Unraveling the Diet-Learning Nexus: a cross-sectional deep dive into Iranian adolescents’ nutrient patterns and learning difficulties

Abstract

Background

learning difficulties (LDs), which encompass deficits in academic achievement and cognitive processing, are associated with significant functional challenges. Research indicates that dietary patterns and nutrient intake represent environmental factors that may exert influence on the phenotypic expression or trajectory of such neurodevelopmental disorders. This study examined the association between nutrient patterns and LDs in school-aged adolescents in Iran.

Methods

A cross-sectional descriptive-analytical study was conducted from March to June 2024 in four secondary schools (two all-female and two all-male) in Jahrom, Iran. A total of 455 male and female adolescents aged 14 to 18 years participated. Adolescents with pre-existing medical conditions (cardiovascular disease, diabetes mellitus, hepatic or renal disease), those following specific dietary regimens, or with incomplete data were excluded. LDs were assessed using the Colorado Learning Difficulties Questionnaire (CLDQ). Dietary intake was assessed via a validated 168-item Food Frequency Questionnaire (FFQ). Seven nutrient patterns (NP) were identified through factor analysis. Anthropometric measurements and socioeconomic variables were also collected. Higher scores of CLDS reflect greater LDs.

Results

Adolescents were classified into tertiles based on CLDS. The first tertile of NPs and CLDS was considered as a reference. Moderate adherence to NP4, characterized by higher glutamine and lower glucose, fructose, vitamins C, A, and E, and potassium, was associated with increased odds of being in the highest CLDS tertile in the fully adjusted model (OR 2.50; 95% CI 1.32–4.71). High adherence to NP7, characterized by lower intake of sodium, MUFA, and vitamin D, was associated with lower odds of being in the highest CLDS tertile in the full adjusted model (OR 0.48; 95% CI 0.25–0.91).

Conclusion

Adherence to a low antioxidant and glucose-content NP diet characterized by higher glutamine and lower intakes of glucose, fructose, vitamins C, A, and E, and potassium was positively associated with LDs in Iranian adolescents. Additionally, high adherence to a low-sodium diet was associated with lower odds of being in the highest CLDS tertile.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40795-026-01338-9.

Background

Learning difficulties (LDs) are characterized by significant learning challenges that are disproportionate to an individual’s age and typical cognitive abilities [1]. LDs are neurodevelopmental disorders that emerge during formal education and are defined by persistent difficulties in acquiring essential academic skills in reading, writing, and mathematics. These challenges can adversely affect academic performance and daily functioning if appropriate accommodations are not provided [2, 3]. Although multiple factors contribute to the etiology of LDs, genetic and environmental influences are considered two critical determinants in their development and progression [4].

Diet and dietary components are among the environmental factors that previous research has identified as playing a key role in either decreasing or increasing the risk of LDs [5, 6]. Adherence to an unhealthy diet characterized by high consumption of fast foods and simple sugars (such as sweets and soft drinks) has been linked to an elevated risk of LDs [7]. Conversely, adequate consumption of healthy food groups, including dairy products and vegetables, has been associated with a reduced risk of LDs [8]. Furthermore, prior studies have highlighted the pivotal role of specific nutrients, such as n-3 long-chain unsaturated fatty acids, vitamin D, zinc, magnesium, iron, folate, and cobalamin, in learning and cognition disorders [6, 9–18]. However, the majority of these studies have focused on individual food groups and single nutrients, with limited attention given to overall dietary patterns, particularly nutrient patterns (NP). People do not consume nutrients in isolation; rather, they typically consume multiple nutrients simultaneously, which may interact with each other in processes such as bioavailability, absorption, and function [19].

As a result, NP analysis offers a more nuanced understanding of the relationships between diet and disease than traditional food group analysis. Furthermore, unlike food group analysis, NP analysis elucidates the underlying mechanisms linking diet to disease by examining the composition of nutrient patterns. Additionally, NP analysis serves as a bridge between dietary patterns and the food metabolome, effectively integrating measurements of nutrient intake and metabolic processes [20, 21]. Therefore, our study aimed to investigate the relationship between NP and LDs in adolescents for the first time.

Materials and methods

The present study was carried out and reported according to the guidelines established by Strengthening the Reporting of Observational Studies in Epidemiology—Nutritional Epidemiology (STROBE-nut) (Additional file 1) [22].

