The China longitudinal ageing study: overview of the demographic, psychosocial and cognitive data of the Shanghai sample

Household Environments and Cognitive Decline Among Middle-Aged and Older Adults in China: Exploring Gender, Age, and Residential Variations

This study examined the relationship between household environments and trajectories of cognitive function among middle-aged and older adults in China and its urban/rural, gender, and age variations. We estimated multi-level linear growth curve models using a representative sample of 16,111 respondents aged 45 years and over from the China Health and Retirement Longitudinal Study (2011-2018). Older people who lived with a spouse, but not with children, and those with higher living expenditures, better housing quality, and indoor clean fuels for cooking had a slower cognitive decline. Living arrangement more strongly predicted men's cognitive decline, while living expenditure, solid fuel use, and housing quality significantly predicted only women's cognitive decline. Only for older adults and rural residents, those living alone had significantly faster cognitive decline than those living with a spouse only. These findings underscore the importance of improving the living conditions of older adults to help alleviate their cognitive decline.

Lifestyle and incident dementia: A COSMIC individual participant data meta‐analysis

INTRODUCTION

The LIfestyle for BRAin Health (LIBRA) index yields a dementia risk score based on modifiable lifestyle factors and is validated in Western samples. We investigated whether the association between LIBRA scores and incident dementia is moderated by geographical location or sociodemographic characteristics.

METHODS

We combined data from 21 prospective cohorts across six continents (N = 31,680) and conducted cohort-specific Cox proportional hazard regression analyses in a two-step individual participant data meta-analysis.

RESULTS

A one-standard-deviation increase in LIBRA score was associated with a 21% higher risk for dementia. The association was stronger for Asian cohorts compared to European cohorts, and for individuals aged ≤75 years (vs older), though only within the first 5 years of follow-up. No interactions with sex, education, or socioeconomic position were observed.

DISCUSSION

Modifiable risk and protective factors appear relevant for dementia risk reduction across diverse geographical and sociodemographic groups.

HIGHLIGHTS

A two-step individual participant data meta-analysis was conducted. This was done at a global scale using data from 21 ethno-regionally diverse cohorts. The association between a modifiable dementia risk score and dementia was examined. The association was modified by geographical region and age at baseline. Yet, modifiable dementia risk and protective factors appear relevant in all investigated groups and regions.

Associations between Informant-Reported Cognitive Complaint and Longitudinal Cognitive Decline in Subjective Cognitive Decline A 7-Year Longitudinal Study

OBJECTIVE

This study aimed to determine the predictive values of informant-reported memory decline (IMD) among subjective cognitive decline (SCD) older adults from a 7-year community-based cohort study.

METHOD

Ninety SCD participants were included. Demographic data and neuropsychological test scores at both baseline and 7-year follow-up were collected. Differences between SCD with IMD (+IMD) and SCD without IMD (-IMD) were compared. Logistic regression models were used to determine whether baseline IMD could predict diagnostic outcomes at 7-year follow-up.

RESULTS

Forty-one percent of SCD adults had IMD. At baseline, the +IMD group showed more depressive symptoms (p = 0.016) than the -IMD group. Furthermore, the Beijing-version Montreal Cognitive Assessment (MoCA), Digit Span Test-Forward, Visual Matching and Reasoning, and Wechsler Adult Intelligence Scale-RC Picture Completion (WAIS-PC) scores in the +IMD group were significantly lower than those in the -IMD group. Fifty-four percent of +IMD participants converted to mild cognitive impairment (MCI) or dementia at follow-up, and 22.6% of the -IMD participants converted to MCI. Follow-up Mini-Mental State Examination, MoCA, and Verbal Fluency Test scores of the +IMD group were significantly lower than those in the -IMD group. The +IMD group was more likely to progress to cognitive impairment at 7-year follow-up (OR = 3.361, p = 0.028).

CONCLUSIONS

SCD participants with +IMD may have poorer cognition and are more likely to convert to cognitive impairment over time. Our long-term follow-up study confirmed the importance of informants' perceptions of SCD, which can help clinicians identify individuals at risk of cognitive decline.

Trends in the disease burden of anxiety disorders in middle-aged and older adults in China

BACKGROUND

Anxiety disorders in middle-aged and older adults are an important public health concern in China. Based on the data in the global disease burden (GDB) research database, this study evaluated and analyzed the trend of the disease burden of middle-aged and older patients living with anxiety in China in the past 30 years.

METHODS

The incidence and disability-adjusted life years (DALYs) data of anxiety disorders in China for individuals aged 45-89 years were collected from the Global Burden of Disease Study 2019, and the effects of age, period, and cohort on the incidence of and DALY rate for anxiety disorders were analysed using an age-period-cohort model. Because of the COVID-19 pandemic, the global disease burden research database has not been updated since 2019. However, this did not affect the analysis of future trends in this study, which combined data in the past three decades from 1990 to 2019.

RESULTS

(1) The overall age-standardised incidence rate (ASIR) and age-standardised DALY rate (ASDR) for anxiety disorders in middle-aged and older adults in China decreased by 4.0 and 7.7% from 1990 to 2019, respectively, and the ASIR and ASDR were always higher in women than in men. (2)Age-period-cohort analysis showed that the net drifts for incidence and DALY rate were - 0.27% and - 0.55% per year, respectively. For both genders, the local drifts for incidence were lower than zero in those aged 45-79 years and higher than zero in those aged 80-89 years; the local drifts for the DALY rate were lower than zero in all groups. (3) From the 1990-1994 to 2015-2019, the relative risks of anxiety disorder incidence and DALY decreased by 5.6 and 7.3% in men and 4.3 and 11.7% in women, respectively.

CONCLUSION

The disease burden of anxiety disorders in middle-aged and older adults in China has been relieved over the past 30 years; however, recent ASDR, ASDR, period, and cohort effects have shown adverse trends. The incidence and DALY rate decreased with age in women, while men showed a trend of increasing first and decreasing afterwards.

Taking precautions in advance: a lower level of activities of daily living may be associated with a higher likelihood of memory-related diseases

Introduction

Memory-related diseases (MDs) pose a significant healthcare challenge globally, and early detection is essential for effective intervention. This study investigates the potential of Activities of Daily Living (ADL) as a clinical diagnostic indicator for MDs. Utilizing data from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS), encompassing 10,062 Chinese individuals aged 45 or older, we assessed ADL using the Barthel Index (BI) and correlated it with the presence of MDs. Statistical analysis, supplemented by machine learning algorithms (Support Vector Machine, Decision Tree, and Logistic Regression), was employed to elucidate the relationship between ADL and MDs.

