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Review

Gestational Weight Gain: Is the Role of Genetic Variants a Determinant? A Review

by
Reyna Sámano
1,2,*,
Hugo Martínez-Rojano
3,
Gabriela Chico-Barba
1,
Ricardo Gamboa
4,
María Eugenia Mendoza-Flores
1,
Francisco Javier Robles-Alarcón
5,
Itzel Pérez-Martínez
5 and
Irma Eloisa Monroy-Muñoz
6,*
1
Coordinación de Nutrición y Bioprogramación, Instituto Nacional de Perinatología, Secretaría de Salud, Mexico City 11000, Mexico
2
Programa de Posgrado Doctorado en Ciencias Biológicas y de la Salud, División de Ciencias Biológicas y de la Salud, Universidad Autónoma Metropolitana, Mexico City 04960, Mexico
3
Sección de Posgrado e Investigación de la Escuela Superior de Medicina del Instituto Politécnico Nacional, Mexico City 11340, Mexico
4
Departamento de Fisiología, Instituto Nacional de Cardiología “Ignacio Chávez”, Mexico City 14080, Mexico
5
Facultad de Nutrición, Universidad Autónoma del Estado de Morelos, Cuernavaca 62350, Mexico
6
Departamento de Investigación Clínica en Salud Reproductiva y Perinatal, Instituto Nacional de Perinatología, Secretaría de Salud, Mexico City 11000, Mexico
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(5), 3039; https://doi.org/10.3390/ijms25053039
Submission received: 25 December 2023 / Revised: 1 March 2024 / Accepted: 3 March 2024 / Published: 6 March 2024
(This article belongs to the Special Issue Leptin and Obesity Associated Diseases)
Browse Figure
Versions Notes

Abstract

:
Excessive or insufficient gestational weight gain (GWG) leads to diverse adverse maternal and neonatal outcomes. There is evidence that pregestational body mass index (pBMI) plays a role in GWG, but no genetic cause has been identified. In this review, we aim to analyze genotype variants associated with GWG. Results: We identified seven genotype variants that may be involved in GWG regulation that were analyzed in studies carried out in Brazil, Romania, the USA, Turkey, Ukraine, and Canada. Some genetic variants were only associated with GWG in certain races or depending on the pBMI. In women who were obese or overweight before gestation, some genetic variants were associated with GWG. Environmental and genetic factors together showed a greater association with GWG than genetic factors alone; for example, type of diet was observed to have a significant influence. Conclusions: We found little scientific evidence of an association between genotype variants in countries with a high prevalence of women of reproductive age who are overweight and obese, such as in Latin America. GWG may be more dependent on environmental factors than genetic variants. We suggest a deeper study of genetic variants, cytokines, and their possible association with GWG, always with the respective control of potential cofounding factors, such as pBMI, diet, and race.

1. Introduction

Pregnancy results in changes to maternal metabolism, which can be influenced by environmental, genetic [1], and inflammatory factors [2,3]; the combination of these factors could result in altered gestational weight gain (GWG) [3] and/or an increased risk of developing gestational diabetes [4], preeclampsia [5], and pregnancy-induced hypertension [6], among others.
Worldwide, excessive or inadequate GWG affects up to 70% of pregnancies [7,8], with excessive GWG being more frequent, accounting for 35 to 60% [8,9]. Excessive GWG increases the risk of morbidity and the costs for those who experience these adverse effects [10]. Pregestational obesity and overweight are factors associated with high odds of maternal and neonatal morbidity, even mortality [11]. Nevertheless, some genetic variants [12] interact with cytokines and adipokines [2,13] to modify pregnancy homeostasis, including GWG [14].
Maternal health during pregnancy may be affected by the interaction and mutual influence between genetic and environmental factors such as food intake, sleep quality, and physical activity. Increased knowledge of the role of how GWG increases the risk of adverse pregnancy outcomes is essential to reduce these risks in the future, as well as to understand how these risk factors may vary depending on pregestational BMI (pBMI) [13,15,16] or race, as was reported by Groth et al. for Caucasian and African American women [17]. The role of genetic variants with respect to GWG is uncertain. Nevertheless, overweight and obesity are increasing in low- and middle-income countries. We aimed to analyze genotype variants associated with GWG reported in the scientific literature to understand their roles in gestational weight gain.

2. Relevant Sections

2.1. What Is the Role of Genomic Sciences in Body Weight?

Genomic sciences allow for a better understanding of the role energy from food intake in the human body plays via signaling pathways that could explain the accumulation of normal and abnormal body fat. Genome-wide association studies contribute by facilitating the identification of genes present in all chromosomes. Single-nucleotide variants (SNV), formerly known as single-nucleotide polymorphisms (SNPs), are classified as functionals (rSNP and srSNP) and encoders (cSNP). rSNPs are located in the promoter sequence, and rsSNPs can be located in introns or in the codifying sequence [18]. Specific functional genetic variants are recognized as being genetic markers for diabetes, hypertension, and development. Genetic expression depends on cis regulators, such as the promoter sequence, and trans regulators, like transcriptional factors. The latter can join DNA, RNA polymerase II, and other molecules that modulate the genetic expression of specific tissues like fat or muscle [19].
Single-nucleotide variants (SNVs) are the result of replacing one nucleotide with another and could result in a structural and biological function change in the protein encoded by the gene in question. An SNV in the promoter region of a gene can increase or decrease the production of relevant proteins [20]. These inherited single-nucleotide variants could be associated with a higher or lower risk of developing a particular disease because SNVs can affect proteins or protein derivatives, such as neurotransmitters, protein-related obesity, and protein receptor leptin, among others. Therefore, their function can be altered.

2.1.1. Which Geographical Areas Are Studied, and What Are the Principal Genetic Variants Associated with Gestational Weight Gain?

The association between specific genetic variants and excessive GWG has been evaluated in 6974 women. The most recent studies came from the United States of America [17,21,22,23,24], Brazil [25,26,27,28], Romania [29,30], Turkey [31,32], Ukraine [33,34], Canada [35], Germany [36], China [37], and Poland [38]. These studies were performed on pregnant adult women (aged 22–35). As can be observed, only three countries from America, five from Europe, and one from Asia analyzed the probable association between genetic variants and gestational weight gain. Whether excessive or insufficient GWG, the two conditions have high rates, with 14–33% being insufficient and 26–61% being excessive [7,8]. This association is not fully addressed in regions like Latin America, characterized by countries with a high prevalence of overweight and obesity in reproductive-age women like Mexico. Table 1 shows the characteristics of the papers included in this review, further developed in the following text.

2.1.2. Genotype Variants Associated with GWG

Of all genetic variants in this manuscript, some are associated with GWG, while others are only associated with pregestational BMI. One of these genetic variants is located in LEPR (leptin receptor); this gene codes for the leptin receptor (an adipocyte-specific hormone that regulates body weight) and controls fat metabolism and other essential functions. Changes in this gene have been associated with obesity and pituitary dysfunction [39,40]. In a sample of 147 adult women aged 27, it was observed that LEPR rs7799039 GG carriers had a lower body weight throughout pregnancy than AA or GA carriers. The authors concluded that the rs7799039 genotype was associated with a high risk of developing excessive GWG [25]. As explained, 98% of placental secreted leptin enters circulation, and the placenta and adipose tissue express this gene during pregnancy [41]. In other studies, the AA genotype was more associated with greater amounts of adipose tissue in obese people than the GG genotype, but this association is not present in other tissues [42,43].
Another studied gene is the fat mass and obesity-associated (FTO) gene, which controls energy balance [44]. It codes for the 2-oxoglutarate-dependent nucleic acid demethylase and is expressed in the hypothalamus. Female carriers of the AT FTO rs9939609 genotype have a greater risk of excessive GWG than those with other genotypes, but only during the early stages of pregnancy. In females with type 1 or 2 diabetes, this genotype is associated with an increased risk of excessive GWG, as reported in a study performed on a group of 70 Brazilian adults with type 1 or 2 diabetes mellitus [26]. Among women who self-identify as black with pregestational obesity, the FTO rs99339609 genetic variant showed an association with GWG. In 194 African American women, the AA FTO rs 9939609 genotype was associated with higher GWG compared to the TT genotype. The AA and AT genotype carriers gained 7.6 kg and 3.6 kg more than the TT carriers, respectively [17].
Similarly, in 97 low-income black women, the AA genotype was associated with greater GWG (16.4 kg) compared with the AT and TT genotypes (14.0 kg and 12.2 kg, respectively) [21]. Nevertheless, for Caucasian women, the results were unexpected because they did not show any associations; only African American women with obesity gained 6.7 kg more than those with TT or AT genotypes [17]. Race may play a role in the expression of the genetic variants; this may, however, vary depending on pBMI, and thus further studies, with larger sample sizes, are necessary to clarify this issue.
The monoamine oxidase A (MAO-A) gene has also been associated with GWG. MAO-A is a member of a family that encodes mitochondrial enzymes, which catalyze the oxidative deamination of amines such as dopamine, norepinephrine, and serotonin. MAO-A maintains neurotransmitter metabolism and hence influences appetite and food intake. Only one study performed on 93 Canadian female carriers of the MAO-A 4/4 genotype observed a higher frequency of excessive GWG than 3/4 and 3/3 carriers; the possible explanation for this is reduced activity of dopamine caused by its deamination. The presence of MAO-A 4/4 (with high activity) in women was associated with excessive GWG when compared with 3/3, 3/4, or 3/3.5 (19.3 ± 4.1 vs. 17 ± 5 kg) [35]. The dopamine pathway could be the cause of this phenomenon.
Other studies explored the association of the GNB3 gene with GWG. In 2018, Groth and Cols. Ref. [17] reported that African American women with pregestational obesity and the CC GNB3 rs5443 genotype were associated with greater GWG (kg) than TT and CT carriers. Nevertheless, Caucasians did not exhibit an association with GWG.
On the other hand, the peroxisome proliferator-activated γ-receptor (PPARγ) rs1801282 SNV and its relationship with GWG were analyzed in 97 Ukrainian [34] and 162 Turkish women [32]; the authors concluded that based on adjusted models, Ala carriers had a greater probability of undergoing excessive GWG. In addition, the TT genotype from endothelial nitric oxide synthase (eNOS) Glu298Asp SNV in the Ukrainian sample was related to an increment of 5 kg compared to the other genotypes (GT and GG) [33].

2.1.3. Are Foods Important, in Addition to Genetic Variants?

It is possible that, as a consequence of the decontrolling of serum lipids and other substances, GWG is affected by genetic variants. Regarding the Glu298Asp (G894T) SNV of the eNOS gene (TT), in 97 adult women during the first and third trimester of pregnancy, TT was associated with a 1.5-fold increment in GWG compared to women with the GG genotype. The adequate, insufficient, and excessive amounts of GWG were 34, 19.6, and 46.4%, respectively [33].
Like others, the potassium channel tetramerization domain containing 15 (KCTD15) gene has been associated with the selection and intake of food providing high amounts of energy and with high carbohydrate and fat content [45]. Like other obesity-related genes, it is highly expressed in the hypothalamus [46]. In KCTD15 rs11084753 AG carriers, an association with GWG was observed, resulting in a gain of 16.9 kg vs. 12.3 for the GG genotype (p = 0.018) and vs. 10.9 kg for AA carriers (p = 0.026). Women carriers of KCTD15 rs11084753 gained more weight during pregnancy, highlighting that this occurred when the women consumed more fatty foods than those with other genotypes [22]. The probable explanation is that fat intake could modify the effect of the KCTD15 rs11084753 genetic variant gene on GWG [23]. Then, the environment could modify the effect of the SNV, which is demonstrated by an attenuated effect on body weight in FTO rs9939609 carriers that maintain a healthy lifestyle [47] and an optimal food intake [48]. Parallel findings were reported by Lawlor in adult UK women [49] and in African American women: diet plus SNV presence explained the observed GWG [22], with only 9% variability [23]. Genetic variants are intrinsic factors that pose a risk of developing excessive GWG. However, maintaining a healthy lifestyle and nutrition during pregnancy could reduce this risk and lead to a healthy childhood and adulthood [44].
Additionally, the effect of different types of diet on GWG in the presence of FTO gene (rs9939609, rs17817449) and ADRB2 (rs1042713, rs1042714) variants has been addressed in Brazilian pregnant women with pregestational diabetes. This nutrigenetic trial randomly assigned 70 pregnant women to a traditional diet (n = 41) or DASH diet (n = 29). Santos et al. found that regardless of type of diet, AT carriers of FTO rs9939609 and AA carriers of ADRB2 rs1042713 had higher risk of earlier excessive GWG [26].
To date these are the only studies where the effect of nutritional factors has been evaluated on GWG in the presence of genetic variants; further investigation must be addressed to clarify the impact diet has on weight gain.

2.1.4. Are Genetic Variants More Associated with Pregestational BMI Than with GWG?

Some genetic variants are associated with GWG, but others are not. For example, in Brazilian women, FTO rs17817494 was not associated with GWG, while women carriers of AT, AA, and TT gained 13.0 kg, 13.2 kg, and 12.3 kg, respectively (p = 0.15); the low sample size could explain this finding. This finding corresponds to 93 Caucasian women from Canada [35].
In Brazilian pregnant women, the LEPR rs1137101 genetic variant was associated with pregestational overweight or obesity. Hence, this alteration can be expressed before pregnancy, but the effects could be different for women carriers and their offspring during gestation [25]. For example, in another SNV, the FTO rs17817449, women carriers with different diets exhibited no associations with GWG. Goldfield et al. demonstrated that there was a null association between DAT-1 gene variants and GWG [35].
Information with more consistency shows that FTO genetic variants are associated with pregestational status, as shown in a study carried out on 205 Turkish adult women; this study reported that the FTO AA genotype was associated with a risk of pre-pregnancy overweight/obesity (OR = 1.43, 95% CI [1.25–3.4], p = 0.035). The authors concluded that the AA FTO rs9939609 genotype was not associated with excessive GWG after adjusting for pre-pregnancy weight (p > 0.05). It seems that FTO rs9939609 affects pregestational weight and GDM, an effect that could be explained by the insulin resistance pathway [31]. In the same way, FTO rs9939609 could have different effects or expression in diverse races. In a study on 406 Brazilian adult women, it was reported that there was a higher frequency of excessive GWG in those with the TT genotype (51%) compared to those with other genotypes; however, this finding did not have statistical significance, and Caucasian women did not report any associations. Only this genetic variant was associated with pBMI and the period after delivery [27] until 2–4 years postpartum. This phenomenon could begin to occur at young ages via the LEPR SNV, which could modify some metabolic indicators [50]. The MAOA genetic variant is associated with BMI in female adolescents [51] due to an effect deriving from stressor factors.
Another relevant gene is the melanocortin-4-receptor (MC4R). The protein encoded by this gene is a membrane-bound receptor and a member of the melanocortin receptor family. Defects in this gene and other heterogeneous factors cause autosomal dominant obesity [52]. The MC4R is located in the brain. It has an affinity with melanocyte alpha, beta hormone stimulators, and melanocortin and corticotropin hormones. Regarding GWG, in a sample of 185 Romanian pregnant women, MC4R rs17782313 showed no associations with GWG, only with pBMI [29]. Lawlor et al. reported parallel findings for adult women from the UK [49].
Melanocortin 4 receptor (MC4R) deficiency due to the disruption of one or both MC4R alleles has a relationship with the frequency of obesity [44]. Nevertheless, the role of the genetic variant GNB3 rs5443 (guanine nucleotide-binding protein rs5443) in GWG is uncertain, as was observed in a study incorporating women from the USA. Of the 674 African American and Caucasian women who participated in the study, in the African American group of women with pregestational overweight or obesity, those with TT or CT genotypes gained 7.3 and 6.0 kg, respectively, more than women with CC genotypes. Caucasian women did not show any association of GNB3 rs5443 with GWG [17]. However, in a sample of 158 Romanian pregnant adults, there was a marginally positive association between the variant genotype GNB3 rs5443 and excessive GWG [30]. Hence, more research is needed to confirm these findings. In this study, pBMI was a better predictor of GWG than the GNB3 SNV.
It is possible that pregestational diabetes type 1 or 2 is associated with previous BMI and that this, in turn, is associated with GWG, but there is not enough scientific evidence; for example, in a case–control association study on the SNV of peroxisome proliferator-activator receptor-gamma2 (PPAR-γ2) carried out in Turkey, women with gestational mellitus diabetes had greater gestational weight gain than women without diabetes. The conclusion was that PPAR-γ2 could influence glucose metabolism and pBMI [32]. This hypothesis has been inconsistent with other studies. The effect of this SNV could be affected by race; for example, in 97 Caucasian Ukrainian women, the genetic variant of PPAR-γ Pro12Ala was associated with greater GWG and a high frequency of excessive GWG [34].
Previous reports complement the following finding. The adrenoceptor beta 2 (ADRB2) gene predisposes one to a high probability of developing obesity and is expressed in adipose tissue and during lipolysis [53]. To give another example, 70 Brazilian women with type 1 or 2 diabetes mellitus and who also carried the AA genotype of ADRB2 rs1042713 had a greater risk of high gestational weight gain than GG carriers (aHR 3.91; CI 95% 1.12–13.70; p = 0.03) during the early stages of pregnancy, regardless of their diet [26]. Even MAOA genetic variants have been associated with BMI in female adolescents [51].
As previously mentioned, the eNOS TT single-nucleotide variant is related to greater levels of triglycerides, cholesterol and low-density lipoprotein, glucose, and insulin and with greater HOMA-IR scores when compared with the GT genotype during the third trimester due to decreased activity of eNOS, particularly among those with excessive GWG [33]. It is possible that the activity of the PPAR-γ gene toward lipids and carbohydrate metabolism could have a positive association with GWG [34].
Several polyunsaturated fatty acids can be modified and affect adipocyte storage. The FADS2 gene codes for a desaturase enzyme that interacts in the process of endogenously converting 18-carbon polyunsaturated fatty acids into very-long-chain fatty acids [36]. Some single-nucleotide variants in these genes are related to the bioavailability of polyunsaturated fatty acids [36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54]. In a sample of 185 Brazilian adult women, the GG FADS2 rs174575 genotype was associated positively with gestational weight gain (coefficient 0.11; p = 0.016; 95% CI 0.10–0.19) [28]. The alteration in genetic transcription probably reduces the bioavailability of ω-3 and promotes the storage of ω-6 and its derivatives [28]. On the other hand, the association between GCKR rs780094 (TT reference, T > C risk) and GWG has been studied. Nevertheless, the results were different. In 158 Romanian adult women, the GCKR (glucokinase receptor) rs 780094 genetic variant was not associated with GWG. As can be seen, the effect of the SNVs is not the same in all races. This gene codes for a protein that inhibits glucokinase in liver and pancreatic islet cells by binding non-covalently to form an inactive complex with the enzyme [30].

2.2. Genetic Variants and Cytokines and Their Probable Interaction with Gestational Weight Gain

Mature adipocytes and a stromal vascular constitute adipose tissue; they function as a unit regulating multiple endocrine and metabolic activities through adipokines [55,56]. It has been shown that the adipose tissue of obese subjects releases mainly proinflammatory adipokines, of which the most important are leptin, visfatin, resistin, TNF-α, IL-6, angiotensin II, and plasminogen activator inhibitor 1. Anti-inflammatory cytokines, such as TGF-β, adiponectin, IL-10, IL-4, IL-13, IL-1 receptor antagonist (IL-1Ra), and adipolin, are secreted by lean subjects [57].
Leptin was the first adipokine to be discovered [56]. LEPR rs7799039 is a nucleotide variant located in the promoter region of the LEPR gene, and it has been associated with a higher risk of GWG and type 2 diabetes mellitus development. LEPR rs1137101 (Q223R), is located in the region that codes for the extracellular domain of the leptin receptor; therefore, the amino acid change affects all isoforms of the receptor, resulting in an amino acid variation in the extracellular region of the leptin receptor, which can change the functional characteristics of the receptor [58,59]. Both variants reduce the function of LEPR, leading to a higher risk of obesity development. It is known that leptin and its receptor can induce IL-6 and TNF-α gene expression, so any change in LEPR function could lead to an altered expression of these two cytokines.
It has been reported that there is a possible association between the FTO gene and leptin. FTO rs9939609 and rs17817449 are single-nucleotide variants located in the intronic region of the FTO gene. FTO rs9939609 has been associated with leptin gene expression [60]. The FTO rs178117119 is implicated in the BMI-mediated risk of developing type 2 diabetes mellitus among obese children and adolescents. Therefore, if FTO can regulate LEP and LEPR expression, this could also lead to an altered expression of IL-6 and TNF-α and a significant susceptibility to the development of chronic diseases such as type 2 diabetes mellitus.
KCTD15, during embryogenesis, inhibits binding protein 2 (AP-2) transcriptional activities via interaction with its activation domain. The probable molecular basis of KCTD15 in obesity may involve some important factors in adipogenesis regulated by AP-2 [61]. The higher risk of GWG precipitated by rs11084753 KCTD15 could lead to the dysregulation of AP-2 [62]. In skin cultures designed to mimic cutaneous inflammation and wound healing, it has been demonstrated that the AP-2 protein is functionally important in TGF alpha-induced VPF/VEGF gene expression. Consequently, as obesity induces an inflammatory state, the mechanism could be similar to the one observed in skin diseases and the healing process [63].
MAO-A’s role consists of generating ROS (H2O2) in alternatively activated monocytes/macrophages. Interesting studies have shown that MAO-produced H2O2 is an essential inhibitor of inducible nitric oxide synthase (NOS2) expression. Although NOS2 generation of NO is differently regulated in murine and human macrophages, it has been speculated that the expression of MAO-A in anti-inflammatory macrophages could partially stabilize this phenotype by preventing the expression of NOS2 and the generation of NO [64]. Excessive amounts of adipose tissue (specifically visceral) generate high quantities of reactive oxygen species (ROS) associated with obesity-related pathologies. Nevertheless, MAO-A’s role in obesity development has not been fully elucidated. The association of MAO-A variants with GWG constitutes a practical initial approach [65].
Variable number tandem repeat (VNTR) variants have been described in the 3′ region of dopamine transporter (DAT-1), with 9-repeat and 10-repeat forms being the most common. The 9-repeat allele could induce lower levels of postsynaptic dopamine. As dopamine regulates food intake, it has been proposed that genetic variants found in dopamine-associated molecules, like DAT-1, could indirectly influence weight gain. However, this association has not yet been verified. This could be due to the modulating effect of other proteins like MAO. It is well understood that MAO is involved in degrading biological amines, including dopamine, and thereby deaminates dopamine that has been taken back up by DAT-1 [64,66].
Another regulatory factor of nutrient metabolism is PPARγ; it controls cellular lipid metabolism with anti-inflammatory activities. PPARγ rs1801282 is a missense mutation resulting in an amino acid substitution of alanine for proline at codon 12 (Pro12Ala); this variant is located in the ligand-independent activation domain that reduces PPARγ transcriptional activity. In a study involving pregnant women and their offspring in Spain, no statistically significant differences were identified in TNF-α, IL-6, and IL-10 cytokine levels at any time point in pregnancy between CG carriers and those carrying the CC genotype, and no effect was observed on their offspring [67].
Regarding GNB3 rs5443, it is located on exon 10 of GNB3 and is associated with the generation of a splice variant named Gβ3. This variant is characterized by a deletion of 41 amino acids and one WD repeat domain in the G beta subunit; both characteristics seem to enhance its function with respect to signal transduction, including lipolysis [64]. GNB3 rs5443 has also been associated with chemotaxis, antigen-specific cellular immune response, and IL-2-induced proliferation [68].
Finally, in an in vitro study, the effect of eNOS Glu298Asp on cytokine production was evaluated, demonstrating increased levels of inflammatory cytokines (IL-1, IL-6, and TNF-α) in the TT genotype. This could explain why people with the T allele present chronic inflammation and/or do not respond to anti-inflammatory treatments or exhibit inflammation and obesity [69].
Figure 1 shows the genetic variants associated and unassociated with GWG; here, we identify that some genetic variants are associated with pBMI but not with GWG. Among neurotransmitters, serotonin, dopamine, and norepinephrine were highlighted. Nevertheless, the role of cytokines (leptin, visfatin, resistin, TNF-α, IL-6, angiotensin II, plasminogen activator inhibitor 1, TGF-β, adiponectin, IL-10, IL-4, IL-13, IL-1 receptor antagonist (IL-1Ra), and adipolin) is not clear. In our analysis, environment was a determinant in the effect of genetic variants on GWG.

