Evidence map›Paper›PMID 40269748›Full record

ArticleBMC geriatrics2025

Associations of the triglyceride-glucose index and triglyceride-glucose/body mass index with all-cause mortality in Chinese centenarians.

Shiju Yan, Wenjing Dong, Yue Niu, Lingyun Song, Ping Pang, Di Sun, Yue Zhang, Wei Wang, Hongyan Hu, Xinye Jin and 19 more

Abstract read
In one paragraph

Article in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

29 authors.

Shiju Yan *Department of Orthopedics, Hainan Hospital of Chinese PLA General Hospital, Hainan, Sanya, 572013, China.
Wenjing Dong *Department of Endocrinology, Hainan Hospital of Chinese PLA General Hospital, Hainan, Sanya, 572013, China.
Yue Niu *Senior Department of Nephrology, State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing Key Laboratory of Digital Intelligent TCM for the Prevention and Treatment of Pan-vascular Diseases, First Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Kidney Diseases, Key Disciplines of National Administration of Traditional Chinese Medicine(zyyzdxk-2023310), Beijing, 100853, China.
Lingyun SongDepartment of Endocrinology, Hainan Hospital of Chinese PLA General Hospital, Hainan, Sanya, 572013, China.
Ping PangDepartment of Endocrinology, Hainan Hospital of Chinese PLA General Hospital, Hainan, Sanya, 572013, China.
Di SunDepartment of Endocrinology, Hainan Hospital of Chinese PLA General Hospital, Hainan, Sanya, 572013, China.
Yue ZhangDepartment of Endocrinology, Hainan Hospital of Chinese PLA General Hospital, Hainan, Sanya, 572013, China.
Wei WangDepartment of Endocrinology, Hainan Hospital of Chinese PLA General Hospital, Hainan, Sanya, 572013, China.
Hongyan HuDepartment of Laboratory Medicine, Hainan Hospital of Chinese PLA General Hospital, Sanya, 572013, China.
Xinye JinDepartment of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Academician Chen Xiangmei of Hainan Province Kidney Diseases Research Team Innovation Center, Sanya, 572013, China.
Jie ZhangDepartment of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Academician Chen Xiangmei of Hainan Province Kidney Diseases Research Team Innovation Center, Sanya, 572013, China.
Qing LuoDepartment of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Academician Chen Xiangmei of Hainan Province Kidney Diseases Research Team Innovation Center, Sanya, 572013, China.
Ding SunDepartment of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Academician Chen Xiangmei of Hainan Province Kidney Diseases Research Team Innovation Center, Sanya, 572013, China.
Hao LiDepartment of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Academician Chen Xiangmei of Hainan Province Kidney Diseases Research Team Innovation Center, Sanya, 572013, China.
Zehao ZhangDepartment of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Academician Chen Xiangmei of Hainan Province Kidney Diseases Research Team Innovation Center, Sanya, 572013, China.
Zeyu QuDepartment of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Academician Chen Xiangmei of Hainan Province Kidney Diseases Research Team Innovation Center, Sanya, 572013, China.
Qiao ZhuCentral Laboratory, Hainan Hospital of Chinese PLA General Hospital, Sanya, 572013, China.
Yujian ChenCentral Laboratory, Hainan Hospital of Chinese PLA General Hospital, Sanya, 572013, China.
Chaoxue NingCentral Laboratory, Hainan Hospital of Chinese PLA General Hospital, Sanya, 572013, China.
Shihui FuDepartment of Cardiology, Hainan Hospital of Chinese PLA General Hospital, Sanya, 572013, China.
Shanshan YangDepartment of Disease Prevention and Control, First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China.
Shengshu WangInstitute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Second Medical Center, State Key Laboratory of Kidney Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
Yao HeInstitute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Second Medical Center, State Key Laboratory of Kidney Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
Bin WangSenior Department of Nephrology, State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing Key Laboratory of Digital Intelligent TCM for the Prevention and Treatment of Pan-vascular Diseases, First Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Kidney Diseases, Key Disciplines of National Administration of Traditional Chinese Medicine(zyyzdxk-2023310), Beijing, 100853, China.
Yali ZhaoCentral Laboratory, Hainan Hospital of Chinese PLA General Hospital, Sanya, 572013, China.
Guoqing YangDepartment of Endocrinology, Hainan Hospital of Chinese PLA General Hospital, Hainan, Sanya, 572013, China.
Xiangmei ChenSenior Department of Nephrology, State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing Key Laboratory of Digital Intelligent TCM for the Prevention and Treatment of Pan-vascular Diseases, First Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Kidney Diseases, Key Disciplines of National Administration of Traditional Chinese Medicine(zyyzdxk-2023310), Beijing, 100853, China. xmchen301@126.com.
Miao LiuInstitute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Second Medical Center, State Key Laboratory of Kidney Diseases, Chinese PLA General Hospital, Beijing, 100853, China. liumiaolmbxb@163.com.
Yizhi ChenSenior Department of Nephrology, State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing Key Laboratory of Digital Intelligent TCM for the Prevention and Treatment of Pan-vascular Diseases, First Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Kidney Diseases, Key Disciplines of National Administration of Traditional Chinese Medicine(zyyzdxk-2023310), Beijing, 100853, China. yizchen@126.com.

Funding

Beijing Natural Science Foundation 7242033Capital's Funds for Health Improvement and Research CFH 2024-1-5021National Key Research and Development Program of China 2022YFC3602900National Natural Science Foundation of China 32141005National Natural Science Foundation of China 82270769Sanya science and technology innovation special project 2022KJCX02Science & Technology Project of Beijing Z221100007422121Specialized Project of Military Logistics Scientific Research and Health Care 21BJZ37
6 · The paper itself

Abstract

backgroundThe triglyceride-glucose (TyG) index and triglyceride-glucose/body mass index (TyG-BMI) have been shown to be associated with cardiovascular and cerebrovascular disorders and the risk of death. The aim of this study was to explore the relationships of the TyG index and TyG-BMI with all-cause mortality among Chinese centenarians.

methodsData from the China Hainan Centenarian Cohort Study (CHCCS) were analyzed. Eligible centenarians were divided into quartiles on the basis of their TyG and TyG-BMI indices. Kaplan‒Meier analysis was used to compare survival times across groups. The associations of the TyG index and TyG-BMI with all-cause mortality were investigated using restricted cubic splines (RCSs) and Cox proportional hazards regression models. Moreover, the concordance of the associations of the TyG index and TyG-BMI with all-cause mortality in different subgroups was further explored by subgroup analysis.

resultsA total of 921 centenarian participants were included in this study. During a median follow-up of 29.70 months, 852 (92.5%) centenarians died. The results of the RCS analysis demonstrated that the TyG index and TyG-BMI were both linearly and negatively associated with all-cause mortality. Compared with that for the highest the TyG index and TyG-BMI quartile groups, higher risks of death were found for the lowest quartile groups (TyG Q1 vs. Q4, HR 1.27, 95% CI 1.03-1.56, P = 0.024; TyG-BMI Q1 vs. Q4, HR 1.60, 95% CI 1.30-1.96, P < 0.001). Centenarians with lower TyG index and TyG-BMI values had significantly greater mortality risks according to the Kaplan‒Meier analysis (log-rank P = 0.020, log-rank P < 0.001, respectively). Subgroup analysis demonstrated that blood pressure could influence the linear negative correlation between the TyG-BMI and all-cause mortality.

conclusionBoth lower TyG and TyG-BMI indices were significantly associated with higher all-cause mortality in Chinese centenarians, whereas the TyG-BMI was superior to the TyG index in predicting the mortality risk of centenarians.

Indexed as

Blood GlucoseBody Mass IndexMortalityTriglyceridesAged, 80 and overCause of DeathChinaCohort StudiesEast Asian PeopleFemaleFollow-Up StudiesHumansMaleBlood GlucoseTriglyceridesAll-cause mortalityCentenariansThe triglyceride–glucose/Body mass index (TyG–BMI)The triglyceride–glucose index (TyG)

Identifiers

PMID40269748
PMCPMC12016446

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