GRAPHICAL ABSTRACT

INTRODUCTION

Metabolic syndrome (MetS) is a clinical condition characterized by a cluster of abnormalities, including obesity (particularly abdominal obesity), elevated blood glucose resulting from diabetes or insulin resistance (IR), dyslipidemia, and hypertension [1]. The global rise in obesity has been accompanied by a parallel increase in the incidence of MetS [2]. Emerging evidence indicates that MetS elevates the risk of type 2 diabetes mellitus (DM), cardiovascular disease, premature death, hypogonadism, and colon polyps [3]. The pathogenesis of MetS is thought to be primarily driven by IR and excessive fatty acid flux [4]. As a syndrome involving multiple metabolic disturbances, MetS can be characterized by numerous physiochemical indicators [5], including body mass index (BMI), fasting insulin, triglycerides (TG), apolipoprotein B, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and waist circumference, all of which are critical determinants [6].

Colonic polyps are mucosal protrusions that extend into the intestinal lumen and are referred to as polyps prior to histopathological classification. Histologically, they are categorized as neoplastic or non-neoplastic lesions [7]. Polyps are recognized as precursors to colon adenocarcinoma, with the majority of colorectal cancer (CRC) cases developing from preexisting polyps [8]. Although the precise pathogenesis of colon polyps remains unclear, several risk factors have been identified, including a high-fat diet, physical inactivity, obesity, smoking, and excessive alcohol consumption [9]. Multiple studies have identified shared risk factors between MetS and colon polyps, including central obesity, IR, dyslipidemia, and chronic inflammation [10,11]. This potential overlap warrants further investigation to elucidate the mechanisms underlying the relationship between MetS and colon polyps.

Extensive epidemiological evidence has suggested that MetS increases the risk of colon polyps. A prospective European study found that MetS was a risk factor for adenomatous colonic polyps in White individuals, particularly those under 50 years of age [3]. Several cohort studies have shown that visceral adipose tissue, abdominal obesity, and higher BMI levels are associated with an increased risk of adenomatous colon polyps [12,13]. Although the precise mechanisms underlying this relationship remain uncertain, abnormal lipid profiles commonly observed in obesity may play a crucial role in the development of colon polyps [14]. In a retrospective study of U.S. adults, Budzynska et al. [15] reported that increased BMI was significantly associated with adenomatous polyps, and even the presence of a single MetS component significantly elevated the risk of developing adenomatous polyps. Similarly, a retrospective observational study in Korea demonstrated that MetS was associated with a higher prevalence of adenomas and CRC [16]. In contrast, some case-control studies have produced inconclusive findings regarding the relationship between MetS and colon polyps. For instance, a study conducted in a Korean population found no significant correlation between MetS markers and colon polyps [17]. In a Chinese population, patients with MetS or serum uric acid levels exceeding 340 μmol/L exhibited a higher likelihood of developing colonic polyps; however, the association was not statistically significant [18]. These inconsistencies underscore the need for further investigation into the relationship between MetS and colon polyps, with rigorous control for potential confounding factors.

To date, most studies exploring the association between MetS and colon polyps have employed prospective or retrospective cohort designs, case-control studies, or meta-analyses. However, these study designs are inherently susceptible to confounding by genetic and environmental factors, limited database sizes, observational bias, and sample selection bias [19]. Although randomized controlled trials are considered the gold standard for establishing causality, they require substantial time and resources [20]. Consequently, the causal relationship between MetS and colon polyps remains unresolved.

Mendelian randomization (MR) is a contemporary epidemiological approach that infers causality by using genetic variants, such as single-nucleotide polymorphisms (SNPs) identified through genome-wide association studies (GWAS), as instrumental variables (IVs) [21]. Because these genetic variants are randomly and independently assigned at conception, MR allows a more robust assessment of causal relationships between MetS and colonic polyps than traditional observational studies [22]. Additionally, the use of large, independent GWAS datasets enhances statistical power and minimizes bias. The present study applied a two-sample MR design to examine the causal relationships between MetS and its individual components with colon polyps.

