AJCN 19th International Congress of Nutrition
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Millen, B. E
Right arrow Articles by D'Agostino, R. B
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Millen, B. E
Right arrow Articles by D'Agostino, R. B
Agricola
Right arrow Articles by Millen, B. E
Right arrow Articles by D'Agostino, R. B
American Journal of Clinical Nutrition, Vol. 84, No. 2, 434-441, August 2006
© 2006 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Nutritional risk and the metabolic syndrome in women: opportunities for preventive intervention from the Framingham Nutrition Study1,2,3

Barbara E Millen, Michael J Pencina, Ruth W Kimokoti, Lei Zhu, James B Meigs, Jose M Ordovas and Ralph B D'Agostino

1 From the Department of Family Medicine (BEM and RWK) and the Graduate Medical Sciences Division (BEM and RWK), Boston University School of Medicine, Boston, MA; the Department of Mathematics, Boston University, Boston, MA (MJP, LZ, and RBD); the General Internal Medicine Division, Department of Medicine, Massachusetts Hospital and Harvard Medical School, Boston, MA (JBM); and the Lipid Metabolism Laboratory, US Department of Agriculture Human Nutrition Research Center on Aging, Tufts University School of Medicine, Boston, MA (JMO)

2 Supported by grants R01-HL-60700 and R01-HL-54776 from the National Heart, Lung, and Blood Institute (NHLBI); N01-HC-25195 from NHLBI, National Institutes of Health (to The Framingham Study); and by a Career Development Award from the American Diabetes Association (to JBM).

3 Reprints not available. Address correspondence to BE Millen, Department of Family Medicine, Boston University School of Medicine, One Boston Medical Center Plaza, Dowling 5, Boston, MA 02118. E-mail: bmillen{at}bu.edu.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Diet is recognized as a key factor in the cause and management of the metabolic syndrome (MetS). However, policies to guide preventive clinical nutrition interventions of the condition are limited.

Objectives: We examined the relation between dietary quality and incident MetS in adult women and identified foci for preventive nutrition interventions.

Design: This was a prospective study of 300 healthy women (aged 30–69 y) in the Framingham Offspring-Spouse study who were free of MetS risk factors at baseline. The development of individual MetS traits and overall MetS status during 12 y of follow-up were compared in women by tertile of nutritional risk, based on intake of 19 nutrients. Multivariate logistic regression models considered age, smoking, physical activity, and menopausal status.

Results: Baseline age-adjusted mean nutrient intake and ischemic heart disease risk profiles differed by tertile of nutritional risk. Women with higher nutritional risk profiles consumed more dietary lipids (total, saturated, and monounsaturated fats) and alcohol and less fiber and micronutrients; they had higher cigarette use and waist circumferences. Compared with women with the lowest nutritional risk, those in the highest tertile had a 2- to 3-fold risk of the development of abdominal obesity and overall MetS during 12 y of follow-up [odds ratio: 2.3 (95% CI: 1.2, 4.3) and 3.0 (95% CI: 1.2, 7.6), respectively].

Conclusions: Higher composite nutritional risk predicts the development of abdominal obesity and MetS during long-term follow-up in healthy women, independent of lifestyle and ischemic heart disease risk factors. Preventive nutrition interventions for obesity and MetS risk reduction should focus on the overall nutritional quality of women's dietary profiles.

Key Words: Composite nutritional risk • dietary quality • ischemic heart disease risk • metabolic syndrome • abdominal obesity


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The metabolic syndrome (MetS), a clustering of ≥3 metabolic risk factors including impaired fasting glucose, elevated blood pressure, low HDL cholesterol, elevated triacylglycerol, and abdominal obesity, affects 29% of American women (1–5). Diabetes risk increases 10-fold in women with MetS, and the development of ischemic heart disease (IHD) in women aged <65 y occurs primarily in those with MetS or multiple IHD risk factors (4, 6). An 8-y prospective study of women and men participating in the Framingham Offspring Study (6) showed that MetS accounted for one-fifth of IHD events and more than half of newly diagnosed type 2 diabetes cases.

