Ethology of Gestational Diabetes Mellitus

#### **Chapter 1**

## Biomarkers in GDM, Role in Early Detection and Prevention

*Samar Banerjee*

#### **Abstract**

Gestational Diabetes Mellitus (GDM) happens to be a very frequent and major complication of pregnancy because of higher morbidity and mortality, both for the mother and the baby. After delivery, GDM carries the risk of higher maternal morbidity due to post pregnancy obesity, development of diabetes mellitus, obesity and also cardiovascular diseases in significant number in both the mother and child for future. As per current guidelines, GDM is diagnosed at the end of the second trimester by elevated blood glucose values when, foetal damages by metabolic and epigenetic changes had already started. As a result, treatments cannot be started before the late second or third trimester, when the process of high risk of foetal morbidity and mortality has been set in. If by any method we can predict development of GDM at earliest part of first trimester or even more overjealously, we can predict, before pregnancy, then and then only we can avoid many disasters induced by GDM. With this idea many biomarkers, both clinical and laboratory based like clinical, metabolic, inflammatory and genetic markers etc., related with early pregnancy metabolic alterations have been studied for their potential to help in the prediction of later pregnancy glucose intolerance. Though promises are seen with some biomarker-enhanced risk prediction models for GDM, but lack of external validation and translation into day-to-day clinical applications, cost effectiveness, with which they may be utilized in routine prenatal care has limited their clinical use. But future is very promising and incorporating the biomarkers which precede the onset of hyperglycaemia into a risk prediction model for GDM and may help us for earlier risk assessment, screening, and diagnosis of GDM and also prevention of its both the immediate and remote complications. This review highlights the current knowledge of the understanding of the candidacy and practical utility of these biomarkers for GDM with recommendations for further research.

**Keywords:** Biomarkers, gestational diabetes mellitus (GDM), macrosomia, foetal abnormalities

#### **1. Introduction**

Norman Freinkel once told that "No single period in human development, provides a greater potential (than pregnancy) for long – range 'pay – off' via a relatively short – range period of enlightened metabolic manipulation".

During pregnancy, the body systems of the woman, must support nutrient and oxygen supply for the proper growth and development of the foetus and subsequently during lactation. Inability to adopt the changes in maternal physiology may lead to complications, such as gestational diabetes mellitus (GDM). The

International Association of Diabetes and Pregnancy Study Groups (IADPSG) shows that, GDM may complicate 15–20% pregnancies, and has increased in the last 20 years in all ethnic groups as much as 27% [1].

GDM originates from interplay of factors like specific gene mutations, dysregulation of placental hormones and β-cell injury, favored by advanced age, gynecological alterations and diabetogenic factors. GDM mostly develop after the 2nd trimester of pregnancy, between the 24th and the 28th week of gestation. GDM may precipitate serious and long-term complications for foetal and maternal health, in particular, metabolism and cardiovascular in nature [2].

Currently, in most cases, the diagnosis of Gestational Diabetes Mellitus (GDM) is done around the late phase of second trimester, which may expose the foetus to the hazards of intrauterine metabolic alterations and also epigenetic changes for the period of exposure. Many documented evidences indicate that the metabolic alterations may subject the new born vulnerable to many long-term pathologies. Detection and management of GDM in pregnancy, can reduce the frequency of adverse pregnancy outcome. Hence, we need to predict and identify GDM earlier in pregnancy even if possible before the pregnancy, in order to limit the exposure to impaired glucose metabolism.

American Diabetes Association (ADA) recommends initial screening for GDM at 24–28 weeks [3]. But Seshiah V et al. from India has detected 62.1% cases of GDM before 24 weeks. Moreover, if we do not test before 24 weeks, we will miss earliest intervention for all the cases of undetected diabetes existing before pregnancy [4].

The aim of this review was to find out the useful and possible markers or guides to detect GDM early in pregnancy before rise of blood sugar and if possible, even before pregnancy to avoid all complication for mother and child arising from effects of GDM on gestation.

#### **1.1 Search strategy and selection criteria**

References for this review were identified by searching PubMed, Embase for articles in English with no language restrictions for articles published mainly from 2000 to 2021. The search terms used were GDM biomarkers, GDM pathogenesis, GDM prevention and epigenetics of GDM. The final reference list was prepared based on this search, supplemented with references from the authors' own dataset.

#### **2. Biomarkers**

GDM develops when beta cell dysfunction coexists, and is complicated by further abnormalities in adipokine and cytokine profiles, increased free fatty acids (FFA), triglycerides (TG), low vitamin D and endothelial dysfunction. The identification of early biomarkers in pregnancy, who may develop GDM, may lead to an improved understanding of pathogenesis of GDM. Combination of biomarkers and different risk factors into a predictive model, may help in early prediction of GDM. This may also find out effective prevention strategies and finally can limit different complications related with GDM. The first-trimester biochemical predictors of GDM are shown in **Table 1**.

#### **3. Epigenetic footprint**

Metabolic alterations like impaired glucose control during the phase of foetal development, may result in functional and structural alterations in the developing foetus, and may result in a predispose to the development of chronic metabolic

	- Fasting insulin
	- Sex hormone-binding globulin
	- Leptin
	- Adiponectin
	- Resistin
	- Visfatin
	- Omentin-1
	- Ghrelin

#### **Table 1.**

*Showing the first-trimester biochemical predictors of GDM.*

diseases in future life. These alterations are actually the 'foetal programming' and may trigger epigenetic changes [5]. The epigenetic changes are considered as different changes in the biochemical structure of DNA, which alters the gene expression in pregnancy as shown in **Table 2**.

Maternal insulin resistance can also cause insulin resistance in the foetus [6]. Multiple studies have correlated maternal GDM, with the development of obesity and T2DM in children who are eight times more prone to develop T2DM than non-GDM children [7, 8]. This raises the strong need for early detection of GDM preceding the hyperglycaemia which might avoid subsequent harm.


### **Table 2.**

*Showing the epigenetic changes in pregnancy.*

	- Follistatin-like 3
	- Placental growth factor
	- Placental exosomes
	- afamin,
	- fetuin-A,
	- fibroblast growth factors-21/23,
	- ficolin-3 and follistatin,
	- specific micro- RNAs
	- Vitamin D
	- Glycosylated fibronectin
	- Soluble(pro)renin receptor
	- Alanine aminotransferase
	- Ferritin
	- Glucagon
	- PAI-1
	- Adipocyte fatty acid-binding protein
	- SNPs,
	- DNA methylation,

<sup>•</sup> DNA methylation,

#### **4. Obesity, inflammation and GDM**

Now a days, more and more women are becoming pregnant, being either overweight or obese. The obese women show a three-fold risk for developing GDM. The global increase in GDM at present time is largely due to the on-going pandemic of obesity. Obesity is related to an altered production of proinflammatory cytokines from the adipocytes, which may lead to a state of chronic low-grade inflammation. It acts upon the expression and production of different proinflammatory cytokines e.g., TNF-alpha and IL-6 and also many anti-inflammatory cytokines. This also produces adipokines e.g., adiponectin, visfatin and leptin etc. Adipokines can modify insulin secretion & sensitivity, appetite, energy control and inflammation. Sound relationship is evident between obesity, chronic low-grade inflammation and development of T2DM. The normal pregnancy shows a balance between the productions of pro-inflammatory and anti-inflammatory cytokines.

Pregnancies in obese women, further may aggravate the proinflammatory markers and may lead to an imbalance and possible complications. It is now accepted that inflammation is also an associated feature of GDM [9]. During GDM, the increased production of proinflammatory cytokines disturbs the insulin signaling [10]. A down regulation of adiponectin and anti-inflammatory markers such as IL-4 and IL-10 and an enhanced production of proinflammatory cytokines such as IL-6 and TNF-α are usually observed in GDM [11].

#### **5. Adipocyte-derived markers**

#### **5.1 Adipokines or Adiponectin's**

Adiponectin is actually an adipocyte protein and consists of anti-atherogenic, anti-inflammatory and also insulin-sensitizing effects [12]. Adiponectin is inversely correlated with the clinical conditions like hypertension, dyslipidaemia, obesity and also coronary artery disease. Diminished level of adiponectin are usually seen with an increased risk of T2DM [13]. During the normal pregnancies, adiponectin decrease progressively also, probably from a decrease in insulin sensitivity [14]. Many studies have indicated that reduced adiponectin levels during 24–28 weeks in GDM compared to non GDM women, probably corelate low levels of adiponectin with onset of insulin resistance and diminished beta cell function [15, 16]. In one study, adiponectin concentrations in 560 GDM patients and 781 controls revealed a significantly decreased adiponectin level in GDM patients vs. controls [17].

Adiponectin, an adipokine having anti-inflammatory, anti-atherosclerotic and insulin-sensitizing proprieties in another study, was constantly lower along the 1st–3rd trimester of GDM gestations [18]. Hypoadiponectinemia increases the risk of developing GDM by 4.6 times [19], and is inversely correlated with the insulin resistance, BMI and leptin [20]. The ratio of plasma adiponectin and leptin (< 0.33) is also considered as predictor of GDM as early as the period of 6th to 14th week of pregnancy [21]. But probably the assessment of the high molecular weight oligomeric-adiponectin may give better results [22].

Recent prospective studies have addressed the role of adiponectin as a possible early predictor of GDM. Lower levels of adiponectin in the first trimester of pregnancy are associated with a greater risk for developing GDM. This suggests that a down regulation of adiponectin may be a predictor of GDM [23]. In a systematic review and metaanalysis, adiponectin had a moderate effect for predicting future GDM [24]. Again, a case–control study found revealed that low adiponectin levels in pre-pregnancy period is associated with an increased risk of 5.0-fold for developing GDM [25].

*Biomarkers in GDM, Role in Early Detection and Prevention DOI: http://dx.doi.org/10.5772/intechopen.100563*

This association was significant even when adjustment of known risk factors for GDM was done. This is important as it can identify a group of high-risk women, who might be not detected by conventional tests. Therapy with adiponectin in animal models of obesity improves glycaemia and also can reduce hyperinsulinaemia without any changes in body weight [26].

