Chen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

RESEARCH ARTICLE

Open Access

Dairy consumption and risk of type 2 diabetes:
3 cohorts of US adults and an updated
meta-analysis
Mu Chen1,2, Qi Sun1,3, Edward Giovannucci1,2,3, Dariush Mozaffarian1,2,3,4, JoAnn E Manson2,3,5, Walter C Willett1,2,3
and Frank B Hu1,2,3*

Abstract
Background: The relation between consumption of different types of dairy and risk of type 2 diabetes (T2D)
remains uncertain. Therefore, we aimed to evaluate the association between total dairy and individual typesof dairy consumptions and incident T2D in US adults.
Methods: We followed 41,436 men in the Health Professionals Follow-Up Study (1986 to 2010), 67,138 women in
the Nurses’ Health Study (1980 to 2010), and 85,884 women in the Nurses’ Health Study II (1991 to 2009). Diet wasassessed by validated food-frequency questionnaires, and data were updated every four years. Incident T2D was
confirmed by a validated supplementary questionnaire.
Results: During 3,984,203 person-years of follow-up, we documented 15,156 incident T2D cases. After adjustment
for age, body mass index (BMI) and other lifestyle and dietary risk factors, total dairy consumption was not
associated with T2D risk and the pooled hazard ratio (HR) (95% confidence interval (CI)) of T2D for one serving/day
increase in total dairy was 0.99 (0.98, 1.01). Among different types of dairy products, neither low-fat nor high-fat
dairy intake was appreciably associated with risk of T2D. However, yogurt intake was consistently and inversely
associated with T2D risk across the three cohorts with the pooled HR of 0.83 (0.75, 0.92) for one serving/day
increment (P for trend <0.001). We conducted a meta-analysis of 14 prospective cohorts with 459,790 participantsand 35,863 incident T2D cases; the pooled relative risks (RRs) (95% CIs) were 0.98 (0.96, 1.01) and 0.82 (0.70, 0.96) for
one serving total dairy/day and one serving yogurt/day, respectively.
Conclusions: Higher intake of yogurt is associated with a reduced risk of T2D, whereas other dairy foods andconsumption of total dairy are not appreciably associated with incidence of T2D.
Keywords: dairy, type 2 diabetes, cohort, meta-analysis

Background
Type 2 diabetes (T2D) affects approximately 26 million
people in the United States and 366 million people worldwide, and the number will reach an estimated 552 million
worldwide by 2030 [1]. Further, management of diabetes
and its complications, such as cardiovascular disease,
imposes enormous medical and economic burdens [2].
Therefore, primary prevention of T2D has become apublic health imperative.
* Correspondence: frank.hu@channing.harvard.edu
1
Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
2
Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
Full list of author information is available at the end of the article

Because of their high content of calcium, magnesium,
vitamin D, whey protein and specific fatty acids, dairy
products have been suggested to lower risk of T2D [3].
Experimental studies indicated that whey protein has
insulinotropic and glucose-lowering properties [4]. Several
epidemiologic studies, but not all, have suggested thatdairy or calcium consumption was associated with lower
risks for hypertension and coronary artery disease [5]. On
the other hand, saturated fats in some dairy products
might offset the benefits of the potentially protective dairy
components [6], whereas other median chain saturated and ruminant trans fatty acids could reduce risk [7].
Total dairy product intake was associated with marginally© 2014 Chen et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.

Chen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

significantly lower risk of T2D in a recent meta-analysis ofprospective cohort studies [8]. However, three large
Harvard cohorts, Health Professionals’ Follow-up Study(HPFS), Nurses’ Health Study (NHS) and II, accounting
for 38% of participants included in the meta-analysis, haveaccumulated up to 12 additional years of follow-up sinceinitial publications and the analyses have not been updated. Furthermore, the meta-analysis has insufficient data
to evaluate most individual dairy subtypes robustly, especially yogurt, which has been linked to lower obesity andmay influence gut microbiota through probiotics [9]. In aprevious study [10], yogurt consumption was associatedwith the least weight gain among all the food types in ourcohorts. To address these questions, we aimed to update
our previous analyses of total dairy and T2D risk withlonger duration of follow-up in the three large cohorts
and then assess subtypes of dairy consumption in detail.
We also conducted an updated meta-analysis of our results from these three cohorts and previous literature.

Methods
Study population

We used data from three prospective cohort studies:
HPFS, NHS and NHS II. The HPFS was composed of
51,529 US male dentists, pharmacists, veterinarians, optometrists, osteopathic physicians and podiatrists, 40- to
75-years old, who returned a baseline questionnaire thatinquired about detailed medical history, as well as lifestyleand usual diet in 1986. The NHS was initiated in 1976,
when 121,700 female registered nurses, 30- to 55-years
old, who lived in one of eleven states, completed a baseline questionnaire about their lifestyle and medical history.
The NHS II was established in 1989 and consisted of
116,671 younger female registered nurses, 25- to 42-years
old, who responded to a baseline questionnaire similar to
the NHS questionnaire. Detailed descriptions of the threecohorts have been published elsewhere [11-13]. In all
three cohorts, questionnaires were administered at baseline and biennially thereafter, to collect and update information on lifestyle practice and occurrence of chronic
diseases. The follow-up rates of the participants in thesecohorts were all >90%. In the current analysis, we excluded men and women who had diagnoses of diabetes(including type 1 and type 2 diabetes and gestational diabetes only), cardiovascular disease, or cancer at baseline(1986 for HPFS, 1980 for NHS, and 1991 for NHS II,
when we first assessed diet in these cohorts) [14]. In
addition, we excluded participants who left >70 of the 131
food items blank on the baseline food frequency questionnaire (FFQ) or who reported unusual total energy intakes(that is, daily energy intake <800 or >4,200 kcal/day formen and <500 or >3,500 kcal/day for women). We also
excluded participants without baseline information on
dairy consumption or follow-up information on diabetes

Page 2 of 14

diagnosis date. After exclusions, data from 41,479 HPFS
participants, 67,138 NHS participants and 85,884 NHS II
participants were available for analysis. The study protocol
was approved by the institutional review boards ofBrigham and Women’s Hospital and Harvard School of
Public Health. The completion of the self-administeredquestionnaire was considered to imply informed consent.
Assessment of dairy consumption

In 1980, a 61-item FFQ was administered to the NHS
participants to collect information on their usual intake
of foods and beverages in the previous year. In 1984,
1986, 1990, 1994, 1998 and 2002, similar but expanded
131-item FFQs were sent to these participants to update
their diet records. With the use of the expanded FFQ usedin the NHS, dietary data were collected in 1986, 1990,
1994, 1998 and 2002 from the HPFS participants, and in
1991, 1995, 1999 and 2003 from the NHS II participants.
In all FFQs, we asked the participants how often, on
average, they consumed each food of a standard portion
size. There were nine possible responses, which ranged
from ‘never or less than once per month’ to ‘6 or moretimes per day’. Nutrient intake was calculated by multiplication of the frequency of consumption of each foodby the nutrient composition in the standard portion sizeof that food and then summing up the nutrient intake
from all relevant food items. The food composition database was created primarily from USDA sources [15].
Questionnaire items on dairy products included ‘skim/
low fat milk’, ‘whole milk’, ‘ice cream’, ‘yogurt’, ‘cottage/ricotta cheese’, ‘cream cheese’, ‘other cheese’, ‘cream’. From1994 in NHS and HPFS and 1995 in NHS II, yogurt consumption was separated into two items, ‘plain yogurt’(plain or with NutraSweet) and ‘flavored yogurt’ (without
NutraSweet). The standard serving size was an 8 oz. glass
for skim, low fat milk, or whole milk, 1 Tbs for cream, sour
cream, ½ cup for sherbet or frozen yogurt, ice cream,cottage or ricotta cheese, 1 oz. for cream cheese or othercheese. The reproducibility and validity of these FFQs havebeen shown in detail elsewhere [16-20]. The correlation
coefficients between FFQ and multiple dietary records were0.62 both for low-fat dairy foods and for high-fat dairy
foods [20] and ranged from 0.57 for hard cheese to 0.97 foryogurt regarding various dairy products intakes [16].
Assessment of covariates

In the biennial follow-up questionnaires, we inquired
about and updated information on risk factors forchronic diseases, such as body weight, cigarette smoking,physical activity, medication use and family history of
diabetes, as well as history of chronic diseases, including
hypertension and hypercholesterolemia. Among NHS andII participants, we ascertained menopausal status, postmenopausal hormone use and oral contraceptive use.

