Factor analysis of risk variables associated with metabolic syndrome in Asian Indian adolescents

Original Research Article

Factor Analysis of Risk Variables Associated WithMetabolic Syndrome in Asian Indian Adolescents

ARNAB GHOSH*

Palli Charcha Kendra, Visva Bharati University, Santiniketan 731 235, West Bengal, India

ABSTRACT The purpose of the present cross-sectional study was to identify components ofrisk variables associated with metabolic syndrome in Asian Indian adolescents. The sampleincluded 400 adolescents (boys ¼ 200; mean age, 15.0 6 4.5 years; girls ¼ 200; mean age, 14.4 63.8 years) from Calcutta, India. The following variables were considered: body mass index, waistcircumference, sum of four skinfolds, subscapular/triceps ratio, total cholesterol, triglycerides,blood glucose, and systolic, diastolic, and mean arterial pressure. Principal component factoranalysis revealed four uncorrelated factors for adolescent boys that cumulatively explained76.3% of the observed variance of metabolic syndrome. Four factors with overlap between fac-tors 1 and 2 were observed for adolescent girls that cumulatively explained 74.3% of the totalvariation of metabolic syndrome. The four factors identified were central body fat distribution(factor 1), centralized subcutaneous fat (factor 2), lipids-blood glucose (factor 3), and bloodpressure (factor 4). Furthermore, the first two factors, i.e., central body fat distribution and cen-tralized subcutaneous fat, cumulatively explained more than 46% (46.5% for boys; 46.4% forgirls) of the observed variation of metabolic syndrome. Since more than one factor was identifiedfor metabolic syndrome, more than one physiological mechanism could account for the cluster-ing of risk variables of metabolic syndrome in Asian Indian adolescents. Factor analysis ofAsian Indian adults also revealed four uncorrelated factors, similar to the present factors,therefore warranting intervention as early as adolescence. Am. J. Hum. Biol. 19:34–40,2007. ' 2006 Wiley-Liss, Inc.

Metabolic syndrome, often considered as anadult-onset phenotype, is characterized by theconstellation of cardiovascular disease (CVD)risk factors such as dyslipidemia, hyperten-sion, glucose intolerance, hyperinsulinemia,and central or visceral obesity (Reaven, 1988,1992; Eisenmann, 2003; Misra and Vikram,2004; Ghosh, 2005). In adults, metabolic syn-drome is associated with an increased risk ofCVD mortality (Lakka et al., 2002, 2003). How-ever, the current literature hints that meta-bolic syndrome might no longer be consideredan adult-onset phenotype. New evidence sug-gests the appearance of metabolic syndromeas early as adolescence (Dwyer et al., 2002;Ford et al., 2002; Eisenmann, 2003; Morrisonet al., 2005; Scott, 2006; Bacha et al., 2006).Individual CVD risk factors track reasonablywell from adolescence to adulthood. Therefore,identification of children and adolescents withan elevated risk factor profile of CVD is of greatimportance (Katzmarzyk et al., 2001; Eisen-mann et al., 2004; Ghosh, 2004). The UnitedStates ‘‘National Health and Nutrition Exami-nation Survey III’’ (NHANES III) indicated

that the prevalence of metabolic syndrome is4% in 12–19-year-old adolescents vs. 30% inobese adolescents (Ford et al., 2002).

The prevalence of coronary heart disease(CHD) is known to be high in people of SouthAsian descent (people originally from India,Pakistan, and Bangladesh). The Global Bur-den of Diseases (GBD) Study reported the es-timated mortality from CHD in India at 1.6million in the year 2000, and extrapolation ofthese numbers estimates the burden of CHDin India to be more than 32 million patients(Gupta, 2005). One reason for this increasedsusceptibility to CHD could be the clusteringof risk variables (the mechanisms of which arestill unknown to us) of metabolic syndrome.This includes glucose intolerance, hypertri-glyceridemia, dyslipidemia, and central or vis-

