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Metabolic syndrome and increased susceptibility to renal cell carcinoma – a meta-analysis

BMC Nephrology volume 26, Article number: 102 (2025) Cite this article

Abstract

Background

Metabolic syndrome (MetS) has been demonstrated to be associated with various types of cancer, but its specific relationship with kidney cancer remains inconclusive. Therefore, this study conducts a Meta-analysis to systematically evaluate the potential link between metabolic syndrome and the risk of kidney cancer development.

Methods

Observational studies were retrieved from PubMed, Embase, Cochrane Library, and Web of Science. Two independent reviewers extracted study characteristics and assessed the quality of the studies. A random-effects model was employed to account for heterogeneity, and subgroup analyses were conducted to explore the impact of study characteristics on the results. Publication bias was evaluated using funnel plot symmetry and Egger’s regression test.

Results

Six studies were included, with 10 results extracted for the Meta-analysis. The findings indicated that MetS is an independent risk factor for kidney cancer (HR: 1.44, 95% CI: 1.31–1.59, P < 0.001). Heterogeneity between studies was significant (Cochran’s Q test, P < 0.001; I2 = 83.7%), indicating substantial variability. Subgroup analyses revealed consistent associations across gender, follow-up duration, and MetS diagnostic criteria (P > 0.05), but significant variations by race and study design (P < 0.05). The funnel plot appeared symmetrical, and Egger’s regression test (P = 0.425) confirmed a low risk of publication bias.

Conclusion

MetS is independently associated with an increased susceptibility to RCC in the adult population, although the strength of this association varies across different study designs and regions due to the observed heterogeneity.

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Background

Kidney cancer, clinically referred to as Renal Cell Carcinoma (RCC), ranks second only to prostate cancer and bladder cancer in incidence within the male urinary system, accounting for 3% to 5% of malignant tumors in adults [1]. However, it has the highest lethality among urological tumors [2]. Due to its asymptomatic nature in early stages, advanced kidney cancer often leads to a worse prognosis, imposing a significant burden on patients' physical and mental health as well as on the healthcare system [3]. Given these challenges, identifying risk factors for the prevention and early detection of kidney cancer is of paramount importance.

Metabolic syndrome (MetS) is a collection of clinical syndromes characterized by metabolic disorders such as obesity, hyperglycemia, hypertension, and dyslipidemia [4]. Although the diagnostic criteria for MetS vary across different countries, major guidelines include those from the International Diabetes Federation (IDF), National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III), and the Chinese Diabetes Society (CDS), among others. Recently, the incidence of MetS has been rising among younger populations, with a prevalence of approximately 25% in Chinese adult males [5]. The situation in Europe and the United States is also concerning. In the United States, the prevalence rate among older adults exceeds 40% [6], while in Europe and Latin America, the rate is around 25% [7], MetS is not only a significant risk factor for cardiovascular diseases, but it is also closely associated with the development of various cancers, including prostate, colorectal, breast, and kidney cancers [8,9,10,11].

Current research on the relationship between RCC and MetS has yielded mixed findings [12,13,14], possibly due to differences in study design, follow-up duration, and diagnostic criteria for MetS. In 2022, a meta-analysis demonstrated that MetS is associated with an increased risk of kidney cancer [15]; however, the study did not account for the potential impact of differing diagnostic criteria for MetS on the outcome. Moreover, several new prospective studies have recently examined this association in greater detail [10, 16, 17]. By synthesizing the latest evidence, this meta-analysis aims to clarify the strength and consistency of this association, address methodological limitations in previous studies, and provide a more comprehensive understanding of how metabolic syndrome influences the risk of RCC.

Material and methods

Search strategy

This meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement [18] was followed in this systematic review and meta-analysis. The methodology for analysis and reporting was conducted in accordance with the Cochrane Handbook for Systematic Reviews and Meta-Analyses [19]. These frameworks ensure rigorous and transparent reporting, which enhances the reliability and reproducibility of our findings.

The study performed a comprehensive literature search across four major databases: PubMed, Web of Science, Cochrane, and Embase. The final database search was conducted on August 1, 2024, with no restrictions applied to publication language. The search strategy used in PubMed is as follows:

MeSH Terms: Kidney Neoplasms, Metabolic Syndrome, Cohort Study, Case–Control Study.

Title/Abstract: Renal Neoplasm OR Cancer of Kidney OR Kidney Cancer OR Cancer of the Kidney OR Renal Cell Cancer OR Renal Cancer, Reaven Syndrome X OR Metabolic Syndrome X OR Insulin Resistance Syndrome X OR Metabolic X Syndrome OR Dysmetabolic Syndrome X.

Study inclusion

The inclusion criteria for this study are as follows: 1. Participants were aged 18 years or older at the start of the study and had not been diagnosed with cancer; 2. The exposure variable was metabolic syndrome (with varying diagnostic criteria), and the outcome variable was renal cell carcinoma; 3. The study design could be either prospective or retrospective. The exclusion criteria are as follows: 1. Studies that measured risk using odds ratios (OR) or relative risks (RR); 2. Studies that did not specify the diagnostic criteria for metabolic syndrome; 3. Reviews, meta-analyses, preclinical studies, and studies that did not assess the impact of MetS.

Data collection and evaluation of study quality

To ensure minimize potential bias, the database search, collection, and assessment of study quality were conducted independently by two authors. Each step was carried out separately to maintain objectivity, with both authors cross-checking their findings to ensure consistency and accuracy in the results. In cases of disagreement, the two authors engaged in in-depth discussions to resolve the issue. If they were unable to reach a solution, the corresponding author was involved to provide additional perspectives and help facilitate consensus. Data extracted included study characteristics, patient demographics, diagnostic criteria for MetS, study periods, HR and their corresponding 95% confidence intervals (CIs). Study quality was assessed using the Newcastle–Ottawa Scale (NOS), which scores studies from 1 to 9 stars. The NOS evaluates the quality of observational studies across three domains: patient selection, comparability of cohorts with and without exposure, and outcome validation strategies. A score of 7 or above is generally regarded as an indicator of high-quality studies.

Statistical methods

HR (95%CI) was used to assess the association between MetS and RCC. The standard errors (SEs) were calculated from the data provided by the 95% CIs or p-values, and the HRs were logarithmically transformed to ensure a normal distribution of the data. Heterogeneity between studies was evaluated using Cochrane’s Q test and the I2 statistic. A P-value for heterogeneity < 0.10 or I2 > 50% was considered indicative of significant heterogeneity among the studies. To account for this heterogeneity, a random-effects model was employed. Sensitivity analysis, performed by excluding one dataset at a time, was conducted to confirm the robustness of the findings. A series of subgroup analyses were carried out to explore the effects of study characteristics on the associations, based on variables such as gender, ethnicity, follow-up period, study design, and the diagnostic criteria for MetS. The primary variation in diagnostic criteria for MetS was related to the measurement of obesity, which was classified based on Body Mass Index (BMI) or Waist Circumference (WC). As a result, studies were grouped accordingly into BMI-based or WC-based criteria. Publication bias was evaluated through visual inspection of funnel plot symmetry and by applying Egger’s regression test.

A P < 0.05 was considered statistically significant. All the statistical analyses were performed using STATA statistical software version 12.0.

Results

Identification of related studies

Figure 1 shows the process of the literature search. A total of 623 articles were retrieved from four databases (Pubmed: 19, Web of Science: 554, Embase: 50, Cochrane's Library: 0). 25 duplicates were excluded, 583 articles were excluded by title and abstract, and 9 articles were excluded by reading the full-text content. Nine articles were excluded and a total of six papers were included in the study [10, 14, 16, 17, 20, 21].

Fig. 1
figure 1

PRISMA diagram of literature search and study inclusion

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Summary of study characteristics

Table 1 presented the characteristics of the individual studies. These studies were conducted in the United Kingdom, China, and South Korea, and were categorized as prospective or retrospective in design. Two studies reported results exclusively for male patients [14, 20], while four other studies performed subgroup analyses based on gender. The sample sizes ranged from 61,758 to 9,932,670 participants, with a total of 11,313,741 participants across all studies. All studies had a follow-up period of more than 5 years. The diagnostic criteria for MetS varied, including NCEP-ATP III, IDF, CDS, American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI), and the Korean Society for the Study of Obesity (KSSO). One of the papers, although using the NCEP-ATP III diagnostic criteria [14], replaced the reference for obesity from WC to BMI. The NOS scores for study quality ranged from 7 to 8, indicating high quality. A multifactorial analysis was conducted for all findings, controlling for confounding variables such as age, sex, smoking, alcohol intake, BMI, and exercise, to varying extents in the multivariate analyses.