3. Discussion

Previous studies concluded that some genetic variants could be associated with GWG. However, indirectly, as reported by Stuebe [24], genetic variants associated with pregestational weight have some mediators that affect serum lipids and fat storage and are a cause of diabetes [28,29,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54]. Accordingly, this process can be supported by genes related to GWG. Considering their effect on pBMI, they could play a role in insulin signaling, glucose homeostasis, mitochondrial metabolism, and inflammatory responses, at least in women with type 1 diabetes mellitus [38].
Genetic variants can affect GWG, cytokine production, and the birth weight of offspring. In 1114 mother–neonate dyads, MTNR1B was associated with GWG and the offspring’s birth weight. The authors of the associated study proposed that offspring could have a high risk of obesity during childhood [70]. Likewise, a study involving 1025 Chinese women reported that GWG could affect the circadian-cycle-related MTNR1B rs10830963 genetic variant, with severe repercussions on long-term glycemic changes. Thus, as can be seen, the effect of GWG transcends pregnancy [71] in women with a history of DMG.
Nevertheless, generating more enlightening insights into the gene–diet–environmental interaction regarding GWG is necessary. The environment determines the phenotype generated by the SNVs’ presence, as revealed in a study involving 1153 elderly Swedish men. They were followed for 32 years; they were born between 1920 and 1924, and the FTO rs9939609 variants did not reveal any difference in BMI. They were born before ultra-processed foods and fast foods were widely available and before physical activity levels decreased. Thus, food and physical activity could influence the effect of the presence of FTO rs9939609 [72]. It should be highlighted that the previous study was performed only in older men and a different scenario could be present in women at reproductive age. Therefore, these findings need to be interpreted with caution.

3.1. Limitations and Strengths

Our review has several limitations. The first is that for the group of pregnant women, there is no information about the interaction between genetic variants, cytokines, and GWG in the same study. Therefore, using the evidence reported in other age groups of non-pregnant individuals regarding their association with cytokines, we hypothesize that cytokines could have similar effects in some molecules and GWG, but in others, these effects could be different; this line of research needs to be scrutinized and developed during pregnancy. Our review’s strength is that it is the first to explore the genotype variants associated with GWG. We observed that low- and middle-income countries have little or no research on this topic, yet they have high rates of overweight and obesity in their female populations.

3.2. Clinical Implications

Pregnancy is an inflammatory state per se that could be dysregulated by inflammatory conditions like obesity. The genotypification of variants of GWG-related genes during pregnancy not only allows for a better understanding of the balance needed between food intake and weight gain during this stage but could also provide a risk probability for developing overweight or obesity and consequently the risk of developing complex diseases, like type 2 diabetes mellitus, which, in most cases, lead to severe consequences for maternal and child health. It also highlights the need to control the intake of high-calorie foods and maintain routine physical activity to avoid excessive GWG.

4. Conclusions

It has been found that some genetic variants are associated with GWG. Nevertheless, this association is only significant for African American women with overweight or obesity. Due to a lack of information, the role of cytokines in the association between genetic variants and GWG remains to be determined.

5. Future Directions

Further research is needed to understand the probable explanation for the association between genetic variants, environmental factors such as high-energy food intake, and their interaction with excessive or inadequate GWG in some ethnic groups, like those from low- and middle-income countries.

Author Contributions

Conceptualization, R.S. and I.E.M.-M.; methodology, R.S. and I.E.M.-M.; software, R.S.; validation, H.M.-R., G.C.-B., M.E.M.-F. and R.G.; formal analysis, R.S. and I.E.M.-M.; investigation, H.M.-R., F.J.R.-A. and I.P.-M.; resources, R.S.; data curation, R.S. and I.E.M.-M.; writing—original draft preparation, R.S. and I.E.M.-M.; writing—review and editing, R.S. and I.E.M.-M.; visualization, H.M.-R., R.G., F.J.R.-A., I.P.-M., G.C.-B. and M.E.M.-F.; supervision, R.S.; project administration, R.S.; funding acquisition, R.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Instituto Nacional de Perinatología (2017-2-101) and CONACyT FOSSIS SALUD-2018-01-A3-S-40575 (grant number: A3-S-40575, FSSS01-C-2018-1).

Institutional Review Board Statement

This study derived from a protocol approved by the institutional research, biosecurity, and ethics committees of the Instituto Nacional de Perinatología in Mexico City (registration 2017-2-101 INPer).

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

Acknowledgments

We acknowledge all the authors that we consulted to perform this scoping review.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

GWG: Gestational weight gain; BMI: Body mass index; MeSH: Medical subject headings; IOM: Institute of Medicine; pBMI: Pre-pregnancy body mass index; CI: Confidence interval, gw: Gestational week; AM: Adjusted models; PCR: Polymerase chain reaction; aHE: Adjusted hazard ratio; VNTR: Variable number tandem repeat; ROS: Reactive oxygen species; no: Nitric oxide; GDM: Gestational diabetes mellitus; SNV: Single-nucleotide variant; LEPR: Leptin receptor gene; LEPR: Leptin receptor, FTO: Fat mass and obesity-associated, GNB3: Guanine nucleotide-binding protein 3, MAO-A: monoamine oxidase A, PPARγ: Peroxisome proliferator-activated γ-receptors, eNOS: Endothelial nitric oxide synthase, MTNR1B: Melatonin receptor 1B, KCTD15: Potassium channel tetramerization domain containing 15, ADRB2: Adrenoceptor beta2, and DAT-1: dopamine transporter.