MetS is thought to have a genetic basis, but environmental factors—particularly obesity, physical inactivity, and diet—are largely implicated in the cause of the syndrome (1, 7, 8). The emergence of the current obesity epidemic (9) is likely to increase rates of MetS in the United States and cause higher rates of morbidity and mortality in women due to heart disease and diabetes (4, 5). MetS is identified as a target for secondary prevention in the National Cholesterol Education Program's Adult Treatment Panel III (NCEP ATP III; 4) in which emphasis is placed on improved nutrition, increased physical activity, and smoking cessation as central tenets of treatment. To date, however, epidemiologic research has provided only limited information to guide the development of targeted interventions, particularly preventive nutrition.

This study examines the relation between dietary quality, by using a validated measure of composite nutritional risk, and the risk of development of MetS in healthy women aged 30–69 y during 12 y of follow-up. Prospective multivariate analyses considered key diet and lifestyle factors, including physical activity and cigarette use, as well as biological and genetic covariates.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Participants
For >50 y, the Framingham Study has investigated the natural progression of IHD and, more recently, other health problems among residents of Framingham, MA (10; RB D'Agostino, WB Kannel, unpublished observations, 1988–1989). In 1971, a second-generation cohort was recruited, and 5124 Framingham Study offspring and their spouses (2483 men and 2641 women) were invited to participate in the Framingham Offspring-Spouse (FOS) study (11).

Members of the FOS study cohort participate approximately every 4 y in standardized clinical assessments, including a complete physical examination, laboratory tests, noninvasive diagnostic testing, and updating of medical histories and other pertinent information. At FOS study exam 3, in 1984–1988, we fully characterized the nutrient intake of the Framingham Offspring. At exam 4, 1988–1992, risk factor profiles of women in the FOS study were evaluated in a manner that allowed the full evaluation of MetS risk according to the most recent NCEP expert criteria. Women aged 18–76 y (n = 2005) participated at exam 3 (83% of the original offspring cohort women), and 967 (48%) of these women provided complete data on baseline diet (exam 3) and IHD risk factors (exam 4) during 12 y of follow-up (through exam 7, 1998–2001). Approximately one-third of these women (n = 300) were completely free of any MetS characteristics at baseline. Baseline IHD and MetS risk factor profiles of subjects included in these analyses (n = 300) did not differ from those of subjects who were not followed because of missing nutrition or covariate data (n = 310), except that our sample was slightly older (48.6 compared with 46.5 y) and had lower smoking rates (14% compared with 28.6%).

As shown in Table 1Go, women without MetS risk factors at baseline had age and physical activity profiles similar to those of women in the FOS study who had ≥1 MetS risk factors. Dietary intakes did not differ for most nutrients; observed differences were generally 3–6% except alcohol consumption, which was low in both groups (3.5% and 2.7%, respectively). However, the remaining IHD risk profiles of women without MetS risk were lower than those for women with MetS risk, which is consistent with our intent to examine MetS development in healthy women free of endpoint characteristics.


View this table:
[in this window]
[in a new window]
  		TABLE 1. Dietary and risk factor profile of women in the Framingham Offspring-Spouse Study, 1984–1988 (n = 967)1

 
All participants provided written informed consent. The Boston University Medical Center's Human Subjects Institutional Review Board approved all protocols.

Nutrient intake and composite nutrition risk score
Nutrient intake was estimated from 3-d dietary records by using a validated, published method (12, 13). Participants were instructed by a registered dietitian in the clinic to record their intake during 2 weekdays and 1 weekend day, while adhering to their usual eating practices. Subjects were trained to estimate portion sizes by using a validated 2-dimensional food portion visual (13). Dietary records were processed by trained coders who adhered to standardized protocols. Nutrient calculations were performed by using MINNESOTA NUTRITION DATA SYSTEM software (version 2.6; Food Database 6A; Nutrient Database 23; Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN; 14).

The nutritional risk score is a validated 19-nutrient index for assessing diet quality (15, 16). Nutrient intakes in all women in the FOS study (n = 1265) with 3-d dietary records were ranked from 1 to 1265. Ranks were assigned so that a person with a somewhat more desirable intake (eg, lower fat or higher vitamin or mineral intake) received a lower rank, whereas a person with a less desirable intake (eg, higher fat or lower micronutrient intake) received a higher rank. An overall composite nutritional risk rank was computed by using the mean of the ranks of 19 individual nutrients, including energy; protein; total fat; monounsaturated and saturated fats; alcohol; cholesterol; sodium; carbohydrate; polyunsaturated fat; fiber; calcium; selenium; vitamins C, B-6, B-12, and E; folate; and ß-carotene. Among Framingham study participants, a higher intake of monounsaturated fats received a higher rating because it is derived from animal sources (eg, beef fat) rather than from vegetable sources (eg, olive oil).

