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Abstract Few studies have provided data on the metabolomics characteristics of metabolic diseases such as hyperuricemia and hyperbilirubinemia in the Tibetan plateau. In the current study, we sought to investigate the serum metabolomics characteristics of hyperbilirubinemia and hyperuricemia in the Tibetan plateau, with the aim to provide a basis for further research on their pathogenesis, prevention, and treatment. The study participants were born in low-altitude areas below 1000 m and had no prior experience living in a high-altitude area before entering Golmud, Tibet (average elevation: 3000 m) and Yushu, Qinghai (average elevation: 4200 m). Thirty-four participants with hyperbilirubinemia (18 in Golmud and 16 in Yushu), 24 participants with hyperuricemia, and 22 healthy controls were enrolled. The serum samples of subjects were separated and then sent to a local tertiary hospital for biochemical examination. Serum widely targeted technology, based on the ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) platform, was used to detect serum metabolites and differential metabolites. Compared to the healthy controls, hyperbilirubinemia patients from Golmud showed 19 differential metabolites, hyperbilirubinemia patients from Yushu showed 12 differential metabolites, and hyperuricemia patients from Yushu showed 23 differential metabolites. Compared to the hyperbilirubinemia patients from Golmud that is at a low altitude, the Yushu groups had 33 different metabolites. Differential metabolites are primarily classified into amino acids and their derivatives, nucleotides and their derivatives, organic acids and their derivatives, and lipids/fatty acids. These are related to metabolic pathways such as caffeine metabolism, arachidonic acid metabolism, and tyrosine metabolism. Hyperbilirubinemia and hyperuricemia in the Tibetan plateau have unique serum metabolomics characteristics. Glycine derivatives and arachidonic acid and its derivatives were associated with plateau hyperbilirubinemia, and vanillic acid and pentadecafluorooctanoic acid were associated with plateau hyperuricemia.

Abstract Background Colorectal cancer (CRC), a commonly diagnosed cancer often develops slowly from benign polyps called adenoma to carcinoma. Altered gut microbiota is implicated in colorectal carcinogenesis. It is warranted to find non-invasive progressive microbiota biomarkers that can reflect the dynamic changes of the disease. This study aimed to identify and evaluate potential progressive fecal microbiota gene markers for diagnosing advanced adenoma (AA) and CRC. Results Metagenome-wide association was performed on fecal samples from different cohorts of 871 subjects (247 CRC, 234 AA, and 390 controls). We characterized the gut microbiome, identified microbiota markers, and further constructed a colorectal neoplasms classifier in 99 CRC, 94 AA, and 62 controls, and validated the results in 185 CRC, 140 AA, and 291 controls from 3 independent cohorts. 21 species and 277 gene markers were identified whose abundance was significantly increased or decreased from normal to AA and CRC. The progressive gene markers were distributed in metabolic pathways including amino acid and sulfur metabolism. A diagnosis model consisting of four effect indexes was constructed based on the markers, the sensitivities of the Adenoma Effect Index 1 for AA, Adenoma Effect Index 2 for high-grade dysplasia (HGD) adenoma were 71.3% and 76.5%, the specificities were 90.5% and 90.3%, respectively. CRC Effect Index 1 for all stages of CRC and CRC Effect Index 2 for stage III–IV CRC to predict CRC yielded an area under the curve (AUC) of 0.839 (95% CI 0.804–0.873) and 0.857 (95% CI 0.793–0.921), respectively. Combining with fecal immunochemical test (FIT) significantly improved the sensitivity of CRC Effect Index 1 and CRC Effect Index 2 to 96.7% and 100%. Conclusions This study reports the successful diagnosis model establishment and cross-region validation for colorectal advanced adenoma and carcinoma based on the progressive gut microbiota gene markers. The results suggested that the novel diagnosis model can significantly improve the diagnostic performance for advanced adenoma.