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The relation between consumption of different types of red meats and risk of type 2 diabetes (T2D) remains uncertain. We evaluated the association between unprocessed and processed red meat consumption and incident T2D in US adults. We followed 37,083 men in the Health Professionals Follow-Up Study (1986-2006), 79,570 women in the Nurses' Health Study I (1980-2008), and 87,504 women in the Nurses' Health Study II (1991-2005). Diet was assessed by validated food-frequency questionnaires, and data were updated every 4 y. Incident T2D was confirmed by a validated supplementary questionnaire. During 4,033,322 person-years of follow-up, we documented 13,759 incident T2D cases. After adjustment for age, BMI, and other lifestyle and dietary risk factors, both unprocessed and processed red meat intakes were positively associated with T2D risk in each cohort (all P-trend <0.001). The pooled HRs (95% CIs) for a one serving/d increase in unprocessed, processed, and total red meat consumption were 1.12 (1.08, 1.16), 1.32 (1.25, 1.40), and 1.14 (1.10, 1.18), respectively. The results were confirmed by a meta-analysis (442,101 participants and 28,228 diabetes cases): the RRs (95% CIs) were 1.19 (1.04, 1.37) and 1.51 (1.25, 1.83) for 100 g unprocessed red meat/d and for 50 g processed red meat/d, respectively. We estimated that substitutions of one serving of nuts, low-fat dairy, and whole grains per day for one serving of red meat per day were associated with a 16-35% lower risk of T2D. Our results suggest that red meat consumption, particularly processed red meat, is associated with an increased risk of T2D.
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Red meat consumption and risk of type 2 diabetes: 3 cohorts
of US adults and an updated meta-analysis
1–3
An Pan, Qi Sun, Adam M Bernstein, Matthias B Schulze, JoAnn E Manson, Walter C Willett, and Frank B Hu
ABSTRACT
Background: The relation between consumption of different types
of red meats and risk of type 2 diabetes (T2D) remains uncertain.
Objective: We evaluated the association between unprocessed and
processed red meat consumption and incident T2D in US adults.
Design: We followed 37,083 men in the Health Professionals Follow-
Up Study (1986–2006), 79,570 women in the Nurses’ Health Study I
(1980–2008), and 87,504 women in the Nurses’ Health Study II
(1991–2005). Diet was assessed by validated food-frequency ques-
tionnaires, and data were updated every 4 y. Incident T2D was con-
firmed by a validated supplementary questionnaire.
Results: During 4,033,322 person-years of follow-up, we docu-
mented 13,759 incident T2D cases. After adjustment for age, BMI,
and other lifestyle and dietary risk factors, both unprocessed and
processed red meat intakes were positively associated with T2D risk
in each cohort (all P-trend ,0.001). The pooled HRs (95% CIs) for a
one serving/d increase of unprocessed, processed, and total red meat
consumption were 1.12 (1.08, 1.16), 1.32 (1.25, 1.40), and 1.14 (1.10,
1.18), respectively. The results were confirmed by a meta-analysis
(442,101 participants and 28,228 diabetes cases): the RRs (95% CIs)
were 1.19 (1.04, 1.37) and 1.51 (1.25, 1.83) for 100 g of unprocessed
red meat and for 50 g of unprocessed red meat, respectively. We
estimated that substitutions of one serving of nuts, low-fat dairy,
and whole grains per day for one serving of red meat per day were
associated with a 16–35% lower risk of T2D.
Conclusion: Our results suggest that red meat consumption, partic-
ularly processed red meat, is associated with an increased risk of
T2D. Am J Clin Nutr doi: 10.3945/ajcn.111.018978.
INTRODUCTION
Diabetes is highly prevalent in the US population. More than
11% of US adults aged 20 y (25.6 million persons) have di-
abetes; the majority (90–95%) suffer from T2D,
4
and 1.9 million
new cases of diabetes occur each year (1). Although obesity and
physical inactivity are major determinants of T2D and account
for much of the increase in prevalence (2), dietary factors also
play an important role in its development (3).
We have shown previously that processed red meat con-
sumption was associated with an increased risk of T2D in 3
Harvard cohorts (4–6). This was also confirmed by 2 recent meta-
analyses (7, 8), but these meta-analyses did not come to an
agreement as to whether unprocessed red meat was associated
with diabetes risk. No study has so far examined whether sub-
stitution of other dietary components for red meat, such as low-fat
dairy products, nuts, and whole grains, could lower diabetes risk.
These foods are major sources of protein intake and have been
related to a lower risk of T2D (9–11). Therefore, we aimed to 1)
update our previous analyses of meat consumption and diabetes
risk with the use of the same analysis strategy, with longer
follow-up years in the 3 large cohorts (HPFS and NHS I and II);
2) conduct an updated meta-analysis of the results from the 3
cohorts and previous literature; and 3) estimate the effects of
substitution of low-fat dairy products, nuts, and whole grains for
red meat on diabetes risk.
SUBJECTS AND METHODS
Study population
We used data from 3 prospective cohort studies: HPFS, NHS I,
and NHS II. The HPFS was initiated in 1986, when 51,529 US
male dentists, pharmacists, veterinarians, optometrists, osteo-
pathic physicians, and podiatrists, aged 40–75 y, returned
a baseline questionnaire that inquired about detailed medical
history, as well as lifestyle and usual diet. The NHS I consisted of
121,700 female registered nurses, aged 30–55 y, who lived in one
of 11 states and completed a baseline questionnaire about their
lifestyle and medical history in 1976. The NHS II was established
in 1989 and was composed of 116,671 younger female registered
nurses, aged 25–42 y, who responded to a baseline questionnaire
similar to the NHS I questionnaire. Detailed descriptions of the 3
cohorts were introduced elsewhere (4–6). In all 3 cohorts,
questionnaires were administered at baseline and biennially
thereafter, to collect and update information on lifestyle practice
1
From the Departments of Nutrition (AP, QS, AMB, WCW, and FBH)
and Epidemiology (JEM, WCW, and FBH), Harvard School of Public
Health, Boston, MA; Channing Laboratory (QS, WCW, and FBH) and the
Division of Preventive Medicine (JEM), Department of Medicine, Brigham
and Women’s Hospital and Harvard Medical School, Boston, MA; and the
Department of Molecular Epidemiology (MBS), German Institute of Human
Nutrition, Nuthetal, Germany.
2
Supported by NIH grants (DK58845, CA55075, CA87969, and CA50385)
and by a career development award (K99HL098459) from the National Heart,
Lung, and Blood Institute (to QS).
3
Address correspondence to FB Hu, Department of Nutrition, Harvard
School of Public Health, 665 Huntington Avenue, Boston, MA 02115.
E-mail: frank.hu@channing.harvard.edu.
4
Abbreviations used: FFQ, food-frequency questionnaire; HPFS, Health
Professionals Follow-Up Study; NHS, Nurses’ Health Study; T2D, type 2
diabetes.
Received April 28, 2011. Accepted for publication July 12, 2011.
doi: 10.3945/ajcn.111.018978.
Am J Clin Nutr doi: 10.3945/ajcn.111.018978. Printed in USA. Ó2011 American Society for Nutrition 1of9
AJCN. First published ahead of print August 10, 2011 as doi: 10.3945/ajcn.111.018978.
Copyright (C) 2011 by the American Society for Nutrition
and occurrence of chronic diseases. The follow-up proportions
of the participants in these cohorts 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), cardiovascular disease, or cancer at
baseline (1986 for HPFS, 1980 for NHS I, and 1991 for NHS II,
when we first assessed diet in these cohorts). In addition, we
excluded participants who left .70 of the 131 food items blank
on the baseline FFQ or who reported unusual total energy in-
takes (ie, daily energy intake ,800 or .4200 kcal/d for men
and ,500 or .3500 kcal/d for women). We also excluded
participants without baseline information on meat consump-
tion or follow-up information on diabetes diagnosis date (de-
tailed information can be found under "Supplemental data" in
the online issue). After exclusions, data from 37,083 HPFS
participants, 79,570 NHS I participants, and 87,504 NHS II
participants were available for analysis. The study protocol
was approved by the institutional review boards of Brigham
and Women’s Hospital and Harvard School of Public Health.
Assessment of meat consumption
In 1980, a 61-item FFQ was administered to the NHS I par-
ticipants 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 used in the NHS I, 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 9
possible responses, which ranged from “never or less than once
per month” to “6 or more times per day.” Nutrient intake was
calculated by multiplication of the frequency of consumption of
each food by the nutrient composition in the standard portion size
of that food and then summing up the nutrient intake from all
relevant food items. The food composition database was created
primarily from USDA sources (12). Questionnaire items on
unprocessed red meat consumption included “beef or lamb as
main dish,” “pork as main dish,” “hamburger,” and “beef, pork,
or lamb as a sandwich or mixed dish,” and items on processed red
meat included “bacon,” “hot dogs,” and “sausage, salami, bo-
logna, and other processed red meats.” The standard serving size
was 85 g (3 ounces) for unprocessed red meat, 45 g for one hot
dog, 28 g for 2 slices of bacon, or 45 g for one piece of other
processed red meat. The reproducibility and validity of these
FFQs have been shown in detail elsewhere (13–16). The cor-
relation coefficients between FFQ and multiple dietary records
ranged from 0.38 to 0.70 for various red meat intakes (16).
