Review Reports - Dietary Behavior Clustering and Cardiovascular Risk Markers in a Large Population Cohort

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript entitled “Dietary Behavior Clustering and Cardiovascular Risk Markers in a Large Population Cohort” addresses an important and timely topic by integrating eating behavior patterns with body composition and cardioprotective dietary preferences. The study is generally well designed and the topic is relevant for Nutrients readership. The large sample size is a major strength. Nevertheless, clearer justification of methodological choices, improved structure, and careful language editing are required.

Abstract (Lines 17–35):

Scientifically sound but should be simplified and corrected stylistically.
- The abstract is informative but overly dense.
- The term “cardioprotective diet score” should be briefly defined.
- Minor typographical error: double period in “diet scores…” (Line 30).
- Consider explicitly stating the cross-sectional design.

Introduction (Lines 39–53):

Relevant but could better articulate novelty. The research gap should be more explicitly stated at the end of the Introduction.
- The rationale for focusing on eating behaviors rather than dietary intake alone should be strengthened.
- Consider adding literature on behavioral nutrition frameworks and eating behavior phenotyping.
Introduction, Lines ~44–53 (after the paragraph introducing eating behaviors as cardiometabolic determinants)

The Introduction would benefit from a broader conceptual framing that links eating behavior patterns not only to dietary quality but also to body composition phenotypes that may remain undetected by BMI alone. Previous population-based studies in young adults have demonstrated that individuals with normal BMI may still present with excess body fat, unfavorable fat distribution, and reduced lean mass, a phenotype often described as normal weight obesity. This condition has been shown to associate with adverse metabolic and cardiovascular risk markers despite apparently healthy body weight (The prevalence of normal weight obesity in Slovak young adults and its relationship with body composition and lifestyle habits.)
In addition, lifestyle-related eating behaviors such as irregular breakfast consumption, meal timing, and late-night eating have been identified as important predictors of unfavorable body composition profiles in adults (Sex‑specific interrelationships of sleeping and nutritional habits with somatic health indicators in young adults). Integrating this perspective would strengthen the rationale for focusing on behavioral eating traits, as these behaviors may contribute to early cardiometabolic risk through their effects on body composition long before overt disease develops.

Methods:

Generally robust; more transparency needed for PCA and questionnaire validity.
Questionnaire (Lines 75–98):
- The questionnaire is not formally validated; this limitation should be acknowledged earlier and more explicitly.
- Clarify why binary food preference responses were chosen instead of frequency-based measures.
- Consider adding a short subsection on reproducibility and internal consistency.

PCA Methodology (Lines 115–125):
- Please justify the selection of six behavioral variables.
- Report eigenvalues, explained variance, and PCA loading structure in supplementary material.
- Clarify how the number of clusters was determined.

Statistical Analysis (Lines 146–158):
- Clarify whether multiple testing correction was applied.
- Report model diagnostics for regression analyses.

Results:

The Results section is clear and well supported by tables and figures; however, some redundancy between the text and the tabulated data could be reduced to improve readability and conciseness. In particular, the narrative description of Tables 2 and 3 extensively reiterates numerical values that are already clearly presented in the tables (e.g., sex differences in BMI, fat mass, abdominal circumference, and physical activity levels). The text could be streamlined by highlighting only the most relevant or distinctive findings, while referring readers to the tables for detailed numerical information. Similarly, in the description of the PCA-derived behavioral groups, the characteristics of each group are described in detail both in the text and in Table 3. Emphasizing key contrasts (for example, structured versus disordered eaters) rather than repeating all group-specific values would enhance clarity and focus.

Regarding food preferences (Figure 1), the text suggests visual trends across groups but subsequently notes that no statistically significant differences were observed. This section could be condensed to a brief descriptive statement, clearly indicating the exploratory nature of the figure. Finally, the opening paragraphs of the Discussion partially restate the main results before moving to interpretation. Reducing this repetition and shifting more quickly toward comparison with previous literature and discussion of potential mechanisms would strengthen the interpretative value of the section.

Discussion: - consider adding implications for preventive nutrition practice.

Discussion, Lines ~262–274 (after the paragraph describing associations between eating profiles and body composition)

The observed associations between disordered eating behaviors and higher fat mass, abdominal adiposity, and lower fat-free mass are consistent with evidence showing that unfavorable lifestyle habits can promote metabolically adverse body composition even in individuals without overt obesity (The prevalence of normal weight obesity in Slovak young adults and its relationship with body composition and lifestyle habits). Previous studies in adult populations have highlighted that irregular meal patterns and skipping breakfast are associated with increased visceral fat accumulation and reduced lean mass, supporting the notion that behavioral factors may contribute to subclinical cardiometabolic risk.

Discussion, Lines ~301–312 (clinical implications paragraph)

Notably, structured eating behaviors identified in the present study share key features with lifestyle patterns previously associated with more favorable body composition, including regular breakfast consumption and more stable meal structure. Evidence from adult cohorts suggests that regular breakfast intake and higher meal regularity are linked to lower fat mass indices, higher phase angle, and more favorable lean mass distribution, highlighting potential mechanisms through which behavioral eating profiles may influence cardiometabolic health

Conclusions: Appropriate and aligned with results.

Minor comments:

Several formatting inconsistencies appear throughout the manuscript (e.g., duplicated table numbering, inconsistent use of hyphens).
- Line 159–161: This appears to be a template sentence and should be removed.
- Table captions should be standardized and shortened.
- Use consistent terminology for abdominal circumference (AC vs CA).
- Minor grammatical issues are present and professional language editing is recommended.

Comments for author File: Comments.pdf

Author Response

Review Report (Reviewer 1)
This manuscript entitled “Dietary Behavior Clustering and Cardiovascular Risk Markers in a Large Population Cohort” addresses an important and timely topic by integrating eating behavior patterns with body composition and cardioprotective dietary preferences. The study is generally well designed and the topic is relevant for Nutrients readership. The large sample size is a major strength. Nevertheless, clearer justification of methodological choices, improved structure, and careful language editing are required.

We sincerely thank the reviewer for this thoughtful and constructive evaluation. We greatly appreciate recognition of the study's strengths.

Abstract (Lines 17–35):

Scientifically sound but should be simplified and corrected stylistically.
- The abstract is informative but overly dense.
- The term “cardioprotective diet score” should be briefly defined.
- Minor typographical error: double period in “diet scores…” (Line 30).
- Consider explicitly stating the cross-sectional design.

Thank you for your constructive feedback on the Abstract. We have revised it to make it less dense, improve stylistic clarity, briefly define the cardioprotective diet score (as detailed in Methods section 2.4), correct the typographical error (double period after "diet scores"), and explicitly state the cross-sectional design.

Introduction (Lines 39–53):

Relevant but could better articulate novelty. The research gap should be more explicitly stated at the end of the Introduction.
- The rationale for focusing on eating behaviors rather than dietary intake alone should be strengthened.
- Consider adding literature on behavioral nutrition frameworks and eating behavior phenotyping.
Introduction, Lines ~44–53 (after the paragraph introducing eating behaviors as cardiometabolic determinants)

The Introduction would benefit from a broader conceptual framing that links eating behavior patterns not only to dietary quality but also to body composition phenotypes that may remain undetected by BMI alone. Previous population-based studies in young adults have demonstrated that individuals with normal BMI may still present with excess body fat, unfavorable fat distribution, and reduced lean mass, a phenotype often described as normal weight obesity. This condition has been shown to associate with adverse metabolic and cardiovascular risk markers despite apparently healthy body weight (The prevalence of normal weight obesity in Slovak young adults and its relationship with body composition and lifestyle habits.)

In addition, lifestyle-related eating behaviors such as irregular breakfast consumption, meal timing, and late-night eating have been identified as important predictors of unfavorable body composition profiles in adults (Sex‑specific interrelationships of sleeping and nutritional habits with somatic health indicators in young adults). Integrating this perspective would strengthen the rationale for focusing on behavioral eating traits, as these behaviors may contribute to early cardiometabolic risk through their effects on body composition long before overt disease develops.

Thank you for your insightful feedback. We have strengthened the novelty by explicitly stating the research gap at the Introduction's end, emphasizing why eating behaviors merit focus beyond dietary intake (due to their independent effects on body composition and early cardiometabolic risk). We added literature on behavioral nutrition frameworks, eating behavior phenotyping, normal weight obesity, and lifestyle-related eating behaviors (e.g., irregular breakfast, meal timing, late-night eating) with targeted citations, linking these to undetected body composition risks.

Methods:

Generally robust; more transparency needed for PCA and questionnaire validity.
Questionnaire (Lines 75–98):
- The questionnaire is not formally validated; this limitation should be acknowledged earlier and more explicitly.
- Clarify why binary food preference responses were chosen instead of frequency-based measures.
- Consider adding a short subsection on reproducibility and internal consistency.

Thank you for highlighting the need for greater transparency. We have explicitly acknowledged the questionnaire's lack of formal validation earlier in the Methods (now flagged as a methodological choice with strengths/limitations), clarified the rationale for binary food preferences (simplicity, scalability, alignment with liking-based diet quality research), and added a new subsection on reproducibility and internal consistency (reporting Cronbach's α and test-retest data).

PCA Methodology (Lines 115–125):
- Please justify the selection of six behavioral variables.
- Report eigenvalues, explained variance, and PCA loading structure in supplementary material.
- Clarify how the number of clusters was determined.

Thank you for requesting PCA transparency. We justified the selection of 7 behavioral questions and referenced Supplementary Table S1 for technical details confirming the 4-cluster solution.

Statistical Analysis (Lines 146–158):
- Clarify whether multiple testing correction was applied.
- Report model diagnostics for regression analyses.

Thank you for statistical clarifications. We've added explanations for multiple testing and model quality.

Results:

We thank the reviewer for this suggestion aimed at improving readability. The Results section has been significantly simplified to eliminate redundancies between text and tables, reduces narrative repetition of numerical values, and preserves all scientific content.

Thank you for these suggestions aimed at improving conciseness and focus. We have condensed the section on food preferences into a single exploratory statement and simplified the introduction to the discussion to eliminate repetition of results, going straight to interpretation and comparison with the literature.

Discussion: - consider adding implications for preventive nutrition practice.

Discussion, Lines ~262–274 (after the paragraph describing associations between eating profiles and body composition)

The observed associations between disordered eating behaviors and higher fat mass, abdominal adiposity, and lower fat-free mass are consistent with evidence showing that unfavorable lifestyle habits can promote metabolically adverse body composition even in individuals without overt obesity (The prevalence of normal weight obesity in Slovak young adults and its relationship with body composition and lifestyle habits). Previous studies in adult populations have highlighted that irregular meal patterns and skipping breakfast are associated with increased visceral fat accumulation and reduced lean mass, supporting the notion that behavioral factors may contribute to subclinical cardiometabolic risk.

Discussion, Lines ~301–312 (clinical implications paragraph)

Notably, structured eating behaviors identified in the present study share key features with lifestyle patterns previously associated with more favorable body composition, including regular breakfast consumption and more stable meal structure. Evidence from adult cohorts suggests that regular breakfast intake and higher meal regularity are linked to lower fat mass indices, higher phase angle, and more favorable lean mass distribution, highlighting potential mechanisms through which behavioral eating profiles may influence cardiometabolic health

Thank you for this suggestion. We have strengthened preventive nutrition implications by incorporating the recommended evidence at the precise locations suggested. These additions directly address preventive nutrition practice by linking behavioral profiles to clinically modifiable targets (breakfast consumption, meal timing) with established cardiometabolic benefits, exactly as recommended.

Conclusions: Appropriate and aligned with results.

Thank you

Minor comments:

Several formatting inconsistencies appear throughout the manuscript (e.g., duplicated table numbering, inconsistent use of hyphens).
- Line 159–161: This appears to be a template sentence and should be removed.
- Table captions should be standardized and shortened.
- Use consistent terminology for abdominal circumference (AC vs CA).
- Minor grammatical issues are present and professional language editing is recommended.

Thank you for identifying these formatting issues. All inconsistencies have been corrected: table numbering deduplicated and sequenced, template text removed, table captions standardized and shortened, abdominal circumference terminology unified throughout, and minor grammatical issues resolved through comprehensive language editing.

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript presents a cross-sectional study exploring the relationship between eating behavior profiles, body composition, and cardioprotective food preferences in a large adult sample. The use of PCA to derive behavioral profiles is appropriate, and the findings are relevant to the fields of behavioral nutrition and preventive cardiology. The manuscript require clarification and improvement before publication.

The background could be strengthened by more explicitly linking eating behaviors to cardiometabolic outcomes beyond adiposity (glycemic control, lipid profiles).

Consider citing recent meta-analyses on behavioral eating patterns and CVD risk to better contextualize the study’s novelty.

The questionnaire, while based on prior tools, was not formally validated. This limitation should be clearly stated in the methods and discussed as a potential source of bias.

The scoring system for the cardioprotective diet (−2 to +10) lacks validation against clinical endpoints. A brief justification or reference to similar scoring approaches would strengthen this section.

Table 1 (PCA group characteristics) is difficult to interpret due to formatting issues in the submitted text. Please ensure the final version clearly displays group-wise responses.

The non-significant findings for food preferences across groups (Figure 1) should be discussed in the context of the study’s aims. Does this suggest behavior and preference are decoupled, or is it a measurement issue?

The discussion would benefit from a clearer mechanistic explanation of why structured eating correlates with better dietary scores and body composition. Consider incorporating psychological or physiological pathways (appetite regulation, circadian influences).

The clinical implications are noted but could be expanded: how might these profiles be integrated into existing risk-assessment tools or digital health platforms?

Tables 2 and 3 contain formatting errors in the submitted text (repeated lines, misplaced captions). These must be corrected.

The abstract states the study is registered under NCT06654674, but no protocol or registration details are cited in the text. Please include a brief reference in the Methods.

The p-value threshold of <0.001 is strict; consider justifying this choice or reporting exact p-values in supplementary material.

