Effects of Breastfeeding and Formula Feeding on the Metabolic Factors and the Expression Level of Obesity and Diabetes-Predisposing Genes in Healthy Infants

doi:10.21203/rs.3.rs-928055/v1

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Effects of Breastfeeding and Formula Feeding on the Metabolic Factors and the Expression Level of Obesity and Diabetes-Predisposing Genes in Healthy Infants

https://doi.org/10.21203/rs.3.rs-928055/v1

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Background: Diabetes mellitus (DM) and obesity are prevalent disorders diagnosed by glucose metabolism problems and excess weight gain. Breastfeeding is the best feeding way for infants until six months and due to its nutritional properties, has desirable effects on diabetes and obesity. This study aimed to investigate the effects of breastfeeding, formula feeding, and formula-plus breastfeeding (mix-feeding) on the anthropometric indices, metabolic factors, and the expression level of obesity and diabetes-predisposing genes of healthy infants.

Methods: A total of 150 healthy infants were enrolled in this cross-sectional study. All infants (aged 24 months) based on the type of their feeding were divided into three groups, breastfeeding, formula feeding, and mix-feeding. The anthropometric indices, glycemic indexes, lipid profile, and the expression level of acetyl-coenzyme A carboxylase beta (ACACB), brain-derived neurotrophic factor (BDNF), liver X receptor α (LXR-α), peroxisome proliferator-activated receptor γ (PPAR-γ), and phosphatase and tensin homolog (PTEN) genes were assessed for all infants using reverse transcription-polymerase chain reaction (RT-PCR) method.

Results: The anthropometric indices including weight, height, and head circumference, insulin, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were lower in the breastfeeding infants in comparison to other groups. As well, the expression level of the ACACB gene was significantly downregulated in breastfeeding infants, while the PPAR-γ gene was significantly upregulated, but the expression level of LXR- α, PTEN and BDNF didn’t show a significant difference between groups.

Conclusions: Breastfeeding in comparison with formula feeding possessed desirable effects on anthropometric indices, metabolic factors, and diabetes-predisposing genes.

Maternal & Fetal Medicine

Breastfeeding

formula feeding

infant

diabetes

obesity

gene expression

Diabetes mellitus (DM) is a metabolic disorder that appears in the disorder of glucose metabolism (1). In recent years, the prevalence of DM rapidly increased and it has become a global health problem in the world (2). In this way, the global prevalence of DM is estimated %8.8 (2). DM is a multifactorial disorder and various acquisitive and non-acquisitive factors involved in its etiology such as genetics, age, excessive weight, diet and lifestyle (3).

In addition, according to world health organization (WHO) report, the prevalence of overweight and obesity in children has increased dramatically from 4% in 1975 to over 18% in 2016, and if this ascending trend is not stopped, it is expected that the worldwide number of overweight or obese infants and children to double in less than a decade (4, 5). It is proved that obese and overweight infants and children are more likely to become obese adults and to develop major health problems like; type 2 diabetes mellitus, heart disease, asthma, stroke, diabetes mellitus, hypertension, dyslipidemia, and certain types of cancer at younger ages (6, 7).

Among all factors, it seems that lifestyle as a modifiable factor has a principal role in the DM etiology (8). Different studies investigated the interactions between genes and lifestyle and showed that lifestyle and early life eating habits may affect the DM predisposing genes and make the person susceptible to DM (8).

Breastfeeding due to several desirable effects on the child, mother, and health status of the community is considered as the best feeding way for the infants during the first 6 months of life (9, 10). Numerous studies indicated the beneficial effects of breastfeeding on the prevention of chronic non-communicable diseases such as autoimmune disease, cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes (11). In addition, the relationship between breastfeeding and DM has been proved and studies showed that breastfeeding has protective effects against DM during childhood and adulthood (12–14).

Investigations showed that various beneficial compounds of breast milk can cause epigenetic changes in infants in his/her early life. These compounds can change the DNA-methylation involved in the regulation of gene expression (15, 16). Different studies reported that downregulation of some genes like; PTEN (17) and ACACB (18) are able to protect against diabetes. PTEN protein performs as a phosphatase and catalyzes the dephosphorylation of the 3` phosphate of the inositol ring in PIP3, resulting in the biphosphate product PIP2 which involves in different signaling pathways such as survival, cell growth, migration, and tumor suppression (19). Since the insulin function in the body depends on the activation of PI3K and PIP3 levels, it seems that mammalian PTEN proteins may act in the insulin or insulin-like hormonal pathways and can regulate mammalian metabolism. Based on different studies upregulation of the PTEN gene may cause insulin resistance in the downstream receptors of insulin. Therefore, this gene can be a good candidate for human autosomal dominant type II diabetes (20).

ACACB gene encodes acetyl-CoA carboxylase beta, which plays a key role in the fatty acid synthesis and oxidation via carboxylation of acetyl-CoA to malonyl-CoA as a rate-limiting step in mitochondrial oxidation of fatty acids. Additionally, the ACACB gene has an important role in triglyceride synthesis and previous studies showed that the triglyceride content of muscle in obese individuals is higher than non-obese and as an adaptation mechanism the expression level of ACACB in adipose tissue diminished in response to increased body fat (21–23). Numerous in vivo studies showed that mice lacking ACACB gene have a normal life, higher insulin resistance and fatty acid oxidation and lower fat mass (24, 25). Accordingly, since the ACACB gene has a significant role in energy metabolism in the body, it can be proposed as a marker gene associated with child obesity and different metabolic disorders like diabetes (26).

On the other hand, some genes such as PPAR-γ, LXRs, and BDNF showed beneficial effects on glucose metabolism and diabetes (27, 28). PPAR-γ is a ligand-activated transcription factor that belongs to a superfamily of receptors and regulates gene transcription (29). PPAR-γ showed a desirable effect on insulin resistance via activation of transporters of fatty acids to adipose tissue, regulation of the release of adipose tissue hormones, and direct effects on the transfer of glucose to tissues (30). Two isoforms of the LXRs gene (α and β) play a critical role in carbohydrate and lipid metabolism (31). LXR-α is expressed only in tissues that are involved in the metabolism of glucose and lipids such as the liver, kidney, adipose tissue, skeletal muscle, and intestinal tract, but LXR-β is expressed in most of the body tissues (32–34). BDNF is a neurotrophic factor that involves in the development, survival, and maintenance of neurons via high-affinity receptor, tropomyosin-related kinase B (TrkB) (35). This neurotrophic factor affects several metabolism pathways via influencing the hypothalamus or neurotransmitters that mediate food intake (36). Therefore, breast milk can reduce the risk of different metabolic-related disorders and makes long-term effects on health status through epigenetic changes and alteration of effective gene expression in adulthood. The aim of the present study was to investigate the effects of breastfeeding, formula feeding, and mix-feeding on the anthropometric indices, metabolic factors, and the expression level of obesity and diabetes-related genes in healthy infants.