Assessment of covariates
In the biennial follow-up questionnaires, we inquired about and
updated information on risk factors for chronic diseases, such as
body weight, cigarette smoking, physical activity, medication use,
and family history of diabetes, as well as history of chronic dis-
eases, including hypertension and hypercholesterolemia. Among
NHS I and II participants, we ascertained menopausal status,
postmenopausal hormone use, and oral contraceptive use.
Assessment of diabetes
A supplementary questionnaire about symptoms, diagnostic
tests, and hypoglycemic therapy was mailed to participants who
reported that they had received a diagnosis of diabetes. A case of
T2D was considered confirmed if at least one of the following
was reported on the supplementary questionnaire [in accordance
with National Diabetes Data Group criteria (17)]: 1) one or more
classic symptoms (excessive thirst, polyuria, weight loss, hun-
ger) and fasting 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 h 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 Association in
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 (18). Only cases confirmed by the supplemental ques-
tionnaires were included.
The validity of the supplementary questionnaire for the di-
agnosis of diabetes has been documented previously. Of the 59
T2D cases in HPFS and 62 cases in NHS I who were confirmed by
the supplementary questionnaire, 57 (97%) and 61 (98%) were
reconfirmed by medical records (19, 20). 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 (21).
Statistical analysis
We calculated each individual’s person-years from the date
of return of the baseline questionnaire to the date of diagnosis of
T2D, death, or the end of the follow-up (31 January 2006 for
HPFS; 30 June 2008 for NHS I; or 30 June 2007 for NHS II),
whichever came first. We used time-dependent Cox pro-
portional hazard regression to estimate the HR for red meat
consumption in relation to the risk of T2D. In the multivariate
analysis, we simultaneously controlled for various potential
confounding factors in addition to age and calendar time with
updated information at each 2-y questionnaire cycle, including
ethnicity (whites, nonwhites), smoking status [never, past,
current (1–14 cigarettes/d), current (15–24 cigarettes/d), or
current (.24 cigarettes/d)], alcohol intake (0, 0.1–4.9, 5.0–
14.9, or 15 g/d in women; 0, 0.1–4.9, 5.0–29.9, or 30 g/d in
men), physical activity (,3, 3–8.9, 9–17.9, 18–26.9, or 27
metabolic equivalent-hours/week), history of hypertension and
hypercholesterolemia, family history of diabetes, post-
menopausal status and menopausal hormone use (NHS I and II
participants only), oral contraceptive use (NHS II participants
only), and quintiles of total energy intake and dietary score.
We created a low diabetes risk diet score as a diet low in trans
fat and glycemic load and high in cereal fiber and the ratio of
polyunsaturated to saturated fat. The dietary score summed the
quintile values of the 4 components, and 5 represented the
lowest-risk quintile in each dietary factor (2). BMI (in kg/m
2
)
was updated every 2 y and was included in the model as
a time-varying covariate in an additional model because red
2of9 PAN E T AL
meat intake was associated with BMI and weight gain in our
cohorts (22), and thus BMI could be considered an in-
termediate variable between red meat intake and diabetes.
Our primary analysis adjusted for this dietary score and total
energy intake. We also conducted a sensitivity analysis with
adjustment for other major dietary variables (whole grain, fish,
nuts, sugar-sweetened beverages, coffee, egg, potatoes, fruit and
vegetables, all in quintiles) instead of the dietary score. We
conducted another sensitivity analysis to correct for measurement
error (23) in the assessment of red meat intake with the use of data
from validation studies conducted in HPFS (13) and NHS I (15).
This method used a regression calibration approach to correct RR
estimates for measurement error for the time-dependent measures
of dietary variable intake (23).
To better represent long-term diet and to minimize within-
person variation, we created cumulative averages of food and
nutrient intake from baseline to the censoring events (24). We
also conducted a sensitivity analysis with the use of only baseline
dietary variables to predict the future risk of T2D. To minimize
missing values of dietary variables in each follow-up FFQ, we
replaced missing values with the cumulative means before the
missing values. The last value was carried forward for one 2-y
cycle to replace nondietary missing values. We investigated the
effect of a “substitution” of a serving of one food for another by
including both as continuous variables in the same multivariable
model, which contained both nondietary covariates and total
energy intake. The difference in their beta coefficients and their
variances and covariance were used to estimate the beta co-
efficient and variance for the substitution effect, which was used
to calculate HRs and 95% CIs for the substitution effect (25, 26).
Proportional hazards assumption was tested with a time-
dependent variable with the inclusion of an interaction term
between the red meat intake and months to events (P.0.05 for
all tests). To test for linear trend, the median value was assigned
to each quintile and this value was modeled as a continuous
variable. All the analyses were conducted separately in each
cohort, and we also conducted meta-analyses to summarize the
estimates of association across the 3 studies. No significant
heterogeneities were shown when the results were pooled across
the 3 cohorts; therefore, fixed-effect models were used. The data
were analyzed with the Statistical Analysis Systems software
package, version 9.1 (SAS Institute Inc), whereas the meta-
analysis was performed with the use of the STATA statistical
program, version 9.2 (StataCorp).
Updated meta-analysis on red meat intake and risk of
incident T2D
We further conducted an updated meta-analysis that incor-
porated our new results from the 3 cohorts into the findings of
previous studies. The 2 recent meta-analyses involved a search of
literature up to December 2008 (7) or March 2009 (8). Thus, we
conducted additional literature searches on MEDLINE (http://
www.ncbi.nlm.nih.gov/pubmed) and EMBASE (http://www.embase.
com/) from March 2009 to April 2011 (details can be found under
"Supplemental data" in the online issue).
RESULTS
We documented 2438 incident T2D cases during a maximum
of 20 y of follow-up in the HPFS, 8253 cases during a maximum
of 28 y in the NHS I, and 3068 cases during a maximum of 16 y
in the NHS II. The distribution of baseline characteristics
accordingtointakeoftotalredmeatconsumptionisshownin
Tabl e 1. For both men and women, red meat intake was neg-
atively associated with physical activity, but positively asso-
ciated with BMI and smoking. In addition, a high red meat
intake was associated with a high intake of total energy and
a worse diabetes dietary score. Unprocessed and processed red
meat consumption was moderately correlated (Spearman cor-
relation coefficients from 0.38 to 0.53 in the 3 cohorts).
However, red meat consumption was only weakly correlated
with intakes of poultry (coefficients from 20.05 to 0.24) or fish
(coefficients from 20.20 to 0.05).
The HRs of T2D according to unprocessed, processed, and total
red meat consumption are shown in Tabl e 2. In age- and multi-
variate-adjusted models, red meat consumption was positively
associated with the risk of development of T2D across the 3
studies (all Pfor trend: ,0.001). After further adjustment for
updated BMI status, these associations were substantially atten-
uated but remained significant. In the pooled analysis of estimates
from the 3 studies that used fixed-effect models, a one serving/d
increase of unprocessed, processed, and total red meat con-
sumption was associated with a 12% (95% CI: 8%, 16%), 32%
(95% CI: 25%, 40%), and 14% (95% CI: 10%, 18%) elevated risk
of T2D, respectively. Adjustment for other major dietary variables
(whole grain, fish, nuts, sugar-sweetened beverages, coffee, egg,
potatoes, and fruit and vegetables) instead of diabetes dietary
score in the sensitivity analyses did not materially alter the as-
sociations, and the corresponding HRs (95% CIs) for un-
processed, processed, and total red meat consumptions were 1.12
(1.08, 1.16), 1.29 (1.22, 1.37), and 1.14 (1.11, 1.18), respectively.
Sensitivity analyses with the use of energy density (serving 1000
kcal
21
d
21
) or conventional cutoffs were consistent with our
main results (see Tables S2 and S3 under “Supplemental data” in
the online issue). In a sensitivity analysis with only baseline di-
etary variables, the corresponding HRs (95% CIs) were 1.07
(0.98, 1.16), 1.16 (1.11, 1.20), and 1.10 (1.02, 1.18), respectively.
In another sensitivity analysis, which accounted for measurement
error in dietary variables, the results were strengthened, and the
corresponding HRs (95% CIs) were 1.66 (1.28, 2.14), 1.53 (1.34,
1.75), and 1.44 (1.10, 1.99), respectively.
When compared with one daily serving of red meat, one daily
serving of nuts (28 g), low-fat dairy products (240 mL milk, 28 g
cheese, or 120 mL yogurt), and whole grains [32 g (1 slice) of
bread or 200 g (1 cup) of cooked brown rice or cereals] was
associated with a lower risk of T2D (Figure 1). One serving of
nuts per day was associated with a 21% (95% CI: 14%, 26%)
lower risk of T2D when compared with one serving total red
meat/d. Similarly, when compared with one serving total red
meat/d, the risk reductions associated with low-fat dairy prod-
ucts and whole grain were 17% (14%, 20%) and 23% (20%,
27%), respectively. The corresponding risk reductions associ-
ated with nuts, low-fat dairy products, and whole grains were
20% (13%, 26%), 16% (13%, 19%), and 24% (20%, 28%) for
unprocessed red meat, and 32% (26%, 37%), 29% (25%, 33%),
and 35% (30%, 39%) for processed red meat, respectively.