References

  1. Daher, S.; Torloni, M.R.; Mattar, R. Cytokines and Cytokine Gene Polymorphisms in Recurrent Pregnancy Loss. In Recurrent Pregnancy Loss [Internet]; Wiley Online Library: Hoboken, NJ, USA, 2013. [Google Scholar] [CrossRef]
  2. Rugină, C.; Mărginean, C.O.; Meliţ, L.E.; Huţanu, A.; Ghiga, D.V.; Modi, V.; Mărginean, C. Systemic Inflammatory Status–A Bridge between Gestational Weight Gain and Neonatal Outcomes (STROBE-Compliant Article). Medicine 2021, 100, e24511. [Google Scholar] [CrossRef]
  3. Wang, P.; Ma, W.; Zhou, Y.; Zhao, Y.; Shi, H.; Yang, Q.; Zhang, Y. Circulating Metal Concentrations, Inflammatory Cytokines and Gestational Weight Gain: Shanghai MCPC Cohort. Ecotoxicol. Environ. Saf. 2020, 199, 110697. [Google Scholar] [CrossRef]
  4. Yang, X.; Quan, X.; Lan, Y.; Ye, J.; Wei, Q.; Yin, X.; Fan, F.; Xing, H. Serum Chemerin Level during the First Trimester of Pregnancy and the Risk of Gestational Diabetes Mellitus. Gynecol. Endocrinol. 2017, 33, 770–773. [Google Scholar] [CrossRef] [PubMed]
  5. Markova, A.D.; Hadzi-Lega, M.; Dimitrov, G.; Tofovski, G.; Georgievska, J.; Dzikova, E.; Kjaev, I. Indicators of Preeclampsia in Correlation with Maternal Cytokines in Pregnancy. In Autoantibodies and Cytokines; IntechOpen: London, UK, 2019. [Google Scholar]
  6. Li, Y.; Zhu, M.; Hu, R.; Yan, W. The Effects of Gene Polymorphisms in Angiotensin II Receptors on Pregnancy-Induced Hypertension and Preeclampsia: A Systematic Review and Meta-Analysis. Hypertens. Pregnancy 2015, 34, 241–260. [Google Scholar] [CrossRef]
  7. Ukah, U.V.; Bayrampour, H.; Sabr, Y.; Razaz, N.; Chan, W.-S.; Lim, K.I.; Lisonkova, S. Association between Gestational Weight Gain and Severe Adverse Birth Outcomes in Washington State, US: A Population-Based Retrospective Cohort Study, 2004–2013. PLoS Med. 2019, 16, e1003009. [Google Scholar] [CrossRef]
  8. ACOG. Practice Bulletin No 156: Obesity in Pregnancy. Obstet. Gynecol. 2015, 126, e112–e126, Erratum in: Obstet Gynecol. 2016, 128, 1450. [Google Scholar] [CrossRef]
  9. Catalano, P.M.; Shankar, K. Obesity and Pregnancy: Mechanisms of Short Term and Long Term Adverse Consequences for Mother and Child. BMJ 2017, 356, j1. [Google Scholar] [CrossRef] [PubMed]
  10. Leonard, S.A.; Carmichael, S.L.; Abrams, B. Gestational Weight Gain and Severe Maternal Morbidity at Delivery Hospitalization. Obstet. Gynecol. 2019, 134, 420. [Google Scholar] [CrossRef] [PubMed]
  11. Bracken, O.; Langhe, R. Evaluation of Maternal and Perinatal Outcomes in Pregnancy with High BMI. Ir. J. Med. Sci. 2021, 190, 1439–1444. [Google Scholar] [CrossRef] [PubMed]
  12. Martins, M.C.; Trujillo, J.; Farias, D.R.; Struchiner, C.J.; Kac, G. Association of the FTO (rs9939609) and MC4R (rs17782313) Gene Polymorphisms with Maternal Body Weight during Pregnancy. Nutrition 2016, 32, 1223–1230. [Google Scholar] [CrossRef]
  13. Benedetto, C.; Marozio, L.; Ciccone, G.; Chieppa, G.; Quaglia, M.; Matullo, G.; Bertola, L.; Guarrera, S.; Carturan, S.; Stratta, P. Synergistic Effect of Renin-Angiotensin System and Nitric Oxide Synthase Genes Polymorphisms in Pre-Eclampsia. Acta Obstet. Gynecol. Scand. 2007, 86, 678–682. [Google Scholar] [CrossRef] [PubMed]
  14. Naidoo, S.; Taylor, M. The Association of Social Influences, Modeling Behavior, Self-Efficacy, and Communication With Teenage Pregnancies Among School Students in South Africa. J. Sch. Nurs. 2021, 37, 128–138. [Google Scholar] [CrossRef] [PubMed]
  15. Benny, P.; Yamasato, K.; Yunits, B.; Zhu, X.; Ching, T.; Garmire, L.X.; Berry, M.J.; Towner, D. Maternal Cardiovascular-Related Single Nucleotide Polymorphisms, Genes, and Pathways Associated with Early-Onset Preeclampsia. PLoS ONE 2019, 14, e0222672. [Google Scholar] [CrossRef]
  16. Beltcheva, O.; Boyadzhieva, M.; Angelova, O.; Mitev, V.; Kaneva, R.; Atanasova, I. The rs266729 Single-Nucleotide Polymorphism in the Adiponectin Gene Shows Association with Gestational Diabetes. Arch. Gynecol. Obstet. 2014, 289, 743–748. [Google Scholar] [CrossRef]
  17. Groth, S.W.; LaLonde, A.; Wu, T.; Fernandez, I.D. Obesity Candidate Genes, Gestational Weight Gain, and Body Weight Changes in Pregnant Women. Nutrition 2018, 48, 61–66. [Google Scholar] [CrossRef] [PubMed]
  18. Sadee, W. Measuring Cis-Acting Regulatory Variants Genome-Wide: New Insights into Expression Genetics and Disease Susceptibility. Genome Med. 2009, 1, 116. [Google Scholar] [CrossRef]
  19. Kininis, M.; Kraus, W.L. A Global View of Transcriptional Regulation by Nuclear Receptors: Gene Expression, Factor Localization, and DNA Sequence Analysis. Nucl. Recept. Signal. 2008, 6, e005. [Google Scholar] [CrossRef]
  20. de Andrade, D.R., Jr.; de Andrade, D.R. The Influence of the Human Genome on Chronic Viral Hepatitis Outcome. Rev. Inst. Med. Trop. Sao Paulo 2004, 46, 119–126. [Google Scholar] [CrossRef]
  21. Groth, S.W.; Morrison-Beedy, D. GNB3 and FTO Polymorphisms and Pregnancy Weight Gain in Black Women. Biol. Res. Nurs. 2015, 17, 405–412. [Google Scholar] [CrossRef]
  22. Meng, Y.; Groth, S.W.; Li, D. The Association between Obesity-Risk Genes and Gestational Weight Gain Is Modified by Dietary Intake in African American Women. J. Nutr. Metab. 2018, 2018, 5080492. [Google Scholar] [CrossRef]
  23. Meng, Y.; Groth, S.W.; Stewart, P.; Smith, J.A. An Exploration of the Determinants of Gestational Weight Gain in African American Women: Genetic Factors and Energy Expenditure. Biol. Res. Nurs. 2018, 20, 118–125. [Google Scholar] [CrossRef] [PubMed]
  24. Stuebe, A.M.; Lyon, H.; Herring, A.H.; Ghosh, J.; Wise, A.; North, K.E.; Siega-Riz, A.M. Obesity and Diabetes Genetic Variants Associated with Gestational Weight Gain. Am. J. Obstet. Gynecol. 2010, 203, 283.e1–283.e17. [Google Scholar] [CrossRef] [PubMed]
  25. Martins, M.C.; Trujillo, J.; Farias, D.R.; Kac, G. Polymorphisms in the Leptin (rs7799039) Gene are Associated with an Increased Risk of Excessive Gestational Weight Gain but not with Leptin Concentration during Pregnancy. Nutr. Res. 2017, 47, 53–62. [Google Scholar] [CrossRef]
  26. dos Santos, K.; Rosado, E.L.; da Fonseca, A.C.P.; Belfort, G.P.; da Silva, L.B.G.; Ribeiro-Alves, M.; Zembrzuski, V.M.; Martínez, J.A.; Saunders, C. FTO and ADRB2 Genetic Polymorphisms Are Risk Factors for Earlier Excessive Gestational Weight Gain in Pregnant Women with Pregestational Diabetes Mellitus: Results of a Randomized Nutrigenetic Trial. Nutrients 2022, 14, 1050. [Google Scholar] [CrossRef]
  27. Kroll, C.; De França, P.H.C.; Mastroeni, M.F. Association between FTO Gene Polymorphism and Excess Body Weight in Women from before to after Pregnancy: A Cohort Study. Am. J. Hum. Biol. 2018, 30, e23164. [Google Scholar] [CrossRef]
  28. Santana, J.d.M.; Pereira, M.; Carvalho, G.Q.; Peluzio, M.D.C.G.; Louro, I.D.; dos Santos, D.B.; Oliveira, A.M. FADS1 and FADS2 Gene Polymorphisms Modulate the Relationship of Omega-3 and Omega-6 Fatty Acid Plasma Concentrations in Gestational Weight Gain: A NISAMI Cohort Study. Nutrients 2022, 14, 1056. [Google Scholar] [CrossRef]
  29. Mărginean, C.; Mărginean, C.O.; Iancu, M.; Meliț, L.E.; Tripon, F.; Bănescu, C. MC4R and ENPP1 Gene Polymorphisms and Their Implication in Maternal and Neonatal Risk for Obesity. Sci. Rep. 2019, 9, 10858. [Google Scholar] [CrossRef] [PubMed]
  30. Mărginean, C.; Mărginean, C.O.; Bănescu, C.; Meliţ, L.E.; Tripon, F.; Iancu, M.M. The Relationship among GNB3 rs5443, PNPLA3 rs738409, GCKR rs780094 Gene Polymorphisms, Type of Maternal Gestational Weight Gain and Neonatal Outcomes (STROBE-Compliant Article). Medicine 2019, 98, e16414. [Google Scholar] [CrossRef]
  31. Beysel, S.; Eyerci, N.; Ulubay, M.; Caliskan, M.; Kizilgul, M.; Hafızoğlu, M.; Cakal, E. Maternal Genetic Contribution to Pre-pregnancy Obesity, Gestational Weight Gain, and Gestational Diabetes Mellitus. Diabetol. Metab. Syndr. 2019, 11, 37. [Google Scholar] [CrossRef]
  32. Tok, E.C.; Ertunc, D.; Bilgin, O.; Erdal, E.M.; Kaplanoglu, M.; Dilek, S. PPAR-γ2 Pro12Ala Polymorphism is Associated with Weight Gain in Women with Gestational Diabetes Mellitus. Eur. J. Obstet. Gynecol. Reprod. Biol. 2006, 129, 25–30. [Google Scholar] [CrossRef]
  33. Ostafiichuk, S.O.; Prudnikov, P.M.; Volosovskiy, P.R.; Zabolotnov, V.O.; Boichuk, H.; Henyk, N.I. Associations of ENOS GLU298ASP (G894T) endothelial dysfunction gene polymorphisms with metabolic disorders in pathological pregnancy. Wiad. Lek. 2022, 75, 1362–1369. [Google Scholar] [CrossRef]
  34. Ostafiichuk, S.; Henyk, N.; Rossokha, Z. The role of Pro12Ala polymorphism of the PPAR-γ gene in the genesis of the gestational weight gain. Georgian Med. News 2018, 285, 86–92. [Google Scholar] [PubMed]
  35. Goldfield, G.S.; Dowler, L.M.; Walker, M.; Cameron, J.D.; Ferraro, Z.M.; Doucet, E.; Adamo, K.B. Are Dopamine-Related Genotypes Risk Factors for Excessive Gestational Weight Gain? Int. J. Women’s Health 2013, 5, 253–259. [Google Scholar] [CrossRef] [PubMed]
  36. Lattka, E.; Illig, T.; Koletzko, B.; Heinrich, J. Genetic Variants of the FADS1 FADS2 Gene Cluster as Related to Essential Fatty Acid Metabolism. Curr. Opin. Infect. Dis. 2010, 21, 64–69. [Google Scholar] [CrossRef] [PubMed]
  37. Wu, W.; Luo, D.; Ruan, X.; Gu, C.; Lu, W.; Lian, K.; Mu, X. Polymorphisms in Gene MTHFR Modify the Association between Gestational Weight Gain and Adverse Birth Outcomes. Front. Nutr. 2022, 9, 919651. [Google Scholar] [CrossRef] [PubMed]
  38. Ludwig-Słomczyńska, A.H.; Seweryn, M.T.; Kapusta, P.; Pitera, E.; Mantaj, U.; Cyganek, K.; Gutaj, P.; Dobrucka, Ł.; Wender-Ożegowska, E.; Małecki, M.T.; et al. The Transcriptome-Wide Association Search for Genes and Genetic Variants which Associate with BMI and Gestational Weight Gain in Women with Type 1 Diabetes. Mol. Med. 2021, 27, 6. [Google Scholar] [CrossRef] [PubMed]
  39. Boumaiza, I.; Omezzine, A.; Rejeb, J.; Rebhi, L.; Ouedrani, A.; Ben Rejeb, N.; Nabli, N.; Ben Abdelaziz, A.; Bouslama, A. Relationship Between Leptin G2548A and Leptin Receptor Q223R Gene Polymorphisms and Obesity and Metabolic Syndrome Risk in Tunisian Volunteers. Genet. Test. Mol. Biomark. 2012, 16, 726–733. [Google Scholar] [CrossRef]
  40. Heo, M.; Leibel, R.L.; Boyer, B.B.; Chung, W.K.; Koulu, M.; Karvonen, M.K.; Pesonen, U.; Rissanen, A.; Laakso, M.; Uusitupa, M.I.J.; et al. Pooling Analysis of Genetic Data: The Association of Leptin Receptor (LEPR) Polymorphisms With Variables Related to Human Adiposity. Genetics 2001, 159, 1163–1178. [Google Scholar] [CrossRef]
  41. Smolinska, N.; Siawrys, G.; Kaminski, T.; Przala, J. Leptin Gene and Protein Expression in the Trophoblast and Uterine Tissues During Early Pregnancy and the Oestrous Cycle of Pigs. J. Physiol. Pharmacol. 2007, 58, 563–581. [Google Scholar]
  42. Dasgupta, S.; Salman, M.; Siddalingaiah, L.B.; Lakshmi, G.; Xaviour, D.; Sreenath, J. Genetic Variants in Leptin: Determinants of Obesity and Leptin Levels in South Indian Population. Adipocyte 2015, 4, 135–140. [Google Scholar] [CrossRef]
  43. de Oliveira, R.; Cerda, A.; Genvigir, F.D.V.; Sampaio, M.F.; Armaganijan, D.; Bernik, M.M.S.; Dorea, E.L.; Hirata, M.H.; Hinuy, H.M.; Hirata, R.D.C. Leptin Receptor Gene Polymorphisms are Associated with Adiposity and Metabolic Alterations in Brazilian Individuals. Arq. Bras. Endocrinol. Metabol. 2013, 57, 677–684. [Google Scholar] [CrossRef] [PubMed]
  44. Frayling, T.M.; Timpson, N.J.; Weedon, M.N.; Zeggini, E.; Freathy, R.M.; Lindgren, C.M.; Perry, J.R.B.; Elliott, K.S.; Lango, H.; Rayner, N.W.; et al. A Common Variant in the FTO Gene Is Associated with Body Mass Index and Predisposes to Childhood and Adult Obesity. Science 2007, 316, 889–894. [Google Scholar] [CrossRef] [PubMed]
  45. Bauer, F.; Elbers, C.C.; Adan, R.A.; Loos, R.J.; Onland-Moret, N.C.; E Grobbee, D.; van Vliet-Ostaptchouk, J.V.; Wijmenga, C.; van der Schouw, Y.T. Obesity Genes Identified in Genome-Wide Association Studies are Associated with Adiposity Measures and Potentially with Nutrient-Specific Food Preference. Am. J. Clin. Nutr. 2009, 90, 951–959. [Google Scholar] [CrossRef]
  46. Hofker, M.; Wijmenga, C. A Supersized List of Obesity Genes. Nat. Genet. 2009, 41, 139–140. [Google Scholar] [CrossRef] [PubMed]
  47. Ruiz, J.R.; Labayen, I.; Ortega, F.B.; Legry, V.; Moreno, L.A.; Dallongeville, J.; Martínez-Gómez, D.; Bokor, S.; Manios, Y.; Ciarapica, D.; et al. Attenuation of the Effect of the FTO rs9939609 Polymorphism on Total and Central Body Fat by Physical Activity in Adolescents: The HELENA Study. Arch. Pediatr. Adolesc. Med. 2010, 164, 328–333. [Google Scholar] [CrossRef]
  48. Labayen, I.; Ruiz, J.R.; Ortega, F.B.; Dalongeville, J.; Jiménez-Pavón, D.; Castillo, M.J.; De Henauw, S.; González-Gross, M.; Bueno, G.; Molnar, D.; et al. Association between the FTO rs9939609 Polymorphism and Leptin in European Adolescents: A Possible Link with Energy Balance Control. The HELENA study. Int. J. Obes. 2011, 35, 66–71. [Google Scholar] [CrossRef]
  49. A Lawlor, D.; Fraser, A.; Macdonald-Wallis, C.; Nelson, S.M.; Palmer, T.M.; Smith, G.D.; Tilling, K. Maternal and Offspring Adiposity-Related Genetic Variants and Gestational Weight Gain. Am. J. Clin. Nutr. 2011, 94, 149–155. [Google Scholar] [CrossRef]
