The “Hidden Hunger” Paradox Amidst a High-Energy Diet: A Cross-Sectional Assessment of an Adult Cohort Evaluated via a Professional Digital Dietary Tool in Russia

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript addresses a relevant and timely topic: the coexistence of high energy intake, obesity, and inadequate micronutrient intake, often described as “hidden hunger.” The topic is important for public health, preventive medicine, nutrition science, and digital dietary assessment.

The article is clearly structured and based on a relatively large sample of 3267 adults. A notable strength is the use of a digital dietary assessment platform that accounts for nutrient losses during food preparation and cooking. This is potentially valuable, as standard dietary assessment methods often overestimate actual nutrient intake.

However, several important methodological and interpretative issues should be addressed before the manuscript can be considered for publication.

 

1. The study population consists of users of a digital dietary tracking platform, with a strong predominance of women. The authors correctly state that the cohort is not representative of the Russian adult population, but this limitation needs to be reflected more strongly throughout the manuscript. The conclusions should avoid language suggesting that the results describe the adult Russian population in general. A more accurate framing would be: “adult users of a Russian digital dietary assessment platform” rather than: “the adult Russian population.” This issue also affects the title, abstract, discussion, and conclusions.

2. The manuscript frequently uses the term “deficiency.” However, the study appears to assess nutrient intake from dietary records, not biochemical or clinical deficiency. For many nutrients, especially vitamin D, iron, iodine, folate, and omega-3 fatty acids, clinical deficiency cannot be confirmed without biomarkers or clinical assessment. For example, low dietary vitamin D intake does not necessarily equal vitamin D deficiency, because vitamin D status also depends on sun exposure, supplementation, skin pigmentation, season, geography, and other factors. The manuscript should consistently distinguish between: inadequate dietary intake, estimated nutritional inadequacy, biochemical deficiency, clinical deficiency.

3. The authors define deficiency as intake more than 10% below the individual requirement. This threshold may be too strict and may inflate the prevalence of inadequacy, especially when based on short 3–7-day dietary records. The manuscript should justify why a deviation of more than 10% below requirement was selected.

4. The digital platform is central to the manuscript’s novelty. However, the paper does not provide enough evidence that NIAP has been validated against established reference methods, such as weighed food records, repeated 24-hour recalls, biomarkers, or doubly labeled water for energy intake.
5. The manuscript reports that 41.0% of participants showed signs of energy under-reporting. This is a major limitation, particularly in a study examining obesity and dietary intake. The explanation that many participants may have been intentionally following a hypocaloric diet is plausible, but it remains speculative unless the dataset includes information on dieting intention or weight-loss treatment.
6. The study is cross-sectional, so it cannot determine whether poor diet quality led to obesity, whether obesity influenced dietary reporting, or whether participants with obesity changed their diet because of counseling or weight-loss attempts. Some parts of the discussion imply causality, for example that nutrient-poor diets “drive” obesity or “create” the obesity-hidden hunger cycle. These statements should be softened.

Author Response

Response to Reviewer 1 Comments

Dear Reviewer 1,

We would like to express our sincere gratitude for your thorough evaluation and highly constructive feedback. We deeply appreciate the time and effort you dedicated to reviewing our manuscript. Your insights have been instrumental in significantly improving the methodological clarity and terminology of our paper. Below, we provide a detailed, point-by-point response to each of your comments. All modifications in the revised manuscript have been highlighted in yellow for your convenience.

 

Comment 1: The study population consists of users of a digital dietary tracking platform, with a strong predominance of women. The authors correctly state that the cohort is not representative of the Russian adult population, but this limitation needs to be reflected more strongly throughout the manuscript. The conclusions should avoid language suggesting that the results describe the adult Russian population in general. A more accurate framing would be: “adult users of a Russian digital dietary assessment platform” rather than: “the adult Russian population.” This issue also affects the title, abstract, discussion, and conclusions.

Response 1: We fully agree with this important observation. To accurately reflect the specific nature of our cohort and avoid overgeneralization, we have revised the title, abstract, discussion, and conclusions.

Action taken:

• The Titlehas been changed to: “The ‘Hidden Hunger’ Paradox Amidst a High-Energy Diet: A Cross-Sectional Assessment of an Adult Cohort Evaluated via a Professional Digital Dietary Tool in Russia.”
• In the Abstractand Conclusions (Section 5), we specified that the findings apply to "the evaluated cohort" rather than the general population.

