Position-Specific Kinanthropometric Traits of Professional American Football Players: A Study of Mexican LFA Players

Round 1

Reviewer 1 Report (New Reviewer)

Comments and Suggestions for Authors

Dear authors,

you can find my comments in the manuscript.

Comments for author File: Comments.zip

Comments on the Quality of English Language

I am not an English native speaker, but my knowledge is above the one we could consider average-leveled. However, this article needs a proofreading by a professional, and by knowing the fact that this Journal charges extra the proofreading I have to ask the Editor(s) to leave this obligation to the authors (I am sure that you are able to find someone in your home country who could do the proofreading and give you a Certificate).

Author Response

Dear Reviewer,

We would like to express our sincere appreciation for the time and care you devoted to evaluating our manuscript. Your observations were precise, constructive, and highly valuable for improving the clarity, methodological rigor, and overall quality of the work. We carefully revised the manuscript in light of each of your comments, making substantial adjustments to structure, language, and content. Below, we summarize the main revisions made across the sections of the manuscript.

 

Abstract

The abstract was substantially revised to enhance methodological clarity and interpretative precision.
We now explicitly state the cross‑sectional observational design and the use of ISAK‑standardized anthropometric procedures. Effect sizes (η² or ε²) were added to complement the significance values. The description of the discriminant analysis was rewritten to emphasize its exploratory nature, and the classification accuracy (57.7%) is now reported together with a note of caution regarding the absence of cross‑validation. The concluding statement was adjusted to present the practical implications and limitations more clearly, particularly regarding the league‑specific sample and the restricted generalizability of the findings.

 

Introduction

The introduction was reorganized to articulate the research gap more precisely, highlighting the limited morphological data available for professional Mexican players and the novelty of integrating anthropometric fractionation, somatotype, proportionality indices, and discriminant analysis in a single study.
A paragraph on psychological well‑being, which was not directly related to the study design, was removed.
The hypothesis was reformulated to avoid an overly predictable or confirmatory tone.
Statements that could imply performance inference were moderated, since no performance variables were measured.
Finally, a concise justification for the use of kinanthropometric methods—relative to densitometric techniques—was added, as requested.

 

Materials and Methods

Several methodological clarifications were incorporated in response to your comments.

Statistical power:
The manuscript now explicitly states that statistical power was calculated post hoc using the observed effect sizes for each variable.

Discriminant analysis and variable selection:
The description of the discriminant analysis was expanded to clarify its exploratory purpose. We now specify that only variables meeting univariate normality criteria were included, and that assumptions of homogeneity of covariance matrices (Box’s M), absence of multicollinearity (VIF < 5), linearity, and independence of observations were verified. The text also notes that multivariate normality cannot be fully guaranteed, and interpretation was therefore conducted with caution.

Control of hydration and fatigue:
The Methods section now clarifies that all assessments were conducted under standardized conditions, including morning measurements, overnight fasting, avoidance of strenuous exercise and alcohol for 24 hours, and adherence to ISAK protocols.

Ross & Kerr method:
The wording was revised to avoid implying external validation. The manuscript now states that the method is widely applied in athletic populations and that reliability in this sample was supported by high agreement between measured and estimated body mass (Spearman correlation, ICC, RSE).

Type I error and post hoc procedures:
The manuscript now explicitly acknowledges the increased risk of Type I error due to multiple comparisons. To address this, we incorporated adjusted post hoc procedures:
– Games–Howell for parametric variables,
– Dunn for non‑parametric variables.
This modification strengthens the statistical rigor of the study and directly addresses your concern.

Simplification of statistical description:
The Statistical Analysis section was streamlined by removing redundant elements and retaining only the essential components: global tests, adjusted post hoc procedures, effect sizes, statistical power, and the discriminant analysis.

Corrections to annotated PDF:
All highlighted comments and annotations in your PDF were carefully addressed. These included textual clarifications, formatting corrections, and removal of redundant or unclear statements.

 

Results

The Results section was revised to eliminate redundancy. Numerical values already presented in tables were not repeated in the text, and the narrative now focuses on the most relevant patterns.
Figures that duplicated information contained in tables were removed. Table 3 was retained as the primary source for inferential results, while figures were reserved for complementary graphical analyses (somatotype and SAM).
Post hoc results were added for all relevant variables, and effect sizes (η² or ε²) were incorporated.
The narrative was rewritten to emphasize the most salient findings, such as the dominance of linemen in mass‑related variables and the clear contrast between linemen and skill‑position players.

 

Discussion

The discussion was revised to improve precision and avoid overinterpretation.
Comparisons with NFL populations were moderated, and statements regarding mesomorphy and training emphasis were adjusted to avoid causal implications.Repetitive justification of the fractionation method was removed, and speculative statements were softened. The Limitations subsection was rewritten to be more concise, objective, and aligned with your recommendations.

 

Conclusion

The conclusion was reformulated to avoid causal language and functional interpretations.
The revised version presents the findings in a strictly descriptive manner, consistent with the study design, and avoids terms such as adaptations or optimization.The closing statement now emphasizes the population‑specific nature of the data and the exploratory scope of the discriminant analysis.

 

Closing Statement

We are grateful for your detailed and constructive review. Your comments significantly strengthened the manuscript, and we hope that the revised version meets your expectations.

Sincerely,
The Authors

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

The manuscript presents a comprehensive kinanthropometric assessment of professional American football players from the Mexican LFA league, with comparisons across playing positions. The dataset is valuable, the measurements appear carefully conducted, and the authors apply a wide range of statistical techniques. However, in its current form, the manuscript suffers from conceptual overextension, excessive statistical complexity, and insufficient analytical focus.

I recommend authors to make the next changes:

• clarify the primary analytical objectives of the study;
• reduce redundancy in statistical testing where similar conclusions are repeatedly demonstrated;
• emphasize key findings rather than presenting all possible statistical outputs.

Reformulate the hypothesis toward:

• population-specific differentiation;
• practical relevance (e.g., classification usefulness);
• comparative or predictive value.                                                                                                 A more cautious interpretation is necessary:
• critically discuss the limited practical usefulness of the discriminant model;
• avoid overinterpretation of its classificatory power;
• consider reframing this analysis as exploratory rather than confirmatory.                         The repetition of tables, figures, and narrative descriptions reduces readability and inflates the Results section without adding new information. Streamlining the presentation—by prioritizing either tables or figures for specific outcomes.

• The Discussion section is generally well structured but lacks sufficient critical reflection on the study’s limitations.                                                                                                          Minor Comments 

• The manuscript would benefit from language polishing, particularly sentence length and stylistic consistency.
• Some technical terms are repeated excessively; modest lexical variation would improve readability.
• Figures and tables should be cross-checked to ensure each provides unique information. It's necessary to indicate significance between groups in the tables
• Consider shortening overly detailed statistical descriptions that do not contribute to interpretation.                                                                                                                           I wish you success!

Author Response

Dear Reviewer,

We sincerely appreciate the time and attention you dedicated to evaluating our manuscript. Your comments were constructive and highly valuable, and they helped us refine the analytical focus, streamline the statistical presentation, and improve the overall clarity of the work. Below, we summarize the revisions made in response to your observations.

 

1. English language and clarity

We thank you for noting the need for language improvement. The manuscript, including the Methods and Results sections, was carefully revised to enhance clarity, sentence structure, and stylistic consistency. Redundant expressions were removed, and terminology was refined to improve readability. If, after this revision, the Editorial Office considers that additional professional editing is required, we are fully willing to use the journal’s English‑editing service.

 

2. Methods – restructuring and clarity

The Methods section was reorganized to improve coherence and readability. Several paragraphs were rewritten to reduce redundancy and present the procedures in a clearer and more concise manner. This restructuring strengthens the overall presentation of the study design and analytical approach.

 

3. Statistical power

The manuscript now specifies that statistical power was calculated post hoc, based on the observed effect sizes for each variable. This clarification has been added to the Statistical Analysis section to ensure transparency regarding the origin and interpretation of the reported power values.

 

4. Fractionation model (Ross & Kerr)

The description of the Ross & Kerr five‑component model was revised to avoid implying external validation. The manuscript now states that the model is widely applied in athletic populations and that its reliability in this sample was supported by the high agreement between measured and estimated body mass (Spearman correlation, ICC, RSE). This provides a more accurate and evidence‑based justification for its use.

 

5. Multivariate normality and exploratory nature of the discriminant analysis

We added an explicit note acknowledging that multivariate normality cannot be fully guaranteed. For this reason, the discriminant analysis is now clearly described as exploratory, and its interpretation is presented with appropriate caution. This revision directly addresses your recommendation to avoid overinterpreting the classificatory power of the model.

 

6. Potential cluster effect (teams)

A statement was added indicating that players were nested within four different teams, which could introduce a potential cluster effect related to team‑based training environments. The manuscript now clarifies that, although this factor was considered, multilevel modeling was not applied because the primary objective of the study was descriptive profiling rather than inference across clusters.

 

7. Type I error and post hoc procedures

The Statistical Analysis section now explicitly acknowledges the increased risk of Type I error associated with multiple comparisons. To address this, we incorporated adjusted post hoc procedures:

• Games–Howell for parametric variables (robust to unequal variances and sample sizes),
• Dunn for non‑parametric variables.

This modification strengthens the statistical rigor of the study and responds directly to your recommendation.

 

8. Figures, tables, and redundancy

All figures were reviewed and improved for clarity and resolution.
Tables were reorganized to improve readability, including clearer headers, explanatory notes, and explicit post hoc information.
Figures that duplicated information already presented in tables were removed. Only figures providing unique visual information (somatotype and SAM) were retained.

 

9. Significance indicators

p‑values and post hoc results were added directly to the tables. Superscript letters were not necessary because the explicit positional comparisons are already included.

 

10. Reduction of repetitive text

The Results section was rewritten to eliminate repetition and improve clarity. Numerical values already presented in tables were not repeated in the narrative. The section was reorganized into three clear blocks:

• Descriptive Data
• Main Results (Discriminant Analysis)
• Body Composition and Somatotype

Additionally, a dedicated subsection was added for the graphical analysis of somatotype (SAM), improving methodological coherence.

 

11. Analytical focus and hypothesis reformulation

Following your recommendation, the hypothesis was reformulated to emphasize:

• population‑specific morphological differentiation,
• the exploratory nature and limited practical usefulness of the discriminant model,
• and the expected variability in classificatory accuracy.

This adjustment provides a clearer and more realistic analytical framework.

 

12. Discussion and limitations

The Discussion section was revised to incorporate a more explicit and critical reflection on the study’s limitations. We now highlight the league‑specific nature of the sample, the exploratory character of the discriminant analysis, and the constraints associated with anthropometric estimation methods. These additions strengthen the interpretative balance of the manuscript.

Closing statement

We are grateful for your constructive feedback and believe that the revisions have strengthened the manuscript substantially. We hope that the revised version meets your expectations and those of the Editorial Office.

Sincerely,
The Authors

Round 2

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

The new version of the article fully meets the publication requirements. The authors took all the comments into account and almost completely rewrote the text. I have no comments on the new version.

Comments for author File: Comments.pdf

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

GENERAL COMMENTS

Overall, the design of the study is good but the idea is not very original. In addition, there are major issues related to clarity, justification, redundancy, statistical analysis interpretation, and alignment between hypotheses and conclusions.

Some references are more than 20 years ago. Please update them since more recent articles might change the paradigm. See the example below and include it

• https://doi.org/10.3390/jfmk10010019

- To describe the position-specific kinanthropometric traits the authors MUST refer to Phantom Z-scores. They describe proportionality and allows comparison with other population. At least it should be mentioned in the introduction/discussion citing previous investigations in similar sports  (Holway, 2009) and recent studies displaying Z-scores in different goups (López-Plaza et al., 2019) Please, see how this last study displays it.

• https://doi.org/10.1080/02640410903207408
• https://doi.org/10.1080/00913847.2019.1623997

 

SPECIFIC COMMENTS

Introduction

- The information included in the first paragraphs is very general and sometimes not necessary. General statements are good to create background and context. Please, reduce the content and focus on the relevant data/findings from prior research that is associated

- The text jumps between general popularity, health risks, and anthropometry without clearly connecting the ideas (lines 45–77). A more logical structure is needed.

- While the gap in Latin-American data is mentioned (lines 83–87), the importance of studying LFA players specifically is not sufficiently argued. A more complete and clearer statement of the necessity of your research would be appropriate. Why your study is necessary in the current context of psychological wellbeing? Include it, please

- Although a hypothesis is vaguely stated (lines 93–94), it lacks precision. It should be clearly articulated (e.g., “We hypothesized that linemen would exhibit significantly greater adiposity and mesomorphy…”).

Methods

- Regarding participants Inclusion/Exclusion Criteria are missing (lines 104–108). What were the eligibility criteria (age range, injury status, training background)? How were teams/players recruited? Was it all teams or a convenience sample?

Statistical Analysis. The rationale for ANOVA assumptions and data normality check is missing (lines 154–159).

• • Bonferroni test is correctly used but not consistently explained when applied.

  • Multicollinearity risks in discriminant analysis are not discussed (lines 164–171).

  • No mention of power analysis or effect size interpretation thresholds (lines 161–163)

Results

- Tables and text information is redundant.  Many data points are unnecessarily repeated in both the text and tables. For example, lines 176–178 restate what is clear in Table 1; lines 215–219 restate Table 2.

- Lines 274–275 report “equilibrium” of means, which is vague. Use statistical terminology consistently

- Figure 1 is messy and provides no clear information. Please, delete it or include the information (if relevant) in a table or clearer format

- The information of table 3 is not precise/clear. When significant differences were shown, please allocate the differences indicating respect to which group or groups and the magnitude

Discussion

- The discussion restates findings without clearly tying them to the original hypothesis (lines 279–281).

- Differences in methods are mentioned (lines 311–319), but comparative claims are still made across studies with incompatible techniques (e.g., DEXA vs. anthropometry).

- Several comparisons with international populations lack adjustment for sample differences (age, training, nationality)

 

Author Response

Response to Reviewer 1

Dear Reviewer,

We deeply appreciate the time and dedication you have invested in reviewing our manuscript. Your insights have been invaluable in improving the clarity, precision, and methodological rigor of our study. We greatly value your detailed analysis and have incorporated the suggested adjustments into the revised version of the manuscript. Below, we provide detailed responses to each of your questions and comments, although the corresponding changes have already been implemented in the revised manuscript.

 General Comments

Response 1: We have updated more than 60% of the references within the last five years, and a significant percentage within the last ten years. However, we have decided to retain key references that describe the morphological characteristics of American football players despite their age. These references helped us develop a logically structured historical framework for constructing the theoretical background of the study.

Response 2: Since we are analyzing anthropometric profiles, which by definition must include body composition, somatotype, and proportionality measures, we have opted to include indices rather than Phantom proportionality. We believe indices are more informative from a biomechanical perspective. Regarding the recommended articles, we have only included Francis Holway’s work, as his methodology shares some similarities with ours and was particularly relevant to our study. We are familiar with López-Plaza’s article, but it is over five years old, and we chose not to include it because we believe one of the contributions of our work is the formulation of an introduction that reflects a deep, exhaustive literature review based solely on American football research. When examining studies within this sport, few investigations focus deeply on a single sport.

 Introduction

3. The information in the first paragraphs is overly general and, in some cases, unnecessary. It is recommended to reduce the content and focus on relevant findings from previous research.

Response:

   We appreciate your valuable insights regarding the introduction of the manuscript. Following your suggestions, we have made significant adjustments to enhance coherence, justification, and methodological alignment with recognized standards. However, as mentioned in the previous response, we consider the general information relevant to the study. Below is a detailed list of the corrections made, along with information on how we have incorporated STROBE criteria to improve the study’s presentation.

   First, we have refined the logical structure of the introduction to ensure a smooth progression of topics. We have removed redundant content, focusing instead on presenting specific background information relevant to the study’s objectives. The introduction now follows a logical sequence: it begins with the contextualization of American football’s popularity, followed by a review of key research areas, and concludes with the justification for studying Latin American athletes.

   Additionally, we have strengthened the argument regarding the existing knowledge gap concerning Latin American players in professional American football. We emphasize the underrepresentation of these athletes in international leagues and the importance of generating specific anthropometric data for this population. A comparative approach with previous studies on players from other regions has also been included, allowing for better contextualization of the study’s relevance to talent identification and athletic development in Latin America.

   Moreover, we have refined the formulation of the hypothesis to enhance clarity and precision. It is now explicitly stated that "kinanthropometric traits vary significantly by position, aligning with the specific physical demands of each role." This reformulation eliminates ambiguity and establishes a clearer connection with the study’s objectives, making it easier to interpret the expected results.

  We are pleased to inform you that we have now aligned the introduction with STROBE recommendations. This means it now meets the following criteria:

• Providing a well-supported rationale that demonstrates the study's relevance based on existing evidence.

• Including specific background information and appropriate references essential to support the research problem.

• Precisely defining objectives while avoiding vague or overly general formulations.

• Structuring the content coherently and progressively, taking care to avoid abrupt thematic shifts.

4. Although the lack of data on Latin American populations is mentioned (lines 83-87), the importance of studying LFA players is not sufficiently justified. It is recommended to provide a clearer statement on the relevance of this study in the context of psychological well-being.

