Higher Risk Of Cognitive Impairment Research Articles

Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation.

The underdiagnosis of cognitive impairment hinders timely intervention of dementia. Health professionals working in the community play a critical role in the early detection of cognitive impairment, yet still face several challenges such as a lack of suitable tools, necessary training, and potential stigmatization. This study explored a novel application integrating psychometric methods with data science techniques to model subtle inconsistencies in questionnaire response data for early identification of cognitive impairment in community environments. This study analyzed questionnaire response data from participants aged 50 years and older in the Health and Retirement Study (waves 8-9, n=12,942). Predictors included low-quality response indices generated using the graded response model from four brief questionnaires (optimism, hopelessness, purpose in life, and life satisfaction) assessing aspects of overall well-being, a focus of health professionals in communities. The primary and supplemental predicted outcomes were current cognitive impairment derived from a validated criterion and dementia or mortality in the next ten years. Seven predictive models were trained, and the performance of these models was evaluated and compared. The multilayer perceptron exhibited the best performance in predicting current cognitive impairment. In the selected four questionnaires, the area under curve values for identifying current cognitive impairment ranged from 0.63 to 0.66 and was improved to 0.71 to 0.74 when combining the low-quality response indices with age and gender for prediction. We set the threshold for assessing cognitive impairment risk in the tool based on the ratio of underdiagnosis costs to overdiagnosis costs, and a ratio of 4 was used as the default choice. Furthermore, the tool outperformed the efficiency of age or health-based screening strategies for identifying individuals at high risk for cognitive impairment, particularly in the 50- to 59-year and 60- to 69-year age groups. The tool is available on a portal website for the public to access freely. We developed a novel prediction tool that integrates psychometric methods with data science to facilitate "passive or backend" cognitive impairment assessments in community settings, aiming to promote early cognitive impairment detection. This tool simplifies the cognitive impairment assessment process, making it more adaptable and reducing burdens. Our approach also presents a new perspective for using questionnaire data: leveraging, rather than dismissing, low-quality data.

Cognitive function in older adults with stroke: A latent profile analysis

BackgroundPost-stroke cognitive impairment (PSCI) is one of the major complications after a stroke. Therefore, it is necessary to identify different categories of cognitive function in older adults with stroke and to explore factors associated with different levels of cognitive function in older adults after stroke. MethodsA cross-sectional survey was conducted using convenience sampling to collect data from 1076 older adults with stroke from 5 hospitals in Lanzhou City, Gansu Province, China, from June 2022 to August 2023. Latent Profile Analysis (LPA) was used to identify cognitive function subgroups among older adults with stroke, and multinomial logistic regression was used to analyze the related factors. Results1076 older adults with stroke were identified and classified as class-1: cognitive impairment, class-2: mild cognitive impairment, class-3: normal cognitive function. Age, gender, education level, hemorrhagic stroke, cerebrovascular disease are associated with different levels of cognitive function in older adults after stroke. ConclusionsThese findings highlight the heterogeneity of cognitive function in older adults with stroke. Our research could assist healthcare professionals in China to identify older adults with a high risk of cognitive impairment and to develop individualized interventions based on their characteristics.

Neutrophil percentage-to-albumin ratio is associated with cognitive function in adults aged over 60 years: An analysis of data from the NHANES 2011-2014.

The aging global population is increasing the attention to cognitive decline in older individuals. This study sought to examine the potential link between the neutrophil percentage-to-albumin ratio (NPAR) and cognitive function. We analyzed data from the National Health and Nutrition Examination Survey 2011-2014 using multivariate logistic regression and smooth curve fitting, to investigate the correlation between NPAR and cognitive performance. Restricted cubic spline analysis assessed the linear relationship with high-risk cognitive dysfunction, while piecewise linear regression identified thresholds. Subgroup analyses confirmed the consistency and reliability of our findings. Our study included data from 2759 individuals aged >60 years. NPAR showed a significant correlation with Consortium to Establish a Registry for Alzheimer's Disease (CERAD) word learning score, CERAD delayed recall score, total z-score and a high risk of cognitive dysfunction. Furthermore, there were statistically significant trends in the changes in CERAD word learning, digit symbol substitution test, and CERAD delayed recall scores as the NPAR quartile increased, these trends were inverted U-shaped. When the NPAR exceeded 14.57, there was a positive association with the likelihood of a high risk of cognitive impairment. The link between NPAR and cognitive performance was notably stronger in individuals with moderate body mass index and those aged 73-80 years. A strong link was observed between the NPAR and cognitive function. NPAR may serve as a tool to identify individuals at increased risk of cognitive decline.