 

Comment 2: The manuscript frequently uses the term “deficiency.” However, the study appears to assess nutrient intake from dietary records, not biochemical or clinical deficiency. For many nutrients, especially vitamin D, iron, iodine, folate, and omega-3 fatty acids, clinical deficiency cannot be confirmed without biomarkers or clinical assessment. For example, low dietary vitamin D intake does not necessarily equal vitamin D deficiency, because vitamin D status also depends on sun exposure, supplementation, skin pigmentation, season, geography, and other factors. The manuscript should consistently distinguish between: inadequate dietary intake, estimated nutritional inadequacy, biochemical deficiency, clinical deficiency.

Response 2: We thank the reviewer for pointing out this critical terminological distinction. We entirely agree that dietary records can only reflect intake shortfalls rather than true clinical or biochemical deficiencies.

Action taken: We have conducted a comprehensive terminology review throughout the manuscript. The term "deficiency" has been systematically replaced with "dietary inadequacy" or "intake shortfall" when describing our findings (specifically in the Abstract, Methods Section 2.3, Results Sections 3.3 and 3.5, and Discussion). The term "deficiency" was retained only in the Introduction when discussing the global, systemic concept of "hidden hunger" based on existing literature.

 

Comment 3: The authors define deficiency as intake more than 10% below the individual requirement. This threshold may be too strict and may inflate the prevalence of inadequacy, especially when based on short 3-7-day dietary records. The manuscript should justify why a deviation of more than 10% below requirement was selected.

Response 3: We appreciate this methodological question. The 10% threshold was introduced precisely as a buffer to mitigate the margin of error caused by normal day-to-day dietary fluctuations. Moreover, our data show that the observed inadequacies were not marginal (bordering the 10% mark) but profoundly deep, proving that the high prevalence is driven by severe systemic shortfalls rather than the strictness of the threshold.

Action taken: We have added a detailed justification in Section 2.3 (Anthropometric and Nutritional Definitions): "While a 10% threshold may appear conservative for short-term dietary records, it is a standard tolerance margin in clinical dietetics. More importantly, the actual mean deviations observed in our cohort were profoundly larger (e.g., -77.3% for vitamin D, -46.2% for folates), indicating that the high prevalence of inadequacy in this study is driven by severe systemic shortfalls rather than marginal threshold effects."

 

Comment 4: The digital platform is central to the manuscript’s novelty. However, the paper does not provide enough evidence that NIAP has been validated against established reference methods, such as weighed food records, repeated 24-hour recalls, biomarkers, or doubly labeled water for energy intake.

Response 4: We thank the reviewer for requesting this clarification. As a clinical decision support system (CDSS), the NIAP platform is a reference-analytical tool. According to Russian national regulatory guidelines, its validation does not require prospective biomarker trials. Instead, its accuracy is guaranteed by strict algorithmic adherence to state guidelines for nutritional calculations and verified food composition databases (which incorporate standard retention factors).

Action taken: We have clarified the validation status in Section 2.2 (Digital Platform and Dietary Assessment): "As a clinical decision support system (CDSS), the NIAP platform functions as a reference-analytical tool. In accordance with national regulatory guidelines, its safety and algorithmic accuracy are verified not through prospective clinical trials (e.g., doubly labeled water or biomarker validation), but through strict adherence to the approved state guidelines for nutritional calculations (MR 2.3.1.0253-21) and the integration of verified food composition databases."

 

Comment 5: The manuscript reports that 41.0% of participants showed signs of energy under-reporting. This is a major limitation, particularly in a study examining obesity and dietary intake. The explanation that many participants may have been intentionally following a hypocaloric diet is plausible, but it remains speculative unless the dataset includes information on dieting intention or weight-loss treatment.

Response 5: We fully acknowledge that self-reported data are inherently susceptible to reporting bias, and the 41.0% under-reporting rate is a substantial limitation. To rigorously address this and eliminate speculation, we performed a new Sensitivity Analysis, excluding all 1,339 under-reporters. The results showed that the critical dietary inadequacies remained virtually unchanged (e.g., vitamin D inadequacy was 95.9% in the validated sub-cohort vs. 96.7% in the full cohort).

Action taken:

• We have explicitly acknowledged the susceptibility to cognitive and reporting biases in Section 4.5 (Study Limitations).
• We added the results of the comprehensive Sensitivity Analysis to Section 4.5, demonstrating that the "hidden hunger" paradox persists strongly even in the fully energy-sufficient, validated sub-cohort (n= 1928). This crucial addition was also highlighted in the Abstract.

 

Comment 6: The study is cross-sectional, so it cannot determine whether poor diet quality led to obesity, whether obesity influenced dietary reporting, or whether participants with obesity changed their diet because of counseling or weight-loss attempts. Some parts of the discussion imply causality, for example that nutrient-poor diets “drive” obesity or “create” the obesity-hidden hunger cycle. These statements should be softened.

Response 6: We agree with the reviewer. Cross-sectional data preclude causal inferences. We have carefully reviewed the manuscript to ensure that all causal language has been softened to reflect associations rather than causation.

Action taken:

• In Section 4.3, causal phrases were replaced with more cautious terminology (e.g., changed to "may be closely linked", "potentially reinforcing").
• In Section 4.5 (Study Limitations), we significantly expanded the paragraph discussing the cross-sectional design, explicitly stating: "we cannot definitively determine whether poor diet quality preceded and contributed to the development of obesity, or whether obesity itself influenced dietary reporting. Furthermore, individuals with obesity may have already modified their dietary patterns due to previous nutritional counseling..."

Reviewer 2 Report

Comments and Suggestions for Authors

This paper, entitled "The 'Hidden Hunger' Paradox Amidst a High-Energy Diet", investigates the prevalence of micronutrient deficiencies in the adult Russian population, using the NIAP digital platform for precise estimation of food intake with heat treatment losses taken into account. 
However, the paper should definitely be supplemented in the following segments
- The sample is strongly skewed towards women (almost 3/4), which the authors acknowledge as a limitation, however, needs to be addressed more clearly. The results in Table 2 compare individuals with normal BMI and obesity, but it would be useful to present these data stratified by gender to see if there are significant differences in intake patterns between men and women within these categories.
- The problem of under-reporting is also present. As many as 41.0% of participants showed signs of energy underreporting. Although the authors suggest that this could be due to intentional hypocaloric diets in obese individuals, it is therefore recommended to conduct a sensitivity analysis that would exclude these participants to confirm whether the rates of micronutrient deficiencies change significantly.
What is shown in Table 2 - a larger amount resulted in meals with more energy and all observed parameters (except omega-3 and dietary fiber) - expectedly - the same would happen if comparing the values ​​of those whose BMI is 18.5-19 with 24-25 kg/m2)
- The heatmap correlation (Figure 2) is informative, but could be further refined by displaying correlations specifically for the obesity subgroup in order to more accurately identify the drivers of nutritional imbalance in this risk group.
- The authors correctly state that the cross-sectional design does not allow for the establishment of cause-and-effect relationships. The discussion could be extended by theoretical consideration of how micronutrient deficits (such as magnesium or chromium, if measured) can feed back on hunger and metabolic resistance, thus closing the obesity cycle. Therefore,
although the role of digitization is emphasized, the discussion would benefit from more specific guidelines for clinical practice – eg, how to integrate the NIAP platform into routine preventive examinations outside the research context.
- Since some elements (fluorine, bromine) are excluded due to dependence on regional waters, it would be useful to briefly discuss the potential influence of the geographical location of the participants within Russia on the obtained results, if such data are available.

Sincerely

Author Response

Response to Reviewer 2 Comments

Dear Reviewer 2,

We deeply appreciate your insightful comments and excellent methodological recommendations. Your suggestions, particularly regarding the sensitivity analysis and subgroup visualizations, have added significant clinical and scientific value to our analysis. Below, we provide a detailed, point-by-point response to each of your comments. All modifications in the revised manuscript have been highlighted in yellow for your convenience.

 

Comment 1: The sample is strongly skewed towards women (almost 3/4), which the authors acknowledge as a limitation, however, needs to be addressed more clearly. The results in Table 2 compare individuals with normal BMI and obesity, but it would be useful to present these data stratified by gender to see if there are significant differences in intake patterns between men and women within these categories.

Response 1: We thank the reviewer for this highly relevant suggestion. To provide a clearer picture of sex-specific dietary patterns, we have conducted an additional analysis comparing male and female participants.

Action taken: We have added a new Supplementary Table S1 displaying the comprehensive dietary intake comparison between males and females. Furthermore, we added a new paragraph in Section 3.2 (Macronutrient Profile and Energy Imbalance) discussing these findings. While we observed that males consumed significantly more absolute calories and macronutrients, the overarching trend of critical micronutrient inadequacy alongside an energy-dense dietary pattern remained consistent across both sexes. We opted not to conduct a 4-way stratification (Sex × BMI categories) due to the relatively small sample size of males with obesity (n = 140), which could underpower the statistical comparisons, but the general sex-stratified data robustly support our main conclusions.

 

Comment 2: The problem of under-reporting is also present. As many as 41.0% of participants showed signs of energy underreporting. Although the authors suggest that this could be due to intentional hypocaloric diets in obese individuals, it is therefore recommended to conduct a sensitivity analysis that would exclude these participants to confirm whether the rates of micronutrient deficiencies change significantly.

Response 2: We sincerely appreciate this excellent methodological recommendation. We fully agree that addressing the under-reporting bias quantitatively strengthens the paper.

Action taken: We have performed the requested Sensitivity Analysis, excluding all 1,339 under-reporters (Energy < 0.8 × BMR × PAL). The results derived from the validated, energy-sufficient sub-cohort (n = 1928, mean energy intake 2216 ± 576 kcal/day) demonstrated that the critical dietary inadequacies remained virtually unchanged and profoundly high (e.g., vitamin D inadequacy was 95.9%, folates 78.2%, Omega-3 72.1%). We have added a detailed paragraph describing these robust findings to Section 4.5 (Study Limitations).

 

Comment 3: What is shown in Table 2 - a larger amount resulted in meals with more energy and all observed parameters (except omega-3 and dietary fiber) - expectedly - the same would happen if comparing the values ​​of those whose BMI is 18.5-19 with 24-25 kg/m2).

Response 3: We agree with the reviewer's mathematical logic that an increase in absolute macronutrient intake is an expected consequence of higher overall energy consumption. However, the core of our "hidden hunger" paradox relies not on absolute grams, but on the severe disproportion of relative intake (g/kg of body weight) and the critical drop in nutrient density per 1000 kcal.

Action taken: To ensure this distinction is clear to the readers, we have added a clarifying sentence to Section 3.4: "Although an increase in the absolute intake of certain nutrients is mathematically expected alongside a higher overall energy intake, the clinical significance of the observed paradox lies in the severe disproportion of relative intake and nutrient density."

 

Comment 4: The heatmap correlation (Figure 2) is informative, but could be further refined by displaying correlations specifically for the obesity subgroup in order to more accurately identify the drivers of nutritional imbalance in this risk group.

Response 4: We thank the reviewer for this excellent suggestion, which adds significant clinical value to our analysis.

Action taken: We have generated a new correlation heatmap exclusively for the obesity sub-cohort (BMI ≥ 30 kg/m ²; n = 664). This visualization has been included as Supplementary Figure S1. We also added a paragraph to Section 3.5, confirming that the pro-inflammatory dietary patterns observed in the overall cohort were fully replicated within the high-risk subgroup.

 

Comment 5: The authors correctly state that the cross-sectional design does not allow for the establishment of cause-and-effect relationships. The discussion could be extended by theoretical consideration of how micronutrient deficits (such as magnesium or chromium, if measured) can feed back on hunger and metabolic resistance, thus closing the obesity cycle. Therefore, although the role of digitization is emphasized, the discussion would benefit from more specific guidelines for clinical practice – eg, how to integrate the NIAP platform into routine preventive examinations outside the research context.

Response 5: We are grateful for this insightful clinical perspective. We did indeed measure magnesium and chromium, and both showed notable intake shortfalls in our cohort.

Action taken:

• We have added the prevalence data for magnesium and chromium inadequacies to the Results (Section 3.3).
• In Section 4.3, we expanded the pathophysiological discussion, detailing how magnesium and chromium shortfalls can impair insulin sensitivity and glucose tolerance, potentially sustaining a feedback loop of compensatory overeating.
• In Section 4.4, we added concrete, actionable recommendations for clinical practice, suggesting the use of the NIAP platform for rapid 3-5-day dietary profiling during routine preventive check-ups to identify specific nutrient gaps and deliver personalized prescriptions.