Risk factor measurements and interpretation
Risk factors are routinely measured at Framingham study examinations (17) according to extensively published methods, including fasting lipid (17–20) and plasma glucose (3) concentrations; APOE genotypes (21–23); duplicate blood pressure measurements (24); weight and height (25); abdominal obesity (26); self-reported age, smoking, and physical activity; and confirmed menopausal status (27). NCEP ATP III cutoffs (4) were used to evaluate study subjects' IHD risk factor characteristics and to diagnose incident MetS by baseline nutritional risk status.

Statistical analysis
The primary research aim was to examine associations between the dietary quality assessed by our validated composite nutritional risk measure and the development of individual MetS risk factors and overall MetS risk. Multivariable analyses were adjusted for age, smoking, physical activity, and menopausal status. Analyses were restricted to women in the FOS study who were free of IHD, diabetes, MetS, and any MetS risk factors at exam 4. For MetS as an outcome and its individual risk factors in their categorical form [eg, elevated glucose (≥110 mg/dL), elevated blood pressure (≥130/≥85 mm Hg), elevated triacylglycerol (≥150 mg/dL), low HDL cholesterol (<50 mg/dL), and abdominal obesity (waist circumference > 88 cm)], we calculated the covariate-adjusted odds ratios (ORs) for each nutritional risk tertile and used the lowest tertile as the referent group. The tests were conducted in a 2-stage approach: first, we ascertained the significance of the risk rank tertile at the 0.10 level and then compared tertiles 2 and 3 of risk rank with tertile 1 that served as reference at the 0.05 level. Because logistic regression and chi-square testing were used, Tukey's test was not available. However, we also ascertained that our findings remain significant even after Bonferroni's adjustment within each model. The covariates included continuous age and physical activity, and smoking (yes or no) and menopausal status (yes or no) were treated as categorical variables. SAS procedure LOGISTIC was used (28). Secondary analyses that adjusted for energy and APOE genotype did not alter our findings. Analyses were performed by using SAS software (version 8.2; SAS Institute, Cary, NC).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
At baseline, the nutrient intakes of these healthy women varied by dietary quality (Table 2Go). Women with the highest nutritional risk profiles, based on the composite 19-nutrient measure of dietary quality, had lower energy intakes, higher percentages of energy from dietary lipids (total, saturated, and monounsaturated fats) and alcohol, and lower total carbohydrate intakes and fiber density, as well as lower amounts of all micronutrients (excluding vitamin B-12). Relative to women with the lowest nutritional risk, fewer women in the middle and highest tertiles complied with the NCEP ATP III dietary criteria for intakes of total fat, cholesterol, carbohydrate, and fiber (Table 3Go). Compliance for saturated fat and fiber was particularly poor regardless of risk score. IHD risk profiles of women at baseline (Table 4Go) differed by selected characteristics in that those with the highest nutritional risk had a slightly higher waist circumference (74 cm compared with 72 cm) than did the women with a lower nutritional risk. Women with the highest nutritional risk had smoking rates ≥3-fold those of women with the lowest nutritional risk. Tables 5Go and 6, respectively, examine the ORs for MetS risk factors and overall MetS and the rates of development of these characteristics by tertile of nutritional risk. The OR for developing abdominal obesity during 12 y of follow-up in women in the highest tertile of nutritional risk was twice (OR: 2.3; 95% CI: 1.2, 4.3) that in women in the middle (OR: 1.1; 95% CI: 0.6, 1.9) or the lowest (referent) tertile. Development of MetS in women in the highest tertile of nutritional risk was 3 times that in women in the lowest tertile (OR: 3.0, 95% CI: 1.2, 7.6). The OR for developing hypertension and elevated glucose, low HDL-cholesterol, and elevated triacylglycerol concentrations did not vary by baseline nutritional risk during follow-up in these healthy women. More women developed abdominal obesity than developed any other MetS risk factor, and the rate of abdominal obesity development varied by tertile of nutritional risk (44.2%, 45.8%, and 64.6% in tertiles 1, 2 and 3, respectively).


View this table:
[in this window]
[in a new window]
  		TABLE 2. Age-adjusted mean nutrient intake by risk score tertiles of women in the Framingham Offspring-Spouse Study, 1984–1988 (n = 300)1

 

View this table:
[in this window]
[in a new window]
  		TABLE 3. Age-adjusted proportions of women in the Framingham Offspring-Spouse Study who complied with the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP-III) dietary guidelines, 1984–1988 (n = 300)1

 

View this table:
[in this window]
[in a new window]
  		TABLE 4. Age-adjusted risk factor profile by risk score tertiles of women in the Framingham Offspring-Spouse Study, 1988–1992 (n = 300)1

 

View this table:
[in this window]
[in a new window]
  		TABLE 5. Odds ratio for metabolic syndrome (MetS) components and MetS in women in the Framingham Offspring-Spouse study (n = 300)1

 

View this table:
[in this window]
[in a new window]
  		TABLE 6. Multivariate-adjusted rates (per 100 women) of development of metabolic syndrome (MetS) and MetS components in women in the Framingham Offspring-Spouse study (n = 300)1

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Relatively poor dietary quality, characterized by higher composite nutritional risk profiles, was associated with the 12-y development of abdominal obesity and MetS in healthy Framingham study women aged 30–69 y, independent of age, smoking, physical activity, or menopausal status. Higher nutritional risk, attributed to a combination of higher dietary lipids (percentage of energy from total, saturated, and monounsaturated fats) and alcohol consumption and to lower intakes of total carbohydrates, fiber, and micronutrients, was associated with a 2- to 3-fold increase in the rates of abdominal obesity and MetS development. Although we found that poor dietary quality was not related to the development of other MetS risk factors during 12 y of follow-up in these women, we note that it predicted MetS overall and abdominal obesity, the most prevalent emerging MetS risk factor in the United States (5) and these healthy Framingham women.

Most research to date on the relations between indexes of dietary quality and MetS risk or its constituent risk factors is cross-sectional in nature but supports our findings. McKeown et al (29) examined the association between the dietary glycemic index, a ranking of carbohydrate-containing foods based on their effect on blood glucose (30), and MetS in the FOS study cohort. Compared with men and women in the lowest quintile of glycemic index, those in the highest quintile had a 41% greater risk of MetS (OR: 1.41; 95% CI: 1.04, 1.91). In analyses of the third National Health and Nutrition Examination Survey data set, Guo et al (31) used the US Department of Agriculture Healthy Eating Index (HEI), a 10-factor composite measure of dietary quality (32), to examine the relation between compliance with the US Department of Agriculture's Dietary Guidelines for Americans (33) and the Food Guide Pyramid (34) and obesity risk. Subjects with a poor HEI score had a risk of obesity twice that in subjects with a good HEI score; ORs for obesity in women and men were 1.7 (95% CI: 1.2, 2.6) and 1.9 (95% CI: 1.1, 3.3), respectively. Ours is the first study to conduct longitudinal research on overall dietary quality and the development of MetS and its component traits.

The individual nutrients we found to be associated with our composite index of dietary quality have also been examined in relation to MetS risk in previous research, and the findings are largely consistent with our research. Cross-sectional studies suggest that low and moderate intakes of carbohydrate (NCEP ATP III criteria; 35); higher intakes of fiber (29), fruit and vegetables (high in vitamin C and ß-carotene; 36), vitamin C, and ß-carotene (37); and light-to-moderate alcohol consumption (35, 38–41) confer a lower risk of MetS. Conversely, higher total dietary fat intake was found to increase MetS risk in prospective research (42). It is important to note that cross-sectional literature does not distinguish between simple and complex carbohydrates, even though they may differentially affect MetS risk. In terms of individual MetS risk factors, higher consumption of vitamins C and E, ß-carotene, and selenium has been shown to be associated with lower levels of abdominal obesity cross-sectionally (37), as have higher intakes of fiber (43, 44) and fruit and vegetables (45) in prospective analyses. Cross-sectional research also shows heavy alcohol consumption to be associated with higher risk of abdominal obesity (41, 46).

