Biology subjects

Saemann, P. G.

Publications and source records attributed to Saemann, P. G..

5 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↗

bioRxiv2024

Huntingtin CAG repeat size variations outside the Huntington's disease complex: associations with depression and anxiety phenotypes and basal ganglia structure

Huntingtons Disease (HD) is strongly associated with psychiatric symptoms, yet, associations between Huntingtin gene (HTT) CAG repeat size variations and psychiatric phenotypes outside the HD complex are still under-investigated. In this genetic case-control study we compared the distribution of HTT CAG repeat sizes in predefined ranges between patients with major depressive disorder (MDD) (n=2136) and anxiety disorders (ANX) (n=493), and healthy controls (CON) (n=1566). We used regression models to study interactions between the alleles and associations with fine-granular clinical phenotypes and basal ganglia structure. HD mutations in the range of incomplete penetrance (36-39 repeats) were not overrepresented in patients. In participants older than 48 years, 13-20 repeats on both HTT alleles were associated with a reduced ANX risk whereas a 13-20|21-26 combination was associated with an increased ANX risk. Post-hoc analyses confirmed a turning point around 21 repeats and trends in the same direction were detected for MDD. The joint patient|CON analysis of the full spectrum of allele combinations confirmed interaction effects and age-dependent allele|risk profiles. A short-by-long interaction effect and an age-dependent negative correlation of the short allele on the nucleus accumbens volume was detected, independently of the diagnostic group. In conclusion, we revealed that HTT CAG repeat sizes of both alleles in the non-HD range modulate the susceptibility for common psychiatric disorders and basal ganglia structure in an age-dependent way, displaying that normal variation of the functionally diverse wildtype huntingtin protein may already impact brain function.

genetics↗

bioRxiv2023

Stress-induced brain responses are associated with BMI in women

BackgroundStress is associated with elevated risk for overweight and obesity, especially in women. Since body mass index (BMI) is correlated with increased inflammation and reduced baseline cortisol, obesity may lead to altered stress responses. However, it is not well understood whether stress-induced changes in brain function scale with BMI and if peripheral inflammation contributes to this. MethodsWe investigated the subjective, autonomous, endocrine, and neural stress response in a transdiagnostic sample (N=192, 120 women, MBMI=23.7{+/-}4.0 kg/m2; N=148, 89 women, with cytokines). First, we used regression models to examine effects of BMI on stress reactivity. Second, we predicted BMI based on stress-induced changes in activation and connectivity using cross-validated elastic-nets. Third, to link stress responses with inflammation, we quantified the association of BMI-related cytokines with model predictions. ResultsBMI was associated with higher negative affect after stress and an increased response to stress in the substantia nigra and the bilateral posterior insula (pFWE<.05). Moreover, stress-induced changes in activation of the hippocampus, dACC, and posterior insula predicted BMI in women (pperm<.001), but not in men. BMI was associated with higher baseline cortisol while cytokines were not associated with predicted BMI scores. ConclusionsStress-induced changes in the hippocampus and posterior insula predicted BMI in women, indicating that acute brain responses to stress might be more strongly related to a higher BMI in women compared to men. Altered stress-induced changes were associated with baseline cortisol but independent of cytokines, suggesting that the endocrine system and not inflammation contributes to stress-related changes in BMI.

neuroscience↗

bioRxiv2021

Spatio-temporal dynamics of stress-induced network reconfigurations reflect negative affectivity

BackgroundChronic stress is an important risk factor in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk. MethodsUsing an established psycho-social stress task flanked by two resting-state scans, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 unmedicated participants with and without mood and anxiety disorders. To estimate block-wise changes in stress-induced brain activation and FC, we used hierarchical mixed-effects models based on denoised timeseries within a predefined stress network. We predicted inter- and intra-individual differences in stress phases (anticipation vs. acute stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines. ResultsWe identified four subnetworks showing distinct changes in FC over time. Subnetwork trajectories predicted the stress phase (accuracy: 71%, pperm<.001) and increases in pulse rate (R2=.10, pperm<.001). Critically, individual spatio-temporal trajectories of changes across networks also predicted negative affectivity ({Delta}R2=.08, pperm=.009), but not the presence or absence of a mood and anxiety disorder. ConclusionsSpatio-temporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.

neuroscience↗

bioRxiv2020

Threat-induced hippocampal connectivity fingerprints do not generalize to psychosocial stress

Stress is an everyday experience and maladaptive responses play a crucial role in the etiology of affective disorders. Despite its ubiquity, the neural underpinnings of subjective stress experiences have not yet been elucidated, particularly at an individual level. In an important advance, Goldfarb et al.1 showed recently that subjective stress and arousal levels in response to threatening stimuli were successfully predicted based on changes in hippocampal connectivity during the task using a machine learning approach. Crucially, stress responses were predicted by interpretable hippocampal connectivity networks, shedding new light on the role of the hippocampus in regulating stress reactivity2. However, the authors induced stress by displaying aversive pictures, while stress research often relies on the extensively validated Trier social stress task (TSST)3. The TSST incorporates crucial factors such as unpredictability of success and the social-evaluative threat of the stressor thereby eliciting cortisol responses more robustly compared to threatening images4. Towards generalization, cross validation within a sample as conducted by Goldfarb et al.1 or independent replications are important steps, but the generalizability to different stressors allows to draw broader conclusions about the potential use of hippocampal connectivity to predict subjective stress5. Arguably, translating these findings to clinical applications would require a broad generalization of the results or the prediction algorithm to psychosocial stress. Here, we assessed the predictive performance of Goldfarb et als1 algorithm for subjective stress in an independent sample using an MR adaption of the TSST6,7. In line with Goldfarb et al.1, we observed robust stress-induced changes in hippocampal connectivity. However, the spatial correlation of the changes in connectivity was low indicating little convergence across alleged stress paradigms. Critically, stress-induced changes of hippocampal connectivity were not robustly predictive of subjective stress across a multiverse of analyses based on connectivity changes. Collectively, this indicates that the generalizability of the reported stress connectivity fingerprint to other stressors is limited at best, suggesting that specific tasks might require tailored algorithms to robustly predict stress above chance levels.

neuroscience↗