Lower risks of T2D were also shown when substitutions of one
serving of total red meat/d with one serving of poultry/d (85 g,
3 ounces; 10%; 95% CI: 5%, 15%) and one serving of fish/d (85 g,
3 ounces; 10%; 95% CI: 5%, 15%) were made.
RED MEAT AND DIABETES 3of9
TABLE 1
Baseline age-adjusted characteristics of participants in the 3 cohorts according to quintile (Q) of total red meat consumption
1
Characteristics
HPFS (1986) NHS I (1980) NHS II (1991)
Q1 (n= 7187) Q3 (n= 7027) Q5 (n= 7247) Q1 (n= 15,777) Q3 (n= 15,579) Q5 (n= 15,900) Q1 (n= 17,506) Q3 (n= 17,542) Q5 (n= 17,575)
Total red meat intake (servings/d) 0.24 (0.11–0.37)
2
1.01 (0.92–1.10) 2.16 (1.93–2.57) 0.58 (0.44–0.68) 1.50 (1.42–1.63) 2.86 (2.57–3.36) 0.37 (0.24–0.48) 1.03 (0.95–1.10) 2.04 (1.80–2.40)
Age (y) 53.6 69.6
3
52.4 69.5 51.9 69.2 47.2 67.2 45.7 67.2 45.8 67.1 36.2 64.7 36.0 64.7 35.9 64.7
Physical activity (MET-h/wk) 27.8 634.0 20.2 628.3 17.7 625.2 16.9 624.8 13.8 620.1 12.5 617.3 26.4 633.7 19.8 625.2 18.2 624.7
BMI (kg/m
2
) 24.7 63.0 25.4 63.1 25.9 63.4 23.8 64.1 24.2 64.3 24.5 64.6 23.3 64.4 24.5 65.1 25.7 66.0
Race, white (%) 93.0 95.4 96.0 96.9 97.9 97.2 95.5 97.1 96.0
Current smoker (%) 5.1 9.8 14.3 25.4 28.1 31.7 10.2 12.2 14.2
Hypertension (%) 18.7 18.3 19.0 14.3 14.5 15.2 5.1 6.1 7.3
High cholesterol (%) 14.5 9.4 7.7 5.7 4.9 4.2 13.9 14.4 14.5
Family history of diabetes (%) 18.7 18.5 18.1 25.9 27.1 28.7 31.3 33.2 36.4
Postmenopausal (%) NA NA NA 30.6 30.3 30.5 3.0 3.1 3.4
Current menopausal hormone
use (%)
4
NA NA NA 20.9 21.1 21.0 84.4 82.9 83.6
Current oral conceptive use (%) NA NA NA NA NA NA 12.3 10.8 10.2
Total energy (kcal/d) 1661 6507 1889 6490 2400 6514 1202 6396 1522 6386 2027 6473 1439 6466 1743 6460 2240 6511
Alcohol (g/d) 8.5 612.3 11.3 614.8 13.5 617.5 5.9 69.8 6.7 610.8 6.8 611.4 3.3 66.0 3.1 65.9 3.1 66.3
Diabetes dietary score 13.7 62.6 11.8 62.6 10.7 62.2 13.0 62.2 11.9 62.0 11.1 61.8 12.8 62.7 11.9 62.7 11.3 62.6
Cereal fiber (g/d) 7.5 65.3 5.7 63.4 4.6 62.5 2.9 61.9 2.5 61.4 1.9 61.1 6.8 64.0 5.5 62.7 4.7 62.1
Glycemic load 139 629 123 623 111 622 97 627 84 623 73 622 134 624 121 619 110 618
Polyunsaturated to saturated
fat ratio
0.74 60.27 0.55 60.15 0.46 60.12 0.43 60.18 0.34 60.11 0.29 60.09 0.56 60.21 0.50 60.14 0.46 60.12
trans Fat (% of total energy) 0.9 60.5 1.3 60.5 1.5 60.5 1.8 60.8 2.3 60.7 2.5 60.6 1.4 60.7 1.7 60.6 1.8 60.6
Fruit and vegetables (servings/d) 6.1 63.2 5.1 62.5 5.1 62.4 4.1 62.3 3.9 61.9 4.1 62.0 4.9 63.1 4.9 62.7 5.6 62.9
Coffee (cups/d) 1.5 61.6 1.9 61.8 2.3 61.9 2.0 61.9 2.2 61.9 2.4 62.0 1.5 61.6 1.6 61.7 1.6 61.8
Egg (servings/d) 0.19 60.30 0.31 60.33 0.51 60.51 0.37 60.36 0.41 60.34 0.50 60.43 0.12 60.18 0.17 60.18 0.25 60.25
Soft drinks (servings/d) 0.61 60.94 0.79 61.03 0.89 61.05 0.61 60.97 0.71 61.00 0.82 61.09 1.20 61.43 1.46 61.46 1.82 61.61
Dairy (servings/d) 1.7 61.3 1.9 61.3 2.1 61.4 1.8 61.3 1.8 61.2 1.8 61.2 2.1 61.4 2.3 61.4 2.5 61.5
Nuts (servings/d) 0.45 60.41 0.45 60.58 0.48 60.60 0.16 60.33 0.13 60.26 0.15 60.28 0.10 60.26 0.08 60.19 0.09 60.20
Potato (servings/d) 0.38 60.32 0.53 60.36 0.76 60.45 0.32 60.31 0.47 60.37 0.63 60.45 0.34 60.29 0.51 60.34 0.75 60.46
Fish (servings/d) 0.55 60.48 0.38 60.33 0.32 60.30 0.50 60.57 0.39 60.41 0.33 60.37 0.28 60.32 0.28 60.25 0.30 60.29
Poultry (servings/d) 0.63 60.50 0.55 60.39 0.52 60.39 0.63 60.57 0.59 60.46 0.58 60.53 0.54 60.42 0.77 60.46 0.97 60.67
1
Data were age standardized except for age and red meat intake. HPFS, Health Professionals Follow-Up Study; MET, metabolic equivalent; NA, not available; NHS, Nurses’ Health Study.
2
Median; interquartile range in parentheses (all such values).
3
Mean 6SD (all such values).
4
Current menopausal hormone users among postmenopausal women.