To summarize, a lower level of adiponectin is seen with type 2 diabetes, obesity and GDM. Adiponectin may influence the pathophysiology of GDM and also be a promising predictive biomarker for identifying GDM. Subsequent research for lifestyle interventions or adiponectin therapy should be done to finalize the role of adiponectin and diagnostic ability in cases of GDM particularly during the first trimester of GDM. Serum adiponectin in GDM, when is below <8.9 μg/ml shows an odds ratio of 3.3.

#### **5.2 1,5 Alfa anhydroglucitrol, SHBG**

Mean value of 1,5 Alfa anhydroglucitrol level is significantly lower in those destined to develop GDM. In the first trimester, higher SHBG levels are indicating the risk of GDM but this was no longer statistically significant when BMI, ethnicity and family history were considered. A measurement of CRP in the first trimester is not a useful marker of GDM [27].

#### **5.3 Leptin**

Leptin is an adipocyte-derived hormone, mostly produced by adipocytes but is also produced in ovaries and the placenta. It regulates energy balance through hypothalamic pathways. Increased leptin is associated with weight gain, obesity and hyperinsulinaemia.

Leptin is a proinflammatory adipokine and participate in immune responses. It also affects glucose metabolism by antagonistic action on appetite and insulin action. In addition, it can stimulate oxidative stress, atherogenesis and arterial stiffness [28]. Leptin levels is detected to be significantly higher in the 2nd half of pregnancy in both normal and overweight women with later diagnosis of GDM [29]. Menon M et al. did a prospective observational study with three study groups, with two-time points-first and second trimester to detect gestational diabetes mellitus as follows: [30]


They found that found that out of the adipokines, leptin was found to be elevated in GDM2 compared to GDM1 and NGT group with a p value (0.11), adiponectin was reduced only in GDM1 group with p value (0.33), TNFα is almost the same in all the 3 study groups but IL-6 is elevated in first and second trimester GDM group.

Maternal leptin levels increase 2 to 3 times in pregnancy, as a placental secretion. Increased levels of leptin have been seen in GDM.

Inflammatory markers like IL-6 and TNF-α also are involved in the pathophysiology of GDM by promoting both the chronic low-grade inflammation and also leptin concentrations. A prospective study detected elevated values of leptin before 16 weeks of conception, regardless of presence of adiposity and this was accompanied by an increased risk of GDM [31]. In another study leptin was increased in all pregnant women, but with highest concentrations in obese GDM patients [32]. But due to confounding effects of the measures of adiposity, current evidence is limited. Leptin is probably involved in the pathophysiology of GDM but is a poor predictor of GDM.

#### **5.4 Visfatin**

Visfatin an adipokine mostly secreted from visceral fat. It possesses both endocrine, paracrine and autocrine effects. Increased level of visfatin is noted in obesity, metabolic syndrome and T2DM. During pregnancy, visfatin levels increase up to the 2nd trimester, then they decrease and persist in lowest concentrations in the third trimester. During GDM, studies on visfatin levels are is inconsistent, as both decreased and increased levels have been reported [33].

In addition to its insulin-like properties to bind to the insulin receptor-1 and promotion of hypoglycaemic effects, visfatin can activate NFκB signaling and chemotaxis and lead to the development of insulin resistance. In fact, visfatin was found increased at the late 1st trimester [34], but differentially expressed at the 3rd trimester of GDM [35].

One study observed, visfatin was better in the prediction of GDM in the first trimester than CRP, IL-6, adiponectin and leptin [36]. One case–control study found that, visfatin in the 1st trimester was higher in GDM, but when it was added to the other maternal risk factors, the GDM detection rate had no improvement [37]. At present, findings indicate that visfatin is a potential biomarker for GDM, but we need further prospective studies to further asses the relationship between visfatin and GDM.

#### **5.5 Resistin**

Resistin represents an adipose-derived hormone and is expressed from monocytes, macrophages and adipocytes. It is corelated with high LDL-c and proinflammatory molecules and is also positively associated with adiposity. It increases during pregnancy, probably from weight gain. A potential link might exist between resistin, adiposity and insulin resistance during pregnancy, but till now, remains inconclusive as because of conflicting reports from case–control studies [38]. Resistin, is found to be reduced or unchanged during GDM [39, 40].

But, nested case–control studies, investigating resistin levels in early pregnancy, found no differences in resistin levels between GDM and controls (adjusted for BMI) [41]. Currently, there is no solid evidence that resistin is involved in the pathophysiology or prediction of GDM.

#### **5.6 Omentin**

Omentin-1, is an adipokine produced in non-fat cells from the adipose tissues (stromal vascular cells). It is involved in vascular tone relaxation due to the production of endothelial nitric oxide and lowering of both hs-CRP and TNFα signaling [42]. Omentin-1 was lower at the 2nd trimester of GDM similar to adiponectin, and in contrast to IL-6 [43].

#### **5.7 Ghrelin**

Hungarian study reported that fasting serum ghrelin levels were lower in women with GDM compared to non-pregnant healthy controls and pregnant controls without GDM in the 1st trimester and 3rd trimester [44].

#### **6. Inflammatory markers**

#### **6.1 TNFα**

TNFα a proinflammatory cytokine produced by monocytes and macrophages affects insulin sensitivity and secretion. These occurs from impairment of B-cell function and insulin signaling and results in insulin resistance and possibly GDM [45]. Multiple studies showed increased maternal TNFα levels in GDM, predominantly during late pregnancy [46]. Increased TNF-α levels in GDM than controls have been shown. Subgroup analysis detected this relationship to remain significant when they are compared with BMI-matched controls [47].

These increased levels are due to increased oxidative stress and inflammation arising from impaired glucose metabolism [48]. A small case–control study 0f 14 cases and 14 controls to address the predictive value of TNFα found no differences between women with GDM and without [49]. In one study of GDM and controls, TNFα levels measured pre-gravid, at 12–14 weeks and 34–36 weeks were increased at 34–36 weeks of gestation. These were inversely correlated with the insulin sensitivity [50]. We need more prospective studies to assess the predictive value of TNFα during GDM, with due adjustment for measures of adiposity.

#### **6.2 Il-6**

IL-6 is one of the proinflammatory cytokines and is increased in obesity and associated with indices of adiposity and insulin resistance, such as body mass index (BMI). The relationship between IL-6 and insulin action appears to be regulated via adiposity. However, in a case–control study, plasma IL-6 levels were elevated when adjusted for BMI in women with GDM [51].

#### **6.3 High-sensitivity C-reactive protein (hsCRP)**

Wolf and co-workers had found that the first-trimester CRP levels were significantly raised among them who later on developed GDM than the control subjects (3.1 vs. 2.1 mg/L, P < 0.01) [52]. After the adjustment for age, race/ethnicity, blood pressure smoking, parity, and age at gestation at CRP sampling, the increased risk of developing GDM among women was seen in the highest tertile than the lowest tertile and was 3.6 times higher (95% CI: 1.2–11.4). But when adjusted for BMI, this relation was not seen anymore. But Berggren and co-workers examined whether first-trimester hs CRP could predict the third-trimester impaired glucose tolerance (IGT). The hs CRP was positively correlated to (hs)CRP and GDM appears to be partly mediated by BMI.

Another study found that elevated plasma insulin and reduced adiponectin levels during first trimester may improve GDM identification rates than by clinical factors alone [53]. Maternal risk factors alone offer a prediction rate of 61% for GDM, but addition of adiponectin and SHBG, improved detection rates to 74% [54].

#### **7. Glycaemic markers**

#### **7.1 Serum insulin and C-peptide**

O'Malley E G et al. found that, both the serum insulin and C-peptide levels in the third tertile were correlated with GDM development (p < 0.001 if adjusted for maternal obesity). Higher values of ghrelin were showing a lower odd of development of GDM, even after adjustment for maternal obesity. The conclusion of the study was though 3 of the 10 biomarkers were statistically indicating an increased risk of GDM, but the presence of large overlap in values between women with normal and abnormal glucose tolerance reflect that the biomarkers (alone or in combination) were not clinically helpfull [55].

#### **7.2 Glucagon and PAI-1**

Two small studies of 54 and 51 women reported higher levels of glucagon and PAI-1 respectively in women with GDM [56, 57].

#### **8. Serum lipids**

Li et al. compared 379 women in the first trimester who developed GDM subsequently with 2166 healthy women. They found that lipid profile was different between the groups. The GDM patients had higher concentrations of Triglyceride, LDL-Cholesterol and total cholesterol but lower concentrations of HDL [58]. The lipid values at first trimester in the cohort of Correa et al. was altered even when glycaemia and glycated hemoglobin were normal. The first trimester insulin concentration was seen to be also higher in women who developed GDM. Both theses indicate that there is a role of lipid metabolism in the pathogenesis of the disease [59].

#### **9. Placenta-related factors**

Placenta-Related Factors such as sex hormone-binding globulin, afamin, fetuin-A, fibroblast growth factors-21/23, ficolin-3 and follistatin, or specific micro- RNAs may be involved in GDM progression and may help in its recognition [60].

In GDM, some adipose-derived factors such as TNFα, visfatin, omentin and FABP4 may be also expressed and expressed from placenta, resulting to their elevated plasma levels [10]. The sex hormone binding globulin (SHBG) from placenta acting as a regulator of sex steroid hormones had been linked with inversely insulin resistance, metabolic syndrome, obesity and T2DM [61]. A lower level of plasma SHBG in the 1st trimester was a true biomarker for GDM [62, 63].

Nanda et al. showed reduced SHBG in parallel to adiponectin in GDM during 11–13th week of pregnancy, in presence of previous macrosomia, BMI > 30 kg/m2, and family history of DM [63, 64]. Similarly, an hepatokine promoter of insulin resistance, known as fetuin-B, is raised at the 3rd trimester of GDM, but returns after delivery [65]. Again, at the late 1st trimester, a reduction of plasma fetuin-A levels (and elevated hs-CRP) is also noted [66].

FGF-21, responsible for browning of white adipose tissue and an upstream effector of adiponectin, was increased in GDM at the 24th week of gestation [67]. Afamin, a glycoprotein member of the albumin family found in liver and placenta, may be a first trimester biomarker for pathological glucose and lipid metabolism [68].

The decreased levels of ficolin-3 (an activator of the lectin pathway of the complement system expressed in liver and placenta) and the increased ratio of ficolin-3/adiponectin are predictive of GDM at the 16–18th week of gestation [18]. Follistatin, a gonadal regulator of follicular-stimulant hormone and activin-A, having angiogenic, anti-inflammatory and cardioprotective properties, were lower in the 3rd trimester of GDM pregnancy [69].

The non-coding RNAs such as micro-RNAs (miR) can be released from placenta to maternal circulation as early as the 6th week of gestation and may be involved in placenta development, insulin signaling and cardiovascular homeostasis [70]. These miR can regulate trophoblasts proliferation, apoptosis, migration and invasion, and angiogenesis [71].

A significant downregulation of miR-29a, miR-132 and miR-222 had been reported in plasma at the 16th week of pregnant women who developed GDM [72]. Similarly, during the 7th–23rd week of gestation, elevated plasma levels of miR-21-3p were seen with GDM [73].

#### **9.1 Sex hormone-binding globulin (SHBG)**

SHBG a glycoprotein regulates the transport of sex hormones. In vitro, this is a marker in insulin resistance as insulin and insulin-like growth factor inhibit SHBG secretion. Indeed, a relation of low levels of SHBG and T2DM has been observed [74]. A study found its concentrations to be significantly lower in GDM [75]. Moreover, women treated with insulin showed even lower SHBG levels. Probably SHBG may help to differentiate or predict who will require insulin therapy or not.

A prospective study evaluated several biomarkers before 15 weeks of gestation and observed that low levels of SHBG were indicating an increased risk of GDM. Adding hs-CRP increases the specificity to 75.46% [76]. However another prospective cross-sectional study, revealed that low levels of SHBG assessed between 13 and 16 weeks of gestation were positively associated with the development of GDM (n = 30) (P < 0.01) [77]. A case–control study also found that SHBG in the nonfasting state in first trimester had a consistent association with an increased GDM risk [78].

#### **10. Other potential biomarkers**

AFABP or Adipocyte fatty acid-binding protein may be one of the risk predictors for cardiovascular disease, metabolic syndrome and T2DM [79]. Two studies have established its increased levels in GDM. Gestational diabetes mellitus causes changes in the concentrations of adipocyte fatty acid-binding protein and other adipo-cytokines in cord blood [80, 81]. Studies investigating the predictive value of AFABP in GDM have not been performed to date, however.

The fatty acid-binding protein 4 (FABP4) correlates with obesity markers e.g., fat mass and high BMI. FABP4 act on lipid and glucose metabolism via fatty acid transport and uptake [82]. The retinol-binding protein 4 (RBP4) is one of the circulating retinol transporters and id correlated with cardiometabolic markers in inflammatory chronic diseases like T2DM, metabolic syndrome obesity, and atherosclerosis process [83]. Higher levels of FABP4 can predict GDM from the 1st and 3rd trimester of [84, 85]. Upregulated values of plasma RBP4 in the 1st and 2nd trimester may modestly indicate GDM risk, especially among women with obesity and advanced age [18, 86].

#### **10.1 Molecular biomarkers**

Growing evidence suggests the use of SNPs, DNA methylation, and miRNAs as biomarkers that could help in the early detection of GDM. In presence of their potential, these molecular biomarkers pose several challenges that need to be addressed before they can become clinically applicable [87].

Decreased levels of first trimester pregnancy-associated plasma protein A (PAPP-A) and increased levels of second trimester unconjugated estriol (uE3) and dimeric inhibin A (INH) were associated with GDM [88].

#### **10.2 Vitamin D**

Lower levels of vitamin D have been seen in both obesity and type 2 diabetes and also in pregnancy very often. Low levels of Vitamin D levels during first trimester also carry a higher risk for GDM as seen in recent meta-analyses [89]. As the mentioned studies all were not randomized controlled studies, we need future RCTs to confirm the predictive role of vitamin D [90].

#### **10.3 Candidate proteins**

Zhao et al. studied maternal blood prospectively from pregnant women at 12–16 weeks of pregnancy. Among these, 30 women were subsequently diagnosed with GDM at 24 to 28 weeks and were selected as case studies along with 30 normoglycemic women as controls. They found that, four proteins, apolipoprotein E, coagulation factor IX, fibrinogen alpha chain, and insulin-like growth factorbinding protein 5, with a high sensitivity and specificity, may provide effective early screening for GDM. The panel of four candidate proteins could distinguish women subsequently developed with GDM from controls with high sensitivity and specificity [91].

#### **10.4 Genetic markers**

For the first time, Ding M et al. detected 8 variants to be associated with GDM, They are rs7957197 (HNF1A), rs3802177 (SLC30A8), rs10814916 (GLIS3), rs34872471 (TCF7L2), rs9379084 (RREB1), rs7903146 (TCF7L2), rs11787792 (GPSM1) and also rs7041847 (GLIS3). They also confirmed 3 other variants e.g., rs1387153 (MTNR1B), rs10830963 (MTNR1B), and rs4506565 (TCF7L2), which had been earlier identified by them or significant association with GDM risk [92].

#### **10.5 Urine biomarkers**

The study of urine metabolome profile in GDM during the 3rd trimester found relation of 14 metabolites with the steroid hormone biosynthesis and tryptophan metabolism, which were significantly high. They are l-urobilinogen, l-tryptophan, 21-deoxycortisol, cucurbitacin-C, ceramide (d18:0/23:0) and aspartame [93]. Upregulation of these pathways could aggravate insulin resistance and respond to oxidative stress and inflammation during GDM. Earliest at 12th–26th week of pregnancy, augmented levels of AHBA, 3-hydroxybutanoic acid (BHBA), valine, alanine, serotonin and related metabolites like l-tryptophan levels were observed in urine (and plasma) from GDM mothers [94].

#### **11. Clinical prediction models incorporating biomarkers**

Clinical risk prediction models' wave has been investigated in GDM. For example, the development of GDM can be predicted from the ethnicity, family history, history of GDM and body mass index. One large prospective study (n = 7929), found that, based on BMI, ethnicity, family history of diabetes and past history of GDM, there was a sensitivity, specificity and AUC of 73% [66–79], 81% [80–82]

*Biomarkers in GDM, Role in Early Detection and Prevention DOI: http://dx.doi.org/10.5772/intechopen.100563*

and 0.824 (0.793–0.855), respectively, for the identification of GDM patients who required insulin therapy [95].

The introduction of biomarkers if added to a set of clinical risk factors are supposed to increase the predication rates of GDM. In particular, low HDL cholesterol and tissue plasminogen activator (t-PA) appeared as independent significant predictors of GDM. The addition of these 2 biomarkers to a group of clinical and demographic risk factors enhances the ROC (area under the curve) from 0.824 to 0.861 [96]. The t-PA not only is a predictor of GDM, it is also associated with a higher risk of T2DM [97].

Addition of maternal adiponectin and visfatin to a bunch of maternal risk factors, reached a detection rate of 68% [98]. The clinical implementation of these multi-parametric prediction models is determined by factors like practical acceptability, significant reduction in adverse pregnancy outcomes and cost-effectiveness. But these models need prospective validation studies and also further identification of predictive threshold values for the said biomarkers.

#### **12. Metabolomic profiling**

In one study, women with GDM (n = 96) were matched to women with NGT (n = 96) by age, BMI, gravidity and parity and the levels of 91 metabolites measured. Six metabolites (anthranilic acid, alanine, glutamate, creatinine, allantoin and serine) were found to have significantly different levels between the two groups in conditional logistic regression analyses (p < 0.05). Metabolic markers identified as being predictive of type 2 diabetes may not have the same predictive power for GDM [99].

Endogenous galanin as a novel biomarker to predict gestational diabetes mellitus is also observed [100]. The higher level of galanin observed in GDM may represent an adaptation to the rise of glucose, weight, GGT associated with GDMs thriving for clinically useful thresholds [101].

Mean 1,5 AG levels are significantly lower in those that go on to develop GDM. Hs-CRP and SHBG are important early predictors of GDM. Adding SHBG to hs-CRP improves specificity and serves good overall accuracy. Uric acid, creatinine and albumin have no role in GDM prediction [102].

Bivariate logistic regression analysis had shown that both adiponectin and insulin highlight future development of gestational diabetes. Both of them measured at 11 weeks, may predict oncoming GDM. But we need further studies to assess the reliability of these biomarkers [103].

Placental growth factor (PLGF), a vascular endothelial growth factor-like protein, is highly expressed in the placenta. About three studies suggest that higher early pregnancy PLGF levels are associated with GDM [104–106]. Recently, ALT, a liver enzyme, a marker of hepatocellular damage, has been examined as a firsttrimester predictor of GDM [107].

One moderate-sized study (N = 182) showed that glycosylated fibronectin measured in the first trimester could predict GDM with high accuracy [108]. Watanabe et al. assessed the soluble (pro)renin receptor levels in 716 Japanese women at less than 14 weeks of gestation and found increased levels in women who developed subsequent GDM [109]. In a case–control study of 1000 women from the UK, Syngelaki et al. found that maternal serum TNF-alpha measured at 11–13 weeks gestation was associated with subsequent GDM [110].

Donovan et al. in their study, indicated that women diagnosed with GDM have lower first trimester levels of both pregnancies associated free β-hCG and plasma protein-A (PAPP-A) than normoglycemic pregnant women. These two markers may indicate the presence of abnormal glucose metabolism at the beginning of pregnancy and may help for identification of future development of GDM [111].

#### **13. First trimester biomarkers for prediction of gestational diabetes mellitus**

Tenenbaum-Gavish et al. in a cohort of GDM group found that, compared to the normal group BMI and insulin (P = 0.003) were higher (both P < 0.003). The soluble (s)CD163 and multiples of median values of uterine artery pulsatility index (UtAPI) were high (p for both <0.01) but, pregnancy associated plasma protein A, tumor-necrosis factor alpha and placental protein 130, were low (p for all <0.005). There was no significant difference between the groups in placental growth factor, leptin, interleukin 6, soluble mannose receptor or peptide YY. For screening GDM in obese pregnancy a combination of high BMI, TNFα, insulin and sCD163 reached an AUC of 0.95, and the detection rate of 89% with a 10% false positive rate. For nonobese pregnancy, the combination of TNFα, PP13,sCD163 and PAPP-A showed an AUC of 0.94 and the detection rate was 83% at 10% false positive rate [112].

