Comparison of the efficacy and safety of an oral sulfate solution and 3-L polyethylene glycol on bowel preparation before colonoscopy: a phase III multicenter randomized controlled trial.

BACKGROUND AND AIMS: Adequate bowel preparation is crucial for clear mucosal visualization during colonoscopy. We aimed to comprehensively compare oral sulfate solution (OSS) and 3-L split-dose polyethylene glycol (PEG) for bowel preparation before colonoscopy. METHODS: This randomized, active-controlled, noninferiority study was performed in 10 medical centers. Eligible subjects were enrolled to receive OSS or 3-L PEG in a split-dose regimen. The quality of bowel preparation, adverse reactions, and acceptability were evaluated. The quality of bowel preparation was evaluated using the Boston Bowel Preparation Scale. Safety was evaluated by adverse reactions. The study population was divided into the full analysis set (FAS), the safety set, the modified FAS (mFAS), and the per-protocol set (PPS). RESULTS: Three hundred forty-eight potentially eligible subjects were enrolled. Three hundred forty-four subjects were included in the FAS and safety set, 340 subjects were included in the mFAS, and 328 subjects were included in the PPS. Adequate bowel preparation of the OSS was not inferior to 3-L PEG in the mFAS (98.22% vs 97.66%) and the PPS (98.17% vs 98.78%). There was no significant difference in acceptability between the 2 groups (94.74% vs 94.80%, P = .9798). Overall adverse reactions were similar (50.88% vs 44.51%, P = .2370) between the 2 groups. CONCLUSIONS: The split-dose OSS regimen was not inferior to the split-dose 3-L PEG regimen for the quality of bowel preparation in a Chinese adult population. The safety and acceptability of the 2 groups were similar. (Clinical trial registration number: NCT05465889.).

Serum and urine metabolomics study reveals a distinct diagnostic model for cancer cachexia.

BACKGROUND: Cachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance-based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model. METHODS: Eighty-four cancer cachexia patients, 33 pre-cachectic patients, 105 weight-stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model. RESULTS: Forty-six cancer cachexia patients, 22 pre-cachectic patients, 68 weight-stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre-cachectic patients, 37 weight-stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age-matched and sex-matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = -400.53 - 481.88 × log(Carnosine) -239.02 × log(Leucine) + 383.92 × log(Phenyl acetate), and the result showed 94.64% accuracy in the validation set. CONCLUSIONS: This metabolomics study revealed a distinct metabolic profile of cancer cachexia and established and validated a diagnostic model. This research provided a feasible diagnostic tool for identifying at-risk populations through the detection of serum metabolites.