We identified abdominal obesity as the most prevalent MetS risk factor to emerge during long-term follow-up in these healthy women in the FOS study, a finding confirmed in national prevalence data (5). Furthermore, NCEP ATP III recognizes abdominal obesity as a key underlying feature of MetS (47, 48). It is postulated that obesity is a proinflammatory state that contributes to insulin resistance, a condition that is suggested to cause dyslipidemia, glucose intolerance, and elevated blood pressure, in addition to exacerbating obesity. The factors produced by adipose tissue that are assumed to contribute to the inflammatory state are cytokines (eg, tumor necrosis factor {alpha}, interleukin 6, leptin, resistin, C-reactive protein, and plasminogen activator inhibitor 1) and nonesterified fatty acids that induce insulin resistance by interfering with insulin signal transduction and hence glucose transport (47–49). Obesity per se also contributes to the development of hypertension and low HDL cholesterol (50), risk factors that appeared in {approx}20–30% of these healthy women in the FOS study. Hyperglycemia, another risk factor that is influenced by obesity, was uncommon in our subjects. From the dietary perspective, macronutrients, including fat and simple carbohydrates, are thought to produce oxidative stress that also stimulates the inflammatory responses in obesity (49). Other macronutrients and micronutrients such as fiber, fruit, vegetables, alcohol, and vitamin E are anti-inflammatory, and they suppress oxidative stress (49).

Women with the highest nutritional risk had higher smoking rates; had larger waist circumferences; had lower intakes of energy, carbohydrate, fiber, and most micronutrients; and consumed more dietary lipids and alcohol than did women with the lowest nutritional risk. The 2 groups of women did not differ significantly in baseline BMI status. The poor overall quality of their diets contributed to the development of abdominal obesity. Our findings concur with other research, which observes that lifestyle behaviors are related and that persons with better dietary quality consume higher levels of energy and more nutrient-dense diets (51–53).

Our findings provide a response to expert position statements that have urged research on overall dietary quality to improve the understanding of the numerous modifiable determinants of disease risk that may guide the development of innovative, focused, and individualized preventive intervention strategies (4, 54, 55). Risk of MetS was highest in women with a higher composite nutritional risk who consumed both a high dietary lipid density and smaller amounts of carbohydrate, fiber, and micronutrients. As is consistent with current expert preventive nutrition guidelines, each of these components of dietary quality could be targeted along with weight management, increased physical activity, and avoidance of smoking to lower MetS and obesity risk. Our previous research (15, 16) also showed a close association between dietary quality, assessed by composite nutritional risk scores, and 5 habitual dietary patterns of women that were characterized in the FOS study cohort. The unique food preferences of women in each dietary pattern subgroup provide a further framework and specific food behavior targets for preventive nutrition intervention planning at the individual and population levels. Recommendations for such strategies were published previously (15, 16, 27, 56–58).

It is a strength of this research that disease-free women of a broad age range were followed during an extended time for the emergence of MetS risk. Among the limitations of these findings is the lack of follow-up assessment of subjects' nutritional risk over time. Such changes could result in misclassification bias that would attenuate the estimated diet-disease relations. We suggest that the findings reported here may underestimate the true relations between nutritional risk and MetS. Future research should refine the nutritional risk score by incorporating both simple and complex carbohydrates and possibly other dietary factors such as nutrient density. Moreover, results may not be generalizable to minority women because the Framingham cohort is predominantly white. Nevertheless, the finding that poor diet predicted the development of abdominal obesity and MetS in healthy younger and middle-aged women during long-term follow-up underscores the importance of diet in the cause of MetS and should be considered in developing preventive nutrition interventions.


