1.Changing distribution and resistance profiles of common pathogens isolated from urine in the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yanming LI ; Mingxiang ZOU ; Wen'en LIU ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; 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 ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; 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 ; 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 2024;24(3):287-299
Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8%(62 219/261 893),and gram-negative bacteria 76.2%(199 674/261 893).The most common species were E.coli(46.7%),E.faecium(10.4%),K.pneumoniae(9.8%),E.faecalis(8.7%),P.mirabilis(3.5%),P.aeruginosa(3.4%),SS.agalactiae(2.6%),and E.cloacae(2.1%).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2%,52.8%and 37.0%,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7%,18.5%,16.4%,and 40.3%,respectively.Lower than 10%of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90%of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was<2%.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.
2.Changing resistance profiles of Enterococcus in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Na CHEN ; Ping JI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; 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 ; 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 ; 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 ; 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 2024;24(3):300-308
Objective To understand the distribution and changing resistance profiles of clinical isolates of Enterococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Enterococcus according to the unified protocol of CHINET program by automated systems,Kirby-Bauer method,or E-test strip.The results were interpreted according to the Clinical & Laboratory Standards Institute(CLSI)breakpoints in 2021.WHONET 5.6 software was used for statistical analysis.Results A total of 124 565 strains of Enterococcus were isolated during the 7-year period,mainly including Enterococcus faecalis(50.7%)and Enterococcus faecalis(41.5%).The strains were mainly isolated from urinary tract specimens(46.9%±2.6%),and primarily from the patients in the department of internal medicine,surgery and ICU.E.faecium and E.faecalis strains showed low level resistance rate to vancomycin,teicoplanin and linezolid(≤3.6%).The prevalence of vancomycin-resistant E.faecalis and E.faecium was 0.1%and 1.3%,respectively.The prevalence of linezolid-resistant E.faecalis increased from 0.7%in 2015 to 3.4%in 2021,while the prevalence of linezolid-resistant E.faecium was 0.3%.Conclusions The clinical isolates of Enterococcus were still highly susceptible to vancomycin,teicoplanin,and linezolid,evidenced by a low resistance rate.However,the prevalence of linezolid-resistant E.faecalis was increasing during the 7-year period.It is necessary to strengthen antimicrobial resistance surveillance to effectively identify the emergence of antibiotic-resistant bacteria and curb the spread of resistant pathogens.
