Daily Endocrinology Research Analysis
Three studies stand out today across metabolic and endocrine science: a randomized crossover trial shows that sucralose acutely increases hypothalamic blood flow and hunger signaling; a multicenter machine-learning tool (ALADDIN) improves noninvasive identification of significant fibrosis (≥F2) in MASLD to guide resmetirom eligibility; and preclinical work repurposes halofuginone to induce weight loss via integrated stress response activation of GDF15 and FGF21.
Summary
Three studies stand out today across metabolic and endocrine science: a randomized crossover trial shows that sucralose acutely increases hypothalamic blood flow and hunger signaling; a multicenter machine-learning tool (ALADDIN) improves noninvasive identification of significant fibrosis (≥F2) in MASLD to guide resmetirom eligibility; and preclinical work repurposes halofuginone to induce weight loss via integrated stress response activation of GDF15 and FGF21.
Research Themes
- Non-caloric sweeteners and central appetite regulation
- AI-driven noninvasive staging for MASLD and precision therapeutics
- Drug repurposing for obesity via endocrine stress hormones (GDF15/FGF21)
Selected Articles
1. Non-caloric sweetener effects on brain appetite regulation in individuals across varying body weights.
In a randomized crossover study of 75 young adults, sucralose acutely increased hypothalamic blood flow and hunger responses compared with sucrose, without raising glucose. Sucrose elevated glucose and reduced medial hypothalamic perfusion, whereas sucralose strengthened hypothalamic connectivity with motivational and somatosensory regions.
Impact: This mechanistic RCT directly links a widely consumed sweetener to acute hypothalamic activity and hunger signaling, informing dietary guidance and public health policy.
Clinical Implications: Counseling on non-caloric sweetener use may consider potential acute increases in hypothalamic appetite signaling, especially in individuals with obesity; further chronic trials are needed before changing guidelines.
Key Findings
- Sucralose vs sucrose increased hypothalamic blood flow (P<0.018) and hunger responses (P<0.001).
- Sucralose vs water increased hypothalamic blood flow (P<0.019) but did not change hunger ratings.
- Sucrose, but not sucralose, raised peripheral glucose, correlating with reduced medial hypothalamic blood flow (P<0.007).
- Sucralose increased functional connectivity between the hypothalamus and motivation/somatosensory brain regions.
Methodological Strengths
- Randomized crossover design controlling inter-individual variability
- Multimodal outcomes including cerebral blood flow and functional connectivity
Limitations
- Acute, single-exposure study; long-term metabolic and behavioral effects remain unknown
- Young adult sample limits generalizability to older populations or those with comorbidities
Future Directions: Conduct longer-term randomized trials assessing repeated sucralose exposure on appetite, energy intake, body weight, and neuroendocrine responses across BMI strata.
Sucralose, a widely used non-caloric sweetener, provides sweet taste without calories. Some studies suggest that non-caloric sweeteners stimulate appetite, possibly owing to the delivery of a sweet taste without the post-ingestive metabolic signals that normally communicate with the hypothalamus to suppress hunger. In a randomized crossover trial (ClinicalTrials.gov identifier: NCT02945475 ), 75 young adults (healthy weight, overweight or with obesity) consumed a drink containing sucralose, sweetness-matched sucrose or water. We show that acute consumption of sucralose versus sucrose stimulates hypothalamic blood flow (P < 0.018) and greater hunger responses (P < 0.001). Sucralose versus water also increases hypothalamic blood flow (P < 0.019) but produces no difference in hunger ratings. Sucrose, but not sucralose, increases peripheral glucose levels, which are associated with reductions in medial hypothalamic blood flow (P < 0.007). Sucralose, compared to sucrose and water, results in increased functional connections between the hypothalamus and brain regions involved in motivation and somatosensory processing. These findings suggest that non-caloric sweeteners could affect key mechanisms in the hypothalamus responsible for appetite regulation.
2. The clinical antiprotozoal drug halofuginone promotes weight loss by elevating GDF15 and FGF21.
In mouse and pig models, halofuginone produced weight loss by suppressing intake and enhancing energy expenditure, while improving insulin resistance and steatosis. Mechanistically, it activated the integrated stress response, elevating endocrine factors GDF15 and FGF21.
Impact: Repurposes an approved drug to trigger endogenous anorectic and metabolic hormones (GDF15, FGF21), suggesting a translational path for obesity therapy beyond GLP-1–based approaches.
Clinical Implications: If safety and efficacy translate to humans, halofuginone or ISR-tuned analogs could complement or serve as alternatives to incretin-based anti-obesity drugs, particularly in patients intolerant to GLP-1 RAs.
Key Findings
- Halofuginone reduced food intake, increased energy expenditure, and induced weight loss in diet-induced obese mice.
- Improved insulin resistance and hepatic steatosis accompanied weight loss.
- Mechanism: activation of integrated stress response with elevations of FGF21 and GDF15.
- Efficacy signals extended to a large-animal (pig) model, supporting translational potential.
