CoChat
Sign Up
Cited by 236Diabetes, Cardiovascular Risks, and LipoproteinsAdipokines, Inflammation, and Metabolic DiseasesDiet, Metabolism, and Disease

Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: evidence from NHANES 2001–2018

Open access
Duan, Mingxuan, Zhao, Xi, Li, Shaolin, Miao, Guangrui, Bai, Linpeng, Zhang, Qingyang, Yang, Wenxuan, Zhao, Xiaoyan·2024·Cardiovascular Diabetology
DOI 10.1186/s12933-024-02334-8PMC PMC11238348
Open this paper in CoChat — free account

Abstract

Background: The prevalence of obesity-associated insulin resistance (IR) is increasing along with the increase in obesity rates. In this study, we compared the predictive utility of four alternative indexes of IR [triglyceride glucose index (TyG index), metabolic score for insulin resistance (METS-IR), the triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio and homeostatic model assessment of insulin resistance (HOMA-IR)] for all-cause mortality and cardiovascular mortality in the general population based on key variables screened by the Boruta algorithm. The aim was to find the best replacement index of IR. Methods: In this study, 14,653 participants were screened from the National Health and Nutrition Examination Survey (2001–2018). And TyG index, METS-IR, TG/HDL-C and HOMA-IR were calculated separately for each participant according to the given formula. The predictive values of IR replacement indexes for all-cause mortality and cardiovascular mortality in the general population were assessed. Results: Over a median follow-up period of 116 months, a total of 2085 (10.23%) all-cause deaths and 549 (2.61%) cardiovascular disease (CVD) related deaths were recorded. Multivariate Cox regression and restricted cubic splines analysis showed that among the four indexes, only METS-IR was significantly associated with both all-cause and CVD mortality, and both showed non-linear associations with an approximate “U-shape”. Specifically, baseline METS-IR lower than the inflection point (41.33) was negatively associated with mortality [hazard ratio (HR) 0.972, 95% CI 0.950–0.997 for all-cause mortality]. In contrast, baseline METS-IR higher than the inflection point (41.33) was positively associated with mortality (HR 1.019, 95% CI 1.011–1.026 for all-cause mortality and HR 1.028, 95% CI 1.014–1.043 for CVD mortality). We further stratified the METS-IR and showed that significant associations between METS-IR levels and all-cause and cardiovascular mortality were predominantly present in the nonelderly population aged < 65 years. Conclusions: In conjunction with the results of the Boruta algorithm, METS-IR demonstrated a more significant association with all-cause and cardiovascular mortality in the U.S. population compared to the other three alternative IR indexes (TyG index, TG/HDL-C and HOMA-IR), particularly evident in individuals under 65 years old.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12933-024-02334-8

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • IR is significantly associated with all-cause and cardiovascular mortalities and is an important factor in regulating metabolic syndrome and diabetes.

    “IR is significantly associated with all-cause and cardiovascular mortalities and is an important factor in regulating metabolic syndrome and diabetes (37, 38).”

    From The waist-to-height ratio is a good predictor for insulin resistance in women with polycystic ovary syndrome (2024) · cites this paper as [37]

    The Results (Relationship between the IR replacement index and mortality; no page available) report significant associations between insulin-resistance indexes and both all-cause and cardiovascular mortality. The Background says insulin resistance often precedes diabetes, and the Results (Baseline characteristics; no page available) link higher index scores with greater prevalence of diabetes and metabolic syndrome. Neither passage establishes the claimed regulatory role in both conditions. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 41

