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Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN – a multi-view, weakly-supervised deep clustering method – which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer’s disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes. Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.

Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. Using harmonized MRI data from 37,096 participants (45–85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45–64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status. Integrating personalized measurements of CVM-specific brain signatures into phenotypic frameworks could guide early risk detection and stratification in clinical studies. Cardiovascular and metabolic risk factors (CVM) impact brain structure and increase dementia risk. Here, the authors developed and validated machine learning models to measure the neuroanatomical changes in people with cardiovascular and metabolic diseases that are cognitively unimpaired.

Machine learning can unravel heterogeneous patterns of brain aging and neurodegeneration, but existing methods offer limited insights into disease progression due to reliance on cross-sectional data. We introduce Coupled Cross-sectional and Longitudinal Non-negative Matrix Factorization (CCL-NMF) to capture dominant brain aging patterns by simultaneously leveraging cross-sectional and longitudinal neuroimaging data. CCL-NMF allows individuals to co-express multiple patterns, capturing mixed neuropathologic processes. Applied to neuroimaging data from 48,949 individuals from the harmonized iSTAGING study, CCL-NMF identifies seven distinct, reproducible, and biologically relevant neuroanatomical patterns. Subject-specific loading coefficients quantifying the individual expression of these patterns show distinct associations with cognition, genetic, and lifestyle factors. To support broader application, a regression-based tool was developed to estimate loadings in external cohorts without rerunning the full framework. By enabling individualized estimation of distinct brain aging patterns, these findings may improve risk assessment and therapeutic evaluation in neurodegenerative diseases. Although demonstrated using structural MRI, this framework is generalizable to other imaging modalities and biomarker types.

Background Hypertension (HTN) is an established risk factor for neurodegeneration and dementia, supported by epidemiological and neuroimaging studies, although there is large variation in individual outcomes. We developed a machine learning (ML)‐based model, termed SPARE‐HTN, to quantify the spatial pattern of HTN‐related neurodegeneration observable in individual structural magnetic resonance images (sMRI). SPARE‐HTN demonstrates superior sensitivity compared to the most widely‐used measure of HTN‐related brain changes, correlates with cognitive performance, and detects early changes in mid‐life years. This study investigated the predictive capacity of SPARE‐HTN for incident HTN and its mediating role in the relationship between HTN and cognition. Methods SPARE‐HTN, derived from N = 37,098 cognitively unimpaired individuals from diverse cohorts, was evaluated in N = 968 (59% female, mean age 63.0 ± 11 years) individuals with longitudinal clinical data from six studies (Table 1). Baseline SPARE‐HTN values were compared across participants categorized by longitudinal HTN status: persistently normotensive, persistently hypertensive, or incident HTN. The risk of incident HTN was evaluated among baseline normotensive participants using Cox regression model across the baseline SPARE‐HTN quartiles, adjusted for age and sex. The average causal mediated effect (ACME) of SPARE‐HTN and total white matter hyperintensity (WMH) volume on the relationship between hypertension and cognitive test scores were assessed using mediation models with 5000 simulated permutations. Results Table 1 presents the participant characteristics at baseline, stratified by longitudinal HTN status. SPARE‐HTN was significantly elevated (Figure 1A) in participants who were normotensive at baseline but developed HTN within 3‐7 years (+0.44, p = 0.01), but not in those who developed HTN in 8‐12 years (p = 0.65). Normotensive participants with elevated baseline SPARE‐HTN scores exhibited significantly higher Cox proportional hazard ratios for HTN incidence (Figure 1B). Mediation analysis demonstrated that SPARE‐HTN mediated up to 26% of the effect of HTN on cognitive measures, whilst the conventional WMH volumes showed little mediation effect (Figure 2). Conclusion Our ML‐based sMRI marker predicted incident HTN prior to formal clinical diagnosis, suggesting the presence of subclinical cerebrovascular changes possibly associated with blood pressure variations. These markers, particularly relevant in midlife, offer potential for informing dementia prevention trials by enabling individualized risk stratification and potentially more sensitive measurement of therapeutic efficacy.

