Age-dependent obesity paradox in acute myocardial infarction prognosis: a cohort study of body mass index and recurrent myocardial infarction.

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Learn more: PMC Disclaimer | PMC Copyright Notice Int J Obes (Lond) . 2026 Feb 28;50(5):1064–1073. doi: 10.1038/s41366-026-02038-x Age-dependent obesity paradox in acute myocardial infarction prognosis: a cohort study of body mass index and recurrent myocardial infarction Lingling Zhang 1 , Zhican Liu 2, ✉ , Jianping Zeng 3 , Mingxin Wu 3 Author information Article notes Copyright and License information 1 Medical Department, Xiangtan Central Hospital, The Affiliated Hospital of Hunan University, Xiangtan, China 2 Department of Pulmonary and Critical Care Medicine,Xiangtan Central Hospital, The Affiliated Hospital of Hunan University, Xiangtan, China 3 Department of Cardiology, Xiangtan Central Hospital, The Affiliated Hospital of Hunan University, Xiangtan, China ✉ Corresponding author. Received 2025 May 13; Revised 2026 Jan 3; Accepted 2026 Feb 15; Issue date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13226052  PMID: 41764327 Abstract Background Obesity is a known cardiovascular risk factor, but the “obesity paradox” has been observed in patients with acute myocardial infarction (AMI), where obesity may be linked to better survival outcomes. The relationship between body mass index (BMI) and recurrent myocardial infarction, particularly with age-specific effects, remains unclear. Methods This retrospective cohort study included 4023 AMI patients from a tertiary medical center (2015–2023). Patients were stratified by age: ≤60 years ( n = 1277) and >60 years ( n = 2746). Multivariable-adjusted Cox proportional hazards models were used to assess the association between BMI and recurrent myocardial infarction, adjusting for demographics, biomarkers [N-terminal pro–B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-cTnT)], imaging parameters [left ventricular ejection fraction (LVEF)], comorbidities, and treatment regimens. Curve-fitting models were also applied. The median follow-up time was 35 months (Q1–Q3 25–58). Results In the ≤60 years group, higher BMI was associated with a significantly lower risk of recurrent myocardial infarction [adjusted hazard ratio (HR) = 0.965, 95% confidence interval (CI) 0.936–0.994, P = 0.018]. In contrast, the >60 years group showed a trend toward higher risk (unadjusted HR = 1.032, 95% CI 1.012–1.053, P = 0.001), which lost statistical significance after adjustment (adjusted HR = 1.015, 95% CI 0.994–1.037, P = 0.151). Curve fitting revealed a negative linear correlation in the ≤60 years group and a positive relationship in the >60 years group. Conclusions This study presents the first evidence of an age-dependent obesity paradox in AMI. In patients aged ≤60 years, higher BMI reduced recurrent myocardial infarction risk, whereas in those aged >60 years, the protective effect disappeared and reversed, indicating potential harm. These findings highlight the need for age-stratified secondary prevention strategies for AMI. Open in a new tab Summary of Principal Study Outcomes. Subject terms: Risk factors, Epidemiology Introduction Obesity is an established independent risk factor for cardiovascular diseases, and its global burden has been confirmed by several multinational cohort studies [ 1 , 2 ]. Interestingly, in the context of acute myocardial infarction (AMI), epidemiological research has revealed a pathophysiological paradox: although obesity significantly increases the risk of initial cardiovascular events, patients with obesity who are diagnosed with AMI tend to have lower mortality and better prognostic outcomes. This phenomenon is referred to as the “obesity paradox” [ 3 – 5 ]. The potential mechanisms behind this paradox may involve several biological pathways. Individuals with obesity often have higher levels of adiponectin and greater metabolic adaptability, which may help reduce inflammation and improve myocardial repair. Furthermore, patients with obesity are more likely to receive intensive, guideline-directed treatments, such as early revascularization and high-dose statin therapy, which could partly explain their improved survival outcomes [ 6 , 7 ]. While the existence of the obesity paradox in AMI prognosis has been widely reported [ 5 , 8 ], the relationship between body mass index (BMI) and recurrent cardiovascular events remains controversial, particularly with regard to age-related differences. Age is an important independent risk factor for cardiovascular diseases and may