Gender-specific differences in body mass index and cardiovascular risk adversely impact survival after aortic valve replacement

Introduction

There is a growing pandemic of obesity both in the developed and developing world. World Health Organization (WHO) estimates 4 million people dying each year as a result of being overweight or obese. There are 650 million people who are obese in the world and numbers are increasing rapidly after the coronavirus disease 2019 (COVID-19) pandemic due to lifestyle changes, lack of exercise and psychological toll of remaining indoors. Severe obesity is associated with myriad of widely acknowledged physical, cardiovascular and mental health problems (1-3). There is a greater incidence of diabetes, hypertension and cardiovascular disease in these patients and risk increases proportionally with increasing body mass index (BMI) and waist circumference compared to normal weight. More recently, the outcomes of patients with COVID-19 have been found to be worse in obese subset of patients (4,5).

The association between perioperative results and long-term mortality has largely been studied in coronary artery disease with regard to the BMI where the results remain controversial. Various other indices like body mass composition index and body mass fat index have been used to explain the ‘obesity paradox’ and find better correlates for survival after coronary revascularization (6,7). The association between gender specific differences in obesity and cardiovascular risk after isolated surgical aortic valve replacement (AVR) and its impact on outcomes and long-term survival has not been studied previously. The aim of this study was to assess the impact of obesity on perioperative outcomes and long-term survival after isolated AVR. We present this article in accordance with the STROBE reporting checklist (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-113/rc).

Methods

Study design

Data were retrospectively collected from April 2000 to December 2019 for all isolated AVR from the cardiac surgery database of University Hospital Southampton NHS Foundation Trust (Patient Administration System, e-CAMIS, Yeadon, Leeds, UK). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by institutional review board of University Hospital Southampton NHS Foundation Trust (No. SEV8389, 01/03/2025). Consent for individual use of data was waived due to the nature of the study and prior approval for the use of such data at the time of consent for surgery. Patients with infective endocarditis, re-sternotomy, other concomitant cardiac procedures, homografts, autografts and emergency/salvage operations were excluded (Figure S1).

Baseline demographics included gender, age, BMI, previous myocardial infarction, Canadian Cardiovascular Society (CCS) angina class, New York Heart Association (NYHA) dyspnoea class, diabetes mellitus, hypertension, smoking history, chronic obstructive pulmonary disease (COPD), creatinine >200 mmol/L, previous stroke, extracardiac arteriopathy, prior coronary artery bypass grafting (CABG), left ventricle ejection fraction (LVEF) <30% and logistic EuroSCORE (%). These variables have previously been described for EuroSCORE and for the National Institute of Cardiac Outcomes Research (NICOR) Adult Cardiac Surgery Database (ACSD).

Operative data included nature and extent of surgery, cardiopulmonary bypass time (CPB), aortic cross clamp time (XCT) and details of prosthesis implanted. Postoperative data included, re-exploration for bleeding/tamponade, new postoperative transient ischaemic attack (TIA)/stroke, acute kidney injury requiring hemofiltration (RRT), deep sternal wound infection (DSWI), permanent pacemaker implantation, length of stay (LOS) and in-hospital mortality. Predicted patient prosthesis mismatch (PPM) was calculated based on published effective office areas index (EOAi) for different valve prostheses. It was classified as mild or absent (EOAi >0.85 cm²/m²), moderate (EOAi between >0.65 and 0.85 cm²/m²) and severe (EOAi ≤0.65 cm²/m²) (8).

National Institutes of Health (NIH) Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults and the WHO classifications for overweight and obesity based on BMI (body weight in kilograms divided by height in meters squared, or kg/m²) were used and defined as overweight: 25.0–29.9 kg/m², obesity (Class I): 30.0–34.9 kg/m², severe obesity (Class II): 35.0–39.9 kg/m², extreme obesity (Class III): ≥40 kg/m² (9).

Patients were divided into two groups: BMI 25–34.9 kg/m² (overweight and Class I obesity) and BMI ≥35 kg/m² (Class II and III, severe and extreme obesity), based on sensitivity modelling.

Statistical analysis

Categorical variables were compared with Chi-squared test and continuous variables were compared with independent samples median test. Univariable regression analysis was performed to identify predictors of in-hospital mortality. Variables with P<0.20 were entered in the multivariable model to calculate odds ratios via the Firth procedure. The area under ROC curve measured 0.91.

