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Original Investigation |

Low-Fat Dietary Pattern and Risk of Treated Diabetes Mellitus in Postmenopausal Women:  The Women's Health Initiative Randomized Controlled Dietary Modification Trial FREE

Lesley F. Tinker, PhD; Denise E. Bonds, MD, MPH; Karen L. Margolis, MD, MPH; JoAnn E. Manson, MD, DrPH; Barbara V. Howard, PhD; Joseph Larson, MS; Michael G. Perri, PhD; Shirley A. A. Beresford, PhD; Jennifer G. Robinson, MD, MPH; Beatriz Rodríguez, MD, PhD; Monika M. Safford, MD; Nanette K. Wenger, MD; Victor J. Stevens, PhD; Linda M. Parker, DSc
[+] Author Affiliations

Author Affiliations: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (Dr Tinker and Mr Larson); Division of Public Health Sciences and Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina (Dr Bonds); Department of Medicine, University of Minnesota, Minneapolis (Dr Margolis); Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts (Dr Manson); Department of Field Studies, MedStar Research Institute, Washington, DC (Dr Howard); Department of Clinical and Health Psychology, University of Florida, Gainesville (Dr Perri); Department of Epidemiology, University of Washington, Seattle (Dr Beresford); Departments of Epidemiology and Medicine, University of Iowa, Iowa City (Dr Robinson); Department of Geriatric Medicine, University of Hawaii, Honolulu (Dr Rodríguez); Department of Medicine, University of Alabama at Birmingham and Birmingham Veterans Affairs Medical Center (Dr Safford); Department of Medicine, Emory University School of Medicine, Atlanta, Georgia (Dr Wenger); Science Programs Department, Kaiser Permanente Center for Health Research, Portland, Oregon (Dr Stevens); and School of Nursing and Health Sciences, University of Miami, Miami, Florida (Dr Parker). Dr Bonds is now with the Departments of Public Health Sciences and Medicine, University of Virginia, Charlottesville; Dr Margolis is now with HealthPartners Research Foundation, Minneapolis.


Arch Intern Med. 2008;168(14):1500-1511. doi:10.1001/archinte.168.14.1500.
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Background  Decreased fat intake with weight loss and increased exercise may reduce the risk of diabetes mellitus in persons with impaired glucose tolerance. This study was undertaken to assess the effects of a low-fat dietary pattern on incidence of treated diabetes among generally healthy postmenopausal women.

Methods  A randomized controlled trial was conducted at 40 US clinical centers from 1993 to 2005, including 48 835 postmenopausal women aged 50 to 79 years. Women were randomly assigned to a usual-diet comparison group (n = 29 294 [60.0%]) or an intervention group with a 20% low-fat dietary pattern with increased vegetables, fruits, and grains (n = 19 541 [40.0%]). Self-reported incident diabetes treated with oral agents or insulin was assessed.

Results  Incident treated diabetes was reported by 1303 intervention participants (7.1%) and 2039 comparison participants (7.4%) (hazard ratio, 0.96; 95% confidence interval, 0.90-1.03; P = .25). Weight loss occurred in the intervention group, with a difference between intervention and comparison groups of 1.9 kg after 7.5 years (P < .001). Subgroup analysis suggested that greater decreases in percentage of energy from total fat reduced diabetes risk (P for trend = .04), which was not statistically significant after adjusting for weight loss.

Conclusions  A low-fat dietary pattern among generally healthy postmenopausal women showed no evidence of reducing diabetes risk after 8.1 years. Trends toward reduced incidence were greater with greater decreases in total fat intake and weight loss. Weight loss, rather than macronutrient composition, may be the dominant predictor of reduced risk of diabetes.

Trial Registration  clinicaltrials.gov Identifier: NCT00000611

Figures in this Article

The incidence and burden of diabetes continue to rise, particularly in developed countries. The prevalence among US adults 20 years and older is expected to increase to 11.2% by the year 2030.1 In women, the prevalence of diabetes rises sharply with each decade of age after menopause and surpasses that in men after the seventh decade of life.2,3 Women with diabetes who are 50 years and older live fewer years than women without diabetes,4 and comorbidity with cardiovascular disease is common.4,5 A further toll is extracted in health care costs6 and decreased work productivity.7

Lifestyle interventions of weight loss and increased physical activity coupled with a low-fat dietary pattern have been shown to reduce the risk of diabetes mellitus in persons with impaired glucose tolerance.811 When investigated alone, an ad libitum low-fat dietary pattern was found to improve the glucose profile among persons with impaired glucose tolerance; however, the intervention group also lost weight and adopted a higher physical activity level than did the control group.12 Even without changes in physical activity, adoption of an ad libitum low-fat dietary pattern often results in decreased energy intake,1316 which may be beneficial in the prevention of diabetes. The low-fat diet results are encouraging because lowering the proportion of energy from fat typically increases the proportion of energy from carbohydrate and the glycemic load, which may increase the risk of developing diabetes.1719

The Women's Health Initiative (WHI) Dietary Modification Trial (DMT) was a randomized controlled trial designed to measure the effect of an ad libitum low-fat diet on the incidence of diabetes. The objectives of this report are to address the hypothesis that an ad libitum low-fat dietary pattern alone, without goals of exercise or weight loss, reduces the incidence of diabetes in generally healthy postmenopausal women.

STUDY POPULATION

The design and baseline description of the WHI DMT and recruitment methods have been published.2022 Briefly, 48 835 generally healthy postmenopausal women aged 50 to 79 years were recruited and enrolled at 40 clinical centers across the United States. Major exclusion criteria included having a history of breast cancer or colorectal cancer, any cancer within the previous 10 years except nonmelanoma skin cancer, medical conditions with a predicted survival of less than 3 years, adherence or retention concerns, current dietary intake of less than 32% of energy from fat, and type 1 diabetes mellitus. Mean follow-up time for the WHI DMT was 8.1 years, and the trial progressed to its planned conclusion date of March 31, 2005. Approximately 14% of participants in the WHI DMT also participated in 1 of the WHI hormone trials,23,24 and nearly 52% participated in the WHI calcium and vitamin D trial.25 The WHI protocol and consent forms were approved by the institutional review board for each participating institution and the clinical coordinating center. All participants provided informed consent.

RANDOMIZATION, BLINDING, AND INTERVENTION

A randomized permuted block algorithm, stratified by clinical center site and age (50-54, 55-59, 60-69, or 70-79 years) was used, with participants randomized to either a low-fat dietary pattern intervention group (40.0%) or to a usual-diet comparison group (60.0%).26 The WHI DMT dietary intervention group received intensive nutritional and behavioral modification training consisting of 18 group sessions in the first year followed by quarterly sessions throughout the trial. Each participant received an individualized dietary fat gram goal estimating 20% of energy from fat during the intervention and a common dietary goal of 5 or more servings daily of combined vegetables and fruits and 6 or more servings daily of grains. Self-monitoring techniques and group session attendance were emphasized. The diet was not intended to promote reduced energy intake, and there was neither a weight loss goal nor a physical activity goal. Participants in the usual-diet comparison group were given a copy of the Dietary Guidelines for Americans27,28 but were not asked to make dietary changes. Details of the intervention program have been published.29

DATA COLLECTION, FOLLOW-UP, AND OUTCOMES ASCERTAINMENT

Potential participants for the WHI DMT completed 3 prerandomization screening visits during which baseline information was obtained.26 Follow-up for clinical events was scheduled to occur every 6 months. Participants were instructed to bring all current prescription and nonprescription medications in their original containers, and medication was documented in a pharmacy database (Master Drug Database; Medi-Span, Indianapolis, Indiana).

Dietary intake was monitored primarily by a food frequency questionnaire (FFQ) designed for the WHI.30 The FFQ was administered to all DMT participants during screening (considered the baseline measure), 1 year after randomization, and thereafter annually to one-third of the participants on a rotating basis. The methods for assigning the glycemic index and glycemic load values used in the WHI FFQ have been reported elsewhere.31

All participants had blood drawn after an overnight 8-hour fast at baseline and at the first annual visit.26 A total of 2816 (5.8%) of the DMT participants had serum analyzed at baseline and years 1, 3, and 6 for diet-related biomarkers including fasting glucose and insulin levels. Insulin resistance was calculated from fasting glucose and insulin according to the homeostasis model assessment, insulin resistance (HOMA-IR) model: insulin/{22.5 × exp[−ln(glucose/18)]}, where the units for insulin are microunits per milliliter and the units for glucose are millimoles per liter.32

Diabetes at baseline was documented by self-report by asking each woman if she had ever been told by a physician that she had “sugar diabetes” when not pregnant. Participants responding positively were considered to have prevalent diabetes and were removed from the analyses. During the semiannual follow-up contacts, documentation was limited to treated diabetes when participants were asked, “Since the date given on the front of this form, has a doctor prescribed any of the following pills or treatments?” Choices included “pills for diabetes” and “insulin shots for diabetes.” It may be assumed that participants were regularly seen by a health care provider because 92.9% reported at baseline having a regular health care provider, and 79.1% of those participants reported having seen their provider within the past year. During the average 8.1 years of follow-up, participants reported having had an average of 8.5 (comparison group) or 8.4 (intervention group) physical examinations. Fasting blood glucose levels were not used to identify diabetes because only 5.8% of WHI DMT participants had blood samples analyzed. Self-report of treated diabetes among the postmenopausal women of the WHI DMT was likely type 2 diabetes mellitus, although diagnosis was not confirmed by medical record audit. Data from a WHI diabetes confirmation study showed that, in the WHI DMT, 80% of participants with prevalent self-reported treated diabetes brought an oral agent or insulin to the baseline drug inventory, and 77% with incident self-reported treated diabetes during the first year of the trial brought a diabetes medication to the year 1 drug inventory. Furthermore, among the 525 WHI participants who had a fasting blood glucose measurement at baseline and self-reported ever having been told by a physician they had “sugar diabetes,” 73% of the WHI DMT participants had a fasting glucose level greater than 125 mg/dL (to convert glucose to millimoles per liter, multiply by 0.0555).33

STATISTICAL ANALYSIS

Descriptive statistics for baseline demographic and health information, laboratory measures related to diabetes, and dietary intakes were computed and compared by randomization assignment. Means and standard deviations were computed for dietary FFQ data and diabetes risk factor data at baseline, year 1, and year 6. The 6-year time point for dietary data included years 5, 6, and 7 to provide a sampling from all active WHI DMT participants. Differences between intervention and comparison for baseline, year 1, and year 6 were computed and tested for significance at P < .001 by means of a 2-sample t test or appropriate nonparametric techniques. Multiple comparisons were tested by Bonferroni t tests or similar tests. Bivariate comparisons of categorical variables were performed with the χ2 test or Fisher exact test as appropriate.

Primary outcome comparisons of treated diabetes were analyzed as time-to-event hazard ratios and 95% confidence intervals from Cox proportional hazards analyses. Outcomes were those available from the September 2005 database. Significance was analyzed by log-rank tests. Analyses were stratified by, and thus adjusted for, the following: (1) randomization assignment, (2) age at baseline (50-59, 60-69, or 70-79 years), (3) randomization to the Hormone Therapy Trial (not randomized, randomized to active estrogen alone or placebo for women without a uterus, or randomized to active estrogen plus progestin or placebo for women with a uterus). A Kaplan-Meier curve was drawn to depict cumulative hazard estimates over time. Annualized rates of incident diabetes were assessed for absolute disease rate comparisons.

A sensitivity (adherence) analysis was done by inverse probability weighting25,34 for session attendance for DMT intervention participants plus clinic visit and additional factors related to adherence, such as WHI hormone trials randomization, demographics, lifestyle habits, and psychosocial factors, for all participants. Because participants in the DMT intervention group lost a modest amount of weight compared with the DMT comparison group (a difference of 1.9 kg over 7.5 years; P < .001),16 a proportional hazards model with weight change as a time-dependent covariate was used to explore possible effects of postrandomization weight change on diabetes risk.

Subgroup analyses for interactions of randomization assignment with the risk of developing treated diabetes were assessed for baseline demographics, health history, dietary factors, and diabetes risk factors. Additional subgroup analyses compared the risk of developing diabetes with dietary intakes at year 1 of percentage of energy from fat and number of servings of vegetable, fruit, and grain, which were the 3 DMT dietary intervention goals. Dietary intakes at year 1 were chosen to examine the potential interactions in relation to the year when maximum change in dietary intake had been reported by the intervention group. Quartiles of intakes reported by the DMT intervention group were compared with the overall intake reported by the comparison group.

The year 1 subgroup data were analyzed as 1 unadjusted model and 2 adjusted models. Of the 2 adjusted models, one adjusted for baseline factors of age, race/ethnicity, education, smoking status, alcohol use, physical activity, baseline body mass index, functional and psychosocial factors influencing adherence (physical functioning, mental health, optimism, hostility, negative emotional expressiveness),35,36 and dietary intake for the pertinent dietary variable. For example, year 1 quartiles of percentage of energy from total fat were adjusted for baseline intake of percentage of energy from total fat. The other model adjusted for the baseline factors listed for the first model plus change in body weight from baseline to year 1. Diabetes cases reported before year 1 were excluded from the analyses to decrease the possibility of undiagnosed diabetes at baseline. Participants with missing data for any of the adjustment factors were excluded from the 3 year 1 subgroup analyses. Hazard ratios and P values for interaction were computed for the subgroup analyses.

Among the 48 835 WHI DMT participants, 45 887 are represented in these analyses (see Figure 1 for participant flow). Excluded were the 2948 participants (6.0%) who self-reported prevalent diabetes at baseline. Blood analyses were available for 2577 of the participants (5.6%). Participants with prevalent diabetes were older, heavier (higher body mass index), and less physically active and had higher blood pressure than participants not reporting diabetes. Baseline dietary intakes for participants with diabetes were higher for total fat, combined vegetables and fruits, grains, and fiber, whereas intake was lower for saturated fat.

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Figure 1.

Participant flow in the Women's Health Initiative Dietary Modification Trial.

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On average, there were no significant differences (P < .05) in baseline characteristics between the intervention and comparison groups of participants without prevalent diabetes (Table 1). There was a borderline statistically significant difference in systolic blood pressure of 0.3 mm Hg between groups, which is not considered clinically different. Within tertiles of serum glucose level among the 5.8% subsample analyzed, slightly more of the intervention participants had blood glucose levels greater than 98 mg/dL, although mean glucose level did not differ between the intervention and comparison groups. Participants were 62 years of age on average; most were white, not randomized to the hormone therapy trials, not exposed to exogenous estrogen or estrogen plus progestin, and nonsmokers or past smokers.

