Yoo Bin Jung; Hee Kyung Ahn; Hyun-Young Shin; Ji Hyung Hong; Chai Hong Rim

doi:10.4143/crt.2025.110

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Original Article Breast cancer The Impact of Obesity on Treatment Outcomes in Patients with Hormone Receptor–Positive HER2-Negative Metastatic Breast Cancer Receiving CDK 4/6 Inhibitors: Yoo Bin Jung¹, Hee Kyung Ahn², Hyun-Young Shin³, Ji Hyung Hong¹, Chai Hong Rim^4,5; Cancer Research and Treatment : Official Journal of Korean Cancer Association 2026;58(1):198-207.
DOI: https://doi.org/10.4143/crt.2025.110
Published online: April 15, 2025

¹Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea

²Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

³Department of Family Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea

⁴Department of Radiation Oncology, Korea University Ansan Hospital, Korea University Medical College, Ansan, Korea

⁵CURE Analytics, Korea

Correspondence: Chai Hong Rim, Department of Radiation Oncology, Korea University Ansan Hospital, Korea University Medical College, 123 Jeokgeum-ro, Danwon-gu, Ansan 15355, Korea; CURE Analytics, Korea
Tel: 82-31-412-6850 E-mail: crusion3@naver.com

Co-correspondence: Ji Hyung Hong, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea
Tel: 82-2-2258-6362 E-mail: jhhong8107@gmail.com

• Received: January 25, 2025 • Accepted: April 13, 2025

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.

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Abstract

Purpose
Guidelines from the aromatase inhibitor era for early breast cancer (EBC) treatment recommend maintaining a body mass index (BMI) below 25. In the current era of cyclin-dependent kinase (CDK) 4/6 inhibitors, now standard in metastatic breast cancer (MBC), limited data exist on treatment outcomes in obese patients. This study investigates how adiposity affects the treatment outcome of CDK 4/6 inhibitors in patients with hormone receptor–positive, human epidermal growth factor receptor 2–negative MBC.
Materials and Methods
We searched PubMed, MEDLINE, and Embase databases, assessing efficacy outcomes such as progression-free survival (PFS) based on obesity markers, including BMI and visceral adipose tissue (VAT) index.
Results
Twelve studies were reviewed, with seven studies and 1,812 patients included in a pooled meta-analysis. Among patients with BMI ≥ 25, modest improvement in PFS was observed, with a pooled hazard ratio (HR) of 0.944 (95% confidence interval [CI], 0.909 to 0.980; p=0.003). Besides, add-on analysis using VAT to define obesity revealed a notable PFS improvement, with a pooled HR of 0.452 (95% CI, 0.256 to 0.798; p=0.006).
Conclusion
While BMI-defined obesity showed slight PFS improvement with CDK 4/6 inhibitors and endocrine therapy, using VAT to define obesity revealed significant PFS gains. This highlights the need for further research on biomarker to clarify the role of adiposity in MBC, which may differ from its impact in EBC.
Key words: Obesity, Breast neoplasms, Neoplasm metastasis, Body mass index, Cyclin-dependent kinase inhibitors, Visceral fat

Introduction

Breast cancer is the second most commonly diagnosed cancer globally in 2022, with approximately 70% of cases classified as hormone receptor–positive (HR+)/human epidermal growth factor receptor 2–negative (HER2–) [1,2]. Due to the high prevalence of this subtype, identifying its risk factors and optimizing treatment strategies are critical priorities in clinical practice.

Obesity is widely recognized to be strongly linked with cancer progression, primarily through mechanisms involving chronic inflammation and hormonal changes mediated by adipocyte-derived hormones such as leptin in many human solid tumors [3,4]. In the context of early-stage breast cancer (EBC), particularly HR+ subtypes, obesity has been consistently associated with a worse prognosis [5]. This adverse effect is largely attributed to the production of estrogens by adipose tissue, which bind to estrogen receptors on HR+ breast cancer cells, thereby promoting tumor growth enhanced cell division and proliferation [6]. Thus, National Comprehensive Cancer Network guideline recommends invasive breast cancer patients to maintain their body mass index (BMI) below 25 [7].

However, in the metastatic setting, the impact of obesity on prognosis remains uncertain. Some studies link higher BMI to poorer outcomes in metastatic breast cancer (MBC) [8], while others find no significant association [9-11]. A retrospective analysis of 1,456 HR+/HER2– MBC patients treated with endocrine therapy (ET), with or without cyclin-dependent kinase 4/6 (CDK 4/6) inhibitors, found no significant association between obesity and overall survival (OS) or progression-free survival (PFS) [11].

