1. Introduction
The World Health Organization (WHO) and UNICEF recommend that children initiate breastfeeding within the first hour after birth and be exclusively breastfed for the first six months of life [
1]. This is of special concern in developing countries where infants face higher environmental and nutritional challenges due to their limited access to clean water and sanitation, and the high infectious and nutritional disease burden. Indeed, the optimal breastfeeding of infants from birth to 24 months has the potential to prevent over 800,000 deaths (13 percent of all deaths) in children under five years, worldwide [
2].
As the primary nutritional source for neonates, the human milk’s (HM) composition is of paramount importance for the neonate’s survival and development. In addition to its immunologically active molecules and bioactive compounds that provide immediate protection against life-threatening conditions such as necrotizing enterocolitis [
1,
3], HM provides a myriad of nutrients that are essential for optimal development in early life [
4,
5]. Precisely, HM fulfills the energy and nutrient requirements that an infant has during the first six months of life. After this, it continues to provide up to half or more of a child’s necessary nutrients during the rest of the first year, and up to one third during the second year of life [
1]. Due to the tremendous beneficial effects of breastfeeding for infant survival and growth globally, the raising interest in optimizing infant nutrition during early life has brought a plethora of studies that have investigated the main determinants of HM composition.
Research from Germany [
6,
7], Italy [
8,
9], China [
10], New Zealand [
11,
12], Iceland [
13], India [
14], and Spain [
15] has revealed that HM composition varies depending on maternal food consumption habits, in addition to the maternal gestational age, lactation period, genetics, and life-style [
16,
17]. Moreover, maternal diet may play a role in shaping the infant gut microbial colonization by modulating the HM composition [
15,
18]. Albeit this research evidence, the effect of the maternal diet and the maternal undernourishment status at delivery on the HM nutritional composition has not yet been analyzed in a context of high food insecurity.
The Central-African Republic lacks precise undernourishment estimates among pregnant women, even though anemia estimates were over 51.5% in 2019 [
19]. However, the Central-African region is plagued by food insecurity [
20]. For these reasons, we selected the Bangui region to conduct this prospective, observational study. Precisely, the “Mother-to-Infant TransmIssion of microbiota in Central-Africa” (MITICA) study addressed the effect of maternal undernourishment on infant malnutrition, with HM and microbes as a possible link between them. In this article, we aimed to decipher how maternal dietary factors and maternal undernourishment status at delivery might influence the HM composition from the infant’s birth until 6 months, i.e., the timeframe that corresponds to the WHO recommendations on exclusive breastfeeding. Evaluating the differences in the HM nutrients (carbohydrates, fatty acids (FAs), amino acids (AAs), human milk oligosaccharides (HMOs), and retinol) depending on their household food security status, maternal dietary factors, and maternal nutritional status at delivery may help us to better understand the impact of maternal undernutrition and food insecurity on HM composition and infant health during the postnatal period.
3. Results
3.1. Description of the Cohort and Maternal Characteristics at Delivery
Forty-eight women were enrolled in the MITICA study between December 2017 and June 2019. Their age ranged from 15 to 39 years, and the median age was 23 years (
Table 1). Five were primigravidae. At delivery, 16 of the 46 (34.8%) women with a blood test were undernourished (which was defined by albumin plasma levels <35 g/L [
35]). As the analysis of the effect of maternal undernourishment at delivery on HM was one of the purposes of the article, we will focus, here, on the women for whom we conducted an albumin measurement at delivery. The iron-deficiency rate among the women for whom we had an albumin measurement was 20/46 (43.5%), which is similar to or lower than other African cohorts and WHO estimates [
36,
37,
38,
39]. On the contrary, vitamin deficiencies were highly prevalent among these women. Concretely, 23/36 (63.9%) of the women for whom we had an albumin measurement had vitamin A deficiency, 19/44 (43.2%) had vitamin C deficiency, and 5/37 (13.5%) had vitamin E deficiency. The differences in the number of women for whom we conducted a vitamin measurement are due to the lack of blood volume available for all of the analyses. Anemia rates differed significantly between the undernourished and the non-undernourished women. While only two of the thirty non-undernourished women were anemic (7.1%), six of the sixteen undernourished women were anemic (40.0%,
p value = 0.01). The proportion of students in the non-undernourished group almost doubled that of the ones in the undernourished group (18/30 (60.0%) vs. 6/16 (37.5%)). Precisely, the proportion of women with primary, secondary, and higher education in the non-undernourished group was 3/30 (10.0%), 21/30 (70.0%), and 6/30 (20.0%), respectively. In contrast, 5/16 (31.3%) undernourished women attended only primary school and 11/16 (68.8%) had a secondary school degree. None of them had studied at a higher education level.