Study Design and Participants

This cross-sectional study was conducted on healthy infants who referred to the health centers of Kermanshah city in Iran. At first, all procedures of the study were described thoroughly for volunteered parents whose infants were eligible and a written consent form was completed for all participants. In the present study, out of 323 infants that were referred to the health centers in Kermanshah city, after considering the inclusion and exclusion criteria, 150 healthy infants aged 24 months (boys and girls) were recruited. All included infants and their parents were healthy, and infants fed with breast milk or formula milk and if any metabolic disorders in the infants or their parents were detected before and during the study, they were excluded from the study. All infants were categorized into three groups including breastfeeding (n = 50) as control group, formula feeding (n = 50), and mix-feeding (n = 50) as case groups (Fig. 1). This study was approved by the ethics committee of Kermanshah University of medical sciences with code No. IR.KUMS.REC.1397.069 and conducted in accordance with the Declaration of Helsinki.

Measurements

At first, the baseline characteristics of infants and their parents, including age, sex, and type and duration of feeding, were recorded by interviewing with parents and using their valid ID cart. Anthropometric indices of infants including weight, height, and head circumference were measured according to standard procedures and compared with the WHO standard charts to determine the health status of infants (37). Furthermore, their previous anthropometric indices (from birth to 24th month age) data were recorded using their health record and to diminish recall bias, all information of infants were rechecked by their previous recorded data. As well, the weight of parents was measured with light clothes using a digital Seca scale with a precision 0.1 kg and the height was determined using a meter attached on the wall with precision 1cm and their body mass index (BMI) was calculated. To perform biochemical analysis, 5 ml of intravenous blood were drawn from all infants. After centrifuging the blood samples for 15 minutes at 5000 rpm, the isolated serum samples were frozen at -80^°C until they were analyzed. Fasting blood sugar (FBS) was determined by the auto-analyzer (Vita Laboratory) following standard procedures recommended by the Pars Azmoon diagnostic kit (Parsazmun, Tehran, Iran) and serum insulin was measured by the ELISA method (Monobind, USA). Moreover, triglyceride (TG), TC, and HDL-C concentration were measured using the Pars Test kit (Parsazmun, Tehran, Iran) enzymatically. LDL-C concentration was also calculated using Friedewald's formula as follows: LDL-C (mg/dL) = TC (mg/dL) - HDL-C (mg/dL) - TG (mg/dL)/5 (38).

For gene expression analysis, the infants’ blood samples were collected in ethylenediaminetetraacetic acid (EDTA) coated vials. Peripheral blood mononuclear cells (PBMC) were segregated through a density gradient centrifuge using the ficoll histopaque solution gradient (Ficoll-paque, Miltenyi Biotec GmbH, and Germany). The total RNA was extracted from PBMC using Trisol Reagent kit (YTzol pure RNA, Iran), and one µg of extracted RNA was used for the synthesis of complementary DNA (cDNA) by Prime Script RT-Reagent kit (Takara Bio Inc., Tokyo, Japan) according to manufacturer’s instructions. Specific primers were designed and purchased from Metabion Biotechnology Company (Metabion, steinkirchen, Germany) (Table 1). Data were normalized to 18s rRNA expression as housekeeping gene by 2^−∆∆Ct method. All reactions were performed in triplicate and negative controls were included in each experiment.

Table 1

Primers sequences for RT-PCR amplification.
Gene name and symbol	Sequence (5՛ 3՛)	Amplicon size (bp)	TM
			F	R
BDNF	F :5-GGCTTGACATCATTGGCTGAC-3՛	79	61.6	62.9
	R :5-TGTGCAGTGTGAGAAAGGCTT-3՛
ACACB	F : 5-CAAGCCGATCACCAAGAGTAAA-3՛	79	60.3	61.3
	R : 5-CCCTGAGTTATCAGAGGCTGG-3՛
PTEN	F : 5-CAAGATGATGTTTGAAACTATTCCAATG-3՛	100	60.5	60.6
	R : 5-CCTTTAGCTGGCAGACCACAA-3՛
LXR-α	F :5-CCTTCAGAACCCACAGAGATCC-3՛	83	61.7	62.4
	R :5-ACGCTGCATAGCTCGTTCC-3՛
PPAR-γ	F :5-GATGCCAGCGACTTTGACTC-3՛	186	61.1	62.4
	R :5-ACCCACGTCATCTTCAGGGA-3՛
18s rRNA	F :5-ACCCGTTGAACCCCATTCGTG A-3՛	96	61.6	62.1
	R :5-GCCTCACTAAACCATCCAATCGG-3՛
ACACB: acetyl-coenzyme A carboxylase beta; BDNF: Brain-derived neurotrophic factor; bp: base pair; F: forward; LXR- α: Liver X receptors α; PPAR-γ: peroxisome proliferator-activated receptor γ; PTEN: phosphatase and tensin homolog; R: reverse; TM: melting temperature

Statistical Analysis

All statistical analyses in this study were performed using SPSS 23 software sciences (SPSS Inc., Chicago, IL, USA, version 23.0). The normality of variables was assessed by the Kolmogorov-Smirnov test. The Chi-square test was used for comparing quantitative variables between groups. The One-way ANOVA test was used to compare quantitative normal variables and the Kruskal-Wallis test was used for comparing non-normal data. All data were obtained from at least three independent experiments and expressed as mean ± SD for quantitative variables and frequency (percent) for qualitative variables.

In the present study, 150 infants were enrolled, which 50.4% and 49.6% were boys and girls, respectively. According to the type of feeding, all infants were divided into three groups including breastfeeding (n = 50), formula feeding (n = 50), and mix-feeding (n = 50), and all of them completed the study. The baseline characteristics of infants and their parents did not show any significant difference between groups (Table 2).

Table 2

Baseline characteristics and anthropometric indices of infants based on types of feeding.
Variable (mean ± SD) ¹		Breastfeeding (n = 50)	Formula feeding (n = 50)	Mix-feeding ² (n = 50)	P-value ³
Sex	Boy	26	25	28	0.74
Sex	Girl	24	25	22	0.74
Age-birth (week)		38.16 ± 1.74	38.8 ± 1.31	38.58 ± 1.53	0.09
Age of mothers (year)		26.40 ± 5.54	28.64 ± 6.40	27.24 ± 5.88	0.06
Age of fathers (year)		32.30 ± 4.54	34.64 ± 6.40	33.24 ± 5.88	0.07
BMI of mothers		24.60 ± 3.41	27.64 ± 3.82	27.74 ± 4.20	0.22
BMI of fathers		26.28 ± 3.41	26.50 ± 3.50	25.84 ± 2.90	0.32
Birth weight		3.10 ± 0.34	3.10 ± 0.38	3.0 ± 0.46	0.27
24th -month weight (kg)		12.64 ± 1.24	14.27 ± 0.61	13.35 ± 1.01	< 0.0001
Birth height(cm)		50.1 ± 2.9	52.3 ± 2.1	49.5 ± 2.8	0.31
24th -month height		86.52 ± 2.71	88.78 ± 2.32	86.4 ± 2.92	< 0.0001
Birth head circumference		42.8 ± 1.99	44.9 ± 1.4	43.2 ± 1.7	< 0.0001
24th -month head circumference		48.35 ± 1.48	50.20 ± 1.04	49.62 ± 0.99	< 0.0001
¹ Data are represented as means ± SDs and n (%) for continuous and categorical variables, respectively.
² Mix feeding, breastfeeding plus formula feeding.
³ P-values for comparison between groups using One-way ANOVA test.

