Genome-Wide Association Summary Statistics-Based Genetic Mapping of Brain Functional and Structural Networks in Type 2 Diabetes Mellitus and Its Complications.

Genetic evidence links hypertension to accelerated brain aging

Proteomic Pathways Linking Type 2 Diabetes Genetic Clusters to Cardiovascular Diseases: A Network Mendelian Randomisation Study.

BACKGROUND Genetic clusters related to Type 2 diabetes (T2D) have differential impact on cardiovascular diseases (CVDs), although the underlying mechanisms, such as proteomic perturbation, remain unexplored. We conducted a network Mendelian randomisation (MR) study to identify proteomic mediators linking T2D genetic clusters and CVDs. METHODS We assessed the associations between genetic liability to T2D and eight genetic clusters (N = 2 535 601) with 12 CVDs (N ≤ 1 946 349). For significant pairs, we performed a two-step MR (mediation) to identify proteins mediating these associations. Inverse-variance weighted method was the main analysis, with sensitivity analyses including genetic colocalisation. Robustness was evaluated in European-specific analyses as well as using Olink-measured proteomic data. False discovery rate (FDR) was used to correct for multiple comparisons. RESULTS Obesity cluster was linked to most CVDs (except haemorrhage strokes), with similar patterns for body fat, metabolic syndrome and lipodystrophy clusters. Majority of the protein signals (2739 out of 4907) were related to obesity cluster although glycemia-related clusters were only associated with a few proteomic signals (≤ 13). Amongst 16 proteins supported by genetic colocalisation (PPH4 > 0.8), the proportion mediated ranged from 1.2% to 36.8%. APOC3, ADH1B, F11 and KLKB1 appeared to be shared across the different genetic clusters, implicating their shared role in CVDs risk. Sensitivity and replication analyses gave similar estimates. CONCLUSION This proteogenomic MR study delineates protein-mediated pathways linking cluster-stratified T2D genetic predisposition to cardiovascular risk, which can inform preventive strategies by stratifying individuals based on distinct biological pathways represented by these genetic clusters.

Dissecting the Genetic Link Between Insulin Resistance and Coronary Heart Disease: A Multi-Trait GWAS and Proteomic-Metabolomic Mediation Analysis

Genetic evidence for causal relationships between brain functional networks and domain-specific recovery after nondisabling ischemic stroke

Intrinsic brain networks are crucial for post-stroke recovery, but the causal relationships between specific networks and domain-specific recovery outcomes, as well as the role of lipid metabolism, remain unclear. This study leveraged Mendelian randomization (MR) to evaluate 191 resting-state functional magnetic resonance imaging (rs-fMRI) BOLD-derived phenotypes in relation to post-stroke recovery after nondisabling ischemic stroke. Genetic instruments for rs-fMRI phenotypes were derived from a UK Biobank genome-wide association study (n = 34,691). Outcomes included motor, cognitive, and global recovery after nondisabling ischemic stroke, assessed using longitudinal National Institutes of Health Stroke Scale subscales over 2 years (n = 1,270). Primary analyses used the multiplicative random-effects inverse-variance weighted method. A two-step MR analysis investigated whether brain networks mediate the effects of lipids on post-stroke outcomes. Higher BOLD-derived functional connectivity within the triple network (default mode network, central executive network, and salience network) was associated with better motor and cognitive outcomes. Higher genetically predicted orbitofrontal node amplitude in the limbic network correlated with better motor recovery, while stronger parieto-frontal connectivity was associated with cognitive recovery. Genetically proxied higher low-density lipoprotein cholesterol (LDL-C) was associated with poorer cognitive recovery, with evidence suggesting partial mediation through differences in BOLD-derived triple-network connectivity. This MR study supports a potential causal role of BOLD-derived functional network phenotypes, particularly the triple network, in motor and cognitive recovery, and further suggests that differences in triple-network connectivity act as a partial mediator linking elevated LDL-C liability to impaired cognitive recovery. These findings provide hypothesis-generating evidence for future mechanistic studies and for exploring whether specific brain network-targeted interventions could have a role in stroke recovery.

Mapping genetic convergence across brain structure, mental health, and cardiometabolic disease

It is found that schizophrenia and ADHD show distinct genetic patterns: schizophrenia shares more genetic factors with brain structure and diabetes, while ADHD is more strongly linked to metabolic pathways.

Mapping Focal and Generalized Effects of Common Genetic Variants on Human Brain Structure

Genome-wide association studies (GWAS) have advanced the quest to understand how specific genetic variants influence human brain structure and function. Recent work has identified hundreds of common variants associated with subcortical brain volumes, sparking interest in how these genetic markers overlap across brain networks. While this can be estimated by hierarchical clustering of the genetic correlation matrix to identify modular patterns of shared architecture, no brain-wide maps of these effects are available. To address this, we computed polygenic scores (PGS) from loci associated with ten brain volume regions of interest (ROIs): nine major subcortical structures and intracranial volume, with each locus weighted by its association with regional volume. In an independent sample from the discovery GWAS, we performed large-scale segmentation of 3D volumetric T1-weighted MRI scans using voxel-based morphometry (VBM) to map 3D profile of regions where gray matter volume (GMV) was associated with each PGS. We found statistically significant, localized effects for PGS defined for the amygdala, thalamus, and basal ganglia, but PGS for brainstem volume was associated with widespread differences throughout the brain. These brain-wide maps reveal patterns consistent with both localized and distributed genetic influences, offering a novel approach to interpret the genomic architecture of brain structure.