Chen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

Assessment of diabetes

A supplementary questionnaire about symptoms, diagnostic tests, and hypoglycemic therapy was mailed toparticipants who reported that they had received a diagnosis of diabetes. In accordance with National Diabetes Data
Group criteria, a case of T2D was considered confirmed if
at least one of the following was reported on the supplementary questionnaire [21]: 1) one or more classic symptoms (excessive thirst, polyuria, weight loss, hunger) andfasting plasma glucose concentrations ≥7.8 mmol/L or
random plasma glucose concentrations ≥11.1 mmol/L;2) ≥2 elevated plasma glucose concentrations on different
occasions (fasting concentrations ≥7.8 mmol/L, random
plasma glucose concentrations ≥11.1 mmol/L, and/or
concentrations of ≥11.1 mmol/L after ≥2 hours shown by
oral-glucose-tolerance testing) in the absence of symptoms; or 3) treatment with hypoglycemic medication(insulin or oral hypoglycemic agent). The diagnostic criteria were changed by the American Diabetes Associationin June 1998, and the threshold for the diagnosis of
diabetes became a fasting plasma glucose of 7.0 mmol/L,instead of 7.8 mmol/L [22]. Only cases confirmed by the
supplemental questionnaires were included. The validityof the supplementary questionnaire for the diagnosis of
diabetes has been documented previously. Of the 59 T2D
cases in HPFS and 62 cases in NHS who were confirmedby the supplementary questionnaire, 57 (97%) and 61(98%) were reconfirmed by medical records [23,24].
Deaths were identified by reports from next of kin or
postal authorities, or by searching the National Death
Index. At least 98% of deaths among the study participants were identified [10].
Statistical analysis

We calculated each individual’s person-years from thedate of return of the baseline questionnaire to the dateof diagnosis of T2D, death, or the end of the follow-up(31 January 2010 for HPFS, 30 June 2010 for NHS or 30
June 2009 for NHS II), whichever came first. We used
time-dependent Cox proportional hazard regression toestimate the hazard ratio (HR) for dairy consumption in
relation to the risk of T2D. Our basic model (Model 1)
simultaneously controlled for age, calendar time with
updated information at each two-year questionnaire
cycle, body mass index (BMI), and total energy intake.
Model 2 also adjusted for various potential confounding
factors, including race, smoking, physical activity, alcohol consumption, menopausal status and menopausal
hormone use (NHS and II participants only), oral contraceptive use (NHS II participants only), family history of
diabetes and diagnosed hypertension or hypercholesterolemia at baseline. Model 3 further adjusted for trans-fat,glycemic load, and intakes of red and processed meat,nuts, sugar-sweetened beverages (SSBs) and coffee. For

Page 3 of 14

individual dairy foods, we additionally adjusted for othertypes of dairy in model 3.
We used the cumulative average of dietary intakes
from baseline to the censoring events in order to best
represent long-term diet and minimize within-person
variation [14]. In our primary analysis we stopped updating dietary intake when participants developed coronary
heart disease, stroke or cancer because changes in dietafter development of these conditions may confound therelationship between diet and diabetes [14,25]. We conducted a further analysis by stopping updating dietary
information after self-reported diagnosis of hypertension
and hypercholesterolemia during the follow-up because
these diagnoses appeared to alter consumption of dairy
products (see Results).
Proportional hazards assumption was tested with atime dependent variable with the inclusion of an interaction term between the dairy intake and months toevents (P >0.05 for all tests). To test for linear trend, themedian value was assigned to each quintile and thisvalue was modeled as a continuous variable. All the analyses were conducted separately in each cohort, and we
also conducted meta-analyses to summarize the estimatesof association across the three studies. No significant heterogeneities were shown when the results were pooled
across the three cohorts; therefore, fixed-effect models
were used. All statistical tests were two-sided and performed using SAS version 9.2 for UNIX (SAS InstituteInc, Cary, NC, USA).
Updated meta-analysis on dairy products and risk ofincident T2D

We further conducted an updated meta-analysis thatincorporated our new results from the three cohorts into
the findings of previous studies. This meta-analysis was
conducted following a review protocol [26]. For study
selection, we included prospective studies with cohort,case cohort or nested case-control design investigating
the association between intake of dairy products and riskof T2D. The two recent meta-analyses involved a searchof the literature up to March 2013 [27] to June 2013 [8].
Thus, we conducted additional literature searches on
MEDLINE [28] and EMBASE [29] from June 2013 to
October 2013 [see Additional file 1]. In studies that reported the intakes by grams, we used 177 g as a serving
size for total dairy products, and 244 g as a serving sizefor milk and yogurt intake to recalculate the intakes to acommon scale (servings/day).

Results
We documented a total of 15,156 cases of incident
diabetes mellitus (DM), including 3,364 cases during amaximum of 24 years of follow-up in the HPFS, 7,841
cases during a maximum of 30 years in the NHS, andHPFS (1986)

NHS I (1980)

NHS II (1991)

Characteristics

Q1
Q3
Q5
Q1
Q3
Q5
Q1
Q3
Q5
(number = 8,638) (number = 8,823) (number = 8,323) (number = 13,456) (number = 13,397) (number = 13,433) (number = 17,225) (number = 17,147) (number = 17,213)

Total dairy intake(servings/day)

0.6 (0.4 to 0.7)b

1.6 (1.4 to 1.7)

3.8 (3.4 to 4.6)

0.9 (0.7 to 1.1)

1.9 (1.8 to 2.0)

3.4 (3.1 to 3.8)

0.8 (0.5 to 1.1)

2.0 (1.7 to 2.3)

3.9 (3.4 to 4.8)

Age (years)

52.6(9.1)c

52.9(9.5)

53.4(9.7)

46.4 (7.1)

46.1(7.1)

46.3(7.3)

36.8(4.6)

36.0(4.7)

35.2(4.6)

Physical activity(MET-hours/week)

19.9(29.1)

21.6(28.0)

22.1(29.9)

12.3(17.5)

14.0(19.4)

15.4 (21.1)

19.0(27.2)

21.3(26.9)

22.7(28.6)

BMI (kg/m2)

24.8(4.9)

25.0(4.8)

25.0(4.9)

24.0(4.3)

24.3(4.3)

24.3(4.4)

24.4(5.3)

24.6(5.3)

24.5(5.1)

Race, white (%)

91.7

96.0

97.0

95.8

98.5

98.9

93.0

97.6

97.9

Current smoker (%)

10.3

8.6

10.5

34.6

25.9

24.0

15.6

10.7

10.8

Hypertension (%)

21.0

19.6

17.4

16.4

14.7

13.7

6.5

6.0

5.2

High cholesterol (%)

12.6

10.0

8.2

6.1

4.7

4.8

16.0

13.6

12.6

Family history of
diabetes (%)

23.1

24.7

23.9

28.2

28.3

28.6

34.9

34.1

32.3

Postmenopausal (%)

NA

NA

NA

32.1

31.2

31.3

3.55

2.99

2.72

Current menopausal
hormone use (%)d

NA

NA

NA

21.6

22.1

21.6

83.1

80.9

83.8

Current oral conceptiveuse (%)

NA

NA

NA

NA

NA

NA

11.49

12.06

9.14

Total energy (Kcal/day)

1,657(523)

1,957(546)

2,425(626)

1,433(364)1,699(363)

2,013(403)

1,443(475)

1,791(469)

2,180(514)

Alcohol (g/day)

12.3(16.6)

11.5(15.0)

10.4(15.0)

7.1(11.0)

6.1 (8.8)

5.2(8.0)

3.2(6.6)

3.4(6.0)

3.1(6.0)

Cereal fiber (g/day)

5.6(4.0)

6.0(3.8)

5.6(3.5)

4.5 (2.1)

5.0 (2.1)

4.8 (1.9)

5.4 (3.1)

5.8(3.0)

5.4(2.8)

Glycemic load

127(30)125(24)120(230)103 (20)103 (16)101 (15)127 (26)

121 (20)115 (18)

Polyunsaturated tosaturated fat ratio

0.68(0.26)

0.56(0.17)

0.47(0.15)

0.59(0.18)

0.56 (0.14)

0.51 (0.13)

0.60(0.19)

0.52(0.13)

0.45(0.12)

Trans fat (% of totalenergy)2.8 (1.3)

2.9 (1.1)

2.8 (1.0)

1.8 (0.6)

1.7 (0.5)

1.7 (0.4)

1.8(0.7)

1.6(0.6)

1.5(0.5)

Fruit and vegetables(servings/day)

4.9(2.8)

5.4(2.6)

5.8(2.9)

4.3 (1.9)

5.2 (1.9)

5.9 (2.1)

4.1(2.7)

5.2(2.7)

6.0(3.1)

Red and processed meatintake (servings/day)

0.99(0.79)

1.16(0.79)

1.33(0.90)

1.02(0.60)

1.01 (0.51)

1.07 (0.56)

1.03(0.67)

1.17(0.68)

1.24(0.72)

Nuts intake(servings/day)

0.38(0.54)

0.46(0.59)

0.58(0.73)

0.13 (0.18)

0.16(0.19)

0.19 (0.21)

0.07(0.20)

0.09(0.19)

0.10(0.22)

0.34(0.64)

0.36(0.58)

0.40(0.62)

0.33 (0.57)

0.27 (0.42)

0.29 (0.42)

0.54(0.99)

0.45(0.80)

0.45(0.77)

Coffee intake(servings/day)

1.87(1.78)

1.92(1.75)

2.05(1.90)

2.08(1.57)

2.14 (1.42)

2.21 (1.50)

1.43 (1.71)

1.57(1.64)

1.70(1.74)

aData were age standardized except for age and dairy intake. bMedian; interquartile range in parentheses (all such values). cMean SD (all such values). dCurrent menopausal hormone users among postmenopausal
women. HPFS, Health Professionals Follow-Up Study; METs, metabolic equivalent; NA, not available; NHS, Nurses’ Health Study; Q, quintile.

