Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey

Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zenglei Zhang [verfasserIn] Lin Zhao [verfasserIn] Yiting Lu [verfasserIn] Xu Meng [verfasserIn] Xianliang Zhou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Cardiometabolic multi-morbidity TyG index Cardiometabolic disease Insulin resistance Risk factors CHARLS

Übergeordnetes Werk:	In: Diabetology & Metabolic Syndrome - BMC, 2010, 15(2023), 1, Seite 14
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:1 ; pages:14

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s13098-023-01205-8

Katalog-ID:	DOAJ092116167

Internformat


LEADER	01000naa a22002652 4500
001	DOAJ092116167
003	DE-627
005	20240412113626.0
007	cr uuu---uuuuu
008	240412s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1186/s13098-023-01205-8 \|2 doi
035			\|a (DE-627)DOAJ092116167
035			\|a (DE-599)DOAJ226f613801a04e2789bc66bd08759668
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a RC620-627
100	0		\|a Zenglei Zhang \|e verfasserin \|4 aut
245	1	0	\|a Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract
650		4	\|a Cardiometabolic multi-morbidity
650		4	\|a TyG index
650		4	\|a Cardiometabolic disease
650		4	\|a Insulin resistance
650		4	\|a Risk factors
650		4	\|a CHARLS
653		0	\|a Nutritional diseases. Deficiency diseases
700	0		\|a Lin Zhao \|e verfasserin \|4 aut
700	0		\|a Yiting Lu \|e verfasserin \|4 aut
700	0		\|a Xu Meng \|e verfasserin \|4 aut
700	0		\|a Xianliang Zhou \|e verfasserin \|4 aut
773	0	8	\|i In \|t Diabetology & Metabolic Syndrome \|d BMC, 2010 \|g 15(2023), 1, Seite 14 \|w (DE-627)610606689 \|w (DE-600)2518786-7 \|x 17585996 \|7 nnns
773	1	8	\|g volume:15 \|g year:2023 \|g number:1 \|g pages:14
856	4	0	\|u https://doi.org/10.1186/s13098-023-01205-8 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/226f613801a04e2789bc66bd08759668 \|z kostenfrei
856	4	0	\|u https://doi.org/10.1186/s13098-023-01205-8 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1758-5996 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 15 \|j 2023 \|e 1 \|h 14

