PONE-D-22-27539Plasma metabolomics profiling identifies new predictive biomarkers for disease severity in COVID-19 patientsPLOS ONE

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Additional Editor Comments:

The manuscript provides an interesting issue in determining COVID-19 severity using the patient's laboratory investigations combined with significant metabolites from LC-MS/MS. The models provided by the authors seem optimistic, with AUC nearly equal to 1. Unfortunately, there is a concern about needing more statistical analysis. I recommend performing multivariable logistic regression analysis or binary regression analysis as it is more appropriate to finalize the model. Afterward, the author can test the AUC of the model. Nonetheless, a suitable criterion for selecting metabolites that reflect more severe disease in the model is necessary, rather than including acetaminophen or related medication.

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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: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: N/A

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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: No

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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

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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: The authors developed the predictive models of COVID-19 severity based upon the previously common biomarkers together with plasma metabolomes. I have a few questions and suggestions as of the following:

1. The authors should give more details regarding sample collection and treatment history of the enrolled cases, including 1) the blood samples were collected at which day of their symptoms (please give the range), 2) what was the treatment for symptomatic patients (both mild and severe cases)?, and 3) did the patients receive treatment (by physicians or by somebody else) prior to the sample collection? In fact, the treatment and the onset of disease at the time of sample collection may confound the predictive models.

2. Were there any patients who developed the progression of symptoms (from mild to severe) and was there mortality among the severe cases? If there were, the authors should identify the differences of biomarkers and plasma metabolomes between the progressive cases vs. the non-progressive cases and between the survival vs. the non-survival cases. This may contribute to the identification of prognostic markers in this study as well.

3. Besides age, sex, and co-morbidities, did the authors include some demographic information of the patients in the predictive models? For example, body mass index, smoking history, alcohol drinking history, physical activity, and medications since these can affect pulmonary and immune function that may determine the disease severity of COVID-19. In other words, adding these parameters to the models may increase the accuracy of prediction.

4. Please give the rationale why the authors ran only the positive mode on the LC/MS. According to my previous run, the negative mode of this LC/MS condition could provide a lot of additional endogenous metabolomes, especially free fatty acids and phospholipids that play a crucial role in the pathophysiology of several diseases.

5. Considering the positive mode LC/MS used in this study, this protocol is robust for the determination of acylcarnitines and phospholipids in human plasma. Again, these endogenous metabolomes play an important role in pathophysiology of several diseases. However, most of these metabolomes were not included in the 99 identified metabolomes of this study. Therefore, the authors should quantify these metabolomes and add them to the models.

6. Among 99 identified metabolomes, some of them are considered exogenous compounds, such as caffeine, chlorpheniramine, acetaminophen, acetaminophen glucuronide, and paracetamol sulfate. It is less likely that these metabolomes are involved in the pathophysiology of COVID-19, and hence I do not think that they should be included in the predictive models. Specifically, the authors’ results showed that acetaminophen, acetaminophen glucuronide, and paracetamol sulfate were 3 of 5 significant metabolomes that predicted COVID-19 severity. According to these findings, it was likely that the severe cases received more paracetamol than asymptomatic/mild cases. Therefore, the exogenous metabolomes should be reported only in terms of the difference between groups of patients, but they should be excluded from the predictive models.

7. The authors demonstrated that LDL exerted the highest accuracy level in predicting COVID-19 severity among the clinical biomarkers (Page 13, Lines 285-286). This finding was really interesting and should be included in the discussion section.

8. Since the authors showed that LDL exhibited that highest accuracy level in predicting COVID-19 severity, it was likely that lipid status and insulin sensitivity play a critical role in severity of COVID-19. Did the authors include triglyceride, total cholesterol, HDL, VLDL, glucose, insulin, and HOMA-IR in the models? If the author did, what were the results? If the authors did not, I suggest that the authors should include them in the models.

Reviewer #2: I have 4 major comments, added here. The remaining will be uploaded as a document. From my perspective those 4 points are very important blockers to a publication at this stage.

1. As briefly mentioned at the end of the discussion session this study requires a validation. No test / validation population was curved out from the initial cohort and all reported results were calculated on the full set of data. As such they are unreliably optimistic. This is hinted by the unusually high AUC values. I would strongly recommend that the proposed model(s) performance is validated in a new cohort prior to publication.

2. As only few metabolites resulting from LC-MS were retained for the models I recommend that prior to publication the identification of those is validated against a standard. This will give the high confidence level (MSI 1) required to be of use to the scientific community. More specifically in the case of COVID-19 patients a lot of cytosine containing compounds are elevated. For many of those compounds the cytosine sub-structure tends to break easily at the MS source resulting in multitude of cytosine events in an acquired TIC. Each of those events will have a good match to cytosine based on MS/MS, but not on RT. Please make sure that the metabolic feature you selected for you model overlap in RT with a cytosine standard for your method i.e., column and LC-MS conditions.

3. I strongly believe that predictive models should not rely on detecting medication (K_4_aminophenol, acetaminophen) in patient’s blood. This is highly subjective to patient’s lifestyle choices and hospital practices.

4. The study states in the abstract that “almost all such models, which relied on serum/plasma biomarkers, clinical data or a combination of both, were subsequently deemed as cumbersome, inadequate and/or subject to bias”. However, very few predictive models of COVID severity were discussed in this work. I don’t believe that this statement was well defended. A more thorough discussion on the alignment / misalignment of this study findings with previous models will be beneficial to the community. The LC-MS findings more specifically were lacking coverage.

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Reviewer #1: No

Reviewer #2: No

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Plasma metabolomics profiling identifies new predictive biomarkers for disease severity in COVID-19 patients

PONE-D-22-27539R1

Dear Dr. Hamad,

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.

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Kind regards,

Konlawij Trongtrakul, MD PhD

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 #1: (No Response)

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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 #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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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 #1: Yes

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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 #1: Yes

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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 #1: (No Response)

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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

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