Page 4 of 14

SSB intake (servings/day)Chen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

Table 1 Baseline age-adjusted characteristics of participants in the three cohorts according to quintile of total dairy consumptiona

Chen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

Page 5 of 14

3,951 cases during a maximum of 16 years in the NHS
II. For both men and women, total dairy intake wasinversely associated with smoking, hypertension andhypercholesterolemia, but positively associated with physical activity, and fruit and vegetable intakes (Table 1).
Different types of dairy products were moderately correlated (Spearman correlation coefficients from -0.13 to
0.27 in the three cohorts).
Total dairy consumption was not associated with riskof T2D in age- and multivariate-adjusted models acrossthe three cohorts (all P for trend >0.05), as shown in

Table 2. In the pooled analysis of estimates from thethree studies that used fixed-effect models, in the age-,
BMI- and energy-adjusted model, one serving/day increment of dairy consumption was significantly associatedwith a 4% lower risk (95% confidence interval (CI): 2%,6%); however, further adjustment for lifestyle and otherdietary factors attenuated the association to null with
the HR of a one serving/day increase of 0.99 (95% CI:
0.98, 1.01). The cohort-specific and combined spline analyses (Figure 1) based on multivariate models also indicated a null association between total dairy consumption

Table 2 HRs (95% CI) of type 2 diabetes risk according to quintile of total dairy intake in HPFS, NHS I and NHS II
Frequency of consumption
a

a

a

a

P-trendb

HR (95% CI) for
one serving/day

a

Q1

Q2

Q3

Q4

Q5

Daily servings(0.64, <0.86)c(1.18, 0.86 to 1.34)(1.64, 1.35 to 1.90)(2.32, 1.91, 2.99)(3.59, ≥3.00)

Cases/person-years

660/161,017

698/157,956

654/159,981

669/160.184

683/159,307

Age-, BMI-andenergy-adjustedd

1.0

1.06 (0.95, 1.18)

0.98 (0.87, 1.09)

0.97 (0.87, 1.09)

0.97 (0.87, 1.09)

0.34

0.98 (0.95, 1.01)

Adjusted fornon-dietary factorsd

1.0

1.10 (0.98, 1.22)

1.02 (0.92, 1.14)

1.00 (0.89, 1.12)

1.01 (0.90, 1.14)

0.60

0.99 (0.96, 1.02)

Adjusted for
dietary factorsd

1.0

1.08 (0.97, 1.21)

1.01 (0.91, 1.13)

0.99 (0.88, 1.11)

0.99 (0.88, 1.11)

0.38

0.98 (0.95, 1.01)

HPFS

NHS
Daily servings(0.91, <1.20)(1.45, 1.20 to 1.66)(1.91, 1.67 to 2.15)(2.45, 2.16, 2.83)(3.37, ≥2.84)

Cases/person-years

1,596/350,963

1,575/350,493

1,531/351,353

1,584/350,639

1,555/351,058

Age-, BMI-andenergy-adjusted

1.00

0.93 (0.87, 1.00)

0.88 (0.82, 0.95)

0.90 (0.84, 0.97)

0.90 (0.83, 0.97)

0.01

0.97 (0.94, 0.99)

Adjusted fornon-dietary factors

1.00

0.97 (0.90, 1.04)

0.93 (0.86, 1.00)

0.94 (0.88, 1.02)

0.94 (0.87, 1.02)

0.13

0.98 (0.95, 1.00)

Adjusted for
dietary factors

1.00

1.00 (0.93, 1.07)

0.98 (0.91, 1.06)

1.03 (0.95, 1.11)

1.05 (0.97, 1.14)

0.15

1.02 (0.99, 1.05)

Daily servings(0.79, <1.06)(1.40, 1.06 to 1.61)(1.96, 1.62 to 2.24)(2.71, 2.25, 3.02)(3.85, ≥3.03)

Cases/person-years

856/283,968

866/285,509

802/286,713

731/286,865

696/288,188

Age-, BMI-andenergy-adjusted

1.00

1.00 (0.91, 1.10)

0.92 (0.83, 1.01)

0.85 (0.77, 0.95)

0.83 (0.75, 0.92)<0.0010.93 (0.90, 0.96)

Adjusted fornon-dietary factors

1.00

1.05 (0.96, 1.16)

1.01 (0.91, 1.11)

0.95 (0.86, 1.06)

0.94 (0.84, 1.04)

0.05

0.96 (0.94, 0.99)

Adjusted for
dietary factors

1.00

1.08 (0.98, 1.18)

1.04 (0.94, 1.15)

0.99 (0.89, 1.10)

1.00 (0.89, 1.11)

0.46

0.98 (0.95, 1.01)

Age-, BMI-andenergy-adjusted

1.00

0.98 (0.93, 1.03)

0.91 (0.86, 0.96)

0.90 (0.86, 0.95)

0.90 (0.85, 0.95)<0.001e0.96 (0.94, 0.98)e

Adjusted fornon-dietary factors

1.00

1.02 (0.97, 1.07)

0.97 (0.92, 1.02)

0.96 (0.91, 1.01)

0.95 (0.90, 1.01)

0.02

0.98 (0.96, 0.99)

Adjusted for
dietary factors

1.00

1.04 (0.98, 1.09)

1.00 (0.95, 1.06)

1.01 (0.96, 1.07)

1.02 (0.96, 1.08)

0.99

0.99 (0.98, 1.01)

NHS II

Pooled analysisa
Q is quintile; bP-trend was calculated by assigning median values to each quintile and was treated as continuous variable; cquintile median and cut-points inparentheses (all such values); dmodel was adjusted for age (continuous), BMI (eight categories), total energy intake (quintiles). Model 2 was additionally adjusted
for race, smoking, physical activity, alcohol consumption, menopausal status and menopausal hormone use (NHS I and II participants only), oral contraceptive use(NHS II participants only), diabetes family history, hypertension, hypercholesterolemia. Model 3 was additionally adjusted for trans-fat intake, glycemic load, redand processed meat intake, nuts intake, SSB intake, and coffee intake. eP for heterogeneity <0.05. BMI, body mass index; CI, confidence interval; HPFS, HealthProfessionals Follow-Up Study; HRs, hazard ratios; NHS, Nurses’ Health Study; Q, quintile.

Chen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

Page 6 of 14

Figure 1 Dose-response relationship between total dairy intake and risk of type 2 diabetes in HPFS, NHS I and NHS II using multivariate
model. a) NHS. b) NHS II. c) HPFS. d) Pooled. HPFS, Health Professionals Follow-up Study; NHS, Nurses Health Study.

and T2D risk. No interactions of total dairy consumption
with age, BMI, vitamin D level, physical activity leveland diabetes family history were observed [see Additional
file 1: Table S1].
When we examined the association with dairy products categorized by their fat contents, there were no significant associations between either low-fat or high-fat
dairy intake and risk of T2D (Table 3). The associations
between individual types of dairy products and risk of
T2D were further assessed, as shown in Table 4. All subtypes of dairy products were mutually adjusted for eachother in the multivariate models. In the pooled analysis
of estimates from the three studies that used fixedeffects models, each one serving/day increase of skim
milk, cheese and whole milk was associated with a 2%
(95% CI: -1%, 4%), 7% (95% CI: 3%, 11%) and 10% (95%
CI: 4%, 16%) higher risk of T2D, respectively (All P for
trend <0.05). Conversely, greater yogurt and ice cream
intakes were significantly associated with lower risk of
T2D with an HR of 0.83 (95% CI: 0.75, 0.92) and 0.78
(95% CI: 0.71, 0.86), respectively. No significant interaction of yogurt consumption with baseline BMI was observed [see Additional file 1: Table S3]. In addition,higher consumption of either plain (HR for each serving:
0.96, 95%CI: 0.88, 1.06) or flavored yogurt (HR: 0.88,
95%CI: 0.77, 1.01) was associated with a nonsignificantly
lower risk of T2D.
We conducted a further analysis by additionally stopping updating dietary information after self-reported
diagnosis of hypertension or hypercholesterolemia during the follow-up as the consumption of ice-cream wasdecreased but consumption of skim milk was increased
after diagnosis of hypertension or hypercholesterolemia
in our three cohorts [see Additional file 1: Table S4]. As
shown in Table 5, the significant associations between

skim milk, cheese, whole milk and risk of T2D became
null, with the corresponding HRs of 1.01 (95%CI: 0.99,
1.03), 1.03 (95%CI: 0.99, 1.07) and 1.03 (95%CI: 0.99, 1.07),
respectively (all P-trend >0.05). The inverse association
between ice cream and T2D risk attenuated with an HR of
0.89 (95%CI: 0.83, 0.96), although still significant. On thecontrary, the inverse association between yogurt intake
and risk of T2D remained significant with an HR of 0.86
(95%CI: 0.78, 0.94) for one serving per day increment.
By incorporating our new results from the three cohorts
together with the findings of previous studies, we conducted an updated meta-analysis. Our updated searchon MEDLINE and EMBASE found 513 potential citations, of which one study [30] met the inclusion criteria,
in addition to the citations in the two previous metaanalyses. Therefore, a total of eleven prospective studies[30-40] for total dairy and six [32-34,36-38] for yogurt
were included in our updated meta-analysis, along withresults from our current analysis. The characteristics of
the included studies are shown in Additional file 1:
Table S3. Total dairy intake was not significantly associated with risk of T2D whereas yogurt intake was associated with a significantly lower risk of T2D, as shownin Figures 2 and 3. Significant heterogeneity was shownfor both total dairy (I2 = 58.8%; P = 0.003) and yogurt(I2 = 63.2%; P = 0.005). The RRs (95% CIs) from therandom-effects model for one serving of total dairy intake
and one serving/day yogurt intake were 0.98 (0.96, 1.01)
and 0.82 (0.70, 0.96), respectively. The RRs (95% CIs) fromthe fixed-effects model for one serving/day of total dairy
intake and one serving/day yogurt intake were 0.99 (0.98,
1.00) and 0.84 (0.78, 0.90), respectively.
After two studies [30,31] that did not adjust for totalenergy intake and other main dietary confounders
were excluded, the heterogeneity for total dairy decreased

