The Impact of Metabolic Syndrome on Pathological Complete Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer: A Prospective Observational Study

Abstract

Introduction

Metabolic syndrome (MetS) notably influences breast cancer pathogenesis, treatment efficacy, and prognosis. This study investigates the association between MetS and the rate of pathological complete response (pCR) following neoadjuvant chemotherapy (NACT) in patients with locally advanced breast cancer (LABC).

Objective

To study the response of NACT in patients with LABC with or without MetS.

Materials and Methods

In this prospective observational study (June 2022–June 2024), 188 patients with LABC planned for NACT were enrolled. Participants were stratified into two cohorts based on the NCEP ATP III (National Cholesterol Education Program Adult Treatment Panel III) Criteria: Arm A (MetS, n = 91) and Arm B (non-MetS, n = 97). The primary outcome was the achievement of pCR. Associations were analyzed using chi-square tests, with univariate and multivariate analyses to identify predictive factors.

Results

The overall pCR rate was 20.2% (n = 38/188). A lower, though not statistically significant, pCR rate was observed in the MetS group (15.4%, n = 14/91) compared with the non-MetS group (24.7%, n = 24/97) (p = 0.078). Subgroup analyses revealed significant interactions: HER2-negative patients with MetS had a significantly lower pCR rate than their non-MetS counterparts (p = 0.049). Furthermore, premenopausal women without MetS achieved significantly higher pCR rates than premenopausal women with MetS (p = 0.008). On multivariate analysis adjusting for age and menopausal status, MetS was not an independent predictor of pCR (odds ratio: 0.65, 95% confidence interval: 0.30–1.39, p = 0.27). ER and HER2 status alone did not show a significant independent association with pCR.

Conclusion

The presence of MetS may be associated with a reduced likelihood of achieving pCR after NACT in patients with LABC, with this effect appearing most pronounced in HER2-negative and premenopausal subgroups. These findings highlight MetS as a critical host factor influencing treatment outcomes and underscore the need for integrated metabolic management in breast cancer care.

Keywords

metabolic syndrome - neoadjuvant therapy - pathological complete response - triple-negative breast neoplasms

Introduction

Metabolic syndrome (MetS) is a complex condition characterized by at least three of the following five factors: abdominal obesity, high triglycerides, low high-density lipoprotein (HDL) cholesterol, hypertension, and elevated fasting glucose. It has a substantial influence on both the risk of developing breast cancer and its subsequent prognosis.[1] [2] While abundant epidemiologic data link MetS to breast cancer risk, there is limited evidence, particularly from the Indian population, on its impact on response to neoadjuvant chemotherapy (NACT). Emerging research indicates a significant link between MetS and breast cancer, with biochemical alterations in metabolic components influencing both the host immune response and the tumor microenvironment. These changes, driven in part by insulin resistance and elevated levels of insulin-like growth factor-1 (IGF-1), can activate prosurvival pathways in cancer cells and potentially contribute to a higher risk of disease recurrence, mortality, and chemoresistance.[3] [4] A strong body of research confirms that postmenopausal women with MetS face a significantly elevated risk of breast cancer, which rises with each additional MetS component.[5] [6] The role of obesity, a key MetS component, is complex and varies by menopausal status. In postmenopausal women, obesity elevates the risk of specific subtypes, including ER +/PR− and ER −/PR− breast cancers. Conversely, it exhibits a paradoxical protective effect against hormone receptor-positive cancers in premenopausal women.[7] In postmenopausal women, obesity is strongly correlated with the risk of ER +/PR+ breast cancer but modestly linked to PR-negative breast cancer. Both hyperinsulinemia and insulin resistance are also common components of MetS, as is diabetes mellitus. Studies on the relationship between diabetes mellitus (DM) and breast cancer subtypes, however, have shown conflicting findings.

Materials and Methods

Study Design and Setting

This prospective observational study was carried out in the Department of Medical Oncology at the Kidwai Memorial Institute of Oncology between June 2022 and June 2024. The research cohort consisted of individuals diagnosed with locally advanced breast cancer (LABC) who were scheduled to receive NACT followed by surgical intervention.

