1.Causal Inference on Association Between Metabolic Syndrome and Breast Cancer: A Bidirectional Two-Sample Mendelian Randomization Study
Yi DU ; Mengyao XUE ; Huiying CHEN ; Ying SUN ; Tianyu LUO ; Haidong SUN
Cancer Research on Prevention and Treatment 2026;53(4):267-273
Objective To investigate the causal relationship between metabolic syndrome and breast cancer by using a bidirectional two-sample Mendelian randomization (MR) approach. Methods Genome-wide association study (GWAS) summary statistics for metabolic syndrome and breast cancer were acquired from the Integrative Epidemiology Unit GWAS database and the GWAS Catalog, with populations encompassing the United States and East Asia. A bidirectional causal design was employed: a forward analysis with metabolic syndrome as the exposure and breast cancer as the outcome, followed by a reverse analysis wherein their roles were interchanged. The inverse-variance weighting (IVW) method was primarily used for effect estimation, supplemented by MR-Egger regression, the weighted median method, the simple mode method, and the weighted mode method. Instrument variable strength was screened using the F-statistic (F>10). Robustness of the results was assessed through heterogeneity tests, horizontal pleiotropy tests, forest plots, and leave-one-out sensitivity analyses. Results The IVW analysis indicated no significant causal relationship between metabolic syndrome and breast cancer (OR=1.00, 95%CI: 0.97-1.03), P>0.05). Sensitivity analyses yielded consistent results, suggesting the good robustness of the study findings. Conclusion This study found no evidence to support a causal relationship, either positive or negative, between metabolic syndrome and breast cancer.
2.Isolation,identification and antimicrobial susceptibility of a strain of Haemophi-lus parasuis
Xi LIU ; Geng WANG ; Zhengdan LIN ; Xiuxiu SUN ; Xinxin JIN ; Li LI ; Junjie YANG ; Xue-ying HU ; Changqin GU ; Wanpo ZHANG ; Xiaoli LIU ; Teng YU ; Guofu CHENG
Chinese Journal of Veterinary Science 2025;45(2):219-226
Porcine arthritis,one of the common chronic diseases in large-scale pig farms,can signifi-cantly reduce the production performance of meat pigs.In this study,a strain of Haemophilus pa-rasuis(HPS)was isolated from the joint fluid of a lame pig.The HPS was analyzed in terms of se-rotypes,virulence genes,and resistance genes.Additionally,it was treated with sensitive antibiotics to provide a theoretical basis for the comprehensive prevention and treatment of arthritis in meat pigs in future production settings.A strain of HPS type 14 was isolated from the joint fluid of dis-eased pigs.The HPS isolate demonstrated sensitivity to β-lactams and tetracyclines,while florfeni-col and polymyxin effectively inhibited its growth at low concentrations.However,the bacteria ex-hibited resistance to sulfonamides and ciprofloxacin.The treatment of affected pigs with clinical ar-thritis using doxycycline and enrofloxacin injections proved effective.Compared to the infected group,in which the sick pigs experienced difficulty flexing their carpal and tarsal joints and exhibi-ted significant lameness,the pigs in the treatment group showed marked improvement.Their joints were only slightly swollen,and the clinical symptoms of arthropathy were alleviated.