*Correspondence to: Dr. Arnab Ghosh, Palli CharchaKendra, Visva Bharati University, Sriniketan 731 236,West Bengal, India. E-mail: [email protected]

Received 23 May 2006; Revision received 13 July 2006;Accepted 16 July 2006

Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ajhb.20570

AMERICAN JOURNAL OF HUMAN BIOLOGY 19:34–40 (2007)

VVC 2006 Wiley-Liss, Inc.

ceral obesity (Ward et al., 2003; Deedwani andSingh, 2005; Vikram et al., 2006). Moreover,CHD among them is often premature and occursa decade earlier than that seen in Europeansand/or Americans (Enas, 2000; Gupta, 2005).The manifestation of multiple CVD risk fac-tors in adulthood (metabolic syndrome) pre-disposes subjects to increased risk of CVD andpremature death. Since clusters of risk factorsfor CVD are fairly stable characteristics thattend to track fairly well from adolescents intoadulthood (Eisenmann et al., 2004; Ghosh,2004), identification of the components of themetabolic syndrome phenotype in Asian In-dian adolescents is of great importance.Various statistical techniques (e.g., multiple

logistic regression) could be used to identifyrisk variables of the metabolic syndrome phe-notype. Principal component factor analysis(PCFA) is one approach that groups quantita-tively measured variables into clusters knownas factors, based on the association among thevariables. Factor analysis attempts to identifyunderlying variables, or factors, that explainthe pattern of correlations within a set ofobserved variables. Factor analysis is oftenused in data reduction to identify a smallnumber of factors that explain most of thevariance observed in a much larger number ofmanifest variables (Stevens, 1996; Shen et al.,2003). PCFA was used to identify the domainsof risk variables of metabolic syndrome (Meigset al., 1997; Gray et al., 1998; Chen et al.,1999; Young et al., 2002; Ghosh, 2005). Forexample, if there is a single underlying causeof the clustering of risk variables of metabolicsyndrome, then factor analysis should pro-duce only one major factor or component.As far as Asian Indians are concerned, very

few studies have been undertaken to identifythe components of metabolic syndrome in adultAsian Indians (Snehalatha et al., 2000; Hodgeet al., 2001; Ghosh, 2005). And to the best ofthe author’s knowledge, no study has beenundertaken to identify the components of riskvariables associated with metabolic syndromein Asian Indian adolescents. Hence factor anal-ysis was undertaken to identify the componentsof risk variables associated with metabolic syn-drome in adolescent Asian Indians.

SUBJECTS AND METHODS

Study population

The present cross-sectional study was under-taken between August 2005–February 2006.A total of 400 apparently healthy Asian Indian

adolescents (boys ¼ 200; girls ¼ 200) aged 12–16 years participated in the study. They wereselected from three schools in Calcutta. To ob-tain a better picture, three different types ofschools, i.e., public, private, and government-sponsored, were considered randomly from Cal-cutta. Written consent was obtained from pa-rents and principals before actual commence-ment of the work. This sample was sufficient totest all research hypotheses at a 5% level of sig-nificance, with a power of 80% (b ¼ 0.80). A de-mographic and socioeconomic profile, includingname, date of birth, educational level of pa-rents, and subject’s and gross annual familyincome, were obtained using an open-endedschedule. Subjects’ ages were ascertained sub-sequently from date of birth to nearest month.No subject received any medication for diabe-tes, hypertension, and/or dyslipidemia duringthe study period.

Anthropometric measures

Height (to nearest 0.1 cm), weight (to near-est 0.5 kg), and skinfolds at biceps, triceps,subscapular, and suprailiac (to nearest 0.2mm) were recorded following standard proce-dures (Lohman et al., 1988). Waist circumfer-ence was taken as the narrowest part of thetorso, as seen from the anterior aspect. Bodymass index (BMI), sum of four (biceps þ tri-ceps þ subscapular þ suprailiac) skinfolds (SF4),and subscapular/triceps ratio (STR) were sub-sequently computed.

