Skip to main content
Top
Published in: Diabetologia 7/2006

01-07-2006 | Article

Practical way to assess metabolic syndrome using a continuous score obtained from principal components analysis

The D.E.S.I.R. Cohort*

Authors: T. A. Hillier, A. Rousseau, C. Lange, P. Lépinay, M. Cailleau, M. Novak, E. Calliez, P. Ducimetière, B. Balkau

Published in: Diabetologia | Issue 7/2006

Login to get access

Abstract

Aims/hypothesis

We devised a practical continuous score to assess the metabolic syndrome, and assessed whether this syndrome score predicts incident diabetes and cardiovascular disease.

Subjects and methods

Among 5,024 participants of the Data from an Epidemiological Study on the Insulin Resistance Syndrome (D.E.S.I.R.) cohort, we defined a metabolic syndrome score by the first principal component (PC1), using only the correlations between continuous metabolic syndrome measures (glucose, waist circumference, triglycerides, and systolic blood pressure). This metabolic syndrome score was highly correlated with a similar score also including insulin and HDL cholesterol (r s =0.94). Over 9 years of follow-up, incident diabetes and cardiovascular disease (CVD) were predicted by logistic regression using the simpler metabolic syndrome score.

Results

The means of the metabolic syndrome measures differed between men and women. Nevertheless, as the degree of variance explained and the PC1 coefficients were remarkably similar, we used a common metabolic syndrome score. The metabolic syndrome score explained 50% of the variance of the metabolic syndrome measures, and waist circumference had the highest correlation (0.59) with this score. Each standard deviation increase in the metabolic syndrome score was associated with a markedly increased age-adjusted risk of developing diabetes (odds ratios: men 3.4 [95% CI 2.6–4.4]; women 5.1 [3.6–7.2]) and with increased incident CVD of 1.7 (1.4–2.1) in men and 1.7 (1.0–2.7) in women.

