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Abstract
Background The early identification of individuals at risk of mild cognitive impairment (MCI) has major public health implications for Alzheimer’s disease prevention.
Objective This study aims to develop and validate a risk assessment tool for MCI with a focus on modifiable factors and a suggested risk stratification strategy.
Methods Modifiable 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.
Results Nine 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’.
Conclusion A 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.
- AGING
- COGNITION
- DEMENTIA
- GERIATRICS
Data availability statement
Data are available on reasonable request.
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Data availability statement
Data are available on reasonable request.
Footnotes
QW and SZ contributed equally.
Contributors QioW drafted the manuscript. GS and YZ framed the concept and designed the study. The data collection and material preparation were conducted by QioW, SZ, JZ and QinW, and the data analysis was performed by FH and XH. All authors meet the criteria for authorship according to their contributions to the manuscript. All authors contributed to the article and approved the submitted version. YZ is responsible for the overall content as guarantor.
Funding This research was funded by the National Natural Science Foundation of China, Grant Number 72004003 to YZ, the Key Project of Science and Technology of Anhui Province, Grant Number 202004b11020019 to GS, and the Hefei Municipal Natural Science Foundation, grant number 2021005 to GS.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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