The Harmful Impact of COVID-19 on Adiposity Markers and Cognitive Development in Maya Children

Round 1

Reviewer 1 Report

This is an interesting manuscript that evaluates the impact of COVID-19 pandemic on adiposity markers and cognitive development among Maya children. The authors found that COVID-19 pandemic had a negatively impact on body composition and cardiometabolic risks. However, there are some points should be addressed in this manuscript:

1. How to assess the 24-hour dietary records by children?
2. There is a little confused on Figure 1 and Figure 2 for the composition of micronutrient and dietary patterns.
3. What is the date of dietary record for pre- and post- COVID-19 epidemic?
4. How about the total energy intake for children during pre- and post- COVID-19 epidemic?
5. It would be more interesting to evaluate the difference of all parameters between pre- and post- COVID-19 epidemic, but not just using the mean difference.
6. It would be more meaningful it the study could explore the causes that made the difference between boys and girls for the body fat mass, waist circumference and cognitive score.

This is an interesting manuscript that evaluates the impact of COVID-19 pandemic on adiposity markers and cognitive development among Maya children. The authors found that COVID-19 pandemic had a negatively impact on body composition and cardiometabolic risks. However, there are some points should be addressed in this manuscript:

1. How to assess the 24-hour dietary records by children?
2. There is a little confused on Figure 1 and Figure 2 for the composition of micronutrient and dietary patterns.
3. What is the date of dietary record for pre- and post- COVID-19 epidemic?
4. How about the total energy intake for children during pre- and post- COVID-19 epidemic?
5. It would be more interesting to evaluate the difference of all parameters between pre- and post- COVID-19 epidemic, but not just using the mean difference.
6. It would be more meaningful it the study could explore the causes that made the difference between boys and girls for the body fat mass, waist circumference and cognitive score.

Author Response

How to assess the 24-hour dietary records by children?

We thank the reviewer for this valuable observation. In response, we added the following lines in section 2.4 Dietetic evaluation, line 153.

Line 153: We recorded details on all foods and beverages consumed in the previous 24 hours. The study collects details on type, preparation, portion size, and timing, using visual aids. Frequencies of dietetic alterations were calculated based on Recommended Dietary Allowances (RDAs) for the Mexican school-age population²⁹.

There is a little confused on Figure 1 and Figure 2 for the composition of micronutrient and dietary patterns.

We thank the reviewer for pointing out this issue. According to this, we have reviewed the order of the figures and adjusted both the titles and content, a new paragraph was included: Section 3.3. Dietetic evaluation lines 204 to 214 and lines 220 to 228.

Line 204: Tahdziú is considered one of the poorest communities in Latin America. In this study, the dietary quality of children and their families was evaluated both before and after the COVID-19 pandemic. The findings are concerning; prior to the pandemic, the average dietary quality score was 57 points, which declined to 46 points after the pandemic (Figure 1). In both periods, dietary quality remained well below the minimum recommended threshold of 80 points. Energy intake was within the recommended values for the group before or after the pandemic (1681[1181, 2185] kcal and 1667[1302, 2022] kcal, respectively). However, when considered alongside dietary quality, these findings indicate that the consumed calories came predominantly from energy-dense, high-fat foods that were poor sources of essential nutrients crucial for child growth and development.

Line 220: Given the poor dietary quality observed among children before the pandemic and considering that a year had passed since its onset, special permission was obtained during 2021 (inter-COVID period), to enter the community and conduct a 24-hour dietary recall. This assessment aimed to identify nutritional deficiencies in children and provide targeted food support from the Yucatan health services.

Figure 2. The results revealed excessive intake of carbohydrates, simple sugars, total fat, and saturated fatty acids, exceeding recommended levels. Concurrently, there were marked deficiencies in essential nutrients such as proteins, polyunsaturated fatty acids, calcium, fiber, and vitamin B12. In addition, 100% of boys and 96.2% of girls had a dietary quality score below the minimum recommended threshold of 80 points (HEI score <80)

What is the date of dietary record for pre- and post- COVID-19 epidemic?

We thank the reviewer for this suggestion. The dates were specified in section 2.1. Study design and participants, line 99; and the section 3.3. Dietetic evaluation line 220.

Line 99: The two periods pre- and post-COVID included somatometric, dietary, and cognitive indicators that were assessed before the COVID-19 pandemic (pre-COVID). In addition, due to COVID restriction, an inter-COVID evaluation was performed only for the dietary record.

Line 220: Given the poor dietary quality observed among children before the pandemic and considering that a year had passed since its onset, special permission was obtained during 2021 (inter-COVID period), to enter the community and conduct a 24-hour dietary recall.

How about the total energy intake for children during pre- and post- COVID-19 epidemic?

We thank the reviewer for this question. In response, we have added some lines in section 3.3. Dietetic evaluation line 209.

Line 209: Energy intake was within the recommended values for the group before or after the pandemic (1681[1181, 2185] kcal and 1667[1302, 2022] kcal, respectively). However, when considered alongside dietary quality, these findings indicate that the consumed calories came predominantly from energy-dense, high-fat foods that were poor sources of essential nutrients crucial for child growth and development.

It would be more interesting to evaluate the difference of all parameters between pre- and post- COVID-19 epidemic, but not just using the mean difference.

We thank the reviewer for this suggestion. We added an explanation to this comment in section 2.1 Study design and participants line 99.

Line 99: The two periods pre- and post-COVID included somatometric, dietary, and cognitive indicators that were assessed before the COVID-19 pandemic (pre-COVID). In addition, due to COVID restriction, an inter-COVID evaluation was performed only for the dietary record.

It would be more meaningful it the study could explore the causes that made the difference between boys and girls for the body fat mass, waist circumference and cognitive score.

We sincerely appreciate your valuable observation. In the reviewed manuscript, we have addressed the differences between boys and girls in the Discussion section, specifically in lines 269-285, 337-341, 352-355, and 361-362. Furthermore, in line 380, when discussing Figure 2 (inter-pandemic period), we highlight the differences in nutrient consumption between boys and girls. In particular, boys show a higher intake of total fats, while girls present a greater consumption of carbohydrates and simple sugars, which may increase their susceptibility to the accumulation of visceral fat.

Lines 269-274: HAZ values in boys remained altered, and the same trend that was observed in girls (Table 2). These data are in line with previous reports that have shown for almost 30 years, short height problems in Maya children’s populations^4,32. It is worth mentioning that this scenario was expected because stunting in children reflects exposure to an environment of chronic undernutrition, poverty, and marginalization³³.

Lines 275-285: The scenario observed in both boys and girls may be due to cell plasticity. However, in girls, the apparent improvement may have been at the expense of their nutritional reserves, most likely leading to deficiencies in later life. In boys, this utilization of nutritional reserves is also likely; however, it was sufficient only to keep the child on the same growth trend. According to the allocation principal organisms have limited time and energy to maximize growth. In the short and long term, energy allocation can result in fitness costs, especially when resources are scarce. Nevertheless, this adaptive advantage observed can lead to the appearance of chronic diseases at later ages. In this sense, it has been evidenced that growth deficiencies during childhood, in response to an adverse environment, condition both short stature and metabolic alterations, such as diabetes, dyslipidemias, hyperglycemia, and hypertension in adulthood⁵.

Lines 337-341: Regarding FMI values in both boys and girls, significant increases were also found in them after the pandemic (p<0.001) (Table 2). These increases in the various adiposity markers evaluated could be the result of the factors associated with the pandemic lockdown, such as a sedentary lifestyle, dietary quality, and sleep disturbances³⁴.

Lines 352-355: In boys, a significant increase in waist circumference percentile was observed (p=0.010) (Table 2); as will be discussed later, this finding may be associated with significantly higher consumption of fats identified in them (Figure 2).

Lines 361-362: Particularly in boys, presented a reduction in the cognitive score (p=0.021) (Table 2) which exacerbates pre-existing vulnerability and can predict an educational lag.