METHODS

Overall study design

We used a two-sample MR method to assess the relationship between MetS and colon polyps, as illustrated in Fig. 1. The exposure factors included diseases such as hypertension, hyperlipidemia, type 1 DM, type 2 DM, and their metabolic indicators, such as diastolic blood pressure, systolic blood pressure, BMI, body fat percentage, waist-to-hip ratio adjusted for BMI, 2-hour glucose, fasting glucose, fasting insulin, glycated hemoglobin A1c (HbA1c), HDL-C, LDL-C, total cholesterol (TC), TG, apolipoprotein A levels, apolipoprotein B levels, omega-3 fatty acid levels, omega-6 fatty acid levels, omega-6 to omega-3 fatty acid ratio, and serum uric acid levels. Three key assumptions must be satisfied for MR analyses: (1) the SNPs should be significantly associated with MetS; (2) the SNPs should not be related to other confounding factors; and (3) the SNPs should influence colon polyps only through MetS, without direct associations, thereby ensuring causal inference.

Data sources

The exposure and outcome data sources are summarized in Table 1. Most exposure data were obtained from the Integrative Epidemiology Unit (IEU) database, while data for 2-hour glucose, fasting glucose, fasting insulin, and HbA1c were sourced from the MAGIC consortium (https://magicinvestigators.org/downloads/). To prevent sample overlap, outcome data were derived from the Department of Veterans Affairs Million Veteran Program (MVP) GWAS in the United States, which included 73,737 cases and 241,931 controls of European ancestry [23]. All data are from publicly available GWAS data and have been approved by the respective ethics review committees. The current analysis does not require additional ethical approval.

The selection of IVs

For each exposure, SNPs serving as potential IVs were required to meet the following criteria: genome-wide significance (P< 5×10^–8); independence based on linkage disequilibrium (LD) thresholds from the 1000 Genomes European reference panel, with r²<0.001 and a clumping window of 10,000 kb; and strong instrument strength defined by an F-statistic >10 and minor allele frequency >0.01 [24]. The strength of the SNP is evaluated by the F-statistic, calculated as F=Beta2SE2, where beta is the effect size between the effect allele and the other allele, and standard error (SE) is the SE of the effect size [25]. LDtraits (https://ldlink.nih.gov/?tab=ldtrait) was used to identify phenotypic characteristics associated with SNPs and remove SNPs that were associated with confounding variables or outcomes. Palindromic or ambiguous SNPs were excluded during the harmonization process to prevent strand alignment errors.

Two-sample MR and multiple testing

The relationships between each exposure and outcome were evaluated using several complementary MR methods, including MR Egger regression, weighted median, maximum likelihood (ML), constrained maximum likelihood with model averaging (cML-MA), Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), and inverse variance-weighted (IVW) approaches. The IVW method combines results from each SNP using a meta-analytic framework to yield an overall causal estimate. A significance threshold of P<0.05 was used. In the absence of horizontal pleiotropy, IVW estimates are considered unbiased [26]. The weighted median method provides consistent causal estimates even when up to 50% of the IVs are invalid [27]. MR Egger regression was used to detect directional pleiotropy among IVs and to provide adjusted causal estimates [28]. When horizontal pleiotropy was detected, MR Egger results were prioritized for causal inference. The ML approach, similar to IVW, assumes no heterogeneity or pleiotropy and typically produces unbiased estimates with smaller SEs when these assumptions hold [29]. The cML-MA approach was incorporated to account for both correlated and uncorrelated pleiotropic effects [30]. MR-PRESSO was employed to detect and remove outlier SNPs, thereby minimizing the influence of horizontal pleiotropy [31].

