Published: Jan. 1, 2024
Language: Английский
Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)
Published: March 23, 2024
Abstract The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three age gaps (BAG) derived from gray matter volume (GM-BAG), white microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10 −8 ). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative neuropsychiatric disorders WM-BAG cancer therapy. displayed most pronounced heritability enrichment in variants within conserved regions. Oligodendrocytes astrocytes, but not neurons, exhibited notable WM FC-BAG, respectively. Mendelian randomization potential causal effects several chronic diseases on aging, such as type 2 diabetes AD WM-BAG. Our results provide insights into genetics with clinical implications lifestyle therapeutic interventions. All are publicly available at https://labs.loni.usc.edu/medicine .
Language: Английский
Citations
21
Science Advances, Journal Year: 2025, Volume and Issue: 11(11)
Published: March 12, 2025
Brain age gap (BAG), the deviation between estimated brain and chronological age, is a promising marker of health. However, genetic architecture reliable targets for aging remains poorly understood. In this study, we estimate magnetic resonance imaging (MRI)–based using deep learning models trained on UK Biobank validated with three external datasets. A genome-wide association study BAG identified two unreported loci seven previously reported loci. By integrating Mendelian Randomization (MR) colocalization analysis eQTL pQTL data, prioritized genetically supported druggable genes, including MAPT , TNFSF12 GZMB SIRPB1 GNLY NMB C1RL as aging. We rediscovered 13 potential drugs evidence from clinical trials several strong support. Our provides insights into basis aging, potentially facilitating drug development to extend health span.
Language: Английский
Citations
1
medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown
Published: June 12, 2023
Abstract Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized architecture age gap (BAG) across nine human organ 377,028 individuals European ancestry from UK Biobank. We discovered 393 genomic loci-BAG pairs (P-value<5×10 -8 ) linked to brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, renal systems. observed BAG-organ specificity inter-organ connections. Genetic variants associated with BAGs are predominantly specific respective system while exerting pleiotropic effects on traits multiple A gene-drug-disease network confirmed involvement metabolic BAG-associated genes drugs targeting various disorders. correlation analyses supported Cheverud’s Conjecture 1 – between mirrors their phenotypic correlation. causal revealed potential linking chronic diseases (e.g., Alzheimer’s disease), body weight, sleep duration BAG Our findings shed light promising interventions enhance health within a complex network, including lifestyle modifications drug repositioning strategies treating diseases. All results publicly available at https://labs-laboratory.com/medicine .
Language: Английский
Citations
12
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown
Published: Sept. 19, 2022
Abstract Alzheimer’s disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised clustering technique known as Surreal-GAN, through which we identified two dominant dimensions of brain in symptomatic mild cognitive impairment (MCI) and AD patients: the “diffuse-AD” (R1) dimension shows widespread atrophy, “MTL-AD” (R2) displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was widely sporadic genetic risk factors (e.g., APOE ε4 ) MCI patients at baseline. then independently detected presence early stages by deploying trained model general population cognitively unimpaired cohorts asymptomatic participants. In population, genome-wide association studies found 77 genes unrelated to differentially R1 R2. Functional analyses revealed that these were overrepresented expressed gene sets organs beyond (R1 R2), including heart pituitary gland, muscle, kidney (R2). These enriched biological pathways implicated dendritic cells (R2), macrophage functions (R1), cancer R2). Several them “druggable genes” for inflammation cardiovascular diseases nervous system The longitudinal progression showed , amyloid, tau stages, but this occurs late R1. Our findings deepen our understanding multifaceted pathogenesis brain. are diverse pathological mechanisms, diseases, inflammation, hormonal dysfunction – driven different from may collectively contribute AD.