The comparison of the trend for weight, height, and head circumference of infants from birth to 24th month of age between the three groups revealed a significant difference between breastfeeding and formula feeding infants (Fig. 2). As shown in Table 2, all anthropometric values including 24th -month weight (p < 0.0001), 24th -month height (p < 0.0001), birth head circumference (p < 0.0001), and 24th -month head circumference (p < 0.0001) showed a significant difference between three groups. Statistical analysis between breastfeeding and formula feeding groups revealed a significant difference for 24th-month weight (p < 0.0001), birth height (p = 0.019), 24th -month height (< 0.0001), and 24th -month head circumference (p < 0.0001). As well, there was a significant difference between breastfeeding and mix-feeding groups for the 24th -month weight (p = 0.015), birth head circumference (p < 0.0001), and 24th -month head circumference (p < 0.0001). Moreover, the differences between formula feeding and mix-feeding infants about all anthropometric variables except for birth weight and height were significant (Table 3) (Fig. 2).

Table 3

Comparison of anthropometric variables between groups based on type of feeding.
Variable	Breastfeeding		Formula feeding
Variable	Formula feeding ¹	Mix-feeding ¹	Mix-feeding ¹
Birth weight	P = 0.11	P = 0.30	P = 0.21
24th -month weight	P < 0.0001	P = 0.01	P < 0.0001
Birth height	P = 0.01	P = 0.05	P = 0.08
24th -month height	P < 0.0001	P = 0. 83	P < 0.0001
Birth head circumference	P = 0.60	P < 0.0001	P < 0.0001
24th -month head circumference	P < 0.0001	P < 0.0001	P = 0.005
¹ P-values for comparison between groups using One-Way ANOVA test.

According to Table 4, biochemical variables including Insulin (p < 0.0001), TC (p < 0.0001), LDL-C (p < 0.0001), and HDL-C (p = 0.004) were significantly different between the three groups. In addition, the statistical analysis between breastfeeding and formula feeding infants showed that insulin (p < 0.0001), TC (p < 0.0001), LDL-C (p < 0.0001), and HDL-C (p = 0.001) were significantly different (Table 5). Besides, breastfeeding infants in comparison to mix-feeding had a significantly lower median for TC (p < 0.0001) and lower means for LDL-C (p < 0.0001). This difference between formula feeding and mix-feeding groups was significant just for insulin (p < 0.0001) and TC (p < 0.0001) (Table 6).

Table 4

Biochemical variables of infants based on type of feeding.
Type of feeding Variable ¹	Breastfeeding (n = 50)	Formula feeding (n = 50)	Mix-feeding (n = 50)	P-value ²
Blood glucose ³	67.50 (58.75, 76.25)	60.50 (54.00, 70.00)	61.50 (54.75, 71.00)	0.05
Insulin	8.71 ± 3.42	13.13 ± 2.40	9.42 ± 3.46	< 0.0001
HbA₁c	5.16 ± 3.64	4.72 ± 2.19	4.89 ± 3.79	0.79
Triglyceride	75.94 ± 6.43	77.13 ± 5.91	74.72 ± 6.18	0.15
Total cholesterol ³	120 (110.50, 129.25)	150.50 (136.25, 167.00)	130.00 (118.50, 142.50)	< 0.0001
LDL-C	39.78 ± 7.43	49.02 ± 5.22	48.7 ± 3.05	< 0.0001
HDL-C	47.78 ± 9.25	50.49 ± 9.34	48.84 ± 8.91	0.004
¹ Data are represented as means ± SDs for continuous variables.
² P-values for comparison between groups using One-way ANOVA test.
³ Data are represented as median (25th -percentile, 75th -percentile) and P-values for comparison between groups using Kruskal-Wallis test.
HbA₁c: hemoglobin A1c; HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol

Table 5

Comparison of biochemical variables between groups based on type of feeding.
Type of feeding		Insulin ¹	Total cholesterol ²	LDL-C ¹	HDL-C ¹
Breastfeeding	Formula feeding	P < 0.0001	P < 0.0001	P < 0.0001	P = 0.001
Breastfeeding	Mix-feeding	P = 0.30	P < 0.001	P < 0.0001	P = 0.09
Formula feeding	Mix-feeding	P < 0.0001	P < 0.0001	P = 0.70	P = 0.09
¹ P-values for comparison between groups using One-way ANOVA test.
² P-values for comparison between groups using Mann-Whitney U-test.
HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol

Table 6

Gene expression of infants based on type of feeding.
Variable ¹	Breastfeeding (n = 50)	Formula feeding (n = 50)	Mix-feeding (n = 50)	P-value ²
BDNF	3.24 ± 2.84	2.77 ± 2.01	3.06 ± 2.27	0.61
LXR-α	8.37 ± 4.64	8.29 ± 2.37	7.87 ± 1.80	0.70
PPAR-γ	8.1 ± 2.03	6.54 ± 3.52	7.62 ± 1.42	0.007
ACACB ³	5.10 (2.65, 9.65)	19.26 (12.07, 40.50)	14.05 (9.37, 28.32)	< 0.0001
PTEN	2.39 ± 2.42	2.17 ± 1.35	2.02 ± 1.97	0.63
¹ Data are represented as means ± SDs for continuous variables.
² P-values for comparison between groups using One-way ANOVA test.
³ Data are represented as median (25th -percentile, 75th -percentile) and P-values for comparison between groups using Kruskal-Wallis test.
ACACB: acetyl-coenzyme A carboxylase beta; BDNF: Brain-derived neurotrophic factor; LXR- α: Liver X receptors α; PPAR-γ: peroxisome proliferator-activated receptor γ; PTEN: phosphatase and tensin homolog

Based on Table 6, the median expression values of ACACB and the mean expression values of PPAR-γ genes were significantly different between groups. Furthermore, the expression level of the ACACB gene between breastfeeding and two other groups (formula feeding and mix-feeding) showed significant differences (P < 0.0001). The same comparison for PPAR-γ gene expression was significant just between breastfeeding and formula feeding groups (P = 0.008). On the other hand, the comparison between formula feeding and mix-feeding groups showed a significant difference just for the expression level of the PPAR-γ gene (P = 0.047) (Table 7).