3.Changing resistance profiles of Enterobacter isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Shaozhen YAN ; Ziyong SUN ; Zhongju CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yi XIE ; Mei KANG ; Fengbo ZHANG ; Ping JI ; Zhidong HU ; Jin LI ; Sufang GUO ; Han SHEN ; Wanqing ZHOU ; Yingchun XU ; Xiaojiang ZHANG ; Xuesong XU ; Chao YAN ; Chuanqing WANG ; Pan FU ; Wei JIA ; Gang LI ; Yuanhong XU ; Ying HUANG ; Dawen GUO ; Jinying ZHAO ; Wen'en LIU ; Yanming LI ; Hua YU ; Xiangning HUANG ; Bin SHAN ; Yan DU ; Shanmei WANG ; Yafei CHU ; Yuxing NI ; Jingyong SUN ; Yunsong YU ; Jie LIN ; Chao ZHUO ; Danhong SU ; Lianhua WEI ; Fengmei ZOU ; Yan JIN ; Chunhong SHAO ; Jihong LI ; Lixia ZHANG ; Juan MA ; Yunzhuo CHU ; Sufei TIAN ; Jinju DUAN ; Jianbang KANG ; Ruizhong WANG ; Hua FANG ; Fangfang HU ; Yunjian HU ; Xiaoman AI ; Fang DONG ; Zhiyong LÜ ; Hong ZHANG ; Chun WANG ; Yong ZHAO ; Ping GONG ; Lei ZHU ; Jinhua MENG ; Xiaobo MA ; Yanping ZHENG ; Jinsong WU ; Yuemei LU ; Ruyi GUO ; Yan ZHU ; Kaizhen WEN ; Yirong ZHANG ; Chunlei YUE ; Jiangshan LIU ; Wenhui HUANG ; Shunhong XUE ; Xuefei HU ; Hongqin GU ; Jiao FENG ; Shuping ZHOU ; Yan ZHOU ; Yunsheng CHEN ; Qing MENG ; Bixia YU ; Jilu SHEN ; Rui DOU ; Shifu WANG ; Wen HE ; Longfeng LIAO ; Lin JIANG
Chinese Journal of Infection and Chemotherapy 2024;24(3):309-317
Objective To examine the changing antimicrobial resistance profile of Enterobacter spp.isolates in 53 hospitals across China from 2015 t0 2021.Methods The clinical isolates of Enterobacter spp.were collected from 53 hospitals across China during 2015-2021 and tested for antimicrobial susceptibility using Kirby-Bauer method or automated testing systems according to the CHINET unified protocol.The results were interpreted according to the breakpoints issued by the Clinical & Laboratory Standards Institute(CLSI)in 2021(M100 31st edition)and analyzed with WHONET 5.6 software.Results A total of 37 966 Enterobacter strains were isolated from 2015 to 2021.The proportion of Enterobacter isolates among all clinical isolates showed a fluctuating trend over the 7-year period,overall 2.5%in all clinical isolates amd 5.7%in Enterobacterale strains.The most frequently isolated Enterobacter species was Enterobacter cloacae,accounting for 93.7%(35 571/37 966).The strains were mainly isolated from respiratory specimens(44.4±4.6)%,followed by secretions/pus(16.4±2.3)%and urine(16.0±0.9)%.The strains from respiratory samples decreased slightly,while those from sterile body fluids increased over the 7-year period.The Enterobacter strains were mainly isolated from inpatients(92.9%),and only(7.1±0.8)%of the strains were isolated from outpatients and emergency patients.The patients in surgical wards contributed the highest number of isolates(24.4±2.9)%compared to the inpatients in any other departement.Overall,≤ 7.9%of the E.cloacae strains were resistant to amikacin,tigecycline,polymyxin B,imipenem or meropenem,while ≤5.6%of the Enterobacter asburiae strains were resistant to these antimicrobial agents.E.asburiae showed higher resistance rate to polymyxin B than E.cloacae(19.7%vs 3.9%).Overall,≤8.1%of the Enterobacter gergoviae strains were resistant to tigecycline,amikacin,meropenem,or imipenem,while 10.5%of these strains were resistant to polycolistin B.The overall prevalence of carbapenem-resistant Enterobacter was 10.0%over the 7-year period,but showing an upward trend.The resistance profiles of Enterobacter isolates varied with the department from which they were isolated and whether the patient is an adult or a child.The prevalence of carbapenem-resistant E.cloacae was the highest in the E.cloacae isolates from ICU patients.Conclusions The results of the CHINET Antimicrobial Resistance Surveillance Program indicate that the proportion of Enterobacter strains in all clinical isolates fluctuates slightly over the 7-year period from 2015 to 2021.The Enterobacter strains showed increasing resistance to multiple antimicrobial drugs,especially carbapenems over the 7-year period.
