Bryl et al.
Journal of Physiological Anthropology(2023) 42:29
https://doi.org/10.1186/s40101-023-00344-1

ORIGINAL ARTICLE

Journal of
Physiological Anthropology

Open Access

FTO and MC4R polymorphisms, and selected
pre‑, peri‑ and postnatal factors as determinantsof body mass index and fatness in children:
a thorough analysis of the associations
Ewa Bryl1* , Paula Szcześniewska1, Agata Dutkiewicz2, Agnieszka Słopień2, Monika Dmitrzak‑Węglarz3 andTomasz Hanć1

Abstract
Background Overweight and obesity among children have become significant global health concerns. Previous
studies have highlighted the potential role of genetic factors, particularly polymorphisms in the FTO and MC4R genes,
as well as environmental factors in the development of childhood obesity. This study aimed to investigate the rela‑tionships between genetic, socioeconomic and perinatal factors, adverse childhood events (ACEs), and lifestyle,and their impact on overweight, obesity and body composition parameters in children. Additionally, we exploredpotential interactions between genetic factors and ACEs.
Methods Four hundred fifty-six children aged 6–12 years participated in our study. Information on the socioeco‑
nomic status, perinatal factors, ACEs and lifestyle of the children was collected with a questionnaire completed
by their parents/guardians. We examined the children’s body weight and conducted an electrical bioimpedance
analysis. Overweight and obesity were diagnosed based on the International Obesity Task Force and McCarthy criteria.
We genotyped two selected polymorphisms in the FTO and MC4R genes using the TaqMan SNP allelic discrimination
method.
Results Higher BMI (Body Mass Index) z scores were related to higher paternal BMI and lower maternal age
at the child’s birth. Higher FMI (Fat Mass Index) z scores were associated with higher paternal BMI, increased gesta‑
tional weight, lower maternal education and the presence of the FTO risk allele. Higher FatM (fat mass in kg) z scores
were linked to lower maternal education, lower maternal age at the child’s birth, higher maternal body weight gain,
paternal BMI and the presence of the FTO risk allele. Moreover, interaction effects were observed on BMI z scores
between ACE and FTO AA, and on FMI z scores and FatM z scored between ACE and MC4R CC.
Conclusions The contribution of environmental factors is more strongly related to changes in body composition
than genetic ones. Additionally, the presence of the risk allele combined with unfavourable environmental factors
like ACEs leads to visible interaction effects, resulting in increased BMI z scores and FMI z scores in children.
Keywords Body composition, Children, BMI, FTO rs9939609, MC4R rs17782313, Environmental factors, ACE*Correspondence:
Ewa Bryl
ewa.bryl@amu.edu.pl
Full list of author information is available at the end of the article© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Bryl et al. Journal of Physiological Anthropology(2023) 42:29

Introduction
The problem of overweight and obesity affects anincreasing number of people, including children. According to the latest data from the World Health Organization (WHO), 39 million children under the age of 5 were
overweight or obese [1]. Research is ongoing to identify
factors influencing this phenomenon, which can be categorised as genetic and environmental ones. Genetic factors include gene polymorphisms that may be associatedwith a higher risk of developing overweight and obesity.
The previous meta-analysis indicated that the polymorphism of the FTO rs9939609 gene is related to bodyweight, BMI (Body Mass Index) and body fat content not
only in adults but also in children [2]. In a group of children and adolescents aged 6–19 years, the presence of atleast one risk allele of the FTO gene was associated withhigher BMI, BMI z scores and adiposity [3, 4]. In the children, the presence of the unfavourable allele nearly doubled the risk of binge eating [3]. Similar findings emerged
concerning the MC4R gene polymorphism. The resultsof studies carried out by Ho-Urriola et al. on a cohortof children also revealed that the MC4R rs17782313
polymorphism may be associated with the risk of overweight and increased food consumption in children aged
6–12 years. The presence of the C allele was associatedwith increased eating pleasure, reduced satiety and agreater tendency to eat without hunger, which may contribute to childhood obesity [5].
Family environment is a major factor for the children’s
health. Children raised by a single parent have a higher
risk of experiencing overweight and obesity [6]. In children older than 7, the association between the family
structure and the child’s BMI increases and is mediatedby household income levels [7]. Low income itself is a risk
factor for higher BMI among children [8]. Various parental factors hold importance in shaping children’s BMI andnutritional status. For instance, the mother’s BMI both
before and after the child’s birth, the father’s BMI, andthe parents’ level of education significantly contribute tothis aspect [9, 10]. The children’s BMI values were more
frequently within the recommended range for their age
when their parents had a higher level of education and ahigher income per family member [9, 10]. In Serbia, forinstance, mothers’ educational attainment proved to be
the most influential factor in determining the children’s
nu’ritional status [11]. Additionally, there is a positive
correlation between parental BMI and the BMI of theirchildren, indicating that higher parental BMI is linked toincreased BMI in the children [12]. Specifically, a higher
paternal BMI has been identified as a factor contribu’ing
to elevated body weight and body fat percentage in thechildren [13], while a higher maternal BMI before pregnancy increases the risk of overweight and obesity in the

Page 2 of 35

offspring [14]. The maternal age also exerts an impact
on the children’s body weight. A study conducted by
Potocka et al. found that younger maternal age was associated with higher BMI z scores and a higher percentage
of energy intake in the children’s diets [15].
Perinatal factors represent additional variables that
can have implications for children’s weight and bodycomposition. Both a low (< 2500 g) and high (> 4000 g)
birth weight have been associated with potential adverse
effects on children’s future health. A low birth weight
may be associated with a higher risk of metabolic syndrome and central obesity [16]. On the other hand, a highbirth weight may also contribute to a higher risk of overweight and abdominal obesity among children in Poland[17]. Another factor related to the child’s health is breastfeeding. The results of a study conducted in 12 countries
show that breastfeeding may protect from obesity andexcessive body fat levels in children aged 9–11 years [18].
It is an important component of reducing BMI in children and lowering the risk of obesity [14]. The type ofbirth also matters. A recent meta-analysis indicates thata c-section (caesarean section) is associated with childhood obesity, yet not overweight [19].
In addition to genetic, socioeconomic and perinatal factors, lifestyle plays an important role in weightchanges among children. A healthy diet, regular meals[20, 21], physical activity [21] and limited screen time areprotective factors against the development of overweight
and obesity in children. Spending more than 2 h [22] ormore than 4 h [23] in front of the screen was associatedwith greater likelihood of overweight/obesity. The lengthof sleep may also be related to the children’s weight. In
a study by Resiak et al., sleeping less than 5 h was associated with a higher risk of overweight/obesity in thechildren [9]. The sleep length is positively affected bya bedtime routine, which is less often observed in households with many children and among the children of
mothers with a low education [24], highlighting how allthese factors relate.
Previous studies have shown that adverse life events inchildhood (ACEs) may have a significant role in changes
in the weight and body composition of children [25–29].
Most studies focused on the effect of ACEs on the occurrence of changes in body weight in adulthood [30]. However, there are still no clear conclusions about the impactof ACEs on changes in body weight in childhood. Metaanalyses have shown that ACEs are associated with bothchildhood obesity [25–27] and underweight [31, 32].
Even less is known about the relationship between ACEs
and body composition parameters. A study by Derkset al. conducted among Dutch children shows that ACEs
do not have a significant effect on the body composition,and the most important factors are the socioeconomic

Bryl et al. Journal of Physiological Anthropology(2023) 42:29

conditions [28]. However, a study by Deng et al. findsthat ACEs are associated with a greater increase in BMI
and FMI (Fat Mass Index) in children aged 5–17 years
with significant gender differences [29].
The aim of our study was to investigate the potential
associations between the BMI z scores, FMI z scores,
FFMI (Fat Free Mass Index) z scores, FatM (Fat Massin kg) z scores and the socioeconomic status, perinatal
factors, parental factors, the children’s lifestyle, ACEs
and FTO rs9939609 and MC4R rs17782313 polymorphisms of Polish children aged 6–12 years. The study was
exploratory.

Material and methods
The size of the study group was determined based ondata published by the Statistics Poland [33], which indicated an approximate population of 36,000 primaryschool children in Poznan at the time of the research.
To calculate the specified confidence level of 95%, astructure index of 18%, and an estimation error of 5%, asample size of 225 or greater was calculated to ensure arobust representation. The study group consisted of 456
children with a comparable number of boys and girls(52.19 and 47.81%, respectively). The children were aged
6 to 12 years (mean = 8.99, SD = 1.32). The study was conducted between March 2017 and November 2019 across
11 randomly selected state primary schools in Poznan.
It was funded by the National Science Centre, Poland,
grant number: 2016/21/B/NZ5/00492 and approved by
the Institutional Bioethics Board of Poznan University of
Medical Sciences (approval no. 542/14). Prior to participation, the parents or legal guardians of the participantsreceived written information about the study along withconsent cards and questionnaires.
The collected material included information and measurements of 530 individuals (276 boys and 254 girls)
aged 6–12 years (mean = 8.99 SD = 1.32). After eliminating cases with missing information on body composition
parameters or the FTO and MC4R gene polymorphisms
determination, the final database included 456 children.
The data on the socioeconomic status, ACEs and gene
polymorphisms were collected for each child, however,there was a small rate of missing data for certain variables, such as family type (n = 12, 2.63%), place of residence (n = 27, 5.92%), parental subjective assessment of
economic situation of the family (n = 17, 3.73%), gaining
weight during pregnancy (n = 20, 4.39%), pregnancy duration (n = 31, 6.80%), single/twin delivery (n = 19, 4.17%),
type of delivery (n = 18, 3.95%), birthweight (n = 13,
2.85%), breastfeeding (n = 13, 2.85%), maternal BMI category before pregnancy (n = 28, 6.14%), maternal current
BMI category (n = 23, 5.04%), maternal educational level(n = 15, 3.29%), paternal current BMI category (n = 27,

Page 3 of 35

10.31%), paternal educational level (n = 32, 7.02%), siblings (n = 20, 4.39%), life threat (n = 28, 6.14%), witnessof life threat (n = 28, 6.14%), violence victim (n = 28,
6.14%), violence witness (n = 30, 6.58%), death of someone close (n = 28, 6.14%), family conflicts (n = 29, 6.36%),
separation from parents (n = 30, 6.58%), school problems(n = 3, 6.80%), other ACEs (n = 33, 7.24%).
Anthropometric measurements

Each child was measured by qualified medical personnel.
Body height was checked with a Seca 213 stadiometer(with an accuracy of 1 mm), and body weight (with anaccuracy of 0.01 kg) and electrical bioimpedance with theTanita MC-780 body composition analyser. During
the body composition examination the children wearing
light clothing (a blouse or shirt and skirt or trousers), withno outer clothing (a jacket or coat). They were barefootand placed appropriately on the electrodes while ensuring
proper electrode positioning. The values of raw indicators, such as FatM in kg, were analyzed. Subsequently,
BMI, FMI, and FFMI were calculated using the following
equations: BMI = body weight [kg]2 , FMI = body fat mass [kg]2 ,(body height[m])FFMI = fat free body mass [kg]
2 .

