Abstract
Extracellular vesicles are critical mediators of cell communication. They encapsulate a variety of molecular cargo such as proteins, lipids, and nucleic acids including miRNAs, lncRNAs, circular RNAs, and mRNAs, and through transfer of these molecular signals can alter the metabolic phenotype in recipient cells. Emerging studies show the important role of extracellular vesicle signaling in the development and progression of cardiovascular diseases and associated risk factors such as type 2 diabetes and obesity. Gestational diabetes mellitus (GDM) is hyperglycemia that develops during pregnancy and increases the future risk of developing obesity, impaired glucose metabolism, and cardiovascular disease in both the mother and infant. Available evidence shows that changes in maternal metabolism and exposure to the hyperglycemic intrauterine environment can reprogram the fetal genome, leaving metabolic imprints that define life-long health and disease susceptibility. Understanding the factors that contribute to the increased susceptibility to metabolic disorders of children born to GDM mothers is critical for implementation of preventive strategies in GDM. In this review, we discuss the current literature on the fetal programming of cardiovascular diseases in GDM and the impact of extracellular vesicle (EV) signaling in epigenetic programming in cardiovascular disease, to determine the potential link between EV signaling in GDM and the development of cardiovascular disease in infants.
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Introduction
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide [1]. Risk factors that predispose to CVD include hypertension and multiple metabolic disorders such as obesity, dyslipidemia, and insulin resistance (including type 2 diabetes). The alarming increase in the incidence of type 2 diabetes now predisposes young people to earlier onset of metabolic complications and increased burden of disease [2].
CVD has been considered to be a consequence of adult lifestyle choices (i.e., modifiable risk factors) and genetic predisposition (i.e., non-modifiable risk factors). Attempts to alter lifestyle to reduce risk factors, however, has not mitigated the high rates of CVD [3,4,5]. Furthermore, genetic polymorphisms associated with diabetes and obesity fail to explain the increase in childhood obesity and type 2 diabetes, both of which are risk factors for CVD [6]. In contrast, there is substantial evidence supporting the involvement of prenatal and postnatal exposure to environmental risk factors in determining life-long disease susceptibility [7,8,9,10,11]. For example, newborns of pregnancies complicated by gestational diabetes mellitus (GDM) are at increased risk of developing type 2 diabetes and CVD in adult life [12]. Despite its high prevalence and association with complications of pregnancy, the underlying pathophysiology of GDM and its effects on fetal metabolism is poorly understood. Current data suggest that the fetus responds to excess maternal nutrition and that GDM affects cellular, molecular, and epigenetic pathways, both in the placenta and in the fetus, predisposing the offspring to subsequent metabolic disease [13,14,15]. Thus, the effects of a diabetic pregnancy can be considered as an inter-generational vicious cycle, with consequences for the offspring that extend far beyond the neonatal period [16].
Recent studies have identified novel extracellular vesicle (EV) signaling pathways, including exosomal signaling, that mediate maternal–fetal communication. EVs are vesicles released from cells and contain bioactive molecules including proteins and miRNAs that, once released, are capable of regulating proximal and distal cell function [17].The International Society for Extracellular Vesicles (ISEV) endorses the term ‘extracellular vesicles’ (EV) as a generic name for the particles naturally released from cells, delimited by a lipid bilayer, without nucleus, and inability to replicate. However, there are different sub-types of EVs such as EVs of endosome-origin known as “exosomes”, EVs that bud from the plasma membrane known as “ectosomes or microvesicles or microparticles” and EVs released from dying or apoptotic cells known as “apoptotic bodies”. These EV subtypes have overlapping physical and biochemical characteristics and hence assigning an EV to a particular biogenesis pathway based on their physical and biochemical characteristics is difficult [18]. Hence, in this review, we will be using the term small EVs (sEVs) when they are less than 200 nm and medium or large EVs (m/l EVs) when greater than 200 nm.
During pregnancy, EVs have been identified in both the maternal and fetal compartments, and gestational changes in their concentration, contents and bioactivity have been documented [19]. It is now well-established that EV signaling represents a significant pathway in cell-to-cell communication and in regulating cellular functions [20]. Most importantly, EV signaling plays pivotal roles in the pathophysiology of metabolic disorders as key mediators of molecular cross talk between metabolic organs [21,22,23,24,25]. Available data suggest the inevitable implications of EV signaling in the maternal metabolic changes in pregnancy and development of GDM [26,27,28,29,30,31] Interestingly, the maternal changes in metabolism during pregnancy regulate epigenetic programing during fetal life, leaving a metabolic imprint that defines life-long health and disease susceptibility [32,33,34,35,36]. The focus of this review is to discuss the role of GDM-induced changes in EV signaling that may predispose to postnatal metabolic syndrome and CVD.