#### **14. Conclusion**

By blood sugar estimation when GDM is diagnosed, adverse foetal changes have already set in. So, we will have to attempt to diagnose GDM, before the foetal changes take place. It would be more rewarding if we can diagnose impending GDM and alert the person even when she plans for pregnancy.

Different biomarkers e.g., glycemic, insulin resistance, inflammatory, adipocyte and placenta-derived, had been evaluated as the first-trimester predictors of GDM. The majority of these studies are smaller in size and was based on case–control designs. But some large studies of glycemic markers indicated that hemoglobin A1C and/or fasting glucose help in detecting women without diagnosis of previous diabetes and they may be benefited from early detection and treatment of GDM, though these observations should be confirmed by interventional studies.

The improvement of GDM development and outcomes is possible by earlier and more specific identification of GDM accompanied by metabolic and cardiovascular risks. In line with these, first or second trimester-related biomarkers seen in maternal plasma like adipose tissue-derived factors like adiponectin, omentin-1, visfatin, fatty retinol binding-protein-4 and acid-binding protein-4 reflect correlations with development of GDM. In addition, placenta-related factors e.g., sex hormonebinding globulin, afamin, fetuin-A, ficolin-3 and follistatin, fibroblast growth factors-21/23 and specific micro-RNAs may be important in detecting progression of GDM and its recognition. Finally, urinary metabolites related to non-polar amino-acids and ketone bodies, serotonin system, may help in completing a predictive or early diagnostic group of GDM biomarkers.

To transform the observations obtained from observational studies into clinical practice, we need also more clinical trials or cost-effectiveness analyses of screening and treatment c.onsidering the first-trimester biochemical GDM predictors. Further studies should examine the first-trimester biochemical markers for adverse outcomes in GDM by prospective trials to find its prevention or early treatment.

GDM involves a significant proportion of pregnant women and is becoming more prevalent as rates of obesity rise globally. Its development and complications could be arrested if accurately predicted in early pregnancy even if possible before conception and effective interventions initiated. Many Several biomarkers have

#### *Biomarkers in GDM, Role in Early Detection and Prevention DOI: http://dx.doi.org/10.5772/intechopen.100563*

been studied to understand pathogenesis of GDM, but till date none are showing adequate robustness to be used for clinical algorithms for prediction of GDM.

Application of the high methodologies gives novel insights about the role of genetic variants, metabolomics and epigenetics regarding the pathogenesis of GDM. This option for using a predictive model during the subclinical phase of GDM appears to be promising as an important arena of future research and development. These modern technologies are off course complex and not applicable to mass level screening. There are also issues related to validity across populations, reproducibility, and selectivity. We will have to find out methods with cost-effectiveness and universal access, otherwise the present complex biomarkers are likely to prove invaluable in the diagnosis of GDM.

The emerging evidences suggest that the assessment at eleven and thirteen weeks of gestation, should be the platform towards a new approach in antenatal care. The data from the maternal history should be added to the results of biochemical and biophysical tests to examine the patient-specific risk related to a wide variety of pregnancy complications. Ideal GDM biomarkers appears to be a combination of several molecular biomarkers to balance the lack of sensitivity and specificity of individual factors. But targeted rapid technological advances will overcome these challenges and develop a quick, cost-effective point-of-care test that can accurately identify women at high risk for GDM during early pregnancy even if before conception.

#### **Author details**

Samar Banerjee

Department of Medicine and Specialist Diabetes Clinic, Vivekananda Institute of Medical Sciences, Kolkata, India

\*Address all correspondence to: drsamarbanerjee@gmail.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 2**

## Epigenetic: New Insight in Gestational Diabetes Mellitus

*Maria Grazia Dalfrà, Silvia Burlina and Annunziara Lapolla*

#### **Abstract**

Gestational diabetes mellitus (GDM) is the more frequent metabolic complication of pregnancy with a prevalence that is significantly increased in the last decade accounting for 12–18% of all pregnancies. Recent evidences strongly suggests that epigenetic profile changes could be involved in the onset of GDM and its related maternal and fetal complications. In particular, the unfavorable intrauterine environment related to hyperglycemia, a feature of GDM, has been evidenced to exert a negative impact on the establishment of the epigenome of the offspring. Furthermore the adverse in utero environment could be one of the mechanisms engaged in the development of adult chronic diseases. The purpose of this article is to review a number of published studies to fill the gap in our understanding of how maternal lifestyle and intrauterine environment influence molecular modifications in the offspring, with an emphasis on epigenetic alterations.

**Keywords:** gestational diabetes, epigenetic, maternal complications, fetal complications

#### **1. Introduction**

Gestational diabetes mellitus (GDM), defined as a glucose intolerance developing or first recognized during pregnancy that is not clearly overt diabetes [1], is increasingly worldwide due mainly to a rising rates of obesity [2–7].

GDM, if not properly diagnosed and/or treated can lead to adverse outcomes for the mother and the child both during and after pregnancy [8–10]. Of note women experiencing GDM and their children are at high risk to develop cardiometabolic diseases (type 2 diabetes, obesity, hyperlipemia, metabolic syndrome, hypertension, cardiovascular disease) later in life [8–10].

Insulin resistance and beta-cell disfunction are the main physiopathological mechanisms involved in GDM development [4–7]. However all the actors involved are not completely understood as an intricate network of metabolic pathways work in pregnancy complicated by GDM, that includes an abnormal expression of proteins involved in glucose and lipid metabolism, inflammation, oxidative stress, immune response, organ development, and cell death regulation. In this context recent studies have suggested that genetic, epigenetic and environmental factors contributes in GDM development [11–14], (**Figure 1**). In addition, the adverse intrauterine environment in patients with GDM could also have a negative impact on the establishment of the epigenomes of the offspring [15, 16].

**Figure 1.** *Factors contributing to GDM development.*

The purpose of this article is to review a number of published studies to fill the gap in our understanding how the intrauterine environment can determine molecular modifications in the offspring, with an emphasis on epigenetic alterations.

#### **2. Epigenetic: the meaning**

Epigenetic is the study of changes in gene expression caused by mechanisms not involving variations in DNA sequences but determining changes as DNA methylation, histone modifications, and messenger RNA (mRNA) binding by microRNAs (miRNAs).

The study of epigenetic modifications can therefore be useful in deepening and clarifying the pathogenesis of GDM as well as in the use of markers for diagnosis, risk prediction and follow up of different types of pathologies as GDM.

Methylation of cytosine on CpG in the DNA so determining the formation of methylcytosine (5-mc) is the first studied DNA modification. Methylation can determine an increased gene expression by silencing some repressor elements, but, in some regions of DNA as the promoter ones, it can reduce gene expression by inhibiting the activity of enhancer elements [17].

*Epigenetic: New Insight in Gestational Diabetes Mellitus DOI: http://dx.doi.org/10.5772/intechopen.100854*

Histone modification can influence the gene expression by modifying the chromatin packing [18].

Micro RNAas are small non-coding single stranded RNAs of about 22 nucleotides that are involved in post-trancriptional regulation of gene expression. It has been evidenced that miRNAs can affect the stability and translation of RNA [19]. Interestingly, some recently-identified miRNAs have been associated with insulin secretion, insulin resistance, and inflammation in patients affected by type 2 diabetes [14].

#### **3. Epigenetic and GDM**

It has been demonstrated that even slight increases in glycemia can be associated with epigenetic adaptations via the so-called "metabolic memory", and in this context few studies have examined the association between methylation and GDM development. Of note Whu and coworkers firstly identified two differently methylated genes in plasma, umbilical cord and placenta samples of pregnant women that develop GDM, the Hook Microtubule Tethering Protein 2 (HOOK2), and Retinol Dehydrogenase 12 (RDH12). HOOK2 is a protein that mediates binding to organelles, and is involved in cilia morphogenesis and endocytosis. RDH12 encodes a retinal reductase involved in short-chain aldehyde metabolism [20] (**Tables 1** and **2**).

In this frame more studies have been performed evaluating microRNA: Zhao and coworkers [21], evidenced that the expression of miR222, miR-132 and miR-29a was significantly lower in women who were diagnosed as affected by GDM at 24–28 WG with respect to non GDM control pregnant women. MiR-29 has a role in glucose homeostasis, in particular when overexpressed reduce the insulin-stimulated glucose uptake and the gluconeogenesis [22]. MiR-132 is involved in the regulation of cytochrome P450,mediated by insulin, furthermore when its expression is reduced impairs the correct development of trophoblast, [14, 22].

Successively, as omental adipose tissue is known to play a role in insulin resistance in GDM, the differential expression patterns of miRNAs in omental adipose tissues from GDM patients and pregnant women with normal glucose tolerance was studied [23]. MiR-222 was found to be significantly up-regulated in GDM by quantitative real-time PCR and its expression was related with serum estradiol levels, whereas the expressions of estrogen receptor (ER)-α protein and insulinsensitive membrane transporter glucose transporter 4 (GLUT4) protein were markedly reduced. Then in order to silence miR-222 in 3 T3-L1 adipocytes the antisense transfection oligonucleotides of miR-222 was applied. An important increase of the expressions of ERα and GLUT4, the insulin-stimulated translocation of GLUT4 from the cytoplasm to the cell membrane and of the uptake of glucose was evidenced in mature adipocites. On the basis of their results the authors conclude that: "miR-222 is a potential regulator of ERα expression in estrogen-induced insulin resistance in GDM and could be a candidate biomarker and therapeutic target for GDM".

Cao and coworkers [24], in 85 pregnant women with GDM found that the relative and absolute expression of plasma microRNA-16-5p, −17-5p, −20a-5p were significantly upregulated, with respect to 72 pregnant women without GDM. During pregnancy, the expression of those microRNAs from GDM women were also positively correlated with insulin resistance. Furthermore, significative differences were found in GDM women with respect to normal pregnant ones in the plasma levels of microRNA-16-5p, −17-5p, −20a-5p and in the areas under the curve (0.92, 0.88, and 0.74, respectively). The authors conclude that plasma microRNA-16-5p, −17-5p and -20a-5p are potential diagnostic biomarkers in GDM. MiR16.5


#### **Table 1.**

*Gene methylation in gestational diabetes mellitus.*

is implicated in the insulin sensitivity regulation and it is upregulated in type 2 diabetes. MiR17–5 has a role is the proliferation of smooth muscle cell. MiR20a-5p is upregulated in preeclampsia, a well known complication of diabetic pregnancy.

Wander and coworkers [25] analyzed the role of miRNA in women affected by GDM and different body mass index. MiR155-5p, and 21–3p were found positively associated with GDM. The miR-21-3p and miR-210-3p were positively associate only in GDM overweight/obese women. MiR-155 and MiR21–3 have a role in pathways that regulate cell survival, and inflammation. MiR210-3p is associated with angiogenesis [14].

As for histone modification, Michalczyk and coworkers [26], analyzed several epigenetic markers during and after pregnancy in a small, multiethnic population. The evaluation of the proportion of total H3 histone methylated GDM women who developed type 2 diabetes after pregnancy showed a significantly lower H3K27 (50%)with respect to non-diabetic women; furthermore type2 diabetic women with previous GDM had also significantly lower H3K4 (75%) with respect to GDM with normal glucose tolerance after pregnancy. A study evaluating a large sample size for a longer post partum follow up is however necessary to confirm that histone methylation could be a useful predictor of type 2 diabetes in women with GDM.

*Epigenetic: New Insight in Gestational Diabetes Mellitus DOI: http://dx.doi.org/10.5772/intechopen.100854*


**Table 2.**

*Studies assessing the role of mRNAs in gestational diabetes.*

#### **4. Epigenetic and placenta**

The placenta undergoes a number of structural and functional changes in pregnant women affected by diabetes due to the increased production of inflammatory cytokines determined by the high levels of maternal glucose [27]. In this frame, utilizing different mass spectrometry approaches - such as MALDI-MS and LC-MS<sup>E</sup> – in the evaluation of placental samples from women with and without GDM, it has been showed that if well controlled, GDM induces only minor changes in the placental proteome [28]. So it is of interest to verify if epigenetic modifications can however occur at the placental level even with relatively low maternal glucose levels and if the extent of these modifications is in some way related to glycemic levels (**Tables 1** and **2**).

Lesseur and coworkers investigate the relations between prepregnancy obesity and GDM and placental leptin DNA methylation on 535 mother-neonate enrolled in the Rhode Island Child health Study. The results of the study showed that neonates of mothers affected by GDM had higher placenta leptin methylation levels similar to those of the mothers with prepregnancy obesity. So maternal metabolic milieu before and during pregnancy can determine impairment of placenta methylation so contributing to the metabolic fetal programming of obesity [29]. These data well fit with those reported by Bouchard et al. [30]. In a subsequent paper Bouchard et al. [31], evaluated the possible association between the methylation of adiponectin gene (ADIPOQ ) in plasma cord blood and placenta tissue and plasma glucose levels of pregnant women. They found low DNA methylation levels in the ADIPOQ promoter on the fetal side of the placenta that were positively related with high maternal glucose levels in the second trimester of pregnancy. Furthermore, the low DNA methylation levels on the maternal side of the placenta were also positively related to insulin resistance, assessed with the homeostasis model assessment method (HOMA), and to high circulating adiponectin levels during pregnancy.

Furthermore, a negative correlation between DNA methylation of the ATPbinding cassette transporter A1 (ABCA1) gene on the placenta maternal site and HDL and 2 hour OGTT plasma glucose was found in 26 GDM women. When looking at the placenta fetal site, DNA methylation of ABCA1 was negatively associated with cord blood tryglicerides [32].

In a well conducted study, Cao et al. aim to verify the role of miRNA-98 in placental tissues from GDM patients, considering that MiRNA-98 is implicated in the correct embryo implantation [33]. They showed that, in the placentas of GDM patients miR-98 is upregulated and total DNA methylation levels are reduced with respect to normal pregnant women. These results,considering that MiRNA-98 regulates the Mecp2 target gene a key protein for embrio development, coud have important consequences for fetal growth.

More recently Cardenas and coworkers [34] conducted an elegant epigenomewide association study (involving 850,000 CpG sites) on samples of placenta and plasma glucose, and related them to 2 h post-OGTT plasma glucose levels in 448 mother-and-infant pairs at 24–30 weeks of gestation. They found a lower DNA methylation of 4 CpG sites within the phosphodiesterase 4b gene that are positively correlated with plasma glucose at 2 h OGTT. Furthermore, a differentially methylation behavior in relation with maternal glucose was found for 3 CpG sites in the TNFRSF1B, LDLR and BLM.

DNA methylation correlated with expression of its respective genes in placental tissue at three out of four independent identified loci:PDE4B, TNFRSF1B, and LDLR. TNFRSF1B is involved in apoptosis, LDLR encodes a lipoprotein receptor that mediates LDL endocytosis in the cells, and is also expressed in the placenta. BLM is associated with genome stability and maintenance. So maternal glycemic levels during pregnancy were associated with placental DNA methylation of inflammatory genes, the expression of which depends on epigenetic changes.

#### **5. Epigenetic and offspring**

The Developmental Origins of Health and Disease, largely derived from the Barker hypothesis [16], strongly suggests that not only undernutrition but also overnutrition, maternal obesity and diabetes can determine chronic diseases in the offspring through an early exposure to a suboptimal fetal environment; in this context epigenetic modifications have been showed to contribute mainly to this (**Table 1**).

Hajj et al. [35], have evaluated the effect of GDM on the epigenome of the offspring. To reach this aim they analyzed cord blood and placental tissue from the newborn of GDM patients 88 of them treated with diet and 98 with insulin. The results of the study show meaningful lower methylation levels in GDM compared with pregnant women without GDM in the levels of the maternal imprinting MEST gene and the non-imprinting glucocorticoid receptor NR3C1 gene. It is to notice that these genes are associated with placental and fetal growth. Low levels of MEST methylation have also been found in plasma of adults with obesity with respect to normal-weight controls. So the intrauterine exposure to GDM has effects on the epigenome of the offspring, and epigenetic malprogramming of MEST can contribute to predisposing individuals to obesity later in life.

The effect of the exposure to maternal diabetes in utero has been investigated by a genome-wide methylation analysis on peripheral mononuclear cell's DNA in 21 healthy children of GDM mothers, by utilizing a mediation analysis [36]. A series of genes have been identified to be associated with cardiometabolic risk among that the ubiquitin proteasome system (UPS) was the most important. An increased methylation of PYGO1 and CLN 8 showed the most important mediation effect on VCAM-1 levels of the children. TheVCAM-1 protein mediates the adhesion of lymphocytes, monocytes, eosinophils, and basophils to vascular endothelium. It also functions in leukocyte-endothelial cell signal transduction, and it may play a role in the development of atherosclerosis.

*Epigenetic: New Insight in Gestational Diabetes Mellitus DOI: http://dx.doi.org/10.5772/intechopen.100854*

A 2 step epigenetic Mendelian randomization approach was used by Allard et al. on data of 485 mothers and their children [37]. To take into consideration maternal glycemia, a genetic risk score, based on 10 known genetic variant related to glycemia, was firstly developed (GRs 10). The results of the study showed that an high GRs 10 was associated with a lower methylation of cg 12083122 that is located near the leptin gene. The low methylation levels at cg12083122 was associated with high cord leptin levels, so evidencing that maternal glycemia can influence offspring leptin epigenetic modulation. In this frame, to evaluate the possible relation of maternal hyperglycemia and DNA methylation of genes involved in brown adipose tissue activation, the DNA methylation levels were measured in placenta samples from normal and GDM women and compared to results of maternal plasma glucose levels. The values of maternal plasma glucose, at the second and third trimester of pregnancy, resulted correlated with the methylation levels of PRDM16, BMP 7 and PPARGC1a and with cord blood leptin levels. These results suggest that maternal glycemia can determine modification in genes related to obesity development in the offsring [38]. More recently, an Illumina 450 K methylation arrays was utilized to analyze genome-wide methylation patterns in fetal cord blood of pregnant women with and without GDM. Significant differences in methylation were found between the GDM patients and the normal pregnant women; furthermore, these differences were more significant in GDM women treated with insulin. A series of genes were found modified by methylation and in particular: ATPSA1, which encodes a subunit of mitochondrial ATP synthetase that acts also reducing mitochondrial oxidation; MFAP4, which is engaged in the process of cell adhesion and intercellular interaction; PRKCH, a component of the protein C family engaged in numerous signaling pathways; and SLC17A, or sodium/phosphate cotransporter involved in hypoxia events. It is to emphasize that these methylation modifications even if had a small effect size, affects many genes/loci [39]. Furthermore, methylation that affects a series of genes that can impair insulin secretion and increase the risk of diabetes and obesity has been reported in offspring of mother affected by type 2 diabetes a condition that shares the same physiopathological mechanisms of GDM [40].

#### **6. Conclusions**

The studies taken into consideration made a significant contribution to the knowledge of the physiopathological basis of GDM and of its complications, however methodological problems, small sample size, different GDM diagnostic criteria, make difficult to have final conclusion.

Further researches with high study power need to be undertaken in order to be more confident on the role of epigenetic in GDM disease, bearing also in mind that epigenetic expression in pregnancy varies with weeks of gestation, sex of the fetus, ethnicity, type of sample considered. These studies must be able to determine new road for intervention so to reduce in GDM patients and their children the development of the chronic metabolic diseases [41, 42].

#### **Abbreviations**



### **Author details**

Maria Grazia Dalfrà, Silvia Burlina and Annunziara Lapolla\* DPT Medicine-Padova University, Padova, Italy

\*Address all correspondence to: annunziata.lapolla@unipd.it

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Epigenetic: New Insight in Gestational Diabetes Mellitus DOI: http://dx.doi.org/10.5772/intechopen.100854*

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#### **Chapter 3**

## The Interaction between the Gut Microbiota and Chronic Diseases

*Temitope Sanusi-Olubowale*

#### **Abstract**

The world is experiencing an increase in chronic diseases like diabetes, inflammatory bowel diseases, cancer, cardiovascular diseases, obesity, and diabetes preceding disease like gestational diabetes. Most of these diseases can be prevented and mitigated if individuals pay attention to the causative factors. One of such factors is the type of microorganisms in an individual's gut. Even though there are innate beneficial microorganisms in the human gut, pathogenic microorganisms can invade the gut, changing the inborn population of the gut microbiota. The changes in the gut microbiota population have been linked to several diseases. This chapter, therefore, describes gut microbiota and their interaction with specific diseases. Also discussed in this chapter are the changes to gut microbiota composition that pose a risk to the host. There is substantial evidence that diseases are initiated or worsened with a change in the gut microbiota composition. Therefore, the gut microbiota plays a crucial role in individuals' health and requires human efforts to keep them in the right population. Furthermore, making lifestyle changes, particularly food choices and behaviors such as the misuse of medications and excessive alcohol consumption, should be monitored and controlled to support gut health.

**Keywords:** Gut microbiota, Bacteria phylum, Gestational diabetes, Chronic diseases, Gut dysbiosis

#### **1. Introduction**

Chronic diseases (CD) are unfavorable health statuses lasting for over one year or more [1, 2]. Such diseases require continual medical attention and activities that could mitigate the severity [1, 2]. The diseases are the leading cause of death and incapacity worldwide, with influences on all socio-economic setups. In 2002, the CD was reported as the cause of 60% of death and 43% global distress [1, 3]. In 2020, the cause of death through CD had risen to 73% and universal distress of 60%, as shown in **Figure 1** [3]. The cause of some of these diseases was attributed to different factors such as genes, poor diet, and lifestyle [1, 4]. The common CD includes diabetes, cancer, cardiovascular diseases, chronic pulmonary diseases, obesity, arthritis, stroke, Alzheimer's diseases, chronic kidney diseases, inflammatory bowel diseases, tooth decay, and epilepsy [1, 3, 5]. Some diseases are signals to the potential development of chronic diseases. Women who have gestational diabetes are at risk of developing type 2 diabetes, hypertension, cardiovascular diseases, and obesity, just as high blood cholesterol could be indicative of future coronary heart diseases, obesity, and hypertension [6, 7].

**Figure 1.** *Chart showing the rate of increase in death and distress as caused by chronic diseases.*

Asides from death and health difficulties associated with CD, there are several negative impacts socially and economically. The family of people suffering from one or more CD reported increased personal life burden, financial difficulties, impaired social relations, and intrinsic rewards [8, 9]. Likewise, treating CD and helping people with such diseases significantly impact different countries' finances. It has become a substantial financial burden to nations [10, 11]. In the United States of America, \$327 billion is spent annually on medical costs for diabetes [5]. About \$147 billion per year for the health cost of obesity, \$164 billion for arthritis, \$500 billion for Alzheimer's diseases, epilepsy takes \$8.6 billion annually, and \$45 billion is spent on annual health care for tooth decay [5]. In 2015, a forecasted percentage of Gross Domestic Product (GDP) loss was reported for different countries worldwide. Brazil was expected to lose 3.21% of GDP, Canada, 0.64%, China, 3.94%, India, 5.05%, Nigeria, 3.07%, Russia, 12.35%, Tanzania, 4.19%, United Kingdom, 5.18% GDP losses from death caused by diseases. In another report, the United States loses \$1.1trillion annually from the lack of productivity of citizens living with CD. Reducing the rate of obesity alone in the country would increase productivity by \$254 billion and \$60 billion in reductions in treatment costs [10, 11].

With the national, family, and personal losses associated with CD, methods of curbing the rising rate call for more research, government and non-governmental initiatives, and policies [10]. One research aspect was to figure out the genesis of all these diseases [4, 12]. From different findings, diverse components contribute to the incidence of diseases or increase the risks of developing these diseases. For example, the cause of some of these diseases was attributed to individual genes, other influences such as unhealthy diet, overweight, sedentary lifestyle (lack of physical activities), and risk behaviors such as tobacco use and excessive alcohol were identified. One crucial discovery on factors contributing to disease incidence was the gut microbiota [13–15].

#### **2. The gut microbiota**

The human body consists of several microorganisms which were innately beneficial to the host. These colonies of microorganisms that have settled in the human's gastrointestinal tract (GIT, gut) over many years include bacteria, eukaryotes, bacteriophages, archaea, and fungi, and they are called the gut microbiota (GM) [13, 16]. The GM has evolved and established a commensal relationship with the human host

*The Interaction between the Gut Microbiota and Chronic Diseases DOI: http://dx.doi.org/10.5772/intechopen.99657*

over many years. Hence, they are equally referred to as commensals microorganisms because they provide health benefits to the host while the commensals get nutrients from the host without harming the host [13, 16].

There are trillions of microorganisms colonizing humans, but research focuses on the bacteria community [17, 18]. Some years ago, scientists reported that the bacteria cells in the GIT are numerous, much more than the number of cells in the body. Some other researchers claimed that the bacterial cells in the GIT are ten times more than body cells [13, 19]. Recent studies, however, showed human cells and bacterial cells are at a ratio of 1:1 [13, 19, 20].

Humans' GM is developed from birth [19, 21, 22]. Particularly for babies delivered vaginally, the microbiota in the mother's cervix is passed on to the babies. This GM received from birth builds the first wall of defense in children, and the population of GM gradually changes as the child grows. In addition, gut bacteria aid adaptive immunity, a crucial function of the GM in the human's body [13, 21, 22].

#### **2.1 Functions of gut microbiota**

The GM proffer benefits to the host. The first known function is to assist in building immunity after birth. The GM has other functions in the body; they contribute advantages anatomically, physiologically, and immunologically [19, 23].

#### *2.1.1 Anatomical and physiological functions*

The GM is known for the breakdown of carbohydrates, particularly the indigestible dietary fibers, like cellulose, resistant starch, pectin, oat, wheat bran, and inulin [24, 25]. The absorption of nutrients from dietary fiber has been associated with satiety feeling after eating, thus preventing overeating. The GM metabolizes protein by the secretion of digestive enzymes. Synthesis of vitamin K and B vitamins such as vitamin B12, riboflavin, niacin, and folate, and other digestive enzymes are also performed by GM [23, 24]. The GM also performs physiological benefits of strengthening the gut, shaping the intestinal epithelium, and harvesting energy [13].

Anatomically, the GM assists in maintaining the mucosal barrier's strength by shaping the intestinal epithelium, thereby sustaining gut integrity. Furthermore, the metabolites produced during the breakdown of dietary fibers are absorbed by the epithelial cells to assist cell proliferation, differentiation, and apoptosis of harmful cells, like the cancer cells [23, 26]. The GM is sometimes called the second brain because of its effect on the brain. This is because the metabolites released during the breakdown of fibers are absorbed to support brain activities [19, 27].

#### *2.1.2 Immunological functions*

The GM provides immunity to the host by building colonization resistance, a situation in which the innate GM antagonizes foreign microorganisms' colonization and prompts the preservation of structural and functional protective mucosal barriers [14, 15]. The GM in humans also invades and takeover foreign pathogens in the gut [19, 21]. The metabolites produced by GM, the short-chain fatty acids (SCFAs), reduces the intestinal pH, thus making survival difficult for foreign microorganisms [14, 15]. The functions of SCFAs will be discussed in subsequent sections of this chapter. In addition, the GM improves the integrity of intestinal mucosal to prevent invasion by foreign organisms. Another function of GM is the metabolism of xenobiotics [14, 15]. Xenobiotics, which are foreign materials to the bodies' biology, include drugs or chemicals that could be toxic [15]. Thus, the GM prevents the body from toxins by breaking them down from their harmful state.

#### **2.2 The pathogenicity of the gut microbiota**

Even though gut bacteria are beneficial, they could become pathogenic in their interaction with changes in the host environment and vectors. For example, if microorganisms giving benefits to the host invades other sites they do not usually colonize, that change in their environment could result in them acting as pathogens which could cause diseases [28, 29]. Furthermore, microorganisms in the body can also contribute to polymicrobial infections. Polymicrobial infection occurs when different microorganisms in the body interact and cooperate to create diseases in the host [28, 29]. Therefore, treatment has to be offered to take care of all microorganisms contributing to the infection.

One salient way the GM becomes harmful to the body is if its population in the gut undergoes unusual changes due to drug use, aging, sicknesses, lifestyle, and unhealthy food choices. This abnormal change in the population can result in gut dysbiosis (GD), which would be discussed in the subsequent section. In addition, changes to GM populations have been related to autoimmune situations, allergies, and chronic diseases. The incidence of diseases like autism, asthma, colitis, obesity, gestational diabetes, type 1 & 2 diabetes has been linked to GM's activity in its natural state or an altered nature [19]. Studies have equally shown a disparity in the types of GM or their populations present in a healthy and sick adult. For example, the type and population of GM in people living with gestational diabetes and type 2 diabetes differs from individuals free from the disease [24, 30]. The composition and population of the GM are identified to be influenced by factors such as diet, feeding type, birth delivery mode, and age of hosts [30, 31].

#### **3. Phylum of gut bacteria in human body**

Researchers identified the prominent type of GM colonizing the human gut, which is bacteria; therefore, subsequent sections of this chapter will focus on gut bacteria. Recently, scientists compiled data that shows there are about 2172 species of microorganisms isolated from humans. These species were classified into 12 phyla. The predominant phyla that make up about 93.5% of humans' colonies are bacteria, Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes [13, 28]. Out of these four predominant phyla, 31·1% are phylum Firmicutes, particularly families Bacillaceae, 15.7%, and Clostridiaceae, 11·4% [28]. Proteobacteria occupies 29.5% of GM species isolated in humans, with 20% belonging to the family Enterobacteriaceae. Phylum Actinobacteria constitutes 25·9% of the GM isolated in humans, with 24·2% identifies as family Mycobacteriaceae. Bacteroidetes amount to 7·1% of the total species cultured from humans, and 29% of the phylum belongs to the family Prevotellaceae [15]. About 386 of the isolated species were identified to be anaerobic. Such microbiota would be found in the oral cavity and GIT – the mucosal regions [13].

