title: Modeling multisystem biological risk in later life: allostatic load in the Lothian birth cohort study 1936.
abstract: OBJECTIVE
  To investigate and replicate a multisystem model of biological risk, or allostatic load, in a sample of generally healthy older adults.
  
  
  METHODS
  Multigroup confirmatory factor analysis (MG-CFA) was applied to data from the Lothian Birth Cohort 1936 (n = 726). Blood samples were taken at a physical examination. Three markers of inflammation (fibrinogen, interleukin-6, and C-reactive protein), five metabolic markers (high- and low-density lipoprotein, body mass index, glycated hemoglobin, and triglyceride), and blood pressure (mean sitting systolic and diastolic blood pressure) were used to estimate a second-order CFA model of allostatic load. Our sample was split into those taking antihypertensive, anti-inflammatory, lipid-lowering, and diabetes medications (n = 470), and those who were not (n = 256), in order to test the stability of the CFA model across groups.
  
  
  RESULTS
  In the nonmedicated sample, a second-order allostatic load model showed good fit to the data. However, the second-order model failed to estimate in the medicated group. The factor correlations between blood pressure and inflammation and metabolism were smaller in magnitude in the medicated group. Invariance analysis on the first-order measurement model suggested significant differences across groups in the associations of low-density lipoprotein and HbA1c with metabolism.
  
  
  CONCLUSIONS
  Reliable measurement of allostatic load is possible in ageing samples free of medications but is complicated in the presence of medications. MG-CFA represents a highly versatile method for the analysis of allostatic load.
authors.affiliation: Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, EH8 9JZ, UK. tom.booth@ed.ac.uk
authors.forename: Tom
authors.initials: T
authors.lastname: Booth
authors.forename: John M
authors.initials: JM
authors.lastname: Starr
authors.forename: Ian
authors.initials: I
authors.lastname: Deary
mesh_terms: D000368:Aged; D000375:Aging / Q000097:blood / Q000378:metabolism*; D052636:Allostasis / Q000502:physiology*; D000893:Anti-Inflammatory Agents / Q000008:administration & dosage; D000959:Antihypertensive Agents / Q000008:administration & dosage; D015415:Biomarkers; D001794:Blood Pressure; D015992:Body Mass Index; D002097:C-Reactive Protein / Q000032:analysis / Q000378:metabolism; D015331:Cohort Studies; D005163:Factor Analysis, Statistical; D005260:Female; D005340:Fibrinogen / Q000032:analysis / Q000378:metabolism; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage; D000960:Hypolipidemic Agents / Q000008:administration & dosage; D018836:Inflammation Mediators / Q000097:blood / Q000378:metabolism*; D015850:Interleukin-6 / Q000097:blood / Q000378:metabolism; D008055:Lipids / Q000097:blood; D008297:Male; D012606:Scotland
chemical_list: D000893:Anti-Inflammatory Agents; D000959:Antihypertensive Agents; D015415:Biomarkers; D002097:C-Reactive Protein; D005340:Fibrinogen; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; D000960:Hypolipidemic Agents; D018836:Inflammation Mediators; D015850:Interleukin-6; D008055:Lipids