Why the study?
Depressive symptoms are common in mild cognitive impairment, AD dementia, and cognitively unimpaired older adults, but whether they can help identify cognitive impairment in ageing is unclear.
Can depressive symptoms assessed by the GDS accurately distinguish between healthy cognitive ageing and cognitive impairment compared to the MMSE?
Can depressive symptoms assessed by the GDS accurately distinguish between healthy cognitive ageing and cognitive impairment compared to the MMSE?
Depressive symptoms, when analyzed at the item level using machine learning, may serve as a useful adjunct in identifying cognitive impairment in older adults.
Depressive symptoms show modest diagnostic utility via machine learning; leaves open incremental value beyond MMSE in prospective studies.
Depressive symptoms are common in mild cognitive impairment (MCI), dementia caused by Alzheimer's disease (AD dementia) and in cognitively unimpaired older adults. However, it is unclear whether they could contribute to the identification of cognitive impairment in ageing. To assess the potential utility of depressive symptoms to distinguish between healthy cognitive ageing and MCI and AD dementia. The diagnostic workup of the cognitive function of 1737 older cognitively unimpaired individuals, 334 people with MCI and 142 individuals with AD dementia relied on a comprehensive neuropsychiatric assessment, including the Mini Mental State Examination (MMSE). Depressive symptoms were tapped with the 15-item Geriatric Depression Scale (GDS). Proportional odds logistic regression (POLR) models and the machine learning technique Adaptive Boosting algorithm (AdaBoost) were employed. Stratified repeated random subsampling (stratified bootstrap resampling) was used to recursive partitioning to training- and validation set (70/30 ratio). The average accuracy of the POLR models for the GDS total score in distinguishing between cognitive impairment and healthy cognitive ageing exceeded 78% and was inferior to that of MMSE. Of note, the sensitivity of GDS total score was very low. By employing the AdaBoost algorithm and considering GDS items separately, the average accuracy was higher than 0.72 and comparable to that of the MMSE, while sensitivity- and specificity values were more balanced. The findings of the study provide initial evidence that depressive symptoms may contribute to distinguishing between cognitive impairment and cognitively healthy ageing.
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Alexopoulos et al. (2025) studied this question.
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