Study design and participant

This cross-sectional descriptive-analytical study assesses the relationship between nutrient patterns (NPs) and LDs among adolescents in Jahrom from March to June 2024. Based on World Health Organization (WHO) standards, individuals aged 10 to 19 are classified as adolescents [23]. The current study includes male and female adolescents aged 14 to 18 years, selected from the adolescent population in Jahrom city [24]. Seven NPs were identified from the FFQ data using principal component analysis (PCA) with varimax rotation and then divided into tertiles. For determining the sample size, we utilized G*Power software (version 3.1) for logistic regression, assuming a minimum clinically significant odds ratio (OR) of 1.8, with a significance level (α) of 0.05 and statistical power of 0.80. To adjust for variance inflation from including multiple dietary patterns and demographic covariates in the models, we accounted for a covariate adjustment of 0.1 (R² = 0.1), ensuring sufficient power after controlling for confounders, which led to a primary sample size estimate of 420. We set an initial target sample size of 476 adolescents. Twenty-one participants were excluded due to missing data on nutrition or CLDS. A two-stage cluster sampling methodology was employed. In the first stage, four educational institutions, two all-female and two all-male, were randomly selected based on predetermined inclusion criteria. These criteria included: the willingness of institutional administrators to facilitate participation; absence of prior sequential nutrition and health program implementation; socioeconomic comparability; adherence to equivalent health regulatory standards; and geographical proximity to minimize inter-institutional adolescents’ interaction. In the second stage, a comprehensive enumeration was conducted, inviting all adolescents enrolled in the selected institutions to participate. Stratification by institutional type (i.e., single-gender status) was used to ensure adequate gender representation within the sample. This sampling strategy was chosen for its logistical feasibility, ability to yield a sufficient sample size, and effectiveness in reducing selection bias.

Parental guardians received written documentation outlining the study’s objectives and potential benefits. Written informed consent was subsequently obtained from those who agreed to permit their adolescent’s participation. Inclusion of participants required both voluntary adolescents’ assent and documented parental consent. Exclusion criteria included participants with documented pre-existing medical conditions (such as cardiovascular disease, diabetes mellitus, hepatic disease, or renal disease); use of medications related to medical conditions influencing body composition or nutritional intake; adolescents who engage in strenuous exercise; individuals following dietary regimens known to significantly alter typical nutrient intake; participants who voluntarily withdrew from the study; and cases where essential data could not be obtained.

Dietary assessment

A validated 168-item FFQ was used by two trained nutritionists to gather adolescents’ dietary information in 3 steps [25, 26]. The dietary assessment methodology began with documenting the consumption frequency of each food item. Next, the quantity consumed per eating occasion was determined. For seasonal foods, the duration of consumption over the previous year was recorded in months. To improve measurement accuracy, standardized household measures were used as visual aids during the quantification of each food item. Daily intake for each food item (expressed in grams per day, g/day) was then calculated by integrating three parameters: consumption frequency, quantity per eating occasion, and duration of consumption (in months) for seasonal foods within the past year. Additionally, daily nutrient intake for each participant was calculated by applying nutrient composition data for all consumed foods. Nutrient composition values were primarily obtained from the United States Department of Agriculture’s (USDA) National Nutrient Database. The selection and application of this FFQ methodology were justified by prior research demonstrating its ability to provide reasonably valid and reliable estimates of long-term dietary intake patterns within the Iranian population [25, 26].

Assessment of adolescents’ weight and demographic information

We assessed adolescents’ weight using a SECA standard scale, which has a precision of 100 g [27]. The scale was positioned on a flat surface, and the adolescents’ weights were recorded without shoes and with minimal additional clothing. Furthermore, the demographic data gathered in our research encompassed factors like gender, parents’ marital status, monthly earnings, tobacco usage, and intake of multivitamins. This information was collected by questionnaires that addressed the participants’ personal backgrounds and medical histories.

Assessment of learning difficulties

The Colorado Learning Difficulties Questionnaire (CLDQ), a reliable screening tool completed by parents, was utilized to evaluate learning challenges. It examines 20 distinct factors, with each item rated on a scale from 1 (lowest) to 5 (highest) according to the responses provided. The overall score is determined by summing the ratings of all answered questions [28, 29].

