Evidence map›Paper›PMID 41968314›Full record

ArticleCardiovascular diabetology2026

Cumulative exposure and dynamic trajectories of the C-reactive protein-triglyceride-glucose index (CTI) versus the cholesterol, high-density lipoprotein, and glucose index (CHG) for incident hypertension prediction: a national cohort study.

Jiaqing Dou, Shuya Zhang, Chaofan Ding, Haoquan Li, Pengfei Zhang

Abstract readComparative Study
In one paragraph

Article in Cardiovascular diabetology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

5 authors.

Jiaqing DouState Key Laboratory for Innovation and Transformation of Luobing Theory; Key Laboratory of Cardiovascular Remodeling and Function Research, Department of Cardiology, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China.
Shuya ZhangDepartment of Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
Chaofan DingState Key Laboratory for Innovation and Transformation of Luobing Theory; Key Laboratory of Cardiovascular Remodeling and Function Research, Department of Cardiology, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China.
Haoquan LiState Key Laboratory for Innovation and Transformation of Luobing Theory; Key Laboratory of Cardiovascular Remodeling and Function Research, Department of Cardiology, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China.
Pengfei ZhangShenzhen Research Institute of Shandong University, Shenzhen, China. pengf-zhang@163.com.

Funding

National Key R&D Program of China 2023YFC2506502Shenzhen Science and Technology Program JCYJ20220818103407015; GJHZ20240218113401003
6 · The paper itself

Abstract

backgroundThe C-reactive protein-triglyceride-glucose index (CTI) and cholesterol-high-density lipoprotein-glucose index (CHG) are emerging composite biomarker indices, but their cumulative exposure and comparative value for predicting incident hypertension remain unclear.

methodsLeveraging a nationwide longitudinal cohort from the China Health and Retirement Longitudinal Study (CHARLS), the cumulative burden of CTI and CHG was modeled. These cumulative indices were defined as the average of the values measured at Wave 1 and Wave 3, multiplied by the time interval between these two assessments. Then, multivariable Cox proportional hazards models were used to assess associations and restricted cubic splines (RCS) for dose–response relationships. Group-based trajectory modeling (GBTM) was used to identify trajectory patterns. To gauge the predictive performance at 7 and 9 years, we analyzed time-dependent receiver operating characteristic (ROC) curves, alongside the C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI), and cuCTI was further compared with triglyceride-glucose (TyG). Finally, the findings were further subjected to subgroup and sensitivity analyses to test their robustness.

resultsOver a median follow-up of 9 years, 408 of 2673 participants (15.3%) developed hypertension. Both elevated cuCTI and cuCHG significantly increased hypertension risk. In the fully adjusted model, participants in the highest quartile had a higher risk of hypertension for both cuCTI (HR = 2.16; 95% CI 1.63–2.87) and cuCHG (HR = 1.63; 95% CI 1.24–2.15). Crucially, cuCTI demonstrated superior predictive accuracy in time-dependent ROC analysis (9 years DeLong P = 0.025). In the fully adjusted model, adding cuCTI improved the 7 years C-index from 0.572 to 0.609 (P = 0.014) and the 9 years C-index from 0.582 to 0.618 (P = 0.003), compared with 0.590 and 0.597 for cuCHG (P = 0.110 and 0.094, respectively). Furthermore, cuCTI showed stronger gains in NRI and IDI compared to cuCHG (NRI: 30.31% vs. 12.61% at 7 years and 35.47% versus 15.77% at 9 years; IDI: 0.86% versus 0.47% at 7 years and 1.32% vs. 0.71% at 9 years; all P < 0.05). Further comparison with TyG also suggested superior predictive performance of cuCTI. Subgroup and sensitivity analyses also showed consistent results.

conclusionBoth indices were independently associated with incident hypertension, and cuCTI showed better long-term predictive performance than cuCHG. These findings support the value of monitoring cumulative inflammatory-metabolic burden for early hypertension risk stratification.

Indexed as

Blood GlucoseBlood PressureCholesterolCholesterol, HDLC-Reactive ProteinDyslipidemiasHypertensionTriglyceridesAgedBiomarkersChinaFemaleHumansIncidenceLongitudinal StudiesMaleBiomarkersBlood GlucoseCholesterolCholesterol, HDLC-Reactive ProteinTriglyceridesCHARLSCholesterolC-reactive protein-triglyceride-glucose indexCumulative exposureGlucose indexHigh-density lipoproteinHypertension

Identifiers

PMID41968314
PMCPMC13195972

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.