Inspecting the risk of heart failure in general population using metabolic health status and obesity profiles

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Phenotypes derived from obesity and metabolic unhealth have promising clinical relevance. Their predictive effect for different subtypes of HF is to be investigated. Methods and findings: Totally 8018 participants from the ARIC study were classified into four phenotypes: metabolic healthy non-obesity (MHNO), metabolic healthy obesity (MHO), metabolic unhealthy non-obesity (MUNO) and metabolic unhealthy obesity (MUO). Cox models were applied to explore the relationship between these phenotypes and the risk of HF with preserved ejection fraction (HFpEF, left ventricular ejection fraction [LVEF] ≥50%) or HF with reduced or mildly reduced LVEF (HFrEF/HFmrEF, LVEF <50%) in total population and subgroups. Association between phenotypes transition and HF was further analyzed. Compared with MHNO, participants with MHO (hazard ratio and 95% confidence interval, 2.04 [1.61-2.59]), MUNO (1.80 [1.40-2.32]) and MUO (2.50 [1.95-3.20]) were related to higher HFpEF risk, MUNO (1.74 [1.36-2.22]) and MUO (1.92 [1.49-2.49]) were associated with higher HFrEF/HFmrEF risks. Subgroup analyses revealed that the associations between the phenotypes and HF risk were more distinct ( P -interaction < 0.009) in participants < 55 years. Serum lipid might impact the relationship of the phenotypes with HFrEF/HFmrEF ( P -interaction =0.033). From a dynamic aspect, persistent MHO, MUNO or MUO was associated with increased HFpEF risk, whereas progression from MHNO to MHO didn’t exhibit higher HFrEF/HFmrEF risk. Conclusions: Both metabolic unhealth and obesity independently and cumulatively contributed to HFpEF risk, while metabolic unhealth rather than obesity are more influential in HFmrEF/HFrEF risk. metabolic unhealth obesity heart failure Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Obesity and metabolic unhealth are two major modifiable risk factors of cardiovascular disease (CVD)( 1 ). They are closely connected and normally taken for granted for their co-existence in individuals. However, a proportion of obese individuals was indeed with healthy metabolic status which accounts for over 3–57% of the obese population( 2 – 4 )and not all individuals with metabolism syndrome were obese. Based on metabolic health and obesity status, four clinical phenotypes are derived: metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO) and metabolically unhealthy obesity (MUO)( 5 ). Individuals with different phenotypes may exhibit discrepant cardiovascular outcomes. Among MHNO, MHO and MUO, people with MUO exhibited the highest risk of coronary heart disease (CHD) and overall CVD risk, followed by those with MHO and MHNO( 6 – 9 ). However, the relevance of these phenotypes in the risk of heart failure (HF) hasn't been clarified. Given that there exists obesity paradox between obesity and HF( 10 ), the associations interwinding the four phenotypes and HF will be more complicated and fascinating. Furthermore, among the HF subtypes classified by left ventricular ejection fraction (LVEF), HF with preserved LVEF (HFpEF) is more related with obesity and metabolic unhealth( 11 – 13 ). How metabolic health and obesity profiles being related to HF subtypes also warrants exploration( 14 ). Therefore, we aimed to investigate the associations of the four phenotypes regarding metabolic status and obesity with the risk of different subtypes of HF in general population. Owing to the attributes of metabolic health status and obesity are changing overtime, the relations of phenotype transition with the risk of HF are further to be studied. METHODS Study population The Atherosclerosis Risk in Communities Study (ARIC) is a prospective heart health study recruiting 15004 participants aged 44 through 66 from 4 US communities from 1987 to 1989( 15 ). We observed the participants’ status of metabolic health and obesity in a time span of 15 years beginning from their enrollment, and followed up for the incident HF events after the 15-year observation window. Thus, the participants with prevalent HF or died within 15 years after enrollment were excluded. The 15-year observation was fulfilled by using the demographic characteristics, medical histories, vital signs and laboratory tests collected in the 1st to 4th visits of ARIC. In our primary analysis, the participants with missing value of body mass index (BMI), waist girth, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting glucose, triglycerides, high density lipoprotein-cholesterol (HDL-C) or covariates including age, sex, race, estimated glomerular filtration rate (eGFR), low density lipoprotein-cholesterol (LDL-C), ever smoke, ever drink, prevalent coronary heart disease, prevalent peripheral artery disease and cholesterol lowering medication use at visit 1 were excluded. Finally, 8018 participants were included for the primary analysis (Fig. 1 ). We further conducted an ancillary analysis to account for the transition of metabolic health and obesity status between the 1st and 4th visit. Participants with missing value of aforesaid variables at the 4th visit were further excluded, resulting in a total of 6546 participants were included in the ancillary analysis. The investigation conforms with the principles outlined in the Declaration of Helsinki . The study protocol was approved by the Medical Ethical Committee of the First Affiliated Hospital, Sun Yat-sen University (ID: [2021]453). Definition of metabolically healthy and obese phenotypes We defined the four phenotypes of metabolic health and obesity based on the metabolic syndrome criteria of Adult Treatment Panel-III and obesity criteria proposed by the World Health Organization( 16 , 17 ). Participants who met fewer than three of following criteria were considered metabolically healthy else metabolically unhealthy: 1) abdominal obesity (waist girth > 88cm in women or > 102cm in men); 2) increased blood pressure (SBP ≥ 130mmHg, DBP ≥ 85mmHg or use of high blood pressure medication; 3) increased fasting glucose (fasting glucose ≥ 100mg/dL) or use of blood glucose regulating medication; 4) increased fasting triglycerides (fasting triglycerides ≥ 150mg/dL); 5) decreased HDL-C (HDL-C < 50mg/dL in women or < 40mg/dL in men. Obesity was defined as body mass index ≥ 30kg/m 2 . On the basis of the combination of metabolic health and obesity status, participants were classified into 4 phenotypes: 1) metabolically healthy non-obesity (MHNO); 2) metabolically healthy obesity (MHO); 3) metabolically unhealthy non-obesity (MUNO); 4) metabolically unhealthy obesity (MUO). In the primary analysis, we used the phenotypes assessed at the 1st visit as independent variable. In the ancillary analysis, we set up 16 transition patterns of phenotypes by permutating and combining the phenotypes assessed at the 1st and 4th visit. Follow-up and Outcomes After the 15-year observation window, the participants entered prediction window, where all incident HF events were traced. The prediction window of each participant started later than 2005. Beginning in 2005, ARIC HF Classification Committee conducted ascertainment and LVEF recording for HF events as previously described( 18 ), so that we could fully utilize the record to realize the classification of HF. Incident HFpEF was defined as incident heart failure with LVEF ≥ 50%. Incident HF with mid-range LVEF (HFmrEF) was defined as incident heart failure with LVEF between 40% and 50%. Incident HF with reduced LVEF (HFrEF) was defined as incident heart failure with LVEF < 40%. Statistical Analysis Baseline data were presented as means with standard deviations (SD) for normally distributed continuous variables and as median with interquartile ranges for non-normal distribution continuous variables. One-way ANOVA and Kruskal-Wallis test were used for comparison of normally and non-normally distributed continuous variables, respectively. Categorical variables were reported as numbers with percentages and compared with Pearson χ2 test. Multivariate Cox regression models were constructed to estimate the association between metabolic health and obesity phenotypes and incident HF. We adjusted age, sex, race in model 1 and added eGFR, LDL-C, smoking status, drinking history, prevalent CHD, prevalent peripheral artery diseases (PAD) and cholesterol lowering medication use in model 2. Kaplan-Meier curves for HFpEF, HFrEF/HFmrEF were plotted and differences were determined by the log-rank test. For subgroup analyses, Cox regression models were repeated in participants stratified by age, sex, race, smoking status, drinking history, CKD (eGFR 130mg/dL), CHD and PAD. A two-tailed P-value of < 0.05 was considered statistically significant. All the statistical analyses were performed using SPSS software, version 27 (SPSS Inc) and R. RESULTS Baseline characteristics Of the 8018 participants in our primary analysis, the mean age was 53.2 years, 44.0% were male and 82.6% were white. As shown in Table 1 , among these participants, 5647 were MHNO, 924 were MHO, 755 were MUNO and 692 were MUO. Prevalent CHD, PAD, AF and use of cholesterol lowering medication were similar among groups. The proportion of male in MHO is significantly the smallest as well as the proportion of white in MUNO. MHO and MUO population showed higher waist girth and BMI compared with MHNO and MUNO counterparts. MUNO and MUO exhibited higher blood pressure, worse metabolic