Language: Английский
Citations
11
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown
Published: April 15, 2023
The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging artificial intelligence to examine the genetic architecture of age gap (BAG) derived from gray matter volume (GM-BAG,
Language: Английский
Citations
6
medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown
Published: Aug. 24, 2023
Abstract Disease heterogeneity poses a significant challenge for precision diagnostics in both clinical and sub-clinical stages. Recent work leveraging artificial intelligence (AI) has offered promise to dissect this by identifying complex intermediate phenotypes – herein called dimensional neuroimaging endophenotypes (DNEs) which subtype various neurologic neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer’s disease (AD1-2) 1 , autism spectrum disorder (ASD1-3) 2 late-life depression (LLD1-2) 3 schizophrenia (SCZ1-2) 4 general population 39,178 participants UK Biobank study. Phenome-wide associations revealed prominent between related brain other human organ systems. This phenotypic landscape aligns with SNP-phenotype genome-wide associations, revealing 31 genomic loci associated (Bonferroni corrected P-value < 5×10 -8 /9). The exhibited genetic correlations, colocalization, causal relationships multiple systems chronic A effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×10 -4 ) was established AD2, characterized focal medial temporal lobe atrophy, AD. their polygenic risk scores significantly improved prediction accuracy 14 systemic categories mortality. These findings underscore potential identify individuals at high developing four diseases during preclinical stages diagnostics. All results are publicly available at: http://labs.loni.usc.edu/medicine/ .
Language: Английский
Citations
6
Translational Psychiatry, Journal Year: 2024, Volume and Issue: 14(1)
Published: Oct. 5, 2024
Language: Английский
Citations
1
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: May 5, 2024
An approach to age modeling involves the supervised prediction of using machine learning from subject features. The derived metrics are used study relationship between healthy and pathological aging in multiple body systems, as well interactions them. We lack a standard for this type modeling. In work we developed AgeML, an OpenSource software age-prediction any tabular clinical data following well-established tested methodologies. objective is set standards reproducibility standardization reporting tasks. AgeML does modeling, calculates deltas, difference predicted chronological age, measures correlations deltas factors, visualizes differences different populations classifies based on deltas. With able reproduce published unveil novel relationships organs polygenetic risk scores. made easy reproducibility.
Language: Английский
Citations
0
medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: June 16, 2024
Abstract Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e., endophenotypes) that bridge the and clinical manifestations of human disease. However, genetic architecture these AI endophenotypes remains largely unexplored in context multi-organ system diseases. Using publicly available GWAS summary statistics from UK Biobank, FinnGen, Psychiatric Genomics Consortium, we comprehensively depicted 2024 multi- organ (MAEs). Two AI- imaging-derived subtypes 1 showed lower polygenicity weaker negative selection effects than schizophrenia disease diagnoses 2 , supporting endophenotype hypothesis 3 . Genetic correlation Mendelian randomization analyses reveal both within-organ relationships cross-organ interconnections. Bi-directional causal were established between chronic diseases MAEs across multiple systems, including Alzheimer’s for brain, diabetes metabolic system, asthma pulmonary hypertension cardiovascular system. Finally, derived polygenic risk scores individuals not used calculate returned Biobank. Our findings underscore promise as new instruments ameliorate overall health. All results are encapsulated MUTATE atlas at https://labs-laboratory.com/mutate Graphical abstract Key points neuroimaging-derived (MAE-SCZ1 MAE- SCZ2) show signatures endpoint/diagnosis schizophrenia, hypothesis. Brain more other systems. Most exhibit signatures, whereas a small proportion brain patterns structural covariance networks positive signatures. The genetically causally associated with endpoints/diagnoses.
Language: Английский
Citations
0
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Aug. 22, 2024
The environment, especially social features, plays a key role in shaping the development of brain, notably during adolescence. To better understand variation brain-environment coupling and its associated outcomes, we identified ''envirotypes,'' or different patterns environment experience, Adolescent Brain Cognitive Development Study by hierarchically clustering subjects. Two focal clusters, which accounted for 89.3% all participants, differed significantly on eight out nine youth-report quality measures, representing almost perfect complements. We then applied tools from network neuroscience to show envirotypes are with whole brain functional connectivity. Differences were distributed across but prominent Default Somatomotor Hand systems these clusters. Finally, examined how change over suite outcomes. resulting dynamic along dimensions stability quality, outcomes diverged based stability. Specifically, stable, high envirotype was most easily distinguished improving envirotype, while unstable worst Altogether, our findings represent significant contributions both developmental neuroscience, emphasizing variability dynamicity consequences.
Language: Английский
Citations
0