   ACKNOWLEDGMENTS
 
BEM provided overall direction to this research and to the preparation of the manuscript; MJP carried out the statistical analyses and wrote the analytical methods section of the manuscript; RWK summarized the data and contributed to writing the manuscript; JBM contributed to writing the manuscript; JMO was the Principal Investigator of one of the funded research projects that supported this work; RBD oversaw the statistical analyses and the interpretation of the data; LZ contributed to the statistical analyses. None of the authors had personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Meigs JB. Epidemiology of the metabolic syndrome, 2002. Am J Manag Care 2002; 8: S283–92.[Medline]
  2. Liese AD, Mayer-Davies EJ, Haffner SM. Development of the multiple metabolic syndrome: an epidemiologic perspective. Epidemiol Rev 1998; 20: 157–72.[Free Full Text]
  3. Meigs JB, D'Agostino RB Sr, Wilson PW, Cupples LA, Nathan DM, Singer DE. Risk variable clustering in insulin resistance syndrome: the Framingham Offspring Study. Diabetes 1997; 46: 1594–600.[Abstract]
  4. NCEP Expert Panel on the Detection, Evaluation, and Treatment of High Blood Pressure in Adults. Executive summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001; 285: 2486–97.[Free Full Text]
  5. Ford ES, Giles WH, Mokdad AH. Increasing prevalence of the metabolic syndrome among US adults. Diabetes Care 2004; 27: 2444–9.[Abstract/Free Full Text]
  6. Wilson PWF, D'Agostino RB Sr, Parise H, Meigs JB. The metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Diabetes 2002; 51: A242 (abstr).
  7. Willett WC. Will high-carbohydrate/low-fat diets reduce the risk of coronary heart disease? Proc Soc Exp Biol Med 2000; 225: 187–90.[Free Full Text]
  8. Wirfalt E, Hedblad B, Gullberg 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; 154: 1150–9.[Abstract/Free Full Text]
  9. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM. Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA 2004; 29: 2847–50.
  10. Dawber TR. The Framingham Study: the epidemiology of atherosclerotic disease. Cambridge, MA: Harvard University Press, 1980.
  11. Kannel WB, Feinleib M, McNamara PM, Garrison RJ, Castelli WP. An investigation of coronary heart disease in families: the Framingham Offspring Study. Am J Epidemiol 1979; 110: 281–90.[Abstract/Free Full Text]
  12. Posner BM, Martin-Munley SS, Smigelski C, et al. Comparison of techniques for estimating nutrient intake: the Framingham Study. Epidemiology 1992; 3: 171–7.[Medline]
  13. Posner BM, Smigelski C, Duggal A, Morgan JL, Cobb J, Cupples LA. Validation of two-dimensional models for estimation of portion size in nutrition research. J Am Diet Assoc 1992; 92: 738–41.[Medline]
  14. Schakel SF, Sievert YA, Buzzard IM. Sources of data for developing and maintaining a nutrient database. J Am Diet Assoc 1988; 88: 1268–71.[Medline]
  15. Millen BE, Quatromoni PA, O'Horo CE, Demissie S, D'Agostino RB, Copenhafer DL. Validation of a dietary pattern approach for evaluating nutritional risk: the Framingham Nutrition Studies. J Am Diet Assoc 2001; 101: 187–94.[Medline]
  16. Quatromoni PA, Copenhafer DL, Demissie S, et al. The internal validity of the dietary pattern analysis: the Framingham Nutrition Studies. J Epidemiol Community Health 2002; 56: 381–8.[Abstract/Free Full Text]
  17. Cupples LA, D'Agostino RB. Some risk factors related to the annual incidence of cardiovascular disease and death by using pooled repeated biennial measurements: Framingham Heart Study, 30-year follow-up. In: Kannel WB, Wolf PA, Garrison RJ, eds. The Framingham Study: an epidemiological investigation of cardiovascular disease. Washington, DC: Department of Health and Human Services; 1987. [NIH publication 87-2703 (NTIS PB87-177499).]
  18. McNamara JR, Schaefer EJ. Automated enzymatic standardized lipid analyses for plasma and lipoprotein fractions. Clin Chem Acta 1987; 166: 1–8.[Medline]
  19. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without the use of preparative ultracentrifuge. Clin Chem 1972; 18: 499–502.[Abstract]
  20. Warnick GR, Benderson J, Albers JJ. Dextran sulfate-magnesium precipitation procedure for quantification of high-density lipoprotein cholesterol. Clin Chem 98228: 1379–88.
  21. Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 1989; 16: 1215.
  22. Austin MA, Ordovas JM, Eckfeldt JH, et al. Guidelines of the National Heart, Lung, and Blood Institute Working Group on Blood Drawing, Processing, and Storage for Genetic Studies. Am J Epidemiol 1996; 144: 437–41. Erratum published in Am J Epidemiol 1997;145:570.[Free Full Text]
  23. Hixson JE, Vernier DT. Restriction isotyping of human apolipoprotein E by gene amplification and cleavage with HhaI. J Lipid Res 1990; 31: 545–8.[Abstract]
  24. Thomas JE, Schirger A, Fealey RD, Sheps SG. Orthostatic hypotension. Mayo Clin Proc 1981; 56: 117–25.[Medline]
  25. Abraham S, Johnson CL, Najjar MF. Weight and height of adults 18–74 years of age. United States, 1971–1974. Vital Health Stat 11 1979; 211: 1–49.
  26. Stoudt H, Damon A, McFarland R. Skinfolds, body girths, biacromial diameter and selected anthropometric indices of adults, United States, 1960–1962. Vital Health Stat 11 1970; 35.
  27. Quatromoni PA, Copenhafer DL, D'Agostino RB, Millen BE. Dietary patterns predict the development of overweight in women: the Framingham Nutrition Studies. J Am Diet Assoc 2002; 102: 1240–6.
  28. SAS/STAT user's guide, version 6, vols 1 and 2. 4th ed. Cary, NC: SAS Institute, 1989: 1641–59.
  29. McKeown NM, Meigs JB, Liu S, Saltzman E, Wilson PW, Jacques PF. Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring cohort. Diabetes Care 2004; 27: 538–46.[Abstract/Free Full Text]
  30. Jenkins DJ, Wolever TM, Taylor RH, et al. Glycemic index of foods: a physiologic basis for carbohydrate exchange. Am J Clin Nutr 1981; 34: 362–6.[Abstract/Free Full Text]
