
==== Front
Investig Clin Urol
Investig Clin Urol
ICU
Investigative and Clinical Urology
2466-0493
2466-054X
The Korean Urological Association

39249914
10.4111/icu.20240052
Special Article
Impact of pretreatment body mass index on clinical outcomes in patients with metastatic renal cell carcinoma receiving first-line immune checkpoint inhibitor-based therapy: A systematic review and meta-analysis
https://orcid.org/0000-0002-1651-6269
Lee Kunwoo 1*
https://orcid.org/0000-0003-2147-2915
Yu Jiwoong 2*
https://orcid.org/0000-0003-0971-1805
Song Wan 2
https://orcid.org/0000-0002-8287-9383
Sung Hyun Hwan 2
https://orcid.org/0000-0002-5613-8389
Jeon Hwang Gyun 2
https://orcid.org/0000-0002-5399-2184
Jeong Byong Chang 2
https://orcid.org/0000-0002-9792-7798
Seo Seong Il 2
https://orcid.org/0000-0002-3265-6261
Jeon Seong Soo 2
https://orcid.org/0000-0002-6966-8813
Kang Minyong 234
1 Inha University College of Medicine, Incheon, Korea.
2 Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
3 Samsung Genome Institute, Samsung Medical Center, Seoul, Korea.
4 Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.
Corresponding Author: Minyong Kang. Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea. TEL: +82-2-3410-1138, FAX: +82-2-3410-3992, dr.minyong.kang@gmail.com
*These authors contributed equally to this study and should be considered co-first authors.

9 2024
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30 4 2024
01 7 2024
© The Korean Urological Association
2024
The Korean Urological Association
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
This study aimed to assess the prognostic role of body mass index (BMI) in patients with metastatic renal cell carcinoma (mRCC) treated with first-line immune checkpoint inhibitor (ICI)-based therapy. We searched for relevant studies in the MEDLINE, Embase, and Cochrane Library databases. The initial search yielded 599 records, of which seven articles (2,517 patients) were selected for analysis. Patients with a high BMI had a favorable overall survival (OS) based on hazard ratio (HR) (crude HR 0.69, 95% confidence interval [CI] 0.57–0.83, p<0.0001; adjusted (a)HR 0.75, 95% CI 0.59–0.95, p=0.02), but not relative risk (RR 0.88, 95% CI 0.67–1.16, p=0.37). In the subgroup analysis, patients with a high BMI had better OS in the ICI with tyrosine kinase inhibitor (TKI) subgroup (aHR 0.71, 95% CI 0.55–0.92, p=0.01), while no significant difference was found in the ICI-only subgroup (aHR 1.02, 95% CI 0.56–1.87, p=0.95). Adjusted statistics for progression-free survival (PFS) were assessable in predominantly ICI-only studies and demonstrated a favorable outcome for patients with a low BMI (aHR 1.67, 95% CI 1.14–2.45, p=0.01). In conclusion, the impact of high BMI varies depending on the treatment type, exhibiting a favorable correlation with OS within ICI with TKI subgroup, but indicating an adverse association with PFS in the ICI-only subgroup. Further research is needed to clarify the influence of BMI by stratifying patients into ICI-only and ICI with TKI treatment to provide more insights.

Graphical Abstract

Body mass index
Immune checkpoint inhibitors
Meta-analysis
Renal cell carcinoma
Systematic review
Korean Urological Association 2023-KUA-005 Korea Health Industry Development Institute https://doi.org/10.13039/501100003710 HR20C0025
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pmcINTRODUCTION

Recently, the therapeutic approach to metastatic renal cell carcinoma (mRCC) has changed from the use of tyrosine kinase inhibitors (TKIs) to an immune checkpoint inhibitor (ICI)-based strategy [12]. Despite advancements facilitated by ICI-based therapy in improving mRCC prognosis [3], the variability in response and duration of efficacy among patients necessitates the identification of prognostic factors. The established International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) risk criteria, while considered the gold standard [4], may fall short in reflecting the host’s immunity in the expanding landscape of treatment options, especially ICI-based therapies. Previous explorations of factors such as PD-L1 expression and tumor mutational burden have not conclusively established their relationship with the prognoses of mRCC patients treated with ICI-based therapies [567]. The searching for predictive parameters is an ongoing challenge.