(body height[m])(body height[m])The BMI was adjusted for sex and age on the basis of
WHO growth charts [34] with the use of WHO AnthroPlus software and presented as z scores. FMI, FFMI and
FatM were standardised by age and sex within the sample. The diagnosis of overweight, obesity, and underweight was determined following the guidelines of theInternational Obesity Task Force (IOTF) [35, 36] andbased on body fat cut-off points obtained using the bioimpedance method (McCarthy criteria) [37].
Socioeconomic and lifestyle factors

In the parental questionnaire, questions pertaining to thesocioeconomic status enabled the classification of families into three types: both biological parents, a biologicalparent with a partner, or a single parent. The place of residence was determined based on the number of residents
and categorised as a village (< 10 000 residents), small
and medium-sized town (10 000 – 100 000 residents) orlarge city (> 100 000 residents) [38]. According to Gross
Domestic Product (GDP) per capita data, the financial
situation in the region was comparable across all placesof residency [39]. The economic situation was assessedsubjectively by the parent and categorised as: bad, average or good. The ‘bad economic situation’ group comprised the children whose parents reported facing serious
difficulty or difficulty in meeting the family’s needs, whilethe ‘average economic situation’ group included children whose parents reported some difficulty. The ‘good
economic situation’ group consisted of the children

Bryl et al. Journal of Physiological Anthropology(2023) 42:29

whose parents indicated that meeting the family’s needs
was quite easy or easy. The maternal weight gain during pregnancy was assessed based on the gynaecologist’s
opinion and categorised as: ‘exceeded’ or ‘non-exceeded’in accordance with the guidelines from the Institute ofMedicine. The recommended amount of weight gain
depends on the pre-pregnancy BMI (for women who had
a BMI < 18.5 before pregnancy, the recommended weightgain is 12.5–18 kg; for women with a pre-pregnancy BMIof 18.5–24.9 the recommended weight gain is 11.5–16;
women with pre-pregnancy BMI 25–29.9 should aim
for weight gain between 7–11.5, and women with prepregnancy BMI ≥ 30 should not exceed weight gain
between the range of 5–7 kg) [40]. Pregnancy duration
was divided into three categories:: < 37 weeks (preterm
birth), 37–42 weeks (term birth) and > 42 weeks (postterm birth) [41]. The information on maternal healthconditions before and during pregnancy, such as hypertension, diabetes, thyroid diseases, kidney diseases andheart failure was collected from parental responses (yes/no). We distinguished a single or twin pregnancy and avaginal or c-section delivery. The birthweight was classified into 3 categories: < 2500 g, 2500-4000 g, > 4000 g [42].
Additionally, information on breastfeeding (yes/no) was
obtained.
The parents declared their weight and height, on thebasis of which we calculated BMI. Maternal BMI before
pregnancy, maternal current BMI and paternal current
BMI were classified according to WHO guidelines as:
underweight, proper weight, overweight or obesity [43].
The educational level of the parents was categorised as:
primary (8 years of education), vocational (10 years),secondary (12 years) or university qualifications (bachelor’s – 15 years or master’s degree – 17 years). We asked
about the presence of siblings in the household (yes/no). Regarding the children’s lifestyle, sleep length wascategorised as ≥ 9 h or < 9 h [44], eating behaviours wereclassified as either at least 3 regular meals/day + a snack
or irregular meals. Physical activity was divided into
exercise 3 days/week for ≥ 3 h or less [45], and screen
time as ≤ 2 h or more than 2 h [46].
Measurements of adverse life events in childhood

Measurements of ACEs in the children were collectedusing a survey method. The parents or legal guardians
received forms containing questions related to the occurrence of specific experiences in the child’s life. The questionnaire was developed based on selected questions
from the Traumatic Events Screening Inventory (TESI)
questionnaire, which originally consists of 24 questions. In order to simplify the form, we condensed thequestionnaire to 9 questions concerning adverse events

Page 4 of 35

experienced by the children. The parents were asked toindicate whether:
1. The life or health of the child was threatened.
2. The child experienced an event in which the life or
health of another person was endangered or someone died.
3. The child was assaulted physically (e.g. hitting, pushing, choking, shaking, biting, burning, forced into any
type of sexual activity) or psychologically (e.g. mocking, gossiping, shouting, threatening, being rejectedby someone close).
4. The child witnessed physical or psychological assault.
5. The child experienced the death of someone close.
6. The child experienced serious family problems (e.g.
quarrels, conflicts, fighting, parting, alcohol problems
or other addictions, emotional or mental problems offamily members).
7. The child was separated from parents for an extended
period.
8. The child encountered serious problems at school,such as being at the risk of failing or repeating a
grade.
9. The child experienced other stressors that were notmentioned in the questionnaire.
Genetic tests (FTO rs993960, MC4R rs177823139)

Genes and polymorphisms were selected based on theirpreviously established relationship with vulnerability tooverweight and obesity (FTO rs9939609) and changes
in food intake (MC4R rs17782313). Saliva samples werecollected by qualified medical personnel and DNA wasextracted from the saliva following the designatedprotocol. The selected polymorphisms were genotyped using
the Taq-Man single-nucleotide polymorphism (SNP)
allelic discrimination method with the ABI 7900HT system (Applied Biosystems). The Real-Time PCR reaction
employed commercially available TaqMan Genotyping
assays for accurate results.

Statistical methods
All statistical analyses were performed using STATISTICA 13 software. The threshold of statistical significance was set at p < 0.05. Analyses were conducted ontindividuals with complete information on the FTO andMC4R gene polymorphisms and body composition. For
comparison analyses involving continuous dependent
variables (BMI z scores, FMI z scores, FFMI z scores,
and FatM z scores), we utilized the two-tailed t-test
for independent groups. When comparing continuous
dependent variables across more than two groups, weemployed analysis of variance (ANOVA). To assess group

Bryl et al. Journal of Physiological Anthropology(2023) 42:29

Page 5 of 35

differences in the BMI z scores (F = 2.72, p = 0.07), and
FFMI z scores (F = 2.74, p = 0.07) (Table 2).
In the group of girls, obesity diagnosed on the basis of
BMI was more frequent among AT heterozygotes compared to TT homozygotes (4.59% vs 1.38%, χ2 = 14.14,
p = 0.03) of the FTO rs9939609 gene. BMI z scores(F = 3.17, p = 0.04) and FatM z scores (F = 3.41, p = 0.04)
were higher in heterozygotes than in TT homozygotesof the FTO gene. Girls homozygous for the CC genotype
had higher FatM z scores than those homozygous for
the TT genotype of the MC4R gene (F = 3.24, p = 0.04)(Table 3).
In the group of boys, the incidence of underweight,
overweight and obesity diagnosed on the basis of BMI
and adipose tissue did not differ depending on the FTO
and MC4R gene variant (Table 4). There were no differences in the BMI z scores, FMI z scores, FFMI z scores,
and FatM z scores between risk homozygous, heterozygous, and homozygous children without the risk allele of
the FTO and MC4R genes (Table 4).

differences in the ANOVA, Tukey’s multiple comparisonstest was performed. The chi-square test (χ2) was applied
for comparative analysis of categorical variables. In order
to identify the most important variables associated withBMI z scores, FMI z scores, FFMI z scores and FatM zscores the forward stepwise multiple regression wasapplied. Two-way ANOVA was used to assess the effects
of interaction between ACEs, FTO and MC4R gene polymorphisms on the BMI z scores, FMI z scores and FatM zscores. The analysis was applied for ACE as a categorizedvariable (0,1,2,3 +) and for the types of ACEs (occurrence
vs non-occurence). The size of effects was assessed with
Cohen’s d.

Results
Differences according to the sex

There were no differences between the prevalence ofunderweight, overweight and obesity diagnosed according to IOTF between the boys and girls; however, there
were differences when the diagnosis was based on bodyfat percentage. Obesity diagnosed according to body fat
tissue was slightly more common in the boys than in the
girls (8.81% vs 5.95%, χ2 = 11.70, p = 0.01) (Table 1).

Socioeconomic factors

The type of family (χ2 = 9.08, p = 0.44), place of residence(χ2 = 7.26, p = 0.22), subjective assessment of economic
status (χ2 = 4.52, p = 0.61) were not associated with theprevalence of underweight, overweight and obesity. The
type of family was not associated with the BMI z scores(F = 0.20, p = 0.90), FMI z scores (F = 0.36, p = 0.78), FFMIz scores (F = 0.05, p = 0.99) or FatM z scores (F = 0.26,
p = 0.85). There were no differences in the BMI z scores(F = 1.75, p = 0.18), FMI z scores (F = 1.28, p = 0.28), FFMIz scores (F = 0.55, p = 0.56) or FatM z scores (F = 1.37,
p = 0.25) depending on the place of residence. The parental subjective assessment of the economic situation of
the family was not related to the BMI z scores (F = 0.53,
p = 0.59), FMI z scores (F = 0.31, p = 0.73), FFMI z scores(F = 0.90, p = 0.41) or FatM z scores (F = 0.37, p = 0.69)(Table 5).

Gene polymorphisms

There were no differences between the occurrence ofunderweight, overweight and obesity diagnosed according to IOTF and McCarthy norms in children with therisk allele of FTO rs9939609 or MC4R rs17782313. No
significant differences were observed in the BMI z scores(F = 0.98, p = 0.38), FMI z scores (F = 2.26, p = 0.11), FFMIz scores (F = 0.19, p = 0.82), and FatM z scores (F = 2.63,
p = 0.07) between the children who were homozygotes
AA, heterozygotes AT and homozygotes TT of the FTO
gene. However, the children who were CC homozygotes
had higher FMI z scores (F = 3.11, p = 0.04) and FatMz scores (F = 4.09, p = 0.02) than the children without
the risk allele (TT) of the MC4R gene. There were no

Table 1 Prevalence of underweight, overweight and obesity diagnosed according to IOTF (Body weight status) and according to
McCarthy criteria (Body fat status) for both sexes
Variable

Body weight statusn

Sex

Body fat status

Underweight

Proper weight

Overweight

Obesity

Underweight

Proper weight

Overweight

Obesity

17

169

41

11

3.73%

37.06%

1

148

48

40

8.99%

2.41%

0.22%

32.60%

10.57%

28

8.81%

148

29

13

0

166

24

6.14%

27

32.46%

6.36%

2.85%

0.00%

36.56%

5.29%

5.95%

456

n
454

Boys
Girls
χ2

5.44

11.70

p

0.14

0.01

454

456

14.68%

31.65%

2.75%

21.56%

6.88%

5.30

0.51

χ2

p

208

45.81%

30

6.61%

95

20.93%

13

3.08%

0.44%

2.86%

14

0.40

p

23.95%

2

6.25

0.84%

χ2

57

47

15

22.81%

3.73%

32.35%

104

3.78%

17

Boys 2

Girls

All

77

69

6

32.02%

3.29%

14.71%

146

2.52%

15

Boys 9

Girls

All

35

32

3.21%

14.69%

2.85%

7

67

13

Boys 6

Girls

All

8.81%

40

5.07%

23

1.1%

5

6.3%

15

4.59%

10

5.48%

25

7.14%

17

6.88%

15

7.02%

32

3.78%

9

1.83%

4

2.85%

13

Overweight

2.64%

12

1.98%

9

0.66%

3

0.84%

2

1.38%

3

1.1%

5

2.1%

5

4.59%

10

3.29%

15

1.68%

4

0,00%

0

0.88%

4

Obesity

455

452

073

3.63

0.22%

1

0,00%

0

0,00%

0

0.89

2.27

0,00%

0

29.03%

63

0,00%

0

0.42%

1

32.72%

71

0.22%

1

0,00%

0

14.75%

32

0,00%

0

Underweightn

Properweightn

Underweight

Body fat status

Body weight statusasignificant differences in the post hoc test

45.13%

204

21.24%

96

3.1%

14

18.14%

43

2.3%

5

23.35%

106

28.69%

68

5.07%

11

30.62%

139

15.61%

37

3.69%

8

15.2%

69

Properweight10.18%

46

4.87%

22

0.88%

4

8.02%

19

3.23%

7

5.29%

24

8.86%

21

7.83%

17

7.05%

32

3.38%

8

1.38%

3

3.52%

16

Overweight

8.19%

37

4.87%

22

1.33%

6

5.91%

14

29.03%

63

4.63%

21

7.17%

17

32.72%

71

7.49%

34

3.8%

9

14.75%

32

2.64%

12

Obesity

F

p

F

p

SD

x

2.26
0.11

0.07

2.72

x

2.63
0.07

2.74
0.07

CC vs CT
CCvs ­T Ta

0.02

4.09

0.90 0.02 0.88 -0.05

0.92 0.15 0.89 -0.02

1.32 0.41 1.02 0.54

0.82

0.19

0.83 0.12 0.92 -0.07

3.11
CCvsTTa

SD

0.95

0.92

1.46

0.87

1.11

0.87

SD

FatM z scores

1.05 0.08 0.90 0.11

0.04

0.24 1.21 -0.13

0.38 1.19 -0.06

0.81 1.32 0.35

0.38

0.98

0.29 1.20 -0.15

0.40 1.24 0.02

x

FFMI zscores0.86 0.05 0.85 -0.13

SD

FMI z scores

0.20 1.22 -0.18

x

BMI z
scores(2023) 42:29

x – mean, BMI Body Mass Index, FMI Fat Mass Index, FFMI Fat Free Mass Index, FatM Fat Mass in kg

TT

CT

CC

MC4R

TT

AT

AA

FTO

Variable

Table 2 Prevalence of underweight, overweight and obesity diagnosed according to IOTF (Body weight status) and according to McCarthy criteria (Body fat status), andassociation between body composition parameters and genetic factors

