-->PONE-D-25-65918-->-->Depression as a Risk Factor for Cardiometabolic Multimorbidity: A Protocol for a Systematic Review and Meta-Analysis-->-->PLOS One

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Additional Editor Comments:

Thank you for submitting this protocol on depression and cardiometabolic multimorbidity. The topic is important and the proposed review has clear potential; however, major revision is required to ensure methodological rigor, clarity, and reproducibility.

Please address the following key issues:

- Conceptual clarity: Clearly distinguish between association, prevalence, and temporally interpretable risk throughout the manuscript. Revise the title and objectives accordingly if cross-sectional evidence is included.

- Definition of CMM: Provide a consistent and explicit definition of cardiometabolic multimorbidity, including a definitive list of eligible conditions, and clarify how discordant study definitions will be handled analytically.

- Depression ascertainment: Specify how different definitions (clinical diagnosis, screening tools, administrative codes) will be handled, and consider prespecified subgroup or sensitivity analyses.

- Risk-of-bias assessment: Clearly define which tools will be used for each study design and how assessments will be applied.

- Statistical analysis plan: Strengthen and specify methods for meta-analysis, including handling of different effect measures (OR, RR, HR), choice of models, heterogeneity metrics, and criteria for subgroup analyses and meta-regression.

- Prevalence synthesis: Clarify transformation methods, handling of zero cells, and stratification by study setting.

- Confounding and sensitivity analyses: Prespecify key confounders and outline planned sensitivity analyses.

- Handling of overlapping data: Describe how duplicate populations or overlapping cohorts will be identified and managed.

- Reporting and consistency: Resolve inconsistencies across sections (e.g., risk-of-bias description, timeline, figures, supplementary materials, and transparency statements).

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Reviewers' comments:

Reviewer's Responses to Questions-->

-->Comments to the Author

1. Does the manuscript provide a valid rationale for the proposed study, with clearly identified and justified research questions?

The research question outlined is expected to address a valid academic problem or topic and contribute to the base of knowledge in the field.-->

Reviewer #1: Yes

Reviewer #2: Yes

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-->2. Is the protocol technically sound and planned in a manner that will lead to a meaningful outcome and allow testing the stated hypotheses?

The manuscript should describe the methods in sufficient detail to prevent undisclosed flexibility in the experimental procedure or analysis pipeline, including sufficient outcome-neutral conditions (e.g. necessary controls, absence of floor or ceiling effects) to test the proposed hypotheses and a statistical power analysis where applicable. As there may be aspects of the methodology and analysis which can only be refined once the work is undertaken, authors should outline potential assumptions and explicitly describe what aspects of the proposed analyses, if any, are exploratory.-->

Reviewer #1: Yes

Reviewer #2: Yes

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-->3. Is the methodology feasible and described in sufficient detail to allow the work to be replicable?

Descriptions of methods and materials in the protocol should be reported in sufficient detail for another researcher to reproduce all experiments and analyses. The protocol should describe the appropriate controls, sample size calculations, and replication needed to ensure that the data are robust and reproducible.-->

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above and, if applicable, provide comments about issues authors must address before this protocol can be accepted for publication. You may also include additional comments for the author, including concerns about research or publication ethics.

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Reviewer #1:    This is a timely and relevant protocol that addresses an important intersection between mental health and cardiometabolic multimorbidity. The registration, broad database coverage without language restrictions, and high-level methodological plan are appropriate and commendable. However, for a protocol publication, key methodological specifications need strengthening to ensure transparency and reproducibility: explicit risk-of-bias tools, detailed synthesis methods for effect-size harmonization and prevalence pooling, prespecified confounder sets and sensitivity analyses, a consistent and comprehensive CMM definition, and clear separation of incident risk versus cross-sectional association. I recommend major revisions.

“Risk factor” framing in the title implies temporality that cross-sectional and case-control designs cannot establish; plans do not explicitly separate incident CMM analyses from cross-sectional associations.

The CMM definition is inconsistently operationalized (e.g., allowing cardiovascular-only clusters if a study labels them CMM) and the list of included metabolic/cardiovascular conditions is incomplete or ambiguous (e.g., hypertension classification, dyslipidemia, obesity, metabolic syndrome).

No explicit plan to handle overlapping cohorts and duplicate populations across studies.

The risk-of-bias tool(s) are not specified; “established tools” is too vague for a protocol.

The statistical synthesis plan lacks detail: reconciliation of effect measures (OR, RR, HR), random-effects estimator choice (e.g., REML, DL), heterogeneity metrics (I2, tau2), small-study effects tests (e.g., Egger), and handling of adjusted vs unadjusted estimates.

For prevalence meta-analysis, no transformation plan (e.g., Freeman–Tukey), treatment of zero cells, or strategy to stratify by sampling frame (population-based vs clinical) is provided.

Confounding control is not prespecified (e.g., minimal adjustment set), and no sensitivity analyses are outlined (e.g., by depression ascertainment, CMM definition, setting, risk of bias).

Temporality for cohort analyses (baseline depression, incident CMM, excluding prevalent CMM) is not clearly prespecified.

Minor inconsistencies and placeholders remain (e.g., “please insert figure,” table artifacts, search strategy not included in the supplement within this text).

The planned search end date (March 2026) should be clarified vis-à-vis the protocol timeline.

The background could better situate the protocol relative to existing systematic reviews on depression and multimorbidity, and clarify how this work will differ (e.g., focus on cardiometabolic clusters, stricter CMM definition, incident outcomes).

Overall, the protocol is sound and appropriately structured, but key methodological details require specification to ensure reproducibility and interpretability. Please predefine: (a) risk-of-bias tools (e.g., ROBINS-I for non-randomized exposures; Newcastle–Ottawa Scale or JBI tools as appropriate), (b) analytic choices for meta-analysis (random-effects estimator, heterogeneity metrics and interpretation thresholds), (c) approach to harmonizing effect measures (e.g., HR approximated as RR when incidence is low, convert OR to RR as feasible or meta-analyze separately by measure), and (d) separate analytical plans for incidence (cohort) vs cross-sectional prevalence/association.

Given the central role of definitions, provide a definitive list of metabolic (e.g., type 2 diabetes, dyslipidemia, obesity, metabolic syndrome, NAFLD) and cardiovascular (e.g., CHD, stroke, heart failure, atrial fibrillation, PAD, hypertension—if included, justify) conditions eligible for CMM. Consider a sensitivity analysis excluding hypertension-only combinations, given classification debates.

Clarify inclusion of lifetime vs current depression, clinical diagnosis vs screening positive (with thresholds, e.g., PHQ-9 ≥10), and plan sensitivity analyses by ascertainment method and severity.

For association meta-analysis, prespecify: preference for maximally adjusted estimates, handling of heterogeneous covariate sets (e.g., stratify by presence of key confounders: age, sex, SES, smoking, BMI, physical activity, baseline CMD), and strategy for residual confounding assessment (e.g., E-value as optional).

For prevalence meta-analysis, define the statistical transformation (e.g., Freeman–Tukey double arcsine), model (random-effects with appropriate variance estimator), and subgroup/sensitivity analyses by sampling frame (community vs clinical vs high-risk cohorts), geography/HDI, and ascertainment methods for both exposure and outcomes.

Describe plans for small-study effects/publication bias (funnel plot, Egger/Begg tests) for association meta-analyses, noting limitations of these tools for prevalence studies and when k < 10.

Outline approaches for outlier and influence diagnostics, leave-one-out analyses, and meta-regression (e.g., by mean age, percent female, region, depression ascertainment, CMM definition) if k permits.

Address overlapping samples (e.g., UK Biobank used in multiple reports): define a hierarchy to select non-overlapping estimates or combine appropriately.

The related work summaries are largely focused on digital phenotyping and AI-enabled depression detection (e.g., PHQ-based studies, wearable data), which underscores the heterogeneity and potential misclassification in depression ascertainment across studies—a relevant concern for your inclusion and sensitivity analyses. Your plan to include validated tools (PHQ-9, BDI, clinical codes) is appropriate; consider stratifying by clinical diagnosis versus screening tools and, where possible, by severity thresholds.

There is no direct prior systematic review in the provided summaries on depression and CMM specifically, which supports the novelty of your focus on multimorbidity rather than single conditions. Please expand the manuscript’s related work section to explicitly contrast your protocol with prior multimorbidity-focused reviews (if any) and articulate how your stricter CMM definition adds value.

The review could meaningfully inform integrated care models, screening strategies (e.g., prioritizing CMM risk assessment among those with depression), and tailored prevention policies. It also has potential to highlight disparities by age/sex and region; consider adding subgroup analyses by income setting and healthcare context to improve global relevance.

Clarify plans for data/code sharing of the extracted dataset and meta-analytic scripts to enhance reproducibility and uptake.

Questions for Authors

Which specific risk-of-bias tools will you use for each study design (cohort, case-control, cross-sectional, prevalence-only), and how will ROBINS-I/JBI/NOS decisions be operationalized?

Will you analyze incident CMM (from cohort studies with baseline depression and no CMM at baseline) separately from cross-sectional associations, and will you adjust the title to avoid causal language (“risk factor”) if substantial cross-sectional evidence predominates?

How will you harmonize OR, RR, and HR across studies—will you analyze them separately or convert to a common metric? What estimator and heterogeneity metrics (I2, tau2) will you use?

Please provide a definitive list of cardiometabolic conditions considered “metabolic” versus “cardiovascular,” and clarify whether hypertension will be classified as cardiovascular, metabolic, or handled in sensitivity analyses. Will dyslipidemia, obesity, metabolic syndrome, and NAFLD be included?

For prevalence meta-analysis, what transformation (e.g., Freeman–Tukey) and models will you use? How will you handle zero-event studies and stratify by sampling frame (population-based vs clinical)?

What is your minimal confounder set for selecting adjusted estimates (e.g., age, sex, SES, smoking, BMI, physical activity)? Will you conduct sensitivity analyses by adjustment level?

How will you handle overlapping cohorts and duplicate populations across publications (e.g., sample hierarchy rules)?

Will you stratify analyses by depression ascertainment (clinical diagnosis vs screening, instrument thresholds, current vs lifetime depression) and by depression severity or duration?

Will you examine dose–response relationships (e.g., number of cardiometabolic conditions ≥2 vs ≥3) and conduct meta-regression by mean age, sex distribution, region, and study setting?

Please clarify the planned search end date (March 2026) relative to registration and publication timelines, and whether you plan to update the search prior to final submission.

Reviewer #2:    The manuscript addresses a clinically and epidemiologically relevant question, and the topic has sufficient potential interest for publication. However, in its current form, the protocol still requires careful minor revision before it can be considered methodologically sound and editorially ready. My first concern is conceptual. The title and stated objective frame depression as a “risk factor” for cardiometabolic multimorbidity, yet the eligibility criteria include cross-sectional studies and the secondary outcome is prevalence among individuals with depression. This creates an unresolved tension between causally oriented language and designs that cannot establish temporality. The authors should therefore refine the wording throughout the manuscript so that the protocol clearly distinguishes association, prevalence, and temporally interpretable risk estimates. At present, the manuscript moves too freely between these concepts, which weakens the conceptual discipline of the protocol.

My second concern is the operational definition of cardiometabolic multimorbidity itself. The manuscript states that CMM will generally require at least one metabolic and one cardiovascular condition, yet it also allows cardiovascular-only clusters when the original study defines them as CMM. That exception substantially dilutes the internal consistency of the review and risks introducing avoidable heterogeneity at the level of outcome definition. The same problem appears again in the extraction plan, where study-level definitions will be “compared” with the review definition, but the protocol does not explain how discordant definitions will be handled analytically. This issue is not minor in substance, because the review’s validity depends heavily on whether all included studies are truly measuring the same construct. The authors should clarify the hierarchy of acceptable definitions, the treatment of borderline phenotypes, and whether sensitivity analyses will exclude studies that do not meet the preferred operational standard.

Third, the protocol remains insufficiently precise in how depression will be classified and synthesized. The manuscript combines structured diagnostic criteria, standardized symptom scales, and administrative or clinical codes under a single exposure category. While this may be practical at the search stage, it is too broad for synthesis without a more explicit plan for handling differential exposure validity. A diagnosis based on DSM/ICD interview is not epistemically equivalent to elevated symptom burden on PHQ-9 or BDI, and neither is interchangeable with routine coding data. Without a prespecified strategy for subgrouping or sensitivity analysis by depression ascertainment method, the pooled estimates may become difficult to interpret. The manuscript would benefit from a clearer analytical plan specifying whether diagnostic interviews, screening-defined depression, and code-based depression will be synthesized together or examined separately.

Fourth, the statistical plan requires greater methodological discipline. The current text proposes pooling adjusted ORs, RRs, and HRs, but does not explain under what assumptions these measures will be combined, whether conversions will be undertaken, or how studies with materially different outcome incidence will be handled. This is particularly important because the review includes mixed observational designs and may combine cross-sectional prevalence-type associations with longitudinal risk estimates. In parallel, the use of the Freeman–Tukey double-arcsine transformation for prevalence pooling should be justified more carefully, as this transformation is not universally preferred and may complicate interpretability. The protocol also mentions meta-regression, subgroup analysis, and publication bias testing, but the decision rules remain underdeveloped. The analysis section should therefore be revised to specify a more rigorous hierarchy of effect measures, transformation rules, conditions for meta-regression, and the exact circumstances under which publication-bias methods will be considered appropriate.

Fifth, there are several protocol-reporting and editorial inconsistencies that should not remain in a finalized submission. The abstract states that risk of bias will be assessed using “established tools,” but the main Methods later specify NOS and either adapted NOS or JBI for cross-sectional studies; the abstract should be aligned with the full protocol. More importantly, the manuscript states that no review stages have been initiated, yet the search is said to run from inception to 31 March 2026 while the timeline also indicates that the review will begin in early 2026 and be completed by late 2026. The reporting of dates therefore feels imprecise and potentially confusing. In addition, Figure 1 remains an uncompleted template, and the Supplementary material section still contains placeholder text rather than a finalized citation to the search strategy file. These points are editorial, but they materially affect the professionalism and reproducibility of the protocol and should be corrected before acceptance.

Sixth, there is a nontrivial inconsistency in the transparency statements. The manuscript declares that the authors received no funding for the research, yet the acknowledgements state that Chiang Mai University provided invaluable partial support of the study. Those two statements are not obviously compatible and should be reconciled. Likewise, the Data Availability Statement says that no datasets were generated or analyzed during the present study, but then promises that all relevant data will be made fully available without restriction upon study completion. For a protocol, this wording should be made more precise and internally consistent with journal expectations. The authors should also ensure that the ethics, dissemination, and data-sharing language are presented in a form appropriate for a protocol rather than a completed review.

The authors may also wish to consider citing a few recent publications that could provide useful contextual support for the broader discussion of cardiometabolic disease complexity, metabolic therapeutics, and multimodal risk stratification. Some studies appear the most appropriate for possible inclusion: 1. PMID: 40005319. DOI: 10.3390/medicina61020202. This study is relevant because it addresses a major cardiometabolic therapeutic class used across chronic disease populations and may help situate the discussion of cardiometabolic disease burden and complexity. 2. PMID: 41038126. DOI: 10.1016/j.jdiacomp.2025.109178. This article may be helpful because it concerns a lipid-modifying intervention tied to metabolic disease and may enrich the manuscript’s framing of cardiometabolic pathways. 3. PMID: 41189017. DOI: 10.1186/s40942-025-00724-y. This study may be considered because it addresses statin-related therapeutic implications within a disease context linked to metabolic and vascular dysregulation. 4. PMID: 40775566. DOI: 10.1007/s12020-025-04374-w. This publication may also be useful because it reflects the broader systemic consequences of diabetes and may support the manuscript’s discussion of cardiometabolic disease clustering and long-term burden.

Overall, the manuscript has merit and the proposed review may be of interest to readers, but the present version still contains notable deficiencies in conceptual framing, outcome definition, exposure classification, statistical planning, reporting consistency, and transparency statements. I therefore recommend minor revision. The work could become suitable for publication after careful correction of the points above.

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Reviewer #1: Yes:    Ricardo Ney Cobucci

Reviewer #2: No

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Association between Depression and Cardiometabolic Multimorbidity: A Protocol for a Systematic Review and Meta-Analysis

PONE-D-25-65918R1

Dear Dr. Oo,

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.

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Kind regards,

Mohamed Baklola

Academic Editor

PLOS One

Additional Editor Comments (optional):

Thank you for your careful revision of the manuscript and for addressing the comments raised during peer review. The authors have adequately responded to the reviewers’ concerns, and the revised protocol has been strengthened accordingly.

The objectives are clearly defined, the proposed methodology is appropriate and transparent, and the manuscript is presented in a clear and organized manner. I have no further substantive comments.

I recommend the manuscript for publication in its current form.

Reviewers' comments: