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The Impact of Eating Frequency on Facial Measurements and Aggressive Behavior in Adolescents
*Corresponding author: Rajen Dey, Department of Medical Laboratory Technology, School of Allied Health Sciences, Swami Vivekananda University, Telinipara, Barasat-Barrackpore Rd, Bara Kanthalia, West Bengal, India. rdrajen422@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Majumder T, Dey R, Bysack M, Ghosh S. The Impact of Eating Frequency on Facial Measurements and Aggressive Behavior in Adolescents. Acad Bull Ment Health. 2026;4:59-69. doi: 10.25259/ABMH_56_2025
Abstract
Objectives:
Picky or fussy eating is a common issue in adolescents, often leading to malnutrition and, in some cases, polyphagia. Facial anthropometry serves as a valuable tool to assess physiological processes and behavioral variations in children. However, no prior study has examined the correlation between eating frequency (EF) and facial anthropometric parameters.
Material and Methods:
This study investigated the relationship between eating habits, facial measurements, and aggression, along with hormonal parameters, in adolescents aged 11–14 years from West Bengal, India. Participants were divided into control and experimental groups. Facial anthropometry was assessed using Farkas landmarks, aggression was measured with the Buss and Perry Aggression Questionnaire (BPAQ), and serum cortisol, testosterone, and brain-derived neurotrophic factor (BDNF) levels were determined via enzyme-linked immunosorbent assay (ELISA).
Results:
Findings revealed that higher EF positively influenced the vertex-to-tragus distance and ear inclination angle. Conversely, low EF was associated with increased anger and hostility, elevated serum BDNF, and reduced testosterone levels. Cortisol appeared to increase EF, likely through stress-induced mechanisms.
Conclusion:
This study highlights the novel association between EF, aggression, and facial anthropometry in adolescents. The findings provide insights for managing adolescent eating behavior and aggression through the integration of physiological and behavioral parameters.
Keywords
Adolescent
Aggression
Cortisol
Facial anthropometry
Fussy eating
INTRODUCTION
Picky eating, namely fussy eating, is very common for everyone; even children are not exempt from this. Fussy eating can be defined as the consumption of inadequate variety and amount of food through rejection. Moreover, various behavioral signs can also be identified by observing the pattern of eating in the post-pubertal age group.[1] Sometimes, environment, socioeconomic status, and family aesthetics promote changes in eating habits.[2] But these can be the external factors and cannot be the only factors to be considered as the primary reasons behind fussy eating. Especially in the post-pubertal age, children often become choosy regarding their diets, and at several times, it shows avoidance of eating green vegetables. Researchers have identified another reason for it to be the food neophobia, which is mainly the reluctance to eat by avoiding uncommon food items in their daily diet.[3] Nevertheless, much research showed that picky eating or fussy eating not only promotes their characteristic stubbornness but also ensures some physiological, cognitive, or anthropometric changes within them.[4] Regardless of the degree and pattern of fussy eating, it had a prevalence of 5.6- 59%.[5] A combination of fussy eating and food neophobia is strongly associated with the development of irritation and anxiety among children.[6] Mainly, stress hormones can be found as one of the key regulators for this type of behavior.[7] It is also claimed that the fussy eaters are very much prone to becoming overweight or obese, especially in post pubertal age.[8] On the other hand, a recent study among young Japanese women claimed that an imbalanced low-calorie diet significantly correlated with increased face height. Hence, facial anthropometry could be influenced by the specific eating pattern or behavior through the alteration of female sex hormones.[9]
However, in India, such work was performed to measure the prevalence of picky eating among children aged 5. Those were found to be related to the parental feeding pattern and genetic features.[5] It can be interpreted from such a randomized, controlled study that daily oral nutritional supplements (ONS) can be used as an effective way to promote so-required growth in young children who tend to an extreme fussy eating tendency consisting of a milk-based ONS & another lactose-free ONS. The simultaneous effects of both the ONS and proper dietary counselling influence outcomes, along with catch-up growth and a healthy lifestyle. Apart from that, proper adaptation of health-related policies with routine nutritional screening and professional training can overcome the hazards of fussy eating-related malpractices.[10]