Table 1 Characteristic of the included studies
Full size table

Association between MetS and RCC

Of the six papers included, two studies reported results exclusively for male populations [14, 20], and four of the studies examined the association between MetS and RCC based on the sex of the participants, so a total of 8 results were extracted from these studies, and 10 final results were included in the meta-analysis. Significant between-study heterogeneity was observed (P for Cochrane’s Q test < 0.001, I2 = 83.7%). Pooled results using a random-effects model indicated that MetS was independently associated with an increased risk of RCC in the adult population (HR: 1.44, 95% CI: 1.31 to 1.59, P < 0.001; Fig. 2). Sensitivity analyses, conducted by omitting one dataset at a time, yielded similar results (HR: 1.39 to 1.48, all P < 0.05; Fig. 3).

Fig. 2
figure 2

Forest plots for the meta-analysis of the association between MetS and RCC

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Fig. 3
figure 3

Sensitivity analysis of the meta-analysis results

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Table 2 presents the results of the subgroup analysis. The predefined subgroup analysis demonstrated a consistent association between MetS and RCC in both men (HR: 1.47 [1.26, 1.72], P < 0.001) and women (HR: 1.42 [1.35, 1.51], P < 0.001; P for subgroup difference = 0.068). However, the association varied significantly across different geographic regions, including China (HR: 1.63 [1.01, 2.63], P = 0.045), England (HR: 1.31 [1.02, 1.69], P = 0.032), and Korea (HR: 1.43 [1.28, 1.60], P < 0.001; P for subgroup difference = 0.022), indicating that the strength of association was not consistent among region. Similarly, significant differences were observed between prospective and retrospective studies, with prospective studies showing a stronger association (HR: 1.50 [1.33, 1.70], P < 0.001) compared to retrospective studies (HR: 1.34 [1.27, 1.41], P < 0.001; P for subgroup difference < 0.001). In contrast, a consistent association was observed in subgroup analyses based on the diagnostic criteria for MetS and the duration of follow-up (P for subgroup difference in both cases > 0.05).

Table 2 Subgroup analysis results for various factors
Full size table

Publication bias

Funnel plots assessing the association between MetS and RCC appeared symmetrical upon visual inspection (Fig. 4), suggesting a low risk of publication bias. This observation was further supported by the results of Egger's regression test (p = 0.425), which confirmed the absence of significant publication bias.

Fig. 4
figure 4

Funnel plot for assessing publication bias

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Discussion

In this study, a total of 10 outcomes from 6 studies were included, and the initial analysis demonstrated that MetS was independently associated with an increased risk of developing kidney cancer. Sensitivity analyses did not significantly alter the results, indicating that the findings were relatively stable. High intergroup heterogeneity was observed among the 10 outcomes, as revealed by the subgroup analyses. These analyses showed greater variability between different study designs and ethnic groups, which were identified as the primary sources of intergroup heterogeneity. In contrast, subgroup analyses by gender, follow-up duration, and diagnostic criteria for MetS consistently demonstrated an association between MetS and RCC. Finally, the results of all subgroup analyses confirmed that MetS was a risk factor for RCC, with HRs consistently greater than 1.

The methodology of this study is similar to that of Du et al.; however, the eight studies included in their analysis employed varying diagnostic criteria for metabolic syndrome [15]. Furthermore, these studies used three distinct measures of risk—HR, Standardized Incidence Ratio (SIR) and Relative Risk (RR). Du et al. did not address the heterogeneity introduced by these variations in diagnostic criteria. Moreover, it is generally inappropriate to combine different risk measures within the same meta-analysis model. A key difference in the diagnostic criteria for metabolic syndrome lies in the assessment of obesity, which is measured using either WC or BMI [14]. Although no universally accepted diagnostic standard exists, it is crucial to account for its potential impact on the results during analysis. In the current study, further adjustments were made to address these issues. A large cross-sectional study from the United States demonstrated a significant association between MetS and kidney cancer (OR = 5.44 [5.17–5.72]) [22]. Another study showed that kidney cancer was associated with MetS by calculating a composite score of all five metabolic factors [23], conversely, some studies have reported no association between MetS and kidney cancer [13], however, these studies did not meet the inclusion criteria for the current analysis and were excluded. The two studies, Oh et al. (2019) with 761,386 participants and Ko et al. (2016) with 61,758 participants, may have overlapping populations [17, 21]. However, we did not exclude either study because the first cohort included both males and females, whereas the Ko et al. (2016) cohort only included males. In our analysis, we extracted sex-stratified results from each study. Furthermore, the two studies differ in sample size and the diagnostic criteria for metabolic syndrome. Finally, although there may be overlap in the male populations, we performed sensitivity analyses by sequentially excluding each cohort to evaluate the impact.

Currently, numerous pathophysiologic mechanisms related to MetS have been identified that promote the development of renal cancer. Diabetes mellitus is closely associated with insulin resistance (IR), and elevated insulin levels can inhibit the synthesis of insulin-like growth factor (IGF)-binding proteins, resulting in increased IGF activity. Insulin-like growth factor plays a crucial role in tumor initiation and progression by promoting mitosis, enhancing cell migration, and inhibiting apoptosis through the activation of the MAPK and PI3K pathways, particularly via the binding of IGF-1 and IGF-1R [24]. Kidney cells are histologically characterized by sterol storage, suggesting that lipid metabolism plays a pivotal role in renal cancer formation. Laboratory studies studies have demonstrated [25] that statins can inhibit tumor cell growth and invasion, possibly due to their effect on lowering lipid levels, thereby inhibiting renal cancer progression. Antihypertensive medications, in addition to lipid-lowering drugs, may also have an impact on kidney cancer. Research has shown that certain antihypertensive drugs, such as calcium channel blockers and diuretics, are associated with an increased risk of papillary renal cell carcinoma (pRCC) [26], whereas their relationship with clear cell renal cell carcinoma (ccRCC) is not significant. This may be due to the long-term effects of diuretics on renal tubular cells, alterations in renal hemodynamics, and the potential carcinogenic transformation of certain diuretics in the body [27, 28]. Additionally, a large-scale prospective cohort study has shown that calcium channel blockers are associated with an increased cancer risk, and a dose–response relationship exists [29]. However, the reasons for the lack of association with ccRCC and the absence of a clear link between other antihypertensive medications and kidney cancer remain unclear. Patients with MetS have reduced levels of lipocalin, which may be linked to obesity and insulin resistance. Low circulating lipocalin is involved in the pathogenesis of various obesity-associated cancers [30, 31]. Moreover, tissue cells in obese individuals are often hypoxic, leading to a chronic inflammatory state in systemic tissues and the release of inflammatory cytokines, which create a microenvironment favourable for tumor survival [32, 33]. Metabolic disorders are also associated with chronic systemic inflammation and oxidative stress, both of which contribute to the carcinogenic process [34].

As seen in the previous text, the impact of medications on cancer is complex, especially for antihypertensive drugs like diuretics and calcium channel blockers. On one hand, these medications reduce blood pressure, alleviating the negative effects of hypertension on kidney cancer. On the other hand, long-term use of these drugs may promote cancer development, particularly in relation to a specific histological subtype of kidney cancer. The underlying mechanisms remain unclear. Unfortunately, the studies included in this article did not account for the use of medications, which undoubtedly affects the interpretation of the results. In studies examining the relationship between MetS and kidney cancer survival, there has been disagreement. Some studies suggest that MetS may shorten cancer survival [35, 36], while others indicate that it may actually contribute to better survival outcomes [37, 38], possibly due to the use of anti-metabolic disorder medications in these patients.