4of9 PAN E T AL
TABLE 2
HRs (95% CI) of type 2 diabetes risk according to quintile (Q) of red meat intake in the Health Professionals Follow-Up Study (HPFS), the Nurses’ Health
Study (NHS) I, and NHS II
1
Frequency of consumption
P-trend
2
HR (95% CI)
for one serving/dQ1 Q2 Q3 Q4 Q5
HPFS
Unprocessed red meat
Servings/d 0.17 (0.08, 0.24)
3
0.43 (0.37, 0.47) 0.65 (0.57, 0.73) 0.94 (0.86, 1.02) 1.44 (1.29, 1.65)
Cases/person-years 375/129,461 394/127,874 526/134,407 489/128,901 654/132,331
Age-adjusted model 1.00 1.07 (0.93, 1.24) 1.41 (1.23, 1.60) 1.33 (1.16, 1.52) 1.79 (1.58, 2.04) ,0.001 1.38 (1.29, 1.48)
Multivariate model 1 1.00 1.03 (0.89, 1.19) 1.31 (1.14, 1.50) 1.23 (1.06, 1.42) 1.65 (1.42, 1.91) ,0.001 1.33 (1.23, 1.44)
Multivariate model 2 1.00 0.95 (0.82, 1.09) 1.14 (0.99, 1.31) 1.05 (0.90, 1.21) 1.29 (1.11, 1.50) ,0.001 1.16 (1.06, 1.26)
Processed red meat
Servings/d 0.02 (0, 0.05) 0.12 (0.09, 0.13) 0.21 (0.20, 0.25) 0.38 (0.33, 0.44) 0.72 (0.60, 0.97)
Cases/person-years 340/138,550 409/121,238 441/131,831 593/131,520 655/129,834
Age-adjusted model 1.00 1.38 (1.20, 1.60) 1.40 (1.22, 1.62) 1.88 (1.65, 2.15) 2.08 (1.82, 2.37) ,0.001 1.55 (1.43, 1.68)
Multivariate model 1 1.00 1.32 (1.14, 1.53) 1.32 (1.14, 1.52) 1.75 (1.51, 2.01) 1.96 (1.69, 2.27) ,0.001 1.47 (1.34, 1.62)
Multivariate model 2 1.00 1.18 (1.02, 1.37) 1.12 (0.97, 1.30) 1.44 (1.25, 1.66) 1.55 (1.33, 1.79) ,0.001 1.34 (1.21, 1.48)
Total red meat
Servings/d 0.25 (0.12, 0.36) 0.60 (0.52, 0.69) 0.94 (0.86, 1.03) 1.34 (1.23, 1.46) 2.02 (1.78, 2.43)
Cases/person-years 346/129,959 398/131,492 488/130,148 545/130,744 661/130,631
Age-adjusted model 1.00 1.16 (1.01, 1.34) 1.44 (1.25, 1.65) 1.60 (1.39, 1.83) 1.96 (1.72, 2.24) ,0.001 1.33 (1.27, 1.39)
Multivariate model 1 1.00 1.11 (0.96, 1.29) 1.37 (1.18, 1.58) 1.54 (1.32, 1.79) 1.92 (1.64, 2.25) ,0.001 1.33 (1.25, 1.41)
Multivariate model 2 1.00 1.01 (0.87, 1.17) 1.17 (1.01, 1.35) 1.25 (1.08, 1.45) 1.44 (1.23, 1.68) ,0.001 1.19 (1.12, 1.27)
NHS I
Unprocessed red meat
Servings/d 0.37 (0.28, 0.45) 0.61 (0.56, 0.67) 0.84 (0.75, 0.98) 1.09 (0.97, 1.26) 1.57 (1.33, 1.95)
Cases/person-years 1278/404,906 1534/402,338 1592/403,148 1793/394,652 2056/409,128
Age-adjusted model 1.00 1.23 (1.14, 1.32) 1.28 (1.19, 1.37) 1.46 (1.36, 1.57) 1.64 (1.53, 1.75) ,0.001 1.29 (1.24, 1.34)
Multivariate model 1 1.00 1.17 (1.08, 1.26) 1.18 (1.10, 1.28) 1.29 (1.19, 1.39) 1.39 (1.28, 1.50) ,0.001 1.15 (1.10, 1.20)
Multivariate model 2 1.00 1.10 (1.02, 1.19) 1.10 (1.02, 1.18) 1.16 (1.08, 1.25) 1.23 (1.14, 1.33) ,0.001 1.09 (1.04, 1.14)
Processed red meat
Servings/d 0.05 (0.02, 0.07) 0.14 (0.13, 0.16) 0.23 (0.21, 0.27) 0.35 (0.32, 0.42) 0.64 (0.52, 0.83)
Cases/person-years 1174/403,239 1426/381,686 1632/420,660 1885/404,142 2136/404,445
Age-adjusted model 1.00 1.26 (1.17, 1.36) 1.42 (1.32, 1.53) 1.65 (1.53, 1.77) 1.93 (1.80, 2.08) ,0.001 1.85 (1.74, 1.97)
Multivariate model 1 1.00 1.19 (1.10, 1.29) 1.32 (1.22, 1.42) 1.46 (1.36, 1.58) 1.60 (1.48, 1.72) ,0.001 1.54 (1.44, 1.65)
Multivariate model 2 1.00 1.08 (1.00, 1.17) 1.15 (1.06, 1.24) 1.23 (1.14, 1.33) 1.30 (1.20, 1.40) ,0.001 1.30 (1.21, 1.41)
Total red meat
Servings/d 0.50 (0.36, 0.61) 0.83 (0.75, 0.95) 1.12 (0.99, 1.30) 1.44 (1.27, 1.68) 2.07 (1.75, 2.55)
Cases/person-years 1212/401,534 1444/405,038 1586/401,953 1863/403,092 2148/402,555
Age-adjusted model 1.00 1.22 (1.13, 1.31) 1.35 (1.25, 1.46) 1.59 (1.48, 1.71) 1.84 (1.71, 1.97) ,0.001 1.32 (1.28, 1.36)
Multivariate model 1 1.00 1.16 (1.07, 1.25) 1.25 (1.16, 1.35) 1.42 (1.31, 1.54) 1.55 (1.43, 1.68) ,0.001 1.21 (1.16, 1.25)
Multivariate model 2 1.00 1.08 (1.00, 1.17) 1.13 (1.04, 1.22) 1.25 (1.15, 1.35) 1.31 (1.21, 1.42) ,0.001 1.13 (1.08, 1.17)
NHS II
Unprocessed red meat
Servings/d 0.17 (0.07, 0.25) 0.43 (0.37, 0.47) 0.61 (0.56, 0.66) 0.84 (0.76, 0.92) 1.29 (1.12, 1.53)
Cases/person-years 368/271,759 462/266,716 562/278,014 690/276,425 986/273,261
Age-adjusted model 1.00 1.28 (1.12, 1.47) 1.54 (1.35, 1.76) 1.89 (1.66, 2.14) 2.70 (2.39, 3.04) ,0.001 1.88 (1.77, 1.99)
Multivariate model 1 1.00 1.17 (1.01, 1.34) 1.31 (1.14, 1.50) 1.39 (1.21, 1.59) 1.69 (1.47, 1.93) ,0.001 1.36 (1.26, 1.47)
Multivariate model 2 1.00 1.01 (0.88, 1.16) 1.13 (0.99, 1.29) 1.12 (0.98, 1.28) 1.27 (1.11, 1.46) ,0.001 1.18 (1.09, 1.28)
Processed red meat
Servings/d 0 (0, 0.03) 0.07 (0.07, 0.10) 0.14 (0.13, 0.17) 0.25 (0.21, 0.28) 0.49 (0.39, 0.64)
Cases/person-years 389/273,513 464/254,002 582/296,610 626/262,102 1007/279,949
Age-adjusted model 1.00 1.29 (1.13, 1.48) 1.54 (1.35, 1.75) 1.78 (1.57, 2.02) 2.81 (2.50, 3.16) ,0.001 2.53 (2.34, 2.73)
Multivariate model 1 1.00 1.10 (0.96, 1.26) 1.24 (1.08, 1.41) 1.32 (1.16, 1.51) 1.72 (1.52, 1.96) ,0.001 1.76 (1.58, 1.96)
Multivariate model 2 1.00 0.93 (0.81, 1.06) 1.00 (0.88, 1.14) 1.02 (0.89, 1.16) 1.21 (1.07, 1.38) ,0.001 1.37 (1.21, 1.55)
Total red meat
Servings/d 0.35 (0.21, 0.45) 0.67 (0.60, 0.73) 0.92 (0.85, 1.00) 1.23 (1.13, 1.34) 1.80 (1.58, 2.13)
Cases/person-years 324/271,508 443/272,488 564/273,770 678/273,938 1059/274,471
Age-adjusted model 1.00 1.40 (1.21, 1.61) 1.79 (1.56, 2.05) 2.16 (1.89, 2.46) 3.38 (2.98, 3.83) ,0.001 1.82 (1.74, 1.91)
Multivariate model 1 1.00 1.23 (1.07, 1.42) 1.46 (1.27, 1.68) 1.58 (1.37, 1.81) 2.07 (1.80, 2.38) ,0.001 1.42 (1.34, 1.50)
Multivariate model 2 1.00 1.05 (0.91, 1.21) 1.16 (1.01, 1.34) 1.17 (1.02, 1.35) 1.37 (1.19, 1.57) ,0.001 1.18 (1.11, 1.26)
(Continued)
RED MEAT AND DIABETES 5of9
We further conducted an updated meta-analysis incorporating
our new results from the 3 cohorts together with the findings of
previous studies. Our updated search on MEDLINE and
EMBASE found 300 potential citations, of which 2 studies met
the inclusion criteria, in addition to the citations in the 2 previous
meta-analyses. Therefore, a total of 6 prospective studies (27–32)
were included in our updated meta-analysis, along with results
from our current analysis (see Table S4 under “Supplemental
data” in the online issue). The detailed characteristics of the
included studies are shown in Table S5 under “Supplemental
data” in the online issue. Unprocessed and processed red meat
intakes were both associated with a significantly increased risk
of T2D, as shown in Figures 2 and 3. The RRs (95% CIs) from
the random-effects model for 100 g/d of unprocessed red meat
(3.5 ounces/d, 1.2 serving/d), and 50 g/d of processed red meat
(1.8 ounces/d, 1.8 serving/d) were 1.19 (1.04, 1.37), and 1.51
(1.25, 1.83), respectively. No significant publication bias was
shown for the association between unprocessed red meat and
risk of T2D (see Figure S1 under “Supplemental data” in the
online issue), although a significant publication bias was de-
tected for processed red meat (see Figure S2 under “Supple-
mental data” in the online issue). With the use of the trim and fill
method, the RR for processed red meat was 1.23 (1.01, 1.52)
(see Figure S3 under “Supplemental data” in the online issue).
Significant heterogeneity was shown for both unprocessed and
processed red meat (I
2
= 93.3% and 94.3%, respectively; both
P,0.001; Figures 2 and 3).
