scholarly journals Temporal changes in serum biomarkers and risk for progression of gastric precancerous lesions: A longitudinal study

2014 ◽  
Vol 136 (2) ◽  
pp. 425-434 ◽  
Author(s):  
Huakang Tu ◽  
Liping Sun ◽  
Xiao Dong ◽  
Yuehua Gong ◽  
Qian Xu ◽  
...  
BMC Obesity ◽  
2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Abdullah Alkandari ◽  
Hutan Ashrafian ◽  
Thozhukat Sathyapalan ◽  
Peter Sedman ◽  
Ara Darzi ◽  
...  

2020 ◽  
Author(s):  
Bo Liu ◽  
Guanqun Chen ◽  
Ruijie Zhao ◽  
Dan Huang ◽  
Lixin Tao

Abstract Background: Metabolic syndrome (MetS) is a major risk factor for cardiovascular diseases. The objective of the study was to evaluate the updated prevalence of MetS and provide a comprehensive illustration of the possible temporal changes in MetS prevalence in China from 2011 to 2015.Methods: The data for this study are from the 2011 and 2015 waves of the China Health and Retirement Longitudinal Study (CHARLS). CHARLS is a nationally representative survey targeting populations aged 45 and above from 28 provinces in mainland China. A total of 11847 and 13013 participants were eligible for data analysis at the two time points.Results: The estimated prevalence of MetS in 2015 was 20.41% (95% CI: 19.02%-21.8%) by the ATP III criteria, 34.77% (95% CI: 33.12%-36.42%) by the International Diabetes Federation (IDF) criteria, 39.68% (95% CI: 37.88%-41.47%) by the revised ATP III criteria, and 25.55% (95% CI: 24.19%-26.91%) by the Chinese Diabetes Society (CDS) criteria. The prevalence was higher among women and elderly adults and in urban and northern populations. Furthermore, the trends in the prevalence decreased significantly between 2011 and 2015 by the ATP III, revised ATP III and CDS criteria. However, trends increased significantly from 2011 to 2015 by the IDF criteria.Conclusions: In China, elderly women living in northern urban areas should receive more attention. Notably, temporal changes in the prevalence of MetS varied somewhat according to different criteria.


2021 ◽  
Vol 108 (Supplement_9) ◽  
Author(s):  
Stephen Lam ◽  
Wing Chou ◽  
Giles Toogood ◽  
Simon Wemyss-Holden ◽  
Alexia Tsigka ◽  
...  

Abstract Background A metaplasia-dysplasia–carcinoma sequence is the most plausible carcinogenic pathway for gallbladder cancer. Although the incidence of gallbladder carcinoma is increasing, little is known about its precancerous lesions. The aim of this study was to determine temporal changes in the prevalence of low-grade dysplasia (LGD), high-grade dysplasia (HGD) and gallbladder adenocarcinoma and associated risk factors. Methods We retrospectively identified consecutive patients who underwent cholecystectomy between January 2011 and March 2020. Patients were grouped according to histology: no dysplasia; LGD; HGD; and adenocarcinoma. Fitted linear models estimated temporal trends in prevalence and mean age for all histological outcomes. Logistic regression estimated associated risk factors. Results A total of 5 835 patients were included in the analysis. The prevalence of LGD was 1.47%, HGD 0.17% and adenocarcinoma 0.19%. Prevalence for all diseases increased over time, and mean age at diagnoses decreased over time. In a multivariate logistic regression model, with no dysplasia as the reference group, female sex increased the odds of LGD (OR 4.57, 95% CI 3.07-10.10, p = <0.0001).  BMI was not associated with disease risk. Conclusions Our data suggests the prevalence of precancerous gallbladder lesions are increasing in younger patients. Although a risk factor for cholelithiasis, BMI was not associated with disease progression.  If occurring in a dysplasia-carcinoma sequence, mean age of diagnoses suggests a progression period of 20 years. Further research is required to explain both the significant sex disparity and potential environmental risk factors for gallbladder dysplasia.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bo Liu ◽  
Guanqun Chen ◽  
Ruijie Zhao ◽  
Dan Huang ◽  
Lixin Tao

Abstract Background Metabolic syndrome (MetS) is a major risk factor for cardiovascular diseases. The objective of the study was to evaluate the updated prevalence of MetS and provide a comprehensive illustration of the possible temporal changes in MetS prevalence in China from 2011 to 2015. Methods The data for this study are from the 2011 and 2015 waves of the China Health and Retirement Longitudinal Study (CHARLS). CHARLS is a nationally representative survey targeting populations aged 45 and above from 28 provinces in mainland China. A total of 11,847 and 13,013 participants were eligible for data analysis at the two time points. Results The estimated prevalence of MetS in 2015 was 20.41% (95% CI: 19.02–21.8%) by the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (ATP III) criteria, 34.77% (95% CI: 33.12–36.42%) by the International Diabetes Federation (IDF) criteria, 39.68% (95% CI: 37.88–41.47%) by the revised ATP III criteria, and 25.55% (95% CI: 24.19–26.91%) by the Chinese Diabetes Society (CDS) criteria. The prevalence was higher among women and elderly adults and in urban and northern populations. Furthermore, the trends in the prevalence decreased significantly between 2011 and 2015 by the ATP III, revised ATP III and CDS criteria. However, trends increased significantly from 2011 to 2015 by the IDF criteria. Conclusions A higher prevalence of MetS is found in those who reported being middle aged and elderly, women, residing in northern China or living in urban areas. Additionally, temporal changes in the prevalence of MetS varied according to different criteria. Increased attention to the causes associated with populations who have higher levels of MetS is warranted.


2012 ◽  
Vol 30 (2) ◽  
pp. 311-321 ◽  
Author(s):  
Matthew Zabel ◽  
Matthew Schrag ◽  
Claudius Mueller ◽  
Weidong Zhou ◽  
Andrew Crofton ◽  
...  

2020 ◽  
Vol 17 (17) ◽  
pp. 2653-2662 ◽  
Author(s):  
Meng Dai ◽  
Xiaoming Liu ◽  
Xiqi Zhu ◽  
Tiejun Liu ◽  
Cihao Xu ◽  
...  

Radiology ◽  
2020 ◽  
Vol 296 (2) ◽  
pp. E55-E64 ◽  
Author(s):  
Yuhui Wang ◽  
Chengjun Dong ◽  
Yue Hu ◽  
Chungao Li ◽  
Qianqian Ren ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document