Post-discharge survival was calculated from the date of discharge to death or last follow-up. Hazard ratios were calculated using a Cox proportional hazards model using LASSO regularization with 10-fold cross-validation and the 1-SE rule for lambda selection to reduce the risk of variable overfitting. Pre-discharge categorical (dichotomized) and continuous variables including the composite risk of BMI ≥30 kg/m², smoking, hypertension, diabetes mellitus was used for analysing predictors of long-term survival. Harrell’s C concordance statistic measured 0.71. Kaplan Meier survival curves were generated and stratified actuarial survival was obtained from life tables. Long-term survival data were collected from the electronic GP database—NHS Spine Portal Summary Care Records (SCR) and the hospital database—Patient Administration System (e-CAMIS). Long-term survival was compared between the groups (BMI 25–34.9 kg/m² and BMI ≥35 kg/m²) and additionally between those with and without the composite cardiovascular risk. Log-rank (Mantel Cox) was used for testing the equality of survivor function. A two-tailed P value of <0.05 was considered statistically significant. The analysis was generated using Statistical Analysis Software (SAS), Version 3.8, SAS University Edition (SAS Institute Inc., Cary, NC, USA) and SPSS version 27 (Armonk, NY, USA).

Results

Total of 2,398 patients were included in the study (BMI 25–34.9 kg/m²: 2,000 patients and BMI ≥35 kg/m²: 398 patients). Overall median follow-up via reverse KM method was 7.6 years [interquartile range (IQR): 3.8–11.7 years].

Demographic characteristics

The demographics for both groups are shown in the Table 1. Median BMI was 29.6 kg/m² (IQR: 27.2–33.1 kg/m²). Patients with BMI ≥35 kg/m² were younger (70 vs. 73 years, P<0.001) and more likely to be female as compared to patients with BMI 25–34.9 kg/m² (62.3% vs. 40.4%, P<0.001). They were more commonly symptomatic with NYHA class ≥3 and had a higher incidence of diabetes mellitus, hypertension, extracardiac arteriopathy and pulmonary disease despite a lower incidence of smoking. The incidence of composite cardiovascular risk factors was higher in patients with BMI ≥35 kg/m².

Perioperative characteristics and in-hospital survival

In both groups, CPB and XCT were similar (Table 2). There were no differences in re-exploration for bleeding/tamponade, postoperative RRT, deep sternal wound infection and postoperative TIA/stroke between the groups. The median LOS was 7.6 vs. 8.4 days (P=0.10) for the BMI 25–34.9 kg/m² and BMI ≥35 kg/m² groups respectively. There were significantly higher incidences of moderate and severe PPM in the group BMI ≥35 kg/m² (P<0.001) as compared to the group BMI 24–34.9 kg/m².

Multivariable logistic regression identified angina class, hypertension, logistic EuroSCORE and XCT as pre-operative predictors of in-hospital mortality (Table 3). Re-exploration for bleeding/tamponade, RRT, TIA/stroke and increased LOS were peri-operative/post-operative predictors of in-hospital mortality.

Long-term survival

Adverse long-term survival was associated with angina (P=0.25), NYHA class ≥3 (P=0.16), hypertension (P=0.08), diabetes mellitus (P=0.08), current smoking (P=0.006), preoperative atrial fibrillation (P<0.001), high BMI (P=0.03), logistic EuroSCORE (P=0.20), bioprosthesis (P=0.01), and PPM (P=0.08) (Table 4). Actuarial survival was comparable across BMI groups at 12.5 and 12.7 years for BMI 25–34.9 kg/m² and BMI ≥35 kg/m², respectively (P=0.75 log-rank, Table 2).

Long-term survival was significantly worse for:

• Patients with high BMI (Figure 1: log-rank P=0.02).
• Patients with NYHA class ≥3 at presentation (Figure 2: log-rank P<0.001).
• Female patients with moderate-to-severe PPM (Figure 3: log-rank P<0.01).

Figure 1 A Kaplan-Meier survival analysis demonstrated that higher BMI (kg/m²) was associated with worse long-term survival overall (P=0.02). BMI, body mass index; CI, confidence interval.

Figure 2 A Kaplan-Meier survival analysis demonstrated that in patients with a BMI of 25-34.9 kg/m², and BMI of ≥35 kg/m², there was an association between adverse long-term survival outcomes and NYHA class ≥3 (moderate-severe dyspnoea) (log-rank P<0.001). BMI, body mass index; CI, confidence interval.