#### **3.1 Proteobacteria**

Proteobacteria (PBAC) was initially called purple bacteria because of their reddish pigmentation, **Figure 2**. In 1988, a group of scientists studied the purple bacteria and their relatives. The scientists discovered that most of the bacteria and their relatives were neither purple nor photosynthetic. However, the bacteria group had great biological significance through physiological features. Therefore, scientists named these groups of microorganisms PBAC [31, 33, 34]. Characteristics of PBAC are:

*The Interaction between the Gut Microbiota and Chronic Diseases DOI: http://dx.doi.org/10.5772/intechopen.99657*

#### **Figure 2.**

*Proteobacteria are called purple bacteria because of reddish pigmentation [32].*


#### **3.2 Actinobacteria**

Actinobacteria (ABAC), another populous bacteria in the human gut, are gramnegative bacteria and are mainly aerobic, even though some can survive under anaerobic conditions [37, 38]. The ABAC has other characteristics;


#### **Figure 3.**

Mycobacterium tuberculosis*, one of the species of Actinobacteria hosted by humans [39].*

#### **3.3 Firmicutes**

Firmicutes are gram-positive bacteria, though some have a pseudo outer membrane that makes them stain gram-negative [35, 40]. Phylum Firmicutes is notable for microorganisms that profer health benefits. Some genera of Firmicutes are administered as probiotics to profer gut health benefits. Characteristics include;

Firmicutes could be round (cocci) or rod-like (bacillus) in shape. Unlike the ABAC, they have a low level of Guanine and Cytosine in their DNA; they are acidtolerant and take part in metabolic and physiological activities [35, 40].

There are two major classes; the anaerobic Clostridia and the obligate or facultative aerobic Bacilli [35, 40]. These are notable pathogens and beneficial microorganisms within this phylum. One crucial order of this phylum is the Lactobacillales (Lactic Acid Bacteria). Lactic acid bacteria produce lactic acid as a metabolite during glucose fermentation. Lactic Acid Bacteria appear everywhere in the food and are therefore regarded safe for consumption. In addition, they are known to contribute health benefits to the human gut [35, 40].

The genera for Lactic Acid Bacteria include *Lactococcus*, *Enterococcus*, and *Streptococcus*. The genus *Lactobacillus* (**Figure 4**) is the most common microbe used as probiotics [35, 40]. Firmicutes are either anaerobic, particularly *Clostridia*, while class Bacilli is an obligate or facultative aerobe. Therefore, bacteria belonging to class Bacilli would not grow or populate in an anaerobic environment [35, 40].

#### **3.4 Bacteroidetes**

Bacteroidetes are gram-negative, non-spore-forming, and anaerobic bacteria [42]. Bacteroidetes can survive in several environments, including the gut and skin. The class Bacteroidia is the most studied class of this phylum GM [42]. One common genus in this class is the *Bacteroides*. *Bacteroides* are clinically significant and have a mutualistic relationship with the host [42, 43]. The mutualistic behavior of *Bacteroides* occurs if they are retained in the gut. Once *Bacteroides* escape their familiar environment, they become pathogenic and can cause

**Figure 4.** Lactobacillus paracasei *[41].*

diseases such as an abscess in different parts of the body [43]. Other features of *Bacteroides* include:


### **4. Short-chain fatty acid producing bacteria**

The GM is a balanced environment of different microorganisms such as bacteria, viruses, bacteriophages, archaea, and fungi; however, the bacteria community preserves the homeostasis of the gut. The bacteria community contains some groups of gut bacteria that produce SCFAs mentioned in Section 2.1.2. These fatty acids include acetate, propionate, and butyrate. The SCFAs are an essential fuel for the intestinal epithelial cells, and they assist the gut barrier functions and sustain homeostasis in the intestine [23, 44].

These groups of gut bacteria ferment indigestible dietary fibers to produce SCFAs. Dietary fibers such as resistant starch, inulin, wheat, oat bran, cellulose, pectin, and Guam gum are suitable substrates for bacteria activities and fermentation. Out of all prominent bacteria phylum identified in the human body, the Firmicutes and Bacteroidetes are more of the SCFAs producers [23, 44]. The acetate and propionate are produced by phylum Bacteroidetes, while the Firmicutes produce more of the butyrate [23, 45]. Another genus, such as *Bifidobacterium*, from phylum Actinobacteria and some Proteobacteria, could also produce butyrate. It is also important to note that some butyrate producers like Bacteroides are anaerobic bacteria and would not be active in aerobic situations; however, class Bacilli of Firmicutes would thrive because they are aerobes [23, 35, 46]. The aerobic environment in a human gut will suppress the growth of some butyrate-producing bacteria but allow the growth of aerobic pathogens like *Salmonella typhimurium* [23].

#### **4.1 Functions of short-chain fatty acids in the gastrointestinal tract**


#### **5. Interactions of gut microflora and diseases**

The colonization of GM starts from childbirth; however, the composition starts changing based on different factors. For example, the birth delivery method, the mode of feeding, and the type of food offered to an infant determine GM's population [14, 22, 50]. For example, researchers reported that the type of bacteria composition in children fed with breast milk differs from children fed with the formula [14, 22]. In the same way, children born via natural birth have different GM compositions from children born via assisted delivery, such as caesarian surgery [14, 22]. As infants are introduced to solid food, GM composition makes another change [14, 22]. It is also important to note that the composition of GM also differs based on the part of GIT. For instance, the types of GM in the colon are different from the types in the stomach. This difference is because of factors such as the redox condition of the different organs. Other factors are the pH of the organ environment, allergies, the motility of organs, secretions in each organ such as the gastric acid secretion of the stomach, and the undamaged ileocaecal valve [14, 22]. Bacteria colonizing the guts from birth are also referred to as commensal bacteria because they benefit the host [14, 51]. For instance, Bacteroidetes and Firmicutes are the major phyla involved in breaking down macromolecules into simpler forms, particularly the indigestible fibers. However, abnormal changes can occur to the GM, leading to an abnormal composition of bacteria. This condition can be the onset of chronic diseases such as type 1 and 2 diabetes, obesity, cardiovascular diseases, cancer, and inflammatory bowel diseases [14, 52].

*The Interaction between the Gut Microbiota and Chronic Diseases DOI: http://dx.doi.org/10.5772/intechopen.99657*

Surprisingly, abnormal bacteria composition has been linked with diseases that are considered temporary due to physiological changes like metabolic and immunological changes [7, 53]. An example is a gestational diabetes. During pregnancy, some women who cannot produce enough insulin develop gestational diabetes. The physiological changes occurring during pregnancy, such as weight gain, reduce the effective use of insulin, resulting in insulin resistance, as shown in **Figure 5**. The development of insulin resistance makes the body of the pregnant woman demands more insulin production. Even though gestational diabetes occurs late in pregnancy, some women experience insulin resistance before pregnancy [53, 55]. Women with gestational diabetes, if not well managed, might have their unborn babies at risk of being over 9lbs weight birth, which could bring delivery hazards to the mother. Also, the baby might be born earlier than anticipated, which could cause health problems for the baby. In addition, the baby might be born with low blood sugar. Women who have gestational diabetes are equally at a 40% risk of developing type 2 diabetes. About 5 to 20% of all world pregnancies are affected by gestational diabetes, and the percentage is increasing [7, 53, 56]. These statistics suggest a possible increase in people at risk of developing or living with type 2 diabetes, one of the world's major chronic diseases. Changes in the GM population are noticed in people who have gestational diabetes and chronic diseases. This change in population is the gut dysbiosis.

#### **5.1 Gut dysbiosis**

Gut dysbiosis is a condition in which there is a change in the balance of GM composition. Some phyla become highly populated while some reduce in population. This condition creates abnormality in the human GIT, and the pathogenesis of commensals bacteria starts [52]. Most bacteria in the gut are beneficial; however, when the balance in population changes in these bacteria gut colonies, as shown in **Figure 6**, dysbiosis occurs [7, 52]. Some symptoms of dysbiosis are mild and temporary; however, leaving dysbiosis untreated could result in severe symptoms associated with chronic diseases [52]. Even though commensals bacteria antagonize invading microorganisms, sometimes foreign microorganisms can seize the epithelium and overthrow the commensals, destabilizing the immune response [52]. After that, the invading pathogens induce inflammation to which they would be resistant, facilitating their growth and changing the balance of commensals bacteria. Viruses create series of mechanisms that regulate the activities of the

**Figure 5.** *Insulin resistance during gestational diabetes [54].*

**Figure 6.** *Gut dysbiosis [57].*

commensals, making them harmful to the host [16, 58]. Factors causing dysbiosis include a dietary change, chemical consumption like insecticides, alcoholism, improper use of medications, particularly antibiotics, poor dental hygiene, unprotected sex, stress, and anxiety, psychological stress. All these factors could change the balance of GM. In addition, the genotype and immune metabolic functions can alter the population of commensal microbes [52, 59, 60].

Symptoms of dysbiosis are dependent on the location of GM imbalance development and the types of bacteria involved. Symptoms could be gas, bloating, diarrhea, constipation, and cramps [52, 61]. To determine the imbalance of GM, most researchers make use of a human stool [52, 62]. The collected sample is then tested to determine the type of bacteria in the host's body. The organic acid test is another test used medically to determine imbalance [52, 62]. Some bacteria produce organic acids as metabolites. Therefore, the concentration of the organic acid in the urine sample determines the host's bacteria population. The hydrogen breath test is another test conducted to determine dysbiosis. In this case, gases from the mouth are tested for imbalance [52, 62]. Unfortunately, diseases tolerance varies in people; not everyone with dysbiosis shows severe symptoms that call for urgent attention or medical checkup, particularly at a young age. Ignoring or leaving the dysbiosis untreated, however, can result in many severe diseases.

#### **5.2 Diseases associated with dysbiosis**

The GM is partly responsible for the physiology of the body systems. Changes in the balance or population of GM have been linked to bowel diseases, allergies, and chronic metabolic diseases such as diabetes, obesity, cardiovascular diseases, and short-term disease like gestational diabetes.

#### *5.2.1 Type 1 diabetes*

According to research, Firmicutes such as *Lactobacillus*, Actinobacteria such as *Bifidobacterium* decreased in populations in children diagnosed with type 1 diabetes. In contrast, the population of Firmicutes such as *Clostridium* and *Veillonella*, Bacteroidetes like *Bacteroides* and *Prevotella* increased [14, 15]. Patients with Type 1 diabetes had low butyrate-producing and mucin degrading microbes, while pathogenic bacteria increased in population in the gut. Butyrate-producing bacteria and mucin degrading microbes are good for gut health [14, 63]. The functions of SCFAs, of which butyrate is one, are discussed in Section 4.1. Mucin degradation releases complex carbohydrates and produces SCFAs like acetate and propionate [64].

#### *5.2.2 Type 2 diabetes*

In patients with type 2 diabetes, Clostridia and Bacilli Firmicutes decreased in population while PBAC increased. Butyrate-producing microorganisms like Firmicutes are known to produce SCFAs. In addition, butyrate has the energy that provides 5–15% of the calories needed per individual daily [47, 65]. Therefore, Firmicutes' absence or reduced population in type 2 diabetes could elevate the blood glucose level. The increase in blood glucose is because the host will seek the missing calories by consuming more food. Lack or low butyrate concentration could also reduce satiety, making the host eat more, thus raising blood glucose [30, 47, 66].

Proteobacteria, which are more dominant in type 2 diabetes, induce inflammatory responses [36, 47]. An alteration in PBAC composition is common in metabolic syndromes causing diseases. In a study where the fecal samples of patients with type 2 diabetes were analyzed, a significant number of Enterobacteriaceae, a family from the phylum PBAC, were found [14, 31]. At the initiation of an inflammatory response, specific proteins are released into the bloodstream. These proteins inhibit insulin secretion and build insulin resistance in the body [14, 67].

#### *5.2.3 Gestational diabetes*

The profile of GM in women with gestational diabetes is similar to patients who have type 2 diabetes [53, 68]. In a study to determine the onset of dysbiosis in pregnant women, GM was typical in pregnant women in their first semester trimester. However, by the third trimester, the population of Proteobacteria and Actinobacteria increased while butyrate-producing microorganisms like *Faecalibacterium* and *Eubacterium* from phylum Firmicutes reduced. In addition, the Enterobacteriaceae family and *Streptococcus* were also numerous. Even though scientists observed Bacteroidetes and Firmicutes throughout all three trimesters of the pregnancy [68, 69]; however, the strong negative relationship between Bacteroidetes and Firmicutes phyla in healthy pregnant women was missing in women with gestational diabetes [7].

Most of the GM reduced were SCFA producing bacteria. The absence of these bacteria in pregnant women reduced the physiological function of the intestine. The gut permeability was not regulated, insulin sensitivity was reduced, inflammatory response that can lessen the development of diabetes was equally reduced [7]. Gut dysbiosis could be a biomarker for gestational diabetes, and a test of dysbiosis could be early detection before the pregnancy reaches the third trimester [7]. Changes in the GM population was associated with diet and weight gain during pregnancy [53, 68].

#### *5.2.4 Inflammatory bowel disease*

The two major bowel diseases associated with dysbiosis are Crohn's disease and ulcerative colitis [14, 15, 23]. In patients with Crohn's disease, the composition of GM was different from healthy individuals. Bacteria belonging to Firmicutes and Actinobacteria were decreased, some of which had a probiotic effect, while PBAC and *Ruminococcus gnavus*, another Firmicutes associated with inflammation, were increased [14, 24, 31, 70]. Increased susceptibility to Crohn's disease is attributed to a lack of the production of SCFAs. The PBAC is signaled as a pointer to instability in the microbiota. Therefore, an increase in the PBA**C** population is found in

people with IBD disease. Even though the exact reason for the increase in PBAC is unknown, it is hypothesized that PBAC, with its inflammatory effect, creates anaerobic conditions in the gut. The beta-oxidation process reduces when proinflammation occurs. Therefore, anaerobic conditions contribute to the growth of PBAC, which are facultative anaerobe, thereby allowing dysbiosis [14, 15]. Anaerobic conditions increase the growth of pathogenic Firmicutes but reduce the population of the beneficial Firmicutes, like the Lactic Acid Bacteria [35, 40]. Another IBD due to dysbiosis is ulcerative colitis [14, 15]. Scientists discovered that *Lactobacilli* were low in composition in patients with ulcerative colitis at an active stage, while the Clostridiales order of Firmicutes was more prominent. High *Escherichia coli* was equally identified in people with active ulcerative colitis. Inflammation seen in IBD is associated with the decreased colonization resistance [14, 15].

Inflammatory Bowel Diseases (IBD) occur when genetic and environmental factors encourage the growth of pathogens that can decrease the population of commensals, thereby causing inflammation [15]. In addition, IBD can occur when there is an unusual immune response against commensal bacteria. For example, sometimes immune cells such as macrophages could not recognize GM and trigger an immune response which attacks the intestinal wall [15]. Hence, Firmicutes and Bacteroidetes decrease while PBAC increases.

#### *5.2.5 Obesity*

When the GM composition of healthy and patients with obesity were compared, anaerobic Firmicutes and Proteobacteria were increased in the fecal samples of patients. At the same time, Bacteroidetes decreased compared to a healthy individual. The high ratio of Firmicutes and Bacteroidetes has been linked to obesity [14, 71]. A significant increase in Enterobacteriaceae, PBAC family, was equally found in patients with obesity. This population of PBAC family reduced after the patient lost weight [31]. In a study on mice, a toll-like receptor 5, a sensor that detects microbial infection to initiate an immune response, was deficient when fed with a high-fat diet. The masking of toll-like receptor 5 concealed the changes occurring in the GM, and the body could not produce an immune response to fight the strange invading organisms [51, 72]. Deficiency of this receptor has been linked to hypertension, insulin resistance, and weight gain, though the exact reason for the masking was uncertain [15, 73].

#### *5.2.6 Cardiovascular diseases*

Microbiota dysbiosis was related to the development of cardiovascular diseases. A high level of PBAC was found in arteriosclerosis plaque, indicating PBAC has the pro-inflammatory effect that can cause plaque [15, 74]. In addition, some scientists reported that GM converts choline, an essential body nutrient, to trimethylamine, an organic compound. Trimethylamine is further processed in the liver to trimethylamine N-oxide which is known to increase arterial plaques. An increase in arterial plaque can cause arteriosclerosis diseases [14, 15]. In another study, Gammaproteobacteria, a class of PBAC, was connected with endogenous alcohol production linked to the cause of non-alcoholic fatty liver diseases, which is associated with increased risk of cardiovascular failure incidence [75].

#### *5.2.7 Cancer*

Inflammation caused by some phylum of GM could create a grave environment for the development and growth of cancer cells. Even though cancer linked to microbiota

*The Interaction between the Gut Microbiota and Chronic Diseases DOI: http://dx.doi.org/10.5772/intechopen.99657*

so far occurs in body parts that house the GM, notably the GIT, the colon [15, 76]. Commensals sometimes take up pathogenic features when invaded, giving them pathogenic effects; such commensals are called pathobionts. In a study on mice, pathobionts and pathogens contributed to the uncontrolled epithelial cell growth of the colorectal region [15]. Some scientists also suggested that some GM like *Bacillus fragilis*, a Bacteroidetes are virulent and can modify the GM to favor inflammatory responses. These inflammatory responses could cause alterations in the epithelial cells, and this could result in cancer. The inflammatory response will equally allow the invasion of cancer allies' microorganisms [15, 43]. In addition, people with chronic inflammatory disarray have been discovered to have a high susceptibility to gastric cancer and cancer of the lymphatic system associated with the mucosa [76].

#### **6. Conclusion**

Research continues on how GM and its activities cause chronic diseases. From completed studies, it is apparent that the composition of the gut differs between diseased and healthy individuals. While a significant population of commensals and SCFAs producing bacteria reduced, the pathogenic population increased and influenced the commensals, making them turn against the host. Pathogens equally created unfavorable conditions such as inflammatory or anaerobic conditions. The change in the environment favored the growth of pathogens but reduced the growth of commensals.

To take care of gut health, the types of food consumed determines the type of GM. Fermentable dietary fibers such as pectin, inulin, and resistant starch undergo fermentation by GM to produce 15% butyrate, 60% acetate, and 25% propionate. Butyrate maintains colonic homeostasis and prevents inflammation, and maintains mucosal integrity [44], thereby playing a role in reducing dysbiosis. Therefore, food rich in these fermentable dietary fibers would be suitable for gut health [25, 44]. The types of protein consumed matter to gut health. Animal protein fermentation decreases the production of SCFAs, increasing the risk of IBD [25]. However, consuming plantbased protein is associated with an increase in beneficial GM like *Bifidobacterium*, *Lactobacillus,* and it increases the production of SCFA [25, 44]. High consumption of polyunsaturated fatty acids has been associated with an increased healthy GM population like *Lactobacillus* and *Roseburia*, thus increasing the production of SCFAbutyrate [25, 44]. In contrast, high consumption of sodium and food additives such as sweeteners are associated with changes in the composition of GM. When food is high in sodium, it reduces the population of commensal microorganisms like *Lactobacillus*. Also, food additives cause a significant change in the population of the balanced gut system [25, 44]. What humans eat can determine gut health, and the composition of GM in the gut contributes to overall well-being.

Asides healthy diet, there are other ways to improve gut health. One of these ways is the use of probiotics as supplements. Probiotics are live microorganisms made into pills that profer health benefits when administered in adequate amounts. In addition, the use of prebiotics has equally been suggested. Prebiotics are not microorganisms but non-digestible substances that benefit the host by improving the growth and activities of selected bacteria in the gut [77–79]. Other methods are requiring medical experts and scientists. One is fecal microbiota transplantation, which involves infusing stool from a healthy donor to a recipient by delivering the stool through the upper GIT [80]. This method requires adequate care to ensure the feces transferred to other patients do not have infectious microorganisms. Other methods being used include phage therapy, bacteria consortium transplantation, and the use of predatory bacteria [81].

### **Acknowledgements**

Special thanks to Olukayode Olubowale and Oluwaseun Sanusi for their contribution to the content of the chapter.

### **Author details**

Temitope Sanusi-Olubowale1,2

1 Saybrook University, Oakland, California, USA

2 Albany State University, Albany, Georgia, USA

\*Address all correspondence to: olutopy@gmail.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*The Interaction between the Gut Microbiota and Chronic Diseases DOI: http://dx.doi.org/10.5772/intechopen.99657*

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### Section 2