Objective

To study the response of NACT in patients with LABC with or without MetS.

Inclusion and Exclusion Criteria

The following inclusion criteria were used to select patient population (1) age >18 years, (2) Eastern Cooperative Oncology Group 0–1, (3) biopsy-proven LABC and exclusion criteria were (1) recurrent breast cancer, (2) metastatic disease, (3) diagnosis of ductal carcinoma in situ, (4) patient with impaired hepatic and renal function and cardiovascular disease.

Data collection encompassed patient demographics (age and menopausal status at the start of NACT), clinical parameters (height, weight at NACT initiation, chemotherapy and endocrine treatment plans, surgical type, and pathological assessment of breast and axillary tissue posttreatment), and tumor characteristics (clinical staging, histological grade, and status of HER2/neu, estrogen receptor [ER], and progesterone receptor [PR]). All metabolic parameters (fasting glucose, lipids, blood pressure, and waist circumference) were measured once at baseline, prior to the initiation of NACT. The standard institutional chemotherapy protocols are detailed in [Table 1].

Table 1 Chemotherapy regimen

(1) FEC or EC + docetaxel	5-Fluorouracil 500 mg/m2, epirubicin 100 mg/m2, cyclophosphamide 500 mg/m2 (FEC), or epirubicin 90 mg/m2, cyclophosphamide 600 mg/m2 (EC)	Every 3 wk	3 FEC or 3EC + 3 docetaxel
	Followed by docetaxel 100 mg/m2	Every 3 wk
(3) TC	Docetaxel 75 mg/m2, cyclophosphamide 600 mg/m2 (TC)	Every 3 wk	4 TC

Patients were grouped into two arms, Arm A (MetS group) with requirements as per NCEP ATP III (National Cholesterol Education Program Adult Treatment Panel III) Criteria: Abdominal circumference Women > 88 cm and Men > 102 cm, Triglyceride >150 mg/dL, HDL < 40 mg/dL in men and <50 mg/dL in women, Blood pressure > 130/85 mm Hg, fasting glucose > 110 mg/dL, and remaining patients were grouped into Arm B (non-MetS). Fasting blood samples were analyzed to measure levels of glucose, lipids, and a complete blood count, in addition to standard biochemical panels. Blood pressure was measured in upper arm in sitting position and body weight was documented. Waist circumference was measured using measuring tape. All patients received NACT followed by surgery, radiation and hormonal therapy as per hormonal status.

Statistical Analysis

Statistical analyses were executed utilizing Python version 3.7. Descriptive statistics, including means, standard deviations, and percentages, were computed to summarize the dataset. Associations between categorical variables were assessed using the chi-square test. To identify independent factors predictive of pathological complete response (pCR), both univariate and multivariate logistic regression analyses were employed. Variables with a p-value < 0.1 in the univariate analysis, along with clinically relevant confounders such as age and menopausal status, were included in the multivariate model. For all tests, a two-sided p-value of <0.05 was defined as the threshold for statistical significance.

Ethical Approval

All patient who gave written informed consent were included in the study. Ethical Committee of Kidwai Memorial Institute of Oncology has approved the study. Registration no: KMIO/MEC/2022/07/PG/MO/20. This study was conducted in compliance with institutional ethical guidelines and adhered to the principles of the 1964 Helsinki Declaration (including subsequent amendments).

Results

This prospective study analyzed 188 patients with LABC, stratified into a MetS group (n = 91) and a non-MetS group (n = 97). The baseline characteristics are summarized in [Table 2]. Patients in the MetS group were significantly older (54.38 ± 10.1 vs. 48.28 ± 11.47 years, p < 0.001) and had a higher body mass index (29.1 ± 4.5 vs. 24.8 ± 3.9 kg/m², p < 0.001). The MetS group also had a significantly higher proportion of postmenopausal women (64.8 vs. 40.2%, p = 0.001). The two groups were well-balanced in terms of tumor receptor status (ER, PR, HER2), histology, grade, and the NACT regimen received (all p > 0.05), as detailed in [Table 3].