Metabolic variables

A fasting blood sample was collected fromeach subject for the determination of meta-bolic profiles. All subjects maintained an over-night fast of �10 hr prior to blood collection.Total cholesterol (TC), triglyceride (TG), andblood glucose (BG) was measured using a GCTmonitor on total blood after pricking of theleft-hand index finger. All metabolic variableswere measured in mg/dl units and then con-verted into mmol/l units, using a standardconversion formula.

Blood pressure measures

Left arm blood pressure was taken fromeach participant with the help of an OmronM1 digital electronic blood pressure/pulse mo-nitor (Omron Corp., Tokyo, Japan). Two bloodpressure measurements were taken and aver-aged for analysis. A third measurement wasonly taken when the difference between thetwo measurements was �5 mm Hg, and sub-

35FACTORS FOR METABOLIC SYNDROME IN ADOLESCENTS

American Journal of Human Biology DOI 10.1002/ajhb

sequently the mean was calculated. A 5-minrelaxation period between measurements wasmaintained for all participants. The workingcondition of the instrument was checked peri-odically with the help of a mercury sphygmo-manometer and stethoscope (auscultator pro-cedure). Mean arterial pressure (MAP) wasthen calculated from systolic blood pressure(SBP) and diastolic blood pressure (DBP).

Statistical analyses

The distributions of variables were checkedfor normality. Log (10) transformation wasundertaken to normalize the distribution ofpositively skewed variables (STR, TC, TG,and BG). Descriptive statistics such as themean and standard deviation (SD) of anthro-pometric, metabolic, and blood pressure meas-ures were undertaken separately for sexes.Sex differences in variables were tested usinganalysis of variance (ANOVA). Factor analysiswas then undertaken to group quantitativelymeasured variables into clusters known asfactors, based on the correlation between vari-ables. This was done in three steps: computa-tion of a correlation matrix for all variablesincluded, factor extraction, and orthogonalrotation to make factors readily interpretable.Factors were extracted by principal compo-nent analysis (PCA), in which linear combina-tions of variables were formed, with the firstprincipal component accounting for the larg-est amount of variance in the sample. Varimaxrotation is an orthogonal rotation in which thefactors are assumed to act independently

(maximum likelihood), and was used in thisstudy. Components were all uncorrelated. Fac-tor loading, which was equivalent to Pearson’scorrelation coefficient between each variableand the factor, was used to interpret each fac-tor. Variables with a factor loading of at least0.3 are generally considered for interpreta-tion, although it is often suggested that onlyloading �0.4, therefore sharing at least 15%of the variance with the factor, should be usedin interpretation (Long, 1983; Comrey andLee, 1992; Stevens, 1996; Hodge et al., 2001).Previous studies also used a factor loading of0.4 or greater to interpret the final rotatedfactor pattern (Meigs et al., 1997; Gray et al.,1998; Chen et al., 1999; Hodge et al., 2001;Young et al., 2002; Ghosh, 2005). A factorloading of 0.4 or greater was used to interpretthe final rotated factor pattern in the presentstudy. Extracted factors (extraction by PCA)were such that each explained at least asmuch (eigenvalue �1) or nearly as much var-iance as any one observed variable (eigen-value ¼ 1). All statistical analyses were per-formed using SPSS, version 10. P < 0.05 (two-tailed) was considered statistically significant.

RESULTS

The means and SDs of anthropometric, met-abolic, and blood pressure measures are shownin Table 1. The mean age for boys and girlswas 15.0 6 4.5 and 14.4 6 3.8 years, respec-tively. Significant sex differences were observedfor height (P < 0.01), weight (P < 0.01), WC

TABLE 1. Descriptive statistics of study population (n ¼ 400)

Variable

Boys (n ¼ 200) Girls (n ¼ 200)