Table 7

The comparison of gene expression between groups.
Type of feeding		ACACB ¹	PPAR-γ ²
Breastfeeding	Formula feeding	P < 0.0001	P = 0.008
Breastfeeding	Mix-feeding	P < 0.0001	P = 0.19
Formula feeding	Mix-feeding	P = 0.07	P = 0.04
¹ P-values for comparison between groups using Mann-Whitney U-test.
² P-values for comparison between groups using Post-Hoc test.
ACACB: acetyl-coenzyme A carboxylase beta; PPAR-γ: peroxisome proliferator-activated receptor γ

To our knowledge, this is the first study that assesses the effects of breastfeeding and formula feeding on the expression level of BDNF, LXR-α, PPAR-γ, ACACB, and PTEN genes in infants. Based on the results of this study, anthropometric indices and biochemical variables including 24th -month weight, 24th -month height, birth head circumference, 24th -month head circumference, insulin, TC, LDL-C, and HDL-C in infants that fed exclusively with breast-milk were lower than others (Tables 3 and 5). Moreover, the expression levels of BDNF, LXR-α, and PPAR-γ genes were higher in breastfeeding infants in comparison to others, while ACACB gene expression was lower (Table 7).

Breastfeeding has reciprocal health beneficial effects on mothers and their children. Different studies revealed that the risk of type 2 diabetes, metabolic and cardiovascular disease, ovarian, and breast cancer may reduce in mothers with regular breastfeeding. On the other hand, the risk of childhood and adolescent overweight in infants who exclusively breastfeed is 22–24% lower than formula-fed infants (39).

Numerous investigations have demonstrated that breastfeeding can prevent obesity and adiposity in adolescence and adulthood via desirable effects on anthropometric indices and biochemical parameters (40–42). In this way, Gopinath et al. showed that the duration of breastfeeding is significantly associated with lower BMI levels and each month the increase in breastfeeding is associated with an average reduction in BMI about 0.04 kg/m² and 0.03 kg/m² among children aged 1–2 years and 3–4 years, respectively. As well, each month increase in breastfeeding duration is associated with a 0.06 cm decrease in waist circumference in 12–24 month-year-old children (43). Moreover, different meta-analysis and high-quality studies showed that breastfeeding can reduce the risk of diabetes and significantly decrease the incidence of overweight/obesity by 13% in both high-income and low- or middle-income countries in childhood (40, 44, 45). Similarly, our investigation showed that exclusive breastfeeding can prevent obesity in early life via modification of anthropometric indices.

Besides, Kimpimäki et al. revealed that short-term exclusive breastfeeding and early initiation of formula feeding that is based on cow’s milk can trigger progressive signs of beta-cell autoimmunity in children who are genetically susceptible to type Ι diabetes (13). There are different proteins and constituents in the cow’s milk like; α- and β-lactoglobulin, Ig E, casein, and bovine serum albumin antibodies that stimulate the immune system in a damaging way on β-cells and trigger type Ι diabetes in the first year of life (46–49).

In addition, the results from a cohort study demonstrated that the risk of elevated weight gain at the age of 2 years between infants who breastfed for less than 6 months is more than infants who fed for 6 months or more. These findings showed that the duration of breastfeeding is highly related to the risk of overweight, so that breastfeeding for at least 6 months decreases the risk of weight gain in comparison to infants who were exclusively breastfed 1 month at most (50).

The first few months of life is an important period of life that growth is very fast and the body weight doubles during 4–6 months (51, 52). Therefore, in this period of life, the type of feeding has a key role in the healthy growth of infants and prevention of overweight and obesity in the future. According to different studies, fast weight gain during the first 4 months of life was associated with a higher risk of overweight at the age of 7 years, independent of birth weight and weight at the age of 1 year (53, 54).

The particular role of breastfeeding on future overweight and obesity is related to human milk’s exceptional biochemical constituents. The nutritive and non-nutritive components of breast milk provide all the requirements of infants and moderate their energy balance. The unique bioactive substances and lower protein, fat, and insulin content of breast milk can regulate appetite, fat deposition, and metabolic responses (50, 55). Moreover, some components of breast milk such as hormones, growth factors, neuropeptides, and anti-inflammatory and immune-modulating agents affect the growth, development, and function of the gastrointestinal (GI) tract during early infancy (55). One of the exclusive components of breast milk is the human milk oligosaccharides (HMOs) that do not exist in infant formula. These complex sugars improve the gut microbiome and immune system of infants and recently have been suggested as a possible link between breastfeeding and lower obesity risk (56).

Besides, the energy density of human milk is lower than formula milk, and children who breastfed have better self-control on energy intake compared to formula feeding infants (57–59). Some investigations suggested that the intake of high protein and nutrients reduces the basal metabolic rate and rose adiposity in rats and humans more than 10 percent (60, 61). Generally, formula-fed infants intake more protein than breastfed infants, which is related to an increased risk of obesity via higher adipogenic activity and adipocyte differentiation (62). High protein intake may decrease the secretion of growth hormone (GH) that leads to lower lipolysis of fat mass and affects the plasma free fatty acids (62, 63). Moreover, consuming formula has some growth accelerating factors that accelerate the growth rate of infants in early life and make them overweight in their later life (54, 64).

It seems that the most important factor that makes the difference between the two types of feeding is feeding behavior and mother-child contact, so that formula-fed infants have a lower frequency of meals, longer time between meals, and different suckling patterns in comparison to breastfed infants (60, 65, 66). On the other hand, breastfed infants can control the quantity, duration, and frequency of their meals based on their feeling of hunger and satiation (60, 67).

Besides, the type of feeding can influence the insulin level and lipid profile of infants. The results of our study showed a significant difference in the insulin and lipid profiles of infants based on their type of feeding. The exclusively breastfed infants have significantly lower insulin, TC, LDL-C, and HDL-C levels compared to formula-fed and mixed-fed groups.

The high protein content of the formula can stimulate the insulin secretion of formula-fed infants and is positively related to the risk of overweight. Different investigations showed that the infants who fed with the formula possess higher postprandial plasma insulin level and have a prolonged insulin response on day 6 of their life compared to breastfed infants (53, 68). Intake of higher protein levels lead to the production of higher insulin and insulin-like growth factor (IGF-I) concentrations, which raises adipose tissue deposition and the risk of overweight, obesity, and type 2 diabetes in the coming years (69).

