Hemorrhoids are one of the most common anorectal disorders encountered in colorectal practice. According to epidemiological data, as many as half of the population suffers from hemorrhoids before reaching the age of 50 (1). Both males and females are affected equally (2). In hemorrhoids, the most common symptom is painless rectal bleeding during defection (3). Depending on their location, hemorrhoids are generally divided into external, internal, and mixed type (3). Hemorrhoids treatment may include a variety of non-operative and surgical options. Approximately 10% of cases require surgical procedures (4). In the United Kingdom (UK), more than 20,000 hemorrhoidal procedures are performed annually (5). Given that hemorrhoids are associated with a heavy healthcare and economic burden, increasing attention has been given to hemorrhoids prevention in recent years (5).

The cause of hemorrhoids remains unclear. Epidemiological studies have suggested several risk factors for the development of hemorrhoids including adiposity (6). However, evidence concerning the relationship between adiposity and hemorrhoids is scarce and inconclusive (7–11), and it is not yet established whether the association is causal. The practical inability to perform randomized controlled trials (RCTs) for evaluating the causality between adiposity and hemorrhoids makes it necessary to conduct a Mendelian randomization (MR) analysis. MR is a form of causal inference method that utilizes single nucleotide polymorphisms (SNPs) as instrumental variables to explore the potential causal relationship between an exposure and a disorder (12). In MR, the biases including residual confounding and reverse causation are significantly reduced (13). Because identifying modifiable risk factors can offer potential for hemorrhoids prevention, it is of importance to infer causation between adiposity and hemorrhoids. The aim of this study is to apply the MR causal framework to evaluate whether four adiposity traits including body mass index (BMI), body fat percentage, waist circumference, and waist-to-hip ratio have causal effects on hemorrhoids development.

A summary of the instrument SNP selection process is shown in Figure 1. Of the 458 SNPs that predicted BMI, 19 SNPs were removed after linkage disequilibrium clumping. Harmonization with the corresponding hemorrhoids summary-level data resulted in 421 independent SNPs for the MR analysis (F-statistic = 61.13). Of the 395 SNPs predicting body fat percentage, linkage disequilibrium clumping removed 18 SNPs. Three hundred and sixty-six SNPs were obtained for the MR analysis after harmonizing the body fat percentage dataset with the hemorrhoids dataset (F-statistic = 32.86). Of the 374 SNPs that predicted waist circumference, 18 correlated SNPs were removed after linkage disequilibrium clumping. Harmonization with the corresponding hemorrhoids summary statistics produced 344 SNPs for the MR analysis (F-statistic = 45.00). The 29 SNPs predicting waist-to-hip ratio were uncorrelated (r² < 0.001). Harmonization with the corresponding hemorrhoids summary statistics obtained 28 SNPs for the MR analysis (F-statistic = 76.56). The SNPs for each exposure had enough strength; the F-statistics were greater than 10. Supplementary Tables S2–S5 show the details on the instrumental genetic variants for each exposure.

For each 1-SD kg/m² increase in genetically determined BMI, the IVW MR analysis indicated enhanced risk of hemorrhoids [odds ratio (OR) = 1.005, 95% confidence interval (CI): 1.003–1.008, p = 7.801 × 10⁻⁵] (Figures 2A, 3A). This finding was consistent across sensitivity analyses applying the maximum likelihood (OR = 1.004, 95% CI: 1.002–1.006, p = 0.001) and MR-PRESSO (OR = 1.004, 95% CI: 1.002–1.006, p = 9.470 × 10⁻⁵) models. We observed heterogeneity across the SNPs (Cochran’s Q p = 9.470 × 10⁻⁵). The MR-Egger intercept was centered around zero (intercept = 2.845 × 10⁻⁵, p = 0.658), which was suggestive of the absence of a directional pleiotropic effect. MR-PRESSO detected one outlying SNP, but the MR estimate was unchanged when removing it (OR = 1.005, 95% CI: 1.003–1.008, p = 4.707 × 10⁻⁵). Results of the leave-one-out sensitivity analysis supported the observed causal association (Supplementary Table S6).

The IVW MR analysis showed that the OR per 1-SD increase in genetically determined body fat percentage for the risk of hemorrhoids was 1.005 (95% CI: 1.001–1.008, p = 0.008) (Figures 2B, 3A). The causal estimates were also significant by sensitivity analyses using the maximum likelihood (OR = 1.005, 95% CI: 1.002–1.008, p = 0.004) and MR-PRESSO (OR = 1.005, 95% CI: 1.001–1.008, p = 0.008) methods. Heterogeneity was noted among studies (p = 0.005). However, this was likely not due to horizontal pleiotropy, because the MR-Egger regression intercept analysis yielded a large p-value (intercept = 7.297 × 10⁻⁷, p = 0.992). No outliers were identified using the MR-PRESSO method. The leave-one-out sensitivity analysis did not detect any individual SNPs that significantly affected the causal estimates (Supplementary Table S7).