The School Years Screening Test for Evaluation of Mental Status-Revised (SYSTEMS-R) as a cognitive function screening tool in children with epilepsy

Background Children with epilepsy are at high risk of cognitive impairment that can affect quality of life. Intelligence quotient (IQ) measurement using the Wechsler Intelligence Scale for Children (WISC) is the gold standard test of cognitive function, but it is time-consuming and costly. The School Years Screening Test for Evaluation of Mental Status-Revised (SYSTEMS-R) is a potential cognitive function screening tool that can be used in children with epilepsy. Objective To assess the performance of SYSTEMS-R as a cognitive function screening tool in children aged 6-15 years with epilepsy. Methods This cross-sectional diagnostic test study was conducted in children aged 6-15 years with epilepsy. All subjects were assessed using both SYSTEMS-R and WISC 4th edition. The sensitivity, specificity, positive and negative predictive value, likelihood ratios of the positive and negative tests, and accuracy of SYSTEMS-R was calculated, with WISC as the gold standard test. Results Based on the SYSTEMS-R, the prevalence of cognitive impairment in children aged 6-15 years with epilepsy in our population was 86.4%. With WISC as the gold SYSTEMS-R had 84% sensitivity, 91% specificity, 98% positive predictive value, and 47% negative predictive value. The likelihood ratio of a positive SYSTEMS-R test was 10.11 and the likelihood ratio of a negative test was 0.17. The overall accuracy of SYSTEMS-R to detect cognitive impairment was 85%. Conclusion SYSTEMS-R has good sensitivity and specificity to assess cognitive function in children 6-15 years with epilepsy. It can be considered for widespread use in the early detection of cognitive impairment in pediatric epilepsy patients aged 6-15 years.

Sedentary Lifestyle Is a Modifiable Risk Factor for Cognitive Impairment in Patients on Dialysis and after Kidney Transplantation

Background: Chronic kidney disease (CKD) is a risk factor for cognitive impairment (CI), and this risk is the highest in patients with end-stage kidney disease (ESKD). As a multifactorial disease, CI may be influenced by several potentially modifiable lifestyle and behavioral factors that may reduce or increase the risk of dementia. The aim of this study was to evaluate the associations between the known modifiable risk factors for dementia and the risk of CI in patients with ESKD treated with renal replacement therapy. The Charlson Comorbidity Index and the risk of CI in patients with ESKD were also assessed. Methods: In this cross-sectional study, 225 consecutive patients with ESKD treated with different modalities of renal replacement therapy were assessed for cognitive decline using the Addenbrooke’s Cognitive Examination (ACE III) test. Information was also collected on modifiable risk factors for dementia, medical history and demographics. Results: This study included 117 patients after kidney transplantation (KT) and 108 patients with ESKD undergoing peritoneal dialysis and hemodialysis. The prevalence of modifiable risk factors for dementia differed between the groups; KT patients were more likely to be physically active, residing in cities with populations of less than 500,000 inhabitants, and were less likely to suffer from depression. Furthermore, the KT group had a lower Charlson Comorbidity Index score, indicating less severe comorbidities, and a lower risk of CI (3.6 ± 1.67 vs. 5.43 ± 2.37; p = 0.001). In both the KT and dialysis groups, patients with CI were more likely to have a sedentary lifestyle (45% vs. 9%, p = 0.001 and 88% vs. 48%, p = 0.001, respectively), whereas lower educational attainment and depression had a significant negative impact on ACE III test results, but only in KT patients. Finally, cognitive function in dialysis patients was negatively affected by social isolation and living in urban areas. Conclusions: Modifiable risk factors for dementia, particularly a sedentary lifestyle, are associated with a higher risk of CI in patients treated with different renal replacement therapy modalities. As CI is an irreversible condition, it is important to identify lifestyle-related factors that may lead to dementia in order to improve or maintain cognitive function in patients with ESKD.

Sensory impairments associated with cognitive impairment among older adults in China: A community-based, 10-year prospective cohort study.

