Anne Hoffmann

Genetic variants may affect the body mass index (BMI) and the effectiveness of obesity treatments. However, it remains largely unclear whether these effects differ between men and women. Thus, this study investigates genetic variants in incretin-related genes ( GIP , GIP-R , GCG , GCG-R and GLP-1R ) using sex-stratified genetic and functional approaches. First, sex-specific genome-wide association study data were analysed to identify variants with a sex-specific effect on BMI. Identified variants were then examined in an independent cohort of adults undergoing bariatric surgery and functionally assessed in a cellular model. Finally, all genes were screened for rare variants in children and adolescents with severe obesity. Nine highly linked variants within GLP-1R showed stronger effects on BMI in men than in women. Among these, only rs1042044 (p.Leu260Phe) is located in the coding region. In a cohort of 807 bariatric surgery patients, rs1042044 showed a nominal association with achieving a weight loss of more than 10%, particularly in women. However, no differences in receptor internalisation were observed between wildtype and mutant GLP-1R in vitro . Additionally, 26 variants were identified across the genes in a paediatric cohort. Future studies should incorporate sex-stratified analyses to uncover putative sex-dependent genetic modifiers of BMI and weight loss outcomes.

Endothelial cells (ECs) line blood vessels, regulate organ homeostasis, and their dysfunction is central to the development of cardiovascular diseases. Metabolites are critical regulators of EC health and function, yet only a limited number of metabolites that impact ECs have been identified. Here, we undertook a high-throughput screen of 884 metabolites and common drugs found in human biofluids to systematically identify compounds that impact barrier function in primary human umbilical vein ECs (HUVECs). Seven metabolites that deregulate vascular barrier function without causing cell death were shortlisted. These include (-)-epigallocatechin gallate, (±)-3-methyl-2-oxovaleric acid, taurocholic acid, myricetin, meloxicam, hesperetin and trans-3-indoleacrylic acid. These metabolites elicited differential effects on HUVECs, coronary artery ECs and pulmonary ECs. Transcriptional profiling revealed that each of the seven metabolites triggered distinct transcriptional responses in ECs, suggesting distinct mechanisms triggering EC barrier disruption. Notably, trans-3-indoleacrylic acid deregulated VE-cadherin junctions and increased EC sprouting, suggesting a potential role in angiogenesis. Together, these results provide a screening resource for metabolite-endothelial interactions and identify candidate circulating metabolites that regulate endothelial function.

Objective: BMI alone does not capture obesity‐related health heterogeneity. The Edmonton Obesity Staging System (EOSS) grades obesity severity based on comorbidities and functional impairment, whereas the Lancet Commission Diagnostic Model for Obesity (DMO) distinguishes preclinical from clinical obesity based on organ dysfunction. We compared classification patterns and concordance across both obesity frameworks in a large population‐based cohort. Methods: A modified EOSS and DMO were applied to the UK Biobank ( N ≈ 411,000). Stage distributions, cross‐classification, and the impact of combining BMI with fat distribution on obesity categorization were analyzed. Results: About one‐quarter of participants were classified with obesity under both frameworks. Most were assigned to advanced stages, with high concordance for established disease. Differences were most pronounced in early stages: DMO captured a broader spectrum of mild/subclinical organ dysfunction, whereas EOSS emphasized more advanced manifestations of obesity‐related disease. Discrepancies reflected differences in operationalization of, for example, metabolic, cardiovascular, and mental health domains. Obesity thresholds influenced classification, with ~50% reclassified when BMI was combined with different fat distribution parameters, highlighting the sensitivity of early‐stage assignment. Conclusions: EOSS and DMO provide complementary approaches to obesity classification, with differing classification patterns highlighting distinct concepts of obesity‐related disease severity.

Fluoxetine is a selective serotonin reuptake inhibitor (SSRI) commonly prescribed for the treatment of depressive disorders. Recent clinical reports and studies in animal models have suggested that fluoxetine increases the risk of cardiovascular diseases, but the underlying mechanisms remain unknown. Here, we uncover that fluoxetine disrupts lipid and cholesterol metabolism in primary human endothelial cells (ECs). Fluoxetine triggered an upregulation of cholesterol metabolism genes, leading to the accumulation of lipid droplets in ECs. We find higher levels of cholesterol esters, ceramides, sphingolipids and fatty acids in ECs treated with fluoxetine. The disruption of lipid homeostasis was driven by increased cholesterol biosynthesis, as well as low-density lipoprotein (LDL) uptake and transcytosis via the LDL receptor. Fluoxetine accumulated in ECs in the endoplasmic reticulum (ER), caused ER expansion and reduced protein translation, without inducing ER stress markers. Mechanistically, fluoxetine activated the SREBP2 transcription factor in an INSIG-dependent manner. SREBP2 inhibition attenuated the fluoxetine-mediated upregulation of the LDL receptor and lipid accumulation. Our findings reveal that fluoxetine reprograms lipid metabolism and leads to endothelial dysfunction.