Comments on the Quality of English Language

Several sentences are overly long or awkwardly phrased (in the Discussion, lines 259–265).

Author Response

Review Report (Reviewer 2)

Thank you for recognizing the appropriateness of our PCA methodology and the relevance of these findings to behavioral nutrition and preventive cardiology. We appreciate your constructive feedback and have comprehensively addressed all clarifications requested.

The background could be strengthened by more explicitly linking eating behaviors to cardiometabolic outcomes beyond adiposity (glycemic control, lipid profiles).

Thank you for this valuable suggestion. We have strengthened the background by explicitly linking eating behaviors to cardiometabolic outcomes beyond adiposity. Specifically, in the Introduction, we now reference evidence showing that lifestyle-related eating behaviors—such as irregular breakfast consumption, meal timing, and late-night eating—are associated not only with unfavorable body composition but also with adverse metabolic markers including impaired glycemic control and altered lipid profiles.

Consider citing recent meta-analyses on behavioral eating patterns and CVD risk to better contextualize the study’s novelty.

Thank you for this recommendation. We have incorporated citations to recent systematic reviews and meta-analytic evidence linking behavioral eating patterns (e.g., meal frequency, eating speed, meal timing) to cardiovascular disease risk.

The questionnaire, while based on prior tools, was not formally validated. This limitation should be clearly stated in the methods and discussed as a potential source of bias.

Thank you. We have expanded the limitations section to explicitly address questionnaire validation, self-report bias, diet score validation, and absence of clinical measures, while acknowledging BIA limitations and residual confounding.

Thank you. We added justification in Methods 2.4. This cross-references the expanded limitations section.

Table 1 (PCA group characteristics) is difficult to interpret due to formatting issues in the submitted text. Please ensure the final version clearly displays group-wise responses.

Thank you for noting formatting concerns. Table 1 has been completely reformatted with clear group-wise behavioral responses. Additionally, full PCA technical details are now provided in Supplementary Table S1

Thank you for this insightful comment. We added a dedicated paragraph in the Discussion explicitly addressing the non-significant Figure 1 findings in context of study aims. The discussion clarifies that while individual food preferences showed no differences, the composite cardioprotective diet score demonstrated significant gradients across behavioral profiles. We explore both measurement limitations of binary preferences and potential behavioral decoupling from preference translation into consistent dietary patterns, ultimately validating the utility of composite preference-based assessment for cardiometabolic profiling.

Thank you. We have added mechanistic explanations detailing the circadian alignment of peripheral clocks, ghrelin-leptin stabilisation through morning hunger patterns, and self-regulatory effects that explain the superior body composition and dietary score results of structured eating compared to the energy imbalance pathways of disordered profiles.

The clinical implications are noted but could be expanded: how might these profiles be integrated into existing risk-assessment tools or digital health platforms?

Thank you for this helpful suggestion. We have expanded the Discussion to better clarify how the identified behavioural profiles could be operationally integrated into clinical practice and digital health settings. Specifically, we now describe how questionnaire-derived behavioural scores could complement traditional risk-assessment tools (e.g., BMI, waist circumference, lifestyle indicators) during routine screening, and how their incorporation into electronic health records or mobile health platforms could enable automated scoring, longitudinal monitoring, and personalised behavioural feedback.

Tables 2 and 3 contain formatting errors in the submitted text (repeated lines, misplaced captions). These must be corrected.

Thank you for pointing out the formatting issues. We have carefully reviewed and fixed Tables 2 and 3 in the revised submission: duplicate lines have been deleted, captions are now correctly placed above each table with complete titles, and all table elements render cleanly without artifacts. Should the manuscript be accepted, the MDPI editorial team will handle any final formatting, copy-editing, and production adjustments as per standard procedure.

The abstract states the study is registered under NCT06654674, but no protocol or registration details are cited in the text. Please include a brief reference in the Methods.

Thank you for this observation. We have added the following sentence to the Methods section (2.1 Subjects): “This study was registered prospectively on ClinicalTrials.gov (identifier: NCT06654674).”

The p-value threshold of <0.001 is strict; consider justifying this choice or reporting exact p-values in supplementary material.

Thank you for this observation. We have clarified in the Statistical Analysis section (2.5) that we used the standard significance threshold of p < 0.05 for primary comparisons, consistent with nutritional epidemiology practice. Exact p-values are reported in all tables for transparency.

Comments on the Quality of English Language

Several sentences are overly long or awkwardly phrased (in the Discussion, lines 259–265).

We appreciate the feedback on phrasing. We revised much of the Discussion in response to prior comments and we have refined lines 259–265 for conciseness.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The reviewer thanks the authors for the revisions that have significantly improved the quality of the manuscript.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have adequately addressed my comments and concerns.