Researchers are increasingly exploring body composition as a potential personalized prognostic indicator. Specifically, obesity is a major risk factor for RCC, yet it paradoxically presents as a favorable prognostic factor in mRCC, a phenomenon known as the “obesity paradox” [8]. This paradoxical relationship reveals underlying mechanisms, such as decreased expression levels of tumor genetic factors involved in fatty acid synthesis and differential gene expression levels related to vascular formation and inflammation in the tumor microenvironment, in obese patients [9]. In this context, the role of body mass index (BMI) has been the subject of investigation regarding its prognostic implications in patients with mRCC treated with ICI-based therapies. Despite ongoing discussions regarding the prognostic significance of BMI in patients with mRCC [10], particularly in the era of ICI-based therapy, a notable research gap remains.

Previous systematic reviews have primarily focused on the evaluation of BMI in patients receiving ICI-based therapy across various treatment lines, with only 14% of patients in the first-line setting, while the remaining patients had received prior TKI therapy [111213]. TKI administration has been linked to distinctive effects on fat and muscle metabolism, causing decreases in skeletal muscle, subcutaneous adipose tissue, and weight [14151617], further complicating the assessment of the prognostic impact of BMI. Moreover, a previous report suggested varied prognostic implications of BMI in patients with melanoma, based on whether ICI-based therapy was administered as a first-line or later-line treatment, with an overweight or obese status moderately associated with improved outcomes in first-line cases and linked to worse outcomes in later-line ICI recipients [18].

The complexity surrounding the influence of BMI on responses to ICI-based therapy underscores the need for a focused investigation. Our study aims to address this gap by conducting a systematic review and meta-analysis of the prognostic implications of pre-treatment BMI in systemic-therapy-naive patients with mRCC treated with first-line ICI-based therapy. We analyzed the potential impact of BMI on the key outcomes of overall survival (OS), progression-free survival (PFS), and overall response rate (ORR) in patients with mRCC. Additionally, we performed subgroup analyses to evaluate the specific effects of different types of first-line ICI-based therapy, comparing the outcomes between ICI-only and the combination of ICI with TKI.

This systematic review was registered in PROSPERO (CRD42023474225). There was no amendment to the protocol after registration.

MATERIALS AND METHODS

1. Eligibility criteria

1) Inclusion criteria

Randomized controlled trials (RCTs), cohort studies, and case-control studies were assessed for potential inclusion in the review. Studies were deemed eligible if they included patients diagnosed with mRCC, who received ICI-based therapy as their initial systemic treatment. To enhance the diversity of the data, various BMI cutoff values were taken into consideration. The analysis also encompassed studies reporting both response outcomes (i.e., ORR) and survival outcomes, specifically the duration from the initiation of ICIs to either death (i.e., OS) or disease progression (i.e., PFS).

2) Exclusion criteria

Review articles, case reports, editorial comments, and letters were excluded, but no language restrictions were imposed. In keeping with the specific objectives of this review, studies involving patients who did not receive ICI-based therapy as their first-line treatment were also excluded. Additionally, studies lacking a comparison based on BMI categories (where BMI was treated as a continuous variable) were excluded. Moreover, studies without relative risk (RR) or hazard ratio (HR) estimates, confidence intervals (CIs), or adequate data for estimate calculations were also excluded.

2. Information sources and search strategy

We conducted a systematic literature search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist [19]. Peer-reviewed literature and grey literature were gathered through a database search of Ovid MEDLINE, Embase, and the Cochrane Library on October 12, 2023. To identify eligible studies, the search terms were categorized into three main components: BMI, RCC, and ICIs, which included various drug names. A comprehensive search strategy was developed for each database, as provided in Supplementary Table 1.

3. Selection process

Two investigators (KL and JY) independently reviewed all titles and abstracts. In cases where the abstract did not provide sufficient information to ascertain whether a study met the inclusion or exclusion criteria, full-text screening was conducted. Cohen’s κ coefficient was computed to evaluate the extent of agreement between these two investigators throughout both the title/abstract screening and full-text screening processes. Any discrepancies were resolved through discussion. The compiled list of relevant studies resulting from this screening was then subjected to risk of bias (RoB) assessment and meta-analysis.

