry of Health (Instituto de Salud Carlos III) and the Ministerio de
Economía y Competitividad (projects G03/140, CIBER 06/03, RD06/0045 PI07-0954,
CNIC-06, PI11/02505, SAF2009-12304, AGL2010-22319-C03-03 and PRX14/00527),
Fondo Europeo de Desarrollo Regional, by the University Jaume I (Project P1-1B2013-
54) and by the Generalitat Valenciana (AP111/10, AP-042/11, BEST/2015/087,
GVACOMP2011-151, ACOMP/2011/145, ACOMP/2012/190 and ACOMP/2013/159).
The BPRHS was supported by the National Institutes of Health grants P01 AG023394
and P50 HL105185. The GOLDN Study was supported by National Heart, Lung, and
Blood Institute (NHLBI) grant no. U01HL072524 (Genetic and Environmental
Determinants of Triglycerides), NHLBI R01 HL091357 (Genomewide Association Study
of Lipid Response to Fenofibrate and Dietary Fat), NHLBI grant number HL54776 and
HL078885; and by contracts 53-K06-5-10 and 58-1950-9-001 from the US Department
of Agriculture, Agriculture Research Service. The Raine Study was supported by the
National Health and Medical Research Council of Australia [grant numbers 403981 and
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003209] and the Canadian Institutes of Health Research [grant number MOP-82893]. The
authors are grateful to the Raine Study participants and their families, and to the Raine
Study research staff for cohort coordinaion and data collection. The authors gratefully
acknowledge the NH&MRC for their long term funding to the study over the last 25
years and also the following institutes for providing funding for Core Management of the
Raine Study: The University of Western Australia (UWA) , Curtin University, the Raine
Medical Research Foundation, the UWA Faculty of Medicine, Dentistry and Health
Sciences, the Telethon Kids Institute, the Women's and Infant's Research Foundation
(King Edward Memorial Hospital) and Edith Cowan University). The authors gratefully
acknowledge the assistance of the Western Australian DNA Bank (National Health and
Medical Research Council of Australia National Enabling Facility). We would also like
to acknowledge the Raine Study participants for their ongoing participation in the study,
the Raine Study Team for study co-ordination and data collection, the UWA Centre for
Science for utilisation of the facility and the Sleep Study Technicians. The 22 year Raine
Study follow-up was funded by NHMRC project grants 1027449, 1044840 and 1021855.
Funding was also generously provided by Safework Australia. The InCHIANTI study
baseline (1998-2000) was supported as a "targeted project" (ICS110.1/RF97.71) by the
Italian Ministry of Health and in part by the U.S. National Institute on Aging (Contracts:
263 MD 9164 and 263 MD 821336).
Competing interests
All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the
submitted work; no financial relationships with any organisations that might have an
interest in the submitted work in the previous three years; no other relationships or
activities that could appear to have influenced the submitted work.
Licence for publication
I, Lu Qi, The Corresponding Author of this article contained within the original
manuscript which includes any diagrams & photographs within and any related or stand
alone film submitted (the Contribution”) has the right to grant on behalf of all authors
and does grant on behalf of all authors, a licence to the BMJ Publishing Group Ltd and its
licencees, to permit this Contribution (if accepted) to be published in the BMJ and any
other BMJ Group products and to exploit all subsidiary rights, as set out in our licence set
out at: http://www.bmj.com/about-bmj/resources-authors/forms-policies-and-
checklists/copyright-open-access-and-permission-reuse.”
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Table 1. Baseline characteristics of included cohorts listed by number of participants
Study name Ethnicity Country Number of
participants
Follow
-up
years
Male
, %
Age
,
year
SBP,
mmHg
Hypert
ension
, %
Antihypert
ensive
medication,
%
rs4988235,
n (%)
CC CT TT
CGPS Caucasian Denmark 74,219 0 45 57 140 6 20 6 36 58
WGHS Caucasian US 19,743 4 0 54 126 22 13 11 38 51
GESUS Caucasian Denmark 14,815 0 46 57 142 10 23 6 36 58
NHS Caucasian US 11,287 26 0 53 NA 9 NA 14 41 45
ARIC (Caucasian) Caucasian US 8,233 6 47 54 118 29 19 9 39 52
ARIC (African
American)
African
Caucasian US 1,889 6 36 53 127 55 41 74# 25
# 2
#
HPFS Caucasian US 6,914 24 100 55 NA 22 NA 18 39 43
INTER99 Caucasian Denmark 6,514 5 49 46 130 31 7 6 36 57
D.E.S.I.R. Caucasian France 3,378 9 50 47 131 35 9 22 49 30
Rotterdam Study Caucasian Netherlands 3,215 7 41 66 136 54 27 9 39 52
MDCS Caucasian Sweden 3,199 17 40 56 139 53 14 6&
33&
61&
GLACIER Caucasian Sweden 2,763 10 37 45 124 19 4 7 36 58
MESA Mixed US 2,424 10 47 61 132 35 28 20 49 30
FamHS Caucasian US 2,167 8 45 51 127 44 36 12 42 46
CHS Caucasian US 1,964 9 38 71 133 49 35 4 43 53
YFS Caucasian Finland 1,370 0 43 38 120 9 7 15 47 38
DCH Study Caucasian Denmark 1,297 0 45 56 135 47 13 5 32 63
DIOGENES-
controls Caucasian Denmark 1,002 0 51 54 135 38 7 6 36 58
DIOGENES-
weight gainers Caucasian Denmark 813 0 49 53 135 42 10 5 35 60
PREDIMED- Caucasian Spain 940 2 36 67 147 84 63 38 46 16
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VALENCIA
BPRHS
Puerto
Rican US 845 0 28 57 136 78 56 61 34 5
GOLDN Caucasian US 818 0 50 49 118 26 21 10 40 50
Raine Study Mixed* Australia 728 2 48 20 117 4 0 15 39 46
InCHIANTI Caucasian Italy 647 0 45 64 142 64 49 2 30 68
# Rs1446585 was used as a proxy.
& Rs309137 was used as a proxy.