Aggressiveness can also develop different eating patterns amongst the post-pubertal age group[11] and can be identified with the help of cognitive ergonomics. On the other hand, various facial anthropometric parameters have already established as markers of aggression and are considered to be key biomarkers for cognitive ergonomics.[12] Numerous studies promote the fact that stressful eating leads to obesity for an individual, irrespective of any population [13], whereas increased levels of cortisol and testosterone may lead to the regulation of aggression within an individual.[14] Previous research suggested that higher testosterone levels, along with lower cortisol, are directly linked to aggressive behavior in human subjects.[15] Moreover, brain-derived neurotrophic factor (BDNF) could play a crucial role in synaptic plasticity and cognitive behavior.[16] Apart from that, BDNF also regulates eating behavior and body weight. Human and rodent studies suggested that BDNF can influence the signaling pathways in appetite-regulating centers of the brain.[17]
Though anthropometry may help to measure the type of obesity in an individual to justify the nature of eating, especially waist and hip circumference, body mass index (BMI), ponderal index (PI), somatotyping, and many more.[18]But correlating anthropometries with biochemical markers may give the major findings in this area of research. An anthropometric study with Chinese school-going children confirmed that fussy eating behavior had a negative effect on body growth.[19] However, no such studies were conducted that could correlate any facial anthropometric parameter with fussy eating behavior, particularly in the post-pubertal stage. Considering this scenario, the following double-blinded study has been designed. Where the findings will ensure that alteration of some facial anthropometric parameters in adolescent (11 to 14 years) subjects may help to identify their pattern of eating in correlation with the regulation of some of the biochemical parameters namely testosterone, cortisol, and BDNF, or else aggression can be said as instrumental in relation with picky eating within the post puberty needs to be hypothesized. Therefore, the main objective of this study is to figure out whether fussy eating is a habit or a stress-induced manifestation and to identify the suitable markers correlated with it. Interestingly, we have tried to correlate fussy eating with a few facial anthropometric parameters along with the potent biochemical markers that have not been investigated so far. Additionally, Buss and Perry Aggression Questionnaire (BPAQ) parameters were also identified to justify the possible alterations in the eating pattern as well as facial anthropometry for the adolescent population.
MATERIAL AND METHODS
Selection of area and human subjects
This cross-sectional study was conducted at Barrackpore and its associated areas of West Bengal suburbs, where children aged 11 to 14 years were arbitrarily/randomly selected. The sample size of the adolescent age group was arbitrarily chosen from the semi-urban part of Bengal, based on our inclusion criteria. At first, 110 students were targeted by different schools, and their parents’ consents were taken. Later on, depending upon their age, availability, and medical history, only 86 subjects aged 11 to 14 years were selected to run this particular research. The inclusion criteria were determined to avoid factorial interferences in drawing a hypothesis. The study intervened on both genders (male and female), irrespective of one, to get a broader perception in finding out significant changes in eating frequencies, correlating hormonal as well as behavioral discrepancies.
The sample size estimation was guided by the formula for comparison of two independent means, as the primary outcomes (facial anthropometric parameters and aggressive behavior scores) were continuous variables and the study design involved comparison between two groups.
The formula used was n = [2 (Zα + Zβ)2 σ2] / d2
Where,
n = sample size per group
Zα = 1.96 (95% confidence interval)
Zβ = 0.84 (80% power)
σ = estimated standard deviation of behavioral/anthropometric scores
d = minimum expected difference between groups
Based on prior literature and pilot observations in adolescent behavioral and anthropometric studies, a moderate effect size was assumed. Using conservative estimates for standard deviation and detectable difference, the calculated minimum sample size ranged between 35 and 45 participants per group. Considering feasibility, availability of participants, and ethical constraints, an initial target of 110 adolescents was approached.
After obtaining written informed consent from parents/guardians, screening was performed based on age, medical history, and data completeness. Finally, 86 healthy adolescents fulfilling the inclusion criteria were enrolled for analysis.
