BACKGROUND: The mechanisms distinguishing metabolically healthy from unhealthy phenotypes within the same BMI categories remain unclear. This study aimed to investigate the associations between regional fat distribution and metabolically unhealthy phenotypes in Chinese adults across different BMI categories. METHODS: This cross-sectional study involving 11833 Chinese adults aged 20 years and older. Covariance analysis, adjusted for age, compared the percentage of regional fat (trunk, leg, or arm fat divided by whole-body fat) between metabolically healthy and unhealthy participants. Trends in regional fat percentage with the number of metabolic abnormalities were assessed by the Jonckheere-Terpstra test. Odds ratios (ORs) and their 95% confidence intervals (CIs) were estimated by logistic regression models. All analyses were performed separately by sex. RESULTS: In non-obese individuals, metabolically unhealthy participants exhibited higher percent trunk fat and lower percent leg fat compared to healthy participants. Additionally, percent trunk fat increased and percent leg fat decreased with the number of metabolic abnormalities. After adjustment for demographic and lifestyle factors, as well as BMI, higher percent trunk fat was associated with increased odds of being metabolically unhealthy [highest vs. lowest quartile: ORs (95%CI) of 1.64 (1.35, 2.00) for men and 2.00 (1.63, 2.46) for women]. Conversely, compared with the lowest quartile, the ORs (95%CI) of metabolically unhealthy phenotype in the highest quartile for percent arm and leg fat were 0.64 (0.53, 0.78) and 0.60 (0.49, 0.74) for men, and 0.72 (0.56, 0.93) and 0.46 (0.36, 0.59) for women, respectively. Significant interactions between BMI and percentage of trunk and leg fat were observed in both sexes, with stronger associations found in individuals with normal weight and overweight. CONCLUSIONS Trunk fat is associated with a higher risk of metabolically unhealthy phenotype, while leg and arm fat are protective factors. Regional fat distribution assessments are crucial for identifying metabolically unhealthy phenotypes, particularly in non-obese individuals.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Adipose Tissue ; Body Fat Distribution ; Body Mass Index ; China/epidemiology* ; Cross-Sectional Studies ; Health Surveys ; Phenotype

Background Bacteria are the most diverse and widely sourced microorganisms in the indoor air of subway stations, where pathogenic bacteria can spread through the air, leading to increased health risks. Objective To understand the status and distribution characteristics of indoor air bacterial pollution in subway stations and compartments in a city of Central South China, and to provide a scientific basis for formulating intervention measures to address indoor air bacteria pollution in subways. Methods Three subway stations and the compartments of trains parking there in a city in Central South China were selected according to passenger flow for synchronous air sampling and monitoring. Temperature, humidity, wind speed, carbon dioxide (CO₂), fine particulate matter (PM_2.5), and inhalable particulate matter (PM₁₀) were measured by direct reading method. In accordance with the requirements of Examination methods for public places-Part 3: Airborne microorganisms (GB/T 18204.3-2013), air samples were collected at a flow rate of 28.3 L·min⁻¹, and total bacterial count was estimated. Bacterial microbial species were identified with a mass spectrometer and pathogenic bacteria were distinguished from non-pathogenic bacteria according to the Catalogue of pathogenic microorganisms transmitted to human beings issued by National Health Commission. Kruskal-Wallis H test was used to compare the subway hygiene indicators in different regions and time periods, and Bonferroni test was used for pairwise comparison. Spearman correlation test was used to evaluate the correlation between CO₂ concentration and total bacterial count. Results The pass rates were 100.0% for airborne total bacteria count, PM_2.5, and PM₁₀ in the subway stations and train compartments, 94.4% for temperature and wind speed, 98.6% for CO₂, but 0% for humidity. The overall median (P₂₅, P₇₅) total bacteria count was 177 (138,262) CFU·m⁻³. Specifically, the total bacteria count was higher in station halls than in platforms, and higher during morning peak hours than during evening peak hours (P<0.05). A total of 874 strains and 82 species were identified by automatic microbial mass spectrometry. The results of identification were all over 9 points, and the predominant bacteria in the air were Micrococcus luteus (52.2%) and Staphylococcus hominis (9.8%). Three pathogens, Acinetobacter baumannii (0.3%), Corynebacterium striatum (0.1%), and Staphylococcus epidermidis bacilli (2.2%) were detected in 23 samples (2.6%), and the associated locations were mainly distributed in train compartments during evening rush hours. Conclusion The total bacteria count in indoor air varies by monitoring sites of subway stations and time periods, and there is a risk of opportunistic bacterial infection. Attention should be paid to cleaning and disinfection during peak passenger flow hours in all areas.