4.Changing resistance profiles of Proteus,Morganella and Providencia in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yunmin XU ; Xiaoxue DONG ; Bin SHAN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Ping JI ; Fengbo ZHANG ; 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 ; 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 ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Hongyan ZHENG ; 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 2024;24(4):410-417
Objective To understand the changing distribution and antimicrobial resistance profiles of Proteus,Morganella and Providencia in hospitals across China from January 1,2015 to December 31,2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods Antimicrobial susceptibility testing was carried out following the unified CHINET protocol.The results were interpreted in accordance with the breakpoints in the 2021 Clinical & Laboratory Standards Institute(CLSI)M100(31 st Edition).Results A total of 32 433 Enterobacterales strains were isolated during the 7-year period,including 24 160 strains of Proteus,6 704 strains of Morganella,and 1 569 strains of Providencia.The overall number of these Enterobacterales isolates increased significantly over the 7-year period.The top 3 specimen source of these strains were urine,lower respiratory tract specimens,and wound secretions.Proteus,Morganella,and Providencia isolates showed lower resistance rates to amikacin,meropenem,cefoxitin,cefepime,cefoperazone-sulbactam,and piperacillin-tazobactam.For most of the antibiotics tested,less than 10%of the Proteus and Morganella strains were resistant,while less than 20%of the Providencia strains were resistant.The prevalence of carbapenem-resistant Enterobacterales(CRE)was 1.4%in Proteus isolates,1.9%in Morganella isolates,and 15.6%in Providencia isolates.Conclusions The overall number of clinical isolates of Proteus,Morganella and Providencia increased significantly in the 7-year period from 2015 to 2021.The prevalence of CRE strains also increased.More attention should be paid to antimicrobial resistance surveillance and rational antibiotic use so as to prevent the emergence and increase of antimicrobial resistance.
5.Changing resistance profiles of Staphylococcus isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yuling XIAO ; Mei KANG ; Yi XIE ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Ping JI ; Fengbo ZHANG ; 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 ; 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 ; 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 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 2024;24(5):570-580
Objective To investigate the changing distribution and antibiotic resistance profiles of clinical isolates of Staphylococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Staphylococcus according to the unified protocol of CHINET(China Antimicrobial Surveillance Network)using disk diffusion method and commercial automated systems.The CHINET antimicrobial resistance surveillance data from 2015 to 2021 were interpreted according to the 2021 CLSI breakpoints and analyzed using WHONET 5.6.Results During the period from 2015 to 2021,a total of 204,771 nonduplicate strains of Staphylococcus were isolated,including 136,731(66.8%)strains of Staphylococcus aureus and 68,040(33.2%)strains of coagulase-negative Staphylococcus(CNS).The proportions of S.aureus isolates and CNS isolates did not show significant change.S.aureus strains were mainly isolated from respiratory specimens(38.9±5.1)%,wound,pus and secretions(33.6±4.2)%,and blood(11.9±1.5)%.The CNS strains were predominantly isolated from blood(73.6±4.2)%,cerebrospinal fluid(12.1±2.5)%,and pleural effusion and ascites(8.4±2.1)%.S.aureus strains were mainly isolated from the patients in ICU(17.0±7.3)%,outpatient and emergency(11.6±1.7)%,and department of surgery(11.2±0.9)%,whereas CNS strains were primarily isolated from the patients in ICU(32.2±9.7)%,outpatient and emergency(12.8±4.7)%,and department of internal medicine(11.2±1.9)%.The prevalence of methicillin-resistant strains was 32.9%in S.aureus(MRSA)and 74.1%in CNS(MRCNS).Over the 7-year period,the prevalence of MRSA decreased from 42.1%to 29.2%,and the prevalence of MRCNS decreased from 82.1%to 68.2%.MRSA showed higher resistance rates to all the antimicrobial agents tested except trimethoprim-sulfamethoxazole than methicillin-susceptible S.aureus(MSSA).Over the 7-year period,MRSA strains showed decreasing resistance rates to gentamicin,rifampicin,and levofloxacin,MRCNS showed decreasing resistance rates to gentamicin,erythromycin,rifampicin,and trimethoprim-sulfamethoxazole,but increasing resistance rate to levofloxacin.No vancomycin-resistant strains were detected.The prevalence of linezolid-resistant MRCNS increased from 0.2%to 2.3%over the 7-year period.Conclusions Staphylococcus remains the major pathogen among gram-positive bacteria.MRSA and MRCNS were still the principal antibiotic-resistant gram-positive bacteria.No S.aureus isolates were found resistant to vancomycin or linezolid,but linezolid-resistant strains have been detected in MRCNS isolates,which is an issue of concern.
6.Korean Thyroid Association Guidelines on the Management of Differentiated Thyroid Cancers; Overview and Summary 2024
Young Joo PARK ; Eun Kyung LEE ; Young Shin SONG ; Bon Seok KOO ; Hyungju KWON ; Keunyoung KIM ; Mijin KIM ; Bo Hyun KIM ; Won Gu KIM ; Won Bae KIM ; Won Woong KIM ; Jung-Han KIM ; Hee Kyung KIM ; Hee Young NA ; Shin Je MOON ; Jung-Eun MOON ; Sohyun PARK ; Jun-Ook PARK ; Ji-In BANG ; Kyorim BACK ; Youngduk SEO ; Dong Yeob SHIN ; Su-Jin SHIN ; Hwa Young AHN ; So Won OH ; Seung Hoon WOO ; Ho-Ryun WON ; Chang Hwan RYU ; Jee Hee YOON ; Ka Hee YI ; Min Kyoung LEE ; Sang-Woo LEE ; Seung Eun LEE ; Sihoon LEE ; Young Ah LEE ; Joon-Hyop LEE ; Ji Ye LEE ; Jieun LEE ; Cho Rok LEE ; Dong-Jun LIM ; Jae-Yol LIM ; Yun Kyung JEON ; Kyong Yeun JUNG ; Ari CHONG ; Yun Jae CHUNG ; Chan Kwon JUNG ; Kwanhoon JO ; Yoon Young CHO ; A Ram HONG ; Chae Moon HONG ; Ho-Cheol KANG ; Sun Wook KIM ; Woong Youn CHUNG ; Do Joon PARK ; Dong Gyu NA ;
International Journal of Thyroidology 2024;17(1):1-20
Differentiated thyroid cancer demonstrates a wide range of clinical presentations, from very indolent cases to those with an aggressive prognosis. Therefore, diagnosing and treating each cancer appropriately based on its risk status is important. The Korean Thyroid Association (KTA) has provided and amended the clinical guidelines for thyroid cancer management since 2007. The main changes in this revised 2024 guideline include 1) individualization of surgical extent according to pathological tests and clinical findings, 2) application of active surveillance in low-risk papillary thyroid microcarcinoma, 3) indications for minimally invasive surgery, 4) adoption of World Health Organization pathological diagnostic criteria and definition of terminology in Korean, 5) update on literature evidence of recurrence risk for initial risk stratification, 6) addition of the role of molecular testing, 7) addition of definition of initial risk stratification and targeting thyroid stimulating hormone (TSH) concentrations according to ongoing risk stratification (ORS), 8) addition of treatment of perioperative hypoparathyroidism, 9) update on systemic chemotherapy, and 10) addition of treatment for pediatric patients with thyroid cancer.
7.Metabolic Disease Management Guideline for National Metabolic Management Center(2nd edition)
Weiqing WANG ; Yufan WANG ; Guixia WANG ; Guang NING ; Dalong ZHU ; Ping LIU ; Libin LIU ; Jianmin LIU ; Zhaoli YAN ; Xulei TANG ; Bangqun JI ; Sunjie YAN ; Heng SU ; Jianling DU ; Sheli LI ; Li LI ; Shengli WU ; Jinsong KUANG ; Yubo SHA ; Ping ZHANG ; Yifei ZHANG ; Lei CHEN ; Zunhai ZHOU ; Chao ZHENG ; Qidong ZHENG ; Zhongyan SHAN ; Dong ZHAO ; Zhigang ZHAO ; Ling HU ; Tingyu KE ; Yu SHI ; Yingfen QIN ; Mingjun GU ; Xuejiang GU ; Fengmei XU ; Zuhua GAO ; Qijuan DONG ; Yi SHU ; Yuancheng DAI
Chinese Journal of Endocrinology and Metabolism 2023;39(6):538-554
The latest epidemiological data suggests that the situation of adult diabetes in China is severe, and metabolic diseases have become significant chronic illnesses that have a serious impact on public health and social development. After more than six years of practice, the National Metabolic Management Center(MMC) has developed distinctive approaches to manage metabolic patients and has achieved a series of positive outcomes, continuously advancing the standardized diagnosis and treatment model. In order to further improve the efficiency, based on the first edition, the second edition guideline was composed by incorporating experience of the past six years in conjunction with the latest international and domestic guidelines.
