Next Article in Journal
Distinct Clinical and Biological Features of Diffusely Metastatic Versus Bulky Localized Lung Cancer: Real-World Outcomes from a University Cancer Center in Germany
Previous Article in Journal
Superparamagnetic Iron Oxide Used Alone Is Non-Inferior to the Combination of Radioactive Tracer and Superparamagnetic Iron Oxide in Detecting Sentinel Lymph Nodes in Early-Stage Vulvar Cancer
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 17
Issue 23
10.3390/cancers17233727
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Inverse Association Between METS-IR and Lung Cancer Risk: The Role of BMI in a Nationwide Korean Cohort

by
Bo-Kyung Shine
1,†,
In Hwa Jeong
2,†,
Minkook Son
3,4,
Bongjo Kim
3,
Sang Yi Moon
4,5,
Jong Yoon Lee
5,
Hye Ryeon Kim
6 and
Seok Jae Huh
6,*
1
Department of Family Medicine, Dong-A University College of Medicine, Dong-A University Hospital, Busan 49201, Republic of Korea
2
Department of Laboratory Medicine, Dong-A University College of Medicine, Dong-A University Hospital, Busan 49315, Republic of Korea
3
Department of Physiology, College of Medicine, Dong-A University, Busan 49315, Republic of Korea
4
Interdisciplinary Program, Department of Data Sciences Convergence, Dong-A University, Busan 49315, Republic of Korea
5
Division of Gastroenterology, Department of Internal Medicine, Dong-A University College of Medicine, Busan 49315, Republic of Korea
6
Division of Hematology and Oncology, Department of Internal Medicine, Dong-A University College of Medicine Busan 49315, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2025, 17(23), 3727; https://doi.org/10.3390/cancers17233727
Submission received: 15 October 2025 / Revised: 14 November 2025 / Accepted: 18 November 2025 / Published: 21 November 2025
(This article belongs to the Section Cancer Epidemiology and Prevention)
Browse Figures
Versions Notes

Simple Summary

The metabolic score for insulin resistance (METS-IR) is a simple index calculated from routine blood tests and body measurements. Although it is widely used as an indirect marker of metabolic health, METS-IR alone may not fully identify individuals who are truly at high risk. In our nationwide cohort study, people with low METS-IR values had a higher risk of developing lung cancer, particularly those with low body mass index. This suggests that some individuals may appear healthy by weight or laboratory values but still have underlying vulnerabilities such as low muscle mass or hidden visceral fat. Therefore, combining METS-IR with direct assessments of body composition—such as DEXA or BIA—may improve the ability to detect high-risk individuals and refine risk-stratification strategies. Our findings highlight the importance of evaluating metabolic health in a more comprehensive way when considering lung cancer prevention.

Abstract

Background: Although insulin resistance has been implicated in cancer development, its specific role in lung cancer remains unclear. The metabolic score for insulin resistance (METS-IR) is a novel surrogate marker that integrates multiple metabolic parameters and has demonstrated strong predictive value for metabolic disorders. This study aimed to investigate the association between METS-IR and lung cancer incidence in a large-scale nationwide cohort. Methods: We analyzed data from 322,624 participants of the National Health Insurance Service-National Health Screening Cohort in Republic of Korea. Participants were stratified into METS-IR quartiles, and lung cancer incidence was assessed using Kaplan–Meier survival curves and Cox proportional hazards regression models. Subgroup analyses were conducted to examine the impact of body composition, particularly sarcopenia, on the association between METS-IR and lung cancer. Results: Over a median follow-up of 9.5 years, 5912 lung cancer cases were identified. Lung cancer incidence per 1000 person-years was highest in the lowest METS-IR quartile (Q1: 2.27) and decreased across quartiles (Q2: 1.93, Q3: 1.81, Q4: 1.72). In fully adjusted Cox regression models, using Q1 as the reference group, higher METS-IR quartiles were associated with a significantly lower risk of lung cancer (Q2: HR 0.91, 95% CI 0.85–0.98; Q3: HR 0.86, 95% CI 0.79–0.92; Q4: HR 0.80, 95% CI 0.74–0.86; p for trend < 0.001). Subgroup analyses revealed that the inverse association was more pronounced in male participants and individuals with a low body mass index. Conclusions: In this nationwide cohort study, we observed a significant inverse association between METS-IR and lung cancer risk. However, METS-IR showed limitations in fully explaining lung cancer risk based on insulin resistance alone. These findings highlight the need for future studies incorporating body composition assessments to better evaluate metabolic vulnerability.

1. Background

Lung cancer is the leading cause of cancer-related mortality worldwide [1], accounting for approximately 1.79 million deaths annually [2]. Although smoking remains the most well-established risk factor, other factors such as secondhand smoke exposure, air pollution, occupational carcinogens, chronic pulmonary diseases, and genetic susceptibility have also been implicated in its pathogenesis [1]. In addition to these traditional factors, recent attention has focused on metabolic dysfunction, particularly insulin resistance, as an emerging contributor to cancer development, including lung cancer [3,4,5].
Insulin resistance has been widely implicated in the development of several cancers [6,7], including colorectal [8,9], breast [10,11], endometrial [12], and prostate cancers [13], through mechanisms involving hyperinsulinemia, chronic inflammation, and altered metabolic signaling pathways [14]. However, despite increasing attention to its role in cancer development, evidence of its association with lung cancer remains limited and inconsistent [14]. Such discrepancies underscore the importance of further investigation to clarify the role of insulin resistance in lung cancer development.
The metabolic score for insulin resistance (METS-IR) is a novel composite index developed to estimate insulin resistance using routine clinical parameters, including fasting glucose, triglycerides (TGs), body mass index (BMI), and high-density lipoprotein cholesterol (HDL-C) [15]. Unlike traditional markers such as HOMA-IR or fasting insulin levels, METS-IR does not require insulin assays and is therefore more applicable in large-scale epidemiologic studies [16,17]. By integrating multiple metabolic variables, the METS-IR offers a practical population-based approach for quantifying insulin resistance.
Given the uncertain role of insulin resistance in lung cancer and the limitations of traditional insulin resistance markers in large-scale studies, further investigations using practical and scalable measures are required. In this study, we aimed to evaluate the association between insulin resistance, as measured using the METS-IR, and the incidence of lung cancer in a nationally representative Korean cohort.