modify the relationship between BMI and cardiovascular events [ 9 ]. A study on cardiogenic shock showed that BMI had significant age-related heterogeneity: low BMI increased 28-day all-cause mortality in elderly patients, while it had no significant effect on younger individuals [ 10 ]. Another study found that a high BMI was associated with improved survival in elderly patients with renal cell carcinoma, but showed no significant effect in younger patients [ 11 ]. These findings underscore the importance of age stratification in analyzing the relationship between BMI and prognosis. Therefore, understanding how BMI influences recurrent myocardial infarction risk at different ages, particularly in relation to the obesity paradox, is essential for optimizing individualized care in AMI patients. Building on our previous work, we found that mild obesity was associated with a reduced risk of major adverse cardiovascular events in male patients with ST-elevation myocardial infarction (STEMI) [ 12 ]. This study extends our previous research in two key areas: (1) it is the first to systematically assess the age-specific effects of BMI on recurrent myocardial infarction, and (2) it uses a larger sample size ( n = 4023 vs. n = 664) to develop an age-stratified risk prediction model. Through multivariable Cox regression analysis, we aim to: (1) identify the age-dependent nature of the obesity paradox in recurrent events, and (2) develop individualized risk management pathways based on age. These findings will provide important evidence to help refine secondary prevention strategies for AMI. Methods Study design and population This retrospective cohort study included 4023 patients with acute myocardial infarction (AMI) treated at the cardiovascular center of a tertiary hospital between January 1, 2015, and June 30, 2023. The inclusion criteria were: (1) a diagnosis of AMI based on established criteria [ 13 ]; (2) age ≥18 years; (3) non-pregnant status. The exclusion criteria were: (1) active malignancy; (2) life expectancy <6 months due to non-cardiovascular conditions; (3) missing key laboratory or imaging data; (4) severe obesity (BMI ≥ 35 kg/m²). A total of 4023 patients meeting these criteria were included and divided into two age groups: ≤60 years ( n = 1277) and >60 years ( n = 2746) (Fig. 1 ). Fig. 1. Flowchart of study participant enrollment. Open in a new tab The flowchart details the inclusion and exclusion criteria for patients admitted with chest pain and diagnosed with Acute Coronary Syndrome (ACS). Ultimately, 4023 patients with myocardial infarction (MI) were enrolled and stratified into two age groups (≤60 years and 60 years) for statistical analysis. ACS acute coronary syndrome, MI myocardial infarction, Ua uric acid, BMI body mass index. Data collection and quality control Data were collected using a standardized case report form (CRF) and extracted from the hospital’s electronic medical records (EPIC, v2023) by cardiovascular specialists who received professional training. The data included: (1) demographic characteristics; (2) laboratory results [high-sensitivity cardiac troponin T (hs-cTnT), N-terminal pro–B-type natriuretic peptide (NT-proBNP), etc.]; (3) cardiac imaging parameters [left ventricular ejection fraction (LVEF) measured by transthoracic echocardiography]; (4) comorbidities and treatment information. To ensure data quality, a three-tier verification system was implemented: (a) primary verification: performed by data managers who cross-checked the electronic data against the original medical records; (b) secondary verification: conducted by senior cardiovascular physicians who blindly reviewed 20% of random samples; (c) logical verification: automated validation using Python 3.9 (Pandas 1.5.3 library) for range checks and logical relationships between variables. Endpoint determination and follow-up protocol A standardized endpoint determination process was followed. The primary endpoint of this study was recurrent myocardial infarction (defined according to the 2023 European Society of Cardiology [ESC] Acute Coronary Syndrome Guidelines [ 13 ]). Secondary endpoints included all-cause mortality and heart failure readmissions (defined according to the 2021 ESC Heart Failure Guidelines [ 14 ]). The endpoints were determined by an independent committee of five senior cardiologists who were not involved in the study. The follow-up protocol was hybrid, with 38% of follow-ups conducted in outpatient clinics and 62% by telephone. The median follow-up period was 35 months (Q1–Q3 25–58), with the last follow-up on