To address an existing gap in knowledge due to limited and inconclusive evidence, we aimed to investigate the association between sensory impairments and cognitive decline among older Chinese individuals. We retrieved data on 6862 adults aged ≥65 years that were collected through the Chinese Longitudinal Healthy Longevity Study (CLHLS), a nationwide, prospective, community-based elderly cohort study. Visual or hearing impairment in the CLHLS were identified through self-reported questionnaire. Sensory impairments were categorised as no sensory impairment, hearing impairment only, visual impairment only, and dual sensory impairment according to hearing and vision function. Cognitive impairment was defined as having a score <18 on the Chinese version of the Mini Mental State Examination. We used a Cox proportional hazard model to evaluate the relationship between sensory and cognitive impairments. Among 6862 participants, 5.7% had dual sensory impairment, 7.4% had hearing impairment only, and had 17.2% visual impairment only. Compared with participants with no sensory impairment, those with hearing impairment only (adjusted hazard ratio (aHR) = 1.65; 95% confidence interval (CI) = 1.41, 1.92), visual impairment only (aHR = 1.25; 95% CI = 1.11, 1.41), and dual sensory impairment (aHR = 1.47; 95% CI = 1.25, 1.74) were significantly associated with higher risk of cognitive impairment in the fully adjusted model. Our results show that having hearing impairment only, visual impairment only, and dual sensory impairment was significantly associated with a higher risk of cognitive impairment among Chinese older adults aged ≥65 years. This suggest a need for the timely identification and management of sensory impairments for the elderly to reduce dementia risk.

Association of Seizure Control With Cognition in People With Normal Cognition and Mild Cognitive Impairment.

Seizures are common in dementia and associated with accelerated cognitive decline. However, the impact of active vs remote seizures on cognition remains understudied. This study aimed to investigate the impact of active vs remote seizures on cognition in people with normal cognition and mild cognitive impairment (MCI). This longitudinal, multicenter cohort is based on National Alzheimer's Coordinating Center data of participants recruited from 39 Alzheimer's Disease Centers in the United States from September 2005 to December 2021. All participants with normal cognition and MCI and at least 2 visits were included. Primary outcome, that is, cognitive decline, was determined using Clinical Dementia Rating (CDR) from (1) normal-to-impaired (CDR ≥0.5) and (2) MCI-to-dementia (CDR ≥1) groups. The effect of active seizures (over the preceding 12 months), remote seizures (previous seizures but none over the preceding 12 months), and no seizures (controls) on cognition was assessed. Subgroups of chronic seizures at enrollment and new-onset seizures were further analyzed. Cox regression models assessed the risk of all-cause MCI and/or dementia. All models were adjusted for age, sex, education, race, hypertension, and diabetes. Of the 13,726 participants with normal cognition at enrollment (9,002 [66%] female; median age 71 years), 118 had active seizures and 226 had remote seizures. Of the 11,372 participants with MCI at enrollment (5,605 [49%] female; median age 73 years), 197 had active seizures and 226 had remote seizures. Active seizures were associated with 2.1 times higher risk of cognitive impairment (adjusted hazard ratio [aHR] 2.13, 95% CI 1.60-2.84, p < 0.001) in cognitively healthy adults (median years to decline: active seizures = ∼1, remote seizures = ∼3, no seizures = ∼3) and 1.6 times higher risk of dementia (aHR 1.58, 95% CI 1.24-2.01, p < 0.001) in those with MCI (median years to decline: active seizures = ∼1, remote seizures = ∼2, controls = ∼2). This risk was not observed with remote seizures. In this study, active seizures but not remote seizures were associated with earlier cognitive decline in both cognitively normal adults and those with MCI, independent of other dementia risk factors. Therefore, early identification and management of seizures may present a path to mitigation of cognitive decline in the aging epileptic population. A limitation is that causality cannot be confirmed in our observational longitudinal study.