Background

MDs represent a significant public health concern, necessitating early detection and intervention to mitigate their impact on individuals and society. Identifying reliable clinical diagnostic signs for MDs is imperative. ADL have garnered attention as a potential marker. This study aims to rigorously analyze clinical data and validate machine learning algorithms to ascertain if ADL can serve as an indicator of MDs.

Methods

Data from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS) were employed, encompassing responses from 10,062 Chinese individuals aged 45 or older. ADL was assessed using the BI, while the presence of MDs was determined through health report questions. Statistical analysis was executed using SPSS 25.0, and machine learning algorithms, including Support Vector Machine (SVM), Decision Tree Learning (DT), and Logistic Regression (LR), were implemented using Python 3.10.2.

Results

Population characteristics analysis revealed that the average BI score for individuals with MDs was 70.88, significantly lower than the average score of 87.77 in the control group. Pearson's correlation analysis demonstrated a robust negative association (r = -0.188, p < 0.001) between ADL and MDs. After adjusting for covariates such as gender, age, smoking status, drinking status, hypertension, diabetes, and dyslipidemia, the negative relationship between ADL and MDs remained statistically significant (B = -0.002, β = -0.142, t = -14.393, 95% CI = -0.002, -0.001, p = 0.000). The application of machine learning models further confirmed the predictive accuracy of ADL for MDs, with area under the curve (AUC) values as follows: SVM-AUC = 0.69, DT-AUC = 0.715, LR-AUC = 0.7. Comparative analysis of machine learning outcomes with and without the BI underscored the BI's role in enhancing predictive abilities, with the DT model demonstrating superior performance.

Conclusion

This study establishes a robust negative correlation between ADL and MDs through comprehensive statistical analysis and machine learning algorithms. The results validate ADL as a promising diagnostic indicator for MDs, with enhanced predictive accuracy when coupled with the Barthel Index. Lower levels of ADL are associated with an increased likelihood of developing memory-related diseases, underscoring the clinical relevance of ADL assessment in early disease detection.

High prevalence of subjective cognitive decline in older Chinese adults: a systematic review and meta-analysis

Background

Subjective cognitive decline (SCD) is considered a preclinical stage of Alzheimer's disease. However, reliable prevalence estimates of SCD in the Chinese population are lacking, underscoring the importance of such metrics for policymakers to formulate appropriate healthcare strategies.

Objective

To systematically evaluate SCD prevalence among older Chinese adults.

Methods

PubMed, Web of Science, The Cochrane Library, Embase, CNKI, Wanfang, VIP, CBM, and Airiti Library databases were searched for studies on SCD in older Chinese individuals published before May 2023. Two investigators independently screened the literature, extracted the information, and assessed the bias risk of the included studies. A meta-analysis was then conducted using Stata 16.0 software via a random-effects model to analyze SCD prevalence in older Chinese adults.

Results

A total of 17 studies were included (n = 31,782). The SCD prevalence in older Chinese adults was 46.4% (95% CI, 40.6-52.2%). Further, subgroup analyzes indicated that SCD prevalence was 50.8% in men and 58.9% among women. Additionally, SCD prevalence in individuals aged 60-69, 70-79, and ≥ 80 years was 38.0, 45.2, and 60.3%, respectively. Furthermore, SCD prevalence in older adults with BMI <18.5, 18.5-24.0, and > 24.0 was 59.3, 54.0, and 52.9%, respectively. Geographically, SCD prevalence among older Chinese individuals was 41.3% in North China and 50.0% in South China. In terms of residence, SCD prevalence was 47.1% in urban residents and 50.0% among rural residents. As for retired individuals, SCD prevalence was 44.2% in non-manual workers and 49.2% among manual workers. In the case of education, individuals with an education level of "elementary school and below" had an SCD prevalence rate of 62.8%; "middle school, "52.4%; "high school, "55.0%; and "college and above, "51.3%. Finally, SCD prevalence was lower among married individuals with surviving spouses than in single adults who were divorced, widowed, or unmarried.

Conclusion

Our systematic review and meta-analysis identified significant and widespread SCD prevalence in the older population in China. Therefore, our review findings highlight the urgent requirement for medical institutions and policymakers across all levels to prioritize and rapidly develop and implement comprehensive preventive and therapeutic strategies for SCD.Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023406950, identifier: CRD42023406950.

Impairment of delayed recall as a predictor of amnestic mild cognitive impairment development in normal older adults: a 7-year of longitudinal cohort study in Shanghai

BACKGROUND

Amnestic mild cognitive impairment (aMCI) is considered a prodromal phase of Alzheimer's disease (AD). However, little is known about the neuropsychological characteristic at pre-MCI stage. This study aimed to investigate which neuropsychological tests could significantly predict aMCI from a seven-year longitudinal cohort study.

METHODS

The present study included 123 individuals with baseline cognitive normal (NC) diagnosis and a 7-year follow-up visit. All the subjects were from the China Longitudinal Aging Study (CLAS) study. Participants were divided into two groups, non-converter and converter based on whether progression to aMCI at follow-up. All participants underwent standardized comprehensive neuropsychological tests, including the mini-mental state examination (MMSE), Montreal Cognitive Assessment (MoCA), auditory verbal learning test (AVLT), the digital span test, the verbal fluency test, the visual recognition test, the WAIS picture completion task, and WAIS block design. Logistic regression analysis was used to evaluate the predictive power of baseline cognitive performance for the transformation of amnestic mild cognitive impairment. Receiver operating characteristic (ROC) curve was used to test the most sensitive test for distinguishing different groups.

RESULTS

Between the non-converter group and converter group, there were significant differences in the baseline scores of AVLT-delayed recall (AVLT-DR) (8.70 ± 3.61 vs. 6.81 ± 2.96, p = 0.001) and WAIS block design (29.86 ± 7.07 vs. 26.53 ± 8.29, p = 0.041). After controlling for gender, age, and education level, converter group showed lower baseline AVLT-DR than non-converter group, while no significant difference was found in WAIS block design. Furthermore, converter group had lower AVLT-DR score after controlling for somatic disease. The area under the curve of regression equation model was 0.738 (95%CI:0.635-0.840), with a sensitivity 83.9%, specificity of 63.6%.

CONCLUSIONS

Our results proved the value of delayed recall of AVLT in predicting conversion to aMCI. Early and careful checking of the cognitive function among older people should be emphasized.

An explainable machine learning based prediction model for Alzheimer's disease in China longitudinal aging study

Alzheimer's disease (AD) is the most common cause of dementia. Accurate prediction and diagnosis of AD and its prodromal stage, i.e., mild cognitive impairment (MCI), is essential for the possible delay and early treatment for the disease. In this paper, we adopt the data from the China Longitudinal Aging Study (CLAS), which was launched in 2011, and includes a joint effort of 15 institutions all over the country. Four thousand four hundred and eleven people who are at least 60 years old participated in the project, where 3,514 people completed the baseline survey. The survey collected data including demographic information, daily lifestyle, medical history, and routine physical examination. In particular, we employ ensemble learning and feature selection methods to develop an explainable prediction model for AD and MCI. Five feature selection methods and nine machine learning classifiers are applied for comparison to find the most dominant features on AD/MCI prediction. The resulting model achieves accuracy of 89.2%, sensitivity of 87.7%, and specificity of 90.7% for MCI prediction, and accuracy of 99.2%, sensitivity of 99.7%, and specificity of 98.7% for AD prediction. We further utilize the SHapley Additive exPlanations (SHAP) algorithm to visualize the specific contribution of each feature to AD/MCI prediction at both global and individual levels. Consequently, our model not only provides the prediction outcome, but also helps to understand the relationship between lifestyle/physical disease history and cognitive function, and enables clinicians to make appropriate recommendations for the elderly. Therefore, our approach provides a new perspective for the design of a computer-aided diagnosis system for AD and MCI, and has potential high clinical application value.

Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used to forecast progression trajectories of cognitive decline caused by preclinical and prodromal Alzheimer's disease (AD). Many existing studies have explored the potential of these two distinct modalities with diverse machine and deep learning approaches. But successfully fusing MRI and PET can be complex due to their unique characteristics and missing modalities. To this end, we develop a hybrid multimodality fusion (HMF) framework with cross-domain knowledge transfer for joint MRI and PET representation learning, feature fusion, and cognitive decline progression forecasting. Our HMF consists of three modules: 1) a module to impute missing PET images, 2) a module to extract multimodality features from MRI and PET images, and 3) a module to fuse the extracted multimodality features. To address the issue of small sample sizes, we employ a cross-domain knowledge transfer strategy from the ADNI dataset, which includes 795 subjects, to independent small-scale AD-related cohorts, in order to leverage the rich knowledge present within the ADNI. The proposed HMF is extensively evaluated in three AD-related studies with 272 subjects across multiple disease stages, such as subjective cognitive decline and mild cognitive impairment. Experimental results demonstrate the superiority of our method over several state-of-the-art approaches in forecasting progression trajectories of AD-related cognitive decline.

Use of Antihypertensives, Blood Pressure, and Estimated Risk of Dementia in Late Life: An Individual Participant Data Meta-Analysis

Importance

The utility of antihypertensives and ideal blood pressure (BP) for dementia prevention in late life remains unclear and highly contested.

Objectives

To assess the associations of hypertension history, antihypertensive use, and baseline measured BP in late life (age >60 years) with dementia and the moderating factors of age, sex, and racial group.

Data Source and Study Selection

Longitudinal, population-based studies of aging participating in the Cohort Studies of Memory in an International Consortium (COSMIC) group were included. Participants were individuals without dementia at baseline aged 60 to 110 years and were based in 15 different countries (US, Brazil, Australia, China, Korea, Singapore, Central African Republic, Republic of Congo, Nigeria, Germany, Spain, Italy, France, Sweden, and Greece).

Data Extraction and Synthesis

Participants were grouped in 3 categories based on previous diagnosis of hypertension and baseline antihypertensive use: healthy controls, treated hypertension, and untreated hypertension. Baseline systolic BP (SBP) and diastolic BP (DBP) were treated as continuous variables. Reporting followed the Preferred Reporting Items for Systematic Review and Meta-Analyses of Individual Participant Data reporting guidelines.

Main Outcomes and Measures

The key outcome was all-cause dementia. Mixed-effects Cox proportional hazards models were used to assess the associations between the exposures and the key outcome variable. The association between dementia and baseline BP was modeled using nonlinear natural splines. The main analysis was a partially adjusted Cox proportional hazards model controlling for age, age squared, sex, education, racial group, and a random effect for study. Sensitivity analyses included a fully adjusted analysis, a restricted analysis of those individuals with more than 5 years of follow-up data, and models examining the moderating factors of age, sex, and racial group.

Results

The analysis included 17 studies with 34 519 community dwelling older adults (20 160 [58.4%] female) with a mean (SD) age of 72.5 (7.5) years and a mean (SD) follow-up of 4.3 (4.3) years. In the main, partially adjusted analysis including 14 studies, individuals with untreated hypertension had a 42% increased risk of dementia compared with healthy controls (hazard ratio [HR], 1.42; 95% CI 1.15-1.76; P = .001) and 26% increased risk compared with individuals with treated hypertension (HR, 1.26; 95% CI, 1.03-1.53; P = .02). Individuals with treated hypertension had no significant increased dementia risk compared with healthy controls (HR, 1.13; 95% CI, 0.99-1.28; P = .07). The association of antihypertensive use or hypertension status with dementia did not vary with baseline BP. There was no significant association of baseline SBP or DBP with dementia risk in any of the analyses. There were no significant interactions with age, sex, or racial group for any of the analyses.

Conclusions and Relevance

This individual patient data meta-analysis of longitudinal cohort studies found that antihypertensive use was associated with decreased dementia risk compared with individuals with untreated hypertension through all ages in late life. Individuals with treated hypertension had no increased risk of dementia compared with healthy controls.

Prevalence of depression and anxiety in older people in low- and middle- income countries in Africa, Asia and South America: A systematic review and meta-analysis

BACKGROUND

There is rapid growth of older people in Low- and Middle- Income Countries (LMICs). The aim of this review was to assess the literature on prevalence of anxiety and depression in this demographic, which to our knowledge, has not yet been conducted.

METHODS

Databases including Medline, PsychInfo, Embase, Scielo and African Journals Online were searched for terms including "mental disorders", "neurotic disorders", "mood disorders" and "anxiety disorders". Studies published between 1990 and 2020 providing data on older people (≥50 years) in LMICs (defined by World Bank Criteria) were included and quality-assessed. Meta-analysis was conducted on a subset of higher-quality studies to derive pooled prevalence estimates of depression.

RESULTS

One hundred and forty relevant studies were identified, of which thirty-two were included in meta-analysis. One hundred and fifteen studies reported depression prevalence only, 19 reported both depression and anxiety, and six reported anxiety only. In all studies identified, depression prevalence ranged from 0.5 % to 62.7 %, and Generalised Anxiety Disorder prevalence ranged from 0.2 % to 32.2 %. The pooled prevalence of depression on meta-analysis was 10.5 % (95 % CI, 8.9 % - 11.2 %). Reported prevalence rates of depression were significantly different in studies using ICD-10 compared with DSM criteria, and between community and clinical settings.

LIMITATIONS

The search strategy contained bias towards English language papers and high income country (HIC) publications. There is significant heterogeneity within the meta-analysis.

DISCUSSION

A wide range of methodologies and clinical criteria are used in prevalence studies of depression and anxiety in older people. Studies using screening tools found higher prevalence rates; clinicians and researchers should ensure diagnosis is made with gold-standard clinical criteria. Meta-analysis data suggest that rates of depression are similar in older people in LMICs compared to HICs but mental healthcare resources are limited, suggesting a large potential treatment gap.

Does the Internet Moderate the Neighborhood Effect? Internet Use, Neighborhoods, and Mental Health among Older Adults in Shanghai

Internet use may reduce the impact of the neighborhood on residents' well-being by helping people utilize resources beyond their immediate neighborhoods or strengthen neighborhood influences by widening the digital divide across neighborhoods. This study investigates how internet use moderates neighborhood effects on mental health among older adults in Shanghai. Using data from the Shanghai Urban Neighborhood Survey (SUNS) and population census, hierarchical linear models reveal that older adults who more frequently use the internet report lower levels of mental distress. Internet use attenuates the negative effects of living in low-socioeconomic status (SES) neighborhoods. We also examine the roles of three types of internet use: social networking, leisure, and information seeking. The results show that only social networking and leisure internet use are significantly associated with improved mental health among older adults. The results suggest that social programs are needed to increase internet literacy among older adults to promote active aging, and priority should be given to relatively disadvantaged neighborhoods.

Do Sensory Impairments Portend Cognitive Decline in Older Chinese Adults? Longitudinal Evidence from a Nationally Representative Survey, 2011-2018

Previous studies on longitudinal sensory-cognition association are limited and have yielded inconsistent conclusions in western and developed countries. The present study obtained data from the China Health and Retirement Longitudinal Survey (CHARLS, 2011−2018) and aimed to investigate the longitudinal effects of sensory impairments including single vision impairment (SVI), single hearing impairment (SHI), and dual sensory impairment (DSI) on cognitive decline in middle-aged and older Chinese population. In total, 11,122 participants accomplished all 4 interviews over 8 years and were included. Cognitive performances were assessed using Mini-Mental Status Examination (MMSE) and self-reported sensory status were accepted as well. Confounding variables included age, sex, educational level, marital status, medical, and lifestyle related information. The impact of sensory impairment on cognitive decline over time was assessed using linear mixed-effects models (LMM). After being adjusted for multiple confounders, SVI/SHI/DSI were all shown to be significantly associated with executive functions, episodic memory impairment, and global cognitive decline over 8 years (all p < 0.05). Such associations become less significant among female and relatively younger populations (45−59 years old). Single vision and hearing impairments, along with dual sensory impairment, are all independently associated with subsequent cognitive decline among middle-aged and older Chinese populations over 8 years of longitudinal observation.

Longitudinal Association Between Depressive Symptoms and Cognitive Function Among Older Adults: A Latent Growth Curve Modeling Approach

Objectives: Although the evidence from numerous longitudinal studies has indicated a remarkable change in cognitive function (CF) and depressive symptoms (DS) over time, the parallel latent growth curve model (LGCM) has seldom been used to simultaneously investigate the relationship between their change trajectories. This study aimed to examine whether a change in DS was associated with CF over time using an LGCM.

Methods: Data were collected from the Chinese Longitudinal Healthy Longevity Survey’s 2011, 2014, and 2018 waves. A parallel LGCM examined the association between CF and DS.

Results: The multivariate conditioned model’s goodness of fit supported the validity of the longitudinal model (Tucker-Lewis index [TLI] = 0.90, comparative fit index [CFI] = 0.96, root mean square error of approximation [RMSEA] = 0.04). The results showed that the CF intercept was positively to the DS slope (β = 0.42, p = 0.004). The CF and DS slopes were significantly linked (β = −0.65, p = 0.002).

Conclusion: The findings expand the knowledge about CF’s effect on DS in older adults.

Domain-Prior-Induced Structural MRI Adaptation for Clinical Progression Prediction of Subjective Cognitive Decline

Growing evidence shows that subjective cognitive decline (SCD) among elderly individuals is the possible pre-clinical stage of Alzheimer's disease (AD). To prevent the potential disease conversion, it is critical to investigate biomarkers for SCD progression. Previous learning-based methods employ T1-weighted magnetic resonance imaging (MRI) data to aid the future progression prediction of SCD, but often fail to build reliable models due to the insufficient number of subjects and imbalanced sample classes. A few studies suggest building a model on a large-scale AD-related dataset and then applying it to another dataset for SCD progression via transfer learning. Unfortunately, they usually ignore significant data distribution gaps between different centers/domains. With the prior knowledge that SCD is at increased risk of underlying AD pathology, we propose a domain-prior-induced structural MRI adaptation (DSMA) method for SCD progression prediction by mitigating the distribution gap between SCD and AD groups. The proposed DSMA method consists of two parallel feature encoders for MRI feature learning in the labeled source domain and unlabeled target domain, an attention block to locate potential disease-associated brain regions, and a feature adaptation module based on maximum mean discrepancy (MMD) for cross-domain feature alignment. Experimental results on the public ADNI dataset and an SCD dataset demonstrate the superiority of our method over several state-of-the-arts.

Subthreshold depression - concept, operationalisation and epidemiological data. A scoping review

Purpose: In diagnostic systems (e.g., DSM-5, ICD-10), depression is defined categorically. However, the concept of subthreshold depression (SD) has gained increasing interest in recent years. The purpose of the present paper was to review, based on a scoping review, the relevant papers in this field published between October 2011 and September 2020.Materials and methods: Of the 1,160 papers identified, 64 records could be included in further analysis. The scoping review was conducted using both electronic and manual methods.Results: The main result of the analysis is that the operationalisation criteria used are highly heterogeneous, which also leads to very heterogenous epidemiological data.Conclusions: Clear conclusions are not possible scrutinising the reported results. Most definitions seem to be arbitrary, with considerable overlap (e.g., between SD and minor depression). The review also revealed that the impact of SD on quality of life and related parameters appear to be in the range of the respective impact of major depression (MD) and therapeutic approaches might be helpful for SD and also for the prevention of conversion from SD to MD. Keeping the presented difficulties in mind, a proposal for the definition of SD is made in the present paper in order to facilitate the discussion leading to more homogeneous criteria.

An Increased Aspartate to Alanine Aminotransferase Ratio Is Associated With a Higher Risk of Cognitive Impairment

Background

Recent Alzheimer's disease (AD) hypotheses implicate that hepatic metabolic disorders might contribute to the disease pathogenesis of AD, but the mechanism remains unclear.

Aims

To investigate whether the elevated aspartate aminotransferase (AST) and Alanine aminotransferase (ALT) ratio is associated with future cognitive decline, and to explore the possible mechanisms of liver enzymes affecting cognitive function.

Methods

Three different clinical cohorts were included in the current study, including one cross-sectional study (Cohort 1) and two longitudinal follow-up studies (Cohort 2 and 3). All participants completed a detailed clinical evaluation, neuropsychological tests, and liver enzyme tests. In addition, some of them also underwent structural magnetic resonance imaging (MRI) scans.

Results

Cohort 1 was derived from the CRC2017ZD02 program, including 135 amnestic mild cognitive impairment (aMCI) patients, 22 AD patients, and 319 normal controls. In this cross-sectional study, we found that the AST/ALT ratio was associated with AD (p = 0.014, OR = 1.848, 95%CI: 1.133∼3.012), but not aMCI; Cohort 2 was derived from the Shanghai Brain Health Program. A total of 260 community elderly people with normal cognitive function were included in the study and followed up for 2 years. In this 2-year longitudinal follow-up study, we found that a higher AST/ALT ratio was a risk factor for future development of aMCI (p = 0.014, HR = 1.848, 95%CI: 1.133∼3.021); Cohort 3 was derived from the China longitudinal aging study (CLAS) Program. A total of 94 community elderly people with normal cognitive function were followed up for 7 years, and all of them completed MRI scans. In this 7-year longitudinal follow-up study, we found that a higher AST/ALT ratio was a risk factor for future development of aMCI (p = 0.006, HR = 2.247, 95%CI: 1.248∼4.049), and the AST/ALT ratio was negatively correlated with right hippocampal volume (r = -0.148, p = 0.043).

Conclusion

An increased ratio of AST to ALT is associated with a higher risk of cognitive impairment and may impair cognitive function by affecting hippocampal volume.

Association of residential greenness with geriatric depression among the elderly covered by long-term care insurance in Shanghai

Residential greenness exposure has been linked to a number of physical and mental disorders. Nevertheless, evidence on the association between greenness and geriatric depression was limited and focused on developed countries. This study was aimed to investigate whether the relationship between residential greenness exposure and geriatric depression exists among the elderly with long-term care insurance (LTCI) in Shanghai, China. In 2018, a total of 1066 LTCI elderly from a cross-sectional survey completed a questionnaire in Shanghai. Residential greenness indicators, including normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI), were calculated from the Landsat 8 imagery data in different buffers (100-m, 300-m, and 500-m). Mediation analysis by perceived social support was conducted to explore potential mechanisms underlying the associations. In the fully adjusted model, one IQR increase of NDVI and SAVI in the 300-m buffer size was associated with an 11.9% (PR: 0.881, 95% CI: 0.795, 0.977) and 14.7% (PR: 0.853, 95% CI: 0.766, 0.949) lower prevalence of geriatric depression, respectively. Stronger association was observed in the elderly with lower education level, living in non-central area, and lower family monthly income. Perceived social support significantly mediated 40.4% of the total effect for NDVI 300-m buffer and 40.3% for SAVI 300-m buffer to the greenness-depression association, respectively. Our results indicate the importance of residential greenness exposure to geriatric depression, especially for the elderly with lower education level, living in non-central area, and lower family monthly income. Perceived social support might mediate the association. Well-designed longitudinal studies are warranted to confirm our findings and investigate the underlying mechanisms.

Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages

Accurately assessing clinical progression from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) is crucial for early intervention of pathological cognitive decline. Multi-modal neuroimaging data such as T1-weighted magnetic resonance imaging (MRI) and positron emission tomography (PET), help provide objective and supplementary disease biomarkers for computer-aided diagnosis of MCI. However, there are few studies dedicated to SCD progression prediction since subjects usually lack one or more imaging modalities. Besides, one usually has a limited number (e.g., tens) of SCD subjects, negatively affecting model robustness. To this end, we propose a Joint neuroimage Synthesis and Representation Learning (JSRL) framework for SCD conversion prediction using incomplete multi-modal neuroimages. The JSRL contains two components: 1) a generative adversarial network to synthesize missing images and generate multi-modal features, and 2) a classification network to fuse multi-modal features for SCD conversion prediction. The two components are incorporated into a joint learning framework by sharing the same features, encouraging effective fusion of multi-modal features for accurate prediction. A transfer learning strategy is employed in the proposed framework by leveraging model trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) with MRI and fluorodeoxyglucose PET from 863 subjects to both the Chinese Longitudinal Aging Study (CLAS) with only MRI from 76 SCD subjects and the Australian Imaging, Biomarkers and Lifestyle (AIBL) with MRI from 235 subjects. Experimental results suggest that the proposed JSRL yields superior performance in SCD and MCI conversion prediction and cross-database neuroimage synthesis, compared with several state-of-the-art methods.

Reduced Peripheral Nerve Conduction Velocity is Associated with Alzheimer's Disease: A Cross-Sectional Study from China

PURPOSE

Elderly individuals with degenerative diseases of the central nervous system are more likely to develop peripheral neuropathy; however, research is limited as to whether the decline in peripheral nerve conduction can be used as a biomarker of Alzheimer's disease (AD).

PATIENTS AND METHODS

This study enrolled 74 patients with mild cognitive impairment (MCI), 21 with AD, and 82 healthy elderly individuals. All participants underwent a peripheral nerve conduction and neuropsychological evaluation. Nicolet EDX was used to assess peripheral nerve conduction in the limbs and comparisons were made between the three cognitive groups. Furthermore, the relationship between peripheral nerve conduction and cognitive function was investigated.

RESULTS

A ladder-shaped difference was found in the median (p < 0.001) and common peroneal (p < 0.001) motor nerve velocity, with the control group > MCI group > AD group, even after controlling for variables. The median motor nerve amplitude in the AD group was lower than that in the control group (P = 0.017). After controlling for age, sex, education, and height, the median motor nerve velocity was positively correlated with the Montreal Cognitive Assessment (r = 0.196, p = 0.015), and the common peroneal motor nerve velocity was positively correlated with verbal fluency task-idioms (r = 0.184, p = 0.026). The median (AUC: 0.777, p < 0.001) and common peroneal motor nerve velocities (AUC: 0.862; p < 0.001) were significantly associated with the diagnosis of AD. The accuracy rate of these two motor nerve velocities to predict AD was 51.5%.

CONCLUSION

Our study found that peripheral motor nerve velocity may correlate with early cognitive impairment in AD. However, the accuracy of different cognitive classifications and the value of early diagnosis are not ideal when peripheral motor nerve velocity is used alone. Whether peripheral nerve function can be used as a marker for early diagnosis of AD needs further clarification but provides a new possibility for the future of biomarker research.

Associations of sensory impairment and cognitive function in middle-aged and older Chinese population: The China Health and Retirement Longitudinal Study

Background

Little is known about the associations between vision impairment, hearing impairment, and cognitive function. The aim of this study was to examine whether vision and hearing impairment were associated with a high risk for cognitive impairment in middle-aged and older Chinese adults.

Methods

A total of 13 914 Chinese adults from the China Health and Retirement Longitudinal Study (CHARLS) baseline were selected for analysis. Sensory impairment was assessed from a single self-report question, and we categorized sensory impairment into four groups: no sensory impairment, vision impairment, hearing impairment, and dual sensory impairment. Cognitive assessment covered memory, mental state, and cognition, and the data was obtained through a questionnaire.

Results

Memory was negatively associated with hearing impairment (β = -0.043, 95% confidence interval (CI) = -0.076, -0.043) and dual sensory impairment (β = -0.033, 95% CI = -0.049, -0.017); mental status was negatively associated with vision impairment (β = -0.034, 95% CI = -0.049, -0.018), hearing impairment (β = -0.070, 95% CI = -0.086, -0.055), and dual sensory impairment (β = -0.054, 95% CI = -0.070, -0.039); and cognition was negatively associated with vision impairment (β = -0.028, 95% CI = -0.044, -0.013), hearing impairment (β = -0.074, 95% CI = -0.090, -0.059), and dual sensory impairment (β = -0.052, 95% CI = -0.067, -0.036), even after adjusting for demographics, social economic factors, and lifestyle behavior.

Conclusions

Vision and hearing impairment are negatively associated with memory, mental status, and cognition for middle-aged and elderly Chinese adults. There were stronger negative associations between sensory impairment and cognitive-related indicators in the elderly compared to the middle-aged.

Cognitive Function Assessment and Prediction for Subjective Cognitive Decline and Mild Cognitive Impairment

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative dementia. Recent studies found that subjective cognitive decline (SCD) may be the early clinical precursor that precedes mild cognitive impairment (MCI) for AD. SCD subjects with normal cognition may already have some medial temporal lobe atrophy. Although brain changes by AD have been widely studied in the literature, it is still challenging to investigate the anatomical subtle changes in SCD. This paper proposes a machine learning framework by combination of sparse coding and random forest (RF) to identify the informative imaging biomarkers for assessment and prediction of cognitive functions and their changes in individuals with MCI, SCD and normal control (NC) using magnetic resonance imaging (MRI). First, we compute the volumes from both the regions of interest from whole brain and the subregions of hippocampus and amygdala as the features of structural MRIs. Then, sparse coding is applied to identify the relevant features. Finally, the proximity-based RF is used to combine three sets of volumetric features and establish a regression model for predicting clinical scores. Our method has double feature selections to better explore the relevant features for prediction and is evaluated with the T1-weighted structural MR images from 36 MCI, 112 SCD, 78 NC subjects. The results demonstrate the effectiveness of proposed method. In addition to hippocampus and amygdala, we also found that the fimbria, basal nucleus and cortical nucleus subregions are more important than other regions for prediction of Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores and their changes.

The Influences of Community-Enriched Environment on the Cognitive Trajectories of Elderly People

To examine the influences of community-enriched environment on the cognitive trajectories of the elderly in China, using panel data of 10,057, 3994, 2387, and 1749 older persons aged 65–104 years of the 2005, 2008, 2011, and 2014 waves from the Chinese Longitudinal Health Longevity Survey (CLHLS) and a growth curve model, the authors analyzed the changing trend of elderly people’s cognitive abilities with age. The influences of community-enriched environments on cognitive abilities were also investigated. Results show that when all the factors are out of consideration except age, for an older person aged 82.5 years, as he/she grows one year older, his/her cognitive abilities will be reduced by 0.139 points, while for one aged 92.5 years, they will be reduced by 0.199 points, which means cognitive abilities decline rapidly as the individuals grow older. The elderly people from communities with enriched environments have higher cognitive levels and slower declining speeds of cognitive abilities than the other elderly people, proving the long-term ability of such environments to facilitate cognitive abilities. An increase in the stimulation of the enriched environment is needed to prevent or slow down the degeneration of cognitive abilities.

Prevalence of mild cognitive impairment in community-dwelling Chinese populations aged over 55 years: a meta-analysis and systematic review

Background

Mild cognitive impairment (MCI) is an intermediate phase between normal cognitive ageing and overt dementia, with amnesic MCI (aMCI) being the dominant subtype. This study aims to synthesise the prevalence results of MCI and aMCI in community-dwelling populations in China through a meta-analysis and systematic review.

Methods

The study followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol. English and Chinese studies published before 1 March 2020 were searched from ten electronic bibliographic databases. Two reviewers screened for relevance of the studies against the pre-defined inclusion and exclusion criteria and assessed the quality of the included studies using the Risk of Bias Tool independently. A random-effect model was adopted to estimate the prevalence of MCI and aMCI, followed by sub-group analyses and meta-regression. Sensitivity and publication bias tests were performed to verify the robustness of the meta-analyses.

Results

A total of 41 studies with 112,632 participants were included in the meta-analyses. The Chinese community-dwelling populations over 55 years old had a pooled prevalence of 12.2% [95% confidence interval (CI): 10.6, 14.2%] for MCI and 10.9% [95% CI, 7.7, 15.4%] for aMCI, respectively. The prevalence of MCI increased with age. The American Psychiatric Association’s Diagnostic tool (DSM-IV) generated the highest MCI prevalence (13.5%), followed by the Petersen criteria (12.9%), and the National Institute on Aging Alzheimer’s Association (NIA-AA) criteria (10.3%). Women, rural residents, and those who lived alone and had low levels of education had higher MCI prevalence than others.

Conclusion

Higher MCI prevalence was identified in community-dwelling older adult populations in China compared with some other countries, possibly due to more broadened criteria being adopted for confirming the diagnosis. The study shows that aMCI accounts for 66.5% of MCI, which is consistent with findings of studies undertaken elsewhere.

Systematic review registration number

PROSPERO CRD42019134686.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-020-01948-3.

Prediction of 7-year's conversion from subjective cognitive decline to mild cognitive impairment

Subjective cognitive decline (SCD) is a high‐risk yet less understood status before developing Alzheimer's disease (AD). This work included 76 SCD individuals with two (baseline and 7 years later) neuropsychological evaluations and a baseline T1‐weighted structural MRI. A machine learning‐based model was trained based on 198 baseline neuroimaging (morphometric) features and a battery of 25 clinical measurements to discriminate 24 progressive SCDs who converted to mild cognitive impairment (MCI) at follow‐up from 52 stable SCDs. The SCD progression was satisfactorily predicted with the combined features. A history of stroke, a low education level, a low baseline MoCA score, a shrunk left amygdala, and enlarged white matter at the banks of the right superior temporal sulcus were found to favor the progression. This is to date the largest retrospective study of SCD‐to‐MCI conversion with the longest follow‐up, suggesting predictable far‐future cognitive decline for the risky populations with baseline measures only. These findings provide valuable knowledge to the future neuropathological studies of AD in its prodromal phase.

Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View

Subjective cognitive decline (SCD) is considered the earliest preclinical stage of Alzheimer’s disease (AD) that precedes mild cognitive impairment (MCI). Effective and accurate diagnosis of SCD is crucial for early detection of and timely intervention in AD. In this study, brain functional connectome (i.e., functional connections and graph theory metrics) based on the resting-state functional magnetic resonance imaging (rs-fMRI) provided multiple information about brain networks and has been used to distinguish individuals with SCD from normal controls (NCs). The consensus connections and the discriminative nodal graph metrics selected by group least absolute shrinkage and selection operator (LASSO) mainly distributed in the prefrontal and frontal cortices and the subcortical regions corresponded to default mode network (DMN) and frontoparietal task control network. Nodal efficiency and nodal shortest path showed the most significant discriminative ability among the selected nodal graph metrics. Furthermore, the comparison results of topological attributes suggested that the brain network integration function was weakened and network segregation function was enhanced in SCD patients. Moreover, the combination of brain connectome information based on multiple kernel-support vector machine (MK-SVM) achieved the best classification performance with 83.33% accuracy, 90.00% sensitivity, and an area under the curve (AUC) of 0.927. The findings of this study provided a new perspective to combine machine learning methods with exploration of brain pathophysiological mechanisms in SCD and offered potential neuroimaging biomarkers for diagnosis of early-stage AD.

Neighborhood Environments and Cognitive Decline Among Middle-Aged and Older People in China

OBJECTIVES

Despite the growing interest in the effects of neighborhood environments on cognitive function, most studies on older people are based on cross-sectional survey data from developed countries. This study examines the relationship between neighborhood environments and decline in cognitive function over time among middle-aged and older people in China and whether this relationship varies between rural and urban residents.

METHODS

The three waves of China Health and Retirement Longitudinal Study (CHARLS 2011-2015) were used. The sample included 12,131 respondents living in 298 rural villages and 4,059 respondents living in 150 urban communities. Three-level linear growth curve models were estimated to track trajectories of cognitive change over a 4-year period.

RESULTS

Chinese older people who lived in neighborhoods with more handicap access, more bus lines, employment service, and higher socioeconomic status (SES) had slower cognitive decline. Neighborhood basic infrastructures, number of days that roads were unpassable, outdoor exercise facilities, and average social activity participation were associated with baseline cognitive function in both rural and urban areas, but neighborhood environments had more impact on cognitive decline among rural older adults than urban older adults.

DISCUSSIONS

Findings from this study call for increased infrastructure development and community building programs in rural China.

Decreased Levels of Serum IGF-1 and Vitamin D Are Associated With Cognitive Impairment in Patients With Type 2 Diabetes

PURPOSE

To determine the association of serum insulin-like growth factor 1 (IGF-1) and vitamin D levels with cognition status in patients with type 2 diabetes mellitus (T2DM).

METHODS

A total of 173 patients with T2DM were recruited and divided into mild cognitive impairment (MCI) group (n = 94) and normal cognition (NC) group (n = 79). Levels of IGF-1 and 25(OH)D were measured and compared, and the correlations among IGF-1, 25(OH)D, and cognitive function were analyzed.

RESULTS

Insulin-like growth factor 1 and 25(OH)D levels significantly decreased in MCI group than those in the NC group (both P < .001). Multiple stepwise regression analysis revealed that IGF-1 (β = .146, P < .001) and 25(OH)D (β = .199, P < .001) independently predicted Montreal Cognitive Assessment (MoCA) scores. Partial least square regression showed that contributions of both 25(OH)D (P < .001) and IGF-1 (P < .001) to MoCA scores were significant, while no cross-effect was observed between them (P = .714).

CONCLUSIONS

Low serum IGF-1 and 25(OH)D levels may separately predict poor cognitive performance in patients with diabetes.

Age-Normative MMSE Data for Older Persons Aged 85 to 93 in a Longitudinal Swedish Cohort

Background/Objectives

Normative Mini‐mental state examination (MMSE) reference values in elderly are scarce. Therefore, the aim is to present normative MMSE values for 85–93 year olds.

Design

A longitudinal age cohort study.

Setting

A population study of the residents in the municipality of Linköping, Sweden.

Participants

Residents (n = 650) born in 1922 during the course of 2007. In total, 374 individuals participated and were tested with MMSE at age 85, 280 of these were willing and able to also participate at age 86, 107 at age 90 and 51 at age 93.

Measurements

MMSE, from 0–30, with lower scores denoting more impaired cognition.

Results

Median MMSE values for the total population over the ages 85, 86, 90 and 93 years was 28 for all ages investigated. The 25th percentile values were 26, 26, 26 and 27, respectively. For a “brain healthy” sub‐group median values were 28, 29, 28, and 28. The 25th percentile values were 27, 28, 26 and 27, respectively. Comparisons for age‐effects showed no differences when all individuals for each age group were compared. When only the individuals reaching 93 years of age (n = 50) were analyzed, there was a significant lowering of MMSE in that age group.

Conclusion

The literature is variable and in clinical practice a low (24) MMSE cut off is often used for possible cognitive impairment in old age. The present data indicate that MMSE 26 is a reasonable cut off for possible cognitive decline in older persons up to the age of 93. J Am Geriatr Soc 67:534–538, 2019.

Secondhand Smoke and Women's Cognitive Function in China

Exposure to secondhand smoke (SHS) is known to be harmful to health. However, the association between household SHS and cognitive function among middle-aged and older women in China is understudied. Lagged dependent variable regression was used to examine the association between household SHS exposure and the cognitive function of married women who had been exposed to SHS, using data from 2 waves of the China Health and Retirement Longitudinal Study (CHARLS, 2011-2013). Controlling for age, educational attainment, geographic residence, household expenditures, and chronic conditions (i.e., hypertension, diabetes, and depressive symptoms), the results indicated that longer SHS exposure was associated with a greater decline in memory over 2 years. After comparing differences across age groups, this pattern was significant for women aged 55-64 years. Furthermore, those who were illiterate, lived in rural areas, and reported depressive symptoms had a greater decline in memory. With evidence linking household SHS exposure with a higher risk of cognitive decline, effective education and public health intervention programs are urgently needed. Stronger tobacco control regulations and education about the dangers of household SHS are viable strategies to reduce the impending dementia epidemic in China.

Prevalence, Risk Factors, and Complaints Screening Tool Exploration of Subjective Cognitive Decline in a Large Cohort of the Chinese Population

BACKGROUND

Substantial studies have reported the prevalence and the affecting factors of subjective cognitive decline (SCD). The complaints screening scale has also been used for probing. However, little is known in China.

OBJECTIVE

To investigate the prevalence and risk factors of SCD, and explore an SCD complaints screening scale in China.

METHODS

Stratified cluster random sampling was conducted. 2,689 residents aged 60-80 years completed questionnaire 1. 814 residents were included for clinical and neuropsychological evaluations. Two standards were used to make the diagnosis of mild cognitive impairment (MCI) and SCD, and a preliminary screening rate comparison was carried out. Finally, we assessed the risk factors of SCD and the correlation between the SCD-questionnaire 9 (SCD-Q9) and the Auditory Verbal Learning Test-Long Delay Free Recall (AVLT-LR).

RESULTS

1) Standard 1 (ADNI2): the prevalence of SCD was 18.8% (95% CI = 14.7-22.9%) and zero conformed to six criteria (SCD plus). 2) Standard 2 (Jak/Bondi): the prevalence of SCD was 14.4% (95% CI = 10.7-18.1%). 3) Standard 1 had a relatively higher "false" positive rate, whereas Standard 2 had higher "false" negative rate. 4) Age, low education, fewer close friends, and daily drinking were independent risk factors for SCD progressing to MCI. 5) Total points of SCD-Q9 were negatively correlated to the value of AVLT-LR.

CONCLUSIONS

The prevalence of SCD is high in the ShunYi District in Beijing, China. Age, low education, less social support, and daily drinking are independent risk factors. The brief SCD-Q9 can be used as a reference.

Asymmetry of Hippocampus and Amygdala Defect in Subjective Cognitive Decline Among the Community Dwelling Chinese

Background: Subjective cognitive decline (SCD) may be the first clinical sign of Alzheimer's disease (AD). SCD individuals with normal cognition may already have significant medial temporal lobe atrophy. However, few studies have been devoted to exploring the alteration of left-right asymmetry with hippocampus and amygdala in SCD. The aim of this study was to compare SCD individuals with amnestic mild cognitive impairment (MCI) patients and the normal population for volume and asymmetry of hippocampus, amygdala and temporal horn, and to assess their relationship with cognitive function in elderly population living in China. Methods: 111 SCD, 30 MCI, and 67 healthy controls (HC) underwent a standard T1-weighted MRI, from which the volumes of the hippocampus and amygdala were calculated and compared. Then we evaluated the pattern and extent of asymmetry in hippocampus and amygdala of these samples. Furthermore, we also investigated the relationship between the altered brain regions and cognitive function. Results: Among the three groups, SCD showed more depressive symptoms (p < 0.001) and higher percentage of heart disease (16.4% vs. 35.1%, p = 0.007) than controls. In terms of brain data, significant differences were found in the volume and asymmetry of both hippocampus and amygdala among the three groups (P < 0.05). In logistic analysis controlled by age, gender, education level, depression symptoms, anxiety symptom, somatic disease and lifestyle in terms of smoking, both SCD and MCI individuals showed significant decreased right hippocampal and amygdala volume than controls. For asymmetry pattern, a ladder-shaped difference of left-larger-than-right asymmetry was found in amygdala with MCI>SCD>HC, and an opposite asymmetry of left-less-than-right pattern was found with HC>SCD>MCI in hippocampus. Furthermore, correlation was shown between the volume of right hippocampus and right amygdala with MMSE and MoCA in SCD group. Conclusion: Our results supported that SCD individuals are biologically distinguishable from HC, and this may relate to cognitive impairment, although more longitudinal studies are need to investigate this further.Moreover, different levels of asymmetry in hippocampus and amygdala might be a potential dividing factor to differentiate clinical diagnosis.

The prevalence and progression of mild cognitive impairment among clinic and community populations: a systematic review and meta-analysis

BACKGROUND

It has been reported that up to 42% of the population aged over 60 are affected by mild cognitive impairment (MCI) worldwide. This study aims to investigate the prevalence and progression of MCI through a meta-analysis.

METHODS

We searched Embase and PubMed for relevant literature. Stable disease rate (SR), reversion rate (RR), dementia rate (DR), and Alzheimer's disease rate (AR) were used to evaluate the progression of MCI. The prevalence and progression rates were both obtained by reported percentile and indirect data analysis. Additionally, we carried out sensitivity analysis of each index by excluding some studies due to influence analysis with the most publication bias.

RESULTS

Effect size (ES) was used to present adjusted overall prevalence (16%) and progression rates including SR (45%), RR (15%), DR (34%), and AR (28%) of MCI. Compared with clinic-based outcomes, MCI prevalence, SR, and RR are significantly higher in community, while DR and AR are lower. Despite significant heterogeneity found among the studies, no publication bias was observed.

CONCLUSIONS

Age and gender were observed to be associated with MCI, in which age was considered as an impact factor for DR. The strong heterogeneity may result from variations in study design and baselines. Standardized MCI criteria were suggested to systematically evaluate MCI in the future.