In line with our results, Hui et al. revealed that breastfeeding for the first 3 months is able to decrease TC, LDL-C, and TG at adolescence. Furthermore, the difference between the lipid profile of mixed feeding and formula feeding groups wasn’t significant, except for HDL-C that was lower in the mix-fed group (70). On the contrary, Thorsdottir et al. showed that the LDL-C level in boy infants who breastfed for 48 months was significantly higher and the TC level was not significantly more in 12 months’ infants who breastfed at the ages of 2, 4, 6, 9, and 12 months. The HDL-C level was significantly lower in infants who had been breastfed at the ages of 4 and 9 months. As well, in this study, the TG level was not significantly associated with breastfeeding (71). Similarly, Harit et al. demonstrated that exclusively breastfed infants had significantly higher TC and LDL-C compared to mixed-fed infants at both 14 weeks and 6 months. Besides, at 14 weeks, TG was significantly higher in breastfed infants compared to mixed fed. Moreover, Teller et al. revealed that cholesterol and lipoprotein concentrations in breastfed infants were higher than formula-fed infants (72). It seems that the high lipid profile in exclusively breastfed infants is beneficial for cognitive growth and lipid metabolism (73). The results of our study showed that breastfeeding can significantly reduce TC and LDL-C in comparison to formula feeding and mix-feeding. The controversy between our findings and other studies may be due to different methodologies, type of blood sample (venous/capillary), the time interval between last feeding and sampling, type of formula, preparation of formula (dilution), and genetic variations. Furthermore, our findings showed that the expression levels of PPAR-γ and ACACB genes were significantly different between groups. In contrast, the statistical analysis between the three groups for BDNF, LXR-α, and PTEN genes was not significant (Table 7). The comparison of the breastfeeding group with formula feeding and mix-feeding groups indicated significant differences for ACACB gene expression (P < 0.0001). This comparison for PPAR-γ gene expression was significant just for breastfeeding and formula feeding groups (P = 0.008). The family of peroxisome proliferation-activated receptors consists of three isoforms: 1) PPAR-α, PPAR-β/δ, and PPAR-γ, which differ from each other in terms of their physiological roles, ligand specificity, and tissue distribution. PPAR-γ is abundantly expressed in white and brown adipose tissue, the large intestine and spleen (74). These isoforms play an important roles in controlling the expression of genes involved in glucose and lipid homeostasis, cell differentiation, morphogenesis, and inflammatory response (75). Due to the significant role of PPAR-γ in macronutrient metabolism, it is a good target for synthetic insulin sensitizers like; thiazolidinediones in the treatment of type 2 diabetes mellitus (76). Based on different studies, the secretion of some effective adipocytokines on insulin sensitivity such as leptin and adiponectin, was maintained via the activation of PPAR-γ in adipocytes (77).

Acetyl-CoA carboxylase (ACAC) is an important enzyme in the synthesis of malonyl-CoA that involves in fatty acid metabolism. Two isoforms of ACAC are ACACA and ACACB, which encode by genes located in chromosomes 17 and 12, respectively. ACACB is predominantly expressed in the heart and skeletal muscle and catalyzes the carboxylation of acetyl-CoA to malonyl-CoA, which regulates the amount of fatty acid entrance into the mitochondria and fatty acid oxidation via modulation of carnitine palmitoyltransferase-1. Therefore, ACACB plays an important role in fatty acid synthesis and oxidation pathways, and any disruption in these pathways is related to impaired insulin sensitivity and metabolic syndrome (MetS) (18, 78, 79). Our investigation showed that breastfeeding in comparison to formula feeding and mix-feeding can decrease the expression level of the ACACB gene. In this way, Ma et al. revealed that ACACB single-nucleotide polymorphism (SNP rs2268388) is related to BMI in general population and to obesity in subjects with type 2 diabetes and affects gene expression in adipose and hepatic tissue. They suggested that ACACB has a significant role in obesity and a potential role in lipid metabolism changes in type 2 diabetes (T2DM)-associated nephropathy (80). Moreover, Abu-Elheiga et al. demonstrated that ACACB knock-out mice are morphologically normal, grow at the expected rate, and breed normally, whereas they have higher fatty acid oxidation rate and lower fat and glycogen storage in their adipose tissue and liver respectively (24). Tang et al. showed that a SNP in the ACACB gene is associated with susceptibility to type 2 diabetic nephropathy (T2DN). They suggested that targeting this pathway can be a new treatment option for the prevention of diabetic nephropathy (81). In addition, Riancho et al. revealed that different polymorphisms of the ACACB gene play an important role in energy metabolism in postmenopausal women and predispose them to obesity and type 2 diabetes (18). It seems that the ACACB gene plays an important role in the development of obesity and diabetes by reducing fat oxidation, and breastfeeding in childhood can be a good way to prevent its adverse effects in adulthood. There are some effective biological compounds in humane milk that have epigenetic effects via triggering some processes such as; histone modification, DNA methylation, and chromatin remodeling. In addition, these active compounds are able to decrease or increase the expression level of different key genes in the metabolic pathways and may prevent weight gain or subsequent non-communicable diseases in later life. As well, our previous investigation revealed that breastfeeding can influence the key genes in the obesity pathway and decrease the expression level of fat mass and obesity-associated (FTO) and carnitine palmitoyltransferase IA (CPT1A) genes and increase the expression level of the PPAR-α gene in 5–6 month’s infants (82). Likewise, in the present study, we investigated the effects of breastfeeding on other important genes in obesity and diabetes pathways like; BDNF, LXR-α, PPAR-γ, ACACB, and PTEN and found that breastfeeding significantly affected PPAR-γ and ACACB genes that confirmed the epigenetic effects of humane milk which is deficient or absent in formula feeding. These findings elucidated some unclear mechanisms of humane milk against obesity and diabetes. There are some limitations with this study, including low sample size and recall bias. By assessment of more infants, we could acquire more reliable and accurate outcomes. Since the duration and type of feeding were obtained via recall, the possibility of recall bias may exist. Also, due to the nature of cross-sectional studies, we couldn’t found cause-effect relationships. In this study, the data about the volume, calories, and components of the consumed formula feeding was not available, and so we couldn’t achieve the association between calorie intake, formula components, and the expression level of investigated genes. In addition, due to financial constraints, we were unable to examine all obesity-related genes in infants. The strength of this study was the infants which consisted of both sexes, acceptable response and participation rate, and standardized anthropometric measurement protocol.

In summary, our findings indicated that anthropometric indices like; 24th-month weight, 24th-month height, birth head circumference, and 24th-month head circumference in breastfed infants are lower than formula and mix-feeding infants. Additionally, some biochemical variables including insulin, TC, LDL-C, and HDL-C in infants that fed exclusively with breast milk were lower than the two other groups. In this study, human milk was significantly decreased the expression level of the ACACB gene and increased the expression level of the PPAR-γ gene. Our findings didn’t show a significant difference between the three groups (breastfeeding, formula feeding, and mix-feeding) about the expression level of BDNF, LXR-α, and PTEN genes. Thus, breastfeeding can modulate some obesity and diabetes-predisposing genes and together with other environmental factors can be a potential effector to prevent weight gain during early life. Overall, the recommendation of exclusive breastfeeding during the first six months of life is the best strategy to ensure the healthy physical growth of infants and prevention of communicable and non-communicable diseases in adulthood. However, further investigations are required to elucidate the effects of humane milk on key genes related to obesity and diabetes and its molecular mechanisms on childhood and adulthood obesity.