  Response: I greatly appreciate this observation, as I believe it is of considerable importance. In the penultimate paragraph of the introduction, we have reinforced the justification for this research with this consideration.

Methods

8. The inclusion/exclusion criteria for participants are not mentioned (lines 104-108). What were the eligibility criteria (age range, injury status, training background)? How were teams/players selected? Were all teams included, or was it a convenience sample?

  Response: In the second paragraph of the methodology section in the revised version, this aspect is clearly stated.

9. The justification for the assumptions of the ANOVA and the verification of data normality are not clearly explained (lines 154-159).

  Response: I will combine this response with the other questions related to statistical processing. In the revised version, all aspects related to the statistical procedure have been addressed. After reconciling the comments from the other reviewer with yours, we decided to review the database to eliminate some distortions, particularly those related to the Ross and Kerr fractionation method, and conducted the statistical analyses following the reviewers' recommendations.

 We are satisfied with the improvements, as your feedback has helped enhance the clarity and statistical robustness of the study. Regarding discriminant analysis, we have clarified that it is exploratory rather than confirmatory; therefore, the literature suggests that it is more robust when considering certain assumptions. Nevertheless, we recalculated the analyses and obtained similar results, but highlighting new variable combinations that meet normality requirements, Box's M test, and other conditions referenced in the text.

13. The information in the tables and text is redundant. Many data points are repeated unnecessarily. For example, lines 176-178 reiterate what is already in Table 1, and lines 215-219 duplicate Table 2.

  Response: Regarding Table 1, the amount of information in the text is minimal (only three lines for an extensive dataset), and I consider it necessary. If it were removed entirely, the table would lack emphasis, and its relevance might be questioned since there would be no textual reference to it. As for Table 2, the redundant paragraph was removed, leaving only minimal necessary information, which was later expanded upon in Figure 2 and further clarified with the ANOVA results in Table 3.

14. The expression "equilibrium" of means (lines 274-275) is vague. It is recommended to use consistent statistical terminology.

 Response: This term has been removed from the text in the revised version.

16. The information in Table 3 is not precise or clear. When significant differences are presented, it is recommended to indicate which group(s) they were found in and the magnitude of those differences.

   Response: In the revised version, it has been clarified that the marked difference refers to other playing positions. The table now clearly shows the predominance of offensive linemen compared to all other positions.

Discussion

17. The discussion repeats the findings without clearly linking them to the original hypothesis (lines 279-281).

Response: In the revised version of the manuscript, these findings are now linked by stating: Notably, differentiation was observed in arm relaxed girth, biiliocristal breadth, and sitting height, highlighting how specific anthropometric traits contribute to positional specialization.

18. Differences in methodologies are mentioned (lines 311-319), but comparisons are still made between studies using incompatible techniques (e.g., DEXA vs. anthropometry).

 Response: At no point do we compare different methodologies; we only highlight that our approach is novel because previous studies have examined body composition characteristics using other techniques. Therefore, in the next paragraph, we compare our findings with the only population group evaluated through anthropometric fractionation, whose characteristics resemble those of American football players.

   Unfortunately, international literature has not comprehensively explored the anthropometric characteristics of these athletes in a study as detailed as ours. As a result, there are limited comparative options beyond isolated data from studies that have reported body mass, stature, and BMI. In many countries—especially in Latin America anthropometry remains crucial for athlete assessment since access to more expensive techniques is limited. This practical approach is strongly supported by ISAK.

19. Several comparisons with international populations do not properly account for differences in sample characteristics (age, training, nationality).

   Response: To the extent possible, we have included the age of all the samples we compared in the revised manuscript. All samples come from the same competitive level as the studied population—namely, the NFL or the Japanese national team in the World Championship.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear authors "Position-Specific Kinanthropometric Traits of Professional American Football Players: A Study of Mexican LFA Players",

Although the topic is interesting, this manuscript has issues related to scientific robustness, reported anthropometric outcomes, and the statistical approach. A strong data bias is detected, given that the selected reporting variables (e.g., body composition model) omit absolute related variables (raw data) that are more relevant for positional differences. This compromises the validity and generalizability of this work. In fact, although several co-authors are ISAK members (even Level 3 instructors), it is sad to see that the terminology is misleading, so they are respectfully requested to revise it and align it with current ISAK standards. The authors are biased toward the importance of muscle-to-bone ratio without data that support this for their study population.

Unfortunately, if these changes are not addressed, this manuscript cannot be accepted for publication.

 

MAJOR ISSUES

1. TERMINOLOGY AND CONCEPTS

1.1. Although "height" and "circumference" are frequently used terms, they are NOT technically correct. Please refer to "stature" and "girths", respectively. 

1.2. Authors are strongly encouraged to adhere to the standards of the International Society for the Advancement of Kinanthropometry (ISAK), especially because according to Lines 119-120 "qualified anthropometrists ISAK Level 3" performed the anthropometry assessment in this study.

For example, what is "extended arm"? Did you mean "arm relaxed" (as appear in the ISAK manual)? Same with "flexed arm", "front thigh", "medial calf", and several others. Please, use the ISAK terms of the manual.

1.3. What is a "sliding compass"? Do you mean a large sliding caliper? Regarding "anthropometer," do you mean a small sliding caliper?
Please adhere to the terms and concepts defined in the ISAK Manual.

Important: Avoid using the term lean mass. In agreement with Heymsfield (2024), "Avoiding the use of the term LBM additionally limits confusion surrounding similar widely used body composition terms such as lean mass, lean soft tissue mass, and lean muscle mass." Please read and apply concepts described in the article "Are Lean Body Mass and Fat-Free Mass the Same or Different Body Components? A Critical Perspective"
https://pubmed.ncbi.nlm.nih.gov/39510253/

2. MANUSCRIPT STRUCTURE:
Authors have to adhere to MDPI guidelines and use the Strengthening the Reporting of Observational Studies in Epidemiology-Nutritional Epidemiology (STROBE-nut) to enhance the manuscript's clarity, rigor, and methodological transparency. Cf, https://pubmed.ncbi.nlm.nih.gov/27270749/

Introduction: Writing is cumbersome.
Citations are missing. Support your statements with references. For example, are you certain that "Research on American football athletes has particularly focused on areas such as orthopedics, neurology, and epidemiology"? Also, why mix former players with current athletes? Be specific—what is the purpose of using BMI as a metric? A metric of which outcome?

In addition, some current references are not suitable for the statements. For example, "American football demands a 57
unique combination of height, speed, and power [10],..." but the cited article "Longitudinal Body Composition Changes in NCAA Division I College Football Players" do NOT support this. Could the authors clarify why they included stature in their statement?

Please elaborate and explain: "The anthropometric profiling of American football players plays a pivotal role in advancing athletic performance and development." Why pivotal?

Lines 72-74: If this constitutes part of the study's rationale, could the authors explain the timeline discrepancy? The study was approved in 2019, data collected in 2020, yet is only now being submitted in 2026. What factors contributed to this six-year delay between data collection and publication?

Line 94: "… and align closely with the specific physical demands of each role." Which ones? Are there previous report of the physical performance-related variables in the American football players in Mexico’s Liga de Fútbol Americano?

Please adhere to the STROBE-Nut guidelines and structure the following subsections of the manuscript accordingly.

3. REPORTED ANTHROPOMETRIC VARIABLES
There is strong bias. This study lacks of scientific robustness in the selección of the main variables. Detailed as follows:

3.1. Has the 5C model been validated for the study population? I think not. Therefore, this body composition information is not useful, as it is not valid for the population—especially since several components of the body composition model can not be contrasted with DXA (fat mass). Additionally, asking practitioners, coaches, and nutritionists/dietitians to always measure 23 anthropometric variables is not practical in real life for elite athletes. It would be preferable to base all analyses on absolute values, such as the sum of six and eight skinfolds. 
In fact, authors are requested to analyze and report the sum of skinfolds by upper-, mid-, and lower-body regions. 

3.2. Skinfold-corrected girths and proportionality indices (e.g., biacromial, Manouvier, Locomotive index) are missing. Please report all and several others. Consider these are more interpretable for positional differences. Avoid unvalidated regression equations.

3.3. Importantly, authors MUST report the mathematical functions, corrections, and any other relevant calculation. This is absolutely necessary for transparency and reproducibility.

4. STATISTICAL ANALYSIS
Misleading. Neither statistical power nor sample size was calculated a priori. 

4.1. The presented data is pure descriptive (Table 2). Please report always 95% CIs for mean differences.

4.2. In addition, considering the unequal and small sample sizes when comparing positions, authors are requested to adhere to an approach other than frequentist, such as robust, estimation-based, or Bayesian statistics. Avoid relying only on NHST!!