4. Data collection process, data items, and effect measures

Data extraction was carried out for each selected study in accordance with the following predefined data extraction format: first author’s name, journal name, study period, publication year, study design, location, ethnicity, cohort size, BMI cutoff, age distribution, IMDC risk categories, histological types, treatment regimens, confounding factors adjusted for, median follow-up period, and the RR or HR for OS, PFS and objective responses along with the corresponding 95% CIs and p-values. In the case where the HR was not provided, but only the Kaplan–Meier curve was presented, the HR for OS was calculated using data extracted up to 21 months of follow-up from the respective Kaplan–Meier curves using established methodologies [20]. Two investigators (KL and JY) independently conducted the data extraction process. We contacted the corresponding authors of the included studies via email to request any additional data or clarification that may have been relevant to our analysis.

5. Study risk of bias assessment

We evaluated the quality of the included studies using the Quality in Prognostic Studies (QUIPS) tool, an assessment tool recommended by the Cochrane Prognosis Methods Group for appraising the RoB in prognostic factor studies. The QUIPS tool evaluates six crucial domains to assess the validity of the findings and bias in studies of prognostic factors: (1) study participation, (2) study attrition, (3) prognostic factor measurement, (4) outcome measurement, (5) study confounding, and (6) statistical analysis and reporting [21]. In each domain, three to seven items are assessed using a four-grade scale (yes, partial; no, unsure). While certain items require direct responses (e.g., “reasons for loss to follow-up are provided”), others necessitate more subjective judgments (e.g., “there is adequate participation in the study by eligible individuals”). Ultimately, the rater makes an overall judgment of the RoB within each domain, expressed on a three-grade scale (high, moderate, or low RoB). Two investigators (KL and JY) independently performed the quality assessment, and any conflicts were resolved through discussion.

6. Synthesis methods

Associations between BMI and OS, PFS, and ORR were assessed by measuring the RR or HR with 95% CIs and p-values. The RR or HR for each outcome was analyzed separately. An HR less than 1 indicated superior survival outcomes for patients with a high BMI, while an HR greater than 1 suggested inferior survival outcomes. Statistical heterogeneity among the included studies or subgroups was assessed using Cochran’s Q test and the I2 statistic under a random-effects model (DerSimonian–Laird method), with a two-sided p-value less than 0.10 and an I2 statistic greater than 50% considered statistically significant based on established guidelines. The random-effects model was employed due to variations in treatment regimens, ethnicity and BMI cutoff values across the included studies [2223]. Subgroup and sensitivity analyses were conducted when appropriate. Subgroup analysis was performed for prespecified subgroups based on the treatment type (predominantly ICI-only subgroup vs. ICI with TKI subgroup). Sensitivity analysis was performed by excluding specific studies, such as those with HR estimated from Kaplan–Meier curves, those with a BMI cutoff value of 30, those encompassing predominantly an ICI with TKI treatment regimen, and those conducted exclusively in Asian populations, to assess the impact of their exclusion on overall estimates. Asymmetry tests, such as Egger’s test, were not conducted due to their limited reliability in meta-analyses with fewer than 10 studies [24]. Additionally, since each analysis comprised fewer than 10 studies, funnel plots, employed to examine potential publication bias, were not generated [25]. The authors, however, thoroughly assessed and discussed the potential presence of publication bias in the included studies. All statistical analyses were carried out using STATA/MP 18.0 software (StataCorp LLC). The robvis package in R was used for the visualization of the QUIPS RoB assessment [26].

7. Certainty assessment

The assessment of certainty in the body of evidence for effects on outcomes was conducted through the creation of “Summary of findings” tables according to the GRADE approach. It encompasses five criteria, extending beyond internal validity factors (RoB, inconsistency, imprecision, publication bias) to include aspects of external validity, such as the directness of results [27]. These tables offer essential information regarding the certainty or quality of evidence, reflecting the confidence or certainty in the estimated effect range or association. Two investigators (KL and JY) independently graded the quality of evidence for six outcome measures and disagreement between authors were resolved through discussion. Evidence for downgrading the quality of evidence is described in the footnotes. The tables also encapsulate details regarding the magnitude of the interventions’ effects and they compile the available data regarding the principal outcomes [2527]. The “Summary of findings” tables were generated using GRADEpro GDT [28].

