Daily Endocrinology Research Analysis
An open adipose tissue knowledge portal integrates multi-omics clinical and experimental datasets to enable cross-study analyses. A mechanistic human study shows that even brief high-calorie overeating disrupts brain insulin action and promotes liver fat with effects persisting beyond the overeating period. In type 2 diabetes, advanced 1H-NMR lipoprotein and glycoprotein profiling significantly improves cardiovascular event prediction with external validation.
Summary
An open adipose tissue knowledge portal integrates multi-omics clinical and experimental datasets to enable cross-study analyses. A mechanistic human study shows that even brief high-calorie overeating disrupts brain insulin action and promotes liver fat with effects persisting beyond the overeating period. In type 2 diabetes, advanced 1H-NMR lipoprotein and glycoprotein profiling significantly improves cardiovascular event prediction with external validation.
Research Themes
- Adipose tissue multi-omics integration
- Diet-induced brain insulin resistance
- Precision cardiometabolic risk prediction in type 2 diabetes
Selected Articles
1. adiposetissue.org: A knowledge portal integrating clinical and experimental data from human adipose tissue.
This paper introduces a curated portal that unifies clinical and experimental adipose tissue datasets, including multi-depot, cell-type, and perturbation studies, with transcriptomic/proteomic layers from over 6,000 individuals. It streamlines access and enables integrative, single-cell–level analyses, providing a foundational resource for adipose biology and metabolic disease research.
Impact: Provides a widely usable data infrastructure that can accelerate discovery of mechanisms, biomarkers, and targets across obesity and metabolic disease research.
Clinical Implications: While not directly altering care, the portal can speed translation by enabling robust cross-study validation of adipose biomarkers and therapeutic targets for obesity, insulin resistance, and related disorders.
Key Findings
- Centralizes and integrates clinical and experimental adipose datasets with transcriptomic/proteomic data from >6,000 individuals.
- Covers multiple adipose depots, resident cell types, and adipocyte perturbation studies.
- Enables streamlined access for integrative analyses down to the single-cell level.
Methodological Strengths
- Large-scale multi-omics integration across numerous cohorts and study types.
- Standardized, streamlined access that supports reproducibility and cross-study comparisons.
Limitations
- Scientific insights depend on the heterogeneity and quality of contributing datasets.
- Clinical impact is indirect and contingent on community adoption and continuous updates.
Future Directions: Expand longitudinal and interventional datasets, harmonize metadata, and embed analytic tools to facilitate hypothesis testing and clinical translation.
We developed the Adipose Tissue Knowledge Portal by centralizing previously dispersed datasets, integrating clinical and experimental results with transcriptomic and proteomic data from >6,000 women and men. The platform includes multiple adipose depots, resident cell types, and adipocyte perturbation studies. By providing streamlined data access, the portal enables integrative analyses and serves as a powerful tool to interrogate various dimensions of adipose biology down to the single-cell level.
2. A short-term, high-caloric diet has prolonged effects on brain insulin action in men.
In healthy-weight men, brief overeating with calorie-dense sweet and fatty foods induced liver fat accumulation and impaired brain insulin action, with disruptions persisting beyond the overeating period. These findings suggest that brain insulin responsiveness adapts rapidly to dietary excess before weight gain, potentially priming pathways toward obesity and metabolic disease.
Impact: Links short-term dietary excess to persistent central insulin dysfunction and hepatic steatosis, reframing early prevention windows for obesity and metabolic disease.
Clinical Implications: Counseling should emphasize that even brief high-calorie overeating may durably impair brain insulin action and increase liver fat, supporting earlier lifestyle interventions and monitoring in at-risk individuals.
Key Findings
- Short-term overeating with calorie-rich sweet and fatty foods induced liver fat accumulation in healthy-weight men.
- Brain insulin action was disrupted and the impairment outlasted the overeating period.
- Brain insulin responsiveness can adapt to short-term diet changes before weight gain, potentially facilitating obesity development.
Methodological Strengths
- Prospective mechanistic human study directly assessing brain insulin responsiveness.
- Concurrent assessment of hepatic fat accumulation provides coherent physiological context.
Limitations
- Sample size and male-only population limit generalizability.
- Short-term follow-up without long-term clinical outcomes; non-randomized design.
Future Directions: Test reversibility and mitigation strategies (e.g., exercise, diet composition), include women and diverse populations, and elucidate neural mechanisms and exposure thresholds.
Brain insulin responsiveness is linked to long-term weight gain and unhealthy body fat distribution. Here we show that short-term overeating with calorie-rich sweet and fatty foods triggers liver fat accumulation and disrupted brain insulin action that outlasted the time-frame of its consumption in healthy weight men. Hence, brain response to insulin can adapt to short-term changes in diet before weight gain and may facilitate the development of obesity and associated diseases.
3. Advanced serum lipoprotein and glycoprotein profiling for cardiovascular event prediction in type 2 diabetes mellitus: the LIPOCAT study.
In 933 T2D participants from four Spanish cohorts, advanced 1H-NMR Liposcale/Glycoscale profiling identified higher VLDL-C, remnant IDL-TG, LDL-TG, and glycoproteins A/B in those with CV events. Adding these variables to traditional risk models raised AUROC to 0.756, with similar gains in an external validation cohort.
Impact: Demonstrates that NMR-derived lipoprotein and glycoprotein metrics add predictive value beyond traditional risk factors for CV events in T2D, supporting precision risk stratification.
Clinical Implications: If validated across settings and cost-effective, NMR-based lipoprotein/glycoprotein profiling could refine CV risk assessment in T2D to guide preventive therapies.
Key Findings
- Among 933 T2D participants, 104 experienced a CV event during follow-up.
- Elevated VLDL-C, remnant IDL-TG, LDL-TG, and glycoproteins A and B were associated with CV events.
- Adding advanced NMR-derived variables improved AUROC to 0.756, replicated in an external validation cohort.
Methodological Strengths
- Prospective multi-cohort design with external validation.
- Use of machine learning (random forest) and AUROC analysis to quantify incremental predictive value.
Limitations
- Observational design with potential residual confounding and limited generalizability beyond Spanish cohorts.
- Clinical utility and cost-effectiveness of NMR profiling were not evaluated in care pathways.
Future Directions: Prospective implementation studies to test clinical impact and cost-effectiveness, calibration across laboratories, and integration with genomics/omics to build multimodal risk models.
BACKGROUND: Traditional risk factors cannot accurately predict cardiovascular events (CVE) in type 2 diabetes (T2D). The LIPOCAT study aimed to prospectively evaluate the clinical utility of advanced lipoprotein characteristics and glycoproteins to predict future cardiovascular events (CVE) in a large cohort of subjects with type 2 diabetes mellitus (T2D). METHODS: From four different Spanish prospective cohorts, a total of 933 T2D subjects were selected to form the LIPOCAT study. Advanced 1H-Nuclear Magnetic Resonance (1H-NMR) analysis included lipoprotein (Liposcale®) and glycoprotein (Glycoscale) profiling. Random forest classification models and Area Under the Receiver Operating Characteristics (AUROC) analysis were used to assess the differential contribution of advanced variables in predicting CVE. Validation was performed using an external cohort. RESULTS: Out of 933 T2D subjects, 104 reported a CVE during follow-up. Analysis of Liposcale®/Glycoscale uncovered elevations in the circulating VLDL-cholesterol(C), remnant IDL-triglycerides (TG) and LDL-TG in subjects with CVE, along with glycoproteins (Glyc) A and B. Moreover, the incorporation of advanced Liposcale® variables to a base model constructed with traditional risk factors significantly improved the prediction of CVE, as evidenced by 1.5-fold increase in the C statistic (AUROC), reaching AUROC values of 0.756. In the independent validation cohort, similar improvements in AUROC values were observed by adding the advanced variables to the traditional models. CONCLUSIONS: Advanced 1H-NMR analysis revealed previously hidden lipoprotein and glycoprotein characteristics associated with CVE in T2D subjects.