After U.S. Food and Drug Administration approval of palbociclib in 2015, CDK 4/6 inhibitors became an integral part of treatment for HR+/HER2– MBC. Key clinical trials, such as PALOMA, MONALEESA, and MONARCH, have established the benefits of CDK 4/6 inhibitors, including palbociclib, ribociclib, and abemaciclib, for this patient population [12-19]. However, studies on the influence of obesity on outcomes in HR+/HER2– MBC patients receiving CDK 4/6 inhibitors are still limited, and results have been inconsistent [20-23]. Given the role of CDK 4/6 inhibitors in regulating lipogenesis and their interactions with obesity-related metabolic pathways, it is essential to investigate how obesity may impact treatment outcomes and prognosis in HR+/HER2–MBC patients [24].

Therefore, this study aims to clarify the impact of obesity on treatment outcomes in HR+/HER2– MBC patients treated with CDK 4/6 inhibitors and ET through a meta-analysis of prior studies.

Materials and Methods

1. Study design and eligibility criteria

This study has been reported in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) [25] and AMSTAR (Assessing the methodological quality of systematic reviews) [26]. The hypothetical question was: “Do HR+/HER2– MBC patients with obesity (as compared to a non-obese population) have poorer oncologic outcomes when treated with a combination of CDK 4/6 inhibitors and ET?” The eligibility criteria for study inclusion are: (1) clinical studies including ≥ 10 HR+/HER2– MBC treated with CDK 4/6 inhibitors; (2) studies providing comparative data of PFS, OS, or response rate (RR) according to obesity markers (e.g., BMI, visceral adipose tissue [VAT] index).

2. Data source and collection process

Three databases including PubMed, MEDLINE, and Embase were searched and the last date of search was August 5, 2024. The search term was designed to encompass studies that use CDK 4/6 inhibitors, which are primary systemic treatment modality for HR+/HER2– MBC, and report on oncologic outcomes, specifically survival. Because obesity data and RR might be mentioned within the text but not explicitly stated in the title or abstract, we chose not to narrow the search by including it as a search term. Therefore, we used search terms of: (breast cancer) AND ((palbociclib) OR (ribociclib) OR (abemaciclib)) AND ((prognosis) OR (survival)). Conference abstracts meeting the inclusion criteria were also included. Language restriction was not applied. Period restriction was not applied, considering the availability period of CDK 4/6 inhibitors. When multiple studies were published from one institution, all studies were included in the meta-analysis unless the patient recruitment periods overlapped. In cases where the recruitment periods overlapped, the study with the larger patient population was selected; if the patient numbers were similar, the more recent study was chosen. For data collection, a pre-standardized sheet was used, including: (1) general information including author names, year of publication, and patient recruitment period; (2) clinical information including obesity index and comparative index (e.g., hazard ratio) for PFS, OS (for reference), and RR. Two independent researchers conducted data collection and study searches. In case of disagreement, the literature was reinvestigated and conflicts were resolved through mutual discussion.

3. Risk of bias and quality assessment

In the preliminary investigation, no randomized studies designed to compare oncologic outcomes based on obesity index were found. Therefore, study quality was assessed using the Newcastle-Ottawa scale [27]. Studies with a score of 8 to 9 were evaluated as high quality, those scoring 5 to 7 as medium quality, and those scoring less than 5 as low-quality studies. According to the Cochrane handbook, which advises against meta-analyzing observational studies with a high risk of bias [28], low-quality studies were excluded from the meta-analysis with the consent of the authors.

4. Statistics

The main effect measure was the pooled HR for PFS or RR, according to obesity index. In the preliminary search, the majority of relevant studies were non-randomized; thus, a random effects model was applied to pooled analyses to account for the possible clinical heterogeneity and variance in treatment details across institutions. In the pooled analyses, heterogeneity between studies was assessed using the Cochran’s Q test [29] and I² statistics [30]. I² values of 25%, 50%, and 75% were regarded as low, moderate, and high heterogeneity, respectively. Publication bias was assessed using visual funnel plot analysis and quantitative Egger’s test [31]. If asymmetry was observed in the funnel plot analysis and the two-tailed p-value in the Egger’s test was < 0.1, adjusted values using Duval and Tweedie’s trim and fill method [32] were presented as references. All statistical analyses were performed using the Comprehensive Meta-Analysis software ver. 4 (Biostat Inc.).