Lines 380-388: In an attempt to monitor alterations development during the pandemic, we carried out a nutritional analysis in the inter-pandemic period. The high frequencies of deficient fiber intake and excessive consumption of carbohydrates, simple sugars, and saturated fatty acids (Figure 2) can explain both frequencies of excess weight and alterations in adiposity markers (Table 1 and 2). Moreover, significantly higher consumption of total fat (p=0.0001) observed in boys (Figure 2) can explain the waist circumference percentile increase in them after the pandemic (Table 2). The reason for the above is that low fiber intake and excess of both sugar and fats (total and saturated) are dietetic factors strongly associated with adiposity gain⁴⁵

Reviewer 2 Report

This prospective study examines the impact of the COVID-19 pandemic on body composition and cognitive development in 80 Maya children from Tahdziú, Yucatán, México. This research sheds light on a critically important yet often overlooked population, offering valuable insights into how the pandemic intensified the challenges faced by Indigenous children already burdened by significant socioeconomic adversity.

Strengths

• The focus on Maya children represents a significant contribution to understanding pandemic impacts on indigenous communities and provides crucial data on a community with the highest poverty rate in Yucatán
• Prospective longitudinal design with pre- and post-pandemic measurements
• Comprehensive assessment including anthropometric, body composition, and cognitive measures
• Appropriate statistical methods for paired comparisons
• The authors have successfully identified significant increases in adiposity markers and a worsening of cognitive performance in this cohort of Maya children, providing a valuable contribution to the understanding of the pandemic's broader public health impacts

Weaknesses and Areas for Improvement

• Methods: The study began with 114 children but was reduced to 80 for the paired analysis, representing a loss of approximately 30%. The authors mention this loss in the results section and later in the limitations section, attributing it to families migrating due to the pandemic crisis. This reduction in sample size raises concerns about potential selection bias. For example, families with greater resilience to the pandemic’s economic and social challenges were able to stay in the community, whereas those most impacted by job losses or food insecurity were forced to migrate. This could mean the remaining cohort is not a truly representative sample of the original population, potentially biasing the results toward a more favourable or stable outcome than what the community as a whole experienced. In addition, no control group from non-indigenous or urban populations was considered. The authors' discussion of these points is commendable, but the implication of this selection bias warrants a more prominent and critical discussion, particularly in the Methods and Discussion sections.
• Cognitive evaluation: As the authors acknowledge in the discussion section, the use of a single cognitive test, while practical for a field study in a remote location, is a significant limitation. The discussion references the fact that children with "borderline IQ" often show deficits in specific executive functions like working memory and problem-solving. A single test may not be sensitive enough to capture these nuanced changes. While the chosen methodology is a valid approximation of cognitive ability, a more comprehensive test profile would have provided a more detailed and accurate assessment. This limitation should be explicitly considered when interpreting the cognitive findings. For example, authors could discuss alternative approaches to cognitive evaluation and potential cultural bias in assessment tools not adequately addressed.
• Dietetic evaluation: The dietetic evaluation relied on 24-hour dietary records and the Healthy Eating Index (HEI). The authors candidly admit a major limitation in this part of the study: due to the community's refusal to provide data during the first year of the pandemic, the diet was only evaluated mid-pandemic and not in a pre-pandemic state. This aspect prevents a direct, paired comparison of dietary changes, which weakens the ability to establish a direct causal link between dietary alterations and the observed health outcomes. Consider improving this aspect in the discussion of study limits.
• Statistical Analysis: Cohen's Kappa results for cognitive assessment inter-rater reliability were not reported. Please consider adding these results to the manuscript.
• Grammar error: Consider revising grammatically all the manuscript to improve its clarity.

Author Response

Weaknesses and Areas for Improvement

Methods: The study began with 114 children but was reduced to 80 for the paired analysis, representing a loss of approximately 30%. The authors mention this loss in the results section and later in the limitations section, attributing it to families migrating due to the pandemic crisis. This reduction in sample size raises concerns about potential selection bias. For example, families with greater resilience to the pandemic’s economic and social challenges were able to stay in the community, whereas those most impacted by job losses or food insecurity were forced to migrate. This could mean the remaining cohort is not a truly representative sample of the original population, potentially biasing the results toward a more favourable or stable outcome than what the community as a whole experienced.