Table 1 presents the most significant elements of the information of the cohort.
3.2. Food Insecurity Indexes
At the beginning of the follow-up, 47/48 (97.9%) of the women that were included in the study lived in non-food-secure households according to the Household Food Insecurity Access Scale (HFIAS). While one out of the thirty (3.3%) non-undernourished women lived in a food-secure household, 16/30 (53.3%) and 13/30 (43.3%) lived in households with moderate and severe food insecurity, respectively. None of the undernourished women lived in food-secure households. Concretely, 6/16 (37.5%) and 10/16 (62.5%) of the undernourished mothers lived in moderately food-insecure and severely food-insecure households, respectively. Neither the HFIAS nor the Household Hunger Score (HHS) evolved significantly during the follow-up.
3.3. Maternal Diet Characteristics
The median number of meals per day of these women was three (Inter-quartile ratio (IQR) = 2; 3) and the median number of snacks per day that they ate was 0 (IQR = 0; 1). The total number of meals per day that the mothers ate was significantly higher during the follow-up, compared to that which was recorded one week after delivery. Compared to that which was recorded one week after delivery (median = 2, IQR = 2; 3), the women had a significantly higher number of meals at the week 4 (median = 3, IQR = 3; 3, ß-coef = 0.3,
p value = 0.03), week 11 (median = 3, IQR = 3; 4, ß-coef = 0.6,
p value < 0.001), week 18 (median = 3, IQR = 3; 4, ß-coef = 0.6,
p value < 0.001), and week 25 after delivery(median = 3, IQR = 3; 4, ß-coef = 0.9,
p value < 0.001). The maternal diet of these women was monotonous according to both the food consumption questionnaire and the 24-h recall during the entire follow-up (
Table S2). Nevertheless, the WDDS was significantly lower one week after delivery, compared to later periods. According to the food consumption questionnaire, compared to that which was recorded one week after delivery (median = 3, IQR = 4; 6), the WDDSs were significantly higher at the week 4 (median = 5, IQR = 4; 6, ß-coef = 0.8,
p value = 0.01), week 11 (median = 5, IQR = 4; 6, ß-coef = 0.7,
p value = 0.03), week 18 (median = 5, IQR = 4; 6, ß-coef = 0.9,
p value = 0.01), and week 25 (median = 5, IQR = 4; 6, ß-coef = 1.1,
p value = 0.001). According to the 24-h recall, the WDDS was also significantly higher at the week 25 after birth (median = 1, IQR = 1; 5, ß-coef = 0.9,
p value = 0.04), compared to that recorded one week after delivery (median = 1, IQR = 1; 2). In parallel, the WDDS did not vary significantly during the follow-up, according to the 24-h recalls (
n = 161). Finally, the HHS was significantly lower at the week 18 after birth (median = 0, IQR = 0; 1, ß-coef = −0.4,
p value = 0.02), compared to that which was recorded one week after birth (median = 0, IQR = 0; 1).
Figure S1 summarizes the precise composition of the maternal diet during the follow-up. Grains, white roots, and tubers (mainly tapioca starch and cassava) were the basis of the maternal diet, and these constituted the most consumed food groups among the mothers during the entire follow-up. Meat, poultry, and fish (dried meat, dried fish, and chicken), condiments (bouillon cubes and tomato paste and chili peppers), other vegetables (okra, onion, and tomato), and sweet foods (biscuits, home-made "pancakes", also called "local doughnuts" in
Figure S1) were also paramount elements of the diet of these Central-African women after they birthed a child.