Conclusions/interpretation

Our results, which should be confirmed in other populations, suggest that it is possible to evaluate the risk of the metabolic syndrome in a pragmatic fashion with a continuous score, obtained from principal components analysis of the basic, continuous syndrome measures.
Literature
1.
go back to reference Reaven GM (1988) Banting lecture. Role of insulin resistance in human disease. Diabetes 37:1595–1607PubMedCrossRef Reaven GM (1988) Banting lecture. Role of insulin resistance in human disease. Diabetes 37:1595–1607PubMedCrossRef
2.
go back to reference DeFronzo RA, Ferrannini E (1991) Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care 14:173–194PubMedCrossRef DeFronzo RA, Ferrannini E (1991) Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care 14:173–194PubMedCrossRef
3.
go back to reference Stern MP (1995) Diabetes and cardiovascular disease. The “common soil” hypothesis. Diabetes 44:369–374PubMedCrossRef Stern MP (1995) Diabetes and cardiovascular disease. The “common soil” hypothesis. Diabetes 44:369–374PubMedCrossRef
4.
go back to reference World Health Organization Consultation (1999) Part 1: Diagnosis and classification of diabetes mellitus. In: Report of a WHO Consultation (eds) Definition, diagnosis and classification of diabetes mellitus and its complications. WHO, Geneva, Switzerland World Health Organization Consultation (1999) Part 1: Diagnosis and classification of diabetes mellitus. In: Report of a WHO Consultation (eds) Definition, diagnosis and classification of diabetes mellitus and its complications. WHO, Geneva, Switzerland
5.
go back to reference Balkau B, Charles MA (1999) Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med 16:442–443PubMedCrossRef Balkau B, Charles MA (1999) Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med 16:442–443PubMedCrossRef
6.
go back to reference Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (2001) 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 285:2486–2497CrossRef Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (2001) 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 285:2486–2497CrossRef
7.
go back to reference Alberti KG, Zimmet P, Shaw J; IDF Epidemiology Task Force Consensus Group (2005) The metabolic syndrome—a new worldwide definition. Lancet 366:1059–1062PubMedCrossRef Alberti KG, Zimmet P, Shaw J; IDF Epidemiology Task Force Consensus Group (2005) The metabolic syndrome—a new worldwide definition. Lancet 366:1059–1062PubMedCrossRef
8.
go back to reference Grundy SM, Cleeman JI, Daniels SR et al (2005) Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation 112:2735–2752PubMedCrossRef Grundy SM, Cleeman JI, Daniels SR et al (2005) Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation 112:2735–2752PubMedCrossRef
9.
go back to reference Ford ES (2005) Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence. Diabetes Care 28:1769–1778PubMedCrossRef Ford ES (2005) Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence. Diabetes Care 28:1769–1778PubMedCrossRef
10.
go back to reference Kahn R, Buse J, Ferrannini E, Stern M (2005) The metabolic syndrome: time for a critical appraisal. Joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetologia 48:1684–1699PubMedCrossRef Kahn R, Buse J, Ferrannini E, Stern M (2005) The metabolic syndrome: time for a critical appraisal. Joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetologia 48:1684–1699PubMedCrossRef
11.
go back to reference Balkau B, Vernay M, Mhamdi L et al (2003) D.E.S.I.R. Study Group. The incidence and persistence of the NCEP (National Cholesterol Education Program) metabolic syndrome. The French D.E.S.I.R. study. Diabetes Metab 29:526–532PubMedCrossRef Balkau B, Vernay M, Mhamdi L et al (2003) D.E.S.I.R. Study Group. The incidence and persistence of the NCEP (National Cholesterol Education Program) metabolic syndrome. The French D.E.S.I.R. study. Diabetes Metab 29:526–532PubMedCrossRef
12.
go back to reference Jackson JE (1991) A user’s guide to principal components analysis. Wiley, New York Jackson JE (1991) A user’s guide to principal components analysis. Wiley, New York
13.
go back to reference Jolliffe IT (2002) Principal components analysis, 2nd edn. Springer, Berlin Heidelberg New York Jolliffe IT (2002) Principal components analysis, 2nd edn. Springer, Berlin Heidelberg New York
14.
go back to reference Meigs JB (2000) Invited commentary: insulin resistance syndrome? Syndrome X? Multiple metabolic syndrome? A syndrome at all? Factor analysis reveals patterns in the fabric of correlated metabolic risk factors. Am J Epidemiol 152:908–911PubMedCrossRef Meigs JB (2000) Invited commentary: insulin resistance syndrome? Syndrome X? Multiple metabolic syndrome? A syndrome at all? Factor analysis reveals patterns in the fabric of correlated metabolic risk factors. Am J Epidemiol 152:908–911PubMedCrossRef
15.
go back to reference Lawlor DA, Ebrahim S, May M, Davey Smith G (2004) (Mis)use of factor analysis in the study of insulin resistance syndrome. Am J Epidemiol 159:1013–1018PubMedCrossRef Lawlor DA, Ebrahim S, May M, Davey Smith G (2004) (Mis)use of factor analysis in the study of insulin resistance syndrome. Am J Epidemiol 159:1013–1018PubMedCrossRef
16.
