Geda, Yonas E

Low self-esteem (LoST) is a latent yet critical psychosocial risk factor that predisposes individuals to depressive disorders. Although structured tools exist to assess self-esteem, their limited clinical adoption suggests that relevant indicators of LoST remain buried within unstructured clinical narratives. The scarcity of annotated clinical notes impedes the development of natural language processing (NLP) models for its detection. Manual chart reviews are labor-intensive and large language model (LLM)-driven (weak) labeling raises privacy concerns. Past studies demonstrate that NLP models trained on LLM-generated synthetic clinical notes achieve performance comparable to, and sometimes better than those trained on real notes. This highlights synthetic data’s utility for augmenting scarce clinical corpora while reducing privacy concerns. Prior efforts have leveraged social media data, such as Reddit, to identify linguistic markers of low self-esteem; however, the linguistic and contextual divergence between social media and clinical text limits the generalizability of these models. To address this gap, we present a novel framework that generates context-enhanced synthetic clinical notes from social media narratives and evaluates the utility of small language models for identifying expressions of low self-esteem. Our approach includes a mixed-method evaluation framework: (i) structure analysis, (ii) readability analysis, (iii) linguistic diversity, and (iv) contextual fidelity of LoST cues in source Reddit posts and synthetic notes. This work offers a scalable, privacy-preserving solution for synthetic data generation for early detection of psychosocial risks such as LoST and demonstrates a pathway for translating mental health signals in clinical notes into clinically actionable insights, thereby identifying patients at risk.

ABSTRACT Background Previous studies on the relationship between prediabetes, HbA1c and cognitive trajectories show mixed results. Therefore, we investigated the association of prediabetes, diabetes and HbA1c with change in global and domain‐specific cognitive scores in cognitively unimpaired (CU) adults, as well as the potential effect modification by sex or age. Methods This longitudinal study included 4236 CU persons aged ≥ 50 years from the population‐based Mayo Clinic Study of Aging. We ran linear mixed‐effect models with baseline prediabetes, diabetes and HbA1c predicting longitudinal global and domain‐specific (i.e., memory, language, attention/executive function, and visuospatial skills) cognitive z‐scores and raw scores. Models were adjusted for age, sex, education, medical comorbidity, repeated cognitive testing, and ApoEɛ4. We additionally ran models with sex and age interactions. Results Compared to normoglycemic individuals, individuals with prediabetes and diabetes showed poorer performance on cognitive tests, i.e., TMT‐B, category fluency, and block design subtest over time. Additionally, the presence of prediabetes, diabetes, longer diabetes duration and higher HbA1c were associated with faster global and domain‐specific cognitive decline over a median follow‐up of 6.4 years (range 1–19). Three‐way interactions showed that the effect of diabetes on decline in global cognition, attention and visuospatial domains was more pronounced in women than in men. Age did not modify the effect of diabetes or higher HbA1c on cognition. Conclusions The results suggest that prediabetes and diabetes significantly shorten the timeframe before a potentially clinically noticeable change in global and domain‐specific cognition is reached. Thus, lifestyle modification to reverse prediabetes and prevent diabetes could potentially reduce the rate of cognitive decline in aging populations. The findings differed by sex (i.e., the effect of diabetes on decline in global cognition, attention and visuospatial domains was more pronounced in women than men).

Background: The potential long-term effects of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) infection on the brain structure have not yet been fully elucidated. Even though existing studies have reported structural changes in the post-COVID-19 period, the results remain highly inconsistent and controversial. As such, identifying an imaging biomarker for post-COVID brains is still under investigation. This review aims to comprehensively summarize the structural MRI (sMRI) studies that focus on volumetric brain changes at least two weeks following COVID-19 infection. Methods: A systematic literature search was conducted on PubMed, SCOPUS, Web of Science, EMBASE, and Google Scholar up to 9 September 2025. Studies that utilized sMRI to assess volumetric brain changes post-COVID at greater than two weeks following infection were included. Exclusion criteria encompassed research involving pediatric or adolescent populations and imaging modalities other than sMRI. Preprints, reviews, case reports, case series and post-mortem studies were also excluded. Results: Forty-one studies satisfied the inclusion criteria and consisted of 2895 patients and 1729 healthy controls. Despite the wide variability in image acquisition protocols, data processing methods, and comorbidities between studies, multiple studies reported statistically significant volumetric reductions in the hippocampus, amygdala, thalamus, basal ganglia, nucleus accumbens and the cerebellum months to years after infection, especially in older hospitalized patients with severe COVID-19. Conclusions: The emerging literature reports long-term volume changes across various brain regions in individuals previously infected with COVID-19; however, the evidence is inconsistent. Specific imaging biomarkers following exposure to SARS-CoV-2 infection and the underlying mechanisms of these changes are yet to be identified. Future studies with harmonized imaging protocols, longitudinal designs, and integrated biomarker and clinical data are needed to define robust biomarkers and elucidate the pathophysiology of these findings.