The consumption of dark green, leafy vegetables (mainly gnetum africanum and “goussa” or “lalo”, a plant belonging to the Amaranthaceae family) increased significantly along the first 6 months after birthing a child, according to both the food consumption questionnaire (p value < 0.001) and the 24-h recall (p value = 0.03). The consumption of nuts and seeds was also significantly decreased 1 week after delivery, compared to that of later periods according to the food consumption questionnaire (ß-coef = −1.6, p value = 0.01), and the 24-h recall (ß-coef = −1.2, p value = 0.04). The consumption of "other vegetables" (vegetables that have not been counted as dark green, leafy vegetables or as other vitamin A-rich vegetables) increased significantly during the 6 months after delivery according to the food consumption questionnaire (p value = 0.01). On the contrary, eggs were less frequently consumed according to both the food consumption questionnaire (only eaten in 5/181 (2.76%)), and the 24-h recall (2/161 (1.24%), during the entire follow-up.
Seasonality was also significantly associated with the consumption of meat, poultry, and fish, and insects according to the food consumption questionnaire and the 24-h recalls. The consumption of meat, poultry, and fish was significantly higher during the dry season, compared to the rainy season, in both the food consumption questionnaire and the 24-h recalls (75/87 (86.2%) vs. 65/91 (71.4%), p value = 0.02 (food consumption questionnaire); 28/94 (29.7%) vs. 15/97 (15.5%) 24-h recalls, p value = 0.02, respectively). On the contrary, insects were significantly more frequently consumed during the rainy season, compared to the dry season, according to the food consumption questionnaire and the 24-h recalls (16/92 (17.4%) vs. 5/87 (5.8%), p value = 0.03; 13/97 (13.4%) vs. 2/94 (2.1%), p value = 0.004, respectively).
There were significant differences in diet composition between the women who were undernourished at delivery and women who were not. According to both the food consumption questionnaire and the 24-h recalls, the consumption of dark green, leafy vegetables was significantly higher among the undernourished women compared to the women who were not undernourished at delivery (
p value = 0.01 (food consumption questionnaire), and
p value = 0.02 (24-h recalls)). The results of the food consumption questionnaire also show that “other beverages and foods” (represented mainly by instant coffee and undefined foods) were more frequently consumed by the undernourished women, compared to the non-undernourished women (
p value < 0.001). The twenty-four-hour recalls displayed a significantly higher consumption of red-palm oil and sweet beverages among the undernourished women at delivery, compared to the non-undernourished mothers (
p value = 0.01 and
p value = 0.04, respectively). On the contrary, the consumption of powdered milk and dairy products was significantly higher among the non-undernourished women at delivery (
p value = 0.004), according to the 24-h recalls. Further differences in diet depending on the undernourishment status of the women at delivery, the collection support tool (food consumption or 24-h recalls), and the lactation period are presented in
Table S2.
3.4. Determinants of Lactose Levels and HM Oligosaccharides
Lactose, a disaccharide, is the sugar with the highest concentration in HM. Its concentration increased significantly during the follow-up (
p < 0.001), according to the Skillings-Mack test. In the multivariate multilevel analyses, meat and fish consumption was associated with lower levels of lactose (aß-coef = −15.6,
p value = 0.01), adjusted on the infant’s age. Compared to food-secure households, the women from households with moderate food insecurity had significantly higher levels of lactose (aß-coef = 3.3,
p value = 0.02). On the contrary, food insecurity was significantly associated with reduced levels of 3-FL (severe hunger, aß-coef = −0.3,
p value = 0.048), 6′-SL (moderate hunger, aß-coef = −0.1,
p value = 0.01), LNT (moderate hunger, aß-coef = −0.4,
p value = 0.01), LNnT (mild food insecurity, aß-coef = −0.1,
p value = 0.03), LNFP-V (moderate hunger, aß-coef = −0.02,
p value = 0.046), and LNDFH-I (mild food insecurity, aß-coef = −0.1,
p value = 0.03) (
Table 2,
Table 3 and
Table 4). The HHS index was also inversely associated with 2′FL levels (aß-coef = −0.2,
p value = 0.03) in the multilevel models’ analyses.