Statistical analysis

First, the energy-adjusted amount of forty-two nutrients and bioactive compounds was determined by the residual method, and then factor analysis with the orthogonal transformation (varimax procedure) was applied to determine the major NP. To examine if the distribution of the different nutrients allows the use of principal components, the Kaiser–Meyer–Olkin test was used. The obtained factors were retained for further analysis based on eigenvalues on the Scree plot. In this study, we retained NPs before the slope plateaued on the scree-plot, selecting those with eigenvalues greater than or equal to 1 to identify the major components. We identified seven NP with a complex array of nutrients, which complicated interpretation when dividing them into tertiles. Therefore, tertiles 1 to 3 were labeled as low, moderate, and high adherence.

In the current study, adolescents were classified into three tertiles using the CLDS. Furthermore, linear regression was used to evaluate continuous P-trends across CLDS tertiles. The chi-square test was employed to identify general differences among the tertiles, while multinomial logistic regression was utilized in three models to assess the relationship between nutrition patterns and the tertiles of CLDS. In the first model, age (in years), calorie intake (Kcal), and sex (male/female) were adjusted. Additionally, the second model adjusted for tobacco consumption (yes/no), multivitamin supplementation (yes/no), parental marital status (married/divorced), monthly household income (<$100, $100-$214, and >$214), and education level (middle/high). Finally, the last model included weight (in kg) as an additional adjustment, along with the factors from the previous models. The first tertile of NPs intake was considered as a reference point in the analysis. Furthermore, the first tertile of CLDS, which indicates adolescents with the lowest risk of LDs, was used as a reference in the analysis. P values less than 0.05 were considered statistically significant. All analyses were performed using Statistical Package for Social Sciences (SPSS Corp, version 27, Chicago, IL, USA).

Results

The general characteristics of study participants across various categories of CLDS are presented in Table 1. In the first tertile of CLDS, 43.4% of adolescents consumed multivitamins. Additionally, higher tertiles of CLDS were associated with increased tobacco consumption, highlighting the potential progressive relationship of tobacco use among individuals with CLDS.

Table 1.

General Characteristics of study participants across categories of Colorado Learning Difficulties¹

Variable	CLDS			P-Value2
	T1 (n = 142)	T2 (n = 158)	T3 (n = 155)
Gender(male)	75.8	71.1	74.5	0.608
Marital status of parents (Marriage)	91.4	94	91	0.560
Monthly household income (< 100$)	23.8	17.2	20.8	0.427
Monthly household income (100–214$)	43.5	43.6	48.1
Monthly household income (> 214$)	32.7	39.3	31.2
Tobacco (Positive)	3.9	6	7.1	0.480
Multivitamin (Positive)	43.4	34.5	39.5	0.267
Education Level (Elementary)	68	60.8	62.2	0.378
Age(year)	14.52 ± 1.54	14.57 ± 1.65	14.55 ± 1.58	0.895
Hight(cm)	166.59 ± 9.58	167.00 ± 10.60	166.55 ± 10.33	0.965
Weight(kg)	62.85 ± 15.05	65.23 ± 18.97	64.47 ± 17.24	0.446
Calorie (Kcal)	3218.73 ± 1362.56	3106.68 ± 1203.05	3113.39 ± 1287.34	0.517

¹All Values are presented as percentages unless indicated

^2.Obtained from linear regression for continuous variables and the Chi-square test for categorical variables

The factor loadings of food groups in dietary patterns are presented in Table 2. Based on the factor analysis, seven NP were identified. The Kaiser-Meyer-Olkin value was 0.78, indicating good sampling adequacy. The first nutrient pattern (NP1) was rich in vitamins A, B2, and B12, calcium, lactose, galactose, saturated fatty acids (SFA), leucine, isoleucine, valine, cholesterol, oleic acid, potassium, and zinc. The second pattern (NP2) was heavily loaded with SFA, manganese, magnesium, polyunsaturated fatty acids (PUFA), copper, total fat, phosphorus, vitamin B9, monounsaturated fatty acids (MUFA), and selenium. The third pattern (NP3) was primarily loaded with iron, vitamins B1, C, E, A, D, and K, carbohydrates, glucose, fructose, and chromium. The fourth pattern (NP4) was characterized by higher levels of glutamine and lower intakes of glucose, fructose, vitamins C, A, and E, and potassium. The fifth pattern (NP5) was rich in docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA). The sixth pattern (NP6) was significantly loaded with fluoride and caffeine. The seventh pattern (NP7) was associated with lower vitamin D, oleic acid, MUFA, and sodium intakes.

Table 2.

