👤 Robert Wolfgang Schwenk

To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D). Bayesian network analyses and complementary two-sample Mendelian randomization were used to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with liver fat in the IMI-DIRECT prospective cohort study. Data included frequently sampled metabolic challenge tests, MRI-derived abdominal and hepatic fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults without diabetes, with harmonized protocols enabling replication. High basal insulin secretion rate (BasalISR), estimated via C-peptide deconvolution, emerged as the primary potential causal driver of liver fat accumulation in both cohorts. BasalISR, a clearance-independent measure of β-cell insulin output distinct from peripheral insulin levels, was independently linked to hepatic steatosis. Visceral adipose tissue exhibited bidirectional associations with liver fat, suggesting a self-reinforcing metabolic loop. Of 446 analyzed proteins, 34 mapped to these metabolic networks (27 in the non-diabetes network, 18 in the T2D network, and 11 shared). Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses identified GUSB in females and LEP in males as the strongest protein predictors of liver fat. BasalISR may better capture early β-cell-driven disturbances contributing to MASLD. These findings outline a multifactorial, sex- and disease stage-specific proteo-metabolic architecture of hepatic steatosis and identify potential biomarkers or therapeutic targets. Show less

To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D). We used Bayesian network analyses to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with fatty liver using data from the IMI-DIRECT prospective cohort study. Measurements were made of glucose and insulin dynamics (using frequently-sampled metabolic challenge tests), MRI-derived abdominal and liver fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults free from diabetes at enrolment. The common protocols used in these two cohorts provided the opportunity for replication analyses to be performed. When the direction of the effect could not be determined with high probability through Bayesian networks, complementary two-sample Mendelian randomization (MR) was employed. High basal insulin secretion rate (BasalISR) was identified as the primary causal driver of liver fat accumulation in both diabetes and non-diabetes. Excess visceral adipose tissue (VAT) was bidirectionally associated with liver fat, indicating a self-reinforcing metabolic loop. Basal insulin clearance (Clinsb) worsened as a consequence of liver fat accumulation to a greater degree before the onset of T2D. Out of 446 analysed proteins, 34 mapped to these metabolic networks and 27 were identified in the non-diabetes network, 18 in the diabetes network, and 11 were common between the two networks. Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses revealed distinct proteomic drivers: GUSB and LEP were most predictive of liver fat in females and males, respectively. Basal insulin hypersecretion is a modifiable, causal driver of MASLD, particularly prior to glycaemic decompensation. Our findings highlight a multifactorial, sex- and disease-stage-specific proteo-metabolic architecture of hepatic steatosis. Proteins such as GUSB, ALDH1A1, LPL, and IGFBPs warrant further investigation as potential biomarkers or therapeutic targets for MASLD prevention and treatment. Show less

Mouse CA1 pyramidal neurons express apamin-sensitive SK2-containing channels in the post-synaptic membrane, positioned close to NMDA-type (N-methyl-D-aspartate) glutamate receptors. Activated by synaptically evoked NMDAR-dependent Ca(2+) influx, the synaptic SK2-containing channels modulate excitatory post-synaptic responses and the induction of synaptic plasticity. In addition, their activity- and protein kinase A-dependent trafficking contributes to expression of long-term potentiation (LTP). We have identified a novel synaptic scaffold, MPP2 (membrane palmitoylated protein 2; p55), a member of the membrane-associated guanylate kinase (MAGUK) family that interacts with SK2-containing channels. MPP2 and SK2 co-immunopurified from mouse brain, and co-immunoprecipitated when they were co-expressed in HEK293 cells. MPP2 is highly expressed in the post-synaptic density of dendritic spines on CA1 pyramidal neurons. Knocking down MPP2 expression selectively abolished the SK2-containing channel contribution to synaptic responses and decreased LTP. Thus, MPP2 is a novel synaptic scaffold that is required for proper synaptic localization and function of SK2-containing channels. Show less

Obesity is characterized as an excess accumulation of body fat resulting from a positive energy balance. It is the major risk factor for type 2 diabetes (T2D). The evidence for familial aggregation of obesity and its associated metabolic diseases is substantial. To date, about 150 genetic loci identified in genome-wide association studies (GWAS) are linked with obesity and T2D, each accounting for only a small proportion of the predicted heritability. However, the percentage of overall trait variance explained by these associated loci is modest (~5-10% for T2D, ~2% for BMI). The lack of powerful genetic associations suggests that heritability is not entirely attributable to gene variations. Some of the familial aggregation as well as many of the effects of environmental exposures, may reflect epigenetic processes. This review summarizes our current knowledge on the genetic basis to individual risk of obesity and T2D, and explores the potential role of epigenetic contribution. Show less