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Original Investigation |

Dietary Patterns, Meat Intake, and the Risk of Type 2 Diabetes in Women FREE

Teresa T. Fung, ScD; Matthias Schulze, DrPH; JoAnn E. Manson, MD, DrPH; Walter C. Willett, MD, DrPH; Frank B. Hu, MD, PhD
[+] Author Affiliations

Author Affiliations: Department of Nutrition, Simmons College (Dr Fung); Departments of Nutrition (Drs Fung, Schulze, Willett, and Hu) and Epidemiology (Drs Manson, Willett, and Hu), Harvard School of Public Health; Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital (Drs Manson, Willett, and Hu), and Division of Preventive Medicine (Dr Manson), Harvard Medical School, Boston, Mass.


Arch Intern Med. 2004;164(20):2235-2240. doi:10.1001/archinte.164.20.2235.
Text Size: A A A
Published online

Background  Although obesity is the most important risk factor for type 2 diabetes, evidence is emerging that certain foods and dietary factors may be associated with diabetes. To examine the association between major dietary patterns and risk of type 2 diabetes mellitus in a cohort of women.

Methods  We prospectively assessed the associations between major dietary patterns and risk of type 2 diabetes in women. Dietary information was collected in 1984, 1986, 1990, and 1994 from 69 554 women aged 38 to 63 years without a history of diabetes, cardiovascular disease, or cancer in 1984. We conducted factor analysis and identified 2 major dietary patterns: “prudent” and “Western.” We then calculated pattern scores for each participant and examined prospectively the associations between dietary pattern scores and type 2 diabetes risks.

Results  The prudent pattern was characterized by higher intakes of fruits, vegetables, legumes, fish, poultry, and whole grains, while the Western pattern included higher intakes of red and processed meats, sweets and desserts, french fries, and refined grains. During 14 years of follow-up, we identified 2699 incident cases of type 2 diabetes. After adjusting for potential confounders, we observed a relative risk for diabetes of 1.49 (95% confidence interval [CI], 1.26-1.76, P for trend, <.001) when comparing the highest to lowest quintiles of the Western pattern. Positive associations were also observed between type 2 diabetes and red meat and other processed meats. The relative risk for diabetes for every 1-serving increase in intake is 1.26 (95% CI, 1.21-1.42) for red meat, 1.38 (95% CI, 1.23-1.56) for total processed meats, 1.73 (95% CI, 1.39-2.16) for bacon, 1.49 (95% CI, 1.04-2.11) for hot dogs, and 1.43 (95% CI, 1.22-1.69) for processed meats.

Conclusion  The Western pattern, especially a diet higher in processed meats, may increase the risk of type 2 diabetes in women.

Figures in this Article

The United States is experiencing an alarming increase in the incidence of type 2 diabetes mellitus.1 The resulting morbidity, economic costs, reduced quality of life, and risk for complications make preventive strategies indispensable. Although obesity is the most important risk factor for type 2 diabetes,1 evidence is emerging that certain foods and dietary factors may be associated with diabetes. In particular, a diet low in whole grains and high in glycemic load2 and processed meats appears to increase the risk.3 Therefore, from a preventive perspective, it would be useful to examine the association of food combinations, or self-selected dietary patterns, with risk of type 2 diabetes. Factor analysis has been commonly used to identify dietary patterns in studies of cardiovascular disease4 and colon cancer.5 Most recently, the “Western” dietary pattern has been shown to increase the risk of diabetes in a cohort of men.6 Therefore, the purpose of the present analysis is to examine the association between major dietary patterns and risk of type 2 diabetes among women in the ongoing Nurses’ Health Study (NHS) cohort.

STUDY POPULATION

The NHS began in 1976, when 121 700 female nurses aged 30 to 55 years living in 11 US states responded to a questionnaire regarding medical, lifestyle, and other health-related information.7 Since 1976, questionnaires have been sent biennially to update this information. Follow-up was complete for greater than 95% of the potential person time up to 1994. In 1980, the participants completed a 61-item food frequency questionnaire (FFQ). In 1984, the FFQ was expanded to 116 items. Similar FFQs were sent to the women in 1986, 1990, and 1994. We used the 1984 FFQ as baseline for this study because the expanded number of items was critical in characterizing dietary patterns. The NHS is approved by the institutional review board of the Brigham and Women’s Hospital, Boston, Mass.

