Skip to main content
NCBI home page
PLOS Medicine logoLink to PLOS Medicine
. 2026 Feb 10;23(2):e1004906. doi: 10.1371/journal.pmed.1004906

Systematic assessment of obesity-related risk factors in renal cancer etiology: A longitudinal risk and Mendelian randomization analysis

Karine Alcala 1, Daniela Mariosa 1, Sara Jacobson 2, Claudia Coscia-Requena 1, Niki Dimou 1, Oskar Franklin 2, Richard M Martin 3,4, George Davey Smith 3, Marc J Gunter 5, Paul Brennan 1, Michael Pollak 6, Ryan Langdon 1, Mattias Johansson 1,*
Editor: Steven C Moore7
PMCID: PMC12919923  PMID: 41666231

Abstract

Background

Excess body adiposity is an established cause of renal cancer, but underlying molecular pathways mediating this relationship remain unclear. This study aimed to systematically evaluate a panel of obesity-related risk factors as potential mediators in renal cancer etiology.

Methods and findings

We used two complementary approaches to evaluate obesity-related risk factors in renal cancer etiology: (i) direct risk factor assessment in longitudinal cohorts and (ii) genetically proxied risk factors through two-sample Mendelian randomization (MR). Direct risk-factor association-analyses (i.e., cohort analyses) were based on the UK Biobank cohort study (472,337 cohort participants, including 1,382 incident renal cancer cases diagnosed during 5,586,414 person years of follow-up) and the Northern Sweden Health and Disease Study (NSHDS) for fasting insulin (204 pairs of cases and controls, ongoing recruitment and follow-up since 1985). We used Cox proportional hazards regression models to evaluate the association between risk factors and renal cancer risk with adjustment for age, sex, center of recruitment, education, smoking and alcohol drinking status. Two-sample MR analyses were based on a genome-wide association study (GWAS) of renal cancer (27,213 cases, 486,846 controls). We used the inverse-variance weighted (IVW) approach to estimate the association between risk factors and renal cancer risk. Mediation analyses were performed for traits displaying directionally consistent associations with renal cancer risk in both the cohort and MR analyses using the product method. We found consistent positive associations with renal cancer risk for fasting insulin (odds ratio per standard deviation increment [ORMR]: 2.24, 95% confidence interval [95% CI]: 1.19, 4.22; p = 0.01; hazard ratio per standard deviation increment [HRcohort]: 1.43, 95% CI: 1.02, 2.00; p = 0.04), triglycerides (ORMR: 1.11, 95% CI: 1.05, 1.17; p < 0.001, HRcohort: 1.23, 95% CI: 1.11, 1.38; p < 0.001), diastolic blood pressure (DBP) (ORMR: 1.14, 95% CI: 1.04, 1.26; p < 0.001, HRcohort: 1.11, 95% CI: 1.05, 1.17; p < 0.001) and consistent inverse associations with renal cancer risk for sex-hormone binding globulin (SHBG) (ORMR: 0.80, 95% CI: 0.70, 0.90; p < 0.001, HRcohort: 0.67, 95% CI: 0.58, 0.76; p < 0.001) and high-density lipoprotein (HDL) cholesterol (ORMR: 0.93, 95% CI: 0.88, 0.98; p < 0.001, HRcohort: 0.72, 95% CI: 0.66, 0.77; p < 0.001). The main limitation of this study was that we had limited statistical power to evaluate some risk factors.

Conclusions

Our study highlights roles for fasting insulin, HDL cholesterol, DBP, triglycerides and SHBG in mediating the relationship between body adiposity and renal cancer risk.

Author summary

Why was this study done?

  • The importance of excess body adiposity and renal caner etiology is well established, but the underlying mechanisms mediating this relationship are unclear.

What did the researchers do and find?

  • We used two complementary observational approaches to evaluate 20 potential obesity-related risk factors in relation to renal cancer risk, including

  • direct risk factor assessment in longitudinal cohorts, and

  • genetically proxied risk factor assessment through Mendelian randomization (MR) in large genome-wide association studies.

  • We found consistent evidence for associations with renal cancer risk for fasting insulin, diastolic blood pressure (DBP), high-density lipoprotein (HDL) cholesterol, triglycerides and sex-hormone binding globulin (SHBG).

What do these findings mean?

  • Our findings highlighted important roles for fasting insulin, HDL cholesterol, DBP, triglycerides and SHBG in mediating the relationship between body adiposity and renal cancer risk.

  • Limitations of our study include a relatively small sample size for the risk analysis of fasting insulin, and an incomplete assessment of sex hormones.

  • This study advances our understanding of obesity in renal cancer etiology.

Using evidence from both observational and Mendelian randomization analyses, Karine Alcala and colleagues systematically assess obesity‑related risk factors as potential mediators in the development of renal cancer.

Introduction

Excess body adiposity—commonly estimated as high body mass index (BMI)—is a well-established cause of renal cancer [1,2]. The proportion of renal cancer cases attributable to high BMI (>25 kg/m2) was estimated to be 17% in 2012 [3], but the underlying biomolecular pathways mediating this association remain unclear [4].

Renal cell carcinoma (RCC) is histologically characterized by its accumulation of glycogen and lipids [5,6], yet its mechanistic link to body fatness remains elusive. Intra-abdominal adipose tissue—previously considered inert—is now recognized as an important endocrine organ with wide-ranging influences in the body, including on hormones, inflammatory biomarkers and lipids, all of which might mediate the obesity link with cancer development [7–11]. There is some evidence from in-vitro and population-based genomics studies implicating insulin in RCC etiology [12,13]. Another important and consistent observation is the 2-fold higher RCC incidence in males versus females [1], suggesting a potential role of sex hormones in RCC [14–17]. There is, however, a paucity of studies evaluating the importance of a wider range of potential factors mediated the well-established link between obesity and RCC development.

The aim of this study was to systematically assess the etiological relevance in RCC etiology for a panel of potential obesity-related mediators. We considered evidence generated by analyzing direct exposure assessment in longitudinal data from a large population cohort, along with genetically proxied exposure assessment in a Mendelian randomization (MR) framework based on genetic data from a large genome-wide association study (GWAS).

Methods

Analytical strategy

This study was designed to evaluate the importance of potential obesity-related risk factors in renal cancer etiology, and to estimate the extent to which they may mediate the risk-increasing effect of obesity on renal cancer risk. We considered risk factors previously implicated in the etiology of obesity related cancers, including fasting insulin [13] glucose [18], glycated hemoglobin (HbA1c) [18], estimated glomerular rate (eGFR) [19], blood pressure [13], IGF-1 [20], leptin [21], lipids components [22,23], sex hormones [17] and Interleukin-6 [24,25]. We used two complementary approaches; i) direct risk factor assessment in longitudinal cohorts and ii) genetically proxied risk factors in a large GWAS (i.e., through MR). These approaches were applied in parallel, initially by (1) evaluating the association of higher BMI on a set of potential mediators and (2) assessing the extent to which the potential mediators were associated with renal cancer risk. For potential mediators with statistical and directional concordant associations with renal cancer risk in both the cohort (1) and MR (2) analyses, we finally (3) estimated the proportion of the BMI-effect on renal cancer risk that they could explain (Fig 1).

Fig 1. Flowchart depicts analytical strategy and data sources for (i) directly measured risk factors in longitudinal cohorts, and (ii) genetically proxied risk factors in genome-wide association studies.

Fig 1

Open in a new tab

NSHDS: Northern Sweden Health and Disease Study. GWAS: Genome-wide association study.

Study population

UK Biobank

UK Biobank (UKB) is a prospective cohort that enrolled over 500,000 participants, aged 40–60 years, from 2006 to 2010 in the United Kingdom. Descriptions of enrollment, data collection and details on lifestyle and biomarkers have been described previously [26]. We excluded 27,013 participants diagnosed with cancer (except non-melanoma skin cancer, ICD10: C44) before enrollment. Deaths, incident renal cancer (ICD10: C64) were obtained through data linkage to national cancer and mortality registries. Participants were followed until the first primary malignant cancer, death or end of follow-up (defined as the date of lost to follow-up, last date of cancer diagnosis or death by center) (Details of study population in S1 Appendix). All participants provided written informed consent, and the study protocol was approved by the Northwest Multicenter Research Ethics Committee of the United Kingdom. This study accessed relevant UKB data under application number 97846.

Northern Sweden Health and Disease Study

The Västerbotten Intervention Study (VIP) is a prospective cohort study within the Northern Sweden Health and Disease Study (NSHDS) that is an ongoing cohort study since 1985 [27]. As of April 2023, VIP included more than 114,000 participants from the general Västerbotten population between 40 and 60 years of age. Participants of VIP with a subsequent renal cancer diagnosis (ICD10: C64) between 1987 and 2018 were identified using the Swedish Cancer Registry. Individuals with a previous diagnosis of malignant disease (except non-melanoma skin cancer) were excluded. Equal numbers of control individuals were randomly selected from the same cohort, matched for sex, age (±6 months), date of sampling (±3 months), and freeze/thaw status of plasma samples (S1 Appendix). This study was approved by the regional ethical committee at Umeå University (2016/384-31) and the Swedish National Review Authority (2020-02179 and 2021-06764-02).

Genetic instruments

We obtained summary statistics for renal cancer (clear cell RCC [ccRCC], papillary RCC [pRCC], and all types of RCC) from 27,213 cases and 486,846 controls in a multi-ancestry GWAS (mainly European ancestry—24,083 cases and 394,824 controls), excluding individuals in UKB [28]. Summary statistics for all potential risk factors and BMI were obtained from publicly available data (all publication details in Tables A–C in S1 Text). Diastolic and systolic blood pressure GWAS were performed in UKB, based on 375,091 participants of European ancestry [13].

We selected each set of genetic instruments according to the gold standard for MR analyses, previously described (Detailed methods in S1 Appendix) [29]. In brief, for each set of genetic instruments, we excluded non-genome-wide significant SNPs (p > 5.10-8) and correlated SNPs with linkage disequilibrium (r2 > 0.01 and separated by less than 10,000kb).

Statistical analyses

Cohort analysis.