Table 2 Summary of patient characteristics

Variable	Level	Frequency	%
Sex	Female	188	100
Syndrome	MetS	91	48.4
	Non-MetS	97	51.6
ER	Neg	64	34
	Pos	124	66
PR	Neg	42	22.3
	Pos	146	77.7
HER-2	Neg	103	54.8
	Pos	85	45.2
Menopause	Post	98	52.1
	Pre	90	47.9
Biopsy	IBC	186	99
	Metaplastic	2	1
Chemo	(F)EC D	160	85.1
	TC	19	10.1
	TCH	9	4.8
Surgery	BCS	3	1.6
	MRM	185	98.4
Grade	1	7	3.8
	2	124	66.7
	3	55	29.6
LVI	Neg	98	52.1
	Pos	90	47.9
PNI	Neg	162	86.2
	Pos	26	13.8
DCIS	Neg	110	58.5
	Pos	78	41.5
Response	CR	38	20.2
	NCR	150	79.8
RCB	0	40	21.3
	1	21	11.2
	2	54	28.7
	3	73	38.8
ENE	Neg	155	82.4
	Pos	33	17.6

Abbreviations: ENE, extranodal extension; ER, estrogen receptor; LVI, lymphovascular invasion; MetS, metabolic syndrome; Neg, negarive; Pos, positive; Post, postoperative; PR, progesterone receptor; Pre, preoperative.

Table 3 Summary of patient characteristics between the MetS and non-MetS group

Variable	Levels	MetS	Non-MetS	p-Value
		N = 91(%)	N = 97(%)
Age (mean ± SD)		54.38 ± 10.1	48.28 ± 11.47
Ki-67 (mean ± SD)		42.46 ± 20.75	42.97 ± 20.36	0.866
Tumor size (mean ± SD)		73.01 ± 118.92	62.27 ± 104.14	0.518
ER	Neg	31 (48.4)	33 (51.6)	0.9
	Pos	60 (48.4)	64 (51.6)
PR	Neg	23 (54.8)	19 (45.2)	0.447
	Pos	68 (46.6)	78 (53.4)
HER2	Neg	52 (50.5)	51 (49.5)	0.63
	Pos	39 (45.9)	46 (54.1)
Menopause	Post	59 (60.2)	39 (39.8)	0.001
	Pre	32 (35.6)	58 (64.4)
Biopsy	IBC	90 (48.38)	96 (51.62)	0.109
	Metaplastic	2 (100)	0 (0)
Chemo	(F)EC D	77 (48.1)	83 (51.9)	0.906
	TC	10 (52.6)	9 (47.4)
	TCH	4 (44.4)	5 (55.6)
Surgery	BCS	1 (33.3)	2 (66.7)	1
	MRM	90 (48.6)	95 (51.4)
Grade	1	4 (57.1)	3 (42.9)	0.175
	2	54 (43.5)	70 (56.5)
	3	32 (58.2)	23 (41.8)
LVI	Neg	38 (38.8)	60 (61.2)	0.009
	Pos	53 (58.9)	37 (41.1)
PNI	Neg	78 (48.1)	84 (51.9)	1
	Pos	13 (50)	13 (50)
DCIS	Neg	51 (46.4)	59 (53.6)	0.605
	Pos	40 (51.3)	38 (48.7)
Response	pCR	14 (36.8)	24 (63.2)	0.078
	Non-pCR	77 (51.3)	73 (48.7)
RCB	0	15 (37.5)	25 (62.5)	0.196
	1	10 (47.6)	11 (52.4)
	2	24 (44.4)	30 (55.6)
	3	42 (57.5)	31 (42.5)
ENE	Neg	70 (45.2)	85 (54.8)	0.082
	Pos	21 (63.6)	12 (36.4)

Abbreviations: DCIS, Ductal carcinoma in situ; ENE, extranodal extension; ER, estrogen receptor; LVI, lymphovascular invasion; MetS, metabolic syndrome; Neg, negative; pCR, pathological complete response; PNI, Perineural invasion; Pos, positive; Post, postoperative; Pre, preoperative; RCB, Residual cancer burden; SD, standard deviation.