For the present analysis, women were included if they completed the 1984 FFQ with fewer than 70 missing items and a total caloric range (as calculated from the FFQ) between 500 and 3500 kcal/d. We excluded women with a history of cancer, cardiovascular disease, and diabetes. We thus included in this analysis 69 554 women with follow-up for up to 14 years, from 1984 to 1998.

ASSESSMENT OF DIETARY INTAKE

Dietary intake information was collected by FFQs designed to assess average food intake over the previous year. A standard portion size was given for each food item. Cohort members were asked to choose from 9 possible frequency responses, ranging from “never” to “more than 6 times a day” for each food. Total energy intake was calculated by summing up energy intakes from all foods. For this analysis, we used information from the FFQs administered in 1984, 1986, 1990, and 1994. Foods from the FFQ were classified into 36 to 38 food groups based on nutrient profiles or culinary usage. This classification follows that of a study in men with a similar dietary assessment instrument.4 Foods that did not fit into any of the groups or that may represent distinctive dietary behaviors were left as individual categories (eg, pizza, tea, and beer). Vitamin and mineral supplements were not included in the definition of the patterns, but they were included as covariates in the analysis. Previous validation studies among members of the NHS cohort revealed good correlations between nutrients assessed by the FFQ and multiple weeks of food records completed over the previous year.8 For example, correlation coefficients between 1986 FFQ and diet records obtained in 1986 were 0.68 for saturated fat, 0.76 for vitamin C, and 0.73 for dietary cholesterol. The mean correlation coefficient between frequencies of intake of 55 foods from 2 FFQs administered 12 months apart was 0.57.9

CASE ASCERTAINMENT

Our end points included incident type 2 diabetes mellitus that occurred between the return of the 1984 questionnaire and June 1, 1998. When a participant reported a diagnosis of diabetes in the biennial questionnaires, we mailed them a supplementary questionnaire that assessed symptoms, diagnostic tests, and treatment to confirm the diagnosis. Diabetes was confirmed when the participant fulfilled 1 or more of the following criteria: (1) manifestation of classic symptoms (eg, excessive thirst, polyuria, weight loss, and hunger) plus an elevated fasting glucose level (>140 mg/dL [>7.8 mmol/L]), or elevated nonfasting level (>200 mg/dL [>11.1 mmol/L]); (2) asymptomatic but plasma glucose level was elevated on at least 2 different occasions (as defined herein) or abnormal oral glucose tolerance test result (>200 mg/dL 2 hours after glucose load); and (3) receiving any hypoglycemic treatment for diabetes. These criteria for diabetes classification were consistent with those of the National Diabetes Data Group during our follow-up period.10 A validation study has shown a high level of accuracy in self-reporting of diabetes.11 Medical records were successfully obtained for 62 women among a random sample of 84 with diabetes confirmed by the supplemental questionnaire. Review of records by an endocrinologist blinded to the questionnaire information confirmed 61 (98%) of the 62 women. Deaths were reported by family members, the postal service, or through searches in the National Death Index.

STATISTICAL ANALYSIS

Dietary patterns were generated by factor analysis (principal components) based on predefined food groups and using an orthogonal rotation procedure.12 This results in uncorrelated factors, which are easier to interpret. We determined the number of factors to retain by eigenvalue (>1), Scree test, and factor interpretability. The factor score for each pattern was calculated by summing intakes of food groups weighted by their factor loadings,13 and each woman received a factor score for each identified pattern. Good reproducibility of the patterns generated by this method has been demonstrated in a parallel cohort of men.14 The correlations between the scores of the 2 major patterns (“prudent” and “Western”) generated from FFQ and diet records were 0.52 for the prudent pattern and 0.74 for the Western pattern. Factor analysis was conducted using SAS PROC FACTOR.15

We used Cox proportional hazard models to examine the associations between major dietary patterns and diabetes risk. To reduce random within-person variation and best represent long-term dietary intake, we calculated cumulative averages of dietary pattern scores from our repeated dietary measurements.4 For example, dietary intake in 1984 was used to predict diabetes occurrence from 1984 to 1986, and the average of 1984 and 1986 intake was used to predict risk from 1986 to 1990, and so on. The regression analyses were adjusted for age (<49 y, 50-54 y, 55-59 y, 60-64 y, and ≥65 y), family history of diabetes (yes vs no), history of hypercholesterolemia (yes vs no), smoking (never, past, current with 1-14 cigarettes per day, current with 15-24 cigarettes per day, current with ≥25 cigarettes per day, and missing), hormone therapy use (premenopausal, never, past, and current hormone use), caloric intake, history of hypertension (yes vs no), physical activity (<1 h/wk, 1-1.9 h/wk, 2-3.9 h/wk, 4-6.9 h/wk, and ≥7 h/wk), alcohol intake (abstainer, 0.1-5.0 g/d, 5.1-15.0 g/d, 15.1-30.0 g/d, and >30g/d), body mass index (BMI; continuous and quadratic terms), and missing FFQ. The proportions with a missing FFQ in 1986, 1990, and 1994 were 17%, 16%, and 14%, respectively. In separate analyses, we only included symptomatic cases. In addition, we examined the association between different forms of red meat and diabetes risk, since these are the major contributors to the Western dietary pattern. We also conducted analyses jointly classifying women by processed meats and family history of diabetes, obesity status (BMI [calculated as weight in kilograms divided by the square of height in meters] ≥30), physical activity, alcohol intake, and smoking status. To minimize confounding by adiposity, we additionally adjusted for waist-hip ratio among women with the available data.

During 14 years of follow-up, we documented 2699 incident cases of type 2 diabetes mellitus, including 1604 symptomatic cases. Two major dietary patterns emerged from factor analysis. The prudent pattern was characterized by higher intake of fruits, vegetables, whole grains, fish, poultry, and low-fat dairy products, while the Western pattern was characterized by higher intakes of red and processed meats, refined grains, sweets and desserts, and high-fat dairy products. Women who scored high on the Western pattern tended to smoke more and consumed less folate, fiber, and protein (Table 1).

Table Graphic Jump LocationTable 1. Age-Standardized Baseline Characteristics of NHS Participants in 1984 by Quintiles of Dietary Pattern Scores

After multivariate adjustment, we observed a modest inverse association between the prudent pattern and type 2 diabetes (Table 2). Limiting the analysis to symptomatic cases only, a significant inverse association was observed (P for trend, .03). Women in the highest quintile of the prudent pattern score had a relative risk (RR) of 0.80 (95% confidence interval [CI], 0.67-0.95) when compared with the lowest quintile. In contrast, we observed a significant positive association with the Western pattern. The RR after multivariate adjustment was 1.49 (95% CI, 1.26-1.76; P for trend, <.001) when we compared the women in the top with the bottom quintile. When we stratified the analysis by BMI, family history of diabetes, and physical activity level, a positive association with the Western pattern score was present within all strata (Figure). Of interest is that the inverse association observed with alcohol was essentially negated by a high Western pattern score. Although the risk of diabetes was higher among individuals with the risk factor and higher Western pattern score, significant interactions between both risk factors were not observed.

Place holder to copy figure label and caption
Figure.

Relative risk (RR) for type 2 diabetes mellitus according to joint classifications of risk factors by quintile (Q) of Western pattern score. A, Body mass index (BMI; calculated as weight in kilograms divided by the square of height in meters); B, physical activity level; and C, family history of diabetes.

Graphic Jump Location
Table Graphic Jump LocationTable 2. Relative Risk of Quintiles of Dietary Pattern Scores for Risk of Type 2 Diabetes*

To explore whether the association of diabetes with the Western pattern is independent of the latter’s major contributors, red and processed meats, we additionally adjusted for total red and processed meats. The resulting association between Western pattern score and diabetes risk was somewhat attenuated (RR, 1.28 [95% CI, 1.05-1.57] when we compared the top with the bottom quintile), but the test for trend remained statistically significant (P<.001). In secondary analysis, we also restricted our analysis to those with waist and hip circumference data and additionally adjusted for waist-hip ratio. A significant positive association was observed in each quintile, although the RR at the fifth quintile was somewhat attenuated. Significant trends remained for total processed meats and bacon.