ACAC

Acetyl-CoA carboxylase

ACACB

acetyl-coenzyme A carboxylase beta

BDNF

brain-derived neurotrophic factor

BMI

body mass index

cDNA

complementary DNA

CPT1A

Carnitine Palmitoyltransferase IA

diabetes mellitus

EDTA

ethylenediaminetetraacetic acid

FBS

fasting blood sugar

FTO

fat mass and obesity-associated

growth hormone

gastrointestinal

HDL-C

high-density lipoprotein-cholesterol

HMOs

human milk oligosaccharides

IGF-I

insulin-like growth factor

LDL-C

low-density lipoprotein-cholesterol

LXR-α

liver X receptors-α

MetS

metabolic syndrome

PBMC

peripheral blood mononuclear cells

PPAR-γ

peroxisome proliferator-activated receptorγ

PTEN

phosphatase and tensin homolog

RT-PCR

reverse transcription-polymerase chain reaction

SNP

single-nucleotide polymorphism

total cholesterol

triglyceride

TrkB

tropomyosin-related kinase B

T2DM

type 2 diabetes mellitus

T2DN

type 2 diabetic nephropathy

WHO

world health organization

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Kermanshah University of medical sciences with code No. IR.KUMS.REC.1397.069 and in accordance with the World Medical Association Declaration of Helsinki. An informed consent was obtained from all subjects and/or their legal guardian(s). All methods were carried out in accordance with relevant guidelines and regulations under Ethics Committee approval.

Consent for publication

Not applicable.

Availability of data and materials

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

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by the Ministry of Health and Medical Education of Kermanshah University of Medical Sciences.

Authors’ Contributions

AS and SMN- are the corresponding authors and are primarily responsible for writing and editing the manuscript. SCH, NH, NE, NHE- contributed with data acquisition and reviewing the manuscript text. SCH- analyzed the data and drafted the manuscript. AS- is the supervising author of the study and contributed with writing and editing the manuscript, as well as data acquisition and analysis. All authors have approved the final manuscript submission and have agreed to be fully accountable for their contributions.

Acknowledgments

We are grateful to all parents and infants who took part in this study. This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors. The financial support of the Kermanshah University of Medical Sciences is gratefully acknowledged.