September 15, 2024, and a loss-to-follow-up rate of less than 5%. Ethical standards and data management This study was approved by the Ethics Committee of Xiangtan Central Hospital (Approval No. 2023-02-001) and adhered to the principles outlined in the Declaration of Helsinki and the Guidelines for Ethical Review of Health Research Involving Humans. Because this was a retrospective observational study, the ethics committee waived the requirement for informed consent. All data were stored on encrypted hospital servers to ensure data security and traceability. Statistical analysis The normality of continuous variables was confirmed using the Shapiro–Wilk test. Normally distributed data were presented as means ± standard deviation and compared using independent t -tests. Non-normally distributed data were expressed as medians (interquartile range) and compared using the Wilcoxon rank-sum test. Categorical variables were presented as frequencies (percentages) and analyzed using Pearson’s χ² test or Fisher’s exact test. All statistical analyses were performed using R version 4.2.1, with a two-sided significance level of α = 0.05. Stratified Cox proportional hazards models were used to analyze the relationship between BMI and recurrent myocardial infarction, with stratification by age groups. Multivariable-adjusted models were developed: (A) unadjusted model; (B) demographic-adjusted model (sex, age); (C) fully adjusted model, incorporating N-terminal pro–B-type natriuretic peptide (NT-proBNP), left ventricular ejection fraction (LVEF), Killip class, hemoglobin, low-density lipoprotein cholesterol (LDL-C), comorbidities [hypertension, diabetes, chronic obstructive pulmonary disease (COPD), etc.], and guideline-directed medical therapy (GDMT; dual antiplatelet therapy, high-dose statins, etc.). Curve fitting models were used to explore the relationship between BMI and recurrent myocardial infarction risk. Results Baseline characteristics A total of 4023 patients with acute myocardial infarction (AMI) were included in this study, and their baseline characteristics are detailed in Table 1 . Age stratification revealed significant differences:the >60 years group displayed a typical metabolic-inflammatory phenotype, with NT-proBNP levels 2.45 times higher than those in the ≤60 years group (4589.99 ± 8048.73 vs. 1873.70 ± 5688.51 pg/mL, P < 0.001). This group also had a lower left ventricular ejection fraction (51.99 ± 12.29% vs. 53.81 ± 11.06%, P < 0.001) and a higher comorbidity burden (hypertension: 71.52% vs. 55.76%, P < 0.001; atrial fibrillation: 12.13% vs. 2.98%, P < 0.001; diabetes: 35.07% vs. 30.46%, P = 0.004; COPD: 22.91% vs. 15.35%, P < 0.001; renal insufficiency: 28.00% vs. 12.61%, P < 0.001; stroke: 21.52% vs. 8.14%, P < 0.001). Table 1. Baseline characteristics stratified by age. Variables Age group (>60 vs ≤60 years old) ≤60 N = 1277 >60 N = 2746 P -value Demographics Age, years 52.06 ± 6.80 73.13 ± 7.41 <0.001 Male, N (%) 1096 (85.83%) 1743 (63.47%) <0.001 BMI, kg/m 2 20.40 ± 3.08 19.34 ± 3.01 <0.001 Clinical conditions at admission NT-proBNP, pg/ml 1873.70 ± 5688.51 4589.99 ± 8048.73 <0.001 hsTnT, ng/mL 3.10 ± 2.95 3.18 ± 3.02 0.39 LVEF, % 53.81 ± 11.06 51.99 ± 12.29 <0.001 Hemoglobin, g/dL 138.82 ± 20.01 123.09 ± 19.97 <0.001 Low-Density Lipoprotein, mmol/L 2.89 ± 0.98 2.67 ± 0.93 <0.001 AMI classification, N (%) <0.001 NSTEMI 632 (49.49%) 1752 (63.80%) STEMI 645 (50.51%) 994 (36.20%) Killip classification, N (%) <0.001 1 452 (35.40%) 677 (24.65%) ≥2 825 (64.60%) 2069 (75.35%) Medical history, N (%) Atrial fibrillation 38 (2.98%) 333 (12.13%) <0.001 Hypertension 712 (55.76%) 1964 (71.52%) <0.001 Diabetes mellitus 389 (30.46%) 963 (35.07%) 0.004 COPD 196 (15.35%) 629 (22.91%) <0.001 Renal insufficiency 161 (12.61%) 769 (28.00%) <0.001 Stroke 104 (8.14%) 591 (21.52%) <0.001 Treatment, N (%) Operative treatments a 906 (70.95%) 1360 (49.53%) <0.001 Antiplatelet 1267 (99.22%) 2685 (97.78%) 0.001 Statins 1265 (99.06%) 2690 (97.96%) 0.012 Beta-blocker 1153 (90.29%) 2327 (84.74%) <0.001 SGLT2i 136 (10.65%) 197 (7.17%) <0.001 ARB 703 (55.05%) 1454 (52.95%) 0.214 ARNI 488 (38.21%) 967 (35.21%) 0.065 ACEI 417 (32.65%) 875 (31.86%) 0.617 Spironolactone 301 (23.57%) 840 (30.59%) <0.001 Clinical endpoints, N (%) Recurrent myocardial infarction 549 (42.99%) 1053 (38.35%) 0.005 In-hospital death 13 (1.02%) 135 (4.92%) <0.001 Hospital-onset heart failure 367 (28.74%) 1327 (48.32%) <0.001 Open in a new tab Categorical variables were presented as n (%). Values for continuous variables are given as means ± SD or medians with interquartile ranges. Bold represent significant values ( p < 0.05). BMI Body mass index, NT-proBNP N-terminal pro b-type natriuretic peptide, hsTnT high-sensitivity troponin T, LVEF left ventricular ejection fraction, AMI Acute myocardial infarction, NSTEMI Non-st-segment elevation myocardial infarction, STEMI St-segment elevation myocardial infarction, COPD Chronic Obstructive Pulmonary Disease, SGLT2i sodium-dependent glucose transporters 2, ACEI angiotensin-converting enzyme inhibitors, ARB angiotensin receptor blockers, ARNI angiotensin receptor - enkephalase inhibitors. a Percutaneous coronary intervention/percutaneous transluminal coronary angioplasty/ coronary artery bypass grafting. Notably, the ≤60 years group had a significantly higher proportion of patients undergoing revascularization [percutaneous coronary intervention (PCI): 70.95% vs. 49.53%, P < 0.001] and better adherence to guideline-directed medical therapy (GDMT) (β-blockers: 84.3% vs. 76.8%, P < 0.001; antiplatelet drugs: 99.22% vs. 97.78%, P = 0.001; statins: 99.06% vs. 97.96%, P = 0.012; sodium–glucose cotransporter 2 (SGLT2) inhibitors: 10.65% vs. 7.17%, P < 0.001) (Table 1 ). Primary effect of BMI on recurrent myocardial infarction Multivariable Cox regression analysis revealed an age-dependent effect of BMI on the risk of recurrent myocardial infarction (Table 2 ). In the ≤60 years group, an increase in BMI by 1 kg/m² was significantly associated with a 3.5% reduction in the risk of recurrent myocardial infarction (fully adjusted hazard ratio [HR] = 0.965, 95% confidence interval [CI] 0.936–0.994, P = 0.018). Table 2. Multivariate Cox regression model for BMI effect on recurrent myocardial infarction in AMI patients. Model Age ≤60 years old Age >60 years old Total HR (95% CI) P -value HR (95% CI) P -value HR (95% CI) P -value Model A 0.969 (0.942, 0.997) 0.030 1.032 (1.012, 1.053) 0.001 1.012 (0.996, 1.028) 0.157 Model B 0.968 (0.940, 0.997) 0.030 1.033 (1.011, 1.054) 0.002 1.011 (0.994, 1.028) 0.212 Model C 0.965 (0.936, 0.994) 0.018 1.015 (0.994, 1.037) 0.151 1.001 (0.985, 1.019) 0.865 Open in a new tab Bold represent significant values ( p < 0.05). CI confidence interval, HR Hazard ratio; Other abbreviations are shown in Table 1 . Model A adjust for: None. Model B adjust for: Sex; Age. Model C adjust for: Sex; Age; NTproBNP; hsTnT; LVEF; Hemoglobin; Low-Density Lipoprotein; Killip classification; Atrial fibrillation; Hypertension; AMI classification; Diabetes mellitus; COPD; Renal insufficiency; Stroke; Operative treatments; Antiplatelet; Statins; Beta-blocker; SGLT1i; ARB; ARNI; ACEI; Spironolactone. This protective effect remained statistically significant after adjusting for gender, age, biomarkers (NT-proBNP, hs-cTnT), left ventricular ejection fraction (LVEF), and comorbidities ( P trend = 0.030 → 0.018). In contrast, in the >60 years group, an increase in BMI by 1 kg/m² was associated with a 3.2% higher risk of recurrent events in the unadjusted model (HR = 1.032, 95% CI 1.012–1.053, P = 0.001). However, after multivariable adjustment, this association weakened and lost statistical significance (adjusted HR = 1.015, 95% CI 0.994–1.037, P = 0.151). As shown in Fig. 2 , after adjusting for all confounders, curve fitting analysis revealed a linear negative correlation between BMI and recurrent myocardial infarction risk in the ≤60 years group, while a positive relationship was observed in the >60 years group. Fig. 2. Age-stratified curve fi tting analysis of BMI and the risk of recurrent myocardial infarction. Open in a new tab The y-axis represents the log relative risk (RR) for recurrent myocardial infarction, and the x-axis represents body mass index (BMI). The red solid line indicates the trend for patients aged ≤60 years, while the blue dashed line indicates the trend for patients aged 60 years. BMI and secondary outcomes in AMI patients Cox regression models adjusted for age and sex showed no significant association between BMI and all-cause mortality (≤60 years group: HR = 0.95, 95% CI 0.89–1.02, P = 0.191; >60 years group: HR = 0.99, 95% CI 0.96–1.01, P = 0.220), and the overall analysis remained neutral (HR = 0.98, 95% CI 0.96–1.00, P = 0.118). Similarly, BMI was not significantly associated with heart failure readmission risk (≤60 years group: HR = 1.00, 95% CI 0.93–1.09, P = 0.927; >60 years group: HR = 0.99, 95% CI 0.94–1.03, P = 0.521), and the overall analysis remained neutral (HR = 0.99, 95% CI 0.95–1.03, P = 0.597) (Table 3 ). Table 3. Cox regression model adjusted for age and sex on the impact of BMI on other outcomes in AMI patients. Other outcomes Age ≤60 years old Age >60 years old Total HR (95% CI) P -value HR (95% CI) P -value HR (95% CI) P -value All-cause mortality 0.95 (0.89, 1.02) 0.191 0.99 (0.96, 1.01) 0.220 0.98 (0.96, 1.00) 0.118 Readmission for heart failure 1.00 (0.93, 1.09) 0.927 0.99 (0.94, 1.03) 0.521 0.99 (0.95, 1.03) 0.597 Open in a new tab Adjust model adjust for: Age; Sex. Abbreviations are shown in Tables 1 and 2 . Clinical subgroup analysis of BMI and risk of recurrent myocardial infarction Age and Gender Stratification: As shown in Table 4 , in patients aged ≤60 years, BMI was associated with a borderline significant protective effect against recurrent myocardial infarction in males (HR = 0.969, 95% CI 0.939–1.000, P = 0.052), while in females, this effect was not statistically significant (HR = 0.954, 95% CI 0.878–1.037, P = 0.268). In contrast, for patients aged >60 years, BMI showed a significant positive correlation with recurrent risk in females (HR = 1.046, 95% CI 1.009–1.084, P = 0.013), and a similar trend was observed in males (HR = 1.026, 95% CI 1.001–1.052, P = 0.043). Table 4. Age and clinical characteristics stratified analysis of BMI and recurrent myocardial infarction. AGE ≤ 60 years old AGE > 60 years old Outcomes/total HR(95% CI) P -value Outcomes/total HR(95% CI) P -value Female 75/181 0.954 (0.878, 1.037) 0.268 368/1003 1.046 (1.009, 1.084) 0.013 Male 474/1096 0.969 (0.939, 1.000) 0.052 685/1743 1.026 (1.001, 1.052) 0.043 NSTEMI 205/632 0.972 (0.927, 1.019) 0.234 554/1752 1.039 (1.011, 1.068) 0.006 STEMI 344/645 0.971 (0.937, 1.006) 0.104 499/994 1.017 (0.989, 1.046) 0.225 Operative treatments NO 116/371 1.016 (0.956, 1.080) 0.602 423/1386 1.029 (0.997, 1.061) 0.073 YES 433/906 0.953 (0.923, 0.984) 0.003 630/1360 1.020 (0.994, 1.046) 0.130 Killip classification 1 193/452 0.992 (0.943, 1.044) 0.759 278/677 1.030 (0.991, 1.071) 0.135 ≥2 356/825 0.958 (0.926, 0.992) 0.015 775/2069 1.033 (1.010, 1.057) 0.005 Atrial fibrillation NO 529/1239 0.969 (0.941, 0.997) 0.033 927/2413 1.039 (1.018, 1.061) <0.001 YES 20/38 0.979 (0.821, 1.167) 0.813 126/333 0.979 (0.921, 1.040) 0.485 Hypertension NO 237/565 0.962 (0.920, 1.006) 0.087 324/782 1.036 (1.001, 1.073) 0.041 YES 312/712 0.973 (0.937, 1.009) 0.143 729/1964 1.031 (1.006, 1.055) 0.013 Diabetes mellitus NO 394/888 0.960 (0.928, 0.994) 0.020 697/1783 1.023 (0.999, 1.048) 0.056 YES 155/389 0.989 (0.939, 1.041) 0.667 356/963 1.054 (1.018, 1.092) 0.003 COPD NO 453/1081 0.967 (0.936, 0.999) 0.040 808/2117 1.041 (1.018, 1.065) <0.001 YES 96/196 0.970 (0.917, 1.026) 0.291 245/629 1.001 (0.962, 1.041) 0.970 Renal insufficiency NO 467/1116 0.963 (0.934, 0.993) 0.017 761/1977 1.032 (1.008, 1.056) 0.008 YES 82/161 1.012 (0.944, 1.084) 0.740 292/769 1.033 (0.995, 1.072) 0.085 Stroke NO 510/1173 0.963 (0.935, 0.992) 0.014 826/2155 1.035 (1.012, 1.058) 0.002 YES 39/104 1.018 (0.910, 1.138) 0.754 227/591 1.023 (0.981, 1.067) 0.290 Antiplatelet NO 3/10 0.998 (0.718, 1.388) 0.991 9/61 0.943 (0.731, 1.216) 0.651 YES 546/1267 0.968 (0.941, 0.996) 0.027 1044/2685 1.032 (1.012, 1.052) 0.002 Statins NO 3/12 1.485 (0.921, 2.396) 0.105 13/56 1.059 (0.888, 1.263) 0.522 YES 546/1265 0.965 (0.938, 0.994) 0.017 1040/2690 1.031 (1.011, 1.052) 0.002 Beta-blocker NO 44/124 0.942 (0.851, 1.043) 0.251 122/419 1.057 (0.996, 1.121) 0.067 YES 505/1153 0.970 (0.942, 1.000) 0.048 931/2327 1.028 (1.007, 1.050) 0.008 SGLT1i NO 492/1141 0.977 (0.948, 1.007) 0.131 965/2549 1.031 (1.011, 1.052) 0.003 YES 57/136 0.933 (0.864, 1.007) 0.075 88/197 1.011 (0.933, 1.095) 0.792 ARB NO 234/574 0.961 (0.919, 1.005) 0.079 435/1292 1.036 (1.005, 1.068) 0.021 YES 315/703 0.964 (0.929, 1.000) 0.052 618/1454 1.012 (0.986, 1.039) 0.360 ARNI NO 317/789 0.970 (0.934, 1.007) 0.109 613/1779 1.024 (0.998, 1.051) 0.066 YES 232/488 0.966 (0.924, 1.010) 0.124 440/967 1.019 (0.989, 1.050) 0.213 ACEI NO 359/860 0.946 (0.914, 0.979) 0.002 700/1871 1.036 (1.011, 1.061) 0.005 YES 190/417 1.008 (0.959, 1.059) 0.761 353/875 1.025 (0.992, 1.060) 0.141 Spironolactone NO 409/976 0.956 (0.925, 0.988) 0.008 700/1906 1.034 (1.009, 1.060) 0.007 YES 140/301 1.006 (0.951, 1.065) 0.827 353/840 1.028 (0.995, 1.063) 0.098 NT-proBNP ≤900 pg/ml 383/923 0.961 (0.929, 0.995) 0.025 432/1232 1.050 (1.018, 1.082) 0.002 >900 pg/ml 166/354 0.995 (0.947, 1.046) 0.857 621/1514 1.019 (0.993, 1.045) 0.146 hsTnT ≤1 ng/mL 131/369 0.981 (0.923, 1.042) 0.524 241/758 1.058 (1.016, 1.102) 0.007 >1 ng/mL 418/908 0.962 (0.932, 0.994) 0.019 812/1988 1.022 (0.999, 1.045) 0.058 LVEF <50% 201/410 0.987 (0.942, 1.034) 0.584 468/1137 1.003 (0.974, 1.032) 0.867 ≥50% 348/867 0.956 (0.923, 0.991) 0.015 