When we examined major contributors to the Western pattern, positive associations with red and processed meats emerged (Table 3). Intake of total processed meats showed the strongest positive association, with an RR of 1.60 (95% CI, 1.39-1.83; P for trend, <.001) when we compared the top with bottom quintile of intake. When these meat products were analyzed as a continuous variable, we observed an RR of 1.26 (95% CI, 1.21-1.42) for each serving increase in red meat consumption, 1.38 (95% CI, 1.23-1.56) for total processed meats, 1.73 (95% CI, 1.39-2.16) for bacon, 1.49 (95% CI, 1.04-2.11) for hot dogs, and 1.43 (95% CI, 1.22-1.69) for processed meats. This suggests that processed meats intake confers a higher risk for diabetes than red meats. The associations between various meat intake and diabetes were slightly attenuated but generally remained statistically significant even after additionally adjusting for Western pattern score, suggesting that these foods are associated with diabetes risk independently of the overall Western pattern. In addition, when the analysis was restricted to those with waist-hip ratio information and controlled for this index of adiposity, the association did not change substantially.

Table Graphic Jump LocationTable 3. Multivariate* RR of Intake (Servings per Day) of Selected Contributors of the Western Pattern for Diabetes

We observed positive associations between the Western pattern, intake of several meat products, and risk of type 2 diabetes mellitus in women. Although red and processed meats are major contributors to the Western pattern, the association with the Western pattern was not fully explained by the various meats. This indicates that the remaining characteristics of the Western pattern, such as high intake of other highly processed foods, may contribute to an increased risk of diabetes. The increased risk from the different types of processed meat was similar but in general higher compared with red meat.

Few studies have prospectively investigated diet patterns or meat intake. Our findings confirm those of a cohort of men followed for up to 12 years.3,6 A cross-sectional study of Seventh-Day Adventists in California showed a lower risk of diabetes among vegetarians, who consumed more legumes, fruits, and nuts in the absence of meat intake.16 The relationship observed with the Western pattern may be mediated through multiple mechanisms, including saturated fat, glycemic load, nitrites, and heme iron. In a group of US male health professionals, the Western pattern was correlated with fasting insulin levels.17 In a Swedish population, dietary patterns that resembled the Western pattern showed associations with hyperglycemia or hyperinsulinemia.18 One major characteristic of the Western pattern is the processed meat and most red meat intake. A higher red and processed meat intake was found to be associated with a higher risk of diabetes in a cohort of men.3 Processed meats were also associated with risk of diabetes in an earlier follow-up of this cohort.19 Nitrites are used in processed meats as a preservative, and it can be converted into nitrosamines in the gastrointestinal tract. Ecological studies suggest a relationship between nitrates and nitrite consumption with type 1 diabetes in children.20,21 Nitrites and some nitrous compounds have been shown to reduce insulin secretion in rats.22 However, whether they have a role in type 2 diabetes is unclear. Another possible mediator is advanced glycation end products. These are easily formed in meat and high fat products through heating and processing.23 Advanced glycation end products has been shown to increase oxidative stress and the levels of tumor necrosis factor α in vitro,24 and inflammation is believed to have a role in diabetes development.25 In mice, administration of an advanced glycation end product formation inhibitor reduced the development of diabetes,26 and restriction of advanced glycation end products in the diet has improved insulin sensitivity.27

The prospective design of this study renders recall bias unlikely. This cohort has demonstrated accurate report of disease and because of the study participants’ access to health care, underreporting of diabetes is expected to be less than that in the general population. We used repeated measurements of various potential confounders and statistically controlled for BMI, a strong risk factor for diabetes. Dietary patterns across time have shown to be consistent in our cohort, and our use of cumulative averages of dietary pattern scores reduce the influence of random error. The principal components method of pattern analysis identifies existing patterns; therefore, the Western pattern observed in our study does not necessarily represent food choices that would pose the highest diabetes risk, nor does the prudent pattern represent the lowest risk. Although few studies have used this method to examine the association between diet and diabetes risk, dietary patterns generated with this method have been shown to be associated with other diseases.4,28 An advantage to using dietary patterns is the potential to detect the combined effect of foods, especially if the individual components of a pattern contribute to only a small amount of risk.

