Comparison of factor loadings for anthropometric and physiometric measures among type 2 diabetic males, pre and postmenopausal females in North Indian Punjabi population

Badarud Doza

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DOI: 10.4236/ns.2010.27093                

Abstract

Background: The objective of the present study was to compare the relationship of anthropometric and physiometric characteristics using principal component factor analysis among three groups of type 2 diabetic subjects such as males, pre and postmenopausal females in North Indian Punjabi population. Method: A total of 349 type 2 diabetic subjects (males 157; females 192; 88 pre and 104 postmenopausal) were ascertained for the present study. Different anthropometric and physiometric measurements were taken. Principal component factor analysis (PCFA) was applied to identify the components which are more close to type 2 diabetes among the three groups. Results: PCFA revealed five uncorrelated components which explained 79% of the total variance among diabetic males and six unrelated components which explained 78% of the total variance among pre and postmenopausal females. The important two factors could be identified as central obesity (factor 1) and blood pressure (factor 2) among these three groups. Conclusion: Higher clustering of obesity and blood pressures were found in diabetic males as com pared to pre and postmenopausal diabetic females in North Indian Punjabi population whereas, waist to hip ratio (WHR) has maximum loading in postmenopausal females as compared to others.

Keywords

Factor Analysis; Blood Pressure; Type 2 Diabetes; Anthropometry; Punjabi Population

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Meigs, J.B. (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. American Journal of Epidemiology, 152(10), 908911.
[2]	Hanley, A.J., Karter, A.J. and Festa, A. (2002) Factor analysis of metabolic syndrome using directly measured insulin sensitivity: The insulin resistance atherosclerosis study. Diabetes, 51(8), 26422647.
[3]	Ghose, A., Bose, K. and Das Chaudhari, A.B. (2003) Association of food pattern, central obesity measures and metabolic risk factors for coronary heart disease (CHD) in middle aged Bengalee Hindu men, Calcutta, India. Asia Pacific Journal of Clinical Nutrition, 12(2), 166 171.
[4]	Ghose, A. (2005) Factor analysis of metabolic syndrome among the middleaged Bengalee Hindu men of Calcutta, India. Diabetes/Metabolism Research and Reviews, 21(1), 5864.
[5]	Singh, I.P. and Bhasin, M.K. (1968) Anthropometry, Kamla Raj Enterprises, Delhi.
[6]	Weiner, J.S. and Lourie, J.A. (1981) Practical Human Biology, Academic Press, London.
[7]	Lemieux, S., Prud’homme, D., Bouchard, C., Tremblay, A. and Despres, J.P. (1996) A single threshold value of waist girth identifies normal weight and overweight subjects with excess visceral adipose tissue. American Journal of Clinical Nutrition, 64(5), 685693.
[8]	Badaruddoza and Afzal, M. (1999) Age specific differences in blood pressure among inbred and noninbred North Indian children. Journal of Biosciences, 24(2), 177184.
[9]	Perusse, L., Rice, T., Bouchard, C., Vogler, G.P. and Rao, D.C. (1989) Cardiovascular risk factors in the French Canadian population: Resolution of genetic and familial environmental effects on blood pressure by using extensive information on environmental correlates. American Journal of Human Genetics, 45(2), 240251.
[10]	Truxillo, C. (2003) Multivariate statistical methods: Practical research applications course notes. SAS Institute, USA.
[11]	Wu, C.Z., Lin, J.D., Li, J.C., Hsiao, F.C., Hsich, C.H., Kuo, S.W., Hung, Y.J., Lu, C.H., He, C.T. and Pei, D. (2008) Factor analysis of metabolic syndrome using direct measurement of insulin resistance in Chinese with different degrees of glucose tolerance. Indian Journal of Medical Research, 127(1), 336343.
[12]	Singh, R., Shaw, J. and Zimmet, P. (2004) Epidemiology of childhood type 2 diabetes in the developing world. Pediatric Diabetes, 5(3), 154168.
[13]	Li, J.K., Ng, M.C., So, W.Y., Chiu, C.K., Ozaki, R., Tong, P.C., Cockram, C.S. and Chan, J.C. (2006) Phenotypic and genetic clustering of diabetes and metabolic syndrome in Chinese families with type 2 diabetes mellitus. Diabetes/Metabolism Research and Reviews, 22(6), 4652.
[14]	Comuzzie, A.G., Tejero, M.E., Funahash, T., Martin, L.J., Kisseban, A., Takahashi, S., Tanaka, S., Rainwater, D.L., Matsuzawa, Y., MacCluer, J.W. and Blangero, J. (2007) The genes influencing adiponectin levels also influence risk factors for metabolic syndrome and type 2 diabetes. Human Biology, 79(2), 191200.
[15]	Rai, E., Sharma, S., Koul, A., Bhat, A.K., Bhanwer, A.J. and Bamezai, R.N. (2007) Interaction between the UCP2866G/A, mtDNA 10398G/A and PGC1alpha p.Thr394Thr and p.Gly482Ser polymorphisms in type 2 diabetes in North Indian population. Human Genetics, 122(5), 535540.
[16]	Franceschini, N., Almasy, L., MacCluer, J.W., Goring, H.H., Cole, S.A., Diego, V.P., Laston, S., Howard, B.V., Lee, E.T., Best, L.G., Fabsitz, R.R. and North, K.E. (2008) Diabetesspecific genetic effects on obesity traits in American Indian populations: The strong heart family study. BMC Medical Genetics, 9(1), 9096.
[17]	van’t Riet, E., Alsseme, M., Nijpels, G. and Dekker, J.M. (2008) Estimating the individual risk of diabetes: Not on the grounds of overweight only. Nederlands Tijdschr Geneeskd, 152(44), 23852388.
[18]	Dunkley, A.J., Taub, N.A., Davis, M.J., Stone, M.A. and Khunti, K.C. (2009) Is having a family history of type 2 diabetes or cardiovascular disease a predictive factor for metabolic syndrome? Primary Care Diabetes, 3(1), 4956.
[19]	Matharoo, K., Kumar, A., Randhawa, N.K., Arora, P. and Bhanwer, A.J.S. (2006) Angiotensin converting enzyme gene insertion/deletion polymorphism in type 2 diabetes (T2DM) patients from Punjab. Journal of Punjab Academy of Sciences, 3(3), 712.
[20]	Sanghera, D.K., Bhatti, J.S., Bhatti, G.K., Ralhan, S.K., Wander, G.S., Singh, J.R., Bunker, C.H., Weeks, D.E., Kamboh, M.I. and Ferrell, R.E. (2006) The Khatri Sikh Diabetes Study (SDS): Study design, methodology, sample collection, and initial results. Human Biology, 78(1), 4363.
[21]	Sanghera, D.K., Ortega, L., Han, S., Singh, J., Ralhan, S.K., Wander, G.S., Mehra, N.K., Mulvihill, J.J., Ferrell R.E., Nath, S.K. and Kamboh, M.I. (2008) Impact of nine common type 2 diabetes risk polymorphisms in Asian Indian Sikhs: PPARG2 (Pro12Ala), IGF2BP2, TCF7L2 and FTO variants confer a significant risk. BMC Medical Genetics, 9(1), 5967.