With the advent of population aging,Alzheimer's disease(AD)has placed a heavy social and economic burden on the global public health system.A series of pathophysiological changes may occur in the brain tissues more than 20 years before the typical symptoms of AD appear.The cognitive impairment that occurs during this process is called AD-derived cognitive disorders when is the most important window for early detection,diagnosis and prevention of AD.Reliable estimates of the incidence of early-onset dementia and AD(before the age of 65 years)are scarce,and about 30%of these cases are attributed to AD.The epidemiology of AD-derived cognitive impairment is influenced by a variety of factors,including age,sex,genetics,environment and geographic differences.These factors are intertwined,resulting in significant differences in incidence and presentation across populations,regions and cultural contexts.Therefore,an in-depth study of these differences can help to develop more targeted prevention and intervention strategies.In order to meet the needs of clinical practice and further improve the cognition of medical workers on AD-derived cognitive disorders,the expert group has formulated the expert consensus on the early diagnosis and treatment of AD-derived cognitive disorders after several discussions,in order to provide consensus and guidance for the early diagnosis,early intervention and precise treatment of AD.

With the advent of population aging,Alzheimer's disease(AD)has placed a heavy social and economic burden on the global public health system.A series of pathophysiological changes may occur in the brain tissues more than 20 years before the typical symptoms of AD appear.The cognitive impairment that occurs during this process is called AD-derived cognitive disorders when is the most important window for early detection,diagnosis and prevention of AD.Reliable estimates of the incidence of early-onset dementia and AD(before the age of 65 years)are scarce,and about 30%of these cases are attributed to AD.The epidemiology of AD-derived cognitive impairment is influenced by a variety of factors,including age,sex,genetics,environment and geographic differences.These factors are intertwined,resulting in significant differences in incidence and presentation across populations,regions and cultural contexts.Therefore,an in-depth study of these differences can help to develop more targeted prevention and intervention strategies.In order to meet the needs of clinical practice and further improve the cognition of medical workers on AD-derived cognitive disorders,the expert group has formulated the expert consensus on the early diagnosis and treatment of AD-derived cognitive disorders after several discussions,in order to provide consensus and guidance for the early diagnosis,early intervention and precise treatment of AD.

Objective To observe the value of machine learning model for predicting the risk of dementia in patients with depression.Methods Totally 31 587 depression patients from UK Biobank database were retrospectively enrolled and divided into dementia group(n=896)or non-dementia group(n=30 691)based on follow-up data showed developed dementia or not,also divided into training set(n=18 952)or test set(n=12 635)at the ratio of 6:4.Based on interviews and questionnaire surveys,a total of 190 factors including demographic characteristics,lifestyle,health status,physical indicators and imaging data were included and screened to establish models with light gradient boosting machine(LightGBM),ridge regression(Ridge)and adaptive boosting(AdaBoost),and the value for predicting the risk of dementia in patients with depression was observed.Results A total of 10 factors were ultimately enrolled,including age,waist circumference,employment status,daytime rest,daytime doze or drowsiness,duration of mobile phone use,number of family members,duration of depression,guilt and seeking medical attention due to psychological issues.Based on the above factors,the models were established.In training set,the area under the curve(AUC)of LightGBM,Ridge and AdaBoost model for predicting dementia risk in patients with depression was 0.914,0.832 and 0.889,respectively,and the differences between each 2 models were significant(all P＜0.05);while in test set,the AUC was 0.866,0.842 and 0.859,respectively,except for LightGBM and AdaBoost,the other with significant differences between each two(both P＜0.05).The calibration curve showed that the LightGBM model had the best fit.Conclusion LightGBM machine learning model was helpful for predicting the risk of dementia in patients with depression.