Results

1. Study selection and characteristics

The selection process is illustrated in Fig. 1. A total of 4,825 studies were identified through database searches, with one additional study included from further web searching. After excluding duplicates and studies with irrelevant formats, 2,027 studies were retained. These studies were subsequently screened based on their abstracts of full texts, guided by the authors’ judgement. Twelve studies met the inclusion criteria and are summarized in Table 1.

The first pooled meta-analysis was conducted using seven studies that met both criteria: categorization based on a BMI threshold near 25 or an estimated threshold near 25, and the provision of HR for PFS obtained from univariate Cox regression analysis. Additionally, two studies reporting HRs of PFS according to VAT index were analyzed in a separate add-on meta-analysis. One study was common to both analyses [33]. Finally, the remaining four studies, which did not provide HRs for PFS, were excluded from the meta-analysis but were included in the narrative review.

2. Narrative review of selected studies

The included studies consisted of eight retrospective studies, two prospective observational studies, one pooled analysis of two randomized controlled trials, and one ambispective study. The studies predominantly focused on the impact of specific CDK 4/6 inhibitors on PFS in relation to obesity: three studies investigated palbociclib, one examined ribociclib, and two studied abemaciclib, while three studies included cohorts treated with either ribociclib or palbociclib, and three studies did not specify the type of CDK 4/6 inhibitor used. Most studies utilized a BMI threshold of approximately 25 to categorize patients into high and low BMI groups.

One pooled analysis of MONARCH-2 and 3 trials examined BMI’s impact on treatment outcomes in patients receiving abemaciclib plus ET [20]. Although there was no significant difference in PFS between normal-weight (BMI < 25 kg/m²) and overweight/obesity (BMI ≥ 25 kg/m²) (hazard ratio, 1.03; p=0.81), normal-weight patients showed a numerically greater benefit from abemaciclib plus ET compared to ET alone.

Fasching et al. [22] evaluated real-world ribociclib use in the RIBECCA study and found no significant association between BMI and PFS (hazard ratio, 0.947; p=0.97), suggesting that BMI may have limited influence on treatment efficacy in this patient population. However, since only multivariate hazard ratio values were provided, the study was excluded from the pooled meta-analysis. Caglayan et al. [21] categorized HR+/HER2– MBC patients into normal (BMI < 25 kg/m²), overweight (30 kg/m² > BMI ≥ 25 kg/m²), and obese (BMI ≥ 30 kg/m²) groups. The study reported the longest PFS in the overweight group, while both the normal and obese group showed shorter outcomes, suggesting a potential advantage for patients in the overweight range. Wu et al. [34] analyzed BMI using a threshold of 24, showing a numerically longer median PFS in higher BMI patients, though not statistically significant (14.2 vs. 13.6 months, p=0.743). Zhang et al. [35], available only in abstract form, observed that patients classified as obese (BMI ≥ 30 kg/m²) at three months after initiating CDK 4/6 inhibitor therapy had a significantly longer OS, suggesting that BMI at early stage of treatment may influence treatment outcomes.

Other studies utilized non-BMI–based metrics to compare treatment outcomes in HR+/HER2– MBC patients treated with CDK 4/6 inhibitors. Yucel et al. [36] used VAT index, finding that higher visceral fat levels were associated with improved PFS (reversed hazard ratio, 0.465; p=0.042). Kripa et al. [37] explored multiple indices such as VAT index and skeletal muscle index (SMI) to assess their impact on treatment outcomes in HR+/HER2– MBC patients treated with CDK 4/6 inhibitors. The study reported higher VAT was linked to improved PFS (hazard ratio, 0.476; p=0.008), and that higher SMI was linked to significantly better outcomes (hazard ratio, 0.687; p < 0.001), underscoring sarcopenia as a negative prognostic factor.

3. Quality assessment

The included studies were evaluated based on eight queries across three categories: selection (4 queries), comparability (1 query), and outcome (3 queries). In the selection category, all studies received scores for the four queries. In detail, as the studies focused on a specific population—patients with HR+/HER2– MBC receiving a particular drug—the representativeness of the cohorts was rated as good. Patients were compared based on obesity status, and each group consisted of individuals from the same hospital source, and data were sourced from tertiary hospital records. Since PFS and RR were the outcomes of interest, they could not have occurred at baseline.

In the comparability category, although obesity served as the primary comparative factor, no specific adjustments were made to control for confounders in all studies; therefore, no studies were scored for the comparability query.

Regarding the outcome category, outcome assessments were based on tertiary hospital records, so all studies were rated accordingly for the outcome assessment query. Studies with a median follow-up period of one year or more received scores for the sufficient follow-up query, whereas studies with shorter follow-up or lacking relevant data did not receive scores. No study experienced follow-up loss that would significantly bias the results; hence, all the studies scored for the adequacy of the follow-up query.