Methodological Strengths
- Multi-species preclinical validation (mouse and pig)
- Mechanistic dissection with transcriptomics and pharmacologic tools
Limitations
- Preclinical; human safety, dose, and efficacy remain untested for obesity endpoints
- Potential ISR-related toxicities and off-target effects require careful evaluation
Future Directions: First-in-human dose-finding studies assessing GDF15/FGF21 pharmacodynamics, tolerability, and weight/metabolic outcomes; exploration of ISR-selective analogs to optimize therapeutic index.
Obesity is a debilitating global pandemic with a huge cost on health care due to it being a major underlying risk factor for several diseases. Therefore, there is an unmet medical need for pharmacological interventions to curb obesity. Here, we report that halofuginone, a Food and Drug Administration-approved anti-scleroderma and antiprotozoal drug, is a promising anti-obesity agent in preclinical mouse and pig models. Halofuginone suppressed food intake, increased energy expenditure, and resulted in weight loss in diet-induced obese mice while also alleviating insulin resistance and hepatic steatosis. Using molecular and pharmacological tools with transcriptomics, we identified that halofuginone increases fibroblast growth factor 21 (FGF21) and growth differentiation factor 15 (GDF15) levels via activating integrated stress response. Using
3. ALADDIN: A Machine Learning Approach to Enhance the Prediction of Significant Fibrosis or Higher in Metabolic Dysfunction-Associated Steatotic Liver Disease.
Across 2,630 biopsy-characterized MASLD patients, the ALADDIN ensemble (with VCTE) improved external validation AUC to 0.791 for ≥F2 fibrosis and outperformed VCTE-only and established scores; a labs-only version (AUC 0.706) performed best among non-VCTE methods. Web calculators support biopsy-free selection of resmetirom-eligible patients.
Impact: Provides a validated, accessible AI tool for a pressing clinical need—noninvasive identification of significant fibrosis in MASLD to target newly approved therapy.
Clinical Implications: ALADDIN-F2-VCTE can refine referrals for treatment and reduce unnecessary biopsies; ALADDIN-F2-Lab offers a pragmatic alternative where VCTE access is limited.
Key Findings
- External validation AUC 0.791 (95% CI 0.764–0.819) for ALADDIN-F2-VCTE, superior to VCTE alone (0.745), FAST (0.710), and Agile-3 (0.740).
- ALADDIN-F2-Lab (no VCTE) achieved AUC 0.706, outperforming FIB-4 and other lab-based scores.
- Decision curve analysis and calibration favored ALADDIN models, supporting clinical utility.
- Public web calculators enable immediate clinical integration and reproducibility.
Methodological Strengths
- Multicenter training, testing, and external validation across 15 centers
- Algorithm comparison with ensemble modeling and calibration/decision-curve analyses
Limitations
- Retrospective datasets and potential spectrum bias of referred populations
- Biopsy reference standard subject to sampling variability; generalizability beyond study centers needs further proof
Future Directions: Prospective impact studies to test ALADDIN-guided care pathways on biopsy rates and treatment outcomes; local recalibration and fairness assessments across demographics.
INTRODUCTION: The recent US Food and Drug Administration approval of resmetirom for treating metabolic dysfunction-associated steatohepatitis in patients necessitates patient selection for significant fibrosis or higher (≥F2). No existing vibration-controlled transient elastography (VCTE) algorithm targets ≥F2. METHODS: The mAchine Learning ADvanceD fibrosis and rIsk metabolic dysfunction-associated steatohepatitis Novel predictor (ALADDIN) study addressed this gap by introducing a machine-learning-based web calculator that estimates the likelihood of significant fibrosis using routine laboratory parameters with and without VCTE. Our study included a training set of 827 patients, a testing set of 504 patients with biopsy-confirmed metabolic dysfunction-associated steatotic liver disease from 6 centers, and an external validation set of 1,299 patients from 9 centers. Five algorithms were compared using area under the curve (AUC) in the test set: ElasticNet, random forest, gradient boosting machines, XGBoost, and neural networks. The top 3 (random forest, gradient boosting machines, and XGBoost) formed an ensemble model. RESULTS: In the external validation set, the ALADDIN-F2-VCTE model, using routine laboratory parameters with VCTE (AUC 0.791, 95% confidence interval [CI]: 0.764-0.819), outperformed VCTE alone (0.745, 95% CI 0.717-0.772, P < 0.0001), FibroScan-aspartate aminotransferase (0.710, 0.679-0.748, P < 0.0001), and Agile-3 model (0.740, 0.710-0.770, P < 0.0001) regarding the AUC, decision curve analysis, and calibration. The ALADDIN-F2-Lab model, using routine laboratory parameters without VCTE, achieved an AUC of 0.706 (95% CI: 0.668-0.749) and outperformed Fibrosis-4, steatosis-associated fibrosis estimator, and LiverRisk scores. DISCUSSION: Along with the steatosis-associated fibrosis estimator model developed to target significant fibrosis or higher, ALADDIN-F2-VCTE ( https://aihepatology.shinyapps.io/ALADDIN1 ) uniquely supports a refined noninvasive approach to patient selection for resmetirom without the need for liver biopsy. In addition, ALADDIN-F2-Lab ( https://aihepatology.shinyapps.io/ALADDIN2 ) offers an effective alternative when VCTE is unavailable.