parameter including fasting glucose and blood lipids, more prevalence of hypertension and diabetes than MHNO and MHO participants. Metabolic health and obesity phenotypes and risk of HF subtypes There were 559 cases of incident HFpEF and 546 cases of incident HFrEF/HFmrEF during a median follow-up of 12.2 years. Among the four phenotypes, MUO had the highest event rate (11.4%) for HFpEF, while MUNO had the highest event rate (10.6%) for HFrEF/HFmrEF. As shown in Table 2 , after adjusting potential confounders, participants with MHO (HR 2.04, 95% CI 1.61–2.59, p < 0.001), MUNO (HR 1.80, 95% CI 1.40–2.32, p < 0.001) and MUO (HR 2.50, 95% CI 1.95–3.20, p < 0.001) were at higher risk of incident HFpEF than those with MHNO. Individuals with MUNO (HR 1.74, 95% CI 1.36–2.22, p < 0.001) and MUO (HR 1.92, 95% CI 1.49–2.49, p < 0.001) also had a higher risk of HFrEF/HFmrEF than MHNO counterparts. Kaplan-Meier curves were shown in Fig. 2 . Table 1 Baseline characteristics by metabolic health and obesity phenotypes at Visit 1. Metabolically healthy non-obesity (MHNO) Metabolically healthy obesity (MHO) Metabolically unhealthy non-obesity (MUNO) Metabolically unhealthy obesity (MUO) P-value N = 5647 N = 924 N = 755 N = 692 Demographics Age at Visit 1 (years) 53.0 (5.6) 52.5 (5.5) 55.1 (5.5) 53.4 (5.5) < 0.001 Male sex, n/N% 2546 (45.1) 319 (34.5) 344 (45.6) 320 (46.2) < 0.001 Race, white, n/N% 6626 (82.6) 4824 (85.4) 616 (66.7) 651 (86.2) < 0.001 Ever smoker, n/N% 3196 (56.6) 436 (47.2) 454 (60.1) 374 (54.0) < 0.001 Ever drinker, n/N% 4497 (79.6) 621 (67.2) 570 (75.5) 495 (71.5) < 0.001 Clinical character Waist girth (cm) 89.2 (9.4) 109.0 (11.1) 98.3 (7.2) 112.4 (9.3) < 0.001 BMI (kg/m2) 24.7 (2.8) 33.6 (3.9) 26.8 (2.2) 34.0 (3.7) < 0.001 SBP (mmHg) 113.8 (15.2) 119.3 (15.3) 126.1 (17.9) 127.3 (16.6) < 0.001 DBP (mmHg) 70.2 (9.9) 73.9 (9.6) 75.0 (10.4) 77.5 (10.4) < 0.001 Laboratory measurements Fasting glucose (mg/dL) 97.5 (15.4) 100.1 (17.1) 112.1 (29.0) 118.7 (41.8) < 0.001 Triglyceride (mg/dL) 102.2 (47.9) 105.0 (39.9) 190.4 (67.5) 176.5 (66.2) < 0.001 HDL-C (mg/dL) 56.3 (17.2) 53.5 (13.6) 40.9 (10.8) 40.0 (10.3) < 0.001 LDL-C (mg/dL) 133.8 (37.5) 135.5 (37.0) 147.4 (39.3) 143.9 (36.8) < 0.001 eGFR (ml/min*1.73m 2 ) 72.0 (10.9) 70.2 (10.8) 71.3 (11.0) 70.6 (11.5) < 0.001 Medical history CHD, n/N% 112 (2.0) 11 (1.2) 16 (2.1) 15 (2.2) 0.374 Hypertension, n/N% 828 (14.7) 201 (21.8) 372 (49.3) 339 (49.0) < 0.001 Diabetes, n/N% 390 (6.9) 69 (7.5) 343 (45.4) 325 (47.0) < 0.001 Atrial fibrillation, n/N% 3 (0.1) 0 (0.0) 2 (0.3) 1 (0.1) 0.161 Cholesterol lowering medication, n/N% 107 (1.9) 23 (2.5) 20 (2.6) 11 (1.6) 0.302 PAD, n/N% 151 (2.7) 32 (3.5) 29 (3.8) 25 (3.6) 0.139 Presented as number (percentage) or mean (SD). Abbreviations: BMI: body mass index; SBP, systolic blood pressure; DBP: diastolic blood pressure; eGFR, estimated glomerular filtration rate; CHD, coronary heart diseases; PAD, peripheral artery disease. Table 2 Cox regression analysis on the association between metabolic health and obesity phenotypes and incident heart failure. Endpoints n/ (n/N, %) Unadjusted Adjusted Model 1 Adjusted Model 2 HR (95% CI) P-value HR (95% CI) P-value HR (95% CI) P-value Occurrence of HFpEF 559 Metabolically healthy non-obesity (MHNO) 310(5.5%) Reference Reference Reference Metabolically healthy obesity (MHO) 92(10.0%) 1.92 (1.52–2.42) < 0.001 1.98 (1.56–2.51) < 0.001 2.04 (1.61–2.59) < 0.001 Metabolically unhealthy non-obesity (MUNO) 78(10.3%) 2.16 (1.69–2.77) < 0.001 1.82 (1.42–2.34) < 0.001 1.80 (1.40–2.32) < 0.001 Metabolically unhealthy obesity (MUO) 79(11.4%) 2.49 (1.95–3.19) < 0.001 2.45 (1.91–3.14) < 0.001 2.50 (1.95–3.20) < 0.001 Occurrence of HFrEF/HFmrEF 546 Metabolically healthy non-obesity (MHNO) 335(5.9%) Reference Reference Reference Metabolically healthy obesity (MHO) 59(6.4%) 1.10 (0.84–1.45) 0.491 1.18 (0.89–1.56) 0.257 1.21 (0.92–1.61) 0.177 Metabolically unhealthy non-obesity (MUNO) 80(10.6%) 2.01 (1.57–2.56) < 0.001 1.76 (1.38–2.25) < 0.001 1.74 (1.36–2.22) < 0.001 Metabolically unhealthy obesity (MUO) 72(10.4%) 2.02 (1.57–2.61) < 0.001 1.93 (1.49–2.48) < 0.001 1.92 (1.49–2.49) < 0.001 Model 1: adjust for age, sex, race; Model 2: adjust for Model 1 + EGFR, LDL-C, ever smoke, ever drink, prevalent coronary heart disease, prevalent peripheral artery disease and cholesterol lowering medication use. Abbreviations: HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; HFmrEF, heart failure with midrange ejection fraction; HR, hazard ratio; CI, confidence interval. Interaction with conventional HF comorbidities Subgroups analyses stratified by well-announced HF risk factors were conducted (Figure 3). Gender, race, ever smoker or not, LDL-c level did not have impact on the higher HFpEF risk of MHO, MUNO and MUO individuals compared with MHNO. Age demonstrated a significant interaction with metabolic health and obesity status in relation to HF risk (p for interaction: 0.009 for HFpEF, 0.004 for HFrEF/HFmrEF, respectively). In participants who age ≥ 55 years or had drinking history, MUNO and MUO were still associated with incidence HFpEF whereas MHO wasn’t. In almost all subgroups except for high LDL-C (HR 1.49, 95% CI 1.01-2.21, p for interaction 0.033), participants with MHO did not yield higher risk of incident HFrEF/HFmrEF compared with MHNO individuals. Phenotype transition and the risk of HF subtypes During a median follow-up of 8.9 years, 11.5%, 2.1%, 1.5% of MHNO individuals transited to MHO, MUNO, MUO, respectively. In participants with MHO, 7.5% transited to MHNO, only 1 participant turned to MUNO, the other 16.1% transited to metabolically unhealthy and kept obese. Finally, HFpEF cases occurred in 6.7% of total population during a follow-up of 11.2 years and HFrEF/HFmrEF cases occurred in 7.1% of total population during a median follow-up of 12.6 years (Figure 4). Persistent MHO, MUNO and MUO is associated with increased HFpEF risk compared with their transition to MHNO. In MHNO individuals, transition to obesity without metabolic deterioration did not have higher HFrEF/HFmrEF risk, while metabolic deterioration is significantly associated with HFrEF/HFmrEF risk despite of obesity transition. MUNO transition to MHO or MHO transition to MUNO didn’t have higher risk of HFpEF while their HFpEF risk increased when transited to MUO. Expect for MHO participants, transition from other three phenotypes to MUO was profoundly associated with higher HFrEF/HFmrEF risk. DISCUSSION In a community-dwelling cohort, we found that: first, both metabolic unhealth and obesity might independently and cumulatively contribute to the risk of HFpEF and HFrEF/HFmrEF, except for the absence of association between MHO phenotype and HFrEF/HFmrEF risk. Second, in specific subgroups of the studied population featured by different HF comorbidities, the relationship of MUNO and MUO phenotypes with HFpEF risk were still significant, while the associations between the phenotypes and HF risk were more distinct (P-interaction < 0.009) in participants < 55 years. Serum lipid might impact the relationship of the phenotypes with HFrEF/HFmrEF. Third, maintaining unhealthy metabolic status or transition to unhealthy metabolic status rather than transition to obesity might augment the risk for all HF subtypes. The outcome-specific association between phenotypes and HF subtype risk Although obesity and metabolic unhealth have been respectively established for their independent predictive value of CVD as well as HF(19, 20), there’s disparity of their link with HFrEF and HFpEF risk. Unlike the association between obesity and HFpEF risk rather than HFrEF(13, 21), metabolic unhealth predicted both HFpEF and HFrEF(13, 22). Given to this disparity and their intrinsic influence in HF subtype pathogenesis, phenotypes based on metabolic health and obesity profiles arise to better understand the substantial association between these two factors and HF. Similar with the conclusion derived from studies about CVD outcome(23), our study also demonstrated that MUO individuals had higher risk of all HF subtypes and this predictive value remained consistent across all subgroup, giving the message that co-existence of obesity and unhealthy metabolic status is no doubt the worst status. Previous studies show that how metabolic health and obesity profile impacted prognosis was outcome-specific. Obesity outweighed the impact of metabolic health status in predicting CVD(24, 25), whereas metabolic health status was more predictable than obesity in type 2 diabetes risk. In HF background, outcome-specific association was still observed. In our study, MHO, MUNO and MUO individuals exhibited higher HFpEF risk. When it comes to HFmrEF/HFrEF, MHO lost its association with HF risk despite of the consistent relation between MUNO and MUO and higher HFmrEF/HFrEF risk. Our study indicated that metabolic unhealth and obesity are risk factors for the onset of HFpEF which is more heterogenetic, while metabolism syndrome may be more harmful than obesity to the onset of HFrEF which is more related to atherosclerosis due to metabolic disorder. The Implication from subgroup analysis and subtypes transition analysis MUO steadily related with HF incidence across LVEF spectrums and subgroups, suggesting the independently worst impact of co-existence phenotype on HF. Subgroup analyses revealed that in individuals age <55 years, the association between metabolic health status and risk of HF subtypes is more prominent. This might indicate