Depressive Symptoms and Cognitive Decline Among Chinese Rural Elderly Individuals: A Longitudinal Study With 2-Year Follow-Up

Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study

Background

The societal burden of cognitive impairment in China has prompted researchers to develop clinical prediction models aimed at making risk assessments that enable preventative interventions. However, it is unclear what types of risk factors best predict future cognitive impairment, if known risk factors make equally accurate predictions across different socioeconomic groups, and if existing prediction models are equally accurate across different subpopulations.

Objective

This paper aimed to identify which domain of health information best predicts future cognitive impairment among Chinese older adults and to examine if discrepancies exist in predictive ability across different population subsets.

Methods

Using data from the Chinese Longitudinal Healthy Longevity Survey, we quantified the ability of demographics, instrumental activities of daily living, activities of daily living, cognitive tests, social factors and hobbies, psychological factors, diet, exercise and sleep, chronic diseases, and 3 recently published logistic regression-based prediction models to predict 3-year risk of cognitive impairment in the general Chinese population and among male, female, rural-dwelling, urban-dwelling, educated, and not formally educated older adults. Predictive ability was quantified using the area under the receiver operating characteristic curve (AUC) and sensitivity-specificity curves through 20 repeats of 10-fold cross-validation.

Results

A total of 4047 participants were included in the study, of which 337 (8.3%) developed cognitive impairment 3 years after baseline data collection. The risk factor groups with the best predictive ability in the general population were demographics (AUC 0.78, 95% CI 0.77-0.78), cognitive tests (AUC 0.72, 95% CI 0.72-0.73), and instrumental activities of daily living (AUC 0.71, 95% CI 0.70-0.71). Demographics, cognitive tests, instrumental activities of daily living, and all 3 recreated prediction models had significantly higher AUCs when making predictions among female older adults compared to male older adults and among older adults with no formal education compared to those with some education.

Conclusions

This study suggests that demographics, cognitive tests, and instrumental activities of daily living are the most useful risk factors for predicting future cognitive impairment among Chinese older adults. However, the most predictive risk factors and existing models have lower predictive power among male, urban-dwelling, and educated older adults. More efforts are needed to ensure that equally accurate risk assessments can be conducted across different socioeconomic groups in China.

Linear brain measurement: a new screening method for cognitive impairment in elderly patients with cerebral small vessel disease

Background

The old adults have high incidence of cognitive impairment, especially in patients with cerebral small vessel disease (CSVD). Cognitive impairment is not easy to be detected in such populations. We aimed to develop clinical prediction models for different degrees of cognitive impairments in elderly CSVD patients based on conventional imaging and clinical data to determine the better indicators for assessing cognitive function in the CSVD elderly.

Methods

210 CSVD patients were screened out by the evaluation of Magnetic Resonance Imaging (MRI). Then, participants were divided into the following three groups according to the cognitive assessment results: control, mild cognitive impairment (MCI), and dementia groups. Clinical data were collected from all patients, including demographic data, biochemical indicators, carotid ultrasound, transcranial Doppler (TCD) indicators, and linear measurement parameters based on MRI.

Results

Our results showed that the brain atrophy and vascular lesions developed progressive worsening with increased degree of cognitive impairment. Crouse score and Interuncal distance/Bitemporal distance (IUD/BTD) were independent risk factors for MCI in CSVD patients, and independent risk factors for dementia in CSVD were Crouse Score, the pulsatility index of the middle cerebral artery (MCAPI), IUD/BTD, and Sylvian fissure ratio (SFR). Overall, the parameters with high performance were the IUD/BTD (OR 2.28; 95% CI 1.26-4.10) and SFR (OR 3.28; 95% CI 1.54-6.91), and the AUC (area under the curve) in distinguishing between CSVD older adults with MCI and with dementia was 0.675 and 0.724, respectively. Linear brain measurement parameters had larger observed effect than other indexes to identify cognitive impairments in CSVD patients.

Conclusion

This study shows that IUD/BTD and SFR are good predictors of cognitive impairments in CSVD elderly. Linear brain measurement showed a good predictive power for identifying MCI and dementia in elderly subjects with CSVD. Linear brain measurement could be a more suitable and novel method for screening cognitive impairment in aged CSVD patients in primary healthcare facilities, and worth further promotion among the rural population.