3.Development and Validation of a Nomogram Prediction Model for Subtherapeutic Voriconazole Concentrations in Allogeneic Hematopoietic Stem Cell Transplantation Recipients
Hongchun WANG ; Meng LI ; Wenli SUN ; Rui LIU ; Ying ZHAO ; Jinyan GUO ; Guangze LU ; Yang XUE ; Ruigeng YANG ; Lei WANG
Journal of Modern Laboratory Medicine 2025;40(6):74-79,85
Objective To identify determinants of subtherapeutic voriconazole(VRCZ)concentrations in allogeneic hematopoietic stem cell transplantation(allo-HSCT)recipients and to develop/validate a nomogram-based risk prediction model.Methods This study retrospectively analyzed 310 VRCZ therapeutic drug monitoring(TDM)measurements from allo-HSCT recipients at 310 patients who under went allo-HSCT surgery at Hebei Yanda Ludaopei Hospital from October 2022 to October 2024 and received VRCZ for the prevention and treatment of invasive fungal infections before transplantion were selected as the study subjects.Cases were stratified into target-concentration group(0.5~5.0μg/ml)and subtherapeutic group(<0.5μg/ml).Through single factor and multiple factor Logistic regression analysis,indeipendent predictive factors forvecz plasma concentration non-compliance were screened,and a column chart prediction model(NPM)was constructed.The performance of the model was evaluateding area under the receiver operating characteristic curve(AUC),Hosmer-Lemeshow(H-L)goodness-of-fit test,and decision curve analysis(DCA).Results Among 310 VRCZ-TDM measurements,71.61%(222/310)achieved target concentrations.Multivariate analysis showed that CYP2C19 intermediate metabolite,daily dose of cyclosporine A(CSA),daily dose of VRCZ,creatinine(Cr)>97 μmol/L,albumin(Alb)and C-reactive protein(CRP)were independent influencing factors for VRCZ blood drug concentration non-compliance(Wald χ2=4.046~13.221,all P<0.05).The nomogram demonstrated excellent discrimination,calibration(H-L goodness of fit test χ2=2.663,P=0.954),and clinical utility with net benefit across 0.05~0.96 risk thresholds.Conclusion The nomogram incorporating CYP2C19 gene phenotype,daily CSA dosing,daily VRCZ dosing,Cr levels,Alb and CRP provides a validated tool for optimizing VRCZ therapy in allo-HSCT recipients,enabling precision dosing strategies.
4.Changing antimicrobial resistance profiles of Burkholderia cepacia in hospitals across China:results from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Chunyue GE ; Yunjian HU ; Xiaoman AI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Hui LI ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Wenhui HUANG ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WENG ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2025;25(5):557-562
Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1%(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5%,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3%(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1%)were isolated from sputum and respiratory secretions,followed by urine(10.7%)and blood samples(8.1%).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1%.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4%in the patients<18 years old to 20.6%in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.
5.Changing distribution and antimicrobial resistance profiles of clinical isolates in children:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Qing MENG ; Lintao ZHOU ; Yunsheng CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Chuanqing WANG ; Aimin WANG ; Lei ZHU ; Jinhua MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Zhiyong LÜ ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Fangfang HU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Wei JIA ; Gang LI ; Kaizhen WEN ; Yirong ZHANG ; Yan JIN ; Chunhong SHAO ; Yong ZHAO ; Ping GONG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Sufang GUO ; Jiao FENG ; Ziyong SUN ; Zhongju CHEN ; Wen'en LIU ; Yanming LI ; Xiaobo MA ; Yanping ZHENG ; Dawen GUO ; Jinying ZHAO ; Ruizhong WANG ; Hua FANG ; Lixia ZHANG ; Juan MA ; Jihong LI ; Zhidong HU ; Jin LI ; Yuxing NI ; Jingyong SUN ; Ruyi GUO ; Yan ZHU ; Yi XIE ; Mei KANG ; Yuanhong XU ; Ying HUANG ; Shanmei WANG ; Yafei CHU ; Hua YU ; Xiangning HUANG ; Lianhua WEI ; Fengmei ZOU ; Han SHEN ; Wanqing ZHOU ; Yunzhuo CHU ; Sufei TIAN ; Shunhong XUE ; Hongqin GU ; Xuesong XU ; Chao YAN ; Bixia YU ; Jinju DUAN ; Jianbang KANG ; Jiangshan LIU ; Xuefei HU ; Yunsong YU ; Jie LIN ; Yunjian HU ; Xiaoman AI ; Chunlei YUE ; Jinsong WU ; Yuemei LU
Chinese Journal of Infection and Chemotherapy 2025;25(1):48-58
Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1%of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9%)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5%)was lower than that in inpatient children(31.5%).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1%)than in inpatient children(59.4%).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14%,11.7%,47.8%in outpatients,but 24.2%,20.6%,and 52.8%in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.
