Overweight and obesity association with mortality in patients with heart failure and reduced or preserved ejection fraction-a cohort study

Nicolas Garin; David Carballo; Jonathan Dash; Jérôme Stirnemann; Jean-Luc Reny; Nicolas Vuilleumier; Sebastian Carballo

doi:10.1371/journal.pone.0341606

Peer Review History

Original SubmissionAugust 12, 2025
29 Oct 2025 Decision Letter - Yoshiaki Taniyama, Editor Dear Dr. Garin, Please submit your revised manuscript by Dec 13 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols . We look forward to receiving your revised manuscript. Kind regards, Yoshiaki Taniyama, MD, PhD Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. When completing the data availability statement of the submission form, you indicated that you will make your data available on acceptance. We strongly recommend all authors decide on a data sharing plan before acceptance, as the process can be lengthy and hold up publication timelines. Please note that, though access restrictions are acceptable now, your entire data will need to be made freely accessible if your manuscript is accepted for publication. This policy applies to all data except where public deposition would breach compliance with the protocol approved by your research ethics board. If you are unable to adhere to our open data policy, please kindly revise your statement to explain your reasoning and we will seek the editor's input on an exemption. Please be assured that, once you have provided your new statement, the assessment of your exemption will not hold up the peer review process. 3. Please upload a new copy of Figures 1A, 1B, and 1C as the details are not clear. Please follow the link for more information: https://journals.plos.org/plosone/s/figures 4. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes ****** Reviewer #1: The manuscript addresses the so-called “obesity paradox” in patients hospitalized with acute heart failure. The study is timely, clinically relevant, and based on a large, prospectively collected cohort with detailed characterization and long-term follow-up. The manuscript is clearly written and methodologically sound. Nevertheless, I have several concerns that should be addressed before the manuscript can be considered for publication. Major comments 1. Definition and distribution of obesity Patients with BMI ≥30 kg/m² were grouped into a single “obese” category. However, Table 1 shows that the mean BMI in this group was 34.7 (SD 4.3) kg/m², suggesting that most patients had only mild obesity (class I). Please provide the median and interquartile range of BMI within the obese subgroup. If possible, stratify the obese category into class I vs. class II–III obesity. This would test whether the “paradox” truly applies to patients with more severe obesity. The Discussion and Conclusions should clearly state that the findings mainly apply to patients with overweight or mild obesity, while extrapolation to severe obesity remains at least uncertain. 2. Model complexity and risk of overfitting While I appreciate the clarity of the analyses and the consistency of the results across sensitivity models, I remain concerned about the possibility of model overfitting, especially in the subgroup analyses where the number of events is more limited (e.g., HFrEF). Please confirm whether proportional hazards assumptions were formally tested for the Cox models. I would also kindly suggest that the authors strengthen their results by including internal validation procedures. In particular, bootstrap resampling (e.g., 1000 repetitions) could be used to quantify optimism-corrected performance measures such as Harrell’s C-index, Brier score at 2 years, integrated Brier score, and calibration slope. Calibration plots (apparent vs. optimism-corrected) would also be informative. Reporting these metrics separately for the overall cohort and for HFpEF/HFrEF subgroups would provide reassurance that the findings are not driven by model instability. If the optimism-corrected calibration slope is found to be <1, a global shrinkage factor or penalized Cox regression (ridge) could be considered as a sensitivity analysis. 3. Adjustment for NT-proBNP The attenuation of the association between BMI and mortality after adjusting for NT-proBNP is a crucial finding. In the overall cohort, the adjusted HR was 0.99 (95% CI 0.96–1.01), i.e., no significant association. This should be highlighted, as the manuscript currently emphasizes the persistence of the paradox in HFpEF, but the global adjusted analysis is essentially negative. NT-proBNP is strongly influenced by adiposity. It may act as both a confounder and a mediator. This issue deserves explicit discussion, ideally with a causal framework. In addition, considering alternative functional forms for NT-proBNP (e.g., log-transformation or low-degree splines) could further exclude residual model misspecification. 4. Residual confounding and limitations of BMI BMI is a crude surrogate for adiposity and does not differentiate between lean and fat mass, nor does it account for fat distribution. Evidence suggests that the “paradox” attenuates when using waist-to-height ratio or direct measures of body composition (please cite 10.1186/S12933-025-02778-6). Also, C-reactive protein (CRP), the available marker of systemic inflammation, was not significantly associated with BMI in either HFpEF or HFrEF (Tables 3–4). Given that obesity is commonly linked to low-grade inflammation (please cite 10.1093/cvr/cvac133), I think it would be important for the authors to comment on this apparent lack of association. A brief discussion of potential explanations (e.g., confounding, limited sensitivity of CRP, or phenotype-specific mechanisms) would add clarity and context to the findings. Minor comments 1. In the Methods, clarify that height was self-reported, which may have introduced bias in BMI calculation. 