Clinical associations between GDM and cardiovascular diseases in children
Cardiovascular disease is the leading cause of mortality and morbidity worldwide [1] and its effects are significantly compounded in diabetic patients [37]. Although incidence and mortality rates have decreased in some countries, the prevalence of CVD has increased in children and young adults in the last decade [3, 4]. Adults with diabetes have a higher risk of developing CVD than those without diabetes, and the risk of CVD increases with fasting glucose concentrations, even before reaching diagnostic threshold concentrations [38]. The International Diabetes Federation (IDF) estimates that there are 425 million people in the world with diabetes and this number is expected to reach 642 million in the year 2040. Significantly, 91% of all cases are type 2 diabetes [39]. The increased incidence of type 2 diabetes has been attributed to interacting genetic, environmental, and metabolic risk factors. Consistent with this proposal, maternal conditioning before and during pregnancy, including the onset of GDM [40], predisposes to subsequent maternal type 2 diabetes and CVD [41, 42] and also to diabetes and CVD in the offspring [43,44,45,46]. A better understanding of this association is crucial to reducing the risk of CVD among children and young adults.
Gestational diabetes mellitus is one of the most common complications of pregnancy and has been defined as glucose intolerance that begins or is first diagnosed during pregnancy [47, 48]. The estimated global prevalence of GDM is between 1 to 14%, depending on the diagnostic criteria, and the population studied [49]. Recent studies identified an increase in the prevalence of GDM that correlates with an increase in urbanization, reduction in physical activity, excess weight gain during pregnancy, and exposure to obesogenic environments [50,51,52].
Alterations in the metabolism of lipids and carbohydrates are common conditions experienced by women with GDM. Several studies report a high cardio-metabolic risk in children exposed to GDM in the womb, considering it an independent risk factor for glucose intolerance and CVD [53, 54]. Prenatal exposure to maternal diabetes is associated with congenital heart disease, obesity and diabetes in the offspring [55, 56]. Exposure to altered metabolic states at critical periods during fetal development, thus, may have long-term impact, generating a metabolic memory that increases the risk of CVD in adulthood [43, 57,58,59,60].
Consistent with the hypothesis that GDM predisposes to CVD in the offspring, biomarkers of CVD risk (including blood lipid profile, obesity or overweight, alterations in blood pressure, and insulin resistance) are evident in children born of mothers with GDM [12, 42, 44, 61,62,63,64]. Furthermore, children (3 years and older) born of pregnancies complicated by GDM have increased systolic and diastolic blood pressure [12, 53], low plasma high-density lipoprotein (HDL) concentrations, hypertriglyceridemia [65], glucose intolerance [63], or insulin resistance [53, 63, 66], as well as increased adiposity [12, 63, 67, 68].
In the adolescent offspring of pregnancies complicated by GDM, abnormal blood pressure, lipid profiles [69] and glucose tolerance [70, 71], high cholesterol concentrations (in females) and increased systolic blood pressure (in males), and a predisposition to type 2 diabetes or pre-diabetes [72] have been reported. The higher risk of developing metabolic syndrome in offspring born to GDM mothers could be attributed to the increase in fetal adiposity or birth weight as a consequence of GDM [73]. As obesity is a major risk factor that contributes directly to dyslipidemia, type 2 diabetes, hypertension and CVD [74], increased birth weight and adiposity can be contributing factors to the development of childhood and early adulthood metabolic disorders. Hyperinsulinemia in-utero is associated with a 17-fold increase in incidence of metabolic syndrome and a tenfold increase in overweight, independent of birth weight [62]. Overweight is observed in children of 4 to 5 years of age, born to mothers with GDM and is associated with increased birth weight, maternal obesity [75], and/or altered lifestyle during childhood [76]. Children with a high birth weight who are exposed to an intrauterine environment of diabetes or maternal obesity have an increased risk of developing metabolic syndrome [56, 77].
Further, pregnancies complicated with GDM and maternal obesity pose a higher risk for the development of metabolic syndrome in offspring, including CVD [69, 76, 78,79,80]. Maternal pre-gestational [66, 81, 82] and gestational body weight are strong predictors of body mass index (BMI) and percentage total body fat [67] in female but not male offspring [42]. Prenatal exposure to GDM concomitant with maternal obesity or overweight is a high-risk factor for high BMI in offspring [79, 83]. However, for the development of offspring overweight and obesity, the risk attributed from maternal obesity is higher compared to GDM [76, 80]. In addition, in a larger study, of 2,432,000 children over a 40-year period, the offspring of GDM pregnancies had a 29% increased rate of early-onset CVD [78] that was further increased in association with comorbidities such as maternal obesity [78]. Table 1 summarizes the clinical consequences of gestational diabetes in the offspring and its impact on CVD development.