Bryl et al. Journal of Physiological Anthropology
Page 6 of 35

218

218

n

3.63

0.73

p

41.28%

χ2

90

7.34%

22.94%

16

50

4.59%

3.67%

0.92%

10

8

9.17%

20

3.21%

7

0.92%

2

2.75%

6

2.29%

5

0.92%

2

217

0.84

1.42

46.54%

101

25.35%

55

4.61%

10

0.10

2

7.79

29.03%

63

32.72%

71

14.75%

32

Underweight

0.03

1.38%

3

217

n

14.14

4.59%

10

4.59%

10

0,00%

0

Obesityp

21.56%

6.88%

15

1.83%

4

Overweight

Body fat status

χ2

47

6.88%

31.65%

15

69

2.75%

14.68%

3.21%

6

32

Properweight7

Underweight

Body mass statusasignificant differences in the post hoc testx – mean, BMI Body Mass Index, FMI Fat Mass Index, FFMI Fat Free Mass Index, FatM Fat Mass in kg

TT

CT

CC

MC4R

TT

AT

AA

FTO

Variable

6.45%

14

4.15%

9

0.46%

1

2.3%

5

5.07%

11

3.69%

8

Properweight

7.37%

16

3.69%

8

1.38%

3

3.23%

7

7.83%

17

1.38%

3

Overweight46.54%

101

25.35%

55

4.61%

10

29.03%

63

32.72%

71

14.75%

32

Obesity

p

F

p

F

0.40

0.93

0.18

0.26

0.62

1.11

1.16

0.06

2.81

-0.13

-0.08

0.49

0.86

092

1.51

no ­differencesa

0.83

1.06

0.72

SD

AT vs ­T Ta

3.75

-0.24

0.11

-0.22

x

0.03

1.44

1.18

1.17

0.96

SD

FMI z scores

0.04

3.17

0.03

0.44

0.09

x

BMI z scores

0.40

1.00

0.002

0.12

0.26

0.76

0.28

0.07

0.08

-0.02

x

0.73

0.87

1.07

0.95

0.75

0.63

SD

CC vs ­T Ta

0.04

3.24

-0.04

-0.05

0.64

AT vs ­T Ta

0.03

3.41

-0.15

0.18

-0.17

x

0.90

0.92

1.69

0.91

1.11

0.72

SD

FFMI z scores FatM z scores

Table 3 Prevalence of underweight, overweight and obesity diagnosed according to IOTF (Body weight status) and according to McCarthy criteria (Body fat status), andassociation between body composition parameters and genetic factors in girls

Bryl et al. Journal of Physiological Anthropology(2023) 42:29
Page 7 of 35

FTO

236

238

8.36

0.21

χ2

50,00%

p

118

5.93%

19.07%

14

45

1.27%

2.54%

3

6

0,00%

0.42

0

6.02

p

21.56%

χ2

47

6.88%

31.65%

15

69

2.75%

14.68%

3.21%

6

32

Properweight7

Underweight

8.47%

20

6.78%

16

1.27%

3

4.59%

10

6.88%

15

1.83%

4

2.54%

6

1.69%

4

0.42%

1

1.38%

3

4.59%

10

0,00%

0

235

217

Overweight Obesity n

056

4.85

0.43%

1

0,00%

0

0,00%

0

0.70

3.80

0,00%

0

0.42%

1

0,00%

0

43.83%

103

17.45%

41

1.7%

4

18.14%

43

28.69%

68

15.61%

37

Underweight Proper
weightBody fat statusx – mean, BMI Body Mass Index, FMI Fat Mass Index, FFMI Fat Free Mass Index, FatM Fat Mass in kg

TT

CT

CC

MC4R

TT

AT

AA

n

Variable Body mass status

13.62%

32

5.53%

13

1.28%

3

8.02%

19

8.86%

21

3.38%

8

8.94%

21

5.96%

14

1.28%

3

5.91%

14

7.17%

17

3.8%

9

Overweight Obesity

p

F

p

F

SD
x

FMI z
scoresSD

x

SD

FFMI zscoresx

FatM zscores
SD

1.10

0.86

0.15

0.72

0.33

1.03 0.63 0.94 0.41

0.84

0.18

0.92

1.12

0.11

2.23

0.52

0.66

0.13

2.05

0.34

1.07

0.29 1.30 -0.14 0.93 0.03 0.99 -0.05 0.99

0.51 1.22 -0.04 0.93 0.17 0.92 0.01

1.08 1.14 0.16

0.52

0.65

054 1.18 -0.07 0.82 0.16 0.91 0.003 0.82

0.37 1.31 -0.06 1.03 0.07 1.02 0.04

0.29 1.40 -0.14 0.96 0.10 0.99 -0.09 0.98

x

BMI z
scoresTable 4 Prevalence of underweight, overweight and obesity diagnosed according to IOTF (Body weight status) and according to McCarthy criteria (Body fat status), andassociation between body composition parameters and genetic factors in boys

Bryl et al. Journal of Physiological Anthropology(2023) 42:29
Page 8 of 35

444

Good

437

427

34

248

46

10.48%

10

2.28%

10

2.28%

1

0.23%

0.22

p

56.21%

8.26

7.26%

χ2

8.20%

240

35

5

1.17%

5.39%

1.64%

31

23

p

7

9.08

0.44

χ2

7.21%

1.58%

4.05%

32

1.35%

7

18

6

58.56%

6.98%

51

2.73%

12

0.91%

4

11.71%

50

2.58%

11

0.70%

3

1.58%

7

1.35%

6

12.16%

54

16

1.37%

6

0.23%

1

3.75%

16

1.17%

5

0.23%

1

1.13%

5

0.23%

1

3.60%

16

Obesity

438

427

442

1

0,00%

0

0,00%

0

0.13

9.79

0,00%

0

0,23%

1

0.00%

0

0.09

15.16

0.00%

0

0.23%

1

0.00%

0

Underweight242

10.96%

48

2.97%

13

55.50%

237

8.20%

35

5.62%

24

7.47%

33

4.52%

20

57.01%

252

Properweight

54

3.42%

15

0.23%

1

13.35%

57

1.64%

7

0.94%

4

1.81%

8

1.13%

5

13.12%

58

Overweight

51

2.51%

11

0.46%

2

10.54%

45

2.58%

11

1.41%

6

2.26%

10

1.13%

5

11.09%

49

Obesity

t

p

F

p

SD

x

0.28 1.20

0.41 1.33

0.49 1.35

0.18

1.75

0.30 1.16

0.52 1.44

0.03 1.28

0.90

0.20

0.78

0.36

0.42 1.40 0.03

0.24 1.37 -0.05

-0.10

-0.01

-0.07

0,28

1,28

-0.10

0.09

-0.20

FMI z scores

0.30 1.17 -0.11

x

BMI z
scores

1.04

0.89

0.91

SD

0.94

0.98

0.73

0.88

1.21

0.86

0.99

0.05

0.11

0.06

0.06

x

0.07

0.04

0.36

0.56

0.58

0.06

0.17

-0.03

x

FatM zscores

0.88 -0.03

0.85 0.07

1.04 0.06

0.25

1.37

0.88 -0.03

0.86 0.17

0.91 -0.15

0.85

0.26

0.98 0.10

0.92 -0.04

0.86 -0.03

SD

FFMI z scores

0.98

1.00

0.76

0.92

1.28

0.90

1.05

1.02

0.94

SD

(2023) 42:29

Average

Bad

Parentalsubjective
assessmentof economic
situation

Large city(> 100 000
residents)

Smalland mediumsized town
(0–100 000
residents)

Village

Place of residenceSingle parent

Biological parents
with partner

260

31

Overweightn

Properweightn

Underweight

Body fat status

Body weight status

Both biological parents

Type of family

Variable

Table 5 Prevalence of underweight, overweight and obesity diagnosed according to IOTF (Body weight status) and according to McCarthy criteria (Body fat status), andassociation between body composition parameters and socioeconomic factors

Bryl et al. Journal of Physiological Anthropology
Page 9 of 35

Overweight

Obesity0.61

p56.49%

7.74%

4.52

χ2

11.62%

3.64%
0.85

2.7

0.23%

Underweightn

Properweightn

Underweight

Body fat status

Body weight statusx – mean, BMI Body Mass Index, FMI Fat Mass Index, FFMI Fat Free Mass Index, FatM Fat Mass in kg

Variable

Table 5 (continued)

55.25%

Properweight12.33%

Overweight
11.64%

Obesity

F
p

0.59

0.53

x

SD

BMI z
scores

x

0.73

0.31

FMI z scores
SD

x

0.90
0.41

SD

FFMI z scores

0.69

0.37

x

FatM zscoresSD

Bryl et al. Journal of Physiological Anthropology(2023) 42:29
Page 10 of 35

Bryl et al. Journal of Physiological Anthropology(2023) 42:29

Parental body weight status

The children whose mothers had their body mass higher
before pregnancy were diagnosed with overweight(IOTF) and obesity (McCarthy criteria) more often thanthe children whose mothers had a proper weight before
pregnancy (54.55% vs 13.37%, χ2 = 34.32, p < 0.001; 36.36%
vs 12.99%, χ2 = 20.47, p = 0.02, respectively). Maternalcurrent BMI was associated with the body weight status
in the children. The children of mothers who experienced
obesity had higher risk of being overweight compared tothe children of the mothers with a healthy weight (34.38%
vs 11.45%, χ2 = 34.92, p < 0.001). The children whose
mothers suffered from obesity before pregnancy had
higher BMI z scores than the children of mothers who
had underweight (1.35 vs -0.06) or had a proper bodyweight before pregnancy (1.35 vs 0.27, F = 5.11, p < 0.001).
They also had higher FMI z scores than the children of
mothers who had a proper weight before pregnancy(0.59 vs -0.14, F = 3.28, p = 0.02). The children of mothers
who experienced obesity had higher BMI z scores (0.92
vs 0.17, F = 6.23, p < 0.001), FMI z scores (0.34 vs -0.18,
F = 4.48, p = 0.004), FFMI z scores (0.43 vs -0.04, F = 6.68,
p < 0.001) and FatM z scores (0.42 vs -0.12, F = 4.83,
p = 0.002) than the children of the mothers with a proper
body weight. The fathers with obesity were more likely tohave children with obesity than the fathers with a proper
body weight (13.92% vs 2.29%, χ2 = 27.88, p < 0.001).
The children of fathers suffering from overweight or obesity had higher BMI z scores (F = 13.44, p < 0.001), FMI
z scores (F = 9.48, p < 0.001), FFMI z scores (F = 10.36,
p < 0.001) and FatM z scores (F = 8.50, p < 0.001) thanthe children whose fathers had a proper body weight(Table 6).
Parental education level

The mothers with a university education were more likely
to have children with a healthy body weight comparedto the mothers with a vocational education (74.01% vs
56.52%, χ = 29.64, p < 0.001). Conversely, the motherswith a vocational education had children with a higher
prevalence of excessive body fat than those with a university education (28.26% vs 9.35%, χ2 = 20.24, p = 0.02).
The children of mothers with a university education
had lower BMI z scores (0.20 vs 0.78, F = 3.37, p = 0.02),
FMI z scores (-0.23 vs 0.47, F = 8.72, p < 0.001) and FatMz scores (-0.15 vs 0.52, F = 7.20, p < 0.001) than the children of mothers with a vocational education. The fatherswith a vocational education had children with overweight more often than the fathers with a university
education (26.74%vs11.94%, χ2 = 18.51, p = 0.03). The
children of fathers with a university education had lower
BMI z scores (F = 3.38, p = 0.02), FMI z scores (F = 5.21,
p = 0.002) and FatM z scores (F = 4.60, p = 0.004) than

Page 11 of 35

the children whose fathers had a vocational education(Table 7).
Lifestyle

Eating at least 3 meals and 1 snack per day was not
associated with the prevalence of abnormal bodyweight, body fat status or body composition parameters(p > 0.05). The children who exercised at least 3 h 3 days
per week had a proper body weight more frequently thanthe children who exercised less often (76.84% vs 67,41%,
χ2 = 8.08, p = 0.04). Spending time in front of the screen
was not associated with the prevalence of an abnormal body weight or body fat status (χ2 = 7.38, p = 0.06;
χ2 = 2.86, p = 0.41, respectively). The children who spent
more than 2 h in front of the screen had higher BMI z
scores (0.42 vs 0.18, t = 2.03, p = 0.04), FMI z scores (0.01
vs -0.20, t = 2.44, p = 0.02), and FatM z scores (0.11 vs-0.16, t = 2.92, p = 0.003). Sleep length was not associated with underweight, overweight and obesity diagnosed according to IOTF and McCarthy norms (χ2 = 0.37,
p = 0.94; χ2 = 0.41, p = 0.93, respectively) or body composition parameters (p > 0.05) (Table 8).
Perinatal factors