8.The distribution of blood pressure and associated factors of the elderly with type 2 diabetes in Jiangsu Province.
Jia Hui LIU ; Han Kun XIE ; Jian SU ; Zheng ZHU ; En Chun PAN ; Yan LU ; Fu Ping WAN ; Qing Yang YAN ; Ning ZHANG ; Shu Jun GU ; Ming WU ; Jin Yi ZHOU ; Chong SHEN
Chinese Journal of Preventive Medicine 2023;57(5):614-625
Objective: To investigate the distribution of blood pressure and analyze the associated factors of blood pressure of the elderly with type 2 diabetes in Jiangsu Province. Methods: The elderly over 60 years old participants with type 2 diabetes in the communities of Huai'an City and Changshu City, Jiangsu Province were selected in this study. They were divided into two groups: taking antihypertensive drugs and not taking antihypertensive drugs. The demographic characteristics, such as age and sex, and relevant factors were collected by questionnaire. The systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by physical examination. The percentile of SBP and DBP in each age group of men and women were described. The kernel density estimation curve was used to show the blood pressure distribution. The trend of blood pressure with age was fitted by locally weighted regression. The logistic regression model was used to analyze relevant factors of blood pressure. Results: A total of 12 949 participants were included in this study, including 7 775 patients in the antihypertensive drug group and 5 174 patients in the group without antihypertensive drugs. The SBP of participants was concentrated at 140-160 mmHg, and their DBP was concentrated at 75-85 mmHg. There were significant differences in the distribution of blood pressure among the subgroups of body mass index (BMI) and rural areas whether taking antihypertensive drugs and not. For participants aged under 80 years old, the SBP showed an increasing trend with age and the DBP showed a decreasing trend with age. Age, BMI ≥24 kg/m2, fasting blood glucose ≥7.0 mmol/L, living in rural areas and no smoking were influencing factors of the elevated SBP; BMI ≥24 kg/m2, male, living in rural areas, no smoking, drinking alcohol and not receiving drug hypoglycemic treatment were influencing factors of the elevated DBP. Conclusion: The SBP of older diabetic adults in Jiangsu Province is at a high level, and the distribution of blood pressure is significantly different between men and women in taking antihypertensive drugs group. The SBP presents a rising trend and the DBP is decreasing at the age of 60-80 years. The blood pressure level of this population are mainly affected by age, BMI, urban and rural areas, smoking.
Adult
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Aged
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Humans
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Male
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Female
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Middle Aged
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Aged, 80 and over
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Blood Pressure/physiology*
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Diabetes Mellitus, Type 2/epidemiology*
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Antihypertensive Agents/therapeutic use*
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Smoking
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Body Mass Index
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Hypertension/epidemiology*
9.Cinnamomi Cortex Regulates Incretin Effect in Diabetic Rats
Jiaqi GU ; Lilan QIN ; Rong SU ; Min HUANG ; Yi WEI ; Qiang XU
Chinese Journal of Experimental Traditional Medical Formulae 2023;29(12):104-111