DISCUSSION
In these 3 large prospective cohorts of US men and women, we
observed that red meat consumption was positively associated
TABLE 2 (Continued )
Frequency of consumption
P-trend
2
HR (95% CI)
for one serving/dQ1 Q2 Q3 Q4 Q5
Pooled results
4
Unprocessed red meat 1.00 1.06 (1.00, 1.12) 1.11 (1.05, 1.18) 1.13 (1.07, 1.20) 1.25 (1.17, 1.33) ,0.001 1.12 (1.08, 1.16)
Processed red meat 1.00 1.06 (1.00, 1.13) 1.11 (1.05, 1.18) 1.22 (1.15, 1.29) 1.32 (1.24, 1.40) ,0.001 1.32 (1.25, 1.40)
Total red meat 1.00 1.06 (1.00, 1.13) 1.14 (1.07, 1.21) 1.23 (1.16, 1.31) 1.34 (1.26, 1.43) ,0.001 1.14 (1.10, 1.18)
1
One serving of unprocessed red meat equals to 85 g (3 ounces) pork, beef, or lamb; one serving of processed red meat equals to 28 g bacon or 45 g hot
dog, sausage, salami, bologna, or other processed red meats. Multivariate model 1 was adjusted for age (continuous), alcohol consumption (0, 0.1–4.9, 5.0–
14.9, 15 g/d), physical activity level (,3, 3–8.9, 9–17.9, 18–26.9, 27 metabolic equivalent task hours/wk), smoking status (never; past; current: 1–14, 15–
24, or .24 cigarettes/d), race (white, nonwhite), menopausal status and hormone use in women (premenopausal, postmenopausal never users, postmenopausal
past users, postmenopausal current users), family history of diabetes, history of hypertension and hypercholesterolemia, quintiles of total calories, and dietary
score. Multivariate model 2 was the same as model 1 with the addition of a BMI category (in kg/m
2
;,23, 23–24.9, 25–29.9, 30–34.9, 35).
2
P-trend was calculated by assigning median values to each quintile and was treated as continuous variable.
3
Median; interquartile range in parentheses (all such values).
4
Results from multivariate model 2 were combined with the use of a fixed-effects model given that the Egger’s tests for heterogeneity were not
significant for all 3 meta-analyses (all Pvalues .0.15).
FIGURE 1. HRs and 95% CIs for diabetes associated with replacement of other food groups for red meat intake. Adjusted for age (continuous), BMI
category (in kg/m
2
;,23, 23–24.9, 25–29.9, 30–34.9, or 35), alcohol consumption (0, 0.1–4.9, 5.0–14.9, or 15 g/d), physical activity level (,3, 3–8.9,
9–17.9, 18–26.9, or 27 metabolic task hours/wk), smoking status (never; past; current: 1–14, 15–24, or .24 cigarettes/d), race (white or nonwhite),
menopausal status and hormone use in women (premenopausal, postmenopausal never hormone users, postmenopausal past hormone users, or postmenopausal
current hormone users), family history of diabetes, history of hypertension and hypercholesterolemia, and quintile of total energy intake.
6of9 PAN E T AL
with the risk of T2D, and this association was observed for both
unprocessed and processed red meat, with a relatively higher risk
for the latter. Substitution of nuts, low-fat dairy products, and
whole grains for red meat was associated with a significantly
lower risk of diabetes.
Red meat is a major food source of protein and fat, and its
health effects have attracted much attention with regard to its
association with cardiovascular disease and diabetes (8). Our
results are largely consistent with our previously published
results in these 3 cohorts. Processed red meat consumption has
been associated consistently with a higher risk of T2D in our
previous investigations in HPFS (4) and NHS I (5) and II (6). The
positive association between processed red meat intake and T2D
has also been reported by other cohort studies (27, 31, 32). For
unprocessed red meat consumption, inconsistent results were
reported in our previous analyses (4–6). In the current updated
analysis with longer follow-up years, we observed that a one
serving/d increase of unprocessed red meat consumption was
associated with a 16%, 9%, and 18% higher risk of T2D in HPFS,
NHS I, and NHS II, respectively. The 2 meta-analyses reported
FIGURE 2. HRs for 100 g unprocessed red meat consumption per day and type 2 diabetes. The RR of each study is represented by a square, and the size of
the square represents the weight of each study to 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 Professionals Follow-Up Study; NHS, Nurses’ Health Study.
FIGURE 3. HRs for 50 g processed red meat consumption per day and type 2 diabetes. The RR of each study is represented by a square, and the size of the
square represents the weight of each study to 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 Professionals Follow-Up Study; NHS, Nurses’ Health Study.
RED MEAT AND DIABETES 7of9
similar risk estimates of development of T2D associated with red
meat consumption: an RR of 1.20 (95% CI: 1.04, 1.38) for
a 120-g/d red meat increase in consumption in Aune et al’s (7)
meta-analysis from 9 cohorts, and 1.16 (95% CI: 0.92, 1.46) for
a 100-g/d red meat increase in consumption in Micha et al’s (8)
meta-analysis from 5 cohorts. The 2 meta-analyses differed in the
quantity of red meat intake (120 g/d compared with 100 g/d), and
also in the number of included studies (9 compared with 5). Our
updated meta-analysis suggested that a 100-g/d unprocessed red
meat increase was associated with a 19% (95% CI: 4%, 37%)
increased risk.
Several mechanisms may explain the adverse effect of red
meat intake on T2D. First, the association may be mediated
through the effects of heme-iron derived from red meats (33, 34).
Iron is a strong prooxidant that catalyses several cellular reactions
in the production of reactive oxygen species, and increases the
level of oxidative stress (35). This can cause damage to tissues, in
particular the pancreatic beta cells, and high body iron stores have
been shown to be associated with an elevated risk of T2D (35). In
our analysis, red meat consumption was highly correlated with
heme-iron intake (correlation coefficients ranged from 0.53 to
0.66), and the risk estimates for diabetes were further attenuated
after adjustment for dietary heme-iron intake (data not shown).
Second, although unprocessed and processed meats contain
similar amounts of saturated fat, other constituents in processed
meat, particularly sodium and nitrites, might partially explain the
higher RR associated with processed red meats. A prospective
study in Finland suggested that the association between pro-
cessed meat and diabetes was largely explained by sodium (32).
Nitrites and nitrates are used frequently in the preservation of
processed meat, and they can be converted into nitrosamines
through interaction with amino compounds either in the stomach
or within the food product. Nitrosamines have been shown to be
toxic to pancreatic beta cells and to increase the risk of diabetes in
animal studies (36), and blood nitrite concentrations in adults
have been related to endothelial dysfunction (37) and impaired
insulin response (38). Other potential mechanisms may involve
advanced glycation end-products (39) or increased concen-
trations of inflammatory mediators (40) and gamma-glutamyl-
transferase (41) with high red meat intake. Lastly, in the present
study, adjustment for updated BMI somewhat attenuated the
association between red meat intake and diabetes risk, which
suggests that the association between red meat intake and di-
abetes risk may be partly mediated through weight gain and
obesity. In our cohorts (22) and a large European cohort (42), red
meat intake was positively associated with future risk of weight
gain. For example, an increase in meat intake of 250 g/d would
lead to a 2-kg greater weight gain after 5 y (42).
The strengths of the current study include a large sample size,
the high proportions of follow-up, and repeated assessments of
dietary and lifestyle variables. The consistency of the results
across all 3 cohorts indicates that our findings are unlikely to be
due to chance. The current study was subject to a few limitations
as well. First, our study populations primarily consisted of
working health professionals with European ancestry. Although
the homogeneity of socioeconomic status helps reduce con-
founding, the observed associations may not be generalizable to
other populations. However, in a secondary analysis, we did not
find any significant differences between whites and nonwhites on
all the associations (data not shown). Second, because diet was
assessed by FFQs, some measurement error of meat intake as-
sessment is inevitable. However, the FFQs used in these studies
were validated against multiple diet records, and reasonable
correlation coefficients between these assessments of meat intake
were observed (13–16). Because we used a prospective study
design, any measurement errors of meat intake are independent of
study outcome ascertainment, and, therefore, are more likely to
attenuate the associations toward the null. In a sensitivity anal-
ysis, correction for measurement errors strengthened the asso-
ciations somewhat. 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. Lastly, because of the observational nature of
our cohorts, the observed associations do not necessarily mean
causation; although we adjusted for established and potential risk
factors for T2D, unmeasured and residual confounding is still
possible.
In conclusion, we showed that a greater consumption of un-
processed and processed red meat is consistently associated with
a higher risk of T2D. Compared with red meat, other dietary
components, such as nuts, dairy products, and whole grains, were
associated with lower risks. Therefore, from a public health point
of view, reduction of red meat consumption, particularly pro-
cessed red meat, and replacement of it with other healthy dietary
components, should be considered to decrease T2D risk.
We are indebted to the participants in the Health Professionals Follow-Up
Study and the Nurses’ Health Study I and II for their continuing outstanding
support and to our colleagues working in these studies for their valuable help.
We also thank Rong Chen and Tricia Li for their help with the statistical anal-
ysis and programming. We are grateful to Janine Kroeger for providing un-
published data from the EPIC-Potsdam study.
The authors’ responsibilities were as follows—AP and FBH: designed and
conducted the analysis; JEM, WCW, and FBH: obtained funding; AP, QS,
AMB, MBS, JEM, WCW, and FBH: interpreted the data; AP: wrote the
manuscript; and QS, AMB, MBS, JEM, WCW, and FBH: edited the manu-
script. The sponsors had no role in the study design, data collection and anal-
ysis, decision to publish, or preparation of the manuscript. None of the
authors had a conflict of interest.