In summary, all studies included in this meta-analysis were rated as medium quality. A detailed evaluation table can be found in S1 Table.

4. Synthesized results

The pooled analysis of all studies indicated that the high BMI group demonstrated improved PFS with a pooled hazard ratio of 0.944 (95% confidence interval [CI], 0.909 to 0.980; p=0.003), exhibiting low heterogeneity (p=0.548, I² < 0.001%) (Fig. 2). The add-on analysis included studies that explored the relationship between VAT index and PFS, specifically Franzoi et al. [33] and Yucel et al. [36]. Both studies were retrospective analyses with small patient cohorts, investigating the influence of body composition on treatment outcomes. In this add-on analysis, the pooled HR was 0.452 (95% CI, 0.256 to 0.798; p=0.006) favoring high visceral obesity group with statistical significance (Fig. 3). The analysis also showed low heterogeneity (p=0.925, I² < 0.001%).

5. Publication bias

To assess publication bias, a funnel plot was generated (Fig. 4). Visual inspection of the funnel plot showed no obvious asymmetry, suggesting the absence of significant publication bias. Additionally, Egger’s regression test supported this finding, with a p-value of 0.575 (Fig. 4). These results collectively indicate that publication bias is unlikely to have affected the outcomes of the meta-analysis.

Discussion

In our meta-analysis, we found that a higher BMI was associated with modest benefit of PFS and a higher VAT index demonstrated an even more pronounced improvement in PFS, suggesting that adiposity may have a protective role in patients with HR+/HER2– MBC receiving CDK 4/6 inhibitor plus ET. These findings align with emerging evidence in MBC or the CDK 4/6 inhibitor era, which have suggested that obesity does not universally confer a poor prognosis in advanced cancer [9-11,22,35,38,39]. Unlike in EBC, where obesity is associated with worse outcomes due to mechanisms such as estrogen-driven tumor proliferation, the role of obesity in MBC appears to be less straightforward. This contrast represents the so-called ‘obesity paradox.’ Our results contribute to this emerging evidence, supporting the hypothesis that obesity may play a more complex role in MBC treated with CDK 4/6 inhibitor.

Several mechanisms can be proposed to explain obesity paradox in EBC versus MBC. In early-stage disease, long-term hormonal stimulation and chronic inflammation may contribute to poorer outcomes in obese patients. Estrogens produced by adipose tissue, along with inflammatory mediators resulting from adipocyte hypoxia, are thought to promote estrogen receptor–positive breast cancer growth [6]. In contrast, in the metastatic setting, where treatment goals are palliative, short-term host-related factors may play a more important role in prognosis. Excess adipose tissue may serve as an energy reserve during palliative cancer therapy, potentially mitigating cancer-related cachexia, which can have a greater negative impact on survival than the adverse effects of obesity [42]. Additionally, underweight patients may have a higher risk of sarcopenia, which has been associated with poor outcomes in several studies [36,37]. Although the relationship between muscle mass and obesity remains unclear in cancer patients, Kripa et al. [37] reported that no patients in their study were both sarcopenic and obese, suggesting a possible protective effect of higher body mass.

Higher BMI may also improve PFS in HR+/HER2– MBC patients treated with CDK 4/6 inhibitors due to pharmacokinetics and reduced toxicity. Lower BMI patients often exhibit higher plasma drug concentrations, increasing hematologic toxicities like neutropenia, leading to treatment interruption or poor adherence [40]. The large distribution volumes of CDK4/6 inhibitors suggest higher BMI patients have lower plasma drug concentrations, potentially reducing side effects [43-46]. Studies, including data from PALOMA-3, reported higher incidence of hematologic toxicity in lower BMI or Asian patients, who generally have lower BMIs [20,40,47]. While dose reductions typically do not compromise efficacy [48-51], frequent treatment modifications or poor adherence in lower BMI groups could negatively impact outcomes [40]. Higher BMI patients, experiencing fewer side effects, are more likely to maintain consistent treatment schedule, which may contribute to improved PFS [52].