When multiple tests are conducted, the likelihood of false-positive results increases, especially when applying the conventional significance threshold (P<0.05) to each test. To mitigate this issue, false discovery rate (FDR) correction was applied to adjust P values. This approach balances false positives and false negatives by controlling the expected proportion of false discoveries among the significant results. FDR-adjusted P values (q-values) were calculated using the formula q=p×n/rank, where P is the unadjusted P value, n is the total number of tests, and rank corresponds to the ascending order of the P values [32]. A q-value <0.05 was considered statistically significant, indicating a robust causal relationship between exposure and outcome. Heterogeneity among IVs was assessed using Cochran’s Q test under the IVW model, with P>0.05 indicating no significant heterogeneity. Horizontal pleiotropy was evaluated using Egger’s intercept test, with P>0.05 suggesting the absence of directional pleiotropy. Additionally, the I²_GX statistic was calculated to evaluate the strength of association between IVs and exposures, where values approaching 1 indicate minimal bias in MR estimates [33]. To ensure adequate statistical power and prevent misinterpretation of non-significant findings, power calculations were performed using the mRnd tool (https://shiny.cnsgenomics.com/mRnd/).

A leave-one-out sensitivity analysis was conducted to determine whether any single IV disproportionately influenced the overall causal estimate. Causal effects of genetically predicted exposures on colon polyps were expressed as odds ratios (ORs) with 95% confidence intervals (CIs). All statistical analyses were conducted using R version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria) [34], and the TwoSampleMR package 0.5.10 version (MRC Integrative Epidemiology Unit, Bristol, UK) was used for MR analysis [24].

Ethics approval

The data we used were obtained from published studies approved by the corresponding ethics committee; thus, no further ethical approval was required for this study.

RESULTS

Selected SNPs

In this study, we identified independent SNPs associated with MetS and colon polyps. The selected SNPs are shown in Fig. 2, Supplemental Table S1. All IVs had F-statistics greater than 10, indicating sufficient strength and the absence of weak instrument bias. The range and median of the F-statistics are illustrated in Supplemental Table S2.

Causal associations between metabolic indicators and colonic polyps

IVW was employed as the primary analysis method for MR, and the MR Egger, weighted median, ML, cML-MA, and MR-PRESSO methods were used for supplementary explanation. An initial MR analysis using the selected SNPs revealed significant horizontal pleiotropy for BMI and 2-hour glucose. Consequently, all significant outliers were identified and removed using MR-PRESSO (Supplemental Table S3). The corrected results are presented in Supplemental Tables S4, S5.

Overall, 11 metabolic indicators—BMI, body fat percentage, waist-to-hip ratio adjusted for BMI, 2-hour glucose, HbA1c, HDL-C, TC, TG, omega-3 fatty acid levels, omega-6 fatty acid levels, and the omega-6 to omega-3 fatty acid ratio—were found to be associated with colon polyps in at least four MR methods (Fig. 2). The IVW estimates were largely consistent with those obtained from MR-PRESSO, confirming the robustness of our findings and the limited influence of horizontal pleiotropy.

Both IVW and MR Egger methods indicated that several metabolic traits were significantly associated with the risk of colonic polyps. Specifically, high BMI (OR, 1.33; 95% CI, 1.17 to 1.51; P<0.001), body fat percentage (OR, 1.11; 95% CI, 1.04 to 1.19; P=0.002), waist-to-hip ratio adjusted for BMI (OR, 1.1; 95% CI, 1.04 to 1.17; P=0.001), TC (OR, 1.07; 95% CI, 1.03 to 1.12; P=0.002), TG (OR, 1.11; 95% CI, 1.06 to 1.16; P<0.001), omega-3 fatty acid levels (OR, 1.08; 95% CI, 1.04 to 1.13; P<0.001), and omega-6 fatty acid levels (OR, 1.06; 95% CI, 1.01 to 1.1; P=0.02) were associated with an increased risk of colon polyps. In contrast, higher 2-hour glucose (OR, 0.74; 95% CI, 0.63 to 0.87; P=0.004), HbA1c (OR, 0.82; 95% CI, 0.71 to 0.94; P=0.004), HDL-C (OR, 0.94; 95% CI, 0.91 to 0.98; P= 0.005), and ratio of omega-6 to omega-3 fatty acids (OR, 0.91; 95% CI, 0.86 to 0.95; P<0.001) were identified as protective factors against colon polyps (Fig. 2, Supplemental Table S4). The associations between these metabolic indicators and colon polyps remained significant even after FDR correction (Fig. 2). Scatter plots depicting the primary MR results are shown in Fig. 3, Supplemental Fig. S1. In addition, all I2 GX values were close to 1, indicating strong validity of the IVs and minimal bias in the causal estimates (Supplemental Table S2).

