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1. Predicting 5‐year dementia conversion in veterans with mild cognitive impairment

   https://doi.org/10.1002/dad2.12572  

   Irwin, Chase; Tjandra, Donna; Hu, Chengcheng; Aggarwal, Vinod; Lienau, Amanda; Giordani, Bruno; Wiens, Jenna; Migrino, Raymond Q (March 2024, Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring)

   INTRODUCTION Identifying mild cognitive impairment (MCI) patients at risk for dementia could facilitate early interventions. Using electronic health records (EHRs), we developed a model to predict MCI to all-cause dementia (ACD) conversion at 5 years. METHODS Cox proportional hazards model was used to identify predictors of ACD conversion from EHR data in veterans with MCI. Model performance (area under the receiver operating characteristic curve [AUC] and Brier score) was evaluated on a held-out data subset. RESULTS Of 59,782 MCI patients, 15,420 (25.8%) converted to ACD. The model had good discriminative performance (AUC 0.73 [95% confidence interval (CI) 0.72–0.74]), and calibration (Brier score 0.18 [95% CI 0.17–0.18]). Age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors, while body mass index, alcohol abuse, and sleep apnea were protective factors. DISCUSSION EHR-based prediction model had good performance in identifying 5-year MCI to ACD conversion and has potential to assist triaging of at-risk patients. Highlights * Of 59,782 veterans with mild cognitive impairment (MCI), 15,420 (25.8%) converted to all-cause dementia within 5 years. * Electronic health record prediction models demonstrated good performance (area under the receiver operating characteristic curve 0.73; Brier 0.18). * Age and vascular-related morbidities were predictors of dementia conversion. * Synthetic data was comparable to real data in modeling MCI to dementia conversion. Key Points * An electronic health record–based model using demographic and co-morbidity data had good performance in identifying veterans who convert from mild cognitive impairment (MCI) to all-cause dementia (ACD) within 5 years. * Increased age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors for 5-year conversion from MCI to ACD. * High body mass index, alcohol abuse, and sleep apnea were protective factors for 5-year conversion from MCI to ACD. * Models using synthetic data, analogs of real patient data that retain the distribution, density, and covariance between variables of real patient data but are not attributable to any specific patient, performed just as well as models using real patient data. This could have significant implications in facilitating widely distributed computing of health-care data with minimized patient privacy concern that could accelerate scientific discoveries. 

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2. Estimated Effects of Comorbidities on Risk of All-cause Dementia in Patients with Mild Cognitive Impairment

   https://doi.org/10.1177/30495334251347053  

   Tjandra, Donna; Irwin, Chase; Migrino, Raymond Q; Giordani, Bruno; Wiens, Jenna (June 2025, Sage Open Aging)

   Introduction:Estimating the effects of comorbidities on risk of all-cause dementia (ACD) could potentially better inform prevention strategies and identify novel risk factors compared to more common post-hoc analyses from predictive modeling. Methods:In a retrospective cohort study of patients with mild cognitive impairment (MCI) from US Veterans Affairs Medical Centers between 2009 and 2021, we used machine learning techniques from the treatment effect estimation literature to estimate individualized effects of 25 comorbidities (e.g., hypertension) on ACD risk within 10 years of MCI diagnosis. Age and healthcare utilization were adjusted for using exact matching. Results:After matching, of 19,797 MCI patients, 6,767 (34.18%) experienced ACD onset. Dyslipidemia (percentage point increase of ACD risk range across different treatment effect estimation techniques = 0.009–0.044), hypertension (range = 0.007–0.043), and diabetes (range = 0.007–0.191) consistently had non-zero average effects. Discussion:Our findings support known associations between dyslipidemia, hypertension, and diabetes that increase the risk of ACD in MCI patients, demonstrating the potential for these approaches to identify novel risk factors. 

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3. Plasma Protein Risk Scores for Mild Cognitive Impairment and Alzheimer Disease in the Framingham Heart Study

   Rehman, H; Ang, TFA; Tao, Q; Au, R; Farrer, LA; Qiu, W; Zhang, X (April 2025, Alzheimers dementia)

   INTRODUCTION: It is unclear whether aggregated plasma protein risk scores (PPRS) could be useful to predict the risks of mild cognitive impairment (MCI) and Alzheimer’s disease (AD). METHODS: The Cox proportional hazard model with the LASSO penalty was used to build the PPRS for MCI and AD in 1,515 Framingham Heart Study Generation2 with 1,128 proteins measured in plasma at exam 5 [cognitive normal (CN)=1,258, MCI=129, AD=128]. RESULTS: MCI PPRS had a hazard ratio (HR) of 6.97[5.34,9.12], with a discriminating power (C-index=82.52%). AD PPRS had an HR of 5.74[4.67,7.05] (C-index=88.15%). Both PPRSs were also significantly associated with cognitive changes, brain-atrophy, and plasma AD biomarkers. Proteins in the MCI and AD PPRSs were enriched in several pathways related to leukocyte, chemotaxis, immunity, inflammation, and cellular migration. DISCUSSION: This study suggests that PPRS serve well to predict the risk of developing MCI and AD as well as cognitive changes and AD related pathogenesis in the brain. 