Chen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

Page 7 of 14

Table 3 Relative risk (RR) of type 2 diabetes among men according to low-fat versus high-fat dairy food intake
Dairy intake (daily servings)
Variable

Q1a

Q2a

Q3a

Q4a

Q5a

(0.43, 0.14 to 0.49)(0.86, 0.50 to 0.99)(1.28, 1.00 to 1.49)(2.57, ≥1.50)

P-trendb

HR (95% CI) for
one serving/day

Low-fat dairy intake
HPFS
Daily servings(0.07, <0.14)c

Cases/person-years

709/154,402

681/161,216

666/163,722

663/159,761

645/159,343

Age-, BMI-andenergy-adjustedd

1.00

0.93 (0.84, 1.04)

0.89 (0.80, 0.99)

0.92 (0.82, 1.02)

0.88 (0.79, 0.99)

0.06

0.97 (0.93, 1.00)

Unadjusted for
dietary factorsd

1.00

0.95 (0.85, 1.06)

0.92 (0.83, 1.02)

0.94 (0.84, 1.05)

0.90 (0.80, 1.00)

0.09

0.97 (0.93, 1.00)

Adjusted for
dietary factorsd

1.00

0.97 (0.87, 1.07)

0.95 (0.85, 1.06)

0.98 (0.88, 1.09)

0.95 (0.84, 1.06)

0.45

0.98 (0.95, 1.02)

Daily servings(0.23, <0.42)(0.64, 0.43 to 0.82)(1.03, 0.83 to 1.21)(1.50, 1.22 to 1.85)(2.42, ≥1.86)

Cases/person-years

1,652/351,416

1,504/350,553

1,620/350,912

1,479/350,718

1,586/350,907

Age-, BMI-andenergy-adjusted

1.00

0.89 (0.83, 0.96)

0.93 (0.87, 1.00)

0.84 (0.78, 0.90)

0.90 (0.84, 0.96)

0.004

0.96 (0.94, 0.99)

Unadjusted for
dietary factors

1.00

0.93 (0.87, 1.00)

0.97 (0.91, 1.04)

0.89 (0.83, 0.96)

0.93 (0.87, 1.00)

0.06

0.98 (0.95, 1.00)

Adjusted for
dietary factors

1.00

0.97 (0.91, 1.04)

1.03 (0.96, 1.10)

0.98 (0.91, 1.06)

1.05 (0.97, 1.14)

0.17

1.02 (0.99, 1.06)

Daily servings(0.21, <0.34)(0.67, 0.35 to 0.87)(1.10, 0.88 to 1.21)(1.68, 1.22 to 2.01)(2.75, >2.02)

Cases/person-years

848/283,696

871/286,724

789/287,700

749/286,955

694/286,167

Age-, BMI-andenergy-adjusted

1.00

1.00 (0.91, 1.10)

0.90 (0.82, 0.99)

0.88 (0.79, 0.97)

0.82 (0.74, 0.91)<0.0010.92 (0.89, 0.96)

Unadjusted for
dietary factors

1.00

1.05 (0.95, 1.15)

0.99 (0.89, 1.09)

0.96 (0.87, 1.06)

0.90 (0.81, 1.00)

0.01

0.95 (0.92, 0.99)

Adjusted for
dietary factors

1.00

1.07 (0.97, 1.17)

1.03 (0.93, 1.13)

1.01 (0.91, 1.12)

0.95 (0.85, 1.06)

0.13

0.97 (0.93, 1.01)

1.00

1.00 (0.95, 1.05)

1.01 (0.96, 1.06)

0.99 (0.94, 1.04)

1.00 (0.94, 1.05)

0.72

1.00 (0.98, 1.02)e(0.64, 0.50 to 0.72)(0.97, 0.73 to 1.14)(1.78, >1.15)

NHS

NHS II

PooledMultivariate
RR (95% CI)

High-fat dairy intake
HPFS
Daily servings(0.16, <0.28)(0.42, 0.28 to 0.49)

Cases/person-years

621/159,021

615/161,726

695/159,337

694/159,123

739/159,238

Age-, BMI-andenergy-adjusted

1.00

0.92 (0.82, 1.03)

1.02 (0.92, 1.14)

0.97 (0.86, 1.08)

1.04 (0.93, 1.16)

0.24

1.01 (0.96, 1.05)

Unadjusted for
dietary factors

1.00

0.95 (0.85, 1.06)

1.08 (0.96, 1.20)

1.03 (0.92, 1.15)

1.09 (0.98, 1.23)

0.05

1.02 (0.97, 1.06)

Adjusted for
dietary factors

1.00

0.90 (0.80, 1.01)

0.99 (0.88, 1.11)

0.94 (0.84, 1.05)

0.97 (0.86, 1.09)

0.87

0.98 (0.94, 1.03)

Daily servings(0.21, <0.31)(0.42, 0.32 to 0.50)(0.62, 0.51 to 0.73)(0.89, 0.74 to 1.10)(1.53, >1.11)

Cases/person-years

1,620/351,081

1,557/350,717

1,530/350,871

1,551/350,817

1,583/351,020

Age-, BMI-andenergy-adjusted

1.00

0.92 (0.86, 0.98)

0.89 (0.83, 0.96)

0.89 (0.82, 0.95)

0.96 (0.89, 1.04)

0.83

1.01 (0.97, 1.05)

Unadjusted for
dietary factors

1.00

0.95 (0.88, 1.02)

0.93 (0.86, 1.00)

0.92 (0.86, 0.99)

0.99 (0.92, 1.07)

0.80

1.01 (0.97, 1.05)

NHS

Chen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

Page 8 of 14

Table 3 Relative risk (RR) of type 2 diabetes among men according to low-fat versus high-fat dairy food intake (Continued)
Adjusted for
dietary factors

1.00

0.95 (0.89, 1.02)

0.93 (0.86, 1.00)

0.92 (0.85, 0.99)

1.01 (0.93, 1.09)

0.51

1.03 (0.99, 1.07)

Daily servings(0.20, <0.28)(0.43, 0.29 to 0.52)(0.64, 0.53 to 0.77)(0.98, 0.78 to 1.17)(1.71, >1.18)

Cases/person-years

792/289,456

775/274,246

843/293,070

811/286,254

730/288,217

Age-, BMI-andenergy-adjusted

1.00

0.95 (0.86, 1.05)

1.01 (0.91, 1.11)

1.01 (0.91, 1.12)

0.94 (0.85, 1.05)

0.40

0.96 (0.92, 1.01)

Unadjusted for
dietary factors

1.00

0.98 (0.89, 1.09)

1.07 (0.97, 1.19)

1.09 (0.98, 1.20)

1.02 (0.92, 1.14)

0.49

0.99 (0.94, 1.03)

Adjusted for
dietary factors

1.00

0.97 (0.88, 1.07)

1.05 (0.95, 1.16)

1.07 (0.96, 1.18)

1.03 (0.92, 1.15)

0.35

1.00 (0.95, 1.05)

1.00

0.94 (0.90, 0.99)

0.97 (0.92, 1.02)

0.96 (0.91, 1.01)

1.00 (0.95, 1.06)

0.30

1.01 (0.98, 1.03)

NHS II

PooledMultivariate Modela

Q is quintile; bP-trend was calculated by assigning median values to each quintile and was treated as continuous variable; cquintile median and cut-points inparentheses (all such values); dModel 1 was adjusted for age (continuous), BMI (eight categories), total energy intake (quintiles). Model 2 was additionally adjusted
for race, smoking, physical activity, alcohol consumption, menopausal status and menopausal hormone use (NHS I and II participants only), oral contraceptive use(NHS II participants only), diabetes family history, hypertension, hypercholesterolemia. Model 3 was additionally adjusted for trans-fat intake, glycemic load, redand processed meat intake, nuts intake, SSB intake, and coffee intake. eP for heterogeneity <0.05. BMI, body mass index; CI, confidence interval; HPFS, HealthProfessionals Follow-up Study; NHS, Nurses Health Study.