that early-life exposure to metabolic disturbances may confer greater risk in younger individuals. In individuals with high LDL-C, MHO showed higher risk of HFrEF/HFmrEF, indicating a potential modifying effect of lipid profiles on association between metabolic health and obesity phenotype and HFrEF/HFmrEF risk. Our study also indicated that maintaining unhealthy metabolic state or transition to unhealthy metabolic status rather than obesity augment the risk for all subtypes of HF. Similar to our study, several other studies have found that transited to metabolically unhealthy phenotype have higher risk of HF as well as other CVDs(5, 26). Maintaining unhealthy metabolic status for long time might be more harmful than maintaining status of obesity. These findings collectively highlight the importance of unhealthy metabolic status as a key driver of cardiovascular risk, suggesting that interventions aimed at improving metabolic health profiles may be more effective in reducing the incidence of HF and other CVDs than focusing solely on weight management. Mechanistic prospects regarding the association between subtypes and HF risk. The disparity of association between profiles and incidence HF mainly falls on the absence relation between MHO and HFmrEF/HFrEF risk, indicative of the sole obesity-related mechanism involved(27). Previous studies have indicated that obesity may lead to impaired myocardial function through mechanisms such as insulin resistance and systemic inflammatory responses, neurohormonal dysregulation and altered hemodynamic loading conditions, and such mechanisms were also reported as potential mechanism of the association between obesity and HFpEF(28). Preclinical animal models have revealed that the HFpEF group exhibits nearly twice the amount of visceral mesenteric white adipose tissue compared to the control group, a finding that aligns with imaging-based observations in human populations(29, 30). These results underscore the potential causal role of visceral adipose tissue (VAT) in driving the HFpEF phenotype. In contrast, HFrEF appears to be less associated with VAT accumulation. Collectively, these findings highlight the distinct pathophysiological mechanisms linking obesity, particularly visceral fat deposition, to HFpEF, and further emphasize the need for targeted interventions to address obesity-related metabolic and inflammatory pathways in HFpEF management. Strengths and limitations The study's strengths include leveraging the ARIC study with a 15-year observation period and prediction window, providing robust longitudinal data. It also assessed baseline phenotypes and transitions over time, offering a dynamic prospect on how changes in metabolic health and obesity profiles influence HF risk. However, we have to acknowledge that there exist several limitations. First, as an observational study, it cannot establish causal relationships, though we minimized reverse causality by excluding participants with prior heart failure (HF). Second, HF subtype classification based on LVEF measurements may introduce variability and potential misclassification. Third, the lack of data on inflammatory biomarkers (e.g., C-reactive protein, Interleukin-6), NT-pro BNP, and body fat distribution limits insights into the biological mechanisms linking metabolic health, obesity, and HF, necessitating further mechanistic research. Fourth, potential confounders such as medications affecting HF incidence (e.g., renin-angiotensin system inhibitors, beta blockers) were not accounted for. CONCLUSIONS In this community-dwelling cohort study, metabolic unhealth and obesity independently and collectively contributed to the HF risk, particularly HFpEF. Individuals with metabolic unhealth status regardless of obesity (MUNO or MUO) significantly elevating the risk of all HF subtypes. It’s worth noting that, although MHO was associated with increased HFpEF risk, it didn’t confer a higher HFrEF/HFmrEF risk, suggesting distinct pathophysiological mechanisms among HF subtypes. The study also highlights the importance of maintaining metabolic health, as transitioning to or maintaining an unhealthy metabolic state significantly augments HF risk, irrespective of obesity status. These findings emphasize the need for targeted interventions aimed at improving metabolic health and managing obesity to mitigate the risk of heart failure. Declarations Funding The present study was funded by the National Natural Science Foundation of China (Nos. 82170384, 82370383, 82100273, 82100387, 82270399, 82200415, 82304491, 82300429), Guangdong Natural Science Foundation (Nos.2021A1515010755, 2022A1515012161, 2022A1515010227, 2022A1515010785, 2023A1515011794, 2023A1515012798, 2023A1515010627), Guangdong Basic and Applied Basic Research Foundation (Nos. 2022A1515111120, 2023A1515110547, 2023A1515110248, 2023A1515111097), Key R&D Projects of Guangzhou Science and Technology Program (Nos. 2023B03J1243, 2023B01J1011), China Postdoctoral Science Foundation (No.2024M753754), Hina Heart House-Chinese Cardiovascular Association-ACCESS fund (2020-CCA-ACCESS-122 and 2020-CCA-ACCESS-138), Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2023-PT320-03), Chinese Society of Cardiology’s Foundation (CSCF2022B06) and China Heart House-Chinese Cardiovascular Association TCM fund (2022-CCA-TCM-034). Authors’ Contributors Under the direction of Xue R-C, Zhang J-C, Dong B performed the study design, data extraction, and statistical analysis. Zhang J-C wrote the original draft, while Li J-Y, Wu Y-Z revised the draft. All other authors checked the data to ensure accuracy and edited the manuscript prior to submission to ensure the precise demonstration. Xue R-C is the guarantor. All authors read and approved the final manuscript. Acknowledgements We gratefully acknowledge the participants, investigators, research coordinators and committee members of the Atherosclerosis Risk in Communities Study. References Ma C, Han Y, Fu X, Xu T. Thoughts on Future Trends in Cardiology. Cardiol Discovery. 2021;01(01):9–11. Zembic A, Eckel N, Stefan N, Baudry J, Schulze MB. An Empirically Derived Definition of Metabolically Healthy Obesity Based on Risk of Cardiovascular and Total Mortality. JAMA Netw Open. 2021;4(5):e218505. Schulze MB, Stefan N. Metabolically healthy obesity: from epidemiology and mechanisms to clinical implications. Nat Rev Endocrinol. 2024;20(11):633–46. Sun J, Qu Q, Yuan Y, Sun G, Kong X, Sun W, et al. Normal-Weight Abdominal Obesity: A Risk Factor for Hypertension and Cardiometabolic Dysregulation. Cardiol Discovery. 2022;02(01):13–21. Lee YB, Kim DH, Kim SM, Kim NH, Choi KM, Baik SH, et al. Hospitalization for heart failure incidence according to the transition in metabolic health and obesity status: a nationwide population-based study. Cardiovasc Diabetol. 2020;19(1):77. Eckel N, Meidtner K, Kalle-Uhlmann T, Stefan N, Schulze MB. Metabolically healthy obesity and cardiovascular events: A systematic review and meta-analysis. Eur J Prev Cardiol. 2016;23(9):956–66. Opio J, Croker E, Odongo GS, Attia J, Wynne K, McEvoy M. Metabolically healthy overweight/obesity are associated with increased risk of cardiovascular disease in adults, even in the absence of metabolic risk factors: A systematic review and meta-analysis of prospective cohort studies. Obes Rev. 2020;21(12):e13127. Putra ICS, Kamarullah W, Prameswari HS, Pramudyo M, Iqbal M, Achmad C, et al. Metabolically unhealthy phenotype in normal weight population and risk of mortality and major adverse cardiac events: A meta-analysis of 41 prospective cohort studies. Diabetes Metab Syndr. 2022;16(10):102635. Li C, Meng X, Zhang J, Wang H, Lu H, Cao M, et al. Associations of metabolic changes and polygenic risk scores with cardiovascular outcomes and all-cause mortality across BMI categories: a prospective cohort study. Cardiovasc Diabetol. 2024;23(1):231. Bahniwal RK, Sadr N, Schinderle C, Avila CJ, Sill J, Qayyum R. Obesity Paradox and the Effect of NT-proBNP on All-Cause and Cause-Specific Mortality. Clin Cardiol. 2024;47(11):e70044. Shah SJ, Borlaug BA, Kitzman DW, McCulloch AD, Blaxall BC, Agarwal R, et al. Research Priorities for Heart Failure With Preserved Ejection Fraction: National Heart, Lung, and Blood Institute Working Group Summary. Circulation. 2020;141(12):1001–26. Mentz RJ, Kelly JP, von Lueder TG, Voors AA, Lam CS, Cowie MR, et al. Noncardiac comorbidities in heart failure with reduced versus preserved ejection fraction. J Am Coll Cardiol. 2014;64(21):2281–93. Savji N, Meijers WC, Bartz TM, Bhambhani V, Cushman M, Nayor M, et al. The Association of Obesity and Cardiometabolic Traits With Incident HFpEF and HFrEF. JACC Heart Fail. 2018;6(8):701–9. Horwich TB, Fonarow GC, Hamilton MA, MacLellan WR, Woo MA, Tillisch JH. The relationship between obesity and mortality in patients with heart failure. J Am Coll Cardiol. 2001;38(3):789–95. Wright JD, Folsom AR, Coresh J, Sharrett AR, Couper D, Wagenknecht LE, et al. The ARIC (Atherosclerosis Risk In Communities) Study: JACC Focus Seminar 3/8. J Am Coll Cardiol. 2021;77(23):2939–59. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP). Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97. Obesity. preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i–xii. Rosamond WD, Chang PP, Baggett C, Johnson A, Bertoni AG, Shahar E, et al. Classification of heart failure in the atherosclerosis risk in communities (ARIC) study: a comparison of diagnostic criteria. Circ Heart Fail. 2012;5(2):152–9. Ndumele CE, Matsushita K, Lazo M, Bello N, Blumenthal RS, Gerstenblith G et al. Obesity and Subtypes of Incident Cardiovascular Disease. J Am Heart Assoc. 