Diabetes Care
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Published online April 17, 2007
Diabetes Care 30:1851-1856, 2007
DOI: 10.2337/dc06-2152
© 2007 by the American Diabetes Association
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
dc06-2152v1
30/7/1851    most recent
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
Services
Right arrow Email this article to a friend
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 Request Permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by de Simone, G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by de Simone, G.
Social Bookmarking
Add to CiteULike   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

Cardiovascular and Metabolic Risk
Original Article

Prognostic Impact of Metabolic Syndrome by Different Definitions in a Population With High Prevalence of Obesity and Diabetes

The Strong Heart Study

Giovanni de Simone, MD1,2, Richard B. Devereux, MD1, Marcello Chinali, MD2, Lyle G. Best, MD3, Elisa T. Lee, PHD4, James M. Galloway, MD, MPH5, Helaine E. Resnick, PHD6 for the Strong Heart Study Investigators

1 Division of Cardiology, Weill Medical College of Cornell University, New York, New York
2 Department of Clinical and Experimental Medicine, Federico II University, Naples, Italy
3 Missouri Breaks Industries Research, Timber Lake, South Dakota
4 Center for American Indian Health Research, University of Oklahoma, Oklahoma City, Oklahoma
5 Indian Health Service, University of Arizona, Tucson, Arizona
6 Medstar Research Institute, Washington, DC

Address correspondence and reprint requests to Giovanni de Simone, MD, Department of Clinical and Experimental Medicine, Federico II University Hospital, v.S.Pansini 5, 80131 Naples, Italy. E-mail: simogi{at}unina.it


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESEARCH DESIGN AND METHODS--
 RESULTS--
 CONCLUSIONS--
 References
 
OBJECTIVE— This study analyzed which definition of the metabolic syndrome is more predictive of cardiovascular events in both diabetic and nondiabetic members of a population-based sample.

RESEARCH DESIGN AND METHODS— A 10-year, longitudinal follow-up of the Strong Heart Study cohort has been evaluated. The analysis included 3,945 participants (2,384 female) with complete data (1,700 with diabetes and 1,468 with arterial hypertension) for evaluation of metabolic syndrome. Those with prevalent cardiovascular disease were excluded (n = 287, of whom 127 were female). Prevalence of metabolic syndrome was assessed based on the World Health Organization (WHO), the National Cholesterol Education Program Adult Treatment Panel (NCEP ATP) III, and International Diabetes Federation (IDF) definitions. The main outcome was 10-year incidence of combined fatal and nonfatal cardiovascular events, including stroke, coronary heart disease, and congestive heart failure.

RESULTS— Fatal and nonfatal cardiovascular events occurred in 1,120 participants. After adjusting for age, sex, and diabetes, metabolic syndrome by all definitions was significantly associated with higher incidence of cardiovascular events (all P < 0.0001). In nondiabetic individuals, incident cardiovascular event rates were about 30–40% higher in those with metabolic syndrome, without a significant difference among definitions (0.03 < P < 0.001), and remained significant in WHO and NCEP ATP III definitions even after further adjustment for obesity, hypertension, and low HDL cholesterol. In the diabetic group, metabolic syndrome risk for cardiovascular events was greatest using the WHO definition (P < 0.002 vs. other models).

CONCLUSIONS— In individuals without diabetes, metabolic syndrome is associated with incident cardiovascular disease, especially with WHO and NCEP ATP III definitions. Metabolic syndrome also predicts higher cardiovascular event rates in diabetic participants, a prediction that is greatest using the WHO definition.

Abbreviations: HOMA, homeostasis model assessment • IDF, International Diabetes Federation • NCEP ATP, National Cholesterol Education Program Adult Treatment Panel • SHS, Strong Heart Study • WHO, World Health Organization


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESEARCH DESIGN AND METHODS--
 RESULTS--
 CONCLUSIONS--
 References
 
The metabolic syndrome represents clusters of cardiovascular risk factors, assuming that cardiovascular risk is amplified more than is expected from the effect of single risk factors (1). Many studies support the utility of this definition (2,3), but others have questioned the incremental utility of this approach (4).

An element of potential confusion concerning the real prognostic utility of defining metabolic syndrome is the availability of several definitions (5–9), reflecting different strategies, either identifying a main characteristic as a necessary factor (5,7,9) associated with other variable risk factors or accepting varied combinations of characteristics (6,8). Other differences may also be important, including partition values and methods to define abnormalities.

At present, few data exist comparing the correlates and prognostic significance of the definitions of metabolic syndrome in the same population. Accordingly, we compared the ability of the most used definitions of metabolic syndrome to predict cardiovascular events in the Strong Heart Study (SHS) cohort.


   RESEARCH DESIGN AND METHODS—
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS--
RESULTS--
 CONCLUSIONS--
 References
 
The SHS is a population-based cohort study of cardiovascular risk factors and disease in 4,549 American Indians from three communities in Arizona, seven in Southwestern Oklahoma, and three in South and North Dakota, as extensively described (10–14). Participants seen during the baseline exam, in 1989–1992, were representative of the source population (15).

For the present analysis, individuals with prevalent cardiovascular disease (n = 287, 127 of whom were female) were excluded. Prevalent and incident cardiovascular events (cardiovascular death, stroke, congestive heart failure, myocardial infarction, and coronary heart disease [coronary angiography, combination of typical symptoms with positive treadmill tests or abnormal imaging stress test, or revascularization procedures]) were confirmed by the SHS mortality and morbidity committees, using specified criteria for causes of fatal and nonfatal cardiovascular events (16).

Diabetic participants were included; participants with fasting triglyceride levels >750 mg/dl were excluded. Thus, 3,945 participants (2,384 women) without prevalent cardiovascular disease and available data (1,700 with diabetes and 1,468 with hypertension) were included in the analysis.

Laboratory tests and definitions of metabolic syndrome
Fasting plasma glucose and lipid profile were measured by standard methods (12). Diabetes (fasting plasma glucose ≥126 mg/dl or antidiabetes treatment) and impaired fasting glucose (≥110 mg/dl) were diagnosed by 1997 American Diabetes Association recommendations (17). Obesity was classified based on the 1998 National Institutes of Health guidelines (18) (BMI ≥30 kg/m2). Central fat distribution was based on waist circumference and defined in relation to sex-specific cutoff points used in the different definitions examined in this study (Table 1). For the International Diabetes Federation (IDF) definition, the values proposed for Europids have been adopted. A random urine sample was used to measure albumin and creatinine (11).


View this table:
[in this window]
[in a new window]

  		Table 1— Definition of metabolic syndrome according according to the WHO, NCEP ATP III, and IDF

 
Table 1 shows the criteria used to define metabolic syndrome by three guidelines: those by the World Health Organization (WHO) (5,9), the National Cholesterol Education Program Adult Treatment Panel (NCEP ATP) III (6), and the IDF (9). Insulin-glucose homeostasis for the WHO definition was estimated by the homeostasis model assessment (HOMA) equation (19). Based on the WHO recommendation, a partition value for HOMA index was arbitrarily determined in the nondiabetic SHS participants as the lower boundary of the highest tertile (4.3). Thus, insulin resistance status was defined as the presence of type 2 diabetes or fasting glucose ≥110 mg/l or HOMA index >4.3. Hypertension was defined by Joint National Committee VII criteria (blood pressure ≥140/90 mmHg or use of antihypertensive treatment).

Statistical analysis
Data were analyzed using SPSS (version 12.0; SPSS, Chicago, IL). Data are expressed as means ± SD. All variables deviating from normal distribution were log transformed before parametric statistics. The urinary albumin-to-creatinine ratio is presented as median (interquartile range). An indicator variable was included for the three field centers: Arizona, South and North Dakota, and Oklahoma. Participants were categorized into groups according to the presence of metabolic syndrome, defined by each definition.