(I2 = 35.6%; P = 0.11); additionally, heterogeneity for
yogurt was reduced when one study [36] was removed(I2 = 40.7%; P = 0.063). Both exclusions did not change thesummary RRs materially. On the basis of a funnel plot[see Additional file 1: Figure S2] and Begg’s test, no significant publication bias was shown for the association
between total dairy (P = 0.19) or yogurt (P = 0.92) intake
and risk of T2D.
We also classified studies according to duration of followup as short-term (≤10 years) or long-term (>10 years). Total
dairy consumption was marginally significantly associated with a lower T2D risk in the short-term studies(RR: 0.95, 95%CI: 0.91, 1.00) but not in the long-term
studies (RR: 1.00, 95%CI: 0.98, 1.02). Yogurt consumption was associated with a lower T2D risk in both shortterm (RR: 0.86, 95%CI: 0.69, 1.08) and long-term studies(RR: 0.76, 95%CI: 0.58, 0.98).

Discussion
In three prospective cohorts of US men and women, we
found that intakes of total dairy products were not significantly associated with the risk of T2D, but higher
consumption of yogurt was significantly associated witha lower risk. An updated meta-analysis of our cohorts
and published literature suggest a marginally lower riskof T2D with higher dairy consumption and a consistent
inverse association between yogurt consumption andT2D risk.
Dairy is a complex food with many bioactive compounds
that have divergent health effects, and its association with
T2D has attracted much attention [5]. Our results on
total dairy intake and T2D risk are consistent with some
[33,34,36,37,40], but not all previous studies. Total dairy
consumption was associated with a lower risk of T2D in

our earlier investigations in HPFS [11] and NHS [12],but not in NHS II [13]. The reason for the discrepancy
between our earlier and current results is probably due
to longer follow-up (10 more years) of the NHS and
HPFS cohorts, and our meta-analysis suggests that
potential benefits of dairy were less evident with longterm follow-up. For yogurt consumption, we observed a
consistent and robust inverse association with T2D in ourcohorts and the meta-analysis. A previous meta-analysis[8] reported a similar but nonsignificant risk estimate
of development of T2D associated with 200 g yogurt
consumption with an RR of 0.78 (95%CI: 0.60, 1.02).
Our updated meta-analysis suggested that each one
serving/day yogurt increase was significantly associatedwith a 18% lower risk. Surprisingly, consumption of ice
cream was inversely associated with T2D risk whereasskim milk was associated with higher T2D risk; however,these associations either became null or attenuated infurther analysis when we stopped updating dietary information after self-reported diagnosis of hypertension
or hypercholesterolemia during the follow-up. Since consumption of ice cream was decreased but consumption ofskim milk was increased after diagnosis of hypertension or
hypercholesterolemia in our three cohorts [see Additional
file 1: Table S4], reverse causation may explain the findings that did not take into account changes in diet after
diagnosis of these conditions.
Certain components in dairy products, such as calcium,
vitamin D, magnesium, lactose and dairy protein, have beensuggested to have a favorable impact on metabolic factors,
including body weight, hypertension [41,42] and glucose
homeostasis [43]. Calcium supplement has been showed tohave a small but significant reduction in body weight over aplacebo in a recent meta-analysis [44] of seven trials, butChen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

Page 9 of 14

Table 4 Multivariate relative risk (RR) of type 2 diabetes among men and women according to intakes of dairy foods
Dairy intake (servings)
Variable

Category 1

Category 2

Category 3

Category 4

Skim/low-fat milk

<1/week

1 to 4/week

5/week to 1.4/day≥1.5/day

HPFS
Cases/person-years
Multivariate modelb
NHS
Cases/person-years
Multivariate modelc

2.3/week

0.9/day

2.5/day

800/187,455

878/211,190

1,095/263,155

591/136,644

0/week

1.00

1.01 (0.91, 1.11)

1.05 (0.96, 1.16)

1.05 (0.94, 1.18)

0.2/week

2.6/week

1.0/day

2.4/day

1,434/324,753

2,022/478,343

2,932/652,108

1,453/299,303

P for trenda

HR (95% CI) for
one serving/day

0.35

0.99 (0.96, 1.03)<0.0011.06 (1.02, 1.09)

1.00

1.00 (0.93, 1.08)

1.05 (0.98, 1.12)

1.13 (1.04, 1.23)

NHS II

0.2/week

3.0/week

1.0/day

2.5/day

Cases/person-years

629/222,580

1,181/414,562

1,490/521.220

651/272,879

Multivariate model

1.00

1.05 (0.95, 1.16)

1.15 (1.04, 1.27)

1.02 (0.91, 1.15)

0.92

0.97 (0.93, 1.01)

1.00

1.02 (0.97, 1.07)

1.07 (1.02, 1.12)

1.08 (1.02, 1.15)

0.006d

1.02 (0.99, 1.04)d<1/week

1 to 4/week

5/week to 1.4/day≥1.5/day

0.002

1.07 (0.99, 1.15)

0.09

1.09 (1.02, 1.17)

PooledMultivariate model
Cheese intake
HPFS

0.6/week

2.6/week

0.8/day

1.8/day

Cases/person-years

407/110,338

1,658/415,482

1,167/250,081

132/22,544

Multivariate Model

1.00

1.07 (0.96, 1.20)

1.16 (1.02, 1.31)

1.31 (1.07, 1.62)

NHS

0.7/week

2.6/week

0.8/day

2.3/day

Cases/person-years

850/166,664

4,223/990,161

2,623/568,288

145/29,393

Multivariate Model

1.00

0.95 (0.90, 0.99)

1.00 (0.97, 1.04)

1.06 (0.90, 1.26)

NHS II

0.7/week

2.6/week

0.8/day

1.7/day

Cases/person-years

364/118,132

1,964/751,355

1,499/520,280

124/41,475

Multivariate Model

1.00

0.90 (0.80, 1.02)

0.97 (0.85, 1.10)

0.89 (0.72, 1.10)

0.80

1.04 (0.96, 1.12)

1.00

0.96 (0.92, 1.00)d

1.01 (0.98, 1.05)d

1.08 (0.96, 1.20)d0.004

1.07 (1.03, 1.11)<1/month

1 to 3/month

1/week≥2/week

0.30

0.85 (0.68, 1.06)<0.0010.75 (0.65, 0.86)

PooledMultivariate Model
Yogurt
HPFS
Cases/person-years
Multivariate model
NHS

0/month2.1/month

1.0/week

3.0/week

1,894/413,496

669/160,207

485/134,656

316/90,085

1.00

0.97 (0.88, 1.06)

0.89 (0.80, 0.99)

0.95 (0.84, 1.08)

0/month1.8/month

1.2/week

2.9/week

Cases/person-years

3,118/608,342

1,762/360,994

1,930/476,985

1,031/308,184

Multivariate model

1.00

1.02 (0.96, 1.08)

0.91 (0.85, 0.97)

0.84 (0.78, 0.91)

0/month2.1/month

1.0/week

2.7/week

NHS II
Cases/person-years

1,153/354,086

867/299,194

1,174/438,017

757/339,945

Multivariate model

1.00

1.00 (0.91, 1.09)

1.00 (0.91, 1.09)

0.90 (0.81, 1.00)

0.02

0.94 (0.80, 1.10)

1.00

1.00 (0.96, 1.05)

0.93 (0.89, 0.97)

0.88 (0.83, 0.93)<0.0010.83 (0.75, 0.92)

Whole milk

<1/month

1 to 3/month

1/week≥2/week

HPFS

0/month2.1/month

1.5/week

5.2/week

2,492/606,198

323/70,991

215/51,858

334/69,398

1.00

1.05 (0.93, 1.18)

0.96 (0.83, 1.11)

1.11 (0.98, 1.25)

0.13

1.07 (0.98, 1.17)

0/month1.5/month

1.05/week

3.5/week

0.05

1.14 (1.06, 1.23)

PooledMultivariate model

Cases/person-years
Multivariate model
NHS
Cases/person-years

4,736/107,7815

959/207,604

1,091/245,503

1,055/223,583

Multivariate model

1.00

1.01 (0.94, 1.09)

0.99 (0.93, 1.06)

1.08 (1.00, 1.15)

0/month2.1/month

1.5/week

4.7/week

NHS IIChen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

Page 10 of 14

Table 4 Multivariate relative risk (RR) of type 2 diabetes among men and women according to intakes of dairy foods(Continued)
Cases/person-years

3,373/122,3599

242/84,815

190/66,854

146/55,973

Multivariate model

1.00

0.96 (0.84, 1.09)

1.04 (0.90, 1.21)

1.03 (0.87, 1.22)

0.65

0.98 (0.83, 1.14)

0.02

1.10 (1.04, 1.16)<0.0010.84 (0.71, 0.99)<0.0010.71 (0.62, 0.82)

PooledMultivariate model

1.00

1.01 (0.96, 1.07)

1.00 (0.94, 1.05)

1.08 (1.02, 1.14)

Ice Cream<1/month

1 to 3/month

1/week≥2/week

HPFS

0/month2.1/month

1.0/week

3.0/week

Cases/person-years

778/165,934

1,262/287,954

851/218,100

473/126,457

1.00

0.83 (0.75, 0.91)

0.68 (0.62, 0.76)

0.63 (0.56, 0.71)

Multivariate RR (95% CI)
NHS

0.6/month

1.8/month

1.1/week

2.8/week

Cases/person-years

1,282/286,271

2,822/626,515

2,518/570,309

1,219/271,411

Multivariate RR (95% CI)
NHS II
Cases/person-years

1.00

0.90 (0.84, 0.96)

0.83 (0.78, 0.89)

0.79 (0.72, 0.86)