Traumatic Brain Injury, Seizures, and Cognitive Impairment Among Older Adults

Traumatic brain injury (TBI), seizures, and dementia increase with age. There is a gap in understanding the associations of TBI, seizures, and medications such as antiseizure and antipsychotics with the progression of cognitive impairment across racial and ethnic groups. To investigate the association of TBI and seizures with the risk of cognitive impairment among cognitively normal older adults and the role of medications in moderating the association. This multicenter cohort study was a secondary analysis of the Uniform Data Set collected between June 1, 2005, and June 30, 2020, from the National Alzheimer's Coordination Center. Statistical analysis was performed from February 1 to April 3, 2024. Data were collected from participants from 36 Alzheimer's Disease Research Centers in the US who were 65 years or older at baseline, cognitively normal at baseline (Clinical Dementia Rating of 0 and no impairment based on a presumptive etiologic diagnosis of AD), and had complete information on race and ethnicity, age, sex, educational level, and apolipoprotein E genotype. Health history of TBI, seizures, or both conditions. Progression to cognitive impairment measured by a Clinical Dementia Rating greater than 0. Among the cohort of 7180 older adults (median age, 74 years [range, 65-102 years]; 4729 women [65.9%]), 1036 were African American or Black (14.4%), 21 were American Indian or Alaska Native (0.3%), 143 were Asian (2.0%), 332 were Hispanic (4.6%), and 5648 were non-Hispanic White (78.7%); the median educational level was 16.0 years (range, 1.0-29.0 years). After adjustment for selection basis using propensity score weighting, seizure was associated with a 40% higher risk of cognitive impairment (hazard ratio [HR], 1.40; 95% CI, 1.19-1.65), TBI with a 25% higher risk of cognitive impairment (HR, 1.25; 95% CI, 1.17-1.34), and both seizure and TBI were associated with a 57% higher risk (HR, 1.57; 95% CI, 1.23-2.01). The interaction models indicated that Hispanic participants with TBI and seizures had a higher risk of cognitive impairment compared with other racial and ethnic groups. The use of antiseizure medications (HR, 1.23; 95% CI, 0.99-1.53), antidepressants (HR, 1.32; 95% CI, 1.17-1.50), and antipsychotics (HR, 2.15; 95% CI, 1.18-3.89) was associated with a higher risk of cognitive impairment, while anxiolytic, sedative, or hypnotic use (HR, 0.88; 95% CI, 0.83-0.94) was associated with a lower risk. This study highlights the importance of addressing TBI and seizures as risk factors for cognitive impairment among older adults. Addressing the broader social determinants of health and bridging the health divide across various racial and ethnic groups are essential for the comprehensive management and prevention of dementia.

Exercise and Cognitive Function in Chronic Kidney Disease: A Systematic Review and Meta-Analysis of Efficacy and Harms.

People living with chronic kidney disease (CKD) are at higher risk of cognitive impairment. Exercise may improve cognitive function. This systematic review and meta-analysis of randomized controlled trials (RCTs) was completed to determine the efficacy and harms of exercise in improving cognitive function in people living with CKD. A systematic literature review identified RCTs of people with any stage of CKD, with an intervention that exercised large-muscle groups, and with a validated outcome measure of cognitive function. First, harms were analyzed. Then a random-effects meta-analysis was completed with subsequent planned subgroup analyses to investigate heterogeneity between CKD stages and treatments, between different exercise types, durations and intensities, and between different outcome methodologies. Finally, quality of evidence was rated. Nineteen trials randomized 1160 participants. Harms were reported on 94 occasions in intervention groups vs. 83 in control. The primary analysis found that exercise had a small but statistically significant effect on cognition in CKD (effect size (ES) = 0.22; 95% confidence intervals (CI95) = 0.00, 0.44; P = 0.05). However, the quality of evidence was rated as low. Subgroup analyses found that type of exercise moderated the effect on cognition (χ2 = 7.62; P = 0.02), with positive effects only observed following aerobic exercise (ES = 0.57; CI95 = 0.21, 0.93; P = 0.002). Across the spectrum of CKD, exercise had a small but positive and clinically meaningful effect on cognitive function and did not appear to be harmful. Aerobic exercise was particularly beneficial. However, results must be interpreted cautiously due to the low quality of evidence. Nevertheless, care teams may choose to recommend aerobic exercise interventions to prevent cognitive decline. Researchers should design unbiased studies to clarify what intensity and duration of exercise is required to maximize efficiency of such exercise interventions.

Relationship between triglyceride-glucose index and cognitive function among community-dwelling older adults: a population-based cohort study.

The global increase in the aging population presents considerable challenges, particularly regarding cognitive impairment, a major concern for public health. This study investigates the association between the triglyceride-glucose (TyG) index, a measure of insulin resistance, and the risk of cognitive impairment in the elderly. This prospective cohort study enrolled 2,959 participants aged 65 and above from the 2015 and 2020 waves of the China Health and Retirement Longitudinal Study (CHARLS). The analysis employed a logistic regression model to assess the correlation between the TyG index and cognitive impairment. The study included 2,959 participants, with a mean age of 71.2 ± 5.4 years, 49.8% of whom were female. The follow-up in 2020 showed a decrease in average cognitive function scores from 8.63 ± 4.61 in 2015 to 6.86 ± 5.45. After adjusting for confounding factors, a significant association was observed between TyG index quartiles and cognitive impairment. Participants in the highest quartile (Q4) of baseline TyG had a higher risk of cognitive impairment compared to those in the lowest quartile (Q1) (odds ratio [OR]: 1.97, 95% confidence intervals [CI]: 1.28-2.62, P<0.001). The study highlights a significant connection between elevated TyG index levels and cognitive impairment among older adults in China. These findings suggest that targeted interventions to reduce the TyG index could mitigate cognitive impairment and potentially lower the incidence of dementia.