Scheen A, De Hert M. Abnormal glucose metabolism in patients treated with antipsychotics. Diabete Metab. 2007;33(3):169–75.
Weisman A, Fazli GS, Johns A, Booth GL. Evolving trends in the epidemiology, risk factors, and prevention of type 2 diabetes: a review. Can J Cardiol. 2018;34(5):552–64.
Beckman JA, Creager MA, Libby P. Diabetes and atherosclerosis: epidemiology, pathophysiology, and management. Jama. 2002;287(19):2570–81.
Freitas AI, Moreira C, Santos AC. Time trends in prevalence and incidence rates of childhood overweight and obesity in Portugal: Generation XXI birth cohort. Int J Obes. 2019;43(2):424–7.
Report of the commission on ending childhood obesity. World Health Organization. 2016.
Friedemann C, Heneghan C, Mahtani K, Thompson M, Perera R, Ward AM. Cardiovascular disease risk in healthy children and its association with body mass index: systematic review and meta-analysis. Bmj. 2012;345:e4759.
Lenz M, Richter T, Mühlhauser I. The morbidity and mortality associated with overweight and obesity in adulthood: a systematic review. Dtsch Arztebl Int. 2009;106(40):641.
Temelkova-Kurktschiev T, Stefanov T. Lifestyle and genetics in obesity and type 2 diabetes. Exp Clin Endocrinol Diabetes. 2012;120(01):1–6.
James D, Lessen R. Position of the American Dietetic Association: promoting and supporting breastfeeding. J Am Diet Assoc. 2009;109(11):1926–42.
Pediatrics AAo. American Academy of Pediatrics Section on Breastfeeding: Brestfeeding and the Use of Human Milk. Pediatrics. 2005;115(2):496–506.
Kelishadi R, Farajian S. The protective effects of breastfeeding on chronic non-communicable diseases in adulthood: A review of evidence. Adv Biomed Res. 2014;3.
Norris JM, Scott FW. A meta-analysis of infant diet and insulin-dependent diabetes mellitus: do biases play a role? Epidemiology. 1996:87–92.
Kimpimäki T, Erkkola M, Korhonen S, Kupila A, Virtanen SM, Ilonen J, et al. Short-term exclusive breastfeeding predisposes young children with increased genetic risk of Type I diabetes to progressive beta-cell autoimmunity. Diabetologia. 2001;44(1):63–9.
Virtanen SM, Kenward MG, Erkkola M, Kautiainen S, Kronberg-Kippilä C, Hakulinen T, et al. Age at introduction of new foods and advanced beta cell autoimmunity in young children with HLA-conferred susceptibility to type 1 diabetes. Diabetologia. 2006;49(7):1512–21.
Boddicker R, Koltes J, Fritz-Waters E, Koesterke L, Weeks N, Yin T, et al. Genome‐wide methylation profile following prenatal and postnatal dietary omega‐3 fatty acid supplementation in pigs. Anim Genet. 2016;47(6):658–71.
Pathak R, Feil R. Oocyte-derived histone H3 lysine 27 methylation controls gene expression in the early embryo. Nat Struct Mol Biol. 2017;24(9):685–6.
Li Y-y, Xiao R, Li C-p, Huangfu J, Mao J-f. Increased Plasma Levels of FABP4 and PTEN are Associated with More Severe Insulin Resistance in Women with Gestational Diabetes Mellitus. Med Sci Mon Int Med J Exp Clin Res. 2015;21:426.
Riancho J, Vázquez L, Garcia-Perez M, Sainz J, Olmos J, Hernández J, et al. Association of ACACB polymorphisms with obesity and diabetes. Mol Genet Metab. 2011;104(4):670–6.
Maehama T, Dixon JE. The tumor suppressor, PTEN/MMAC1, dephosphorylates the lipid second messenger, phosphatidylinositol 3, 4, 5-trisphosphate. J Biol Chem. 1998;273(22):13375–8.
Ogg S, Ruvkun G. The C. elegans PTEN homolog, DAF-18, acts in the insulin receptor-like metabolic signaling pathway. Mol Cell. 1998;2(6):887–93.
Wang J-C, Gray NE, Kuo T, Harris CA. Regulation of triglyceride metabolism by glucocorticoid receptor. Cell Biol. 2012;2(1):1–9.
Sinha R, Dufour S, Petersen KF, LeBon V, Enoksson S, Ma Y-Z, et al. Assessment of skeletal muscle triglyceride content by 1H nuclear magnetic resonance spectroscopy in lean and obese adolescents: relationships to insulin sensitivity, total body fat, and central adiposity. Diabetes. 2002;51(4):1022–7.
Ma L, Mondal AK, Murea M, Sharma NK, Tönjes A, Langberg KA, et al. The effect of ACACB cis-variants on gene expression and metabolic traits. PloS one. 2011;6(8):e23860.
Abu-Elheiga L, Matzuk MM, Abo-Hashema KA, Wakil SJ. Continuous fatty acid oxidation and reduced fat storage in mice lacking acetyl-CoA carboxylase 2. Science. 2001;291(5513):2613–6.
Oh W, Abu-Elheiga L, Kordari P, Gu Z, Shaikenov T, Chirala SS, et al. Glucose and fat metabolism in adipose tissue of acetyl-CoA carboxylase 2 knockout mice. Proc Natl Acad Sci. 2005;102(5):1384–9.
Li L, Wang G, Li N, Yu H, Si J, Wang J. Identification of key genes and pathways associated with obesity in children. Exp Ther Med. 2017;14(2):1065–73.
Murphy GJ, Holder JC. PPAR-γ agonists: therapeutic role in diabetes, inflammation and cancer. Trends Pharmacol Sci. 2000;21(12):469–74.
Krabbe K, Nielsen A, Krogh-Madsen R, Plomgaard P, Rasmussen P, Erikstrup C, et al. Brain-derived neurotrophic factor (BDNF) and type 2 diabetes. Diabetologia. 2007;50(2):431–8.
Dreyer C, Krey G, Keller H, Givel F, Helftenbein G, Wahli W. Control of the peroxisomal β-oxidation pathway by a novel family of nuclear hormone receptors. Cell. 1992;68(5):879–87.
Rangwala SM, Lazar MA. Peroxisome proliferator-activated receptor γ in diabetes and metabolism. Trends Pharmacol Sci. 2004;25(6):331–6.
Edwards PA, Kennedy MA, Mak PA. LXRs;: Oxysterol-activated nuclear receptors that regulate genes controlling lipid homeostasis. Vasc Pharmacol. 2002;38(4):249–56.
Song C, Kokontis JM, Hiipakka RA, Liao S. Ubiquitous receptor: a receptor that modulates gene activation by retinoic acid and thyroid hormone receptors. Proc Natl Acad Sci. 1994;91(23):10809–13.
Willy PJ, Umesono K, Ong ES, Evans RM, Heyman RA, Mangelsdorf DJ. LXR, a nuclear receptor that defines a distinct retinoid response pathway. Genes Dev. 1995;9(9):1033–45.
Apfel R, Benbrook D, Lernhardt E, Ortiz MA, Salbert G, Pfahl M. A novel orphan receptor specific for a subset of thyroid hormone-responsive elements and its interaction with the retinoid/thyroid hormone receptor subfamily. Mol Cell Biol. 1994;14(10):7025–35.
Mattson MP, Maudsley S, Martin B. BDNF and 5-HT: a dynamic duo in age-related neuronal plasticity and neurodegenerative disorders. Trends Neurosci. 2004;27(10):589–94.
Gray J, Yeo GS, Cox JJ, Morton J, Adlam A-LR, Keogh JM, et al. Hyperphagia, severe obesity, impaired cognitive function, and hyperactivity associated with functional loss of one copy of the brain-derived neurotrophic factor (BDNF) gene. Diabetes. 2006;55(12):3366–71.
Bammann K, Sioen I, Huybrechts I, Casajus J, Vicente-Rodriguez G, Cuthill R, et al. The IDEFICS validation study on field methods for assessing physical activity and body composition in children: design and data collection. Int J Obes. 2011;35(1):S79-S87.
Krishnaveni P, Gowda VM. Assessing the Validity of Friedewald's Formula and Anandraja's Formula For Serum LDL-Cholesterol Calculation. J Clin Diagn Res. 2015;9(12):BC01-BC4.
Gunderson EP. Breast-feeding and diabetes: long-term impact on mothers and their infants. Curr Diab Rep. 2008;8(4):279–86.
Horta BL, Loret de Mola C, Victora CG. Long-term consequences of breastfeeding on cholesterol, obesity, systolic blood pressure and type 2 diabetes: a systematic review and meta-analysis. Acta Paediatrica. 2015;104(S467):30–7.
Fenger-Grøn J, Fenger-Grøn M, Blunck CH, Schønemann-Rigel H, Wielandt HB. Low breastfeeding rates and body mass index in Danish children of women with gestational diabetes mellitus. Int Breastfeed J. 2015;10(1):26.
Marques RdFdSV, Taddei JAdAC, Konstantyner T, Lopez FA, Marques ACV, de Oliveira CS, et al. Anthropometric indices and exclusive breastfeeding in the first six months of life: a comparison with reference standards NCHS, 1977 and WHO, 2006. Int Breastfeed J. 2015;10:20-.
Gopinath B, Subramanian I, Flood VM, Baur LA, Pfund N, Burlutsky G, et al. Relationship between breast-feeding and adiposity in infants and pre-school children. Public Health Nutr. 2012;15(9):1639–44.
Arenz S, Rückerl R, Koletzko B, von Kries R. Breast-feeding and childhood obesity–a systematic review. Int J Obes Relat Metab Disord. 2004;28(10):1247–56.
Kramer MS, Kakuma R. The optimal duration of exclusive breastfeeding: a systematic review. Adv Exp Med Biol. 2004;554:63–77.
Hochwallner H, Schulmeister U, Swoboda I, Spitzauer S, Valenta R. Cow's milk allergy: from allergens to new forms of diagnosis, therapy and prevention. Methods. 2014;66(1):22–33.
Saukkonen T, Virtanen SM, Karppinen M, Reijonen H, Ilonen J, Räsänen L, et al. Significance of cow's milk protein antibodies as risk factor for childhood IDDM: interactions with dietary cow's milk intake and HLA-DQB1 genotype. Childhood Diabetes in Finland Study Group. Diabetologia. 1998;41(1):72–8.
Xinias I, Cassimos D, Trypsianis G, Nivatsi M, Mavroudi A. Immediate vs delayed cow's milk protein allergy in terms of tolerance at year 1. Ann Allergy Asthma Immunol. 2019;123(3):304–6.
Natale M, Bisson C, Monti G, Peltran A, Perono Garoffo L, Valentini S, et al. Cow's milk allergens identification by two-dimensional immunoblotting and mass spectrometry. Mol Nutr Food Res. 2004;48(5):363–9.
Kalies H, Heinrich J, Borte N, Schaaf B, Von Berg A, Von Kries R, et al. The effect of breastfeeding on weight gain in infants: results of a birth cohort study. Eur J Med Res. 2005;10(1):36–42.
Ong KK, Ahmed ML, Emmett PM, Preece MA, Dunger DB. Association between postnatal catch-up growth and obesity in childhood: prospective cohort study. Bmj. 2000;320(7240):967–71.
Plagemann A, Harder T, Franke K, Kohlhoff R. Long-term impact of neonatal breast-feeding on body weight and glucose tolerance in children of diabetic mothers. Diabetes care. 2002;25(1):16–22.
Papatesta E-M, Iacovidou N. Breastfeeding reduces the risk of obesity in childhood and adolescence. J Pediatric Neonatal Individ Med. 2013;2.
Stettler N, Zemel BS, Kumanyika S, Stallings VA. Infant weight gain and childhood overweight status in a multicenter, cohort study. Pediatrics. 2002;109(2):194–9.
Gridneva Z, Kugananthan S, Hepworth AR, Tie WJ, Lai CT, Ward LC, et al. Effect of Human Milk Appetite Hormones, Macronutrients, and Infant Characteristics on Gastric Emptying and Breastfeeding Patterns of Term Fully Breastfed Infants. Nutrients. 2016;9(1):15.
Maessen SE, Derraik JGB, Binia A, Cutfield WS. Perspective: Human Milk Oligosaccharides: Fuel for Childhood Obesity Prevention? Adv Nutr. 2019;11(1):35–40.
Koletzko B, Schiess S, Brands B, Haile G, Demmelmair H, von Kries R, et al. [Infant feeding practice and later obesity risk. Indications for early metabolic programming]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2010;53(7):666–73.
Li L, Parsons TJ, Power C. Breast feeding and obesity in childhood: cross sectional study. Bmj. 2003;327(7420):904–5.
Ryan AS. Breastfeeding and the risk of childhood obesity. Coll Antropol. 2007;31(1):19–28.
Koletzko B, Kries R, Closa R, Monasterolo R, Escribano J, Scaglioni S, et al. Can infant feeding choice modulate later obesity risk? Am J Clin Nutr. 2009;89:1502S-8S.
Daenzer M, Ortmann S, Klaus S, Metges CC. Prenatal high protein exposure decreases energy expenditure and increases adiposity in young rats. J Nutr. 2002;132(2):142–4.
Koletzko B, Broekaert I, Demmelmair H, Franke J, Hannibal I, Oberle D, et al. Protein intake in the first year of life: a risk factor for later obesity? The E.U. childhood obesity project. Adv Exp Med Biol. 2005;569:69–79.
Marcus C, Margery V, Kamel A, Brönnegård M. Effects of growth hormone on lipolysis in humans. Acta Paediatrica. 1994;83(s406):54–8.
Ong KK, Loos RJ. Rapid infancy weight gain and subsequent obesity: systematic reviews and hopeful suggestions. Acta Paediatrica. 2006;95(8):904–8.
Bosma JF, Hepburn LG, Josell SD, Baker K. Ultrasound demonstration of tongue motions during suckle feeding. Dev Med Child Neurol. 1990;32(3):223–9.
Mathew OP, Bhatia J. Sucking and breathing patterns during breast- and bottle-feeding in term neonates. Effects of nutrient delivery and composition. Am J Dis Child. 1989;143(5):588–92.
Priego T, Sánchez J, Picó C, Ahrens W, Bammann K, De Henauw S, et al. Influence of breastfeeding on blood-cell transcript-based biomarkers of health in children. Pediatr Obes. 2014;9(6):463–70.
Lucas A, Boyes S, Bloom SR, Aynsley-Green A. Metabolic and endocrine responses to a milk feed in six-day-old term infants: differences between breast and cow's milk formula feeding. Acta Paediatr Scand. 1981;70(2):195–200.
Putet G, Labaune JM, Mace K, Steenhout P, Grathwohl D, Raverot V, et al. Effect of dietary protein on plasma insulin-like growth factor-1, growth, and body composition in healthy term infants: a randomised, double-blind, controlled trial (Early Protein and Obesity in Childhood (EPOCH) study). Br J Nutr. 2016;115(2):271–84.
Hui LL, Kwok MK, Nelson EAS, Lee SL, Leung GM, Schooling CM. Breastfeeding in Infancy and Lipid Profile in Adolescence. Pediatrics. 2019;143(5):e20183075.
Thorsdottir I, Gunnarsdottir I, Palsson GI. Birth weight, growth and feeding in infancy: relation to serum lipid concentration in 12-month-old infants. Eur J Clin Nutr. 2003;57(11):1479–85.
Teller IC, Schoen S, van de Heijning B, van der Beek EM, Sauer PJ. Differences in Postprandial Lipid Response to Breast- or Formula-feeding in 8-Week-Old Infants. J Pediatr Gastroenterol Nutr. 2017;64(4):616–23.
Harit D, Faridi M, Aggarwal A, Sharma S. Lipid profile of term infants on exclusive breastfeeding and mixed feeding: a comparative study. Eur J Clin Nutr. 2008;62(2):203–9.
and JB, Moller DE. The Mechanisms of Action of PPARs. Annu Rev Med. 2002;53(1):409–35.
Janani C, Ranjitha Kumari BD. PPAR gamma gene-a review. Diabetes Metab Syndr. 2015;9(1):46–50.
Feige JN, Gelman L, Michalik L, Desvergne B, Wahli W. From molecular action to physiological outputs: Peroxisome proliferator-activated receptors are nuclear receptors at the crossroads of key cellular functions. Prog Lipid Res. 2006;45(2):120–59.
Kintscher U, Law RE. PPARγ-mediated insulin sensitization: the importance of fat versus muscle. Am J Physiol Endocrinol Metab. 2005;288(2):E287-E91.
Abu-Elheiga L, Brinkley WR, Zhong L, Chirala SS, Woldegiorgis G, Wakil SJ. The subcellular localization of acetyl-CoA carboxylase 2. Proc Natl Acad Sci. 2000;97(4):1444–9.
Phillips CM, Goumidi L, Bertrais S, Field MR, Cupples LA, Ordovas JM, et al. ACC2 gene polymorphisms, metabolic syndrome, and gene-nutrient interactions with dietary fat. J Lipid Res. 2010;51(12):3500–7.
Ma L, Murea M, Snipes JA, Marinelarena A, Krüger J, Hicks PJ, et al. An ACACB variant implicated in diabetic nephropathy associates with body mass index and gene expression in obese subjects. PloS one. 2013;8(2).
Tang SCW, Leung VTM, Chan LYY, Wong SSH, Chu DWS, Leung JCK, et al. The acetyl-coenzyme A carboxylase beta (ACACB) gene is associated with nephropathy in Chinese patients with type 2 diabetes. Nephrol Dial Transplant. 2010;25(12):3931–4.
Cheshmeh S, Nachvak SM, Rezvani N, Saber A. Effects of Breastfeeding and Formula Feeding on the Expression Level of FTO, CPT1A and PPAR-α Genes in Healthy Infants. Diabetes Metab Syndr Obes: Targets Ther. 2020;13:2227–37.

No competing interests reported.

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Effects of Breastfeeding and Formula Feeding on the Metabolic Factors and the Expression Level of Obesity and Diabetes-Predisposing Genes in Healthy Infants

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