OBJECTIVE: We sought to examine the relative importance of genetic and environmental factors for the MOS SF-36; a widely used, valid, and reliable measure of health-related quality of life and to discuss incorporating genetic influences into health services research. DATA SOURCES: Data are from a nationally distributed, nonclinical cohort of 2928 middle age, middle-class, male-male twin members of the Vietnam Era Twin Registry. STUDY DESIGN: This was a secondary data analysis, classic twin heritability analysis. DATA COLLECTION: A telephone survey was used to collect information on alcohol-related problems and health services use, including the SF-36. PRINCIPAL FINDINGS: Variance component analyses indicated that additive genetic factors accounted for 17% to 33% of the variance for each of the 8 domains of the SF-36. Shared environment accounted for 0% to 12% of the variance for each domain, with the majority of variance for each domain accounted for by nonshared, or unique environment and error. Physical and mental health summary measures indicated that approximately one-third of the variance was accounted for by additive genetic factors and the remainder accounted for by nonshared environment and error. Clinical condition, history of alcohol dependence, had a small-but-significant influence for all domains. Including condition proved to be a better-fitting model. However, confidence intervals temper uniform statistical significance for genetic factors. CONCLUSIONS: This study assessed the heritability of the SF-36 in a nonclinical, community sample of middle age, middle-class all-male twins. The moderate genetic effects on SF-36 domain and summary measures are new findings and thus may affect interpretations of SF-36 as a measure of health-related quality of life. Ideally, trait-based measures should identify genetic sources of variation and thus help understand any bias of the true effects of SF-36. Still the majority of variance is accounted for by nonshared or unique environmental factors and error. By extension, increased understanding of the importance of genetic and environmental factors that influence either predictors or outcomes of interest will expand the level of scientific debate in health services research and improve predictability.
OBJECTIVE: We sought to examine the relative importance of genetic and environmental factors for the MOS SF-36; a widely used, valid, and reliable measure of health-related quality of life and to discuss incorporating genetic influences into health services research. DATA SOURCES: Data are from a nationally distributed, nonclinical cohort of 2928 middle age, middle-class, male-male twin members of the Vietnam Era Twin Registry. STUDY DESIGN: This was a secondary data analysis, classic twin heritability analysis. DATA COLLECTION: A telephone survey was used to collect information on alcohol-related problems and health services use, including the SF-36. PRINCIPAL FINDINGS: Variance component analyses indicated that additive genetic factors accounted for 17% to 33% of the variance for each of the 8 domains of the SF-36. Shared environment accounted for 0% to 12% of the variance for each domain, with the majority of variance for each domain accounted for by nonshared, or unique environment and error. Physical and mental health summary measures indicated that approximately one-third of the variance was accounted for by additive genetic factors and the remainder accounted for by nonshared environment and error. Clinical condition, history of alcohol dependence, had a small-but-significant influence for all domains. Including condition proved to be a better-fitting model. However, confidence intervals temper uniform statistical significance for genetic factors. CONCLUSIONS: This study assessed the heritability of the SF-36 in a nonclinical, community sample of middle age, middle-class all-male twins. The moderate genetic effects on SF-36 domain and summary measures are new findings and thus may affect interpretations of SF-36 as a measure of health-related quality of life. Ideally, trait-based measures should identify genetic sources of variation and thus help understand any bias of the true effects of SF-36. Still the majority of variance is accounted for by nonshared or unique environmental factors and error. By extension, increased understanding of the importance of genetic and environmental factors that influence either predictors or outcomes of interest will expand the level of scientific debate in health services research and improve predictability.
Authors: Kimberly E Alexander; Suzanne Chambers; Amanda B Spurdle; Jyotsna Batra; Felicity Lose; Tracy A O'Mara; Robert A Gardiner; Joanne F Aitken; Judith A Clements; Mary-Anne Kedda; Monika Janda Journal: Qual Life Res Date: 2015-02-28 Impact factor: 4.147
Authors: Juan R Ordoñana; Meike Bartels; Dorret I Boomsma; David Cella; Miriam Mosing; Joao R Oliveira; Donald L Patrick; Ruut Veenhoven; Gert G Wagner; Mirjam A G Sprangers Journal: Qual Life Res Date: 2012-10-10 Impact factor: 4.147
Authors: Melissa S Y Thong; Mirjam A G Sprangers; Jeff A Sloan; Donald L Patrick; Ping Yang; Cornelis J F van Noorden Journal: Qual Life Res Date: 2014-11-12 Impact factor: 4.147
Authors: Mirjam A G Sprangers; Jeff A Sloan; Andrea Barsevick; Cynthia Chauhan; Amylou C Dueck; Hein Raat; Quiling Shi; Cornelis J F Van Noorden Journal: Qual Life Res Date: 2010-10-14 Impact factor: 4.147
Authors: Mirjam A G Sprangers; Jeff A Sloan; Ruut Veenhoven; Charles S Cleeland; Michele Y Halyard; Amy P Abertnethy; Frank Baas; Andrea M Barsevick; Meike Bartels; Dorret I Boomsma; Cynthia Chauhan; Amylou C Dueck; Marlene H Frost; Per Hall; Pål Klepstad; Nicholas G Martin; Christine Miaskowski; Miriam Mosing; Benjamin Movsas; Cornelis J F Van Noorden; Donald L Patrick; Nancy L Pedersen; Mary E Ropka; Quiling Shi; Gen Shinozaki; Jasvinder A Singh; Ping Yang; Ailko H Zwinderman Journal: Twin Res Hum Genet Date: 2009-06 Impact factor: 1.587