We found that a diet high in red and processed meats, refined grains, and other characteristics of the Western pattern was associated with an elevated risk of type 2 diabetes mellitus in women. Red and processed meats were also independently associated with an increased risk. Therefore, it may be prudent to reduce the consumption of these food items to decrease the risk of type 2 diabetes.

Accepted for Publication: December 17, 2003.

Correspondence: Teresa T. Fung, ScD, Department of Nutrition, Simmons College, 300 The Fenway, Boston, MA 02115 (fung@simmons.edu).

Financial Disclosure: None.

Funding/Support: This study was supported by grants CA87969 from the National Institutes of Health, Bethesda, Md, and 9911DYS from the American Diabetes Association, Alexandria, Va.

Mokdad  AHFord  ESBowman  BA  et al.  Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 2003;28976- 79
PubMed Link to Article
Salmeron  JManson  JEStampfer  MJColditz  GAWing  ALWillett  WC Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women. JAMA 1997;277472- 477
PubMed Link to Article
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;25417- 424
PubMed Link to Article
Hu  FBRimm  EBStampfer  MJAscherio  ASpiegelman  DWillett  WC Prospective study of major dietary patterns and risk of coronary heart disease in men. Am J Clin Nutr 2000;72912- 921
PubMed
Slattery  MLBoucher  KMCaan  BJPotter  JDMa  K-N Eating patterns and risk of colon cancer. Am J Epidemiol 1998;1484- 16
PubMed Link to Article
van Dam  RMRimm  EBWillett  WCStampfer  MJHu  FB Dietary patterns and risk for type 2 diabetes mellitus in US men. Ann Intern Med 2002;136201- 209
PubMed Link to Article
Colditz  GAMartin  PStampfer  MJ  et al.  Validation of questionnaire information on risk factors and disease outcomes in a prospective cohort study of women. Am J Epidemiol 1986;123894- 900
PubMed
Willett  WC Nutritional Epidemiology. 2nd ed. New York, NY Oxford University Press1998;
Salvini  SHunter  DJSampson  L  et al.  Food-based validation of a dietary questionnaire: the effects of week-to-week variation in food consumption. Int J Epidemiol 1989;18858- 867
PubMed Link to Article
National Diabetes Data Group, Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 1979;281039- 1059
PubMed Link to Article
Manson  JERimm  EBStampfer  MJ  et al.  Physical activity and incidence of non-insulin-dependent diabetes mellitus in women. Lancet 1991;338774- 778
PubMed Link to Article
Kleinbaum  DGKupper  LLMuller  KE Variable reduction and factor analysis.  Applied Regression Analysis and Other Multivariable Methods2nd ed. Pacific Grove, Calif Duxbury Press1988;595- 641
Kim  JOMueller  CW Factor Analysis: Statistical Methods and Practical Issues.  Thousand Oaks, Calif Sage Publications Inc1978;
Hu  FBRimm  EBSmith-Warner  SA  et al.  Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr 1999;69243- 249
PubMed
SAS Institute Inc, SAS/STAT User's Guide: Version 6. Vol 24th ed. Cary, NC SAS Institute Inc1989;
Fraser  GE Associations between diet and cancer, ischemic heart disease, and all-cause mortality in non-Hispanic white California Seventh-day Adventists. Am J Clin Nutr 1999;70 ((3 suppl)) 532s- 538s
PubMed
Fung  TRimm  EBSpiegelman  D  et al.  Association between dietary patterns and plasma biomarkers of obesity and cardiovascular disease risk. Am J Clin Nutr 2001;7361- 67
PubMed
Wirfalt  EHedblad  BGullberg  B  et al.  Food patterns and components of the metabolic syndrome in men and women: a cross sectional study within the Malmo Diet and Cancer Cohort. Am J Epidemiol 2001;1541150- 1159
PubMed Link to Article
Colditz  GAManson  JEStampfer  MJRosner  BWillett  WCSpeizer  FE Diet and risk of clinical diabetes in women. Am J Clin Nutr 1992;551018- 1023
PubMed
Parslow  RCMcKinney  PALaw  GRStaines  AWilliams  RBodansky  HJ Incidence of childhood diabetes mellitus in Yorkshire, northern England, is associated with nitrate in drinking water: an ecological analysis. Diabetologia 1997;40550- 556
PubMed Link to Article
Virtanen  SMJaakkola  LRasanen  L  et al. Childhood Diabetes in Finland Study Group, Nitrate and nitrite intake and the risk for type 1 diabetes in Finnish children. Diabet Med 1994;11656- 662
PubMed Link to Article
Portha  BGiroix  MHCros  JCPicon  L Diabetogenic effect of N-nitrosomethylurea and N-nitrosomethylurethane in adult rat. Ann Nutr Aliment 1980;341143- 1151
PubMed
Peppa  MGoldberg  TCai  WRayfield  EVlassara  H Glycotoxins: a missing link in the “relationship of dietary fat and meat intake in relation to risk of type 2 diabetes in men.” Diabetes Care 2002;251898- 1899
PubMed Link to Article
Cai  WGao  QDZhu  LPeppa  MHe  CVlassara  H Oxidative stress-inducing carbonyl compounds from common foods: novel mediators of cellular dysfunction. Mol Med 2002;8337- 346
PubMed
Biondi-Zoccai  GGLAbbate  ALiuzzo  GBiasucci  LM Atherosclerosis, inflammation, and diabetes. J Am Coll Cardiol 2003;411071- 1077
PubMed Link to Article
Piercy  VToseland  CDTurner  NC Potential benefit of inhibitors of advanced glycation end products in the progression of type II diabetes: a study with aminoguanidine in C57/BLKsJ diabetic mice. Metabolism 1998;471477- 1480
PubMed Link to Article
Hofmann  SMDong  HJLi  Z  et al.  Improved insulin sensitivity is associated with restricted intake of dietary glycoxidation products in the db/db mouse. Diabetes 2002;512082- 2089
PubMed Link to Article
Fung  TTHu  FBFuchs  C  et al.  Major dietary patterns and the risk of colorectal cancer in women. Arch Intern Med 2003;163309- 314
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure.