2. Figures 1b and 1c: axis labels should use larger fonts to improve readability. Adding numbers at risk below the Kaplan–Meier curves would also help interpretation. Reviewer #2: This manuscript investigates the association between body mass index (BMI) and mortality among patients hospitalized for acute heart failure (HF), with separate analyses for HF with preserved (HFpEF) and reduced ejection fraction (HFrEF). The authors report that a higher BMI is only associated with better survival only in HFpEF patients, even after multivariable adjustment including NT-proBNP. This topic is clinically and pathophysiologically relevant, especially given the increasing prevalence of obesity and the emerging use of weight-lowering agents in HF management. The study is generally well-written and based on a well-defined cohort. It contributes to our understanding of the heterogeneity of the “obesity paradox” in HF phenotypes. However, several methodological and interpretative issues should be addressed before the manuscript can be considered for publication. Major Comments 1. Adjustment Strategy for NT-proBNP The inclusion of NT-proBNP in the multivariable model is appropriate; however, since its distribution is typically exponential (right-skewed), it should be log-transformed prior to analysis. Additionally, presenting sensitivity models in which NT-proBNP is included as a categorical variable (e.g., tertiles) would further demonstrate the robustness of the findings. 2. Heart Failure Phenotype Classification Patients with “mildly reduced EF (41–49%)” were merged into the HFrEF group. Given recent ESC and AHA classifications that consider HFmrEF as a separate phenotype, a sensitivity analysis excluding or separately analyzing these patients is recommended to confirm that this choice does not influence the results. 3. Nutritional Status Across BMI Categories While BMI is a crude measure of obesity, it does not directly reflect nutritional status. It would strengthen the study to include data (if available) on serum albumin, total cholesterol, or other nutritional indices (e.g., CONUT or PNI score) across BMI groups. This would clarify whether the observed association reflects true adiposity or better nutritional reserve. 4. Distribution of Heart Failure Medications Pharmacologic treatment significantly influences prognosis in HF. Please provide the distribution of key heart failure therapies (e.g., ACEI/ARB/ARNI, β-blockers, MRA, SGLT2 inhibitors) across BMI categories and between HFpEF and HFrEF. Adjustment for medication use, at least in sensitivity analyses, would improve the validity of the observed associations. 5. Underweight Group Exclusion Table 2 and Figure 1 exclude the underweight group, yet their mortality is of interest given their potential frailty and catabolic state. Please provide descriptive and outcome data for this subgroup (even if excluded from multivariable models), or clearly explain the rationale for exclusion from survival analysis. Inclusion of their Kaplan–Meier curve or summary results in supplementary materials would be helpful. 6. Potential Selection and Residual Confounding As this registry includes only hospitalized HF patients, selection bias toward more severe cases may exist. Discuss whether admission criteria or disease severity at presentation could differ by BMI category. Additionally, residual confounding by unmeasured variables—such as inflammation, cardiorespiratory fitness, or socioeconomic factors—should be discussed as alternative explanations of the observed “obesity paradox.” 7. Statistical Power and Interaction Testing The reported interaction between BMI and HF phenotype (p = 0.02) suggests heterogeneity, but subgroup sample sizes may limit statistical power. Please provide the number of events per variable (EPV) in each model and discuss whether this interaction was adequately powered. Minor Comments 1. Tables and Figures The legends in Figure 1 are duplicated; please correct them appropriately. Figure 1 would benefit from including the number at risk at the bottom of each Kaplan–Meier curve. In Table 1, “Rales: moderate or marked” might be replaced with “Pulmonary rales (≥ moderate)” for clarity. ****** what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation. NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. https://doi.org/10.1371/journal.pone.0341606.r001
Revision 1