  50. Domínguez-Reyes, T.; Astudillo-López, C.C.; Salgado-Goytia, L.; Muñoz-Valle, J.F.; Salgado-Bernabé, A.B.; Guzmán-Guzmán, I.P.; Castro-Alarcón, N.; Moreno-Godínez, M.E.; Parra-Rojas, I. Interaction of Dietary Fat Intake with APOA2, APOA5 and LEPR Polymorphisms and Its Relationship with Obesity and Dyslipidemia in Young Subjects. Lipids Health Dis. 2015, 14, 106. [Google Scholar] [CrossRef]
  51. Xie, B.; Li, D.; London, S.J.; Palmer, P.H.; Johnshon, C.A.; Li, Y.; Shih, J.; Bergen, A.W.; Nishita, D.; Swan, G.E.; et al. Gender Difference in Interactions between MAOA Promoter uVNTR Polymorphism and Negative familial Stressors on Body Mass Index among Chinese Adolescents. Pediatr. Obes. 2014, 9, e80–e90. [Google Scholar] [CrossRef]
  52. Vaisse, C.; Clement, K.; Durand, E.; Hercberg, S.; Guy-Grand, B.; Froguel, P. Melanocortin-4 Receptor Mutations are a Frequent and Heterogeneous Cause of Morbid Obesity. J. Clin. Investig. 2000, 106, 253–262. [Google Scholar] [CrossRef]
  53. Steemburgo, T.; Azevedo, M.J.; Martínez, J.A. Gene-nutrient interaction and its association with obesity and diabetes mellitus. Arq. Bras. Endocrinol. Metabol. 2009, 53, 497–508. [Google Scholar] [CrossRef]
  54. De la Garza Puentes, A.; Montes Goyanes, R.; Chisaguano Tonato, A.M.; Torres-Espínola, F.J.; Arias García, M.; de Almeida, L.; Bonilla Aguirre, M.; Guerendiain, M.; Castellote Bargalló, A.I.; Segura Moreno, M.; et al. Association of Maternal Weight with FADS and ELOVL Genetic Variants and Fatty Acid Levels- The PREOBE Follow-Up. PLoS ONE 2017, 12, e0179135. [Google Scholar] [CrossRef]
  55. Kershaw, E.E.; Flier, J.S. Adipose Tissue as an Endocrine Organ. J. Clin. Endocrinol. Metab. 2004, 89, 2548–2556. [Google Scholar] [CrossRef] [PubMed]
  56. Chang, M.-L.; Yang, Z.; Yang, S.-S. Roles of Adipokines in Digestive Diseases: Markers of Inflammation, Metabolic Alteration and Disease Progression. Int. J. Mol. Sci. 2020, 21, 8308. [Google Scholar] [CrossRef] [PubMed]
  57. Artemniak-Wojtowicz, D.; Kucharska, A.; Pyrżak, B. Obesity and Chronic Inflammation Crosslinking. Central Eur. J. Immunol. 2020, 45, 461–468. [Google Scholar] [CrossRef] [PubMed]
  58. Härle, P.; Straub, R.H. Leptin Is a Link between Adipose Tissue and Inflammation. Ann. N. Y. Acad. Sci. 2006, 1069, 454–462. [Google Scholar] [CrossRef]
  59. Zarkesh-Esfahani, H.; Pockley, G.; Metcalfe, R.A.; Bidlingmaier, M.; Wu, Z.; Ajami, A.; Weetman, A.P.; Strasburger, C.J.; Ross, R.J.M. High-Dose Leptin Activates Human Leukocytes Via Receptor Expression on Monocytes. J. Immunol. 2001, 167, 4593–4599. [Google Scholar] [CrossRef] [PubMed]
  60. Mehrdad, M.; Doaei, S.; Gholamalizadeh, M.; Fardaei, M.; Fararouei, M.; Eftekhari, M.H. Association of FTO rs9939609 Polymorphism with Serum Leptin, Insulin, Adiponectin, and Lipid Profile in Overweight Adults. Adipocyte 2020, 9, 51–56. [Google Scholar] [CrossRef] [PubMed]
  61. Lv, D.; Zhang, D.-D.; Wang, H.; Zhang, Y.; Liang, L.; Fu, J.-F.; Xiong, F.; Liu, G.-L.; Gong, C.-X.; Luo, F.-H.; et al. Genetic Variations in SEC16B, MC4R, MAP2K5 and KCTD15 were Associated with Childhood Obesity and Interacted with Dietary Behaviors in Chinese School-Age Population. Gene 2015, 560, 149–155. [Google Scholar] [CrossRef]
  62. Joost, U.; Villa, I.; Comasco, E.; Oreland, L.; Veidebaum, T.; Harro, J. Association between Transcription Factor AP-2B Genotype, Obesity, Insulin Resistance and Dietary Intake in a Longitudinal Birth Cohort Study. Int. J. Obes. 2019, 43, 2095–2106. [Google Scholar] [CrossRef]
  63. Gille, J.; Swerlick, R.A.; Caughman, S.W. Transforming Growth Factor-Alpha-Induced Transcriptional Activation of the Vascular Permeability Factor (VPF/VEGF) Gene Requires AP-2-Dependent DNA Binding and Transactivation. EMBO J. 1997, 16, 750–759. [Google Scholar] [CrossRef]
  64. Cathcart, M.K.; Bhattacharjee, A. Monoamine Oxidase A (MAO-A): A Signature Marker of Alternatively Activated Monocytes/Macrophages. Inflamm. Cell Signal. 2014, 1, e161. [Google Scholar] [CrossRef] [PubMed]
  65. Sturza, A.; Popoiu, C.M.; Ionică, M.; Duicu, O.M.; Olariu, S.; Muntean, D.M.; Boia, E.S. Monoamine Oxidase-Related Vascular Oxidative Stress in Diseases Associated with Inflammatory Burden. Oxidative Med. Cell. Longev. 2019, 2019, 8954201. [Google Scholar] [CrossRef] [PubMed]
  66. Li, H.-L.; Zhang, Y.-J.; Chen, X.-P.; Luo, J.-Q.; Liu, S.-Y.; Zhang, Z.-L. Association between GNB3 c.825C > T Polymorphism and the Risk of Overweight and Obesity: A Meta-Analysis. Meta Gene 2016, 9, 18–25. [Google Scholar] [CrossRef] [PubMed]
  67. García-Ricobaraza, M.; García-Bermúdez, M.; Torres-Espinola, F.J.; Moreno, M.T.S.; Bleyere, M.N.; Díaz-Prieto, L.E.; Nova, E.; Marcos, A.; Campoy, C. Association Study of rs1801282 PPARG Gene Polymorphism and Immune Cells and Cytokine Levels in a Spanish Pregnant Women Cohort and Their Offspring. J. Biomed. Sci. 2020, 27, 101. [Google Scholar] [CrossRef] [PubMed]
  68. Lindemann, M.; Virchow, S.; Ramann, F.; Barsegian, V.; Kreuzfelder, E.; Siffert, W.; Müller, N.; Grosse-Wilde, H. The G Protein β3 Subunit 825T Allele is a Genetic Marker for Enhanced T Cell Response. FEBS Lett. 2001, 495, 82–86. [Google Scholar] [CrossRef] [PubMed]
  69. Maurer, P.; Barbisan, F.; Azzolin, V.F.; Berro, L.F.; Montagner, R.; Duarte, M.M.M.F.; da Cruz, I.B.M.; Manfredini, V.; Piccoli, J.d.C.E. Polymorphism eNOS Glu298Asp Modulates the Inflammatory Response of Human Peripheral Blood Mononuclear Cells. Cytokine 2020, 125, 154812. [Google Scholar] [CrossRef] [PubMed]
  70. Liang, Z.; Liu, H.; Wang, L.; Chen, Y.; Zhou, T.; Heianza, Y.; Li, W.; Leng, J.; Wang, J.; Gao, R.; et al. Maternal MTNR1B Genotype, Maternal Gestational Weight Gain, and Childhood Obesity. Am. J. Clin. Nutr. 2020, 111, 360–368. [Google Scholar] [CrossRef] [PubMed]
  71. Nisa, H.; Qi, K.H.T.; Leng, J.; Zhou, T.; Liu, H.; Li, W.; Wang, L.; Li, N.; Hu, G.; Qi, L. The Circadian Rhythm–Related MTNR1B Genotype, Gestational Weight Gain, and Postpartum Glycemic Changes. J. Clin. Endocrinol. Metab. 2018, 103, 2284–2290. [Google Scholar] [CrossRef]
  72. Jacobsson, J.A.; Risérus, U.; Axelsson, T.; Lannfelt, L.; Schiöth, H.B.; Fredriksson, R. The Common FTO Variant rs9939609 Is Not Associated with BMI in a Longitudinal Study on a Cohort of Swedish Men Born 1920–1924. BMC Med. Genet. 2009, 10, 131. [Google Scholar] [CrossRef]
Ijms 25 03039 g001
Figure 1. Interaction between genotype variants and other factors associated with GWG. Abbreviations. GWG: Gestational weight gain, BMI: Body mass index, LEPR: Leptin receptor, FTO: Fat mass and obesity-associated, GNB3: Guanine nucleotide-binding protein 3, MAO-A: monoamine oxidase A, PPARγ: Peroxisome proliferator-activated γ-receptors, eNOS: Endothelial nitric oxide synthase, MTNR1B: Melatonin receptor 1B, KCTD15: Potassium channel tetramerization domain containing 15, ADRB2: Adrenoceptor beta2, and DAT-1: dopamine transporter.