Relative risk (RR) for type 2 diabetes mellitus according to joint classifications of risk factors by quintile (Q) of Western pattern score. A, Body mass index (BMI; calculated as weight in kilograms divided by the square of height in meters); B, physical activity level; and C, family history of diabetes.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Age-Standardized Baseline Characteristics of NHS Participants in 1984 by Quintiles of Dietary Pattern Scores
Table Graphic Jump LocationTable 2. Relative Risk of Quintiles of Dietary Pattern Scores for Risk of Type 2 Diabetes*
Table Graphic Jump LocationTable 3. Multivariate* RR of Intake (Servings per Day) of Selected Contributors of the Western Pattern for Diabetes

References

Mokdad  AHFord  ESBowman  BA  et al.  Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 2003;28976- 79
PubMed Link to Article
Salmeron  JManson  JEStampfer  MJColditz  GAWing  ALWillett  WC Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women. JAMA 1997;277472- 477
PubMed Link to Article
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;25417- 424
PubMed Link to Article
Hu  FBRimm  EBStampfer  MJAscherio  ASpiegelman  DWillett  WC Prospective study of major dietary patterns and risk of coronary heart disease in men. Am J Clin Nutr 2000;72912- 921
PubMed
Slattery  MLBoucher  KMCaan  BJPotter  JDMa  K-N Eating patterns and risk of colon cancer. Am J Epidemiol 1998;1484- 16
PubMed Link to Article
van Dam  RMRimm  EBWillett  WCStampfer  MJHu  FB Dietary patterns and risk for type 2 diabetes mellitus in US men. Ann Intern Med 2002;136201- 209
PubMed Link to Article
Colditz  GAMartin  PStampfer  MJ  et al.  Validation of questionnaire information on risk factors and disease outcomes in a prospective cohort study of women. Am J Epidemiol 1986;123894- 900
PubMed
Willett  WC Nutritional Epidemiology. 2nd ed. New York, NY Oxford University Press1998;
Salvini  SHunter  DJSampson  L  et al.  Food-based validation of a dietary questionnaire: the effects of week-to-week variation in food consumption. Int J Epidemiol 1989;18858- 867
PubMed Link to Article
National Diabetes Data Group, Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 1979;281039- 1059
PubMed Link to Article
Manson  JERimm  EBStampfer  MJ  et al.  Physical activity and incidence of non-insulin-dependent diabetes mellitus in women. Lancet 1991;338774- 778
PubMed Link to Article
Kleinbaum  DGKupper  LLMuller  KE Variable reduction and factor analysis.  Applied Regression Analysis and Other Multivariable Methods2nd ed. Pacific Grove, Calif Duxbury Press1988;595- 641
Kim  JOMueller  CW Factor Analysis: Statistical Methods and Practical Issues.  Thousand Oaks, Calif Sage Publications Inc1978;
Hu  FBRimm  EBSmith-Warner  SA  et al.  Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr 1999;69243- 249
PubMed
SAS Institute Inc, SAS/STAT User's Guide: Version 6. Vol 24th ed. Cary, NC SAS Institute Inc1989;
