Obesity is currently a major global public health issue. It has been shown by many that gut microbiota and microbial factors regulate the pathogenesis of obesity and metabolic abnormalities, but little is known about their roles in the different degrees of obesity. Here, we sought to investigate the microbial signatures of obesity of various severities. Patients and Methods: We did this by characterizing the intestinal microbiome signature in a Chinese cohort of obese patients and healthy controls using 16S rRNA gene sequencing. To this end, obesity was sub-divided into four subgroups, including "Overweight", Class I, Class II, and Class III obesity, based on body mass index (BMI). Results: Microbial diversity decreased in obese subjects, and the reduction trend was correlated with the severity of obesity. We detected an expansion of Escherichia shigella in obese patients compared to healthy controls. The family Eubacterium coprostanoligenes and Tannerellaceae, the genera Eubacterium coprostanoligenes, Lachnospiraceae NK4A136, Parabacteroides, and Akkermansia, and the species Prevotella copri were microbial biomarkers of healthy people. Gammaproteobacteria and Enterobacterales were biomarkers of being "Overweight". Erysipelatoclostridiaceae was a biomarker of Class I obesity. The class Bacilli and the order Lactobacillales were both biomarkers of Class II obesity. Negativicutes was a biomarker of Class III obesity. We further established relationships between this microbiome data and other biochemical data, including albumin, low-density lipoprotein (LDL), high-density lipoprotein (HDL), vitamin folic acid (FA) and vitamin B12 (VB12), and Interleukin-6 (IL-6) levels. Function prediction results showed a marked energy metabolism dysbiosis in obesity, especially in patients with Class III obesity. Conclusion: These results suggested that people with different levels of obesity had distinct gut microbial signatures. Decreased microbial diversity, depletion of some specific taxa, and deviation in potential functions mirrored the severity of obesity in this cohort.

IntroductionPhase angle (PhA) is a ratio of reactance and resistance {arctangent (reactance (Xc)/resistance (R)) × (180°/π)}, which can be obtained by bioelectrical impedance analysis (BIA). PhA indicates cellular health and integrity, and it is also considered as a prognostic tool in medical disorders and an indicator of nutritional status (especially of muscle quality) in patients with obesity. However, PhA has limited usefulness in clinical practice because of a lackness of reference values for Chinese overweight and obese populations. The main aim of this study was to show PhA reference data in different age and BMI groups by sex. In addition, we also study the association of age, sex, and BMI on PhA.MethodsA total of 1729 overweight and obese participants were included in this study. PhA and body composition were measured using segmental multifrequency BIA. Differences in mean values for variables were tested by one-way analysis of variance. Multiple regression analysis was used to assess the associations of PhA with age, sex and BMI.ResultsMultiple regression analysis showed that age, sex and BMI were significant (P < 0.05) independent influence factors of PhA in Chinese overweight and obese adults when age and BMI were continues variables. The mean PhA value for all participants was 5.5°. Mean BMI, age, weight, height and 50kHz-PhA were significantly higher (P < 0.001) in male participants than female ones. In age groups and BMI groups, mean 50kHz-PhA was significantly higher (P < 0.005) in male participants than female ones. When age groups and BMI groups were categorical variables, multiple regression analysis showed that different age groups (46–55 years and ≥ 56 years) had a significantly lower (P < 0.005) PhA as compared with the baseline group (18-25 years) and different BMI groups (≥ 28 kg/m2) had a significantly higher (P < 0.05) PhA as compared with the baseline group (24–27.9 kg/m2).ConclusionPhA differed according to age, sex and BMI. Reference data in this study can be taken into consideration when deriving the reference values for overweight and obese Chinese populations.

Senescence is an effective barrier to tumor progression. Mutations that inhibit senescence and promote cell division are mandatory for the development of cancer. Therefore, it is particularly important to explore the differences between cutaneous melanoma (CM) patients with severe and mild degrees of senescence. We clustered all the patients with CM in the Cancer Genome Atlas (TCGA) database based on all the genes of the senescence pathway in the CellAge and MSigDB database. The prognosis, immunotherapy effect, tumor microenvironment score, NRAS mutation rate, expression of CD274, CTLA4, and PDCD1, and abundance of CD8+ T and natural killer (NK) cell infiltration in the younger group of patients (YG) were higher than those in the older group (OG). Compared with the American Joint Committee on Cancer (AJCC) stage, the risk scoring system stratified the risk of CM patients and guided immunotherapy more accurately. The nomogram model, which combined the AJCC stage and risk score, greatly improved the ability and accuracy of prognosis prediction. As KIR2DL4 is the core molecule in the risk scoring system (RSS), knocking down the KIR2DL4 of human NK cells in vitro can inhibit the cytotoxicity of NK cells and can also inhibit the secretion of tumor necrosis factor‐α and interferon‐γ by NK cells. In contrast, upregulation of KIR2DL4 can activate the MEK/ERK signaling pathway, which is the activation pathway of NK cells. Our RSS and nomogram model can accurately stratify the risk of CM patients and effectively predict the effect of immunotherapy and prognosis in CM patients.