This systematic review received confirmation of exemption from review by the Institutional Review Board (approval number: SMC 2023-11-156).

RESULTS

1. Literature search results

The study selection process, which is shown in Fig. 1 [29], followed the PRISMA guidelines. The initial search yielded 731 articles (110 from MEDLINE, 586 from Embase, and 35 from the Cochrane Library). After removing 132 duplicate articles, 599 records were screened based on their titles and abstracts, of which 94 articles were selected with moderate interobserver agreement (κ=0.48). The full texts of the articles were retrieved and further evaluated based on predefined exclusion and inclusion criteria. Seven articles were selected for the final analysis with substantial interobserver agreement (κ=0.73). The excluded studies and their corresponding reasons for exclusion are presented in Supplementary Table 2. Efforts were made to obtain additional or clarifying data by reaching out to the authors of the included studies via email; however, no response was received.

2. Summary of studies

This meta-analysis included seven studies involving 2,517 patients with mRCC who underwent first-line ICI-based treatment (Table 1). Of the seven included studies, two were phase III RCTs [3031], four were retrospective studies [32333435], and one was a conference abstract [36]. The baseline characteristics were evaluated across the studies, with three of them considering multiple clinically relevant covariates in the adjustment of BMI. Despite slight variations in BMI classification, the cutoff values (18.5, 25, and 30 kg/m2) uniformly adhered to the World Health Organization definitions. Six studies reported the specific regimens administered to the study cohorts. Notably, four studies included patients who predominantly received ICI-only regimens (i.e., nivolumab+ipilimumab). In three of these studies, all patients received nivolumab+ipilimumab [303234], and in one study, 73.5% of the patients received nivolumab+ipilimumab [33]; these four studies were collectively grouped into predominantly ICI-only subgroup. In contrast, the other two studies predominantly evaluated combination of ICIs with TKIs. In one of these studies, all enrolled patients received ICI with TKI (i.e., avelumab+axitinib) [31], and in the other study, 52% of the patients received ICI with TKI [35]; these two studies were collectively grouped into ICI with TKI subgroup.

Regarding the reported outcomes, one study conducted an analysis of the HR for OS and PFS after segregating the cohort into intermediate- and poor-risk groups based on IMDC criteria [35]. Four (57.1%) of the included studies reported five HRs for OS and three (42.9%) of the studies reported four HRs for PFS as either the crude HR (cHR) or adjusted HR (aHR). Additionally, two studies (28.6%) presented RRs for OS, one study (14.3%) reported the RR for PFS, and three studies (42.9%) studies reported RRs for ORR.

3. Risk assessment

Risk assessment was performed using the QUIPS tool, and the results are presented in Fig. 2. Overall, most studies had a low RoB in participation, attrition, prognostic factors, and outcome domains, but had varying degrees of bias for the confounding and statistical analysis domains. For both the study confounder domain and statistical analysis domain, four out of seven studies had a high RoB due to not adjusting for possible confounding variables and inappropriate statistical design of the study. Two studies were found to have a moderate risk for the confounding domain due to variability in the study treatment regimen.

4. OS according to BMI

1) Meta-analysis of HR

In analysis of cHRs, patients with a high BMI were likely related to a large improvement in OS compared to patients with a low BMI (cHR 0.69, 95% CI 0.57–0.83, p<0.0001, moderate certainty; Fig. 3A). Heterogeneity analysis showed no significant heterogeneity between studies (I2=0.00%, Cochrane’s Q test p=0.84; Supplementary Fig. 1). Moreover, sensitivity analyses, which involved excluding studies specified in the synthesis method, demonstrated a favorable OS in patients with a high BMI (Supplementary Table 3).

Similarly, in analysis of aHRs, patients with a high BMI were probably associated with a large improvement in OS compared to those with a low BMI after adjusting for confounding variables (aHR 0.75, 95% CI 0.59–0.95, p=0.02, moderate certainty; Fig. 3B). Heterogeneity analysis showed no significant heterogeneity between studies (I2=0.00%, Cochrane’s Q test p=0.55).

2) Meta-analysis of RR

For studies reporting OS event counts instead of HRs, the effect size was compared using RR. The evidence is highly uncertain about the effect of patients with a high BMI showing better OS compared to those with a low BMI (RR 0.88, 95% CI 0.67–1.16, p=0.37, very low certainty; Supplementary Fig. 2). No significant heterogeneity was observed between studies (I2=57.19%, Cochrane’s Q test p=0.13).

