Shuangtong Yan

506 total citations
34 papers, 322 citations indexed

About

Shuangtong Yan is a scholar working on Endocrinology, Diabetes and Metabolism, Molecular Biology and Epidemiology. According to data from OpenAlex, Shuangtong Yan has authored 34 papers receiving a total of 322 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Endocrinology, Diabetes and Metabolism, 9 papers in Molecular Biology and 7 papers in Epidemiology. Recurrent topics in Shuangtong Yan's work include Diabetes, Cardiovascular Risks, and Lipoproteins (15 papers), Diabetes Treatment and Management (6 papers) and Liver Disease Diagnosis and Treatment (5 papers). Shuangtong Yan is often cited by papers focused on Diabetes, Cardiovascular Risks, and Lipoproteins (15 papers), Diabetes Treatment and Management (6 papers) and Liver Disease Diagnosis and Treatment (5 papers). Shuangtong Yan collaborates with scholars based in China and United States. Shuangtong Yan's co-authors include Chunlin Li, Minyan Liu, Yanhui Lu, Fusheng Fang, Yanping Gong, Xinyu Miao, Hui Tian, Zhaoyan Gu, Nan Li and Jian Li and has published in prestigious journals such as Atherosclerosis, Experimental Gerontology and Molecular and Cellular Biochemistry.

In The Last Decade

Shuangtong Yan

31 papers receiving 315 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Shuangtong Yan China 11 122 95 62 49 48 34 322
Maria Andrikoula Greece 7 79 0.6× 134 1.4× 61 1.0× 55 1.1× 25 0.5× 15 338
Xianghua Zhuang China 12 174 1.4× 80 0.8× 56 0.9× 44 0.9× 27 0.6× 32 419
Dalia El-Lebedy Egypt 12 98 0.8× 95 1.0× 86 1.4× 71 1.4× 23 0.5× 29 453
Makoto Tsugita Japan 13 102 0.8× 164 1.7× 52 0.8× 67 1.4× 32 0.7× 18 406
Miwa Morita Japan 11 55 0.5× 95 1.0× 53 0.9× 47 1.0× 20 0.4× 17 349
Ruth Boxall United Kingdom 5 197 1.6× 118 1.2× 81 1.3× 109 2.2× 81 1.7× 7 463
Panwei Mu China 12 177 1.5× 165 1.7× 78 1.3× 69 1.4× 30 0.6× 24 449
Luciana S Carmo Brazil 8 71 0.6× 33 0.3× 50 0.8× 23 0.5× 21 0.4× 11 300
Katarzyna Musialik Poland 13 78 0.6× 84 0.9× 57 0.9× 40 0.8× 37 0.8× 29 341
Marc Gregory Yu Philippines 9 92 0.8× 138 1.5× 46 0.7× 94 1.9× 21 0.4× 32 372

Countries citing papers authored by Shuangtong Yan

Since Specialization
Citations

This map shows the geographic impact of Shuangtong Yan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shuangtong Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shuangtong Yan more than expected).

Fields of papers citing papers by Shuangtong Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shuangtong Yan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shuangtong Yan. The network helps show where Shuangtong Yan may publish in the future.