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4. Curve Walking Reveals More Gait Impairments in Older Adults with Mild Cognitive Impairment than Straight Walking: A Kinect Camera-Based Study

   https://doi.org/10.3233/ADR-230149  

   Seifallahi, Mahmoud; Galvin, James E; Ghoraani, Behnaz (January 2024, Journal of Alzheimer's Disease Reports)

   Background:Mild cognitive impairment (MCI) can be an early sign of Alzheimer’s disease and other types of dementia detectable through gait analysis. Curve walking, which demands greater cognitive and motor skills, may be more sensitive in MCI detection than straight walking. However, few studies have compared gait performance in older adults with and without MCI in these conditions. Objective:To compare the capability of curve and straight walking tests for the detection of MCI among older adults. Methods:We employed a Kinect v.2 camera to record the gait of 55 older adults (30 healthy controls, 25 with MCI) during single-task straight and curve walking tests. We examined 50 gait markers and conducted statistical analyses to compare groups and conditions. The trail was approved with protocol No. IR.SEMUMS.REC.1398.237 by the ethics committee of Semnan University of Medical Sciences in Iran. Results:Older adults with MCI exhibited more compromised gait performance, particularly during curve walking. Curve walking outperformed straight walking in MCI detection, with several gait markers showing significant differences between healthy controls and MCI patients. These markers encompass average velocity, cadence, temporal markers (e.g., gait cycle subphase durations), spatial markers (e.g., foot position changes during gait subphases), and spatiotemporal markers (e.g., step and stride velocities). Conclusions:Our study suggests curve walking as a more informative and challenging test for MCI detection among older adults, facilitating early diagnosis using non-invasive, cost-effective tools like the Kinect v.2 camera, complementing cognitive assessments in early diagnosis, and tracking MCI progression to dementia. 

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5. Initiation of Continuous Glucose Monitoring Is Linked to Improved Glycemic Control and Fewer Clinical Events in Type 1 and Type 2 Diabetes in the Veterans Health Administration

   https://doi.org/10.2337/dc22-2189  

   Reaven, Peter D.; Newell, Michelle; Rivas, Salvador; Zhou, Xinkai; Norman, Gregory J.; Zhou, Jin J. (February 2023, Diabetes Care)

   OBJECTIVETo determine the benefit of starting continuous glucose monitoring (CGM) in adult-onset type 1 diabetes (T1D) and type 2 diabetes (T2D) with regard to longer-term glucose control and serious clinical events. RESEARCH DESIGN AND METHODSA retrospective observational cohort study within the Veterans Affairs Health Care System was used to compare glucose control and hypoglycemia- or hyperglycemia-related admission to an emergency room or hospital and all-cause hospitalization between propensity score overlap weighted initiators of CGM and nonusers over 12 months. RESULTSCGM users receiving insulin (n = 5,015 with T1D and n = 15,706 with T2D) and similar numbers of nonusers were identified from 1 January 2015 to 31 December 2020. Declines in HbA1c were significantly greater in CGM users with T1D (−0.26%; 95% CI −0.33, −0.19%) and T2D (−0.35%; 95% CI −0.40, −0.31%) than in nonusers at 12 months. Percentages of patients achieving HbA1c <8 and <9% after 12 months were greater in CGM users. In T1D, CGM initiation was associated with significantly reduced risk of hypoglycemia (hazard ratio [HR] 0.69; 95% CI 0.48, 0.98) and all-cause hospitalization (HR 0.75; 95% CI 0.63, 0.90). In patients with T2D, there was a reduction in risk of hyperglycemia in CGM users (HR 0.87; 95% CI 0.77, 0.99) and all-cause hospitalization (HR 0.89; 95% CI 0.83, 0.97). Several subgroups (based on baseline age, HbA1c, hypoglycemic risk, or follow-up CGM use) had even greater responses. CONCLUSIONSIn a large national cohort, initiation of CGM was associated with sustained improvement in HbA1c in patients with later-onset T1D and patients with T2D using insulin. This was accompanied by a clear pattern of reduced risk of admission to an emergency room or hospital for hypoglycemia or hyperglycemia and of all-cause hospitalization. 

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