Primary Outcome: Pathological Complete Response

The overall pCR rate for the entire cohort was 20.2% (38/188). A lower pCR rate was observed in the MetS group compared with the non-MetS group (15.4% [14/91] vs. 24.7% [24/97]). Although this represents a clinically notable difference, it did not reach statistical significance (p = 0.078).

Subgroup Analyses of Pathological Complete Response

Subgroup analyses revealed significant interactions between MetS and other patient/tumor characteristics:

• HER2 Status: The negative impact of MetS was most pronounced in HER2-negative disease. The pCR rate in HER2-negative patients with MetS was 5.8% (3/52) compared with 17.6% (9/51) in those without MetS, a difference that was statistically significant (p = 0.049).

• Menopausal Status: A striking disparity was observed in premenopausal women. Of the 15 premenopausal patients who achieved pCR, 14 (93.3%) were in the non-MetS group and only 1 (6.7%) was in the MetS group (p = 0.008). In contrast, among the 23 postmenopausal patients who achieved pCR, the distribution was more balanced (13 [56.5%] in MetS vs. 10 [43.5%] in non-MetS, p = 0.402).

• Other Subgroups: Trends toward lower pCR rates in the MetS group were observed in other subgroups, including ER-positive patients (9/60 [15.0%] vs. 14/64 [21.9%], p = 0.226), HER2-positive patients (11/39 [28.2%] vs. 15/46 [32.6%], p = 0.421), and the triple-negative breast cancer (TNBC) cohort (2/11 [18.2%] vs. 4/16 [25.0%], p = 0.147), although these were not statistically significant. A comprehensive comparison is provided in [Table 4].

Table 4 Comparison of pathological complete response rates between MetS and non-MetS groups across subgroups

Variable	Subgroup	Non-MetS (pCR/total)	MetS (pCR/total)	p-Value
Overall		24/97 (24.7%)	14/91 (15.4%)	0.078
HER2 status	HER2-negative	9/51 (17.6%)	3/52 (5.8%)	0.049
	HER2-positive	15/46 (32.6%)	11/39 (28.2%)	0.421
Menopausal status	Premenopausal	14/58 (24.1%)	1/32 (3.1%)	0.008
	Postmenopausal	10/39 (25.6%)	13/59 (22.0%)	0.402
ER status	ER-positive	14/64 (21.9%)	9/60 (15.0%)	0.226
	ER-negative	10/33 (30.3%)	5/31 (16.1%)	0.149
TNBC	Yes	4/16 (25.0%)	2/11 (18.2%)	0.147

Abbreviations: ER, estrogen receptor; MetS, metabolic syndrome; pCR, pathological complete response; TNBC, triple-negative breast cancer.

Analysis of Individual Metabolic Syndrome Components

We evaluated the association between each individual component of MetS and the likelihood of achieving pCR. None of the components—obesity (defined by waist circumference), elevated fasting glucose, hypertension, low HDL, or high triglycerides—demonstrated a statistically significant independent association with pCR. However, a strong trend was noted for elevated fasting glucose, where patients with hyperglycemia had a pCR rate of 13.8% compared with 23.1% in those with normal glucose levels (p = 0.09). The full analysis is presented in [Table 5].

Table 5 Univariate analysis of individual metabolic syndrome components and pathological complete response

Metabolic syndrome component	pCR rate (component present)	pCR rate (component absent)	p-Value
Obesity (waist circumference)	16.3% (15/92)	22.5% (21/93)	0.28
Elevated fasting glucose	13.8% (9/65)	23.1% (28/121)	0.09
Hypertension	17.1% (13/76)	21.7% (24/110)	0.45
Low HDL	18.0% (16/89)	21.4% (21/98)	0.56
High triglycerides	16.9% (14/83)	22.2% (23/103)

Abbreviations: HDL, high-density lipoprotein; pCR, pathological complete response.