6.Surveillance of antimicrobial resistance in clinical isolates of Escherichia coli:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Shanmei WANG ; Bing MA ; Yi LI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Aimin WANG ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Wenhui HUANG ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WEN ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2025;25(1):39-47
Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)%.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0%vs 29.5%).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7%vs 7.8%,0.8%vs 0.1%,both P<0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)%with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9%and 1.8%,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1%).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10%.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P<0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.
7.Epidemiological characteristics and influencing factors of diabetes and pre-diabetes among adult residents in Hainan Province
Juan JIANG ; Changfu XIONG ; Dingwei SUN ; Ying LIU ; Hongying WU ; Xingren WANG ; Xiaohuan WANG ; Tingting OU ; Xue ZHOU ; Shizhu MENG ; Saiku CHEN ; Kanglin WANG ; Lu ZHONG ; Bin HE
Chinese Journal of Epidemiology 2025;46(4):700-708
Objective:To describe epidemiological characteristics and their influencing factors of diabetes and pre-diabetes among adult residents in Hainan Province and provide a theoretical basis to develop epidemic prevention and control strategies for diabetes.Methods:This study used a two-stage unequal proportion cluster sampling method, and 32 857 subjects (≥18 years old) were collected from 24 cities/counties/districts in Hainan Province. All the subjects were investigated with questionnaires, physical examination, and laboratory tests from January to June 2023. The χ2 and Mantel-Haenszel trend χ2 tests were used to analyze the data. Multivariate logistic regression was used to analyze the factors influencing diabetes and pre-diabetes. SPSS 23.0 software was used to analyze the data. Results:The crude prevalence of diabetes and pre-diabetes in adult residents of Hainan Province were 18.1% and 22.8%, while the weighted rates were 13.7% and 20.7%, respectively. The results of multivariate logistic regression analysis showed that: aging (30-39 years old: OR=2.65, 95% CI: 2.06-3.41; 40-49 years old: OR=5.64, 95% CI: 4.40-7.24; 50- 59 years old: OR=9.88, 95% CI: 7.71-12.67; 60-69 years old: OR=18.34, 95% CI: 14.28-23.55; 70-79 years old: OR=21.30, 95% CI: 16.41-27.65; 80 years old and above: OR=24.13, 95% CI: 17.94-32.46), nationality (Li minority group: OR=1.50, 95% CI: 1.38-1.63; other ethnic groups: OR=1.53, 95% CI: 1.20-1.94), urban ( OR=1.12, 95% CI: 1.04-1.21), central obesity ( OR=2.14, 95% CI: 2.01-2.29), higher frequency of alcohol consumption (5-7 day/week: OR=1.24, 95% CI: 1.11-1.38), physical inactivity ( OR=1.09, 95% CI: 1.02-1.17) were risk factors for diabetes, while aging (30-39 years old: OR=1.53, 95% CI: 1.31-1.79; 40-49 years old: OR=2.36, 95% CI: 2.01-2.76; 50-59 years old: OR=3.03, 95% CI: 2.58-3.55; 60-69 years old: OR=4.22, 95% CI: 3.58-4.97; 70-79 years old: OR=5.05, 95% CI: 4.23-6.04; 80 years old and above: OR=6.08, 95% CI: 4.86-7.61), nationality: (Li minority group: OR=1.18, 95% CI: 1.10-1.28; other ethnic groups: OR=1.40, 95% CI: 1.14-1.71), urban ( OR=1.12, 95% CI: 1.04-1.19), central obesity ( OR=1.72, 95% CI: 1.62-1.83), higher frequency of alcohol consumption (1-4 day/week: OR=1.12, 95% CI: 1.01-1.23; 5-7 day/week: OR=1.35, 95% CI: 1.22-1.49) were risk factors for pre-diabetes. Conclusions:The epidemic situation of diabetes and pre-diabetes among adult residents in Hainan Province was not optimistic. In order to control the development of abnormal blood glucose, measures and targeted health education should be carried out to strengthen the screening, treatment, and management of people with abnormal blood glucose among different populations.
