Insulin resistance alone does not explain the clustering of cardiovascular risk factors in southern India

Snehalatha, Chamukuttan; Sivasankari, S.; Kumpatla, Satyavani; Vijay, V.; Ramachandran, Ambady

doi:10.1046/j.1464-5491.2000.00239.x

Cited by 46 publications

(41 citation statements)

References 24 publications

(51 reference statements)

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“…Despite the subjective nature of factor analysis and differences as to which variables are included, results of these studies have shown some consistency. Many of them, like the present study, have identified a four-factor solution with factors representing obesity, insulin-glucose metabolism, lipid metabolism, and blood pressure, with some minor variation in composition of individual factors (7)(8)(9)(10)(11)(12). Others have identified a three-factor solution that combines some of these entities; commonly, obesity is combined with lipid and/or insulin variables (13)(14)(15)(16)(17)(18)(19)(20)(21)(22).…”

Section: Discussionmentioning

confidence: 57%

“…Thus, factor analysis is well suited for identifying components of the metabolic syndrome, and several analyses have been undertaken for this purpose (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). Prospective epidemiological studies of factor "scores" from these analyses can further determine relations between components of the metabolic syndrome and incidence of diabetes.…”

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confidence: 99%

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Components of the “Metabolic Syndrome” and Incidence of Type 2 Diabetes

et al. 2002

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The combination of insulin resistance, dyslipidemia, hypertension, and obesity has been described as a "metabolic syndrome" that is a strong determinant of type 2 diabetes. Factor analysis was used to identify components of this syndrome in 1,918 Pima Indians. Prospective analyses were conducted to evaluate associations of identified factors with incidence of diabetes. Factor analysis identified 4 factors that accounted for 79% of the variance in the original 10 variables. Each of these factors reflected a proposed component of the metabolic syndrome: insulinemia, body size, blood pressure, and lipid metabolism. Among 890 originally nondiabetic participants with follow-up data, 144 developed diabetes in a median follow-up of 4.1 years. The insulinemia factor was strongly associated with diabetes incidence (incidence rate ratio [IRR] for a 1-SD difference in factor scores ‫؍‬ 1.81, P < 0.01). The body size and lipids factors also significantly predicted diabetes (IRR 1.52 and 1.37, respectively, P < 0.01 for both), whereas the blood pressure factor did not (IRR 1.11, P ‫؍‬ 0.20). Identification of four unique factors with different associations with incidence of diabetes suggests that the correlations among these variables reflect distinct metabolic processes, about which substantial information may be lost in the attempt to combine them into a single entity. Diabetes 51:3120 -3127, 2002 T ype 2 diabetes and cardiovascular disease have many risk factors in common, and many of these risk factors are highly correlated with one another (1-3). The relationships among these risk factors may be attributable to a small number of physiological phenomena, perhaps even a single phenomenon. The combination of hypertension, dyslipidemia, insulin resistance, hyperinsulinemia, glucose intolerance, and obesity, particularly central obesity, has been termed the "metabolic syndrome." It has been proposed that this syndrome is a powerful determinant of diabetes and cardiovascular disease (3-6). There are few prospective data, however, on the extent to which this syndrome or its constituent components predict incidence of type 2 diabetes.Factor analysis is a mathematical technique by which a large number of correlated variables can be reduced to fewer "factors" that represent distinct attributes that account for a large proportion of the variance in the original variables. Thus, factor analysis is well suited for identifying components of the metabolic syndrome, and several analyses have been undertaken for this purpose (7-26). Prospective epidemiological studies of factor "scores" from these analyses can further determine relations between components of the metabolic syndrome and incidence of diabetes. In the present study, factor analysis is used to identify components of the metabolic syndrome in Pima Indians, an American Indian population with a high prevalence of type 2 diabetes and obesity (27,28), and relations of these components to incidence of diabetes are examined. RESEARCH DESIGN AND METHODSParticipants and measurements...

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Section: Discussionmentioning

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Components of the “Metabolic Syndrome” and Incidence of Type 2 Diabetes

et al. 2002

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“…8 The ATP III report considers insulin resistance as one of the three underlying factors for the MetS, the others being obesity and 'independent factors'. 7 Multiple studies seeking to clarify the underlying etiology of the MetS have detailed anywhere from two to four factors independently associated with the MetS [9][10][11] These clusters invariably include insulin resistance but also include factors ranging from obesity to hyperglycemia. Recently, an underlying inflammatory process has been linked to the MetS and postulated as an explanation for the association of MetS with CVD.…”

Section: Conceptual Basis Of the Metabolic Syndromementioning

confidence: 99%

“…These studies have invariably found more than one factor and usually 3-4 factors clustering independently. [9][10][11] For example, Hanley et al 9 identified two factors underlying the metabolic abnormalities comprising the MetS using factor analysis. One was termed a 'metabolic' factor, with positive loadings of BMI, waist circumference, fasting and 2-h glucose, and TG and inverse loadings of insulin sensitivity…”

Section: Differences In the Clustering Of Metabolic Parameters In Difmentioning

confidence: 99%

Does using ethnic specific criteria improve the usefulness of the term metabolic syndrome? controversies and suggestions

Banerjee

Misra

2007

Int J Obes

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The metabolic syndrome (MetS) is an important tool that identifies populations at increased risk for cardiovascular disease (CVD) and type 2 diabetes, targeting them for preventive measures. The criteria for the identification of the MetS were initially constructed from data in Caucasian populations. Recent research suggests that the current criteria for the MetS may not accurately characterize disease risk in non-Caucasian populations, either over or underestimating the risk in certain ethnic groups. Altering the criteria for each population by making ethnic-specific cutoffs as has been done with waist circumference will help in more accurate characterization. Using different combinations of the MetS criteria for different ethnic groups based CVD risk and factor analysis needs consideration. With better characterizations of patient populations, the ultimate goal would be to make MetS more accurate for predicting CVD risk while retaining the ease of screening afforded by the MetS. The proposed alterations of definition and criteria of the MetS would ensure its continued viability and sustainability.

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“…Abdominal obesity is a strongly concluded phenotypic companion for a cluster of metabolic abnormalities characterized by insulin resistance [4,5]. However, the association with abdominal obesity and features of the metabolic syndrome has been reported to vary with gender [6,7], and with different degrees of obesity [8,9]. Although abdominal obesity is the best obesity-related predictor of Correspondence: Ulf Lindblad, Community Medicine, Department of Clinical Sciences, Malmö University Hospital, SE-205 02 Malmö, Sweden.…”

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confidence: 99%

Abdominal obesity and insulin resistance in patients with type 2 diabetes in a Swedish community

Bari¹,

Östgren²,

Råstam³

et al. 2006

Scandinavian Journal of Primary Health Care

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Insulin resistance alone does not explain the clustering of cardiovascular risk factors in southern India

Abstract: Insulin resistance is not the only underlying factor for the clustering of CVD risk factors in south Indians. These findings are consistent with the presence of several distinct physiological domains, as shown in other ethnic groups.

Cited by 46 publications

References 24 publications

Components of the “Metabolic Syndrome” and Incidence of Type 2 Diabetes

Components of the “Metabolic Syndrome” and Incidence of Type 2 Diabetes

Does using ethnic specific criteria improve the usefulness of the term metabolic syndrome? controversies and suggestions

Abdominal obesity and insulin resistance in patients with type 2 diabetes in a Swedish community

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