Modified predictive model incorporating the waist-to-hip ratio for advanced colorectal neoplasia: A step toward precision screening

Abstract

This editorial discusses an article by Liu et al, which focuses on the development and evaluation of a modified scoring model incorporating the waist-to-hip ratio for predicting advanced colorectal neoplasia (ACN). This editorial provides an overview of the study, including the background of ACN risk prediction, the study design, key findings, and the significance and limitations of the new model. The study identified independent risk factors for ACN and developed a 7-point scoring model with better predictive performance than existing models. However, challenges, such as generalizability across ethnic groups and selection bias, exist. Further research involving multi-ethnic cohorts and the integration of novel biomarkers is needed to improve the model and its clinical application.

Key Words: Advanced colorectal neoplasia; Abdominal obesity; Colorectal cancer screening; Risk scoring model; Waist-to-hip ratio

Core Tip: The new Asia-Pacific Colorectal Screening model, by incorporating the waist-to-hip ratio (WHR), better captures the metabolic risks driven by visceral fat and significantly improves the prediction accuracy. A high WHR (> 0.9 for men; > 0.85 for women) increases the risk of advanced colorectal neoplasms by 46%, enabling high-risk subgroups to receive prioritized colonoscopy. Future research should be expanded to multi-regional cohorts and incorporate more biomarkers (such as percentage of body fat, blood lipid and blood glucose levels), laboratory (stool DNA, polygenic risk scores, circulating tumor DNA) and imaging indicators (computed tomography-measured visceral fat area) to optimize the model.

INTRODUCTION

Against the backdrop of the continuously increasing global cancer burden, colorectal cancer (CRC), ranks as the third most common malignant tumor, and the innovation of its screening strategies has become a public health priority[1,2]. In clinical practice, timely identification and prophylactic resection of advanced colorectal neoplasia (ACN) are crucial for the prevention and control of CRC[3,4]. However, traditional predictive models, such as the Asia-Pacific Colorectal Screening (APCS), which primarily rely on variables such as age, gender, smoking history, and family history of CRC to predict ACN, exhibit suboptimal prediction efficiency[5,6]. Even after incorporating body mass index (BMI) as an additional parameter to optimize and improve the model, there are still significant limitations in its predictive accuracy[7,8].

The study by Liu et al[9] published in the World Journal of Clinical Oncology confirmed in a prospective study of 6483 Chinese individuals that the modified scoring model incorporating the waist-to-hip ratio (WHR) was significantly superior to traditional screening tools. This breakthrough is not only reflected in the improvement of statistical power but also creates a new paradigm for the organic integration of metabolic parameters and tumor risk models[10,11]. This editorial provides an overview and a profound discussion of the study, including the background of ACN risk factors, the study design, key findings, and the significance and limitations of the new model.

BACKGROUND OF ACN RISK FACTORS

Traditional ACN prediction models (APCS) are mainly based on age, gender, smoking history, and family history of CRC, but their predictive efficacy is limited (the C-statistic is only 0.63)[12]. A previous study involving 4592 asymptomatic Chinese individuals demonstrated that the predictive efficacy of the BMI-modified APCS model was improved compared with that of the original APCS model. The C-statistic was 0.69 ± 0.04 for the modified score, and was 0.67 ± 0.04 for the original score. However, this result is still not satisfactory[7]. Despite these improvements, BMI improvement remains limited by its inability to distinguish subcutaneous adipose tissue from metabolically active visceral fat which directly affects the release of tumor-related adipokines[13,14].

The introduction of abdominal obesity indicators makes up for this deficiency. The WHR serves as a practical and effective proxy for estimating abdominal obesity, offering a more direct reflection of the metabolic and health risks associated with visceral adipose tissue (VAT) compared to BMI, which is a more general measure of simple obesity. Studies have confirmed that VAT drives the malignant transformation of the colonic epithelium by secreting pro-inflammatory cytokines (such as interleukin-6 and tumor necrosis factor-α) and adipokines (leptin, adiponectin and resistin)[15,16]. Dysregulation of adipokine secretion can disrupt normal metabolic and inflammatory pathways[17]. For instance, increased leptin levels associated with excessive visceral fat can promote chronic low-grade inflammation, activate pro-inflammatory signaling pathways, and contribute to the development of insulin resistance[18]. These processes play a significant role in the pathogenesis of ACN. This cohort study shows that individuals with higher WHR (> 0.9 for males; > 0.85 for females) have a 46% higher risk of ACN compared to the normal population (adjusted odds ratio = 1.46), and its predictive ability is significantly better than that of BMI. Indeed, the precise quantification of metabolic parameters may become a new direction for tumor risk stratification, especially suitable for the Asian population with a high incidence of metabolic diseases.

In addition, some studies have evaluated CRC risk and facilitated screening from a genetic perspective through polygenic risk scores (PRS), which integrate genetic variant information across the entire genome[19,20]. Compared to traditional models, PRS exhibit superior predictive value for CRC. Moreover, PRS can be effectively integrated with traditional risk prediction models, leading to a significant enhancement in the predictive efficacy of the combined model. In addition, circulating tumor DNA (ctDNA), also called cell-free DNA or liquid biopsy, consists of sequences of DNA detected in the circulation derived from tumor cells undergoing apoptosis. Some high-quality research has shown that ctDNA exhibits remarkable potential in screening, detecting minimal residual disease, monitoring for early recurrence, molecular profiling, and therapeutic response prediction for CRC. Its combination with traditional APCS is a promising research direction that may further improve the accuracy and comprehensiveness of ACN prediction models.

