Daily Endocrinology Research Analysis
Three studies reshape endocrinology and metabolism this cycle: (1) mechanosensory Piezo2 signaling in sensory neurons restrains systemic hypermetabolism and controls brown/beige fat biology; (2) an epigenetic lncRNA–R-loop–TET3 axis (DSP-AS1) impairs desmoplakin transcription and re-epithelialization in diabetic wounds; and (3) a machine-learning model using longitudinal risk trajectories markedly improves 10-year cardiovascular risk prediction in type 2 diabetes.
Summary
Three studies reshape endocrinology and metabolism this cycle: (1) mechanosensory Piezo2 signaling in sensory neurons restrains systemic hypermetabolism and controls brown/beige fat biology; (2) an epigenetic lncRNA–R-loop–TET3 axis (DSP-AS1) impairs desmoplakin transcription and re-epithelialization in diabetic wounds; and (3) a machine-learning model using longitudinal risk trajectories markedly improves 10-year cardiovascular risk prediction in type 2 diabetes.
Research Themes
- Sensory neurobiology governing energy homeostasis
- Epigenetic regulation of diabetic wound healing
- AI-enabled cardiovascular risk prediction in type 2 diabetes
Selected Articles
1. Piezo2 in sensory neurons regulates systemic and adipose tissue metabolism.
Using multiple genetic mouse models, the authors show that mechanosensory Piezo2 in Runx3/PV sensory neurons constrains thermogenic adipose remodeling and systemic hypermetabolism. Piezo2 deletion protects against high-fat-diet obesity, improves insulin sensitivity, and induces browning/beiging, likely via increased norepinephrine.
Impact: This uncovers a previously unappreciated sensory mechanotransduction pathway controlling adipose biology and whole-body metabolism, opening targets beyond classical sympathetic circuits.
Clinical Implications: If conserved in humans, modulating Piezo2-mediated sensory signaling could complement anti-obesity and insulin-sensitizing strategies by tuning adipose tissue thermogenesis.
Key Findings
- Targeting Runx3/PV sensory neurons yielded reduced adiposity with improved insulin sensitivity and glucose tolerance.
- Piezo2 deletion in PV sensory neurons protected against high-fat-diet-induced obesity and induced browning/beiging of adipose tissue.
- Findings support a model where Piezo2-sensed mechanical signals in sensory neurons restrain systemic hypermetabolism, potentially via elevated norepinephrine.
Methodological Strengths
- Multiple independent genetic mouse models targeting Runx3/PV neurons and Piezo2 deletion.
- Comprehensive in vivo metabolic phenotyping including adipose morphology and insulin sensitivity.
Limitations
- Preclinical mouse data without direct human validation.
- Mechanistic link between mechanosensation and norepinephrine dynamics requires further causal dissection.
Future Directions: Validate Piezo2-sensory neuron control of adipose thermogenesis in human tissues or models; explore pharmacologic or neuromodulatory approaches to modulate this axis safely.
2. The Diminution of R-Loops Generated by LncRNA DSP-AS1 Inhibits DSP Gene Transcription to Impede the Re-Epithelialization During Diabetic Wound Healing.
The study identifies an epigenetic mechanism in diabetic wounds: reduced lncRNA DSP-AS1–mediated R-loop formation at the DSP promoter diminishes TET3 recruitment and demethylation, downregulating desmoplakin and impairing keratinocyte MET and re-epithelialization.
Impact: Reveals a tractable lncRNA–R-loop–TET3 axis driving impaired re-epithelialization, suggesting novel nucleic acid or epigenetic therapies for diabetic foot ulcers.
Clinical Implications: Therapeutically augmenting DSP-AS1 function, stabilizing R-loops at the DSP promoter, or enhancing TET3 recruitment may accelerate re-epithelialization in refractory diabetic wounds.
Key Findings
- Re-epithelialization failure in diabetic wounds is linked to impaired MET of keratinocytes rather than EMT.
- Desmoplakin (DSP) is downregulated due to reduced TET3 occupancy and diminished TET3-dependent demethylation at the DSP promoter.
- lncRNA DSP-AS1 forms R-loops at the DSP promoter to recruit TET3; its downregulation in diabetic skin reduces R-loops and suppresses DSP transcription.
Methodological Strengths
- Mechanistic dissection linking promoter methylation dynamics, TET3 occupancy, and lncRNA-mediated R-loop formation.
- In vivo diabetic models coupled with molecular assays (qRT-PCR, protein analyses, interaction assays).
Limitations
- Predominantly preclinical with species-specific differences likely; human tissue validation scope is limited in the abstract.
- Therapeutic modulation of R-loops/TET3 may carry off-target genomic risks requiring careful safety evaluation.
Future Directions: Validate DSP-AS1/TET3/R-loop signatures in human diabetic ulcer biopsies; develop RNA therapeutics or epigenetic modulators to restore DSP expression and MET; assess safety of R-loop targeting.
3. Predicting cardiovascular outcomes in Chinese patients with type 2 diabetes by combining risk factor trajectories and machine learning algorithm: a cohort study.
In 16,378 Chinese adults with type 2 diabetes, a machine-learning model using 4-year trajectories of risk factors achieved C-index 0.80 for 10-year cardiovascular events, outperforming China-PAR and PREVENT with marked NRI/IDI gains. Dynamic trajectories and ML both contributed to improved discrimination and stratification.
Impact: Demonstrates a practical, trajectory-informed ML approach that substantially outperforms established calculators, enabling personalized cardiovascular risk management in diabetes.
Clinical Implications: Incorporation of longitudinal risk trajectories into EHR-enabled ML tools could refine cardiovascular risk stratification, guide therapy intensity, and monitor risk transitions in type 2 diabetes.
Key Findings
- ML-CVD-C achieved C-index 0.80 (95% CI 0.78–0.82) for 10-year CV outcomes, outperforming China-PAR, PREVENT, and a baseline-only ML model (C=0.62–0.65).
- Substantial reclassification improvements versus comparators (NRI 44–58%; IDI ~8–10%), with both trajectories and ML contributing to performance.
- All models tended to overestimate high-risk prevalence; transition analyses showed risk reductions for those remaining stable or reclassified downward.
Methodological Strengths
- Large prospective cohort with explicit train/test split and head-to-head comparisons with established risk scores.
- Use of longitudinal trajectories (4-year changes) rather than static baselines, improving discrimination and reclassification.
Limitations
- Single-cohort internal validation without external validation; potential overfitting/generalizability concerns.
- Observed overestimation of high-risk prevalence across models; calibration optimization needed before clinical deployment.
Future Directions: External validation across diverse Chinese and non-Chinese T2D populations; EHR integration trials to test clinical utility and impact on outcomes; continuous calibration updating.