中华内科杂志最新文献

To investigate the prevalence and epidemiological characteristics of diabetic retinopathy (DR) in Yunnan Province, explore its risk factors, and provide a basis for the prevention and treatment of chronic complications of diabetes mellitus (DM). This is a large cross-sectional study, in all, 1 524 DM patients in 16 communities and villages of Yunnan Province who were registered in health service centers were included in this study from August to November 2019. All patients completed a uniform questionnaire, anthropometric measurements, biochemical measurements, and auxiliary examinations. Logistic regression analysis was used to screen the risk factors of DR. The prevalence rates of DR, mild non-proliferative DR (mild-NPDR), and referable DR (RDR) were 16.0% (244/1 524), 4.5% (69/1 524), and 11.5% (175/1 524), respectively. Glycated hemoglobin A_1c (HbA_1c)≥7.0% was the risk factor of mild-NPDR (OR=1.872, 95%CI 1.055-3.323) and RDR (OR=4.821, 95%CI 2.917-7.969). Blood pressure≥130/80 mmHg (1 mmHg=0.133 kPa) was the risk factor of mild-NPDR (OR=1.933, 95%CI 1.112-3.358) and RDR (OR=1.505, 95%CI 1.063-2.130). In Yunnan Province, 16.0% DM patients had accompanying DR, wherein about 71.7% of them required an ophthalmology referral, and the high incidence of RDR in DM patients was associated with poor control of blood glucose and blood pressure.

Objective: To observe the characteristics and differences of gut microbiota in asthma patients with different inflammatory types through metagenomic analysis. Methods: Adults aged ≥18 years who visited the Respiratory Clinic of Peking University Third Hospital from August 1, 2021 to August 31, 2022 and were primarily diagnosed with asthma were selected as the study subjects. Finally, 29 patients with stable asthma were included. Fresh fecal samples were collected and the fecal DNA was extracted for high-throughput 16sRNA sequencing of gut microbiota. The diversity and community structure of gut microbiota in different groups of asthma patients were compared, and the species differences were analyzed through random forest and LEfSe analysis. Results: There were sex-based differences in asthma patients with different types of inflammation, and the proportion of female patients was higher in neutrophilic asthma patients (χ²=4.14, P=0.042). There was no significant intergroup difference in the alpha diversity of gut microbiota among asthma patients with different inflammatory types, but there were significant differences in the microbiome. Patients with neutrophilic asthma had higher relative abundance of Bacillales (P=0.029) and Oscillospiraceae (P=0.015). In species LEfSe analysis, patients with eosinophilic asthma had a higher relative abundance of fungi. Conclusion: There are intergroup differences in the gut microbiota of asthma patients with different inflammation types, and fungi are biomarkers that distinguish the differences in gut microbiota between patients with eosinophilic asthma and neutrophilic asthma.

Objective: To study the relationship between hemoglobin glycation index (HGI) and blood lipid indices such as low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), and plasma atherogenic index (AIP). Methods: This cross-sectional study included 16 049 participants from the Beijing Apple Garden community between December 2011 and August 2012. The subjects were divided into three groups based on the HGI quartile: low (n=5 388), medium (n=5 249), and high (n=5 412). The differences in blood lipid indicators between different HGI groups were compared and multivariate logistic regression model was established to analyze the association between HGI and dyslipidemia. And multivariate logistic regression model was established to analyze the relationship between HGI and blood lipid indicators in different glucose metabolism populations. Results: There were 16 049 participants in all (mean age: 56 years), including 10 452 women (65.1%). They were classified into normal glucose tolerance (9 093 cases), prediabetes (4 524 cases), and diabetes (2 432 cases) based on glucose tolerance status. In the general population, with the increase of HGI, LDL-C, non-HDL-C, and AIP gradually increased (all P values for trends were <0.05), and the proportion of abnormalities increased significantly (χ²=101.40, 42.91, 39.80; all P<0.001). A multivariate logistic regression model was established, which suggested a significant correlation between HGI and LDL-C, non-HDL-C, and AIP (all P<0.05), after adjusting for factors such as age, sex, fasting blood glucose, hypertension, body mass index, smoking, and alcohol consumption. In the overall population, normal glucose tolerance group, and diabetes group, HGI had the highest correlation with non-HDL-C (OR values of 1.325, 1.678, and 1.274, respectively); in the prediabetes group, HGI had a higher correlation with LDL-C (OR value: 1.510); and in different glucose metabolism groups, AIP and HGI were both correlated (OR: 1.208-1.250), but not superior to non-HDL-C and LDL-C. Conclusion: HGI was closely related to LDL-C, non HDL-C, and AIP in the entire population and people with different glucose metabolism, suggesting that HGI may be a predictor of atherosclerotic cardiovascular disease.