The children of mothers who had at least one disease(hypertension, diabetes, thyroid diseases, kidney diseases or heart failure) before pregnancy had obesity
diagnosed according to body fat tissue less often (12.5%
vs 21.19%, χ = 8.66, p = 0.03) than the children of mothers without the diseases. The children with a birthweight
above 4000 g had a higher risk of being overweight inchildhood than the children with a birthweight 25004000 g (23.33% vs 14.40%, χ2 = 16.25, p = 0.02). Birthweight had no association with the body fat status.
The pregnancy duration was not associated with the
BMI z scores (t = 0.11, p = 0.90), FMI z scores (t = 0.24,
p = 0.79), FFMI z scores (t = 0.10, p = 0.90) or FatM zscores (t = 0.42, p = 0.66). Prenatal stress had no impact
on the BMI z scores (t = -1.31, p = 0.19), FMI z scores(t = -0.40, p = 0.69), FFMI z scores (t = -0.89, p = 0.38)
or FatM z scores (t = -0.30, p = 0.76). The children who
had a twin had the BMI z scores (0.33 vs -0.31, t = 2.31,
p = 0.02) and the FatM z scores (-0.01 vs -0.43, t = 1.97,
p = 0.04) lower than the single born children. The type ofdelivery had an impact on the FMI z scores and FatM zscores. The children born vaginally had the FMI z scores(-0.02 vs -0.27, t- = 2.64, p = 0.01) lower than the children delivered by a c-section, but they had higher FatMz scores (0.04 vs -0.16, t = 2.06, p = 0.04). Breastfeeding
was not related to the BMI z scores (t = -0.40, p = 0.69),
FMI z scores (t = -0.35, p = 0.73), FFMI z scores (t = -0.80,
p = 0.43), or FatM z scores (t = -0.24, p = 0.81). The children of the mothers with exceeded weight gain during

408

0.89

408

4.35

0,00%

0

< 0.001

0.93%

4

0,00%

0

0.23%

1

0,00%

0

34.92

2.55%

11

1.86%

8

2.32%

10

0,00%

0

P

3.71%

0.23%

3.71%

16

7.89%

34

0.93%

4

χ2

16

1

13.92%

0.93%

50.58%

60

8.12%

4

218

1.39%

0.93%

35

6

4

0.01

432

20.47

0,00%

0

0,00%

< 0.001

0.23%

1

1.17%

0,00%
0

34.32

1.41%

6

1.41%

3.52%
5

0

0.23%

1

P

0.94%

0,00%

10.33%
6

15

0,00%

0

426

Underweight

χ2

4

0

8.22%

0.23%

55.63%

35

7.75%

1

44

1.64%

7

Obesity

4.17%

18

14.12%

61

49.07%

212

2.31%

10

0.7%

3

7.28%

31

55.63%

237

6.34%

27

Properweight

1.62%

7

3.01%

13

11.11%

48

0.46%

2

0.94%

4

1.88%

8

11.97%

51

1.17%

5

Overweight

1.62%

7

3.24%

14

8.56%

37

0.46%

2

0.94%

4

1.41%

6

10.09%

43

1.41%

6

Obesity

p

F

p

F

1.26

1.21

1.16

1.51

0.004

4.48

0.34

0.09

-0.18

-0.16

1.03

1.03

0.85

1.19

proper weightproper weightvs overweight,vs ­obesitya
proper weight vs­obesitya< 0,001

6,23

0.92

0.61

0.17

0.03

proper weightvs ­obesitya

0.87

1.06

0.89

0.90

SD

underweight vsobesity, proper
weight vs­obesitya

3.28

0.59

0.11

-0.14

-0.19

x

0.02

1.18

1.04

1.19

1.38

SD

FMI z scores< 0.001

5.11

1.35

0.59

0.27

-0.06

x

BMI z scores

0.96

0.98

0.82

0.90

1.21

0.82

0.87

0.91

SD

proper weightvs overweight,
proper weight vs­obesitya< 0.001

6.68

0.43

0.36

-0.04

-0.03

0.07

2.34

0.53

0.16

0.07

-0.19

x

FFMI z scores

0.84

1.15

0.91

0.96

SD

1.10

1.12

0.86

1.26

properweight vsoverweight,
proper weightvs ­obesitya

0.002

4,83

0.42

0.19

-0.12

-0.02

no
­differencesa

0.03

3.07

0.66

0.17

-0.06

-0.16

x

FatM z scores(2023) 42:29

Paternal cur‑rent BMI

Obesity

Overweight

ProperweightUnderweightMaternal cur‑ 431
rent BMI

Obesity

Overweight

237

2.35%

33

5.16%

10

Underweight

Properweight22

426

Overweightn

Properweightn

Underweight

Body fat status

Body weight status

Maternal BMI
before preg‑nancy

Variable

Table 6 Prevalence of underweight, overweight and obesity diagnosed according to IOTF (Body weight status) and according to McCarthy criteria (Body fat status), andassociation between body composition parameters and parental body weight status

Bryl et al. Journal of Physiological Anthropology
Page 12 of 35

Overweight

Obesity

0.34
0.11

11.55%

47< 0.001

2.7%

11

33.17%

135

25.55%

104

27.88

3.69%

15

1.23%

5

0.74%

3

χ2

12.78%

0.25%

8.11%

33

3.69%

15

P

52

1

33.66%

5.41%

23.83%

137

3.93%

22

97

16

Underweightn

Properweightn

Underweight

Body fat status

Body weight statusasignificant differences in the post hoc testx – mean, BMI Body Mass Index, FMI Fat Mass Index, FFMI Fat Free Mass Index, FatM Fat Mass in kg

Obesity

Overweight

ProperweightVariable

Table 6 (continued)

4.67%

19

7.37%

30

3.93%

16

Properweight

3.19%

13

7.62%

31

2.95%

12

Overweight11.55%

47

33.17%

135

25.55%

104

Obesityp

Fproper weightvs obesity,
overweight vs­obesitya

0.79

0.87

0.79

SD

proper weightvs obesity,
overweight vs­obesitya

10.36

0.51

0.04

-0.09

x

proper weightvs obesity,
overweight vs­obesitya

1.15

0.77

0.83

SD

< 0.001

9.48

0.35

-0.14

-0.30

x

FFMI z scores< 0.001

1.13

1.16

1.09

SD

FMI z scores< 0.001

13.44

0.99

0.25

0.02

x

BMI z scores

1.17

0.81

0.89

SD

proper weightvs obesity,
overweight vs­obesitya< 0.001

8.50

0.43

-0.07

-0.21

x

FatM z scores

Bryl et al. Journal of Physiological Anthropology(2023) 42:29
Page 13 of 35

409

439

36.41%

4.49%

20.8%

154

2.6%

19

88

11.58%

1.89%

11

49

8

7

1.65%

11

0.24%

5.67%

24

4.02%

17

5.44%

23

0.47%

2

0.95%

4

2.13%

9

1.42%

6

0.24%

1

422

0,00%

0

0,00%

0

0,00%

0

0,00%

0

0.02

0.001

0,00%

0

0.23%

1

0,00%

0

P

1.14%

5

1.82%

8

1.82%

8

0
0,00%

20.24

9.34%

41

4.1%

18

2.05%

9

1
0.23%

29.64

46.7%

0
0,00%

439

Underweight

χ2

5.92%

15.95%

205

26

3.42%

5.92%

70

0.68%

15

26

3

4

0.91%

0

0,00%

Obesityn

Proper Overweightweightn

Underweight

Body fat status

Body weight status

35.07%

148

20.85%

88

12.09%

51

1.66%

7

46.47%

204

16.63%

73

5.69%

25

0.68%

3

8.29%

35

4.27%

18

3.55%

15

0.24%

1

10.93%

48

3.19%

14

1.82%

8

0.23%

1

Proper Overweightweight

4.27%

18

4.5%

19

4.5%

19

0.71%

3

5.92%

26

5.01%

22

2.96%

13

0.23%

1

Obesity

F
p

x

0.15

0.40

0.60

1.04 -0.26

1.24 -0.01

1.32 0.15

1.39 0.18

x

0.76

1.09

1.41

1.19

SD

0.91 0.05

0.88 0.25

1.26 0.20

0.74

1.05

1.08

1.18

Second‑ary vsvocational,
secondaryvs ­highera< 0.001

7.20

0.81 -0.15

1.02 0.09

0.94 0.52

1.08 0.16

SD

FatM zscores0.71 -0.02 0.81 -0.17

1.04 0.16

1.00 0.22

1.19 0.18

secondary vsvocational,
secondary vs­highera

0.16

1.74

0.73 0.03

1.03 0.09

1.36 0.33

secondary vs­highera

0.53

x

FFMI zscores1.11 0.34

SD< 0.001

8.72

1.06 -0.23

1.39 0.02

1.49 0.47

1.37 0.06

SD

FMI z scores

0.02

3.37

0.20

0.37

0.78

0.73

x

BMI z scores(2023) 42:29

Univer‑sity

Second‑ary

Voca‑
tionalPrimary

Paternal
educa‑
tion level

Univer‑sity

Second‑ary

Voca‑
tionalPrimary

Maternal
educa‑
tion level

Variable

Table 7 Prevalence of underweight, overweight and obesity diagnosed according to IOTF (Body weight status) and according to McCarthy criteria (Body fat status), andassociation between body composition parameters and parental educational level

Bryl et al. Journal of Physiological Anthropology
Page 14 of 35

Obesity
11.95
0.22

18.51

0.03

χ2

P

Underweightn

Proper Overweightweightn

Underweight

Body fat status

Body weight statusasignificant differences in the post hoc testx – mean, BMI Body Mass Index, FMI Fat Mass Index, FFMI Fat Free Mass Index, FatM Fat Mass in kg

Variable

Table 7 (continued)

Proper OverweightweightObesityFpsecondary vs­higheraSDsecondary vs­highera

5.21

x

0.002

SD

FMI z scores

0.02

3.38

x

BMI z scores

0.10

2.11

x

SD

FFMI zscoresSDsecondaryvs ­highera

0.004

4.60

x

FatM zscoresBryl et al. Journal of Physiological Anthropology(2023) 42:29
Page 15 of 35

432

454

454

454
44
8

1

0,00%

0

0.22%

0.37

0.94

χ2

P

35
8.1%

6

1.39%

267
61.81%

35

P

8.1%

7.38

0.06

χ2

135
29.74%

22

39.65%

5.07%

4.85%

180

23

1.85%

8

13.66%

59

4.19%

19

11.23%

0.69%

3

4.4%

19

2.42%

11

2.86%

432

0.93

0.41

0,00%

0

0.23%

1

0.41

2.86

0.22%

1

0,00%

0

2.01
454

3.27
0.35

0.57
13

0
0,00%

8.08

1.54%

7

3.74%

17

1.76%

1
0.22%

0.04
51

16
3.52%

454

Underweight

P

2.64%

12

12.78%

58

5.73%

26

9.69%

Obesityχ2

73
16.08%

3

0.66%

242
53.3%

42

P

9.25%

3.61

0.31

χ2

148
32.6%

21

4.63%

167
36.78%

24

5.29%

Overweightn

Properweightn

Underweight

Body fat status

Body weight status

8.8%

38

61.34%

265

28.63%

130

40.53%

184

15.42%

70

53.74%

244

32.82%

149

36.34%

165

Properweight1.62%

7

14.12%

61

7.27%

33

8.59%

39

2.42%

11

13.44%

61

6.17%

28

9.69%

44

Overweight

1.62%

7

12.27%

53

5.29%

24

9.47%

43

3.08%

14

11.67%

53

5.95%

27

8.81%

40

Obesity

t

p

t

p

t

p

t

p

0.60

0.52

0.24

0.34

0.04

2.03

0.18

0.42

0.64

-0.46

0.37

0.31

0.16

1.40

0.23

0.39

x

BMI z
scoresx

SD

x

FFMI zscores

1.40
0.16

-1.63

1.02 0.14

0.10

1.57
0.12

0.71

0.37

0.50

0.68

1.24 -0.13 0.89 0.01

1.21 -0.08 0.93 0.10

0.02

2.44

1.17 -0.20 0.81 0.01

1.25 0.01

0.94

0.08

1.21 -0.08 0.98 0.22

1.23 -0.08 0.93 0.05

0.43

0.79

1.18 -0.12 1.00 0.02

1.25 -0.05 0.89 0.14

SD

FMI z
scores

x

0.89

-0.14

0.99 0.01

0.87 -0.01

0.003

2.92

0.88 -0.16

0.90 0.11

0.51

-0.66

0.94 0.06

0.88 -0.02

0.55

0.59

0.89 -0.03

0.89 0.02

SD

FatM zscores

0.99

0.97

0.79

1.09

1.08

0.96

1.05

0.93

SD

(2023) 42:29

x – mean, BMI Body Mass Index, FMI Fat Mass Index, FFMI Fat Free Mass Index, FatM Fat Mass in kg, ACE Adverse Childhood Events