Factor Loadings of food groups in dietary patterns

Nutrient1,2	NP-1	NP-2	NP-3	NP-4	NP-5	NP-6	NP-7
Vit B2	0.959
Calcium	0.942
Lactose	0.935
Saturated Fatty Acid	0.926
Galactose	0.917
Vit B12	0.916
Leucine	0.901
Valine	0.900
Vit B3	-0.894
Manganese	-0.880	0.258
Cholesterol	0.875
Protein	0.864
Iso Leucine	0.863
Polyunsaturated Fatty Acid	-0.842	0.503
Copper	-0.837	0.483
Oleic	0.823						-0.266
Vit B6	-0.810			-0.213
Iron	-0.807	-0.302	0.373
Potassium	0.743			-0.284
Zinc	0.637
Vit B1		-0.879	0.269
Fat		0.875	-0.268
Phosphorous		0.819	-0.253
Magnesium		0.797
Carbohydrate	-0.308	-0.771	0.344	-0.376
Vit B9	-0.600	0.703
Glutamine	-0.364	-0.695		0.289
Monounsaturated Fatty Acid		0.624	-0.314				-0.459
Selenium		0.585
Vit K			0.810
Chromium	-0.300		0.715
Vit E	-0.296	-0.248	0.691	-0.220
Vit A	0.372		0.688	-0.229
Vit C	-0.307		0.639	-0.592
Glucose	-0.374		0.271	-0.814
Fructose	-0.388		0.289	-0.814
DHA					0.983
EPA					0.972
Fluoride						0.932
Caffeine	-0.200					0.902
Vit D			0.330				-0.495
Sodium	-0.206						-0.425

^1.Obtained from Factor Analysis for nutrient patterns and the varimax with Kaiser normalization method for rotation

² Factor loadings lower than 0.2 were deleted for simplicity

The odds ratio and 95% confidence interval for the association between NPs and LDs are presented in Table 3 (Complete data are provided in Appendix 1, Supplemental Table S1). Compared to those who had the lowest adherence to NP4, a significant positive association was identified between moderate adherence to NP4 and the odds of being in the highest CLDS tertiles, with an increase of over 112% (Crude: OR 2.12; CI 1.22–3.68). This association remained significant even after adjusting for potential confounding factors in Model 3 (OR 2.50; CI 1.32–4.71). Highest adherence to NP7 was associated with lower odds of being in the highest CLDS tertile compared to those with the lowest adherence to NP7, according to the fully adjusted model (OR 0.48; CI 0.25–3.91). Compared to adolescents with the lowest adherence to NP6, adolescents who had moderate adherence to NP6 had higher odds of being in the highest CLDS tertile in Model 2 (OR 1.89; CI 1.01–3.52). However, after adjusting for the weight variable, this association became non-significant in Model 3 (OR 1.75; CI 0.92–3.31).

Table 3.

The odds ratio and 95% confidence interval for the association between Nutrient Patterns and the CLDS¹

		NP 4
	T1(n = 152)	T2(n = 166)	T3(n = 158)	P-trend
T2CLDS
Crude	1.00	1.65(0.96–2.86)	1.29(0.76–2.20)	0.331
Model-1	1.00	1.59(0.90–2.82)	1.27(0.73–2.21)	0.366
Model-2	1.00	1.55(0.85–2.85)	1.30(0.72–2.33)	0.383
Model-3	1.00	1.53(0.82–2.84)	1.29(0.71–2.36)	0.394
T3CLDS
Crude	1.00	2.12(1.22–3.68)	1.29(0.74–2.25)	0.360
Model-1	1.00	2.43(1.35–4.39)	1.47(0.82–2.62)	0.204
Model-2	1.00	2.36(1.27–4.39)	1.43(0.77–2.65)	0.286
Model-3	1.00	2.50(1.32–4.71)	1.50(0.79–2.84)	0.229

		NP 6
	T1(n = 152)	T2(n = 164)	T3(n = 160)	P-trend
T2CLDS
Crude	1.00	1.28(0.74–2.23)	0.62(0.36–1.05)	0.071
Model-1	1.00	1.19(0.67–2.11)	0.58(0.33–1.01)	0.046
Model-2	1.00	1.17(0.64–2.15)	0.54(0.30–0.97)	0.032
Model-3	1.00	1.12(0.60–2.09)	0.52(0.28–0.96)	0.030
T3CLDS
Crude	1.00	1.71(0.97–3.02)	0.83(0.48–1.44)	0.429
Model-1	1.00	1.69(0.94–3.03)	0.70(0.39–1.25)	0.186
Model-2	1.00	1.89(1.01–3.52)	0.74(0.40–1.35)	0.252
Model-3	1.00	1.75(0.92–3.31)	0.73(0.39–1.37)	0.247

		NP 7
	T1(n = 145)	T2(n = 157)	T3(n = 153)	P-trend
T2CLDS
Crude	1.00	1.15(0.66-2.00)	0.76(0.44–1.32)	0.303
Model-1	1.00	0.93(0.52–1.66)	0.64(0.36–1.14)	0.118
Model-2	1.00	0.90(0.49–1.65)	0.64(0.34–1.17)	0.147
Model-3	1.00	0.88(0.47–1.64)	0.55(0.29–1.04)	0.063
T3CLDS
Crude	1.00	0.85(0.49–1.48)	0.56(0.32–0.96)	0.035
Model-1	1.00	0.78(0.44–1.39)	0.46(0.26–0.83)	0.010
Model-2	1.00	0.71(0.39–1.30)	0.50(0.27–0.93)	0.028
Model-3	1.00	0.73(0.39–1.37)	0.48(0.25–0.91)	0.024

¹CLDS Colorado Learning Difficulties

Model 1: adjusted for energy, sex, and age; Model 2: additionally adjusted for Tobacco, multi-vitamin, Marital status of parents, Monthly household income ($), and Education level; Model 3: all variables in Model 2 + weight

Discussion

The relationship between NPs and LDs is significant, as adequate nutrition is essential for cognitive development and academic success. Research indicates that a balanced diet can enhance memory function and reduce LDs in adolescents [30–33].

To our knowledge, it’s the first study to investigate the association between NPs and LDs. Adolescents who had moderate adherence to NP4, which was labeled as low antioxidant and glucose diet, had greater odds of being in the highest CLDS tertile by 150% increase. Similarly, moderate adherence to NP6, labeled as a high fluoride and caffeine diet, was associated with higher odds of being in the highest CLDS tertile. However, in the fully adjusted model, no significant association was found between adherence to NP6 and CLDS. High adherence to NP7, labeled as a low-sodium diet, was associated with lower odds of being in the highest CLDS tertile. Eventually, adolescents with higher CLDS exhibit NP containing lower anti-oxidant and glucose levels. In contrast, adolescents with lower CLDS exhibit NP containing lower sodium levels.

Earlier studies have been mostly limited to individual nutrition intake [30, 34–36]. NP analysis is a new approach in nutrition epidemiology that covers the intake of all nutrients, in addition to their interactions [19]. NP4 was characterized by lower intake of glucose and antioxidants has been observed in NP4. Furthermore, previous studies reported the role of higher serum levels of vitamins C, A, and E in better cognitive function [37–39]. Additionally, a decline in cognitive function has been reported as a result of deficiencies in vitamin C, A, and E intake [40–42]. Although the association between sodium intake and LDs among adolescents has not been properly studied, previous research on other age groups has reported a detrimental effect of high sodium intake on cognitive function [43, 44]. Consequently, the absence of studies exclusively examining the relationship between micro- and macronutrients and LDs have created ambiguity in the literature, underscoring the importance of the findings of the current study.

The precise mechanisms by which nutrients influence LDs remain unclear; however, several pathways suggest their involvement in cognitive function. Glutamine may enhance cognitive function by serving as a precursor for neurotransmitters such as GABA, glutamate, and aspartate [45, 46]. Additionally, glutamine and vitamin C can improve synaptic function through their antioxidant activity [47–49]. Furthermore, vitamin C serves as a cofactor for dopamine-β-hydroxylase, the enzyme that converts dopamine into norepinephrine. This role modulates executive functions and processing speed within the prefrontal cortex. Consequently, vitamin C supports neurotransmitter systems, including catecholaminergic, cholinergic, and glutamatergic pathways, which are essential for cognition and neuronal communication [17, 37, 50]. Moreover, vitamin C contributes to neuronal differentiation, maturation, and myelin formation, thereby helping to maintain neural integrity and efficient signal transmission in the brain [37]. Vitamin A influences cognitive function and neuronal activity primarily through its active metabolite [51, 52]. Vitamin A deficiency (VAD) leads to impaired synaptic plasticity and cognitive deficits, as demonstrated in animal models where VAD resulted in learning and memory impairments [41, 53, 54]. Vitamin E protects neuronal membranes by scavenging lipid peroxyl radicals and preventing lipid peroxidation, thereby supporting cognitive health [55, 56]. Additionally, vitamin E deficiency causes defects in neural development and function, underscoring its essential role in maintaining healthy nervous system physiology, which underpins cognitive capacity [55]. High-salt intake can impair cognitive function through mechanisms including synaptic impairment, the inflammatory pathway, and autophagy dysregulation [57–60]. High-salt intake impedes synaptic function by elevating reactive oxygen species (ROS) and suppressing Brain-Derived Neurotrophic Factor (BDNF) [60]. Furthermore, high-salt intake activates IGF1R/mTOR/p70S6K signaling, which results in loss in the hippocampus [58] (Fig. 1).