Table Graphic Jump LocationTable 1. Baseline Demographics of 45 887 Participants Without Prevalent Diabetesa in the WHI Dietary Modification Trial

As shown in Table 2, at baseline, the mean percentage of energy from total fat was 37.7% (SD, 5.0%). No differences in dietary intake at baseline were seen between the intervention and comparison groups. At year 1, the intervention group reported lower total dietary fat as a percentage of energy and higher intakes of vegetables, fruits, grains, and dietary glycemic index and glycemic load than did the comparison group. The differences between intervention and comparison groups at year 6 were somewhat smaller than those at year 1, but they remained significant, except dietary glycemic index at year 6.

Table Graphic Jump LocationTable 2. Dietary Intake Estimates at Baseline and Follow-up of 45 887 Participants Without Prevalent Diabetesa in the WHI Dietary Modification Trial

Table 3 shows that, on average, participants at baseline were overweight and had systolic blood pressure in the prehypertensive range, although blood glucose levels were in the normal range with no differences between groups. One year after randomization, weight, body mass index, waist circumference, systolic and diastolic blood pressure, and serum glucose level were lower in the intervention group (P < .001). Six years after randomization, weight and body mass index were lower in the intervention group (P < .001).

Table Graphic Jump LocationTable 3. Diabetes Risk Factors at Baseline and Follow-up of 45 887a Participants Without Prevalent Diabetesb in the WHI Dietary Modification Trial

Overall, 1303 participants (7.1%; annual incidence, 0.88%) in the WHI DMT intervention group and 2039 participants (7.4%; annual incidence, 0.91%) in the comparison group self-reported a new (incident) diagnosis of treated diabetes during follow-up. The estimated hazard ratio was 0.96 (95% confidence interval, 0.90-1.03; Figure 2), with essentially the same results if only treated diabetes was excluded at baseline (data not shown). The adherence sensitivity analysis, adjusting for participation in the intervention and DMT protocol, resulted in a hazard ratio of 0.93 (95% confidence interval, 0.81-1.07). The time-dependent weight change proportional hazards model resulted in a hazard ratio of 0.96 (95% confidence interval, 0.89-1.02), nearly identical to the primary hazard ratio.

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Figure 2.

Kaplan-Meier estimates of the cumulative hazard for treated diabetes in the Women's Health Initiative Dietary Modification Trial. CI indicates confidence interval; HR, hazard ratio.

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In baseline subgroup analyses, there were no significant interactions between randomization assignment and dietary intakes (Figure 3). A statistically significant interaction was seen among tertiles of serum insulin levels and HOMA-IR, but no one tertile differed significantly from unity (Figure 4).

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Figure 3.

Treated diabetes hazard ratios and annualized incidence by baseline dietary factors in the Women's Health Initiative Dietary Modification Trial. Limit lines indicate confidence interval.

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Figure 4.

Treated diabetes hazard ratios and annualized incidence by baseline diabetes risk factors in the Women's Health Initiative Dietary Modification Trial. Limit lines indicate confidence interval. BMI indicates body mass index (calculated as weight in kilograms divided by height in meters squared); E, estrogen; HOMA-IR, homeostasis model assessment, insulin resistance; HT, hormone therapy; MET, metabolic equivalent; and P, progestin. To convert glucose to millimoles per liter, multiply by 0.0555; insulin to picomoles per liter, multiply by 6.945.

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For the year 1 subgroup analyses, data from an additional 12 767 participants were excluded because of missing data (n = 10 535) or diabetes reported between baseline and year 1 (n = 2232). Adjusted subgroup analyses between year 1 dietary factors and randomization assignment showed a statistically significant effect for percentage of energy from total fat (P = .04). After additional adjustment for weight change between baseline and year 1, an effect of percentage of energy from dietary fat on incidence of treated diabetes was no longer statistically significant (Table 4). On adjustment for baseline factors and weight change, no apparent effect was seen for changes in intake of vegetables, fruits, or grains.

Table Graphic Jump LocationTable 4. Diabetes Hazard Ratios by Year 1 Dietary Factors of the WHI Dietary Modification Trial Intervention Goalsa

Lifestyle interventions combining a low-fat dietary pattern, weight loss, and exercise have been found to lower risk of developing type 2 diabetes mellitus.811 In the WHI DMT, implemented for an average of 8.1 years among generally healthy postmenopausal women, intervention with a low-fat dietary pattern alone did not result in a statistically significant reduction in treated diabetes incidence compared with participants consuming their usual diet. Neither, however, did the low-fat dietary pattern result in an increased risk of diabetes.

A number of reasons may account for the differences between our findings and those of previous trials. The participants in the Diabetes Prevention Program (DPP),10 the Finnish Diabetes Prevention Study (DPS),8,9 and the Da Qing Diabetes IGT [Impaired Glucose Tolerance] and Diabetes Study11 were at higher risk of developing diabetes at baseline when compared with the participants in the WHI DMT. However, most important, the DPS, DPP, and Da Qing studies included physical activity and weight loss as primary objectives, whereas the goals of the WHI DMT were to analyze the effects of changes in diet composition. In the DPP, weight loss was the strongest predictor of diabetes risk reduction, although physical activity, even without weight loss, also predicted risk reduction.37 The effect of a low-fat dietary pattern on diabetes risk reduction in the DPP and Finnish DPS was no longer statistically significant after adjusting for weight loss and physical activity.37,38

In the WHI DMT, the ad libitum low-fat dietary pattern assigned to the intervention group was intended to be body weight neutral, although participants in the intervention group reported consuming less energy and lost a modest amount of weight (a mean of 2.2 kg [2.8%]) after the first year of the study,16 which is often experienced with ad libitum low-fat dietary patterns.13,15,39 After 7 years of follow-up, participants in the WHI DMT intervention group maintained a mean weight loss of 0.8 kg from baseline, whereas there was an average of a 0.1-kg weight loss in the comparison group.16 By contrast to the WHI DMT, the average weight reductions in the lifestyle intervention groups of the diabetes studies were 5.6 kg in the DPP,10 4.2 kg in the DPS,8 and 3.3 kg in the Da Qing study.11

Dietary total fat, on its own, has overall shown little evidence of being a risk factor for diabetes. Although research by Marshall and colleagues40 suggested that dietary total fat was a risk factor for developing diabetes, much of the prospective research since then has not corroborated such an association.4144 The findings from the WHI are consistent with the prospective studies in not showing an effect of dietary total fat alone.

The impact of early dietary changes can be partially examined through subgroup analyses based on dietary change at year 1. While the statistically significant interaction (P = .04) between intervention and comparison groups after adjustment for baseline factors suggested a reduced risk of diabetes with greater fat reduction, the significance disappeared on further adjustment for weight change. This suggests that weight loss accounts for the protective effect. Thus, in a generally healthy population of postmenopausal women, the role of an ad libitum low-fat dietary pattern in diabetes prevention may be as a mediator of weight loss. Subgroup analyses showed a statistically significant interaction among tertiles of baseline serum insulin level and HOMA-IR, suggesting the possibility of differential effects of a low-fat dietary pattern. However, this should be interpreted cautiously because of the number of subgroup analyses and the small number of cases in the lowest tertile of insulin level and HOMA-IR.

Vegetable and fruit intake in the WHI DMT did not reduce the risk of diabetes after adjustment for baseline factors and weight change. These results are consistent with observational research from the Iowa Women's Health Study and the Nurses' Health Study.45,46 However, among overweight women from the Nurses' Health Study, higher intakes of vegetables and fruits were associated with reduced risk of diabetes. Results from the Finnish Mobile Clinic Health Examination Survey indicated that higher intakes of green vegetables, fruits, and berries were associated with reduced risk of diabetes.47 In the US National Health and Nutrition Examination Survey, a protective effect of vegetables and fruits was seen, although the effect was ameliorated when education level was considered.48

Some studies have shown that a higher dietary glycemic index and glycemic load may predict elevated risk of diabetes.1719 However, other studies have not shown an effect of glycemic index or glycemic load on diabetes incidence.42,49,50 In the WHI DMT, the glycemic index did not differ to a biologically significant degree between the DMT comparison and intervention groups at baseline, year 1, or year 6. Dietary glycemic load was higher in the intervention group at years 1 and 6 but was not found to be associated with risk of diabetes.

Limitations of our study warrant consideration. In the WHI DMT, incident diabetes was limited to self-report of diabetes treated by insulin or oral agents, although the medication inventory agreement with self-report was high33 and the number of physical examinations suggests that health was routinely being monitored. Second, dietary intake was captured by self-report and may present a source of bias.51 Third, the WHI FFQ was designed to be sensitive to sources of dietary fat, changes in fat intake, and vegetables and fruits, not glycemic index or glycemic load. Fourth, in the sensitivity analysis, there was not a method for fully comparing adherence between the 2 groups given that only the intervention group was asked to attend dietary change sessions. Fifth, it is possible that there was an increased awareness of diabetes risk, screening, and hence diagnosis among the intervention group, leading to an underestimation of the association between a low-fat diet and incident diabetes. However, this is unlikely because the intervention and comparison groups reported having received the same number of physical examinations during the follow-up period. Finally, subgroup analyses must be viewed with caution because of the large number of comparisons, the potential for residual confounding, and missing data.

Overall, the WHI DMT ad libitum low-fat dietary intervention was not associated with a reduced or increased risk of treated diabetes in generally healthy postmenopausal women. However, there were suggestions that, among participants with greater reductions in dietary fat, reductions in energy intake resulted in weight loss. Thus, any reduction in diabetes risk appears to be related to weight loss rather than to the macronutrient composition of the diet.

Correspondence: Lesley F. Tinker, PhD, Women's Health Initiative, Fred Hutchinson Cancer Research Center, PO Box 19024, Mail Stop M3-A410, Seattle, WA 98109 (ltinker@whi.org).

Accepted for Publication: December 20, 2007.

Author Contributions: Dr Tinker had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Tinker, Margolis, Manson, and Wenger. Acquisition of data: Bonds, Manson, Howard, Perri, Beresford, Rodríguez, Safford, Stevens, and Parker. Analysis and interpretation of data: Tinker, Bonds, Margolis, Manson, Howard, Larson, Perri, Robinson, Wenger, and Stevens. Drafting of the manuscript: Tinker, Bonds, Larson, and Safford. Critical revision of the manuscript for important intellectual content: Tinker, Bonds, Margolis, Manson, Howard, Larson, Perri, Beresford, Robinson, Rodríguez, Wenger, Stevens, and Parker. Statistical analysis: Larson and Safford. Obtained funding: Manson, Howard, Beresford, Rodríguez, and Stevens. Administrative, technical, and material support: Manson, Perri, Stevens, and Parker. Study supervision: Tinker, Manson, Howard, Perri, Wenger, and Stevens.

Financial Disclosure: Dr Robinson has received grants from Abbott, Andrx Labs, Astra-Zeneca, Atherogenics Inc, Bristol-Myers Squibb, GlaxoSmithKline, Hoffman La Roche, Merck, Pfizer, Procter & Gamble, Sankyo, Schering-Plough, Takeda, and Wyeth Ayerst; speaker honoraria for educational programs from Bristol-Myers Squibb, Merck, and Pfizer; and honoraria from Reliant; and serves on the Consultant/Advisory Board for Bristol-Myers Squibb, Merck, Pfizer, Proliant, Wellmark, and the American Emu Association. Dr Howard is a consultant for Merck and the Egg Nutrition Council; has received research support from Pfizer, Merck, and Schering-Plough; and has lectured for Schering-Plough.

Funding/Support: The WHI program was funded by the National Heart, Lung, and Blood Institute of the National Institutes of Health, US Department of Health and Human Services.

Role of the Sponsor: The funding organization had representation on the steering committee, which governed the design and conduct of the study, the interpretation of the data, and the preparation and approval of the manuscript. The National Heart, Lung, and Blood Institute Program Office reviewed the manuscript before publication.

Independent Statistical Analysis: All statistical analyses for the study were performed by statisticians at the Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, under the direction of the principal investigator of the Coordinating Center, Ross Prentice, PhD.

Box Section Ref ID

WHI Investigators by Clinical Center

Program Office

National Heart, Lung, and Blood Institute, Bethesda, Maryland: Elizabeth Nabel, Jacques Rossouw, Linda Pottern, Shari Ludlam, Joan McGowan, Nancy Geller, and Leslie Ford.

Clinical Coordinating Centers

Fred Hutchinson Cancer Research Center, Seattle, Washington: Ross Prentice, Garnet Anderson, Andrea LaCroix, Ruth Patterson, Anne McTiernan, Barbara Cochrane, Julie Hunt, Lesley Tinker, Charles Kooperberg, Martin McIntosh, C. Y. Wang, Chu Chen, Deborah Bowen, Alan Kristal, Janet Stanford, Nicole Urban, Noel Weiss, and Emily White. Medical Research Labs, Highland Heights, Kentucky: Evan Stein and Peter Laskarzewski. San Francisco Coordinating Center, San Francisco, California: Steven R. Cummings, Michael Nevitt, and Lisa Palermo. University of Minnesota, Minneapolis: Lisa Harnack. Fisher BioServices, Rockville, Maryland: Frank Cammarata and Steve Lindenfelser. University of Washington, Seattle: Bruce Psaty and Susan Heckbert.

Clinical Centers

Albert Einstein College of Medicine, Bronx, New York: Sylvia Wassertheil-Smoller, William Frishman, Judith Wylie-Rosett, David Barad, and Ruth Freeman. Baylor College of Medicine, Houston, Texas: Aleksandar Rajkovic, Jennifer Hays, Ronald Young, and Haleh Sangi-Haghpeykar. Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts: JoAnn E. Manson, Kathryn Rexrode, Brian Walsh, J. Michael Gaziano, and Maria Bueche. Brown University, Providence, Rhode Island: Charles B. Eaton, Michele Cyr, and Gretchen Sloane. Emory University, Atlanta, Georgia: Lawrence Phillips, Vicki Butler, and Vivian Porter. Fred Hutchinson Cancer Research Center: Shirley A. A. Beresford, Vicky M. Taylor, Nancy F. Woods, Maureen Henderson, and Robyn Andersen. George Washington University, Washington, DC: Judith Hsia, Nancy Gaba, and Richard Katz. Harbor-UCLA Research and Education Institute, Torrance, California: Rowan Chlebowski, Robert Detrano, Anita Nelson, and Michele Geller. Kaiser Permanente Center for Health Research, Portland, Oregon: Yvonne Michael, Evelyn Whitlock, Victor Stevens, and Njeri Karanja. Kaiser Permanente Division of Research, Oakland, California: Bette Caan, Stephen Sidney, Geri Bailey, and Jane Hirata. Medical College of Wisconsin, Milwaukee: Jane Morley Kotchen, Vanessa Barnabei, Theodore A. Kotchen, Mary Ann C. Gilligan, and Joan Neuner. MedStar Research Institute/Howard University, Washington, DC: Barbara V. Howard, Lucile Adams-Campbell, Lawrence Lessin, Cheryl Iglesia, and Linda K. Mickel. Northwestern University, Chicago/Evanston, Illinois: Linda Van Horn, Philip Greenland, Janardan Khandekar, Kiang Liu, and Carol Rosenberg. Rush Medical Center, Chicago, Illinois: Henry Black, Lynda Powell, Ellen Mason, and Martha Gulati. Stanford Prevention Research Center, Stanford University, Stanford, California: Marcia L. Stefanick, Mark A. Hlatky, Bertha Chen, Randall S. Stafford, and Sally Mackey. State University of New York at Stony Brook: Dorothy Lane, Iris Granek, William Lawson, Catherine Messina, and Gabriel San Roman. The Ohio State University, Columbus: Rebecca Jackson, Randall Harris, Electra Paskett, W. Jerry Mysiw, and Michael Blumenfeld. University of Alabama at Birmingham: Cora E. Lewis, Albert Oberman, James M. Shikany, and Monika Safford. University of Arizona, Tucson/Phoenix: Cynthia A. Thomson, Tamsen Bassford, Cheryl Ritenbaugh, Zhao Chen, and Marcia Ko. University at Buffalo, Buffalo, New York: Jean Wactawski-Wende, Maurizio Trevisan, Ellen Smit, Susan Graham, and June Chang. University of California at Davis, Sacramento: John Robbins and S. Yasmeen. University of California at Irvine: F. Allan Hubbell, Gail Frank, Nathan Wong, Nancy Greep, and Bradley Monk. University of California at Los Angeles: Lauren Nathan, David Heber, Robert Elashoff, and Simin Liu. University of California at San Diego, La Jolla/Chula Vista: Robert D. Langer, Michael H. Criqui, Gregory T. Talavera, Cedric F. Garland, and Matthew A. Allison. University of Cincinnati, Cincinnati, Ohio: Margery Gass and Nelson Watts. University of Florida, Gainesville/Jacksonville: Marian Limacher, Michael Perri, Andrew Kaunitz, R. Stan Williams, and Yvonne Brinson. University of Hawaii, Honolulu: J. David Curb, Helen Petrovitch, Beatriz Rodríguez, Kamal Masaki, and Santosh Sharma. University of Iowa, Iowa City/Davenport: Robert Wallace, James Torner, Susan Johnson, Linda Snetselaar, and Jennifer Robinson. University of Massachusetts/Fallon Clinic, Worcester: Judith Ockene, Milagros Rosal, Ira Ockene, Robert Yood, and Patricia Aronson. University of Medicine and Dentistry of New Jersey, Newark: Norman Lasser, Baljinder Singh, Vera Lasser, John Kostis, and Peter McGovern. University of Miami, Miami, Florida: Mary Jo O’Sullivan, Linda Parker, JoNell Potter, Diann Fernandez, and Pat Caralis. University of Minnesota, Minneapolis: Karen L. Margolis, Richard H. Grimm, Mary F. Perron, Cynthia Bjerk, and Sarah Kempainen. University of Nevada, Reno: Robert Brunner, William Graettinger, Vicki Oujevolk, and Michael Bloch. University of North Carolina, Chapel Hill: Gerardo Heiss, Pamela Haines, David Ontjes, Carla Sueta, and Ellen Wells. University of Pittsburgh, Pittsburgh, Pennsylvania: Lewis Kuller, Jane Cauley, and N. Carole Milas. University of Tennessee Health Science Center, Memphis: Karen C. Johnson, Suzanne Satterfield, Rongling Li, Stephanie Connelly, and Fran Tylavsky. University of Texas Health Science Center, San Antonio: Robert Brzyski and Robert Schenken. University of Wisconsin, Madison: Gloria E. Sarto, Douglas Laube, Patrick McBride, Julie Mares, and Barbara Loevinger. Wake Forest University School of Medicine: Mara Vitolins, Greg Burke, Robin Crouse, and Scott Washburn. Wayne State University School of Medicine/Hutzel Hospital, Detroit, Michigan: Michael Simon.

Women's Health Initiative Memory Study

Wake Forest University School of Medicine: Sally Shumaker, Stephen Rapp, Claudine Legault, Mark Espeland, and Laura Coker.

Former Principal Investigators and Project Officers

Baylor College of Medicine: Jennifer Hays and John Foreyt. Brown University: Annlouise R. Assaf. Emory University: Dallas Hal. George Washington University: Valery Miller. Kaiser Permanente Center for Health Research, Portland: Barbara Valanis. Kaiser Permanente Division of Research, Oakland: Robert Hiat. National Cancer Institute: Carolyn Clifford.† National Heart, Lung, and Blood Institute: Linda Pottern. University of California at Irvine: Frank Meyskens, Jr. University of California at Los Angeles: Howard Judd.† University of Cincinnati: James Liu and Nelson Watts. University of Miami: Marianna Baum. University of Minnesota, Minneapolis: Richard Grimm. University of Nevada, Reno: Sandra Daugherty.† University of North Carolina, Chapel Hill: David Sheps and Barbara Hulka. University of Tennessee Health Science Center, Memphis: William Applegate. University of Wisconsin, Madison: Catherine Allen.† Wake Forest University School of Medicine: Denise Bonds.

†Deceased.

Wild  SRoblic  GGreen  ASicree  RKing  H Global prevalence of diabetes. Diabetes Care 2004;27 (5) 1047- 1053
PubMed
DECODE Study Group, Age- and sex-specific prevalences of diabetes and impaired glucose regulation in 13 European cohorts. Diabetes Care 2003;26 (1) 61- 69
PubMed
Harris  MIFlegal  KMCowie  CD  et al.  Prevalence of diabetes, impaired fasting glucose, and impaired glucose tolerance in U.S. adults. Diabetes Care 1998;21 (4) 518- 524
PubMed
Franco  OHSteyerberg  EWHu  FBMackenbach  JNusselder  W Associations of diabetes mellitus with total life expectancy and life expectancy with and without cardiovascular disease. Arch Intern Med 2007;167 (11) 1145- 1151
PubMed
Centers for Disease Control and Prevention, National Diabetes Fact Sheet: General Information and National Estimates on Diabetes in the United States, 2003.  Atlanta, GA US Dept of Health and Human Services2004;
Hogan  PDall  TNikolov  PAmerican Diabetes Association, Economic costs of diabetes in the U.S. in 2002. Diabetes Care 2003;26 (3) 917- 932
PubMed
Tunceli  KBradley  CJNerenz  DWilliams  LKPladevall  MElston Lafata  J The impact of diabetes on employment and work productivity. Diabetes Care 2005;28 (11) 2662- 2667
PubMed
Tuomilehto  JLindstrom  JEriksson  JG  et al.  Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001;344 (18) 1343- 1350
PubMed
Lindström  JLouheranta  AMannelin  M  et al.  The Finnish Diabetes Prevention Study (DPS): lifestyle intervention and 3-year results on diet and physical activity Diabetes Care 2003;26 (12) 3230- 3236
PubMed
Knowler  WCBarrett-Connor  EFowler  SE  et al. Diabetes Prevention Program Research Group, Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346 (6) 393- 403
PubMed
Pan  XRLi  GHu  Y  et al.  Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and Diabetes Study. Diabetes Care 1997;20 (4) 537- 544
PubMed
Swinburn  BAMetcalf  PALey  SJ Long-term (5-year) effects of a reduced-fat diet intervention in individuals with glucose intolerance. Diabetes Care 2001;24 (4) 619- 624
PubMed
Henderson  MMKushi  LHThompson  DJGorbach  SL Feasibility of a randomized trial of a low-fat diet for the prevention of breast cancer: dietary compliance in the Women's Health Trial vanguard study. Prev Med 1990;19 (2) 115- 133
PubMed
Insull  WHenderson  MMPrentice  RLThompson  DJ Results of a randomized feasibility study of a low-fat diet. Arch Intern Med 1990;150 (2) 421- 427
PubMed
Hall  WDFeng  ZGeorge  V  et al. Women's Health Trial: Feasibility Study in Minority Populations, Low-fat diet: effect on anthropometrics, blood pressure, glucose and insulin in older women. Ethn Dis 2003;13 (3) 337- 343
PubMed
Howard  BVManson  JEStefanick  ML  et al.  Low-fat dietary pattern and weight change over 7 years—the Women's Health Initiative Dietary Modification Trial. JAMA 2006;295 (1) 39- 49
PubMed
Salmerón  JManson  JEStampfer  MJColditz  GAWing  ALWillett  WC Dietary fiber, glycemic load, and risk of non–insulin-dependent diabetes mellitus in women. JAMA 1997;277 (6) 472- 477
PubMed
Salmerón  JAscherio  ARimm  E  et al.  Dietary fiber, glycemic load, and risk of NIDDM in men. Diabetes Care 1997;20 (4) 545- 550
PubMed
Schulze  MBLiu  SRimm  EBManson  JEWillett  WCHu  FB Glycemic index, glycemic load, and dietary fiber intake and incidence of type 2 diabetes in younger and middle-aged women. Am J Clin Nutr 2004;80 (2) 348- 356
PubMed
Women's Health Initiative Study Group, Design of the Women's Health Initiative Clinical Trial and Observational Study. Control Clin Trials 1998;19 (1) 61- 109
PubMed
Hays  JHunt  JRHubbell  FA  et al.  The Women's Health Initiative recruitment methods and results. Ann Epidemiol 2003;13 (9) ((suppl)) S18- S77
PubMed
Ritenbaugh  CPatterson  REChlebowski  RT  et al.  The Women's Health Initiative Dietary Modification Trial: overview and baseline characteristics of participants. Ann Epidemiol 2003;13 (9) ((suppl)) S87- S97
PubMed
Rossouw  JEAnderson  GLPrentice  RL  et al. Writing Group for the Women's Health Initiative Investigators, Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women's Health Initiative Randomized Controlled Trial. JAMA 2002;288 (3) 321- 333
PubMed
Anderson  GLLimacher  MAssaf  AR  et al. Women's Health Initiative Steering Committee, Effects of conjugated equine estrogen in postmenopausal women with hysterectomy: the Women's Health Initiative Randomized Controlled Trial. JAMA 2004;291 (14) 1701- 1712
PubMed
Prentice  RLCaan  BChlebowski  RT  et al.  Low-fat dietary pattern and risk of invasive breast cancer: the Women's Health Initiative Randomized Controlled Dietary Modification Trial. JAMA 2006;295 (6) 629- 642
PubMed
Anderson  GLManson  JWallace  R  et al.  Implementation of the Women's Health Initiative Study Design. Ann Epidemiol 2003;13 (9) ((suppl)) S5- S17
PubMed
US Department of Agriculture, Dietary Guidelines for Americans. 3rd ed. Washington, DC US Dept of Health and Human Services1990;Home and Garden Bulletin 232
US Department of Agriculture, Dietary Guidelines for Americans. 4th ed. Washington, DC US Dept of Health and Human Services1995;Home and Garden Bulletin 232
Tinker  LFBurrows  ERHenry  HPatterson  RRupp  JVan Horn  L The Women's Health Initiative: overview of the nutrition components. Krummel  DKris-Etherton  PNutrition in Women's Health. Gaithersburg, MD Aspen1996;510- 542
Patterson  REKristal  ARCarter  RAFels-Tinker  LBolton  MPAgurs-Collins  T Measurement characteristics of the Women's Health Initiative Food Frequency Questionnaire. Ann Epidemiol 1999;9 (3) 178- 197
PubMed
Neuhouser  MLTinker  LFThomson  C  et al.  Development of a glycemic index database for food frequency questionnaires used in epidemiologic studies. J Nutr 2006;136 (6) 1604- 1609
PubMed
Matthews  DRHosker  JPRudenski  ASNaylor  BATreacher  DFTurner  RC Homeostasis model assessment: insulin resistance and B-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28 (7) 412- 419
PubMed
Margolis  KLQi  LBrzyski  R  et al.  Validity of diabetes self-reports in the Women's Health Initiative: comparison with medication inventories and fasting glucose values. Clin Trials In press
Robins  JMFinkelstein  DM Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics 2000;56 (3) 779- 781
PubMed
Tinker  LFRosal  MCYoung  AF  et al.  Predictors of dietary change and maintenance in the Women's Health Initiative (WHI) Dietary Modification (DM) Trial. J Am Diet Assoc 2007;107 (7) 1155- 1165
PubMed
Tinker  LFPerri  MGPatterson  RE  et al.  The effects of physical and emotional status factors on adherence to a low-fat dietary pattern in the Women's Health Initiative. J Am Diet Assoc 2002;102 (6) 789- 800
PubMed
Hamman  RFWing  RREdelstein  SL  et al.  Effect of weight loss with lifestyle intervention on risk of diabetes. Diabetes Care 2006;29 (9) 2102- 2107
PubMed
Lindström  JPeltonen  MEriksson  J  et al.  High-fibre, low-fat diet predicts long-term weight loss and decreased type 2 diabetes risk: the Finnish Diabetes Prevention Study. Diabetologia 2006;49 (5) 912- 920
PubMed
Mueller-Cunningham  WMQuintana  RKasim-Karakas  SE An ad libitum, very low-fat diet results in weight loss and changes in nutrient intakes in postmenopausal women. J Am Diet Assoc 2003;103 (12) 1600- 1606
PubMed
Marshall  JAHoag  SShetterly  SHamman  RF Dietary fat predicts conversion from impaired glucose tolerance to NIDDM. Diabetes Care 1994;17 (1) 50- 56
PubMed
Salmerón  JHu  FBManson  JE  et al.  Dietary fat intake and risk of type 2 diabetes in women. Am J Clin Nutr 2001;73 (6) 1019- 1026
PubMed
Meyer  KAKushi  LHJacobs  DR  JrFolsom  AR Dietary fat and incidence of type 2 diabetes in older Iowa women. Diabetes Care 2001;24 (9) 1528- 1535
PubMed
van Dam  RMWillett  WCRimm  EBStampfer  MJHu  FB Dietary fat and meat intake in relation to risk of type 2 diabetes in men. Diabetes Care 2002;25 (3) 417- 424
PubMed
Harding  A-HDay  NShaw  K-T  et al.  Dietary fat and the risk of clinical type 2 diabetes. Am J Epidemiol 2004;159 (1) 73- 82
PubMed
Meyer  KAKushi  LHJacobs  DR  JrSlavin  JSellers  TAFolsom  AR Carbohydrates, dietary fiber, and incident type 2 diabetes in older women. Am J Clin Nutr 2000;71 (4) 921- 930
PubMed
Liu  SSerdula  MJanket  S-J  et al.  A prospective study of fruit and vegetable intake and the risk of type 2 diabetes in women. Diabetes Care 2004;27 (12) 2993- 2996
PubMed
Montonen  JJärvinen  RHeliövaara  MReunanen  AAromaa  AKnekt  P Food consumption and the incidence of type II diabetes mellitus. Eur J Clin Nutr 2005;59 (3) 441- 448
PubMed
Ford  ESMokdad  AH Fruit and vegetable consumption and diabetes mellitus incidence among U.S. adults. Prev Med 2001;32 (1) 33- 39
PubMed
Stevens  JKyungmi  AJuhaeriHouston  DSteffan  LCouper  D Dietary fiber intake and glycemic index and incidence of diabetes in African-American and white adults. Diabetes Care 2002;25 (10) 1715- 1721
PubMed
Hodge  AMEnglish  DRO’Dea  KGiles  GG Glycemic index and dietary fiber and the risk of type 2 diabetes. Diabetes Care 2004;27 (11) 2701- 2706
PubMed
Subar  AFKipnis  VTroiano  RP  et al.  Using intake of biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN Study. Am J Epidemiol 2003;158 (1) 1- 13
PubMed

