Factors, Definitions, Predictive Value & Asian Indian Ethnicity: Complexities of the Metabolic Syndrome

By Misra, Anoop Vikram, Naval K

The metabolic syndrome is a cluster of metabolic abnormalities, including impaired glucose metabolism, hypertension, dyslipidemia and abdominal obesity. Although the pathophysiology of this syndrome is incompletely understood, insulin resistance and abdominal obesity are central to many of the metabolic perturbations. Based on existing estimates the metabolic syndrome affects a nearly 1/4th of the populations in developed countries1. Prevalence of the metabolic syndrome is increasing in developing countries, including India1,2. The cluster of metabolic abnormalities comprising the metabolic syndrome is evident even in Asian Indian children and adolescents3- 5. The rapid increase in the clustering of risk factors in urban Indians is due to increasing affluence of middle class, urbanization, mechanization, marked changes in diet (increased consumption of “calorie-dense foods”) and sedentary habits6. In Asian Indians, increasing pool of the metabolic syndrome is a reason for concern since much of it would convert to type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) if effective interventions are not applied7,8. The metabolic syndrome as a clinical entity continues to be debated. A number of questions regarding this syndrome have been answered only partially. The three issues which have lead to intense debate are as follows. Firstly, how best define the metabolic syndrome? How accurately does it to predict T2DM and CVD? And finally, which are the factor(s) that best comprise the metabolic syndrome? While some discussion follows on these issues, most of it does not lead to firm conclusions; sometimes due to lack of data and other times, due to lack of consensus. Indeed many more research studies are needed before we could arrive at definitive guidelines.

Despite considerable research, there is no clear consensus regarding the optimal definition of the metabolic syndrome yet. As many as five definitions of the metabolic syndrome have been considered over the last decade. In addition, ethnic-specific definitions applicable to Asian Indian adults and children have been reported by us3. Worldwide, two most popular definitions are those given by the National Cholesterol Education Program, Adult Treatment Panel III (NCEP, ATP III)9, and International Diabetes Federation (IDF)10. The latter definition includes waist circumference as a mandatory variable, and also specifies its ethnic specific cut- offs. However, whether even the latest IDF definition identifies the metabolic syndrome optimally and predicts cardiovascular risk in Asian Indians and other Asian populations is not clear11. Lorenzo et al12 showed that all the three definitions of the metabolic syndrome (NCEP, ATP III, IDF, and WHO) have equal ability to predict incidence of coronary events and T2DM in subjects with or without coronary heart disease (CHD) and CHD risk equivalents.

An issue central to defining the metabolic syndrome correctly is whether it independently predicts risks of T2DM and CVD better than its individual components’3. It is also not clear whether construct of the metabolic syndrome is better than other risk prediction engines (e.g., Framingham cardiovascular risk prediction engine, and various risk scoring systems for prediction of diabetes) in context of prediction of T2DM and CVD. For example, Framingham risk score performed better than the metabolic syndrome in prediction of CVD mortality in the multi-ethnic population of Singapore14.

Furthermore, whether all the components of the metabolic syndrome are equally important or some are more important than others is a contentious issue. For example, hypertension is considered as one of the weakest correlate of insulin resistance despite a number of pathophysiological data based on experimental and clinical studies. Investigators have attempted to resolve this issue by using various types of statistical analyses. Factor analysis is a mathematical model which attempts to reduce a large number of inter-correlated variables to a smaller number of unobserved and uncorrelated latent factors or dimensions. The assumption behind factor analysis is that there exist some unmeasured latent factors and the observed variables are dependent upon the latent factors. This analysis helps to detect the underlying structure in the relationships of the factors, especially in setting of a complex clustering of factors in setting of a syndrome. Factor analysis basically involves three steps: (i) extraction of factors; (ii) rotation of factors for better interpretation; and (iii) assigning a particular name to each factor depending on the variables loaded onto it. The most common types of factor analysis are exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The most commonly used method has been EFA in defining the factor structure of the metabolic syndrome both in adults15,16 and children17,18. Recently few investigators have used CFA and suggested that there may be a single factor underlying the metabolic syndrome19,20.