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RED MEAT AND DIABETES 9of9
... Many studies investigated the relationship of protein consumption with diabetes risk, but the results are still controversial (3,4). In this regard, several studies claimed that red and processed meat could increase the risk of T2D (5)(6)(7). In comparison, other studies found that diets rich in plant-based protein food could lower the risk of T2D (8,9). ...
... Many investigations have reported the role of impaired glucose and insulin metabolism in the development of T2D, and different protein sources could affect them in disparate ways (10,11). In contrast, the quantity of meat consumption also plays a substantial part in the formation of T2D (4,9); one meta-analysis showed that the consumption of 100 g red meat/day and 50 g processed meat/day increased the risk of T2D for 19 and 51%, respectively (7). This association remains a question regarding low red and white meat consumption among the Iranian population (12). ...
... We determined the effect size for estimating sample size based on previous literature on the relationship between red and processed meat, poultry, fish, eggs, and legumes with diabetes, which was in the range of 1.0-1.51, so the effect size of 1.25 was estimated as a middle of this range (7,8,(17)(18)(19)(20). ...
Article
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Aim This study aimed to evaluate the association of meats and their substitute food group intakes, including nuts, eggs, and legumes, with type 2 diabetes (T2D). Methods For this secondary analysis, we selected eligible adults ( n = 6,112) from the Tehran Lipid and Glucose Study participants with a median follow-up of 6.63 years. Expert nutritionists assessed dietary intakes using a valid and reliable semiquantitative food frequency questionnaire. Biochemical and anthropometric variables were assessed at baseline and follow-up examinations. We used multivariable Cox proportional hazard regression models to estimate the new onset of T2D concerning meats and their substitute food groups. Results We performed this study on 2,749 men and 3,363 women, aged 41.4 ± 14.2 and 39.1 ± 13.1 years, respectively. The number of participants with incident T2D was 549. After adjusting for confounders, legume [HR: 1, 0.74 (0.58–0.94), 0.69 (0.54–0.90), 0.65 (0.50–0.84), P -trend = 0.01)] was inversely associated with incident T2D. Fish intake [HR: 1, 1.0 (0.79–1.27), 1.17 (0.91–1.50), 1.14 (0.89–1.45), P -trend = 0.01)] was positively associated with incident T2D. In subjects who reported poultry consumption of 36.4–72.8 g/day, a positive association [HR: 1.33 (1.03–1.71)] between poultry intake and T2D risk was observed. Conclusion Our findings revealed that a diet rich in legumes significantly reduced the risk of T2D incidence, while a diet high in poultry increased the risk of T2D incidence, probably due to high-temperature cooking methods and environmental contaminants.
... Weight gain was highly correlated with increased energy intake in ultra-processed diet group increasing the risk of obesity and associated diseases including T2D [7]. On the contrary, an updated meta-analysis using US adults by Panet et al. [44] and two other recent studies (9 year prospective cohort study on Chinees adults and a meta-analysis) [45,46], indicated that although the relative risk for T2D is less compared to processed meat, unprocessed red meat consumption still has a substantial risk of getting T2D [44][45][46]. Nevertheless, it is still unclear whether the later risk of T2D is solely related to meat consumption, or it is partly due to other dietary factors such as fat content, salt and total acid load coming from other foods consumed along with unprocessed meat that could contribute to T2D risk. ...
... Weight gain was highly correlated with increased energy intake in ultra-processed diet group increasing the risk of obesity and associated diseases including T2D [7]. On the contrary, an updated meta-analysis using US adults by Panet et al. [44] and two other recent studies (9 year prospective cohort study on Chinees adults and a meta-analysis) [45,46], indicated that although the relative risk for T2D is less compared to processed meat, unprocessed red meat consumption still has a substantial risk of getting T2D [44][45][46]. Nevertheless, it is still unclear whether the later risk of T2D is solely related to meat consumption, or it is partly due to other dietary factors such as fat content, salt and total acid load coming from other foods consumed along with unprocessed meat that could contribute to T2D risk. ...
Article
Full-text available
(1) Consumption of diets that are caloric dense but not nutrient dense have been implicated in metabolic diseases, in part through low-grade metabolic acidosis. Mitigation strategies through dietary intervention to alleviate acidosis have not been previously reported. Our objective is to determine the effects of pH enhancement (with ammonia) in high fat diet-induced obese mice that were fed beef or casein as protein sources compared to low fat diet-fed mice. (2) Methods: B6 male and female mice were randomized (n = 10) into eight diets that differ in protein source, pH enhancement of the protein, and fat content, and fed for 13 weeks: low fat (11% fat) casein (LFC), LF casein pH-enhanced (LFCN), LF lean beef (LFB), LFBN, high fat (46%) casein (HFC), HFCN, HF beef (HFB), and HFBN. Body weights and composition, and glucose tolerance tests were conducted along with terminal serum analyses. Three-way ANOVA was performed. (3) Results: A significant effect of dietary fat (LF vs. HF) was observed across all variables in both sexes (final body weight, fat mass, glucose clearance, and serum leptin). Importantly, pH enhancement significantly reduced adiposity (males only) and final body weights (females only) and significantly improved glucose clearance in both sexes. Lastly, clear sex differences were observed across all variables. (4) Conclusions: Our findings demonstrate metabolic benefits of increasing dietary pH using ammonia, while high fat intake per se (not protein source) is the major contributor to metabolic dysfunctions. Additional research is warranted to determine mechanisms underlying the beneficial effects of pH enhancement, and interactions with dietary fat content and proteins.
... The baseline time of each participant was defined as the date of their first participation, with complete dietary data since 1997, and the individual follow-up person-years of each participant were calculated from the baseline to the date of first diagnosis with T2DM, death, the last wave before departure from the survey, or the end of the last survey at 2015, whichever came first [37,45]. In order to reduce inter-individual variation and capture long-term dietary patterns, cumulative average values of dietary intake, BMI, urbanization index, and physical activity status data were calculated and used in the analysis [46]. ...
... Time-dependent Cox proportional hazard regression was performed to investigate the associations between dietary Cu or Se intakes and the risk of T2DM [45]. The independent variables were quintiles of dietary Cu or Se intakes, and the lowest quintile was used as the reference group to estimate hazard ratios (HRs) and 95% confidence intervals (95% CI). ...
Article
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The long-term associations between dietary copper (Cu) and selenium (Se) intakes and type 2 diabetes mellitus (T2DM) risk are unclear. We aimed to examine the prospective associations between dietary Cu and Se intakes and T2DM risk in Chinese adults. A total of 14,711 adults from the China Health and Nutrition Survey (1997–2015) were included. Nutrient intakes were assessed by 3 consecutive 24 h recalls and food-weighing methods. T2DM was identified by a validated questionnaire and laboratory examination. Cox regression models were used for statistical analysis. A total of 1040 T2DM cases were diagnosed during 147,142 person-years of follow-up. In fully adjusted models, dietary Cu or Se intake was not associated with T2DM risk. Dietary Se intake significantly modified the association between dietary Cu intake and T2DM risk, and dietary Cu intake was positively associated with T2DM risk when Se intake was lower than the median (p-interaction = 0.0292). There were no significant effect modifications on the associations by age, sex, BMI, or region. Although dietary Cu or Se intake was not independently associated with T2DM risk in Chinese adults free from cardiometabolic diseases and cancer at the baseline, there was a significant interaction between dietary Cu and Se intakes on T2DM risk.
... The red meat metabolite score, as a proxy for red meat intake, showed a positive association with T2D risk consistent with results from several large cohort studies that have reported associations of T2D risk with self-reported intake as dietary exposures (3,4,39,40). The score-derived association appeared to be comparable in magnitude with that using 7dDDmeasured meat intake. ...
Article
Background: Self-reported meat consumption is associated with disease risk but objective assessment of different dimensions of this heterogeneous dietary exposure in observational and interventional studies remains challenging. Objectives: We aimed to derive and validate scores based on plasma metabolites for types of meat consumption. For the most predictive score, we aimed to test whether the included metabolites varied with change in meat consumption, and whether the score was associated with incidence of type 2 diabetes (T2D) and other noncommunicable diseases. Methods: We derived scores based on 781 plasma metabolites for red meat, processed meat, and poultry consumption assessed with 7-d food records among 11,432 participants in the EPIC-Norfolk (European Prospective Investigation into Cancer and Nutrition-Norfolk) cohort. The scores were then tested for internal validity in an independent subset (n = 853) of the same cohort. In focused analysis on the red meat metabolite score, we examined whether the metabolites constituting the score were also associated with meat intake in a randomized crossover dietary intervention trial of meat (n = 12, Lyon, France). In the EPIC-Norfolk study, we assessed the association of the red meat metabolite score with T2D incidence (n = 1478) and other health endpoints. Results: The best-performing score was for red meat, comprising 139 metabolites which accounted for 17% of the explained variance of red meat consumption in the validation set. In the intervention, 11 top-ranked metabolites in the red meat metabolite score increased significantly after red meat consumption. In the EPIC-Norfolk study, the red meat metabolite score was associated with T2D incidence (adjusted HR per SD: 1.17; 95% CI: 1.10, 1.24). Conclusions: The red meat metabolite score derived and validated in this study contains metabolites directly derived from meat consumption and is associated with T2D risk. These findings suggest the potential for objective assessment of dietary components and their application for understanding diet-disease associations.The trial in Lyon, France, was registered at clinicaltrials.gov as NCT03354130.