Enhancing identification performance of cognitive impairment high-risk based on a semi-supervised learning method

BackgroundCognitive assessment plays a pivotal role in the early detection of cognitive impairment, particularly in the prevention and management of cognitive diseases such as Alzheimer’s and Lewy body dementia. Large-scale screening relies heavily on cognitive assessment scales as primary tools, with some low sensitivity and others expensive. Despite significant progress in machine learning for cognitive function assessment, its application in this particular screening domain remains underexplored, often requiring labor-intensive expert annotations. AimsThis paper introduces a semi-supervised learning algorithm based on pseudo-label with putback (SS-PP), aiming to enhance model efficiency in predicting the high risk of cognitive impairment (HR-CI) by utilizing the distribution of unlabeled samples. DataThe study involved 189 labeled samples and 215,078 unlabeled samples from real world. A semi-supervised classification algorithm was designed and evaluated by comparison with supervised methods composed by 14 traditional machine-learning methods and other advanced semi-supervised algorithms. ResultsThe optimal SS-PP model, based on GBDT, achieved an AUC of 0.947. Comparative analysis with supervised learning models and semi-supervised methods demonstrated an average AUC improvement of 8% and state-of-art performance, repectively. ConclusionThis study pioneers the exploration of utilizing limited labeled data for HR-CI predictions and evaluates the benefits of incorporating physical examination data, holding significant implications for the development of cost-effective strategies in relevant healthcare domains.

Very high high-density lipoprotein cholesterol may be associated with higher risk of cognitive impairment in older adults

BackgroundPrevious studies have shown that high-density lipoprotein cholesterol (HDL-C) levels are positively associated with cognitive function across a range of concentrations. However, recent studies have suggested that very high HDL-C levels may lead to poorer outcomes. Therefore, we aimed to investigate the relationship between different concentrations of HDL-C and cognitive impairment risk.MethodsWe collected data from 3632 participants aged over 60 years from the U.S. National Health and Nutrition Examination Survey (NHANES) between 2011 and 2014 to assess the relationship between HDL-C and cognitive function. Cognitive function was evaluated with the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) test, the animal fluency test (AFT), and the digit symbol substitution test (DSST). We used restricted cubic spline models and logistic regression to examine the association between HDL-C and cognitive function.ResultsA U-shaped was observed between HDL-C and cognitive outcomes, individuals with higher risk in those with both low and very high HDL-C levels compared with those with midrange values. Very high HDL-C levels (≥ 2.50 mmol/L) were associated with increased risk of cognitive impairment (OR = 2.19; 95% CI, 1.12–4.28) compared with those with HDL-C levels in the range of 1.50 to 1.99 mmol/L in older adults after adjustment for confounding factors. Interaction test demonstrated that relationship between very high HDL-C and the risk of cognitive impairment was not changed in different sex and race group (P for interaction > 0.05).ConclusionsVery high HDL-C levels were associated with an increased risk of cognitive impairment. HDL-C may not be a protective factor for maintaining brain health in older adults at very high levels.

Association of modified dementia risk score with cerebrospinal fluid biomarkers and cognition in adults without dementia.