To revise your manuscript, I suggest: 
- Welch’s ANOVA (does not assume equal variances) + Games-Howell post-hoc (you can even use Yuen-Dixon for unequal group sizes).
- Non-parametric option: Kruskal-Wallis + Dunn’s test.
- Report effect sizes accordingly.

(Optional)
Bayesian ANOVA (using brms in R). Report Bayes Factors (BF).

4.3. The total n = 189 might be acceptable for discriminant analysis, but group sizes vary: QB=18, RB=16, OL=27, etc. Thus, smaller groups (QB, RB) may be underrepresented in discriminant analysis. 

Suggestion: 
Use stratified sampling or weighted discriminant analysis to avoid bias toward larger groups (e.g., DL, WR).

Alternative useful methods for profiling: 
Use unsupervised machine learning such as k-medoid or hierarhical clustering. Do not select an arbitrary k value and test with different metrics (Dunn, Sillouethe, etc.).

 

5. RESULTS AND DISCUSSION
Findings are generally misleading. Strong bias is detected in several aspects (especially reported anthropometric variables and statistical analysis).

Replace figures. Please use estimation graphics - Plot mean differences with 95% CIs between positions. Cf, https://journals.sagepub.com/doi/10.1177/0956797613504966 

The writing is cumbersome, and the discussion is poor (references and analysis of available data are not accurate; similar to the introduction, several papers are cited but are not related to the outcomes). Please avoid the term circumference—it is girth.

Why "tailoring training programs to the morphological requirements of each position"? Could the authors explain what are morphological requirements? Sports clothes? Training materials?

Lines 357-359: This was not tested at all! The authors did not evaluate physical or sports performance to bring this to matter. Actually, this is a limitation of the study. You should highlight that future studies might test physical and sports performance variables during a pre-competition phase, competitive season, or injury time and their associations with several anthropometric indices (mainly absolute-related values such as the sum of skinfolds and skinfold-corrected girths, as well as proportionality indices beyond the muscle-to-bone ratio [please report all possible since you have a good database]). This might add ideas for more reliable metrics and fewer anthropometric data collections in real-world situations for elite athletes. Less is always better for time efficiency and accuracy.

"Relevance and practical applications"
Misleading.

Line 384: Actually not, because closing the gap would require having absolute data or fewer anthropometric variables. This just uses an old approach to evaluate body composition from anthropometry and does not add value to real-world situations or on-field metrics.

Lines 388-390: Unfortunately, the study merely adheres to ISAK recommendations (failing in terminology as well as in the use of absolute data and related indexes). The authors mentioned ISAK certifications, but as instructors, they fail to use rigorous terminology in anthropometry and body composition and also lack a good data analysis approach (technical error of measurement and confidence intervals were not reported—not to mention the overreliance on obsolete and impractical body composition models for elite athletes, such as the 5C). So they are not actually using ISAK standards.

Actually, why do you have a bias toward the muscle-to-bone ratio? If no studies have been conducted in the LFA Mexican population, why do you conclude that this metric is important? Do you have sport performance data to support this in your population? If so, it should be added to expand the analysis. Contrasting or bringing other populations to the context of this question is not valid.

CONCLUSIONS:

Line 402-405: This should be deleted and rewritten. No medical, physical, or sport performance-related variable was measure in this study. So this statement is unrealistic. 

Conflics of interest: 
As Level 3 instructors, there is an inherent conflict of interest regarding the ISAK certifications. In fact, the authors repeatedly encourage this throughout the manuscript, and for transparency, it should be made clear that they receive money for these private courses. 

MINOR ISSUES

Correct typos: 
- Line 234: "Skinl" in Table 2
- Line 276: Residual is repeated in Table 3

Do not use "mean ± standard deviation." Use "Mean (SD)" instead. Cf, PMID 21206631

The symbol of kilogram is "kg" not "Kg"

 

Author Response

Response to Reviewer 2

Dear Reviewer,

We deeply appreciate the time and dedication you have invested in reviewing our manuscript. Your insights have been invaluable in improving the clarity, precision, and methodological rigor of our study. We greatly value your detailed analysis and have incorporated the suggested adjustments into the revised version of the paper.

Below, we provide detailed responses to each of your questions and comments, although the corresponding changes have already been implemented in the revised manuscript.

  General Comments

   Response 2: Dear colleague, the specific nature of the bias was not explicitly mentioned, which would have helped us address your concern more effectively. In this case, the only course of action we could take was to review the data matrix used for our calculations. We identified and corrected some inconsistencies, particularly those related to the anthropometric fractionation of body mass. The revised version now includes the absolute values.

  Additionally, we have reported a significant number of indices that enrich the available information in our study. Furthermore, we have provided relative fit indices for this model in the statistical processing section, which we consider an added value to this research.

3. Response: All issues related to ISAK terminology have been corrected in accordance with the society's official guidelines.

4. Response: Muscle to bone ratio has only been studied through imaging techniques, as seen in the works of Dengel and Evanoff [47] and, more recently, Studee in a master's thesis supervised by Donal R. Dengel at the University of Minnesota. Without making an absolute claim, this may be one of the first studies to address this index, which has been widely discussed in academia and various media outlets by the renowned nutritionist Francis Holway.

 Key Issues

1. Terminology and Concepts: All ISAK terminology and conceptual errors, such as "Lean Mass," have been corrected according to the appropriate terminology.

2. Manuscript Structure: We have utilized both documents, and the STROBE guidelines are cited in the manuscript as part of the methodology to which we align.

12. We appreciate your valuable observations regarding the introduction of the manuscript. Following your suggestions, we have made significant adjustments to improve coherence, justification, and methodological alignment with recognized standards. However, as I pointed out in the previous response, we consider the general information relevant to the study. Below is a detailed list of corrections made, as well as information on how we have incorporated STROBE criteria to improve the study's presentation.

   First, we have enhanced the logical structure of the introduction, ensuring a clear progression between the topics covered. We have removed redundant content, focusing instead on presenting specific background information relevant to the study’s objectives. The introduction now follows a logical sequence: starting with the contextualization of American football's popularity, followed by a review of key research areas, and concluding with the justification for studying Latin American athletes.

   Additionally, we have strengthened the argument regarding the existing knowledge gap concerning Latin American players in professional American football. We emphasize the underrepresentation of these athletes in international leagues and the importance of generating specific anthropometric data for this population. A comparative approach with previous studies on players from other regions has also been included, allowing for better contextualization of the study’s relevance to talent identification and athletic development in Latin America.

   Furthermore, we have refined the formulation of the hypothesis to enhance clarity and precision. It is now explicitly stated that "kinanthropometric traits vary significantly by position, aligning with the specific physical demands of each role." This reformulation eliminates ambiguity and establishes a clearer connection with the study’s objectives, facilitating the interpretation of expected results.

   We are pleased to inform you that we have now aligned the introduction with STROBE recommendations, ensuring that it meets the following criteria:

• Providing a well-supported rationale demonstrating the relevance of the study based on existing evidence.

• Including specific background information and appropriate references essential to supporting the research problem.

• Defining objectives precisely and avoiding vague or overly general formulations.

• Structuring content coherently and progressively while taking care to avoid abrupt thematic shifts.

   These adjustments have significantly improved the study's presentation and its relevance within the field of anthropometry applied to athletic performance.

14. It is requested to justify the use of BMI as a metric and explain its purpose in this study.

  Response: We have justified its use in the discussion section with a dedicated paragraph.

15. There is indeed a discrepancy in the timeline. The data belongs to active LFA players who currently alternate among different teams in the league. These data are part of a doctoral thesis project that originally aimed to conduct two evaluations, which were halted by the COVID-19 pandemic in 2019. Subsequently, the league underwent significant structural modifications.

   We decided to publish this data because it pertains to the same players who now make up four national selections, with only a 13% renewal rate, and ten players from the sample have retired. Moreover, the professional players in the league typically remain for 5 to 7 years, meaning that 80% of them are still active today.

   Additionally, peak athletic performance in the NFL is reached around age 30, making these data highly relevant given the sample’s average age of 28 years. The league has expanded rapidly and exponentially. Independently of the publication process, the data generated from the initial study have been used for the past three years by team medical staff. They rely on six skinfold measurements and position-specific body fat percentage goals to make decisions during preseason and the regular season.

   Most medical staff members also work at lower levels of American football and have been the primary advocates for having this information published, as it represents a genuine need in the sport.

Major Issues

16. The importance of studying LFA players is not sufficiently justified. It is suggested to strengthen the study’s justification in the context of psychological well-being.

   Response: A paragraph addressing this aspect has been added to the introduction. The authors have welcomed this suggestion.

