OBJECTIVES: Metabolic syndrome (MetS) is a major public health concern that poses a significant threat to human health. Investigating its underlying mechanisms and identifying potential intervention targets has important clinical implications. This study aims to explore the association between serum gastric biomarkers and MetS and its components. METHODS: A cross-sectional study was conducted among 24 635 individuals (aged 18 to 80 years) who underwent routine health examinations from May 2017 to June 2021 at the Health Management Medical Center, Third Xiangya Hospital, Central South University. Demographic data, medical and medication history, height, weight, blood pressure, fasting blood glucose, glycated hemoglobin (HbA1c), total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and creatinine levels were collected. Serum levels of pepsinogen (PG) I, PGII, and gastrin-17 (G-17) were measured using enzyme-linked immunosorbent assay kits. MetS was diagnosed based on the International Diabetes Federation criteria. Logistic regression was used to assess the association between gastric biomarkers and MetS. RESULTS: Among the 24 635 participants, the overall prevalence of MetS was 35.72%, with a higher rate in males than in females (42.41% vs 24.31%). Compared with the non-MetS group, MetS group were older and had higher metabolic-related diseases rate, Helicobacter pylori infection rate, body mass index (BMI), waist circumference, systolic and diastolic blood pressure, total cholesterol, triglycerides, fasting blood glucose, glycated hemoglobin, and creatinine levels (all P<0.05). Serum G-17 levels were significantly elevated in the MetS group, and PGI levels decreased (both P<0.05). Males had higher G-17, PGI, PGII, and PGI/PGII ratios than females (all P<0.05). Subgroup analysis revealed that G-17 was consistently elevated in MetS patients regardless of sex, whereas PGI was decreased. PGII levels exhibited sex-specific differences. After adjusting for confounders, Logistic regression analysis revealed that high G-17 level was independently associated with MetS, with a stronger correlation observed in males. Moreover, G-17 level progressively increased with higher MetS scores (all P<0.05). CONCLUSIONS Serum G-17 level is positively associated with both the presence and severity of MetS, with a more pronounced correlation in males, suggesting its potential involvement in MetS-related metabolic dysregulation.
Humans ; Metabolic Syndrome/epidemiology* ; Female ; Male ; Middle Aged ; Adult ; Cross-Sectional Studies ; Biomarkers/blood* ; Aged ; Young Adult ; Adolescent ; Gastrins/blood* ; Pepsinogen A/blood* ; Pepsinogen C/blood* ; Aged, 80 and over

OBJECTIVES: Microvascular invasion (MVI) is an important predictor of postoperative recurrence or poor outcomes of hepatocellular carcinoma (HCC). Radiomics is able to predict MVI in HCC preoperatively. This study aims to investigate the influence of different region of interest (ROI) sizes on CT-based radiomics model for MVI prediction in HCC. METHODS: Patients with HCC with or without MVI confirmed by pathology and those who underwent preoperative plain or enhanced abdominal CT scans in the Third Xiangya Hospital of Central South University from January 2010 to December 2020 were retrospectively and consecutively included. According to the ratio of 7 to 3, the patients were randomly assigned into a training set and a validation set. Clinical data were collected from medical records, and radiomics features were extracted from the arterial phase (AP) and portal venous phase (PVP) of preoperatively acquired CT in all patients. Six different ROI sizes were employed. The original ROI (OROI) was manually delineated along the visible borders of the tumor layer-by-layer. The OROI was expanded out by 1-5 mm. The OROI was combined with 5 different peritumoral regions to generate the other 5 ROIs, named Plus1-Plus5. Feature extraction, dimension reduction, and model development were conducted in 6 different ROIs separately. Supporter vector machine (SVM) was used for model construction. Model performance was assessed via receiver operating characteristic (ROC) curve. RESULTS: A total of 172 HCC patients were included, in which 83 (48.3%) were MVI positive, and 89 (51.7%) were MVI negative. Three hundred and ninety-six features based on AP or PVP images were extracted from each ROI. After feature selection and dimension reduction, 4, 5, 15, 11, 6, and 3 features of OROI, Plus1, Plus2, Plus 3, Plus4, and Plus5 were selected for model construction, respectively. In the training set, the sensitivity, specificity, and area under the curve (AUC) of OROI were 0.759, 0.806, and 0.855, respectively. The AUC values of Plus2 (0.979) and Plus3 (0.954) were higher than that of OROI. The AUC values of Plus1 (0.802), Plus4 (0.792), and Plus5 (0.774) were not significantly different from those of OROI. In the validation set, the sensitivity, specificity, and AUC value of OROI were 0.640, 0.630, and 0.664, respectively. The AUC value of Plus3 was 0.903, which was higher than that of OROI. The AUC values of Plus1 (0.679), Plus2 (0.536), Plus4 (0.708), and Plus5 (0.757) were not significantly different from that of OROI (P>0.05). CONCLUSIONS The size of ROI significantly inflluences on the performance of CT-based radiomics model for MVI prediction in HCC. Including appropriate area around the tumor into ROI could improve the predictive performance of the model, and 3 mm might be appropriate distance.
Carcinoma, Hepatocellular/pathology* ; Humans ; Liver Neoplasms/pathology* ; Predictive Value of Tests ; Retrospective Studies ; Tomography, X-Ray Computed/methods*

To illustrate the literature distribution, research power distribution, and research hotspots in the radiomics research by using knowledge mapping analysis, and to provide reference for relevant researchers.
 Methods: Bibliographies from literature regarding radiomics in Web of Science database were downloaded. BICOM 2.0.1 and SATI 3.2 were used to clean and caculate the frequency of publication year, journal, author, key word, and research institution. CiteSpace V4.4.R1 was used to build the knowledge map of scientific research collaboration network between countries/regions.Ucinet 6 was used to build the knowledge map of scientific research collaboration network between core authors and institutions. gCLUTO 1.0 was applied to construct high-frequency keywords bi-clustering map.
 Results: A total of 700 literature was screened. Since 2012 the number of publications has been growing rapidly year by year. The United States, China, and Netherlands were leaders in this field. There were 5 major scientific research institution cooperative groups and 10 major author cooperative groups. Eight research hotspots were clustered by using high-frequency key word bi-clustering analysis.
 Conclusion: Radiomics is a new field and develops very fast. More and more countries, research institutions, and researchers with multidisciplinary background are going to participate in this filed. New terminology and new methods are going to appear in the field.
China ; Cluster Analysis