≥9 h

<9 h

Sleep
length≤2 h>2 hScreen time

Good

Bad

Physical
activity

Good

Bad

Eatingbehaviour

Variable

Table 8 Prevalence of underweight, overweight and obesity diagnosed according to IOTF (Body weight status) and according to McCarthy criteria (Body fat status), andassociation between body composition parameters and lifestyle factors

Bryl et al. Journal of Physiological Anthropology
Page 16 of 35

Bryl et al. Journal of Physiological Anthropology(2023) 42:29

pregnancy were more likely to suffer from obesity (12.2
vs 4.5, χ2 = 12.79, p = 0.01). Moreover, they had higher
BMI z scores (0.82 vs 0.27, t = 2.77, p = 0.01), FMI z scores(0.33 vs -0.12, t = 2.96, p < 0.01), FFMI z scores (0.38 vs
0.05, t = 2.29, p = 0.02) and FatM z scores (0.53 vs -0.06,
t = 3.76, p < 0.01) (Table 9).
Adverse childhood experiences

The number of experienced events was associated withoverweight diagnosed according to IOTF (χ2 = 17.27,
p = 0.04). However, the experience of stressors was not
associated with the body fat status (χ2 = 13.0, p = 0.16).
The children who had been exposed to family conflicts
had obesity more often than the children who had notexperienced that stressor (9.46% vs 3.70%, χ2 = 12.44,
p = 0.01). The children who had witnessed violence hadexcessive body fat more often than the children who
had not experienced the stressor (18.18% vs 13.55%,
χ2 = 12.63, p = 0.01). The children who had experienced
death of someone close were diagnosed overweightaccording to body fat tissue more often than the children
who had not experienced the stressor (22.67% vs 13.96%,
χ2 = 8.50, p = 0.03). The children who had been separated
from their parents were more often diagnosed underweight according to BMI (8.17% vs 19.30%, χ2 = 8.44,
p = 0.04) and more often obese according to body fat tissue (12.81 vs 19.30%, χ2 = 8.35, p = 0.04) compared to thechildren who had not been separated from their parents.
Various types of stressors were not associated with the
body composition parameters (Table 10).
Multiple regression models for body composition
parameters

Multiple regression models were prepared for eachdependent variable (BMI z scores, FMI z scores, FFMIz scores, and FatM z scores) including the following
independent variables: the type of family, highest bodyweight during pregnancy, pregnancy duration, prenatal
stress, mother’s age at the child’s birth, maternal BMI
before pregnancy, maternal current BMI, maternal education level, paternal current BMI, paternal educationlevel, ACE experience, eating behaviour, physical activity,
screen time, sleep length, and polymorphisms of the FTO
and MC4R genes. All thes variables were included in the
stepwise multiple regression model. The variables wereadded until the highest value of ­R2 was reached. Multiple
regression was performed for the whole group and separately for boys and girls.
The variables that remained in the model for all groups,
explaining 17.2% (F = 7.02, p < 0.001) of the variability in
BMI z scores were paternal BMI, highest body weight
during pregnancy, maternal age at the child’s birth, paternal education level, prenatal stress, maternal BMI before

Page 17 of 35

pregnancy, eating behaviour and FTO and MC4R gene
polymorphisms. Among these variables, paternal BMI,
highest body weight in pregnancy and maternal age at thechild’s birth made the greatest contribution to the prediction of the dependent variable (ΔR2 = 0.085, p < 0.001;
ΔR2 = 0.031, p = 0.16, ΔR2 = 0.021, p = 0.01, respectively).
Specifically, paternal BMI was positively associated withBMI z scores (β = 0.26, p < 0.001), while the mother’s age
at the child’s birth was negatively associated with BMI z
scores (β = -0.13, p = 0.01) (Table 11).
Thirteen point eight percent (13.8%) of the variancein FMI z scores was explained by paternal BMI, maternal education, highest body weight during pregnancy,maternal age at the child’s birth, FTO polymorphism,and experience of ACE. Among the variables, paternal
BMI (β = 0.21, p < 0.001), maternal education (β = 0.18,
p < 0.001), highest body weight in pregnancy (β = 0.16,
p < 0.001) and FTO polymorphism (β = 0.10, p = 0.04)
were positively connected with FMI z scores. Paternal
BMI, maternal education and highest body weight during pregnancy had the greatest contribution to predicting
the variable (ΔR2 = 0.056, p < 0.001; ΔR2 = 0.043, p < 0.001,
ΔR2 = 0.025, p =  < 0.001, respectively) (Table 11).
The FFMI z scores were predicted by 6 variables:
paternal BMI, maternal pre-pregnancy BMI, maternal
age at the child’s birth, eating behaviour, MC4R gene
polymorphism, and paternal education. The model
explained 12.5% of the variance in the dependent variable(p < 0.001). Among the variables, paternal BMI, maternal
pre-pregnancy BMI and maternal age at the child’s birth
had the greatest contributions (ΔR2 = 0.075, p < 0.001;
ΔR2 = 0.019, p = 0.006, ΔR2 = 0.018, p = 0.01, respectively).
Paternal BMI (β = 0.25, p < 0.001) and maternal pre-pregnancy BMI (β = 0.14, p = 0.006) were positively associated with the dependent variable, while the maternal age
at the child’s birth (β = -0.13, p = 0.01) showed a negative
association with FFMI z scores (Table 11).
The forward regression model for the FatM z scores
included maternal education, highest body weight inpregnancy, paternal BMI, maternal age at the child’sbirth, FTO gene polymorphism, paternal education, experiences of ACE and eating behaviour, which
explained 17.8% of the variance in FatM z scores. The
FatM z scores were positively associated with maternal education (β = 0.16, p = 0.01), highest body weight inpregnancy (β = 0.21, p < 0.001), paternal BMI (β = 0.17,
p < 0.001), and FTO polymorphism (β = 0.11, p = 0.03),
while they were negatively associated with maternal age
at the child’s birth (β = -0.11, p = 0.04). Maternal education, highest body weight in pregnancy and paternal BMI
made the greatest contributions predicting the variable(ΔR2 = 0.059, p = 0.01, ΔR2 = 0.054, p < 0.001, ΔR2 = 0.028,
p < 0.01, respectively) (Table 11).

425

n

No

454

456

229
50.44%

35

p

7.71%

2.43

0.49

χ2

76
16.67%

12

2.63%

241
52.85%

33

p

7.24%

4,74

0.19

χ2

92
20.26%

18

3.96%

223
49.12%

27

5.95%

10.35%

47

4.82%

22

10.53%

48

5.29%

24

10.13%

46

0,00%

0

13.88%

59

1.41%

6

Overweight

4

3.74%

17

1.75%

8

3.51%

16

2.42%

11

2.86%

13

0,00%

0

4.47%

19

0.94%

440

533

454

423

Obesity n

F

p

t

p

0.22%

1

52.2%

9.69%

10.35%

t

47

5.51%

25

9.25%

42

6.39%

29

8.37%

38

0,00%

0

13.24%

56

2.13%

9

Obesity

p

44

3.3%

15

12.56%

57

4.19%

19

11.67%

53

0.47%

2

13.24%

56

2.13%

9

Overweight

8.66

237

16.96%

77

52.2%

237

21.37%

97

47.8%

217

0,00%

0

59.1%

250

9.46%

40

Properweight

0.03

0.22%

1

0,00%

0

0.06

7.24

0.22%

1

0,00%

0

0.09

10.84

0,00%

0

0.24%

1

0,00%

0

Underweight

Body fat status

0.28

0.45

-0.76

0.40

0.30

0.69

-0.40

0.36

0.31

0.90

0.11

0.74

0.34

0.35

x

BMI z
scoresx

1.22 -0.09

0.13

-1.52

1.31 0.04

1.19 -0.12

0.12

-1.54

1.39 0.02

1.14 -0.12

0.79

0.24

0.59 0.20

1.23 -0.05

1.14 -0.13

SDx0.95 0.06

0.45

-0.75

1.06 0.14

0.89 0.06

0.81

-0.24

1.15 0.10

0.82 0.08

0.90

0.10

0.20 0.01

0.95 0.08

0.90 0.13

SDx

0.90 -0.01

0.05

-2.00

0.97 0.16

0.87 -0.05

0.05

-1.93

0.92 0.13

0.88 -0.06

0.66

0.42

0.76 0.36

0.91 0.03

1.00

1.14

0.92

1.22

0.85

0.03

1.00

0.87

SD

FatM z scores

0.77 -0.08

SD

FMI z scores FFMI z
scores(2023) 42:29

PrenatalstressYes

No

At least
one disease
before preg‑
nancy

Yes

No

0.41

p

0.24%

6.15

0,00%

χ2

1

0

276
64.94%

40

9.41%

19
4.47%

1

Properweight0.24%

Underweight

Body weight status

At least454
one diseaseduring preg‑nancy> 42 week

37–42 week

< 37 week

Pregnancy
duration

Variable

Table 9 Prevalence of underweight, overweight and obesity diagnosed according to IOTF (Body weight status) and according to McCarthy criteria (Body fat status), andassociation between body composition parameters and perinatal factors

Bryl et al. Journal of Physiological Anthropology
Page 18 of 35

n

443

441

434

1

18

40

20

1

0,00%

0

0.23%

1

0,00%

0

0.06

7.41

0,00%

0

0.23%

1

3.38
441

441

2.49
0.48

0.97

0.45%

2

4.08%

18

0.45%

2

0.69%

3

4.13%

434

0
0,00%

16.25

60

2
0.46%

1
0.23%

0.02

3.17%

14

11.79%

52

0.45%

2

3.67%

16

11.7%

51

0.23%

19
4.35%

435

0.08

6.64

0,00%

0

Underweight

p

283

65
14.87%

1.54%

7

Obesity n

χ2

42
9.52%

2

0.45%

255
57.82%

36

2.27%

1.36%

8.16%

10

p

6

4.70

0.20

χ2

98
22.48%

11

2.52%

207
47.48%

32

p

7.34%

7.53

0.06

χ2

12
2.75%

5

1.14%

294
67.28%

39

8.92%

5.07%

23

Overweight

Body fat status

282

9.07%

40

56.69%

250

3.63%

16

22.71%

99

46.79%

204

3.91%

17

66.21%

288

16.96%

77

Properweight

64

2.27%

10

13.61%

60

0.45%

2

3.67%

16

12.61%

55

0.23%

1

15.63%

68

6.17%

28

Overweight

54

2.04%

9

11.56%

51

0.45%

2

2.52%

11

11.47%

50

0.46%

2

13.33%

58

4.41%

20

Obesity

t

p

t

p

t

p

t

p

x

1.20 -0.08

0.69

-0.40

1.23 -0.05

SD

0.30

0.06

2.85

0.54

0.30

-0.89

0.89

1.22 -0.10

0.53

0.64

1.12 -0.09

0.92 0.07

0.31

1.18

0.92 0.24

0.92 0.06

0.95 0.01

0.83
0.41

0.67 0.02

0.99 0.10

0.37

2.64

1.21 -0.08

x0.88 -0.01

0.76

-0.30

0.87 0.02

SD

1.97

0.89 -0.02

0.35

1.06

0.82 0.07

0.89 -0.01

1.05 -0.29

0.04

2.06

0.75 -0.16

0.93 0.04

0.04

0.96

0.95

0.97

0.83

0.73

1.02

0.72

0.95

0.95

SD

FatM z scores

0.82 -0.10 0.77 -0.43

0.92 0.08

0.38

0.01

1.07 -0.27

1.26 -0.02-0.20 1.49 -0.32

0.27

1.11

0.21

0.35

1.82
0.07

2.31
0.02

x

0.93 0.14

SD

FMI z scores FFMI z
scores-0.31 1.28 -0.46

0.33

0.19

-1.31

0.44

x

BMI z
scores(2023) 42:29

Yes

Breastfeed‑ing> 40002500–4000< 2500BirthweightC-section

VaginalTypeof delivery

Twin

Single

1.65

0.64

Χ

p

86
18.94%

10

Properweight2.2%

Underweight

Body weight status

Type of preg‑ 437
nancy

Yes

Variable

Table 9 (continued)