go back to reference Maison P, Byrne CD, Hales CN et al (2001) Do different dimensions of the metabolic syndrome change together over time? Evidence supporting obesity as the central feature. Diabetes Care. 24:1758–1763PubMedCrossRef Maison P, Byrne CD, Hales CN et al (2001) Do different dimensions of the metabolic syndrome change together over time? Evidence supporting obesity as the central feature. Diabetes Care. 24:1758–1763PubMedCrossRef
17.
go back to reference Hanson RL, Imperatore G, Bennett PH et al (2002) Components of the ‘metabolic syndrome’ and incidence of type 2 diabetes. Diabetes 51:3120–3127PubMedCrossRef Hanson RL, Imperatore G, Bennett PH et al (2002) Components of the ‘metabolic syndrome’ and incidence of type 2 diabetes. Diabetes 51:3120–3127PubMedCrossRef
18.
go back to reference Kekalainen P, Sarlund H, Pyorala K, Laakso M (1999) Hyperinsulinemia cluster predicts the development of type 2 diabetes independently of family history of diabetes. Diabetes Care 22:86–92PubMedCrossRef Kekalainen P, Sarlund H, Pyorala K, Laakso M (1999) Hyperinsulinemia cluster predicts the development of type 2 diabetes independently of family history of diabetes. Diabetes Care 22:86–92PubMedCrossRef
19.
go back to reference Lempiainen P, Mykkanen L, Pyorala K, Laakso M, Kuusisto J (1999) Insulin resistance syndrome predicts coronary heart disease events in elderly nondiabetic men. Circulation 100:123–128PubMed Lempiainen P, Mykkanen L, Pyorala K, Laakso M, Kuusisto J (1999) Insulin resistance syndrome predicts coronary heart disease events in elderly nondiabetic men. Circulation 100:123–128PubMed
20.
go back to reference Tang W, Miller MB, Rich SS et al (2003) National Heart, Lung, and Blood Institute Family Heart Study. Linkage analysis of a composite factor for the multiple metabolic syndrome: the National Heart, Lung, and Blood Institute Family Heart Study. Diabetes 52:2840–2847PubMedCrossRef Tang W, Miller MB, Rich SS et al (2003) National Heart, Lung, and Blood Institute Family Heart Study. Linkage analysis of a composite factor for the multiple metabolic syndrome: the National Heart, Lung, and Blood Institute Family Heart Study. Diabetes 52:2840–2847PubMedCrossRef
21.
go back to reference Hosmer DW, Lemeshow S (1989) Assessing the fit of the model. Applied logistic regression. Wiley, New York Hosmer DW, Lemeshow S (1989) Assessing the fit of the model. Applied logistic regression. Wiley, New York
22.
go back to reference American Diabetes Association (2004) Diagnosis and classification of diabetes mellitus. Diabetes Care 27:S5–S10CrossRef American Diabetes Association (2004) Diagnosis and classification of diabetes mellitus. Diabetes Care 27:S5–S10CrossRef
23.
go back to reference Genuth S, Alberti KG, Bennett P et al (2003) Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 26:3160–3167PubMedCrossRef Genuth S, Alberti KG, Bennett P et al (2003) Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 26:3160–3167PubMedCrossRef
24.
go back to reference DECODE Study Group (2003) Age- and sex-specific prevalences of diabetes and impaired glucose regulation in 13 European cohorts. Diabetes Care 26:61–69CrossRef DECODE Study Group (2003) Age- and sex-specific prevalences of diabetes and impaired glucose regulation in 13 European cohorts. Diabetes Care 26:61–69CrossRef
25.
go back to reference Barrett-Connor E (1980) The prevalence of diabetes mellitus in an adult community as determined by history or fasting hyperglycemia. Am J Epidemiol 111:705–712PubMed Barrett-Connor E (1980) The prevalence of diabetes mellitus in an adult community as determined by history or fasting hyperglycemia. Am J Epidemiol 111:705–712PubMed
26.
go back to reference Kuulasmaa K, Tunstall-Pedoe H, Dobson A et al (2000) Estimation of contribution of changes in classic risk factors to trends in coronary-event rates across the WHO MONICA Project populations. Lancet 355:675–687PubMedCrossRef Kuulasmaa K, Tunstall-Pedoe H, Dobson A et al (2000) Estimation of contribution of changes in classic risk factors to trends in coronary-event rates across the WHO MONICA Project populations. Lancet 355:675–687PubMedCrossRef
27.
go back to reference Dekker JM, Girman C, Rhodes T et al (2005) Metabolic syndrome and 10-year cardiovascular disease risk in the Hoorn Study. Circulation 112:666–673PubMedCrossRef Dekker JM, Girman C, Rhodes T et al (2005) Metabolic syndrome and 10-year cardiovascular disease risk in the Hoorn Study. Circulation 112:666–673PubMedCrossRef
28.
go back to reference Eberly LE, Prineas R, Cohen JD et al (2006) Metabolic syndrome: risk factor distribution and 18-year mortality in the multiple risk factor intervention trial. Diabetes Care 29:123–130PubMedCrossRef Eberly LE, Prineas R, Cohen JD et al (2006) Metabolic syndrome: risk factor distribution and 18-year mortality in the multiple risk factor intervention trial. Diabetes Care 29:123–130PubMedCrossRef
Metadata
Title
Practical way to assess metabolic syndrome using a continuous score obtained from principal components analysis
The D.E.S.I.R. Cohort*
Authors
T. A. Hillier
A. Rousseau
C. Lange
P. Lépinay
M. Cailleau
M. Novak
E. Calliez
P. Ducimetière
B. Balkau
Publication date
01-07-2006
Publisher
Springer-Verlag
Published in
Diabetologia / Issue 7/2006
Print ISSN: 0012-186X
Electronic ISSN: 1432-0428
DOI
https://doi.org/10.1007/s00125-006-0266-8

Other articles of this Issue 7/2006

Diabetologia 7/2006 Go to the issue