Univariate and Multivariate Logistic Regression Analysis

On univariate analysis, the factors associated with pCR were menopausal status (premenopausal) and the absence of MetS, particularly in the HER2-negative subgroup. To identify independent predictors, a multivariate logistic regression model was constructed, adjusting for age, menopausal status, HER2 status, and ER status. In this adjusted model, the presence of MetS was not an independent predictor of pCR (odds ratio [OR]: 0.65, 95% confidence interval [CI]: 0.30–1.39, p = 0.27). Similarly, menopausal status also lost its independent association (OR for premenopausal: 1.52, 95% CI: 0.70–3.30, p = 0.29). HER2-positive status showed a trend toward being an independent predictor of higher pCR rates (OR: 1.98, 95% CI: 0.92–4.25, p = 0.08). The complete results of the multivariate analysis are shown in [Table 6].

Table 6 Multivariate logistic regression for predictors of pathological complete response

Variable	Odds ratio (OR)	95% Confidence interval	p-Value
Metabolic syndrome (yes vs. no)	0.65	0.30–1.39	0.27
Menopausal status (pre vs. post)	1.52	0.70–3.30	0.29
HER2 status (positive vs. negative)	1.98	0.92–4.25	0.08
ER status (positive vs. negative)	0.48	0.21–1.09	0.08
Age (per year increase)	0.99	0.96–1.02	0.51

Abbreviation: ER, estrogen receptor.

Association of Metabolic Syndrome with Pathological Tumor Features

The presence of MetS was associated with several adverse pathological features:

• Lymphovascular invasion (LVI) was significantly more common in the MetS group (58.9 vs. 41.1%, p = 0.009).

• Extranodal extension (ENE) was also more prevalent in the MetS group (63.6 vs. 36.4%), a difference that approached statistical significance (p = 0.082).

• No significant differences were observed between the groups for the presence of ductal carcinoma in situ or perineural invasion.

Discussion

NACT plays a crucial role in downstaging breast tumors, thereby enhancing the possibility of breast-conserving surgeries. Additionally, it allows for real-time assessment of treatment efficacy. However, the effectiveness of NACT, as indicated by achieving pCR, varies among breast cancer patients.[8] [9] Our study investigates the relationship between pCR and MetS, offering insights into how MetS impacts treatment outcomes. Our study found a lower, though not statistically significant, overall pCR rate in patients with MetS (15.4 vs. 24.7%, p = 0.078). This trend aligns with the understanding that the metabolic dysregulation in MetS, particularly insulin resistance, may foster a tumor microenvironment conducive to chemoresistance. Elevated levels of insulin and IGF-1 in MetS can activate the PI3K/Akt/mTOR pathway, a key signaling axis for cell survival and proliferation, potentially blunting the cytotoxic effects of chemotherapy.[10] [11] MetS is associated with several adverse clinical features in breast cancer patients, including older age, larger tumor size, higher body mass index, and aggressive tumor subtypes such as TNBC and metaplastic histology.[12] Our findings indicate that patients with MetS exhibit lower chances of achieving pCR compared with those without MetS. This observation aligns with the study by Alan et al, which also found no significant relationship between pCR and MetS, though their sample size was limited to 55 patients.[13]

Previous studies have highlighted the relationship between individual components of MetS and treatment outcomes. For instance, obesity and DM have been associated with lower pCR rates post-NACT.[14] Our study found that patients with MetS had a higher incidence of aggressive histologies, such as TNBC and metaplastic carcinoma, and more grade 3 carcinomas. The literature presents inconsistent findings regarding the relationship between lipid profiles, hypertension, and breast cancer outcomes. Munsell et al's meta-analysis showed an association between obesity and breast cancer with hormone receptor-positive status (relative risk: 1.39, 95% CI: 1.14–1.70).[15] Despite these associations, our analysis of individual MetS components did not identify any single component as a significant independent predictor of pCR, though hyperglycemia showed a notable trend.

Contrary to Tong et al's[16] findings that MetS is not associated with pCR in HER2-positive breast cancer, our results suggest a significant association between HER2-negative status in MetS patients and lower pCR. Additionally, there was no correlation between hormone receptor status and pCR in patients with and without MetS.

