Biology subjects

Penninx, B. W. J. H.

Publications and source records attributed to Penninx, B. W. J. H..

6 recordsLinked to original sources

bioRxiv2025

Lifestyle, Early-life, and Genetic Health Risk Factors Underlying the Brain Age Gap: A Mega-Analysis Across 3,934 Individuals from the ENIGMA MDD Consortium

BackgroundLarge-scale studies show that adults with major depressive disorder (MDD) generally have a higher imaging-predicted age relative to their chronological age (i.e., positive brain age gap) compared to controls, though considerable within-group variation exists. This study examines lifestyle, early-life, and genetic health risk factors contributing to the brain age gap. Identifying risk and resilience factors could help protect brain and mental health. MethodsUsing an established model trained on FreeSurfer-derived brain regions (www.photon-ai.com/enigma_brainage), we generated brain age predictions for 1,846 controls and 2,088 individuals with MDD (aged 18-75) from 12 international cohorts. Polygenic risk scores (PRS) were calculated for major depression, C-reactive protein, and body mass index (BMI) using large-scale GWAS results. Linear mixed models were applied to assess lifestyle (BMI, smoking, education), early-life childhood trauma, and genetic (PRS) health risk associations with the brain age gap. Additionally, we evaluated the link between the brain age gap and peripheral biological age indicators (epigenetic clocks). ResultsHigher brain age gaps were significantly associated with BMI ({beta}=0.01, PFDR=0.02) and smoking ({beta}=0.11, PFDR=0.02), while lower brain age gaps were linked to higher education ({beta}=-0.02, PFDR=0.02). Higher childhood trauma scores predicted a higher brain age gap ({beta}=0.04, P=0.01). Higher brain age gaps were positively associated with all PRS ({beta}s=0.04-0.16, PsFDR=0.02-0.03). There were no significant interactions between diagnosis and assessed factors on the brain age gap. In a multivariable model, only modifiable health factors--BMI, smoking, and education--remained uniquely associated with brain age gaps. ConclusionsGenetic liability for depression and related traits is linked to poorer brain health, but health behaviors potentially offer a key opportunity for intervention. This study underscores the importance of targeting modifiable lifestyle factors to mitigate poor brain health in depressed individuals, an approach perhaps under-recognized in clinical practice.

neuroscience↗

bioRxiv2025

Social dysfunction relates to altered default mode network functional integrity across neuropsychiatric disorders: A replication and generalization study

BackgroundSocial dysfunction is an early manifestation of neuropsychiatric disorders that may relate to altered Default Mode Network (DMN) integrity. This study aimed to replicate previous findings linking social dysfunction with diminished resting-state DMN functional connectivity and altered task-based DMN functional activation in response to emotional faces across schizophrenia (SZ), Alzheimers disease (AD), and healthy controls (HC), and to extend these findings to major depressive disorder (MDD). MethodsResting-state fMRI and task-based fMRI data on implicit facial emotional processing were acquired in an overlapping cohort (resting-state fMRI: N=167; SZ=32, MDD=44, AD=29, HC=62. Task-based fMRI: N=152; SZ=30, MDD=42, AD=26, HC=54). Additionally, mega-analyses (N=317 for resting-state fMRI; N=291 for task-based fMRI) of the current and a prior independent sample were conducted. Social dysfunction was indexed with the Social Functioning Scale (SFS) and the De Jong-Gierveld Loneliness (LON) scale. ResultsThe association between higher mean SFS+LON social dysfunction scores and diminished DMN connectivity within the dorsomedial prefrontal cortex across SZ/AD/HC participants was replicated, and extended to MDD patients. Similar observations within the dorsomedial and rostromedial prefrontal cortex were found in the mega-analysis. Associations between social dysfunction and DMN activation in response to sad and happy faces were not replicated or found in the mega-analysis. ConclusionsDiminished dorsomedial prefrontal cortex DMN connectivity emerged as a transdiagnostic neurobiological marker for social dysfunction, suggesting a potential treatment target for precision medicine approaches. DMN functional responses to emotional faces may not be a sensitive biomarker for social dysfunction.