Fig. 1.

Proposed biological pathway linking antioxidants and sodium with Learning Difficulties

This study has several notable strengths. It is the first thorough investigation exploring the link between NP and LDs. We accounted for various potential confounding variables; however, the influence of remaining confounders, such as physical activity, stress, and sleep duration, cannot be completely ruled out. Data were gathered through face-to-face interviews using questionnaires to improve accuracy. Nonetheless, some limitations should be kept in mind when interpreting the results. Moreover, due to the study’s cross-sectional design, a causal relationship between NP and LDs cannot be determined. It is possible that adolescents modified their responses to the CLDS to lower their reported LDs. However, this type of bias would likely reduce the observed effect sizes, indicating that the actual associations might be stronger than those found. Additionally, as with other epidemiological research, measurement errors and participant misclassification cannot be excluded. Conducting multiple analyses across CLDS quartiles may also raise the chance of type I errors. Furthermore, the sample size was exclusively gathered from a single city in Iran which may limit the generalizability of the findings. We adjusted for potential confounders and used a validated FFQ to evaluate dietary intake.

Additional studies are necessary to enhance our comprehension of the connection between diet quality and LDs. This could be supported by creating new measures, like the Macronutrient Quality Index (MQI) [61]. Also, Future RCT studies are needed to investigate the causal effect of glutamine and vitamins C, A, and E, and their optimal dose of intake on LDs. Additionally, future studies should also consider additional confounding factors, including the level of stress, sleep duration, and level of physical activity.

Conclusion

Adherence to a low antioxidant and glucose-content NP diet, characterized by higher glutamine levels and lower intakes of glucose, fructose, vitamins C, A, and E, and potassium, was positively associated with the odds of being in the highest CLDS tertiles. Additionally, high adherence to a low-sodium diet was associated with lower odds of being in the highest CLDS tertile. Further studies, particularly those with prospective designs, are needed to confirm these findings.

Abbreviations

CLDS

Colorado Learning Difficulties Score

NP

Nutrient Pattern

LD

Learning Difficulty

FFQ

Food Frequency Questionnaire

USDA

United States Department of Agriculture

KMO

Kaiser-Meyer-Olkin

OR

Odds Ratio

CI

Confidence Interval

SFA

Saturated Fatty Acids

PUFA

Polyunsaturated Fatty Acids

MUFA

Monounsaturated Fatty Acids

DHA

Docosahexaenoic Acid

EPA

Eicosapentaenoic Acid

Vit

Vitamin

SPSS

Statistical Package for Social Sciences

VAD

Vitamin A Deficiency

GABA

Gamma-Aminobutyric Acid

RCT

Randomized Controlled Trial

MQI

Macronutrient Quality Index

SBMU

Shahid Beheshti University of Medical Sciences

ROS

Reactive Oxygen Species

BDNF

Brain-Derived Neurotrophic Factor

Authors’ contributions

All authors contributed to writing and reviewing the manuscript critically for important intellectual content and final approval of the version to be published. RR: Writing – original draft, Data sampling, Visualization, Project administration, Methodology, Conceptualization. ARP: Writing – original draft, Visualization, Project administration. MST: Writing – original draft. LZBB: Writing – original draft, Data analysis. MRJ: Review & editing, Supervision, Methodology, Conceptualization.

Funding

This study was supported by Shahid Beheshti University of Medical Sciences.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The research was approved by the Ethics Committee of the National Nutrition and Food Technology Research Institute at Shahid Beheshti University of Medical Sciences, Iran (IR.SBMU.NNFTRI.REC.1403.016). The study adheres to the principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants. For participants under 16 years of age, informed consent was also obtained from their parents or legal guardians before enrollment. All personal information about participants will be kept secure in a database.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.