Improved PFS in higher BMI groups treated with CDK 4/6 inhibitors may also be influenced by their interaction with adipose metabolism. CDK 4/6 plays a role in adipogenesis and metabolic regulation. CDK4 promotes adipocyte differentiation by activating peroxisome proliferator-activated receptor γ and peroxisome proliferator-activated receptor gamma coactivator 1-alpha, while CDK6 inhibition induces white adipose tissue browning via RUNX1 activation, enhancing energy expenditure and glucose metabolism [24,53]. CDK 4/6 inhibitors additionally prevent fat mass gain by inhibiting retinoblastoma protein (Rb) phosphorylation, as demonstrated in preclinical studies where abemaciclib reduced fat mass gain in high-fat diet mice without affecting lean mass [54]. This was achieved by preserving energy balance through inhibiting Rb phosphorylation in the hypothalamus and pro-opiomelanocortin neuron function. In line with this, weight loss was more frequently observed in patients treated with abemaciclib plus ET compared to placebo plus ET in post-hoc analysis of MONARCH-2 and -3 trials (odds ratio, 3.23; p < 0.001) [20]. Yucel et al. [36] observed a significant reduction in the VAT index after 6 months of CDK 4/6 inhibitor therapy (mean VAT-index change, –4.21; p=0.02). These findings suggest that CDK 4/6 inhibitors may mitigate the metabolic impact of excess adiposity, potentially offering greater benefits in obese breast cancer patients compare to non-obese patients.

The synergistic effects of immunomodulation from obesity and CDK 4/6 inhibitors may enhance cancer treatment outcomes. Obesity increases pro-inflammatory cytokines secretion and adipose-derived leptin, promoting CD4+ T cell proliferation and reducing regulatory T cell (Treg) function, creating a pro-inflammatory immune environment supportive of CDK 4/6 inhibitor activity [55,56]. CDK 4/6 inhibitors further enhance anti-tumor immune microenvironment by enhancing tumor antigen presentation, inducing tumor cell senescence, and recruiting immune cells via senescence-associated secretory phenotype [56,57]. Additionally, these inhibitors activate CD8+ T cells while reducing the Treg population, shifting the immune balance toward an anti-tumor response [57-59]. Combined, these effects suggest a potential synergy between obesity and CDK 4/6 inhibitors, possibly mediated through shared mechanisms like Rb phosphorylation. However, direct connection between Rb phosphorylation and immune modulation requires further investigation.

The VAT index showed a stronger correlation with improved PFS rather than BMI. While BMI is a simple measure of weight relative to height, the VAT index measures visceral fat area from computed tomography scans toward the level of third lumbar vertebra, adjusted for height. Three studies using the VAT index instead of BMI in HR+/HER2– MBC patients treated with CDK 4/6 inhibitors and ET reported better outcomes in patients with high visceral adiposity (odds ratio/hazard ratio, 0.44 to 0.48; p=0.008-0.063) [33,36,37]. Two studies of these studies found no significant link between BMI and outcomes, highlighting BMI’s limitations. These findings suggest that the VAT index may more accurately assess adiposity. Standardizing its calculation is essential for further investigation.

A key limitation of this meta-analysis is the limited number of included studies, which amplifies the impact of individual studies. Notably, the study by Chen et al. [41] has a large influence; when excluded in the pooled analysis, the p-value increased to 0.667, resulting in a substantially different conclusion. While heterogeneity remains low due to most effect sizes clustering near Chen’s results, this suggests that the pooled outcome is heavily influenced by a single study with high reliability but a small effect size, requiring cautious interpretation. Also, the modest association between BMI and PFS, compared to VAT index, may stem from BMI grouping criteria. Most included studies used a BMI threshold around 25, potentially missing effects of extreme BMI ranges. For instance, Lammers et al. [11] found worse OS with a BMI lower than 18.5 (hazard ratio, 1.45; p=0.07). [11]. Further studies focusing on obesity (BMI ≥ 30) or underweight (BMI < 18.5) groups are needed to clarify these associations.

Importantly, only three studies provided data on VAT, and two of them reported its impact on PFS, limiting our ability to robustly assess its prognostic value. All three studies were retrospective studies with small patient numbers and heterogeneous VAT measurement protocols. Considering that VAT demonstrated stronger and more consistent associations with treatment outcomes compared to BMI, prospective trials using standardized VAT assessment methods are warranted to clarify its prognostic relevance.

Furthermore, in most included studies, baseline characteristics such as comorbidities, performance status, and treatment-related factors such as dose modification were not considered in the analysis of BMI or VAT with respect to PFS. Although some studies reported these variables in the overall study population, relevant data were not specifically provided or adjusted for the subgroup of HR+/HER2– MBC patients treated with CDK 4/6 inhibitors. Future studies should incorporate these clinical factors using multivariable models to better assess the impact of obesity on prognosis.