Background Smoking is a well‐established risk factor for cardiovascular disease, and its association with neurodegeneration and cognitive decline is an area of ongoing research. Critically, the interplay between smoking, Alzheimer's disease (AD) pathology, and cognitive impairment remains incompletely understood. This study investigated the relationship between smoking, AD pathology as indexed by amyloid‐beta (Aβ) deposition, and cognitive performance using SPARE‐SM, a novel machine learning‐based marker that quantifies smoking‐related spatial patterns of abnormalities on individual structural magnetic resonance images (sMRI). Methods SPARE‐Smoking, derived from N = 37,098 cognitively unimpaired individuals from diverse cohorts, was evaluated in N = 222 individuals who had amyloid (Aβ) status available within +/‐ 1 year of the MRI scan in a subset of the training cohort. Amyloid deposition was determined using study‐specific cut‐offs for CSF and PET SUVR measures, categorizing participants as Aβ‐/Aβ+. Multivariable regression models were used to assess interactions between Aβ status, smoking history, and age on SPARE‐SM scores. Multivariable linear regression models, adjusted for age, sex, and years of education, examined associations between SPARE‐SM and cognitive performance. Results While the proportion of smokers was similar between Aβ+ and Aβ‐ participants (Table 1), SPARE‐SM showed a nuanced relationship with both Aβ and smoking status (Figure 1A). Specifically, SPARE‐SM was higher than SM+Aβ‐ individuals in SM+ Aβ+ individuals (p <0.05) but lower in SM‐ Aβ+ individuals (p <0.05). Importantly, higher SPARE‐SM was associated with worse cognitive performance, whereas simply classifying individuals as smokers or non‐smokers showed no associations with cognitive outcomes (Figure 1B). Conclusion These findings suggest a complex relationship between smoking, amyloid pathology, and cognition. The observation that SPARE‐SM differed by Aβ in smoking individuals highlights their potential synergistic effects on neurodegeneration. SPARE‐SM demonstrated associations with cognitive decline, even when clinical smoking status did not, emphasizing its potential for early risk identification. Further research is needed to disentangle the mechanisms linking smoking, brain changes, amyloid, and dementia.

Smoking is a well-established risk factor for cardiovascular disease, and its association with neurodegeneration and cognitive decline is an area of ongoing research. Critically, the interplay between smoking, Alzheimer's disease (AD) pathology, and cognitive impairment remains incompletely understood. This study investigated the relationship between smoking, AD pathology as indexed by amyloid-beta (Aβ) deposition, and cognitive performance using SPARE-SM, a novel machine learning-based marker that quantifies smoking-related spatial patterns of abnormalities on individual structural magnetic resonance images (sMRI). SPARE-Smoking, derived from N = 37,098 cognitively unimpaired individuals from diverse cohorts, was evaluated in N = 222 individuals who had amyloid (Aβ) status available within +/- 1 year of the MRI scan in a subset of the training cohort. Amyloid deposition was determined using study-specific cut-offs for CSF and PET SUVR measures, categorizing participants as Aβ-/Aβ+. Multivariable regression models were used to assess interactions between Aβ status, smoking history, and age on SPARE-SM scores. Multivariable linear regression models, adjusted for age, sex, and years of education, examined associations between SPARE-SM and cognitive performance. While the proportion of smokers was similar between Aβ+ and Aβ- participants (Table 1), SPARE-SM showed a nuanced relationship with both Aβ and smoking status (Figure 1A). Specifically, SPARE-SM was higher than SM+Aβ- individuals in SM+ Aβ+ individuals (p <0.05) but lower in SM- Aβ+ individuals (p <0.05). Importantly, higher SPARE-SM was associated with worse cognitive performance, whereas simply classifying individuals as smokers or non-smokers showed no associations with cognitive outcomes (Figure 1B). These findings suggest a complex relationship between smoking, amyloid pathology, and cognition. The observation that SPARE-SM differed by Aβ in smoking individuals highlights their potential synergistic effects on neurodegeneration. SPARE-SM demonstrated associations with cognitive decline, even when clinical smoking status did not, emphasizing its potential for early risk identification. Further research is needed to disentangle the mechanisms linking smoking, brain changes, amyloid, and dementia.

Background Hypertension (HTN) is an established risk factor for neurodegeneration and dementia, supported by epidemiological and neuroimaging studies, although there is large variation in individual outcomes. We developed a machine learning (ML)‐based model, termed SPARE‐HTN, to quantify the spatial pattern of HTN‐related neurodegeneration observable in individual structural magnetic resonance images (sMRI). SPARE‐HTN demonstrates superior sensitivity compared to the most widely‐used measure of HTN‐related brain changes, correlates with cognitive performance, and detects early changes in mid‐life years. This study investigated the predictive capacity of SPARE‐HTN for incident HTN and its mediating role in the relationship between HTN and cognition. Methods SPARE‐HTN, derived from N = 37,098 cognitively unimpaired individuals from diverse cohorts, was evaluated in N = 968 (59% female, mean age 63.0 ± 11 years) individuals with longitudinal clinical data from six studies (Table 1). Baseline SPARE‐HTN values were compared across participants categorized by longitudinal HTN status: persistently normotensive, persistently hypertensive, or incident HTN. The risk of incident HTN was evaluated among baseline normotensive participants using Cox regression model across the baseline SPARE‐HTN quartiles, adjusted for age and sex. The average causal mediated effect (ACME) of SPARE‐HTN and total white matter hyperintensity (WMH) volume on the relationship between hypertension and cognitive test scores were assessed using mediation models with 5000 simulated permutations. Results Table 1 presents the participant characteristics at baseline, stratified by longitudinal HTN status. SPARE‐HTN was significantly elevated (Figure 1A) in participants who were normotensive at baseline but developed HTN within 3‐7 years (+0.44, p = 0.01), but not in those who developed HTN in 8‐12 years (p = 0.65). Normotensive participants with elevated baseline SPARE‐HTN scores exhibited significantly higher Cox proportional hazard ratios for HTN incidence (Figure 1B). Mediation analysis demonstrated that SPARE‐HTN mediated up to 26% of the effect of HTN on cognitive measures, whilst the conventional WMH volumes showed little mediation effect (Figure 2). Conclusion Our ML‐based sMRI marker predicted incident HTN prior to formal clinical diagnosis, suggesting the presence of subclinical cerebrovascular changes possibly associated with blood pressure variations. These markers, particularly relevant in midlife, offer potential for informing dementia prevention trials by enabling individualized risk stratification and potentially more sensitive measurement of therapeutic efficacy.