3) Subgroup analysis of aHR for OS according to the treatment type

The subgroup analysis by treatment type demonstrated better OS in patients with a high BMI in the ICI with TKI subgroup (aHR 0.71, 95% CI 0.55–0.92, p=0.01), but showed no difference in OS between BMI groups for the predominantly ICI-only subgroup (aHR 1.02, 95% CI 0.56–1.87, p=0.95; Fig. 3C).

5. PFS according to BMI

In the analysis of unadjusted HRs for PFS, patients with a high BMI may have worse PFS compared to those with a low BMI, but the evidence is very uncertain (cHR 1.12, 95% CI 0.81–1.55, p=0.47, very low certainty; Fig. 4A). Moreover, heterogeneity analysis revealed that there was significant heterogeneity between the studies (I2=65.23%, Cochrane’s Q test p=0.03; Supplementary Fig. 3). Sensitivity analysis showed no difference in PFS between the two BMI groups, except when the study using an ICI with TKI treatment regimen was excluded, which showed better PFS for the low-BMI group (cHR 1.63, 95% CI 1.12–2.35, p=0.01; Supplementary Table 3).

In the analysis of aHRs, primarily assessable in predominantly ICI-only studies, a low BMI was associated with a large increase in PFS compared to a high BMI (aHR 1.67, 95% CI 1.14–2.45, p=0.01, high certainty; Fig. 4B). There was no heterogeneity between studies (I=0.00%, Cochrane’s Q test p=0.47).

Subgroup analysis for PFS was unfeasible as the analysis of aHR was limited solely to ICI-only studies.

6. ORR according to BMI

The ORR was compared using RRs for studies reporting response rates by BMI groups. The evidence suggests the ORR may be more favorable in the low BMI group, but the evidence is highly uncertain around this finding (RR 0.90, 95% CI 0.47–1.73, p=0.76, very low certainty), and significant heterogeneity between studies was observed (I2=76.07%, Cochrane’s Q test p=0.02; Supplementary Fig. 4).

Subgroup analysis for ORR was omitted due to the exclusive inclusion of ICI-only studies in the ORR analysis.

7. Summary of findings

The certainty or quality of evidence for each analyzed outcome, as well as the magnitude of the effects of the intervention, are summarized Supplementary Table 4.

DISCUSSION

In the present systematic review and meta-analysis, we reviewed seven studies that reported outcomes of systemic-therapy-naive patients with mRCC treated with first-line ICI-based therapy. To the best of our knowledge, this is the first systematic review focused on the influence of BMI on treatment outcomes in this patient population. Notably, the results from our meta-analyses showed that a high BMI had mixed implications among patients treated with first-line ICI-based therapy. In the population receiving ICI with TKI treatment, a high BMI served as a favorable prognostic factor for OS, while in the population undergoing ICI-only treatment, it concurrently exhibited an association with a poorer prognosis for PFS.

The studies analyzed in our systematic review included four studies that predominantly focused on patients treated with ICI-only (i.e., nivolumab+ipilimumab) and two studies that investigated patients treated with ICI in combination with TKIs (Table 1). When examining OS using both cHR and aHR, a high BMI was consistently identified as a favorable predictor of OS. However, subgroup analysis revealed that in the predominantly ICI-only subgroup, BMI did not serve as a predictor of OS, while in the ICI with TKI subgroup, high BMI emerged as a favorable predictor of OS. In the analysis of PFS using aHRs, the ratios were primarily available for predominantly ICI-only studies, and the PFS was better in the low-BMI group. Thus, setting our research apart from previous studies, the findings of this systematic review indicated that the varying impact of BMI on the outcomes could be largely attributed to the concurrent administration of a TKI.