We thank the reviewer for highlighting this point. We agree with the reviewer, the present study has some limitations. We answered the comment in section 2.1 Study design and participants, line 98, in the section 4. Discussion, line 243 and line 399.

Line 98: The present work was an observational and prospective study consisting of three periods: pre-COVID, inter-COVID, and post-COVID. The two periods pre- and post-COVID included somatometric, dietary, and cognitive indicators that were assessed before the COVID-19 pandemic (pre-COVID). In addition, due to COVID restriction, an inter-COVID evaluation was performed only for the dietary record.

Line 243: In the pre-COVID stage, the study included 100% of the children enrolled in the fourth grade of elementary school. Due to the COVID-19 pandemic, some participants were lost in the two subsequent assessments, which is a limitation of the study. Nevertheless, all children attending elementary school both pre- and post-pandemic periods were included. During the intermediate period, when data were collected through house-to-house visits, only those ones who still residing in the community were assessed. The pandemic, not only in Tahdziú but globally, had severe consequences on living conditions, widening economic disparities, and exacerbating food shortages, which, in some cases, forced families to migrate in search of better opportunities. This study highlights the impact on children who remained in an isolated rural town, characterized by persistent, transgenerational poverty. The consequences for these children extend beyond economic hardship, encompassing educational setbacks that are likely to manifest both in the short and long term.

Line 399: The present study has some limitations. These include the size of follow-up samples and the use of a single cognitive test. The size of the follow-up sample (n = 80) was smaller than the initial sample (n = 114) because, only two months had passed since the decree of return to face-to-face classes by the State Government; therefore, not all children regularly attended schools. In addition, during post-COVID visits, schoolteachers reported the migration of many families due to the pandemic crisis.

In addition, no control group from non-indigenous or urban populations was considered. The authors' discussion of these points is commendable, but the implication of this selection bias warrants a more prominent and critical discussion, particularly in the Methods and Discussion sections.

We thank the reviewer for raising this issue. We answered in section 4. Discussion, line 404.

Line 404: A non-indigenous control group was not included, as the study was designed to evaluate the dietary quality and nutritional status of the school-age Maya population and to compare the findings with national survey report.

Cognitive evaluation: As the authors acknowledge in the discussion section, the use of a single cognitive test, while practical for a field study in a remote location, is a significant limitation. The discussion references the fact that children with "borderline IQ" often show deficits in specific executive functions like working memory and problem-solving. A single test may not be sensitive enough to capture these nuanced changes. While the chosen methodology is a valid approximation of cognitive ability, a more comprehensive test profile would have provided a more detailed and accurate assessment. This limitation should be explicitly considered when interpreting the cognitive findings. For example, authors could discuss alternative approaches to cognitive evaluation and potential cultural bias in assessment tools not adequately addressed.

We appreciated this observation. We added a more detailed explanation in section 2.3 Cognitive evaluation, line 135.

Line 135: The Human figure drawing (HFD) methodology was applied to assess cognitive ability²⁸, considering the child´s sex and age. To evaluate concordance, two independent evaluators assigned cognitive scores, with a high level of agreement (kappa=0.98, p=0.04).

The HFD was selected as the sole cognitive assessment tool because it is a validated, non-verbal instrument that can be administered quickly and without specialized equipment, making it feasible in remote, low-literacy indigenous settings. Although cultural bias is an inherent consideration in cognitive testing, the HFD evaluates developmental maturity through universal human figure components, reducing dependence on language, formal schooling, or culturally specific content.

Dietetic evaluation: The dietetic evaluation relied on 24-hour dietary records and the Healthy Eating Index (HEI). The authors candidly admit a major limitation in this part of the study: due to the community's refusal to provide data during the first year of the pandemic, the diet was only evaluated mid-pandemic and not in a pre-pandemic state. This aspect prevents a direct, paired comparison of dietary changes, which weakens the ability to establish a direct causal link between dietary alterations and the observed health outcomes. Consider improving this aspect in the discussion of study limits.

We acknowledge the reviewer’s observation; we answered in section 2.4. Dietetic evaluation, line 147 and in section 3.3. Dietetic evaluation line 204.