This study aimed to investigate the cognitive profile and prospective cognitive changes in non-demented adults with elevated Modified Dementia Risk Scores (MDRS), while also exploring the potential relationship between these associations and cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathology and neuroinflammation. Within the Chinese Alzheimer's Biomarker and LifestylE (CABLE) database, 994 participants without dementia were assessed on MDRS, CSF biomarkers and cognition. We examined the associations of the MDRS with CSF biomarkers and cognitive scores using linear regressions. Causal mediation analyses were conducted to analyze the associations among MDRS, brain pathologies, and cognition. The Alzheimer's Disease Neuroimaging Initiative (ADNI) study was used to validate the mediation effects and to investigate the longitudinal association between MDRS and cognitive decline. The results revealed that higher MDRS were linked to poorer cognitive performance (Model 1: PFDR < 0.001; Model 2: PFDR < 0.001) and increases in CSF levels of phosphorylated tau (P-tau, Model 1: PFDR < 0.001; Model 2: PFDR < 0.001), total tau (T-tau, Model 1: PFDR < 0.001; Model 2: PFDR < 0.001), P-tau/Aβ42 ratio (Model 1: PFDR = 0.023; Model 2: PFDR = 0.028), T-tau/Aβ42 ratio (Model 1: PFDR < 0.001; Model 2: PFDR < 0.001) and soluble triggering receptor expressed on myeloid cells 2 (sTrem2, Model 1: PFDR < 0.001; Model 2: PFDR < 0.001) in the CABLE study. The impact of MDRS on cognition was partially mediated by neuroinflammation and tau pathology. These mediation effects were replicated in the ADNI study. Baseline MDRS were significantly associated with future cognitive decline, as indicated by lower scores on the Mini-Mental State Examination (MMSE, Model 1: PFDR = 0.045; Model 2: PFDR < 0.001), ADNI composite memory score (ADNI-MEM, Model 1: PFDR = 0.005; Model 2: PFDR < 0.001), ADNI composite executive function score (ADNI-EF, Model 1: PFDR = 0.045; Model 2: PFDR < 0.001), and higher score on the Alzheimer's Disease Assessment Scale (ADAS13, Model 1: PFDR = 0.045; Model 2: PFDR < 0.001). The findings of this study revealed significant associations between MDRS and cognitive decline, suggesting a potential role of tau pathology and neuroinflammation in the link between MDRS and poorer cognitive performance in individuals without dementia. Consequently, the MDRS holds promise as a tool for targeted preventive interventions in individuals at high risk of cognitive impairment.

Language and sensory characteristics are reflected in voice-evoked responses in low birth weight children.

Children born with very low birth weight (VLBW) are at higher risk for cognitive impairment, including language deficits and sensorimotor difficulties. Voice-evoked response (P1m), which has been suggested as a language development biomarker in young children, remains unexplored for its efficacy in VLBW children. Furthermore, the relation between P1m and sensory difficulties in VLBW children remains unclear. 40 children with VLBW were recruited at 5-to-6 years old (26 male, 14 female, mean age of months ± SD, 80.0 ± 4.9). We measured their voice-evoked brain response using child-customized magnetoencephalography (MEG) and examined the relation between P1m and language conceptual inference ability and sensory characteristics. The final sample comprised 36 children (23 boys, 13 girls; ages 61-86 months; gestational ages 24-36 weeks). As a result of multiple regression analysis, voice-evoked P1m in the left hemisphere was correlated significantly with language ability (β = 0.414 P = 0.015) and sensory hypersensitivity (β = 0.471 P = 0.005). Our findings indicate that the relation between P1m and language conceptual inference ability observed in term children in earlier studies is replicated in VLBW children, and suggests P1m intensity as a biomarker of sensory sensitivity characteristics. We investigated brain functions related to language development and sensory problems in very low birth-weight children. In very low birth weight children at early school age, brain responses to human voices are associated with language conceptual inference ability and sensory hypersensitivity. These findings promote a physiological understanding of both language development and sensory characteristics in very low birth weight children.

Associations Between Ultra-Processed Food Consumption and Adverse Brain Health Outcomes.

Ultra-processed foods (UPFs) are linked to cardiometabolic diseases and neurologic outcomes, such as cognitive decline and stroke. However, it is unclear whether food processing confers neurologic risk independent of dietary pattern information. We aimed to (1) investigate associations between UPFs and incident cognitive impairment and stroke and (2) compare these associations with other commonly recommended dietary patterns in the REasons for Geographic and Racial Differences in Stroke study. This prospective, observational cohort study enrolled Black and White adults in the United States from 2003 to 2007. The NOVA system was used to categorize items from a baseline food frequency questionnaire according to the level of processing. Participants with incomplete or implausible self-reported dietary data were excluded. Consumption for each category (grams) was normalized to total grams consumed. Scores quantifying adherence to a Mediterranean, Dietary Approaches to Stop Hypertension (DASH), and Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet were also calculated. Incident cognitive impairment was defined using performance relative to a normative sample on memory and fluency assessments. Incident stroke was identified through adjudicated review of medical records. The cognitive impairment cohort (n = 14,175) included participants without evidence of impairment at baseline who underwent follow-up testing. The stroke cohort (n = 20,243) included participants without a history of stroke. In multivariable Cox proportional hazards models, a 10% increase in relative intake of UPFs was associated with higher risk of cognitive impairment (hazard ratio [HR] = 1.16, 95% CI 1.09-1.24, p = 1.01 × 10-5) and intake of unprocessed or minimally processed foods with lower risk of cognitive impairment (HR = 0.88, 95% CI 0.83-0.94, p = 1.83 × 10-4). Greater intake of UPFs (HR = 1.08, 95% CI 1.02-1.14, p = 1.12 × 10-2) and unprocessed or minimally processed foods (HR = 0.91, 95% CI 0.86-0.95, p = 2.13 × 10-4) were also associated with risk of stroke in multivariable Cox models. The effect of UPFs on stroke risk was greater among Black than White participants (UPF-by-race interaction HR = 1.15, 95% CI 1.03-1.29, p = 1.50 × 10-2). Associations between UPFs and both cognitive impairment and stroke were independent of adherence to the Mediterranean, DASH, and MIND diets. Food processing may be important to brain health in older adults independent of known risk factors and adherence to recommended dietary patterns.