Bryl et al. Journal of Physiological Anthropology
Page 19 of 35

434

n

1.09

0.24

0.23%

1

0,00%

0

0.78

0.01

4.15%

0
0,00%

p

12.44%

18

1
0.23%

4.19

64.29%

54

5
1.15%

434

434

0.75

1.21

0,00%

0

0.23%

Underweight

12.79

9.68%

12
2.76%

0.92%

4

3.69%

16

0.45%

2

4.51%

Obesity n

χ2

279

42

22
5.07%

2

0.46%

p

4.38%

19

10.83%

47

1.81%

8

13.54%

Overweight

Body fat status63.82%

277

5.07%

22

15.67%

68

54.84%

238

5.67%

25

63.95%

Properweight

13.59%

59

2.07%

9

3.92%

17

11.75%

51

1.81%

8

14.51%

Overweightx – mean, BMI Body Mass Index, FMI Fat Mass Index, FFMI Fat Free Mass Index, FatM Fat Mass in kg, ACE Adverse Childhood Events

Nonexceeded

Exceeded

1.73

0.63

χ2

66
15.21%

11

54.84%

7.6%

2.53%

238

33

0.76

0.87

p

5.87%

0.9%

χ2

26

63.88%

9.03%

4

ProperweightUnderweight

Body weight status

Weight gain 434
during preg‑
nancy

No

Yes

Siblings

No

Variable

Table 9 (continued)

13.13%

57

2.07%

9

3.69%

16

9.91%

43

1.59%

7

12.24%

Obesity

t

p

t

p

t

p

0.01

2.77

0.27

0.82

0.55

-0.60

0.35

0.27

0.69

-0.40

0.38

x

BMI z
scoresx< 0.01

2.96

1.21 -0.12

1.30 0.33

0.37

-0.89

1.25 -0.03

1.18 -0.13

0.73

-0.35

1.20 -0.05

SD
x

0.02

2.29

0.91 0.05

1.12 0.38

0.57

-0.57

0.93 0.10

0.91 0.05

0.43

-0.80

0.83 0.18

SD

x

-0.24

< 0.001

3.76

0.85 -0.06

1.17 0.53

0.46

-0.73

0.90 0.04

0.85 -0.05

0.81

0.93

1.22

1.01

0.93

0.87

SD

FatM z scores

0.83 0.02

SD

FMI z scores FFMI z
scoresBryl et al. Journal of Physiological Anthropology(2023) 42:29
Page 20 of 35

Yes

426

426

426

454

n

24

64.32%

6

274

8.22%

0.20

35

4.63

χ2

6.57%

p

28

0,00%

63.38%

0

270

9.62%

0.22

41

4.45

p

5.63%

χ2

24

1.64%

64.32%

7.98%

7

274

34

4

14.79%

63

1.17%

5

14.55%

62

0.94%

4

14.79%

63

3

3.99%

17

0.23%

1

4.46%

19

0.47%

2

4.23%

18

426

0

0.23%

1

0.81

0.97

0,00%

0

0.23%

1

0.95

0.42

0,00%

0

0.23%

1

0.16
426

13.00

0.22%

1

0,00%

0

0,00%

0

0,00%

0

0.04

0.22%

1

454

Underweight

17,27

1.76%

8

1.1%

5

2.2%

10

1.76%

8

Obesity n

χ2

6.83%

1.76%

8

4.19%

19

7.71%

35

Overweight

Body fat status

p

31

1.32%

6.17%

6

28

1.76%

14.76%

8

67

2.86%

41.63%

3.96%

13

189

Properweight18

Underweight

Body weight status28

63.85%

272

4.93%

21

65.49%

279

6.34%

27

64.08%

273

6.83%

31

6.39%

29

16.3%

74

39.65%

180

Properweight6

14.08%

60

1.41%

6

14.08%

60

1.41%

6

14.08%

60

1.76%

8

1.76%

8

4.41%

20

7.93%

36

Overweight3

13.15%

56

1.41%

6

12.44%

53

0.94%

4

12.91%

55

1.32%

6

2.42%

11

3.3%

15

7.71%

35

Obesity

p

t

p

t

p

F

0.04

0.33

0.27

-1.12

0.53

0.29

0.25

1.15

0.09

0.33

0.27

1.33

0.14

0.42

0.49

0.26

x

BMI z
scoresxSDx

1.21 0.18

0.99 0.18

0.41

0.97

0.83

0.21

0.82

-0.23

1.45 -0.18 0.97 0.07

1.17 -0.09 0.91 0.07

0.71

-0.37

0.89 -0.04 0.72 0.11

1.22 -0.10 0.93 0.07

0.32

1.00

1.35 -0.24 0.90 0.04

1.19 -0.08 0.92 0.07

0.33

1.14

1.27 -0.10 0.80 0.00

1.52 0.04

1.29 0.03

x

SD

FatM zscores

1.30

1.03

0.96

0.78

1.16 -0.12 0.99

0.85 -0.01 0.96

0.77

-0.29

0.62 0.03

0.90 -0.02 0.97

0.36

0.92

1.05 -0.15 0.95

0.86 0.00

0.15

1.80

0.93 -0.10 0.83

1.06 0.16

0.91 0.14

0.84 -0.07 0.92

SD

FFMI zscores1.12 -0.14 0.88 0.04

SD

FMI z
scores(2023) 42:29

No

Violence
victimYes

No

Life/healththreat wit‑
ness

Yes

No

Life/healththreat

3+

2

1

0

ACE

Variable

Table 10 Prevalence of underweight, overweight and obesity diagnosed according to IOTF (Body weight status) and according to McCarthy criteria (Body fat status), andassociation between body composition parameters and ACE

Bryl et al. Journal of Physiological Anthropology
Page 21 of 35

424

n

No

425

264

57

16

0

0.12

30

5.83

0.24%

1

0,00%

0

263

11.29%

48

59.29%

57

3.06%

13

12.47%

47

2.82%

12

10.82%

p

t

p

46

2.35%

10

11.5%

t

53

3.99%

17

11.5%

0.03

252

11.03%

47

59.39%

8.50

0.23%

1

0,00%

0

0.01

1.65%

7

425

p

t

p

49

1.42%

6

12.5%

53

0.7%

Obesity

t

49

0.94%

4

14.39%

61

1.41%

Overweight

0.01

253

5.19%

22

65.33%

277

6.57%

Properweight

12.63

0.24%

1

0,00%

0

12.44

2.82%

12

3.06%

13

0.94%

4

3.76%

16

426

424

0.75

1.23

0,00%

Underweight

P

9.88%

12.71%

54

3.52%

15

12.21%

52

0.47%

2

4.25%

18

0.7%

Obesity n

χ2

42

3.06%

60.24%

13

256

6.59%

0.70

28

1.40

P

11.97%

χ2

51

1.41%

57.98%

8.22%

6

247

35

0.69

1.65%

7

14.15%

60

0.94%

Overweight

Body fat status

0.32

0.93

-0.09

0.31

0.30

0.52

-0.65

0.39

0.29

083

0.22

0.26

0.31

0.16

1.41

x

BMI z
scores

0.57

0.57

x

SD

0.97

0.04

x

0.87

-0.16

1.09 0.10

0.61

0.51

0.41

-0.82

1.02 0.15

1.17 -0.10 0.88 0.09

0.29

-1.07

1.41 0.01

1.15 -0.12 0.89 0.05

0.37

-0.90

1.29 -0.01 0.94 0.03

1.18 -0.11 0.91 0.08

0.28

-1.08

1.43 0.07

0.47

0.72

x

SD

FatM zscores

1.20

0.99

1.08

0.87 -0.02 0.92

0.43

-0.79

1.00 0.06

0.85 -0.04 0.93

0.54

-0.62

0.78 0.05

0.90 -0.03 0.95

0.36

-0.92

0.97 0.13

0.88 -0.03 0.94

SD

FFMI zscores1.18 -0.11 0.90 0.07

SD

FMI z
scores(2023) 42:29

Separationfrom par‑
ents

Yes

No

Familyconflicts

Yes

No

1.46

P

4.72%

χ2

20

0.94%

65.09%

8.73%

4

276

37

0.31

p

5.63%

1.41%

3.61

χ2

ProperweightUnderweight

Body weight status

Disease/426
deathof someone
close

Yes

No

Violence
witness

Variable

Table 10 (continued)

Bryl et al. Journal of Physiological Anthropology
Page 22 of 35

423

n

3.04

0.39

P

2.38%

χ2

10

0.71%

67.46%

9.03%

3

284

38

0.50

1.19%

5

14.49%

61

0.71%

3

14.89%

63

0.24%

1

4.51%

19

0.24%

1

4.49%

19

421

0.79

1.04

0,00%

0

0.24%

1

2.85%

12

67.93%

0.71%

3

14.73%

0.95%

4

12.59%

p

t

p

53

0.71%

3

13,00%

55

p

t

t

62

0.47%

2

14.89%

63

2.59%

11

11.08%

Obesity< 0.001

286

1.42%

6

69.27%

293

1.89%

8

13.44%

Overweight

36.16

0.24%

1

0,00%

0

8.73%

37

62.03%

Properweightx -mean, BMI Body Mass Index, FMI Fat Mass Index, FFMI Fat Fress Mass Index, FatM Fat Mass in kg, ACE Adverse Childhood Events

Yes

No

2.36

p

1.42%

χ2

6

0.47%

68.56%

9.22%

2

290

39

0.04
423

8.35

0.24%

0.04

0.94%

1

0,00%

Underweight

8.44

χ

2.12%

4

3.77%

Obesity n

P

7.78%

2.59%

9

13.44%

Overweight

Body fat status2

33

62.26%

7.08%

11

ProperweightUnderweight

Body weight status

Other
421
unspecified
stressors

Yes

No

Schoolproblems

Yes

Variable

Table 10 (continued)

0.71

-0.38

0.41

0.30

0.83

-0.22

0.38

0.30

0.49

0.69

0.20

x

BMI z
scoresx

SD
x

SD

FFMI zscores

x

0.32

0.99

0.22

-1.23

0.95 0.38

0.55

-0.60

1.60 0.03

0.34

-0.95

0.95 0.26

1.19 -0.10 0.92 0.06

0.71

-0.37

1.67 0.00

1.19 -0.10 0.92 0.06

0.58

-0.55

1.07

0.47

-0.72

1.26 0.14

0.97

0.86 -0.02 0.96

0.78

-0.28

1.17 0.06

0.87 -0.02 0.96

0.69

-0.40

1.18

SD

FatM zscores1.42 -0.03 1.14 -0.03 0.94 0.03

SD

FMI z
scoresBryl et al. Journal of Physiological Anthropology(2023) 42:29
Page 23 of 35

Bryl et al. Journal of Physiological Anthropology(2023) 42:29

Page 24 of 35

Table 11 Regression analysis predicting BMI z scores, FMI z scores and FatM z scores in children
VariableR2

βModel 1: BMI z scores

0.172

F

P-value

7.02< 0.001

Paternal BMI

0.26< 0.001

Highest body weight in pregnancy

0.11

0.16

Mother’s age at the child’s birth

-0.13

0.01

Father’s education (Reference: higher education)

0.08

0.11

Prenatal stress at least one (Reference: 0)

0.07

0.17

Maternal BMI before pregnancy

0.11

0.15

FTO (Reference: TT)

0.07

0.18

Eating behaviour (Reference: good)

0.07

0.2

MC4R (Reference: TT)

0.05

Model 2: FMI z scores

0.138

0.31
10.24

< 0.001

Paternal BMI

0.21< 0.001

Maternal education level (Reference: higher education)

0.18< 0.001

Highest body weight in pregnancy

0.16< 0.001

Mother’s age at the child’s birth

-0.10

0.06

FTO (Reference: TT)

0.10

0.04

ACE at least one (Reference: 0)0.07

Model 3: FFMI z scores

0.125

0.19
8.09

< 0.001

Paternal BMI

0.25< 0.001

Maternal pre-pregnancy BMI

0.14

0.006

Mother’s age at the child’s birth

-0.13

0.01

Eating behaviour (Reference: good)

0.08

0.15

MC4R (Reference: TT)

0.06

0.21

Paternal education level (Reference: higher education)

0.05

Model 4: FatM z scores

0.178

0.30
9.17

< 0.001

Maternal education level (Reference: higher education)

0.16

0.01

Highest body weight in pregnancy

0.21< 0.001

Paternal BMI

0.17< 0.001

Mother’s age at the child’s birth

-0.11

0.04

FTO (Reference: TT)

0.11

0.03

Paternal education level (Reference: higher education)

0.06

0.28

ACE at least one (Reference: 0)

0.06

0.25

Eating behaviour (Reference: good)

0.06

0.27

BMI Body Mass Index, FMI Fat Mass Index, FFMI Fat Free Mass Index, FatM Fat Mass in kg