17. The hypothesis is vague and needs greater precision. It is recommended to clearly formulate it, specifying the expected differences between positions.

 Response: In the manuscript revisions, the introduction now emphasizes this aspect. The structure has been adjusted to improve readability and fluency.

Anthropometric Variables Reported

19. We have stated in the text that the method was validated from its inception. The study that introduced this methodology in 1993 clearly demonstrated its validity with Olympic athletes from the MOGAP sample and others. After that, it was validated in approximately six or seven samples in Spain, Cuba, Venezuela, and Chile. We have not cited these studies because the most recent dates back to 2008 with a population of around 1,000 Cuban athletes.

  However, we present the goodness-of-fit values for our own sample in the manuscript. This is precisely why we employed this method, which is widely referenced in Latin America, particularly in Argentina and Mexico. The sum of six skinfolds provides valuable data without the uncertainty associated with other methods, which is why we included it in this study.

20. Proportionality indices (biacromial, Manouvier, locomotor index) are missing. Their inclusion is requested.

   Response: These indices have now been added along with others to further enrich this crucial section in biomechanics.

21. The formulas have not been included, but they are described in detail within the text. The formulas for Ross and Kerr's anthropometric fractionation, as well as the somatotype formula, were not added since they are universally recognized and referenced in most publications.

Statistical Analysis

The statistical analysis has been completely revised according to the reviewer’s suggestions. We acknowledge that the reviewer has made a significant contribution to improving the methodological and analytical quality of the text.

Confidence intervals for mean differences were not reported because tests such as Student’s t-test or the Mann-Whitney U test were not used to compare independent samples. Additionally, reporting full post-hoc test results would be overly extensive, and we believe that the additional explanations included are sufficient.

Results and Discussion

3. Which variables? We have reviewed this information multiple times and, in response, revised the data matrix and corrected errors that have strengthened our new analysis. If the reviewer had identified specific variables, we would have been better able to address the limitations. Nevertheless, we appreciate this observation, as it helped us identify errors in formula input and calculations in the spreadsheet used for fractionation.

4. Response: The graphics have been prepared, but we would need permission for an extension, and the report submission deadline is imminent. We believe that the figures included provide reinforced statistical analysis, but, if necessary, we can make last-minute changes.

5. Response: The discussion has been refined, with improved fluency and depth in our interpretations.

7. Response: Morphological demands is a term introduced by Kevin Norton and Tim Olds in the 1990s for athlete evaluation. This concept is addressed in the section analyzing the added indices.

9. It is recommended to highlight those future studies could evaluate physical and sports performance variables during the preseason, competitive season, or injury period, and their association with anthropometric indices such as skinfold sum and corrected girths, as well as proportionality indices.

   Response: A paragraph regarding future studies has now been included.

10. The Relevance and Practical Applications section is misleading.

   Response: This section has been rewritten, incorporating the latest manuscript adjustments.

12. Response: The necessary corrections have already been made in the manuscript.

13. Response: The significance attributed to muscle to bone ratio has been removed, and it now appears simply as an additional finding that may support sports medicine work in the future within the league.

Conclusions

14. Response: The conclusion has been reformulated, though it is clear that performance was not measured.

15. Response: We do not understand this statement. We do not charge anything for athlete evaluations, as this work in the league is our obligation. The manuscript does not mention courses or payments. That is part of a separate activity conducted in the involved countries, but this work is entirely voluntary as part of a doctoral project, with no financial compensation whatsoever.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

All suggestions and modifications have been addressed. The mansucript meets all the requeriments. However, I would like to recommend a couple of aspects:

• Review grammar and expressions to be more precise.
• Present the Z-Scores as a graph. The visual approach would facilitate the interpretation. There are many the data in the table and it is difficult to interpretate

Anyway, congratulations to the authors

Author Response

I would like to express our sincere gratitude for your thorough review and valuable feedback on our manuscript. Thank you for your time, dedication, and commitment to academic excellence. We hope that the revisions made to the manuscript adequately reflect the growth and refinement achieved, thanks to your evaluation.

Comment: Review grammar and expressions to be more precise. Present the Z-Scores as a graph. The visual approach would facilitate interpretation. There are many data in the table, and it is difficult to interpretate. Anyway, congratulations to the authors.

Response: We have made changes and reviewed grammar and expressions to improve all the manuscript, we ad and changed graphs on this last version. Now the data on the table is easier to interpretate.

Reviewer 2 Report

Comments and Suggestions for Authors

Dear authors "Position-Specific Kinanthropometric Traits of Professional American Football Players: A Study of Mexican LFA Players",

Thank you for your revisions. However, I was unable to clearly identify the changes made. For future submissions, please use tracked changes or highlight modifications in red. Additionally, the manuscript still requires several substantial revisions before it can be considered for publication.

1. STRUCTURE
The authors continue to disregard the STROBE-Nut guidelines, which are essential for ensuring transparency and methodological quality. This is particularly critical in the Methods section, which should follow the structure recommended by STROBE-Nut: i) Study design, ii) Setting, iii) Participants, iv) Variables, v) Data sources/measurement, vi) Bias, vii) Study size; viii) Statistical methods. Please structure your manuscript according to STROBE-Nut (https://www.strobe-statement.org/download/strobe-checklist-cross-sectional-studies-pdf) and MDPI guidelines.

In addition, the authors appear to be confused about the type of study conducted. In line 127 states: "This case study adheres to the STROBE guidelines [35]." This is inaccurate, as the manuscript describes a cross-sectional descriptive study, not a case study. Please revise this statement accordingly and ensure consistency throughout the manuscript.

The Results section should also be reorganized in line with the STROBE-Nut recommendations, using the following structure: i) Participants, ii) Descriptive data, iii) Outcome data, iv) Main results, v) Other analyses. Use the checklist: https://www.strobe-statement.org/download/strobe-checklist-cross-sectional-studies-pdf

2. RATIONALE 
Lines 148–150: The authors state that the 5C model has been validated in athletic populations. In which specific population? Are you referring to Mexican American football players? Additionally, why is the ISAK manual cited here? The 5C model has not been validated in the population studied, and relying solely on this metric may lead to misleading interpretations. Throughout the manuscript, please prioritize reporting absolute values and morphological indices, which offer more robust and interpretable outcomes.

Lines 168–185: This section requires careful revision. There are multiple spacing and grammatical errors. Furthermore, please ensure that you provide the original references for each outcome or metric reported.

Regarding your response: "Muscle-to-bone ratio has only been studied through imaging techniques, as seen in the works of Dengel and Evanoff [47] and, more recently, Studee in a master's thesis supervised by Donal R. Dengel at the University of Minnesota. Without making an absolute claim, this may be one of the first studies to address this index, which has been widely discussed in academia and various media outlets by the renowned nutritionist Francis Holway." — this argument is not valid. First, the muscle-to-bone ratio has not been studied exclusively using imaging techniques. While some recent studies have used DXA, there are previous studies reporting this anthropometric index using traditional anthropometric methods. Second, it is unclear what you mean by “one of the first studies to address this index.” Address in what way? This manuscript does not propose a new conceptual framework nor a proof of concept; it is a cross-sectional study based solely on anthropometric data. Therefore, the rationale is weak—particularly because the study does not incorporate or analyze any sports performance-related outcomes. Third, as mentioned in the previous review, recommending that practitioners, coaches, and nutritionists/dietitians routinely measure more than 20 anthropometric variables is impractical in real-world elite sports settings. This process is time-consuming and requires at least three measurements per variable to minimize technical error. Fourth, although one of the few studies on the muscle-to-bone ratio was published in 2009 by Holway et al. (PMID: 19697229) - an economist (Southern Methodist University, Dallas, Texas) with a Master of Science in Human Nutrition (San Jose State University, California), the authors should carefully read the article. Notably, the study reported no statistically significant differences between positions in the muscle-to-bone ratio (p = 0.060). In conclusion, while this index may hold promise, it must be evaluated alongside sports performance metrics to draw meaningful conclusions regarding its relevance for athletes. This is precisely the value of previous and ongoing research by Dr. Donald Dengel, who has utilized DXA to estimate the muscle-to-bone ratio. However, it is important to also acknowledge that DXA does not directly measure muscle mass, and thus the ratio may require conceptual reformulation (e.g., fat-free mass-to-bone ratio).

3. DATA ANALYSIS
The authors continue to rely on a frequentist approach, which should be reconsidered in the context of current sports science practices. Testing for normality and other assumptions is unnecessary. Given the small sample size (per position), robust statistical methods should be used directly to reduce the risk of Type I error. This section requires revision.

Additionally, why was post hoc statistical power calculated? This was not included in the original manuscript, and the current calculation is incorrect. This not only raises concerns about transparency but also fails to provide meaningful information. All power and sample size calculations should be done before the study is performed. Cf: https://pubmed.ncbi.nlm.nih.gov/35642557/

The symbol of kilogram is "kg" not "Kg". Please do NOT use the capital letter.

Confidence intervals should be reported for both descriptive statistics and group comparisons. Contrary to the authors’ comments, reporting confidence intervals does not require the use of Student’s t-test or the Mann–Whitney U test. Please read: https://pubmed.ncbi.nlm.nih.gov/24220629/; https://doi.org/10.1017/prp.2019.28

Finally, it is essential to change Figures 2, 3, and 5. The current figures do not adequately reflect the sample values, data distribution, or meet acceptable quality standards. Please replace them with estimation graphics that plot mean differences with 95% confidence intervals between positions (no problem with a time extension to complete). Cf: https://journals.sagepub.com/doi/10.1177/0956797613504966; https://pubmed.ncbi.nlm.nih.gov/31217592/ 

4. DISCUSSION
Several statements in the manuscript go beyond the scope of the study’s actual findings. The authors should limit their interpretations accordingly. For example, in lines 452-454, the claim: "These results suggest that sport-specific positional demands are essential in shaping an athlete’s physical profile, with American football favoring greater muscular development for offensive performance," is problematic. Why use “essential,” or refer to the “physical profile,” when no physical or performance-related data were measured?

Lines 455–463: The discussion suggesting that the muscle-to-bone ratio is highly relevant does not align with the current state of the literature. The authors claim that "... the muscle-to-bone ratio remains highly relevant in applied sports sciences, as it helps elucidate the physiological potential and capabilities of football players." However, only about 40 articles on this topic appear in PubMed, many of which involve animal models or non-athlete populations.

- Positional Differences in Muscle-to-bone Ratio in National Football League Players https://pubmed.ncbi.nlm.nih.gov/37160263/ 

- Muscle-to-Bone Ratio in NCAA Division I Collegiate Football Players by Position
https://pubmed.ncbi.nlm.nih.gov/38968202/ (this study specifically included American football players, so please consider specifying this in your manuscript).

- Muscle-to-bone and soft tissue-to-bone ratios in track and field athletes https://pubmed.ncbi.nlm.nih.gov/39904352/

Moreover, the muscle-to-bone ratio studied in practice is typically derived from DXA measurements. As the authors should be aware, the five anthropometric components are not interchangeable with DXA-derived values; therefore, the methods produce fundamentally different outputs and should not be treated as equivalent. Thus, clinical and sports-related research using the muscle-to-bone ratio derived from anthropometry is very limited, which may contribute to data bias in the authors’ claims.

--> For transparency, the following should be reported in the "limitations and future directions" section:

"There is indeed a discrepancy in the timeline. The data belongs to active LFA players who currently alternate among different teams in the league. These data are part of a doctoral thesis project that originally aimed to conduct two evaluations, which were halted by the COVID-19 pandemic in 2019. Subsequently, the league underwent significant structural modifications.

We decided to publish this data because it pertains to the same players who now make up four national selections, with only a 13% renewal rate, and ten players from the sample have retired. Moreover, the professional players in the league typically remain for 5 to 7 years, meaning that 80% of them are still active today.

  Additionally, peak athletic performance in the NFL is reached around age 30, making these data highly relevant given the sample’s average age of 28 years. The league has expanded rapidly and exponentially. Independently of the publication process, the data generated from the initial study have been used for the past three years by team medical staff. They rely on six skinfold measurements and position-specific body fat percentage goals to make decisions during preseason and the regular season."

 

CONCLUSIONS:
Well done on improving the conclusion in this revised version.

CONFLICTS OF INTEREST also encompass overt and direct personal or financial benefits derived from professional roles related to the study. In this case, the authors should disclose their involvement as anthropometry instructors. 
In agreement with Annae et al. (2019): 
"Institutions often consider that there is a risk of academic COI when their personnel have outside activities, such as membership of another entity whether academic or not, or conducting research, educational courses, or expertise for a third party. Such outside academic activities may raise legal concerns, e.g., when they are conducted with the institution’s resources, facilities, or personnel, or when confidential information are disclosed to third party." Cf, Academic conflict of interest: https://pubmed.ncbi.nlm.nih.gov/30426140/ 

 

Author Response

I would like to express our sincere gratitude for your thorough review and valuable feedback on our manuscript. We recognize that the process has been challenging, particularly due to the discrepancies between the reviewers’ recommendations. Striking a balance between differing perspectives has required considerable effort, but it has been a learning opportunity that has strengthened our work.

I especially appreciate the materials and suggestions you provided to enhance the quality of the article. Your guidance has been instrumental not only in refining the structure and precision of the study, but also in elevating its methodological and argumentative rigor. It has prompted us to reflect deeply on each revision and to strive to meet the high standards of the review process.

Thank you for your time, dedication, and commitment to academic excellence. We hope that the revisions made to the manuscript adequately reflect the growth and refinement achieved thanks to your evaluation.

Comment: Thank you for your revisions. However, I was unable to clearly identify the changes made. For future submissions, please use tracked changes or highlight modifications in red. Additionally, the manuscript still requires several substantial revisions before it can be considered for publication.

Response: We have made changes throughout the document highlighting modifications in yellow for ease of identification.

1. STRUCTURE

The authors continue to disregard the STROBE-Nut guidelines, which are essential for ensuring transparency and methodological quality. This is particularly critical in the Methods section, which should follow the structure recommended by STROBE-Nut: i) Study design, ii) Setting, iii) Participants, iv) Variables, v) Data sources/measurement, vi) Bias, vii) Study size; viii) Statistical methods. Please structure your manuscript according to STROBE- Nut (https://www.strobe-statement.org/download/strobe-checklist- cross-sectional-studies-pdf) and MDPI guidelines.

Response: We have followed the author guidelines provided by the journal, which reflect widely accepted standards in scientific writing. As authors, we are committed to adhering to the journal’s formatting and structural requirements. In this version we refined the manuscript structure in accordance with your recommendation by STROBE-Nut  checklist.

Comment: In addition, the authors appear to be confused about the type of study conducted. In line 127 states: "This case study adheres to the STROBE guidelines [35]." This is inaccurate, as the manuscript describes a cross-sectional descriptive study, not a case study. The Results section should also be reorganized in line with the STROBE-Nut recommendations, using the following structure: i) Participants, ii) Descriptive data, iii) Outcome data, iv) Main results, v) Other analyses. Use the checklist: https://www.strobe- statement.org/download/strobe-checklist-cross-sectional-studies- pdf

Please revise this statement accordingly and ensure consistency throughout the manuscript.

Response: Thank you for your valuable feedback. We would like to clarify that the study is a cross-sectional descriptive observational study, and not a case study. We acknowledge the mislabeling and we have clearly stated in the revised version to accurately reflect the study design.

We also confirm that the study was conducted and reported in accordance with the STROBE guidelines (Strengthening the Reporting of Observational Studies in Epidemiology), which are a set of 22 recommendations aimed at improving the transparency and quality of reporting in observational research. These guidelines help ensure that readers can clearly understand the study’s design, execution, and findings.

In addition, we have followed the journal’s author guidelines, which provide a widely accepted framework for scientific writing. We remain fully open to refining the manuscript structure further, in line with the Editor-in-Chief’s recommendations, to ensure clarity and compliance.

Comment: RATIONALE

Lines 148–150: The authors state that the 5C model has been validated in athletic populations. In which specific population? Are you referring to Mexican American football players? Additionally, why is the ISAK manual cited here? The 5C model has not been validated in the population studied, and relying solely on this metric.

Response: Regarding the validity of the anthropometric fractionation of body mass, the method has been validated in multiple international samples since its inception. In the original publication, which is cited in this article, the model was shown to accurately calculate total body mass across 11 samples selected to represent human variability. All correlations between predicted body mass (from the sum of the five fractions) and actual measured mass exceeded 0.93, except in the case of lightweight rowers, where reduced variance around the mean was observed (nonetheless, it is worth noting that this sample also had the lowest standard error for estimated body weight). Subsequently, the method was validated in Spanish athletic populations by Casajus et al. (1993), as well as in Cuban and Chilean athletic populations by Gurovich et al. (1995). It is true that the number of publications using this method as a reference is limited; perhaps fewer than 30 according to previous reviews by one of the authors. However, it remains one of the most widely used methods in Latin America, and it is promoted by the International Society for the Advancement of Kinanthropometry (ISAK) as part of its certification processes. Furthermore, the method has been employed by the LFA for body composition assessments over the past seven years. In this study, we have reported relevant statistical data to support its use. Given the absence of previous validation studies specifically focused on this population, as well as the lack of such data in the study by Holway and Garavaglia (2009) on rugby players, a sport with similar demands. This highlights the relevance of our study in contributing new and important data for Mexican athletes.