4 Dec 2025 Author Response We would like to sincerely thank both reviewers for their genuine interest in this manuscript. Specifically, their comments allowed to strengthen the analyses, which led to a major change in the interpretation of the data. Indeed, BMI lost all association with mortality when adjusting for NT-proBNP, both for patients with reduced and with preserved ejection fraction. This highlights that the so-called obesity paradox is mainly due to confusion, and that previous studies that did not adjust for natriuretic peptides could not entirely deal with residual confusion. Further comments of the reviewers allowed more detailed discussion of the relationships between adipose tissue and HFpEF, specially regarding the effects of GLP agonists in HF. Reviewer #1: The manuscript addresses the so-called “obesity paradox” in patients hospitalized with acute heart failure. The study is timely, clinically relevant, and based on a large, prospectively collected cohort with detailed characterization and long-term follow-up. The manuscript is clearly written and methodologically sound. Nevertheless, I have several concerns that should be addressed before the manuscript can be considered for publication. Thank you for this supporting comment Major comments 1. Definition and distribution of obesity Patients with BMI ≥30 kg/m² were grouped into a single “obese” category. However, Table 1 shows that the mean BMI in this group was 34.7 (SD 4.3) kg/m², suggesting that most patients had only mild obesity (class I). Please provide the median and interquartile range of BMI within the obese subgroup. If possible, stratify the obese category into class I vs. class II–III obesity. This would test whether the “paradox” truly applies to patients with more severe obesity. Median BMI in the obese category was 34 kg/ m2 (IQR 32-37); among the 256 obese patients, 144 had class I and 112 class II or III obesity. Overall and heart failure related mortality decreased according to the class of obesity, confirming that the obesity paradox also applies to severe obesity. This stratification has been added in the method section and the corresponding results have been introduced in the Result section and in Table 2 The Discussion and Conclusions should clearly state that the findings mainly apply to patients with overweight or mild obesity, while extrapolation to severe obesity remains at least uncertain. Thank you for this relevant comment. Actually, our findings are not restrained to obesity but also concern overweight patients. We first modified the Title accordingly, that now reads: “Overweight and obesity association with mortality in patients with heart failure and reduced or preserved ejection fraction-a cohort study « We acknowledge in the limitations that patients with moderate or severe obesity formed a minority of the included population. “Patients with grade II or III obesity were a minority and our findings apply to the whole spectrum of overweight, mild, moderate and severe obesity » 2. Model complexity and risk of overfitting While I appreciate the clarity of the analyses and the consistency of the results across sensitivity models, I remain concerned about the possibility of model overfitting, especially in the subgroup analyses where the number of events is more limited (e.g., HFrEF). Please confirm whether proportional hazards assumptions were formally tested for the Cox models. I would also kindly suggest that the authors strengthen their results by including internal validation procedures. In particular, bootstrap resampling (e.g., 1000 repetitions) could be used to quantify optimism-corrected performance measures such as Harrell’s C-index, Brier score at 2 years, integrated Brier score, and calibration slope. Calibration plots (apparent vs. optimism-corrected) would also be informative. Reporting these metrics separately for the overall cohort and for HFpEF/HFrEF subgroups would provide reassurance that the findings are not driven by model instability. If the optimism-corrected calibration slope is found to be <1, a global shrinkage factor or penalized Cox regression (ridge) could be considered as a sensitivity analysis. Thank you for this insightful comment. The proportional hazards assumption was assessed by direct examination of the log-minus-log plots. The assumption was met. To strengthen confidence in the results of the multivariate analysis, we now present robust 95% CI obtained with bootstrapping. These points have been added in the Methods and Results sections of the manuscript. 3. Adjustment for NT-proBNP The attenuation of the association between BMI and mortality after adjusting for NT-proBNP is a crucial finding. In the overall cohort, the adjusted HR was 0.99 (95% CI 0.96–1.01), i.e., no significant association. This should be highlighted, as the manuscript currently emphasizes the persistence of the paradox in HFpEF, but the global adjusted analysis is essentially negative. NT-proBNP is strongly influenced by adiposity. It may act as both a confounder and a mediator. This issue deserves explicit discussion, ideally with a causal framework. In addition, considering alternative functional forms for NT-proBNP (e.g., log-transformation or low-degree splines) could further exclude residual model misspecification. Thank you for this comment. Appropriate modelling of the data is indeed a crucial issue. NT-proBNP distribution in this study was right-skewed, and log transformation allowed effective normalization of the distribution (see below). NT-proBNP distribution NT-proBNP distribution after log transformation When introducing log transformed NT-proBNP in the fully adjusted model (Model 2), the association between BMI and HR of mortality disappeared completely, both for HFpEF and HFrEF patients. The conclusion is that the obesity paradox is indeed fully explained in our cohort by confounding, and that full adjustment including natriuretic peptides is required to control for differences in severity of heart failure between overweight / obese patients and their leaner counterparts. These findings are more consistent with recent literature results and concepts and lead to major changes in the results and discussion section, as well as in the title and in the abstract. We now state in the Discussion section: “Our findings highlight that the apparent survival benefit of overweight or obese patients with heart failure is confounded by younger age, less comorbidities, and less advanced heart failure, but this demonstration requires extensive adjustment including for differences in natriuretic peptides levels.” The Conclusion now reads: “The seemingly better prognosis conferred by increased BMI in patients with heart failure disappears after extensive adjustment for co-morbidities and NT-proBNP. The mortality benefit associated with increased BMI (obesity paradox) can be fully explained by differences in age, comorbidities and severity of heart failure.” As for the complex relation between natriuretic peptides and obesity, it is extensively discussed in the Discussion section. We added a causal framework as a supplement figure. 