Co-authorship network of co-authors of Shuangtong Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Shuangtong Yan. A scholar is included among the top collaborators of Shuangtong Yan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shuangtong Yan. Shuangtong Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jin, Shuai, et al.. (2025). Global burden and trends of gout incidence and prevalence. Chinese Medical Journal. 138(23). 3153–3162.
2.
Wang, Lin, Jing Xie, Zhaoyan Gu, et al.. (2025). Predicting isolated impaired glucose tolerance without oral glucose tolerance test using machine learning in Chinese Han men. Frontiers in Endocrinology. 16. 1514397–1514397.
3.
Miao, Xinyu, Bo Fu, Shaoyuan Cui, et al.. (2024). Astragalus polysaccharides attenuate rat aortic endothelial senescence via regulation of the SIRT-1/p53 signaling pathway. BMC Complementary Medicine and Therapies. 24(1). 80–80. 9 indexed citations
4.
Sun, Jianhe, et al.. (2023). [Relationship between hemoglobin and serum uric acid in adults with various glucose metabolism status].. PubMed. 57(4). 516–521. 1 indexed citations
5.
Fu, Xiaomin, Yuhan Wang, Nan Li, et al.. (2023). Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes. Frontiers in Endocrinology. 13. 1061507–1061507. 6 indexed citations
6.
Gong, Yanping, et al.. (2023). The role of TyG index in predicting the incidence of diabetes in Chinese elderly men: a 20-year retrospective study. Frontiers in Endocrinology. 14. 1191090–1191090. 14 indexed citations
7.
Miao, Xinyu, Xiaomin Fu, Hongzhou Liu, et al.. (2023). Analysis of clinical features and 7-year all-cause mortality in older male patients with non-thyroidal illness syndrome on general wards. European Geriatric Medicine. 14(2). 363–371.
8.
Liu, Qianqian, Fan Hu, Lichao Ma, et al.. (2023). Islet function changes of post-glucose-challenge relate closely to 15 years mortality of elderly men with a history of hyperglycemia. Heliyon. 9(3). e14100–e14100. 1 indexed citations
9.
Fu, Xiaomin, Hongzhou Liu, Jing Liu, et al.. (2022). Association Between Triglyceride–Glucose Index and the Risk of Type 2 Diabetes Mellitus in an Older Chinese Population Aged Over 75 Years. Frontiers in Public Health. 9. 796663–796663. 14 indexed citations
11.
Liu, Hongzhou, Shuangtong Yan, Gang Chen, et al.. (2021). Association of the Ratio of Triglycerides to High-Density Lipoprotein Cholesterol Levels with the Risk of Type 2 Diabetes: A Retrospective Cohort Study in Beijing. Journal of Diabetes Research. 2021. 1–8. 14 indexed citations
12.
Liu, Minyan, et al.. (2019). <p>Relationship between abnormal glucose metabolism and osteoporosis in Han Chinese men over the age of 50 years</p>. Clinical Interventions in Aging. Volume 14. 445–451. 21 indexed citations
13.
Yan, Shuangtong, Xiaofeng Lv, Xingguang Zhang, et al.. (2019). Glycemic control and comprehensive metabolic risk factors control in older adults with type 2 diabetes. Experimental Gerontology. 127. 110713–110713. 5 indexed citations
14.
Dong, Sheng-Yong, et al.. (2018). Associations of body weight and weight change with cardiovascular events and mortality in patients with coronary heart disease. Atherosclerosis. 274. 104–111. 10 indexed citations
15.
Yan, Shuangtong, Hui Tian, Chunlin Li, et al.. (2015). The cutoffs and performance of glycated hemoglobin for diagnosing diabetes and prediabetes in a young and middle-aged population and in an elderly population. Diabetes Research and Clinical Practice. 109(2). 238–245. 14 indexed citations
16.
Yan, Shuangtong, et al.. (2015). Promotive effect of comprehensive management on achieving blood glucose control in senile type 2 diabetics. Genetics and Molecular Research. 14(2). 3062–3070. 2 indexed citations
17.
Yan, Shuangtong & Hui Tian. (2013). Senile parathyroid dysfunction: Characteristics of its diagnosis and treatment. Journal of Translational Internal Medicine. 1(1). 32–35. 1 indexed citations
18.
Liu, Yu, Suozhu Shi, Zhaoyan Gu, et al.. (2012). Impaired autophagic function in rat islets with aging. AGE. 35(5). 1531–1544. 42 indexed citations
19.
Jin, Nan, Jing–tao Dou, Shuyu Wang, et al.. (2010). A survey of prevalence of diabetes mellitus and metabolic syndrome in Beijing. 2(6). 414–418. 2 indexed citations
20.
Fang, Fusheng, Hui Tian, Chunlin Li, et al.. (2010). [The clinical characteristics and trend of conversion to type 2 diabetes mellitus of individuals with normal glucose tolerance-hyperinsulinemia].. PubMed. 49(6). 480–3. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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