This study was performed following the human ethical guidelines of the Institutional Ethical Committee (Human), Department of Physiology, Hooghly Mohsin College, Chinsurah, West Bengal, as per ICMR (GOI) recommendations. Ethical approval number: HMC/IEC/SG/05 dated 04.10.2015.
Measurement of physiological parameters
The selected 86 subjects were divided into two groups [Figure 1] depending upon a few very basic physiological parameters like: body height (cm), body weight (kg), BMI (kg/m2), PI (kg/m3). Considering the Physical parameters like body height, body weight, subjects who showed significantly higher values were put into the experimental group (n=50), whereas the rest were decided as the control group (n=36).
At first, the body heights of the subjects were measured using an anthropometric rod, and body weights were determined using a digital weighing machine. These parameters were taken for the final calculation of BMI and PI according to the formula described earlier.
BMI was calculated using the following formulae: BMI= Body weight (kg)/Body height (m)2
PI was calculated as [20]: PI=Body weight (kg)/Body height (m)3
As the research refers upon the concept of frequent eating habits amongst the adolescent/post pubertal children of West Bengal, so the physical anthropometric parameters like height in cm, weight in cm, BMI in kg/m2 and PI in kg/m3 rendered to be used as the key indicators for understanding the nature of obesity or more precisely tendencies of increasing body weight in them. Though body weight can be the only key indicator for understanding obesity, to avoid guesswork, parameters mostly coherent with weight, such as likely height, BMI, and PI, have been taken into account. For example, a child with higher weight may be taller than others. So, in those cases, the normal range of BMI and PI will be considered to objectify the child into control groups other than experimental ones. Whereas increased body weight with normal or comparatively lower height will increase the value of BMI and PI, to be kept in the experimental group.
On the contrary, after considering the above-mentioned four physical anthropometric parameters, children with increased weight, BMI & PI were assigned to the experimental group and later on compared and contrasted with the rest/control group to obtain the desired hypothesis. It can also be said that the finding of the novel parameters for facial anthropometry correlating behavioral changes, along with assessment of eating frequencies, may also vary from sample to sample in assessing these fundamental anthropometric parameters. Facial dimensions can be changed for obese children, so that the EF can also vary, and vice versa can also happen for the same study population.
Assessment of cognitive parameters
For the assessment of cognitive parameters like anger, hostility, physical aggression, and verbal aggression, the revised Buss & Perry aggression questionnaire (1992)[21] was performed upon the subjects. It is basically a 5-point scale questionnaire that defines personal traits of individual members, which are related to cognition. The Pattern of scoring in the revised Buss & Perry aggression questionnaire was -1 (extremely uncharacteristic of me), 2 (somewhat uncharacteristic of me), 3 (neither characteristic nor uncharacteristic of me), 4 (somewhat characteristic of me), and 5 (extremely characteristic of me). In this study, physical, verbal aggression, anger, and hostility subscales were used to measure the trait aggressive behavior, trait anger, and trait hostility. The respective higher scores indicate a higher trait.
Measurement of facial anthropometric parameters
To correlate fussy eating with facial anthropometric parameters, all the subjects of the control and experimental groups were taken for five major facial measurements with their written consent as described earlier by Farkas [22] as five major landmarks of Farkas [Table 1]. The measured values were correlated to draw the interpretation.