Indexfelder

author_variant	z z zz l z lz y l yl x m xm x z xz
matchkey_str	article:17585996:2023----::eainhpfrgyeielcsidxihadoeaoimliobdticia
hierarchy_sort_str	2023
callnumber-subject-code	RC
publishDate	2023
allfields	10.1186/s13098-023-01205-8 doi (DE-627)DOAJ092116167 (DE-599)DOAJ226f613801a04e2789bc66bd08759668 DE-627 ger DE-627 rakwb eng RC620-627 Zenglei Zhang verfasserin aut Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract Cardiometabolic multi-morbidity TyG index Cardiometabolic disease Insulin resistance Risk factors CHARLS Nutritional diseases. Deficiency diseases Lin Zhao verfasserin aut Yiting Lu verfasserin aut Xu Meng verfasserin aut Xianliang Zhou verfasserin aut In Diabetology & Metabolic Syndrome BMC, 2010 15(2023), 1, Seite 14 (DE-627)610606689 (DE-600)2518786-7 17585996 nnns volume:15 year:2023 number:1 pages:14 https://doi.org/10.1186/s13098-023-01205-8 kostenfrei https://doaj.org/article/226f613801a04e2789bc66bd08759668 kostenfrei https://doi.org/10.1186/s13098-023-01205-8 kostenfrei https://doaj.org/toc/1758-5996 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 14
spelling	10.1186/s13098-023-01205-8 doi (DE-627)DOAJ092116167 (DE-599)DOAJ226f613801a04e2789bc66bd08759668 DE-627 ger DE-627 rakwb eng RC620-627 Zenglei Zhang verfasserin aut Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract Cardiometabolic multi-morbidity TyG index Cardiometabolic disease Insulin resistance Risk factors CHARLS Nutritional diseases. Deficiency diseases Lin Zhao verfasserin aut Yiting Lu verfasserin aut Xu Meng verfasserin aut Xianliang Zhou verfasserin aut In Diabetology & Metabolic Syndrome BMC, 2010 15(2023), 1, Seite 14 (DE-627)610606689 (DE-600)2518786-7 17585996 nnns volume:15 year:2023 number:1 pages:14 https://doi.org/10.1186/s13098-023-01205-8 kostenfrei https://doaj.org/article/226f613801a04e2789bc66bd08759668 kostenfrei https://doi.org/10.1186/s13098-023-01205-8 kostenfrei https://doaj.org/toc/1758-5996 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 14
allfields_unstemmed	10.1186/s13098-023-01205-8 doi (DE-627)DOAJ092116167 (DE-599)DOAJ226f613801a04e2789bc66bd08759668 DE-627 ger DE-627 rakwb eng RC620-627 Zenglei Zhang verfasserin aut Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract Cardiometabolic multi-morbidity TyG index Cardiometabolic disease Insulin resistance Risk factors CHARLS Nutritional diseases. Deficiency diseases Lin Zhao verfasserin aut Yiting Lu verfasserin aut Xu Meng verfasserin aut Xianliang Zhou verfasserin aut In Diabetology & Metabolic Syndrome BMC, 2010 15(2023), 1, Seite 14 (DE-627)610606689 (DE-600)2518786-7 17585996 nnns volume:15 year:2023 number:1 pages:14 https://doi.org/10.1186/s13098-023-01205-8 kostenfrei https://doaj.org/article/226f613801a04e2789bc66bd08759668 kostenfrei https://doi.org/10.1186/s13098-023-01205-8 kostenfrei https://doaj.org/toc/1758-5996 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 14
allfieldsGer	10.1186/s13098-023-01205-8 doi (DE-627)DOAJ092116167 (DE-599)DOAJ226f613801a04e2789bc66bd08759668 DE-627 ger DE-627 rakwb eng RC620-627 Zenglei Zhang verfasserin aut Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract Cardiometabolic multi-morbidity TyG index Cardiometabolic disease Insulin resistance Risk factors CHARLS Nutritional diseases. Deficiency diseases Lin Zhao verfasserin aut Yiting Lu verfasserin aut Xu Meng verfasserin aut Xianliang Zhou verfasserin aut In Diabetology & Metabolic Syndrome BMC, 2010 15(2023), 1, Seite 14 (DE-627)610606689 (DE-600)2518786-7 17585996 nnns volume:15 year:2023 number:1 pages:14 https://doi.org/10.1186/s13098-023-01205-8 kostenfrei https://doaj.org/article/226f613801a04e2789bc66bd08759668 kostenfrei https://doi.org/10.1186/s13098-023-01205-8 kostenfrei https://doaj.org/toc/1758-5996 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 14
allfieldsSound	10.1186/s13098-023-01205-8 doi (DE-627)DOAJ092116167 (DE-599)DOAJ226f613801a04e2789bc66bd08759668 DE-627 ger DE-627 rakwb eng RC620-627 Zenglei Zhang verfasserin aut Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract Cardiometabolic multi-morbidity TyG index Cardiometabolic disease Insulin resistance Risk factors CHARLS Nutritional diseases. Deficiency diseases Lin Zhao verfasserin aut Yiting Lu verfasserin aut Xu Meng verfasserin aut Xianliang Zhou verfasserin aut In Diabetology & Metabolic Syndrome BMC, 2010 15(2023), 1, Seite 14 (DE-627)610606689 (DE-600)2518786-7 17585996 nnns volume:15 year:2023 number:1 pages:14 https://doi.org/10.1186/s13098-023-01205-8 kostenfrei https://doaj.org/article/226f613801a04e2789bc66bd08759668 kostenfrei https://doi.org/10.1186/s13098-023-01205-8 kostenfrei https://doaj.org/toc/1758-5996 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 14
language	English
source	In Diabetology & Metabolic Syndrome 15(2023), 1, Seite 14 volume:15 year:2023 number:1 pages:14
sourceStr	In Diabetology & Metabolic Syndrome 15(2023), 1, Seite 14 volume:15 year:2023 number:1 pages:14
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cardiometabolic multi-morbidity TyG index Cardiometabolic disease Insulin resistance Risk factors CHARLS Nutritional diseases. Deficiency diseases
isfreeaccess_bool	true
container_title	Diabetology & Metabolic Syndrome
authorswithroles_txt_mv	Zenglei Zhang @@aut@@ Lin Zhao @@aut@@ Yiting Lu @@aut@@ Xu Meng @@aut@@ Xianliang Zhou @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	610606689
id	DOAJ092116167
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ092116167</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412113626.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240412s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13098-023-01205-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ092116167</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ226f613801a04e2789bc66bd08759668</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC620-627</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Zenglei Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cardiometabolic multi-morbidity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">TyG index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cardiometabolic disease</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Insulin resistance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Risk factors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CHARLS</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Nutritional diseases. Deficiency diseases</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lin Zhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yiting Lu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xu Meng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xianliang Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Diabetology & Metabolic Syndrome</subfield><subfield code="d">BMC, 2010</subfield><subfield code="g">15(2023), 1, Seite 14</subfield><subfield code="w">(DE-627)610606689</subfield><subfield code="w">(DE-600)2518786-7</subfield><subfield code="x">17585996</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:14</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13098-023-01205-8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/226f613801a04e2789bc66bd08759668</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13098-023-01205-8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1758-5996</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="h">14</subfield></datafield></record></collection>
callnumber-first	R - Medicine
author	Zenglei Zhang
spellingShingle	Zenglei Zhang misc RC620-627 misc Cardiometabolic multi-morbidity misc TyG index misc Cardiometabolic disease misc Insulin resistance misc Risk factors misc CHARLS misc Nutritional diseases. Deficiency diseases Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey
authorStr	Zenglei Zhang
ppnlink_with_tag_str_mv	@@773@@(DE-627)610606689
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	RC620-627
illustrated	Not Illustrated
issn	17585996
topic_title	RC620-627 Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey Cardiometabolic multi-morbidity TyG index Cardiometabolic disease Insulin resistance Risk factors CHARLS
topic	misc RC620-627 misc Cardiometabolic multi-morbidity misc TyG index misc Cardiometabolic disease misc Insulin resistance misc Risk factors misc CHARLS misc Nutritional diseases. Deficiency diseases
topic_unstemmed	misc RC620-627 misc Cardiometabolic multi-morbidity misc TyG index misc Cardiometabolic disease misc Insulin resistance misc Risk factors misc CHARLS misc Nutritional diseases. Deficiency diseases
topic_browse	misc RC620-627 misc Cardiometabolic multi-morbidity misc TyG index misc Cardiometabolic disease misc Insulin resistance misc Risk factors misc CHARLS misc Nutritional diseases. Deficiency diseases
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Diabetology & Metabolic Syndrome
hierarchy_parent_id	610606689
hierarchy_top_title	Diabetology & Metabolic Syndrome
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)610606689 (DE-600)2518786-7
title	Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey
ctrlnum	(DE-627)DOAJ092116167 (DE-599)DOAJ226f613801a04e2789bc66bd08759668
title_full	Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey
author_sort	Zenglei Zhang
journal	Diabetology & Metabolic Syndrome
journalStr	Diabetology & Metabolic Syndrome
callnumber-first-code	R
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
container_start_page	14
author_browse	Zenglei Zhang Lin Zhao Yiting Lu Xu Meng Xianliang Zhou
container_volume	15
class	RC620-627
format_se	Elektronische Aufsätze
author-letter	Zenglei Zhang
doi_str_mv	10.1186/s13098-023-01205-8
author2-role	verfasserin
title_sort	relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in china: evidence from a national survey
callnumber	RC620-627
title_auth	Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey
abstract	Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract
abstractGer	Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract
abstract_unstemmed	Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey
url	https://doi.org/10.1186/s13098-023-01205-8 https://doaj.org/article/226f613801a04e2789bc66bd08759668 https://doaj.org/toc/1758-5996
remote_bool	true
author2	Lin Zhao Yiting Lu Xu Meng Xianliang Zhou
author2Str	Lin Zhao Yiting Lu Xu Meng Xianliang Zhou
ppnlink	610606689
callnumber-subject	RC - Internal Medicine
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1186/s13098-023-01205-8
callnumber-a	RC620-627
up_date	2024-07-03T23:57:58.343Z
_version_	1803604279526686720
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ092116167</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412113626.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240412s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13098-023-01205-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ092116167</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ226f613801a04e2789bc66bd08759668</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC620-627</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Zenglei Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. Methods We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. Results In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1–4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29–1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05–1.90) and 1.61 (1.18–2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). Conclusions We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index. Graphical Abstract</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cardiometabolic multi-morbidity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">TyG index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cardiometabolic disease</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Insulin resistance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Risk factors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CHARLS</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Nutritional diseases. Deficiency diseases</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lin Zhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yiting Lu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xu Meng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xianliang Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Diabetology & Metabolic Syndrome</subfield><subfield code="d">BMC, 2010</subfield><subfield code="g">15(2023), 1, Seite 14</subfield><subfield code="w">(DE-627)610606689</subfield><subfield code="w">(DE-600)2518786-7</subfield><subfield code="x">17585996</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:14</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13098-023-01205-8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/226f613801a04e2789bc66bd08759668</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13098-023-01205-8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1758-5996</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="h">14</subfield></datafield></record></collection>
score	7.3974915

Nicht das Richtige dabei?

Schreiben Sie uns!

Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?