*Mainly Caucasian, Caucasian-admixed (individuals with at least one Caucasian parent)
CGPS: The Copenhagen General Population Study; WGHS: Women's Genome Health Study; GESUS: The Danish General Suburban
Population Study; NHS: Nurses' Health Study; ARIC: Atherosclerosis Risk in Communities Study; HPFS: Health Professional
Follow-up Study; D.E.S.I.R.: The Data from an Epidemiological Study on the Insulin Resistance syndrome; MDCS: Malmö Diet and
Cancer Study; GLACIER: The GLatiramer Acetate low frequenCy safety and patIent ExpeRience Study; MESA: The Multi-Ethnic
Study of Atherosclerosis; FamHS: Family Heart Study; CHS: Cardiovascular Health Study; YFS: Young Finns Study; DCH Study:
Diet, Cancer and Health Cohort; Diogenes: Diet, Obesity and Genes Study; PREMED:The PREvención con DIeta MEDiterránea
Study; BPRHS: Boston Puerto Rican Health Study; GOLDN: Genetics of Lipid Lowering Drugs and Diet Network; Raine Study: The
Western Australian Pregnancy Cohort (Raine) Study; InCHIANTI: Invecchiare in Chianti Study
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Figure 1. The association of SNP rs4988235 with dairy consumption (serving/day) using additive model
Linear regression adjusted for baseline age, sex, ethnicity, and region.
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Figure 2. The association of SNP rs4988235 with systolic blood pressure (SBP, mmHg) and risk of hypertension using additive model
a. SNP rs4988235 (CT/TT vs. CC) and SBP b. SNP rs4988235 (CT/TT vs. CC) and risk of hypertension
Linear/logistic regression adjusted for baseline age, sex, ethnicity, and region.
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Figure 3. The association of baseline dairy consumption (serving/day) with systolic blood pressure (SBP, mmHg) and risk of
hypertension in observational cohort studies and clinical trials
a. Dairy and SBP in cohort studies b. Dairy and hypertension in cohort studies c. Dairy and SBP in clinical trials
a & b. Linear/logistic regression was used in collaborative cohorts adjusted for sex, ethnicity, region, years of follow-up, as well as
age, body mass index, blood pressure/hypertension, smoking status, physical activity, total energy intake, and alcohol consumption at
baseline
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Table 2. Stratified analysis on the causal estimates of dairy consumption with systolic blood pressure (SBP) and risk of hypertension
SBP (risk difference, 95% CI) Hypertension (relative risk, 95% CI)
Number
of
studies
SNP rs4988235
with dairy intake
(serving/d)
SNP rs4988235
with SBP (mmHg)
Dairy intake
(serving/d) with
SBP (mmHg),
IV estimation
SNP rs4988235
with risk of
hypertension
Dairy intake
(serving/d) with
risk of
hypertension,
IV estimation
CC frequency
< 10 % 12 0.07 (0.04, 0.10) 0.19 (-0.01, 0.38) 2.71 (-0.30, 5.73) 1.01 (0.97, 1.06) 1.15 (0.61, 2.18)
≥ 10 % 12 0.11 (0.06, 0.15) 0.14 (-0.21, 0.49) 1.27 (-1.95, 4.50) 1.01 (0.98, 1.04) 1.09 (0.83, 1.44)
Ethnicity
Caucasian 20 0.08 (0.05, 0.11) 0.19 (0.03, 0.35) 2.38 (0.19, 4.56) 1.01 (0.98, 1.03) 1.13 (0.83, 1.55)
Other races 4 0.12 (0.04, 0.21) -0.38 (-1.11, 0.36) -3.17 (-9.69, 3.36) 1.06 (0.89, 1.25) 1.63 (0.38, 6.97)
Country
U.S. 10 0.12 (0.08, 0.16) 0.11 (-0.18, 0.39) 0.92 (-1.48, 3.31) 1.01 (0.98, 1.04) 1.09 (0.85, 1.39)
Europe 13 0.05 (0.02, 0.09) 0.29 (0.02, 0.56) 5.80 (-0.96, 12.56) 1.01 (0.97, 1.07) 1.22 (0.45, 3.29)
Study design
Cross-sectional 9 0.08 (0.03, 0.12) 0.36 (-0.001, 0.73) 4.50 (-0.72, 9.72) 0.99 (0.92, 1.06) 0.88 (0.36, 2.14)
Cohort 15 0.09 (0.05, 0.12) 0.09 (-0.14, 0.32) 1.00 (-1.58, 3.58) 1.02 (0.99, 1.05) 1.25 (0.89, 1.75)
Measurement of SBP
Self-reported 5 0.10 (0.05, 0.15) 0.01 (-0.31, 0.34) 0.10 (-3.15, 3.35) 1.01 (0.97, 1.07) 1.10 (0.67, 1.81)
Clinical measurement 19 0.08 (0.04, 0.11) 0.22 (0.03, 0.41) 2.75 (0.09, 5.41) 1.01 (0.98, 1.05) 1.13 (0.73, 1.75)
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Supplemental materials
Description of collaborative cohorts
The Copenhagen General Population Study (CGPS): The CGPS is an ongoing
prospective cohort study initiated in 2003 and includes individuals residing in the greater
urban area of Copenhagen. Residence was determined by data from the national Danish
Civil Registration System. All individuals aged > 40 years were invited along with a
random selection of 25% of individuals aged 20-39 years, and the overall response rate
was 45%. Participants in the study completed a general questionnaire and a health
examination including a non-fasting blood sample. In this study we included
approximately 74,000 white participants of Danish descent who had been genotyped for
the rs4988235 genetic variant.