The consumption of meat, poultry, and fish was associated with higher levels of the sum of the HMOs that were measured (aß-coef = 2.9, p value = 0.02), 6′-SL (aß-coef = 0.3, p value < 0.001), LNFP-II (aß-coef = 0.9, p value < 0.001), LNFP-V (aß-coef = 0.1, p value = 0.001), and LNDFH-II + LNnDFH-II (aß-coef = 0.4, p value < 0.001).
3.5. Dietary Determinants of HM Retinol
A high HHS was significantly associated with lower levels of retinol in the HM (aß-coef = −0.2, p value = 0.04), adjusted on the infant’s age. Indeed, retinol levels diminished significantly during the follow-up (p value < 0.001) according to the Skillings-Mack test.
3.6. Dietary Determinants of HM Fatty Acids Levels
The human milk of the women with moderate food insecurity had significantly lower levels of total fatty acids, compared to the women who lived in food-secure households (aß-coef = −7.2,
p value = 0.03). More precisely, some degree of food insecurity was significantly associated with lower HM levels of Omega-3 poly-unsaturated fatty acids (PUFAs) (absolute and relative levels: mild food insecurity aß-coef = −275.8,
p value = 0.04; mild food insecurity aß-coef = −0.7,
p value = 0.002, respectively), docosahexaenoic acid (DHA) (absolute and relative levels: moderate food insecurity aß-coef = −34.9,
p value = 0.04; mild food insecurity aß-coef = −0.4,
p value = <0.001, respectively), and eicosapentaenoic acid (EPA) (absolute and relative levels: mild food insecurity aß-coef = −55.1,
p value = 0.003, moderate food insecurity aß-coef = −16.0,
p value = 0.01; moderate food insecurity aß-coef = −0.03,
p value = 0.04, respectively). Fish was the food group that was most frequently associated with higher levels of HM fatty acids. Concretely, fish consumption was statistically linked with higher HM levels of PUFAs (relative levels: aß-coef = 1.0,
p value = 0.047), Omega-3 PUFAs (relative levels: aß-coef = 0.2,
p value = 0.01), arachidonic acid (ARA) (relative levels: aß-coef = 0.04,
p value = 0.01), DHA (absolute and relative levels: aß-coef = 43.7,
p value = 0.01, and aß-coef = 0.1,
p value = <0.001, respectively); EPA (absolute and relative levels: aß-coef = 15.3,
p value = 0.01, and aß-coef = 0.04,
p value = 0.01, respectively). More globally, the consumption of the food categories “meat, poultry, and fish”, “insects and small rodents” (including insect larvae/grubs, insect eggs, fish roe, spiders, land and sea snails, and any other small invertebrates [
22]), “other vegetables”, and “other oils and fats” were also significantly associated with higher levels of different fatty acids in HM (
Table 3). The food group “other oils and fats” comprises mainly peanut oil, but also lard, suet (tallow), and butter (solid animal fats); margarine and hydrogenated vegetable oil; a range of oils that have been extracted from nuts, seeds, and grains [
22]. Further dietary determinants of HM fatty acids levels are presented in
Table 5 and
Table 6. Only the multilevel models with significant results are shown.