Maternal hyperglycemia results in increased placental transfer of glucose and amino acids to the fetus leading to fetal hyperinsulinemia. This fetal insulinemia results in short-term consequences such as fetal overgrowth, adiposity, and hypoglycaemia [84].This can also induce changes in fetal cardiovascular function characterized by elevated concentrations of endothelial dysfunction markers (E-selectin and VCAM1), and leptin [44], increased thickness of the aortic intima-media, and arterial stiffness [85].The available data support the hypothesis that in-utero exposure to hyperinsulinemia and hyperglycemia has lasting effects on gene expression and known risk factors for CVD in the offspring [85]. Exposures to adverse metabolic environments during early life, thus, leave an imprint that determines the long-term health of an individual [86]. Compelling data implicates epigenetic mechanisms in regulating these effects and their subsequent transmission to future generations [87, 88].
Fetal programming for the development of cardiovascular disease
Until recently, it was widely accepted that the development of chronic metabolic disease is a consequence of inherited genetics and a lifetime of exposure to suboptimal lifestyle factors (e.g., inactivity, diet). These factors, however, only explain 10% of the origin of chronic non-communicable diseases [89]. The polymorphisms associated with diabetes and obesity, similarly, fail to explain the notable increase in childhood obesity and type 2 diabetes, which are risk factors for CVD [6]. Additionally, children born after maternal onset of diabetes may have a higher risk of obesity and type 2 diabetes when compared to their siblings born before maternal disease onset [90]. This observation is significant in that it identifies maternal diabetes as an independent contributing factor for obesity and type 2 diabetes in the offspring. Furthermore, it is consistent with the tenet of developmental origins of the health and diseases (DOHaD) [43] and the role of epigenetics in fetal adaptation to maternal metabolic status [91].
Epigenetic regulation is defined as inheritable changes in gene expression in response to environmental stimuli that do not include changes in nucleotide sequence [92,93,94]. Epigenetic modifications include: DNA methylation on CpG islands [95, 96], post-translational modifications of histone proteins, and regulation of expression by non-coding RNA (such as, miRNA) [97, 98]. Changes in epigenetic patterns can result in impaired gene expression, which itself can have many downstream effects, including changes in disease risk, stress response, and changes in cellular metabolism [99,100,101].
As alluded to above, GDM predisposes offspring to chronic diseases. One mechanism by which this may occur is fetal metabolic reprogramming, involving epigenetic changes [102]. Differential DNA methylation profiles have been identified in umbilical cord blood and /or placenta obtained from infants of GDM pregnancies compared to normoglycemic pregnancies [15, 34, 36, 103,104,105]. Differentially methylated genes include genes associated with CVD, metabolic diseases specifically type 2 diabetes, and immunological and endocrine disorders [51, 103].
For example, leptin, an adipokine involved in energy balance and appetite, is differentially methylated in placental tissues obtained from pregnancies complicated by GDM [106] and is dependent on glucose concentrations [107]. Leptin methylation is associated with decreased leptin mRNA abundance in placental tissues [108] and decreased leptin concentrations in maternal serum and umbilical cord blood, in the third trimester. Leptin concentrations are negatively correlated with glucose concentrations at 2 h post-OGTT. These data highlight an in-utero effector pathway whereby GDM may induce long-lasting epigenetic modification of metabolism that increases the likelihood of obesity in later life. Similarly, GDM-responsive pathways have been reported for other metabolic pathway genes including MEST (human mesoderm-specific transcript, a gene implicated in fetal and placental growth) and ABCA1 (a member of the ATP-binding cassette family).
MEST is an imprinted gene implicated in placental development. During the first trimester, the translational product of MEST, mesoderm-specific transcript homolog protein, is highly expressed in extra-villous trophoblast cells [109]. Aberrant methylation of MEST is associated with dysregulation of placentation and complications of pregnancy [109]. Placental tissue and cord blood obtained from pregnancies complicated by GDM display significantly decreased methylation of MEST when compared to tissues obtained from normoglycemic pregnancies [32]. Decreased methylation of MEST is also characteristic of obese adults. In mice, the loss of MEST differentially methylated domains is strongly associated with the accumulation of triacylglycerol within the placenta and abnormal mitochondrial function [110]. In support of the role of MEST in lipid metabolism, overexpression of MEST in transgenic mice increases the size of adipocytes [111], while global knockdown of MEST reduces adiposity [112]. In the setting of GDM, decreased methylation of MEST may alter placental function and lipid metabolism, having a long-lasting effect on the epigenome of offspring and contributing to a predisposition to obesity throughout life [32, 113].