Prediction of Cognitive Impairment Risk among Older Adults: A Machine Learning-Based Comparative Study and Model Development

Introduction: The prevalence of cognitive impairment and dementia in the older population is increasing, and thereby, early detection of cognitive decline is essential for effective intervention. Methods: This study included 2,288 participants with normal cognitive function from the Ma’anshan Healthy Aging Cohort Study. Forty-two potential predictors, including demographic characteristics, chronic diseases, lifestyle factors, anthropometric indices, physical function, and baseline cognitive function, were selected based on clinical importance and previous research. The dataset was partitioned into training, validation, and test sets in a proportion of 60% for training, 20% for validation, and 20% for testing, respectively. Recursive feature elimination was used for feature selection, followed by six machine learning algorithms that were employed for model development. The performance of the models was evaluated using area under the curve (AUC), specificity, sensitivity, and accuracy. Moreover, SHapley Additive exPlanations (SHAP) was conducted to access the interpretability of the final selected model and to gain insights into the impact of features on the prediction outcomes. SHAP force plots were established to vividly show the application of the prediction model at the individual level. Results: The final predictive model based on the Naive Bayes algorithm achieved an AUC of 0.820 (95% CI, 0.773–0.887) on the test set, outperforming other algorithms. The top ten influential features in the model included baseline Mini-Mental State Examination (MMSE), education, self-reported economic status, collective or social activities, Pittsburgh sleep quality index (PSQI), body mass index, systolic blood pressure, diastolic blood pressure, instrumental activities of daily living, and age. The model demonstrated the potential to identify individuals at a higher risk of cognitive impairment within 3 years from older adults. Conclusion: The predictive model developed in this study contributes to the early detection of cognitive impairment in older adults by primary healthcare staff in community settings.

Prediction Model for Cognitive Impairment among Disabled Older Adults: A Development and Validation Study.

Disabled older adults exhibited a higher risk for cognitive impairment. Early identification is crucial in alleviating the disease burden. This study aims to develop and validate a prediction model for identifying cognitive impairment among disabled older adults. A total of 2138, 501, and 746 participants were included in the development set and two external validation sets. Logistic regression, support vector machine, random forest, and XGBoost were introduced to develop the prediction model. A nomogram was further established to demonstrate the prediction model directly and vividly. Logistic regression exhibited better predictive performance on the test set with an area under the curve of 0.875. It maintained a high level of precision (0.808), specification (0.788), sensitivity (0.770), and F1-score (0.788) compared with the machine learning models. We further simplified and established a nomogram based on the logistic regression, comprising five variables: age, daily living activities, instrumental activity of daily living, hearing impairment, and visual impairment. The areas under the curve of the nomogram were 0.871, 0.825, and 0.863 in the internal and two external validation sets, respectively. This nomogram effectively identifies the risk of cognitive impairment in disabled older adults.

Data-driven clustering approach to identify novel clusters of high cognitive impairment risk among Chinese community-dwelling elderly people with normal cognition: A national cohort study.