In this issue of the Journal Wu et al21 report the results of EFA of the metabolic syndrome in Chinese subjects with normal glucose tolerance (NGT), impaired glucose tolerance (IGT) and T2DM. This is one of the few studies which have included diabetic subjects and also used a direct measure of insulin sensitivity [steady state plasma glucose (SSPG)]. Overall three dimensions, namely, blood pressure, insulin resistance and adiposity/glucose were identified. Two factors were identified in those with NGT and three factors in those with IGT and diabetes. Insulin resistance was related to waist- to-hip ratio (WHR) and serum triglycerides in subjects with NGT and IGT, further highlighting the relationship of abdominal obesity and hypertriglyceridemia to insulin resistance. Another interesting observation in this study is that systolic blood pressure, instead of WHR, consistently loaded onto the first factor in all the three groups. It is also noteworthy that systolic blood pressure and diastolic blood pressures loaded onto separate factors in diabetic patients. This is in variance with other studies where both systolic and diastolic blood pressures have consistently loaded together onto a factor. Wang et al22, in a previous study involving Chinese population, showed that the ‘blood pressure’ factor was most important in diabetic patients but not in non-diabetic subjects. These authors showed that obesity/insulin resistance and glucose/2- h insulin factors, but not the blood pressure factor significantly predicted incident T2DM.

The studies attempting factor analysis of the metabolic syndrome differ in terms of clinical, anthropometric and biochemical variables and the ethnic group included in the analysis, limiting generalisability of the results. Most of the investigators using EFA have reported two to four factors explaining the variance in the metabolic syndrome variables. In most of the studies, adiposity, insulin and glucose were observed to load together onto one factor, highlighting the role of obesity in insulin resistance. Blood pressure consistently loaded onto a separate factor sometimes along with obesity. Another very common factor reported is the metabolic factor, consisting of lipids or other biochemical parameters included in the analysis. In some studies the various factors were linked by insulin17, whereas in others, obesity was observed to be the link between the various factors23. In some studies no common variable was observed to load on more than one factor24.

What is the meaning of these complicated factor analyses? Which is better method, EFA or CFA? The opinions of experts working in this area differ on these issues. In simple words, EFA can be used for hypothesis generation and CFA for hypothesis testing. The number of latent factors is unknown in EFA and has to be determined from the data being analyzed. On the other hand the number of factors is defined beforehand in CFA and it can be used to evaluate the robustness of the results obtained after EFA. Though used frequently, EFA may have certain weaknesses and its use in study of the metabolic syndrome has been criticized25. The results of EFA should be interpreted cautiously since by this method, one is not testing any hypothesis directly and cannot identify causality. The results of EFA are influenced by the number and nature of variables included in the modeling and the loading thresholds used for interpretation. More than one interpretation can be made of the same data using similar procedure. The results of EFA should be further tested by CFA to find out that the latent factor(s) is/ are indeed good fit of the data. Using CFA different investigators have suggested that one factor19 or more than one factor26 provide good fit of the data. It would be better to compare the ability of the factor scores with that of the standard definitions of the metabolic syndrome in predicting T2DM and CHD in prospective studies. From the available data it appears that a single factor does not explain the presence of the metabolic syndrome. In addition, insulin resistance, initially thought to be the sole underlying mechanism may not explain the presence of the metabolic syndrome in totality. In other words, it is likely that different physiological processes are associated with different components of the metabolic syndrome. How these various factors or domains interact and interlink with each other over time in producing the clinical disease needs to be evaluated with the help of longitudinal studies. Differential importance of factors may also be governed by other factors such as ethnicity. Although the NCEP, ATP III, IDF and WHO definitions of the metabolic syndrome may serve to be a prediction tool for CHD, its implications in different ethnic groups may be different. Lower waist circumference cut-off for Asian ethnic group is one such example for which correction has been made for Asians27, 28. These ethnic differences are important research areas for South Asian adults and children1, 4, 29.

Current definitions of the metabolic syndrome include only five factors. Another question is whether one could consider other factors related to insulin resistance, not presently included in the definitions of the metabolic syndrome, in the various models of statistical analysis. For example, C-reactive protein3032, and nonalcoholic fatty liver disease33 are two factors shown to be closely related to cluster of factors comprising the metabolic syndrome in some populations, but again implications for Asian Indians may be different34.

It has taken more than a decade to understand and unravel the complexities and ramifications of insulin resistance and the metabolic syndrome. New statistical tools have helped in better understanding of interrelationship of various metabolic factors and possible pathophysiological path. In this evolution, both basic scientists and clinicians have benefited. Although the path is arduous, the progress is slow but encouraging.

References

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Anoop Misra[dagger] & Naval K. Vikram*

Department of Diabetes & Metabolic Diseases

Fortis Hospitals, New Delhi 110 070

& *Department of Medicine

All India Institute of Medical Sciences

New Delhi 110029, India

[dagger] For correspondence:

anoopmisra@metabolicresearchindia.com

Copyright Indian Council of Medical Research Apr 2008

(c) 2008 Indian Journal of Medical Research. Provided by ProQuest LLC. All rights Reserved.