To the best of our knowledge, this study represents the first comprehensive examination of the causal impact of MetS components, including obesity, BMI, waist circumference, T1DM, T2DM, and hypertension, on PCa using genetic variation data. We conducted causal analysis separately for T1DM and T2DM first through GWAS data, avoiding the dilemma of difficult differentiation between T1DM and T2DM. Because studies on the causal relationship between lipid traits based on genetic prediction and PCa have been fairly standardized by 2022, we did not reanalyze this aspect in our study (Ioannidou et al., 2022). Our MR analysis provided evidence that an increased waist circumference was a protective factor for PCa risk, and no causal relationship with PCa was found in other components.
MetS represents a constellation of risk factors associated with cardiovascular disease and T2DM, posing an increasingly prevalent global challenge (Alberti et al., 2009). MetS plays a crucial role in the development of cancer, particularly obesity, insulin resistance, and adipocytokines generated by adipose tissue (Torres et al., 2019). The prostate possesses a unique metabolism not observed in other tissues, which established a foundation for elucidating the mechanism by which MetS impacts PCa risk (Gillis et al., 2021; Mamouni et al., 2021). Esposito et al. (Esposito et al., 2012) analyzed 116 datasets from 43 articles and observed that MetS is associated with an increased risk of common cancers, including PCa, in US populations (Whites) (relative risk [RR], 0.79; 95% CI, 0.69–0.91; P = 0.001). A retrospective case–control study including 394 cases and 793 healthy controls in Mexico City concluded that MetS and some of its components were identified as potential risk factors for PCa (OR, 1.94; 95% CI, 1.37–2.75) and only the marked weight increase was associated with more poorly differentiated PCa (Gleason score, ≥ 8) (OR, 2.79; 95% CI, 1.50–5.17) (Hernández-Pérez et al., 2022). It is difficult to compare these results with those of other studies because their MetS and MetS component evaluation was retrospective and based on self-report. Monroy-Iglesias et al. (Monroy-Iglesias et al., 2021) conducted a cohort study to investigate the relationship between MetS and PCa risk, which included 220,622 PCa-free men enrolled in UK Biobank. No associations were found between PCa risk and MetS (hazard ratio [HR], 0.99; 95% CI, 0.92–1.06); however, a negative association was observed with increased glycated hemoglobin levels (≥ 42 mmol/mol) (HR, 0.89; 95%, CI, 0.79–0.98), which was partially attributable to hormonal and inflammatory pathways. Given the limitations of observational studies in terms of data bias and inability to determine causal relationships, we used genetic variation instruments to evaluate the causal effects of MetS components and PCa risk.
Our findings in the univariate MR analysis indicated that waist circumference was likely to be a causal determinant of PCa, whereas BMI and obesity were not. However, we did not find consistent results in the multivariate analysis after adjustment for BMI. We considered that the inconsistency between the univariate and multivariate MR analysis results may be due to BMI not being a causal factor for PCa, as the premise of conducting the MVMR analysis is that each exposure will have a certain causal effect on the outcome. Although BMI does not consider body mass composition or the distribution of adipose tissue, it has been consistently shown to be a reliable proxy for obesity at the population level. Previous studies have explored the relationship between BMI and obesity and PCa risk; however, the study conclusions were inconsistent. Allott et al. (Allott et al., 2013) summarized the results of three meta-analyses that reported a positive association between obesity and PCa incidence. The RRs in these studies were modest yet consistent, from 1.03 (95% CI, 1.0–1.07) per 5-kg/m2 increase in BMI to 1.05 (95% CI, 1.01–1.08) and 1.01 (95% CI, 1.0–1.02) per 1-kg/m2 increment in BMI. Nevertheless, the results from individual studies contributing to these meta-analyses varied significantly, with some showing no association between obesity and PCa, others indicating obesity as a risk factor, and still others reporting a protective effect of obesity. The results of the recent MR study by Loh et al. (Loh et al., 2022) indicated that an increased BMI was associated with lower PCa risk (OR, 0.91; 95% CI, 0.85–0.98). Boehm et al. (Boehm et al., 2015) conducted a case–control study in a North American population and observed that men with a BMI of ≥ 30 kg/m2 had a lower PCa risk in both low-grade PCa and high-grade PCa (P < 0.01). This opposite study result may be due to prostate-specific antigen detection. Men with obesity were less likely to undergo biopsy compared with men of normal weight, resulting in fewer early-stage cancers being detected in individuals with obesity (Wright et al., 2007). Previous research has also indicated a lack of association between obesity and PCa. An MR study on 20,848 cases and 20,214 controls from the PRACTICAL consortium was conducted to investigated the effects of height and BMI on PCa incidence and mortality and found little evidence of a causal effect of genetically increased height or BMI on PCa risk, suggesting that previous observational findings reflect shared environmental determinants affecting both height or BMI and PCa risk (Davies et al., 2015). Grotta et al. (Grotta et al., 2015) followed a cohort of 13,109 Swedish men for 13 years and observed no association between a high BMI and PCa risk, which was consistent with our study findings. Given our study findings and considering that BMI may not be a risk factor for PCa, we opted not to exclude BMI in the process of PhenoScanner V2 tool screening. We conducted a new MR analysis, which yielded consistent results with our previous findings. The results of a recent study indicated a negative association with total PCa (HR per 10 cm, 0.95; 95% CI, 0.92–0.99) and localized PCa (HR per 10 cm, 0.93; 95% CI, 0.88–0.96) but no association with advanced PCa (Jochems et al., 2021). Waist circumference represents abdominal obesity, a special pattern of fat distribution in the body, and the specific mechanism differences between its impact on PCa and overall obesity have not been clearly explained. A previous study has indicated that visceral fat may have a stronger correlation with metabolic and hormonal dysfunctions, such as insulin resistance, impaired glucose metabolism, and low-grade inflammation, than subcutaneous fat (Hwang et al., 2015). Consequently, visceral fat may exert a more significant influence on PCa progression, although the precise roles of these factors in PCa remain uncertain.
An MR analysis was conducted to investigate the potential relationship between DM and PCa, and the results indicated that DM was an independent risk factor for PCa (Yuan et al., 2023). However, the GWAS phenotyped selected in this study was “DM,” which did not differentiate the effects of T1DM and T2DM on PCa. In our study, we conducted a univariate MR analysis on the effects of T1DM and T2DM on PCa through genetic variation instruments. No causal association was found between T1DM or T2DM and PCa risk, which is consistent with the results of the most updated meta-analysis of 203 cohorts (Ling et al., 2021). As treatment and survival rates for PCa improve, the presence of comorbid cardiac conditions will notably affect the overall morbidity and mortality associated with the disease (Dolmatova et al., 2023). Hypertension, a widely recognized cardiovascular risk factor, increases the risk of heart failure. In recent years, multiple researchers have investigated whether hypertension is a potential risk factor for PCa, yielding conflicting findings. Some studies indicated that individuals with hypertension had higher PCa risk than those without hypertension, whereas several others did not find a positive correlation between hypertension and PCa risk (Grundmark et al., 2010; Pai et al., 2015; Romero et al., 2012). Liang et al. (Liang et al., 2016) observed that hypertension may be associated with increased PCa risk in their meta-analysis. However, we used genetic variation instruments to investigate the association between hypertension and PCa risk and found no evidence of a causal relationship between the two. More relevant studies are still needed to confirm the roles of blood pressure and glucose in the development of PCa.
This study has some limitations. First, no direct method was used to validate the second and third MR assumptions, and any violations could lead to biased MR estimates. Second, our primary MR analysis exclusively examined samples from individuals of European descent to mitigate concerns regarding heterogeneity, a prerequisite for conducting MR between two samples. However, it is important to acknowledge that this may impact the generalizability of the findings because they are confined to individuals of European ancestry. Third, numerous robust methods and sensitivity analyses were employed to investigate potential violations, primarily stemming from horizontal pleiotropy. However, it remains challenging to completely rule out its presence.
In summary, we observed that among the MetS components, genetically predicted waist circumference was associated with decreased PCa risk, whereas no association was observed between other components and PCa risk. This suggests that regular waist circumference measurement is needed to screen individuals at high risk of PCa in the future, and more clinical studies are needed to investigate the relationship and underlying mechanisms between abdominal obesity and PCa.