BackgroundDepressive symptoms and mild cognitive impairment (MCI) are highly prevalent in rural China. The study aimed to investigate the longitudinal associations between changes in depressive symptoms and cognitive decline and MCI incidence among Chinese rural elderly individuals.MethodsA 2-year follow-up study was conducted among 1,477 participants from the Anhui Healthy Longevity Survey (AHLS). Depressive symptoms were assessed by the 9-item Patient Health Questionnaire (PHQ-9), and cognitive status was evaluated by the Mini Mental State Examination (MMSE). Multivariable linear regression and logistic regression were employed.ResultsEvery 1-unit PHQ-9 score increase was significantly associated with more cognitive decline (β = 0.157, 95% CI: 0.092, 0.221, p < 0.001) and a higher risk of MCI incidence (OR = 1.063, 95% CI: 1.025, 1.103, p = 0.001). The participants who experienced worsening of depression symptoms had a larger decline in the 2-year MMSE score (β = 0.650, 95% CI: 0.039, 1.261, p = 0.037) and elevated risks of incident MCI (OR = 1.573, 95% CI: 1.113, 2.223, p = 0.010).LimitationsScreening tools rather than standard diagnostic procedures were used in the study. Moreover, the long-term associations still need further exploration since the follow-up time was short.ConclusionsIncreased depressive symptoms were associated with more cognitive decline and higher risks of incident MCI among Chinese rural residents.

BackgroundThe early identification of individuals at risk of mild cognitive impairment (MCI) has major public health implications for Alzheimer’s disease prevention.ObjectiveThis study aims to develop and validate a risk assessment tool for MCI with a focus on modifiable factors and a suggested risk stratification strategy.MethodsModifiable risk factors were selected from recent reviews, and risk scores were obtained from the literature or calculated based on the Rothman-Keller model. Simulated data of 10 000 subjects with the exposure rates of the selected factors were generated, and the risk stratifications were determined by the theoretical incidences of MCI. The performance of the tool was verified using cross-sectional and longitudinal datasets from a population-based Chinese elderly cohort.ResultsNine modifiable risk factors (social isolation, less education, hypertension, hyperlipidaemia, diabetes, smoking, drinking, physical inactivity and depression) were selected for the predictive model. The area under the curve (AUC) was 0.71 in the training set and 0.72 in the validation set for the cross-sectional dataset. The AUCs were 0.70 and 0.64 in the training and validation sets, respectively, for the longitudinal dataset. A combined risk score of 0.95 and 1.86 was used as the threshold to categorise MCI risk as ‘low’, ‘moderate’ and ‘high’.ConclusionA risk assessment tool for MCI with appropriate accuracy was developed in this study, and risk stratification thresholds were also suggested. The tool might have significant public health implications for the primary prevention of MCI in elderly individuals in China.

Early identification of individuals with mild cognitive impairment (MCI) is essential to combat worldwide dementia threats. Physical function indicators might be low-cost early markers for cognitive decline. To establish an early identification tool for MCI by combining physical function indicators (upper and lower limb function) via a clinical prediction modeling strategy. A total of 5393 participants aged 60 or older were included in the model. The variables selected for the model included sociodemographic characteristics, behavioral factors, mental status and chronic conditions, upper limb function (handgrip strength), and lower limb function (self-rated squat ability). Two models were developed to test the predictive value of handgrip strength (Model 1) or self-rated squat ability (Model 2) separately, and Model 3 was developed by combining handgrip strength and self-rated squat ability. The 3 models all yielded good discrimination performance (area under the curve values ranged from 0.719 to 0.732). The estimated net reclassification improvement values were 0.3279 and 0.1862 in Model 3 when comparing Model 3 to Model 1 and Model 2, respectively. The integrated discrimination improvement values were estimated as 0.0139 and 0.0128 when comparing Model 3 with Model 1 and Model 2, respectively. The model that contains both upper and lower limb function has better performance in predicting MCI. The final prediction model is expected to assist health workers in early identification of MCI, thus supporting early interventions to reduce future risk of AD, particularly in socioeconomically deprived communities.