Mean SD Range1 Mean SD Range1

Age (years) 15.0 4.5 4.0 14.4 3.8 3.8Height (cm)** 150.1 10.2 12.2 141.2 9.6 10.6Weight (kg)** 49.0 7.6 4.2 42.0 8.2 4.0Waist circumference (cm)* 80.2 4.6 5.4 78.4 5.6 6.2Body mass index (kg/m2) 21.8 2.8 1.8 21.2 3.2 2.2Subscapular/triceps ratio** 1.57 0.21 1.0 1.64 0.19 1.3Sum of four skinfolds (mm)* 76.4 9.6 10.0 78.2 10.0 11.4Metabolic measures (mmol/l)Total cholesterol 3.7 0.94 1.2 3.4 0.89 1.0Triglyceride 1.6 0.68 1.4 1.7 0.74 1.0Blood glucose 4.4 1.4 2.6 4.6 1.2 2.8Blood pressure measures (mm Hg)Systolic blood pressure* 124.2 10.4 12.4 122.0 9.6 11.6Diastolic blood pressure 73.0 8.4 8.2 72.2 8.6 9.4Mean arterial pressure* 90.0 6.8 5.4 88.8 7.2 4.8

1Range was defined as difference between maximum and minimum values.*Significant sex differences at P < 0.05.**Significant sex differences at P < 0.01.

36 A. GHOSH


(P < 0.01), STR (P < 0.01), SF4 (P < 0.05), SBP(P < 0.05), and MAP (P < 0.05).The intercorrelation matrix of variables is

shown in Table 2. BMI had no significant asso-ciation with central obesity measure (i.e., WC),metabolic (TC, TG, and BG), and blood pres-sure variables.The factor-loading pattern of four factors

(components) identified is shown in Table 3.Only variables with loading �0.4 were consid-ered for explanations. After Varimax rotation,PCFA revealed four uncorrelated factors foradolescent boys that cumulatively explained76.3% of the observed variance of metabolicsyndrome. Four factors, with overlap betweenfactors 1 and 2, were observed for adolescentgirls that cumulatively explained 74.3% of thetotal variation of metabolic syndrome. Thefour factors identified were central body fatdistribution (WC, factor 1), centralized subcu-taneous fat (STR, factor 2), lipids-blood glu-cose (TC, TG, and BG, factor 3), and bloodpressure (SBP, DBP, and MAP, factor 4). Fur-

thermore, the first two factors, i.e., centralbody fat distribution and centralized subcuta-neous fat, cumulatively explained more than46% (46.5% for boys; 46.4% for girls) of theobserved variation of metabolic syndrome.The loading of individual risk variables variedfrom 0.65–0.89. Since no significant age differ-ence was observed, age was not considered anindependent variable in factor analysis.

DISCUSSION

This study reports on the identification ofcomponents of metabolic syndrome in adoles-cent Asian Indians. The constellation of cen-tral obesity, glucose intolerance, hypertension,dyslipidemia, and hyperinsulinemia known asmetabolic syndrome was observed in a num-ber of ethnic groups worldwide (Misra et al.,2004; Morrison et al., 2005; Atabek et al.,2006; Vikram et al., 2006). Studies across theworldwide population demonstrated that met-abolic syndrome occupies a pivotal role in the

TABLE 2. Intercorrelation matrix (n ¼ 400)1

WC BMI STR TC TG BG SBP DBP MAP

WC 0.12 0.27 0.43 0.44 0.51 0.24 0.26 0.28BMI 0.11 0.12 0.13 0.14 0.13 0.14 0.18STR2 0.30 0.35 0.39 0.32 0.34 0.37TC2 0.68 0.60 0.34 0.38 0.40TG2 0.48 0.52 0.56 0.62BG2 0.52 0.56 0.60SBP 0.78 0.86DBP 0.82MAP

1WC, waist circumference; BMI, body mass index; STR, subscapular/triceps ratio; TC, total cholesterol; TG, triglyceride; BG, bloodglucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure. Significant at 5% level when coeffi-cient was > 0.18, 1% level when coefficient was > 0.26, and 0.1% level when coefficient was >0.32.2Log (10) transformed values were used.

TABLE 3. Factor loading by principal component analysis with Varimax rotation1

Variables

Boys (n ¼ 200), factors Girls (n ¼ 200), factors

F1 F2 F3 F4 F1 F2 F3 F4

WC 0.8203 0.314 0.324 0.321 0.8043 0.7213 0.316 0.312BMI 0.210 0.200 0.189 0.215 0.311 0.289 0.311 0.287STR 0.321 0.8163 0.223 0.327 0.7743 0.8983 0.265 0.323TC2 0.331 0.312 0.7783 0.311 0.316 0.278 0.7713 0.216TG2 0.314 0.316 0.8873 0.345 0.288 0.322 0.6593 0.287BG2 0.289 0.310 0.8803 0.312 0.311 0.313 0.7273 0.288SBP 0.218 0.310 0.322 0.8853 0.256 0.288 0.352 0.8873

DBP 0.221 0.242 0.312 0.8613 0.315 0.248 0.333 0.8213

MAP 0.289 0.321 0.318 0.8863 0.322 0.254 0.361 0.8893

Variance explained (%) 24.0 22.5 16.2 13.6 23.8 22.6 13.4 14.5Cumulative variance (%) 24.0 46.5 62.7 76.3 23.8 46.4 59.8 74.3

1WC, waist circumference; BMI, body mass index; STR, subscapular/triceps ratio; TC, total cholesterol; TG, triglyceride; BG,blood glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure.2Log (10) transformed values were used.3Loading with absolute value �0.4.



occurrence of cardiovascular risk factors(Invitti et al., 2006). Hence, identification ofthe phenotypic components of metabolic syn-drome, and how its phenotypic expression dif-fers across ethnic groups, would be helpful inunderstanding the etiology of chronic diseaseswhich are ingrained in metabolic abnormal-ities, e.g., ‘‘insulin resistance syndrome’’ (Scott,2006). However, it is noteworthy that moststudies to identify components of the risk vari-ables of metabolic syndrome were predomi-nantly based on adult subjects. Little atten-tion has been paid to children and adolescentsto identify components of risk variables associ-ated with metabolic syndrome, notwithstand-ing the fact that metabolic syndrome in adultsoriginates as early as adolescence.Principal component factor analysis in this

study revealed four uncorrelated factors foradolescent boys that cumulatively explained76.3% of the observed variance of metabolicsyndrome. Four factors with overlap betweenfactors 1 and 2 were observed for adolescentgirls that eventually (cumulatively) explained76.3% of the total variation of metabolic syn-drome. The four factors identified were cen-tral body fat distribution (factor 1), central-ized subcutaneous fat (factor 2), lipids-bloodglucose (factor 3), and blood pressure (factor4). The Bogalusa Heart Study on cardiovascu-lar risk factors, clustering features of meta-bolic syndrome in a biracial (black-white) pop-ulation of children, adolescents, and youngadults, yielded two uncorrelated factors(factor 1: insulin, lipids, glucose, and ponderalindex; factor 2: insulin and blood pressure).These two factors eventually explained 54.6%of the total variance in the entire sample(Chen et al., 1999). Insulin resistance syn-drome (IRS) in a representative sample ofchildren and adolescents from Quebec, Can-ada, revealed three factors (BMI/insulin/lip-ids, BMI/insulin/glucose, and systolic/diastolicblood pressure) consistent across ages, sug-gesting that more than one pathophysiologicprocess underlies IRS (Lambert et al., 2004).A cross-sectional study of South Korean urbanadolescents aged 13–18 years also revealedthree factors in males and females thatexplained 70% and 65%, respectively, of theobserved variance of 10 measured variables.These were obesity/leptin/lipid factor, bloodpressure factor, and glucose/cholesterol factorin males, and obesity/leptin/glucose factor,blood pressure factor, and cholesterol factor infemales. Leptin loaded on only one factor inboth genders, and Park et al. (2004) concluded

that more than one pathophysiological mecha-nism might underlie the full expression ofmetabolic syndrome among South Koreanadolescents. The Princeton School DistrictStudy, a school-based study in Cincinnati,Ohio, yielded four uncorrelated factors: adi-posity (BMI, waist, fibrinogen, and insulin),cholesterol (low-density lipoprotein and totalcholesterol), carbohydrate-metabolic (glucose,insulin, high density lipoprotein, and triglyc-erides), and blood pressure (systolic and dia-stolic). These factors explained approximately67% of the total variance (Goodman et al.,2005). In a study to examine the relationshipbetween nontraditional CVD risk factors andcomponents of metabolic syndrome, fiveunderlying core traits (defined as adiposity,lipids/adiponectin, inflammation, blood pres-sure, and glucose) were evident in a popula-tion-based study of Canadian Oji-Cree chil-dren and adolescents aged 10–19 years(Retnakaran et al., 2006). The results sug-gested that metabolic syndrome is character-ized by the linking of a metabolic entity (insu-lin resistance, hyperlipidemia, and obesity) toa hemodynamic factor (elevated blood pres-sure/hypertension) through shared correlationwith insulin resistance. These summary fac-tors may enable longitudinal studies to estab-lish trajectories of risk, thereby enhancing ourunderstanding of the etiology of metabolicsyndrome.

Furthermore, in Asian Indian adolescents,the first two factors, i.e., central body fat dis-tribution and centralized subcutaneous fat,cumulatively explained more than 46%(46.5% for boys; 46.4% for girls) of theobserved variation of metabolic syndrome inthe present study. Overlap between the first(central body fat) and second (centralized sub-cutaneous fat) factor for girls could be attrib-uted to female sex hormones that develop aGynoid form of fat patterning, along withexcess subcutaneous fat compared to theirmale counterparts (Brook, 1995). It was sug-gested that thick trunk skinfolds with centralobesity for a given level of BMI is the charac-teristic feature of Asian Indians (WorldHealth Organization/International Associa-tion for the Study of Obesity/InternationalObesity Task Force, 2000). The present studyalso corroborated the above fact, since the fac-tor loading pattern of BMI was less than 0.4across factors for both sexes, therefore sharingless than 15% of variance with the factors.The lack of overlap of WC across factors wasquite unexpected, since central obesity is con-

38 A. GHOSH


sidered one of the key determinants of excessCHD occurrences in people of South Asian ori-gin. Furthermore, no overlap of variables onmore than two factors indicated that a keyrole for any one of these variables in metabolicsyndrome is very unlikely.However, it is noteworthy that results from

different factor analyses are limited by differ-ences in the ethnicity, sex, and age composi-tion of the study samples, the number of riskvariables included, sample size, and the cutoffpoints of loadings set by investigators. More-over, prevention of metabolic syndrome mightnot be feasible unless a universally acceptabledefinition of metabolic syndrome in childrenand adolescents is adopted. Further investiga-tion is needed to determine which metabolicfactors and their respective cutpoints shouldbe used to identify children and adolescents atrisk for the development of clinical disease(Chi et al., 2006).This study’s major limitation is that it was

performed on a relatively small sample size,and therefore is not representative of theAsian Indian population. Owing to consider-able ethnic and cultural heterogeneity in theAsian Indian population, it is necessary tostudy other ethnic groups to see if the trendsobserved here also exist among them. Resultsobtained from such studies could be utilized toprevent metabolic syndrome in adulthood.However, to the best of the author’s knowl-edge, no real attempt in this regard has beenattempted for the Indian subcontinent. Thelack of pubertal staging further limits theinterpretation of any sex-related differencesin the clustering of cardiovascular risk factorsin the study.

CONCLUSIONS

This model suggests that the clustering ofvariables in metabolic syndrome is a result ofmultiple factors, and not a single etiology withadiposity playing a pivotal role. Furthermore,as the loaded risk variables in four factors aremodifiable in nature, it seems reasonable toargue that early prevention and interventionshould be highlighted to promote a healthylifestyle and successful weight managementin children and adolescents. This would behelpful in reducing the burden of metabolicsyndrome in adulthood.

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Factor analysis of risk variables associated with metabolic syndrome in Asian Indian adolescents

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