... The DGA as well as guidance from the American Heart Association support consumption of protein from a variety of animal and plant sources, which reflects the preferences of many Americans as assessed in nationwide surveys [1,4]. Americans are, however, encouraged to limit consumption of red meat and in particular processed meat [1], largely based on concerns of chronic disease risk observed in epidemiological studies [5][6][7][8]. Some evidence suggests that adverse effects of meat intake may be attributed to processed meat rather than total meat [9,10], and evidence indicates that effects vary based on the comparator diet [11]. ...
Article
Full-text available
Background Dietary patterns developed by the USDA provide modest levels of protein (14–18% energy) within the Acceptable Macronutrient Distribution Range (AMDR) of 10–35% for adults, though diets providing a higher percentage of energy may be beneficial for some individuals. The purpose of this study was to determine if it is feasible to modify the Healthy U.S.-Style Eating Pattern (“HEP”) to provide a higher percentage of energy from protein. Methods Using the framework implemented by the USDA in developing the HEP, energy from protein was set at 20%, 25%, and 30%. Amounts of protein foods were proportionally increased while amounts of other foods were adjusted iteratively within specified parameters. The models also disaggregated total meat/poultry into fresh and processed forms to develop patterns maintaining current proportions, current levels, reduced, or no processed meat/poultry. Nutrient intakes were compared with nutrient goals for representative U.S. populations with 2,000 kcal needs (females 19–30 years, males 51–70 years), with 90% of the Recommended Dietary Allowance or Adequate Intake regarded as sufficient. Results Dietary patterns with 20% energy from protein were constructed with minor deviations from the current 2,000 kcal HEP. Dietary patterns with 25% energy from protein were constructed for all levels of processed meat/poultry excluding the current proportion model, though relative to the current HEP the constructed patterns reflect substantial reductions in amounts of refined grains and starchy vegetables, and substantial increases in protein foods consumed as beans and peas, seafood, and soy products. It was not possible to develop a pattern with 30% energy from protein without reducing the percentage of energy from carbohydrate below the AMDR or non-compliance with other modeling constraints. Stepwise reductions in processed meat/poultry reduced sodium intake. Conclusions It is feasible to develop dietary patterns in a 2,000 kcal diet while mirroring the HEP that meet recommended intakes of nutrients with 20% or 25% energy from protein, though the pattern with 25% energy from protein may be more idealistic than realistic. Reduced levels of processed meat/poultry may translate to lower sodium intake.
... For instance, the livestock sector is estimated to be responsible for 14.5% of global greenhouse gas emissions, which is more than emissions from fueling all the world's cars, trains, ships, and airplanes combined (Bailey et al., 2014). In terms of the individual, red and processed meat is high in saturated fats and sodium, which are associated with a variety of physical maladies such as obesity (Rouhani et al., 2014), cancer (Chan et al., 2011;Wang et al., 2016), and cardiovascular disease (Bechthold et al., 2019;Pan et al., 2011). As a result, the United States Department of Agriculture and United States Department of Health and Human Services (2020) recommends that we consider consuming more plant-based proteins, and that any intake of animal-based foods should be primarily in lean forms. ...
Article
The production of meat and its consumption are associated with negative consequences for the environment, the animals raised and slaughtered for food, and the health of those who consume animal-based foods. We investigated whether video appeals that addressed these topics affected participants' wanting of meat and intentions to reduce meat relative to a control video. Results indicated only the environmental video led to increased intentions to reduce meat relative to controls. Nevertheless, implicit wanting of meat was lower in all three experimental conditions compared to the control condition. Additionally, moral emotions and agreement with the video's message mediated the relationships between condition and implicit wanting and intentions for the animal welfare and environment conditions. For the health condition, only agreement with the message served as a mediator. These results suggest that although animal welfare-, environmental-, and health-focused video appeals may be effective at shifting immediate desire to consume meat, environmental video appeals may be the most effective for increasing intentions to change future meat intake.
... Our study also demonstrates that diet rich in processed meat is the second most important risk factor for type 2 diabetes-related deaths and DALYs in HICs. Meta-analysis studies have consistently shown that diet rich in processed meat is associated with an increased risk of type 2 diabetes in HICs [46,47]. Over the past decade, despite some HICs having witnessed declining trends in the consumption of red and processed meat, consumption in these countries remains higher than in other income countries [48]. ...
Article
Full-text available
Aims/hypothesis The study aims to quantify the global trend of the disease burden of type 2 diabetes caused by various risks factors by country income tiers. Methods Data on type 2 diabetes, including mortality and disability-adjusted life years (DALYs) during 1990–2019, were obtained from the Global Burden of Disease Study 2019. We analysed mortality and DALY rates and the population attributable fraction (PAF) in various risk factors of type 2 diabetes by country income tiers. Results Globally, the age-standardised death rate (ASDR) attributable to type 2 diabetes increased from 16.7 (15.7, 17.5)/100,000 person-years in 1990 to 18.5 (17.2, 19.7)/100,000 person-years in 2019. Similarly, age-standardised DALY rates increased from 628.3 (537.2, 730.9)/100,000 person-years to 801.5 (670.6, 954.4)/100,000 person-years during 1990–2019. Lower-middle-income countries reported the largest increase in the average annual growth of ASDR (1.3%) and an age-standardised DALY rate (1.6%) of type 2 diabetes. The key PAF attributing to type 2 diabetes deaths/DALYs was high BMI in countries of all income tiers. With the exception of BMI, while in low- and lower-middle-income countries, risk factors attributable to type 2 diabetes-related deaths and DALYs are mostly environment-related, the risk factors in high-income countries are mostly lifestyle-related. Conclusions/interpretation Type 2 diabetes disease burden increased globally, but low- and middle-income countries showed the highest growth rate. A high BMI level remained the key contributing factor in all income tiers, but environmental and lifestyle-related factors contributed differently across income tiers. Data availability To download the data used in these analyses, please visit the Global Health Data Exchange at http://ghdx.healthdata.org/gbd-2019 . Graphical abstract
... In particular, dietary risk factors may be significant; for example recent analyses suggest that high consumption of red meat might increase risk of T2DM by as much as 30%. 39 Trends in dietary risk factors are difficult to model, requiring repeated high quality dietary data and not available in Turkey. Our model intended to capture the contributions of the most significant modifiable risk factors that are associated with the most powerful increases in RR (such as obesity, which increases the risk of T2DM by 4-8 times depending on age and sex), and those that are easiest to measure from routinely available, serial data sources (such as smoking prevalence). ...
Article
Full-text available
Background: Using a previously developed and validated mathematical model, we predicted future prevalence of type 2 diabetes mellitus (T2DM) and major modifiable risk factors (obesity, physical inactivity and smoking) stratified by age and sex in Turkey up to the year 2050. Methods: Our deterministic compartmental model fitted nationally representative demographic and risk factor data simultaneously for Turkish adults (aged 20-79) between 1997 and 2017, then estimated future trends. Our novel approach explored the impact of future obesity trends on these projections, specifically modelling (1) a gradual fall in obesity in women after the year 2020 until it equalled the age-specific levels seen in men and (2) cessation of the rise in obesity after 2020. Results: T2DM prevalence is projected to rise from an estimated 14.0% (95% uncertainty interval (UI) 12.8% to 16.0%) in 2020 to 18.4% (95% UI 16.9% to 20.9%) by 2050; 19.7% in women and 17.2% in men by 2050; reflecting high levels of obesity (39.7% for women and 22.0% for men in 2050). Overall, T2DM prevalence could be reduced by about 4% if obesity stopped rising after 2020 or by 12% (22% in women) if obesity prevalence among women could be lowered to equal that of men. The higher age-specific obesity prevalence among women resulted in 2 076 040 additional women developing T2DM by the year 2050. Conclusion: T2DM is common in Turkey and will remain so. Interventions and policies targeting the high burden of obesity (and low physical activity levels), particularly in women, could significantly impact future disease burdens.
Article
We investigated the effect of health and environmental information messages on purchases of meat and plant‐based alternatives in a non‐hypothetical online supermarket experiment. When controlling for observables, we find the health information nudge to be effective at motivating meat eaters to purchase plant‐based meat alternatives. This effect is absent when providing environmental information or its combination with health information. We also find that meat eaters implicitly perceive meat to be healthier but environmentally unsustainable compared to plant‐based alternatives. Our findings provide insights as to how to steer consumers towards meat alternative purchases under different information types in an online supermarket.