The Women’s Genome Health Study (WGHS): The WGHS is a prospective cohort of
initially healthy, female North American health care professionals at least 45 years old at
baseline representing participants in the Women’s Health Study (WHS) who provided a
blood sample at baseline and consent for blood-based analyses. The WHS was a 2x2 trial
beginning in 1992-1994 of vitamin E and low dose aspirin in prevention of cancer and
cardiovascular disease with about 10 years of follow-up. Since the end of the trial,
follow-up has continued in observational mode. Additional information related to health
and lifestyle were collected by questionnaire throughout the WHS trial and continuing
observational follow-up.
The Danish General Suburban Population Study (GESUS): The GESUS was initiated
in 2010 and concluded in 2013. The GESUS is a study of the suburban general
population in Naestved Municipality located approximately 70 km south of Copenhagen.
All individuals aged > 30 and a random selection of 25% of the younger population aged
20-30 years were invited. Participants in the study completed a general questionnaire and
a health examination including a non-fasting blood sample. In this study, we included
approximately 14,000 white participants of Danish descent, with known rs4988235
genotypes. The response rate for this subset of the study population was 50%, and the
GESUS had an overall response rate of 43% when the study was concluded in 2013.
The Nurses’ Health Study (NHS): The NHS began in 1976, when 121,700 female
registered nurses aged 30 - 55 y residing in 11 states were recruited to complete a
baseline questionnaire about their lifestyle and medical history. Questionnaires were
collected at baseline and biennially thereafter, to update information on lifestyle factors
and the occurrence of chronic diseases. In the current analysis, we used 1990 as baseline
in the NHS, when the earliest complete dietary data were collected. Our analysis included
13,000 women whose genotype data were available. All of the participants were
Caucasians and were free of diabetes, cardiovascular disease, and cancer at baseline. The
study protocol was approved by the institutional review boards of Brigham and Women’s
Hospital and Harvard School of Public Health.
The Atherosclerosis Risk in Communities Study (ARIC): The ARIC is a multi‐center
prospective investigation of atherosclerotic disease in a predominantly bi‐ racial
population conducted in four U.S. communities, involving both cohort and community
surveillance components 2. Study participants aged 45‐64 years at baseline were
recruited from 4 communities: Forsyth County, North Carolina; Jackson, Mississippi;
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suburban areas of Minneapolis, Minnesota; and Washington County, Maryland. A total
of 15,792 individuals participated in the baseline examination in 1987‐1989, with follow‐
up examinations in approximate 3‐year intervals, during 1990‐1992, 1993‐1995, and
1996‐1998. Weight and height were measured. All study participants provided written
informed consent.
The Health Professional Follow-up Study (HPFS): The HPFS was initiated in 1986,
and was composed of 51,529 male dentists, pharmacists, veterinarians, optometrists,
osteopathic physicians, and podiatrists, aged 40-75 y at baseline. The male participants
returned a baseline questionnaire about detailed medical history, lifestyle, and usual diet.
Questionnaires were collected at baseline and biennially thereafter, to update information
on lifestyle factors and the occurrence of chronic diseases. In the current analysis, we
used 1990 as baseline in the HPFS, when the earliest complete dietary data were
collected. Our analysis included 8,000 men whose genotype data were available. All of
the participants were Caucasians and were free of diabetes, cardiovascular disease, and
cancer at baseline. The study protocol was approved by the institutional review boards of
Brigham and Women’s Hospital and Harvard School of Public Health.
The Danish population-based Inter99 study: The INTER99 is a non-pharmacological
intervention study for ischemic heart disease, initiated in 1999 at the Research Centre for
Prevention and Health, Glostrup, Denmark (ClinicalTrials.gov ID-no: NCT00289237). A
random sample of 13,016 individuals living in Copenhagen County from seven different
age groups (30 - 60 years, grouped with five year intervals) was drawn from the Civil
Registration System and 6,784 of these attended the health examination.
The Data from an Epidemiological Study on the Insulin Resistance syndrome
(D.E.S.I.R.): The D.E.S.I.R. began in 1994 and included 5,212 men and women, aged
from 30 to 64 years. Participants were recruited from volunteers insured by the French
Social Security system, which offered periodic health examinations free of charge.
D.E.S.I.R. is a 9-year follow-up study with clinical and biological examinations every 3
years. A medical interview provided information about use of medication, and personal
and familial history of diseases. Our analysis included only Caucasian subjects with
lactase genotype, dietary data and examined 9 years after inclusion (n=3478). The study
was approved by the ethics committee of the Kremlin Bicêtre Hospital and by the CNIL
(Commission Nationale de l'Informatique et des Libertes).
The Rotterdam Study: The Rotterdam Study is a population-based prospective cohort
study ongoing since 1990 in the city of Rotterdam in the Netherlands. At present the The
study was designed to investigate the prevalence and incidence of and risk factors for
chronic diseases in the elderly. All inhabitants of Ommoord, a district of Rotterdam, the
Netherlands, aged 55 years and older were invited. At enrollment, participants were
interviewed at home (2h) and were examined in detail at a dedicated research facility (5h).
These measurements were repeated every 3 to 4 years. The study was approved by the
Medical Ethics Committee of Erasmus Medical Center and all participants provided
written informed consent to participate in the study and to obtain information from their
treating physicians.
Malmö Diet and Cancer Study (MDCS): The MDCS was a population based study
conducted 1991-1996. In total, 28098 individuals (45-74 years) completed all baseline
examinations. The MDC cardiovascular subcohort (n = 6,103) were invited to a follow-
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up study 2007-2012. Individuals from the follow-up study with data available on
genotypes were included in this study (n = 3,199).