 

Comment 6: Since some elements (fluorine, bromine) are excluded due to dependence on regional waters, it would be useful to briefly discuss the potential influence of the geographical location of the participants within Russia on the obtained results, if such data are available.

Response 6: We thank the reviewer for highlighting this geographic nuance. Unfortunately, detailed geographic metadata regarding the participants' region of residence within the Russian Federation was not systematically collected in this study. Consequently, we are unable to perform a regional sub-analysis. However, to minimize the confounding effect of regional water and soil composition on our findings, we deliberately excluded the most water-dependent trace elements (such as fluorine and bromine) from our primary dietary analysis. This methodological safeguard ensures that our findings reflect dietary patterns rather than geographic geochemical anomalies. We had already highlighted this exclusion as a methodological decision in Section 4.5 (Study Limitations).

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript addresses an important and highly relevant topic—the coexistence of obesity and micronutrient deficiencies (“hidden hunger”). The large sample size and the implementation of a digital dietary assessment tool incorporating nutrient retention factors represent clear strengths and add novelty to the work. However, despite these strengths, the study is affected by major methodological limitations that significantly reduce the interpretability and reliability of the findings.

A fundamental limitation of this study is the lack of clinical characterization of the study population, which introduces substantial and uncontrolled confounding.

First, the manuscript does not account for the presence of metabolic disorders, including type 2 diabetes, insulin resistance, thyroid dysfunction, or other chronic conditions. This is particularly problematic given the high prevalence of overweight and obesity in the cohort (~45%), where such conditions are expected to be common. These disorders strongly influence dietary behavior, macronutrient distribution, appetite regulation, and micronutrient metabolism. Without adjustment for these variables, the reported associations between BMI and nutrient density may be confounded and should be interpreted with caution.

Second, the study does not report or control for medication use or dietary supplementation. The absence of information on commonly used drugs (e.g., metformin, insulin, GLP‑1 receptor agonists, thyroid hormones) and supplements (e.g., vitamin D, iron, omega‑3 fatty acids) represents a major limitation. These factors may significantly influence both dietary intake and measured nutrient adequacy, potentially biasing the reported prevalence of deficiencies.

Third, although the authors collected data on dietary patterns (e.g., vegetarian diets), these variables were not incorporated into the statistical analysis. This omission is particularly relevant for interpreting micronutrient deficiencies (e.g., vitamin B12, iron, iodine), where diet type is a primary determinant.

Fourth, the study does not consider sex-specific hormonal status, despite the cohort being predominantly female (84.2%) with a mean age of 40.4 ± 11.4 years. A substantial proportion of participants is therefore likely to be in the perimenopausal or menopausal transition, which is associated with significant changes in body composition, energy metabolism, and micronutrient requirements. Failure to account for menopausal status introduces an important source of bias in analyses linking BMI with dietary quality and nutrient deficiencies.

Taken together, these limitations suggest that the cohort is treated as metabolically homogeneous, which is not realistic. As a result, the observed associations between obesity and micronutrient inadequacy may be substantially confounded by unmeasured clinical and hormonal factors, rather than reflecting purely dietary effects.

Additional Concerns

Selection bias: participants were users of a digital dietary platform, which limits generalizability; strong gender imbalance (84% women)

Cross-sectional design: no causal inference possible

Self-reported dietary intake: despite digital support, still subject to reporting bias

Extremely high prevalence of deficiencies (99.9%) - it requires deeper methodological justification; potential overestimation due to thresholds or modeling assumptions

Minor Comments

1. Clarify the validation status of the NIAP platform

2. Improve readability of figures (especially correlation heatmap)

3. Consider additional subgroup or sensitivity analyses

Summarizing: the authors should: provide additional clinical data (if available); perform adjusted analyses, or explicitly acknowledge these limitations and substantially tone down conclusions.

 

Author Response

Response to Reviewer 3 Comments

Dear Reviewer 3,

We are immensely grateful for your rigorous review and highly valuable clinical observations. Addressing your comments regarding unmeasured confounders, clinical comorbidities, and reporting biases has allowed us to significantly strengthen the scientific rigor and transparency of our study. Below, we provide a detailed, point-by-point response to each of your comments. All modifications in the revised manuscript have been highlighted in yellow for your convenience.

 

Comment 1: First, the manuscript does not account for the presence of metabolic disorders, including type 2 diabetes, insulin resistance, thyroid dysfunction, or other chronic conditions. This is particularly problematic given the high prevalence of overweight and obesity in the cohort (~45%), where such conditions are expected to be common. These disorders strongly influence dietary behavior, macronutrient distribution, appetite regulation, and micronutrient metabolism. Without adjustment for these variables, the reported associations between BMI and nutrient density may be confounded and should be interpreted with caution.

Response 1: We highly appreciate this critical observation. We agree that clinical comorbidities are major determinants of dietary behavior. Fortunately, our dataset included medical histories, allowing us to address this gap.

Action taken: We have added a comprehensive analysis of the cohort's clinical profile to Section 3.1 (Demographic and Anthropometric Profile). The data revealed that 52.8% of participants had at least one diagnosed chronic condition, including hypertension (5.2%), hypothyroidism (3.3%), and type 2 diabetes (2.2%). Importantly, our analysis demonstrated that the phenomenon of multiple dietary inadequacies was universally profound across all these clinical subgroups (e.g., patients with type 2 diabetes and hypertension exhibited an average of 7.22 and 7.88 simultaneous vitamin intake shortfalls, respectively, closely comparable to the overall obesity group). This addition robustly proves that the nutrient-poor nature of the diet persists independently of the underlying metabolic diagnosis.

 

Comment 2: Second, the study does not report or control for medication use or dietary supplementation. The absence of information on commonly used drugs (e.g., metformin, insulin, GLP‑1 receptor agonists, thyroid hormones) and supplements (e.g., vitamin D, iron, omega‑3 fatty acids) represents a major limitation. These factors may significantly influence both dietary intake and measured nutrient adequacy, potentially biasing the reported prevalence of deficiencies.

Response 2: We fully agree with the reviewer. Clinical biochemical status heavily depends on supplementation and pharmacotherapy. However, the primary objective of our study was to evaluate the nutritional quality of the alimentary food matrix itself, not the final biochemical status of the patients.

Action taken: We have explicitly acknowledged this limitation in Section 4.5 (Study Limitations): "Furthermore, this study assessed exclusively dietary (alimentary) intake from food sources and did not control for the use of dietary supplements or medications (e.g., metformin, GLP-1 receptor agonists, thyroid hormones). However, this aligns with the primary objective of our study: to evaluate the baseline nutritional quality of the diet itself... The potential use of supplements does not negate the fundamental finding that the underlying dietary pattern is critically nutrient-poor."

 

Comment 3: Third, although the authors collected data on dietary patterns (e.g., vegetarian diets), these variables were not incorporated into the statistical analysis. This omission is particularly relevant for interpreting micronutrient deficiencies (e.g., vitamin B12, iron, iodine), where diet type is a primary determinant.

Response 3: We thank the reviewer for pointing this out. We have analyzed the dietary preferences within our cohort to address this issue.

Action taken: We added a new paragraph to the beginning of Section 3.5 (Dietary Patterns and Sources of “Empty Calories”) detailing the prevalence of specific diets (e.g., lactose-free, gluten-free, vegan, vegetarian). We demonstrated that regardless of the chosen dietary pattern, the average number of micronutrient intake shortfalls remained critically high across all groups (e.g., 7.68 vitamin inadequacies per person in the unrestricted group vs. 7.25 in the vegan group), indicating that popular restrictive diets in this population do not eliminate the "hidden hunger" problem.

 

Comment 4: Fourth, the study does not consider sex-specific hormonal status, despite the cohort being predominantly female (84.2%) with a mean age of 40.4 ± 11.4 years. A substantial proportion of participants is therefore likely to be in the perimenopausal or menopausal transition, which is associated with significant changes in body composition, energy metabolism, and micronutrient requirements. Failure to account for menopausal status introduces an important source of bias in analyses linking BMI with dietary quality and nutrient deficiencies.

Response 4: This is a highly insightful clinical observation. Menopausal transition indeed alters energy metabolism and micronutrient requirements.

Action taken: We have added a dedicated acknowledgement of this unmeasured confounder to Section 4.5 (Study Limitations). Furthermore, we clarified that the NIAP platform dynamically adjusts individual baseline requirements (BMR and micronutrient norms) based on real-time age, sex, and anthropometric data, which partially mitigates the impact of these physiological and age-related shifts on the calculated adequacy of the diet.