Despite these limitations, this study provides a comprehensive overview of the current evidence regarding the impact of obesity on the treatment outcome of HR+/HER2– MBC patients. Our findings suggest that weight reduction may be unnecessary or even detrimental for patients with MBC receiving CDK 4/6 inhibitors plus ET, and thus approached cautiously on an individualized basis. However, these conclusions are based on limited and heterogeneous evidence and should be interpreted with care. Future prospective studies with standardized adiposity measures and stratified analyses are needed to confirm our findings. Lastly, further research is needed to explore the distinct roles of adiposity in EBC versus MBC outcomes.

Electronic Supplementary Material

Supplementary materials are available at Cancer Research and Treatment website (https://www.e-crt.org).

crt-2025-110_S1_Table.pdf

NOTES

Author Contributions

Conceived and designed the analysis: Jung YB, Hong JH, Rim CH.

Collected the data: Jung YB, Hong JH.

Contributed data or analysis tools: Rim CH.

Performed the analysis: Jung YB, Rim CH.

Wrote the paper: Jung YB, Ahn HK, Shin HY, Hong JH, Rim CH.

Conflicts of Interest

Conflict of interest relevant to this article was not reported.

Funding

This work was supported by The Catholic University of Korea Eunpyeong St. Mary’s Hospital Clinical Research Laboratory Foundation [grant number: EMBRF-2020-09]; National Research Fund of Korea [grant number: NRF-2021R1I1A2047475].

Fig. 1.

Study inclusion plot. Diagram showing the study selection process, including the number of records screened, excluded, and included in the meta-analysis.

Fig. 2.

Forest plot of meta-analysis of hazard ratio for progression-free survival (PFS) based on body mass index (BMI) [20,33,35,38-41]. Forest plot illustrating the pooled hazard ratio for PFS stratified by BMI groups in hormone receptor–positive/human epidermal growth factor receptor 2–negative metastatic breast cancer patients.

Fig. 3.

Forest plot of meta-analysis of hazard ratio for progression-free survival (PFS) based on visceral adipose tissue (VAT) index [33,36]. Forest plot showing the pooled hazard ratio for PFS based on VAT index in hormone receptor–positive/human epidermal growth factor receptor 2–negative metastatic breast cancer patients.

Fig. 4.

Funnel plot of body mass index (BMI) and progression-free survival (PFS) meta-analysis to assess publication bias. Funnel plot evaluating publication bias in the meta-analysis of BMI and PFS.

Table 1.

Study characteristics and reported HR for PFS

Study	Study type	Country	Inclusion period	No. of patients	CDK 4/6 inhibitor	Metrics	Criteria	Hazard ratio for PFS (95% CI)	mPFS (95% CI, mo)
Roncato^a) (2023) [40]	Prospective	Caucasians	2020-2022	134	Palbo	BMI	BMI ≥ 25	0.43 (0.20-0.92)	NA
Roncato^a) (2023) [40]	Prospective	Caucasians	2020-2022	134	Palbo	BMI	BMI < 25	0.43 (0.20-0.92)	NA
Franzoi^a) (2021) [20]	Pooled analysis	Worldwide	2014-2015	757	Abema	BMI	BMI ≥ 25	1.03 (0.83-1.27)	21.7 (17.1-27.5)
Franzoi^a) (2021) [20]	Pooled analysis	Worldwide	2014-2015	757	Abema	BMI	BMI < 25	1.03 (0.83-1.27)	22.0 (17.2-29.1)
Caglayan (2022) [21]	Retrospective	Turkey	2019-2021	116	Palbo, Ribo	BMI	BMI ≥ 30	NA	11.1 (9.7-12.56)
							25 ≤ BMI < 30		Not reached
							18.5 ≤ BMI < 25		9.3 (5.3-13.4) (p=0.02)
Wu (2023) [34]	Retrospective	China	2016-2022	397	Palbo	BMI	BMI ≥ 24	NA	14.17
Wu (2023) [34]	Retrospective	China	2016-2022	397	Palbo	BMI	BMI < 24	NA	13.63 (p=0.743)
Fasching (2024) [22]	Prospective	Germany	2016-2020	481	Ribo	BMI	Not specified	0.99 (0.96-1.01)^b)	NA
Shen^a) (2022) [38]	Ambispective	China	2018-2020	190	Palbo	BMI	BMI ≥ 24	0.98 (0.66-1.47)	NA
Shen^a) (2022) [38]	Ambispective	China	2018-2020	190	Palbo	BMI	BMI < 24	0.98 (0.66-1.47)	NA
Chen^a) (2024) [41]	Retrospective	Taiwan	2018-2023	340^c)	Palbo, Ribo	BMI	BMI ≥ 25	0.94 (0.91-0.98)	NA
Chen^a) (2024) [41]	Retrospective	Taiwan	2018-2023	340^c)	Palbo, Ribo	BMI	BMI < 25	0.94 (0.91-0.98)	NA
Takada^a) (2023) [39]	Retrospective	Japan	2018-2021	120	Abema	BMI	BMI > 22	1.04 (0.50-2.04)	NA
Takada^a) (2023) [39]	Retrospective	Japan	2018-2021	120	Abema	BMI	BMI ≤ 22	1.04 (0.50-2.04)	NA
Zhang^a) (2024) [35]	Retrospective	USA	2015-2022	221	Palbo, Ribo, Abema	BMI	BMI ≥ 30	0.76 (0.35-1.66)	NA
							25 ≤ BMI < 30	0.9 (0.42-1.94)	NA
							18.5 ≤ BMI < 25		NA
Yucel (2024) [36]	Retrospective	Turkey	2018-2021	52	Palbo, Ribo	BMI	BMI ≥ 30	1.22 (0.57-2.59)	13.4 (2.0-26.4)
						BMI	BMI < 30	1.22 (0.57-2.59)	14.6 (4.1-23.8)
						VAT	High	0.46 (0.22-0.97)	20.4 (8.9-28.6)
						VAT	Low	0.46 (0.22-0.97)	9.3 (5.9-12.0)
Franzoi^a) (2020) [33]	Retrospective	Belgium	2016-2019	50	Not specified	BMI	BMI ≥ 25	0.81 (0.35-2.00)	20.8 (18.1-23.4)
						BMI	BMI < 25	0.81 (0.35-2.00)	12.1 (8.2-16.0)
						VAT	High	0.44 (0.18-1.06)	20.8 (17.6-23.9)
						VAT	Low	0.44 (0.18-1.06)	10.4 (6.8-14.0)
Kripa (2022) [37]	Retrospective	Italy	2018-2021	30	Not specified	VAT	VAT ≥ 130 cm²	0.476 (0.28-0.82)	NA
						VAT	VAT < 130 cm²	0.476 (0.28-0.82)	NA
						SMI	SMI ≥ 40 cm²	0.69 (p < 0.001)	NA