Background The literature on dyslipidemia and cognitive trajectories among cognitively unimpaired (CU) persons remains inconclusive. Objective To investigate the association between baseline dyslipidemia and change in global and domain specific (i.e., memory, language, attention/executive function, and visuospatial skills) cognition in a population-based setting and whether the association differs by sex, age, or APOE ɛ4 carrier status. Methods We conducted a longitudinal study derived from the Mayo Clinic Study of Aging, involving 4236 CU persons aged ≥ 50 years. We ran linear mixed-effect models to examine baseline dyslipidemia in predicting longitudinal cognitive global and domain-specific z-scores adjusted for age, sex, education, medical comorbidity, repeated cognitive testing, and APOE ɛ4. We examined interactions among dyslipidemia, years since baseline, and separately by sex, age, and APOE ɛ4 carrier status. Results Over a median follow-up period of 6.4 years, CU individuals with dyslipidemia showed faster decline in z-scores of all domains, i.e., memory, language, attention/executive function, visuospatial and global cognition relative to CU individuals without dyslipidemia. Three-way interactions showed that dyslipidemia's effect on decline in z-scores of global cognition, attention and visuospatial skills was less pronounced in males than females. Higher age increased dyslipidemia's effect on decline in z-scores of attention but not global cognition or any other domain. APOE ɛ4 carrier status did not modify the effect of dyslipidemia on cognitive z-scores. Conclusions Dyslipidemia was associated with faster global and domain-specific cognitive decline over time in older community-dwelling individuals who were cognitively unimpaired at baseline. The effect of dyslipidemia on cognitive trajectories may be sex-influenced.

Background The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults. Objective To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults. Methods The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.e., age, sex, education), NPS (i.e., Neuropsychiatric Inventory Questionnaire; Beck Depression and Anxiety Inventories), at least one AD biomarker (i.e., plasma-, neuroimaging- and/or cerebrospinal fluid [CSF]-derived), and at least 2 repeated neuropsychological assessments. We trained and tested ML models using a stepwise feature addition approach to predict decline versus non-decline in global and domain-specific (i.e., memory, language, visuospatial, and attention/executive function) cognitive scores. Results ML models had better performance when NPS were included along with a) neuroimaging biomarkers for predicting decline in global cognition, as well as language and visuospatial skills; b) plasma-derived biomarkers for predicting decline in visuospatial skills; and c) CSF-derived biomarkers for predicting decline in attention/executive function, language, and memory. Conclusions NPS, added to ML models including demographic and AD biomarker data, improves prediction of downward trajectories in global and domain-specific cognitive scores among community-dwelling older adults, albeit effect sizes are small. These preliminary findings need to be confirmed by future cohort studies.

Alzheimer’s disease (AD) and Alzheimer’s Related Dementias (ADRD) are projected to affect 50 million people globally in the coming decades. Clinical research suggests that Mild Cognitive Impairment (MCI), a precursor to dementia, offers a critical window of opportunity for lifestyle interventions to delay or prevent the progression of AD/ADRD. Previous research indicates that lifestyle changes, including increased physical exercise, reduced caloric intake, and mentally stimulating activities, can reduce the risk of MCI. Early detection of MCI is challenging due to subtle and often unnoticed cognitive decline and is traditionally monitored through infrequent clinical tests. In this research, the Smart Driving System, a novel, unobtrusive, and economical technology to detect early stages of neurodegenerative diseases, is presented. The system comprises a multi-modal biosensing array (MMS) and AI algorithms, including driving performance and driver’s biometrics, offering insights into a driver’s cognitive function. This publication is the first work reported towards the ultimate goal of developing the Smart Driving Device and App, integrating it into vehicles, and validating its effectiveness in detecting MCI through comprehensive pilot studies.

Abstract Former American football players are at risk for developing traumatic encephalopathy syndrome (TES), the clinical disorder associated with neuropathologically diagnosed chronic traumatic encephalopathy (CTE). The objective of this study was to determine whether hyposmia is present in traumatic encephalopathy syndrome. The study included 119 former professional American football players, 60 former college football players, and 58 same-age asymptomatic unexposed men from the DIAGNOSE CTE Research Project. All subjects included in the analysis had completed the Brief Smell Identification Test (B-SIT). Traumatic encephalopathy syndrome and the level of CTE certainty were diagnosed using the 2021 NINDS consensus diagnostic criteria. TES is categorized antemortem by provisional levels of increasing CTE certainty: Suggestive, Possible, and Probable. Former players who had traumatic encephalopathy syndrome and Probable CTE had lower B-SIT scores than those with TES and Suggestive CTE. Hyposmia was more likely in the former players with TES who were either CTE Possible or Probable than in those who did not have TES or had TES but were less likely to have CTE, or CTE Suggestive. There was no difference in B-SIT scores between all former players versus unexposed men nor overall between the football players with and without TES. We conclude that lower B-SIT scores may be a clinical biomarker for underlying CTE in former American football players.