 

Comment 5 (Additional Problem 1): Selection bias: participants were users of a digital dietary platform, which limits generalizability; strong gender imbalance (84% women).

Response 5: We agree. To avoid overgeneralization, we have adjusted the title and the text.

Action taken: The Title was changed to specify "Adult Cohort Evaluated via a Professional Digital Dietary Tool in Russia". We also revised the Abstract and Conclusions to restrict our inferences to the evaluated cohort. The gender imbalance was further addressed by adding a sex-stratified analysis (Supplementary Table S1) and discussed in Section 4.5.

 

Comment 6 (Additional Problem 2): Cross-sectional design: no causal inference possible.

Response 6: We fully agree.

Action taken: Causal language in Section 4.3 was softened (changed to "may be closely linked", "potentially reinforcing"). We also significantly expanded the discussion regarding the impossibility of causal inference in Section 4.5.

 

Comment 7 (Additional Problem 3): Self-reported dietary intake: despite digital support, still subject to reporting bias.

Response 7: We acknowledge this inherent limitation.

Action taken: We added a sentence to Section 4.5 explicitly stating that self-reported assessment remains susceptible to cognitive and reporting biases (such as socially desirable reporting). Crucially, to counter this, we conducted a Sensitivity Analysis excluding all 1,339 under-reporters (detailed in Section 4.5), which proved that the critical dietary inadequacies remained robustly high even in the energy-sufficient sub-cohort.

 

Comment 8 (Additional Problem 4): Extremely high prevalence of deficiencies (99.9%) - it requires deeper methodological justification; potential overestimation due to thresholds or modeling assumptions.

Response 8: We understand the reviewer's concern regarding the magnitude of the reported inadequacies.

Action taken: In Section 2.3, we justified the 10% threshold as a standard tolerance margin. Moreover, we added a clarification stating that the actual mean deviations observed in our cohort were profoundly larger (e.g., -77.3% for vitamin D, -46.2% for folates). This indicates that the 99.9% prevalence is driven by severe systemic shortfalls rather than marginal threshold effects or modeling assumptions.

 

Comment 9 (Minor 1): Clarify the validation status of the NIAP platform.

Response 9: Addressed.

Action taken: We clarified in Section 2.2 that NIAP functions as a clinical decision support system (CDSS) and its accuracy is verified through strict adherence to approved state guidelines for nutritional calculations and verified food composition databases, rather than through separate prospective biomarker trials.

 

Comment 10 (Minor 2): Improve readability of figures (especially correlation heatmap).

Response 10: Addressed.

Action taken: All figures (Figure 1, Figure 2, and Supplementary Figure S1) were re-exported at high resolution (300 DPI) with increased font sizes and improved color contrast for optimal readability in the final layout. The color scale in Figure 2 was adjusted to standard conventions (red for positive, blue for negative correlations).

 

Comment 11 (Minor 3): Consider additional subgroup or sensitivity analyses.

Response 11: Addressed.

Action taken: We conducted extensive additional analyses as requested:

1. A sensitivity analysis excluding under-reporters (Section 4.5).
2. Subgroup analyses by sex (Supplementary Table S1).
3. Subgroup correlation analysis specifically for patients with obesity (Supplementary Figure S1).
4. Subgroup analyses based on clinical diagnoses (Section 3.1) and dietary patterns (Section 3.5).

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for addressing all the points.

Author Response

Dear Reviewer 1,

We would like to express our sincere gratitude for your time and for reviewing our revised manuscript. We are very pleased that our revisions have successfully addressed all of your initial points. Thank you once again for your highly constructive feedback during the first round of review, which significantly improved the methodological clarity and overall quality of our paper.

Best regards,
The Authors

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript has substantially improved following revision, and the authors have adequately addressed the majority of my previous comments. The study is well-designed, clearly presented, and provides valuable insights into nutrient inadequacy using an advanced digital assessment approach.
Before final acceptance, I recommend addressing the following minor points:

Some statements remain slightly too strong given the cross-sectional design and the cohort's non-representative nature. Please moderate expressions such as “universal prevalence” or “systemic characteristic” and replace them with more cautious wording (e.g., “very high prevalence in the studied cohort” or “may reflect a broader trend”).