We initially used linear regression models to estimate the association of BMI on each potential mediator (S1 Fig), and Cox proportional hazards regression models to evaluate the association between BMI, our mediators and renal cancer risk (overall, ccRCC only and pRCC only) [30]. Follow-up time from recruitment to event (diagnosis, death, loss to follow-up, or end of follow-up) was used as the underlying timescale. The end date of follow-up was set as the minimum between the last date of death or cancer diagnosis recorded, by center, from 2020-03-18 for Wrexham to 2022-03-03 for Glasgow. Mediators were transformed for the cohort analyses in accordance with the corresponding GWAS used in our MR analyses (see below, Tables A–C in S1 Text). Each linear regression and Cox model was adjusted for age at baseline, sex (except for sex specific phenotypes), center of recruitment, education, smoking status (never/former/current) and alcohol drinking status (never/former/current). Finally, to calculate the proportion mediated effect, the Cox proportional hazards model of each mediator on renal cancer risk was additionally adjusted for BMI.

For fasting insulin measured in NSHDS, linear regression models were used to investigate the relationship between a standard deviation change in BMI and fasting insulin levels. Odds ratios were calculated using conditional logistic regression models, conditioning on matched case-sets. The natural logarithm of insulin level was used in all analyses. Insulin concentrations below the lower limit of detection (LOD) were replaced with the LOD√2.

Mendelian randomization.

Genetic instrumental variables were identified from GWAS of individuals with European ancestry (Tables A–C in S1 Text) [31,32], adjusted for age, sex and the first 10 principal components. IL-6 and leptin did not have suitable genetic instruments for MR and thus were not further analyzed.

We carried out two-sample two-step MR analysis using the inverse-variance weighted (IVW) approach [31] to estimate the association between (i) BMI and renal cancer risk, (ii) BMI and each potential mediator, and (iii) each potential mediator and renal cancer risk [29,32–34]. Effect estimates were scaled per standard deviation increment in BMI with renal cancer risk and each mediator, and various specific transformation for potential mediators (Tables A–C in S1 Text). Subsequently, we performed multivariable MR analysis to estimate the association for each potential mediator with renal cancer risk with adjustment for BMI. Further details, including the 3 core MR assumptions and procedures for sensitivity analyses, are described in S1 Appendix and S1 Fig.

Mediation analysis.

Mediation analyses were restricted to potential mediators that displayed directionally consistent associations with renal cancer in both the MR and cohort analyses. Mediation analyses were carried out using the product method [33–35] to estimate the indirect and direct effects for BMI through mediators on renal cancer risk, along with the proportion of the effect of BMI on risk mediated by the mediator (with confidence intervals) [36,37] (S1 Fig).

Statistical analyses were performed using R (version 4.1.2) for UKB and MR and STATA 18 (Stata corp. College Station, TX, USA) for the insulin analyses in NSHDS. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (S1 and S2 STROBE Checklist).

Results

In the longitudinal cohort analysis, we included a total of 472,337 participants from UKB. During 5,586,414 person years of follow-up, 1,382 participants were diagnosed with renal cancer. In NSHDS, we included 204 pairs of renal cancer cases and matched controls. Baseline characteristics of the study participants from the longitudinal cohorts are provided in Tables D–F in S1 Text, and by sex in Table E in S1 Text. The MR analysis was based on GWAS summary statistics generated using 27,213 renal cancer cases and 486,846 controls.

The association between BMI and potential risk factors

The association between higher BMI and potential mediators, as assessed using both the direct (i.e., cohort analysis: y-axis) and genetically proxied (i.e., MR: x-axis) risk factor assessment, is depicted in Fig 2. The two methods provided consistent evidence for an important role of higher BMI in influencing most assessed risk factors (r2: 0.86). For instance, one standard deviation increase in BMI (1 SDBMI) was associated with 0.16 and 0.35 nmol/L higher fasting insulin based on the MR and cohort analyses, respectively (p < 0.001 in both approaches). Similar positive associations were seen for Glucose, HbA1c, diastolic blood pressure (DBP), systolic blood pressure (SBP), triglycerides, and testosterone in females (p < 0.001 in both approaches). Most of the other risk factors were inversely associated with BMI, including sex-hormone binding globulin (SHBG), testosterone in males, high-density lipoprotein (HDL) (medium, large and X-large), IGF-1, eGFR and cholesterol HDL (p < 0.001 in both approaches, except for IGF-1 in MR, p = 0.04). The only risk factors that did not provide consistent positive or inverse associations with BMI in the cohort and MR analyses were small HDL particles and LDL cholesterol. All effect estimates with confidence intervals are available in Table G in S1 Text.

Fig 2. Figure depicts the association between one standard deviation increment in BMI and potential mediators as measured directly in cohort studies (y-axis) and through genetic Mendelian randomization studies (x-axis).

Fig 2

Open in a new tab

Error bars indicate 95% confidence intervals for beta estimates (note that most CIs were small in the cohort studies). R2 indicates the correlation between cohort and MR estimates. All linear models were adjusted for age, sex, center of recruitment, education, smoking, and alcohol drinking status.

Potential risk factor and renal cancer risk

We estimated that a one standard deviation increment in BMI was associated with a renal cancer risk increase of between 33% (HRcohort: 1.33, 95% CI: 1.26, 1.39; p < 0.001) based on the cohort analysis and 44% based on the MR analysis (ORMR: 1.44, 95% CI: 1.35, 1.53; p < 0.001) (Table H in S1 Text). Similar associations were observed for BMI with risk of clear-cell renal carcinoma (RCC), but not with risk of pRCC (HRcohort: 1.06, 95% CI: 0.83, 1.35; p = 0.65, ORMR: 1.28, 95% CI: 1.08, 1.51; p = 0.004) (Tables I and J in S1 Text).

In risk analyses of potential risk factors (Fig 3), we found statistical and directionally concordant evidence in both the cohort and MR analyses for positive associations with RCC risk for insulin, DBP, and triglycerides. We found corresponding evidence for inverse associations with RCC risk for HDL cholesterol and SHBG. For instance, the MR analysis suggested that one-standard deviation increment in insulin is associated with a greater than 2-fold increase in RCC risk (ORMR: 2.24, 95% CI: 1.19, 4.22; p = 0.01), with more modest but directionally concordant associations suggested in the cohort analysis (ORcohort: 1.43, 95% CI: 1.02, 2.00; p = 0.04). The associations between triglycerides and RCC risk were modest but similar in the MR and cohort analyses (ORMR: 1.11, 95% CI: 1.05, 1.17; p < 0.001, HRcohort: 1.23, 95% CI: 1.11, 1.38; p < 0.001), with similar results for DBP (ORMR: 1.14, 95% CI: 1.04, 1.26; p < 0.001, HRcohort: 1.11, 95% CI: 1.05, 1.17; p < 0.001). We observed consistent inverse associations with RCC risk for SHBG overall (ORMR: 0.80, 95% CI: 0.70, 0.90; p < 0.001, HRcohort: 0.67, 95% CI: 0.58, 0.76; p < 0.001). We also observed inverse associations with risk for HDL cholesterol (ORMR: 0.93, 95% CI: 0.88, 0.98; p < 0.001, HRcohort: 0.72, 95% CI: 0.66, 0.77; p < 0.001), but found evidence for unbalanced pleiotropy (p < 0.01) which may have biased the MR-based OR estimate for HDL cholesterol (Table H in S1 Text). We observed discrepancies between the cohort (negatively associated with RCC) and MR analyses (no association) for total cholesterol, HDL particles, LDL and testosterone (Table H in S1 Text). As a sensitivity analysis, for sex-hormones in females, we adjusted our models in the observational analysis with hormone-replacement therapy and menopausal status which did not alter the results (Table K in S1 Text).

Fig 3. Forest plot depicts the association between each potential mediator and risk of renal cancer based on MR in light blue and cohort analyses in dark blue.

Fig 3

Open in a new tab

MR estimated relative risks by calculating odds ratios per standard deviation increment for each potential mediator using the inverse-variance weighted approach. Cohort analyses estimated relative risks by calculating hazard ratios per standard deviation increment for each potential mediator, adjusted for age, sex, education, center of recruitment, smoking and alcohol status, except for fasting insulin which was estimated as odds ratios from conditional logistic regression (the NSHDS sample was a nested case-control study). Potential mediators with evidence for directionally consistent associations with renal cancer risk in both the cohort and MR analyses are denoted with a (*).

The proportion of the BMI-effect on renal cancer risk explained by each mediator

To estimate the proportion of the BMI effect on renal cancer risk that is mediated by individual risk factors, we carried out a mediation analysis. This analysis was limited to risk factors for which we found consistent evidence for associations with renal cancer risk in both the cohort and MR analyses (Fig 3). Fig 4 graphically depicts the network of relationship connections between elevated BMI, risk factors, and renal cancer risk. This diagram uses information from the analyses illustrated in Figs 1–3, as well as pair-wise risk factor relations estimated using cohort and MR analyses (Table L in S1 Text). Of note, we found evidence of complex and often bidirectional relations between risk factors.

Fig 4. The diagram summarizes the pairwise associations between BMI, each mediator implicated in renal cancer etiology in this study (i.e., with evidence for association with renal cancer risk in both the MR and cohort-based risk analysis), and renal cancer risk based on univariable MR.

Fig 4

Open in a new tab

Light blue arrows depict positive associations, and dark blue arrows indicate inverse associations. The thickness of the arrows indicates the strength of association. Continuous associations are expressed as beta estimates, and associations with renal cancer risk are expressed as odds ratios per standard deviation increment. The size of the nodes reflects the amount of the association between BMI and renal cancer risk that is mediated (Table M in S1 Text).

When considering individual risk factors as mediators, we estimated that insulin mediates between 67% (based on cohort analyses) and 22% (based on MR analyses) of the effect of elevated BMI on renal cancer risk (Fig 4 and Table M in S1 Text). However, we note that the estimated OR between BMI and renal cancer risk in the NSHDS cohort analysis was weaker than the corresponding HR estimate in UKB, and this might have inflated the corresponding mediation estimate for fasting insulin. The corresponding proportion mediated for SHBG was similar between the cohort and MR analysis. HDL cholesterol and triglycerides were estimated to mediate less than 10% of the effect of BMI on renal cancer risk. However, these mediation estimates were based on uncertain indirect effect estimates for the individual risk factors (S2 Fig and Tables M and N in S1 Text). Whereas some evidence was seen in the cohort analysis for indirect effects for fasting insulin (indirect effect: 1.12, 95% CI: 1.04, 1.20), HDL cholesterol (indirect effect: 1.08, 95% CI: 1.06, 1.10), SHBG overall (indirect effect: 1.04, 95% CI: 1.00, 1.08) and SHBG in females (indirect effect: 1.06, 95% CI: 1.02, 1.09), the indirect effect estimates based on the genetic MR analysis had wide confidence intervals (S2 Fig and Tables M and N in S1 Text). We also estimated mutually adjusted association estimates for each putative mediator using both MR and cohort analyses (Table O in S1 Text).