While the method has its critics, it is also noteworthy that recent studies on American football players have used bioelectrical impedance analyzers that have not been validated for this specific population in the United States (e.g., Mokha et al., 2024). As this special issue of the journal focuses on anthropometry, we considered it appropriate to present this integrative approach, not placing fractionation at the core of the article, but rather promoting its use in this sport for the first time in the international literature.

Regarding the citation of the ISAK manual, it was included to support the need for standardized anthropometric measurement methods to ensure methodological consistency and reliability. Nevertheless, we have corrected the citation, replacing reference number 37 with 38, which also refers to Ross and Kerr (1993). We have made improvements to the highlighted lines in the manuscript to address your concerns.

Comment: Lines 168–185: This section requires careful revision. There are multiple spacing and grammatical errors. Furthermore, please ensure that you provide the original references for each outcome or metric reported.

Response:  Many of the indices referenced in the paragraph originate from the early 20th century, such as the Cormic Index by Giuffridda Ruggeri (1907) or the Manouvrier skeletal index (1902). Our intention was not to rely on the classifications associated with these indices, as they were developed based on populations from over a century ago, whose physical characteristics differ greatly from contemporary populations—and even more so from athletic populations, as evidenced in Martin and Saller (Lehrbuch der Anthropologie in systematischer Darstellung. Stuttgart: G. Fischer Verlag, 1957).

We referred to the literature “Analysis of Proportionality Through Anthropometric Assessment” and “The Locomotive Index, an Anthropometric Perspective of Biomechanical Efficiency” because these are the sources that highlight the functional and structural utility of each index. Norton et al. (1996) also address the use of anthropometric indices and their relationship to sports performance, though their work is somewhat dated. Previous studies, such as Alacid et al. (2014) in canoe-kayak athletes, have cited Pacheco del Cerro (1993); however, this author uses cutoff points based on anthropometric data from over a century ago, which may be influenced by secular trends.

In summary, while we value the reviewer’s observation, we face a dilemma: whether to simply describe these indices as some authors do without citing the original sources, or to follow the common but problematic practice in the literature where many authors use these metrics without referencing their origin (e.g., Saco-Ledo et al., “Body proportions according to stature groups in elite athletes”. Res Sports Med. 2022 Sep-Oct), or cite secondary sources such as Norton et al. (1996). Given this dilemma, we have decided to include a reference to Pacheco del Cerro, who has recommended the use of these metrics in athletic populations. However, in our case, we have chosen not to adopt the classifications of Martin and Saller, as we consider them outdated and contextually inappropriate.

Regarding the grammatical issues, we have revised the text to address the comments raised, and these concerns have been resolved in the updated version of the manuscript.

Comment: Regarding your response: "Muscle-to-bone ratio has only been studied through imaging techniques, as seen in the works of Dengel and Evanoff [47] and, more recently, Studee in a master's thesis supervised by Donal R. Dengel at the University of Minnesota. Without making an absolute claim, this may be one of the first studies to address this index, which has been widely discussed in academia and various media outlets by the renowned nutritionist Francis Holway." — this argument is not valid. First, the muscle-to-bone ratio has not been studied exclusively using imaging techniques. While some recent studies have used DXA, there are previous studies reporting this anthropometric index using traditional anthropometric methods. Second, it is unclear what you mean by “one of the first studies to address this index.” Address in what way? This manuscript does not propose a new conceptual framework nor a proof of concept; it is a cross-sectional study based solely on anthropometric data. Therefore, the rationale is weak—particularly because the study does not incorporate or analyze any sports performance-related outcomes. Third, as mentioned in the previous review, recommending that practitioners, coaches, and nutritionists/dietitians routinely measure more than 20 anthropometric variables is impractical in real-world elite sports settings. This process is time-consuming and requires at least three measurements per variable to minimize technical error. Fourth, although one of the few studies on the muscle-to-bone ratio was published in 2009 by Holway et al. (PMID: 19697229) - an economist (Southern Methodist University, Dallas, Texas) with a Master of Science in Human Nutrition (San Jose State University, California), the authors should carefully read the article. Notably, the study reported no statistically significant differences between positions in the muscle-to-bone ratio (p = 0.060). In conclusion, while this index may hold promise, it must be evaluated alongside sports performance metrics to draw meaningful conclusions regarding its relevance for athletes. This is precisely the value of previous and ongoing research by Dr. Donald Dengel, who has utilized DXA to estimate the muscle-to-bone ratio. However, it is important to also acknowledge that DXA does not directly measure muscle mass, and thus the ratio may require conceptual reformulation (e.g., fat-free mass-to-bone ratio).

Response: 

1.    Use of ISAK and anthropometric methods: The use of ISAK-standardized anthropometric techniques was a deliberate methodological choice made from the outset of the study. These methods are widely recognized for their practicality, cost-effectiveness, and reliability in field-based assessments—particularly in contexts where access to imaging technologies like DXA is limited. While we acknowledge the value of DXA in body composition research, it is important to note that it is not always feasible in real-world sports environments due to cost, availability, and logistical constraints. ISAK methods, on the other hand, allow for standardized, reproducible measurements that are suitable for large-scale assessments and have been validated in athletic populations. This approach aligns with the study’s goal of providing accessible and applicable data for practitioners working in similar settings. We are not recommending or suggesting directly in any part of the manuscript take more than 20 measures, a restricted or complete ISAK profile. Our propose is based on share specific data on this sport using Kinanthropometry and respond to this journal special issue information “In the sports field, kinanthropometry is essential in describing and quantifying athletes, physical characteristics to obtain information on body composition, somatotype, and proportionality. It allows for the evaluation of morphological traits and their monitoring during the competitive season.” The reader can decide how many or what specific measures will take for practical interventions or design future studies focused on new interests. That’s the same reason we have decided to add indices after your first valuable feedback. With this information they can decide to use 2 or 4 measures to understand better some physical characteristics per position, or six skinfold sum for monitoring.

2.    Clarification on muscle-to-bone ratio: We appreciate the clarification regarding the prior use of anthropometric methods to estimate the muscle-to-bone ratio. Our intention was not to claim novelty in the concept itself, but rather to highlight the limited application of this index in Mexican athletic populations. We will revise the manuscript to clarify this point and avoid overstating the originality of the metric.

3.    Sample size and relevance: The athletes included in this study were part of a selective national-level team in a non-mainstream sport in Mexico. Given the scarcity of published data on this population, we believe this study provides valuable baseline information. To our knowledge, this is the first study in the country to report detailed anthropometric and proportionality data for this group at professional level, which we hope will serve as a reference for future research and applied practice in sports science and athlete development in Mexico.

4.    On the preference for DXA: While we recognize the scientific rigor of DXA-based studies, we respectfully note that an overreliance on this technique may overlook the value of validated, field-friendly methods like standardized anthropometric measures in underrepresented or resource-limited contexts. Our study aims to contribute meaningful data using accessible tools that can be realistically implemented by coaches, nutritionists, and sports scientists working directly with professional athletes or lower levels.

5.    In any case, in the revised version of the manuscript, we decided to report only the index values, as other authors have done. We avoided delving into the comparisons with Dengel, which were initially included only to highlight a similar approach. Nonetheless, we are well aware of Dengel’s arguments regarding the need for more refined studies to assess the applicability of the muscle-bone index proposed by Francis Holway in the context of sports performance.

3. DATA ANALYSIS

Comment:
The authors continue to rely on a frequentist approach, which should be reconsidered in the context of current sports science practices. Testing for normality and other assumptions is unnecessary. Given the small sample size (per position), robust statistical methods should be used directly to reduce the risk of Type I error. This section requires revision.

Response: Thank you very much for your thoughtful comments. As you rightly point out, there are simpler or more robust ways to achieve the desired outcomes without requiring all variables to meet the assumptions of normality and homogeneity of variance. However, after reviewing a series of similar studies, the authors considered that the use of such tests is not inappropriate, especially when certain variables do meet the necessary assumptions while others do not.

In the second version of this manuscript, we have formalized the statistical analysis, which was not explicitly done in the original submission. We now clarify the steps taken depending on whether variables met the assumptions for applying parametric or non-parametric tests, as suggested by Reviewer 1. We also included the calculation of statistical power and modified the approach to post hoc testing, again based on Reviewer 1’s recommendations.

Other authors, such as Gualdi-Russo and Zaccagni, have previously used post hoc tests like Tukey’s in anthropometric studies of volleyball players from Italy’s A1 and A2 leagues when comparing playing positions (Gualdi-Russo et al., “Somatotype, role and performance in volleyball players”. J Sports Med Phys Fitness. 2001 Jun), which initially led us to believe that our design was acceptable. Carvajal et al. (2012) also used a similar methodology in their comparison of Olympic champion female volleyball players.