2016;5(8). Pandey A, LaMonte M, Klein L, Ayers C, Psaty BM, Eaton CB, et al. Relationship Between Physical Activity, Body Mass Index, and Risk of Heart Failure. J Am Coll Cardiol. 2017;69(9):1129–42. Ho JE, Lyass A, Lee DS, Vasan RS, Kannel WB, Larson MG, Levy D. Predictors of new-onset heart failure: differences in preserved versus reduced ejection fraction. Circ Heart Fail. 2013;6(2):279–86. Eaton CB, Pettinger M, Rossouw J, Martin LW, Foraker R, Quddus A et al. Risk Factors for Incident Hospitalized Heart Failure With Preserved Versus Reduced Ejection Fraction in a Multiracial Cohort of Postmenopausal Women. Circ Heart Fail. 2016;9(10). Mirzababaei A, Djafarian K, Mozafari H, Shab-Bidar S. The long-term prognosis of heart diseases for different metabolic phenotypes: a systematic review and meta-analysis of prospective cohort studies. Endocrine. 2019;63(3):439–62. Thomsen M, Nordestgaard BG. Myocardial infarction and ischemic heart disease in overweight and obesity with and without metabolic syndrome. JAMA Intern Med. 2014;174(1):15–22. Hinnouho GM, Czernichow S, Dugravot A, Nabi H, Brunner EJ, Kivimaki M, Singh-Manoux A. Metabolically healthy obesity and the risk of cardiovascular disease and type 2 diabetes: the Whitehall II cohort study. Eur Heart J. 2015;36(9):551–9. Bi J, Song L, Wang L, Su B, Wu M, Li D, et al. Transitions in metabolic health status over time and risk of heart failure: A prospective study. Diabetes Metab. 2022;48(1):101266. Campbell P, Rutten FH, Lee MM, Hawkins NM, Petrie MC. Heart failure with preserved ejection fraction: everything the clinician needs to know. Lancet. 2024;403(10431):1083–92. Borlaug BA, Jensen MD, Kitzman DW, Lam CSP, Obokata M, Rider OJ. Obesity and heart failure with preserved ejection fraction: new insights and pathophysiological targets. Cardiovasc Res. 2023;118(18):3434–50. Rao VN, Zhao D, Allison MA, Guallar E, Sharma K, Criqui MH, et al. Adiposity and Incident Heart Failure and its Subtypes: MESA (Multi-Ethnic Study of Atherosclerosis). JACC Heart Fail. 2018;6(12):999–1007. Méndez-Fernández A, Fernández-Mora Á, Bernal-Ramírez J, Alves-Figueiredo H, Nieblas B, Salazar-Ramírez F et al. Distinguishing pathophysiological features of heart failure with reduced and preserved ejection fraction: A comparative analysis of two mouse models. J Physiol. 2024. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6203913","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427728452,"identity":"234774ea-8a64-498b-97df-82c7199eda10","order_by":0,"name":"Jiancheng Zhang","email":"","orcid":"","institution":"the First Affiliated Hospital of Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Jiancheng","middleName":"","lastName":"Zhang","suffix":""},{"id":427728453,"identity":"90d60874-dac3-48d4-acc6-a35046812903","order_by":1,"name":"Bin Dong","email":"","orcid":"","institution":"the First Affiliated Hospital of Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Dong","suffix":""},{"id":427728454,"identity":"4576aa4d-ae9d-4748-8cac-50973beec4c4","order_by":2,"name":"Jiayong Li","email":"","orcid":"","institution":"the First Affiliated Hospital of Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Jiayong","middleName":"","lastName":"Li","suffix":""},{"id":427728455,"identity":"9db5a2af-f41c-4fff-90c6-0947ed136698","order_by":3,"name":"Yu Ning","email":"","orcid":"","institution":"the First Affiliated Hospital of Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Ning","suffix":""},{"id":427728456,"identity":"a2385449-6367-4acd-943c-0150b7b71acc","order_by":4,"name":"Yilong Wang","email":"","orcid":"","institution":"the First Affiliated Hospital of Sun Yat-Sen 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14:23:27","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6203913/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6203913/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78748983,"identity":"e0d85746-f322-41af-920e-81a5c833cade","added_by":"auto","created_at":"2025-03-18 11:18:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38705,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study inclusion and exclusion.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6203913/v1/8513c460c51680a92627a087.png"},{"id":78747926,"identity":"ed4c997a-b4bd-41a5-8cc8-ea47a4bbb78f","added_by":"auto","created_at":"2025-03-18 11:02:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86876,"visible":true,"origin":"","legend":"\u003cp\u003eKM curves for HF risk.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6203913/v1/1447669e27b66992f4e3b416.png"},{"id":78747930,"identity":"d5ceeed8-f313-43e4-be7d-cda44d933e06","added_by":"auto","created_at":"2025-03-18 11:02:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":351173,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of subgroup analysis of association between metabolic health and obesity phenotypes and HF risk.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6203913/v1/81ce6aa23cfd6cead02589e3.png"},{"id":78748665,"identity":"1fecf3c3-69f4-47e2-ad2c-3a27bdc62611","added_by":"auto","created_at":"2025-03-18 11:10:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104289,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots for associations between metabolic health and obesity phenotypes transition and HF risk.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6203913/v1/1b8593f5e7d9ba7f536b1e1b.png"},{"id":78749926,"identity":"a31affa0-0e95-47a2-a13a-c3ccf2947066","added_by":"auto","created_at":"2025-03-18 11:34:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1602836,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6203913/v1/67d9e75a-f9b3-4109-a211-cbfc8a9eaf34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inspecting the risk of heart failure in general population using metabolic health status and obesity profiles","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eObesity and metabolic unhealth are two major modifiable risk factors of cardiovascular disease (CVD)(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). They are closely connected and normally taken for granted for their co-existence in individuals. However, a proportion of obese individuals was indeed with healthy metabolic status which accounts for over 3\u0026ndash;57% of the obese population(\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)and not all individuals with metabolism syndrome were obese. Based on metabolic health and obesity status, four clinical phenotypes are derived: metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO) and metabolically unhealthy obesity (MUO)(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Individuals with different phenotypes may exhibit discrepant cardiovascular outcomes. Among MHNO, MHO and MUO, people with MUO exhibited the highest risk of coronary heart disease (CHD) and overall CVD risk, followed by those with MHO and MHNO(\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, the relevance of these phenotypes in the risk of heart failure (HF) hasn't been clarified. Given that there exists obesity paradox between obesity and HF(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), the associations interwinding the four phenotypes and HF will be more complicated and fascinating. Furthermore, among the HF subtypes classified by left ventricular ejection fraction (LVEF), HF with preserved LVEF (HFpEF) is more related with obesity and metabolic unhealth(\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). How metabolic health and obesity profiles being related to HF subtypes also warrants exploration(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Therefore, we aimed to investigate the associations of the four phenotypes regarding metabolic status and obesity with the risk of different subtypes of HF in general population. Owing to the attributes of metabolic health status and obesity are changing overtime, the relations of phenotype transition with the risk of HF are further to be studied.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe Atherosclerosis Risk in Communities Study (ARIC) is a prospective heart health study recruiting 15004 participants aged 44 through 66 from 4 US communities from 1987 to 1989(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). We observed the participants\u0026rsquo; status of metabolic health and obesity in a time span of 15 years beginning from their enrollment, and followed up for the incident HF events after the 15-year observation window. Thus, the participants with prevalent HF or died within 15 years after enrollment were excluded. The 15-year observation was fulfilled by using the demographic characteristics, medical histories, vital signs and laboratory tests collected in the 1st to 4th visits of ARIC. In our primary analysis, the participants with missing value of body mass index (BMI), waist girth, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting glucose, triglycerides, high density lipoprotein-cholesterol (HDL-C) or covariates including age, sex, race, estimated glomerular filtration rate (eGFR), low density lipoprotein-cholesterol (LDL-C), ever smoke, ever drink, prevalent coronary heart disease, prevalent peripheral artery disease and cholesterol lowering medication use at visit 1 were excluded. Finally, 8018 participants were included for the primary analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We further conducted an ancillary analysis to account for the transition of metabolic health and obesity status between the 1st and 4th visit. Participants with missing value of aforesaid variables at the 4th visit were further excluded, resulting in a total of 6546 participants were included in the ancillary analysis. The investigation conforms with the principles outlined in the \u003cem\u003eDeclaration of Helsinki\u003c/em\u003e. The study protocol was approved by the Medical Ethical Committee of the First Affiliated Hospital, Sun Yat-sen University (ID: [2021]453).