The 10-year relative risk of combined fatal and nonfatal cardiovascular events was estimated for each definition. Log-cumulative hazard functions were computed by Cox regression, adjusting for age (years), field center, sex, and diabetes. Additional models were also adjusted for the other components of metabolic syndrome. Cox regression was also run separately for diabetic and nondiabetic participants.

To compare the independent prognostic effect of metabolic syndrome by the three definitions, likelihood functions were compared (20). The difference between two –2 log likelihoods has a {chi}2 distribution, which, for this comparison, has 1 d.f. (20). Two-tailed {alpha} ≤ 0.01 identified significant differences among the three models.


   RESULTS—
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS--
 RESULTS--
CONCLUSIONS--
 References
 
Table 2 shows that the IDF definition yielded the highest prevalence of metabolic syndrome in both men and women. The WHO definition resulted in a similar proportion of metabolic syndrome in men and women, whereas NCEP ATP III and IDF showed a substantially higher prevalence in women, with the greatest sex difference by the NCEP ATP III definition (both P < 0.0001). The coefficient of concordance (k) among the different definitions in the recognition of metabolic syndrome status was not excellent: 0.56 and 0.59 for men and women between WHO and NCEP ATP III, 0.58 and 0.77 between NCEP ATP III and IDF, and only 0.49 and 0.50 between WHO and IDF definitions, respectively.


View this table:
[in this window]
[in a new window]

  		Table 2— Metabolic syndrome by different definitions (see Table 1): concordance and differences in blood pressure and urinary albumin-to-creatinine ratio

 
Table 2 also shows that, due to differing high blood pressure criteria, the WHO definition was associated with slightly higher blood pressure levels than was NCEP ATP III or IDF. Urinary albumin-to-creatinine ratio was highest in participants with metabolic syndrome by the WHO definition (including this parameter among criteria for definition), but also higher in the NCEP ATP III and IDF definitions (all P < 0.0001), than in participants without metabolic syndrome.

The criteria for adiposity proposed by the WHO identified 83% of participants with central fat distribution in the absence of metabolic syndrome (compared with 96% in those with metabolic syndrome). The difference between groups without (66% with central fat) vs. with metabolic syndrome (100% with central fat) was more accentuated in the IDF and maximal with the NCEP ATP III definition (50 vs. 91%, respectively). In all definitions, one-half or more of individuals free of the syndrome exhibited central fat distribution.

Cardiovascular risk in the metabolic syndrome
Over the follow-up time (119 ± 45 months), 1,157 cardiovascular events were adjudicated, including 176 strokes, 299 myocardial infarctions, 226 other clinical manifestations of coronary heart disease, 394 cases of congestive heart failure, and 62 sudden deaths. The incidence of combined fatal and nonfatal cardiovascular events was 2.38-fold greater in participants with than in those without metabolic syndrome (95% CI 2.04–2.73) by the WHO definition, 2.12-fold greater (1.81–2.47) by the ATP III definition, and 1.92-fold greater (1.62–2.27) by the IDF definition (all P < 0.0001). Table 3 shows that metabolic syndrome was always associated with increased rate of cardiovascular events (all P < 0.0001), even independent of age, sex, field center, and presence of diabetes. The regression model including metabolic syndrome by the WHO definition was significantly more predictive than the other models, including the other definitions (both P < 0.002).


View this table:
[in this window]
[in a new window]

  		Table 3— The 10-year hazard for incident fatal/nonfatal cardiovascular associated with metabolic syndrome, according to three different definitions, adjusting for age, sex, field center, and diabetes

 
Alternative Cox regression models adjusted for obesity, low HDL cholesterol, and hypertension, in addition to the covariates used in the previous model. Although reduced, the hazard ratios (HRs) of metabolic syndrome remained statistically significant for the definitions by the NCEP ATP III (HR 1.28 [95% CI 1.04–1.56], P < 0.02) and the WHO (1.35 [1.11–1.64], P < 0.002) but not by IDF (1.12 [0.92–1.36], P > 0.2).

Cardiovascular risk in diabetic and nondiabetic subjects
In both diabetic and nondiabetic participants, the prognostic effect of metabolic syndrome was confirmed for all three definitions (Table 4). The HR for incident composite fatal and nonfatal events was ~30–40% higher in nondiabetic participants with metabolic syndrome, by all definitions, without significant differences among them (Fig. 1). In contrast, in diabetic participants, the HR for the metabolic syndrome was not statistically significant using the IDF definition (26% increased risk), was higher with the NCEP ATP III definition (43% increased risk), and was highest with the WHO definition (near-doubled risk), a difference that was statistically significantly (P < 0.001).


View this table:
[in this window]
[in a new window]

  		Table 4— The 10-year hazard for incident fatal/nonfatal cardiovascular events in separate nondiabetic and diabetic subgroups of the SHS, in relation to presence of metabolic syndrome, according to 3 different definitions, adjusting for age, sex, and field center

 

Figure 1
View larger version (28K):
[in this window]
[in a new window]

  		Figure 1— Adjusted cumulative hazard in participants with (—) or without (Figure 1) metabolic syndrome, in nondiabetic or diabetic participants, according to diagnostic criteria issued by the WHO (top panel), ATP III (middle panel), or IDF (bottom panel).

 

   CONCLUSIONS—
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS--
 RESULTS--
 CONCLUSIONS--
References
 
This study demonstrates that in the SHS cohort, metabolic syndrome by all three examined definitions is independently associated with a significantly greater rate of incident composite fatal and nonfatal cardiovascular events and is a marker of preclinical cardiovascular disease, represented by the increased urinary albumin-to-creatinine ratio. The independent association of metabolic syndrome with cardiovascular events was seen in population strata with or without diabetes.

In participants without diabetes, increased cardiovascular risk is independently associated with metabolic syndrome, suggesting that this diagnosis can help identify high-risk individuals. Similarly, among diabetic individuals, recognition of the metabolic syndrome impressively enhanced the prediction of cardiovascular events, consistent with National Health and Nutrition Examination Survey III results (21), where the age-adjusted prevalence of coronary heart disease in the diabetic population without metabolic syndrome (8%) was similar to that in subjects without diabetes or metabolic syndrome (9%), while increasing to 14% in nondiabetic subjects with metabolic syndrome and to 19% in those with both conditions. However, independent of the presence of full-fledged metabolic syndrome, attention to all cardiovascular risk factors is paramount in the presence of diabetes. Compared with nondiabetic populations, detecting the full presentation of metabolic syndrome might be less important for decision making when diabetes coexists with even one additional risk factor.

This study identifies similarities among the three definitions of metabolic syndrome but also reveals differences. In the whole population, combining participants with or without diabetes, the model using the WHO definition was a better predictor than models using NCEP ATP III or IDF definitions, with IDF exhibiting the lowest fit. When forcing single cardiovascular risk factors into the proportional hazard model, only the WHO and ATP III definitions maintained an independent prognostic impact, whereas the IDF definition did not, possibly due to lower specificity associated with the generally lower partition values for single factors.

In nondiabetic participants, the risks predicted by the three definitions were not statistically different. In contrast, a substantial difference was evident among diabetic participants, with the WHO definition significantly superior to those of both the NCEP ATP III and IDF (the HR of which was not statistically significant). Although the NCEP ATP III definition was not intended for diabetic individuals, it should be noted that it performed well in participants with or without diabetes, though slightly less well than WHO in those with diabetes.

To evaluate correctly the implications of these findings, both the characteristics of the SHS population and the differences among the different definitions of metabolic syndrome have to be taken into account. The American Indian population of the SHS is characterized by higher prevalence of obesity than the overall population in the U.S. and Europe (12), though this difference is rapidly attenuating (22–25). As a consequence, in the SHS cohort, the distribution of measures of adiposity is skewed toward higher values, similar to findings in hypertensive populations (26). Due to the high prevalence of obesity, the adopted measures of fatness in the WHO definition do not discriminate well between participants with or without the metabolic syndrome. Also with the other definitions, substantial proportions of participants without metabolic syndrome exhibited central fat distribution, maximal with the IDF definition, which uses very low partition values for waist girth. Thus, in the context of a population with very high prevalence of obesity, measures of adiposity or abdominal obesity do not strongly separate individuals with or without metabolic syndrome, since obesity is also highly prevalent among individuals without metabolic syndrome.

The present analysis extends a previous study of metabolic syndrome in nondiabetic SHS participants. That analysis did not report significant independent associations between metabolic syndrome (by the NCEP ATP III definition) and cardiovascular risk (27), but it included only 4.2 years of follow-up and was limited to some cardiac events. In contrast, the present analysis also included diabetic participants, the follow-up was substantially longer, and all clinical manifestations of cardiovascular disease, including cerebrovascular events and congestive heart failure, were considered as end points. In particular, incident congestive heart failure (394 adjudicated events) could be very important, because heart failure is a relevant end point for obesity (28–30). Overall, our findings suggest that the metabolic syndrome may take several years to manifest its effects on clinical events (as Fig. 1 also suggests).

In conclusion, the NCEP ATP III and WHO (but not IDF) definitions of metabolic syndrome predict cardiovascular disease independently of single components of the syndrome; their value may be similar in individuals without diabetes, but in diabetes the WHO definition seems to be more useful. Results of these analyses, obtained in a population with high prevalences of obesity and diabetes, are likely applicable to other populations of different ethnicities in which there is an epidemic rise of prevalences of overweight and obesity (31), triggering diabetes and other metabolic abnormalities (32). The identification of the metabolic syndrome can be valuable to focus aggressive intervention strategies, especially when diabetes has not yet occurred.


   Acknowledgments
 
This work has been supported by grants HL41642, HL41652, HL41654, HL65521, and M10RR0047-34 (General Clinical Research Center) from the National Institutes of Health, Bethesda, Maryland.

The authors wish to thank the Indian Health Service, the SHS participants, the participating tribal communities, and the SHS Center coordinators for their help in the realization of this project.


   Footnotes
 
Published ahead of print at http://care.diabetesjournals.org on 17 April 2007. DOI: 10.2337/dc06-2152.

The views expressed in this paper are those of the authors and do not necessarily reflect those of the Indian Health Service.

A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C Section 1734 solely to indicate this fact.

Received for publication October 18, 2006. Accepted for publication March 30, 2007.


   References
TOP
 ABSTRACT
 INTRODUCTION
 RESEARCH DESIGN AND METHODS--
 RESULTS--
 CONCLUSIONS--
 References
 

  1. Kaplan NM: The deadly quartet and the insulin resistance syndrome: an historical overview. Hypertens Res 19 (Suppl 1):S9–S11, 1996
  2. Gotto AM Jr, Blackburn GL, Dailey GE, III, Garber AJ, Grundy SM, Sobel BE, Weir MR: The metabolic syndrome: a call to action. Coron Artery Dis 17:77–80, 2006[Medline]
  3. Grundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C: Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 109:433–438, 2004[Free Full Text]
  4. Kahn R, Buse J, Ferrannini E, Stern M: The metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 28:2289–2304, 2005[Abstract/Free Full Text]
  5. World Health Organization: Definition, Diagnosis, and Classification of Diabetes and its Complications: Report of a WHO Consultation. Part 1: Diagnosis and Classification of Diabetes Mellitus. Geneva, World Health Organization, 1999 (Tech. Rep. Ser., no.)
  6. 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) final report. Circulation 106:3143–3421, 2002[Free Full Text]
  7. Balkau B, Charles MA, Drivsholm T, Borch-Johnsen K, Wareham N, Yudkin JS, Morris R, Zavaroni I, van Dam R, Feskins E, Gabriel R, Diet M, Nilsson P, Hedblad B: Frequency of the WHO metabolic syndrome in European cohorts, and an alternative definition of an insulin resistance syndrome. Diabetes Metab 28:364–376, 2002[Medline]
  8. Einhorn D, Reaven GM, Cobin RH, Ford E, Ganda OP, Handelsman Y, Hellman R, Jellinger PS, Kendall D, Krauss RM, Neufeld ND, Petak SM, Rodbard HW, Seibel JA, Smith DA, Wilson PW: American College of Endocrinology position statement on the insulin resistance syndrome. Endocr Pract 9:237–252, 2003[Medline]
  9. Alberti G: Introduction to the metabolic syndrome. Eur Heart J (Suppl 7):D3–D5, 2005
  10. Lee ET, Welty TK, Fabsitz R, Cowan LD, Le NA, Oopik AJ, Cucchiara AJ, Savage PJ, Howard BV: The Strong Heart Study: a study of cardiovascular disease in American Indians: design and methods. Am J Epidemiol 136:1141–1155, 1990
  11. Howard BV, Lee ET, Yeh JL, Go O, Fabsitz RR, Devereux RB, Welty TK: Hypertension in adult American Indians: the Strong Heart Study. Hypertension 28:256–264, 1996[Abstract/Free Full Text]
  12. Welty TK, Lee ET, Yeh J, Cowan LD, Go O, Fabsitz RR, Le NA, Oopik AJ, Robbins DC, Howard BV: Cardiovascular disease risk factors among American Indians: the Strong Heart Study. Am J Epidemiol 142:269–287, 1995[Abstract/Free Full Text]
  13. Lee ET, Cowan LD, Welty TK, Sievers M, Howard WJ, Oopik A, Wang W, Yeh J, Devereux RB, Rhoades ER, Fabsitz RR, Go O, Howard BV: All-cause mortality and cardiovascular disease mortality in three American Indian populations, aged 45–74 years, 1984–1988: the Strong Heart Study. Am J Epidemiol 147:995–1008, 1998[Abstract/Free Full Text]
  14. Devereux RB, Roman MJ, Paranicas M, O'Grady MJ, Lee ET, Welty TK, Fabsitz RR, Robbins D, Rhoades ER, Howard BV: Impact of diabetes on cardiac structure and function: the Strong Heart Study. Circulation 101:2271–2276, 2000[Abstract/Free Full Text]
  15. Howard BV, Lee ET, Cowan LD, Fabsitz RR, Howard WJ, Oopik AJ, Robbins DC, Savage PJ, Yeh JL, Welty TK: Coronary heart disease prevalence and its relation to risk factors in American Indians: the Strong Heart Study. Am J Epidemiol 142:254–268, 1995[Abstract/Free Full Text]
  16. Howard BV, Lee ET, Cowan LD, Devereux RB, Galloway JM, Go OT, Howard WJ, Rhoades ER, Robbins DC, Sievers ML, Welty TK: Rising tide of cardiovascular disease in American Indians: the Strong Heart Study. Circulation 99:2389–2395, 1999[Abstract/Free Full Text]
  17. American Diabetes Association: Clinical practice recommendations 1997. Diabetes Care 20 (Suppl.1):S1–S70, 1997
  18. National Institutes of Health: Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report. Obes Res 6:51S–209S, 1998[Medline]
  19. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC: Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28:412–419, 1985[Medline]
  20. Collett D: Modelling Survival Data in Medical Research. London, Chapman & Hill, 1996
