Unraveling the biological link between diabetes mellitus and prostate cancer: Insights and implications

Abstract

This article is a comprehensive study based on research on the connection between diabetes mellitus (DM) and prostate cancer (PCa). It investigates the potential role of DM as an independent risk factor for PCa, delving into the biological links, including insulin resistance and hormonal changes. The paper critically analyzes previous studies that have shown varying results and introduces mendelian randomization as a method for establishing causality. It emphasizes the importance of early DM screening and lifestyle modifications in preventing PCa, and proposes future research directions for further under-standing the DM - PCa relationship.

Key Words: Biological mechanisms, Diabetes mellitus, Mendelian randomization, Prevention prostatic cancer, Treatment

Core Tip: The article emphasizes diabetes mellitus (DM) as a potential independent risk factor for prostate cancer (PCa), and delves into the possible biological mechanisms related to both diseases, such as insulin resistance and hormonal changes. The document also reviews existing research, providing an in-depth analysis of the application of mendelian randomization in the context of the relationship between DM and PCa. It underscores the importance of early DM screening and lifestyle interventions in potentially preventing PCa. Furthermore, it highlights the need for future research to further elucidate this complex relationship.

TO THE EDITOR

Diabetes mellitus (DM) and prostate cancer (PCa) are two diseases that pose a serious threat to global health. DM primarily affects blood sugar regulation, while PCa is one of the most common cancers in men[1,2]. With an aging population structure and changes in lifestyle, the incidence of both diseases is on the rise. They not only impose a sub-stantial disease burden on patients, reducing their quality of life, but also may increase the risk of complications and financial strain[3,4].

Currently, there is much debate and discussion in the academic community about the relationship between DM and PCa. In this context, the research paper by Yuan et al[5], employing the mendelian randomization (MR) method to explore the relationship between these two diseases, has captured our interest. This study finds that DM may be an independent risk factor for PCa, providing a new perspective in understanding the connection between these two conditions. Given the high prevalence of DM and PCa, as well as their significant impact on patients and society, in-depth research into the interactions between these two diseases is crucial for developing effective public health strategies and improving treatment outcomes.

POSSIBLE BIOLOGICAL MECHANISMS OF DM LEADING TO PCA

Although the results of the study of Yuan et al[5] suggest that DM may increase the incidence of PCa, the paper does not discuss in detail the possible biological mechanisms. We have delved into the potential associations between DM and PCa. The relationship between DM and PCa is complex, and the scientific community’s understanding of their direct connection is still evolving. Some studies indicate that DM might be associated with a lower risk of PCa, potentially related to lower circulating insulin levels in diabetic patients. However, from a biological perspective, the following are several plausible mechanisms explaining why diabetic patients may have an increased risk of developing PCa (Figure 1).

Figure 1 Possible biological mechanisms of diabetes mellitus leading to prostate cancer. Insulin-like growth factor-1 can encourage cellular proliferation and hinder cell apoptosis, potentially elevating the risk of various cancers, including prostate cancer (PCa). Elevated blood glucose can accelerate cell oxidation and chronic inflammation, both of which are linked to cancer progression. Diabetes mellitus (DM) often coexists with obesity and metabolic syndrome, conditions that are associated with systematic inflammation and hormone imbalance, potentially increasing the risk of PCa. DM can also influence the levels of sex hormones, including testosterone. Altered testosterone levels can influence the risk of PCa. The diet and lifestyle of diabetics may also contribute to an elevated risk of PCa. Additionally, the disease itself and the immune function changes caused by long-term DM may play a role in the development of PCa. IGF: Insulin-like growth factor.

INSULIN RESISTANCE AND INSULIN-LIKE GROWTH FACTORS

Diabetic patients often exhibit insulin resistance, leading to elevated levels of insulin and insulin-like growth factor-1 (IGF-1) in the body[6,7]. IGF-1 can promote cell proliferation and inhibit apoptosis, potentially increasing the risk of various cancers, including PCa[8,9].