2. Methods

2.1. Data Source and Study Population

Approximately 97% of the Republic of Korea population benefits from the mandatory health insurance system established by the government, with the remaining 3% covered by the Medical Aid Program. In Republic of Korea, a biennial health examination initiative ensures extensive coverage for all employees, regardless of age, as well as individuals aged 40 and above.
The National Health Insurance Service-National Health Screening Cohort (NHIS-HealS) is a population-based database constructed from the national health screening records and medical claims data in Republic of Korea. In this study, we included individuals who underwent a national health screening program between 2010 and 2011. The NHIS-HealS database provides comprehensive information on demographics, diagnoses, procedures, prescriptions, and lifestyle factors, making it suitable for large-scale epidemiological studies [18,19]. For this study, we used the NHIS-HealS database, which encompasses comprehensive medical claims information, including prescription records, procedures, surgeries, insurance premium payments, and other details related to both inpatient and outpatient healthcare services. In addition, the database includes health screening data such as laboratory tests, physical measurements, and self-reported lifestyle questionnaires.
Participants (n = 364,757) who underwent a health screening program between 2010 and 2011 were initially identified. The participants were diagnosed and categorized using the International Classification of Diseases, version 10 (ICD-10). To refine the cohort, we excluded individuals with a previous diagnosis of lung cancer (n = 3335), those diagnosed with cancers other than lung cancer during the observation period (n = 25,895), and those with missing data (n = 3850). Participants with missing values for fasting glucose, triglyceride, HDL-cholesterol, or covariates were excluded from the analysis (complete-case analysis). No imputation was performed. We excluded 6581 participants with METS-IR values below the 1st percentile or above the 99th percentile. The final cohort comprised 322,624 participants. The follow-up period continued until 31 December 2019, or until death or a diagnosis of lung cancer was made (Figure 1).
The Institutional Review Board of Dong-A University College of Medicine exempted this study protocol from review because of its retrospective design. The investigators used only de-identified open clinical data for analytical purposes (DAUHIRB-EXP-25-025).

2.2. Definitions of Surrogate Markers for Insulin Resistance: METS-IR

METS-IR was calculated using the following formula: ln [(2 × fasting blood glucose (FBG) in mg/dL + serum TG level in mg/dL) × BMI in kg/m2]/ln [HDL-C level in mg/dL] [15]. At baseline, 322,624 participants were divided into four quartiles (Q1, Q2, Q3, and Q4) based on their METS-IR results, and the outcome variables were identified during follow-up.

2.3. Study Outcomes: Lung Cancer

The primary outcome of the study was the incidence of lung cancer, which was identified using the ICD-10 codes C33 and C34. Previous research has demonstrated that using ICD-10 codes from the National Health Insurance Service database to diagnose lung cancer has a high sensitivity (95.0%; 95% confidence interval [CI], 94.8%–95.1%) and positive predictive value (88.9%; 95% CI, 88.8%–89.1%) [20].

2.4. Covariates

Information on demographic and clinical characteristics—including age, sex, and major comorbidities such as hypertension, diabetes, and dyslipidemia—was obtained from the linked health screening and claims databases. Additional details are summarized in Supplementary Table S1. Household income was categorized into quartiles, and place of residence was grouped into urban or rural areas. The Charlson Comorbidity Index (CCI) was calculated based on relevant diagnostic codes to reflect the overall burden of chronic illness [21]. Laboratory data, including hemoglobin concentration and estimated glomerular filtration rate (eGFR), were also incorporated. Lifestyle information—including cigarette smoking, alcohol intake, and engagement in regular physical activity—was assessed through standardized self-reported questionnaires.

2.5. Statistical Analyses

Categorical variables were summarized as counts and percentages, whereas continuous variables were reported as means with standard deviations. Kaplan–Meier curves were generated to compare cumulative lung cancer incidence across METS-IR quartiles. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between METS-IR and lung cancer. The proportional hazards assumption was evaluated using Schoenfeld residuals, which indicated no violation of model assumptions. Multivariable models were adjusted for age, sex, income level, residential area, hypertension, diabetes, dyslipidemia, CCI, hemoglobin, eGFR, smoking, alcohol consumption, and physical activity. A restricted cubic spline model was applied to explore potential nonlinear relationships between METS-IR [22]. and lung cancer risk, with knots placed at the 5th, 35th, 65th, and 95th percentiles of METS-IR values and the mean serving as the reference.
All analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R version 3.6.0 (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was defined as a two-sided p-value < 0.05.

3. Results

3.1. Baseline Characteristics

A total of 322,624 participants were enrolled in this study and categorized into four groups using the METS-IR. The corresponding METS-IR values for each quartile (Q1, Q2, Q3, and Q4) were 28.6 ± 2.0, 33.2 ± 1.3, 36.9 ± 1.4, and 42.7 ± 3.0, respectively. Table 1 shows the varied results, such as sex, age, income level, residence, underlying disease, BMI, blood pressure, FBG, total cholesterol, TG, HDL-C, low-density lipoprotein cholesterol, hemoglobin, GFR, smoking, alcohol consumption, and regular exercise, for the quartiles. Significant differences (p < 0.001) were observed among the four groups regarding the characteristics during the 2010–2011 health screening. Additional table files show the baseline characteristics of each sex in detail [see Supplementary Tables S2 and S3].