4. Residual confounding and limitations of BMI BMI is a crude surrogate for adiposity and does not differentiate between lean and fat mass, nor does it account for fat distribution. Evidence suggests that the “paradox” attenuates when using waist-to-height ratio or direct measures of body composition (please cite 10.1186/S12933-025-02778-6). Thank you for raising this crucial point. Unfortunately, no other biometric measure of adiposity was available in our cohort. We extended the discussion section with a paragraph acknowledging the importance of epicardial adipose tissue measurement and its relations with myocardial function. Also, C-reactive protein (CRP), the available marker of systemic inflammation, was not significantly associated with BMI in either HFpEF or HFrEF (Tables 3–4). Given that obesity is commonly linked to low-grade inflammation (please cite 10.1093/cvr/cvac133), I think it would be important for the authors to comment on this apparent lack of association. A brief discussion of potential explanations (e.g., confounding, limited sensitivity of CRP, or phenotype-specific mechanisms) would add clarity and context to the findings. Thank you for this important ref that is now cited in the discussion section. We also comment on the trend towards increased CRP levels with increased BMI, and to the elevated CRP level in our cohort. “Inflammation associated with ageing and common co-morbidities is characteristic of HFpEF, and obesity by itself promotes an inflammatory milieu. Ref C-reactive protein, the circulating biomarker of inflammation available in our study, was elevated in all categories of BMI and tended to rise with increasing BMI. The elevated C-reactive level in our cohort is probably related to the hospital setting, with many admitted patients suffering from multiple conditions, including COPD or concomitant infections.” Minor comments 1. In the Methods, clarify that height was self-reported, which may have introduced bias in BMI calculation. We acknowledge this limitation; the corresponding paragraph in the discussion section now reads: “Weight and height were obtained from routine clinical practice and were not standardized, and inaccuracies are possible. This is especially true for height, that was not measured but self-reported which may introduce bias in BMI calculation.” 2. Figures 1b and 1c: axis labels should use larger fonts to improve readability. Adding numbers at risk below the Kaplan–Meier curves would also help interpretation. Done Reviewer #2: This manuscript investigates the association between body mass index (BMI) and mortality among patients hospitalized for acute heart failure (HF), with separate analyses for HF with preserved (HFpEF) and reduced ejection fraction (HFrEF). The authors report that a higher BMI is only associated with better survival only in HFpEF patients, even after multivariable adjustment including NT-proBNP. This topic is clinically and pathophysiologically relevant, especially given the increasing prevalence of obesity and the emerging use of weight-lowering agents in HF management. The study is generally well-written and based on a well-defined cohort. It contributes to our understanding of the heterogeneity of the “obesity paradox” in HF phenotypes. Again, many thanks for this encouraging comment. However, several methodological and interpretative issues should be addressed before the manuscript can be considered for publication. Major Comments 1. Adjustment Strategy for NT-proBNP The inclusion of NT-proBNP in the multivariable model is appropriate; however, since its distribution is typically exponential (right-skewed), it should be log-transformed prior to analysis. Additionally, presenting sensitivity models in which NT-proBNP is included as a categorical variable (e.g., tertiles) would further demonstrate the robustness of the findings. Thank you for this major point that led to significant changes in the results of the fully adjusted model. See also response to reviewer 1. 2. Heart Failure Phenotype Classification Patients with “mildly reduced EF (41–49%)” were merged into the HFrEF group. Given recent ESC and AHA classifications that consider HFmrEF as a separate phenotype, a sensitivity analysis excluding or separately analyzing these patients is recommended to confirm that this choice does not influence the results. As the results of the fully adjusted model are now homogeneous for both phenotypes of HF, this sensitivity analysis is not yet needed. 