Abema, abemaciclib; BMI, body mass index; CDK, cyclin-dependent kinase; CI, confidence interval; HER2, human epidermal growth factor receptor 2; HR, hormone receptor; mPFS, median progression-free survival; NA, not available; Palbo, palbociclib; PFS, progression-free survival; Ribo, ribociclib; SMI, skeletal muscle index; VAT, visceral adipose tissue.

^a) The seven studies included in the first pooled meta-analysis,

^b) Multivariate Cox hazard ratio is reported, while others are based on univariate Cox hazard ratio,

^c) 3% of patients in the study population are HR+/HER2.

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The Impact of Obesity on Treatment Outcomes in Patients with Hormone Receptor–Positive HER2-Negative Metastatic Breast Cancer Receiving CDK 4/6 Inhibitors

Cancer Res Treat. 2026;58(1):198-207. Published online April 15, 2025

DOI: https://doi.org/10.4143/crt.2025.110

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Abstract
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The Impact of Obesity on Treatment Outcomes in Patients with Hormone Receptor–Positive HER2-Negative Metastatic Breast Cancer Receiving CDK 4/6 Inhibitors

Fig. 1. Study inclusion plot. Diagram showing the study selection process, including the number of records screened, excluded, and included in the meta-analysis.

Fig. 2. Forest plot of meta-analysis of hazard ratio for progression-free survival (PFS) based on body mass index (BMI) [20,33,35,38-41]. Forest plot illustrating the pooled hazard ratio for PFS stratified by BMI groups in hormone receptor–positive/human epidermal growth factor receptor 2–negative metastatic breast cancer patients.

Fig. 3. Forest plot of meta-analysis of hazard ratio for progression-free survival (PFS) based on visceral adipose tissue (VAT) index [33,36]. Forest plot showing the pooled hazard ratio for PFS based on VAT index in hormone receptor–positive/human epidermal growth factor receptor 2–negative metastatic breast cancer patients.

Fig. 4. Funnel plot of body mass index (BMI) and progression-free survival (PFS) meta-analysis to assess publication bias. Funnel plot evaluating publication bias in the meta-analysis of BMI and PFS.