585/1609 1.056 (1.029, 1.084) <0.001 Hemoglobin <110 g/dL 40/94 0.989 (0.879, 1.112) 0.853 215/595 1.059 (1.013, 1.108) 0.012 ≥110 g/dL 509/1183 0.967 (0.939, 0.996) 0.026 838/2151 1.026 (1.004, 1.049) 0.020 Low-density lipoprotein <3.4 mmol/L 391/934 0.967 (0.934, 1.002) 0.062 849/2203 1.022 (1.000, 1.045) 0.048 ≥3.4 mmol/L 158/343 0.973 (0.928, 1.020) 0.258 204/543 1.072 (1.026, 1.121) 0.002 Open in a new tab Bold represent significant values ( p < 0.05). Abbreviations are shown in Tables 1 and 2 . AMI Type and Treatment Approach: Among patients undergoing surgical treatment, the younger group showed a significant protective effect of BMI (HR = 0.953, 95% CI 0.923–0.984, P = 0.003), while no significant association was found in the older group (HR = 1.020, 95% CI 0.994–1.046, P = 0.130). Notably, in patients with a Killip class ≥2, BMI exhibited a significant protective effect in the younger group (HR = 0.958, 95% CI 0.926–0.992, P = 0.015), whereas the older group showed a trend toward increased risk (HR = 1.033, 95% CI 1.010–1.057, P = 0.005). Comorbidities and Biomarkers: In patients without atrial fibrillation, BMI showed a significant protective effect in the younger group (HR = 0.969, 95% CI 0.941–0.997, P = 0.033), while in the older group, BMI was associated with an increased risk (HR = 1.039, 95% CI 1.018–1.061, P < 0.001). In patients with NT-proBNP ≤900 pg/mL, BMI was significantly protective in the younger group (HR = 0.961, 95% CI 0.929–0.995, P = 0.025), whereas in the older group, BMI was associated with increased risk (HR = 1.050, 95% CI 1.018–1.082, P = 0.002). Medication Treatment: In patients using angiotensin-converting enzyme (ACE) inhibitors, BMI showed a significant protective effect in the younger group (HR = 0.946, 95% CI 0.914–0.979, P = 0.002), while in the older group, BMI was associated with increased risk (HR = 1.036, 95% CI 1.011–1.061, P = 0.005). In patients on statin therapy, BMI showed a significant protective effect in the younger group (HR = 0.965, 95% CI 0.938–0.994, P = 0.017), whereas in the older group, BMI was associated with increased risk (HR = 1.031, 95% CI 1.011–1.052, P = 0.002) (details provided in Table 4 ). Stratified Forest Plot: Fig. 3 illustrates the impact of body mass index (BMI) on the risk of recurrent myocardial infarction in acute myocardial infarction (AMI) patients, stratified by age. The ≤60 years group (in blue) and the >60 years group (in red) are shown. In the figure, most clinical factors (such as low-density lipoprotein cholesterol [LDL-C], low hemoglobin, left ventricular ejection fraction [LVEF], etc.) exhibited a protective effect on the risk of recurrent myocardial infarction as BMI increased (HR < 1). This suggests that an increase in BMI in the younger-middle-aged group is associated with a reduced risk of recurrent myocardial infarction. Conversely, in the >60 years group, BMI was positively correlated with the risk of recurrent myocardial infarction. The risk ratios (HR > 1) shown in the figure indicate that increased BMI in older patients may correlate with a higher risk of recurrent myocardial infarction. Notably, the association between BMI and comorbidities such as hypertension and diabetes in older patients further increased the risk of recurrent myocardial infarction. Fig. 3. Forest plot of BMI-associated hazard ratios for recurrent myocardial infarction stratified by clinical characteristics and age groups. Open in a new tab The plot illustrates the hazard ratios (HR) and their corresponding 95% confidence intervals across various clinical subgroups. Blue dots represent the HRs for patients aged ≤60 years, and red dots represent the HRs for patients aged 60 years. The vertical dashed line indicates an HR of 1.00. Discussion This study is the first to systematically evaluate the effect of body mass index (BMI) on the risk of recurrent myocardial infarction in patients with acute myocardial infarction (AMI), revealing an age-dependent bidirectional relationship. Our main finding indicates that, in patients aged ≤60 years, an increase in BMI is significantly associated with a reduced risk of recurrent myocardial infarction. In contrast, in patients aged >60 years, an increase in BMI tends to be associated with an increased risk of recurrent myocardial infarction, although this association did not reach statistical significance after multivariable adjustment (Graphical abstract). Our results provide new insights into the mechanisms underlying the obesity paradox and suggest that clinical practice should incorporate stratified management based on age and BMI