In the group of girls, the variables that remained in
the multiple regression model explaining 26% (F = 11.53,
p < 0.001) of BMI variability in z scores were paternal
BMI, highest body weight during pregnancy, FTO gene
polymorphism, maternal education, and duration ofpregnancy. Paternal BMI, highest body weight during
pregnancy and FTO gene polymorphism had the greatest contribution to the prediction of the dependent variable (respectively: ΔR2 = 0.144, p < 0.001; ΔR2 = 0.07,
p < 0.001, ΔR2 = 0.022, p = 0.04). These variables werepositively correlated with BMI z scores (Table 12).
Twenty-nine percent (29%) of the variance in FMI
scores in the girls’ group was explained by paternal

BMI, highest gestational weight, maternal education,
FTO polymorphism, duration of pregnancy, MC4R
polymorphism, current maternal BMI, prenatal stressexperience and ACE. Father’s BMI (β = 0.25, p < 0.001),
highest gestational weight (β = 0.34, p < 0.001), maternal education (β = 0.26, p < 0.001) and FTO gene polymorphism (β = 0.15, p = 0.03) were positively associatedwith FMI z scores and had the greatest impact on theprediction of the dependent variable (ΔR2 = 0.102,
p < 0.001; ΔR2 = 0.067, p < 0.001, ΔR2 = 0.066, p =  < 0.001,
ΔR2 = 0.002 p = 0.03 respectively) (Table 12).
The girls’ FFMI scores were predicted based on six variables: father’s BMI, highest gestational weight, mother’sBryl et al. Journal of Physiological Anthropology(2023) 42:29

Page 25 of 35

Table 12 Multiple forward regression results predicting BMI z scores, FMI z scores, FFMI z scores, and FatM z scores in girls
VariableR2

βModel 1: BMI z scores

0.260

F

P-value

11.53< 0.001

Paternal BMI

0.329

Highest body weight in pregnancy

0.284< 0.001

FTO (ref. TT)

0.142

0.04

Maternal education level (Reference: higher education)

0.131

0.05

Pregnancy duration-0.07

Model 2: FMI z scores

0.290< 0.001

0.27
7.46< 0.001

Paternal BMI

0.25< 0.001

Highest body weight in pregnancy

0.34< 0.001

Maternal education level (Reference: higher education)

0.26< 0.001

FTO (ref. TT)

0.15

0.03

Pregnancy duration

-0.11

0.10

MC4R (ref. TT)

0.089

0.19

Maternal current BMI

-0.09

0.38

Prenatal stress (ref. 0)

-0.10

0.18

ACE at least one (ref. 0)

0.09

Model 3: FFMI z scores

0.147

0.21
4.68

< 0.001

Paternal BMI

0.27< 0.001

Highest body weight in pregnancy

0.16

0.04

Mother’s age at the child’s birth

-0.10

0.18

Pregnancy duration

-0.07

0.31

Prenatal stress (ref. 0)

-0.09

0.24

Sleep duration

0.08

Model 4: FatM z scores

0.289

0.30
8.17

< 0.001

Highest body weight in pregnancy

0.40< 0.001

Paternal BMI

0.23

0.001

Maternal education level (Reference: higher education)

0.25< 0.001

FTO (ref. TT)

0.14

0.04

Maternal current BMI

-0.13

0.19

Pregnancy duration

-0.09

0.18

Prenatal stress (ref. 0)

-0.12

0.10

ACE at least one (ref. 0)

0.11

0.13

age at birth, duration of pregnancy, prenatal stress experience and sleep duration. The model explained 14.7%of the variance of the dependent variable (p < 0.001).
Paternal BMI and highest body weight during pregnancy showed the strongest influence (ΔR2 = 0.096,
p < 0.001; ΔR2 = 0.024, p = 0.04, respectively). Paternal
BMI (β = 0.27, p < 0.001) and highest gestational weight(β = 0.16, p = 0.04) were positively associated with thedependent variable (Table 12).
The stepwise regression model for FatM z scores in
the girls’ group included highest gestational weight,
paternal BMI, maternal education, FTO gene polymorphism, current maternal BMI, duration of pregnancy,
experience of prenatal stress and ACE, which explained
28.9% of the variance in FatM z scores. FatM scores werepositively associated with highest gestational weight(β = 0.40, p < 0.001), paternal BMI (β = 0.23, p = 0.001),
maternal education (β = 0.25, < 0.001) and FTO gene
polymorphism (β = 0.14, p = 0.04). The variables had
the greatest impact on the prediction of the dependent
variable (respectively, ΔR2 = 0.15, p < 0.001, ΔR2 = 0.064,
p = 0.001, ΔR2 = 0.055, p < 0.01, ΔR2 = 0.018, p = 0.04)(tab. 12).
The boys’ BMI scores were predicted based on 7 variables: paternal BMI, maternal age at the child’s birth,maternal BMI in pregnancy, experience of prenatal
stress, eating habits, maternal education, and MC4R
gene polymorphism. The model explained 14.6% of thevariance in the dependent variable (p < 0.001). Paternal
BMI, maternal age at the child’s birth, and maternal BMIBryl et al. Journal of Physiological Anthropology(2023) 42:29

Page 26 of 35

before pregnancy had the greatest impact (ΔR2 = 0.053,
p = 0.002; ΔR2 = 0.027, p = 0.03, ΔR2 = 0.022, p = 0.02,
respectively). Paternal BMI (β = 0.08, p = 0.002) andmaternal BMI before pregnancy (β = 0.07, p = 0.02) werepositively associated with the dependent variable, whilematernal age at the child’s birth was negatively associated with the dependent variable (β = -0.04, p = 0.03)(Table 13).
In the group of boys, the variables that remained in
the multiple regression model explaining 8% (F = 5.04,
p < 0.002) of the variability of FMI z scores were maternal education, maternal age at the child’s birth, andpaternal BMI (ΔR2 = 0.40, p = 0.02, ΔR2 = 0.019,
p = 0.04, ΔR2 = 0.021, p = 0.046, respectively). Paternal BMI (β = 0.17, p = 0.02) and maternal education(β = 0.15, p = 0.046) were positively associated with FMI
z scores, while maternal age at the child’s birth was negatively associated with FMI z scores (β = -0.15, p = 0.04)(Table 13).
The stepwise regression model for FFMI z scores inthe boys’ group included paternal BMI, eating behavior,current maternal BMI, maternal age at the child’s birth,prenatal stress experience, and paternal education, which
Table 13 Multiple forward regression results predicting BMI z
scores, FMI z scores, FFMI z scores and FatM z scores in boys
Variable

R2

Model 1: BMI z scores

0.146

β

F
4.15

P-value< 0.001

Paternal BMI

0.08

0.002

Mother’s age at the child’s birth

-0.04

0.03

Maternal BMI before pregnancy

0.07

0.02

Prenatal stress (ref. 0)

0.34

0.09

Eating behaviours (ref. good)

0.28

0.12

Maternal educational level (ref. higher)

0.26

0.19

MC4R (ref. TT)

0.21

Model 2: FMI z scores

0.080

0.27
5.04

< 0.002

Maternal educational level (ref. higher)

0.17

0.02

Mother’s age at the child’s birth

-0.15

0.04

Paternal BMI

0.15

Model 3: FFMI z scores

0.165

0.046
5.69

< 0.001

Paternal BMI

0.24

0.001

Eating behaviours (ref. good)

0.16

0.02

Maternal current BMI

0.17

0.02

Mother’s age at the child’s birth

-0.14

0.05

Prenatal stress (ref. 0)

0.10

0.14

Paternal educational level (ref. higher)

0.08

Model 4: FatM z scores

0.085

0.27
4.08

< 0.001

Maternal educational level (ref. higher)

0.16

0.04

Paternal BMI

0.15

0.04

Mother’s age at the child’s birth

-0.16

0.03

Highest body weight in pregnancy

0.08

0.30

explained 16.5% of the dependent variable’s variance.
FFMI z scores were positively related to paternal BMI(β = 0.24, p < 0.001), eating behaviour (β = 0.16, p = 0.02),
and current maternal BMI (β = 0.17, p = 0.02). These variables had the greatest impact on the prediction of the
explained variable (ΔR2 = 0.71, p < 0.001, ΔR2 = 0.028,
p = 0.02, ΔR2 = 0.024, p = 0.02, respectively) (Table 13).
Eight point five percent (8.5%) of the variance in FatM
scores in the boys’ group was explained by maternal education, paternal BMI, maternal age at the child’s birth,and highest weight during pregnancy. Maternal education (β = 0.16, p = 0.04) and paternal BMI (β = 0.15,
p = 0.04) were positively associated with FatM z scores,
and maternal age at the child’s birth was negatively associated with FatM z scores (β = -0.16, p = 0.03). Maternal
education, paternal BMI and maternal age at the child’sbirth had the greatest impact on the prediction of thedependent variable (ΔR2 = 0.037, p = 0.04; ΔR2 = 0.019,
p = 0.04, ΔR2 = 0.023, p = 0.03, respectively) (Table 13).
The effects of ACEs and genetic interactions on bodycomposition parameters

The analysis revealed the effects of the interaction
between the FTO and MC4R gene polymorphisms and
ACE types on the BMI, FMI, FFMI and FatM z scores.
The experience of 3 or more stressors was related to anincreased BMI z scores (1.05 vs -0.24, Cohen’s d = 1.05)(Table 14, Fig. 1) in the children with FTO AA compared
to TT, and increased FMI and FatM z scores in the children with MC4R CC compared to TT (1.26 vs -0.33,
Cohen’s d = 3.20; 0.56 vs -0.36, Cohen’s d = 2.54, respectively). (Table 14, Figs. 2 and 3). Separation from theparents was related to an increase in BMI z scores in thechildren with FTO AA compared to TT (0.73 vs -0.11,
Cohen’s d = 0.62) (Table 14, Fig. 4) and MC4R CC compared to TT. It was also connected with an increase in
FMI z scores (2.55 vs -0.23, Cohen’s d = 1.72) and FatM zscores (2.43 vs 0.27, Cohen’s d = 1.39) (Table 14, Fig. 5) inthe children with MC4R CC compared to TT. Difficulties
at school were related to increased BMI and FMI z scoresin the children with FTO AA compared to TT (2.54 vs
0.80, Cohen’s d = 1.90; 1.47 vs -0.003, Cohen’s d = 2.33,
respectively) (Table 14, Figs. 6 and 7). The experience ofother unspecified stressors was related to higher BMI andFMI z scores in the children with FTO AA compared to
TT (1.59 vs -0.71, Cohen’s d = 1.43; 0.98 vs -0.59, Cohen’sd = 1.60, respectively) (Table 14, Figs. 8 and 9).

Discussion
In the context of previous research, the interactions
between genes and the environment seem to be particularly interesting. One less explored aspect pertains
to the interplay between unfavourable life experiences

Bryl et al. Journal of Physiological Anthropology(2023) 42:29

Page 27 of 35

Table 14 Interactions between ACEs and polymorphisms of FTO
and MC4R genes
FTO
F

MC4R
p

F

p

BMI z scores
ACE 0,1,2,3 +

2.59

0.02

1.80

0.10

Life threatened

2.20

0.11

0.10

0.90

Life threatened witness

1.73

0.17

0.30

0.74

Violence victim

0.58

0.56

0.16

0.85

Violence witness

0.50

0.61

0.22

0.80

Death of someone close

1.65

0.19

0.10

0.91

Family conflicts

1.94

0.15

0.02

0.98

Separation from parent

3.06

0.048

3.07

0.03

School problems

5.73

0.004

1.33

0.27

Other unspecified stressors

4.60

0.01

0.14

0.87

ACE at least one

0.58

0.55

0.06

0.94

FMI z scores
ACE 0,1,2,3 +

1.65

0.13

2.31

0.03

Life threatened

1.32

0.26

0.08

0.92

Life threatened witness

1.96

0.14

0.73

0.48

Violence victim

0.44

0.65

0.11

0.90

Violence witness

0.27

0.76

0.07

0.93

Death of someone close

0.15

0.86

0.55

0.58
0.58

Family conflicts

1.55

0.21

0.55

Separation from parent

0.93

0.40

8.34

< 0.001

School problems

4.09

0.02

0.81

0.44

Other unspecified stressors

3.13

0.04

1.02

0.36

ACE at least one

0.63

0.53

0.26

0.77

FFMI z scores
ACE 0,1,2,3 +

1.70

0.12

1.65

0.13

Life threatened

2.67

0.07

0.18

0.84

Life threatened witness

0.50

0.61

0.39

0.67

Violence victim

1.19

0.30

0.69

0.50

Violence witness

0.01

0.99

0.74

0.48

Death of someone close

2.33

0.10

0.60

0.55

Family conflicts

2.07

0.13

0.68

0.51

Separation from parent

3.27

0.04

2.16

0.11

School problems

4.95

0.007

1.91

0.15

Other unspecified stressors

3.03

0.04

0.54

0.58

ACE at least one

0.66

0.52

1.27

0.28

FatM z scores
ACE 0,1,2,3 +

0.80

0.57

2.27

0.04

Life threatened

0.98

0.38

0.13

0.88

Life threatened witness

0.62

0.54

0.27

0.77

Violence victim

0.41

0.67

0.007

0.99

Violence witness

0.22

0.81

0.13

0.88

Death of someone close

0.31

0.74

0.13

0.88
0.90

Family conflicts

1.34

0.26

0.11

Separation from parent

0.75

0.47

9.73

< 0.001

School problems

2.28

0.10

0.34

0.71

Other unspecified stressors

1.99

0.14

0.22

0.80

ACE at least one

0.64

0.53

0.07

0.93

BMI Body Mass Index, FMI Fat Mass Index, FFMI Fat Free Mass Index, FatM Fat
Mass in kg

and polymorphisms of the FTO rs9939609 and MC4R
rs17782313 genes and their impact on body composition parameters. The results of the Copenhagen General Population Study indicate a positive relationshipbetween BMI and WHR (Waist to Hip Ratio) and distressin adults. However, while examining the same relationships using adiposity related genotypes (FTO rs9939609
and MC4R rs17782313) as instrumental variables inverse
associations were observed [47]. Overweight is influenced by both genetic and environmental factors. In ourstudy we investigated the association of polymorphismsof the FTO rs9939609 and MC4R rs1778231 genes,
which have been well-documented to be related to ahigher prevalence of overweight and obesity [2, 5].
The results of our study reveal that environmental factors have a significant influence on body compositionchanges in school-aged children, while genetic factors
show less significance in the multiple regression model.
This indicates that environmental factors play a more
substantial role in shaping changes in body composition.
The study confirms that the most strongly influential
factors on body composition parameters were the paternal BMI, maternal education, highest body weight inpregnancy and mother’s age at the child’s birth, although
they explain a low variability of the dependent variable.
The limited explanatory capacity of these variables needs
further invastigation and additional research. It is essential to acknowledge that beyond the factors explored inthis study, numerous others may potentially influencebody composition parameters. The available literatureprovides evidence that children of parents with overweight/obesity are more prone to developing obesity[48–51]. Other studies have indicated a stronger maternal intergenerational relationship with the child’s bodymass [48, 49, 52]. However, our findings demonstratethat the paternal BMI has the strongest influence on thechild’s body composition parameters. These results alignwith a meta-analysis that shows a connection between
paternal BMI and the child’s weight and/or body fat [13].
The higher the father’s BMI, the greater the child’s bodyweight and/or the higher the body fat percentage [53].
Our futher analysis reveals that paternal BMI in addition
to BMI z scores significantly affects all other body composition parameters of the child, including FMI, FFMI,
and FatM. A possible explanation for the influence ofpaternal BMI on the children’s body composition could
be the process of learning by imitation. Parents’ weightis frequently related to their lifestyle, and it is well-documented that the dietary habits of individuals with a normal body weight differ from those who have overweightor obesity [54, 55]. Children, being keen observers, often
emulate the dietary choices [56] and leisure activities [57]of their parents.

Bryl et al. Journal of Physiological Anthropology(2023) 42:29

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Fig. 1 Effects of interaction between FTO polymorphism and ACEs on the BMI z scoresFig. 2 Effects of interaction between MC4R polymorphism and ACEs on FMI z scores

A high pregnancy weight is positively correlated withBMI z scores and FatM z scores. Previous research demonstrated that maternal gestational weight gain is associated with the child’s obesity and high waist circumference[58]. Maternal overnutrition may affect the developmentof adipocytes and their capacity to regulate the appetitecontrol system and energy metabolism later in life [59],which might lead to increased body fat in the offspring.
The mothers without a university education had children with higher FMI z scores and FatM z scores. Thereis an inverse relationship between the parents’ level of
education and obesity of their children, with the lowest
level of education corresponding to the highest prevalence of obesity in the children. The parents’educationlevel has a significant impact on the child’s body composition [28]. Education is also related to other components of socioeconomic status. Individuals with lower
education tend to have lower income, which affects
the quality of the food they choose, including the foodserved to their children [60]. Moreover, mothers with

Bryl et al. Journal of Physiological Anthropology(2023) 42:29

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Fig. 3 Effects of interaction between MC4R polymorphism and ACEs on FatM z scoresFig. 4 Effects of interaction between FTO polymorphism and separation from parents on BMI z scores

lower education levels are less likely to breastfeed theirchildren [61], which is also a protective factor against
the child’s overweight and obesity later in life [62]. The
socioeconomic conditions of parents are very importantin shaping the children’s eating habits since they influence the type of food available at home [63]. Studies
indicate that the diet of children whose parents declared
low socioeconomic status exhibit a higher consumptionof high-energy food with low nutritional value [64] and ahigher consumption of sugary drinks [65] at a preschool
age. The mother’s impact on the child’s nutritional

preferences is already present in fetal life, as her dietduring pregnancy and breastfeeding shapes the future
nutritional preferences of her child [66, 67].
The presence of risk alleles of the FTO and MC4R genes
was included in the models describing the variability ofthe body composition parameters, but the relationships
with MC4R were not statistically significant. However,we were interested in how genetic susceptibility and thecoexistence of unfavourable environmental factors, suchas ACEs would affect the body composition parameters.
Experiencing three stressors while being homozygous forBryl et al. Journal of Physiological Anthropology(2023) 42:29

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Fig. 5 Effects of interaction between MC4R polymorphism and separation from parents on FMI z scoresFig. 6 Effects of interaction between FTO polymorphism and school problems on BMI z scores

the risk allele was associated with higher BMI z scores for
the FTO gene and higher FMI z scores and FatM z scoresfor the MC4R gene compared to the children who did
not carry the risk allele. The results indicate interactions
between genetic and environmental factors. The presence of risk alleles alone is not a factor for the occurrenceof changes in children’s weight and body composition,
but it may become one depending on the environment.
The results of our study indicate that the presence of 3
or more unfavourable life experiences could potentiallyserve as a contributing factor. Upon separate examinationof different types of stressors, such as separation fromparents, problems at school and other unspecified typesof stressors significant associations emerged.The children who were separated from their parents and also carried the risk homozygous alleles of the FTO and MC4R
genes exhibited higher BMI z scores, FMI z scores, andFatM z scores. Similarly, those who were homozygous
for the risk allele of the FTO gene and had serious problems in school showed higher BMI z scores, FMI z scores,
and FFMI z scores. The occurrence of other unspecified
stressful situations, combined with being homozygous

Bryl et al. Journal of Physiological Anthropology(2023) 42:29

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Fig. 7 Effects of interaction between FTO polymorphism and school problems on FMI z scoresFig. 8 Effects of interaction between FTO polymorphism and other stressful events on BMI z scoresfor the risk allele of the FTO gene was also related tohigher BMI z scores, FMI z scores and FFMI z scores.
While considering only life stressors as the influencing
factor no significant correlation was found with the bodycomposition parameters in the children. The presenceof the risk allele of the FTO gene did not demonstrate animportant impact on the body composition parameters,
whereas the presence of the risk allele of the MC4R gene
was associated with increased FMI z scores and FFMIz scores. However, when the children were carriers of
the risk allele as well as experienced ACEs, there was astatistically significant effect on all body composition
parameters.
The gene polymorphisms investigated in our study wereselected for analysis based on strong evidence supporting
their association with BMI and body weight in children[2, 5]. It is worth emphasizing, however, that no studies
have so far demonstrated a direct association between
the FTO gene polymorphism and a resting metabolic rate[4]. The development of excess body weight in children
with the unfavourable allele of the FTO gene is attributed to their tendency to consume meals that are higher

Bryl et al. Journal of Physiological Anthropology(2023) 42:29

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Fig. 9 Effects of interaction between FTO polymorphism and other stressful events on FMI z scoresin energy and fat content [68]. Similarly for the MC4R
gene, the presence of the C allele leads to increased eating pleasure, reduced satiety, and a tendency to eat when
not hungry, which may contribute to obesity [5]. Thus themere presence of an unfavourable allele of the gene does
not singularly determine changes in the body weight.
However, if environmental factors, such as ACEs, which
may also affect food choices [69] co-occure, the risk ofchanges in body composition parameters increases.

Conclusions
The results of our study indicate that within the multiple regression model, genetic factors exhibit a lower
level of significance compared to the environment whenelucidating alterations in body composition parameters
among children. The presence of the risk allele does
not determine a decisive influence on changes in bodycomposition. However, together with the simultaneous
occurrence of unfavourable environmental factors, suchas ACEs a discernible interaction effect emerges, leading to an increase in BMI z scores and FMI z scores inchildren.
Strengths and weaknesses of the study
The strength of our study lies in the use of variables
related to body weight normality, which include not only
weight itself and the BMI derived from it but also indicators based on fat mass content (FMI) and fat-free masscontent (FFMI). Another significant aspect is the comprehensive consideration of numerous environmental,
perinatal, ACE and lifestyle factors as well as the FTO

and MC4R gene polymorphisms to examine their relationships with the body composition parameters. The
influence of environmental factors on the risk of overweight and obesity in children has been extensively studied; however, research on ACEs and their association
with body composition remains limited. To our knowledge, there have been only two previously publishedstudies [28, 29] that focused entirely on the relationshipbetween ACEs and the body composition parameters,
and their results were contradictory. Our study, therefore, represents an attempt to further explain this phenomenon. Another strength of the investigation is theexamination of interactions between the genes and theenvironment and their mutual influence on the bodycomposition parameters.
A weakness of the study is the use of BMI as a tool
to assess underweight, overweight, and obesity in children. Based on existing research, the use of BMI may
not be an appropriate method for such an assessment
since it does not consider the content of body fat andfat-free mass [70]. While assessing body weight basedon BMI, there is a possibility of overestimating therisk of excessive weight gain in children with a highamount of lean muscle mass [71, 72]. On the other
hand, it is possible to underestimate the risk in children who, despite their relatively low body weight, havea high fat content hazardous to health [73]. A weakness of the study is our reliance on information about
the parents’ weight and height derived solely from thequestionnaires. This may be associated with an overestimation of height (especially in the case of men) andBryl et al. Journal of Physiological Anthropology(2023) 42:29

an underestimation of body weight (in women andindividuals who are overweight or obese) [74]. We did
not check food intake, physical activity and time of last
urination prior to the body composition analysis. However, we would like to point out that other studies on
this subject indicate that differences in body composition depending on external factors are not clinicallysignificant [75]. Furthermore, limitations include theretrospective study of stressors, which increases therisk of memory error.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40101-​023-​00344-1.
Additional file 1. Correlations between BMI z scores, FMI z scores, FFMIz scores, FatM z scores and covariates. Prevalence of underweight, over‑weight and obesity diagnosed according to IOTF (Body weight status) andaccording to McCarthy criteria (Body fat status), and association between
body composition parameters and parental, environmental and genetic
factors – statistically significant results. Interactions between ACEs andpolymorphisms of FTO and MC4R genes.
Authors’ contributions
Conceptualization, TH, MD-W and AS; methodology, TH, MD-W and AS; investi‑
gation, EB, PS, TH, AD, MD-W; data curation, EB; writing – original draft prepara‑tion, EB, TH; project administration, TH; funding acquisition, TH, MD-W, AS All
authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the National Science Center (grant no.
2016/21/B/NZ5/00492).
Availability of data and materials
Due to the sensitive nature of the data supporting the conclusions of thearticle, only selective access to data is offered on reasonable request to theprincipal investigator (MD-W).

Declarations
Ethics approval and consent to participate
The research was conducted in accordance with the principles outlined in The
Code of Ethics of the World Medical Association (Declaration of Helsinki) for
experiments involving humans and approved by the Institutional Bioethics
Board of Poznan University of Medical Sciences (approval no. 542/14). The
subjects and their legal guardians were fully informed about the research
procedures and they submitted a written consent to participate in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Institute of Human Biology and Evolution, Faculty of Biology, Adam Mickie‑
wicz University, 61‑614 Poznan, Poland. 2 Department of Child and Adolescent
Psychiatry, Poznan University of Medical Sciences, 60‑572 Poznan, Poland.
3
Psychiatric Genetics Unit, Department of Psychiatry, Poznan Universityof Medical Sciences, 60‑806 Poznan, Poland.
Received: 30 April 2023 Accepted: 28 October 2023

Page 33 of 35

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