Risk Prediction Performance of the Thai Cardiovascular Risk Score for Mild Cognitive Impairment in Adults with Metabolic Risk Factors in Thailand

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.

Examining determinants of control of metabolic syndrome among older adults with NCDs receiving service at NCD Plus clinics: multilevel analysis

BACKGROUND

Metabolic syndrome (MetS) in older adults with hypertension, diabetes, and hyperlipidemia increases the risks of cardiovascular diseases by 2.5 times and type 2 diabetes by five times. This study aimed to explain the multilevel relationships between health service system factors and individual-level factors influencing the control of MetS among older adults with NCDs receiving health care services at the NCD Plus clinics of hospitals in 1 year.

METHODS

This cross-sectional analytical study employed a systematic sampling method to have two groups of samples from 4 regions of Thailand: (1) 600 older adults having at least one diagnosis of NCDs receiving services at NCD Plus clinics and (2) 12 nurses in charge of the NCD Plus clinics at the hospitals providing services to these patient samples. Data were analyzed using multilevel logistic regression analysis.

RESULTS

24% of older adults with NCDs can control MetS within one year. The MetS escalation from the initial assessment to 1-year follow-up varied according to the level of the hospitals. The transition from MetS to non-MetS status was rare in older adults with NCDs. Among health service system factors, complete screening for MetS influenced 1-year MetS control (95% CI [1.06, 2.92]). Older adults who were female and who had polypharmacy had a 66% (95% CI [0.22, 0.53]) and a 54% (95% CI [0.29 - 0.71]) reduction chance in MetS control. Older adults, who were ≥ 80 years old, labor-employed, healthy dietary patterns, and medication adherence increased chances of controlling MetS by 2.38 times (95% CI [1.12, 5.05]), 2.14 times (95% CI [1.03, 4.42]), 1.61 times (95% CI [1.06-2.46]), and 3.18 times (95% CI [1.51, 6.70]), respectively.

CONCLUSIONS

NCDs Plus clinics that provide complete screening for MetS significantly enhance their effectiveness in reducing the proportion of older adults with MetS. In addition, the service should pay attention to older adults who are female, are retired, and take multiple medications to achieve MetS control better. The insights gained from such an analysis could be instrumental in pinpointing the resources necessary to bolster the efficacy of NCD Plus clinics.

A household survey of the prevalence of subjective cognitive decline and mild cognitive impairment among urban community-dwelling adults aged 30 to 65

While it is possible to detect cognitive decline before the age of 60, and there is a report indicating that certain cognitive abilities peak in one's 30s, the evidence regarding cognitive problems in populations younger than 65 years is scarce. This study aims to (1) determine the proportion of community-dwelling adults with different cognitive status, and (2) determine the prevalence of neuropsychiatric behaviors. A population-based survey was conducted in Chiang Mai, Thailand. Individuals aged 30 to 65 were recruited and assessed for demographic data, memory complaints, cognitive performance, and neuropsychiatric symptoms using self-reported questionnaires. In a total of 539 participants, 33.95% had mild cognitive impairment (MCI), 7.05% had subjective cognitive decline (SCD), and 52.50% had neuropsychiatric symptoms. The risk of MCI increased with age, and neuropsychiatric symptoms were significantly higher in those with MCI or SCD than in those without (p < 0.001). The most common complaints were sleep problems, anxiety, and irritability. Screening for MCI in adults aged < 65 years might be useful. However, further investigation on the appropriate age to screen and the program's cost-effectiveness is suggested.

Cardiovascular disease risk models and dementia or cognitive decline: a systematic review

Background

Health cognitive promotion and protection is a critical topic. With the world's aging population and rising life expectancy, there will be many people living with highly age-related dementia illnesses. Cardiovascular disease (CVD) and dementia share the same risk factors, such as unhealthy lifestyles and metabolic factors. These recognized risks associated with CVD and dementia frequently co-occur. CVD risk models may have a close association with dementia and cognitive decline. So, this systematic review aimed to determine whether CVD risk models were connected with dementia or cognitive decline and compare the predictive ability of various models.

Methods

PubMed, Web of Science, PsychINFO, Embase, Cochrane Library, CNKI, Sinomed, and WanFang were searched from 1 January 2014 until 16 February 2023. Only CVD risk models were included. We used the Newcastle-Ottawa scale (NOS) for the quality assessment of included cohort studies and the Agency for Healthcare Research and Quality (AHRQ) for cross-sectional studies. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement's guidelines were followed in this systematic study.

Results

In all, 9,718 references were screened, of which 22 articles were included. A total of 15 CVD risk models were summarized. Except for the Cardiovascular Health in Ambulatory Care Research Team (CANHEART) health index, the other 14 CVD risk models were associated with dementia and cognitive decline. In comparison, different CVD risk models and domain-specific cognitive function correlation variation depended on cohort characteristics, risk models, cognitive function tests, and study designs. Moreover, it needed to be clarified when comparing the predicting performance of different CVD risk models.

Conclusion

It is significant for public health to improve disease risk prediction and prevention and mitigate the potential adverse effects of the heart on the brain. More cohort studies are warranted to prove the correlation between CVD risk models and cognitive function. Moreover, further studies are encouraged to compare the efficacy of CVD risk models in predicting cognitive disorders.

Prevention, Intervention and Care of Neurodegenerative Diseases

Chronic neurodegenerative diseases encompass a wide spectrum of disorders and affect millions of people worldwide [...].