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1. Chronic Non-cancer Pain and Associated Risks of Incident Mild Cognitive Impairment and Alzheimer’s Disease and Related Dementias in Middle-Aged and Older Adults

   https://doi.org/10.1177/07334648241237340  

   Hu, Yi-Han; Seo, Dong-Chul; Huber, Lesa; Shih, Patrick C; Lin, Hsien-Chang (October 2024, Journal of Applied Gerontology)

   The goal of this study is to investigate the association between chronic non-cancer pain (CNCP) and mild cognitive impairment (MCI)/Alzheimer’s disease and related dementias (ADRDs) development among adults aged ≥50 using administrative claims data from a national commercial health insurance company during 2007–2017. To reduce selection bias, propensity-score matching was applied to select comparable CNCP and non-CNCP patients. Time-dependent Cox proportional-hazards regressions were conducted to estimate the hazard ratios (HRs) of incident MCI/ADRDs. Of 170,900 patients with/without CNCP, 0.61% developed MCI and 2.33% had been diagnosed with ADRDs during the follow-up period. Controlling for potential confounders, CNCP patients had a 123% increase in MCI risk (HR = 2.23; 95% CI = 1.92–2.58) and a 44% increase in ADRDs risk (HR = 1.44; 95% CI = 1.34–1.54) relative to non-CNCP patients. CNCP is a risk factor for MCI/ADRDs. Promoting awareness and improving early CNCP diagnosis in middle-aged and older adults should be incorporated into cognitive impairment and dementia prevention. 

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2. Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

   https://doi.org/10.1073/pnas.2214634120  

   Yin, Chenzhong; Imms, Phoebe; Cheng, Mingxi; Amgalan, Anar; Chowdhury, Nahian F.; Massett, Roy J.; Chaudhari, Nikhil N.; Chen, Xinghe; Thompson, Paul M.; Bogdan, Paul; et al (January 2023, Proceedings of the National Academy of Sciences)

   The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. 

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3. Comprehensive Anatomical Staging Predicts Clinical Progression in Mild Cognitive Impairment: A Data-Driven Approach

   https://doi.org/10.3390/ijms26125514  

   Tandon, Raghav; Mei, Yajun; Lah, James J; Mitchell, Cassie S (June 2025, International Journal of Molecular Sciences)

   Alzheimer’s disease (AD) presents significant challenges in clinical practice due to its heterogeneous manifestation and variable progression rates. This work develops a comprehensive anatomical staging framework to predict progression from mild cognitive impairment (MCI) to AD. Using the ADNI database, the scalable Subtype and Stage Inference (s-SuStaIn) model was applied to 118 neuroanatomical features from cognitively normal (n = 504) and AD (n = 346) participants. The framework was validated on 808 MCI participants through associations with clinical progression, CSF and FDG-PET biomarkers, and neuropsychiatric measures, while adjusting for common confounders (age, gender, education, and APOE ε4 alleles). The framework demonstrated superior prognostic accuracy compared to traditional risk assessment (C-index = 0.73 vs. 0.62). Four distinct disease subtypes showed differential progression rates, biomarker profiles (FDG-PET and CSF Aβ42), and cognitive trajectories: Subtype 1, subcortical-first pattern; Subtype 2, executive–cortical pattern; Subtype 3, disconnection pattern; and Subtype 4, frontal–executive pattern. Stage-dependent changes revealed systematic deterioration across diverse cognitive domains, particularly in learning acquisition, visuospatial processing, and functional abilities. This data-driven approach captures clinically meaningful disease heterogeneity and improves prognostication in MCI, potentially enabling more personalized therapeutic strategies and clinical trial design. 

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4. 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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5. Association of amyloid and cardiovascular risk with cognition: Findings from KBASE

   https://doi.org/10.1002/alz.14290  

   Chaudhuri, Soumilee; Dempsey, Desarae A; Huang, Yen‐Ning; Park, Tamina; Cao, Sha; Chumin, Evgeny J; Craft, Hannah; Crane, Paul K; Mukherjee, Shubhabrata; Choi, Seo‐Eun; et al (December 2024, Alzheimer's & Dementia)

   Abstract BACKGROUNDLimited research has explored the effect of cardiovascular risk and amyloid interplay on cognitive decline in East Asians. METHODSVascular burden was quantified using Framingham's General Cardiovascular Risk Score (FRS) in 526 Korean Brain Aging Study (KBASE) participants. Cognitive differences in groups stratified by FRS and amyloid positivity were assessed at baseline and longitudinally. RESULTSBaseline analyses revealed that amyloid‐negative (Aβ–) cognitively normal (CN) individuals with high FRS had lower cognition compared to Aβ– CN individuals with low FRS (p < 0.0001). Longitudinally, amyloid pathology predominantly drove cognitive decline, while FRS alone had negligible effects on cognition in CN and mild cognitive impairment (MCI) groups. CONCLUSIONOur findings indicate that managing vascular risk may be crucial in preserving cognition in Aβ– individuals early on and before the clinical manifestation of dementia. Within the CN and MCI groups, irrespective of FRS status, amyloid‐positive individuals had worse cognitive performance than Aβ– individuals. HighlightsVascular risk significantly affects cognition in amyloid‐negative older Koreans.Amyloid‐negative CN older adults with high vascular risk had lower baseline cognition.Amyloid pathology drives cognitive decline in CN and MCI, regardless of vascular risk.The study underscores the impact of vascular health on the AD disease spectrum. 

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