  31. Guo X, Warden BA, Paeratakul S, Bray GA. Healthy Eating Index and obesity. Eur J Clin Nutr 2004; 58: 1580–6.[Medline]
  32. Kennedy ET, Ohls J, Carlson S, Fleming K. The Healthy Eating Index: design and applications. J Am Diet Assoc 1995; 95: 1103–8.[Medline]
  33. US Department of Agriculture, US Department of Health and Human Services. Nutrition and your health: Dietary Guidelines for Americans. Washington, DC: US Government Printing Office, 2000.
  34. US Department of Agriculture. The Food Guide Pyramid. Hyattsville, MD: Human Nutrition Information Service, August 1992. (home and garden bulletin No. 252.)
  35. Zhu S, St-Onge M-P, Heshka S, Heymsfield SB. Lifestyle behaviors associated with lower risk of having the metabolic syndrome. Metabolism 2004; 53: 1503–11.[Medline]
  36. Yoo S, Nicklas T, Baranowski T, et al. Comparison of dietary intakes associated with metabolic syndrome risk factors in young adults: the Bogalusa Heart Study. Am J Clin Nutr 2004; 80: 841–8.[Abstract/Free Full Text]
  37. Ford ES, Mokdad AH, Giles WH, Brown DW. The metabolic syndrome and antioxidant concentrations: findings from the third National Health and Nutrition Examination Survey. Diabetes 2003; 52: 2346–52.[Abstract/Free Full Text]
  38. Freiberg MS, Cabral HJ, Heeren TC, Vasan RS, Ellison CR; third National Health and Nutrition Examination Survey. Alcohol consumption and the prevalence of the metabolic syndrome in the US. A cross-sectional analysis of data from the third National Health and Nutrition Examination Survey. Diabetes Care 2004; 27: 2954–9.[Abstract/Free Full Text]
  39. Park HS, Oh SW, Cho SI, Choi WH, Kim YS. The metabolic syndrome and associated lifestyle factors among South Korean adults. Int J Epidemiol 2004; 33: 328–36.[Abstract/Free Full Text]
  40. Djousse L Arnett DK, Eckfeldt JH, Province MA, Singer MR, Ellison RC. Alcohol consumption and metabolic syndrome: does the type of beverage matter? Obes Res 2004; 12: 1375–85.[Medline]
  41. Yoon YS, Oh SW, Baik HW, Park HS, Kim WY. Alcohol consumption and the metabolic syndrome in Korean adults: the 1998 Korean National Health and Nutrition Examination Survey. Am J Clin Nutr 2004; 80: 217–24.[Abstract/Free Full Text]
  42. Carnethon MR, Loria CM, Hill JO, et al. Risk factors for the metabolic syndrome: the Coronary Artery Risk Development in Young Adults (CARDIA) Study, 1985–2001. Diabetes Care 2004; 27: 2707–15.[Abstract/Free Full Text]
  43. Koh-Banerjee P, Chu NF, Spiegelman D, et al. Prospective study of the association of changes in dietary intake, physical activity, alcohol consumption, and smoking with 9-y gain in waist circumference among 16 587 US men. Am J Clin Nutr 2003; 78: 719–27.[Abstract/Free Full Text]
  44. Liu S, Willett WC, Manson JE, Hu FB, Rosner B, Colditz G. Relation between changes in intakes of dietary fiber and grain products and changes in weight and development of obesity among middle-aged women. Am J Clin Nutr 2003; 78: 920–7.[Abstract/Free Full Text]
  45. He K, Hu FB, Colditz GA, Manson JE, Willett WC, Liu S. Changes in intake of fruits and vegetables in relation to risk of obesity and weight gain among middle-aged women. Int J Obes Relat Metab Disord 2004; 28: 1569–74.[Medline]
  46. Lee WY, Jung CH, Park JS, Rhee EJ, Kim SW. Effects of smoking, alcohol, exercise, education, and family history on the metabolic syndrome as defined by the ATP III. Diabetes Res Clin Pract 2005; 67: 70–7.[Medline]
  47. Grundy SM, Brewer HB Jr, Cleeman JI, et al. Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association Conference on Scientific Issues Related to Definition. Circulation 2004; 109: 433–8.[Free Full Text]
  48. Grundy SM, Hansen B, Smith SC Jr, et al. Clinical management of metabolic syndrome: report of the American Heart Association/National Heart, Lung, and Blood Institute/American Diabetes Association Conference on Scientific Issues Related to Management. Circulation 2004; 109: 551–6.[Free Full Text]
  49. Dandona P, Aljada A, Chaudhuri A, Mohanty P, Garg R. Metabolic syndrome: a comprehensive perspective based on interactions between obesity, diabetes, and inflammation. Circulation 2005; 111: 1448–54.[Free Full Text]
  50. Grundy SM. Obesity, metabolic syndrome, and cardiovascular disease. J Clin Endocrinol Metab 2004; 89: 2595–600.[Free Full Text]
  51. McCullough ML, Feskanich D, Rimm EB, et al. Adherence to dietary guidelines for Americans and risk of major chronic disease in women. Am J Clin Nutr 2000; 72: 1214–22.[Abstract/Free Full Text]
  52. McCullough ML, Feskanich D, Rimm EB, et al. Adherence to dietary guidelines for Americans and risk of major chronic disease in men. Am J Clin Nutr 2000; 72: 1223–31.[Abstract/Free Full Text]
  53. McCullough ML, Feskanich D, Stampfer MJ, et al. Diet quality and major chronic disease risk in men and women: moving toward improved dietary guidance. Am J Clin Nutr 2002; 76: 1261–71.[Abstract/Free Full Text]
  54. Freeland-Graves J, Nitzke S. Position of the American Dietetic Association: total diet approach to communicating food and nutrition information. J Am Diet Assoc 2002; 102: 100–8.[Medline]
  55. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opinion Lipidol 2002; 13: 3–9.[Medline]
  56. Millen BE, Quatromoni PA, Nam BH, O'Horo CE, Polak JF, D'Agostino RB. Dietary patterns and the odds of carotid atherosclerosis in women: the Framingham Nutrition Studies. Prev Med 2002; 35: 540–7.[Medline]
  57. Millen BE, Quatromoni PA, Nam BH, et al. Dietary patterns, smoking, and sub-clinical heart disease in women: opportunities for primary prevention from the Framingham Nutrition Studies. J Am Diet Assoc 2004; 104: 208–14.[Medline]
  58. Sonnenberg L, Pencina M, Kimokoti R, et al. Dietary patterns and the metabolic syndrome in obese and non-obese Framingham women. Obes Res 2005; 13: 153–62.[Medline]
Received for publication January 21, 2006. Accepted for publication March 30, 2006.