3. Nutritional Status Across BMI Categories While BMI is a crude measure of obesity, it does not directly reflect nutritional status. It would strengthen the study to include data (if available) on serum albumin, total cholesterol, or other nutritional indices (e.g., CONUT or PNI score) across BMI groups. This would clarify whether the observed association reflects true adiposity or better nutritional reserve. Only total cholesterol was available. Levels did not differ significantly between BMI categories. Results were added in Table 1 4. Distribution of Heart Failure Medications Pharmacologic treatment significantly influences prognosis in HF. Please provide the distribution of key heart failure therapies (e.g., ACEI/ARB/ARNI, β-blockers, MRA, SGLT2 inhibitors) across BMI categories and between HFpEF and HFrEF. Adjustment for medication use, at least in sensitivity analyses, would improve the validity of the observed associations. Thank you. Prescription of angiotensin converting enzyme inhibitors, angiotensin receptor blockers or mineralocorticoid receptor antagonists was not significantly different between weight categories. The corresponding results have been added to Table 1 and in the Results section. Indeed, these medications modify the prognosis of the disease only in patients with HFrEF. SGLT-2i are effective both in patients with HFpEF and HFrEF. However, when the patients of the cohort were included, SGLT-2i were not yet largely used. 5. Underweight Group Exclusion Table 2 and Figure 1 exclude the underweight group, yet their mortality is of interest given their potential frailty and catabolic state. Please provide descriptive and outcome data for this subgroup (even if excluded from multivariable models), or clearly explain the rationale for exclusion from survival analysis. Inclusion of their Kaplan–Meier curve or summary results in supplementary materials would be helpful. We agree that this subgroup is of special interest, as it certainly includes patients with more advanced heart failure characterized by a catabolic state. However, our data don’t allow to exclude that some of them might be affected by cancer or another chronic inflammatory illness, which can confer them a worse diagnosis. According to the suggestion, we added outcome data of these patients in Table 2 and provide a Kaplan Meier analysis including this category as a supplementary file. 6. Potential Selection and Residual Confounding As this registry includes only hospitalized HF patients, selection bias toward more severe cases may exist. Discuss whether admission criteria or disease severity at presentation could differ by BMI category. Additionally, residual confounding by unmeasured variables—such as inflammation, cardiorespiratory fitness, or socioeconomic factors—should be discussed as alternative explanations of the observed “obesity paradox.” Indeed, the obesity paradox has completely disappeared when log transformation to NT-proBNP levels was applied. The following statement was added to the Study limitations: “Finally, we lacked information on other important variables like cardiorespiratory fitness or socioeconomic factors, so residual confounding cannot be excluded.” 7. Statistical Power and Interaction Testing The reported interaction between BMI and HF phenotype (p = 0.02) suggests heterogeneity, but subgroup sample sizes may limit statistical power. Please provide the number of events per variable (EPV) in each model and discuss whether this interaction was adequately powered. We suppressed this interaction test as the results of the analyses stratified by HF phenotype are now congruent Minor Comments 1. Tables and Figures The legends in Figure 1 are duplicated; please correct them appropriately. Done Figure 1 would benefit from including the number at risk at the bottom of each Kaplan–Meier curve. Done In Table 1, “Rales: moderate or marked” might be replaced with “Pulmonary rales (≥ moderate)” for clarity. Done Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0341606.r002
11 Jan 2026 Decision Letter - Yoshiaki Taniyama, Editor Overweight and obesity association with mortality in patients with heart failure and reduced or preserved ejection fraction-a cohort study PONE-D-25-37278R1 Dear Dr. Garin, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support . If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Yoshiaki Taniyama, MD, PhD Academic Editor PLOS One Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes ****** Reviewer #1: The manuscript addresses the so-called “obesity paradox” in patients hospitalized with acute heart failure. The study is timely, clinically relevant, and based on a large, prospectively collected cohort with detailed characterization and long-term follow-up. The manuscript is clearly written and methodologically sound. The authors answered my previous comments.......... ****** what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy Reviewer #1: No ******** https://doi.org/10.1371/journal.pone.0341606.r003
Formally Accepted
Acceptance Letter - Yoshiaki Taniyama, Editor PONE-D-25-37278R1 PLOS One Dear Dr. Garin, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Yoshiaki Taniyama Academic Editor PLOS One https://doi.org/10.1371/journal.pone.0341606.r004

Open letter on the publication of peer review reports

PLOS recognizes the benefits of transparency in the peer review process. Therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. Reviewers remain anonymous, unless they choose to reveal their names.

We encourage other journals to join us in this initiative. We hope that our action inspires the community, including researchers, research funders, and research institutions, to recognize the benefits of published peer review reports for all parts of the research system.

Learn more at ASAPbio .