ObjectiveTo observe the pharmacodynamic effects of Cinnamomi Cortex on the incretin effect in the rat model of diabetes mellites (DM) induced by streptozotocin (STZ) and explore the underlying mechanism from glucagon-like peptide-1 (GLP-1) and dipeptidyl peptidase-4 (DPP-4). MethodForty SD rats were randomly assigned into blank, model, sitagliptin (0.1 g·kg-1), and low- and high-dose Cinnamomi Cortex (0.45 and 0.9 g·kg-1, respectively) groups. The DM rat model was established by a high-fat diet combined with intraperitoneal injection of 40 mg·kg-1 STZ in other groups except the blank group. The intervention lasted for 8 weeks. The status, body weight, water intake, food intake, and fasting blood glucose (FBG) of the rats were observed and determined. Hematoxylin-eosin staining was employed to reveal the pathological changes of the pancreas, and immunohistochemistry to detect the expression of glucagon in the pancreas. Biochemical assay was employed to measure the serum levels of lipid metabolism indexes such as total cholesterol (TC), triglyceride (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL). Enzyme-linked immunosorbent assay was employed to determine the levels of glycosylated hemoglobin, insulin, glucagon, GLP-1, and glucose-dependent insulinotropic polypeptide (GIP) in rat serum, and Western blot to determine the protein levels of GLP-1 and DPP-4 in the pancreas. ResultAfter 8 weeks of intervention, the model group showed higher body weight, FBG, TC, TG, LDL, glycosylated hemoglobin, glucagon, insulin, and insulin resistance index and lower HDL, GLP-1, and GIP than the blank group (P<0.05, P<0.01). The Cinnamomi Cortex groups showed lower body weight, FBG, TC, TG, LDL, glycosylated hemoglobin, glucagon, insulin, and insulin resistance index and higher HDL, GLP-1, and GIP than the model group (P<0.05, P<0.01). The Cinnamomi Cortex groups showed recovered morphology of islet cells and no nucleus aggregation. Compared with the model group, the Cinnamomi Cortex groups showed declined levels of glucagon in the center of islet cells. Compared with the blank group, the model group showed up-regulated protein level of DPP-4 and down-regulated protein level of GLP-1 (P<0.01). Compared with the model group, the high-dose Cinnamomi Cortex groups showed down-regulated protein level of DPP-4 and up-regulated protein level of GLP-1 (P<0.05). ConclusionCinnamomi Cortex may reduce blood glucose and improve incretin effect to lower the blood glucose level by regulating DPP-4 and GLP-1 in DM rats.
10.A prospective cohort study of long-term fasting blood glucose variability and risk of mortality in patients with type 2 diabetes.
Yi Jia CHEN ; Yu QIN ; Hao YU ; Zheng ZHU ; Chong SHEN ; Yan LU ; Ting Ting CHENG ; Ning ZHANG ; Shu Jun GU ; Jin Yi ZHOU ; Ming WU ; Jian SU
Chinese Journal of Epidemiology 2023;44(7):1099-1105
Objective: To investigate the association between long-term fasting blood glucose (FPG) variability and all-cause mortality in patients with type 2 diabetes. Methods: A total of 7 174 type 2 diabetic patients included in National Basic Public Health Service Program in Changshu of Jiangsu Province were recruited as participants. Long-term glucose variability was assessed using standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and variability independent of the mean (VIM) across FPG measurements at the more than three visits. Death information were mainly obtained from the death registry system in Jiangsu. Then Cox proportional hazards regression models were used to estimate the associations of four variability indicators and all-cause mortality's hazard ratios (HRs) and their 95%CIs. Results: Among 55 058.50 person-years of the follow-up, the mean follow-up time was 7.67 years, and 898 deaths occurred during the follow-up period. After adjustment, compared with T1 group, the Cox regression model showed that HRs of T3 group in SD, CV, ARV and VIM were 1.24 (95%CI: 1.03-1.49), 1.20 (95%CI: 1.01-1.43), 1.28 (95%CI: 1.07-1.55) and 1.20 (95%CI:1.01-1.41), respectively. HRs of per 1 SD higher SD, CV, ARV and VIM were 1.13 (95%CI: 1.06-1.21), 1.08 (95%CI: 1.01-1.15), 1.05 (95%CI: 1.00-1.12) and 1.09 (95%CI: 1.02-1.16) for all-cause mortality, respectively. In the stratified analysis, age, gender, hypoglycemic agent and insulin uses had no effect on the above associations (all P for interaction >0.05). Conclusion: Long-term FPG glycemic variability was positively associated with the risk of all-cause mortality in type 2 diabetes patients.

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