Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus

Andrya J. Durr; Anna S. Korol; Quincy A. Hathaway; Amina Kunovac; Andrew D. Taylor; Saira Rizwan; Mark V. Pinti; John M. Hollander

doi:10.1371/journal.pone.0285512

Peer Review History

Original SubmissionJanuary 23, 2023
20 Mar 2023 Decision Letter - Yoshihiro Fukumoto, Editor PONE-D-23-02008Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitusPLOS ONE Dear Dr. Hollander, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by May 04 2023 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. 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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, Yoshihiro Fukumoto 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. Please make sure that all information entered in the 'Ethics Statement' section regarding ethics approval is also included in the Methods section of the manuscript 3. We noted in your submission details that a portion of your manuscript may have been presented or published elsewhere. [Yes, the week 25 echocardiography assessments in Table 1 are the same week 25 data currently published in the manuscript "Manipulation of the miR-378a/mt-ATP6 regulatory axis rescues ATP synthase in the diabetic heart and offers a novel role for lncRNA Kcnq1ot1". DOI: 10.1152/ajpcell.00446.2021. The inclusion of this data does not constitute dual publication because it is only the same because the same mice were used for both manuscripts, but for two completely separate experiments. We view this overlap as a strategic use of resources, as the related manuscript focuses on echocardiography as a mechanism of testing ejection fraction and fractional shortening, while this submission focuses strictly on the use of echocardiography and machine learning to identify the progressive contractile dysfunction associated with the diabetic condition in a spatial and temporal manner.] Please clarify whether this publication was peer-reviewed and formally published. If this work was previously peer-reviewed and published, in the cover letter please provide the reason that this work does not constitute dual publication and should be included in the current manuscript. [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? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: I Don't Know Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Authors examined whether machine learning could be utilized to reliably describe patterns of the progressive regional and segmental dysfunction that are associated with the development of cardiac contractile dysfunction in the T2DM heart. Their study could suggest that that machine learning may provide a more thorough approach to managing contractile data with the intention of identifying experimental and therapeutic targets. . Their study seems to be acceptable because their data shows enough data to prove above. Reviewer #2: This study by Durr et al. was designed to determine whether machine learning could be used to accurately describe patterns of progressive regional and segmental dysfunction associated with the development of cardiac contractile dysfunction in the heart of individuals with T2DM. The authors concluded that cardiac dysfunction in the T2DM heart follows spatial and temporal patterns, which are characterized by regional and segmental dysfunction identifiable through the use of machine learning methodologies. This study is based on previous publications by the authors and other researchers, although the presented data are preliminary. Therefore, further mechanistic experiments are necessary, especially given recent high-impact publications in PLOS One. Major comments: 1. In the introduction or discussion, the authors could expand on their mention of speckle tracking echocardiography (STE) and its use in evaluating independent spatial alterations in the diabetic heart. They could provide further details on the limitations of previous studies that have used STE to evaluate cardiac dysfunction in T2DM patients, and explain why the progressive manifestation of regional and segmental cardiac dysfunction in T2DM patients remains understudied. 2. In this study, the researchers used non-invasive echocardiography techniques, including conventional M-mode and Pulse-Wave Doppler, to collect data from mice at specific time points (5, 12, 20, and 25 weeks). They then used advanced machine learning algorithms, specifically support vector machines and ReliefF algorithms, to identify and rank cardiac regions, segments, and features that were most strongly associated with cardiac dysfunction. In simpler terms, the researchers took measurements of the heart's function in mice using ultrasound, and then used computer programs to analyze the data and identify which parts of the heart were most affected by disease. Here, please provide more simple explanations in the Abstract for the authors who are not familiar with the ReliefF algorithms. 3. While the authors did not provide any specific reports in patients with diabetes in the abstract, they suggested that their study provides new insights into the progressive manifestation of regional and segmental cardiac dysfunction in the type 2 diabetic (T2DM) heart. The authors noted that speckle tracking echocardiography (STE) more accurately segregated animals as diabetic or non-diabetic when compared with conventional echocardiography, and that the ReliefF algorithm efficiently ranked STE features by their ability to identify cardiac dysfunction. These findings suggest that STE may be a useful tool in identifying early cardiac dysfunction in patients with T2DM. However, further research is necessary to determine the applicability of these findings in human patients, as animal models may not fully represent the complexities of human disease. Please discuss as to this point. ****** 6. PLOS authors have the option to publish the peer review history of their article (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.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0285512.r001