HYPERGLYCEMIC ENVIRONMENT

High blood sugar levels can cause oxidative stress and chronic inflammation in cells, both of which are associated with cancer progression. This low-level chronic inflammation may promote tumor growth[10,11].

OBESITY AND METABOLIC SYNDROME

DM often coexists with obesity and metabolic syndrome, conditions associated with systemic inflammation and hormonal imbalances, which may increase the risk of PCa[12].

SEX HORMONES

DM can affect levels of sex hormones, including testosterone. Changes in testosterone levels may impact the risk of PCa[13].

DIET AND LIFESTYLE

The diet and lifestyle of diabetic patients may be linked to an increased risk of PCa. For example, a high-fat diet and low intake of fruits and vegetables are associated with an increased cancer risk[14,15].

CHRONIC DISEASE STATE AND IMMUNE MODULATION

The disease itself and the immune function changes caused by long-term DM might also be factors in developing PCa[16,17]. It is important to note that these mechanisms may interact with each other and manifest differently in various populations. Moreover, there is still controversy over how DM affects the risk of PCa, with different studies potentially reaching different conclusions. Therefore, more epidemiological and laboratory research is needed to elucidate these potential biological mechanisms.

EXPLORING THE CONTESTED TERRAIN - PREVIOUS RESEARCH INSIGHTS

Research into the relationship between DM and PCa has yielded many inconsistent results, possibly due to the complex interactions between the two conditions and variations in research designs. Some studies have identified DM as a protective factor against PCa[18-20], while others have found DM to be a high-risk factor for PCa[5,21,22] (Table 1).

Table 1 The relationship between diabetes mellitus and prostate cancer.

DM and PCa	Main point	Ref.
DM is a protective factor of PCa	This publication discusses the potential protective effects of diabetes against PCa, focusing on the role of diabetes-related microvascular complications	Zhang and Hu[18], 2010
	This article reviews the epidemiological evidence regarding the reduced risk of PCa in diabetic individuals, offering insights into potential mechanisms	Pierce[19], 2012
	This population-based case-control study examines the association between T2DM, antidiabetic medication, and the risk of PCa	Lin et al[20], 2020
DM is a high-risk factor for PCa	This approach improved the accuracy in estimating the relationship between diabetes and the incidence of PCa	Yuan et al[5], 2023
	The study employed the Cox model and competing risks methods to estimate hazard ratios for various subtypes of PCa	Piffoux et al[21], 2021
	The study discusses the common risk factors for diabetes and how aspects frequently seen in diabetic patients, such as hyperglycemia and hyperinsulinemia, impact the risk of developing PCa	Sousa et al[22], 2022

T2DM: Type 2 diabetes mellitus; PCa: Prostate cancer.

Firstly, a segment of observational studies has found that men with DM have a lower risk of developing PCa[23]. These studies are typically based on large population samples and consider a variety of potential confounding factors. The results suggest that DM patients may be protected by some mechanism from developing PCa. However, these studies also have limitations. For instance, the diagnosis of DM might be inaccurate or underestimated, especially in studies based on self-reporting. Moreover, these studies may not have adequately considered the severity, duration, or treatment of DM and its impact on the risk of PCa.

On the other hand, research also exists that identifies DM as a high-risk factor for PCa. These studies usually focus on specific patient groups, such as elderly men or men of certain races/ethnicities. The findings suggest that DM might increase the risk of PCa by promoting insulin resistance, chronic inflammation, or changes in sex hormone metabolism. However, these studies, too, have their limitations. For example, they might not have sufficiently considered potential confounding factors such as lifestyle, family history, or other chronic diseases. Additionally, the results of these studies might be influenced by selection bias, as DM patients may undergo medical examinations more frequently, thereby increasing the detection rate of PCa. Overall, this contested terrain in research highlights the complexity of the relationship between DM and PCa, and underscores the need for more nuanced and comprehensive studies to unravel these connections.

UNVEILING NEW HORIZONS - MR METHOD AND INSIGHTFUL FINDINGS

MR is a statistical method that uses genetic variations as instrumental variables to study the causal relationship between an exposure (such as DM) and an outcome (such as PCa). It is based on the principle of Mendel’s laws of genetics, where genotypes are randomly assigned to individuals. This allows researchers to assess the direct impact of an exposure on an outcome, free from confounding factors (Table 2).

Table 2 The commonly used mendelian randomization methods in the analysis of the relationship between diabetes mellitus and prostate cancer.

Analytical method	Method name	Description	Application
Association analysis	Univariable MR	Using a single genetic variant as an instrumental variable to estimate the causal relationship between exposure and outcome	Utilizing specific SNPs associated with the development of DM to assess the risk of PCa in diabetic patients
	Multivariable MR	Simultaneously using multiple genetic variations as instrumental variables to consider the potential relationships among multiple exposures	Uncovering potential common genetic paths between DM and PCa
	Two-sample MR	Allowing data on exposure and outcome to come from different study populations can increase the sample size, improve statistical power, and reduce the impact of sample selection bias	It can be used to evaluate whether DM increases the risk of PCa
Statistical efficiency analysis	Reliability analysis	Examine the consistency of estimates and stability of different genetic instrumental variables	MR-Egger regression, the weighted median approach, and the leave-one-out cross-validation
	Sensitivity analysis	Assess the sensitivity of the results to potential confounding factors or violations of instrumental variable assumptions

DM: Diabetes mellitus; MR: Mendelian randomization; PCa: Prostate cancer; SNP: Single nucleotide polymorphism.

In the study of the relationship between DM and PCa, three common types of MR methods include univariable MR[24], multivariable MR[24], and two-sample MR[5]. Univariable MR uses a single genetic variant as the instrumental variable to estimate the causal relationship between exposure and outcome. For instance, specific single nucleotide polymorphisms (SNPs) associated with the development of DM could be used to assess the risk of PCa in diabetic patients. Multivariable MR, on the other hand, employs multiple genetic variants as instrumental variables to consider potential relationships among various exposures. This is particularly important for revealing possible common genetic pathways between DM and PCa, helping to isolate the independent effects of each variable and provide a more comprehensive assessment of the causal relationship. Two-sample MR allows data on exposure and outcome to come from different study populations, thus increasing sample size, enhancing statistical power, and reducing the impact of sample selection bias.

Reliability analysis examines the consistency of results across different genetic instrumental variables and their stability[25], while sensitivity analysis assesses the results’ sensitivity to potential confounders or violations of the instrumental variable assumptions[26]. The application of methods like MR-Egger regression, the weighted median approach, and the leave-one-out cross-validation helps identify and correct potential issues, such as insufficient strength or bias of the instrumental variables.

BRIDGING GAPS AND FUTURE TRAJECTORIES

The current limitations of MR studies, such as those on DM and PCa, include potential biases caused by pleiotropy and population stratification. Future research should focus on diversifying the demographic structure of participants to understand how DM affects PCa across different populations. Additionally, the relationship between different subtypes of DM and PCa needs exploration. Future studies must consider both known and unknown confounding factors to more clearly understand the causal effects. While MR studies can partially explain the causal relationship between DM and PCa, further research is necessary to comprehensively explore this complex relationship. Integrating data from diverse populations and various subtypes will enhance our understanding of the DM-PCa connection[27,28].

CONCLUSION

This article discusses the relationship between DM and PCa, emphasizing DM as a potential independent risk factor for PCa. It explores the biological mechanisms linking DM to PCa, such as insulin resistance, hormonal changes, and chronic inflammation. Our review of previous studies demonstrates contradictory results regarding the DM-PCa relationship and introduces the MR method for understanding causality. The study highlights the importance of early DM screening and lifestyle interventions in preventing PCa. Finally, it proposes future research directions, including diversifying participant demographics and investigating different subtypes of DM, to gain a more comprehensive understanding of the DM-PCa link.

ACKNOWLEDGEMENTS

We thank the reviewers for their comments that helped to improve the manuscript.