STUDY DESIGN

In terms of study design, this research employed a large-sample, training-validation dual-cohort design (training cohort: n = 4592; validation cohort: n = 1891). In the consistency analysis of baseline characteristics (gender, P = 0.381), the risk of selection bias was significantly reduced, ensuring the independent validation of the model's efficacy. Moreover, this study was the first to incorporate the WHR into the colorectal neoplasia risk model. By screening independent predictors with multivariate regression, a new 7-point scoring model for ACN prediction was constructed.

KEY FINDINGS

The study conducted by Liu et al[9] successfully incorporated the WHR into the ACN risk model for the first time. In the multivariate analysis, the WHR was validated as an independent predictive factor (odds ratio = 1.32, P < 0.05), markedly enhancing the discriminative power of the model. Age, being a core risk factor, had the highest weighting (4 points were assigned to those aged 70 years), aligning with the existing research consensus, and strongly bolstering the strategy of prioritizing screening for the elderly population[6,12,21-23]. With the 7-point scoring scale (1 point for gender, smoking and high WHR, and a maximum of 4 points for age), it is possible to efficiently identify high-risk individuals, optimizing the allocation of colonoscopy resources, and well-fitting the screening requirements in China.

SIGNIFICANCE AND LIMITATIONS

This study developed the first risk-stratification model for ACN specifically targeting the Chinese population. Its core innovation lies in integrating WHR, a central obesity indicator, to replace BMI. The modified APCS model demonstrated superior predictive accuracy compared to the traditional APCS model in validation cohorts (C-statistic 0.66 vs 0.63), with enhanced capacity to evaluate visceral adipose-induced metabolic-inflammatory risks[24-26]. The newly constructed model employs a scoring-based risk stratification approach, enabling the optimized allocation of colonoscopy resources in China's primary healthcare institutions. This method effectively prioritizes the identification of high-risk individuals, ensuring that limited medical resources are directed precisely where they are most needed.

However, certain limitations still exist. The data sources were confined to a single medical center in China, failing to cover the samples among populations from different regions and centers, thus being unable to avoid confounding factors and bias. There are significant differences in lifestyle habits, obesity patterns, metabolic diseases, and CRC incidence among different populations in China[27]. Single-center studies cannot accurately reflect the overall characteristics of ACN. In this study, the WHR thresholds for southern Chinese populations were > 0.9 for men and > 0.85 for women, which may not be applicable to populations in other regions or Western countries. The correlation between WHR and ACN may be affected by regional genetic differences, potentially limiting the model's accuracy. In addition, although both BMI and WHR were integrated, other metabolism-related indicators (such as percentage of body fat, computed tomography (CT)-measured visceral fat area, blood lipid and blood glucose levels) were not included, which may affect the comprehensive understanding of the tumorigenic mechanisms of abdominal obesity[28,29]. Stool DNA testing can detect DNA abnormalities in exfoliated intestinal cells, which helps to improve the detection rate of early CRC and adenomas[30,31]. It can serve as one of the important indicators for evaluating the risk of individuals developing CRC and adenomas. Some studies have integrated fecal DNA into the APCS model, significantly increasing the detection rate of ACN[32]. The study omitted CT, stool DNA, etc. which may have been due to the unavailability of non-routine screening data, high costs of advanced imaging/serological tests (especially in resource-limited areas), and risk of overfitting with too many variables. Moreover, as novel molecular markers, PRS and ctDNA are undergoing in-depth research and evaluation for their roles in CRC screening[33]. Both have demonstrated great potential for highly accurate applications. PRS integrates genetic variant information across the genome to assess genetic predisposition, while ctDNA captures real-time genetic alterations from tumor cells. Their unique characteristics suggest that they could significantly enhance the precision of CRC screening, potentially revolutionizing early detection strategies and improving patient outcomes.

Future research expanding to multi-regional cohorts, including inland and coastal populations, is essential to address geographic disparities in diet, lifestyle, and genetic predispositions that may influence the accuracy of the model. Moreover, further research should incorporate in-depth obesity-related biomarkers beyond WHR, such as CT-measured visceral fat area, body fat percentage, and adipokine levels to better characterize metabolic-inflammatory pathways. Concurrently, integrating convenient and validated laboratory indicators, such as stool DNA, has demonstrated utility in detecting early colorectal neoplasms, which would enhance the model’s sensitivity for preclinical detection. Multidimensional molecular tools, such as PRS and ctDNA, may be added to capture genetic susceptibility and real-time tumor-derived signals, respectively. Imaging examinations, including abdominal CT or magnetic resonance imaging, could provide direct quantification of VAT, addressing the limitations of WHR as a surrogate marker. By combining these modalities with machine learning algorithms, researchers can develop personalized risk prediction models that integrate demographic, metabolic, genetic, and molecular data. Such models would enable stratified screening strategies, prioritizing colonoscopy for high-risk subgroups while reducing unnecessary testing for low-risk individuals, thereby optimizing resource allocation and improving early detection rates globally. It is also feasible to develop personalized risk prediction models based on the characteristics of populations in target regions, incorporating multi-dimensional indicators. These may include demographic information, lifestyle factors, metabolic parameters, genetic markers, molecular biomarkers, imaging-derived metrics and so on. This approach aligns with the trend in precision medicine and holds potential to enhance the accuracy and clinical applicability of ACN prediction models across diverse populations.

CONCLUSION

The WHR modified APCS model demonstrates superior discriminative capacity compared to the traditional APCS model. By prioritizing age and central obesity metrics, the model optimizes colonoscopy allocation for high-risk subgroups, addressing critical resource constraints in Chinese primary care. Future research should expand to multi-regional cohorts and incorporate more biomarkers, laboratory and imaging indicators to optimize the model.