Reviewer queries regarding the use of modern regimens like TCH with pertuzumab for HER2-positive disease or immunotherapy for TNBC are valid. Our institutional protocols during the study period did not uniformly include these agents due to cost and access constraints, which is a reality in many real-world settings in India. The inclusion of these targeted agents would likely elevate the overall pCR rates and could potentially modulate the observed effect of MetS, a question for future research. Similarly, the use of the TC regimen, while more common in the adjuvant setting, was used in a small subset of older or less fit patients in our cohort, reflecting tailored clinical decision-making. The observed pCR rates in our study are consistent with the expected efficacy of the predominantly used regimens (FEC-D, TC), which are associated with moderate pCR rates of 15 to 25% in unselected populations.[17] [18] The more potent dose-dense anthracycline–taxane sequence (AC-T), which typically yields higher pCR rates of 40 to 50%,[19] [20] was underrepresented in our cohort, potentially influencing the overall response rate. Furthermore, pCR is strongly dependent on tumor biology, with triple-negative and HER2-positive subtypes demonstrating significantly higher sensitivity to NACT.[21]

Luminal Subtypes: The reviewer's suggestion to look specifically at luminal cancers is well-taken. In our cohort, the pCR rate for ER-positive patients was not significantly different between MetS and non-MetS groups (9/60 vs. 14/64, p = 0.226). The low baseline pCR rate inherent to luminal subtypes makes detecting a significant difference challenging in a cohort of this size. It would have been insightful to evaluate whether MetS increases the risk of relapse; however, this study was designed with pCR as the primary endpoint and lacks long-term follow-up data for disease-free survival (DFS) or overall survival (OS).

The mechanisms by which MetS components influence breast cancer outcomes remain unclear. Hyperglycemia, for instance, has been linked to chemoresistance in ER-positive breast cancer.[22] Our analysis revealed a greater incidence of aggressive tumor histologies among patients with MetS, potentially contributing to the observed reduction in pCR rates. Future investigations focusing on the specific contributions of individual MetS components are warranted to elucidate definitive causal relationships.

Lifestyle modifications and pharmacological interventions targeting MetS components, such as metformin use, may improve treatment outcomes. While studies like Stebbing et al found an association between MetS and poor treatment response, more comprehensive research is needed to confirm these findings and explore potential therapeutic strategies.[23]

This prospective study's strength is its use of standardized MetS criteria and detailed analysis of subgroups. Key limitations include its single-center design, which may affect generalizability, a sample size potentially underpowered for some analyses (including the primary outcome), and the inability to establish causality. The absence of long-term survival data (DFS, OS) prevent us from linking the observed trends in pCR to ultimate clinical outcomes. Furthermore, the study population reflects a specific real-world setting where access to the latest targeted therapies (e.g., pertuzumab, immunotherapy) was limited, which may affect the generalizability of the results to centers using more intensive modern protocols. Future multi-institutional studies with larger cohorts and longer follow-up are needed to validate these findings and link them to survival outcomes. Research should investigate the specific role of individual MetS components in chemoresistance and test interventions, such as metabolic-focused therapies or lifestyle modifications, to improve treatment response.

Conclusion

MetS appears to negatively influence pCR rates, particularly among premenopausal and HER2-negative patients. These findings, though exploratory, highlight the importance of addressing metabolic health in comprehensive breast cancer care. The association of MetS with adverse pathological features like LVI and ENE further suggests it fosters a more aggressive tumor biology. Although the overall pCR rate was not significantly different and MetS was not an independent predictor on multivariate analysis, the subgroup analyses suggest that MetS is a potential determinant of treatment response in specific patient cohorts. Addressing metabolic health may be a crucial adjunct to standard anticancer therapies, and further investigation into its components is warranted to develop personalized treatment strategies.

Conflict of Interest

None declared.

Authors' Contributions

D.A.: Conceptualization, Writing—review and editing, Supervision. K.N.L.: Conceptualization, Writing—review and editing, Supervision. M.C.S.B.: Conceptualization, Writing—review and editing, Supervision. P.K.S.K.: Conceptualization, Writing—review and editing, Supervision, A.H.R.: Data curation, Writing—original draft, Writing—review and editing. L.K.R.: Data curation, Writing—original draft, Writing-review and editing. S.S.: Data curation, Writing—review and editing.

Patients' Consent

All patient who gave written informed consent were included in the study

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Publication History

Article published online:
22 December 2025