A previous systematic review analyzed studies of patients with mRCC who were predominantly treated with ICI-only regimens at any lines of therapy [11]. In that review, a significant association was found between a high BMI and improved OS and PFS compared to these outcomes in patients with a low BMI (aHR 0.77, 95% CI 0.65–0.91, p=0.002; aHR 0.66, 95% CI 0.44–1.00, p=0.050, respectively). However, this study has significant limitations in assessing the impact of BMI on patients exclusively receiving ICI as systemic therapy, as the review included 2,517 patients from seven studies, with only 320 patients (12.7%) receiving first-line ICI therapy. Therefore, the results may have been influenced by the previous administration of a TKI in the earlier lines of therapy. The administration of a TKI before ICI-based therapy may impact fat and muscle metabolism, leading to a reduction in skeletal muscle, subcutaneous adipose tissue, and weight [14151617]. This complicates the evaluation of the prognostic impact of BMI.

Furthermore, various mechanisms have been proposed for the improved outcomes observed in patients with a high BMI who are administered TKIs rather than ICIs. Tumors in obese patients exhibit elevated angiogenesis scores compared with those in individuals with normal weight [9], and patients with higher angiogenesis scores tend to experience greater benefits from TKI therapy [37]. Consequently, the favorable outcomes in patients with a high BMI may be attributed to the targeting of activated angiogenesis by TKIs. Additionally, adiponectin (APN), released by adipocytes, exhibits an inverse correlation with body fat accumulation [38]. The interplay between APN and its receptors has the potential to inhibit the PI3K/AKT pathway, deactivate the NF-κB signaling pathway, and function as a resistance mechanism against the therapeutic effectiveness of TKIs [39404142]. This association may lead to improved outcomes for obese patients with higher BMI treated with concurrent TKIs.

On the other hand, the efficacy of ICIs is influenced by various clinical factors related to the patient’s immune system and the tumor microenvironment [43]. However, biological studies of obese patients treated with ICI-based therapy have provided limited information thus far. A prior investigation demonstrated that obesity leads to heightened immune aging, tumor progression, and PD-1-mediated T-cell dysfunction in tumor models, mice, and humans, influenced in part by leptin [44]. That study also suggested an increased efficacy of PD-1/PD-L1 blockade in obese mice and human cancer patients. However, the findings do not conclusively establish obesity as the sole positive prognostic factor for treatment outcomes, and it does not necessarily indicate a superior treatment response in obese patients compared to non-obese patients. Instead, the authors distinctly emphasize that administering PD-1/PD-L1 blockade through ICI administration may alleviate T-cell dysfunction induced by obesity, thereby partially impeding tumor progression.

Additionally, BMI can be viewed as an indicator of a patient’s overall fitness and biological reserve, which may influence their response to various therapeutic sequences. For instance, patients with a higher BMI might tolerate and respond better to intensive therapies, potentially leading to improved survival [45]. Conversely, those with a lower BMI may experience poorer outcomes due to reduced resilience and increased vulnerability to treatment-related adverse effects. Therefore, even though the follow-up duration of the studies included in our research is short and may not fully capture the impact of subsequent lines of therapy, the interaction between BMI and treatments administered after the first-line cannot be ignored, necessitating further research.

There are some limitations of this study. First, the present systematic review included a relatively small number of studies, with sensitivity analyses indicating notable influence from a few retrospective studies. The findings are primarily situated between those of Ishihara et al. [33] and McManus et al. [34], as well as Santoni et al. [35]. However, the total number of patients in this review encompassed a substantial cohort of 2,517 patients with first-line ICI-based therapy, marking it as the largest such study to date. Furthermore, although important confounding factors such as IMDC risk were adjusted in the studies, potential confounding factors, such as differences in treatment regimens, could still affect the survival outcomes. However, given the limited range of currently available treatment regimens, we believe that the findings of our study can largely be attributed to differences in treatment types (i.e., ICI-only and ICI with TKI treatments) rather than the specific regimens themselves. Second, it should be taken into account, when interpreting the results, that two of the included studies were prospective, while five were retrospective in nature. Despite differences in study design, no statistical heterogeneity was observed for survival outcomes (i.e., OS and PFS), except in the cHRs for PFS. The observed heterogeneity of the cHRs for PFS is likely due to variations among studies employing different treatment types, as confirmed by sensitivity analysis. Third, it is important to acknowledge the possibility of publication bias, where BMI might be selectively reported only when it has an impact on treatment outcomes, across all relevant studies. Additionally, BMI serves as a prognostic factor, albeit its mention may not always be evident in study titles or abstracts, particularly if statistical significance is lacking. Nonetheless, our investigation thoroughly examined full-text resources, including supplementary materials, to rectify any omissions of BMI data in title or abstracts. Finally, with the recent introduction of ICI-based therapy, the follow-up periods in the included studies have been relatively brief. Additionally, this raises concerns about potential time-lag bias, where delays in publishing studies with less favorable outcomes could result in an overestimation of the effectiveness of newly introduced drugs, emphasizing the need for caution in interpreting these findings.

