Using Different Diagnostic Criteria To Detect The Prevalence Of Mild Cognitive Impairment In A Community-based Sample And Analysis Of Predictors

Mild cognitive impairment(MCI)is considered an intermediate state between the cognitive changes of aging and the earliest clinical features of dementia.From the point of MCI diagnosis,6-15% of the patients will convert to dementia per year.The objective neuropsychological assessment identifying cognitive impairment is the key to a clinical criterion for the diagnosis of MCI.However,no consensus of the operationalized criteria has been reached in the community in China.Therefore,participants were recruited from individuals who registered to participant in a community service program for the early detection and management of dementia in two residential districts in Shijiazhuang,Hebei.A comprehensive neuropsychological test battery was administered to detect the cognition of subjects.And multivariate analysis was used to explore the independent risk factors affecting MCI.This study is divided into two parts,and the contents of each part are now summarized as follows.Part I Using different diagnostic criteria to detect the prevalence of mild cognitive impairment in a community-based sampleObjective: The community-based adults from Shijiazhuang were detected by a comprehensive neuropsychological battery.All participants were classified by two types of impairment rule: severity of impairment and extent of impairment.The main aim of this study was to examine the effects of different criteria on the prevalence of MCI in the community-based adults in Shijiazhuang.Methods: 395 non-demented community-based adults were enrolled in this study from July 2018 to October 2019.The cognition of the participants was detected and classified into five domains with a comprehensive neuropsychological battery.All participants were classified by two types of impairment rule: severity of impairment and extent of impairment.The prevalence of MCI and the subtypes were calculated.A κ score was used to identify the agreements of diagnostic criteria.Results: The prevalence of MCI was highly dependent on the diagnostic criteria used,ranging from 23.5% to 79.7%.Single-domain amnestic MCI was the most common subtype,and multiple-domain non-amnestic MCI was the rarest one.Consistency between different diagnostic classifications was from poor to good.And the overall prevalence of MCI was more consistent than that of subtypes.Conclusions: The prevalence of MCI and its subtypes were associated with the diagnostic criteria.The consistency of MCI prevalence using different diagnostic criteria was highly unstable.This part of the study can provide references for clinicians to choose MCI diagnostic criterion and provide basis for early diagnosis of MCI.Part II Analysis of predictors of MCI in a community-based sampleObjective: At present,demographic characteristics,lifestyle,and previous medical history are associated with MCI.The key to prevent MCI is to control the associated risk factors.In this part of the study,the demographic characteristics,lifestyle and previous medical history were enrolled detailedly.And the possible related factors of MCI were explored through multiple logistic regression analysis.Methods: 395 non-demented community-based adults were enrolled in this study from July 2018 to October 2019.The cognition of the participants was detected and classified into five domains with a comprehensive neuropsychological battery.The demographic characteristics(age,sex and educational level),lifestyle(smoking,alcohol drinking,time of asleep,body mass index and sedentary behavior)and previous medical history(hypertension,diabetes,coronary heart disease,atrial fibrillation and hyperlipidemia)were enrolled detailedly.According to the diagnostic criteria in the first part,the subjects were divided into two groups: normal cognitive function group and MCI group.The key factors with statistically significant differences(P≤0.2)in the univariate analysis were incorporated into the multivariate logistic regression analysis model to explore the independent risk factors affecting MCI.Results: Male was an independent risk factor for MCI in all six diagnostic criteria.For most diagnostic criteria,low educational level was an independent risk factor for MCI.In addition to sex and educational level,hypertension and less sleep time(<6h)were also independent risk factors for MCI in the diagnostic criterion of the domain composite score at 1.5 SD.Conclusions: Sex,education level,time of asleep and hypertension were independent related factors for MCI.These findings can provide clues for clinicians to explore the possible pathogenesis of MCI and provide a basis for clinicians to help MCI patients form the early rehabilitation plans.