Colorectal cancer (CRC) continues to pose one of the most significant global challenges in oncology, being consistently ranked among the leading causes of cancer-related mortality. Despite advances in diagnostic and therapeutic strategies, the prognosis remains heavily dependent on the ability to detect metastatic progression early. Metastasis—the spread of cancer cells beyond the primary tumor site—dramatically alters treatment paradigms and patient outcomes. A critical unmet need in CRC management is the identification of reliable biomarkers that can predict metastatic risk with high accuracy before treatment initiation. Recent interest has honed in on the metabolic alterations that accompany tumor progression, particularly those linked to insulin resistance (IR), a metabolic state characterized by impaired cellular responses to insulin.
In a landmark pilot study published in 2025 in BMC Cancer, researchers from India have investigated the prognostic value of lipid-based insulin resistance biomarkers in treatment-naïve CRC patients, examining how these markers correlate with metastatic status and other clinical parameters. The study enrolled 87 patients from four tertiary care hospitals, stratified into metastatic (n = 24) and non-metastatic (n = 63) groups. Comprehensive clinical assessments included TNM cancer staging, performance measures such as ECOG-Performance Status and Karnofsky Performance Scale, serum carcinoembryonic antigen (CEA) levels, and detailed lipid profiles encompassing LDL, HDL, and triglyceride indices.
The cornerstone of this research lies in elucidating the relationship between specific insulin resistance biomarkers and the propensity for CRC metastasis. Notably, the study focused on lipid ratios, such as the low-density lipoprotein to high-density lipoprotein ratio (LHR) and triglyceride-glucose index (TyG), as potential predictive indicators. Statistical analyses—ranging from Fisher’s exact tests to advanced regression models and receiver operating characteristic (ROC) curves—were employed to contextualize the diagnostic power of these markers within clinical data. Importantly, the binary logistic regression pinpointed LHR as a singularly strong predictor of metastatic disease, with increases in LHR corresponding to nearly a 20% heightened risk of metastasis.
These findings signify a breakthrough in understanding the metabolic underpinnings influencing tumor dissemination. The LHR demonstrated superb diagnostic metrics, achieving an area under the curve (AUC) of 0.867, alongside a sensitivity of 83.3% and specificity of 74.6%. Such performance metrics illustrate its potential utility as a non-invasive biomarker, potentially enabling clinicians to identify high-risk CRC patients at diagnosis, before metastasis becomes radiologically or clinically apparent. This advantage could revolutionize stratification strategies, allowing for tailored interventions predicated upon metabolic risk profiling.
Further insights from the study revealed that LHR’s predictive power was not an isolated phenomenon but was intricately associated with established clinical parameters including TNM stage, ECOG-PS, and serum CEA levels. The moderate positive correlations found via Spearman analysis emphasize the complex interdependence between lipid metabolism, tumor biology, and systemic disease status. These associations bolster the hypothesis that metabolic disruptions intrinsic to insulin resistance may facilitate or reflect mechanisms driving metastasis, such as altered cellular energetics, inflammatory cascades, and microvascular remodeling.
Crucially, these results emerge from a population of treatment-naïve patients, underscoring the biomarker’s capability to predict metastatic risk devoid of confounding effects from prior chemotherapy, radiotherapy, or surgical interventions. This clean clinical baseline enhances the reliability of the findings and suggests that the pathways connecting insulin resistance and metastasis are entrenched early in the disease course, possibly reflecting host metabolic milieu as much as tumor-intrinsic factors.
The study’s authors acknowledge the necessity of confirming these promising findings in larger, multi-centric cohorts with diverse ethnic and genetic backgrounds. While the pilot data strongly indicate LHR as a harbinger of metastatic progression, external validation will be pivotal before clinical integration. Furthermore, mechanistic studies exploring how lipid metabolism and insulin resistance drive metastasis at molecular and cellular levels would complement these epidemiological findings, potentially unveiling novel therapeutic targets.
From a clinical perspective, the incorporation of LHR into routine diagnostic algorithms could complement conventional staging approaches, such as imaging and histopathology, by adding a metabolic dimension to risk assessment. This stratification could identify patients who might benefit from intensified surveillance or early systemic therapies aimed at intercepting metastatic spread. Additionally, LHR is derived from commonly measured lipid panels, making it a cost-effective and easily implementable biomarker in diverse healthcare settings, including resource-limited environments where advanced molecular diagnostics are not readily available.
Insulin resistance’s intricate link with cancer biology encompasses various pathways, including hyperinsulinemia-induced cellular proliferation, dysregulated adipokine signaling, and chronic low-grade inflammation. The findings from this study reinforce the concept that metabolic syndrome components, such as dyslipidemia, are not merely comorbid risk factors but active participants in the neoplastic process, particularly in tumor aggressiveness and metastatic potential.
Importantly, the differentiation between LHR and other IR markers such as the TyG index highlights the nuanced landscape of metabolic biomarkers. While TyG did not show a significant correlation with either metastasis or CEA levels, LHR stood out as a robust and independent predictor. This specificity suggests that the balance between LDL and HDL cholesterol might be particularly reflective of biological processes pertinent to CRC progression, like oxidative stress and endothelial dysfunction.
The integration of IR biomarkers with traditional oncological parameters also opens new avenues for comprehensive prognostic models. The study’s multiple linear regression analysis underscores the combined predictive value of TNM staging, performance status scores, and LHR, suggesting that multifactorial models incorporating metabolic parameters could enhance prognostic precision beyond conventional staging alone.
Looking forward, such research may catalyze a paradigm shift in oncology towards metabolically informed cancer management. Interventions targeting insulin resistance, through lifestyle modifications or pharmacologic agents like metformin and statins, might gain prominence not only for metabolic health but also as adjunctive measures in cancer therapy aimed at reducing metastatic risk.
In summary, this pioneering study elucidates the pivotal role of lipid-based insulin resistance biomarkers, especially the LDL/HDL ratio, as powerful predictors of metastatic prognosis in treatment-naïve colorectal cancer patients. The findings herald a new frontier where metabolic profiling intersects with oncological diagnostics, offering hope for earlier detection, personalized treatment strategies, and ultimately improved survival in CRC.
Subject of Research: Association of insulin resistance biomarkers with metastatic prognosis in treatment-naïve colorectal cancer patients.
Article Title: Association between insulin resistance biomarkers and metastatic prognosis in treatment-naïve colorectal cancer patients: a pilot study
Article References: Narayanan, M.P., Sehrawat, A., Goyal, B. et al. Association between insulin resistance biomarkers and metastatic prognosis in treatment-naïve colorectal cancer patients: a pilot study. BMC Cancer 25, 1711 (2025). https://doi.org/10.1186/s12885-025-14669-w
Image Credits: Scienmag.com
DOI: 10.1186/s12885-025-14669-w (05 November 2025)