Figures

Place holder to copy figure label and caption
Figure 1.

Participant flow in the Women's Health Initiative Dietary Modification Trial.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

Kaplan-Meier estimates of the cumulative hazard for treated diabetes in the Women's Health Initiative Dietary Modification Trial. CI indicates confidence interval; HR, hazard ratio.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.

Treated diabetes hazard ratios and annualized incidence by baseline dietary factors in the Women's Health Initiative Dietary Modification Trial. Limit lines indicate confidence interval.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 4.

Treated diabetes hazard ratios and annualized incidence by baseline diabetes risk factors in the Women's Health Initiative Dietary Modification Trial. Limit lines indicate confidence interval. BMI indicates body mass index (calculated as weight in kilograms divided by height in meters squared); E, estrogen; HOMA-IR, homeostasis model assessment, insulin resistance; HT, hormone therapy; MET, metabolic equivalent; and P, progestin. To convert glucose to millimoles per liter, multiply by 0.0555; insulin to picomoles per liter, multiply by 6.945.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Baseline Demographics of 45 887 Participants Without Prevalent Diabetesa in the WHI Dietary Modification Trial
Table Graphic Jump LocationTable 2. Dietary Intake Estimates at Baseline and Follow-up of 45 887 Participants Without Prevalent Diabetesa in the WHI Dietary Modification Trial
Table Graphic Jump LocationTable 3. Diabetes Risk Factors at Baseline and Follow-up of 45 887a Participants Without Prevalent Diabetesb in the WHI Dietary Modification Trial
Table Graphic Jump LocationTable 4. Diabetes Hazard Ratios by Year 1 Dietary Factors of the WHI Dietary Modification Trial Intervention Goalsa

