Illness and intelligence are comparatively strong predictors of individual differences in depressive symptoms following middle age

ABSTRACT

Objective: We compared the importance of socio-demographic, lifestyle, health, and multiple cognitive measures for predicting individual differences in depressive symptoms in later adulthood.

Method: Data came from 6203 community-dwelling older adults (age 41–93 years at study entry) from the United Kingdom. Predictors (36 in total) were assessed up to four times across a period of approximately 12 years. Depressive symptoms were measured with the Geriatric Depression Scale. Statistical methods included multiple imputation (for missing data), random forest analysis (a machine learning approach), and multivariate regression.

Results: On average, depressive symptoms increased gradually following middle age and appeared to accelerate in later life. Individual differences in depressive symptoms were most strongly associated with differences in combined symptoms of physical illness (positive relation) and fluid intelligence (negative relation). The strength of association between depressive symptoms and fluid intelligence was unaffected by differences in health status within a subsample of chronically depressed individuals.

Conclusion: Joint consideration of general health status and fluid intelligence may facilitate prediction of depressive symptoms severity during later life and may also serve to identify sub-populations of community-dwelling elders at risk for chronic depression.

KEYWORDS:

• Depression
• cognition
• fluid intelligence
• aging
• machine learning

Acknowledgments

The authors gratefully acknowledge the UK Medical Research Council, the UK Economic and Social Research Council, and the UK Welcome Trust.

Disclosure statement

The authors report no conflicts of interest.

Notes

1. We could not estimate acceleration (i.e. quadratic change) in depressive symptoms as a random effect due to an insufficient number of measurement occasions. Therefore, we could not predict acceleration in depression symptoms as an outcome in its own right.

Additional information

Funding

This work was supported by the Swiss National Science Foundation [grant number 100019_146535]; Swiss National Centre of Competence in Research LIVES – Overcoming Vulnerability: Life course perspectives (Swiss National Science Foundation) [grant number 51NF40-160590]; the UK Medical Research Council; the UK Economic and Social Research Council; and the UK Welcome Trust.