  1. [1]Ward Z Bleich S Cradock A Projected U.S. state-level prevalence of adult obesity and severe obesity N Engl J Med 2019 381 2440 2450 10.1056/NEJMsa1909301 31851800 · DOI · Paper page
  2. [2]Hill MA Yang Y Zhang L Insulin resistance, cardiovascular stiffening and cardiovascular disease Metabolism 2021 119 154766 10.1016/j.metabol.2021.154766 33766485 · DOI
  3. [3]Tahapary DL Pratisthita LB Fitri NA Challenges in the diagnosis of insulin resistance: focusing on the role of HOMA-IR and Tryglyceride/glucose index Diabetes Metab Syndr: Clin Res Rev 2022 16 102581 10.1016/j.dsx.2022.102581 35939943 · DOI
  4. [4]Zhang Q Xiao S Jiao X The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001–2018 Cardiovasc Diabetol 2023 22 279 10.1186/s12933-023-02030-z 37848879 PMC10583314 · DOI
  5. [5]Mancusi C de Simone G Best LG Myocardial mechano-energetic efficiency and insulin resistance in non-diabetic members of the strong heart study cohort Cardiovasc Diabetol 2019 18 56 10.1186/s12933-019-0862-9 31039789 PMC6492323 · DOI
  6. [6]Badmus OO Hillhouse SA Anderson CD Molecular mechanisms of metabolic associated fatty liver disease (MAFLD): functional analysis of lipid metabolism pathways Clin Sci 2022 136 1347 1366 10.1042/CS20220572 PMC9508552 36148775 · DOI
  7. [7]Minh Huynh V Tien Hoang A Sinh Cao T Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension J Clin Hypertens 2021 23 529 537 10.1111/jch.14155 PMC8029536 33415834 · DOI
  8. [8]Tao L-C Xu J-N Wang T-T Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations Cardiovasc Diabetol 2022 10.1186/s12933-022-01511-x 35524263 PMC9078015 · DOI
  9. [9]Rhee EJ Diabetes in Asians Endocrinol Metab (Seoul, Korea) 2015 30 263 269 10.3803/EnM.2015.30.3.263 PMC4595349 26435131 · DOI
  10. [10]de la Monte SM Therapeutic targets of brain insulin resistance in sporadic Alzheimer’s disease Front Biosci (Elite Ed) 2012 4 1582 1605 10.2741/e482 22201977 PMC4550311 · DOI
  11. [11]Chamroonkiadtikun P Ananchaisarp T Wanichanon W The triglyceride-glucose index, a predictor of type 2 diabetes development: a retrospective cohort study Prim Care Diabetes 2020 14 161 167 10.1016/j.pcd.2019.08.004 31466834 · DOI
  12. [12]Simental-Mendía L Rodríguez-Morán M Guerrero-Romero F The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects Metab Syndr Relat Disord 2008 6 299 304 10.1089/met.2008.0034 19067533 · DOI
  13. [13]Chen J Wu K Lin Y Association of triglyceride glucose index with all-cause and cardiovascular mortality in the general population Cardiovasc Diabetol 2023 22 320 10.1186/s12933-023-02054-5 37993902 PMC10666367 · DOI
  14. [14]Liu X Tan Z Huang Y Relationship between the triglyceride-glucose index and risk of cardiovascular diseases and mortality in the general population: a systematic review and meta-analysis Cardiovasc Diabetol 2022 10.1186/s12933-022-01546-0 35778731 PMC9250255 · DOI
  15. [16]Su Y Yu G Li D Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model Front Physiol 2024 10.3389/fphys.2023.1289976 38260098 PMC10800828 · DOI
  16. [17]Ramdas Nayak V Satheesh P Shenoy M Triglyceride glucose (TyG) index: a surrogate biomarker of insulin resistance JPMA J Pak Med Assoc 2022 72 986 988 10.47391/JPMA.22-63 35713073 · DOI
  17. [18]Widjaja N Irawan R Hanindita M METS-IR vs. HOMA-AD and metabolic syndrome in obese adolescents J Med Invest: JMI 2023 70 7 16 10.2152/jmi.70.7 37164746 · DOI
  18. [19]Abbasi F Reaven G Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides × glucose versus triglyceride/high-density lipoprotein cholesterol Metab: Clin Exp 2011 60 1673 1676 10.1016/j.metabol.2011.04.006 21632070 · DOI
  19. [20]Meyer C Pimenta W Woerle H Different mechanisms for impaired fasting glucose and impaired postprandial glucose tolerance in humans Diabetes Care 2006 29 1909 1914 10.2337/dc06-0438 16873801 · DOI · Paper page
  20. [21]WHO Consultation Obesity: preventing and managing the global epidemic. Report of a WHO consultation World Health Org Tech Rep Ser 2000 894 1 253 11234459
  21. [22]Boutouyrie P Chowienczyk P Humphrey J Arterial stiffness and cardiovascular risk in hypertension Circ Res 2021 128 864 886 10.1161/CIRCRESAHA.121.318061 33793325 · DOI
  22. [23]Qiu Z Geng T Wan Z Serum selenium concentrations and risk of all-cause and heart disease mortality among individuals with type 2 diabetes Am J Clin Nutr 2022 115 53 60 10.1093/ajcn/nqab241 34664061 · DOI
  23. [24](2002) Third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation, 106: 3143–421 12485966
  24. [25]James IC Stephen RD Karen AD Diagnosis and management of the metabolic syndrome: an American heart association/national heart, lung, and blood institute scientific statement Circulation 2005 112 2735 10.1161/CIRCULATIONAHA.105.169404 16157765 · DOI · Paper page
  25. [26]Alberti KGMM Robert HE Scott MG Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity Circulation 2009 120 1640 10.1161/CIRCULATIONAHA.109.192644 19805654 · DOI · Paper page
  26. [27]Wang L Cong H-l Zhang J-X Triglyceride-glucose index predicts adverse cardiovascular events in patients with diabetes and acute coronary syndrome Cardiovasc Diabetol 2020 10.1186/s12933-020-01054-z 32534586 PMC7293784 · DOI
  27. [28]Liu R Li L Wang L Triglyceride-glucose index predicts death in patients with stroke younger than 65 Front Neurol 2023 14 1198487 10.3389/fneur.2023.1198487 37602260 PMC10435085 · DOI
  28. [29]Jin J-L Cao Y-X Wu L-G Triglyceride glucose index for predicting cardiovascular outcomes in patients with coronary artery disease J Thorac Dis 2018 10 6137 6146 10.21037/jtd.2018.10.79 30622785 PMC6297409 · DOI
  29. [30]Guerrero-Romero F Simental-Mendia LE Gonzalez-Ortiz M The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp J Clin Endocrinol Metab 2010 95 3347 3351 10.1210/jc.2010-0288 20484475 · DOI
  30. [31]Shulman GI Ectopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease N Engl J Med 2014 371 2237 2238 10.1056/NEJMra1011035 25470706 · DOI · Paper page
  31. [32]Singh RG Yoon HD Poppitt SD Ectopic fat accumulation in the pancreas and its biomarkers: a systematic review and meta-analysis Diabetes-Metab Res Rev 2017 10.1002/dmrr.2918 28730683 · DOI
  32. [33]Yaxmehen Bello-Chavolla O Almeda-Valdes P Gomez-Velasco D METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes Eur J Endocrinol 2018 178 533 544 10.1530/EJE-17-0883 29535168 · DOI
  33. [34]Almeda-Valdes P Herrera-Mercadillo R Aguilar-Salinas C The role of diet in patients with metabolic syndrome Curr Med Chem 2019 26 3613 3619 10.2174/0929867324666170518095316 28521684 · DOI
  34. [35]Ramírez-Manent JI López-González ÁA Tomás-Gil P Relationship between abdominal volume index and body adiposity index and scales of insulin resistance and metabolic syndrome Diagnostics 2023 13 3356 10.3390/diagnostics13213356 37958252 PMC10649100 · DOI
  35. [36]McLaughlin T Reaven G Abbasi F Is there a simple way to identify insulin-resistant individuals at increased risk of cardiovascular disease? Am J Cardiol 2005 96 399 404 10.1016/j.amjcard.2005.03.085 16054467 · DOI
  36. [37]Abbasi F Reaven GM Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides × glucose versus triglyceride/high-density lipoprotein cholesterol Metab-Clin Exp 2011 60 1673 1676 10.1016/j.metabol.2011.04.006 21632070 · DOI
  37. [38]Matsuda M DeFronzo RA Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp Diabetes Care 1999 22 1462 10.2337/diacare.22.9.1462 10480510 · DOI · Paper page
  38. [39]Somayeh S Esmaeil Yousefi R Mehdi B Serum insulin level, HOMA-IR and prostate cancer risk: a systematic review and meta-analysis Diabetes Metab Syndr 2019 13 110 10.1016/j.dsx.2018.08.031 30641680 · DOI
  39. [40]Lei J Sun T Jiang Y Risk identification of bronchopulmonary dysplasia in premature infants based on machine learning Front Pediatr 2021 9 719352 10.3389/fped.2021.719352 34485204 PMC8415969 · DOI
  40. [41]Li W Zeng L Yuan S Machine learning for the prediction of cognitive impairment in older adults Front Neurosci 2023 17 1158141 10.3389/fnins.2023.1158141 37179565 PMC10172509 · DOI
  41. [42]Sharif S Groenwold RHH van der Graaf Y Mediation analysis of the relationship between type 2 diabetes and cardiovascular events and all-cause mortality: findings from the SMART cohort Diabetes Obes Metab 2019 21 1935 1943 10.1111/dom.13759 31062479 PMC6767388 · DOI

Cited by

Sorted by citations

Authors
Duan, M., Zhao, X., Li, S., Miao, G., Bai, L., Zhang, Q., Yang, W., & Zhao, X.
Affiliations
First Affiliated Hospital of Zhengzhou University (CN)
Corresponding author
Zhao, Xiaoyan
ORCID
Venue
Cardiovascular Diabetology
Publisher
Springer Science and Business Media LLC
Published
2024-07-10
Volume
23
Issue
1
Pages
243-243
ISSN
1475-2840 (ISSN-L)
DOI
10.1186/s12933-024-02334-8
PMC
PMC11238348
OpenAlex
W4400485749
Cited by
236
References
1
Citations by year
2026: 112 · 2025: 117 · 2024: 7
FWCI
77.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Cardiovascular Diabetology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Education Department of Henan Province
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diet, Metabolism, and Disease
Keywords
METS-IR, insulin resistance index, triglyceride-glucose index, HOMA-IR, triglyceride to HDL cholesterol ratio, all-cause mortality, cardiovascular mortality, obesity, NHANES, nonlinear relationship, U-shaped relationship, non-elderly adults, mortality prediction, Boruta feature selection, restricted cubic splines, sex differences in mortality
MeSH terms
Heart Disease Risk Factors, Adult, Aged, Blood Glucose, Cardiovascular Diseases, Cause of Death, Female, Humans, Insulin, Insulin Resistance, Cholesterol, HDL, Male, Middle Aged, Nutrition Surveys, Predictive Value of Tests, Prognosis, Risk Factors, Time Factors, Triglycerides, United States, Biomarkers, Risk Assessment, Metabolic Syndrome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review