Discussion

We carried out a systematic assessment of 20 obesity-related risk factors and their association with renal cancer risk by leveraging both direct risk factor assessment in 500,000 longitudinally followed research participants and an MR analysis of 27,213 renal cancer cases with genetic data. Our results confirmed the important influence of elevated BMI on each risk factor, and highlight directionally consistent evidence of association with renal cancer risk for fasting insulin, HDL cholesterol, triglycerides, and SHBG. Mediation analyses suggested roles of these risk factors in mediating the influence of obesity on renal cancer risk.

The etiology of renal cancer is complex and multiple risk factors are thought to contribute. In addition to genetic predisposition and tobacco exposure, excess body adiposity and related risk factors, including hypertension and diabetes, have been considered in renal cancer etiology. We sought to (1) describe the influence of elevated BMI on a broader panel of potential risk factors, (2) investigate their importance in renal cancer etiology, and (3) estimate the extent to which they may mediate the impact of elevated BMI on renal cancer risk. We systematically considered two lines of evidence: (i) one based on a traditional approach of direct risk factor assessment in longitudinally followed research participants, and (ii) one based on genetically proxied risk factor in a large genetic association study.

  • (1)

    The association of elevated BMI with obesity-related risk factors has been well described, and our results were largely consistent with the literature [38–43]. The results suggest that elevated BMI increases concentrations of insulin, blood pressure and triglycerides, but decreases testosterone in men, SHBG and some cholesterol particles. The results were highly concordant in the cohort and MR analyse (Fig 1).

  • (2)

    The subsequent risk analysis provided consistent evidence of a risk-increasing effect on renal cancer risk for elevated fasting insulin, DBP and triglycerides, with evidence for risk-decreasing effects of elevated SHBG (Fig 2). These observations have some support from literature, albeit primarily from genetic analyses. The associations with risk for fasting insulin and DBP are consistent with previous reports from our group [13,44], although fasting insulin had not been directly assessed in relation to renal cancer risk in a pre-diagnostic cohort analysis before. Went and colleagues recently carried out a phenome-wide MR study across eight common cancers which highlighted inverse associations of SHBG with renal cancer which is consistent with our findings [17]. The inverse association of HDL cholesterol with renal cancer risk is intriguing with a strong inverse association observed with renal cancer risk in the cohort analysis, and a weak inverse association seen in the MR analysis. This observation contrasts with a previous MR study from Riscal and colleagues [45] that suggested a positive association between HDL cholesterol and renal cancer. Our MR analysis used a genetic instrument for HDL with 835 SNPs (compared to 13 SNPs for Riscal and colleagues) that was evaluated in relation to renal cancer risk using genetic data from a GWAS of 27,213 cases (instead of 10,784 cases for Riscal and colleagues) [45].

We adopted a conservative approach in the mediation analysis by only considering risk factors influenced by (1) elevated BMI (Fig 1) and (2) with evidence for association with renal cancer risk in both the cohort and MR analysis (Fig 2). One previous study has evaluated obesity-related risk factors as potential mediators of the effect of obesity on renal cancer risk. This study estimated that 15% of effect of elevated BMI is mediated by the triglycerides glucose index, an insulin resistance indicator estimated using fasting triglycerides and glucose [46]. This may be compared with our estimate for fasting insulin of 67% in the cohort analysis and 22% in the MR analysis (Fig 4). For the other considered risk factors, we observed mediation estimates of between 3% (for triglycerides) to 27% (for HDL cholesterol) (Table M in S1 Text). However, these analyses were based on one-off measurements of BMI and potential mediators, most of which are likely to vary substantially over time which may result in regression dilution bias [47,48]. We note that the confidence intervals for these mediation estimates were wide, particularly those based on the MR analysis. Whereas many published mediation analyses rely on the point estimates, our study highlight the inherent uncertainty in these analyses and that mediation point estimates should be interpreted with caution. Future studies would benefit from incorporating multiple intra-individual risk factor measurements to assess the extent to which regression dilution may have weakened some of the association estimates in our study, and to improve the precision of the mediation analyses.

When evaluating the directionality of relationships between the identified risk factors, we noted that most risk factors were related and often appeared to influence each other bidirectionally (Fig 4). Indeed, it is not clear whether our results reflect the influence on renal cancer development through one or several pathways. In-vitro studies suggest that insulin inhibits SHBG production and that lower insulin increases SHBG levels [49]. However, other studies suggest that tumor necrosis factor-α [50] or glucose may be involved in SHBG regulation, rather than insulin [51]. In addition, SHBG binds androgens and estrogens, both of which are synthetized from cholesterol [52]. This highlights the need for further studies with a specific focus on sex-hormones metabolism in renal cancer etiology. Taken together, our and previous studies emphasize the complexity of the relationship between obesity and renal cancer etiology, as well as the limitations of the observational approach. Well-designed experimental studies may be required to describe the causal pathways by which obesity influences renal cancer risk in detail.

Our study had important strengths and limitations. We relied heavily on UKB to (i) assess the cross-sectional relations between elevated BMI and most of the assessed risk factors, (ii) to establish genetic instruments for risk factors of interest, and (iii) for the risk analysis based on directly measured risk factors. Whereas UKB is a suitable resource for this line of research, using one, relatively homogeneous and healthy study population may limit the external validity of our findings. The large sample size of the UKB provided statistically robust results but did not solve the potential issue of residual confounding inherent to association analyses based on directly measured risk factors (i.e., the cohort analysis) [53]. An important limitation of our analysis relates to fasting insulin which was not available in UKB, but measured in a separate small case-control study using fasting pre-diagnostic samples. The small sample size available, both for the cohort analysis and for the definition of the genetic instrument, hampered the robustness of the both the MR and cohort analyses for fasting insulin. To circumvent this limitation of traditional observational studies, we additionally used an independent large GWAS of 27,000 renal cancer cases with hundreds of thousands of controls to carry out complementary risk analyses along the lines of two-sample MR. By adopting a conservative approach when interpreting the results in focusing on risk factors with consistent associations with renal cancer risk in both the cohort and MR analyses, the specific risk factors implicated in renal cancer etiology by our study would seem robust.

In conclusion, we evaluated a comprehensive panel of obesity-related risk factors in relation to renal cancer risk using two complementary approaches. We quantified the important influence of obesity on each risk factor and found robust evidence for a role in renal cancer etiology for fasting insulin, HDL cholesterol, SHBG, DBP, and triglycerides. Our results reinforce prior evidence that insulin is an important causal factor for RCC, and is likely to represent an important link between obesity and RCC development. Our study also highlighted likely important roles for cholesterol and sex steroid metabolism in renal cancer etiology.

Supporting information

S1 Fig. Flowchart of the mediation analysis.

NSHDS: The Northern Sweden Health and Disease Study. GWAS: Genome-wide association study. SNPs: Single nucleotide polymorphism. Exp: exposure. Med: potential mediator. Out: outcome. MR: Mendelian Randomization. IVW: Inverse Variance Weighted.

(DOCX)

pmed.1004906.s001.docx (477.9KB, docx)
S2 Fig. Indirect effect estimates for BMI in the mediation analysis.

Indirect effect estimates for BMI through each risk factor with renal cancer risk as calculated with the product method, confidence intervals estimated through the Sobel method. MR: Mendelian Randomization. BP: Blood pressure. HDL: High-density lipoprotein. SHBG: Sex-hormone binding globulin. CI: Confidence Interval.

(DOCX)

pmed.1004906.s002.docx (62.4KB, docx)
S1 Text

Table A. Potential obesity-related risk factors for renal cell carcinomas, in the Mendelian Randomization analyses. *Removed due to missingness in the outcome SNPs, outliers identified by MR-PRESSO or removed by steiger filtering. SNP: single-nucleotide polymorphism. GWAS: Genome-wide association study. INTR: Inverse normal transformation of rank. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table B. Potential obesity-related risk factors for clear cell renal cell carcinomas, in the Mendelian randomization analyses. *Removed due to missingness in the outcome SNPs, outliers identified by MR-PRESSO or removed by steiger filtering. SNP: single-nucleotide polymorphism. GWAS: Genome-wide association study. INTR: Inverse normal transformation of rank. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table C. Potential obesity-related risk factors for papillary renal cell carcinoma, in the Mendelian Randomization analysis. *Removed due to missingness in the outcome SNPs, outliers identified by MR-PRESSO or removed by steiger filtering. SNP: single-nucleotide polymorphism. GWAS: Genome-wide association study. INTR: Inverse normal transformation of rank. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table D. Characteristics of the UKB population, by renal cell carcinomas subtypes. 1n (%); Median (IQR). RCC: Renal cell carcinoma. ccRCC: clear cell renal cell carcinoma. pRCC: papillary renal cell carcinoma. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table E. Characteristics of the UKB population, by sex. 1n (%); Median (IQR). HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table F. Characteristics of the Northern Sweden Health and Disease Study (NSHDS) population. 1n (%); Mean (95% confidence interval). Table G. Association of BMI with each potential mediator. Each cox proportional hazard model was adjusted for age, sex, center of recruitment, education, smoking and alcohol drinking status. *log-transformed, **inverse-normal transformation of rank and Z-score, ***Z-score transformation, †standardized and 1-unit increase in Estradiol. Fasting insulin association with BMI was assess in NSHDS. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table H. Association of potential obesity-related risk factors with risk of renal cell carcinoma using Mendelian randomization and prospective cohort analyses. Adjustments for the cox models: Age, sex, center, education, alcohol status and smoking status, • SNPs removed by MR-PRESSO and steiger filtering, *log-transformed, **inverse-normal transformation of rank and Z-score, ***Z-score transformation, †standardized and 1-unit increase in Estradiol. Fasting insulin association with RCC was assess in NSHDS.ª Fasting insulin effect as OR [95% CI]. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table I. Association of potential obesity-related risk factors with risk of clear cell renal cell carcinoma using Mendelian randomization and prospective cohort analyses. Adjustments for the cox models: Age, sex, center, education, alcohol status and smoking status, • SNPs removed by MR-PRESSO and steiger filtering, *log-transformed, **inverse-normal transformation of rank and Z-score, ***Z-score transformation, †standardized and 1-unit increase in Estradiol. Fasting insulin association with RCC was assess in NSHDS. ª Fasting insulin effect as OR [95% CI]. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table J. Association of potential obesity-related risk factors with risk of papillary renal cell carcinoma using Mendelian randomization and prospective cohort analyses. Adjustments for the cox models: Age, sex, center, education, alcohol status and smoking status, • SNPs removed by MR-PRESSO and steiger filtering, *log-transformed, **inverse-normal transformation of rank and Z-score, ***Z-score transformation, †standardized and 1-unit increase in Estradiol. Fasting insulin association with RCC was assess in NSHDS. ª Fasting insulin effect as OR [95% CI]. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table K. Comparison between the adjusted and the non-adjusted model for specific female factors. *Cox proportional hazard model adjusted for age, center of recruitment, education, smoking status and alcohol drinking status. ** Cox proportional hazard model additionally adjusted for Hormone replacement therapy (Yes/no) and menopausal status (Yes/no). RCC: renal cell carcinoma. ccRCC: clear cell renal cell carcinoma. SHBG: Sex-hormone binding globulin. Table L. Beta estimates between potential mediators. Association between each potential mediator, bidirectionally, in cohort and MR analyses. BP: blood pressure. SHBG: Sex-hormone binding globulin. HDL: High-density lipoprotein cholesterol. Each model, in the cohort analyses, was adjusted for age, sex, center of recruitment, education, alcohol and smoking status. Table M. Proportion of BMI effect on renal cell carcinoma mediated. Each cox proportional hazard model was adjusted for age, sex, center of recruitment, education, smoking and alcohol drinking status. ªFasting insulin effect as OR [95% CI]. BP: blood pressure. SHBG: Sex-hormone binding globulin. HDL: High-density lipoprotein cholesterol. Table N. Proportion of BMI effect on ccRCC mediated. Each cox proportional hazard model was adjusted for age, sex, center of recruitment, education, smoking and alcohol drinking status. ª Fasting insulin effect as OR [95% CI]. SHBG: Sex-hormone binding globulin. HDL: High-density lipoprotein cholesterol. Table O. Multivariable analyses of mediators on renal cell carcinomas. Multivariable analyses between each potential mediator adjusted for each other mediator. Each cox proportional hazard model was adjusted for age, sex, center of recruitment, education, smoking and alcohol drinking status. Not enough SNPs in Insulin instruments to run multivariable MR with another mediator. - Not calculated to due lack of power (F-statisticconditional <10). BP: blood pressure. SHBG: Sex-hormone binding globulin. HDL: High-density lipoprotein.

(DOCX)

pmed.1004906.s003.docx (707.9KB, docx)
S1 STROBE Checklist. Checklist of items that should be included in reports of observational studies.

Licensed under CC BY 4.0. Checklist available from https://www.strobe-statement.org/checklists/.

(DOC)

pmed.1004906.s004.doc (124.5KB, doc)
S2 STROBE Checklist. STROBE-MR checklist of recommended items to address in reports of Mendelian randomization studies.

Checklist available from https://www.strobe-mr.org/.

(DOCX)

pmed.1004906.s005.docx (41.1KB, docx)
S1 Appendix. Supplementary methods and materials.

(DOCX)

pmed.1004906.s006.docx (77.9KB, docx)

Acknowledgments

Data on glycemic traits were generated by MAGIC investigators and downloaded from https://www.magicinvestigators.org

All participants provided written informed consent, and the study protocol was approved by the Northwest Multicenter Research Ethics Committee of the United Kingdom. This study accessed relevant UK Biobank data under application number 97846.

IARC disclaimer:

Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization.

Department of Health and Social Care disclaimer:

The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Abbreviations

BMI

body mass index

ccRCC

clear cell RCC

DBP

diastolic blood pressure

eGFR

estimated glomerular filtration rate

GWAS

genome-wide association study

HbA1c

glycated hemoglobin

HDL

high-density lipoprotein

HRcohort

hazard ratio, from cohort analysis

IVW

inverse-variance weighted

MR

Mendelian randomization

NSHDS

Northern Sweden Health and Disease Study

ORMR

odds ratio, from MR analysis

pRCC

papillary RCC

RCC

renal cell carcinoma

SHBG

sex-hormone binding globulin

STROBE

Strengthening the Reporting of Observational Studies in Epidemiology

UKB

UK Biobank

VIP

Västerbotten Intervention Study

Data Availability

The data underlying the results presented in the study are available from UK Biobank upon request (https://www.ukbiobank.ac.uk) and GWAS summary statistics from https://www.nature.com/articles/s41588-024-01725-7. The code used in the analysis is available from Gitlab: https://gitlab.com/Karine.Alcala/obesity_rcc/-/tree/79b0a4566da2a0a29f3bbf08eaf1d75f66691f69/ and archived in Zenodo: https://doi.org/10.5281/zenodo.17640162.

Funding Statement

MJ and RMM were supported by a Cancer Research UK programme grants: the Obesity-related Cancer Epidemiology Programme (grant number PRCPGM-May25/100001) and The Integrative Epidemiology Programme (C18281/A29019). PB and MJ were supported by World Cancer Research Fund (WCRF UK, IIG_2019_1995). OF was supported by grants from the Swedish Society of Medicine (SLS-960379) and Bengt Ihre Research Fellowship. GDS works within the MRC Integrative Epidemiology Unit at the University of Bristol, which is supported by the Medical Council (MC_UU_00032/1). RMM is a National Institute for Health Research Senior Investigator (NIHR202411). RMM is supported by a Cancer Research UK 25 (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). RMM is also supported by the NIHR Bristol Biomedical Research Centre which is funded by the NIHR (BRC-1215-20011) and is a partnership between University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. SJ was supported by grants from the strategic board for research, Umeå University (FS 2.1.6-59-23) and Svensk-Franska Stiftelsen (F0025_230413). The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

References

  • 1.Scelo G, Larose TL. Epidemiology and risk factors for kidney cancer. J Clin Oncol. 2018;36(36):JCO2018791905. doi: 10.1200/JCO.2018.79.1905 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Padala SA, Barsouk A, Thandra KC, Saginala K, Mohammed A, Vakiti A, et al. Epidemiology of renal cell carcinoma. World J Oncol. 2020;11(3):79–87. doi: 10.14740/wjon1279 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Arnold M, Pandeya N, Byrnes G, Renehan PAG, Stevens GA, Ezzati PM, et al. Global burden of cancer attributable to high body-mass index in 2012: a population-based study. Lancet Oncol. 2015;16(1):36–46. doi: 10.1016/S1470-2045(14)71123-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Capitanio U, Bensalah K, Bex A, Boorjian SA, Bray F, Coleman J, et al. Epidemiology of renal cell carcinoma. Eur Urol. 2019;75(1):74–84. doi: 10.1016/j.eururo.2018.08.036 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Gebhard RL, Clayman RV, Prigge WF, Figenshau R, Staley NA, Reesey C, et al. Abnormal cholesterol metabolism in renal clear cell carcinoma. J Lipid Res. 1987;28(10):1177–84. doi: 10.1016/s0022-2275(20)38606-5 [DOI] [PubMed] [Google Scholar]
  • 6.Reuter VE, Tickoo SK. Differential diagnosis of renal tumours with clear cell histology. Pathology. 2010;42(4):374–83. doi: 10.3109/00313021003785746 [DOI] [PubMed] [Google Scholar]
  • 7.Tchernof A, Després J-P. Pathophysiology of human visceral obesity: an update. Physiol Rev. 2013;93(1):359–404. doi: 10.1152/physrev.00033.2011 [DOI] [PubMed] [Google Scholar]
  • 8.James DE, Stöckli J, Birnbaum MJ. The aetiology and molecular landscape of insulin resistance. Nat Rev Mol Cell Biol. 2021;22(11):751–71. doi: 10.1038/s41580-021-00390-6 [DOI] [PubMed] [Google Scholar]
  • 9.Luo L, Liu M. Adipose tissue in control of metabolism. J Endocrinol. 2016;231(3):R77–99. doi: 10.1530/JOE-16-0211 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Calle EE, Kaaks R. Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat Rev Cancer. 2004;4(8):579–91. doi: 10.1038/nrc1408 [DOI] [PubMed] [Google Scholar]
  • 11.Jiang J, Cai X, Pan Y, Du X, Zhu H, Yang X, et al. Relationship of obesity to adipose tissue insulin resistance. BMJ Open Diabetes Res Care. 2020;8(1):e000741. doi: 10.1136/bmjdrc-2019-000741 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Solarek W, Koper M, Lewicki S, Szczylik C, Czarnecka AM. Insulin and insulin-like growth factors act as renal cell cancer intratumoral regulators. J Cell Commun Signal. 2019;13(3):381–94. doi: 10.1007/s12079-019-00512-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Johansson M, Carreras-Torres R, Scelo G, Purdue MP, Mariosa D, Muller DC, et al. The influence of obesity-related factors in the etiology of renal cell carcinoma—a mendelian randomization study. PLoS Med. 2019;16(1):e1002724. doi: 10.1371/journal.pmed.1002724 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Karami S, Daugherty SE, Purdue MP. Hysterectomy and kidney cancer risk: a meta-analysis. Int J Cancer. 2014;134(2):405–10. doi: 10.1002/ijc.28352 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Scelo G, Li P, Chanudet E, Muller DC. Variability of sex disparities in cancer incidence over 30 years: the striking case of kidney cancer. Eur Urol Focus. 2018;4(4):586–90. doi: 10.1016/j.euf.2017.01.006 [DOI] [PubMed] [Google Scholar]
  • 16.Peila R, Arthur RS, Rohan TE. Association of sex hormones with risk of cancers of the pancreas, kidney, and brain in the UK Biobank Cohort Study. Cancer Epidemiol Biomarkers Prev. 2020;29(9):1832–6. doi: 10.1158/1055-9965.EPI-20-0246 [DOI] [PubMed] [Google Scholar]
  • 17.Went M, Sud A, Mills C, Hyde A, Culliford R, Law P, et al. Phenome-wide Mendelian randomisation analysis of 378,142 cases reveals risk factors for eight common cancers. Nat Commun. 2024;15(1):2637. doi: 10.1038/s41467-024-46927-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Peila R, Rohan TE. Diabetes, glycated hemoglobin, and risk of cancer in the UK Biobank Study. Cancer Epidemiol Biomarkers Prev. 2020;29(6):1107–19. doi: 10.1158/1055-9965.EPI-19-1623 [DOI] [PubMed] [Google Scholar]
  • 19.Alcala K, Zahed H, Cortez Cardoso Penha R, Alcala N, Robbins HA, Smith-Byrne K, et al. Kidney function and risk of renal cell carcinoma. Cancer Epidemiol Biomarkers Prev. 2023;32(11):1644–50. doi: 10.1158/1055-9965.EPI-23-0558 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Knuppel A, Fensom GK, Watts EL, Gunter MJ, Murphy N, Papier K, et al. Circulating insulin-like growth factor-I concentrations and risk of 30 cancers: prospective analyses in UK Biobank. Cancer Res. 2020;80(18):4014–21. doi: 10.1158/0008-5472.CAN-20-1281 [DOI] [PubMed] [Google Scholar]
  • 21.Park J, Morley TS, Kim M, Clegg DJ, Scherer PE. Obesity and cancer--mechanisms underlying tumour progression and recurrence. Nat Rev Endocrinol. 2014;10(8):455–65. doi: 10.1038/nrendo.2014.94 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Kitahara CM, Berrington de González A, Freedman ND, Huxley R, Mok Y, Jee SH, et al. Total cholesterol and cancer risk in a large prospective study in Korea. J Clin Oncol. 2011;29(12):1592–8. doi: 10.1200/JCO.2010.31.5200 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Koene RJ, Prizment AE, Blaes A, Konety SH. Shared risk factors in cardiovascular disease and cancer. Circulation. 2016;133(11):1104–14. doi: 10.1161/CIRCULATIONAHA.115.020406 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Jones SA, Jenkins BJ. Recent insights into targeting the IL-6 cytokine family in inflammatory diseases and cancer. Nat Rev Immunol. 2018;18(12):773–89. doi: 10.1038/s41577-018-0066-7 [DOI] [PubMed] [Google Scholar]
  • 25.Coussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420(6917):860–7. doi: 10.1038/nature01322 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. doi: 10.1371/journal.pmed.1001779 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Norberg M, Wall S, Boman K, Weinehall L. The Västerbotten Intervention Programme: background, design and implications. Glob Health Action. 2010;3:10.3402/gha.v3i0.4643. doi: 10.3402/gha.v3i0.4643 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Purdue MP, Dutta D, Machiela MJ, Gorman BR, Winter T, Okuhara D, et al. Multi-ancestry genome-wide association study of kidney cancer identifies 63 susceptibility regions. Nat Genet. 2024;56(5):809–18. doi: 10.1038/s41588-024-01725-7 [DOI] [PubMed] [Google Scholar]
  • 29.Richmond RC, Davey Smith G. Mendelian randomization: concepts and scope. Cold Spring Harb Perspect Med. 2022;12(1):a040501. doi: 10.1101/cshperspect.a040501 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Vanderweele TJ, Vansteelandt S. Odds ratios for mediation analysis for a dichotomous outcome. Am J Epidemiol. 2010;172(12):1339–48. doi: 10.1093/aje/kwq332 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37(7):658–65. doi: 10.1002/gepi.21758 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Hartwig FP, Davies NM, Hemani G, Davey Smith G. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol. 2016;45(6):1717–26. doi: 10.1093/ije/dyx028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Burgess S, Daniel RM, Butterworth AS, Thompson SG, EPIC-InterAct Consortium. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int J Epidemiol. 2015;44(2):484–95. doi: 10.1093/ije/dyu176 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Carter AR, Sanderson E, Hammerton G, Richmond RC, Davey Smith G, Heron J, et al. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021;36(5):465–78. doi: 10.1007/s10654-021-00757-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Relton CL, Davey Smith G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol. 2012;41(1):161–76. doi: 10.1093/ije/dyr233 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Sobel ME. Asymptotic confidence intervals for indirect effects in structural equation models. Sociol Methodol. 1982;13:290. doi: 10.2307/270723 [DOI] [Google Scholar]
  • 37.Vabistsevits M, Davey Smith G, Sanderson E, Richardson TG, Lloyd-Lewis B, Richmond RC. Deciphering how early life adiposity influences breast cancer risk using Mendelian randomization. Commun Biol. 2022;5(1):337. doi: 10.1038/s42003-022-03272-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):122. doi: 10.1186/s12933-018-0762-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Subramanian S, Chait A. Hypertriglyceridemia secondary to obesity and diabetes. Biochim Biophys Acta. 2012;1821(5):819–25. doi: 10.1016/j.bbalip.2011.10.003 [DOI] [PubMed] [Google Scholar]
  • 40.Hautanen A. Synthesis and regulation of sex hormone-binding globulin in obesity. Int J Obes Relat Metab Disord. 2000;24 Suppl 2:S64-70. doi: 10.1038/sj.ijo.0801281 [DOI] [PubMed] [Google Scholar]
  • 41.Hall JE, do Carmo JM, da Silva AA, Wang Z, Hall ME. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. Circ Res. 2015;116(6):991–1006. doi: 10.1161/CIRCRESAHA.116.305697 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Zumoff B, Strain GW, Miller LK, Rosner W, Senie R, Seres DS, et al. Plasma free and non-sex-hormone-binding-globulin-bound testosterone are decreased in obese men in proportion to their degree of obesity. J Clin Endocrinol Metab. 1990;71(4):929–31. doi: 10.1210/jcem-71-4-929 [DOI] [PubMed] [Google Scholar]
  • 43.Grundy SM. Obesity, metabolic syndrome, and cardiovascular disease. J Clin Endocrinol Metab. 2004;89(6):2595–600. doi: 10.1210/jc.2004-0372 [DOI] [PubMed] [Google Scholar]
  • 44.Alcala K, Mariosa D, Smith-Byrne K, Nasrollahzadeh Nesheli D, Carreras-Torres R, Ardanaz Aicua E, et al. The relationship between blood pressure and risk of renal cell carcinoma. Int J Epidemiol. 2022;51(4):1317–27. doi: 10.1093/ije/dyac042 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Riscal R, Bull CJ, Mesaros C, Finan JM, Carens M, Ho ES, et al. Cholesterol auxotrophy as a targetable vulnerability in clear cell renal cell carcinoma. Cancer Discov. 2021;11(12):3106–25. doi: 10.1158/2159-8290.CD-21-0211 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Fritz J, Bjørge T, Nagel G, Manjer J, Engeland A, Häggström C, et al. The triglyceride-glucose index as a measure of insulin resistance and risk of obesity-related cancers. Int J Epidemiol. 2020;49(1):193–204. doi: 10.1093/ije/dyz053 [DOI] [PubMed] [Google Scholar]
  • 47.Smith GD, Phillips AN. Inflation in epidemiology: “the proof and measurement of association between two things” revisited. BMJ. 1996;312(7047):1659–61. doi: 10.1136/bmj.312.7047.1659 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Rutter CE, Millard LAC, Borges MC, Lawlor DA. Exploring regression dilution bias using repeat measurements of 2858 variables in ≤49 000 UK Biobank participants. Int J Epidemiol. 2023;52(5):1545–56. doi: 10.1093/ije/dyad082 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Wallace IR, McKinley MC, Bell PM, Hunter SJ. Sex hormone binding globulin and insulin resistance. Clin Endocrinol (Oxf). 2013;78(3):321–9. doi: 10.1111/cen.12086 [DOI] [PubMed] [Google Scholar]
  • 50.Simó R, Barbosa-Desongles A, Lecube A, Hernandez C, Selva DM. Potential role of tumor necrosis factor-α in downregulating sex hormone-binding globulin. Diabetes. 2012;61(2):372–82. doi: 10.2337/db11-0727 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Peter A, Kantartzis K, Machann J, Schick F, Staiger H, Machicao F, et al. Relationships of circulating sex hormone-binding globulin with metabolic traits in humans. Diabetes. 2010;59(12):3167–73. doi: 10.2337/db10-0179 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Miller WL, Auchus RJ. The molecular biology, biochemistry, and physiology of human steroidogenesis and its disorders. Endocr Rev. 2011;32(1):81–151. doi: 10.1210/er.2010-0013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2017;186(9):1026–34. doi: 10.1093/aje/kwx246 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Alexandra Tosun

7 Jul 2025

Dear Dr Johansson,

Thank you for submitting your manuscript entitled "Obesity and renal cancer etiology: a systematic assessment of potential mediators" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

For clinical studies, please upload a copy of your trial study protocol as a supporting information file. The study protocol should be the version submitted for approval to the institutional review board or ethics committee, should include any amendments to the study protocol, as well as the date of their approval by the institutional review or ethics committee. Please also detail any deviations from the study protocol in the Methods section of your manuscript. The editors will consider the protocol and study conduct prior to a final decision for external review.

Please re-submit your manuscript within two working days, i.e. by Jul 09 2025.

Login to Editorial Manager here: https://www.editorialmanager.com/pmedicine

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email me at atosun@plos.org or us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Alexandra Tosun, PhD

Senior Editor

PLOS Medicine

Decision Letter 1

Alexandra Tosun

24 Oct 2025

Dear Dr Johansson,

Many thanks for submitting your manuscript "Obesity and renal cancer etiology: a systematic assessment of potential mediators" (PMEDICINE-D-25-02391R1) to PLOS Medicine. The paper has been reviewed by subject experts and a statistician; their comments are included below and can also be accessed here: [LINK]

As you will see, while the reviewers find the study to be generally well-conducted, they express concerns about the conceptual framework as well as its novelty. After discussing the paper with the editorial team and an academic editor with relevant expertise, I'm pleased to invite you to revise the paper in response to the reviewers' comments. We plan to send the revised paper to some or all of the original reviewers, and we cannot provide any guarantees at this stage regarding publication.

In addition to these revisions, you may need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests shortly. If you do not receive a separate email within a few days, please assume that checks have been completed, and no additional changes are required.

When you upload your revision, please include a point-by-point response that addresses all of the reviewer and editorial points, indicating the changes made in the manuscript and either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please also be sure to check the general editorial comments at the end of this letter and include these in your point-by-point response. When you resubmit your paper, please include a clean version of the paper as the main article file and a version with changes tracked as a marked-up manuscript. It may also be helpful to check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper.

We ask that you submit your revision by Nov 14 2025. However, if this deadline is not feasible, please contact me by email, and we can discuss a suitable alternative.

Don't hesitate to contact me directly with any questions (atosun@plos.org).

Best regards,

Alexandra

Alexandra Tosun, PhD

Senior Editor

PLOS Medicine

atosun@plos.org

-----------------------------------------------------------

Comments from the academic editor:

This is an interesting and well-done manuscript that helps characterize why obesity may relate to kidney cancer risk. The study is well-powered and rather well-done but the analytes being examined and the methodology employed are not especially novel. The results are important but not groundbreaking. The study has a key limitation in that the most important result--for fasting insulin--was available only in a subset. This makes it harder to use multivariable models and introduces questions about whether the result is stronger simply because of the use of a different dataset.

I think the manuscript is most interesting from a broader perspective--that of a field transitioning from mostly questionnaire-based methods to intensively-biological methods, and currently grappling with how to interpret such dense data and how it can be used to complement traditional hypotheses.

Specific comments are below:

1. Would the authors consider including metabolomics data from the UK Biobank (and/or NHSDS) to add novelty?

2. The lack of fasting insulin in Biobank is a limitation that should be more forthrightly acknowledged.

3. Would the authors consider examining HBA1c and fasting glucose from the UK Biobank?

4. The network depiction (Figure 4) should be described in the Methods and in much greater detail. Is this a Gaussian Graphical Model, with conditional correlations, or are the lines unconditional? Are lines based on observed levels or MR levels? Which is which? How about the circles--observed or MR? Are results from UK Biobank or NSHDS? Perhaps % mediation can be included in the circle, because the size of circles in the key are misleading. It's a good figure but it needs more footnoting and annotating to be clear.

5. For the insulin result, I think it's important to clarify in the results that the BMI-RCC association was weaker in NSHDS than UK Biobank to begin with, which may (partly) explain the higher percent of mediation for insulin.

6. I would omit most or perhaps all instances of bolding in the discussion.

7. Discussion: regarding "regression dilution bias" in previous papers. Did the current paper address this is some way? This point did not appear to support the strength of the current study?

-----------------------------------------------------------

Comments from the reviewers:

Reviewer #1: This study by Alcala et al is a carefully designed analysis of making the most of observational and Mendelian randomisation estimates to elucidate potential pathways underlying the association of BMI with renal cancer through mediation analyses. Parts of these analyses have been previously explored and some results have been established. The authors were very clear on which parts these were, supported by the relevant literature and which parts needed more evidence, where they contributed. From the potential mediators they investigated, they found that fasting insulin, diastolic BP, triglycerides, sex hormone binding globulin and HDL-C mediated to some extent this association.

I thought this was a very relevant research question and the study design was carefully thought through utilising many data sources to make as robust a conclusion as possible. I found the analytical decisions reasonable. I mainly have minor comments related to the presentation of the results.

Overall, throughout the text, tables and figures, it should be made clear that any results from insulin came from a conditional logistic regression analysis (based on my understanding). First, NSHDS is not mentioned at all in the abstract. Despite any word limit, I think there should be an effort to make this clear in a concise way. Second, throughout including tables/figures, the results for insulin from the observational analyses are presented as HR. This is not quite right, so for insulin it needs to be changed to OR. When insulin is presented along with other risk factors in the same table, the title should be something that covers this too .e.g HR/OR [95% CI] with a footnote explaining that for insulin is OR or Effect size [95% CI] with a footnote.

Line 72: Add "." After "RCC".

Lines 104-105: Could you mention that latest censoring date, if the participant did not die, get cancer or was lost to follow-up?

Line 125: The citation number jumps from 26 to 30, so this needs adjustment and changing the order of references.

Lines 133-135: Wouldn't it be relevant for the Cox models to be adjusted for a SES variable e.g. education or deprivation index? Was there any reason in particular that they were not while the conditional logistic regression analyses were (as per the supplementary figure 1)?

Lines 148-149: This is not very clearly written. Would the following rephrasing be accurate "to estimate the association between i) BMI and each potential mediators, ii) each potential mediator and renal cancer risk, and iii) BMI and renal cancer risk."?

Lines 151-153: Since BMI was the exposure of interest, in the multivariable MR, why didn't you frame it as adjustment of the BMI and renal cancer risk association for each potential mediator and then directly estimating from this the direct effect? Then the indirect effect for each potential mediator would be total effect-direct effect. Are the two approaches equivalent or is there any particular reason you did the analysis this way?

Line 156: Replace "was" with "were".

Lines 161-162: Could you rewrite this as "Statistical analyses were performed using R (version 4.1.2) for UKB and MR and STATA 18 (Stata corp. College Station, TX, USA) for the insulin analyses in NSHDS."

Line 189: According to the Supplementary Table 7, this estimate is 1.32 [1.26, 1.39]. Please double-check and correct as appropriate.

Line 199: Similarly, the OR should be HR.

Line 200: In Supplementary Table 7, the 95% CI for the MR are 1.04-1.17. Please double-check and correct accordingly.

Line 202: Similarly, check the upper confidence bound of the HR (0.76 vs 0.75).

Line 204: Aren't these results still refer to Supplementary Table 7 (rather than 8)?

Line 221: Add an "s" after "depict".

Line 284: Add "d" after "evaluate".

Line 287: Add "d" after "influence".

Line 307: Add "d" after "involve".

References 32 and 34 are the same.

Figure 1: Maybe to make this figure more accurate, split steps 1 and 2 into two separate boxes and then make the arrows that go towards the central box of the "Mediator excluded if…" come from the box of step 2 only (Mediators -> outcomes), as this is what you did from my understanding.

Figure 2: All the vertical 95% CIs apart from insulin are missing; could you add them? In addition, it may make the figure clearer if you remove the background grey lines and instead you add a grey line at 45 degrees.

Figure 3: For the insulin CIs, could you put an arrow-head on the right-hand side to show that the lines continue outside the x-axis limits?

Figures 3 and 5: As suggested for figure 2, please remove the grey background lines to make the figures cleaner.

Figure 4: You may want to change the colours of the arrows to make them more colour-blind friendly.

Supplementary methods:

1. The first sentence of the third paragraph looks like it was left from a previous version that it may have been in the main text. Since you talk about SBP and DBP in the previous paragraph, maybe replace this sentence with just a description of the BMI measurement.

2. When you talk about missing biomarker measurements, could you first mention what was the percent missingness and then say "… of which most (80%) occurred due to …".

3. Mendelian randomisation, 5th paragraph, 3rd line: add "d" after "remove".

Supplementary table 7: In the title you mention kidney cancer. Could you keep it to renal cell carcinoma throughout for consistency and to avoid any confusion among the readers?

Reviewer #2: In this paper, the authors applied regression analysis to the UK Biobank Cohort and Swedish cohort as well as MR analysis to GWAS Consortium data to assess the causal relationship of obesity with renal cell cancer (RCC) including clear cell RCC (ccRCC) and papillary cell RCC (pcRCC). They further explored the relationships of putative mediators including insulin, lipid parameteres, testosterone and SHBG along with their explained variance. They compared the effect size and explained variances of these 2 analyses and found similar directions albeit with substantial differences for some biomarkers. In a network analysis, they found bidirectional relationships amongst these biomarkers in their mediation of the causal association between BMI and RCC.

While the researchers have performed extensive and robust analyses using these large datasets, the main concern relates to the hypothesis and the conceptual framework underlying these analyses. The risk associations between obesity and cancer including RCC are well documented in cross sectional and prospective studies supported by biological plausibility. The latter include abnormal cell signalling due to inflammation, oxidative stress, glucolipotoxicity amongst others. Prospective studies and more recently RCTs with anti-obesity medications have also demonstrated the benefits of weight reduction on cancer risk. These consistent data support the causal role of obesity and cancer events.

Although this analysis may add insights into the mediators for such causal relationships, it has not addressed the intriging question whether this obesity-cancer link is true for all cancer with additional factors modifying the risk of site-specific cancer. As eluded by the authors, there is a male prepondrance for RCC and given the divergent relationships between testosterone and RCC in men and women, sex-specific analysis is needed to improve clarity. Besides, the range of testosterone levels in men and women are markedly different making sex-specific analysis imperative. Some researchers also used different assays to detect the low testosterone levels in women and it is not sure how these data were handled in these large databases with considerable heterogeneity in terms of assays and definitions. A table comparing differences between men and women in the UK and Swedish cohorts will be informative, in particular the menopausal states of women and stages of CKD (see later). There are also sex differences in distribution of anthropmetric indexes and analysis of visceral obesity indexes (e.g. waist circumference) will strengthen the results. Likewise, alcohol and tobacco use are different between men and women and thus, the mediators may be different even if the causal relationship between obesity and RCC holds true in both sexes.

Importantly, the confounding or mediating effects of hyperglycemia in obesity-cancer relationship needs to be clarified. There are studies supporting the association between obesity and hyperglycemia independent of obesity, again supported by biological plausibility. The relationship between insulin and cancer may also be different in people with or without diabetes. Likewise, CKD may lie on the causal pathway leading to RCC with low eGFR being associated with low SHBG. The authors need to take these biological factors into considersation when performing these statistical analyses .

The author needs to provide more explanation on the methdology of the network analysis and propose some biological explanation to these bidirectional relationships. General (BMI) or visceral (waist circumfernce) obesity is due to perturbation of energy metabolism in part due to abnormal insulin secretion and action which in turn can affect lipid and glucose metabolism, vascular biology, inflammation, and other hormonal pathways including SHBG and sex hormones. These biological changes can lead to complications such as CKD which can feedback onto these complex loops. These complexies may not be fully addressed by MR analysis even using multiple statistical tools such as horizontal plieotrophy. and that consistent evidence from different study design is probably more important.

Please define ccRCC and pcRCC in the introduction when they first appeared. Please clarify whether the RCC cases in the UK biobankwere cross-sectionally or prospectively ascertained. Likewise, was the case-control cohort of RCC in the Swedish cohort cross sectional or prospective? If the former, then reporting patient-years can be misleading.

In the supplementary text (p3), please clarify the statement: We additionally excluded correlated SNPs in linkage disequilibrium (r2>0.01 and separated by less than 10 000 kb). Is r2>0.01 correct?

Reviewer #3: The paper considers the relationship between BMI and renal cancer, through both observational analysis and using Mendelian randomization with genetic data. Potential mediators of this relationship are identified, and the proportion of the association between BMI and renal cancer risk mediated by these factors is studied. My comments are as follows.

Some of the causal language may be tempered, for example in line 88 it is said the study is evaluating the "impact" of higher BMI on potential mediators, and in line 128 "the effect" of BMI. Whereas the effects measured, particularly in the cohort analysis, are associational rather than causal.

By labelling the potential mediators as such, it is implicit that these factors sit on the causal pathway between BMI and RCC. Bidirectional analyses were performed among the mediators, but not between the mediators and BMI. Is there any evidence of bidirectional relationships between BMI and the mediators?

The MR-Egger intercept test suggests that directional pleiotropy is present for a number of the mediators, as shown in Supplementary tables 7, 8, and 9. E.g., BMI, HDL, triglycerides, SHBG all have low p-values from the intercept test, suggesting the SNPs may not meet the IV assumptions and the MR estimates may be biased. Has this been considered in the subsequent mediation analysis?

Any attachments provided with reviews can be seen via the following link: [LINK]

--------------------------------------------------------- ---

General editorial requests:

(Note: not all will apply to your paper, but please check each item carefully)

* We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. Please do not add or remove authors without first discussing this with the handling editor. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

* Please upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, we strongly recommend that you use PLOS's NAAS tool (https://ngplosjournals.pagemajik.ai/artanalysis) to test your figure files. NAAS can convert your figure files to the TIFF file type and meet basic requirements (such as print size, resolution), or provide you with a report on issues that do not meet our requirements and that NAAS cannot fix.

After uploading your figures to PLOS's NAAS tool - https://ngplosjournals.pagemajik.ai/artanalysis, NAAS will process the files provided and display the results in the "Uploaded Files" section of the page as the processing is complete.

If the uploaded figures meet our requirements (or NAAS is able to fix the files to meet our requirements), the figure will be marked as "fixed" above. If NAAS is unable to fix the files, a red "failed" label will appear above.

When NAAS has confirmed that the figure files meet our requirements, please download the file via the download option, and include these NAAS processed figure files when submitting your revised manuscript.

* Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information (web or email address) for obtaining the data. Please note that a study author cannot be the contact person for the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

* We expect all researchers with submissions to PLOS in which author-generated code underpins the findings in the manuscript to make all author-generated code available without restrictions upon publication of the work. In cases where code is central to the manuscript, we may require the code to be made available as a condition of publication. Authors are responsible for ensuring that the code is reusable and well documented. Please make any custom code available, either as part of your data deposition or as a supplementary file. Please add a sentence to your data availability statement regarding any code used in the study, e.g. "The code used in the analysis is available from Github [URL] and archived in Zenodo [DOI link]" Please review our guidelines at https://journals.plos.org/plosmedicine/s/materials-software-and-code-sharing and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse. Because Github depositions can be readily changed or deleted, we encourage you to make a permanent DOI'd copy (e.g. in Zenodo) and provide the URL.

* Please provide the ethical approval number and ensure to include the ethics statement in the Methods section.

FORMATTING - GENERAL

* Abstract: Please structure your abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions). Please combine the Methods and Findings sections into one section.

* At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Ideally each sub-heading should contain 2-3 single sentence, concise bullet points containing the most salient points from your study. In the final bullet point of 'What Do These Findings Mean?', please include the main limitations of the study in non-technical language. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary.

* Please express the main results with 95% CIs as well as p values. When reporting p values please report as p<0.001 and where higher as the exact p value p=0.002, for example. Throughout, suggest reporting statistical information as follows to improve clarity for the reader "22% (95% CI [13%,28%]; p</=)". Please be sure to define all numerical values at first use.

* Please include page numbers and line numbers in the manuscript file. Use continuous line numbers (do not restart the numbering on each page).

* Please cite the reference numbers in square brackets. Citations should precede punctuation.

FIGURES AND TABLES

* Please provide titles and legends for all figures and tables (including those in Supporting Information files).

* Please define all abbreviations used in each figure/table (including those in Supporting Information files).

* Please consider avoiding the use of red and green in order to make your figure more accessible to those with color blindness.

SUPPLEMENTARY MATERIAL

* Please note that supplementary material will be posted as supplied by the authors. Therefore, please amend it according to the relevant comments outlined here.

* Please cite your Supporting Information as outlined here: https://journals.plos.org/plosmedicine/s/supporting-information

REFERENCES

* PLOS uses the numbered citation (citation-sequence) method and first six authors, et al.

* Please ensure that journal name abbreviations match those found in the National Center for Biotechnology Information (NCBI) databases (http://www.ncbi.nlm.nih.gov/nlmcatalog/journals), and are appropriately formatted and capitalised.

* Where website addresses are cited, please include the complete URL and specify the date of access (e.g. [accessed: 12/06/2023]).

* Please also see https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references for further details on reference formatting.

STUDY TYPE-SPECIFIC REQUESTS

OBSERVATIONAL STUDIES

* Abstract: Please include the study design, population and setting, number of participants, years during which the study took place (enrollment and follow up), length of follow up, and main outcome measures.

* Please ensure that the study is reported according to the STROBE (or appropriate STOBE extension) guideline (available from: https://www.equator-network.org/reporting-guidelines/strobe) and include the completed STROBE (or STROBE extension) checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)." When completing the checklist, please use section and paragraph numbers, rather than page numbers.

* In the manuscript text, please indicate: (1) the specific hypotheses you intended to test, (2) the analytical methods by which you planned to test them, (3) the analyses you actually performed, and (4) when reported analyses differ from those that were planned, transparent explanations for differences that affect the reliability of the study's results. If a reported analysis was performed based on an interesting but unanticipated pattern in the data, please be clear that the analysis was data driven.

* Please state in the Methods section whether the study had a prospective protocol or analysis plan. If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant document(s) with your revised manuscript as a Supporting Information file to be published alongside your study and cite it in the Methods section. A legend for this file should be included at the end of your manuscript. If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place. Changes in the analysis, including those made in response to peer review comments, should be identified as such in the Methods section of the paper, with rationale.

MENDELIAN RANDOMIZATION STUDIES

* Please ensure that the study is reported according to the STROBE-MR guideline (https://www.equator-network.org/reporting-guidelines/strobe/) and include the completed STROBE-MR checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline, specific for mendelian randomization (S1 Checklist)." When completing the checklist, please use section and paragraph numbers, rather than page numbers.

* In the Introduction, please describe the exposure and the evidence for a potential causal relationship between exposure and outcome.

* In the Methods, please explicitly state the 3 core instrumental variable assumptions for the main analysis (relevance, independence, and exclusion restriction), as well assumptions for any additional or sensitivity analysis.

* In the Methods, please describe the MR estimator (e.g., 2-stage least squares, Wald ratio) and related statistics. Detail the included covariates and, in case of 2-sample MR, whether the same covariate set was used for adjustment in the 2 samples.

* If you are presenting an instrumental variable estimate, please compare this to the conventional observational estimate.

* Report the associations between genetic variant and exposure and between genetic variant and outcome, preferably on an interpretable scale.

* Report MR estimates of the relationship between exposure and outcome and the measures of uncertainty from the MR analysis, on an interpretable scale, such as odds ratio or relative risk per SD difference.

* If relevant, please consider translating estimates of relative risk into absolute risk for a meaningful time period.

* Please consider including plots to visualize results (e.g., forest plot, scatterplot of associations between genetic variants and outcome vs between genetic variants and exposure).

Decision Letter 2

Alexandra Tosun

19 Dec 2025

Dear Dr. Johansson,

Thank you very much for re-submitting your manuscript "Obesity and renal cancer etiology: a systematic assessment of potential mediators" (PMEDICINE-D-25-02391R2) for review by PLOS Medicine.

Thank you for your detailed response to the reviewers' and editors’ comments. I have discussed the paper with my colleagues and the academic editor with relevant expertise, and it has also been seen again by all three original reviewers. The changes made to the paper were satisfactory to the reviewers. As such, we intend to accept the paper for publication, pending your attention to the editors' comments below in a further revision. When submitting your revised paper, please once again include a detailed point-by-point response to the editorial comments. The remaining issues that need to be addressed are listed at the end of this email.

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper.

In addition to these revisions, you may need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests shortly. If you do not receive a separate email within a few days, please assume that checks have been completed, and no additional changes are required.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

We ask that you submit your revision by Jan 09 2026. However, if this deadline is not feasible, please contact me or the journal staff (plosmedicine@plos.org) by email, and we can discuss a suitable alternative.

Due to the upcoming holiday season, the journal will operate at reduced capacity from December 22 to January 2. This may cause delays in the manuscript process. We appreciate your understanding.

We look forward to receiving the revised manuscript.

Sincerely,

Alexandra Tosun, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

------------------------------------------------------------

Comments from Academic Editor:

I think the authors did an excellent job with the revision and the study is an excellent example of a mechanistic study.

------------------------------------------------------------

Comments from Reviewers:

Reviewer #1: I would like to thank the authors for responding to my comments in detail.

Reviewer #2: The author has addressed all comments.

Reviewer #3: I thank the authors for their response to my comments, which have been addressed satisfactorily.

------------------------------------------------------------

Requests from Editors:

GENERAL

* Please confirm that your title complies with to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon).

* Statistical reporting: Please revise throughout the manuscript, including tables and figures.

- Please report statistical information as follows to improve clarity for the reader ""22% (95% CI [13,28]; p</=)"".

- Please separate upper and lower bounds with commas instead of hyphens as the latter can be confused with reporting of negative values.

- Please repeat statistical definitions (HR, CI etc.) for each set of parentheses.

* Please ensure that all abbreviations are defined at first use throughout the text (including statistical abbreviations).

* Please ensure that tables and figures, including those in supplementary files, are appropriately referenced in the main text.

* Please review your text for claims of novelty or primacy (e.g. 'for the first time' or ‘novel’) and remove this language.

* Please confirm that any use of statistical terms (such as trend or significant) are supported by the data, and if not please remove them. The term trend should be used only when the test for trend has been conducted.

* Please provide titles and legends for all figures and tables (including those in Supporting Information files).

* Please define all acronyms used in each figure or table in the corresponding legend.

* Please confirm that you used patient-centered language. Please note that patient-centered language is constructed with the use of post-modified nouns putting the person first in the sentence structure.

* Please include the statement on code availability in the data availability statement in the online submission form.

* Please add this statement to the manuscript's Competing Interests: "GDS is an Academic Editor on PLOS Medicine's editorial board."

* Thank you for agreeing to make your data available. At this time, please provide the link to the data repository and accession numbers required for access.

ABSTRACT

* Please confirm that your abstract complies with our requirements, including providing all the information relevant to this study type https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-abstract

* Please confirm that all numbers presented in the abstract are present and identical to numbers presented in the main manuscript text.

* In the abstract, please include the important dependent variables that are adjusted for in the analyses.

* In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

METHODS AND RESULTS

* The terms gender and sex are not interchangeable (as discussed in https://www.who.int/health-topics/gender#tab=tab_1 ); please use the appropriate term.

* “We selected each set of genetic instruments according to the gold standard for Mendelian Randomization analyses, previously described (Detailed methods in S1 Appendix) [29].” – Please provide a summary of the methods with sufficient detail in the main text.

* “As a sensitivity analysis, for sex-hormones in females, we adjusted our models in the observational analysis with hormone-replacement therapy and menopausal status which did not alter the results.” – please provide a reference to the relevant table/figure.

* “When considering individual risk factors as mediators, we estimated that insulin mediates between 67% and 22% of the effect of elevated BMI on renal cancer risk based on the cohort and genetic analyses, respectively (Figure 4).” – please explain where 67% and 22% come from? Are these numbers available outside the figure? If so, please ensure to add a relevant reference.

* Please confirm that you specified the variables controlled for in all relevant Tables/Figures.

------------------------------------------------------------

General Editorial Requests

1) We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

2) Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

3) Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Decision Letter 3

Alexandra Tosun

13 Jan 2026

Dear Dr Johansson,

On behalf of my colleagues and the Academic Editor, Steven C Moore, I am pleased to inform you that we have agreed to publish your manuscript "Systematic assessment of obesity-related risk factors in renal cancer etiology: An observational study using both population cohorts and genetic studies" (PMEDICINE-D-25-02391R3) in PLOS Medicine.

I appreciate your thorough responses to the reviewers' and editors' comments throughout the editorial process. We look forward to publishing your manuscript, and editorially there are only two remaining points that should be addressed prior to publication. We will carefully check whether the changes have been made. If you have any questions or concerns regarding these final requests, please feel free to contact me at atosun@plos.org.

Please see below the minor points that we request you respond to:

* Title: We suggest changing the title to: Systematic assessment of obesity-related risk factors in renal cancer etiology: A mediation analysis using cohort data and Mendelian randomization

* Thank you for including the details of the ethical approvals for the UK Biobank and the Västerbotten Intervention Study. Please confirm that your study did not require separate ethical approval.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email (including the editorial requests above). Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Alexandra Tosun, PhD

Senior Editor

PLOS Medicine

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Flowchart of the mediation analysis.

    NSHDS: The Northern Sweden Health and Disease Study. GWAS: Genome-wide association study. SNPs: Single nucleotide polymorphism. Exp: exposure. Med: potential mediator. Out: outcome. MR: Mendelian Randomization. IVW: Inverse Variance Weighted.

    (DOCX)

    pmed.1004906.s001.docx (477.9KB, docx)
    S2 Fig. Indirect effect estimates for BMI in the mediation analysis.

    Indirect effect estimates for BMI through each risk factor with renal cancer risk as calculated with the product method, confidence intervals estimated through the Sobel method. MR: Mendelian Randomization. BP: Blood pressure. HDL: High-density lipoprotein. SHBG: Sex-hormone binding globulin. CI: Confidence Interval.

    (DOCX)

    pmed.1004906.s002.docx (62.4KB, docx)
    S1 Text

    Table A. Potential obesity-related risk factors for renal cell carcinomas, in the Mendelian Randomization analyses. *Removed due to missingness in the outcome SNPs, outliers identified by MR-PRESSO or removed by steiger filtering. SNP: single-nucleotide polymorphism. GWAS: Genome-wide association study. INTR: Inverse normal transformation of rank. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table B. Potential obesity-related risk factors for clear cell renal cell carcinomas, in the Mendelian randomization analyses. *Removed due to missingness in the outcome SNPs, outliers identified by MR-PRESSO or removed by steiger filtering. SNP: single-nucleotide polymorphism. GWAS: Genome-wide association study. INTR: Inverse normal transformation of rank. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table C. Potential obesity-related risk factors for papillary renal cell carcinoma, in the Mendelian Randomization analysis. *Removed due to missingness in the outcome SNPs, outliers identified by MR-PRESSO or removed by steiger filtering. SNP: single-nucleotide polymorphism. GWAS: Genome-wide association study. INTR: Inverse normal transformation of rank. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table D. Characteristics of the UKB population, by renal cell carcinomas subtypes. 1n (%); Median (IQR). RCC: Renal cell carcinoma. ccRCC: clear cell renal cell carcinoma. pRCC: papillary renal cell carcinoma. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table E. Characteristics of the UKB population, by sex. 1n (%); Median (IQR). HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table F. Characteristics of the Northern Sweden Health and Disease Study (NSHDS) population. 1n (%); Mean (95% confidence interval). Table G. Association of BMI with each potential mediator. Each cox proportional hazard model was adjusted for age, sex, center of recruitment, education, smoking and alcohol drinking status. *log-transformed, **inverse-normal transformation of rank and Z-score, ***Z-score transformation, †standardized and 1-unit increase in Estradiol. Fasting insulin association with BMI was assess in NSHDS. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table H. Association of potential obesity-related risk factors with risk of renal cell carcinoma using Mendelian randomization and prospective cohort analyses. Adjustments for the cox models: Age, sex, center, education, alcohol status and smoking status, • SNPs removed by MR-PRESSO and steiger filtering, *log-transformed, **inverse-normal transformation of rank and Z-score, ***Z-score transformation, †standardized and 1-unit increase in Estradiol. Fasting insulin association with RCC was assess in NSHDS.ª Fasting insulin effect as OR [95% CI]. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table I. Association of potential obesity-related risk factors with risk of clear cell renal cell carcinoma using Mendelian randomization and prospective cohort analyses. Adjustments for the cox models: Age, sex, center, education, alcohol status and smoking status, • SNPs removed by MR-PRESSO and steiger filtering, *log-transformed, **inverse-normal transformation of rank and Z-score, ***Z-score transformation, †standardized and 1-unit increase in Estradiol. Fasting insulin association with RCC was assess in NSHDS. ª Fasting insulin effect as OR [95% CI]. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table J. Association of potential obesity-related risk factors with risk of papillary renal cell carcinoma using Mendelian randomization and prospective cohort analyses. Adjustments for the cox models: Age, sex, center, education, alcohol status and smoking status, • SNPs removed by MR-PRESSO and steiger filtering, *log-transformed, **inverse-normal transformation of rank and Z-score, ***Z-score transformation, †standardized and 1-unit increase in Estradiol. Fasting insulin association with RCC was assess in NSHDS. ª Fasting insulin effect as OR [95% CI]. HbA1c: glycated hemoglobin. IGF-1: Insulin-like growth factor-1. eGFR: estimated glomerular filtration rate. HDL/LDL: High/Low-density lipoproteins. SHBG: Sex-hormone binding globulin. Table K. Comparison between the adjusted and the non-adjusted model for specific female factors. *Cox proportional hazard model adjusted for age, center of recruitment, education, smoking status and alcohol drinking status. ** Cox proportional hazard model additionally adjusted for Hormone replacement therapy (Yes/no) and menopausal status (Yes/no). RCC: renal cell carcinoma. ccRCC: clear cell renal cell carcinoma. SHBG: Sex-hormone binding globulin. Table L. Beta estimates between potential mediators. Association between each potential mediator, bidirectionally, in cohort and MR analyses. BP: blood pressure. SHBG: Sex-hormone binding globulin. HDL: High-density lipoprotein cholesterol. Each model, in the cohort analyses, was adjusted for age, sex, center of recruitment, education, alcohol and smoking status. Table M. Proportion of BMI effect on renal cell carcinoma mediated. Each cox proportional hazard model was adjusted for age, sex, center of recruitment, education, smoking and alcohol drinking status. ªFasting insulin effect as OR [95% CI]. BP: blood pressure. SHBG: Sex-hormone binding globulin. HDL: High-density lipoprotein cholesterol. Table N. Proportion of BMI effect on ccRCC mediated. Each cox proportional hazard model was adjusted for age, sex, center of recruitment, education, smoking and alcohol drinking status. ª Fasting insulin effect as OR [95% CI]. SHBG: Sex-hormone binding globulin. HDL: High-density lipoprotein cholesterol. Table O. Multivariable analyses of mediators on renal cell carcinomas. Multivariable analyses between each potential mediator adjusted for each other mediator. Each cox proportional hazard model was adjusted for age, sex, center of recruitment, education, smoking and alcohol drinking status. Not enough SNPs in Insulin instruments to run multivariable MR with another mediator. - Not calculated to due lack of power (F-statisticconditional <10). BP: blood pressure. SHBG: Sex-hormone binding globulin. HDL: High-density lipoprotein.

    (DOCX)

    pmed.1004906.s003.docx (707.9KB, docx)
    S1 STROBE Checklist. Checklist of items that should be included in reports of observational studies.

    Licensed under CC BY 4.0. Checklist available from https://www.strobe-statement.org/checklists/.

    (DOC)

    pmed.1004906.s004.doc (124.5KB, doc)
    S2 STROBE Checklist. STROBE-MR checklist of recommended items to address in reports of Mendelian randomization studies.

    Checklist available from https://www.strobe-mr.org/.

    (DOCX)

    pmed.1004906.s005.docx (41.1KB, docx)
    S1 Appendix. Supplementary methods and materials.

    (DOCX)

    pmed.1004906.s006.docx (77.9KB, docx)
    Attachment

    Submitted filename: Revisions_answers.docx

    pmed.1004906.s009.docx (43.2KB, docx)
    Attachment

    Submitted filename: Editorial_Revisions_answers.docx

    pmed.1004906.s010.docx (43.2KB, docx)

    Data Availability Statement

    The data underlying the results presented in the study are available from UK Biobank upon request (https://www.ukbiobank.ac.uk) and GWAS summary statistics from https://www.nature.com/articles/s41588-024-01725-7. The code used in the analysis is available from Gitlab: https://gitlab.com/Karine.Alcala/obesity_rcc/-/tree/79b0a4566da2a0a29f3bbf08eaf1d75f66691f69/ and archived in Zenodo: https://doi.org/10.5281/zenodo.17640162.

    Articles from PLOS Medicine are provided here courtesy of PLOS

    RESOURCES