Nonetheless, in this new version, we have followed your suggestions: we removed the post hoc tests and instead included confidence intervals for variables with normal distributions, and Bootstrap intervals for those that did not meet normality assumptions, but only for those variables analyzed using ANOVA.

As previously mentioned in our response, we initially included post hoc tests based on the suggestion of Reviewer 1. However, after carefully considering your recommendations, we now believe your approach is more valid, as it challenges traditional methods historically used in studies of this type. In high performance sports research, as you suggested, it is indeed preferable to rely on more robust statistical techniques.

On another note, we decided not to include confidence intervals for all variables, as you proposed. While we agree that confidence intervals are essential for both descriptive and comparative statistics, regardless of the test applied, inclusion in the tables could confuse readers. Considering the target audience of this manuscript, we opted to simplify the tables as much as possible. Including confidence intervals for all variables would result in an overly lengthy dataset and may reduce the clarity and objectivity of the work. Additionally, a significant number of these variables do not follow a normal distribution, which could lead to misinterpretation.

We also chose not to include confidence intervals for the mean differences between positions, as this would again result in an excessive amount of information that may be difficult to interpret and of limited utility. We have, however, generated a supplementary appendix of nearly 20 pages with these values, which were obtained following ANOVA and other tests. Given that there are seven playing positions and at least 17 dependent variables with significant differences, including all this data in the main text would be impractical.

In practice, studies of this nature more commonly report means and standard deviations, as seen in other works published in this journal ncluding recent articles in this special issue on advances in kinanthropometry. Our intention has been to respond to the comments of both reviewers while maintaining the objectivity and clarity of the study.

To summarize, in the current version of the manuscript, we have included Table 3, which reflects your recommendations, and we have also added several summary figures.

Comment: Additionally, why was post hoc statistical power calculated? This was not included in the original manuscript, and the current calculation is incorrect. This not only raises concerns about transparency but also fails to provide meaningful information. All power and sample size calculations should be done before the study is performed. Cf: https://pubmed.ncbi.nlm.nih.gov/35642557/

Response: We appreciate Reviewer 1’s recommendation regarding the analysis of statistical power and have taken the suggestion into account to assess the sensitivity of our results. While we acknowledge that a priori power calculation is the gold standard for study design, we believe that post hoc power analysis provides complementary information about the sample's ability to detect significant effects.

Nonetheless, in this revised version, we have incorporated your suggestion to include confidence intervals only for the variables that were statistically compared. We are also aware that the use of confidence intervals is becoming increasingly common in discussions of group differences (Winkler et al., 2024: “Calculation of Statistical Power and Sample Size”).

Comment: The symbol of kilogram is "kg" not "Kg". Please do NOT use the capital letter.

Response: We sincerely appreciate your observation. We have made several replacements of this term in the revised version. A few instances had been previously overlooked, but they have now been addressed.

Comment: Confidence intervals should be reported for both descriptive statistics and group comparisons. Contrary to the authors’ comments, reporting confidence intervals does not require the use of Student’s t-test or the Mann–Whitney U test. Please read: https://pubmed.ncbi.nlm.nih.gov/24220629/; https://doi.org/10.1017/prp.2019.28

Response: This comment was addressed previously. We also read and greatly appreciated your contribution, which allowed us to engage with the article by Lyu et al. (2020).

Comment: Finally, it is essential to change Figures 2, 3, and 5. The current figures do not adequately reflect the sample values, data distribution, or meet acceptable quality standards. Please replace them with estimation graphics that plot mean differences with 95% confidence intervals between positions (no problem with a time extension to complete). Cf: https://journals.sagepub.com/doi/10.1177/0956797613504966; https://pubmed.ncbi.nlm.nih.gov/31217592/ 

Response: This point was taken into account and previously explained. Figures 2, 3, and 5 have been replaced with graphs that include confidence intervals to enhance reader understanding.

4. DISCUSSION

Comment: Several statements in the manuscript go beyond the scope of the study’s actual findings. The authors should limit their interpretations accordingly. For example, in lines 452-454, the claim: "These results suggest that sport-specific positional demands are essential in shaping an athlete’s physical profile, with American football favoring greater muscular development for offensive performance," is problematic. Why use “essential,” or refer to the “physical profile,” when no physical or performance-related data were measured?

Response:  We appreciate the detailed review and fully recognize the need to clarify our statements to avoid interpretations that go beyond the study’s findings. In the indicated section, we have revised the wording to more accurately reflect the anthropometric data analyzed, removing terms such as “essential” and “physical profile,” since no direct performance measures were assessed. The revised statement now refers solely to differences in body composition between playing positions, without extrapolating functional or causal effects.

Suggested reformulation: These results indicate that differences in body composition exist between positions in American football, reflecting morphological trends that may be associated with the positional demands of the sport. However, the study did not directly assess athletic performance or its relationship to these characteristics.

In this way, we avoid making causal claims and focus on the actual findings, while introducing a nuanced suggestion of possible associations without asserting a direct causal link.

 

Comment: Lines 455–463: The discussion suggesting that the muscle-to-bone ratio is highly relevant does not align with the current state of the literature. The authors claim that "... the muscle-to-bone ratio remains highly relevant in applied sports sciences, as it helps elucidate the physiological potential and capabilities of football players." However, only about 40 articles on this topic appear in PubMed, many of which involve animal models or non-athlete populations.

- Positional Differences in Muscle-to-bone Ratio in National Football League Players https://pubmed.ncbi.nlm.nih.gov/37160263/ 

- Muscle-to-Bone Ratio in NCAA Division I Collegiate Football Players by Position
https://pubmed.ncbi.nlm.nih.gov/38968202/ (this study specifically included American football players, so please consider specifying this in your manuscript).

- Muscle-to-bone and soft tissue-to-bone ratios in track and field athletes https://pubmed.ncbi.nlm.nih.gov/39904352/

Moreover, the muscle-to-bone ratio studied in practice is typically derived from DXA measurements. As the authors should be aware, the five anthropometric components are not interchangeable with DXA-derived values; therefore, the methods produce fundamentally different outputs and should not be treated as equivalent. Thus, clinical and sports-related research using the muscle-to-bone ratio derived from anthropometry is very limited, which may contribute to data bias in the authors’ claims.

Response: We appreciate the observation and acknowledge the importance of better contextualizing the relevance of the muscle-bone ratio within the field of sports science. We have revised the section and adjusted the wording to avoid broad claims about its impact on athletic performance in American football players, as the volume of studies involving athletic populations remains limited. Revised from 495-518

Comment:  For transparency, the following should be reported in the "limitations and future directions" section:

"There is indeed a discrepancy in the timeline. The data belongs to active LFA players who currently alternate among different teams in the league. These data are part of a doctoral thesis project that originally aimed to conduct two evaluations, which were halted by the COVID-19 pandemic in 2019. Subsequently, the league underwent significant structural modifications.

We decided to publish this data because it pertains to the same players who now make up four national selections, with only a 13% renewal rate, and ten players from the sample have retired. Moreover, the professional players in the league typically remain for 5 to 7 years, meaning that 80% of them are still active today.

  Additionally, peak athletic performance in the NFL is reached around age 30, making these data highly relevant given the sample’s average age of 28 years. The league has expanded rapidly and exponentially. Independently of the publication process, the data generated from the initial study have been used for the past three years by team medical staff. They rely on six skinfold measurements and position-specific body fat percentage goals to make decisions during preseason and the regular season."

Response: We appreciate the observation. This has been taken and corrected in the current version, specifically in lines 619–634.

 

Comment: CONFLICTS OF INTEREST also encompass overt and direct personal or financial benefits derived from professional roles related to the study. In this case, the authors should disclose their involvement as anthropometry instructors. 
In agreement with Annae et al. (2019): 
"Institutions often consider that there is a risk of academic COI when their personnel have outside activities, such as membership of another entity whether academic or not, or conducting research, educational courses, or expertise for a third party. Such outside academic activities may raise legal concerns, e.g., when they are conducted with the institution’s resources, facilities, or personnel, or when confidential information are disclosed to third party." Cf, Academic conflict of interest: https://pubmed.ncbi.nlm.nih.gov/30426140/ 

Response: In these aspects we have referred that: The authors declare that their affiliation with the International Society for the Advancement in Kinanthropometry (ISAK) and their role as medical director of the LFA, as well as other academic positions, have not influenced the study design, data collection, or result interpretation. The research was conducted independently, adhering to ethical principles of informed consent and without the use of institutional resources for personal benefit. There are no financial or academic conflicts of interest related to this study.  

Author Response File: Author Response.pdf