Article
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Introduction The United States has among the highest per capita red meat consumption in the world. Reducing red meat consumption is crucial for minimizing the environmental impact of diets and improving health outcomes. Warning messages are effective for reducing purchases of products like sugary beverages but have not been developed for red meat. This study developed health and environmental warning messages about red meat and explored participants’ reactions to these messages. Methods A national convenience sample of US red meat consumers ( n = 1,199; mean age 45 years) completed an online survey in 2020 for this exploratory study. Participants were randomized to view a series of either health or environmental warning messages (between-subjects factor) about the risks associated with eating red meat. Messages were presented in random order (within-subjects factor; 8 health messages or 10 environmental messages). Participants rated each warning message on a validated 3-item scale measuring perceived message effectiveness (PME), ranging from 1 (low) to 5 (high). Participants then rated their intentions to reduce their red meat consumption in the next 7 days. Results Health warning messages elicited higher PME ratings than environmental messages (mean 2.66 vs. 2.26, p <0.001). Health warning messages also led to stronger intentions to reduce red meat consumption compared to environmental messages (mean 2.45 vs. 2.19, p< 0.001). Within category (health and environmental), most pairwise comparisons of harms were not statistically significant. Conclusions Health warning messages were perceived to be more effective than environmental warning messages. Future studies should measure the impact of these messages on behavioral outcomes.
Article
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Dairy product intake may be inversely associated with risk of type 2 diabetes, but the evidence is inconclusive for total dairy products and sparse for types of dairy products. The objective was to investigate the prospective association of total dairy products and different dairy subtypes with incidence of diabetes in populations with marked variation of intake of these food groups. A nested case-cohort within 8 European countries of the European Prospective Investigation into Cancer and Nutrition Study (n = 340,234; 3.99 million person-years of follow-up) included a random subcohort (n = 16,835) and incident diabetes cases (n = 12,403). Baseline dairy product intake was assessed by using dietary questionnaires. Country-specific Prentice-weighted Cox regression HRs were calculated and pooled by using a random-effects meta-analysis. Intake of total dairy products was not associated with diabetes (HR for the comparison of the highest with the lowest quintile of total dairy products: 1.01; 95% CI: 0.83, 1.34; P-trend = 0.92) in an analysis adjusted for age, sex, BMI, diabetes risk factors, education, and dietary factors. Of the dairy subtypes, cheese intake tended to have an inverse association with diabetes (HR: 0.88; 95% CI: 0.76, 1.02; P-trend = 0.01), and a higher combined intake of fermented dairy products (cheese, yogurt, and thick fermented milk) was inversely associated with diabetes (HR: 0.88; 95% CI: 0.78, 0.99; P-trend = 0.02) in adjusted analyses that compared extreme quintiles. This large prospective study found no association between total dairy product intake and diabetes risk. An inverse association of cheese intake and combined fermented dairy product intake with diabetes is suggested, which merits further study.
Article
The authors compared the ability of the National Death Index and the Equifax Nationwide Death Search to ascertain deaths of participants in the Nurses' Health Study. Each service was sent information on 197 participants aged 60–68 years in 1989 whose deaths were reported by kin or postal authorities and 1,997 participants of the same age who were known to be alive. Neither service was aware of the authors' information regarding participants' vital status. The sensitivity of the National Death Index was 98 percent and that of Equifax was 79 percent. Sensitivity was similar for women aged 65–68 years; however, for women aged 61–64 years, the sensitivity of the National Death Index was 97.7 percent compared with 60.2 percent for Equifax. The specificity of both services was approximately 100 percent. The contrast between the sources of these databases and the matching algorithms they employ has implications for researchers and for those planning health data systems.
Article
Principal results: We identified 1972 incident cases of type 2 DM during a total of 326,581 person-years of follow up. Intake of unprocessed meat, particularly poultry, was associated with a decrease in the risk of type 2 DM in this cohort. The fully adjusted relative risks (RRs) for quintiles of total unprocessed meat intake were 1.00, 0.78, 0.83, 0.74, and 0.83 (P for trend: <0.01). When the joint effect between meat intake and BMI categories was evaluated, high intake of total unprocessed meat appeared to be associated with an increased risk of type 2 DM among obese women but a reduced risk among lean women (P value for the interaction tests = 0.05). Processed meat consumption was positively associated with the risk of type 2 DM. The adjusted RR was 1.15 (95% 1.01-1.32) in women consuming processed meats compared to those who did not consume processed meats (P=0.04). Conclusions: Processed meat intake was positively associated with the risk of type 2 DM. There was an indication that the effect of unprocessed meat intake on type 2 DM may be modified by BMI.
Article
Context Nuts are high in unsaturated (polyunsaturated and monounsaturated) fat and other nutrients that may improve glucose and insulin homeostasis.Objective To examine prospectively the relationship between nut consumption and risk of type 2 diabetes.Design, Setting, and Participants Prospective cohort study of 83 818 women from 11 states in the Nurses' Health Study. The women were aged 34 to 59 years, had no history of diabetes, cardiovascular disease, or cancer, completed a validated dietary questionnaire at baseline in 1980, and were followed up for 16 years.Main Outcome Measure Incident cases of type 2 diabetes.Results We documented 3206 new cases of type 2 diabetes. Nut consumption was inversely associated with risk of type 2 diabetes after adjustment for age, body mass index (BMI), family history of diabetes, physical activity, smoking, alcohol use, and total energy intake. The multivariate relative risks (RRs) across categories of nut consumption (never/almost never, <once/week, 1-4 times/week, and ≥5 times/week) for a 28-g (1 oz) serving size were 1.0, 0.92 (95% confidence interval [CI], 0.85-1.00), 0.84 (0.95% CI, 0.76-0.93), and 0.73 (95% CI, 0.60-0.89) (P for trend <.001). Further adjustment for intakes of dietary fats, cereal fiber, and other dietary factors did not appreciably change the results. The inverse association persisted within strata defined by levels of BMI, smoking, alcohol use, and other diabetes risk factors. Consumption of peanut butter was also inversely associated with type 2 diabetes. The multivariate RR was 0.79 (95% CI, 0.68-0.91; P for trend <.001) in women consuming peanut butter 5 times or more a week (equivalent to ≥140 g [5 oz] of peanuts/week) compared with those who never/almost never ate peanut butter.Conclusions Our findings suggest potential benefits of higher nut and peanut butter consumption in lowering risk of type 2 diabetes in women. To avoid increasing caloric intake, regular nut consumption can be recommended as a replacement for consumption of refined grain products or red or processed meats.
Article
The aim of this study was to investigate the association between processed and other meat intake and incidence of Type 2 diabetes in a large cohort of women. Incident cases of Type 2 diabetes were identified during 8 years of follow-up in a prospective cohort study of 91246 U.S. women aged 26 to 46 years and being free of diabetes and other major chronic diseases at baseline in 1991. We identified 741 incident cases of confirmed Type 2 diabetes during 716276 person-years of follow-up. The relative risk adjusted for potential non-dietary confounders was 1.91 (95% CI: 1.42-2.57) in women consuming processed meat five times or more a week compared with those consuming processed meat less than once a week ( p<0.001 for trend). Further adjustment for intakes of magnesium, cereal fibre, glycaemic index, and caffeine or for a Western dietary pattern did not appreciably change the results and associations remained strong after further adjustment for fatty acid and cholesterol intake. Frequent consumption of bacon, hot dogs, and sausage was each associated with an increased risk of diabetes. While total red meat (beef or lamb as main dish, pork as main dish, hamburger, beef, pork or lamb as sandwich or mixed dish) intake was associated with an increased risk of diabetes, this association was attenuated after adjustment for magnesium, cereal fiber, glycaemic index, and caffeine (relative risk: 1.44; 95% CI: 0.92-2.24). Our data suggest that diets high in processed meats could increase the risk for developing Type 2 diabetes.
Article
Background and aim: A high intake of dairy has been linked to lower risk of type 2 diabetes (T2D). The relationship between dairy intake and glucose metabolism is still not well understood. The aim of this study was to investigate the relation between the intake of total dairy and dairy subgroups and T2D and measures of glucose metabolism. Methods and results: A total of 5953 Danish men and women aged 30-60 years without baseline diabetes or cardiovascular diseases were included in this prospective analysis. The dairy intake at baseline was categorised into low-fat dairy, full-fat dairy, milk and milk products, cheese and fermented dairy. Fasting plasma glucose (FPG), 2-h plasma glucose (2hPG), HbA1c, insulin resistance (HOMA2-IR) and beta-cell function (HOMA2-B) were considered at 5-year follow-up. In the maximally-adjusted model (demographics, lifestyle factors, dietary factors and waist), cheese intake was inversely associated with 2hPG (β = -0.048, 95% CI -0.095; -0.001). Fermented dairy intake was inversely associated with FPG (β = -0.028, 95% CI -0.048; -0.008) and HbA1c (β = -0.016, 95% CI -0.030; -0.001). Total dairy intake and the dairy subgroups were not related to HOMA-IR and HOMA-B in the maximally-adjusted model. Furthermore, there was no significant association between intake of total dairy or any of the dairy subgroups and incidence of T2D. Conclusion: Our data suggest a modest beneficial effect of cheese and fermented dairy on glucose regulation measures; however, this did not translate into a significant association with incident T2D.