Epidemiology of neurocognitive disorders in adults from urban-marginalized areas: a door-to-door population-based study in Puente Piedra, Lima, Peru

Background

In Latin America (LA), the prevalence of dementia is expected to triple to 150 million people by 2050. The 2020 Lancet Commission report identified several modifiable dementia risk factors, yet few social and environmental factors, most relevant to vulnerable regions of LA, were highlighted in this report. We sought to assess the epidemiology of neurocognitive disorders (NCD) in Puente Piedra, one of the most socially and economically vulnerable districts of Lima, the capital of Peru.

Methodology

This was a cross-sectional door-to-door observational study that used two-stage household sampling. One young adult (30-59 years) and one older adult (>60 years) per household were enrolled. We collected demographic, clinical, and neurocognitive data. Addenbrooke's Cognitive Examination (young adults) and the RUDAS-PE (older adults) were used, classifying participants as cognitively normal, possible mild NCD, or possible major NCD.

Results

We enrolled 247 participants (median age 46 years; 67% female). One-fourth had not completed secondary school and more than 50% completed only secondary school. Most participants were housewives (46%) and 21% did not have health insurance. The overall prevalence of possible NCD was 30% (25.6 and 41.8% among younger adults and older adults, respectively). Among younger adults, those ages 55-59 years more frequently had NCD (70%) compared to younger age ranges. Among older adults, only 3 subjects (4.5%) had major NCD.

Conclusion

We found a high frequency of possible NCDs in a socially and economically vulnerable community in Lima, Peru, with younger adults showing levels of NCD higher than expected. Our findings support the need for health systems to incorporate cognitive screenings programs for NCD in younger ages. Future research on NCD would include younger populations, particularly in vulnerable communities.

Relationship between Mild Cognitive Impairment, Pre-Frailty, Physical and Psychological Functioning, and Functional Capacity among Community-Dwelling Older Adults

Mild cognitive impairment (MCI) is associated with a high risk of dementia. Reportedly, 54.6% of older adults with MCI fall into the pre-frailty category. However, it is unclear what differences exist in older adults with regard to their physical, psychological, and functional capacities, which depend on MCI, pre-frailty, or a combination of the two. This study aimed to examine the differences between the association between physical function, psychological function, and functional capacity by examining a combination of MCI and pre-frailty among community-dwelling older Japanese individuals. The participants in the analysis were 236 older people living in the community. They were classified into four groups, as follows: normal, MCI only, pre-frailty only, and pre-frailty/MCI; furthermore, their physical, psychological, and functional capacities were compared. In addition, a multinomial logistic regression analysis was performed using MCI and pre-frailty as dependent variables. No associated factors were identified for MCI only, and a depressive mood was only associated with pre-frailty. When pre-frailty and MCI were combined, knee extension muscle strength, fastest gait speed, the s30 s chair stand test, depressed moods, and the instrumental activities of daily living (IADL) were correlated. Our results suggest that when MCI and pre-frailty are combined, declines in both physical and psychological functions and IADL are likely to occur.

Risk assessment and stratification of mild cognitive impairment among the Chinese elderly: attention to modifiable risk factors

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.

The Value of Handgrip Strength and Self-Rated Squat Ability in Predicting Mild Cognitive Impairment: Development and Validation of a Prediction Model

Early identification of individuals with mild cognitive impairment (MCI) is essential to combat worldwide dementia threats. Physical function indicators might be low-cost early markers for cognitive decline. To establish an early identification tool for MCI by combining physical function indicators (upper and lower limb function) via a clinical prediction modeling strategy. A total of 5393 participants aged 60 or older were included in the model. The variables selected for the model included sociodemographic characteristics, behavioral factors, mental status and chronic conditions, upper limb function (handgrip strength), and lower limb function (self-rated squat ability). Two models were developed to test the predictive value of handgrip strength (Model 1) or self-rated squat ability (Model 2) separately, and Model 3 was developed by combining handgrip strength and self-rated squat ability. The 3 models all yielded good discrimination performance (area under the curve values ranged from 0.719 to 0.732). The estimated net reclassification improvement values were 0.3279 and 0.1862 in Model 3 when comparing Model 3 to Model 1 and Model 2, respectively. The integrated discrimination improvement values were estimated as 0.0139 and 0.0128 when comparing Model 3 with Model 1 and Model 2, respectively. The model that contains both upper and lower limb function has better performance in predicting MCI. The final prediction model is expected to assist health workers in early identification of MCI, thus supporting early interventions to reduce future risk of AD, particularly in socioeconomically deprived communities.