References

Wild  SRoblic  GGreen  ASicree  RKing  H Global prevalence of diabetes. Diabetes Care 2004;27 (5) 1047- 1053
PubMed
DECODE Study Group, Age- and sex-specific prevalences of diabetes and impaired glucose regulation in 13 European cohorts. Diabetes Care 2003;26 (1) 61- 69
PubMed
Harris  MIFlegal  KMCowie  CD  et al.  Prevalence of diabetes, impaired fasting glucose, and impaired glucose tolerance in U.S. adults. Diabetes Care 1998;21 (4) 518- 524
PubMed
Franco  OHSteyerberg  EWHu  FBMackenbach  JNusselder  W Associations of diabetes mellitus with total life expectancy and life expectancy with and without cardiovascular disease. Arch Intern Med 2007;167 (11) 1145- 1151
PubMed
Centers for Disease Control and Prevention, National Diabetes Fact Sheet: General Information and National Estimates on Diabetes in the United States, 2003.  Atlanta, GA US Dept of Health and Human Services2004;
Hogan  PDall  TNikolov  PAmerican Diabetes Association, Economic costs of diabetes in the U.S. in 2002. Diabetes Care 2003;26 (3) 917- 932
PubMed
Tunceli  KBradley  CJNerenz  DWilliams  LKPladevall  MElston Lafata  J The impact of diabetes on employment and work productivity. Diabetes Care 2005;28 (11) 2662- 2667
PubMed
Tuomilehto  JLindstrom  JEriksson  JG  et al.  Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001;344 (18) 1343- 1350
PubMed
Lindström  JLouheranta  AMannelin  M  et al.  The Finnish Diabetes Prevention Study (DPS): lifestyle intervention and 3-year results on diet and physical activity Diabetes Care 2003;26 (12) 3230- 3236
PubMed
Knowler  WCBarrett-Connor  EFowler  SE  et al. Diabetes Prevention Program Research Group, Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346 (6) 393- 403
PubMed
Pan  XRLi  GHu  Y  et al.  Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and Diabetes Study. Diabetes Care 1997;20 (4) 537- 544
PubMed
Swinburn  BAMetcalf  PALey  SJ Long-term (5-year) effects of a reduced-fat diet intervention in individuals with glucose intolerance. Diabetes Care 2001;24 (4) 619- 624
PubMed
Henderson  MMKushi  LHThompson  DJGorbach  SL Feasibility of a randomized trial of a low-fat diet for the prevention of breast cancer: dietary compliance in the Women's Health Trial vanguard study. Prev Med 1990;19 (2) 115- 133
PubMed
Insull  WHenderson  MMPrentice  RLThompson  DJ Results of a randomized feasibility study of a low-fat diet. Arch Intern Med 1990;150 (2) 421- 427
PubMed
Hall  WDFeng  ZGeorge  V  et al. Women's Health Trial: Feasibility Study in Minority Populations, Low-fat diet: effect on anthropometrics, blood pressure, glucose and insulin in older women. Ethn Dis 2003;13 (3) 337- 343
PubMed
Howard  BVManson  JEStefanick  ML  et al.  Low-fat dietary pattern and weight change over 7 years—the Women's Health Initiative Dietary Modification Trial. JAMA 2006;295 (1) 39- 49
PubMed
Salmerón  JManson  JEStampfer  MJColditz  GAWing  ALWillett  WC Dietary fiber, glycemic load, and risk of non–insulin-dependent diabetes mellitus in women. JAMA 1997;277 (6) 472- 477
PubMed
Salmerón  JAscherio  ARimm  E  et al.  Dietary fiber, glycemic load, and risk of NIDDM in men. Diabetes Care 1997;20 (4) 545- 550
PubMed
Schulze  MBLiu  SRimm  EBManson  JEWillett  WCHu  FB Glycemic index, glycemic load, and dietary fiber intake and incidence of type 2 diabetes in younger and middle-aged women. Am J Clin Nutr 2004;80 (2) 348- 356
PubMed
Women's Health Initiative Study Group, Design of the Women's Health Initiative Clinical Trial and Observational Study. Control Clin Trials 1998;19 (1) 61- 109
PubMed
Hays  JHunt  JRHubbell  FA  et al.  The Women's Health Initiative recruitment methods and results. Ann Epidemiol 2003;13 (9) ((suppl)) S18- S77
PubMed
Ritenbaugh  CPatterson  REChlebowski  RT  et al.  The Women's Health Initiative Dietary Modification Trial: overview and baseline characteristics of participants. Ann Epidemiol 2003;13 (9) ((suppl)) S87- S97
PubMed
Rossouw  JEAnderson  GLPrentice  RL  et al. Writing Group for the Women's Health Initiative Investigators, Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women's Health Initiative Randomized Controlled Trial. JAMA 2002;288 (3) 321- 333
PubMed
Anderson  GLLimacher  MAssaf  AR  et al. Women's Health Initiative Steering Committee, Effects of conjugated equine estrogen in postmenopausal women with hysterectomy: the Women's Health Initiative Randomized Controlled Trial. JAMA 2004;291 (14) 1701- 1712
PubMed
Prentice  RLCaan  BChlebowski  RT  et al.  Low-fat dietary pattern and risk of invasive breast cancer: the Women's Health Initiative Randomized Controlled Dietary Modification Trial. JAMA 2006;295 (6) 629- 642
PubMed
Anderson  GLManson  JWallace  R  et al.  Implementation of the Women's Health Initiative Study Design. Ann Epidemiol 2003;13 (9) ((suppl)) S5- S17
PubMed
US Department of Agriculture, Dietary Guidelines for Americans. 3rd ed. Washington, DC US Dept of Health and Human Services1990;Home and Garden Bulletin 232
US Department of Agriculture, Dietary Guidelines for Americans. 4th ed. Washington, DC US Dept of Health and Human Services1995;Home and Garden Bulletin 232
Tinker  LFBurrows  ERHenry  HPatterson  RRupp  JVan Horn  L The Women's Health Initiative: overview of the nutrition components. Krummel  DKris-Etherton  PNutrition in Women's Health. Gaithersburg, MD Aspen1996;510- 542
Patterson  REKristal  ARCarter  RAFels-Tinker  LBolton  MPAgurs-Collins  T Measurement characteristics of the Women's Health Initiative Food Frequency Questionnaire. Ann Epidemiol 1999;9 (3) 178- 197
PubMed
Neuhouser  MLTinker  LFThomson  C  et al.  Development of a glycemic index database for food frequency questionnaires used in epidemiologic studies. J Nutr 2006;136 (6) 1604- 1609
PubMed
Matthews  DRHosker  JPRudenski  ASNaylor  BATreacher  DFTurner  RC Homeostasis model assessment: insulin resistance and B-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28 (7) 412- 419
PubMed
Margolis  KLQi  LBrzyski  R  et al.  Validity of diabetes self-reports in the Women's Health Initiative: comparison with medication inventories and fasting glucose values. Clin Trials In press
Robins  JMFinkelstein  DM Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics 2000;56 (3) 779- 781
PubMed
Tinker  LFRosal  MCYoung  AF  et al.  Predictors of dietary change and maintenance in the Women's Health Initiative (WHI) Dietary Modification (DM) Trial. J Am Diet Assoc 2007;107 (7) 1155- 1165
PubMed
Tinker  LFPerri  MGPatterson  RE  et al.  The effects of physical and emotional status factors on adherence to a low-fat dietary pattern in the Women's Health Initiative. J Am Diet Assoc 2002;102 (6) 789- 800
PubMed
Hamman  RFWing  RREdelstein  SL  et al.  Effect of weight loss with lifestyle intervention on risk of diabetes. Diabetes Care 2006;29 (9) 2102- 2107
PubMed
Lindström  JPeltonen  MEriksson  J  et al.  High-fibre, low-fat diet predicts long-term weight loss and decreased type 2 diabetes risk: the Finnish Diabetes Prevention Study. Diabetologia 2006;49 (5) 912- 920
PubMed
Mueller-Cunningham  WMQuintana  RKasim-Karakas  SE An ad libitum, very low-fat diet results in weight loss and changes in nutrient intakes in postmenopausal women. J Am Diet Assoc 2003;103 (12) 1600- 1606
PubMed
Marshall  JAHoag  SShetterly  SHamman  RF Dietary fat predicts conversion from impaired glucose tolerance to NIDDM. Diabetes Care 1994;17 (1) 50- 56
PubMed
Salmerón  JHu  FBManson  JE  et al.  Dietary fat intake and risk of type 2 diabetes in women. Am J Clin Nutr 2001;73 (6) 1019- 1026
PubMed
Meyer  KAKushi  LHJacobs  DR  JrFolsom  AR Dietary fat and incidence of type 2 diabetes in older Iowa women. Diabetes Care 2001;24 (9) 1528- 1535
PubMed
van Dam  RMWillett  WCRimm  EBStampfer  MJHu  FB Dietary fat and meat intake in relation to risk of type 2 diabetes in men. Diabetes Care 2002;25 (3) 417- 424
PubMed
Harding  A-HDay  NShaw  K-T  et al.  Dietary fat and the risk of clinical type 2 diabetes. Am J Epidemiol 2004;159 (1) 73- 82
PubMed
Meyer  KAKushi  LHJacobs  DR  JrSlavin  JSellers  TAFolsom  AR Carbohydrates, dietary fiber, and incident type 2 diabetes in older women. Am J Clin Nutr 2000;71 (4) 921- 930
PubMed
Liu  SSerdula  MJanket  S-J  et al.  A prospective study of fruit and vegetable intake and the risk of type 2 diabetes in women. Diabetes Care 2004;27 (12) 2993- 2996
PubMed
Montonen  JJärvinen  RHeliövaara  MReunanen  AAromaa  AKnekt  P Food consumption and the incidence of type II diabetes mellitus. Eur J Clin Nutr 2005;59 (3) 441- 448
PubMed
Ford  ESMokdad  AH Fruit and vegetable consumption and diabetes mellitus incidence among U.S. adults. Prev Med 2001;32 (1) 33- 39
PubMed
Stevens  JKyungmi  AJuhaeriHouston  DSteffan  LCouper  D Dietary fiber intake and glycemic index and incidence of diabetes in African-American and white adults. Diabetes Care 2002;25 (10) 1715- 1721
PubMed
Hodge  AMEnglish  DRO’Dea  KGiles  GG Glycemic index and dietary fiber and the risk of type 2 diabetes. Diabetes Care 2004;27 (11) 2701- 2706
PubMed
Subar  AFKipnis  VTroiano  RP  et al.  Using intake of biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN Study. Am J Epidemiol 2003;158 (1) 1- 13
PubMed

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