In the primary MR analysis using the IVW method, the OR per 1-SD increase in genetically determined waist circumference was 1.008 (95% CI: 1.005–1.011, p = 1.051 × 10⁻⁶) for hemorrhoids (Figures 2C, 3A). The sensitivity analyses based on the maximum likelihood (OR = 1.008, 95% CI: 1.005–1.011, p = 6.891 × 10⁻⁸), weight median (OR = 1.006, 95% CI: 1.001–1.011, p = 0.028), and MR-PRESSO (OR = 1.008, 95% CI: 1.005–1.011, p = 1.614 × 10⁻⁶) methods yielded similar results. Although the Cochran’s Q test showed evidence of heterogeneity (p = 0.002), it was not likely because of horizontal pleiotropy (MR-Egger intercept = 2.687 × 10⁻⁶, p = 0.970). MR-PRESSO did not identify potential outliers. The leave-one-out sensitivity analysis found no individual SNPs significantly affecting the causal estimates (Supplementary Table S8).

For each 1-SD increase in genetically determined waist-to-hip ratio, increased risk of hemorrhoids was identified in the IVW estimate (OR = 1.010, 95% CI: 1.003–1.017, p = 0.003) (Figures 2D, 3A). The maximum likelihood (OR = 1.010, 95% CI: 1.004–1.017, p = 0.001) and MR-PRESSO (OR = 1.010, 95% CI: 1.003–1.017, p = 0.007) analyses supported this association with consistent effect sizes. There was no evidence for heterogeneity (p = 0.203). The MR-Egger intercept indicated absence of horizontal pleiotropy (intercept = 2.670 × 10⁻⁴, p = 0.512). No significant outliers were detected using the MR-PRESSO method. The leave-one-out sensitivity analysis showed that no single SNPs could dramatically drive the observed causal association (Supplementary Table S9).

The funnel plots showed symmetry, supporting the reliability of the MR analyses (Figure 4). The MR Steiger directionality test was used to test the causal direction between the adiposity measures and hemorrhoids. Table 2 shows the results. The variance in the outcome (snp_r2.outcome) was less than each exposure (snp_r2.exposure), confirming the causal direction.

Finally, we performed multivariable MR analysis to adjust for physical activity. The results showed statistically significant causal associations of genetically determined BMI (OR = 1.005, 95% CI: 1.002–1.008, p = 5.821 × 10⁻⁴), body fat percentage (OR = 1.004, 95% CI: 1.000–1.008, p = 0.043), waist circumference (OR = 1.008, 95% CI: 1.005–1.012, p = 3.446 × 10⁻⁶), and waist-to-hip ratio (OR = 1.011, 95% CI: 1.004–1.019, p = 0.001) with hemorrhoids (Figure 3B).

In order to evaluate the effects of adiposity traits including BMI, body fat percentage, waist circumference, and waist-to-hip ratio on hemorrhoids risk, we used large-scale GWAS summary-level data within the MR framework. Our MR analyses showed that genetically determined BMI, body fat percentage, waist circumference, and waist-to-hip ratio were causally associated with increased risk of hemorrhoids. These findings indicated an important role of adiposity in hemorrhoids development. As far as we know, this is the first MR study to establish a causal relationship between adiposity and hemorrhoids.

Our results were in line with most previously published observational studies evaluating the relationship between obesity and hemorrhoids. These studies were carried out among Europeans (7, 8), Asians (9, 10), and Americans (25). Using data from the Dutch Health Interview Surveys, Seidell and colleagues reported a positive association between severe overweight (body mass index [BMI] 30.0–40.0 kg/m²) and hemorrhoids in women (7). Similarly, in a large Italian survey study of 72,284 participants (34,787 men and 37,497 women) aged 15 years and above, Negri et al. (8) found that the prevalence of hemorrhoids or varices was significantly associated with body weight (relative risk = 1.2 for obese men, 1.5 for women). In addition to these European studies, a Korean National Health and Nutrition Examination Survey (KNHANES) study including a total of 17,228 individuals aged ≥19 years identified a link between obesity and enhanced hemorrhoids risk [odds ratio (OR) = 1.13, 95% CI: 1.01–1.26] (9). This finding was supported by another Korean study of 194,620 adults (10). However, not all observational studies reported a positive relationship between adiposity and hemorrhoids. In a cross sectional study involving 2,813 participants who underwent a colonoscopy, Peery and colleagues did not find any associations of overweight (OR = 0.89, 95% CI: 0.72–1.09) and obese (OR = 0.86, 95% CI: 0.70–1.06) with hemorrhoids risk (11). Conventional statistical approaches including the Mantel–Haenszel procedure (26) and regression models were the main methods used in these observational studies for evaluating the relationship. Large sample sizes were applied in several studies such as the study of Negri et al. (8) and Hong et al. (10), which increased the statistical power for discovering a potential relationship. However, due to the limitations of study design and conventional statistical methods, these observational studies were vulnerable to biases including confounding and reverse causation. It was unclear if their observed association between adiposity and hemorrhoids was causal.

In our study, the causal effects of adiposity on hemorrhoids were estimated in univariable and multivariable MR analyses. The MR framework effectively minimized the risk of biases that were major concerns in the previously published observational studies. Unlike these studies that mainly used BMI for evaluation, we used a range of adiposity measures including BMI, body fat percentage, waist circumference, and waist-to-hip ratio. We took into account not only total adiposity (BMI) but also specific adiposity indicators. For instance, waist circumference and waist-to-hip ratio are indicators of central adiposity, while body fat percentage is a proxy for total body fatness. The inclusion of these adiposity indicators could provide more valuable information regarding the association between adiposity and hemorrhoids. Our univariable IVW analyses revealed significant causal effects of the four adiposity indicators on hemorrhoids risk, which was supported by several sensitivity analyses and leave-one-out analyses. The causal direction was confirmed by the Steiger directionality test. Further multivariable MR suggested that the effects of BMI, body fat percentage, waist circumference, and waist-to-hip ratio on hemorrhoids were independent of physical activity. Our results encouraged future clinical studies to focus on body weight and fat reduction in hemorrhoids prevention.

The association between adiposity and hemorrhoids may be attributed to a variety of potential biological mechanisms. Firstly, adiposity was found to be associated with increased intrabdominal pressure (18, 27), which could contribute to hemorrhoids development. In individuals with excess adiposity, enhanced intrabdominal pressure may lead to impaired venous return and engorgement of the venous structure in the distal rectum. These changes could promote hemorrhoids development. Secondly, adiposity has a link with chronic inflammation and oxidative stress (28, 29). Adiposity-associated inflammation and oxidative stress triggered the synthesis of oxygen radicals, reactive lipids, and proinflammatory cytokines including interleukin (IL)-6 and tumor necrosis factor (TNF)-ɑ, activated tissue remodeling proteins such as matrix metalloproteinases, induced C-reactive protein production, and promoted mitochondrial dysfunction and DNA damage (30–32). Complex interactions between adiposity, chronic inflammation, and oxidative stress may have harmful effects on the supporting tissues of the anal cushions, causing their impairments. Thirdly, low fiber intake might play a role. According to a number of epidemiological studies, obese individuals usually have less intake of fiber than the general population (33–35). There was evidence showing that a low-fiber diet might be implicated in hemorrhoids development, although the reported results were conflicting (36, 37).

Our MR study has certain drawbacks that need to be acknowledged. Firstly, given that our findings are based on GWAS data derived from individuals of European ancestry, it is necessary to exercise caution when attempting to extrapolate our results and conclusions to other ethnic populations. Secondly, since we only used summary statistics for estimation, we did not evaluate the causal association in males and females separately. In a cross-sectional study of healthy young and middle-aged adults, Hong et al. (10) found a positive association of overweight with hemorrhoidal disease only in women but not in men. However, Lee and colleagues observed that high BMI (≥25) was associated with self-reported hemorrhoids in both males and females (9). The study of Negri et al. (8) also revealed a link between high BMI (≥30) and hemorrhoids or varices in both men and women. Thirdly, we mainly evaluated the effects of adiposity during adulthood on hemorrhoids in this MR study. This was consistent with most observational studies. It would be valuable to assess the impact of excess childhood adiposity at hemorrhoids in future analyses. Fourthly, we were unable to investigate the effect of adiposity on the severity of hemorrhoids.

In summary, our comprehensive MR study provided evidence for causal effects of genetically determined BMI, body fat percentage, waist circumference, and waist-to-hip ratio on hemorrhoids. These findings encouraged future clinical investigations to focus on body weight and fat reduction in the prevention of hemorrhoids.

The analysis in this research relied on publicly available datasets, which can be accessed through the IEU Open GWAS Project (https://gwas.mrcieu.ac.uk/).

Due to the exclusive analysis of publicly available GWAS summary-level data, ethical approval was not applicable to this Mendelian randomization study.

JH contributed to study concept and design, acquisition and interpretation of data, statistical analyses, and manuscript writing. YG and HQ assisted in data interpretation and reviewing the manuscript. YX supervised the study, contributed to funding acquisition, and assisted in reviewing the manuscript. All authors read and approved the final manuscript.