Evidence map›Paper›PMID 39152445›Full record

ArticleCardiovascular diabetology2024

Relationship between changes in the triglyceride glucose-body mass index and frail development trajectory and incidence in middle-aged and elderly individuals: a national cohort study.

Kai Guo, Qi Wang, Lin Zhang, Rui Qiao, Yujia Huo, Lipeng Jing, Xiaowan Wang, Zixuan Song, Siyu Li, Jinming Zhang and 3 more

Erratum issuedAbstract read
In one paragraph

Article in Cardiovascular diabetology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

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

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Kai Guo *The School of public health, Inner Mongolia University of Science and Technology Baotou Medical College, Baotou, China.
Qi Wang *The School of public health, Inner Mongolia University of Science and Technology Baotou Medical College, Baotou, China.
Lin ZhangThe School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Rui QiaoThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical College, Baotou, China.
Yujia HuoThe School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Lipeng JingThe School of Public Health, Lanzhou University, Lanzhou, China.
Xiaowan WangThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical College, Baotou, China.
Zixuan SongThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical College, Baotou, China.
Siyu LiThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical College, Baotou, China.
Jinming ZhangThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical College, Baotou, China.
Yanfang YangThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical College, Baotou, China.
Jinli MaheThe School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. mahejinli@163.com.
Zhengran LiuThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical College, Baotou, China. Lzr13039570839@sina.com.

Funding

the Central Guidance Fund for Local Science and Technology Development Projects 2022ZY0180
6 · The paper itself

Abstract

backgroundInsulin resistance is linked to an increased risk of frailty, yet the comprehensive relationship between the triglyceride glucose-body mass index (TyG-BMI), which reflects weight, and frailty, remains unclear. This relationship is investigated in this study.

methodsData from 9135 participants in the China Health and Retirement Longitudinal Study (2011-2020) were analysed. Baseline TyG-BMI, changes in the TyG-BMI and cumulative TyG-BMI between baseline and 2015, along with the frailty index (FI) over nine years, were calculated. Participants were grouped into different categories based on TyG-BMI changes using K-means clustering. FI trajectories were assessed using a group-based trajectory model. Logistic and Cox regression models were used to analyse the associations between the TyG-BMI and FI trajectory and frail incidence. Nonlinear relationships were explored using restricted cubic splines, and a linear mixed-effects model was used to evaluate FI development speed. Weighted quantile regression was used to identify the primary contributing factors.

resultsFour classes of changes in the TyG-BMI and two FI trajectories were identified. Individuals in the third (OR = 1.25, 95% CI: 1.10-1.42) and fourth (OR = 1.83, 95% CI: 1.61-2.09) quartiles of baseline TyG-BMI, those with consistently second to highest (OR = 1.49, 95% CI: 1.32-1.70) and the highest (OR = 2.17, 95% CI: 1.84-2.56) TyG-BMI changes, and those in the third (OR = 1.20, 95% CI: 1.05-1.36) and fourth (OR = 1.94, 95% CI: 1.70-2.22) quartiles of the cumulative TyG-BMI had greater odds of experiencing a rapid FI trajectory. Higher frail risk was noted in those in the fourth quartile of baseline TyG-BMI (HR = 1.42, 95% CI: 1.28-1.58), with consistently second to highest (HR = 1.23, 95% CI: 1.12-1.34) and the highest TyG-BMI changes (HR = 1.58, 95% CI: 1.42-1.77), and those in the third (HR = 1.10, 95% CI: 1.00-1.21) and fourth quartile of cumulative TyG-BMI (HR = 1.46, 95% CI: 1.33-1.60). Participants with persistently second-lowest to the highest TyG-BMI changes (β = 0.15, 0.38 and 0.76 respectively) and those experiencing the third to fourth cumulative TyG-BMI (β = 0.25 and 0.56, respectively) demonstrated accelerated FI progression. A U-shaped association was observed between TyG-BMI levels and both rapid FI trajectory and higher frail risk, with BMI being the primary factor.

conclusionA higher TyG-BMI is associated with the rapid development of FI trajectory and a greater frail risk. However, excessively low TyG-BMI levels also appear to contribute to frail development. Maintaining a healthy TyG-BMI, especially a healthy BMI, may help prevent or delay the frail onset.

Indexed as

BiomarkersBlood GlucoseBody Mass IndexFrail ElderlyFrailtyGeriatric AssessmentTriglyceridesAgedAged, 80 and overAge FactorsChinaFemaleHumansIncidenceInsulin ResistanceLongitudinal StudiesBiomarkersBlood GlucoseTriglyceridesCohort studyFrailty indexGroup-based trajectory modelK-means clusteringTyG-BMI

Identifiers

PMID39152445
PMCPMC11330012

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.