Gene-Lifestyle interactions And Complex traits Involved in Elevated disease Risk
(GLACIER): The GLACIER Study (N ~ 19,000) is a population-based cohort study of
initially non-diseased adults living in the county of Västerbotten in Northern Sweden,
nested within the Northern Sweden Health and Disease Study. Clinical characteristics and
lifestyle data were obtained as part of a population-wide health screening initiative called
the Västerbotten’s Health Survey (also called the Västerbotten Intervention Program),
where habitants are invited to attend an extensive health examination the year of their
40th, 50th, and 60th birthday. The total number of GLACIER participant with genotype
and phenotype data available for the current analysis was 2,763. Ethical approval for the
GLACIER Study was obtained from the Regional Ethical Review Board in Umeå.
The Multi-Ethnic Study of Atherosclerosis Study (MESA): The MESA is a study of
the characteristics of subclinical cardiovascular disease (disease detected non-invasively
before it has produced clinical signs and symptoms) and the risk factors that predict
progression to clinically overt cardiovascular disease or progression of the subclinical
disease. MESA researchers study a diverse, population-based sample of 6,814
asymptomatic men and women aged 45 - 84. Thirty-eight percent of the recruited
participants were white, 28 percent African-American, 22 percent Hispanic, and 12
percent Asian, predominantly of Chinese descent. Participants were recruited from six
field centers across the United States and followed-up three times with an average time
period of follow-up of 2 years between each visit. Data from four visits (exam1 to exam 5)
was used for the analysis. The tenets of the Declaration of Helsinki were followed and
institutional review board approval was granted at all MESA sites. Written informed
consent was obtained from each participant.
The Family Heart Study (FamHS): The FHS began in 1992 with the ascertainment of
1,200 families (50% randomly sampled, and 50% high risk for CHD). The families (~
6,000 individuals,) were sampled on the basis of information on probands from four
population-based parent studies: the Framingham Heart Study, the Utah Family Tree
Study, and two ARIC centers (Minneapolis, and Forsyth County, NC). Approximately
eight years later, study participants belonging to the largest pedigrees were invited for a
second clinical exam. A total of 2,767 participants of European descent in 510 extended
families were examined. A total of 2,167 adults with available DNA and who provided
valid dietary information were eligible for the current study.
The Cardiovascular Health Study (CHS): The CHS is a population-based cohort study
of risk factors for coronary heart disease and stroke in adults ≥65 years conducted across
four field centers. The original predominantly European ancestry cohort of 5,201 persons
was recruited in 1989-1990 from random samples of the Medicare eligibility lists;
subsequently, an additional predominantly African-American cohort of 687 persons was
enrolled for a total sample of 5,888. The analyses included 1,964 white study participants
without cardiovascular disease at baseline, with available genotype data and with follow-
up data 9 years after baseline. CHS was approved by institutional review committees at
each site, the subjects gave informed consent, and those included in the present analysis
consented to the use of their genetic information for the study of cardiovascular disease.
The Cardiovascular Risk in Young Finns (YFS) Study: The YFS is a population-
based 27 year follow up-study. The first cross-sectional survey was conducted in 1980,
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when 3,596 Caucasian subjects aged 3-18 years participated. In adulthood, the latest 27-
year follow-up study was conducted in 2007 (ages 30-45 years) with 2,204 participants.
The study cohort for the present analysis comprised subjects who had participated in the
study in 2007 and had validated dietary data from FFQ, available genotype and other risk
factor data. The dietary intake of nutrients was assessed using a modified 131-item food
frequency questionnaire developed by the Finnish National Institute for Health and
Welfare. The study was approved by the local Ethical Committees and was performed
according to Helsinki declaration.
The Diet, Cancer and Health Cohort (DCH) and the Diet, Obesity and Genes Study
(DIOGENES): The DCH and the DIOGENES cohorts are both subsamples from the
same larger cohort, the Danish Diet, Cancer and Health cohort. The larger cohort was
composed of 57,053 individuals in the age of 50-64 years and born in Denmark. At
baseline, anthropometric measurements were taken and blood pressure was measured.
Information on usual diet and lifestyle was obtained using self-administered
questionnaires, and biological samples were collected. At 5-year follow-up, the
participants completed a repeat questionnaire and self-measured their BMI and waist
circumference. The DCH included a subsample of 1,297 randomly selected individuals
who were free of diabetes, cardiovascular disease, and cancer at baseline and at the 5-
year follow-up. The DIOGENES included 1,815 individuals, of which 813 were weight-
gainers and 1,002 were randomly selected control individuals. Weight gainers were
defined as those individuals who had experienced the greatest degree of unexplained
weight gain and were identified by using the residuals from a regression model of annual
weight change on baseline values of age, weight, height, and smoking status
(current/former/nonsmokers) and follow-up time. The participants were younger than 60
years at baseline and younger than 65 years at follow-up, with stable smoking habits,
without cancer, cardiovascular disease, or diabetes at baseline and at the 5 year follow-up,
and with a weight change not more than 5 kg/year.
The PREvención con DIeta MEDiterránea Study (PREDIMED-VALENCIA study):
The PREDIMED-VALENCIA study was initiated in 2003 y was composed of 1094
participants. LCT genotypes were available for 940. PREDIMED-VALENCIA is a
randomized intervention trial with Mediterranean diet versus a control diet. FFQ
questionnaires were completed at baseline and yearly. BMI and blood pressure were
directly measured yearly. We used data corresponding to the 2-y follow-up period.
The Boston Puerto Rican Health Study (BPRHS): The Boston Puerto Rican Health
Study is an ongoing longitudinal cohort study designed to examine the role of
psychosocial stress on presence and development of allostatic load and health outcomes
in Puerto Ricans, and potential modification by nutritional status, genetic variation, and
social support. Individuals who were self-identified Puerto Ricans, aged 45-75 years and
residing in the Boston, MA metro area, were recruited through door-to-door enumeration
and community approaches. Data including demographics, medical history, physical
function, cognition and dietary data were collected through a comprehensive set of
questionnaires and tests. Blood, urine and salivary samples were extracted for biomarker
and genetic analysis. Measurements were repeated at a two-year follow-up and a five-
year follow up.
Genetics of Lipid Lowering Drugs and Diet Network Study (GOLDEN): The
GOLDN Study belongs to the PROgram for GENetic Interaction (PROGENI) Network,
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which includes family studies examining gene-environment interactions via controlled
interventions. GOLDN participants were recruited from three-generational pedigrees
previously identified in the Minneapolis, MN, and Salt Lake City, UT, field centers of the
National Heart, Lung, and Blood Institute Family Heart Study. Clinical, dietary and
biochemical measurements were collected at baseline and following an intervention with
fenofibrate. A postprandial study fat-challenge was also conducted pre - and post-
fenofibrate. Individuals were >= 18 years with fasting triglycerides < 1500 mg/dl.
The Western Australian Pregnancy Cohort (Raine) Study: Raine Study is a
prospective pregnancy cohort where 2900 women were recruited from King Edward
Memorial Hospital between 1989 and 1991. Data were collected during pregnancy and
the children have been followed-up at ages 1, 2, 3, 5, 8, 10, 14, 17, 18, 20 and 22. Human
Research Ethics approval for this study was obtained from King Edward Memorial
Hospital, Princess Margaret Hospital and University of Western Australia. Parent of the
participants and participants from age 18 were consented at each follow-up. In the current
analysis, the 20 year follow-up was used as baseline. Analyses were performed at the 22
year follow-up on 728 participants who had genotyping data, food frequency data and
other co-variates.
The Invecchiare in Chianti Study (InCHIANTI): The InCHIANTI study is a
population-based epidemiological study aimed at evaluating the factors that influence
mobility in the older population living in the Chianti region in Tuscany, Italy. Overnight
fasted blood samples were for genomic DNA extraction and genotyping using Illumina
Infinium HumanHap 550K SNP arrays were used for genotyping. Dairy consumption
was assessment using a questionnaire designed for the EPIC study. The analysis was
restricted to those with data on dairy intake, BMI, blood pressure, and genotyping. The
study protocol was approved by the Italian National Institute of Research and Care of
Aging Institutional Review and Medstar Research Institute (Baltimore, MD).
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Methods of study selection
PUBMED search query
#1
"dairy products"[Mesh] OR "dairy"[tiab] OR "milk"[tiab] OR "calcium"[tiab] OR
"cheese"[tiab] OR "yogurt"[tiab]
#2
"hypertension"[Mesh] OR "blood pressure"[Mesh]
#3
(#1 AND #2))
Publication bias of included RCTs
We examined publication bias of included RCTs using Egger test, with P < 0.05 to
indicate significant asymmetry [1].
Dose-response analysis within each included cohort study
To obtain the association of per serving/day increase in dairy consumption with SBP and
risk of hypertension, we performed dose-response analysis within studies using
categorical dairy intake as main exposure.
We assigned the median dairy consumption in each category of consumption to the
corresponding outcome for each study. If the upper boundary for the highest category
was not provided, the assigned median value was 25% higher than the lower boundary of
that category. If the lower boundary for the lowest category was not provided, the
assigned median value was half of the upper boundary of that category.
To examine the association of continuous dairy consumption with risk of
hypertension, we used two-stage fixed/random-effects dose response models to combine
studies that reported results for categorized dairy consumption and studies with reported
results for continuous dairy consumption. Specifically, the RR of risk of hypertension per
unit increase of dairy consumption for each study was first estimated separately by
generalized least squares models (GLST) [2], and then the RRs from all of the studies
were pooled together by a fixed/random-effects model. We fit a fixed-effects generalized
linear model first, and changed to a random-effects generalized linear model if the p
value for the goodness of fit/heterogeneity of the previous model was < 0.05. We used
the STATA command GLST for model fitting.
To examine the association of continuous dairy consumption with blood pressure, for
studies with categorized dairy consumption, we calculated the change of blood pressure
comparing the highest category of dairy consumption to the lowest. We assumed a linear
association of continuous dairy consumption with change of blood pressure.
Reference
1. Egger M, Davey Smith G, Schneider M, Minder C: Bias in meta-analysis detected by a
simple, graphical test. BMJ 1997, 315(7109):629-634.
2. Greenland S, Longnecker MP: Methods for trend estimation from summarized dose-
response data, with applications to meta-analysis. American journal of epidemiology
1992, 135(11):1301-1309.
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Supplemental Figure 1. Study selection process of cohort studies and randomized trials (RCT)
Articles identified initially (n = 3,755)
Articles excluded on basis of title and abstract (n = 3,723)
Articles retrieved for further evaluation (n = 32)
Articles excluded (n=13):
Cohort studies excluded due to duplicity with ours (n=3)
RCT excluded using fermented milk or soy milk as intervention group (n = 7)
RCT excluded not presenting the exact number of SBP and unable to contact the
author (n = 1)
RCT excluded presenting extreme small standard deviation (SD) of SBP (SD < 2
mmHg for both interventional and control group) (n = 1)
Articles finally included in the analysis (n = 20)
Randomized trials on dairy consumption with SBP (n = 8)
Cohort studies on dairy consumption with SBP (n = 7)
Cohort studies on dairy consumption with risk of hypertension (n = 5)
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Supplemental table 1. Measurements of dairy products, systolic blood pressure, and hypertension and genotype method of included
cohorts
Study Dairy products Systolic blood
pressure,
hypertension
Genotype
Measur
ement
Time of
assessme
nt
Dairy products included
Mean
(servi
ng/d)
Measure
ment
Time of
assessme
nt
Genotypi
ng
method
Sample
call rate
Proxy
SNP/
R2
HWE
p-
value
CGPS
General
questio
nnaire Baseline
Milk (whole, semi-
skimmed, skimmed), cheese 1.69
Clinical
measure
ment Baseline TaqMan
>
99.9%. NA 0.001
WGHS FFQ Baseline
Skim/low fat milk, whole
milk, ice cream, yogurt,
cottage cheese, cream
cheese, other cheese,
cream, sour cream, and
sherbert 1.98
Self-
reported Baseline TaqMan 97% NA NA
GESUS
General
questio
nnaire Baseline
Milk (whole, semi-
skimmed, skimmed, butter
milk, lactose free), cheese,
fermented milk 2.38
Clinical
measure
ment
End of
follow-
up
Competit
ive
Allele-
Specific
PCR-
based
assay
(KASP)
>
99.9%. NA 0.19
NHS FFQ Baseline
Skim/low fat milk, whole
milk, ice cream, yogurt, 1.37 NA
End of
follow- TaqMan 97% NA 0.35
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cottage/ricotta cheese,
cream cheese, other cheese,
and cream
up
ARIC
(Caucasi
an) FFQ Baseline
Skim/low fat milk, whole
milk, ice cream, yogurt,
cottage/ricotta cheese,
cream cheese, other cheese,
and cream 1.8
Clinical
measure
ment
End of
follow-
up Imputed NA NA 0.92
ARIC
(African
America
n) FFQ Baseline
Skim/low fat milk, whole
milk, ice cream, yogurt,
cottage/ricotta cheese,
cream cheese, other cheese,
and cream 1.25
Clinical
measure
ment
End of
follow-
up Imputed NA
rs144
6585/
1.00 0.96
HPFS FFQ Baseline
Skim/low fat milk, whole
milk, ice cream, yogurt,
cottage/ricotta cheese,
cream cheese, other cheese,
and cream 1.37 NA
End of
follow-
up TaqMan 97% NA 0.37
INTER9
9 FFQ Baseline
Skim/low fat milk, whole
milk, chocolate milk, ice
cream, yogurt,
cottage/ricotta cheese,
cream cheese, other cheese,
and cream 1.43
Clinical
measure
ment
End of
follow-
up
Illumina
HiScan
system 99.3% NA 0.16
D.E.S.I.
R. FFQ Baseline
milk, yogurt, cottage/fresh
soft cheese, custard type
desserts 1.36
Clinical
measure
ment
End of
follow-
up Kaspar 97% NA 0.36
Rotterda
m Study FFQ Baseline
Whole milk, skimmed milk,
yogurt, hard cheese, soft
cheese, whip cream, coffee
cream, ice cream, custard 3.99
Clinical
measure
ment
End of
follow-
up
Illumina
550 > 97% NA 0.13
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MDCS
Diet
recall Baseline
Skim/low fat milk, whole
milk, ice cream, yogurt,
cottage/ricotta cheese,
cream cheese, other cheese
and cream 4.8
Clinical
measure
ment
End of
follow-
up
Illumina
HumanO
mniExpr
ess
BeadChi
p
>
99.9%.
rs309
137/0.
77 0.02
GLACIE
R FFQ Baseline
cream, sour cream, hard
cheese 28% fat, hard cheese
10-17% fat, sour milk and
yoghurt (0.5 and 3% fat),
ice cream, milk (0.5, 1.5 and
3% fat) 3.32
Clinical
measure
ment
End of
follow-
up
Metaboc
hip array
100% NA
0.000
25
MESA FFQ Baseline
Whole milk, regular cheese,
cottage or ricotta cheese,
whole-fat yogurt, and ice
cream, 2% milk, 1% or
skim milk, and low-fat
yogurt 1.63
Self-
reported
End of
follow-
up Affy 6.0 > 95% NA NA
FamHS FFQ Baseline
Skim/low fat milk, whole
milk, ice cream, yogurt,
cottage/ricotta cheese, and
other cheese 2.04
Clinical
measure
ment
End of
follow-
up
Illumina
GWAS
arrays 100% NA 0.019
CHS FFQ Baseline
Skim milk/buttermilk, low
fat milk/beverages with low
fat milk, whole
milk/beverages with whole
milk, ice cream, yogurt,
cottage cheese, other
cheese/cheese spread 1.37
Clinical
measure
ment
After 9
years of
follow-
up
Illumina
370CNV
BeadChi
p ≥ 97% NA NA
YFS FFQ
End of
follow-
Skim/low fat milk, whole
milk, ice cream, yogurt, 4.3
Clinical
measure
End of
follow-
custom
Illumina 95% NA 0.19
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up cottage/ricotta cheese,
cream cheese, other cheese,
and cream
ment up BeadChi
p
Human6
70K
DCH
Study FFQ Baseline
Skimmed milk, Semi-
skimmed milk, Whole fat
milk, Buttermilk, Fermented
dairy (low-fat), Fermented
dairy (whole-fat), Ice cream
(low fat), Ice cream (whole
fat), Cream 1.64
Clinical
measure
ment Baseline
Illumina
HumanC
oreExom
e
BeadChi
p
genotype
s >95% NA 0.36
DIOGE
NES FFQ Baseline
Skimmed milk, Semi-
skimmed milk, Whole fat
milk, Buttermilk, Fermented
dairy (low-fat), Fermented
dairy (whole-fat), Ice cream
(low fat), Ice cream (whole
fat), Cream 1.59
Clinical
measure
ment Baseline
Illumina
Metaboc
hip >95% NA 0.64