The issue of selection bias should be further emphasized. Although mentioned, it would be beneficial to more clearly highlight that the study population consists predominantly of women and represents a self-selected group of digital platform users, which may limit generalizability.

Please explicitly acknowledge the lack of biomarker validation. As the study is based solely on dietary intake data, it should be clearly stated that intake does not necessarily reflect physiological or biochemical status.

Some parts of the Discussion repeat results already presented in the Results section. Please consider shortening these fragments and focusing more on interpretation rather than reiteration of numerical findings.

The NIAP platform description is informative; however, in some sections, the tone comes across as slightly promotional. Please ensure a neutral and balanced scientific tone throughout.

A final minor language editing pass would further improve clarity and readability, particularly in the Discussion section.

 

 

Author Response

Response to Reviewer 3 Comments

Dear Reviewer 3,

We are immensely grateful for your positive assessment of our revised manuscript and your highly valuable final recommendations. Addressing these minor points has allowed us to significantly polish the text and improve the overall readability and scientific neutrality of our paper. Below, we provide a point-by-point response to your comments. All final modifications in the manuscript have been highlighted in yellow for your convenience.

 

Comment 1: Some statements remain slightly too strong given the cross-sectional design and the cohort's non-representative nature. Please moderate expressions such as “universal prevalence” or “systemic characteristic” and replace them with more cautious wording (e.g., “very high prevalence in the studied cohort” or “may reflect a broader trend”).

Response 1: We fully agree. To ensure appropriate caution, we have moderated these expressions throughout the manuscript.

Action taken: Phrases such as "universal" and "systemic characteristic" were replaced with more cautious wording, such as "highly prevalent", "widespread", and "may reflect a broader trend" (e.g., in the Abstract, Section 3.3, Section 4.1, and Section 5).

 

Comment 2: The issue of selection bias should be further emphasized. Although mentioned, it would be beneficial to more clearly highlight that the study population consists predominantly of women and represents a self-selected group of digital platform users, which may limit generalizability.

Response 2: Thank you for this suggestion. We have expanded the limitations section to explicitly address this specific bias.

Action taken: In Section 4.5 (Study Limitations), we added: "Furthermore, the study population represents a self-selected group of digital platform users. This inherent selection bias... significantly limits the generalizability of the findings to the broader or male population."

 

Comment 3: Please explicitly acknowledge the lack of biomarker validation. As the study is based solely on dietary intake data, it should be clearly stated that intake does not necessarily reflect physiological or biochemical status.

Response 3: We completely agree and have added an explicit statement to clarify this distinction.

Action taken: In Section 4.5, we added: "Importantly, we must explicitly acknowledge the lack of biomarker validation in our methodology. Because the study relies solely on self-reported dietary intake data, it must be clearly stated that calculated dietary inadequacies do not necessarily reflect the actual physiological or biochemical nutritional status of the individuals."

 

Comment 4: Some parts of the Discussion repeat results already presented in the Results section. Please consider shortening these fragments and focusing more on interpretation rather than reiteration of numerical findings.

Response 4: This is a very helpful comment. We have thoroughly reviewed the Discussion section to eliminate redundant numerical data.

Action taken: Exact percentages, grams, and numbers that were already presented in the Results were removed from the Discussion (specifically in Section 4.1, Section 4.2, and Section 4.3). The text has been streamlined to focus purely on the interpretation of the observed clinical and dietary trends.

 

Comment 5: The NIAP platform description is informative; however, in some sections, the tone comes across as slightly promotional. Please ensure a neutral and balanced scientific tone throughout.

Response 5: We appreciate this feedback. We have carefully revised the manuscript to remove any adjectives that could be perceived as promotional.

Action taken: Words such as "precision", "highly accurate", and "professional" when referring to the platform were systematically removed or replaced with neutral scientific terms (e.g., "digital", "detailed") in the Introduction, Section 2.2, and Section 4.4.

 

Comment 6: A final minor language editing pass would further improve clarity and readability, particularly in the Discussion section.

Response 6: We have conducted a final comprehensive proofreading of the entire manuscript. Additionally, we corrected minor grammatical artifacts and typographical errors to ensure smooth readability. The removal of redundant numerical data and the moderation of the tone (as addressed in Comments 4 and 5) have substantially improved the overall flow, clarity, and readability of the Discussion section.