Genetic correlation and causal associations between metabolic traits and lung cancer risk.

10599 Background: Lung cancer is the leading cause of cancer-related mortality worldwide. We evaluated whether associations between metabolic traits and lung cancer risk reflect shared genetic susceptibility or causal effects of metabolic dysregulation. Methods: GWAS summary statistics from European-ancestry participants were obtained for lung cancer (TRICL–ILCCO; n = 29,266 cases, 56,450 controls), body mass index (BMI; GIANT; n = 681,275), blood lipids, triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL), from the Global Lipids Genetics Consortium (GLGC; n = 1,320,016), and type 2 diabetes (T2D; T2DGGI; n = 242,283 cases, 1,569,734, controls). Genome-wide genetic correlations were estimated using LD score regression. Two-sample Mendelian randomization analyses were conducted with lung cancer as the outcome using inverse-variance weighted models, with weighted median, MR-Egger, and weighted mode sensitivity analyses. Heterogeneity and pleiotropy were assessed using Cochran’s Q, MR-Egger intercept, and MR-PRESSO. Results: Lung cancer demonstrated significant positive genetic correlations (r g ) with BMI (r g = 0.16, P = 1.0 × 10⁻⁴), TG (r g = 0.14, P = 3.0 × 10⁻⁴), and T2D (r g = 0.09, P = 0.017), and a significant inverse correlation with HDL (r g = −0.14, P = 2.4 × 10⁻⁵). No significant genetic correlation was observed for LDL. In MR analyses, genetically predicted BMI was associated with increased lung cancer risk (475 SNPs; IVW β = 0.23, SE = 0.05, P = 2.0 × 10⁻⁶; OR = 1.26, 95% CI 1.14–1.38), and remained statistically significant after outlier correction (β = 0.21, P = 7.2 × 10⁻⁶), with no evidence of distortion. In contrast, no supporting evidence of causal associations between lung cancer risk and genetically predicted HDL, TG, LDL, or T2D liability across primary and sensitivity estimators (all P > 0.05). Cochran’s Q tests indicated heterogeneity across genetic instruments; however, MR-Egger intercept tests did not provide evidence of directional horizontal pleiotropy. Conclusions: These findings suggest that metabolic health, particularly obesity, may play a clinically meaningful role in lung cancer risk assessment. Considering obesity alongside established smoking-related determinants may enhance lung cancer prevention efforts.

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Publication Details

Journal
Journal of Clinical Oncology
Published
2026-05-27
DOI
https://doi.org/10.1200/jco.2026.44.16_suppl.10599
Primary Topic
Cancer, Lipids, and Metabolism
Type
article
Field-Weighted Citation Impact
0.00

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article

Genetic correlation and causal associations between metabolic traits and lung cancer risk.

Sally N. Adebamowo, Michael Zhong, Clement Adebamowo, Knightess Oyibo
Journal of Clinical Oncology
Cancer, Lipids, and Metabolism
article

Genetic correlation and causal associations between metabolic traits and lung cancer risk.

Sally N. Adebamowo, Michael Zhong, Clement Adebamowo, Knightess Oyibo
article en

Abstract

10599 Background: Lung cancer is the leading cause of cancer-related mortality worldwide. We evaluated whether associations between metabolic traits and lung cancer risk reflect shared genetic susceptibility or causal effects of metabolic dysregulation. Methods: GWAS summary statistics from European-ancestry participants were obtained for lung cancer (TRICL–ILCCO; n = 29,266 cases, 56,450 controls), body mass index (BMI; GIANT; n = 681,275), blood lipids, triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL), from the Global Lipids Genetics Consortium (GLGC; n = 1,320,016), and type 2 diabetes (T2D; T2DGGI; n = 242,283 cases, 1,569,734, controls). Genome-wide genetic correlations were estimated using LD score regression. Two-sample Mendelian randomization analyses were conducted with lung cancer as the outcome using inverse-variance weighted models, with weighted median, MR-Egger, and weighted mode sensitivity analyses. Heterogeneity and pleiotropy were assessed using Cochran’s Q, MR-Egger intercept, and MR-PRESSO. Results: Lung cancer demonstrated significant positive genetic correlations (r g ) with BMI (r g = 0.16, P = 1.0 × 10⁻⁴), TG (r g = 0.14, P = 3.0 × 10⁻⁴), and T2D (r g = 0.09, P = 0.017), and a significant inverse correlation with HDL (r g = −0.14, P = 2.4 × 10⁻⁵). No significant genetic correlation was observed for LDL. In MR analyses, genetically predicted BMI was associated with increased lung cancer risk (475 SNPs; IVW β = 0.23, SE = 0.05, P = 2.0 × 10⁻⁶; OR = 1.26, 95% CI 1.14–1.38), and remained statistically significant after outlier correction (β = 0.21, P = 7.2 × 10⁻⁶), with no evidence of distortion. In contrast, no supporting evidence of causal associations between lung cancer risk and genetically predicted HDL, TG, LDL, or T2D liability across primary and sensitivity estimators (all P > 0.05). Cochran’s Q tests indicated heterogeneity across genetic instruments; however, MR-Egger intercept tests did not provide evidence of directional horizontal pleiotropy. Conclusions: These findings suggest that metabolic health, particularly obesity, may play a clinically meaningful role in lung cancer risk assessment. Considering obesity alongside established smoking-related determinants may enhance lung cancer prevention efforts.

Journal of Clinical OncologyVol. 44(16_suppl)
University of Maryland, Baltimore (US), U-M Rogel Cancer Center (US), University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center
American Cancer Society
Good health and well-being
Openalex Percentile: Top 11%
Cancer, Lipids, and Metabolism
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