Daily Endocrinology Research Analysis
Analyzed 128 papers and selected 3 impactful papers.
Summary
Major advances span metabolism and diabetes care: a mechanistic study identifies an MTARC1–glycerophospholipid–lipid droplet axis that protects against MASLD, a meta-analysis finds that adding SGLT2i, DPP-4i, or GLP-1 RA to automated insulin delivery improves time-in-range without excess DKA, and a diabetes-specific neural network (RenoTrue) outperforms CKD-EPI for estimating GFR against measured GFR.
Research Themes
- Hepatic lipid metabolism and MASLD mechanisms
- Adjunct pharmacotherapy with automated insulin delivery in type 1 diabetes
- Improved kidney function estimation in diabetes using machine learning
Selected Articles
1. MTARC1 Inactivation Remodels Lipid Droplets to Protect Against Metabolic Fatty Liver Disease.
Global and liver-specific Mtarc1 knockout reduced diet-induced steatosis, injury, inflammation, and fibrosis. Mechanistically, MTARC1 deficiency post-transcriptionally upregulated CEPT1 and PEMT, remodeling lipid droplet phospholipids to shrink droplets and enhance lipophagy/lipolysis-dependent triglyceride degradation. Silencing CEPT1/PEMT reversed protection, highlighting an MTARC1–GPL biosynthesis–LD degradation axis.
Impact: This work uncovers a mechanistic axis linking MTARC1 to lipid droplet remodeling and triglyceride clearance, directly explaining human genetic protection from MASLD and nominating MTARC1 as a drug target.
Clinical Implications: MTARC1 inhibition may represent a therapeutic strategy for MASLD by enhancing lipid droplet turnover via phospholipid remodeling; translational studies and pharmacologic inhibitors should be pursued.
Key Findings
- Global and liver-specific Mtarc1 knockout mice were protected from diet-induced steatosis, injury, inflammation, and fibrosis.
- Protection required triglyceride degradation via lipophagy and lipolysis.
- MTARC1 deficiency post-transcriptionally upregulated CEPT1 and PEMT, altering lipid droplet phospholipid composition to reduce droplet size and increase degradation.
- Knockdown of CEPT1 and PEMT reversed hepatoprotection, defining an MTARC1–GPL biosynthesis–LD degradation axis.
Methodological Strengths
- Use of both global and liver-specific knockout models with in vivo and in vitro validation
- Integrated multi-omics and genetic epistasis (CEPT1/PEMT knockdown) to establish mechanism
Limitations
- Preclinical mouse and cell models; human translation and safety of MTARC1 inhibition remain untested
- Potential off-target or compensatory effects of chronic MTARC1 inhibition are unknown
Future Directions: Develop selective MTARC1 inhibitors; test efficacy/safety in NASH/MASLD models and human systems; identify biomarkers of target engagement and lipid droplet remodeling.
BACKGROUND AND AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health burden with limited treatment options. Human genetic studies have identified mitochondrial amidoxime-reducing component (MTARC1) variants associated with a reduced risk of MASLD, but the underlying mechanisms remain unclear. Here, we investigated the role of MTARC1 in fatty liver disease using in vitro and in vivo models. METHODS: We generated global and liver-specific Mtarc1 knockout mice, as well as models with genetic inhib
2. Efficacy and Safety of Dipeptidyl Peptidase-4 Inhibitors, Glucagon-Like Peptide-1 Receptor Agonists, and Sodium-Glucose Cotransporter-2 Inhibitors as Adjunctive Therapy to Automated Insulin Delivery System in Type 1 Diabetes: A Systematic Review and Meta-Analysis of Randomized Clinical Trials.
Across nine RCTs, adding SGLT2 inhibitors, DPP-4 inhibitors, or GLP-1 receptor agonists to AID improved time-in-range (70–180 mg/dL), reduced hyperglycemia, and lowered insulin requirements without evidence of increased diabetic ketoacidosis or severe hypoglycemia.
Impact: This meta-analysis synthesizes RCTs to guide adjunct therapy with AID in T1D, supporting combined strategies that improve glycemic quality without apparent safety trade-offs.
Clinical Implications: For selected patients using AID, adjunct SGLT2i/GLP-1 RA/DPP-4i may improve glycemic metrics and reduce insulin needs. Careful education and monitoring for euglycemic ketosis remain prudent, especially with SGLT2 inhibitors.
Key Findings
- Nine RCTs showed adjunct noninsulin agents with AID increased time-in-range and reduced hyperglycemia.
- Insulin requirements were reduced without signals of increased DKA or severe hypoglycemia.
- Results were synthesized under a registered protocol with risk of bias and GRADE assessment.
Methodological Strengths
- Systematic review and meta-analysis restricted to randomized controlled trials
- Registered protocol with PRISMA adherence and GRADE evidence assessment
Limitations
- Heterogeneity in adjunct drug classes, trial durations, and patient characteristics
- Limited power to detect rare adverse events and limited long-term data
Future Directions: Head-to-head trials of adjunct classes with AID, longer follow-up for safety (e.g., DKA risk), and evaluation in diverse populations and real-world settings.
AIMS: The efficacy of adding various noninsulin hypoglycemic drugs to automated insulin delivery (AID) systems in patients with type 1 diabetes (T1D) was investigated in randomized controlled trials (RCTs), yet no meta-analysis has been conducted. This study aimed to systematically analyze the existing evidence. METHODS: Four datasets were searched up to August 31, 2025. Inclusion criteria were as follows: T1D populations of any age; comparing any type of noninsulin hypoglycemic drug added to AID systems or not; and reportin
3. RenoTrue: a diabetes-specific machine learning model to estimate glomerular filtration rate for people with diabetes.
In 5,619 adults with type 1 or type 2 diabetes across five international cohorts, a neural network (RenoTrue) using age, sex, and serum creatinine achieved higher agreement and accuracy (p30) with measured GFR than CKD-EPI 2009, improving performance across the full GFR range.
Impact: Accurate GFR estimation underpins drug dosing and risk stratification in diabetes; this diabetes-specific model, validated against measured GFR, offers immediate translational potential.
Clinical Implications: RenoTrue may enhance CKD detection and medication dosing decisions in diabetes clinics; external implementation and calibration across diverse populations are the next steps.
Key Findings
- In a 5,619-participant, five-cohort dataset with measured GFR, RenoTrue outperformed CKD-EPI 2009 in agreement and p30 accuracy.
- The model uses only age, sex, and serum creatinine, enhancing practicality for clinical deployment.
- Performance gains were observed across the full spectrum of GFR.
Methodological Strengths
- Large multi-cohort dataset with measured GFR gold standard and predefined train/validation/test splits
- Robust comparative evaluation using mixed-effects modeling
Limitations
- Creatinine-only inputs may not capture non-GFR determinants across diverse populations
- Prospective clinical implementation and impact on decision-making were not assessed
Future Directions: External validation and calibration in varied ancestries and care settings; exploration of cystatin C or combined biomarkers; prospective impact studies on clinical decisions and outcomes.
BACKGROUND: Existing methods for estimating GFR in people with diabetes have shown inaccuracies when compared to mGFR measurements. We developed and validated an artificial neural network - RenoTrue to improve estimating GFR in people with diabetes. METHODS: 5,619 individuals from five international cohorts with type 1 and type 2 diabetes was split into training (70%), validation (10%) and test (20%) datasets. RenoTrue was developed to estimate GFR using age, sex, and serum creatinine. The performance was evaluated