Although this study found that different diagnostic criteria for MetS did not have a significant impact on the results, the importance of diagnostic criteria should not be overlooked. The subgroup analysis in this study was based solely on the concept of obesity; however, MetS consists of four metabolic disturbances: glucose, lipid, blood pressure, and obesity. Some studies have shown that as the number of metabolic disturbances increases, the risk of renal cell carcinoma also rises [10], and even the combination of different components may result in varying levels of risk [16, 39]. As studies on MetS progress, its core mechanism—IR—is well recognized, but clinically, measuring IR is complex [40]. Therefore, most studies on MetS use surrogate markers for IR, such as METS-IR, TyG, and TyG-BMI [41,42,43], which incorporate multiple metabolic indicators to best represent IR. However, the relationship between these surrogate markers and the risk of renal cell carcinoma remains unclear. Further research is needed to clarify the role of these markers in assessing the relationship between MetS and renal cancer.

The results of this meta-analysis confirm the conclusions of most previous studies, showing that MetS is a potential risk factor for RCC in both male and female populations. This also suggests that clinical interventions, such as adjusting dietary habits, improving lifestyle, and implementing early interventions, may help reduce cancer risk in high-risk populations, while also providing new strategies for the treatment of RCC patients.

The study also has some limitations. First, none of the studies accounted for the use of medications, such as those for hypertension or diabetes, which may have influenced the results. In addition, there was significant heterogeneity across the included studies. Although sensitivity and subgroup analyses were conducted, this heterogeneity remains a potential issue that may impact the interpretation of the meta-analysis results. Finally, the power of Egger's regression test is relatively low when the number of studies is small, meaning that publication bias might not be reliably detected.

Conclusion

Based on the results of this meta-analysis, MetS is independently associated with an increased risk of RCC in adults, although the strength of this association varies across different study designs and regions due to the observed heterogeneity, suggesting that individuals with MetS may represent a high-risk population for RCC. Future research should focus on determining whether the use of medications aimed at treating metabolic disorders could potentially reduce or increase this risk.

Data availability

The datasets used during the current study are available from the corresponding author on reasonable request.

Abbreviations

MetS:

Metabolic syndrome

RCC:

Renal Cell Carcinoma

IDF:

International Diabetes Federation

NCEP ATP III:

National Cholesterol Education Program Adult Treatment Panel III

CDS:

Chinese Diabetes Society

AHA/NHLBI:

American Heart Association/National Heart, Lung, and Blood Institute

KSSO:

Korean Society for the Study of Obesity

NOS:

Newcastle-Ottawa Scale

BMI:

Body Mass Index

WC:

Waist Circumference

SIR:

Standardized Incidence Ratio

IR:

Insulin resistance

PC:

Prospective Cohort

RC:

Retrospective Cohort

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Acknowledgements

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Funding

This study was supported by the National Natural Science Foundation of China (No. 82060467, 81460389).

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Authors and Affiliations

  1. Department of Urology, the 1st Affiliated Hospital, Jiangxi Medical College, Nanchang University, 17 YongWai Street Surgery Building, 17th Floor, Nanchang, Jiangxi, 330006, China

    Yanyu Zhou, Yujun Chen, Heng Yang, Zhiqi Xu, Jinbiao Zhuang, Qitao Bian & Gongxian Wang

  2. Jiangxi Provincial Key Laboratory of Urinary System Diseases, Nanchang, Jiangxi, 330000, China

    Yujun Chen & Gongxian Wang

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  1. Yanyu Zhou
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Contributions

ZY and CY contributed in conception and design of the work; Data analysis was performed by ZY, BQ and YH. The first draft of the manuscript was written by ZY, XZ and ZJ. It was critically revised by WG. All authors reviewed and commented on previous versions of the manuscript. They approved the final manuscript and agreed to take responsibility for the accuracy and integrity of the work, ensuring that any issues are properly investigated and resolved.

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Correspondence to Gongxian Wang.

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Not applicable. This study is a meta-analysis based on previously published data, and it does not involve the collection of new data from human participants, human tissue, or animals.

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The authors declare no competing interests.

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Zhou, Y., Chen, Y., Yang, H. et al. Metabolic syndrome and increased susceptibility to renal cell carcinoma – a meta-analysis. BMC Nephrol 26, 102 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12882-025-04013-6

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Keywords

BMC Nephrology

ISSN: 1471-2369

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