0/month2.1/month

1.1/week

3.0/week

1,185/436,526

1,679/598,547

830/306,473

257/89,696

1.00

0.88 (0.82, 0.95)

0.77 (0.70, 0.84)

0.79 (0.69, 0.91)<0.0010.90 (0.73, 1.12)

1.00

0.87 (0.84, 0.91)

0.78 (0.74, 0.82)d0.74 (0.70, 0.79)d<0.001d0.78 (0.71, 0.86)<1/month

1 to 3/month

1/week≥2/week

0.84

0.94 (0.87, 1.02)

Multivariate RR (95% CI)
PooledMultivariate RR (95% CI)
Cream
HPFS

0/month1.5/month

1.0/week

4.5/week

Cases/person-years

2,145/501,934

509/127,177

261/66,686

449/102,648

Multivariate model

1.00

0.95 (0.86, 1.05)

0.94 (0.82, 1.07)

0.98 (0.88, 1.09)

NHS
Cases/person-years

0/month1.8/month

1.1/week

4.8/week

4,271/822,910

1,257/269,337

820/178,495

956/212,976

Multivariate model

1.00

0.96 (0.90, 1.03)

0.92 (0.85, 0.99)

0.99 (0.92, 1.07)

NHS II

0/month1.5/month

0.7/week

3.5/week

0.92

1.01 (0.95, 1.07)

Cases/person-years

1,703/590,798

874/305,399

618/215,329

756/319,716

Multivariate model

1.00

1.00 (0.92, 1.09)

1.02 (0.93, 1.13)

1.04 (0.94, 1.14)

0.46

1.02 (0.95, 1.09)

0.88

0.99 (0.95, 1.03)

0.13

0.98 (0.80, 1.21)

0.79

0.96 (0.82, 1.12)

PooledMultivariate model

1.00

0.97 (0.93, 1.02)

0.95 (0.90, 1.01)

1.00 (0.95, 1.05)

Sherbet<1/month

1-3/month

1/week≥2/week

HPFS

0/month1.8/month

1.0/week

2.4/week

Cases/person-years

1,386/325,655

1,050/251,524

558/136,755

370/84,511

Multivariate model

1.00

1.01 (0.93, 1.10)

1.05 (0.94, 1.17)

1.09 (0.97, 1.23)

NHS

0.1/month

1.8/month

1.1/week

2.8/week

Cases/person-years

3,052/609,546

2,081/402,796

1,576/335,068

595/136,309

Multivariate model

1.00

1.06 (1.00, 1.13)

1.02 (0.95, 1.09)

1.00 (0.91, 1.10)

NHS II

0/month2.1/month

1.3/week

3.0/week

Cases/person-years

1,292/501,292

1,413/499,003

946/321,704

300/109,243

Multivariate model

1.00

1.03 (0.95, 1.11)

1.00 (0.92, 1.10)

0.93 (0.81, 1.06)

0.24

0.94 (0.78, 1.14)

1.00

1.04 (1.00, 1.09)

1.02 (0.97, 1.07)

1.01 (0.94, 1.07)

0.88

0.96 (0.87, 1.07)

PooledMultivariate modelaP-trend was calculated by assigning median values to each quintile and was treated as continuous variable; bmultivariate model was adjusted for age(continuous), BMI (eight categories), total energy intake (quintiles), race, smoking, physical activity, alcohol consumption, menopausal status and menopausal
hormone use (NHS I and II participants only), oral contraceptive use (NHS II participants only), diabetes family history, hypertension, hypercholesterolemia, trans-fat
intake, glycemic load, red and processed meat intake, nuts intake, SSB intake, and coffee intake, and other dairy types for individual dairy types; ccategory median intake(all such values); dP for heterogeneity <0.05. CI, confidence interval; HPFS, Health Professionals Follow-up Study; HR, hazard ratio; NHS, Nurses Health Study.

Chen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

Page 11 of 14

Table 5 Multivariate relative risk (RR) of type 2 diabetes among men and women according to specific dairy foods
using different methods of updating diets
Dairy Intake (servings)
Variable
Skim/low-fat milkbCategory 1

Category 2

Category 3

Category 4<1/week

1 to 4/week

5/week to 1.4/day≥1.5/day

P for trenda

HR (95% CI) for
one serving/day

Direct update

1.00

1.11 (1.05, 1.17)

1.16 (1.10, 1.22)

1.19 (1.12, 1.26)<0.0011.02 (1.00, 1.05)

Stop updating CVD and cancer

1.00

1.02 (0.97, 1.07)

1.07 (1.02, 1.12)

1.08 (1.02, 1.15)

0.006

1.02 (0.99, 1.04)

0.25

1.01 (0.99, 1.03)

cStop updating + HTHC

1.00

0.94 (0.90, 0.99)

0.97 (0.93, 1.01)

1.02 (0.97, 1.07)

Cheese intake<1/week

1 to 4/week

5/week to 1.4/day≥1.5/dayDirect update

1.00

0.97 (0.93, 1.01)

1.07 (1.03, 1.12)

1.19 (1.06, 1.33)<0.0011.10 (1.05, 1.14)

Stop updating CVD and cancer

1.00

0.96 (0.92, 1.00)

1.01 (0.98, 1.05)

1.08 (0.96, 1.20)

0.004

1.07 (1.03, 1.11)

0.41

1.03 (0.99, 1.07)

Stop updating + HTHC

1.00

0.96 (0.92, 1.00)

1.00 (0.98, 1.03)

1.02 (0.92, 1.13)<1/month

1 to 3/month

1/week≥2/week

Direct update

1.00

1.01 (0.97, 1.06)

0.96 (0.92, 1.01)

0.91 (0.86, 0.96)<0.0010.83 (0.76, 0.92)

Stop updating CVD and cancer

1.00

1.00 (0.96, 1.05)

0.93 (0.89, 0.97)

0.88 (0.83, 0.93)<0.0010.83 (0.75, 0.92)<0.0010.86 (0.78, 0.94)

YogurtStop updating + HTHC

1.00

0.97 (0.93, 1.02)

0.92 (0.88, 0.97)

0.87 (0.82, 0.91)

Ice cream<1/month

1 to 3/month

1/week≥2/week

Direct update

1.00

0.85 (0.82, 0.89)

0.77 (0.73, 0.81)

0.68 (0.63, 0.72)<0.0010.73 (0.66, 0.81)

Stop updating CVD and cancer

1.00

0.87 (0.84, 0.91)

0.78 (0.74, 0.82)

0.74 (0.70, 0.79)<0.0010.78 (0.71, 0.86)<0.0010.89 (0.83, 0.96)

Stop updating + HTHC

1.00

0.96 (0.92, 1.00)

0.89 (0.85, 0.94)

0.89 (0.84, 0.94)

Whole milk

<1/month

1 to 3/month

1/week≥2/week

Direct update

1.00

1.03 (0.98, 1.10)

0.99 (0.94, 1.05)

1.12 (1.05, 1.19)<0.0011.10 (1.03, 1.17)

Stop updating CVD and cancer

1.00

1.01 (0.96, 1.07)

1.00 (0.94, 1.05)

1.08 (1.02, 1.14)

0.02

1.10 (1.04, 1.16)

Stop updating + HTHC

1.00

0.96 (0.90, 1.01)

1.04 (0.98, 1.11)

1.03 (0.98, 1.08)

0.15

1.03 (0.99, 1.07)

aP-trend was calculated by assigning median values to each quintile and was treated as continuous variable; bdirect update: cumulative averages of dairy intakes
were calculated without stopping updating diets. All multivariate models were adjusted for age (continuous), BMI (eight categories), total energy intake (quintiles),smoking, physical activity, alcohol consumption, menopausal status, race, diabetes family history, baseline hypertension, hypercholesterolemia, trans-fat intake,glycemic load, red and processed meat intake, nuts intake, SSB intake, and coffee intake, and other dairy types for individual dairy types; cHTHC: hypertension andhypercholesterolemia. BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio; SSB, sugar-sweetened beverages.

the largest study [45] included in the meta-analysis did not
find a significant effect of two-year calcium supplement use
compared to the placebo. Conjugated linoleic acid, createdby bacteria in the gut of ruminants, has been shown to
reduce body weight in animals [46]. However, findings
from randomized trials did not provide clear support
for a role of dairy products in weight reduction [47].
Milk proteins, such as whey, may have insulinotropic
properties with a relatively low glycemic load (GL), which
may improve glucose tolerance [48]. Circulating transpalmitoleate concentrations [7] have been inversely associated with insulin resistance, atherogenic dyslipidemia andincident diabetes. Whole-fat dairy product consumption
was strongly associated with higher trans-palmitoleate
which may offset the unfavorable effect of saturated fat in
high-fat dairy product intake.
Several mechanisms may explain the inverse association between yogurt intake and risk of T2D. Probiotic
bacteria have been shown to improve lipid profile andantioxidant status in T2D patients [49,50] and have beneficial effects on cholesterol levels [51]. In addition, ourprevious study [10] of the three cohorts showed that increased consumption of yogurt was inversely associatedwith weight gain. However, adjusting for BMI in the multivariate model did not alter the inverse association between
yogurt intake and T2D risk.
The strengths of the current study include a large sample size, high rates of follow-up and repeated assessmentsof dietary and lifestyle variables. The current study was
subject to several limitations as well. First, our study
populations primarily consisted of health professionals ofEuropean ancestry. Although the homogeneity of socioeconomic status helps reduce confounding, the observed
associations may not be generalizable to other populations. However, the relatively high educational status isan advantage because high quality and reliable data canbe collected from our study participants. Second, because diet was assessed by FFQs, some measurement
error of dairy intake assessment is inevitable. However,the FFQs used in these studies were validated against
multiple diet records, and reasonable correlation coefficients between these assessments of dairy intake wereChen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