CONCLUSIONS

In conclusion, our systematic review and meta-analysis revealed variations in the prognostic role of pretreatment BMI among patients undergoing first-line ICI-based therapy, depending on the treatment type. Specifically, in studies involving patients receiving ICI with TKI, a high BMI was more frequently linked to a favorable OS. Conversely, in studies predominantly including ICI-only patients, a high BMI showed no prognostic value for OS and was even associated with poor PFS. The current evidence, combined with our findings, underscores the need for further detailed research to elucidate the influence of high BMI in patients receiving ICI-only therapy, thereby excluding the impact of previously or concurrently administered TKIs. Future studies should consider stratifying patients into ICI-only and ICI with TKI groups to provide more insights into this matter.

SUPPLEMENTARY MATERIALS

Supplementary materials can be found via https://doi.org/10.4111/icu.20240052.

Supplementary Table 1

Search strategy

Supplementary Table 2

Excluded studies and their reasons for exclusion

Supplementary Table 3

Sensitivity analysis results

Supplementary Table 4

Summary of findings

Author(s): Jiwoong Yu, Kunwoo Lee

Question: High BMI compared to Normal BMI for Survival Outcomes

Setting: mRCC patients

Supplementary Fig. 1

Funnel plot of the meta-analysis of the crude hazard ratio for overall survival. CI, confidence interval; IV, inverse variance.

Supplementary Fig. 2

Relative risk for overall survival by body mass index (BMI). IV, inverse variance; CI, confidence interval.

Supplementary Fig. 3

Funnel plot of the meta-analysis of the crude hazard ratio for progression-free survival. CI, confidence interval; IV, inverse variance.

Supplementary Fig. 4

Relative risk for overall response rate by body mass index (BMI). IV, inverse variance; CI, confidence interval.

Fig. 1 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. Data from the article of Page et al. [29] (BMJ 2021;372:n71).

Fig. 2 Risk assessment based on the QUIPS (Quality in Prognostic Studies) tool. (A) Risk of bias traffic-light plot. (B) Risk of bias summary plot.

Fig. 3 Meta-analysis for overall survival (OS) according to body mass index (BMI). (A) Forest plot of the meta-analysis of the crude hazard ratio (HR) for OS comparing a high BMI versus a low BMI. (B) Forest plot of the meta-analysis of the adjusted HR for OS comparing high BMI versus low BMI. (C) Subgroup analysis of the adjusted HR for OS according to the treatment type. IV, inverse variance; CI, confidence interval; ICI, immune checkpoint inhibitor; TKI, tyrosine kinase inhibitor.

Fig. 4 Meta-analysis for progression-free survival (PFS) according to body mass index (BMI). (A) Forest plot of the meta-analysis of the crude hazard ratio (HR) for PFS comparing high BMI versus low BMI. (B) Forest plot of the meta-analysis of the adjusted HR for PFS comparing high BMI versus low BMI. IV, inverse variance; CI, confidence interval.

Table 1 Summary of studies

Study	Study period	Study design	BMI cutoff (kg/m2)	Cohort (n)	IMDC risk (%)	Pathology (%)	Median follow-up (mo)	Therapeutic regimen	Cofounding factors	HR (95% CI) or RR	
Total	≥Cutoff	<Cutoff	FAV	INT	Poor	CC	NCC	OS	PFS	ORR	
Basso et al. [32] (2022)	2019–2021	RS	≥25 vs. <25	324	143	181	0	68	32	83	17	12	Nivolumab+ipilimumab (100%)	NA	cHR 0.6907 (0.49–0.9737)	NA	NA	
RR 0.74	
Motzer et al. [30] (2022)	2014–2017	PS	≥30 vs. <30	547	165	382	22	60	18	100	-	67.7	Nivolumab+ipilimumab (100%)	NA	RR 0.98	NA	NA	
Ishihara et al. [33] (2023)	2016–2021	RS	≥25 vs. <25	98	23	75	6	65	29	78	22	12.1	Nivolumab+ipilimumab (73.5%)	OS: KPS, IMDC risk score, serum CRP level	cHR 0.78 (0.22–2.75)	cHR 1.39 (0.74–2.61)	RR 0.82	
Pembrolizumab+axitinib (18.4%)	PFS: sex, LN metastasis, serum CRP level	aHR 1.17 (0.31–4.5)	aHR 1.39 (0.74–2.62)	
Avelumab+axitinib (6.1%)	
McManus et al. [34] (2023)	2015–2021	RS	≥30 vs. 18.5–29.9	99	31	68	12	69	19	81	19	25.7	Nivolumab+ipilimumab (100%)	Age, sex, and IMDC risk score	cHR 0.916 (0.489–1.718)	cHR 1.765 (1.119–2.783)	RR 0.49	
aHR 0.9841 (0.4988–1.9417)	aHR 1.859 (1.156–2.989)	
Santoni et al. [35] (2024)	2016–2022	RS	≥25 vs. <25	930	443	487	0	70	30	87	13	18.7	Nivolumab+ipilimumab (47.8%)	OS: nephrectomy, sarcomatoid features, bone metastasis, liver metastasis, ICI+ICI vs. ICI+TKI	INT risk:	INT risk: cHR 0.86 (0.68–1.09)	NA	
Pembrolizumab+axitinib (43.2%)		cHR 0.61 (0.44–0.85)	
Nivolumab+cabozantinib (7.2%)	PFS: nephrectomy, sarcomatoid features, bone metastasis, brain metastasis, ICI+ICI vs. ICI+TKI		aHR 0.71 (0.55–0.92)	Poor risk: cHR 0.96 (0.69–1.33)	
Pembrolizumab+lenvatinib (1.7%)	Poor risk:	
	cHR 0.72 (0.5–1.05)	
Motzer et al. [31] (2019)	2016–2017	PS	≥25 vs. <25	269	176	93	20	68	12	100	-	11.6	Avelumab+axitinib (100%)	NA	NA	RR 0.71	NA	
Gan et al. [36] (2020)	NA	RS	≥30 vs. 18.5–24.9	250	140	110	16	84	NA	NA	First-line immuno-oncology agent	NA	NA	NA	RR 1.52	
BMI, body mass index; IMDC, International Metastatic Renal Cell Carcinoma Database Consortium; FAV, favorable; INT, intermediate; CC, clear cell; NCC, non-clear cell; HR, hazard ratio; CI, confidence interval; RR, relative risk; OS, overall survival; PFS, progression-free survival; ORR, objective response rate; RS, retrospective study; PS, prospective study; NA, not available; KPS, Karnofsky performance status; CRP, C-reactive protein; LN, lymph node; ICI, immune checkpoint inhibitor; TKI, tyrosine kinase inhibitor; cHR, crude hazard ratio; aHR, adjusted hazard ratio.

CONFLICTS OF INTEREST: The authors have nothing to disclose.

FUNDING: This research was funded by the Korean Urological Association in 2023 (2023-KUA-005). This study was also supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HR20C0025).

AUTHORS’ CONTRIBUTIONS: Research conception and design: Kunwoo Lee, Jiwoong Yu, and Minyong Kang.

Data acquisition: Kunwoo Lee and Jiwoong Yu.

Statistical analysis: Kunwoo Lee and Jiwoong Yu.

Data analysis and interpretation: Kunwoo Lee and Jiwoong Yu.

Drafting of the manuscript: Kunwoo Lee and Jiwoong Yu.

Critical revision of the manuscript: Wan Song, Hyun Hwan Sung, Hwang Gyun Jeon, Byong Chang Jeong, Seong Il Seo, Seong Soo Jeon, and Minyong Kang.

Obtaining funding: Jiwoong Yu and Minyong Kang.

Administrative, technical, or material support: Kunwoo Lee, Jiwoong Yu, and Minyong Kang.

Supervision: Minyong Kang.

Approval of the final manuscript: Kunwoo Lee, Jiwoong Yu, Wan Song, Hyun Hwan Sung, Hwang Gyun Jeon, Byong Chang Jeong, Seong Il Seo, Seong Soo Jeon, and Minyong Kang.
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