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Fang Hu

Publications and source records attributed to Fang Hu.

2 recordsLinked to original sources
PubMed2026

Efficacy and Safety of Anti-Obesity Medications for Weight Loss Maintenance in Adults With Overweight or Obesity: A Systematic Review and Meta-Analysis of Randomised Controlled Trials.

AIMS: This systematic review and meta-analysis aimed to evaluate the efficacy and safety of anti-obesity medications (AOMs) for long-term weight maintenance following initial weight loss in adults with overweight or obesity. METHODS: We searched PubMed, Embase, Web of Science and the Cochrane Library from inception to 31 October 2025. Eligible studies were randomised controlled trials (RCTs) comparing AOMs with placebo during the weight-maintenance phase in adults with overweight or obesity after initial weight loss achieved through lifestyle, dietary, surgical or pharmacological interventions. Outcomes included anthropometric indices, cardiometabolic measures, safety endpoints, quality of life and neuropsychiatric adverse events. Weighted mean differences (WMDs) and odds ratios (ORs), each with 95% confidence intervals (CIs), were pooled for continuous and categorical outcomes, respectively, using fixed-effect or random-effects models as appropriate. Risk of bias was assessed using the Cochrane RoB 2 tool, and certainty of evidence was evaluated using the GRADE framework. The protocol was registered with PROSPERO (CRD420261293040). RESULTS: A total of 12 RCTs comprising 4915 participants met the inclusion criteria. Compared with placebo, AOM therapy during the weight-maintenance phase was associated with prevention of weight regain and additional improvements in anthropometric and cardiometabolic outcomes. AOMs led to further reductions in body weight (BW, WMD: -8.68 kg, 95% CI: -13.25 to -4.11), waist circumference (WC, WMD: -7.16 cm, 95% CI: -10.34 to -3.98) and body mass index (BMI, WMD: -3.58 kg/m2, 95% CI: -5.69 to -1.46). Additional benefits were observed for triglycerides, total cholesterol, low-density lipoprotein cholesterol, systolic blood pressure and diastolic blood pressure. Gastrointestinal adverse events were more common with AOMs, whereas serious adverse events were not significantly increased. Neuropsychiatric adverse events were generally comparable between groups, and available quality-of-life measures favoured AOM treatment. CONCLUSIONS: AOM therapy during the weight-maintenance phase can help prevent weight regain, provide further reductions in body weight and improve cardiometabolic risk factors in adults with overweight or obesity. Gastrointestinal adverse events were more common with AOMs, whereas serious adverse events were not significantly increased. Further long-term trials are needed to clarify optimal treatment strategies and safety.

Humans↗
PubMed2026

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans↗