Figure 3 A Kaplan-Meier survival analysis demonstrated that moderate-severe patient prosthesis mismatch was associated with worse long-term survival in both men (A; P=0.21) and women (B; P<0.01). CI, confidence interval; PPM, patient-prosthetic mismatch.

Gender-specific differences

There were several significant gender-specific differences within our cohort. Female patients were around 5 years older than male patients (Table S1). They were more symptomatic despite a lower composite risk profile (hypertension, active smoking, diabetes mellitus). They had a higher predicted risk of death as compared to male patients (logistic EuroSCORE; 7.4 vs. 5.1, P<0.001) and were more likely to receive a valve <21 mm despite higher BMI (30.4 kg/m² for females vs. 29.1 kg/m² for males, P<0.001).

Using Kaplan-Meier survival estimations, females had worse outcomes than their male counterparts overall. Median survival for females was 11.5 years (IQR: 10.3–12.3 years) vs. 14.2 years (IQR: 12.7–15.7 years) for males (P=0.006) (Table S1). However, female gender was not a significant predictor of worse perioperative outcomes or long-term survival after adjusting for covariates using cox regression modelling (Tables 3,4). Interestingly, moderate-severe PPM was associated with significantly worse survival in females (P<0.01), compared to males for whom this difference was not significant (P=0.21) (Figure 3).

Long-term survival was significantly worse in obese patients, BMI 25–34.9 kg/m² (log-rank P=0.02) as compared to the age- and gender-matched general population in England (Office of National Statistics data for England, 2021) (Figure 4).

Figure 4 A Kaplan-Meier survival analysis. Long-term survival for both BMI (kg/m²) groups were compared to an age- gender- matched general population cohort based on the 2021 Office of National Statistics data for England data (log-rank P=0.02). BMI, body mass index; CI, confidence interval.

Discussion

BMI does not directly measure body fat, although it strongly correlates with adverse health outcomes associated with direct measurements of body weight with skinfold thickness, bioelectrical impedance, underwater weighing and dual energy X-ray absorptiometry (10-12). Obesity has been associated with cardiovascular morbidity, but its impact on perioperative results and long-term survival after isolated AVR remains unclear.

The demographic profile of the group with BMI ≥35 kg/m² showed more commonly NYHA class ≥3, hypertension, diabetes mellitus, pulmonary disease and preoperative atrial fibrillation despite younger age and better LVEF. Unsurprisingly, smoking incidence was higher in non-obese as nicotine use increases metabolic rate, decreases appetite and causes weight loss (only in mild-moderate smokers) (13). However heavy smoking is a major confounder as it is associated with reduced activity, poor diet, central obesity, insulin resistance and metabolic syndromes, which all contribute to reduced survival.

Surgery in obese were technically not different as compared to non-obese patients (similar XCT and CPB). Despite more co-morbidities, postoperative complications and the LOS were similar between the groups. Several previous publications have referred to an ‘obesity paradox’ with better survival in obese patients with chronic kidney disease, after cardiac arrest, coronary revascularization and cardiac surgery (14,15). Ma et al. in a meta-analysis of 865,774 participants observed a U-shaped association across BMI categories for all-cause mortality after coronary revascularization (16). All-cause mortality was higher for underweight patients [relative risk (RR): 2.4; 95% credibility interval (CrI): 2.1–2.7] and lower for overweight, obese, and severely obese with a reference to normal weight patients. Major adverse cardiovascular events (MACE) were the lowest among overweight patients. A nationwide study and meta-analysis of obese patients focused on the obesity paradox observed in cardiac surgery reported an U-shaped association between mortality and BMI classes (17). Effect of obesity was less protective in patients with severe chronic renal, lung, or cardiac diseases, and greater in older patients and those with complications related to the obesity, including metabolic syndrome and atherosclerosis. In both meta-analyses, underweight patients had high cardiovascular event rates and higher mortality after cardiac surgery (16,17). BMI <25 kg/m² was associated with greater risks of myocardial infarction (RR: 1.9; 95% CrI: 1.4–2.5), cardiovascular-related mortality (RR: 2.8; 95% CrI: 1.6–4.7), stroke (RR: 2.0; 95% CrI: 1.3–3.3) and heart failure (RR: 1.7; 95% CrI: 1.1–2.7) compared with normal weight patients; no significant association was observed among individuals with higher BMI. Most studies have included underweight patients in their analysis that might have skewed the overall results for these studies. The reverse epidemiology with better survival in a clinically worse group has correctly been variously ascribed to statistical bias, inexact analysis, index event bias, residual confounding and misinterpretation of association and causality (18-21). Also, association of BMI with survival in isolation has been previously studied without considering the impact of modifiable and non-modifiable risk factors for heart disease, activity status, gender and cardiorespiratory fitness (22). BMI is also a crude indicator for obesity compared to more sensitive indices like waist circumference and total body fat that have better prognostic implications.