3.2. Incidence of Lung Cancer According to METS-IR

The study population was followed up for a median of 9.5 years, during which 5912 lung cancer cases occurred. During the observation period, we conducted Kaplan–Meier analysis to compare the cumulative incidence rates of lung cancer among the four quartiles of the METS-IR. The results indicated a sequential decrease in the incidence of lung cancer from Q1 to Q4 (p < 0.001, log-rank test) (Figure 2).
Table 2 shows the crude and adjusted HRs with 95% CIs for the association between lung cancer incidence rate and the METS-IR quartiles. In the crude analysis, the incidence rates of lung cancer for the METS-IR quartiles were as follows: 2.27, 1.93, 1.81, and 1.72 cases per 1000 person-years for Q1, Q2, Q3, and Q4, respectively. The corresponding HRs and 95% CIs for lung cancer incidence rates were 1 (reference) for Q1, 0.85 (95% CI, 0.79–0.91) for Q2, 0.79 (95% CI, 0.74–0.85) for Q3, and 0.75 (95% CI, 0.70–0.81) for Q4, in the same groups. After adjustment for variables, the corresponding HRs and 95% CIs for lung cancer incidence rates were 1 (reference) for Q1, 0.91 (95% CI, 0.85–0.98) for Q2, 0.86 (95% CI, 0.79–0.92) for Q3, and 0.80 (95% CI, 0.74–0.86) for Q4. Both crude and adjusted analyses showed a statistically significant decrease in the incidence of lung cancer from Q1 to Q4 in the METS-IR.
Figure 3 shows a restricted cubic spline for the correlation between METS-IR and lung cancer incidence. The nonlinear relationship between METS-IR and the incidence of lung cancer was evaluated by a restricted cubic spline using a multivariable adjusted model to depict the trend of HR with respect to METS-IR. The restricted cubic spline curves showed that the incidence of lung cancer decreased with increasing METS-IR for all participants.

3.3. Subgroup Analysis

Figure 4 shows HR comparisons from Q4 to Q1 of the METS-IR within subgroups categorized by age (<65 years and ≥65 years), obesity (defined as BMI ≥ 25), diabetes, and smoking status (current smoker, ex-smoker, and non-smoker). According to the subgroup analysis, all examined subgroups showed a decrease in HR at Q4 compared to that at Q1 of the METS-IR, consistent with the overall population. Notably, this decrease was most pronounced in the obesity subgroup.

4. Discussion

In this nationwide cohort of 322,624 participants, 5912 incident lung cancer cases were identified over a median follow-up of 9.5 years. Participants in the highest METS-IR quartile (Q4) showed a significantly lower risk of lung cancer than those in the lowest quartile (Q1), with an adjusted HR of 0.80 (95% CI: 0.74–0.86). Subgroup analysis revealed that the inverse relationship between METS-IR and lung cancer risk was consistently observed across all subgroups and was particularly pronounced in participants with obesity. This quartile-based categorization of METS-IR follows a widely used analytical structure in previous epidemiologic research, where METS-IR quartiles were employed to assess metabolic risk gradients and disease incidence [23,24].
Recent studies have highlighted the complex relationship between changes in body weight and the risk of lung cancer. A U-shaped association has been reported, with both major weight loss and gain linked to increased risk, even among never-smokers [25]. Similarly, a prospective analysis of the PLCO Trial found that an increase in BMI over time was associated with a lower risk of non-small cell lung cancer [26]. These findings suggest that BMI fluctuations may reflect deeper metabolic disturbances; however, BMI alone does not distinguish between fat and muscle mass or account for metabolic quality [27,28]. Supporting this, a recent meta-analysis of 31 studies reported that while insulin resistance and diabetes mellitus were positively associated with lung cancer risk, a high BMI was inversely associated with lung cancer risk [3]. These findings further underscore that BMI alone may inadequately reflect underlying metabolic vulnerability. Moreover, weight fluctuation itself has been associated with an increased risk of cardiovascular disease and mortality, suggesting that dynamic changes in body weight may reflect underlying metabolic instability [29]. In addition, normal-weight obesity, characterized by excess fat mass despite a normal BMI, has been associated with increased cardiometabolic risk [30]. These hidden forms of adiposity highlight that BMI alone is inadequate to fully capture the complex metabolic disturbances underlying lung cancer risk.
Supporting this finding, a Japanese study using bioelectrical impedance analysis found that the fat-to-muscle mass ratio, rather than either component alone, was independently associated with insulin resistance [31], underscoring the importance of body composition quality in assessing metabolic vulnerability. A comparative study of lean and obese individuals with metabolic syndrome showed similar levels of insulin resistance across both groups [32], underscoring that body size alone may not accurately capture metabolic vulnerability—an insight consistent with our findings regarding low METS-IR and lung cancer risk. The more pronounced inverse association observed in males may reflect sex-related differences in fat distribution, hormonal balance, and behavioral factors such as smoking or alcohol consumption [33]. Men generally exhibit greater visceral fat accumulation and a higher prevalence of smoking, both of which may influence metabolic profiles and inflammatory responses differently compared with women [34]. These differences could partially explain the stronger association between lower METS-IR and lung cancer risk observed in men.
Although METS-IR is widely used as a practical surrogate of insulin resistance in epidemiological studies, it may not fully capture the heterogeneity of metabolic health in lean populations. Individuals with low BMI or METS-IR values may still exhibit metabolic vulnerability due to underlying factors such as mitochondrial dysfunction [35,36], systemic inflammation [37], or inadequate nutrient sensing [38]. In this context, “metabolically unhealthy lean” (MUL) refers to individuals who have a normal BMI but display metabolically adverse characteristics—including insulin resistance, visceral adiposity, sarcopenia, or chronic inflammation—despite their lean appearance. This notion of “metabolically unhealthy lean” phenotypes is supported by previous studies linking low adiposity and poor metabolic profiles with increased morbidity and mortality [39,40]. Therefore, when interpreting the inverse association between METS-IR and lung cancer, it is important to consider that low scores may, in some cases, reflect underlying frailty or nontraditional forms of metabolic dysfunction rather than a truly healthy metabolic state.
This study had several strengths, including the use of a large-scale nationally representative dataset, long-term follow-up, and the novel application of METS-IR as a metabolic marker.
METS-IR has previously demonstrated predictive value in various contexts, including all-cause and cardiovascular mortality in the general US population using National Health and Nutrition Examination Survey data [41], as well as mortality among older adults in Republic of Korea [42]. Moreover, it has been associated with the development of non-alcoholic fatty liver disease [43], chronic kidney disease [44], and polycystic ovary syndrome [45]. Given its simplicity and reproducibility, METS-IR can be easily applied in both research and clinical practice as a marker of metabolic vulnerability. Because it relies solely on routine laboratory and anthropometric parameters, it provides a cost-effective approach for population-based risk stratification and early identification of individuals at higher metabolic or oncologic risk. However, METS-IR alone may not be sufficient to fully capture metabolic heterogeneity. Combining METS-IR with measures of body composition—such as DEXA or BIA—is essential for enhancing the precision of lung cancer risk prediction and for developing more personalized preventive strategies.
This study also has several limitations. First, direct measurements of muscle mass were not available, which limited our ability to confirm the role of sarcopenia in our findings. Future studies incorporating direct assessments of sarcopenia, such as dual-energy X-ray absorptiometry (DEXA) or bioelectrical impedance analysis (BIA), are necessary to validate these observations. Second, as this was an observational study, we could not establish causality between METS-IR and lung cancer risk. Although we adjusted for multiple confounders, residual confounding from unmeasured variables such as nutritional status and inflammatory markers may still be present. These findings suggest that METS-IR may serve as an associated indicator for cancer risk stratification. Future studies should integrate additional metabolic biomarkers and direct muscle mass assessments to provide a more comprehensive understanding of this association.
From a clinical standpoint, our findings highlight the need to incorporate both metabolic indices and body composition assessments into lung cancer risk-prediction models. Traditional screening frameworks that focus on BMI or insulin resistance may overlook high-risk individuals with low muscle mass or altered metabolic profiles. Therefore, integrating METS-IR with methods such as DEXA or BIA can improve early detection and prevention strategies.