Background: Blood-based DNA methylation has been linked to obesity and metabolic health, yet its relationship to adipose tissue function remains incompletely understood. This study aimed to investigate the epigenetic regulation of EIF5A (Eukaryotic translation initiation factor 5A-1 ) and TGIF1 (TGFB Induced Factor Homeobox 1) across blood and adipose tissue in obesity. Methods: Candidate genes were identified using a multi-step gene selection approach integrating transcriptomic and epigenomic data from blood and adipose tissue samples obtained from children and adults across four diverse population- and disease-focused cohorts. Genes were prioritized based on differential DNA methylation and gene expression in obesity. Targeted bisulfite sequencing of EIF5A and TGIF1 was conducted in blood samples from adults across BMI-defined groups and from children prior to the development of obesity, to validate candidate loci and to examine associations with metabolic and adipocyte-related phenotypes. Results: In adults from the Leipzig Obesity BioBank, blood DNA methylation ( N = 150) of both EIF5A and TGIF1 was significantly increased in individuals with obesity. EIF5A mRNA expression ( N = 1554) was significantly higher in omental-visceral adipose tissue compared with subcutaneous adipose tissue. Blood DNA methylation of EIF5A was associated with body mass index (BMI), glycated hemoglobin (HbA1c), and leukocyte counts, particularly among individuals with type 2 diabetes mellitus. In children, blood DNA methylation ( N = 75) of EIF5A was associated with longitudinal HbA1c trajectories. For TGIF1 , DNA methylation levels were increased in subcutaneous adipose tissue ( N = 219) of children with obesity and correlated with fasting serum insulin concentrations. Across cohorts, TGIF1 regulation in both blood and adipose tissue showed consistent associations with adipocyte size. Notably, blood DNA methylation of TGIF1 in childhood was associated with body fat mass and HbA1c at later follow-up despite normal weight at baseline. Conclusion: EIF5A and TGIF1 DNA methylation represent cross-tissue epigenetic signatures linking blood-based DNA methylation to adipose tissue dysfunction, adipocyte hypertrophy, and early metabolic risk. These findings support the potential of blood DNA methylation markers to reflect adipose tissue health and metabolic outcomes across the lifespan.

Background: Obesity arises from a complex interplay of genetic and environmental factors, with alterations of transcriptional networks that integrate metabolic, immune, and regulatory pathways. Conventional measures such as body mass index (BMI) quantify body size but fail to capture the molecular heterogeneity underlying divergent metabolic outcomes. We therefore sought to construct a gene expression-based transcriptomic representation of obesity, using BMI as a practical training anchor, and to use this framework to delineate transcriptional programs associated with metabolically healthy and pathogenic obesity, with subsequent projection to mouse transcriptomic data for cross-species validation. Methods: Transcriptome data of human visceral adipose tissue (N= 1,298) were used to derive the transcriptomic BMI model, and genes contributing to the model were functionally annotated by gene set enrichment analysis. The human-trained model was subsequently applied to mouse selection lines (N = 18) with divergent obesity phenotypes. In the human cohort, post hoc stratification into metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) groups was performed using a downstream classification framework incorporating observed BMI together with predicted BMI, to assess whether model-derived predicted BMI reflected obesity-related pathophysiologic status. Results: Model-selected genes were involved in coordinated regulation of lipid metabolism, immune activation, and growth signaling, extending to mitochondrial and translational pathways. Cross-species analyses uncovered conserved metabolic polarization: DU6 mice exhibited lipid-anabolic and inflammatory remodeling, whereas DU6P mice displayed oxidative, mitochondrial, and GH-axis–enriched transcriptional states. In human cohorts, MHO individuals showed upregulation of mitochondrial energetics and protein synthesis, while MUO individuals were characterized by increased autophagy, lipid catabolism, and stress-adaptive signaling on the transcriptional level. Together, these findings define a conserved molecular continuum linking oxidative efficiency to metabolic health and inflammation to metabolic vulnerability. Conclusions: This integrative transcriptomic framework bridges human and mouse adipose biology to uncover conserved mechanisms underlying obesity phenotypes. By contrasting mitochondrial and translational programs with inflammatory and catabolic pathways, it provides mechanistic insight into metabolic resilience and a foundation for precision approaches to obesity management.

