Longitudinal studies predictably experience non-random attrition over time. Among older adults, risk factors for attrition may be similar to risk factors for outcomes such as cognitive decline and dementia, potentially biasing study results.

To characterize participants lost to follow-up which can be useful in the study design and interpretation of results.

In a longitudinal aging population study with 10 years of annual follow-up, we characterized the attrited participants (77%) compared to those who remained in the study. We used multivariable logistic regression models to identify attrition predictors. We then implemented four machine learning approaches to predict attrition status from one wave to the next and compared the results of all five approaches.

Multivariable logistic regression identified those more likely to drop out as older, male, not living with another study participant, having lower cognitive test scores and higher clinical dementia ratings, lower functional ability, fewer subjective memory complaints, no physical activity, reported hobbies, or engagement in social activities, worse self-rated health, and leaving the house less often. The four machine learning approaches using areas under the receiver operating characteristic curves produced similar discrimination results to the multivariable logistic regression model.

Attrition was most likely to occur in participants who were older, male, inactive, socially isolated, and cognitively impaired. Ignoring attrition would bias study results especially when the missing data might be related to the outcome (e.g. cognitive impairment or dementia). We discuss possible solutions including oversampling and other statistical modeling approaches.

Patients with dementia follow different trajectories of progression. We aimed to investigate which factors at the time of diagnosis could predict trajectory group membership.

Longitudinal observational study.

Specialized memory clinic, Oslo University Hospital in Norway.

Patients assessed at the memory clinic, between 12 January 2009 and 31 July 2016, who were registered in the Norwegian Registry of persons assessed for cognitive symptoms (NorCog) and diagnosed with dementia after the baseline examination period (n = 442). The patients were followed up to 3 years, with an average of 3.5 examinations.

Clinical Dementia Rating Scale Sum of Boxes (CDR-SB), Mini-Mental State Examination (MMSE), the Consortium to Establish a Registry of Alzheimer’s disease (CERAD) 10-item word list delayed recall, the Clock Drawing Test, (CDT) Trail Making Test A (TMT-A), and Neuropsychiatric Inventory Questionnaire (NPI-Q). Based on changes in scores on the CDR-SB, we used group-based trajectory modeling (GBTM) to explore the presence of trajectory groups. Multinomial logistic regression was used to explore whether a set of baseline variables could predict trajectory group membership.

Three trajectory groups were identified, one with a slow progression rate and two with more-rapid progression. Rapid progression was associated with older age, lower cognitive function (MMSE and TMT-A), and more-pronounced neuropsychiatric symptoms (NPI-Q) at the time of diagnosis.

Our findings demonstrate the heterogeneity of dementia progression and describe risk factors for rapid progression, emphasizing the need for individual follow-up regimes. For future intervention studies, our results may guide the selection of patients.

Geriatric depression complicates cognitive health in older adults. This study aims to investigate the impact of depressive symptoms on cognitive impairment in community-dwelling older adults, depending on whether cognitive dysfunction accompanied.

A community-based longitudinal cohort study.

This study analyzed data from the Korean Longitudinal Study of Aging (KLoSA) from 2006 to 2018.

Among 10,254 individuals who were registered in the KLoSA study, a total of 9119 subjects met the criteria, and 4547 subjects were included in the final analysis. The subjects were grouped into 4 categories based on depressive symptoms and cognitive dysfunction at baseline assessment: “normal control” (NC, n = 3341), “depression only” (Dep-only, n = 652), “cognitive dysfunction only” (CD-only, n = 393), and “depression with cognitive dysfunction” (Dep-CD, n = 161).

Cognitive impairment 10 years later was defined as K-MMSE scores below two percentile on demographically adjusted norms.

Ten-year survival, that is, not experiencing cognitive impairment, was 80 $$ \pm \,$$1% in NC group, 72 $$ \pm $$ 2% in Dep-only group, 52 $$ \pm $$ 3% in CD-only group, and 44 $$ \pm $$ 5% in Dep-CD group. The hazard ratio (HR) of the Dep-only group (HR = 1.18, 95% CI, 0.97–1.43, n.s.) did not differ from that of the NC group, but the HR of the Dep-CD group was significantly higher (HR = 2.85, 95% CI, 2.23–3.66, p < 0.001) than the NC group. When the Dep-CD group was compared to the CD-only group, the HR was 1.13 (95% CI, 0.85–1.49, n.s.), which indicates that it did not significantly differ from the CD-only group.

Our findings suggest that depressive symptoms with cognitive dysfunction are associated with a higher risk of cognitive impairment. Furthermore, cognitive dysfunction occurring with depressive symptoms is as much a risk for cognitive impairment as is pure cognitive dysfunction. Thus, healthcare providers should pay close attention to the community-dwelling elderly when depressive symptoms occur with cognitive dysfunction.

To investigate if depression risk modifies the association between frailty and mortality in older adults.

Ongoing cohort study.

Albacete city, Spain

Eight hundred subjects, 58.8% women, over 70 years of age from the Frailty and Dependence in Albacete (FRADEA) study.

Frailty phenotype, Geriatric Depression Scale (GDS), comorbidity, disability, and drug use were collected at baseline. Six groups were categorized: (G1: non-frail/no depression risk; G2: non-frail/depression risk; G3: prefrail/no depression risk; G4: prefrail/depression risk; G5: frail/no depression risk; and G6: frail/depression risk). Mean follow-up was 2542 days (SD 1006). GDS was also analyzed as a continuous variable. The association between frailty and depression risk with 10-year mortality was analyzed.

Mean age was 78.5 years. Non-frail was 24.5%, prefrail 56.3%, frail 19.3%, and 33.5% at depression risk. Mean GDS score was 3.7 (SD 3.2), increasing with the number of frailty criteria (p < 0.001). Ten-year mortality rate was 44.9%. Mortality was 21.4% for the non-frail, 45.6% for the prefrail, and 72.7% for the frail participants, 56% for those with depression risk, and 39.3% for those without depression risk. Mean survival times for groups G1 to G6 were, respectively, 3390, 3437, 2897, 2554, 1887, and 1931 days. Adjusted mortality risk was higher for groups G3 (HR 2.1; 95% confidence interval (CI) 1.4–3.1), G4 (HR 2.5; 95% CI 1.7–3.8), G5 (HR 3.8; 95% CI 2.4–6.1), and G6 (HR 4.0; 95% CI 2.6–6.2), compared with G1 (p < 0.001). Interaction was found between frailty and depression risk, although they were independently associated with mortality.

Depression risk increases mortality risk in prefrail older adults but not in non-frail and frail ones. Depression should be monitored in these older adults to optimize health outcomes. Factors modulating the relationship between frailty and depression should be explored in future studies.

To examine whether the educational level moderates the relationship between baseline depressive symptoms and cognitive functioning at 5- and 10-year follow-ups in older adults, considering the association between cognitive functioning and difficulty with activities of daily living (ADL).

Using a prospective design, a path analysis was performed.

In-home, face-to-face interviews and self-administered questionnaires, within the National Social Life, Health, and Aging Project.

In total, 1,461 participants (mean age = 66.62) were followed up from Wave 1 (baseline) to Wave 2 (at 5 years) and Wave 3 (at 10 years).

Depressive symptoms were assessed at baseline. Cognitive functioning and difficulty with ADL were assessed at baseline and at 5 and 10 years.

Educational level moderates the relationship between depressive symptoms and cognitive functioning at 5 years (β = 0.07, SE = 0.03, p = 0.04, Cohen’s f2 = 0.02), being depressive symptoms related to poor cognitive functioning only at low educational levels. Cognitive functioning predicts difficulty with ADL at 5 and 10 years (β = −0.08, SE = 0.03, p = 0.008, Cohen’s f2 = 0.01; β = −0.09, SE = 0.03, p = 0.006, Cohen’s f2 = 0.02). The proposed model yielded excellent fit (CFI = 1.00, RMSEA = 0.0001, 90% CI 0.0001–0.03, SRMR = 0.004, and χ2(8) = 7.16, p = 0.52).

Cognitive reserve may act as a protective factor against the effect of depressive symptoms on cognition in older adults, which, in turn, is relevant to their functional independence.

Prior neuropsychiatric disturbances are risk factors for stroke. There is a knowledge gap on the predictors of prestroke psychopathology, as well as their association with stroke outcomes in survivors living in low- and middle-income countries (LMICs). We estimated prevalence, predictors, and association of prestroke neuropsychiatric symptoms with poststroke depression (PSD), disability, and mortality.

Prospective observation.

Nigeria.

Adult ischemic and hemorrhagic stroke survivors.

Prestroke psychopathology were ascertained using the Neuropsychiatric Inventory Questionnaire (NPI-Q). Outcomes were assessed using validated tools, including the Centre for Epidemiologic Studies – Depression Scale (CES-D 10) and modified Rankin scale (mRS). Independent associations were investigated using regression models with Bonferroni corrections, and presented as standardized mean differences (SMD) and odds ratios (OR) within 95% confidence intervals (CI).

Among 150 participants, prestroke neuropsychiatric symptoms were found in 78 (52%). In multivariate logistic regression analyses, prestroke sleep disturbance was associated with systemic hypertension (OR = 5.39, 95% CI = 1.70–17.08). Prestroke neuropsychiatric symptoms independently predicted worse motor disability scores (SMD = 0.92, 95% CI = 0.21–1.62) and greater odds of poststroke mortality (OR = 2.7, 95% CI = 1.1–7.0) at 3 months. However, prestroke depression was not significantly associated with PSD.

Prestroke sleep disturbances was associated with systemic hypertension, a key index of high cardiovascular risk profile and stroke. The findings should energize before-the-stroke identification and prioritization of limited treatment resources in LMICs to persons with sleep symptoms who have multiple, additional, risks of stroke.