Biology subjects

Davis, O. S. P.

Publications and source records attributed to Davis, O. S. P..

2 recordsLinked to original sources

bioRxiv2018

Testing the causal effects between subjective wellbeing and physical health using Mendelian randomisation

ObjectivesTo investigate whether the association between subjective wellbeing (subjective happiness and life satisfaction) and physical health is causal.\n\nDesignWe conducted two-sample bidirectional Mendelian randomisation between subjective wellbeing and six measures of physical health: coronary artery disease, myocardial infarction, total cholesterol, HDL cholesterol, LDL cholesterol and body mass index (BMI).\n\nParticipantsWe used summary data from four large genome-wide association study consortia: CARDIoGRAMplusC4D for coronary artery disease and myocardial infarction; the Global Lipids Genetics Consortium for cholesterol measures; the Genetic Investigation of Anthropometric Traits consortium for BMI; and the Social Science Genetics Association Consortium for subjective wellbeing. A replication analysis was conducted using 337,112 individuals from the UK Biobank (54% female, mean age =56.87, SD=8.00 years at recruitment).\n\nMain outcome measuresCoronary artery disease, myocardial infarction, total cholesterol, HDL cholesterol, LDL cholesterol, BMI and subjective wellbeing.\n\nResultsThere was evidence of a causal effect of BMI on subjective wellbeing such that each 1 kg/m2 increase in BMI caused a 0.045 (95%CI 0.006 to 0.084, p=0.023) SD reduction in subjective wellbeing. Replication analyses provided strong evidence of an effect of BMI on satisfaction with health ({beta}=0.034 (95% CI: -0.042 to -0.026) unit decrease in health satisfaction per SD increase in BMI, p<2-16). There was no clear evidence of a causal effect between subjective wellbeing and the other physical health measures in either direction.\n\nConclusionsOur results suggest that a higher BMI lowers subjective wellbeing. Our replication analysis confirmed this finding, suggesting the effect in middle-age is driven by satisfaction with health. BMI is a modifiable determinant and therefore, our study provides further motivation to tackle the obesity epidemic because of the knock-on effects of higher BMI on subjective wellbeing.

epidemiology↗

bioRxiv2018

Identifying Critical Points of Trajectories of Depressive Symptoms From Childhood to Young Adulthood: Evidence of Sex Differences from the Avon Longitudinal Study of Parents and Children (ALSPAC)

Depression is a common mental illness associated with increased substance misuse and risk of suicide. Potential risk factors for depression include sex and depressive symptoms in early life, however the mechanisms responsible are not yet understood. Research has focused on late childhood and adolescence as this developmental period may be a modifiable risk factor that prevents or reduces depression at a later stage. It is also important to establish at what ages the level of depression is changing as this will help identify critical points to intervene with treatment. We used multilevel growth-curve models to explore adolescent trajectories of depressive symptoms in the Avon Longitudinal Study of Parents and Children, a UK based pregnancy cohort. Using data from 9301 individuals, trajectories of depressive symptoms were constructed for males and females between 10.6 and 22.8 years old. We calculated the age of peak velocity for depressive symptoms (the age at which depressive symptoms increases most rapidly) and the age of maximum depressive symptoms. Adjusted results suggested that being female was associated with a steeper trajectory compared to being male (per 1 year increase in relation to depressive symptoms: 0.128, SE = 0.035, [95% CI: 0.059, 0.198]; p <0.001). We found evidence suggesting that females had an earlier age of peak velocity of depressive symptoms (females 13.7 years old, SE = 0.321, [95% CI: 12.9, 14.4] and males 16.4 years old, SE = 0.096, [95% CI: 16.2, 16.6]; p <0.001), but weak evidence of an earlier age of maximum depressive symptoms (p = 0.125). Possible mechanisms that underlie this sex difference include the roles of pubertal development and timing. Using multilevel growth curve models to estimate the age of peak velocity and maximum depressive symptoms for different population subgroups may provide useful knowledge for treating and preventing later depression.

epidemiology↗