Brown adipose tissue is an evolutionary innovation in placental mammals that regulates body temperature through adaptive thermogenesis. Cold exposure activates brown adipose tissue thermogenesis through coordinated induction of brown adipogenesis, angiogenesis, and sympathetic innervation; however, how these processes are coordinated remains unclear. Here, we show that fragments of Slit guidance ligand 3 (SLIT3) drive crosstalk among adipocyte progenitors, endothelial cells, and sympathetic nerves. Adipocyte progenitors secrete SLIT3, which is cleaved into functionally distinct SLIT3-N and SLIT3-C fragments that independently promote angiogenesis and sympathetic innervation. We identify PLXNA1 as a receptor for SLIT3-C and demonstrate its essential role in sympathetic innervation of brown adipose tissue. Moreover, we identify BMP1 as the first SLIT protease described in vertebrates. Coordinated neurovascular expansion mediated by distinct SLIT3 fragments provides a bifurcated yet integrated mechanism that ensures a synchronized brown adipose tissue response to environmental challenges. Finally, this study reveals a previously unrecognized role for adipocyte progenitors in regulating tissue innervation.

Objective: BMI alone does not capture obesity-related health heterogeneity. The Edmonton Obesity Staging System (EOSS) grades obesity severity based on comorbidities and functional impairment, whereas the Lancet Commission Diagnostic Model for Obesity (DMO) distinguishes preclinical from clinical obesity based on organ dysfunction. We assessed whether both frameworks identify overlapping phenotypes and how they classify obesity severity. Methods: A modified EOSS and DMO were applied to the UK Biobank (N ≈ 411,000). Stage distributions, cross-classification, and the impact of combining BMI with fat distribution on obesity categorization were analyzed. Results: About one quarter of participants were classified with obesity under both frameworks. Most were assigned to advanced stages, with high concordance for established disease. Differences were most pronounced in early stages: DMO captured a broader spectrum of mild/subclinical organ dysfunction, whereas EOSS emphasized established disease with prognostic relevance. Discrepancies reflected differences in operationalization of e.g. metabolic, cardiovascular, and mental health. Obesity thresholds influenced classification, with ∼50% reclassified when BMI was combined with different fat distribution parameters, highlighting sensitivity of early-stage assignment. Conclusion: EOSS and DMO provide complementary perspectives on obesity severity. Integrating EOSS’s prognostic granularity with DMO’s multidimensional approach may improve risk stratification and identify individuals most suitable for intensive interventions. STUDY IMPORTANCE: What is already known?: BMI alone poorly reflects obesity-related health risk; comorbidities, organ dysfunction, and functional impairments are crucial for precise staging. Two major frameworks exist: EOSS focuses on prognostic severity, while DMO identifies early/preclinical obesity—but their agreement and clinical implications were unclear. What does this study add?: Demonstrates that EOSS emphasizes established disease and prognostic severity, whereas DMO captures a broader spectrum of early or subclinical organ dysfunction, revealing distinct phenotypes within the same BMI-defined population. Highlights that combining BMI with anthropometric measures can reclassify up to ∼50% of individuals, illustrating the sensitivity of early-stage assignment to diagnostic thresholds. How might these results change the direction of research or the focus of clinical practice?: Integrating EOSS’s prognostic detail with DMO’s broad, multidimensional approach enables targeted intervention, helping clinicians prioritize patients for intensive obesity management or treatment. Provides evidence for harmonizing obesity classification beyond BMI, emphasizing the need for multidimensional assessment in both research cohorts and routine clinical practice.

Altered activity of brown adipose tissue (BAT) contributes to obesity, insulin resistance, and cardiovascular disease. BAT secretes endocrine factors (“batokines”) that regulate thermogenesis. We identify the serpin vaspin as a batokine that modulates adrenergic control of lipolysis and thermogenesis. Adipocyte-specific vaspin overexpression in mice reduces BAT activation and impairs thermoregulation during cold exposure or fasting. Mechanistically, vaspin binds low-density lipoprotein receptors (LRP1, LDLR, vLDLR), inhibiting adrenergic signaling and lipolysis in brown and white adipocytes by modulating phosphodiesterase activity and endocytic lipid uptake. Gene set enrichment analyses in human subcutaneous adipose tissue and in vitro studies confirm vaspin’s anti-lipolytic effects in humans. Overall, vaspin emerges as a regulatory BATokine that fine-tunes BAT thermogenic activity to limit excessive energy expenditure and preserve metabolic balance.