3.7. Dietary Determinants of Amino Acids
3.7.1. Total Amino Acids
Moderate hunger in the household (according to the HHS), compared to “no to little hunger in the household”, was significantly associated with low levels of each of the amino acids that were analyzed in the study (
Table 7 and
Table 8). Arginine levels were also inversely correlated with the HHS (aß-coef = −26.7,
p value = 0.03). An elevated consumption of meat, poultry, and fish, and red palm oil was also significantly associated with higher levels of HM amino acids (aß-coef = 5484.4,
p value < 0.001; aß-coef = 2825.1,
p value = 0.003, respectively). On the contrary, the consumption of nuts (tree nuts but also groundnuts (peanuts), certain seeds, and seed butters, such as pounded groundnut/peanut butter or peanut paste, cashew butter, or sesame butter (tahini) when it was consumed in doses that were bigger than 15 g) (aß-coef = −4798.2,
p value < 0.001), other vegetables (aß-coef = − 1205.7,
p value = 0.03), insects (aß-coef = −1209.9,
p value = 0.03), and condiments (aß-coef = −842.6,
p value = 0.01) were inversely associated with the HM amino acid levels. Indeed, these dietary determinants of HM amino acids were almost unanimously homogeneous among the different amino acids (
Table 7 and
Table 8). Additionally, the concentration of total amino acids in HM decreased significantly during the follow-up. Compared to the HM one week after birth, the total content of all of the amino acids diminished significantly and progressively for 25 weeks.
Increased food insecurity indexes were associated with lower relative values of aspartic acid and asparagine (aß-coef = −0.01, p value = 0.049), histidine (aß-coef = −0.1, p value = 0.002), and phenylalanine (aß-coef = −0.1, p value = 0.03).
3.7.2. Free Amino Acids
Human milk free amino acids were also significantly influenced by food insecurity levels. Precisely, compared to the category “no hunger in the household”, women who lived in households with “moderate hunger in the household” (according to HHS) had significantly lower levels of total free amino acids in HM (aß-coef = −62.8, p value = 0.04). Moreover, the indexes of food insecurity were significantly correlated with lower levels of free arginine (aß-coef = −0.1, p value = 0.02), asparagine (aß-coef = −1.3, p value < 0.001), histidine (aß-coef = −1.4, p value = 0.03), lysine (aß-coef = −0.7, p value = 0.04), taurine (aß-coef = −7.9, p value = 0.03), threonine (aß-coef = −4.9, p value = 0.002), tryptophan (aß-coef = −0.04, p value = 0.04), tyrosine (aß-coef = −1.8, p value = 0.02), and valine (aß-coef = −3.0, p value = 0.03).
The consumption of pulses was also associated with higher levels of the sum of the free amino acids (aß-coef = 116.6,
p value = 0.04), alanine (aß-coef = 16.3,
p value = 0.003), lysine (aß-coef = 3.9,
P value = 0.01), and threonine (aß-coef = 7.5,
p value = 0.01). Other dietary elements that were associated with the free amino acid concentration in the breastmilk are detailed in
Table 9 and
Table 10.
4. Discussion
The MITICA study is one of the only studies linking HM composition, maternal diet, and maternal nutritional status at delivery in Central-Africa. We found a high food insecurity burden in addition to a low diverse maternal diet that was based on grains, tubers, and white roots. Even in the context of extended food insecurity in our cohort, high household food insecurity levels were significantly associated with reduced levels of fatty acids, total amino acids, free amino acids, retinol, and up to seven different HMOs in the HM. This is particularly worrisome as the “breastfeeding paradox” shows that the households with an increased risk of food insecurity tend to reduce breastfeeding in quantity and duration in Western and African countries [
40,
41,
42,
43,
44,
45]. Hence, adverse consequences may arise in the infants that live in these already vulnerable households. Unfortunately, further evidence on the influence of food insecurity on HM nutritional content is extremely limited. Yet, a recent study showed that the percentage of energy from both carbohydrates and fats in the maternal diet was significantly associated with the HM total energy [
46]. Some authors have postulated that HM composition might rather be related with maternal body composition [
47]. Indeed, in samples that were taken at the first (
n = 40), third (
n = 22), and sixth (
n = 15) lactation months, their nutrient intake was not correlated with the HM composition among Polish women, but the variance in milk fat was significantly correlated with the body mass index (BMI) in the first month postpartum, thereby underling the association of maternal body composition with the nutritional content of HM [
47]. In any case, it is reasonable to consider that diet is one of the major determinants of maternal body composition. In our study, the maternal diet differed significantly between undernourished and non-undernourished women at delivery. Undernourished women at delivery were significantly more likely to consume dark green, leafy vegetables, red palm oil, and sweet beverages, whereas milk and dairy product consumption prevailed among the non-undernourished women. In parallel, the food insecurity indexes were also significantly associated with the undernourishment status of the women at delivery.
In the broadest review articles investigating the influence of maternal diet on HM composition, the maternal dietary intakes of FA and some micronutrients—including fat soluble vitamins, vitamin B1, and vitamin C—were significantly associated with their concentration in HM [
48,
49,
50,
51]. Concretely, in the majority of studies FAs in the maternal diet are, with some exceptions [
52,
53], positively associated with FAs in HM [
11,
13,
54,
55,
56,
57,
58,
59,
60,
61,
62,
63,
64,
65,
66,
67,
68,
69,
70]. Also, interventional studies have shown that supplementation with FAs resulted in a significant increase of FAs in HM [
71,
72]. Furthermore, studies from very different countries, worldwide, have shown that lactating women who consume fish and other foods with high PUFA levels display relatively higher HM fatty acids, especially DHA [
62,
73,
74,
75,
76]. In our study, fish consumption was also significantly associated with higher levels of FAs in HM (
Table 5). Further, the consumption of “fats and oils”, “other vegetables”, and “insects and small rodents” was also associated with higher FA levels in HM. These results are also coherent with the previous literature [
11,
51,
77,
78]. However, the FA levels in HM were lower than they were in pre-existing cohorts [
79]. Additionally, the total FA concentration in HM remained constant during the first 6 months of the infant’s life, which is in contrast with other cohorts [
6,
80]. In sum, dietary determinants of HM FAs do not differ significantly from other cohorts despite the low FAs levels in HM and the high rates of food insecurity. Nevertheless, the meaningful effect of food insecurity on HM FAs is likely fundamental to explaining the low levels of FAs that are in this cohort, compared to those of others cohorts. Indeed, these low levels of FAs cannot be ascribed solely to the ethnic variations in HM FA content that have already been observed in other populations, including in African cohorts [
11,
81]. Therefore, the effect of food insecurity on HM FA composition may be greater than it has been previously postulated.
In parallel to the FAs and compared to the category “no hunger in the household”, a “moderate level of hunger in the household” was significantly associated with reduced levels of both free amino acids and the total level of amino acids. While certain studies suggest that there is a lack of evidence associating maternal diet with HM amino acids [
47,
48], the dietary determinants of the total amino acids were homogeneous among the different amino acids that were analyzed in our study. Precisely, we found that “meat, poultry, and fish”, and red-palm oil consumption was associated with high levels of total amino acids [
82]. In previous research, protein intake has also been associated with higher amino acid levels in HM [
48,
50]. On the contrary, in our cohort, women who reported a high consumption of “nuts”, “other vegetables”, and “insects and small rodents” displayed significantly lower levels of total amino acids in their HM. Some studies report a positive correlation between maternal egg intake and amino acid levels in HM [
82,
83]. The effect of egg intake on amino acids’ levels in HM could not be properly assessed in our cohort as egg consumption was extremely rare among the women in Bangui (only five eggs were reported to be eaten the previous day during the entire follow-up). Further limitations of the study include the limited sample-size, the lack of a homogeneous schedule for the foremilk sampling (sampling time fluctuated from 10 to 12 AM), and the limitation of the food intake assessment to the 24-h preceding the sampling.
The effect of diet on HMO composition has been seldom analyzed as it is an emerging research field and HM types are genetically influenced. More concretely, in a 2021 survey on HM determinants, only three studies considered the maternal diet [
84]. Further, some authors have reported no significant association of maternal diet with HMO levels [
85,
86,
87]. Azad et al. found that, independent of the secretor status and lactation stage, seasonal variation, geographic location, parity, ethnicity, and exclusive breastfeeding were significant determinants of some HMO levels in the CHILD Canadian cohort [
85]. On the contrary, diet quality and the mode of delivery were not significantly associated with the HMOs analyzed in the CHILD cohort study. However, in this same cohort, maternal diet and body mass were interrelated and associated with HM microbiota [
88]. In parallel, in another study including both Swedish and Gambian women, some HMOs were significantly associated with maternal age, postpartum period, weight, and body-mass index [
89]. Further, the HMOs from ethnically similar populations varied geographically, suggesting that HMO levels might also be influenced by the environment.