| Parameters | Measuring tools | Unit |
|---|---|---|
| Distance between endocanthion and exocanthion (en-ex) | Digital slide calipers | mm (converted to cm for statistical calculation) |
| Distance between vertex and tragus (v-tr) | Digital slide calipers | mm (converted to cm for statistical calculation) |
| Distance between cheilion and tragion (ch-t) | Digital slide calipers | mm (converted to cm for statistical calculation) |
| Ear inclination | Goniometer | values expressed in degrees |
| Mento cervical angle | Goniometer | values expressed in degrees |
Quantification of serum cortisol, testosterone, and BDNF level
Some biochemical parameters, like serum cortisol level, serum testosterone level, and serum BDNF ,were also measured by the enzyme-linked immunosorbent assay (ELISA) method as per the guidelines provided by the manufacturer. At first, 5 ml of intravenous blood was collected in a vial without an anticoagulant, and serum was collected from the individual subjects of the two groups mentioned earlier. The net protein content of the individual serum sample was normalized before performing ELISA. Cortisol, testosterone, and BDNF levels were measured using the Corticosterone ELISA kit (ab108821), Abcam, the Human Testosterone ELISA Kit (KA0236), Novus Biologicals, and the Human BDNF ELISA Kit (RAB0026), Sigma Aldrich, respectively. The minimum detectable values were cortisol >280 pg/ml, testosterone >50 pg/ml, and BDNF >80 pg/ml as given in the manual. The overall intra-assay precision was <10%, and the inter-assay precision was <12%.
Analysis of the frequency of eating in specific intervals
The frequency of eating in the several intervals of the control and experimental groups was also recorded at three different parts of the day. Other than the normal timings of breakfast, lunch, and dinner, three basic intervals were considered as the intervals for picky eating within a day, which might affect body composition, metabolism, or behavioral alterations.[23]The first part of the interval was from 10.00 am to 1.00 pm, the second part of the interval was from 3.00 pm to 8.00 pm, and the third part of the interval was from 11.00 pm to 2.00 am [Table 2]. The following set of data was collected and correlated statistically to draw a satisfactory interpretation.
| Frequency of eating at three different times of day | ||
|---|---|---|
| In between breakfast & lunch | In between lunch & dinner | After dinner (late-night eating) |
| 10.00 am to 1.00 pm | 3.00 pm to 8.00 pm | 11.00 pm to 2.00 am |
Statistical analysis
All the data were collected and statistically analyzed using Minitab statistics software, Malavida, version 19.1.1.0, and correlation analyses were done by SPSS statistics software, IBM, version 25.0. p<0.05 was found to be the significance level. One-way model 1 analysis of variance (ANOVA) was performed between the groups. Scheffe's F-test had been done as a post hoc test for multiple comparisons when significant F values were found. 2 tailed t-tests were performed for eating occasions. The Odd’s ratio (OR) was evaluated to assess the association between the two variables.
RESULTS
The dimensions of the research in drawing hypotheses about the analyses of the outlined objectives have shown both remarkable and significant findings for the specific targeted age group. Firstly, the entire dataset of 16 parameters, namely physical parameters, facial anthropometric parameters, biochemical parameters, and cognitive parameters, has been examined and recorded from the children of the age group 11years to 14 years. It has been believed that at this age, children physically, biochemically, and behaviorally go through an immense transition, so this particular age has been chosen to proceed with this piece of research. Considering the physical parameters like height, weight, BMI & PI, children having higher values are placed in experimental groups, and the rest of the students with comparatively lower values are placed in the control group. So before going to examine the secondary parameters like aggression questionnaire of Buss & Perry (Revised 1992), Farkas landmark for facial anthropometric parameters, hormonal assays and quantification of BDNF, the primary physical parameters had been assessed to make two clear uneven groups with control and experimental subjects. The observations inferred like:
Comparisons in terms of anthropometry, biochemical analyses, and cognitive scores
Height and weight were the keys for categorizing children into two groups: overweight & normal. Primarily, the children were selected in an arbitrary manner, and after assessing their height and weight, the mean was calculated. Children with height and weight below the average (mean) or equivalent score are to be placed as controls, whereas those with height and weight above the average or mean value are to be considered as experimental. After forming groups, the individual means for the height and weight of each group were compared by one-way ANOVA, and it was observed that the mean height (cm) and weight (kg) for experimental children were significantly higher than their control counterparts [Table 3] [Figure 2A, B].