Objective:To investigate the clinical characteristics and predisposing factors associated with preoperative anemia in adult femoral shaft fractures.Methods:A retrospective analysis of clinical data from 157 patients presenting with femoral shaft fractures admitted to the department of orthopedics at Nanjing Drum Tower Hospital between June 2018 and June 2022 was conducted. The study cohort comprised 106 males and 51 females, with an average age of 45.06 ± 14.32 years (range: 18-65 years). Based on hemoglobin levels measured within 2 days of admission, patients were stratified into two groups: anemia group (Hb<120 g/L in adult males and Hb<110 g/L in adult females) and non-anemia group. General demographic information, AO fracture types, and clinical characteristics, as well as the results of laboratory examinations for both groups were collected. Subsequently, univariate and multivariate logistic regression analyses were conducted.Results:Out of the 157 patients with femoral shaft fractures, 118 (75.2%) exhibited preoperative anemia (the anemia group). Among them, 75 cases were male, and 43 cases were female, with an average age of 45.84±14.23 years (range: 18-65 years). In terms of fracture AO type, 41 cases were classified as 32A, 19 as 32B, and 58 as 32C. Regarding fracture location, 14 were situated in the upper 1/3 of the femoral shaft, 67 in the middle 1/3, and 37 in the lower 1/3. The causes of injury included 63 cases of motor vehicle accidents, 5 cases of blunt trauma, 40 cases of falls, and 10 cases of other falls, with 65 cases involving multiple injuries. Conversely, 39 patients (24.8%) did not exhibit preoperative anemia (the non-anemia group). Of these, 31 were male, and 8 were female, with an average age of 42.72 ± 14.51 years (range: 19-65 years). In terms of fracture AO type, 24 cases were classified as 32A, 5 as 32B, and 10 as 32C. Regarding fracture location, 3 were situated in the upper 1/3 of the femoral shaft, 19 in the middle 1/3, and 17 in the lower 1/3. The causes of injury included 13 cases of motor vehicle accidents, 5 cases of blunt trauma, 20 cases of falls, and 1 other fall, with 8 cases involving multiple injuries. Univariate analysis revealed statistically significant differences between preoperative anemia and AO fracture type, mechanism of injury, multiple injuries, time from injury to hospital admission, albumin levels, and age ( P< 0.05). Multifactorial logistic regression analysis identified AO type 32C ( OR=3.12, P=0.020), blunt trauma injuries ( OR=0.13, P=0.021), reduced albumin levels ( OR=9.90, P=0.037), and multiple injuries ( OR=3.65, P=0.016) as risk factors for preoperative anemia. Multifactorial logistic regression further revealed that multiple injuries ( OR=5.20, P=0.004) and reduced albumin levels ( OR=5.47, P=0.001) were risk factors for the severity of anemia. Conclusion:AO type 32C fractures, blunt trauma injuries, reduced albumin levels, and multiple injuries were identified as potential contributors to the development of preoperative anemia, with multiple injuries and reduced albumin levels exacerbating the severity of anemia. Clinicians should be vigilant for the occurrence of preoperative anemia in adult femoral shaft fracture patients, particularly those with blunt trauma injuries, multiple injuries, hypoalbuminemia, and AO type 32C fractures.