Line 147: Before and after the COVID pandemic period, a month of dietary data by the frequency of dietary consumption was recorded. The Healthy Eating Index (HEI)³⁰ was estimated according to the Food and Nutrition Service of the U.S. Department of Agriculture (USDA). A total score of HEI ≤80 was defined as “low dietary quality”.

Once the COVID-19 pandemic began, the indigenous community decided to restrict outside access. It was only after the first year of the pandemic that special permission was approved to enter and collect data on the children's food consumption frequency. We recorded details on all foods and beverages consumed in the previous 24 hours. The study collected details on type, preparation, portion size, and timing, using visual aids. Frequencies of dietetic alterations were calculated based on Recommended Dietary Allowances (RDAs) for the Mexican school-age population²⁹.

Line 204: Tahdziú is considered one of the poorest communities in Latin America. In this study, the dietary quality of children and their families was evaluated both before and after the COVID-19 pandemic. The findings are concerning, prior to the pandemic, the average dietary quality score was 57 points, which declined to 46 points after the pandemic (Figure 1). In both periods, dietary quality remained well below the minimum recommended threshold of 80 points. Energy intake was within the recommended values for the group before or after the pandemic (1681[1181, 2185] kcal and 1667[1302, 2022] kcal, respectively). However, when considered alongside dietary quality, these findings indicate that the consumed calories came predominantly from energy-dense, high-fat foods that were poor sources of essential nutrients crucial for child growth and development.

Statistical Analysis: Cohen's Kappa results for cognitive assessment inter-rater reliability were not reported. Please consider adding these results to the manuscript.

In response to the reviewer’s concern, we added in section 2.3 Cognitive evaluation, line 136.

Line 136: To evaluate concordance, two independent evaluators assigned cognitive scores, with a high level of agreement (kappa=0.98, p=0.04).

Grammar error: Consider revising grammatically all the manuscript to improve its clarity.

We thank the reviewer for this recommendation. The grammar throughout the manuscript has been carefully revised. 

Round 2

Reviewer 2 Report

Accept in this form

Accept in this form

Author Response

We sincerely appreciate your valuable comments and suggestions, which have greatly contributed to improving our manuscript. Please find attached the revised PDF file, in which all the observations have been carefully addressed.

 

Reviewer

In pediatric evaluations, due to the rapid growth rate of children, it is necessary to use internationally validated references to ensure that different populations can be compared under the same criteria. Therefore, the literature recommends the use of Z-scores and percentiles, which are calculated based on age and sex. These standardized parameters allow comparisons across children regardless of ethnicity, since the reference values are validated according to both sex and age.

 

Table 1: In addition to percentages, please, could you prepare a table with absolute numbers with mean +/- SD for each item? If you wish you may prepare categories per quartiles or the percentiles you think that are more representative - I see you use different percentiles (25-75-85-90, depending on the variable). It's ok if you clearly indicate them.

Reviewer,

We sincerely appreciate your valuable comments. Table 1 was designed to present the percentage of children exhibiting alterations in somatometric and biochemical parameters, as well as cardiometabolic risk factors. The percentiles reported are calculated according to sex and age, using reference populations as described in the methodology. Based on these criteria, the percentages in Table 1 reflect the proportion of children classified as altered according to the risk cut-offs established in the literature.

For this reason, mean values with standard deviations or quartile distributions are not included in Table 1, but rather in Table 2, which specifically addresses the central tendency and dispersion of the continuous variables. The main purpose of Table 1 is to highlight the prevalence of metabolic alterations in the children studied, both in the pre- and post-COVID periods

Why do you use again a different percentile depending on the gender?

Different cut-off points were applied according to sex and age, as indicated in the literature. For boys, a threshold of less than 20% body fat is accepted, while for girls the corresponding threshold is 25%, as detailed in the references. First, all parameters were calculated using age- and sex-specific percentiles. Then, in Table 2, the objective was to show the differences between boys and girls. Since the data did not follow a normal distribution, values were expressed as medians and quartiles, and comparisons were performed using the appropriate non-parametric test (Wilcoxon). To facilitate interpretation, the cut-off points established in the literature were applied, specifying whether they are general or stratified by sex, according to the criteria validated for boys and girls.

Author Response File: Author Response.pdf