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
Abstract Background Insulin 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. Methods Data from 913...
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2024-08-01
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| Series: | Cardiovascular Diabetology |
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| Online Access: | https://doi.org/10.1186/s12933-024-02373-1 |
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| author | Kai Guo Qi Wang Lin Zhang Rui Qiao Yujia Huo Lipeng Jing Xiaowan Wang Zixuan Song Siyu Li Jinming Zhang Yanfang Yang Jinli Mahe Zhengran Liu |
| author_facet | Kai Guo Qi Wang Lin Zhang Rui Qiao Yujia Huo Lipeng Jing Xiaowan Wang Zixuan Song Siyu Li Jinming Zhang Yanfang Yang Jinli Mahe Zhengran Liu |
| author_sort | Kai Guo |
| collection | DOAJ |
| description | Abstract Background Insulin 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. Methods Data 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. Results Four 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. Conclusion A 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. |
| format | Article |
| id | doaj-art-5a04c3f07c0f49a3a262476e38c29581 |
| institution | Kabale University |
| issn | 1475-2840 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | BMC |
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| series | Cardiovascular Diabetology |
| spelling | doaj-art-5a04c3f07c0f49a3a262476e38c295812025-02-02T12:07:23ZengBMCCardiovascular Diabetology1475-28402024-08-0123111410.1186/s12933-024-02373-1Relationship between changes in the triglyceride glucose-body mass index and frail development trajectory and incidence in middle-aged and elderly individuals: a national cohort studyKai Guo0Qi Wang1Lin Zhang2Rui Qiao3Yujia Huo4Lipeng Jing5Xiaowan Wang6Zixuan Song7Siyu Li8Jinming Zhang9Yanfang Yang10Jinli Mahe11Zhengran Liu12The School of public health, Inner Mongolia University of Science and Technology Baotou Medical CollegeThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical CollegeThe School of Public Health and Preventive Medicine, Monash UniversityThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical CollegeThe School of Public Health and Preventive Medicine, Monash UniversityThe School of Public Health, Lanzhou UniversityThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical CollegeThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical CollegeThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical CollegeThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical CollegeThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical CollegeThe School of Public Health and Preventive Medicine, Monash UniversityThe School of public health, Inner Mongolia University of Science and Technology Baotou Medical CollegeAbstract Background Insulin 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. Methods Data 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. Results Four 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. Conclusion A 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.https://doi.org/10.1186/s12933-024-02373-1TyG-BMIFrailty indexGroup-based trajectory modelCohort studyK-means clustering |
| spellingShingle | Kai Guo Qi Wang Lin Zhang Rui Qiao Yujia Huo Lipeng Jing Xiaowan Wang Zixuan Song Siyu Li Jinming Zhang Yanfang Yang Jinli Mahe Zhengran Liu 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 Cardiovascular Diabetology TyG-BMI Frailty index Group-based trajectory model Cohort study K-means clustering |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | TyG-BMI Frailty index Group-based trajectory model Cohort study K-means clustering |
| url | https://doi.org/10.1186/s12933-024-02373-1 |
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