PREDI
MED-
VALEN
CIA-
Study FFQ Baseline
Skim/low fat milk, whole
milk, ice cream, yogurt,
cottage/ricotta cheese,
cream cheese, other cheese,
and cream 1.85
Clinical
measure
ment
End of
follow-
up TaqMan 97% NA 0.221
BPRHS FFQ Baseline
Skim/low fat milk, whole
milk, ice cream, yogurt,
cottage/ricotta cheese,
cream cheese, other cheese,
and cream 2.3
Clinical
meausre
ment Baseline TaqMan >97% NA 0.5
GOLDN FFQ Baseline
Skim/low fat milk, whole
milk, ice cream, yogurt, 1.97
Clinical
measure Baseline TaqMan >97% NA 0.06
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cottage/ricotta cheese,
cream cheese, other cheese,
and cream
ment
Raine
Study FFQ Baseline
Full cream milk, Reduced
fat milk, Skim milk, hard
cheese, firm cheese, ricotta,
low fat cheese, flavoured
milk, ice cream, yoghurt,
cream cheese, soft cheese 1.9
Clinical
measure
ment
End of
follow-
up
Illumina
660W
Quad
Array;
Human
Omni
Express
BeadChi
p 97% NA
660W
: 1.8
E-05;
Omni:
1
InCHIA
NTI FFQ Baseline
whole milk, low fat milk,
icecream, yogurt, latte,
butter, cheese, soft cheeseer 1.09
Clinical
measure
ment Baseline
Illumina
550K 97% NA 0.23
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Supplemental Table 2. Baseline characteristics of published randomized trials
Study
design Author Year
Sample
size Sex
Age
(year)
Health
status Intervention Control Period Results
Difference
of SBP
changes
comparing
interventio
n to
control
group
(mmHg)
Crosso
ver
Drouin-
Chartier 2015 27
Wo
men 57
Postmenopa
usal women
with
abdominal
obesity
3.2 servings/d of
2% fat milk per
2000 kcal
Diet without
milk or other
dairy
6
weeks
Intervention
group
End of trial:
109.9 mmHg
95% CI (105.1,
114.6)
Control group
End of trial:
112.1 mmHg
95% CI (107.9,
116.5)
Parallel Tanaka 2014 200 Both
20 -
60
Two or
more
components
of the
metabolic
syndrome
Both dietary
intervention and
regular home
dairy delivery of
400 g/d
Dietary
intervention
24
weeks
-1.0
(-4.0, 2.1)
Crosso
ver
Drouin-
Chartier 2014 89 Both 53
Prehyperten
sion
Three daily
servings of dairy
products
Control
products
equivalent in
4
weeks
0
(-1, 1)
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macronutrients
and sodium
Crosso
ver Rideout 2013 23 Both
Mean
53 Healthy
4 servings of
dairy per day
No more than
2 servings of
dairy per day
12
months
Intervention
group
End of trial:
122 ± 15
(mmHg,
mean ± SD)
Control group
End of trial:
124 ± 16
(mmHg,
mean ± SD)
Crosso
ver Crichton 2012 36 Both
18 -
75
Overweight
or obese
adults
A high dairy
diet (4 serves of
reduced fat
dairy/day)
A low dairy
control diet
(≤1 serve/day)
6
months
0.9
(-1.8, 3.6)
Crosso
ver
van
Meijl 2009 35 Both 50 Healthy
500 mL low-fat
milk and 150 g
low-fat yogurt
600 mL fruit
juice and 3
fruit biscuits
8
weeks
-2.9
(-5.5, -0.3)
Parallel
Wenners
berg 2009 121 Both
30 -
65
Metabolic
syndrome
3 - 5 portions of
dairy products
daily Habitual diet
6
months
Intervention
group (56
participants)
Baseline:
134 ± 16
(mmHg,
mean ± SD)
End of trial:
132 ± 16
Control group
(55 participants)
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Baseline:
134 ± 16
End of trial:
133 ± 17
Parallel Barr 2000 204 Both
55 -
85 Healthy
Skim or 1%
milk intake by 3
cups per day Usual diet
12
weeks
Intervention
group (98
participants)
Baseline:
126 ± 12
(mmHg,
mean ± SD)
End of trial:
124 ± 13
Control group
(102
participants)
Baseline:
128 ± 15
End of trial:
125 ± 14
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Supplemental Table 3. Baseline characteristics of published cohorts on dairy consumption with change of systolic blood pressure and
risk of hypertension
Systolic blood pressure
Cohort
name
Author Year Sample
size
Sex Age
(year
s)
Foll
ow-
up
year
Dairy
consump
tion
Change of
SBP (mmHg)
Confounding adjustment
Framingha
m Heart
Study Wang 2015 2,075 Both
28-
62 15
0.64
serving/d
ay
1.07
(0.95, 1.19)
Sex, age and systolic blood pressure at
the beginning of each exam interval and
average total energy intake during each
exam interval, smoking status and
physical activity at the beginning of
each exam interval and the average
caffeine coffee intake and Dietary
Guidelines Adherence Index (DGAI)
sub-score (i.e. DGAI score excluding
sub-scores for assessing the
consumption amount of milk and milk
products and the likelihood of choosing
low-fat milk and milk products) during
each exam interval, BMI at the
beginning of each exam interval.
3.53
serving/d
ay
0.47
(0.23, 0.71)
Nutrition
and Health
of Aging
Population
in China Zong 2014 2,091 Both
50-
70 6
0
serving/d
ay
-0.62
(-2.07, 0.83)
Age, sex, region, residence, smoking
status, family history of diabetes, BMI
(not for BMI and waist circumference),
dietary fiber intake, and baseline values
of SBP. 1.25
serving/d
ay
-4.15
(-6.27, -2.03)
Caerphilly
Prospective
Livingst
one 2013 2,512 Men
45-
59 5
Per
serving
-10.40
(-19.38, -
Age, alcohol consumption, smoking
habits, social class, physical activity,
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Study increase 1.42) total energy intake, fat intake, heart rate,
mean arterial pressure, and drug use,
and protein intake.
STANISLA
S Samara 2012 588 Both
28-
60 5
Per
serving
increase
Men:
-0.68
(-2.56, 1.20)
Women:
0.81
(-0.84, 2.46)
Adjusted for age, physical activity,
alcohol and cigarette consumption,
energy intake without alcohol, education
level, mean adequacy ratio index, and
value of metabolic syndrome-related
variable at entry.
1946 Birth
Cohort
Heraclid
es 2012 1,750 Both
43-
53 10
0.93
serving/d
ay
0 (0,0) Fruit and vegetable intake, wholegrain
cereal intake, coffee and tea
consumption, total energy intake,
alcohol consumption, physical activity,
smoking status 1.29
serving/d
ay
-0.67
(-5.39,4.04)
The Hoorn
Study Snijder 2008 1,124 Both
50–
75 6.4
Per
serving
increase
0.27
(-0.22, 0.76)
Age, sex, total energy intake, baseline
value of the outcome variable, lifestyle
factors (alcohol intake, smoking,
physical activity).
SU.VI.MA
X Dauchet 2007 2,341 Both
35–
63 5.4
84 g 0 (0, 0) Adjusted for age, sex, group (treatment
vs placebo), total energy intake
(excluding alcohol), number of dietary
records completed, and SBP or DBP at
first clinical examination, tobacco,
alcohol, physical activity, education
level, BMI, dietary sodium, other
dietary variables (fruit and vegetables,
dairy products, and Keys score) if they
456 g 0.1 (-1.2, 1.5)
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are not part of a score included in the
model, and SBP or DBP at first clinical
examination.
Hypertension
Cohort
name
Author Year Sample
size
Sex Age
(year
s)
Foll
ow-
up
year
Dairy
consum
ption
Case
s
Obser
vation
s
Risk of
hypertensio
n
Confounding adjustment
Framingha
m Heart
Study Wang 2015 2,075 Both
28–
62 15
Per
serving
0.92
(0.86, 0.99)
Sex, age and systolic blood
pressure at the beginning
of each exam interval and
average total energy intake
during each exam interval,
smoking status and
physical activity at the
beginning of each exam
interval and the average
caffeine coffee intake and
Dietary Guidelines
Adherence Index (DGAI)
sub-score (i.e. DGAI score
excluding sub-scores for
assessing the consumption
amount of milk and milk
products and the likelihood
of choosing low-fat milk
and milk products) during
each exam interval, BMI at
the beginning of each
exam interval.
1946 Birth Heraclid 2012 1,750 Both 43- 10 224 g 208 577 Reference
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Cohort es 53 275 g 200 571 0.88
(0.68,1.14)
Fruit and vegetable intake,
wholegrain cereal intake,
coffee and tea
consumption, total energy
intake, alcohol
consumption, physical
activity, smoking status
309 g 313 602 0.93
(0.72,1.18)
Morgan
Study
Engberi
nk 2009 3454 both
20–
65 5
206 g 185 863 Reference Adjusted for age, sex, total
energy intake (MJ/d),
socioeconomic status (5
categories), BMI (kg/m2),
smoking (yes/no), alcohol
intake (6 categories), and
daily intake of fruit (g),
vegetables (g), fish (g),
meat (g), bread (g), coffee
(mL), and tea (mL).
359 g 189 864 1.08
(0.84, 1.38)
510 g 162 864 0.95
(0.73, 1.22)
757 g 177 863 1.11
(0.85, 1.44)
CARDIA Steffen 2005 4304 both
18–
30 15
0.6
serving/
day
259 860 Reference Adjusted for baseline age,
sex, race, education,
center, and energy intake,
physical activity, alcohol
intake, baseline smoking,
vitamin supplement use,
and simultaneous
adjustment of all food
groups, explanatory
nutrients (sodium,
saturated fat, calcium,
magnesium, potassium,
and dietary fiber) and
physiological
1.4 227 861 1.02
(0.83, 1.25)
2 181 861 0.82
(0.65, 1.04)
2.9 170 861 0.85
( 0.66, 1.11)
4.25 160 861 0.82
(0.59, 1.14)
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measurements (baseline
systolic blood pressure,
BMI, and fasting insulin).
SUN Alonso 2005 5880 both 37 2.3
156 g 49 2709 Reference Cox regression model
adjusted for age
(continuous variable), sex,
BMI (lineal and quadratic
term), physical activity,
alcohol consumption,
sodium intake, total energy
intake, smoking (never,
former, or current),
hypercholesterolemia (yes
or no), quintiles of fruit,
vegetable, fiber, caffeine,
magnesium, potassium,
monosaturated fatty acid,
and saturated fatty acid
intakes.
292 g 38
2708 0.84
(0.54, 1.29)
386 g 39 2708 0.85
(0.54, 1.32)
530 g 24 2705 0.57
(0.34,0.95)
799 g 30 2694 0.75
(0.45,1.27)
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Supplemental Figure 2. The associations of SNP (rs4988235) with dairy consumption (serving/day), systolic blood pressure (SBP),
and risk of hypertension using recessive models (TT vs. CC/ CT)
a. SNP (rs4988235) and dairy consumption b. SNP (rs4988235) and SBP c. SNP (rs4988235) and risk of hypertension
Linear/logistic regression adjusted for baseline age, sex, ethnicity, and region.
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Supplemental Figure 3. The associations of SNP (rs4988235) with dairy consumption (serving/day), systolic blood pressure (SBP),
and risk of hypertension using dominant models (CT/TT vs. CC)
a. SNP (rs4988235) and dairy consumption b. SNP (rs4988235) and SBP c. SNP (rs4988235) and risk of hypertension
Linear/logistic regression adjusted for baseline age, sex, ethnicity, and region.
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Supplemental Table 4. The causal estimate of per serving increase in dairy consumption on systolic blood pressure (SBP) and risk of
hypertension using recessive and additive models by instrumental variable method
Change of SBP (mmHg) Relative risk of hypertension
Recessive models 3.00 (-0.56, 6.56) 1.00 (0.66, 1.51)
Dominant 1.35 (-0.28, 2.97) 1.04 (0.88, 1.24)
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