Behavioural and biological predictors of depression in older age : the role of Internet use, insulin-like growth factor 1, and cardiovascular disease risk factors in the English Longitudinal Study of Ageing

• Main Author: Chigogora, Sungano
• Published: University College London (University of London) 2018
• Subjects: 614.4
• Online Access: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.747555

BACKGROUND: Depression is a leading cause of disability and morbidity. Its determinants are not fully understood. In the present thesis I investigated the potential role of selected behavioural and biological characteristics in predicting depression in older people: Insulin-like Growth Factor 1 (IGF-1), Internet use and cardiovascular disease (CVD) risk factors. METHODS: Participants were recruited from the English Longitudinal Study of Ageing, an ongoing prospective cohort study of adults aged 50 years and over which was established in 2002. With six waves of biennial data collection up to 2012, serum IGF-1 levels were measured from 2008 at each nurse visit. Internet use was ascertained from 2002, and characterised again with greater detail in 2012. Risk factors for CVD were measured from 2004 and investigated according to the QRISK2, Framingham and SCORE algorithms. Depression symptoms were captured using the 8-item Centre for Epidemiologic Studies Depression Scale. Prospective analyses were carried out in individuals free from depressive symptoms at baseline (range dependent on analyses: 3,435 to 7,524 participants). RESULTS: A ‘U’-shaped association between IGF-1 and depression was observed, where both lower and higher levels were associated with elevated risk. For instance, relative to men in the lowest quintile of IGF-1, the age-adjusted odds ratio [OR] (95 confidence interval [CI]) for depression symptoms after 4 years of follow-up for increasing quintiles of IGF-1 were: 0.51 (0.28, 0.91), 0.50 (0.27, 0.92), 0.63 (0.35, 1.15) and 0.63 (0.35, 1.13) (P-value for quadratic association 0.002). In multivariable logistic regression analyses, compared with Internet users, non-users were 1.73 times (CI; 1.56, 1.95) more likely to develop depression after 10 years of follow-up. In particular, using the Internet for email communication was associated with a lower risk of depression. Increased risk of depression was observed in women only for all CVD risk algorithms per standard deviation change in score as follows; QRISK2 (OR 1.60: CI; 1.30, 2.00), Framingham model (OR 1.34: CI; 1.13, 1.58), SCORE chart (OR 1.40; CI; 1.11, 1.77). CONCLUSION: The present study adds to the understanding of the aetiology of depressive symptoms by suggesting a potential role for IGF-1, Internet use, and elevated CVD risk.