In our cohort, in parallel to the rest of nutrients, a large number of HMOs were significantly associated with food insecurity levels. In Bangladesh, the mothers of undernourished children also showed significantly lower levels of HMOs, even if neither the maternal undernourishment status nor the maternal diet were assessed [
90].
Here, meat and poultry consumption were associated with higher HMO levels. Other studies have also shown similar results. Qiao et al. showed that a higher dietary intake of milk, beef, egg, mutton, and pork was associated with higher milk sialic acid levels [
91]. Recent research has demonstrated that maternal dietary carbohydrate and energy sources alter HMO concentrations significantly, including fucosylated species [
92]: fucose and galactose might be recycled by specific monosaccharide metabolic pathways that are in mammalian cells [
93,
94]. Furthermore, the previous study reveals that this dynamic process, by which maternal diet modifies the HMO composition during lactation, also modulates the HM-associated microbiota [
92]. In the context of maternal undernutrition, this might entail meaningful differences in the infant oral and gastrointestinal bacterial colonization, thereby resulting in an impaired metabolism and immune development [
95,
96,
97,
98,
99,
100]. HMOs provide fucose and sialic acid which are essential for brain development [
101,
102]. Sialic acid also plays a significant role in the formation of synapses and its concentration in HM is influenced by the maternal diet [
103,
104,
105,
106]. Therefore, maternal food insecurity might have long-term effects with long-lasting consequences for the child. However, evidence on how food insecurity or maternal undernourishment might influence lactose levels (inversely, compared to the rest of HM nutrients) remains a topic for further research.
Maternal diets modulates the maternally secreted micro-RNAs (miRNAs) in HM that are stable in HM fat globules [
107]. These miRNAs are involved in DNA methylation, histone modification, and chromatin remodeling and might have important regulatory functions in the infant’s development and metabolism, such as FTO, INS, and IGF1 modulation [
108,
109,
110,
111,
112], in addition to their essential immune properties [
113,
114]. Moreover, recent studies have described the meaningful role of miRNAs in neurodevelopment [
112,
115,
116,
117]. Indeed, they constitute a substantial part of the health benefits of HM [
5], and they might be reduced due to the low levels of fat in the cohort. The effect of low fat levels in HM on the infant’s metabolism and neurodevelopment needs to be assessed in prospective cohorts.
5. Conclusions
Food insecurity and maternal diet, via nutrient intake reduction in HM, might exert a considerable impact on the infant’s undernourishment risk.
Beyond the direct effect of nutrient deficiencies in HM, epigenetic alterations affecting the infant’s metabolism and development might arise in the context of maternal undernourishment.
Nutritional alterations in HM—especially in HMO—might alter the physiological assembly of the gut microbiota of the infant, thereby resulting in an impaired immune priming and metabolic function [
95,
96,
97,
98,
99,
100]. Infant gut colonization should be investigated in prospective clinical follow-ups in the context of altered HM composition to assess its consequences.
In conclusion, our results plead for consistent actions on food security as an effective manner to influence the nutritional content of HM and thereby, potentially improve the infants’ survival and healthy growth. Human milk is the most unique nutritional source for infants, therefore, food security, maternal nutritional status, and maternal dietary factors might entail founding effects and diverse trajectories for the infant’s growth and their immune and neurocognitive development. In parallel, the pathological pathways of maternal undernourishment and its influence on HM biosynthesis remain deeply elusive. Supplementary research to unravel the molecular mechanisms that are responsible for the fluctuations in HM nutrient concentration in the context of maternal undernutrition remain, therefore, of paramount importance.