This study is unique in assessing the composite of other cardiovascular risk factors with obesity on long-term survival and the gender-specific differences. Our analysis and findings in fact strongly refuted that higher BMI was associated with better survival. The study group is confined to isolated aortic valve disease and hence removes the confounding effects of associated ischemic heart disease. We identified four major confounders for impact of high BMI on survival (I) major cardiovascular risk factors, (II) cardiorespiratory fitness, (III) patient prosthesis mismatch and (IV) gender.

We did not have the data points for cardiorespiratory fitness or accelerometer assessed physical activity grades to evaluate the ‘fat but fit’ paradigm (23,24). However, we used high dyspnoea grades despite preserved cardiac function and absence of pulmonary disease as a surrogate clinical marker for physical activity and cardiorespiratory fitness. In both BMI groups, we observed worse survival among patients with NYHA class ≥3. Diastolic dysfunction and heart failure with preserved ejection fraction in severe calcific aortic stenosis which is difficult to assess may cause high dyspnoea grades in some patients.

It would seem that with increasing BMI, there is a higher likelihood of PPM as the size of the aorta and aortic annulus do not significantly change with increasing body fat (25). It is already well established that PPM is significantly more common in female patients, especially those who are obese, due to the smaller annular size of the aortic valve in women. Smaller predicted effective and indexed effective orifice areas after AVR are associated with adverse long-term outcomes (25-28). We tested this hypothesis in our study and found that the incidence of PPM does increase with increasing BMI. The incidence of moderate PPM was 15.6% versus 33.4% and severe PPM was 1.6% versus 3.8% for overweight and obese patients with BMI 25–34.9 and ≥35 kg/m² respectively. High BMI was associated with a higher incidence of PPM. PPM however did not affect perioperative outcomes or long-term survival in patients with BMI ≥35 kg/m². High BMI itself was associated with worse long-term survival in our study. Therefore, it seems that weight loss and bariatric surgery could improve outcomes in obese patients. Reduction in body weight after cardiac surgery has already been shown to positively influence long term outcomes. Interestingly, another study showed that PPM has a significant negative impact on survival in patients with a BMI <30 kg/m², but this was not observed in obese patients (29).

Adverse outcomes in females after coronary artery bypass surgery are already known due to biological, socioeconomic and cultural factors and biases in care delivery and referrals (30). Guideline-based revascularization rates were much lower, and females were underrepresented 1:4 in the Society of Thoracic Surgeons database (20011-19) among 1.2 million patients (31,32). Although, gender was not a risk factor for adverse survival in this study, we have found similar patterns, where female patients presented almost a half a decade later compared to male patients and they were much more symptomatic with breathlessness at presentation. Although we did not have the data to investigate this further, it may be possible that there may be a referral bias (self-referral for seeking medical care and physician referral for specialist investigations). Also, female patients were getting smaller valves despite higher BMI. These may have been due to perception of frailty and tissue weakness among elderly females rather than biased decision making (33).

Limitations

Limitations are inherent to the retrospective observational study design and its restriction to a single institution. When analysing long-term survival, we could not account for non-cardiac comorbidities or other treatments as potentially relevant baseline or post-baseline confounders. Additionally, we could not analyse patients that required re-intervention or hospital admission due to cardiac cause, and we did not obtain echocardiography data during follow-up.

Conclusions

Obesity with composite risk factors (hypertension, diabetes mellitus and active smoking) is associated with adverse survival. Although we did not observe significant gender-specific differences in long-term survival among specific BMI groups of patients, obese patients had lower survival as compared to matched population in England.

Acknowledgments

We thank Mr Bradley Yee (Senior Data Manager, Department of Cardiac Surgery, University Hospital Southampton NHS Foundation Trust) for help with the collection of data.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-113/rc

Data Sharing Statement: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-113/dss

Peer Review File: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-113/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-113/coif). D.S. serves as an unpaid editorial board member of Cardiovascular Diagnosis and Therapy from August 2024 to July 2026. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by institutional review board of University Hospital Southampton NHS Foundation Trust (No. SEV8389, 01/03/2025). Consent for individual use of data was waived due to the nature of the study and prior approval for the use of such data at the time of consent for surgery.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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