A study on factors associated with age of Alzheimer's disease onset / 中华流行病学杂志

Objective: To understand the distribution characteristics of age of Alzheimer's disease (AD) onset and influencing factors. Methods: Based on the follow-up data of Alzheimer's Disease Neuroimaging Initiative from 2005 to 2022, participants with normal cognition (CN) or mild cognitive impairment (MCI) at baseline survey, and those with progression to AD during follow-up period were selected as study subjects. Univariate analysis and multiple linear regression analysis were performed to explore the associations of gender, race, number of ApoE ε4 genes carried, family history, years of education and marital status with the age of AD onset. Results: A total of 405 participants, with an average age of (74.0±6.9) years at baseline survey, progressed to AD during follow up period. The age of AD onset was (76.6±7.5) years, and age of onset in men was about 1.9 years later than women. Multiple linear regression analysis showed that for each increase in ApoE ε4 gene number, the age of AD onset was about 0.344 years earlier. The age of AD onset was 4.007 years earlier for those with MCI at baseline survey compared with those with CN. Years of education were not significantly associated with the age of onset of AD (P>0.05). Conclusion: Those who carry ApoE ε4 gene, and have MCI at baseline survey might have earlier age of AD onset.

Transition rules of cognitive frailty and influencing factors in the elderly in China / 中华流行病学杂志

Objective: To understand the transition rules of cognitive frailty and its influencing factors in the elderly in China and provide evidence for the early intervention of cognitive frailty. Methods: Data were retrospectively collected from China Health and Retirement Longitudinal Study with 3 round consecutive survey (2011, 2013, 2015) and the state of the subjects were classified into four categories: robust-normal cognitive, cognitive impairment, physical frailty, and cognitive frailty. A multi-state Markov model was established to explore the transition rules of cognitive frailty and its influencing factors. Results: A total of 3 470 older adults were included, and 350 (10.09%) had cognitive frailty at baseline. After two years, the probability of cognitive frailty in the cognitive impairment population was higher than that in people with physical frailty (31.6% vs. 7.6%). Persons with cognitive frailty were more likely to become physical frailty (29.7% vs. 15.6%). Being women (HR=1.599, 95%CI: 1.058-2.417), comorbidity (HR=3.035, 95%CI: 1.090-8.450), and depression (HR=1.678, 95%CI: 1.153-2.441) were the risk factors associated with cognitive frailty in the elderly, while being educated (HR=2.367, 95%CI: 1.567-3.575) was a protective factor for the transition of cognitive frailty to physical frailty. Conclusions: The prevalence of cognitive frailty is relatively high in the elderly in China. Those with cognitive impairment have a higher probability of cognitive frailty. Gender, education level, comorbidity, and depression are the main influencing factors for the occurrence and transition of cognitive frailty.

Research on predicting the risk of mild cognitive impairment in the elderly based on the joint model / 中华流行病学杂志

Objective: To construct and compare the dynamic prediction models of the risk of mild cognitive impairment (MCI) in the elderly based on six different cognitive function scales. Methods: Based on longitudinal data from the Alzheimer's Disease Neuroimaging Initiative from 2005 to 2020, Mini-mental state examination (MMSE), functional activities questionnaire (FAQ), Alzheimer's disease assessment scale-cognitive (ADAS-Cog) 11, ADAS-Cog13, ADAS delayed word recall (ADASQ4), and Rey auditory verbal learning test (RAVLT)_immediate were used as longitudinal cognitive function evaluation indicators to assess the longitudinal changes in cognitive function. The joint model was used to analyze association between indicators variation trajectory and survival outcome MCI, and construct the risk prediction model of MCI in the elderly, the linear mixed model was constructed the longitudinal sub-model which described the evolution of a repeated measure over time, a proportional hazards model was constructed the survival sub-model, and the two sub-models were connected through the correlation parameter (α). The areas under the receiver operator characteristic curve (AUC) were used to evaluate the predictive efficacy of the model in the follow-up period of (t, t+Δt). The starting point t was selected at the 30th, 42nd, and 54th month, and the Δt was selected as 15 and 21 months. Based on the prediction model, an example of the research object was selected for dynamic individual predictions of the risk of MCI. Results: Finally, 544 older adults (aged 60 years and above) with normal baseline cognitive status were included, of which 119 cases (21.9%) had MCI during the follow-up process were regarded as the case group, and 425 cases remained normal as the control group. The joint model suggests that the longitudinal trajectories of the six evaluation indicators are all related to the risk of MCI (P<0.001). The risk of MCI decreased by 32.3% (HR=0.677, 95%CI: 0.541-0.846) and 10.8% (HR=0.892, 95%CI: 0.865-0.919) for each one-point increase of MMSE and RAVLT_immediate longitudinal scores. The risk of MCI increased by 53.2% (HR=1.532, 95%CI: 1.393-1.686), 36.2% (HR=1.362, 95%CI: 1.268-1.462), 23.2% (HR=1.232, 95%CI: 1.181-1.285), and 85.1% (HR=1.851, 95%CI:1.629-2.104) for each one-point increase of FAQ, ADAS-Cog11, ADAS-Cog13, and ADASQ4 longitudinal scores. AUC results show that RAVLT_immediate (0.760 2) and ADASQ4 (0.755 8) have higher average prediction efficiency, followed by ADAS-Cog13 (0.743 7), ADAS-Cog11 (0.715 3), FAQ (0.700 8) and MMSE (0.629 5). ADASQ4 joint model was used to provide a dynamic individual prediction of the risk of MCI. The average probability of MCI after five years of follow-up and ten years of follow-up in the example individuals were 8% and 40%, respectively. Conclusions: The RAVLT_immediate and ADASQ4 scales, which are only for memory tests, have high accuracy in predicting the risk of MCI. Using the RAVLT_immediate and ADASQ4 scales as longitudinal cognitive function evaluation indicators to construct a joint model, the results can provide a basis for realizing MCI risk prediction for the elderly.