title: Cluster analysis reveals important determinants of cardiometabolic risk patterns in Filipino women.
abstract: With modernization, the Philippines has experienced increasing rates of obesity and related cardiometabolic diseases. Studying how risk factors cluster in individuals may offer insight into cardiometabolic disease etiology.  We used cluster analysis to group women who share the following cardiometabolic biomarkers: fasting triglycerides, HDL-C and LDL-C, C-reactive protein, systolic and diastolic blood pressure, homeostasis model assessment of insulin resistance, and fasting glucose. Participants included 1,768 women (36-69 years) in the Cebu Longitudinal Health and Nutrition Survey. We identified five distinct clusters characterized by: 1) low levels of all risk factors (except HDL-C and LDL-C) or "healthy"; 2) low HDL-C in the absence of other risk factors; 3) elevated blood pressure; 4) insulin resistance; and 5) high C-reactive protein. We identified predictors of cluster membership using multinomial logistic regression. Clusters differed by age, menopausal status, socioeconomic status, saturated fat intake, and combinations of overweight (BMI >23) and high waist circumference (>80 cm).  In comparison to the healthy cluster, overweight women without high waist circumference were more likely to be in the high CRP cluster (OR=2.26, 95% CI=1.24-4.11), while women with high waist circumference and not overweight were more likely to be in the elevated blood pressure (OR=2.56, 95% CI=1.20-5.46) or insulin resistant clusters (OR=4.05, 95% CI=1.39-11.8). In addition, a diet lower in saturated fat uniquely increased the likelihood of membership to the low HDL-C cluster. Cluster analysis identified biologically meaningful groups, predicted by modifiable risk factors; this may have implications for the prevention of cardiometabolic diseases.
authors.affiliation: Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27516, USA. zubair@email.unc.edu
authors.forename: Niha
authors.initials: N
authors.lastname: Zubair
authors.forename: Chris W
authors.initials: CW
authors.lastname: Kuzawa
authors.forename: Thomas W
authors.initials: TW
authors.lastname: McDade
authors.forename: Linda S
authors.initials: LS
authors.lastname: Adair
mesh_terms: D000328:Adult; D000368:Aged; D015415:Biomarkers / Q000097:blood; D015992:Body Mass Index; D002097:C-Reactive Protein / Q000032:analysis; D016000:Cluster Analysis; D015331:Cohort Studies; D005260:Female; D006306:Health Surveys; D006801:Humans; D006973:Hypertension / Q000453:epidemiology / Q000208:ethnology / Q000503:physiopathology*; D007333:Insulin Resistance; D008137:Longitudinal Studies; D024821:Metabolic Syndrome / Q000097:blood / Q000208:ethnology / Q000209:etiology* / Q000378:metabolism; D008875:Middle Aged; D009767:Obesity, Morbid / Q000453:epidemiology / Q000208:ethnology / Q000503:physiopathology; D050177:Overweight / Q000453:epidemiology / Q000208:ethnology / Q000503:physiopathology*; D010679:Philippines / Q000453:epidemiology; D015995:Prevalence; D012307:Risk Factors; D055105:Waist Circumference
chemical_list: D015415:Biomarkers; D002097:C-Reactive Protein