Figure 1. Interaction between genotype variants and other factors associated with GWG. Abbreviations. GWG: Gestational weight gain, BMI: Body mass index, LEPR: Leptin receptor, FTO: Fat mass and obesity-associated, GNB3: Guanine nucleotide-binding protein 3, MAO-A: monoamine oxidase A, PPARγ: Peroxisome proliferator-activated γ-receptors, eNOS: Endothelial nitric oxide synthase, MTNR1B: Melatonin receptor 1B, KCTD15: Potassium channel tetramerization domain containing 15, ADRB2: Adrenoceptor beta2, and DAT-1: dopamine transporter.
Ijms 25 03039 g001
Table 1. Analysis of the information on genetic variants in women with GWG.
Table 1. Analysis of the information on genetic variants in women with GWG.
Author/Year/CountrySample
(n and Phenotype)		DesignStudy PeriodGenetic Variant (Exposure)Gestational Weight Gain (kg)Findings
Martins [25]/2018/Brazil147
Age range:
20–40 years old		Prospective cohortFrom gw 5 to deliveryLEPR rs1137101
(blood) real-time PCR		AA: 12.1
AG: 13.2
GG: 11.7		No association of AG + GG vs. AA with GWG.
RR 1.63 (95% CI 0.78–3.42) AM
Martins [25]/2018/Brazil147
Age range:
20–40 years old		Prospective cohortFrom gw 5 to deliveryLEPR rs7799039 (blood) real-time PCRAA: 12.8
AG: 11.7
GG: 13.2		Association of GA + AA vs. GG with excessive GWG.
RR: 2.18 (95% CI 1.27–3.71) AM
Groth et al. [21]/2015/USA97
Black women, low-income Age range:
18–36 years old		Prospective cohortFrom gw 20 to 6 months postpartum FTO rs993609
(buccal epithelial cells) real-time PCR		AA: 16.4
AT: 14.0
TT: 12.2		Association with greater GWG in AA and AT vs. TT.
TT gene predicted GWG (b = −13.48, p = 0.007) AM
Groth et al. [17]/2018/
USA		770
580 Caucasian
194 African American
Age range:
12–35 years old		Prospective cohortFrom gw 20 to 1 year postpartumFTO rs993609
(buccal epithelial cells) real-time PCR		Obese African American had greater GWG
AA: 7.6
AT: 3.6 		Obese African American women homozygous for the AA genotype had higher GWG vs. TT genotype (p = 0.006).
Meng Y et al. [23]/2018/USA55 women. Age range:
18–36 years old		Prospective cohortFrom gw 20 to 6 months postpartumGNB3, FTO, MC4R, KCTD15, NEGR1, SH2B1, and GNPDA2 (buccal epithelial cells) real-time PCR. Only KCTD15
rs11084753
showed association		KCTD15
rs11084753
GG: 12.3
GA: 16.9
AA: 10.9		Gene risk score explained 9.1 of variability in GWG.
Values for GA (mean: 16.9 kg; 95% CI: 14.5 to 19.4 kg), GG (mean: 12.3 kg; 95% CI: 10.2 to 14.5 kg; p = 0.018); and AA carriers (mean: 10.9 kg; 95% CI: 7.3 to 15.0 kg; and p = 0.026) were obtained when the individuals consumed more fat than those with other genotypes.
Goldfield [35]/2013/Canada93 Caucasian women.
Age range:
27.5–35.9
years old		Prospective cohortFrom gw 14–20 to deliveryDAT-1 gene
(blood) determined via end-point PCR		10/10 genotype
17.5 ± 4.7
9/9 genotype
17.5 ± 4.9		No association of DAT-1 variants with excessive GWG:
10/10 genotype vs. 9/9 genotype (17.5 ± 4.7 vs. 17.5 ± 4.9, p = 0.98)
Goldfield [35]/2013/Canada93 Caucasian women.
Age range:
27.5–35.9
years old		Prospective cohortFrom gw 14–20 to deliveryMAO-A
(blood) determined via end-point PCR		4/4 genotype
19.3 ± 4.1
3/3, 3/4, or 3/3.5 pooled genotypes 17.0 ± 5.0		Association of 4/4 genotype with greater GWG:
4/4 vs. 3/3,3/4,3/3.5
19.3 ± 4.1 kg versus 17.0 ± 5.0, p = 0.03
Groth et al. [17]/2018/
USA		770
580 Caucasian
194 African American
Age range:
12–35 years old		Prospective cohortFrom gw 20 to 1 year postpartumGNB3 rs5443
(buccal epithelial cells) determined via real-time PCR		African American with obesity CT genotype
−6.6 vs. TT genotype		Greater association of
GNB3 CT genotype with decreased GWG compared to those with the TT genotype (−6.6 kg., p = 0.011) in obese African American
Ostafiichuk et al. [34]/2018/Ukraine97 Ukrainian women with a normal pregestational BMI.
Age range:
24–30 years old		Prospective cohortFrom 10 gw to deliveryPPAR- γ Pro 12 rs1801282
(Blood) real-time PCR		Homozygous
Pro/pro 12.1 ± 2.6 (CI 11.1–13.1)
Ala carriers: 19.5 ± 2.7 (CI 17.8–21.2)		Association of Ala carriers with increased GWG.
GWG is 1.6 times higher in Ala carriers compared to the Pro/Pro genotype (p < 0.05)
Ostafiichuk et al. [33]/2022/Ukraine97 Ukrainian women with a normal pregestational BMI.
Age range:
24–30 years old 		Prospective cohortFrom gw 9 to 38–40 eNOS Glu298Asp
(Blood) real-time PCR		GG: 12.5 ± 2.5 (95%CI 11.3–13.4)
GT: 14.4 ± 3.1 (95%CI 13.1–15.7) TT 19.3 ± 2.3 (95%CI)		Association of TT with GWG. GWG is 1.5 times higher in women with TT than in GG carriers
(OR = 4.52; 95%CI: 1.18–17.32; p < 0.05)
Tok et al. [32]/2006/Turkey162 Caucasian women.
Cases: 62 patients with gestational diabetes.
Age range: 28.4–38.2
Years old
Control: 100 patients with non-gestational diabetes.
Age range: 27.1–38.5 years old		Cases and controlsFrom 28 to deliveryPPAR- γ Pro 12 rs1801282
(Blood) real-time PCR		Control
Pro12Ala: 10.8
Pro12Pro: 9.9, p = 0.64
Case
Pro12Ala: 12.2
Pro12Pro: 17.9 p = 0.017		Women with GDM and Pro12 Ala from PPAR-gamma2 were associated with GWG.
AM: 17.9 ± 13.9, p = 0.017
gw: gestational week. AM: Adjusted models. PCR: Polymerase chain reaction. GWG: Gestational weight gain. aHE: Adjusted hazard ratio RCT: randomized clinical trial. CI: confidence interval. GDM: Gestational Diabetes Mellitus.
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Sámano, R.; Martínez-Rojano, H.; Chico-Barba, G.; Gamboa, R.; Mendoza-Flores, M.E.; Robles-Alarcón, F.J.; Pérez-Martínez, I.; Monroy-Muñoz, I.E. Gestational Weight Gain: Is the Role of Genetic Variants a Determinant? A Review. Int. J. Mol. Sci. 2024, 25, 3039. https://doi.org/10.3390/ijms25053039

AMA Style

Sámano R, Martínez-Rojano H, Chico-Barba G, Gamboa R, Mendoza-Flores ME, Robles-Alarcón FJ, Pérez-Martínez I, Monroy-Muñoz IE. Gestational Weight Gain: Is the Role of Genetic Variants a Determinant? A Review. International Journal of Molecular Sciences. 2024; 25(5):3039. https://doi.org/10.3390/ijms25053039

Chicago/Turabian Style

Sámano, Reyna, Hugo Martínez-Rojano, Gabriela Chico-Barba, Ricardo Gamboa, María Eugenia Mendoza-Flores, Francisco Javier Robles-Alarcón, Itzel Pérez-Martínez, and Irma Eloisa Monroy-Muñoz. 2024. "Gestational Weight Gain: Is the Role of Genetic Variants a Determinant? A Review" International Journal of Molecular Sciences 25, no. 5: 3039. https://doi.org/10.3390/ijms25053039

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