Horizontal pleiotropy analysis

We observed significant horizontal pleiotropy for BMI and 2-hour glucose, both before and after outlier removal by MR-PRESSO (Supplemental Tables S2, S5). IVW estimation indicated that there was a significant relationship between BMI and 2-hour glucose and colon polyps (Supplemental Tables S1, S4). Importantly, these associations remained robust after accounting for pleiotropy using the MR Egger and cML-MA methods (BMI: MR Egger OR, 1.0022 [95% CI, 1.0008 to 1.0036; P=0.002]; cML-MA OR, 1.0022 [95% CI, 1.0008 to 1.0036; P=0.002]; 2-hour glucose: MR Egger OR, 1.0022 [95% CI, 1.0008 to 1.0036; P=0.002]; cML-MA OR: 1.0022 [95% CI, 1.0008 to 1.0036; P=0.002]) (Supplemental Table S4). For all other metabolic indicators, MR Egger intercept testing did not detect evidence of horizontal pleiotropy (P_intercept>0.05), suggesting that the IVs did not significantly affect the results through pathways other than exposure (Supplemental Tables S2, S5, Supplemental Fig. S2).

Heterogeneity analysis

Supplemental Tables S2, S5 present heterogeneity tests with P<0.05, which may be attributed to the limited number of included SNPs. To assess the influence of individual SNPs on the findings, we conducted a leave-one-out analysis by sequentially excluding each SNP (Supplemental Fig. S3). The results demonstrated that omitting any single SNP had minimal impact on the overall causal estimates, indicating that heterogeneity among SNPs did not substantially bias the findings and supporting the reliability of the selected IVs.

Statistical power analysis

We further evaluated the statistical power of the MR analyses. As shown in Supplemental Table S6, the power for all positive associations exceeded 90%, indicating minimal risk of weak instrument bias. Conversely, the low power (<50%) observed for non-significant associations suggests that true associations with the outcomes cannot be entirely excluded.

DISCUSSION

The relationship between MetS and the development of colon polyps remains unclear, as prior studies have reported inconsistent findings. Given the increasing prevalence of MetS and its components, clarifying their potential causal effects on colon polyp formation is of clinical importance. In this study, a two-sample MR analysis was conducted to comprehensively examine the causal effects of MetS and its metabolic indicators on colon polyps. MR analysis showed that MetS-related diseases, such as hypertension, hyperlipidemia, and diabetes, were not associated with colon polyps. However, specific MetS components, including BMI, body fat percentage, waist-to-hip ratio adjusted for BMI, TC, TG, omega-3 fatty acid levels, and omega-6 fatty acid levels, were associated with an increased risk of colon polyps, whereas 2-hour glucose, HbA1c, HDL-C, and the ratio of omega-6 to omega-3 fatty acids were protective factors against colon polyps.

Our findings showed that the presence of hypertension, hyperlipidemia, and diabetes alone was not significantly associated with the occurrence of colon polyps. The effect of individual diseases on colon polyp development may be minimal within the broader context of the syndrome, suggesting that the relationship between these conditions and colon polyps cannot be explained by a single factor. Importantly, our results demonstrated significant associations between metabolic indicators related to these diseases and the presence of colon polyps.