Fraser  GE Associations between diet and cancer, ischemic heart disease, and all-cause mortality in non-Hispanic white California Seventh-day Adventists. Am J Clin Nutr 1999;70 ((3 suppl)) 532s- 538s
PubMed
Fung  TRimm  EBSpiegelman  D  et al.  Association between dietary patterns and plasma biomarkers of obesity and cardiovascular disease risk. Am J Clin Nutr 2001;7361- 67
PubMed
Wirfalt  EHedblad  BGullberg  B  et al.  Food patterns and components of the metabolic syndrome in men and women: a cross sectional study within the Malmo Diet and Cancer Cohort. Am J Epidemiol 2001;1541150- 1159
PubMed Link to Article
Colditz  GAManson  JEStampfer  MJRosner  BWillett  WCSpeizer  FE Diet and risk of clinical diabetes in women. Am J Clin Nutr 1992;551018- 1023
PubMed
Parslow  RCMcKinney  PALaw  GRStaines  AWilliams  RBodansky  HJ Incidence of childhood diabetes mellitus in Yorkshire, northern England, is associated with nitrate in drinking water: an ecological analysis. Diabetologia 1997;40550- 556
PubMed Link to Article
Virtanen  SMJaakkola  LRasanen  L  et al. Childhood Diabetes in Finland Study Group, Nitrate and nitrite intake and the risk for type 1 diabetes in Finnish children. Diabet Med 1994;11656- 662
PubMed Link to Article
Portha  BGiroix  MHCros  JCPicon  L Diabetogenic effect of N-nitrosomethylurea and N-nitrosomethylurethane in adult rat. Ann Nutr Aliment 1980;341143- 1151
PubMed
Peppa  MGoldberg  TCai  WRayfield  EVlassara  H Glycotoxins: a missing link in the “relationship of dietary fat and meat intake in relation to risk of type 2 diabetes in men.” Diabetes Care 2002;251898- 1899
PubMed Link to Article
Cai  WGao  QDZhu  LPeppa  MHe  CVlassara  H Oxidative stress-inducing carbonyl compounds from common foods: novel mediators of cellular dysfunction. Mol Med 2002;8337- 346
PubMed
Biondi-Zoccai  GGLAbbate  ALiuzzo  GBiasucci  LM Atherosclerosis, inflammation, and diabetes. J Am Coll Cardiol 2003;411071- 1077
PubMed Link to Article
Piercy  VToseland  CDTurner  NC Potential benefit of inhibitors of advanced glycation end products in the progression of type II diabetes: a study with aminoguanidine in C57/BLKsJ diabetic mice. Metabolism 1998;471477- 1480
PubMed Link to Article
Hofmann  SMDong  HJLi  Z  et al.  Improved insulin sensitivity is associated with restricted intake of dietary glycoxidation products in the db/db mouse. Diabetes 2002;512082- 2089
PubMed Link to Article
Fung  TTHu  FBFuchs  C  et al.  Major dietary patterns and the risk of colorectal cancer in women. Arch Intern Med 2003;163309- 314
PubMed Link to Article

Correspondence

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The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
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For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
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