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of metabolically healthy and obese phenotypes\u003c/h3\u003e\n\u003cp\u003eWe defined the four phenotypes of metabolic health and obesity based on the metabolic syndrome criteria of Adult Treatment Panel-III and obesity criteria proposed by the World Health Organization(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Participants who met fewer than three of following criteria were considered metabolically healthy else metabolically unhealthy: 1) abdominal obesity (waist girth\u0026thinsp;\u0026gt;\u0026thinsp;88cm in women or \u0026gt;\u0026thinsp;102cm in men); 2) increased blood pressure (SBP\u0026thinsp;\u0026ge;\u0026thinsp;130mmHg, DBP\u0026thinsp;\u0026ge;\u0026thinsp;85mmHg or use of high blood pressure medication; 3) increased fasting glucose (fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;100mg/dL) or use of blood glucose regulating medication; 4) increased fasting triglycerides (fasting triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150mg/dL); 5) decreased HDL-C (HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;50mg/dL in women or \u0026lt;\u0026thinsp;40mg/dL in men. Obesity was defined as body mass index\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e. On the basis of the combination of metabolic health and obesity status, participants were classified into 4 phenotypes: 1) metabolically healthy non-obesity (MHNO); 2) metabolically healthy obesity (MHO); 3) metabolically unhealthy non-obesity (MUNO); 4) metabolically unhealthy obesity (MUO). In the primary analysis, we used the phenotypes assessed at the 1st visit as independent variable. In the ancillary analysis, we set up 16 transition patterns of phenotypes by permutating and combining the phenotypes assessed at the 1st and 4th visit.\u003c/p\u003e\n\u003ch3\u003eFollow-up and Outcomes\u003c/h3\u003e\n\u003cp\u003eAfter the 15-year observation window, the participants entered prediction window, where all incident HF events were traced. The prediction window of each participant started later than 2005. Beginning in 2005, ARIC HF Classification Committee conducted ascertainment and LVEF recording for HF events as previously described(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), so that we could fully utilize the record to realize the classification of HF. Incident HFpEF was defined as incident heart failure with LVEF\u0026thinsp;\u0026ge;\u0026thinsp;50%. Incident HF with mid-range LVEF (HFmrEF) was defined as incident heart failure with LVEF between 40% and 50%. Incident HF with reduced LVEF (HFrEF) was defined as incident heart failure with LVEF\u0026thinsp;\u0026lt;\u0026thinsp;40%.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eBaseline data were presented as means with standard deviations (SD) for normally distributed continuous variables and as median with interquartile ranges for non-normal distribution continuous variables. One-way ANOVA and Kruskal-Wallis test were used for comparison of normally and non-normally distributed continuous variables, respectively. Categorical variables were reported as numbers with percentages and compared with Pearson χ2 test. Multivariate Cox regression models were constructed to estimate the association between metabolic health and obesity phenotypes and incident HF. We adjusted age, sex, race in model 1 and added eGFR, LDL-C, smoking status, drinking history, prevalent CHD, prevalent peripheral artery diseases (PAD) and cholesterol lowering medication use in model 2. Kaplan-Meier curves for HFpEF, HFrEF/HFmrEF were plotted and differences were determined by the log-rank test. For subgroup analyses, Cox regression models were repeated in participants stratified by age, sex, race, smoking status, drinking history, CKD (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e), high LDL-C (LDL-C\u0026thinsp;\u0026gt;\u0026thinsp;130mg/dL), CHD and PAD. A two-tailed P-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. All the statistical analyses were performed using SPSS software, version 27 (SPSS Inc) and R.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n \u003cp\u003eOf the 8018 participants in our primary analysis, the mean age was 53.2 years, 44.0% were male and 82.6% were white. As shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, among these participants, 5647 were MHNO, 924 were MHO, 755 were MUNO and 692 were MUO. Prevalent CHD, PAD, AF and use of cholesterol lowering medication were similar among groups. The proportion of male in MHO is significantly the smallest as well as the proportion of white in MUNO. MHO and MUO population showed higher waist girth and BMI compared with MHNO and MUNO counterparts. MUNO and MUO exhibited higher blood pressure, worse metabolic parameter including fasting glucose and blood lipids, more prevalence of hypertension and diabetes than MHNO and MHO participants.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMetabolic health and obesity phenotypes and risk of HF subtypes\u003c/h3\u003e\n\u003cp\u003eThere were 559 cases of incident HFpEF and 546 cases of incident HFrEF/HFmrEF during a median follow-up of 12.2 years. Among the four phenotypes, MUO had the highest event rate (11.4%) for HFpEF, while MUNO had the highest event rate (10.6%) for HFrEF/HFmrEF. As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, after adjusting potential confounders, participants with MHO (HR 2.04, 95% CI 1.61\u0026ndash;2.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MUNO (HR 1.80, 95% CI 1.40\u0026ndash;2.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and MUO (HR 2.50, 95% CI 1.95\u0026ndash;3.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were at higher risk of incident HFpEF than those with MHNO. Individuals with MUNO (HR 1.74, 95% CI 1.36\u0026ndash;2.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and MUO (HR 1.92, 95% CI 1.49\u0026ndash;2.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) also had a higher risk of HFrEF/HFmrEF than MHNO counterparts. Kaplan-Meier curves were shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics by metabolic health and obesity phenotypes at Visit 1.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetabolically healthy non-obesity (MHNO)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetabolically healthy obesity (MHO)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetabolically unhealthy non-obesity (MUNO)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetabolically unhealthy obesity (MUO)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;5647\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;924\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;755\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;692\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge at Visit 1 (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.0 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.5 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.1 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.4 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale sex, n/N%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2546 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace, white, n/N%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6626 (82.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4824 (85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e616 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e651 (86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEver smoker, n/N%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3196 (56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e436 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e454 (60.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374 (54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEver drinker, n/N%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4497 (79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e621 (67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e570 (75.