The aggregation of MetS components significantly increases the risk of adenomatous polyps [35], suggesting that colon polyps may arise from the combined effects of multiple metabolic abnormalities. Previous studies have identified obesity (BMI, body fat percentage, and waist-to-hip ratio) and lipid levels (TG and TC) as risk factors for colon polyps, while HDL-C has been shown to play a protective role [36,37]. These observations are consistent with our findings. Specifically, we found that for every one standard deviation (SD) increase in BMI (OR, 1.33; 95% CI, 1.17 to 1.51; P<0.001), the risk of developing colon polyps increased by 33%. For instance, if the mean BMI of a population is 25 kg/m² and the SD is 4 kg/m², then an increase in BMI from 25 to 29 kg/m² would correspond to a 33% increase in colon polyp risk. Likewise, for every SD increase in body fat percentage, TG, TC, and waist-to-hip ratio adjusted for BMI, the risk of colon polyps increased by 11% (OR, 1.11; 95% CI, 1.04 to 1.19), 11% (OR, 1.11; 95% CI, 1.06 to 1.16), 7% (OR, 1.07; 95% CI, 1.03 to 1.12), and 10% (OR, 1.10; 95% CI, 1.04 to 1.17), respectively. Conversely, for every SD increase in HDLC, the risk of colon polyps decreased by 6% (OR, 0.94; 95% CI, 0.91 to 0.98). These findings suggest that colon polyps may develop through the synergistic effects of multiple metabolic abnormalities. Clinically, although the contribution of any single metabolic indicator may appear limited, these abnormalities frequently cluster in the same population, collectively heightening overall risk. A case-control study also reported that serum lipid levels and obesity were related to colon polyps, suggesting that hyperlipidemia and obesity may represent important risk factors [14]. For BMI, significant horizontal pleiotropy persisted despite correction with MR-PRESSO, MR Egger, and cML-MA. This finding is biologically plausible, as BMI is a complex phenotype reflecting multiple metabolic and inflammatory pathways—including IR, cytokine activity, and adipokine signaling—that may influence colorectal polyp risk independently of BMI itself. Consequently, MR analysis cannot entirely exclude pleiotropic effects, and these results should be interpreted with caution. Future studies should incorporate more precise metabolic markers and multivariable MR approaches to further clarify the relationship between BMI and colon polyp risk.

Numerous studies have shown that omega-3 and omega-6 fatty acids, as polyunsaturated fatty acids, have positive effects on hyperlipidemia, hyperglycemia, and hypertension [38]. Experimental data have also demonstrated that marine-derived omega-3 fatty acids may confer protective effects against colon polyp formation [39]. However, our findings regarding omega-3 and omega-6 fatty acids contrast sharply with previous observational studies [40]. In the present study, omega-3 and omega-6 fatty acids were found to promote the formation of colon polyps. In addition, we observed a protective effect of HDL-C against colon polyps, consistent with prior reports [41]. By contrast, our results showing that HbA1c and 2-hour glucose levels were negatively correlated with colon polyp risk differed from earlier findings [42]. Previous research has indicated that abnormal glucose metabolism is closely linked to increased risk of colon polyps. For instance, several studies have demonstrated that poor glycemic control in patients with type 2 DM is an independent predictor of colon polyp progression [43], and that elevated HbA1c and 2-hour glucose levels are associated with higher colon polyp risk [13]. The results related to omega-3 and omega-6 fatty acids, HbA1c, and 2-hour glucose therefore contradict prior findings and may represent potential false-positive associations. We found that the number of SNPs contributing to these associations was fewer than 100, a relatively small number compared with the SNPs supporting other significant findings. Consequently, the limited sample size for these variables may have reduced statistical power, increasing the likelihood of false-positive results. Larger and higher-quality prospective studies are needed to validate these observations.

Potential mechanisms underlying the development of colon polyps in individuals with MetS include chronic inflammation, IR, activation of insulin-like growth factor 1, and oxidative stress [44-47]. Our findings indicate that both obesity and elevated serum lipid levels are important risk factors for colon polyps. Adipose tissue is now recognized as an active endocrine organ rather than a passive energy reservoir. It secretes free fatty acids, growth factors, sex steroids, adipokines, and inflammatory cytokines into the systemic circulation [48]. Specifically, TG can activate insulin-like growth factors, inhibit apoptosis, and promote tumor formation [46]. Dyslipidemia induces proinflammatory cytokines such as interleukin 6 (IL-6) and tumor necrosis factor-alpha while suppressing anti-inflammatory cytokines such as IL-10, thereby creating an environment conducive to tumor cell proliferation [46]. Chronic inflammation can impair cholesterol transport, leading to increased LDL-C synthesis and TG accumulation in intestinal cells, which may trigger downstream carcinogenic cascades [44]. Elevated TG levels are also associated with increased bile acid synthesis, and excessive dietary fat intake stimulates bile acid secretion, enhancing epithelial cell proliferation and impairing carcinogen detoxification in the colon [49]. Furthermore, fatty acids can promote oxidative stress, increasing reactive oxygen species production, inflammation, and aberrant expression of cancer-related genes [50]. In contrast, HDL-C, identified as a protective factor in this study, may exert antioxidant effects that inhibit lipid peroxidation and oxidative stress in the colon [47]. Together, these findings suggest that serum lipid levels play a central role in modulating the malignant transformation of colon polyps.