Page 12 of 14

Figure 2 HRs for a serving total dairy consumption per day and type 2 diabetes. The RR of each study is represented by a square, and thesize of the square represents the weight of each study of the overall estimate. The 95% CIs are represented by the horizontal lines, and the diamond
represents the overall estimate and its 95% CI. HPFS, Health Professional Follow-Up Study; NHS, Nurses’ Health Study. CI, confidence interval; HRs,
hazard ratios; RR; relative risk.

observed. Moreover, we calculated cumulative averages
for dietary variables to minimize the random measurement error caused by within-person variation and to accommodate diet changes over time. Nonetheless, since
we did not specifically assess types or brands of yogurt

consumed by the participants, it is difficult to attributethe observed benefits to various components of yogurt.
Lastly, because of the observational nature of our cohorts, the observed associations do not necessarily meancausation; although we adjusted for established andFigure 3 HRs for a serving yogurt consumption per day and type 2 diabetes. The RR of each study is represented by a square, and the size ofthe square represents the weight of each study of the overall estimate. The 95% CIs are represented by the horizontal lines, and the diamond represents
the overall estimate and its 95% CI. HPFS, Health Professional Follow-Up Study; NHS, Nurses’ Health Study. CI, confidence interval; HRs, hazard ratios; RR,
relative risk.

Chen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

potential risk factors for T2D, unmeasured and residual
confounding is still possible. This is especially true for
yogurt consumption, which is typically associated with ahealthy diet and lifestyle.

Conclusions
We found that higher intake of yogurt is associated witha reduced risk of T2D, whereas other dairy foods andconsumption of total dairy are not appreciably associated with incidence of T2D. The consistent findings for
yogurt suggest that it can be incorporated into a healthy
dietary pattern. However, randomized clinical trials are
warranted to further examine the causal effects of yogurt
consumption as well as probiotics on body weight andinsulin resistance.
Additional file
Additional file 1: Supplemental Methods. Table S1: Baseline
age-adjusted characteristics of participants in the 3 cohorts according tocategories of yogurt consumptiona. Table S2: Type 2 diabetes according
to total dairy intake: stratified analyses. Table S3: Type 2 diabetes according
to yogurt consumption stratified by baseline BMI. Table S4: Changes indairy products intake after hypertension and hypercholesterolemia inthe 3 cohorts. Table S5: Multivariate relative Risk (RR) of type 2 diabetes
among men and women according to different types of cheese.
Table S6: Characteristics of studies included in the meta-analysis of
the association of dairy intake with type 2 diabetes. Figure S1:
Age-standardized trends of dairy foods consumption in three cohorts.
Figure S2A: Test for publication bias for the association between total
dairy intake and type 2 diabetes. Figure S2B: Test for publication bias
for the association between yogurt intake and type 2 diabetes.

Abbreviations
BMI: body mass index; CI: confidence interval; FFQ: food frequency
questionnaire; HPFS: Health Professionals Follow-up Study; HR: hazard ratio;
NHS: Nurses’ Health Study; RR: relative risk; SSBs: sugar sweetened beverages;
T2D: type 2 diabetes.
Competing interests
Dr. Dariush Mozaffarian declared that he holds membership of UnileverNorth America Scientific Advisory Board. The other authors declare that they
have no competing interests.
Authors’ contributions
MC, QS, EG, DM, JEM, WCW, and FBH conceived and designed theexperiments. MC, QS, EG, DM, JEM, WCW, and FBH performed theexperiments. MC analyzed the data. MC, QS, EG, DM, JEM, WCW, and FBHcontributed reagents/materials/analysis tools. MC wrote the first draft of themanuscript. MC, QS, EG, DM, JEM, WCW, and FBH contributed to the writingof the manuscript. All authors revised the manuscript for important
intellectual content. All authors read and approved the final manuscript.
Acknowledgements
We thank Lisa Li for her valuable programming assistance.
Funding
The study was supported by research grants DK58845, P01 CA87969, P01
CA055075, R01 HL034594 and HL60712 from the National Institutes of
Health. The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript. Qi Sun was supported
by a career development award R00HL098459 from the NHLBI.

Page 13 of 14

Author details
1
Department of Nutrition, Harvard School of Public Health, Boston, MA, USA.
2
Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
3
Channing Division of Network Medicine, Brigham and Women’s Hospital andHarvard Medical School, Boston, MA, USA. 4Division of Cardiovascular Medicine,and Division of Preventive Medicine, Department of Medicine, Brigham and
Women’s Hospital and Harvard Medical School, Boston, MA, USA. 5Division ofPreventive Medicine, Department of Medicine, Brigham and Women’s Hospitaland Harvard Medical School, Boston, MA, USA.
Received: 13 August 2014 Accepted: 15 October 2014
Published: 25 November 2014

References
1. Whiting DR, Guariguata L, Weil C, Shaw J: IDF diabetes atlas: global
estimates of the prevalence of diabetes for 2011 and 2030.
Diabetes Res Clin Pract 2011, 94:311–321.
2. American Diabetes Association: Economic costs of diabetes in the U.S. in2012. Diabetes Care 2013, 36:1033–1046.
3. Rice BH, Cifelli CJ, Pikosky MA, Miller GD: Dairy components and risk
factors for cardiometabolic syndrome: recent evidence andopportunities for future research. Adv Nutr 2011, 2:396–407.
4. Jakubowicz D, Froy O: Biochemical and metabolic mechanisms bywhich dietary whey protein may combat obesity and Type 2 diabetes.
J Nutr Biochem 2013, 24:1–5.
5. Elwood PC, Pickering JE, Givens DI, Gallacher JE: The consumption of milk
and dairy foods and the incidence of vascular disease and diabetes: an
overview of the evidence. Lipids 2010, 45:925–939.
6. ᅟ: Diet, nutrition and the prevention of chronic diseases. World HealthOrgan Tech Rep Ser 2003, 916:1–149. Backcover.
7. Mozaffarian D, Cao H, King IB, Lemaitre RN, Song X, Siscovick DS,
Hotamisligil GS: Trans-palmitoleic acid, metabolic risk factors, andnew-onset diabetes in U.S. adults: a cohort study. Ann Intern Med 2010,
153:790–799.
8. Aune D, Norat T, Romundstad P, Vatten LJ: Dairy products and the risk oftype 2 diabetes: a systematic review and dose-response meta-analysis ofcohort studies. Am J Clin Nutr 2013, 98:1066–1083.
9. Ridaura VK, Faith JJ, Rey FE, Cheng J, Duncan AE, Kau AL, Griffin NW,
Lombard V, Henrissat B, Bain JR, Muehlbauer MJ, Ilkayeva O, Semenkovich
CF, Funai K, Hayashi DK, Lyle BJ, Martini MC, Ursell LK, Clemente JC, Van
Treuren W, Walters WA, Knight R, Newgard CB, Heath AC, Gordon JI: Gut
microbiota from twins discordant for obesity modulate metabolism inmice. Science 2013, 341:1241214.
10. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB: Changes in diet andlifestyle and long-term weight gain in women and men. N Engl J Med
2011, 364:2392–2404.
11. Choi HK, Willett WC, Stampfer MJ, Rimm E, Hu FB: Dairy consumption andrisk of type 2 diabetes mellitus in men: a prospective study. Arch Intern
Med 2005, 165:997–1003.
12. Pittas AG, Dawson-Hughes B, Li T, Van Dam RM, Willett WC, Manson JE,
Hu FB: Vitamin D and calcium intake in relation to type 2 diabetes inwomen. Diabetes Care 2006, 29:650–656.
13. Malik VS, Sun Q, van Dam RM, Rimm EB, Willett WC, Rosner B, Hu FB:
Adolescent dairy product consumption and risk of type 2 diabetes in
middle-aged women. Am J Clin Nutr 2011, 94:854–861.
14. Hu FB, Stampfer MJ, Rimm E, Ascherio A, Rosner BA, Spiegelman D, Willett
WC: Dietary fat and coronary heart disease: a comparison of approaches
for adjusting for total energy intake and modeling repeated dietarymeasurements. Am J Epidemiol 1999, 149:531–540.
15. Watt BK, Merrill AL: Composition of Foods: Raw, Processed, Prepared, Rev. Dec.
1963. Washington: Consumer and Food Economics Research Division,
Agricultural Research Service, U.S. Dept. of Agriculture: for sale by theSuperintendent of Documents; 1964.
16. Salvini S, Hunter DJ, Sampson L, Stampfer MJ, Colditz GA, Rosner B, Willett WC:
Food-based validation of a dietary questionnaire: the effects of week-toweek variation in food consumption. Int J Epidemiol 1989, 18:858–867.
17. Feskanich D, Rimm EB, Giovannucci EL, Colditz GA, Stampfer MJ, Litin LB,
Willett WC: Reproducibility and validity of food intake measurementsfrom a semiquantitative food frequency questionnaire. J Am Diet Assoc
1993, 93:790–796.