8.Epidemiological characteristics and influencing factors of diabetes and pre-diabetes among adult residents in Hainan Province
Juan JIANG ; Changfu XIONG ; Dingwei SUN ; Ying LIU ; Hongying WU ; Xingren WANG ; Xiaohuan WANG ; Tingting OU ; Xue ZHOU ; Shizhu MENG ; Saiku CHEN ; Kanglin WANG ; Lu ZHONG ; Bin HE
Chinese Journal of Epidemiology 2025;46(4):700-708
Objective:To describe epidemiological characteristics and their influencing factors of diabetes and pre-diabetes among adult residents in Hainan Province and provide a theoretical basis to develop epidemic prevention and control strategies for diabetes.Methods:This study used a two-stage unequal proportion cluster sampling method, and 32 857 subjects (≥18 years old) were collected from 24 cities/counties/districts in Hainan Province. All the subjects were investigated with questionnaires, physical examination, and laboratory tests from January to June 2023. The χ2 and Mantel-Haenszel trend χ2 tests were used to analyze the data. Multivariate logistic regression was used to analyze the factors influencing diabetes and pre-diabetes. SPSS 23.0 software was used to analyze the data. Results:The crude prevalence of diabetes and pre-diabetes in adult residents of Hainan Province were 18.1% and 22.8%, while the weighted rates were 13.7% and 20.7%, respectively. The results of multivariate logistic regression analysis showed that: aging (30-39 years old: OR=2.65, 95% CI: 2.06-3.41; 40-49 years old: OR=5.64, 95% CI: 4.40-7.24; 50- 59 years old: OR=9.88, 95% CI: 7.71-12.67; 60-69 years old: OR=18.34, 95% CI: 14.28-23.55; 70-79 years old: OR=21.30, 95% CI: 16.41-27.65; 80 years old and above: OR=24.13, 95% CI: 17.94-32.46), nationality (Li minority group: OR=1.50, 95% CI: 1.38-1.63; other ethnic groups: OR=1.53, 95% CI: 1.20-1.94), urban ( OR=1.12, 95% CI: 1.04-1.21), central obesity ( OR=2.14, 95% CI: 2.01-2.29), higher frequency of alcohol consumption (5-7 day/week: OR=1.24, 95% CI: 1.11-1.38), physical inactivity ( OR=1.09, 95% CI: 1.02-1.17) were risk factors for diabetes, while aging (30-39 years old: OR=1.53, 95% CI: 1.31-1.79; 40-49 years old: OR=2.36, 95% CI: 2.01-2.76; 50-59 years old: OR=3.03, 95% CI: 2.58-3.55; 60-69 years old: OR=4.22, 95% CI: 3.58-4.97; 70-79 years old: OR=5.05, 95% CI: 4.23-6.04; 80 years old and above: OR=6.08, 95% CI: 4.86-7.61), nationality: (Li minority group: OR=1.18, 95% CI: 1.10-1.28; other ethnic groups: OR=1.40, 95% CI: 1.14-1.71), urban ( OR=1.12, 95% CI: 1.04-1.19), central obesity ( OR=1.72, 95% CI: 1.62-1.83), higher frequency of alcohol consumption (1-4 day/week: OR=1.12, 95% CI: 1.01-1.23; 5-7 day/week: OR=1.35, 95% CI: 1.22-1.49) were risk factors for pre-diabetes. Conclusions:The epidemic situation of diabetes and pre-diabetes among adult residents in Hainan Province was not optimistic. In order to control the development of abnormal blood glucose, measures and targeted health education should be carried out to strengthen the screening, treatment, and management of people with abnormal blood glucose among different populations.
9.Guideline for Adult Weight Management in China
Weiqing WANG ; Qin WAN ; Jianhua MA ; Guang WANG ; Yufan WANG ; Guixia WANG ; Yongquan SHI ; Tingjun YE ; Xiaoguang SHI ; Jian KUANG ; Bo FENG ; Xiuyan FENG ; Guang NING ; Yiming MU ; Hongyu KUANG ; Xiaoping XING ; Chunli PIAO ; Xingbo CHENG ; Zhifeng CHENG ; Yufang BI ; Yan BI ; Wenshan LYU ; Dalong ZHU ; Cuiyan ZHU ; Wei ZHU ; Fei HUA ; Fei XIANG ; Shuang YAN ; Zilin SUN ; Yadong SUN ; Liqin SUN ; Luying SUN ; Li YAN ; Yanbing LI ; Hong LI ; Shu LI ; Ling LI ; Yiming LI ; Chenzhong LI ; Hua YANG ; Jinkui YANG ; Ling YANG ; Ying YANG ; Tao YANG ; Xiao YANG ; Xinhua XIAO ; Dan WU ; Jinsong KUANG ; Lanjie HE ; Wei GU ; Jie SHEN ; Yongfeng SONG ; Qiao ZHANG ; Hong ZHANG ; Yuwei ZHANG ; Junqing ZHANG ; Xianfeng ZHANG ; Miao ZHANG ; Yifei ZHANG ; Yingli LU ; Hong CHEN ; Li CHEN ; Bing CHEN ; Shihong CHEN ; Guiyan CHEN ; Haibing CHEN ; Lei CHEN ; Yanyan CHEN ; Genben CHEN ; Yikun ZHOU ; Xianghai ZHOU ; Qiang ZHOU ; Jiaqiang ZHOU ; Hongting ZHENG ; Zhongyan SHAN ; Jiajun ZHAO ; Dong ZHAO ; Ji HU ; Jiang HU ; Xinguo HOU ; Bimin SHI ; Tianpei HONG ; Mingxia YUAN ; Weibo XIA ; Xuejiang GU ; Yong XU ; Shuguang PANG ; Tianshu GAO ; Zuhua GAO ; Xiaohui GUO ; Hongyi CAO ; Mingfeng CAO ; Xiaopei CAO ; Jing MA ; Bin LU ; Zhen LIANG ; Jun LIANG ; Min LONG ; Yongde PENG ; Jin LU ; Hongyun LU ; Yan LU ; Chunping ZENG ; Binhong WEN ; Xueyong LOU ; Qingbo GUAN ; Lin LIAO ; Xin LIAO ; Ping XIONG ; Yaoming XUE
Chinese Journal of Endocrinology and Metabolism 2025;41(11):891-907
Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.
10.Analysis of the fairness of medical resource allocation in prefecture-level regions across the country: based on agglomeration degree method
Fei HAN ; Yang ZHAO ; Ying SUN ; Baojuan XUE ; Junshu GE ; Yuanyuan SU
Chinese Journal of Hospital Administration 2025;41(4):289-293
Objective:To systematically evaluate the fairness of traditional Chinese medicine (TCM) healthcare resource allocation at the prefecture-level in China using the healthcare resource agglomeration degree (HRAD) method, so as to provide empirical evidence for optimizing resource distribution.Methods:Data on TCM healthcare resources (including the number of TCM institutions, public TCM hospitals, TCM hospital beds, and TCM healthcare technicians) were collected from 333 prefecture-level regions in 2023. The HRAD method was employed to assess fairness in geographic allocation (HRAD i) and population-based allocation (HRAD i/PAD i). A multi-indicator comprehensive evaluation was conducted using the entropy weight method to determine weighting coefficients. Results:Significant disparities were observed in the geographic agglomeration of TCM resources (HRAD i) in China. Resource-rich regions (HRAD i>5) were primarily concentrated in eastern and some central-western provinces, while resource-scarce regions (HRAD i<1) were mainly distributed in western, northeastern, and parts of central and eastern provinces. Analysis of population-based fairness (HRAD i/PAD i) revealed that most prefecture-level cities nationwide had ratios<1, with only 8 provinces having more cities with ratios>1 than<1. The comprehensive evaluation showed that top-ranked cities in the east (e.g., Hangzhou, Dongying, Shenzhen), central region (e.g., Taiyuan, Zhengzhou), and west (e.g., Hainan Prefecture, Alxa League) were predominantly core cities or sparsely populated areas. Conclusions:China′s prefecture-level TCM healthcare resource allocation exhibits significant geographic and population-based inequities, with excessive concentration in provincial capitals and developed cities. Urgent strategies are needed to optimize resource allocation, enhance fairness and accessibility, including promoting the decentralization of high-quality resources, strengthening regional collaborative support, enhancing talent attraction in underdeveloped areas, and leveraging information technology to improve efficiency.

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