Predicting Global Cognitive Decline in the General Population Using the Disease State Index

Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population: A machine learning approach

Inter-individual cognitive variability, influenced by genetic and environmental factors, is crucial for understanding typical cognition and identifying early cognitive disorders. This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. We included 199 subjects, aged 20 to 70, and measured cognitive performance with the Wisconsin Card Sorting Test. Differentially expressed proteins (DEPs) were identified using label-free proteomics and analyzed with the Linear Model for Microarray Data. We discovered 213 DEPs between lower and higher cognition groups, with 155 upregulated in the lower cognition group and enriched in the IL-17 signaling pathway. Subsequent bioinformatic analysis linked these DEPs to neuroinflammation-related cognitive impairment. A random forest model classified cognitive ability groups with an accuracy of 81.5%, sensitivity of 65%, specificity of 85.9%, and an AUC of 0.79. By targeting a specific Thai cohort, this research provides novel insights into the link between neuroinflammation and cognitive performance, advancing our understanding of cognitive variability, highlighting the role of biological markers in cognitive function, and contributing to developing more accurate machine learning models for diverse populations.

Patients reported outcome of cognitive function scale: a psychometric evaluation

BACKGROUND

Assessment of cognitive function is essential to identify the impact of brain aging, disease or injury on individuals. The Short Form Cognitive Function Scale is a brief instrument, easy to use in clinical and research settings with simple interpretation. However, its psychometric properties have not been confirmed in the general Mexican population. The aim of this study was to determine the psychometric properties of the Short Form Cognitive Function Scale in the general Mexican population.

METHODS

An instrumental design was conducted with 600 participants. Analyses were performed to evaluate factor structure (exploratory and confirmatory factor analysis), reliability (internal consistency), measurement invariance, construct validity (convergent and divergent) and Know-Groups Validity.

RESULTS

A unifactorial structure of 8 items was verified with an internal consistency of α = 0.945 and a McDonald Omega index of Ω = 0.946. Measurement invariance was confirmed (ΔCFI & TLI ≤ 0.01; ΔRMSEA & SRMR ≤ 0.015), with respect to gender, age groups and geographic area of residence. Finally, the Short Form Cognitive Function Scale showed adequate convergent validity with the Subjective Well-Being variable (r =.507, p <.001), divergent with the GAD 5 (r = -.517, p <.001), and discriminant between younger and older participants (t = -5.304, p <.001).

CONCLUSIONS

The Short Form Cognitive Function Scale version for the general Mexican population presented adequate psychometric properties that make it a valid and reliable instrument for use in non-clinical and research settings in Mexico.

The impact of retirement on executive functions and processing speed: findings from the Canadian Longitudinal Study on Aging

We used data from the Comprehensive cohort of the Canadian Longitudinal Study on Aging to compare the cognitive performance of retirees and workers (n = 1442), 45-85 years of age at baseline. Speed processing and executive functioning were assessed using standardized assessment tools at baseline and at follow-up, measured 3 years later. Retirees and workers were matched for age, sex, and education using the nearest neighbor propensity score method with a caliper of 0.02. Mixed ANOVA and post hoc analyses were conducted separately for the English- and French-speaking samples. Results for the English-speaking sample showed a significant decline on both the Stroop and the Mental Alternation tasks for retirees compared to workers from baseline to follow-up. These results support previous cross-sectional studies that have demonstrated a negative effect of retirement on executive functioning. The absence of significant results in the French-speaking sample are discussed in terms of sample size and professional occupation.

Sociodemographic Factors Predict Incident Mild Cognitive Impairment: A Brief Review and Empirical Study

OBJECTIVES

Mild cognitive impairment (MCI) is a transitional stage between normal cognitive aging and dementia that increases the risk of progressive cognitive decline. Early prediction of MCI could be beneficial for identifying vulnerable individuals in the community and planning primary and secondary prevention to reduce the incidence of MCI.

DESIGN

A narrative review and cohort study.

SETTING AND PARTICIPANTS

We review the MCI prediction based on the assessment of sociodemographic factors. We included participants from 3 surveys: 8915 from wave 2011/2012 of the China Health and Retirement Longitudinal Study (CHARLS), 9765 from the 2011 Chinese Longitudinal Healthy Longevity Survey (CLHLS), and 1823 from the 2014 Rugao Longevity and Ageing Study (RuLAS).

METHODS

We searched in PubMed, Embase, and Web of Science Core Collection between January 1, 2019, and December 30, 2022. To construct the composite risk score, a multivariate Cox proportional hazards regression model was used. The performance of the score was assessed using receiver operating characteristic (ROC) curves. Furthermore, the composite risk score was validated in 2 longitudinal cohorts, CLHLS and RuLAS.

RESULTS

We concluded on 20 articles from 892 available. The results suggested that the previous models suffered from several defects, including overreliance on cross-sectional data, low predictive utility, inconvenient measurement, and inapplicability to developing countries. Our empirical work suggested that the area under the curve for a 5-year MCI prediction was 0.861 in CHARLS, 0.797 in CLHLS, and 0.823 in RuLAS. We designed a publicly available online tool for this composite risk score.

CONCLUSIONS AND IMPLICATIONS

Attention to these sociodemographic factors related to the incidence of MCI can be beneficially incorporated into the current work, which will set the stage for better early prediction of MCI before its incidence and for reducing the burden of the disease.

Factors associated with healthy aging in Latin American populations

Latin American populations may present patterns of sociodemographic, ethnic and cultural diversity that can defy current universal models of healthy aging. The potential combination of risk factors that influence aging across populations in Latin American and Caribbean (LAC) countries is unknown. Compared to other regions where classical factors such as age and sex drive healthy aging, higher disparity-related factors and between-country variability could influence healthy aging in LAC countries. We investigated the combined impact of social determinants of health (SDH), lifestyle factors, cardiometabolic factors, mental health symptoms and demographics (age, sex) on healthy aging (cognition and functional ability) across LAC countries with different levels of socioeconomic development using cross-sectional and longitudinal machine learning models (n = 44,394 participants). Risk factors associated with social and health disparities, including SDH (β > 0.3), mental health (β > 0.6) and cardiometabolic risks (β > 0.22), significantly influenced healthy aging more than age and sex (with null or smaller effects: β < 0.2). These heterogeneous patterns were more pronounced in low-income to middle-income LAC countries compared to high-income LAC countries (cross-sectional comparisons), and in an upper-income to middle-income LAC country, Costa Rica, compared to China, a non-upper-income to middle-income LAC country (longitudinal comparisons). These inequity-associated and region-specific patterns inform national risk assessments of healthy aging in LAC countries and regionally tailored public health interventions.

Multi-domain prognostic models used in middle-aged adults without known cognitive impairment for predicting subsequent dementia

BACKGROUND

Dementia, a global health priority, has no current cure. Around 50 million people worldwide currently live with dementia, and this number is expected to treble by 2050. Some health conditions and lifestyle behaviours can increase or decrease the risk of dementia and are known as 'predictors'. Prognostic models combine such predictors to measure the risk of future dementia. Models that can accurately predict future dementia would help clinicians select high-risk adults in middle age and implement targeted risk reduction.

OBJECTIVES

Our primary objective was to identify multi-domain prognostic models used in middle-aged adults (aged 45 to 65 years) for predicting dementia or cognitive impairment. Eligible multi-domain prognostic models involved two or more of the modifiable dementia predictors identified in a 2020 Lancet Commission report and a 2019 World Health Organization (WHO) report (less education, hearing loss, traumatic brain injury, hypertension, excessive alcohol intake, obesity, smoking, depression, social isolation, physical inactivity, diabetes mellitus, air pollution, poor diet, and cognitive inactivity). Our secondary objectives were to summarise the prognostic models, to appraise their predictive accuracy (discrimination and calibration) as reported in the development and validation studies, and to identify the implications of using dementia prognostic models for the management of people at a higher risk for future dementia.

SEARCH METHODS

We searched MEDLINE, Embase, PsycINFO, CINAHL, and ISI Web of Science Core Collection from inception until 6 June 2022. We performed forwards and backwards citation tracking of included studies using the Web of Science platform.  SELECTION CRITERIA: We included development and validation studies of multi-domain prognostic models. The minimum eligible follow-up was five years. Our primary outcome was an incident clinical diagnosis of dementia based on validated diagnostic criteria, and our secondary outcome was dementia or cognitive impairment determined by any other method.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened the references, extracted data using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and assessed risk of bias and applicability of included studies using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We synthesised the C-statistics of models that had been externally validated in at least three comparable studies.  MAIN RESULTS: We identified 20 eligible studies; eight were development studies and 12 were validation studies. There were 14 unique prognostic models: seven models with validation studies and seven models with development-only studies. The models included a median of nine predictors (range 6 to 34); the median number of modifiable predictors was five (range 2 to 11). The most common modifiable predictors in externally validated models were diabetes, hypertension, smoking, physical activity, and obesity. In development-only models, the most common modifiable predictors were obesity, diabetes, hypertension, and smoking. No models included hearing loss or air pollution as predictors. Nineteen studies had a high risk of bias according to the PROBAST assessment, mainly because of inappropriate analysis methods, particularly lack of reported calibration measures. Applicability concerns were low for 12 studies, as their population, predictors, and outcomes were consistent with those of interest for this review. Applicability concerns were high for nine studies, as they lacked baseline cognitive screening or excluded an age group within the range of 45 to 65 years. Only one model, Cardiovascular Risk Factors, Ageing, and Dementia (CAIDE), had been externally validated in multiple studies, allowing for meta-analysis. The CAIDE model included eight predictors (four modifiable predictors): age, education, sex, systolic blood pressure, body mass index (BMI), total cholesterol, physical activity and APOEƐ4 status. Overall, our confidence in the prediction accuracy of CAIDE was very low; our main reasons for downgrading the certainty of the evidence were high risk of bias across all the studies, high concern of applicability, non-overlapping confidence intervals (CIs), and a high degree of heterogeneity. The summary C-statistic was 0.71 (95% CI 0.66 to 0.76; 3 studies; very low-certainty evidence) for the incident clinical diagnosis of dementia, and 0.67 (95% CI 0.61 to 0.73; 3 studies; very low-certainty evidence) for dementia or cognitive impairment based on cognitive scores. Meta-analysis of calibration measures was not possible, as few studies provided these data.

AUTHORS' CONCLUSIONS

We identified 14 unique multi-domain prognostic models used in middle-aged adults for predicting subsequent dementia. Diabetes, hypertension, obesity, and smoking were the most common modifiable risk factors used as predictors in the models. We performed meta-analyses of C-statistics for one model (CAIDE), but the summary values were unreliable. Owing to lack of data, we were unable to meta-analyse the calibration measures of CAIDE. This review highlights the need for further robust external validations of multi-domain prognostic models for predicting future risk of dementia in middle-aged adults.

Capsaicin: A Potential Treatment to Improve Cerebrovascular Function and Cognition in Obesity and Ageing

Impaired cognition is the primary symptom of dementia, which can lead to functional disability and reduced quality of life among an increasingly ageing population. Ageing is associated with increased oxidative stress, chronic low-grade systemic inflammation, and endothelial dysfunction, which reduces cerebrovascular function leading to cognitive decline. Chronic low-grade systemic inflammatory conditions, such as obesity, exacerbate this decline beyond normal ageing and predispose individuals to neurodegenerative diseases, such as dementia. Capsaicin, the major pungent molecule of chilli, has recently demonstrated improvements in cognition in animal models via activation of the transient receptor potential vanilloid channel 1 (TRPV1). Capsaicin-induced TRPV1 activation reduces adiposity, chronic low-grade systemic inflammation, and oxidative stress, as well as improves endothelial function, all of which are associated with cerebrovascular function and cognition. This review examines the current literature on capsaicin and Capsimax, a capsaicin supplement associated with reduced gastrointestinal irritation compared to capsaicin. Acute and chronic capsaicin treatment can improve cognition in animals. However, studies adequately assessing the effects of capsaicin on cerebrovascular function, and cognition in humans do not exist. Capsimax may be a potentially safe therapeutic intervention for future clinical trials testing the effects of capsaicin on cerebrovascular function and cognition.

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

BACKGROUND

In the older general population, neurodegenerative diseases (NDs) are associated with increased disability, decreased physical and cognitive function. Detecting risk factors can help implement prevention measures. Using deep neural networks (DNNs), a machine-learning algorithm could be an alternative to Cox regression in tabular datasets with many predictive features. We aimed to compare the performance of different types of DNNs with regularized Cox proportional hazards models to predict NDs in the older general population.

METHODS

We performed a longitudinal analysis with participants of the English Longitudinal Study of Ageing. We included men and women with no NDs at baseline, aged 60 years and older, assessed every 2 years from 2004 to 2005 (wave2) to 2016-2017 (wave 8). The features were a set of 91 epidemiological and clinical baseline variables. The outcome was new events of Parkinson's, Alzheimer or dementia. After applying multiple imputations, we trained three DNN algorithms: Feedforward, TabTransformer, and Dense Convolutional (Densenet). In addition, we trained two algorithms based on Cox models: Elastic Net regularization (CoxEn) and selected features (CoxSf).

RESULTS

5433 participants were included in wave 2. During follow-up, 12.7% participants developed NDs. Although the five models predicted NDs events, the discriminative ability was superior using TabTransformer (Uno's C-statistic (coefficient (95% confidence intervals)) 0.757 (0.702, 0.805). TabTransformer showed superior time-dependent balanced accuracy (0.834 (0.779, 0.889)) and specificity (0.855 (0.0.773, 0.909)) than the other models. With the CoxSf (hazard ratio (95% confidence intervals)), age (10.0 (6.9, 14.7)), poor hearing (1.3 (1.1, 1.5)) and weight loss 1.3 (1.1, 1.6)) were associated with a higher DNN risk. In contrast, executive function (0.3 (0.2, 0.6)), memory (0, 0, 0.1)), increased gait speed (0.2, (0.1, 0.4)), vigorous physical activity (0.7, 0.6, 0.9)) and higher BMI (0.4 (0.2, 0.8)) were associated with a lower DNN risk.

CONCLUSION

TabTransformer is promising for prediction of NDs with heterogeneous tabular datasets with numerous features. Moreover, it can handle censored data. However, Cox models perform well and are easier to interpret than DNNs. Therefore, they are still a good choice for NDs.

External validation of existing dementia prediction models on observational health data

BACKGROUND

Many dementia prediction models have been developed, but only few have been externally validated, which hinders clinical uptake and may pose a risk if models are applied to actual patients regardless. Externally validating an existing prediction model is a difficult task, where we mostly rely on the completeness of model reporting in a published article. In this study, we aim to externally validate existing dementia prediction models. To that end, we define model reporting criteria, review published studies, and externally validate three well reported models using routinely collected health data from administrative claims and electronic health records.

METHODS

We identified dementia prediction models that were developed between 2011 and 2020 and assessed if they could be externally validated given a set of model criteria. In addition, we externally validated three of these models (Walters' Dementia Risk Score, Mehta's RxDx-Dementia Risk Index, and Nori's ADRD dementia prediction model) on a network of six observational health databases from the United States, United Kingdom, Germany and the Netherlands, including the original development databases of the models.

RESULTS

We reviewed 59 dementia prediction models. All models reported the prediction method, development database, and target and outcome definitions. Less frequently reported by these 59 prediction models were predictor definitions (52 models) including the time window in which a predictor is assessed (21 models), predictor coefficients (20 models), and the time-at-risk (42 models). The validation of the model by Walters (development c-statistic: 0.84) showed moderate transportability (0.67-0.76 c-statistic). The Mehta model (development c-statistic: 0.81) transported well to some of the external databases (0.69-0.79 c-statistic). The Nori model (development AUROC: 0.69) transported well (0.62-0.68 AUROC) but performed modestly overall. Recalibration showed improvements for the Walters and Nori models, while recalibration could not be assessed for the Mehta model due to unreported baseline hazard.

CONCLUSION

We observed that reporting is mostly insufficient to fully externally validate published dementia prediction models, and therefore, it is uncertain how well these models would work in other clinical settings. We emphasize the importance of following established guidelines for reporting clinical prediction models. We recommend that reporting should be more explicit and have external validation in mind if the model is meant to be applied in different settings.

Arterial stiffness and age moderate the association between physical activity and global cognition in older adults

BACKGROUND

Evidence supports that time spent on physical activity has beneficial effects on cognition in older adults. Nevertheless, whether these beneficial effects are still present at the intersection of different levels of arterial stiffness and age is uncertain.

METHODS

One hundred and ten healthy older adults aged 60-75 years were examined for arterial stiffness [carotid-femoral pulse wave velocity (cf-PWV)], global cognition (composite score of Montreal Cognitive Assessment, and Mini-Mental State Examination), and self-reported physical activity (PACED diary). Using PROCESS macro for SPSS, we evaluated if cf-PWV (moderator 1), and age (moderator 2) moderate the relationship between physical activity (X) and global cognition (Y). The threshold for high stiffness was set at 8.5 m/s based on previous studies that reported this cut-off as more appropriate for classifying cerebrovascular risk groups.

RESULTS

Physical activity had a positive effect on cognition in young-elderly adults (<68.5 years) with a cf-PWV of at least 8.5 m/s (β = 0.48, SE = 0.193, P = 0.014, 95% CI = 0.100--0.868) and in elderly adults (≥68.5 years) with a cf-PWV of less than 8.5 m/s (β = 0.56, SE = 0.230, P = 0.017, 95% CI = 0.104-1.018). This was not the case in elderly adults with a cf-PWV of at least 8.5 m/s (β = 0.00, SE = 0.193, P = 0.998, 95% CI = -0.362 to 361), or in young-elderly adults with a cf-PWV of less than 8.5 m/s (β = 0.16, SE = 0.247, P = 0.501, 95% CI = -0.326 to 656).

CONCLUSION

The interaction between arterial stiffness and age moderated the effect of physical activity on global cognition. Time spent on physical activity alone might not be sufficient to achieve cognitive benefit over a specific threshold of arterial stiffness and age.