Evidence map›Paper›PMID 41484691›Full record

ArticleJournal of health, population, and nutrition2026

Global trends and the case of China in Cardio-Kidney-Metabolic multimorbidity: development and validation of a composite risk index using GBD 1990-2021 data.

Xiaojing Lu, Xin Peng, Yunxiang Zhang, Hongming Zhu, Yu Zheng, Yue Sun, Heyu Ji, Junmin Li, Haijiao Jin, Xiaoyang Li

Abstract readValidation Study
In one paragraph

Article in Journal of health, population, and nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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.

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

10 authors.

Xiaojing Lu *Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, Ruijin Hospital, National Research Center for Translational Medicine at Shanghai, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xin Peng *Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, Ruijin Hospital, National Research Center for Translational Medicine at Shanghai, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yunxiang ZhangShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, Ruijin Hospital, National Research Center for Translational Medicine at Shanghai, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Hongming ZhuShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, Ruijin Hospital, National Research Center for Translational Medicine at Shanghai, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yu ZhengShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, Ruijin Hospital, National Research Center for Translational Medicine at Shanghai, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yue SunDepartment of Medical Affairs, Ruijin Hospital Affifiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Heyu JiDepartment of Medical Affairs, Ruijin Hospital Affifiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Junmin LiShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, Ruijin Hospital, National Research Center for Translational Medicine at Shanghai, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Haijiao JinRen Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. jinhaijiao1108@126.com.
Xiaoyang LiDepartment of Medical Affairs, Ruijin Hospital Affifiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. woodslee429@126.com.ORCID 0000-0003-4309-8709

Funding

Shanghai Science Technology and Innovation Action Plan Key Program on Medical Innovation Research 21Y31920400Shanghai Shenkang Hospital Development Center's Project SHDC12024141
6 · The paper itself

Abstract

backgroundThe syndemic burden of cardiovascular disease (CVD), chronic kidney disease (CKD), and type 2 diabetes mellitus (T2DM) represents a growing global health challenge, yet standardized tools for quantifying integrated disease burden remain lacking. We developed and preliminarily evaluated a novel Cardio-Kidney-Metabolic (CKM) Index to assess multimorbidity trends and to explore its potential to inform evidence-based policy interventions.

methodsUsing Global Burden of Disease Study 2021 data spanning 204 countries and territories from 1990 to 2021, we extracted age-standardized disability-adjusted life years (DALYs) for CVD, CKD, and T2DM. The CKM Index was constructed through logarithmic transformation (log[x + 1]), min-max normalization, and weighted integration based on global burden proportions and clinical significance (CVD = 0.5, CKD = 0.2, T2DM = 0.3) to generate a standardized 0-100 scale. We analyzed temporal trends using estimated annual percentage change (EAPC), performed joinpoint regression to identify trajectory inflection points, and conducted forecasting through 2030 using ARIMA and Bayesian ridge regression models. Sensitivity analyses evaluated index robustness across alternative weighting schemes, and clustering analysis identified distinct country trajectory patterns across socio-demographic index (SDI) regions.

resultsGlobal CKM-related DALYs increased dramatically by 60% from 343 million person-years in 1990 to 548 million in 2021 (EAPC + 1.51%). T2DM demonstrated the most explosive growth (EAPC + 3.57%), followed by CKD (+ 2.46%) and CVD (+ 1.18%). The CKM Index revealed marked global health inequalities, with high-SDI countries exhibiting the highest burden (70.7 in 2021) despite advanced healthcare systems, while middle-SDI regions showed concerning acceleration (reaching 77.1). Low-SDI regions demonstrated lower absolute burden (22.7) but concerning stagnation since 2005. China's CKM Index rose substantially from 27.7 in 1990 to 67.6 in 2021, with joinpoint analysis revealing acceleration phases in 1998 and 2006, reflecting rapid epidemiological transition during economic development. Age-stratified analysis revealed 35% of total burden concentrated in individuals aged 60-74 years, with consistent male predominance. Pairwise comorbidity analysis showed CVD + T2DM combinations increasing fastest globally (EAPC + 1.45%), while CKD + T2DM burden nearly tripled. Forecasting models projected continued escalation through 2030, with middle-SDI regions reaching 86.6-87.5 and China plateauing at 66.7-77.5, though COVID-19 highlighted forecasting limitations under structural disruptions.

interpretationThe CKM Index provides a promising methodological prototype for quantifying multimorbidity burden and reveals alarming global escalation trends with pronounced health inequalities. The syndemic nature of CKM diseases demands paradigmatic shifts from specialty-siloed to integrated chronic disease management. Countries achieving burden reductions within similar development contexts demonstrate the potential for coordinated policy interventions. Urgent global action is required to address the growing CKM crisis through enhanced surveillance, integrated care delivery, and targeted interventions addressing social determinants of health.

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2Global Burden of DiseaseGlobal HealthMultimorbidityRenal Insufficiency, ChronicAdultAgedChinaDisability-Adjusted Life YearsFemaleHumansMaleMiddle AgedRisk FactorsCardiovascular diseaseChinaChronic kidney diseaseCKM multimorbidityDiabetesGlobal burden of diseaseHealth policy

Identifiers

PMID41484691
PMCPMC12866153

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LicenceCC BY-NC-ND
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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.