Revision 1
20 Apr 2023 Author Response Responses to the Editor/Reviewers Responses to the Editor 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. Thank you for this opportunity to revise our manuscript, and for this recommendation. We have uploaded the three files necessary to replicate the protocols followed for both conventional and speckle tracking strain-based echocardiography analyses to Protocol.io. as a collection titled “Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus” For reference, the link is provided here: https://www.protocols.io/private/D62464FFD7AA11EDB9470A58A9FEAC02 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 We have reformatted the manuscript to meet these additional requirements. Please note, the original citations were inserted using EndNote reference manager, and the first author no longer uses this software. All citations, including those for revision, were inserted using Mendeley reference manager and edited to meet the requirements of the Vancouver reference style. 2. Please make sure that all information entered in the 'Ethics Statement' section regarding ethics approval is also included in the Methods section of the manuscript We believe that we have corrected this issue. The ethics statements that were listed in the Methods section under “Experimental Animals” have now been moved under a separate section heading within the Methods section titled “Ethics statement” on line numbers 161 – 168 of the “Manuscript” file. 3. We noted in your submission details that a portion of your manuscript may have been presented or published elsewhere. [Yes, the week 25 echocardiography assessments in Table 1 are the same week 25 data currently published in the manuscript "Manipulation of the miR-378a/mt-ATP6 regulatory axis rescues ATP synthase in the diabetic heart and offers a novel role for lncRNA Kcnq1ot1". DOI: 10.1152/ajpcell.00446.2021. The inclusion of this data does not constitute dual publication because it is only the same because the same mice were used for both manuscripts, but for two completely separate experiments. We view this overlap as a strategic use of resources, as the related manuscript focuses on echocardiography as a mechanism of testing ejection fraction and fractional shortening, while this submission focuses strictly on the use of echocardiography and machine learning to identify the progressive contractile dysfunction associated with the diabetic condition in a spatial and temporal manner.] Please clarify whether this publication was peer-reviewed and formally published. If this work was previously peer-reviewed and published, in the cover letter please provide the reason that this work does not constitute dual publication and should be included in the current manuscript. Thank you for this clarification. This publication has not been formally reviewed and formally published elsewhere. 1. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: I Don't Know Reviewer #2: Yes We hope that we have further clarified the statistical analyses used and addressed Reviewer 1’s uncertainty. Specifically, we now state that raw data values were collected during conventional and speckle tracking echocardiography on line numbers 380 – 381 of the “Manuscript” file. Responses to Reviewers Reviewer #1: Authors examined whether machine learning could be utilized to reliably describe patterns of the progressive regional and segmental dysfunction that are associated with the development of cardiac contractile dysfunction in the T2DM heart. Their study could suggest that that machine learning may provide a more thorough approach to managing contractile data with the intention of identifying experimental and therapeutic targets. . Their study seems to be acceptable because their data shows enough data to prove above. Reviewer #2: This study by Durr et al. was designed to determine whether machine learning could be used to accurately describe patterns of progressive regional and segmental dysfunction associated with the development of cardiac contractile dysfunction in the heart of individuals with T2DM. The authors concluded that cardiac dysfunction in the T2DM heart follows spatial and temporal patterns, which are characterized by regional and segmental dysfunction identifiable through the use of machine learning methodologies. This study is based on previous publications by the authors and other researchers, although the presented data are preliminary. Therefore, further mechanistic experiments are necessary, especially given recent high-impact publications in PLOS One. Major comments: 1. In the introduction or discussion, the authors could expand on their mention of speckle tracking echocardiography (STE) and its use in evaluating independent spatial alterations in the diabetic heart. They could provide further details on the limitations of previous studies that have used STE to evaluate cardiac dysfunction in T2DM patients, and explain why the progressive manifestation of regional and segmental cardiac dysfunction in T2DM patients remains understudied. Thank you for your comment. In the Introduction, we have expanded on the limitations of previous studies assessing speckle tracking echocardiography in T2DM patients. Briefly, we discuss three limitations: many studies exploring cardiovascular dysfunction were performed in animal models with established T2DM or human subjects with T2DM, rather than in populations or individuals at risk of diabetes mellitus and/or cardiovascular dysfunction, the primary focus of studies evaluating the use of STE to detect cardiac dysfunction have often focused on STE’s ability to detect subclinical cardiovascular dysfunction rather than its ability to identify and monitor progressive cardiovascular dysfunction over time, and few studies focus on the nuances of segmental or regional changes in cardiac function, with many utilizing aggregate results summarizing the totality of each cardiac suction, such as global longitudinal strain. These additions can be found on line numbers 126 – 143 of the “Manuscript” file. Through this discussion, we hope we have sufficiently explained why the progressive manifestation of regional and segmental cardiac dysfunction in T2DM patients is understudied. 2. In this study, the researchers used non-invasive echocardiography techniques, including conventional M-mode and Pulse-Wave Doppler, to collect data from mice at specific time points (5, 12, 20, and 25 weeks). They then used advanced machine learning algorithms, specifically support vector machines and ReliefF algorithms, to identify and rank cardiac regions, segments, and features that were most strongly associated with cardiac dysfunction. In simpler terms, the researchers took measurements of the heart's function in mice using ultrasound, and then used computer programs to analyze the data and identify which parts of the heart were most affected by disease. Here, please provide more simple explanations in the Abstract for the authors who are not familiar with the ReliefF algorithms. Thank you for your comment. We altered the abstract to include a description of both the support vector machine model, which classifies data using a single line, or hyperplane, that best separates each class, and the ReliefF algorithm, which ranks features by how well each feature lends to the classification of data. (line numbers 66 – 68 of the “Manuscript” file). We believe this addition will ensure that readers have the necessary context on the primary machine learning algorithms used to understand the study. 3. While the authors did not provide any specific reports in patients with diabetes in the abstract, they suggested that their study provides new insights into the progressive manifestation of regional and segmental cardiac dysfunction in the type 2 diabetic (T2DM) heart. The authors noted that speckle tracking echocardiography (STE) more accurately segregated animals as diabetic or non-diabetic when compared with conventional echocardiography, and that the ReliefF algorithm efficiently ranked STE features by their ability to identify cardiac dysfunction. These findings suggest that STE may be a useful tool in identifying early cardiac dysfunction in patients with T2DM. However, further research is necessary to determine the applicability of these findings in human patients, as animal models may not fully represent the complexities of human disease. Please discuss as to this point. Thank you for your comment. We agree that this is a very important point of discussion and have added text to the discussion describing this topic. Specifically, we address the notion that animal models may not fully represent the complexities of human disease as mentioned in your comment, and discuss several reasons why what we observe in animal models may or may not be reflected in human subjects. This addition can be found on line numbers 868 – 877 in the “Manuscript” file. Additional response While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. Figs 1-8 were uploaded to PACE and a PACE corrected version was downloaded, reviewed, and submitted with the revised manuscript. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0285512.r002
25 Apr 2023 Decision Letter - Yoshihiro Fukumoto, Editor Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus PONE-D-23-02008R1 Dear Dr. Hollander, 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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. 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, Yoshihiro Fukumoto Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #2: The authors improved the manuscript according to the Reviewer's comments. There is no further comment for this Reviewer. ****** 7. PLOS authors have the option to publish the peer review history of their article (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 #2: No ******** https://doi.org/10.1371/journal.pone.0285512.r003
Formally Accepted
28 Apr 2023 Acceptance Letter - Yoshihiro Fukumoto, Editor PONE-D-23-02008R1 Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus Dear Dr. Hollander: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. 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. If we can help with anything else, please email us at plosone@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. Yoshihiro Fukumoto Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0285512.r004

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