This article has been cited by other articles:


Home page
 Eur J EndocrinolHome page
S. Bo, G. Ciccone, S. Guidi, R. Gambino, M. Durazzo, L. Gentile, M. Cassader, P. Cavallo-Perin, and G. Pagano
Diet or exercise: what is more effective in preventing or reducing metabolic alterations?
Eur. J. Endocrinol., December 1, 2008; 159(6): 685 - 691.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Clin. Nutr.Home page
D. Giugliano, A. Ceriello, and K. Esposito
Are there specific treatments for the metabolic syndrome?
Am. J. Clinical Nutrition, January 1, 2008; 87(1): 8 - 11.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Chem.Home page
E. K. Kabagambe, M. Y. Tsai, P. N. Hopkins, J. M. Ordovas, J. M. Peacock, I. B. Borecki, and D. K. Arnett
Erythrocyte Fatty Acid Composition and the Metabolic Syndrome: A National Heart, Lung, and Blood Institute GOLDN Study
Clin. Chem., January 1, 2008; 54(1): 154 - 162.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Millen, B. E
Right arrow Articles by D'Agostino, R. B
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Millen, B. E
Right arrow Articles by D'Agostino, R. B
Agricola
Right arrow Articles by Millen, B. E
Right arrow Articles by D'Agostino, R. B

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS