Tom Booth1, John M Starr, Ian Deary. 1. Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, EH8 9JZ, UK. tom.booth@ed.ac.uk
Abstract
OBJECTIVES: 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.
OBJECTIVES: 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.
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Authors: Tom Booth; Natalie A Royle; Janie Corley; Alan J Gow; Maria del C Valdés Hernández; Susana Muñoz Maniega; Stuart J Ritchie; Mark E Bastin; John M Starr; Joanna M Wardlaw; Ian J Deary Journal: Neurobiol Aging Date: 2014-12-22 Impact factor: 4.673
Authors: Zander Crook; Tom Booth; Simon R Cox; Janie Corley; Dominika Dykiert; Paul Redmond; Alison Pattie; Adele M Taylor; Sarah E Harris; John M Starr; Ian J Deary Journal: PLoS One Date: 2018-02-16 Impact factor: 3.240
Authors: V Deary; S P Hagenaars; S E Harris; W D Hill; G Davies; D C M Liewald; A M McIntosh; C R Gale; I J Deary Journal: Mol Psychiatry Date: 2017-02-14 Impact factor: 15.992
Authors: J H Cole; S J Ritchie; M E Bastin; M C Valdés Hernández; S Muñoz Maniega; N Royle; J Corley; A Pattie; S E Harris; Q Zhang; N R Wray; P Redmond; R E Marioni; J M Starr; S R Cox; J M Wardlaw; D J Sharp; I J Deary Journal: Mol Psychiatry Date: 2017-04-25 Impact factor: 15.992