5. Conclusions

This study provides novel insights into the association between insulin resistance and the risk of lung cancer in a nationwide population. An inverse relationship between METS-IR and lung cancer was consistently observed across subgroups and was particularly pronounced in older adults and individuals with obesity. These findings indicate that low METS-IR is associated with, but may not directly determine, lung cancer risk, particularly among lean individuals who may harbor hidden metabolic vulnerabilities. To improve cancer risk prediction, both stratified analyses and body composition assessments should be routinely incorporated into standard metabolic evaluations as part of regular health screenings. Furthermore, future research should consider body composition alongside insulin resistance when evaluating cancer risk to better understand the potential contribution of metabolic dysfunction to cancer development and guide more precise preventive strategies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers17233727/s1, Supplementary Table S1. Definitions for clinical variables. Supplementary Table S2. Baseline characteristics of study population according to METS-IR (male). Supplementary Table S3. Baseline characteristics of study population according to METS-IR (female).

Author Contributions

B.-K.S. and I.H.J. conceived and designed the study. B.-K.S. and I.H.J. drafted the manuscript. M.S. and S.Y.M. analyzed and interpreted the data. B.K., H.R.K., and J.Y.L. provided critical inputs for the manuscript and assisted with writing. S.J.H. critically reviewed the manuscript and supervised the study. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2024-00342613).

Institutional Review Board Statement

This study was performed in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Dong-A University Hospital (DAUHIRB-EXP-25-025, 27 March 2025). Informed consent was obtained from all participants before their inclusion in this study.

Informed Consent Statement

Not applicable. This manuscript does not contain any individual person’s data in any form (including individual details, images, or case reports).

Data Availability Statement

The datasets generated and/or analyzed in the current study are available from the corresponding author upon reasonable request. Requests should be directed to BKS.

Conflicts of Interest

The authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. The funders had no role in the design of the study; collection, analyses, or interpretation of data; writing of the manuscript; or decision to publish the results.

Abbreviations

METS-IRMetabolic score for insulin resistance
BMIBody mass index
HDL-CHigh-density lipoprotein cholesterol
TGTriglyceride
NHIS-HealSNational Health Insurance Service-National Health Screening Cohort
ICD-10International Classification of Diseases, version 10
FBGFasting blood glucose
CIConfidence interval
CCICharlson comorbidity index
GFRGlomerular filtration rate
HRHazard ratio
BIABioelectrical impedance analysis
DEXADual-energy X-ray absorptiometry

References

  1. Leiter, A.; Veluswamy, R.R.; Wisnivesky, J.P. The global burden of lung cancer: Current status and future trends. Nat. Rev. Clin. Oncol. 2023, 20, 624–639. [Google Scholar] [CrossRef]
  2. Chhikara, B.S.; Parang, K. Global Cancer Statistics 2022: The trends projection analysis. Chem. Biol. Lett. 2023, 10, 451. [Google Scholar]
  3. Liu, J.; Wang, R.; Tan, S.; Zhao, X.; Hou, A. Association between insulin resistance, metabolic syndrome and its components and lung cancer: A systematic review and meta-analysis. Diabetol. Metab. Syndr. 2024, 16, 63. [Google Scholar] [CrossRef]
  4. Wang, G.; Zhu, Z.; Wang, Y.; Zhang, Q.; Sun, Y.; Pang, G.; Ge, W.; Ma, Z.; Ma, H.; Gong, L. The association between METS-IR, an indirect index for insulin resistance, and lung cancer risk. Eur. J. Public Health 2024, 34, 800–805. [Google Scholar] [CrossRef] [PubMed]
  5. Cho, I.Y.; Chang, Y.; Sung, E.; Park, B.; Kang, J.-H.; Shin, H.; Wild, S.H.; Byrne, C.D.; Ryu, S. Glycemic status, insulin resistance, and mortality from lung cancer among individuals with and without diabetes. Cancer Metab. 2024, 12, 17. [Google Scholar] [CrossRef]
  6. Szablewski, L. Insulin resistance: The increased risk of cancers. Curr. Oncol. 2024, 31, 998–1027. [Google Scholar] [CrossRef] [PubMed]
  7. Chiefari, E.; Mirabelli, M.; La Vignera, S.; Tanyolaç, S.; Foti, D.P.; Aversa, A.; Brunetti, A. Insulin resistance and cancer: In search for a causal link. Int. J. Mol. Sci. 2021, 22, 11137. [Google Scholar] [CrossRef] [PubMed]
  8. Son, M.; Moon, S.Y.; Koh, M.; Kang, Y.; Lee, J.Y. Association between Surrogate Markers of Insulin Resistance and the incidence of Colorectal Cancer in Korea: A Nationwide Population-based study. J. Clin. Med. 2024, 13, 1628. [Google Scholar] [CrossRef]
  9. Farahani, H.; Mahmoudi, T.; Asadi, A.; Nobakht, H.; Dabiri, R.; Hamta, A. Insulin resistance and colorectal cancer risk: The role of elevated plasma resistin levels. J. Gastrointest. Cancer 2020, 51, 478–483. [Google Scholar] [CrossRef]
  10. Srinivasan, M.; Arzoun, H.; Gk, L.B.; Thangaraj, S.R. A systematic review: Does insulin resistance affect the risk and survival outcome of breast cancer in women? Cureus 2022, 14, e21712. [Google Scholar] [CrossRef]
  11. Gallagher, E.J.; Fei, K.; Feldman, S.M.; Port, E.; Friedman, N.B.; Boolbol, S.K.; Killelea, B.; Pilewskie, M.; Choi, L.; King, T. Insulin resistance contributes to racial disparities in breast cancer prognosis in US women. Breast Cancer Res. 2020, 22, 40. [Google Scholar] [CrossRef] [PubMed]
  12. Li, X.; Fan, Y.; Wang, J.; Zhou, R.; Tian, L.; Wang, Y.; Wang, J. Insulin resistance and metabolic syndrome increase the risk of Relapse for Fertility preserving treatment in atypical endometrial hyperplasia and early endometrial Cancer patients. Front. Oncol. 2021, 11, 744689. [Google Scholar] [CrossRef] [PubMed]
  13. Lutz, S.Z.; Hennenlotter, J.; Franko, A.; Dannecker, C.; Fritsche, L.; Kantartzis, K.; Wagner, R.; Peter, A.; Stefan, N.; Fritsche, A. Diabetes and the prostate: Elevated fasting glucose, insulin resistance and higher levels of adrenal steroids in prostate cancer. J. Clin. Med. 2022, 11, 6762. [Google Scholar] [CrossRef] [PubMed]
  14. Kim, D.-S.; Scherer, P.E. Obesity, diabetes, and increased cancer progression. Diabetes Metab. J. 2021, 45, 799–812. [Google Scholar] [CrossRef]
  15. Bello-Chavolla, O.Y.; Almeda-Valdes, P.; Gomez-Velasco, D.; Viveros-Ruiz, T.; Cruz-Bautista, I.; Romo-Romo, A.; Sánchez-Lázaro, D.; Meza-Oviedo, D.; Vargas-Vázquez, A.; Campos, O.A. 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. [Google Scholar] [CrossRef]
  16. Cao, S.; Meng, L.; Lin, L.; Hu, X.; Li, X. The association between the metabolic score for insulin resistance (METS-IR) index and urinary incontinence in the United States: Results from the National Health and Nutrition Examination Survey (NHANES) 2001–2018. Diabetol. Metab. Syndr. 2023, 15, 248. [Google Scholar] [CrossRef]
  17. Li, H.; Tan, H.; OuYang, Z.; Hu, X.; Bao, Y.; Gao, T.; Hua, W. Association between METS-IR and female infertility: A cross-sectional study of NHANES 2013–2018. Front. Nutr. 2025, 12, 1549525. [Google Scholar] [CrossRef]
  18. Moon, S.Y.; Son, M.; Cho, J.H.; Kim, H.I.; Han, J.M.; Bae, J.C.; Suh, S. Association Between Metabolic Dysfunction-Associated Steatotic Liver Disease and Thyroid Cancer. Thyroid 2025, 35, 79–86. [Google Scholar] [CrossRef]
  19. Kim, K.S.; Kim, B.; Han, K. Big Data Research for Diabetes-Related Diseases Using the Korean National Health Information Database. Diabetes Metab. J. 2025, 49, 13–21. [Google Scholar] [CrossRef]
  20. Yang, M.S.; Park, M.; Back, J.H.; Lee, G.H.; Shin, J.H.; Kim, K.; Seo, H.J.; Kim, Y.A. Validation of Cancer Diagnosis Based on the National Health Insurance Service Database versus the National Cancer Registry Database in Korea. Cancer Res. Treat. 2022, 54, 352–361. [Google Scholar] [CrossRef]
  21. Quan, H.; Li, B.; Couris, C.M.; Fushimi, K.; Graham, P.; Hider, P.; Januel, J.M.; Sundararajan, V. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am. J. Epidemiol. 2011, 173, 676–682. [Google Scholar] [CrossRef]
  22. Lin, H.; Wong, G.L.-H.; Zhang, X.; Yip, T.C.-F.; Liu, K.; Tse, Y.K.; Hui, V.W.-K.; Lai, J.C.-T.; Chan, H.L.-Y.; Wong, V.W.-S. U-shaped relationship between urea level and hepatic decompensation in chronic liver diseases. Clin. Mol. Hepatol. 2022, 28, 77–90. [Google Scholar] [CrossRef]
  23. Choi, H.L.; Yang, J.; Lee, H.S.; Lee, J.-W. Non-insulin-based indices of insulin resistance for predicting incident albuminuria: A nationwide population-based study. Korean J. Fam. Med. 2024, 45, 324–330. [Google Scholar] [CrossRef]
  24. Yoon, J.; Jung, D.; Lee, Y.; Park, B. The metabolic score for insulin resistance (METS-IR) as a predictor of incident ischemic heart disease: A longitudinal study among Korean without diabetes. J. Pers. Med. 2021, 11, 742. [Google Scholar] [CrossRef] [PubMed]
  25. Wu, Z.; Xie, S.; Wang, F.; Chen, S.; Su, K.; Li, F.; Cui, H.; Cao, W.; Yu, Y.; Qin, C. BMI changes and the risk of lung cancer in male never—Smokers: A prospective cohort study. Cancer Med. 2022, 11, 1336–1346. [Google Scholar] [CrossRef] [PubMed]
  26. You, D.; Wang, D.; Wu, Y.; Chen, X.; Shao, F.; Wei, Y.; Zhang, R.; Lange, T.; Ma, H.; Xu, H. Associations of genetic risk, BMI trajectories, and the risk of non-small cell lung cancer: A population-based cohort study. BMC Med. 2022, 20, 203. [Google Scholar] [CrossRef] [PubMed]
  27. Shah, U.A.; Ballinger, T.J.; Bhandari, R.; Dieli-Conwright, C.M.; Guertin, K.A.; Hibler, E.A.; Kalam, F.; Lohmann, A.E.; Ippolito, J.E. Imaging modalities for measuring body composition in patients with cancer: Opportunities and challenges. JNCI Monogr. 2023, 2023, 56–67. [Google Scholar] [CrossRef]
  28. Snoke, D.B.; Atwood, G.S.; Bellefleur, E.R.; Stokes, A.M.; Toth, M.J. Body composition alterations in patients with lung cancer. Am. J. Physiol.-Cell Physiol. 2025, 328, C872–C886. [Google Scholar] [CrossRef]
  29. Bangalore, S.; Fayyad, R.; Laskey, R.; DeMicco, D.A.; Messerli, F.H.; Waters, D.D. Body-weight fluctuations and outcomes in coronary disease. N. Engl. J. Med. 2017, 376, 1332–1340. [Google Scholar] [CrossRef]
  30. Romero-Corral, A.; Somers, V.K.; Sierra-Johnson, J.; Korenfeld, Y.; Boarin, S.; Korinek, J.; Jensen, M.D.; Parati, G.; Lopez-Jimenez, F. Normal weight obesity: A risk factor for cardiometabolic dysregulation and cardiovascular mortality. Eur. Heart J. 2010, 31, 737–746. [Google Scholar] [CrossRef] [PubMed]
  31. Matsui, M.; Fukuda, A.; Onishi, S.; Ushiro, K.; Nishikawa, T.; Asai, A.; Kim, S.K.; Nishikawa, H. Insulin resistance: A marker for fat-to-lean body composition in Japanese adults. Nutrients 2023, 15, 4724. [Google Scholar] [CrossRef] [PubMed]
  32. Pandit, K.; Mukhopadhyay, P.; Chatterjee, P.; Majhi, B.; Chowdhury, S.; Ghosh, S. Assessment of insulin resistance indices in individuals with lean and obese metabolic syndrome compared to normal individuals: A population based study. J. Assoc. Physicians India 2020, 68, 29–33. [Google Scholar] [PubMed]
  33. Kim, H.; Kim, S.-E.; Sung, M.-K. Sex and gender differences in obesity: Biological, sociocultural, and clinical perspectives. World J. Men’s Health 2025, 43, 758–772. [Google Scholar] [CrossRef]
  34. McClements, L.; Kautzky-Willer, A.; Kararigas, G.; Ahmed, S.B.; Stallone, J.N. The role of sex differences in cardiovascular, metabolic, and immune functions in health and disease: A review for “Sex Differences in Health Awareness Day”. Biol. Sex Differ. 2025, 16, 33. [Google Scholar] [CrossRef]