| Parameters | n | Mean ± SD | F value | p value |
|---|---|---|---|---|
| Comparison of physical anthropometric parameters | ||||
| Height (cm)-control | 36 | 129.89±4.93 | 7.92 | 0.015 |
| Height(cm)-experimental | 50 | 138.81±6.99* | ||
| Weight (kg)-control | 36 | 28.43±2.76 | 27.79 | <0.001 |
| Weight (kg)-experimental | 50 | 36.88±3.36* | ||
| BMI (kg/m2) -control | 36 | 16.89±1.98 | 3.20 | 0.097 |
| BMI (kg/m2)-experimental | 50 | 19.39±3.21 | ||
| PI (kg/m3)- control | 36 | 13.07±1.87 | 0.44 | 0.520 |
| PI (kg/m3)-experimental | 50 | 13.90±2.81 | ||
| Comparison of facial anthropometric parameters | ||||
| En-ex (cm)- control | 36 | 2.63±0.31 | 0.05 | 0.824 |
| En-ex (cm)-experimental | 50 | 2.59±0.37 | ||
| V-tr (cm)- control | 36 | 9.66±0.89 | 8.71 | 0.011 |
| V-tr (cm)-experimental | 50 | 10.91±0.75* | ||
| Ch-t (cm)- control | 36 | 10.07±1.06 | 2.77 | 0.120 |
| Ch-t (cm)-experimental | 50 | 10.85±0.76 | ||
| Ear inclination (degrees)- control | 36 | 6.86±0.38 | 6.50 | 0.024 |
| Ear inclination (degrees)-experimental | 50 | 7.88±0.99* | ||
| Mentocervical angle (degrees)- control | 36 | 51.14±2.04 | 0.38 | 0.551 |
| Mentocervical angle (degrees)-experimental | 50 | 50.00±4.54 | ||
p<0.05 was found to be the significant. SD: Standard deviation, BMI: Body mass index, PI: Ponderal index, ANOVA: Analysis of variance.
Moving on to the Farkas Landmarks of facial anthropometry, it has been observed that the parameters like distance between vertex and tragus (v-tr) were significantly higher amongst the experimental subjects than those of the control subjects (p<0.05). Even the value of ear inclination angle was found to be significantly higher (p<0.05) amongst experimental subjects than that of the control ones [Table 3] [Figure 2C, D].
In terms of hormonal data, serum cortisol (ng/dl) and serum testosterone (ng/dl) levels were found to be significantly higher amongst the experimental subjects than those of the control subjects (p<0.05) [Table 4] [Figure 3A, B].
Cognitive scores also showed remarkable outcomes for both groups. Each and every parameter, like physical aggression,verbal aggression, anger, and hostility scores, was found to be significantly higher amongst the experimental children than that of the control group [Table 4] [Figure 4A-D]. These signify extreme effects of psychosocial influences upon the growing parameters of this transitional age group. It also signifies that certain behavioral changes among the experimental children at this age were alarming.
| Parameters | n | Mean ± SD | F value | p value |
|---|---|---|---|---|
| Comparison of biochemical parameters | ||||
| Serum cortisol (ng/dl)- control | 36 | 10.12±0.81 | 8.50 | 0.012 |
| Serum cortisol (ng/dl)- experimental | 50 | 18.38±7.42* | ||
| Serum testosterone (ng/dl)- control | 36 | 22.47±5.70 | 7.69 | 0.013 |
| Serum testosterone (ng/dl)-experimental | 50 | 49.85±7.36* | ||
| Serum BDNF (ng/dl)- control | 36 | 0.31±0.17 | 0.973 | |
| Serum BDNF (ng/dl)- experimental | 50 | 0.31±0.12 | ||
| Comparison of behavioral/cognitive parameters [Buss & Perry aggression questionnaire (1992)] | ||||
| Physical aggression-control | 36 | 15.86±3.71 | 16.31 | 0.001 |
| Physical aggression-experimental | 50 | 28.85±7.74* | ||
| Verbal aggression-control | 36 | 14.91±8.26 | 23.64 | <0.001 |
| Verbal aggression-experimental | 50 | 30.05±2.94* | ||
| Anger- control | 36 | 13.91±5.26 | 29.84 | <0.001 |
| Anger- experimental | 50 | 18.09±5.96* | ||
| Hostility- control | 36 | 21.33±5.70 | 7.30 | 0.018 |
| Hostility-experimental | 50 | 32.48±9.50* | ||
p<0.05 was found to be the significant. SD: Standard deviation, BDNF:Brain-derived neurotrophic factor
Comparison of eating frequencies in specific time intervals
After an illustrative comparison of the assessed parameters, the analyses turn out to trigger many critical modes of judgement in correspondence to the eating occasions the two groups had followed. As the control group, which has been said to fall within the normal range of Height, Weight, BMI, and PI, showed comparatively fewer eating occasions than the overweight experimental data set. Though the study has not been suggested to evaluate gender specific variations, some common modes of interest to identify the speculations in terms of gender have also been evaluated in the study.
At first, the average number of meals for all the participants in the study was at least 4 (±1) times (calculating all the Eating occasions). So, the participants having fewer than 4 meals per day were codified as having low EFs, while those with 4 or more meals were designated to have high EFs. Now, depending upon the eating frequencies, the groups of control and experimental cannot be denoted due to some physiological reasons. Some of the subjects, irrespective of higher eating frequencies, also possess BMI & PI due to many factors, like higher metabolism, may be engaged in sports or any other extracurricular activities, but considering the facts, the eating behavior for each of them was then utilized in subsequent analyses.
Low EFs were found to be present for 33.3% (n=28) of the participants in the total participants/samples, of whom 80% (n=22) were females.
When the eating occasions were further investigated, it was observed that those with low EFs had only one eating occasion from 10 am to 1 pm, while those with high EFs had 2(±1) (rounded up) EFs, as seen in Table 5. This difference is significant when evaluated by an independent samples t-test [p < 0.05, Table 6]. Also, there was a significantly higher number of EFs during 3-8 pm for the high EF group compared to the low EF group.
| Intervals | EF_l/h | Mean | Std. deviation | Std. error mean |
|---|---|---|---|---|
| 10 AM TO 1.00 PM | Low EF | 1.0000 | - | - |
| High EF | 1.5000 | 0.52705 | 0.16667 | |
| 3.00 PM TO 8.00 PM | Low EF | 1.8000 | 0.44721 | 0.20000 |
| High EF | 2.7000 | 0.67495 | 0.21344 |
EF: Eating frequency
| T-test for equality of means | t | df | Significance level (2-tailed) | |
|---|---|---|---|---|
| 10 AM TO 1.00 PM | Equal variances not assumed | -3.000 | 9.000 | 0.015 |
| 3.00 PM TO 8.00 PM | Equal variances not assumed | -3.077 | 11.608 | 0.010 |
df: Degrees of freedom, EF: Eating frequency
Evaluation of odds ratio (OR) for high and low eating occasions against different parameters
The entire dataset was dichotomized based on the average value of all the parameters studied. The individual data points, if greater than the average, were marked as ‘high’ while the ones lower than the mean value of the variable studied were marked as ‘low’. Subsequently, the effect of the total no of meals per day was investigated with respect to the dichotomized dataset, and the evaluated OR was focused on drawing logical interpretations instead of common comparisons by one-way ANOVA. The results have been represented in Table 7.
| OR evaluated against dichotomized | OR value | 95% Confidence interval (CI) | |
|---|---|---|---|
| Lower limit | Upper limit | ||
| En-ex(cm) | 4.00 | 0.323 | 49.596 |
| V-Tr (cm) | 2.25 | 0.251 | 20.131 |
| Ch-t (cm) | 0.107 | 0.008 | 1.407 |
| Ear inclination | 1.714 | 0.131 | 22.513 |
| Mentocervical angle | 0.667 | 0.076 | 5.878 |
| BDNF (ng/dl) | 0.286 | 0.03 | 2.692 |
| Anger | 0.667 | 0.076 | 5.878 |
| Hostility | 0.444 | 0.05 | 3.976 |
BDNF: Brain-derived neurotrophic factor
The OR analysis revealed that for the participants with 4 or more eating occasions, there was a 4-times higher chance, a 2.25-times higher chance, and a 1.7-times higher chance that their en-ex, v-tr, and ear inclination were also higher than the average value in the dataset, respectively. While these measures demonstrated proportionality within this dataset, the ch-t was found to be lower in participants with higher than 3 eating occasions (EO). This optimization, irrespective of traditional comparisons between the control and experimental sets, is considered to be one of the major findings relating the facial anthropometry to eating patterns. These outcomes suggested that the increase in the number of eating timings shows an increase in the Farkas landmarks like en-ex, v-tr, and ear inclination, whereas the other parameters remain unchanged.