Chen et al. BMC Medicine 2014, 12:215
http://www.biomedcentral.com/1741-7015/12/215

18. Rimm EB, Giovannucci EL, Stampfer MJ, Colditz GA, Litin LB, Willett WC:
Reproducibility and validity of an expanded self-administeredsemiquantitative food frequency questionnaire among male healthprofessionals. Am J Epidemiol 1992, 135:1114–1126. discussion 1127-1136.
19. Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, Hennekens
CH, Speizer FE: Reproducibility and validity of a semiquantitative foodfrequency questionnaire. Am J Epidemiol 1985, 122:51–65.
20. Hu FB, Rimm E, Smith-Warner SA, Feskanich D, Stampfer MJ, Ascherio A,
Sampson L, Willett WC: Reproducibility and validity of dietary patterns
assessed with a food-frequency questionnaire. Am J Clin Nutr 1999,
69:243–249.
21. Classification and diagnosis of diabetes mellitus and other categories of
glucose intolerance. National Diabetes Data Group. Diabetes 1979,
28:1039–1057.
22. Report of the Expert Committee on the Diagnosis and Classification of
Diabetes Mellitus. Diabetes Care 1997, 20:1183–1197.
23. Manson JE, Rimm EB, Stampfer MJ, Colditz GA, Willett WC, Krolewski AS,
Rosner B, Hennekens CH, Speizer FE: Physical activity and incidence of
non-insulin-dependent diabetes mellitus in women. Lancet 1991,
338:774–778.
24. Hu FB, Leitzmann MF, Stampfer MJ, Colditz GA, Willett WC, Rimm EB:
Physical activity and television watching in relation to risk for type 2
diabetes mellitus in men. Arch Intern Med 2001, 161:1542–1548.
25. Hu FB, Stampfer MJ, Manson JE, Rimm E, Colditz GA, Rosner BA, Hennekens
CH, Willett WC: Dietary fat intake and the risk of coronary heart disease
in women. N Engl J Med 1997, 337:1491–1499.
26. Moher D, Liberati A, Tetzlaff J, Altman DG: Preferred reporting items for
systematic reviews and meta-analyses: the PRISMA statement. BMJ 2009,339:b2535.
27. Gao D, Ning N, Wang C, Wang Y, Li Q, Meng Z, Liu Y: Dairy products
consumption and risk of type 2 diabetes: systematic review anddose-response meta-analysis. PLoS One 2013, 8:e73965.
28. PubMed. [http://www.ncbi.nlm.gov/pubmed]
29. Embase. [http://www.embase.com/]
30. Zong G, Sun Q, Yu D, Zhu J, Sun L, Ye X, Li H, Jin Q, Zheng H, Hu FB, Lin X:
Dairy consumption, type 2 diabetes, and changes in cardiometabolic
traits: a prospective cohort study of middle-aged and older Chinese in
Beijing and Shanghai. Diabetes Care 2014, 37:56–63.
31. Elwood PC, Pickering JE, Fehily AM: Milk and dairy consumption, diabetes
and the metabolic syndrome: the Caerphilly prospective study.
J Epidemiol Community Health 2007, 61:695–698.
32. Grantham NM, Magliano DJ, Hodge A, Jowett J, Meikle P, Shaw JE:
The association between dairy food intake and the incidence ofdiabetes in Australia: the Australian Diabetes Obesity and Lifestyle Study(AusDiab). Public Health Nutr 2013, 16:339–345.
33. Kirii K, Mizoue T, Iso H, Takahashi Y, Kato M, Inoue M, Noda M, Tsugane S:
Calcium, vitamin D and dairy intake in relation to type 2 diabetes risk ina Japanese cohort. Diabetologia 2009, 52:2542–2550.
34. Liu S, Choi HK, Ford E, Song Y, Klevak A, Buring JE, Manson JE: A
prospective study of dairy intake and the risk of type 2 diabetes inwomen. Diabetes Care 2006, 29:1579–1584.
35. Louie JC, Flood VM, Rangan AM, Burlutsky G, Gill TP, Gopinath B, Mitchell P:
Higher regular fat dairy consumption is associated with lower incidenceof metabolic syndrome but not type 2 diabetes. Nutr Metab Cardiovasc
Dis 2013, 23:816–821.
36. Margolis KL, Wei F, de Boer IH, Howard BV, Liu S, Manson JE, MossavarRahmani Y, Phillips LS, Shikany JM, Tinker LF: A diet high in low-fat dairy
products lowers diabetes risk in postmenopausal women. J Nutr 2011,
141:1969–1974.
37. Sluijs I, Forouhi NG, Beulens JW, van der Schouw YT, Agnoli C, Arriola L,
Balkau B, Barricarte A, Boeing H, Bueno-de-Mesquita HB, Clavel-Chapelon F,
Crowe FL, de Lauzon-Guillain B, Drogan D, Franks PW, Gavrila D, Gonzalez C,
Halkjaer J, Kaaks R, Moskal A, Nilsson P, Overvad K, Palli D, Panico S,
Quirós JR, Ricceri F, Rinaldi S, Rolandsson O, Sacerdote C, Sánchez MJ, et al:
The amount and type of dairy product intake and incident type 2
diabetes: results from the EPIC-InterAct Study. Am J Clin Nutr 2012,
96:382–390.
38. Soedamah-Muthu SS, Masset G, Verberne L, Geleijnse JM, Brunner EJ:
Consumption of dairy products and associations with incident
diabetes, CHD and mortality in the Whitehall II study. Br J Nutr 2013,
109:718–726.

Page 14 of 14

39. Struijk EA, Heraclides A, Witte DR, Soedamah-Muthu SS, Geleijnse JM, Toft U,
Lau CJ: Dairy product intake in relation to glucose regulation indices andrisk of type 2 diabetes. Nutr Metab Cardiovasc Dis 2013, 23:822–828.
40. van Dam RM, Hu FB, Rosenberg L, Krishnan S, Palmer JR: Dietary calcium
and magnesium, major food sources, and risk of type 2 diabetes in U.S.
black women. Diabetes Care 2006, 29:2238–2243.
41. Pereira MA, Jacobs DR Jr, Van Horn L, Slattery ML, Kartashov AI, Ludwig DS:
Dairy consumption, obesity, and the insulin resistance syndrome inyoung adults: the CARDIA Study. JAMA 2002, 287:2081–2089.
42. Sacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D, Obarzanek E,
Conlin PR, Miller ER 3rd, Simons-Morton DG, Karanja N, Lin PH, DASH-Sodium
Collaborative Research Group: Effects on blood pressure of reduced dietarysodium and the Dietary Approaches to Stop Hypertension (DASH) diet.
DASH-Sodium Collaborative Research Group. N Engl J Med 2001, 344:3–10.
43. Tremblay A, Gilbert JA: Milk products, insulin resistance syndrome andtype 2 diabetes. J Am Coll Nutr 2009, 28:91S–102S.
44. Onakpoya IJ, Perry R, Zhang J, Ernst E: Efficacy of calcium supplementation
for management of overweight and obesity: systematic review of
randomized clinical trials. Nutr Rev 2011, 69:335–343.
45. Yanovski JA, Parikh SJ, Yanoff LB, Denkinger BI, Calis KA, Reynolds JC,
Sebring NG, McHugh T: Effects of calcium supplementation on bodyweight and adiposity in overweight and obese adults: a randomized
trial. Ann Intern Med 2009, 150:821–829. W145-826.
46. van Meijl LE, Vrolix R, Mensink RP: Dairy product consumption and themetabolic syndrome. Nutr Res Rev 2008, 21:148–157.
47. Chen M, Pan A, Malik VS, Hu FB: Effects of dairy intake on body weight
and fat: a meta-analysis of randomized controlled trials. Am J Clin Nutr
2012, 96:735–747.
48. King JC: The milk debate. Arch Intern Med 2005, 165:975–976.
49. Ejtahed HS, Mohtadi-Nia J, Homayouni-Rad A, Niafar M, Asghari-Jafarabadi
M, Mofid V: Probiotic yogurt improves antioxidant status in type 2
diabetic patients. Nutrition 2012, 28:539–543.
50. Ejtahed HS, Mohtadi-Nia J, Homayouni-Rad A, Niafar M, Asghari-Jafarabadi
M, Mofid V, Akbarian-Moghari A: Effect of probiotic yogurt containing
Lactobacillus acidophilus and Bifidobacterium lactis on lipid profile inindividuals with type 2 diabetes mellitus. J Dairy Sci 2011, 94:3288–3294.
51. Parvez S, Malik KA, Ah Kang S, Kim HY: Probiotics and their fermented
food products are beneficial for health. J Appl Microbiol 2006,
100:1171–1185.
doi:10.1186/s12916-014-0215-1
Cite this article as: Chen et al.: Dairy consumption and risk of type 2
diabetes: 3 cohorts of US adults and an updated meta-analysis. BMC
Medicine 2014 12:215.

Submit your next manuscript to BioMed Central
and take full advantage of:• Convenient online submission• Thorough peer review• No space constraints or color ﬁgure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit