title: Clustering of cardiovascular risk factors in a middle-income country: a call for urgency.
abstract: BACKGROUND
  This study aimed to estimate the prevalence of cardiovascular risk factors and its clustering. The findings are to help shape the Malaysian future healthcare planning for cardiovascular disease prevention and management.
  
  
  METHODS
  Data from a nationally representative cross-sectional survey was used. The survey was conducted via a face-to-face interview using a standardised questionnaire. A total of 37,906 eligible participants aged 18 years and older was identified, of whom 34,505 (91%) participated. Focus was on hypertension, hyperglycaemia (diabetes and impaired fasting glucose), hypercholesterolaemia and central obesity.
  
  
  RESULTS
  Overall, 63% (95% confidence limits 62, 65%) of the participants had at least one cardiovascular risk factor, 33% (32, 35%) had two or more and 14% (12, 15%) had three risk factors or more. The prevalence of hypertension, hyperglycaemia, hypercholesterolaemia and central obesity were 38%, 15%, 24% and 37%, respectively. Women were more likely to have a higher number of cardiovascular risk factors for most age groups; adjusted odds ratios ranging from 1.1 (0.91, 1.32) to 1.26 (1.12, 1.43) for the presence of one risk factor and 1.07 (0.91, 1.32) to 2.00 (1.78, 2.25) for two or more risk factors.
  
  
  CONCLUSIONS
  Cardiovascular risk-factor clustering provides a clear impression of the true burden of cardiovascular disease risk in the population. Women displayed higher prevalence and a younger age shift in clustering was seen. These findings signal the presence of a cardiovascular epidemic in an upcoming middle-income country and provide evidence that drastic measures have to be taken to safeguard the health of the nation.
authors.affiliation: Clinical Research Centre, Ministry of Health, Malaysia. sharm@crc.gov.my
authors.forename: Sharmini
authors.initials: S
authors.lastname: Selvarajah
authors.forename: Jamaiyah
authors.initials: J
authors.lastname: Haniff
authors.forename: Gurpreet
authors.initials: G
authors.lastname: Kaur
authors.forename: Tee Guat
authors.initials: TG
authors.lastname: Hiong
authors.forename: Kee Chee
authors.initials: KC
authors.lastname: Cheong
authors.forename: Chiao Mei
authors.initials: CM
authors.lastname: Lim
authors.forename: Michiel L
authors.initials: ML
authors.lastname: Bots
mesh_terms: D000367:Age Factors; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000191:economics / Q000453:epidemiology* / Q000517:prevention & control; D016009:Chi-Square Distribution; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D003906:Developing Countries / Q000191:economics / Q000706:statistics & numerical data*; D005260:Female; D006306:Health Surveys; D006801:Humans; D006937:Hypercholesterolemia / Q000191:economics / Q000453:epidemiology; D006943:Hyperglycemia / Q000191:economics / Q000453:epidemiology; D006973:Hypertension / Q000191:economics / Q000453:epidemiology; D007182:Income / Q000706:statistics & numerical data*; D016015:Logistic Models; D008296:Malaysia / Q000453:epidemiology; D008297:Male; D015999:Multivariate Analysis; D056128:Obesity, Abdominal / Q000191:economics / Q000453:epidemiology; D016017:Odds Ratio; D015995:Prevalence; D012111:Residence Characteristics / Q000706:statistics & numerical data; D018570:Risk Assessment; D012307:Risk Factors; D012423:Rural Health / Q000706:statistics & numerical data; D012737:Sex Factors; D011795:Surveys and Questionnaires; D014504:Urban Health / Q000706:statistics & numerical data