to optimize individualized treatment strategies. Mechanisms of the obesity paradox Although the mechanisms underlying the obesity paradox remain unclear, previous studies suggest that individuals with overweight or mild obesity may have greater metabolic reserves, enabling them to better withstand acute cardiovascular events [ 15 , 16 ]. However, this does not appear to apply to patients with severe obesity. Our study further supports this hypothesis, particularly in patients aged ≤60 years, where an increase in BMI correlates with a lower risk of recurrent myocardial infarction. This trend does not hold for patients with severe obesity. The protective effect observed in these patients may be linked to metabolic adaptations caused by obesity and lower levels of chronic inflammation [ 17 ], which could enhance recovery after acute coronary syndromes [ 16 , 18 ]. Additionally, individuals with obesity are often administered more intensive treatment, which may contribute to improved short-term prognosis [ 6 , 7 ]. However, as age increases, the protective effect of obesity seems to diminish and may even transform into a risk factor. This shift is closely associated with the higher comorbidity burden observed in older patients. Comorbidities such as diabetes and hypertension can increase the risk of cardiovascular events [ 19 , 20 ]. Our findings suggest that in patients aged >60 years, BMI increases are linked to worsening comorbidities, particularly hypertension and diabetes, which may contribute to the higher risk of recurrent myocardial infarction. Expansion of existing evidence Our study is consistent with previous literature, emphasizing the unique nature of the obesity paradox in AMI patients [ 3 – 5 , 8 , 12 , 16 , 18 ]. Through multivariable-adjusted analysis, we have demonstrated for the first time a significant age-dependent effect of BMI in AMI patients. In patients aged ≤60 years, BMI increases may be associated with changes in metabolic factors such as adiponectin, which is believed to promote myocardial repair by regulating metabolic responses [ 21 ]. However, in older patients, some studies suggest that the effect of adiponectin is not linear but instead follows a U-shaped curve in relation to cardiovascular events, which differs from the response observed in patients aged >60 years [ 22 ]. Other research has suggested that interventions targeting adiponectin biosynthesis could protect the heart from ischemia-reperfusion injury [ 23 ]. Nonetheless, some studies question the existence of the obesity paradox in all cardiovascular disease patients, proposing that it may not apply universally across all conditions [ 24 ]. With the advancement of artificial intelligence, future research into BMI, adiponectin, and other metabolic markers in relation to cardiovascular prognosis is expected to become more refined [ 25 ]. Regional differences in obesity cut-points Regional differences in BMI distribution and obesity cut-points may also help to interpret our findings. In our Chinese AMI cohort, the mean BMI was in the low-to-normal range (around 20 kg/m²), whereas large US population data show that more than one third of adults meet criteria for obesity (BMI ≥ 30 kg/m²), indicating a substantially higher BMI burden in Western populations [ 26 ]. In addition, a WHO expert consultation has highlighted that Asian populations develop cardiometabolic risk at lower BMI levels than European populations and has proposed lower BMI cut-points for overweight and obesity in Asian populations [ 27 ]. Taken together, these differences highlight the importance of considering region-specific BMI distributions and cut-points when interpreting the obesity paradox across diverse populations. Clinical translational value Based on the significant age-dependent effects observed in this study, we propose a new management strategy to optimize weight management in AMI patients: Patients aged ≤60 years: For patients with a BMI between 25 and 35 kg/m², we suggest moderately relaxing weight management targets, especially for those with a left ventricular ejection fraction (LVEF) ≥ 50% or those undergoing PCI. This approach aims to avoid excessive weight loss that may lead to muscle wasting. Patients aged >60 years: BMI should be strictly maintained below 28 kg/m², with particular attention to high-risk subgroups, such as females, patients with non-ST-segment elevation myocardial infarction (NSTEMI), and those with LDL levels ≥3.4 mmol/L. Limitations and future directions This study has several limitations. First, as a retrospective study, it may be subject to selection and information biases, particularly concerning BMI measurements and baseline characteristics. Second, since the data were collected from a single center, external validity must be confirmed through multi-center cohort studies. Additionally, due to the observational design, we cannot establish a causal relationship between BMI and recurrent myocardial infarction. Third, because this registry was based on routinely collected clinical data, we did not have systematic information on body composition (e.g., body fat distribution), indices of muscle mass and muscle strength, or cardiorespiratory fitness and habitual physical activity. Cardiorespiratory fitness and physical activity have been shown to be powerful, independent predictors of all-cause and cardiovascular mortality and other major outcomes in both general and high-risk populations [ 28 – 33 ]. In addition, recent reviews on obesity and the obesity paradox have emphasized that the interaction between adiposity and cardiorespiratory fitness may be crucial for interpreting apparently paradoxical associations between BMI and prognosis [ 34 – 37 ]. Therefore, BMI in our study cannot distinguish between adiposity and lean mass or between fit and unfit phenotypes, and residual confounding by these unmeasured factors cannot be excluded. Although adjusting for a large number of variables in Model C might reduce statistical power, it is crucial to control for key confounders such as NT-proBNP, hsTnT, LVEF, and comorbidities (e.g., hypertension, diabetes). These variables are clinically relevant and have been shown to significantly influence the prognosis of AMI patients. Reducing the number of variables could introduce omitted variable bias, potentially distorting the true relationship between BMI and recurrent myocardial infarction. Lastly, due to the limitations of the retrospective database, we set the upper BMI limit at 35 kg/m², excluding patients with severe obesity. Therefore, it remains unclear whether the obesity paradox persists in this group. Future studies should not only incorporate more accurate body composition measurements, such as bioelectrical impedance analysis and cardiac MRI, but also objective or questionnaire-based assessments of muscle mass, muscle strength, cardiorespiratory fitness, and physical activity, in order to more comprehensively evaluate the interplay between adiposity, fitness, and prognosis in AMI patients. Conclusion This study is the first to systematically examine the age-dependent nature of the obesity paradox following AMI. Our findings reveal a bidirectional effect: an increase in BMI significantly reduces the risk of recurrent myocardial infarction in ≤60 years patients, but this protective effect diminishes and may even become harmful in >60 years patients. These results challenge the “one-size-fits-all” approach to weight management and provide strong evidence for personalized secondary prevention strategies based on age, comorbidities, and treatment responses. This discovery has the potential to influence clinical guidelines and contribute to the development of cardiovascular risk management in precision medicine. Acknowledgements We appreciate the assistance provided by the Information Systems Department and Department of Scientific Research of Xiangtan Central Hospital in ethical review and data collection. Author contributors Lingling Zhang: established the hypothesis, performed the statistical analysis, wrote the manuscript. Jianping Zeng and Mingxin Wu: interpreted statistical analysis and conducted multivariate analysis. Zhican Liu: data collection and participated follow-up. Zhican Liu: initiated the study hypothesis, edited the manuscript. Funding This study is supported by: 1. Scientific Bureau of Xiangtan City (SF-ZDJH20231037), Xiangtan City, Hunan Province, China. 2. Medical Association of Xiangtan City (2023-xtyx-35), Xiangtan City, Hunan Province, China. 3. Medical Association of Xiangtan City (2024-xtyx-38), Xiangtan City, Hunan Province, China. Data availability The datasets generated and analyzed during the current study are not publicly available due to the database owner is reluctant to make them public, but are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of Xiangtan Central Hospital (Xiangtan, China, No.2023-02-001) and conformed to the principles outlined in the Declaration of Helsinki. The need for informed consent was waived by the Ethics Committee Review Board of Xiangtan Central Hospital, because of the retrospective nature of the study. 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