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

The Impact of Obesity on Treatment Outcomes in Patients with Hormone Receptor–Positive HER2-Negative Metastatic Breast Cancer Receiving CDK 4/6 Inhibitors

Study	Study type	Country	Inclusion period	No. of patients	CDK 4/6 inhibitor	Metrics	Criteria	Hazard ratio for PFS (95% CI)	mPFS (95% CI, mo)
Roncato^a) (2023) [40]	Prospective	Caucasians	2020-2022	134	Palbo	BMI	BMI ≥ 25	0.43 (0.20-0.92)	NA
Roncato^a) (2023) [40]	Prospective	Caucasians	2020-2022	134	Palbo	BMI	BMI < 25	0.43 (0.20-0.92)	NA
Franzoi^a) (2021) [20]	Pooled analysis	Worldwide	2014-2015	757	Abema	BMI	BMI ≥ 25	1.03 (0.83-1.27)	21.7 (17.1-27.5)
Franzoi^a) (2021) [20]	Pooled analysis	Worldwide	2014-2015	757	Abema	BMI	BMI < 25	1.03 (0.83-1.27)	22.0 (17.2-29.1)
Caglayan (2022) [21]	Retrospective	Turkey	2019-2021	116	Palbo, Ribo	BMI	BMI ≥ 30	NA	11.1 (9.7-12.56)
							25 ≤ BMI < 30		Not reached
							18.5 ≤ BMI < 25		9.3 (5.3-13.4) (p=0.02)
Wu (2023) [34]	Retrospective	China	2016-2022	397	Palbo	BMI	BMI ≥ 24	NA	14.17
Wu (2023) [34]	Retrospective	China	2016-2022	397	Palbo	BMI	BMI < 24	NA	13.63 (p=0.743)
Fasching (2024) [22]	Prospective	Germany	2016-2020	481	Ribo	BMI	Not specified	0.99 (0.96-1.01)^b)	NA
Shen^a) (2022) [38]	Ambispective	China	2018-2020	190	Palbo	BMI	BMI ≥ 24	0.98 (0.66-1.47)	NA
Shen^a) (2022) [38]	Ambispective	China	2018-2020	190	Palbo	BMI	BMI < 24	0.98 (0.66-1.47)	NA
Chen^a) (2024) [41]	Retrospective	Taiwan	2018-2023	340^c)	Palbo, Ribo	BMI	BMI ≥ 25	0.94 (0.91-0.98)	NA
Chen^a) (2024) [41]	Retrospective	Taiwan	2018-2023	340^c)	Palbo, Ribo	BMI	BMI < 25	0.94 (0.91-0.98)	NA
Takada^a) (2023) [39]	Retrospective	Japan	2018-2021	120	Abema	BMI	BMI > 22	1.04 (0.50-2.04)	NA
Takada^a) (2023) [39]	Retrospective	Japan	2018-2021	120	Abema	BMI	BMI ≤ 22	1.04 (0.50-2.04)	NA
Zhang^a) (2024) [35]	Retrospective	USA	2015-2022	221	Palbo, Ribo, Abema	BMI	BMI ≥ 30	0.76 (0.35-1.66)	NA
							25 ≤ BMI < 30	0.9 (0.42-1.94)	NA
							18.5 ≤ BMI < 25		NA
Yucel (2024) [36]	Retrospective	Turkey	2018-2021	52	Palbo, Ribo	BMI	BMI ≥ 30	1.22 (0.57-2.59)	13.4 (2.0-26.4)
						BMI	BMI < 30	1.22 (0.57-2.59)	14.6 (4.1-23.8)
						VAT	High	0.46 (0.22-0.97)	20.4 (8.9-28.6)
						VAT	Low	0.46 (0.22-0.97)	9.3 (5.9-12.0)
Franzoi^a) (2020) [33]	Retrospective	Belgium	2016-2019	50	Not specified	BMI	BMI ≥ 25	0.81 (0.35-2.00)	20.8 (18.1-23.4)
						BMI	BMI < 25	0.81 (0.35-2.00)	12.1 (8.2-16.0)
						VAT	High	0.44 (0.18-1.06)	20.8 (17.6-23.9)
						VAT	Low	0.44 (0.18-1.06)	10.4 (6.8-14.0)
Kripa (2022) [37]	Retrospective	Italy	2018-2021	30	Not specified	VAT	VAT ≥ 130 cm²	0.476 (0.28-0.82)	NA
						VAT	VAT < 130 cm²	0.476 (0.28-0.82)	NA
						SMI	SMI ≥ 40 cm²	0.69 (p < 0.001)	NA

Table 1. Study characteristics and reported HR for PFS

The seven studies included in the first pooled meta-analysis,

Multivariate Cox hazard ratio is reported, while others are based on univariate Cox hazard ratio,

3% of patients in the study population are HR+/HER2.