  35. Petersen, K.F.; Befroy, D.; Dufour, S.; Dziura, J.; Ariyan, C.; Rothman, D.L.; DiPietro, L.; Cline, G.W.; Shulman, G.I. Mitochondrial dysfunction in the elderly: Possible role in insulin resistance. Science 2003, 300, 1140–1142. [Google Scholar] [CrossRef]
  36. Seyfried, T.N.; Flores, R.E.; Poff, A.M.; D’Agostino, D.P. Cancer as a metabolic disease: Implications for novel therapeutics. Carcinogenesis 2014, 35, 515–527. [Google Scholar] [CrossRef]
  37. Ruan, G.-T.; Xie, H.-L.; Zhang, H.-Y.; Liu, C.-A.; Ge, Y.-Z.; Zhang, Q.; Wang, Z.-W.; Zhang, X.; Tang, M.; Song, M.-M. A novel inflammation and insulin resistance related indicator to predict the survival of patients with cancer. Front. Endocrinol. 2022, 13, 905266. [Google Scholar] [CrossRef]
  38. Hotamisligil, G.S.; Erbay, E. Nutrient sensing and inflammation in metabolic diseases. Nat. Rev. Immunol. 2008, 8, 923–934. [Google Scholar] [CrossRef]
  39. Stefan, N. Metabolically healthy and unhealthy normal weight and obesity. Endocrinol. Metab. 2020, 35, 487–493. [Google Scholar] [CrossRef]
  40. Park, D.; Shin, M.-J.; Magkos, F. When Being Lean Is Not Enough: The Metabolically Unhealthy Normal Weight Phenotype and Cardiometabolic Disease. CardioMetabolic Syndr. J. 2024, 4, 57–69. [Google Scholar] [CrossRef]
  41. Duan, M.; Zhao, X.; Li, S.; Miao, G.; Bai, L.; Zhang, Q.; Yang, W.; Zhao, X. Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: Evidence from NHANES 2001–2018. Cardiovasc. Diabetol. 2024, 23, 243. [Google Scholar] [CrossRef]
  42. Ryu, H.E.; Jung, D.H.; Heo, S.-J.; Park, B.; Lee, Y.J. METS-IR and all-cause mortality in Korean over 60 years old: Korean genome and epidemiology study-health examinees (KoGES-HEXA) cohorts. Front. Endocrinol. 2024, 15, 1346158. [Google Scholar] [CrossRef]
  43. Ma, X.; Ji, B.; Du, W.; Shi, S.; Zhao, H.; Sheng, J.; Jiang, X.; Ban, B.; Gao, G. METS-IR, a Novel simple insulin Resistance Index, is Associated with NAFLD in patients with type 2 diabetes Mellitus. Diabetes Metab. Syndr. Obes. 2024, 17, 3481–3490. [Google Scholar] [CrossRef]
  44. Yoon, J.; Heo, S.-J.; Lee, J.-H.; Kwon, Y.-J.; Lee, J.E. Comparison of METS-IR and HOMA-IR for predicting new-onset CKD in middle-aged and older adults. Diabetol. Metab. Syndr. 2023, 15, 230. [Google Scholar] [CrossRef]
  45. Jabczyk, M.; Nowak, J.; Jagielski, P.; Hudzik, B.; Kulik-Kupka, K.; Włodarczyk, A.; Lar, K.; Zubelewicz-Szkodzińska, B. Metabolic deregulations in patients with polycystic ovary syndrome. Metabolites 2023, 13, 302. [Google Scholar] [CrossRef] [PubMed]
Cancers 17 03727 g001
Figure 1. The flow of study population.
Figure 1. The flow of study population.
Cancers 17 03727 g001
Cancers 17 03727 g002
Figure 2. Kaplan–Meier curve for association between METS-IR and lung cancer.
Figure 2. Kaplan–Meier curve for association between METS-IR and lung cancer.
Cancers 17 03727 g002
Cancers 17 03727 g003
Figure 3. Restricted cubic spline of hazard ratio with 95% confidence intervals for lung cancer according to METS-IR.
Figure 3. Restricted cubic spline of hazard ratio with 95% confidence intervals for lung cancer according to METS-IR.
Cancers 17 03727 g003
Cancers 17 03727 g004
Figure 4. Subgroup analyses according to METS-IR.
Figure 4. Subgroup analyses according to METS-IR.
Cancers 17 03727 g004
Table 1. Baseline characteristics of study population according to METS-IR.
Table 1. Baseline characteristics of study population according to METS-IR.
All Subjects
(N = 322,624)		METS-IRp-Value
1st Quartile, Q1
(N = 80,722)		2nd Quartile, Q2
(N = 80,602)		3rd Quartile, Q3
(N = 80,707)		4th Quartile, Q4
(N = 80,593)
Demographics     
Age (years)58.4 (9.1)58.5 (8.6)58.9 (8.5)59.2 (8.6)<0.001
Sex (%)    1.00
Male43,545 (53.9)43,459 (53.9)43,542 (54.0)43,451 (53.9) 
Female37,177 (46.1)37,143 (46.1)37,165 (46.0)37,142 (46.1) 
Income Level (%)    <0.001
1st quartile11,572 (14.3)11,184 (13.9)11,105 (13.8)11,295 (14.0) 
2nd quartile17,814 (22.1)16,675 (20.7)16,069 (19.9)16,062 (19.9) 
3rd quartile23,198 (28.7)23,374 (29.0)23,914 (29.6)24,670 (30.6) 
4th quartile28,138 (34.9)29,369 (36.4)29,619 (36.7)28,566 (35.4) 
Residence (%)    <0.001
Urban53,251 (66.0)52,349 (64.9)51,938 (64.4)50,273 (62.4) 
Rural27,471 (34.0)28,253 (35.1)28,769 (35.6)30,320 (37.6) 
Underlying disease     
Hypertension (%)26,472 (32.8)34,026 (42.2)40,506 (50.2)49,054 (60.9)<0.001
Diabetes (%)5112 (6.3)8514 (10.6)12,498 (15.5)19,536 (24.2)<0.001
Dyslipidemia (%)18,676 (23.1)26,325 (32.7)34,599 (42.9)48,544 (60.2)<0.001
Charlson Comorbidity Index    <0.001
042,623 (52.8)38,925 (48.3)36,075 (44.7)31,887 (39.6) 
121,672 (26.8)22,307 (27.7)22,424 (27.8)21,939 (27.2) 
29481 (11.7)10,540 (13.1)11,429 (14.2)12,523 (15.5) 
≥36946 (8.6)8830 (11.0)10,779 (13.4)14,244 (17.7) 
Health Screening     
Body Mass Index (kg/m2)21.1 (1.6)23.2 (1.4)24.8 (1.5)27.0 (2.1)<0.001
Systolic Blood Pressure (mmHg)121.5 (15.3)124.3 (15.0)126.4 (14.8)128.8 (14.8)<0.001
Diastolic Blood
Pressure (mmHg)		75.4 (9.9)77.0 (9.8)78.2 (9.8)79.7 (9.8)<0.001
Fasting Blood Glucose (mg/dL)94.1 (16.5)98.1 (20.2)102.0 (23.7)109.3 (32.3)<0.001
Total Cholesterol (mg/dL)197.7 (35.3)200.5 (36.8)201.6 (38.0)201.6 (39.0)<0.001
Triglyceride (mg/dL)93.0 (48.7)117.4 (56.0)144.3 (70.7)193.4 (103.2)<0.001
HDL Cholesterol (mg/dL)64.7 (27.3)55.8 (12.0)50.9 (10.8)45.1 (9.7)<0.001
LDL Cholesterol (mg/dL)115.8 (35.6)121.2 (35.9)122.0 (37.6)118.2 (39.3)<0.001
Hemoglobin (g/dL)13.6 (1.4)13.8 (1.5)13.9 (1.5)14.1 (1.5)<0.001
Glomerular Filtration Rate (mL/min/1.73 m2)80.4 (30.3)79.1 (30.3)77.8 (30.9)76.9 (32.4)<0.001
Current Smoker (%)15,015 (18.6)12,925 (16.0)12,877 (16.0)13,610 (16.9)<0.001
Alcohol Drink (%)32,892 (40.7)32,877 (40.8)32,220 (39.9)30,843 (38.3)<0.001
Regular Exercise (%)3909 (4.8)3948 (4.9)3724 (4.6)3418 (4.2)<0.001
METS-IR28.6 (2.0)33.2 (1.3)36.9 (1.4)42.7 (3.0)<0.001
Table 2. Hazard ratio and 95% confidence interval for incidence of lung cancer according to METS-IR.
Table 2. Hazard ratio and 95% confidence interval for incidence of lung cancer according to METS-IR.
Subjects
(N = 322,624)		EventsFollow-up Duration (Person-Years)Incidence Rate (per 1000 Person-Years)Hazard Ratio (95% Confidence Intervals)
Crudep-ValueAdjusted *p-Value
Total        
Q1
(N = 80,722)		1722758,7872.271.00
(reference)		 1.00
(reference)		 
Q2
(N = 80,602)		1480765,7191.930.85
(0.79–0.91)		<0.0010.91
(0.85–0.98)		0.009
Q3
(N = 80,707)		1390766,7171.810.79
(0.74–0.85)		<0.0010.86
(0.79–0.92)		<0.001
Q4
(N = 80,593)		1320765,6341.720.75
(0.70–0.81)		<0.0010.80
(0.74–0.86)		<0.001
Male       
Q1
(N = 43,545)		1318400,6143.291.00
(reference)		 1.00
(reference)		 
Q2
(N = 43,459)		1068404,1692.640.80
(0.74–0.87)		<0.0010.92
(0.85–0.99)		0.04
Q3
(N = 43,542)		938409,2952.290.70
(0.64–0.76)		<0.0010.85
(0.78–0.93)		<0.001
Q4
(N = 43,451)		861408,4392.110.64
(0.59–0.70)		<0.0010.80
(0.73–0.88)		<0.001
Female       
Q1
(N = 37,177)		404356,8991.131.00
(reference)		 1.00
(reference)		 
Q2
(N = 37,143)		412360,2871.141.01
(0.88–1.16)		0.870.94
(0.82–1.07)		0.35
Q3
(N = 37,165)		452360,5011.251.11
(0.97–1.27)		0.140.94
(0.82–1.08)		0.36
Q4
(N = 37,142)		459360,2771.271.13
(0.98–1.29)		0.080.88
(0.76–1.01)		0.07
* The model was adjusted for age, sex, income level, residence, hypertension, diabetes, dyslipidemia, Charlson comorbidity index, hemoglobin level, glomerular filtration rate, smoking, alcohol drink, and regular exercise status.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Shine, B.-K.; Jeong, I.H.; Son, M.; Kim, B.; Moon, S.Y.; Lee, J.Y.; Kim, H.R.; Huh, S.J. Inverse Association Between METS-IR and Lung Cancer Risk: The Role of BMI in a Nationwide Korean Cohort. Cancers 2025, 17, 3727. https://doi.org/10.3390/cancers17233727

AMA Style

Shine B-K, Jeong IH, Son M, Kim B, Moon SY, Lee JY, Kim HR, Huh SJ. Inverse Association Between METS-IR and Lung Cancer Risk: The Role of BMI in a Nationwide Korean Cohort. Cancers. 2025; 17(23):3727. https://doi.org/10.3390/cancers17233727

Chicago/Turabian Style

Shine, Bo-Kyung, In Hwa Jeong, Minkook Son, Bongjo Kim, Sang Yi Moon, Jong Yoon Lee, Hye Ryeon Kim, and Seok Jae Huh. 2025. "Inverse Association Between METS-IR and Lung Cancer Risk: The Role of BMI in a Nationwide Korean Cohort" Cancers 17, no. 23: 3727. https://doi.org/10.3390/cancers17233727

APA Style

Shine, B.-K., Jeong, I. H., Son, M., Kim, B., Moon, S. Y., Lee, J. Y., Kim, H. R., & Huh, S. J. (2025). Inverse Association Between METS-IR and Lung Cancer Risk: The Role of BMI in a Nationwide Korean Cohort. Cancers, 17(23), 3727. https://doi.org/10.3390/cancers17233727

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop