Junhao Wen

INTRODUCTION: The association of sleep duration with Alzheimer's disease (AD) ‐ related brain atrophy and cognition remains unclear. METHODS: Among 38,816 participants in the UK Biobank, we examined the association between sleep duration and a validated machine‐learning‐derived magnetic resonance imaging (MRI) signature of AD‐related atrophy (Spatial Pattern of Abnormality for Recognition of Early AD [SPARE‐AD]) and three cognitive test scores using generalized additive models. Independently, electronic health records (EHR) from TriNetX were used to examine the 10‐year AD risk associated with insomnia and hypersomnia. RESULTS: Sleep duration exhibited a U‐shaped association with SPARE‐AD ( p = 0.001), Trail Making Tests A and B, and the Digit Symbol Substitution Test (all p < 0.001). Short (5–6 hours) and long (9–10 hours) sleep duration were associated with poorer cognitive performance. Both insomnia and hypersomnia showed a high risk for Alzheimer's disease. CONCLUSION: This study demonstrates a U‐shaped association between sleep duration and AD‐like atrophy and cognition. DISCUSSION: These findings show that excessive or insufficient sleep is linked to worse brain health.

Recent work leveraging omics and imaging data now enables the estimation of aging at the level of individual organs. Emerging findings suggest that organs age at different rates, which may be linked to environmental exposures and genetic factors. However, premature aging in one organ may also drive aging in connected organs within multi-organ aging networks. Here, we outline methods for measuring organ-specific biological age and discuss insights derived from recent progress in multi-organ aging research. We put forward recommendations, best practices and research priorities, including the importance of longitudinal tracking, biomarkers with high organ specificity and reference ranges for organ age gaps. We envision routine organ-specific biological age assessments as tools for developing personalized organ aging maps and tracking organ aging across the life course, thereby facilitating early, targeted interventions to delay organ-specific decline and interorgan consequences.

Optimal sleep has a vital role in promoting healthy ageing and enhancing longevity. Here we propose Sleep Chart to assess the relationship between self-reported sleep duration and 23 biological ageing clocks derived from in vivo imaging¹, plasma proteomics² and metabolomics³. First, a systemic, U-shaped pattern emerges between sleep duration and biological age gaps across nine brain and body systems and three omics technologies. The sample-specific lowest biological age gaps are achieved between 6.4 and 7.8 h of sleep duration, varying by organ and sex in the UK Biobank (aged 37–84 years). Furthermore, short (8 h) sleep duration, compared with a normal sleep duration (6–8 h), are associated with increased risk of systemic diseases beyond the brain and all-cause mortality, with evidence from genetic correlations and time-to-incident survival predictions, such as depression and diabetes. Finally, the pathways by which long and short sleep duration are associated with late-life depression differ: ageing clocks may partially mediate the pathway for long sleep duration, while short sleep duration shows a more direct link. Although Mendelian randomization does not provide strong evidence that disease causally affects sleep, it cannot completely exclude such reverse causality. Our findings suggest a cross-organ, multi-omics U-shaped relationship between sleep duration and biological ageing clocks, highlighting the potential of sleep optimization to promote healthy ageing, lower disease risk and extend longevity.

Biological heterogeneity presents a formidable challenge in understanding psychiatric illness, yet it remains underexplored at scale or in a pan-disease setting. We characterized biochemical and brain heterogeneity in 445,374 individuals, considering 9 psychiatric disorders and 30 non-psychiatric medical conditions. Psychiatric and non-psychiatric illnesses were characterized by greater biological heterogeneity than predicted by healthy variation. Notably, heterogeneity in psychiatric disorders was comparable to non-psychiatric illnesses and often diverged from diagnostic classification. Circulating glucose and glycated hemoglobin levels were the most heterogeneous traits in 65% of diagnoses, suggesting that glucose dysregulation is pervasive yet variable across diseases. Diagnosis-specific patterns also surfaced—e.g., in schizophrenia, developmental brain markers ranked among the most heterogeneous traits. These characterizations of biochemical and brain heterogeneity lay the groundwork for biological focal points to aid precision psychiatry research. To support this effort, we provide an interactive online resource for exploring and downloading illness-specific heterogeneity measures.

Neuroimagerie structurelle et microstructurelle pour le diagnostic et le suivi de maladies neurodégénératives: L’identification et le suivi de biomarqueur de la démence sont essentiels pour mieux comprendre les mécanismes pathologiques et la trajectoire de la maladie. Le diagnostic précoce de la démence constitue un autre défi. Cette thèse a deux objectifs principaux. Premièrement, nous cherchons à identifier les biomarqueurs les plus prometteurs au stade présymptomatique de la démence. Plus spécifiquement, nous avons étudié ce phénomène dans le cas de la dégénérescence lobaire frontotemporale due à la mutation C9orf72. Le deuxième objectif est de faire progresser le diagnostic et le pronostic précoces en utilisant des méthodes d’apprentissage machine et des données d’imagerie par résonance magnétique. Nous abordons cette question dans le contexte de la maladie d’Alzheimer sporadique. Suivant ces deux objectifs, la thèse se compose de deux parties principales, chaque partie comprenant deux études. Dans la première étude, les biomarqueurs ont été identifiés à partir de l’IRM conventionnelle pondérée T1 et du modèle d’imagerie du tenseur de diffusion. La deuxième étude a comparé la sensibilité et la spécificité du modèle NODDI et celle de techniques conventionnelles, à savoir l’IRM pondérée en T1 et le DTI. La deuxième partie porte sur le diagnostic précoce de la MA et comprend les deux dernières études. La troisième étude propose un cadre open source pour une évaluation reproductible de la classification de la MA à l’aide de l’IRM de diffusion et des méthodes classiques d’apprentissage. La dernière étude étend ce cadre aux méthodes d’apprentissage profond et démontre son utilisation sur l’IRM pondérée en T1.