Background Smoking is a well‐established risk factor for cardiovascular disease, and its association with neurodegeneration and cognitive decline is an area of ongoing research. Critically, the interplay between smoking, Alzheimer's disease (AD) pathology, and cognitive impairment remains incompletely understood. This study investigated the relationship between smoking, AD pathology as indexed by amyloid‐beta (Aβ) deposition, and cognitive performance using SPARE‐SM, a novel machine learning‐based marker that quantifies smoking‐related spatial patterns of abnormalities on individual structural magnetic resonance images (sMRI). Methods SPARE‐Smoking, derived from N = 37,098 cognitively unimpaired individuals from diverse cohorts, was evaluated in N = 222 individuals who had amyloid (Aβ) status available within +/‐ 1 year of the MRI scan in a subset of the training cohort. Amyloid deposition was determined using study‐specific cut‐offs for CSF and PET SUVR measures, categorizing participants as Aβ‐/Aβ+. Multivariable regression models were used to assess interactions between Aβ status, smoking history, and age on SPARE‐SM scores. Multivariable linear regression models, adjusted for age, sex, and years of education, examined associations between SPARE‐SM and cognitive performance. Results While the proportion of smokers was similar between Aβ+ and Aβ‐ participants (Table 1), SPARE‐SM showed a nuanced relationship with both Aβ and smoking status (Figure 1A). Specifically, SPARE‐SM was higher than SM+Aβ‐ individuals in SM+ Aβ+ individuals (p <0.05) but lower in SM‐ Aβ+ individuals (p <0.05). Importantly, higher SPARE‐SM was associated with worse cognitive performance, whereas simply classifying individuals as smokers or non‐smokers showed no associations with cognitive outcomes (Figure 1B). Conclusion These findings suggest a complex relationship between smoking, amyloid pathology, and cognition. The observation that SPARE‐SM differed by Aβ in smoking individuals highlights their potential synergistic effects on neurodegeneration. SPARE‐SM demonstrated associations with cognitive decline, even when clinical smoking status did not, emphasizing its potential for early risk identification. Further research is needed to disentangle the mechanisms linking smoking, brain changes, amyloid, and dementia.

Hypertension (HTN) is an established risk factor for neurodegeneration and dementia, supported by epidemiological and neuroimaging studies, although there is large variation in individual outcomes. We developed a machine learning (ML)-based model, termed SPARE-HTN, to quantify the spatial pattern of HTN-related neurodegeneration observable in individual structural magnetic resonance images (sMRI). SPARE-HTN demonstrates superior sensitivity compared to the most widely-used measure of HTN-related brain changes, correlates with cognitive performance, and detects early changes in mid-life years. This study investigated the predictive capacity of SPARE-HTN for incident HTN and its mediating role in the relationship between HTN and cognition. SPARE-HTN, derived from N = 37,098 cognitively unimpaired individuals from diverse cohorts, was evaluated in N = 968 (59% female, mean age 63.0 ± 11 years) individuals with longitudinal clinical data from six studies (Table 1). Baseline SPARE-HTN values were compared across participants categorized by longitudinal HTN status: persistently normotensive, persistently hypertensive, or incident HTN. The risk of incident HTN was evaluated among baseline normotensive participants using Cox regression model across the baseline SPARE-HTN quartiles, adjusted for age and sex. The average causal mediated effect (ACME) of SPARE-HTN and total white matter hyperintensity (WMH) volume on the relationship between hypertension and cognitive test scores were assessed using mediation models with 5000 simulated permutations. Table 1 presents the participant characteristics at baseline, stratified by longitudinal HTN status. SPARE-HTN was significantly elevated (Figure 1A) in participants who were normotensive at baseline but developed HTN within 3-7 years (+0.44, p = 0.01), but not in those who developed HTN in 8-12 years (p = 0.65). Normotensive participants with elevated baseline SPARE-HTN scores exhibited significantly higher Cox proportional hazard ratios for HTN incidence (Figure 1B). Mediation analysis demonstrated that SPARE-HTN mediated up to 26% of the effect of HTN on cognitive measures, whilst the conventional WMH volumes showed little mediation effect (Figure 2). Our ML-based sMRI marker predicted incident HTN prior to formal clinical diagnosis, suggesting the presence of subclinical cerebrovascular changes possibly associated with blood pressure variations. These markers, particularly relevant in midlife, offer potential for informing dementia prevention trials by enabling individualized risk stratification and potentially more sensitive measurement of therapeutic efficacy.