neuroscience↗

bioRxiv2019

Genetic underpinnings of sociability in the UK Biobank

Difficulties with sociability include a tendency to avoid social contacts and activities, and to prefer being alone rather than being with others. While sociability is a continuously distributed trait in the population, decreased sociability represent a common early manifestation of multiple neuropsychiatric disorders such as Schizophrenia (SCZ), Bipolar Disorder (BP), Major Depressive Disorder (MDD), Autism Spectrum Disorders (ASDs), and Alzheimers disease (AD). We aimed to investigate the genetic underpinnings of sociability as a continuous trait in the general population. In this respect, we performed a genome-wide association study (GWAS) using a sociability score based on 4 social functioning-related self-report questions in the UK Biobank sample (n=342,461) to test the effect of individual genetic variants. This was followed by LD score analyses to investigate the genetic correlation with psychiatric disorders (SCZ, BP, MDD, ASDs) and a neurological disorder (AD) as well as related phenotypes (Loneliness and Social Anxiety). The phenotypic data indeed showed that the sociability score was decreased in individuals with ASD, (probable) MDD, BP and SCZ, but not in individuals with AD. Our GWAS showed 604 genome-wide significant SNPs, coming from 18 independent loci (SNP-based h2=0.06). Genetic correlation analyses showed significant correlations with SCZ (rg=0.15, p=9.8e-23), MDD (rg=0.68, p=6.6e-248) and ASDs (rg=0.27, p=4.5e-28), but no correlation with BP (rg=0.01, p=0.45) or AD (rg=0.04, p=0.55). Our sociability trait was also genetically correlated with Loneliness (rg=0.45, p=2.4e-8) and Social Anxiety (rg=0.48, p=0.002). Our study shows that there is a significant genetic component to variation in population levels of sociability, which is relevant to some psychiatric disorders (SCZ, MDD, ASDs), but not to BP and AD.

genetics↗

bioRxiv2019

Beyond accuracy: Measures for assessing machine learning models, pitfalls and guidelines

Pattern recognition predictive models have become an important tool for analysis of neuroimaging data and answering important questions from clinical and cognitive neuroscience. Regardless of the application, the most commonly used method to quantify model performance is to calculate prediction accuracy, i.e. the proportion of correctly classified samples. While simple and intuitive, other performance measures are often more appropriate with respect to many common goals of neuroimaging pattern recognition studies. In this paper, we will review alternative performance measures and focus on their interpretation and practical aspects of model evaluation. Specifically, we will focus on 4 families of performance measures: 1) categorical performance measures such as accuracy, 2) rank based performance measures such as the area under the curve, 3) probabilistic performance measures based on quadratic error such as Brier score, and 4) probabilistic performance measures based on information criteria such as logarithmic score. We will examine their statistical properties in various settings using simulated data and real neuroimaging data derived from public datasets. Results showed that accuracy had the worst performance with respect to statistical power, detecting model improvement, selecting informative features and reliability of results. Therefore in most cases, it should not be used to make statistical inference about model performance. Accuracy should also be avoided for evaluating utility of clinical models, because it does not take into account clinically relevant information, such as relative cost of false-positive and false-negative misclassification or calibration of probabilistic predictions. We recommend alternative evaluation criteria with respect to the goals of a specific machine learning model.

neuroscience↗

bioRxiv2019

Genetic comorbidity between major depression and cardio-metabolic disease, stratified by age at onset of major depression.

IntroductionIts imperative to understand the specific and shared aetiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression.\n\nMethodsPolygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic aetiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in eleven data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank.\n\nResultsAll cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression.\n\nConclusionsThe phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic aetiology irrespective of the age depression first presents.

genetics↗

bioRxiv2018

Cell-type-specific methylome-wide association studies implicate neurodegenerative processes and neuroimmune communication in major depressive disorder

We studied the methylome in three collections of human postmortem brain (N=206) and blood samples (N=1,132) of subjects with major depressive disorder (MDD) and controls. Using an epigenomic deconvolution approach we performed cell-type-specific methylome-wide association studies (MWAS) within sub-populations of neurons/glia and granulocytes/T-cells/B-cells/monocytes for bulk brain and blood data, respectively. Multiple MWAS findings in neurons/glia replicated across brain collections (ORs=509-538, P-values<1x10-5) and were reproducible in an array-based MWAS of sorted neurons/glia from a fourth brain collection (N=58). Pathway analyses implicated p75NTR/VEGF signaling, neurodegeneration, and blood-brain barrier perturbation. Cell-type-specific analysis in blood identified associations in CD14+ monocytes -- a cell type strongly linked to neuroimmune processes and stress. Top results in neurons/glia/bulk and monocytes were enriched for genes supported by GWAS for MDD (ORs=2.02-2.87, P-values=0.003 to <1x10-5), neurodegeneration and other psychiatric disorders. In summary, we identified novel MDD-methylation associations by using epigenomic deconvolution that provided important mechanistic insights for the disease.

genomics↗