Our findings have several important clinical implications. This study identified a range of biomarkers—such as BMI, body fat percentage, TG, and TC—that influence the formation of colon polyps. Moreover, the MR analysis highlighted the protective role of HDL-C. These results underscore the importance of assessing lipid metabolism comprehensively when developing prevention and management strategies for colon polyps, particularly among individuals with MetS or those at elevated risk. Maintaining appropriate levels of BMI, body fat percentage, TG, and TC remains a cornerstone of public health guidance, reinforcing the clinical significance of weight and lipid control in the prevention of colon polyps.

Compared with traditional observational studies, a major strength of the present study is the use of MR, which capitalizes on the random allocation of genetic variants. This approach minimizes confounding from genetic and environmental factors and provides stronger evidence for causality. Nonetheless, several limitations should be acknowledged. First, the MVP cohort has distinct demographic characteristics, which may limit the generalizability of our findings to other populations. Second, some associations—particularly those involving omega-3 and omega-6 fatty acids, HbA1c, and 2-hour glucose—may represent false-positive findings due to the limited number of SNPs and the small proportion of variance explained. These constraints could contribute to heterogeneity among IVs and reduce the stability of the results. Therefore, replication in larger and more representative datasets is warranted. Third, MR analyses rely on three fundamental assumptions, and violation of any of these may bias the findings. In this study, significant horizontal pleiotropy persisted for BMI and 2-hour glucose despite correction with MR-PRESSO, MR Egger, and cML-MA. Consequently, although our results suggest a potential causal relationship between BMI and the risk of colon polyps, the conclusions should be interpreted with caution. All findings should be validated in independent cohorts before clinical application.

In conclusion, the findings of this study emphasize the critical importance of comprehensive weight and lipid management in preventing colon polyps. Lifestyle changes, particularly weight reduction, represent a practical and effective strategy for reducing colon polyp risk. The protective effect of HDL-C also highlights its potential as a therapeutic target. Although these findings provide new insights into the metabolic determinants of colon polyp formation, further validation in larger, multiethnic populations is necessary to confirm their broader applicability.

Supplementary Material

Article information

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

ACKNOWLEDGMENTS

This work was supported by the Zhejiang Medical and Health Science and Technology project (No. 2023KY194).

AUTHOR CONTRIBUTIONS

Conception or design: D.C., F.P. Acquisition, analysis, or interpretation of data: D.C., H.X., Z.Z., F.C., Q.L., F.P. Drafting the work or revising: D.C., F.P. Final approval of the manuscript: D.C., H.X., Z.Z., F.C., Q.L., F.P.

Fig. 1.

Schematic diagram of the study design. LD, linkage disequilibrium; SNP, single-nucleotide polymorphism.

Fig. 2.

Causal relationship forest plot. The forest plot shows the odds ratios (ORs) and P values derived from the inverse variance-weighted (IVW) analysis, which were false discovery rate (FDR)-corrected. SNP, single-nucleotide polymorphism; CI, confidence interval; MR, Mendelian randomization; HbA1c, glycated hemoglobin A1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein.

Fig. 3.

Scatter plot of significant results of Mendelian randomization (MR) analysis. cML-MA, constrained maximum likelihood with model averaging; MR-PRESSO, Mendelian Randomization Pleiotropy RESidual Sum and Outlier; SNP, single-nucleotide polymorphism; BMI, body mass index; HbA1c, glycated hemoglobin A1c; HDL, high-density lipoprotein.

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