title: Proximal correlates of metabolic phenotypes during 'at-risk' and 'case' stages of the metabolic disease continuum.
abstract: OBJECTIVE
  To examine the social and behavioural correlates of metabolic phenotypes during 'at-risk' and 'case' stages of the metabolic disease continuum.
  
  
  METHODS
  Cross-sectional study of a random population sample.
  
  
  METHODS
  A total of 718 community-dwelling adults (57% female), aged 18-92 years from a regional South Australian city.
  
  
  METHODS
  Total body fat and lean mass and abdominal fat mass were assessed by dual energy x-ray absorptiometry. Fasting venous blood was collected in the morning for assessment of glycated haemoglobin, plasma glucose, serum triglycerides, cholesterol lipoproteins and insulin. Seated blood pressure (BP) was measured. Physical activity and smoking, alcohol and diet (96-item food frequency), sleep duration and frequency of sleep disordered breathing (SDB) symptoms, and family history of cardiometabolic disease, education, lifetime occupation and household income were assessed by questionnaire. Current medications were determined by clinical inventory.
  
  
  RESULTS
  36.5% were pharmacologically managed for a metabolic risk factor or had known diabetes ('cases'), otherwise were classified as the 'at-risk' population. In both 'at-risk' and 'cases', four major metabolic phenotypes were identified using principal components analysis that explained over 77% of the metabolic variance between people: fat mass/insulinemia (FMI); BP; lipidaemia/lean mass (LLM) and glycaemia (GLY). The BP phenotype was uncorrelated with other phenotypes in 'cases', whereas all phenotypes were inter-correlated in the 'at-risk'. Over and above other socioeconomic and behavioural factors, medications were the dominant correlates of all phenotypes in 'cases' and SDB symptom frequency was most strongly associated with FMI, LLM and GLY phenotypes in the 'at-risk'.
  
  
  CONCLUSIONS
  Previous research has shown FMI, LLM and GLY phenotypes to be most strongly predictive of diabetes development. Reducing SDB symptom frequency and optimising the duration of sleep may be important concomitant interventions to standard diabetes risk reduction interventions. Prospective studies are required to examine this hypothesis.
authors.affiliation: 1] Division of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide, South Australia, Australia [2] Spencer Gulf Rural Health School (SGRHS), University of South Australia and The University of Adelaide, Whyalla Norrie, South Australia, Australia [3] Centre for Rural Health and Community Development (CRHaCD), University of South Australia, Whyalla Norrie, South Australia, Australia.
authors.forename: M T
authors.initials: MT
authors.lastname: Haren
authors.forename: G
authors.initials: G
authors.lastname: Misan
authors.forename: J F
authors.initials: JF
authors.lastname: Grant
authors.forename: J D
authors.initials: JD
authors.lastname: Buckley
authors.forename: P R C
authors.initials: PR
authors.lastname: Howe
authors.forename: A W
authors.initials: AW
authors.lastname: Taylor
authors.forename: J
authors.initials: J
authors.lastname: Newbury
authors.forename: R A
authors.initials: RA
authors.lastname: McDermott