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e495 (71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical character\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist girth (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.2 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109.0 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.3 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.4 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.7 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.6 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.8 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.0 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113.8 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119.3 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.1 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.3 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.2 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.9 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.0 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.5 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory measurements\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFasting glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.5 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.1 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.1 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.7 (41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102.2 (47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.0 (39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190.4 (67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176.5 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.3 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.5 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.9 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.0 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133.8 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135.5 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147.4 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143.9 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR (ml/min*1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.0 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.2 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.3 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.6 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHD, n/N%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n/N%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e828 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e201 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e339 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n/N%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e390 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtrial fibrillation, n/N%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCholesterol lowering medication, n/N%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAD, n/N%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePresented as number (percentage) or mean (SD).\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI: body mass index; SBP, systolic blood pressure; DBP: diastolic blood pressure; eGFR, estimated glomerular filtration rate; CHD, coronary heart diseases; PAD, peripheral artery disease.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCox regression analysis on the association between metabolic health and obesity phenotypes and incident heart failure.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEndpoints\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003en/ (n/N, %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAdjusted Model 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAdjusted Model 2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccurrence of HFpEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolically healthy non-obesity (MHNO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e310(5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolically healthy obesity (MHO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92(10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92 (1.52\u0026ndash;2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.98 (1.56\u0026ndash;2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04 (1.61\u0026ndash;2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolically unhealthy non-obesity (MUNO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78(10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16 (1.69\u0026ndash;2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82 (1.42\u0026ndash;2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80 (1.40\u0026ndash;2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolically unhealthy obesity (MUO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79(11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49 (1.95\u0026ndash;3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45 (1.91\u0026ndash;3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50 (1.95\u0026ndash;3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccurrence of HFrEF/HFmrEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolically healthy non-obesity (MHNO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e335(5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolically healthy obesity (MHO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59(6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (0.84\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (0.89\u0026ndash;1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21 (0.92\u0026ndash;1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolically unhealthy non-obesity (MUNO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80(10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01 (1.57\u0026ndash;2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76 (1.38\u0026ndash;2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74 (1.36\u0026ndash;2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolically unhealthy obesity (MUO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72(10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02 (1.57\u0026ndash;2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93 (1.49\u0026ndash;2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92 (1.49\u0026ndash;2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eModel 1: adjust for age, sex, race;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eModel 2: adjust for Model 1\u0026thinsp;+\u0026thinsp;EGFR, LDL-C, ever smoke, ever drink, prevalent coronary heart disease, prevalent peripheral artery disease and cholesterol lowering medication use.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAbbreviations: HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; HFmrEF, heart failure with midrange ejection fraction; HR, hazard ratio; CI, confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eInteraction with conventional HF comorbidities\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eSubgroups analyses stratified by well-announced HF risk factors were conducted (Figure 3). Gender, race, ever smoker or not, LDL-c level did not have impact on the higher HFpEF risk of MHO, MUNO and MUO individuals compared with MHNO. Age demonstrated a significant interaction with metabolic health and obesity status in relation to HF risk (p for interaction: 0.009 for HFpEF, 0.004 for HFrEF/HFmrEF, respectively). In participants who age \u0026ge; 55 years or had drinking history, MUNO and MUO were still associated with incidence HFpEF whereas MHO wasn\u0026rsquo;t. In almost all subgroups except for high LDL-C (HR 1.49, 95% CI 1.01-2.21, p for interaction 0.033), participants with MHO did not yield higher risk of incident HFrEF/HFmrEF compared with MHNO individuals.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003ePhenotype transition and the risk of HF subtypes\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eDuring a median follow-up of 8.9 years, 11.5%, 2.1%, 1.5% of MHNO individuals transited to MHO, MUNO, MUO, respectively. In participants with MHO, 7.5% transited to MHNO, only 1 participant turned to MUNO, the other 16.1% transited to metabolically unhealthy and kept obese. Finally, HFpEF cases occurred in 6.7% of total population during a follow-up of 11.2 years and HFrEF/HFmrEF cases occurred in 7.1% of total population during a median follow-up of 12.6 years (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePersistent MHO, MUNO and MUO is associated with increased HFpEF risk compared with their transition to MHNO. In MHNO individuals, transition to obesity without metabolic deterioration did not have higher HFrEF/HFmrEF risk, while metabolic deterioration is significantly associated with HFrEF/HFmrEF risk despite of obesity transition. MUNO transition to MHO or MHO transition to MUNO didn\u0026rsquo;t have higher risk of HFpEF while their HFpEF risk increased when transited to MUO. Expect for MHO participants, transition from other three phenotypes to MUO was profoundly associated with higher HFrEF/HFmrEF risk.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn a community-dwelling cohort, we found that: first, both metabolic unhealth and obesity might independently and cumulatively contribute to the risk of HFpEF and HFrEF/HFmrEF, except for the absence of association between MHO phenotype and HFrEF/HFmrEF risk. Second, in specific subgroups of the studied population featured by different HF comorbidities, the relationship of MUNO and MUO phenotypes with HFpEF risk were still significant, while the associations between the phenotypes and HF risk were more distinct (P-interaction \u0026lt; 0.009) in participants \u0026lt; 55 years. Serum lipid might impact the relationship of the phenotypes with HFrEF/HFmrEF. Third, maintaining unhealthy metabolic status or transition to unhealthy metabolic status rather than transition to obesity might augment the risk for all HF subtypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe outcome-specific association between phenotypes and HF subtype risk\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough obesity and metabolic unhealth have been respectively established for their independent predictive value of CVD as well as HF(19, 20), there’s disparity of their link with HFrEF and HFpEF risk. Unlike the association between obesity and HFpEF risk rather than HFrEF(13, 21), metabolic unhealth predicted both HFpEF and HFrEF(13, 22). Given to this disparity and their intrinsic influence in HF subtype pathogenesis, phenotypes based on metabolic health and obesity profiles arise to better understand the substantial association between these two factors and HF.\u003c/p\u003e\n\u003cp\u003eSimilar with the conclusion derived from studies about CVD outcome(23), our study also demonstrated that MUO individuals had higher risk of all HF subtypes and this predictive value remained consistent across all subgroup, giving the message that co-existence of obesity and unhealthy metabolic status is no doubt the worst status. Previous studies show that how metabolic health and obesity profile impacted prognosis was outcome-specific. Obesity outweighed the impact of metabolic health status in predicting\u0026nbsp;CVD(24, 25),\u0026nbsp;whereas metabolic health status was more predictable than obesity in type 2 diabetes risk. In HF background, outcome-specific association was still observed. In our study, MHO, MUNO and MUO individuals exhibited higher HFpEF risk. When it comes to HFmrEF/HFrEF, MHO lost its association with HF risk despite of the consistent relation between MUNO and MUO and higher HFmrEF/HFrEF risk. Our study indicated that metabolic unhealth and obesity are risk factors for the onset of HFpEF which is more heterogenetic, while metabolism syndrome may be more harmful than obesity to the onset of HFrEF which is more related to atherosclerosis due to metabolic disorder.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe Implication from subgroup analysis and subtypes transition analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMUO steadily related with HF incidence across LVEF spectrums and subgroups, suggesting the independently worst impact of co-existence phenotype on HF. Subgroup analyses revealed that in individuals age \u0026lt;55 years, the association between metabolic health status and risk of HF subtypes is more prominent. This might indicate that early-life exposure to metabolic disturbances may confer greater risk in younger individuals. In individuals with high LDL-C, MHO showed higher risk of HFrEF/HFmrEF, indicating a potential modifying effect of lipid profiles on association between metabolic health and obesity phenotype and HFrEF/HFmrEF risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study also indicated that maintaining unhealthy metabolic state or transition to unhealthy metabolic status rather than obesity augment the risk for all subtypes of HF. Similar to our study, several other studies have found that transited to metabolically unhealthy phenotype have higher risk of HF as well as other CVDs(5, 26). Maintaining unhealthy metabolic status for long time might be more harmful than maintaining status of obesity. These findings collectively highlight the importance of unhealthy metabolic status as a key driver of cardiovascular risk, suggesting that interventions aimed at improving metabolic health profiles may be more effective in reducing the incidence of HF and other CVDs than focusing solely on weight management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMechanistic prospects regarding the association between subtypes and HF risk.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe disparity of association between profiles and incidence HF mainly falls on the absence relation between MHO and HFmrEF/HFrEF risk, indicative of the sole obesity-related mechanism involved(27). Previous studies have indicated that obesity may lead to impaired myocardial function through mechanisms such as insulin resistance and systemic inflammatory responses, neurohormonal dysregulation and altered hemodynamic loading conditions, and such mechanisms were also reported as potential mechanism of the association between obesity and HFpEF(28). Preclinical animal models have revealed that the HFpEF group exhibits nearly twice the amount of visceral mesenteric white adipose tissue compared to the control group, a finding that aligns with imaging-based observations in human populations(29, 30). These results underscore the potential causal role of visceral adipose tissue (VAT) in driving the HFpEF phenotype. In contrast, HFrEF appears to be less associated with VAT accumulation. Collectively, these findings highlight the distinct pathophysiological mechanisms linking obesity, particularly visceral fat deposition, to HFpEF, and further emphasize the need for targeted interventions to address obesity-related metabolic and inflammatory pathways in HFpEF management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStrengths and limitations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study's strengths include leveraging the ARIC study with a 15-year observation period and prediction window, providing robust longitudinal data. It also assessed baseline phenotypes and transitions over time, offering a dynamic prospect on how changes in metabolic health and obesity profiles influence HF risk. However, we have to acknowledge that there exist several limitations. First, as an observational study, it cannot establish causal relationships, though we minimized reverse causality by excluding participants with prior heart failure (HF). Second, HF subtype classification based on LVEF measurements may introduce variability and potential misclassification. Third, the lack of data on inflammatory biomarkers (e.g., C-reactive protein, Interleukin-6), NT-pro BNP, and body fat distribution limits insights into the biological mechanisms linking metabolic health, obesity, and HF, necessitating further mechanistic research. Fourth, potential confounders such as medications affecting HF incidence (e.g., renin-angiotensin system inhibitors, beta blockers) were not accounted for.\u0026nbsp;\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn this community-dwelling cohort study, metabolic unhealth and obesity independently and collectively contributed to the HF risk, particularly HFpEF. Individuals with metabolic unhealth status regardless of obesity (MUNO or MUO) significantly elevating the risk of all HF subtypes. It’s worth noting that, although MHO was associated with increased HFpEF risk, it didn’t confer a higher HFrEF/HFmrEF risk, suggesting distinct pathophysiological mechanisms among HF subtypes. The study also highlights the importance of maintaining metabolic health, as transitioning to or maintaining an unhealthy metabolic state significantly augments HF risk, irrespective of obesity status. These findings emphasize the need for targeted interventions aimed at improving metabolic health and managing obesity to mitigate the risk of heart failure.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was funded by the National Natural Science Foundation of China (Nos. 82170384, 82370383, 82100273, 82100387, 82270399, 82200415, 82304491,\u0026nbsp; 82300429), Guangdong Natural Science Foundation (Nos.2021A1515010755, 2022A1515012161, 2022A1515010227, 2022A1515010785, 2023A1515011794, 2023A1515012798, 2023A1515010627), Guangdong Basic and Applied Basic Research Foundation (Nos. 2022A1515111120, 2023A1515110547, 2023A1515110248, 2023A1515111097), Key R\u0026amp;D Projects of Guangzhou Science and Technology Program (Nos. 2023B03J1243, 2023B01J1011), China Postdoctoral Science Foundation (No.2024M753754), Hina Heart House-Chinese Cardiovascular Association-ACCESS fund (2020-CCA-ACCESS-122 and 2020-CCA-ACCESS-138), Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2023-PT320-03), Chinese Society of Cardiology\u0026rsquo;s Foundation (CSCF2022B06) and China Heart House-Chinese Cardiovascular Association TCM fund (2022-CCA-TCM-034).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnder the direction of Xue R-C, Zhang J-C, Dong B performed the study design, data extraction, and statistical analysis. Zhang J-C wrote the original draft, while Li J-Y, Wu Y-Z revised the draft. All other authors checked the data to ensure accuracy and edited the manuscript prior to submission to ensure the precise demonstration.\u0026nbsp;Xue R-C is the guarantor. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the participants, investigators, research coordinators and committee members of the Atherosclerosis Risk in Communities Study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMa C, Han Y, Fu X, Xu T. Thoughts on Future Trends in Cardiology. Cardiol Discovery. 2021;01(01):9\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZembic A, Eckel N, Stefan N, Baudry J, Schulze MB. An Empirically Derived Definition of Metabolically Healthy Obesity Based on Risk of Cardiovascular and Total Mortality. JAMA Netw Open. 2021;4(5):e218505.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchulze MB, Stefan N. Metabolically healthy obesity: from epidemiology and mechanisms to clinical implications. Nat Rev Endocrinol. 2024;20(11):633\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun J, Qu Q, Yuan Y, Sun G, Kong X, Sun W, et al. Normal-Weight Abdominal Obesity: A Risk Factor for Hypertension and Cardiometabolic Dysregulation. Cardiol Discovery. 2022;02(01):13\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee YB, Kim DH, Kim SM, Kim NH, Choi KM, Baik SH, et al. Hospitalization for heart failure incidence according to the transition in metabolic health and obesity status: a nationwide population-based study. Cardiovasc Diabetol. 