Measurement of BDNF concentration and subsequent ORs evaluation portrayed that an increase in BDNF was associated with a subsequent low no of EFs. There was no association found (OR=1) when cortisol and testosterone levels were investigated with respect to a high or low number of EFs [Table 7].
Buss and Perry's aggression analysis questionnaire analysis revealed that anger and hostility were likely to be higher in participants with lower EFs.
Correlation analysis
Spearman’s Rank Correlation was performed to analyze the association between the various parameters included in the study. Higher concentration of BDNF was associated with lower concentration of testosterone (Spearman’s rho = -0.81, p=0.015 or p<0.05). Higher cortisol levels were associated with higher EF (Spearman’s rho = 0.74, p=0.035 or p<0.05) [Table 8].
| Spearman's rho correlation | Testost- erone (ng/dl) | BDNF ng/dl) | Cortisol (ng/dl) | EOs | |
|---|---|---|---|---|---|
| Testosterone (ng/dl) | Correlation coefficient | 1.000 | -0.810* | -0.048 | 0.074 |
| Sig. (2-tailed) | - | 0.015 | 0.911 | 0.862 | |
| BDNF (ng/dl) | Correlation coefficient | -0.810* | 1.000 | -0.357 | -0.358 |
| Sig. (2-tailed) | 0.015 | - | 0.385 | 0.384 | |
| Cortisol (ng/dl) | Correlation coefficient | -0.048 | -0.357 | 1.000 | 0.741* |
| Sig. (2-tailed) | 0.911 | 0.385 | - | 0.035 | |
| EOs | Correlation coefficient | 0.074 | -0.358 | 0.741* | 1.000 |
| Sig. (2-tailed) | 0.862 | 0.384 | 0.035 | - | |
BDNF:Brain-derived neurotrophic factor, EOs: Eating occasions.
DISCUSSION
Intermittent eating or polyphagia is a very common eating disorder in children and the adolescent population throughout the world. Such kinds of eating disorders (ED) may affect cognition, body structure, and composition.[24]Apart from the common anthropometric measurements like body height, weight, BMI, PI, and waist-to-hip ratio, the facial morphology could play a crucial role in physiological processes, evolutionary science, or forensic investigation.[25]It has been well documented that facial characteristics are dependent upon nutritional status, hormonal levels, stress, lifestyle factors, and oral function.[26] Previous research suggests that testosterone levels can actively modulate the facial structures in both male and female candidates. Moreover, testosterone is also considered a potent inducer of aggressive and modified social behavior.[27] Although several researchers had tried to find out the relationship between eating behavior and sex hormones.[28]
But no experiments were conducted to correlate intermittent eating behavior with facial anthropometry, particularly in the adolescent population of West Bengal. Therefore, our current study aimed to answer this unresolved question, which also includes the measurement of aggressive behavior (both physical and verbal), and some hormonal parameters among the experimental population. Moreover, this basic study could justify the possible reasons behind the dependency of those parameters, which will definitely enrich the existing knowledge of the particular domain.