  21. Alexander CM, Landsman PB, Teutsch SM, Haffner SM: NCEP-defined metabolic syndrome, diabetes, and prevalence of coronary heart disease among NHANES III participants age 50 years and older. Diabetes 52:1210–1214, 2003[Abstract/Free Full Text]
  22. Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP: The continuing epidemics of obesity and diabetes in the United States. JAMA 286:1195–1200, 2001[Abstract/Free Full Text]
  23. Okosun IS, Chandra KM, Boev A, Boltri JM, Choi ST, Parish DC, Dever GE: Abdominal adiposity in U.S. adults: prevalence and trends, 1960–2000. Prev Med 39:197–206, 2004[Medline]
  24. Silventoinen K, Sans S, Tolonen H, Monterde D, Kuulasmaa K, Kesteloot H, Tuomilehto J: Trends in obesity and energy supply in the WHO MONICA Project. Int J Obes Relat Metab Disord 28:710–718, 2004[Medline]
  25. Haftenberger M, Lahmann PH, Panico S, Gonzalez CA, Seidell JC, Boeing H, Giurdanella MC, Krogh V, Bueno-de-Mesquita HB, Peeters PH, Skeie G, Hjartaker A, Rodriguez M, Quiros JR, Berglund G, Janlert U, Khaw KT, Spencer EA, Overvad K, Tjonneland A, Clavel-Chapelon F, Tehard B, Miller AB, Klipstein-Grobusch K, Benetou V, Kiriazi G, Riboli E, Slimani N: Overweight, obesity and fat distribution in 50- to 64-year-old participants in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 5:1147–1162, 2002[Medline]
  26. de Simone G, Palmieri V, Bella JN, Celentano A, Hong Y, Oberman A, Kitzman DW, Hopkins PN, Arnett DK, Devereux RB: Association of left ventricular hypertrophy with metabolic risk factors: the HyperGEN study. J Hypertens 20:323–331, 2002[Medline]
  27. Resnick HE, Jones K, Ruotolo G, Jain AK, Henderson J, Lu W, Howard BV: Insulin resistance, the metabolic syndrome, and risk of incident cardiovascular disease in nondiabetic American Indians: the Strong Heart Study. Diabetes Care 26:861–867, 2003[Abstract/Free Full Text]
  28. Kasper EK, Hruban RH, Baughman KL: Cardiomyopathy of obesity: a clinicopathologic evaluation of 43 obese patients with heart failure. Am J Cardiol 70:921–924, 1992[Medline]
  29. Eckel RH: Obesity and heart disease: a statement for healthcare professionals from the Nutrition Committee, American Heart Association. Circulation 96:3248–3250, 1997[Free Full Text]
  30. Kenchaiah S, Evans JC, Levy D, Wilson PW, Benjamin EJ, Larson MG, Kannel WB, Vasan RS: Obesity and the risk of heart failure. N Engl J Med 347:305–313, 2002[Abstract/Free Full Text]
  31. Kraja AT, Rao DC, Weder AB, Mosley TH, Turner ST, Hsiung CA, Quertermous T, Cooper R, Curb JD, Province MA: An evaluation of the metabolic syndrome in a large multi-ethnic study: the Family Blood Pressure Program. Nutr Metab (Lond) 2:17, 2005[Medline]
  32. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, Marks JS: Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 289:76–79, 2003[Abstract/Free Full Text]

Add to CiteULike CiteULike   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?


This article has been cited by other articles:


Home page
 NDT PlusHome page
P. K.-T. Li, B. C.-H. Kwan, C. C. Szeto, and G. T.-C. Ko
Metabolic syndrome in peritoneal dialysis patients
NDT Plus, August 1, 2008; 1(4): 206 - 214.
[Abstract] [Full Text] [PDF]

Home page
 J. Clin. Pathol.Home page
R Olufadi and C D Byrne
Clinical and laboratory diagnosis of the metabolic syndrome
J. Clin. Pathol., June 1, 2008; 61(6): 697 - 706.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
dc06-2152v1
30/7/1851    most recent
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
Services
Right arrow Email this article to a friend
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 Request Permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by de Simone, G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by de Simone, G.
Social Bookmarking
Add to CiteULike   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Diabetes Diabetes Care Clinical Diabetes Diabetes Spectrum