2020;19(1):77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEckel N, Meidtner K, Kalle-Uhlmann T, Stefan N, Schulze MB. Metabolically healthy obesity and cardiovascular events: A systematic review and meta-analysis. Eur J Prev Cardiol. 2016;23(9):956\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOpio J, Croker E, Odongo GS, Attia J, Wynne K, McEvoy M. Metabolically healthy overweight/obesity are associated with increased risk of cardiovascular disease in adults, even in the absence of metabolic risk factors: A systematic review and meta-analysis of prospective cohort studies. Obes Rev. 2020;21(12):e13127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePutra ICS, Kamarullah W, Prameswari HS, Pramudyo M, Iqbal M, Achmad C, et al. Metabolically unhealthy phenotype in normal weight population and risk of mortality and major adverse cardiac events: A meta-analysis of 41 prospective cohort studies. Diabetes Metab Syndr. 2022;16(10):102635.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Meng X, Zhang J, Wang H, Lu H, Cao M, et al. Associations of metabolic changes and polygenic risk scores with cardiovascular outcomes and all-cause mortality across BMI categories: a prospective cohort study. Cardiovasc Diabetol. 2024;23(1):231.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahniwal RK, Sadr N, Schinderle C, Avila CJ, Sill J, Qayyum R. Obesity Paradox and the Effect of NT-proBNP on All-Cause and Cause-Specific Mortality. Clin Cardiol. 2024;47(11):e70044.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah SJ, Borlaug BA, Kitzman DW, McCulloch AD, Blaxall BC, Agarwal R, et al. Research Priorities for Heart Failure With Preserved Ejection Fraction: National Heart, Lung, and Blood Institute Working Group Summary. Circulation. 2020;141(12):1001\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMentz RJ, Kelly JP, von Lueder TG, Voors AA, Lam CS, Cowie MR, et al. Noncardiac comorbidities in heart failure with reduced versus preserved ejection fraction. J Am Coll Cardiol. 2014;64(21):2281\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavji N, Meijers WC, Bartz TM, Bhambhani V, Cushman M, Nayor M, et al. The Association of Obesity and Cardiometabolic Traits With Incident HFpEF and HFrEF. JACC Heart Fail. 2018;6(8):701\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorwich TB, Fonarow GC, Hamilton MA, MacLellan WR, Woo MA, Tillisch JH. The relationship between obesity and mortality in patients with heart failure. J Am Coll Cardiol. 2001;38(3):789\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright JD, Folsom AR, Coresh J, Sharrett AR, Couper D, Wagenknecht LE, et al. The ARIC (Atherosclerosis Risk In Communities) Study: JACC Focus Seminar 3/8. J Am Coll Cardiol. 2021;77(23):2939\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eExecutive Summary of The Third Report of The National Cholesterol Education Program (NCEP). Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObesity. preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i\u0026ndash;xii.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosamond WD, Chang PP, Baggett C, Johnson A, Bertoni AG, Shahar E, et al. Classification of heart failure in the atherosclerosis risk in communities (ARIC) study: a comparison of diagnostic criteria. Circ Heart Fail. 2012;5(2):152\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNdumele CE, Matsushita K, Lazo M, Bello N, Blumenthal RS, Gerstenblith G et al. Obesity and Subtypes of Incident Cardiovascular Disease. J Am Heart Assoc. 2016;5(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey A, LaMonte M, Klein L, Ayers C, Psaty BM, Eaton CB, et al. Relationship Between Physical Activity, Body Mass Index, and Risk of Heart Failure. J Am Coll Cardiol. 2017;69(9):1129\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo JE, Lyass A, Lee DS, Vasan RS, Kannel WB, Larson MG, Levy D. Predictors of new-onset heart failure: differences in preserved versus reduced ejection fraction. Circ Heart Fail. 2013;6(2):279\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEaton CB, Pettinger M, Rossouw J, Martin LW, Foraker R, Quddus A et al. Risk Factors for Incident Hospitalized Heart Failure With Preserved Versus Reduced Ejection Fraction in a Multiracial Cohort of Postmenopausal Women. Circ Heart Fail. 2016;9(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirzababaei A, Djafarian K, Mozafari H, Shab-Bidar S. The long-term prognosis of heart diseases for different metabolic phenotypes: a systematic review and meta-analysis of prospective cohort studies. Endocrine. 2019;63(3):439\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomsen M, Nordestgaard BG. Myocardial infarction and ischemic heart disease in overweight and obesity with and without metabolic syndrome. JAMA Intern Med. 2014;174(1):15\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinnouho GM, Czernichow S, Dugravot A, Nabi H, Brunner EJ, Kivimaki M, Singh-Manoux A. Metabolically healthy obesity and the risk of cardiovascular disease and type 2 diabetes: the Whitehall II cohort study. Eur Heart J. 2015;36(9):551\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBi J, Song L, Wang L, Su B, Wu M, Li D, et al. Transitions in metabolic health status over time and risk of heart failure: A prospective study. Diabetes Metab. 2022;48(1):101266.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell P, Rutten FH, Lee MM, Hawkins NM, Petrie MC. Heart failure with preserved ejection fraction: everything the clinician needs to know. Lancet. 2024;403(10431):1083\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorlaug BA, Jensen MD, Kitzman DW, Lam CSP, Obokata M, Rider OJ. Obesity and heart failure with preserved ejection fraction: new insights and pathophysiological targets. Cardiovasc Res. 2023;118(18):3434\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao VN, Zhao D, Allison MA, Guallar E, Sharma K, Criqui MH, et al. Adiposity and Incident Heart Failure and its Subtypes: MESA (Multi-Ethnic Study of Atherosclerosis). JACC Heart Fail. 2018;6(12):999\u0026ndash;1007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026eacute;ndez-Fern\u0026aacute;ndez A, Fern\u0026aacute;ndez-Mora \u0026Aacute;, Bernal-Ram\u0026iacute;rez J, Alves-Figueiredo H, Nieblas B, Salazar-Ram\u0026iacute;rez F et al. Distinguishing pathophysiological features of heart failure with reduced and preserved ejection fraction: A comparative analysis of two mouse models. J Physiol. 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"metabolic unhealth, obesity, heart failure","lastPublishedDoi":"10.21203/rs.3.rs-6203913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6203913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eObesity and metabolic unhealth don’t always co-exist as the risk factors of heart failure (HF). Phenotypes derived from obesity and metabolic unhealth have promising clinical relevance. Their predictive effect for different subtypes of HF is to be investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and findings: \u003c/strong\u003eTotally 8018 participants from the ARIC study were classified into four phenotypes: metabolic healthy non-obesity (MHNO), metabolic healthy obesity (MHO), metabolic unhealthy non-obesity (MUNO) and metabolic unhealthy obesity (MUO). Cox models were applied to explore the relationship between these phenotypes and the risk of HF with preserved ejection fraction (HFpEF, left ventricular ejection fraction [LVEF] ≥50%) or HF with reduced or mildly reduced LVEF (HFrEF/HFmrEF, LVEF \u0026lt;50%) in total population and subgroups. Association between phenotypes transition and HF was further analyzed. Compared with MHNO, participants with MHO (hazard ratio and 95% confidence interval, 2.04 [1.61-2.59]), MUNO (1.80 [1.40-2.32]) and MUO (2.50 [1.95-3.20]) were related to higher HFpEF risk, MUNO (1.74 [1.36-2.22]) and MUO (1.92 [1.49-2.49]) were associated with higher HFrEF/HFmrEF risks. Subgroup analyses revealed that the associations between the phenotypes and HF risk were more distinct (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e-interaction \u003c/sub\u003e\u0026lt; 0.009) in participants \u0026lt; 55 years. Serum lipid might impact the relationship of the phenotypes with HFrEF/HFmrEF (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e-interaction \u003c/sub\u003e=0.033). From a dynamic aspect, persistent MHO, MUNO or MUO was associated with increased HFpEF risk, whereas progression from MHNO to MHO didn’t exhibit higher HFrEF/HFmrEF risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eBoth metabolic unhealth and obesity independently and cumulatively contributed to HFpEF risk, while metabolic unhealth rather than obesity are more influential in HFmrEF/HFrEF risk.\u003c/p\u003e","manuscriptTitle":"Inspecting the risk of heart failure in general population using metabolic health status and obesity profiles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-18 11:02:09","doi":"10.21203/rs.3.rs-6203913/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9fd555e-4ec8-4ead-9958-7dd9dcd09d29","owner":[],"postedDate":"March 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-18T11:02:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-18 11:02:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6203913","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6203913","identity":"rs-6203913","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}