Cognitive impairment is a highly heterogeneous disorder that necessitates further investigation into the distinct characteristics of populations at varying risk levels of cognitive impairment. Using a large-scale registry cohort of elderly individuals, we applied a data-driven approach to identify novel clusters based on diverse sociodemographic features. A prospective cohort of 6398 elderly people from the Chinese Longitudinal Healthy Longevity Survey, followed between 2008-14, was used to develop and validate the model. Participants were aged ≥60 years, community-dwelling, and the Chinese version of the Mini-Mental State Examination (MMSE) score ≥18 were included. Sixty-nine sociodemographic features were included in the analysis. The total population was divided into two-thirds for the derivation cohort (n = 4265) and one-third for the validation cohort (n = 2133). In the derivation cohort, an unsupervised Gaussian mixture model was applied to categorise participants into distinct clusters. A classifier was developed based on the most important 10 factors and was applied to categorise participants into their corresponding clusters in a validation cohort. The difference in the three-year risk of cognitive impairment was compared across the clusters. We identified four clusters with distinct features in the derivation cohort. Cluster 1 was associated with the worst life independence, longest sleep duration, and the oldest age. Cluster 2 demonstrated the highest loneliness, characterised by non-marital status and living alone. Cluster 3 was characterised by the lowest sense of loneliness and the highest proportions in marital status and family co-residence. Cluster 4 demonstrated heightened engagement in exercise and leisure activity, along with independent decision-making, hygiene, and a diverse diet. In comparison to Cluster 4, Cluster 1 exhibited the highest three-year cognitive impairment risk (adjusted odds ratio (aOR) = 3.31; 95% confidence interval (CI) = 1.81-6.05), followed by Cluster 2 and Cluster 3 after adjustment for baseline MMSE, residence, sex, age, years of education, drinking, smoking, hypertension, diabetes, heart disease and stroke or cardiovascular diseases. A data-driven approach can be instrumental in identifying individuals at high risk of cognitive impairment among cognitively normal elderly populations. Based on various sociodemographic features, these clusters can suggest individualised intervention plans.

Hemodynamic Reactivity to Mental Stress and Cognitive Function in Coronary Artery Disease.

People with coronary artery disease (CAD) are at higher risk of cognitive impairment than those without CAD. Psychological stress is a risk factor for both conditions, and assessing the hemodynamic reactivity to mental stress could explain the link between stress and cognitive function. A total of 779 individuals with stable CAD from two prospective cohort studies were included. All individuals underwent acute mental stress testing, as well as conventional stress testing. Cognitive function was assessed both at baseline and at a 2-year follow-up. The rate-pressure product (RPP) was calculated as the mean systolic blood pressure times the mean heart rate at rest. RPP reactivity was defined as the maximum RPP during standardized mental stress test minus the RPP at rest. After multivariable adjustment, every standard deviation decrease in RPP reactivity with mental stress was associated with slower completion of Trail-A and Trail-B in both cohorts (13% and 11% in cohort 1, and 15% and 16% in cohort 2, respectively; p for all <.01). After a 2-year follow-up period, every standard deviation decrease in RPP reactivity with mental stress was associated with a 8% and 9% slower completion of Trail-A and Trail-B, respectively ( p for all <.01). There was no significant association between RPP reactivity with conventional stress testing and any of the cognitive tests. In the CAD population, a blunted hemodynamic response to mental stress is associated with slower visuomotor processing and worse executive function at baseline and with greater decline in these abilities over time.

White matter hyperintensities mediate the association between frailty and cognitive impairment in moyamoya disease

ObjectivesThe relationship between cognitive function and frailty in moyamoya disease (MMD) remains unclear, and the underlying mechanism is poorly understood. This study aims to investigate whether white matter hyperintensities (WMHs) mediate the association between frailty and cognitive impairment in MMD. MethodsPatients with MMD were consecutively enrolled in our study from January 2021 to May 2023. Pre-admission frailty and cognition were assessed using the Clinical Frailty Scale (CFS) and cognitive tests, respectively. Regional deep WMH (DWMH) and periventricular WMH (PWMH) volumes were calculated using the Brain Anatomical Analysis using Diffeomorphic deformation toolbox based on SPM 12 software. Multivariate logistic regression analysis was conducted to evaluate the association between frailty and cognitive function in MMD. Mediation analysis was performed to assess whether WMHs explained the association between frailty and cognition. ResultsA total of 85 patients with MMD were enrolled in this study. On the basis of the CFS scores, 24 patients were classified as frail, 38 as pre-frail, and 23 as robust. Significant differences were observed in learning, memory, processing speed, executive functions, and semantic memory among the three groups (p < 0.001). Frailty was independently associated with memory and executive functions (p < 0.05); even after controlling for WMH. Mediation analysis indicated that the associations of frailty with memory and executive functions were partially mediated by WMH, DWMH, and PWMH (p < 0.05). ConclusionFrailty is significantly correlated with a higher risk of cognitive impairment in MMD, even after adjusting for other covariates. WMHs partially mediate the association between frailty and cognitive impairment.