For conducting the experiments, the children of the age group 11 to 14 years were selected and divided into control and experimental groups, considering some physical parameters (height, weight, etc.). When coming to the facial anthropometric parameters, the distance between the vertex and tragus (v-tr) and the ear inclination angle were found to be markedly higher in the experimental groups. So, it can be assumed as an indicator of obesity. However, it was observed that the size, shape, and projection of the ear can influence the attitude and personality of an individual.[29] Here, it was observed that a higher value of ear inclination is proportional to anger and aggression. Literature also supports that specific craniofacial measurements could predict the degree of aggression in men.[30] Simultaneously, biochemical analyses of serum indicated that higher levels of stress hormone cortisol and sex hormone testosterone modulate the degree of physical and verbal aggression, hostility in the experimental group.[31]
After calculating the EF among the control and experimental groups, it can be concluded that the experimental groups having higher eating frequencies showed higher values of the distance between endocanthion and exocanthion (enex), vertex to tragus distance (v-tr), and ear inclination angle with respect to that of control subjects. Therefore, it can be speculated that the eating patterns of experimental groups are somehow correlated with the facial anthropometric parameters, which were supported by a previous study.[32]From the correlation analysis, it was also evident that a higher cortisol level is associated with higher eating occasions. This observation indicates that cortisol induced stress response could facilitate the polyphagia in children.[33] which is assumed to be the reason behind the early onset of diabetes. On the other hand, lower EF is associated with higher BDNF levels, anger, and hostility. It was justified from the existing literature that eating disorders can deplete the serum BDNF level compared to healthy individuals.[34] Psychiatric studies revealed that serum BDNF level is positively correlated with the intensity of psychiatric manifestations, which might be a reason behind the elevated anger and hostility in the presence of an augmented level of BDNF, as depicted from our experimental data. It could also be a possible reason behind the lower eating frequencies in those subjects. The higher concentration of BDNF was also found to be associated with the lower concentration of testosterone, which could be taken as a potent biomarker of depression, especially in the adolescent population.[35]
Therefore, from this study, we can conclude that higher EF among the adolescent population of West Bengal positively influenced the vertex to tragus distance and ear inclination angle. However, the anger and hostility were elevated when the eating frequencies were low in the presence of higher serum concentrations of BDNF and lower testosterone levels. Cortisol could elevate the eating frequencies, possibly as a part of the stress response in the adolescent population. This research work was restricted to only a particular area to avoid geographical sequels upon the subjects, and also due to the shortage of manpower, whereas to improve the hypothesis, extensive research is required. Even the age group was also restricted to 11 to 14 years, which involves various hormonal changes, and is able to justify the kind of stress they are having in their day-to-day adolescent life. It was presumed that the significant ups and downs in the regulations of physiological, anthropometric, and cognitive parameters found in this particular dataset are valid for this part of the world.
CONCLUSION
From the above research, it is confirmed that growing age alters the values of various body dimensions remarkably, as well as facial anthropometry. These anthropometric parameters can be novel predictors not only for the biochemical markers but also for many other endogenous parameters. In this particular study, it has been observed that elevated values of V-Tr, i.e., distance between vertex and tragus, along with the ear inclination joint, can be considered as novel indicators of facial dimensions to identify the tendency of fussy eating amongst the boys of 11 to 14 years in Bengal suburbs. On the other side, boys with higher serum cortisol levels showed increased frequencies of eating with significantly higher anger and hostility scores, confirming their behavioral assortments. So, leading to this bridge formed within facial anthropometry, the behavior of eating and biochemical outputs like serum cortisol portray a predictive hypothetical triad satisfying the objective for the assessed population of this particular study. Though sample size and geographical restrictions limit the use of more experimental evidence, the experimental basis of special relativity suggests exploring a clear indication of correlations between facial anthropometry, biochemical triggers, and behavioral manifestations in terms of eating habits amongst the adolescent population.
Acknowledgment:
The authors are highly thankful to the Department of Medical Laboratory Technology, Swami Vivekananda University, Barrackpore and the Department of Physiology, Hooghly Mohsin College, West Bengal.
Authors’ contributions:
TM, SG: Participated in the conception and design of the study; TM: Performed all experiments; TM, RD: Undertook all statistical analyses; TM, RD, MB: Wrote the manuscript. All approved the final manuscript before submission.
Ethical approval:
The research/study was approved by the Institutional Review Board at Hooghly Mohsin College, Chinsurah, West Bengal, number HMC/IEC/SG/05, dated 04th October 2015.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient has given consent for clinical information to be reported in the journal. The patient understands that the patient’s names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Financial support and sponsorship: Nil.
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