DNA methylation in the ABCA1 transporter gene has previously been associated with CVD. ABCA1 is a transporter that transfers cholesterol from cells to apolipoproteins A-1 (apo-A1), which contributes to the formation of nascent HDL [114]. In a study by Houde and collaborators, the association between the maternal metabolic profile and the methylation levels of the ABCA1 transporter gene, in placenta and umbilical cord blood in pregnancies with GDM was evaluated [35]. Results indicated that the methylation levels of the ABCA1 gene on the maternal side of the placenta correlated with the maternal HDL cholesterol levels and glucose levels observed at 2 h on the diagnostic OGTT. On the fetal side of the placenta, methylation levels of the ABCA1 gene were associated with triglyceride levels in umbilical cord blood. Variability of DNA methylation on both sides of the placenta was also associated with ABCA1 mRNA levels. In contrast, cord blood ABCA1 gene methylation levels were negatively correlated with maternal glucose levels 2 h after OGTT [35]. These results suggest that epigenetic adaptations to the uterine environment may contribute to triggering long-term susceptibility to dyslipidemia and developing CVD (Fig. 1).
Offspring consequences due to maternal GDM, and exposure to environmental factors throughout its lifetime, leading to alterations in epigenetic programming
In animal models, pancreas from offspring of mothers with GDM showed changes in DNA methylation patterns of several genes involved in signaling pathways associated with glucose and lipid metabolism, such as, Agap2, Plcbr, Hnf1b, Gnas, Fbp2, Cdh13, Wnt2, Kcnq1, Lhcgr, and Irx3 [115]. This suggests that changes in the DNA methylation profile in the pancreas genome are associated with metabolic alterations and increased susceptibility to developing type 2 diabetes, obesity, and cardiovascular risk in adult life [115].
DNA methylation is not the sole epigenetic mechanism involved in GDM. More recently, miRNAs have also been investigated as possible mediators of epigenetic modifications in GDM. During GDM, maternal hyperglycemia can favor the transfer of glucose from the mother to the fetus through the fetal placental circulation, inducing endothelial dysfunction in the fetal micro- and macro-circulation, which is associated with an increase in the susceptibility of developing type 2 diabetes along with CVD [116]. Exposure to high glucose levels has been proposed to induce epigenetic changes that negatively affect endothelial function. It has been demonstrated in tests carried out on human umbilical vein endothelial cells (HUVEC) cells, obtained from pregnancies with GDM, that both high glucose and GDM induce endothelial dysfunction through a mechanism mediated by an increase in the expression of miRNA-101 and a reduced expression of histone methyl transferase enhancer of zester homolog-2 (EZH2-ß), which reduces trimethylation of lysine 27 in Histone 3 [117]. These results also showed that the inhibition of miRNA-101 reestablishes cell function and increases the expression of EZH2-ß along with favoring cell survival. Therefore, an altered miRNA-101/EZH2-ß ratio could contribute to the endothelial dysfunction observed in GDM.
In addition, a key event in cardiac myogenesis has been determined to occur during pregnancy, in which fetal exposure to metabolic diseases may dramatically alter postnatal muscle development and metabolism [118, 119]. miRNA133a has been shown to participate in muscle differentiation and regulate the mitochondrial function of muscle cells. Alterations in miRNA133a expression were associated with severe myopathies and mitochondrial dysfunction [120]. In addition, murine models exposed to GDM during fetal development showed muscle lipotoxicity, which was evidenced by diacylglycerol accumulation, insulin resistance, reduced miR-133a expression, and elevated Nix expression [121], a known mediator of mitophagy and programmed cell death [122].
Members of the miR-15 family can alter the expression or function of distinct proteins involved in the insulin signaling pathway, thus affecting insulin sensitivity and secretion. Coincidentally, skeletal muscle biopsies of people between 26 and 35 years of age whose mothers were previously diagnosed with GDM have shown increased expression in skeletal muscle of miR-15a and miR-15b. Additionally, miR-15a expression was positively associated with fasting plasma glucose, 2 h plasma glucose and HbA1c levels, which may contribute to the development of metabolic diseases and high cardiovascular risk [123].
miRNAs have been implicated in the epigenetic regulation of key metabolic and inflammatory pathways and their expression is modified in the face of stress situations or metabolic adaptations [124]. The majority of cells selectively secrete miRNAs. In addition, changes in the profile of miRNAs secreted into the blood have been associated with several pathologies, where they have been considered as potential biomarkers of health or disease status [125, 126]. There are numerous publications in which changes in the circulating miRNA profile have been observed in pre-diabetic and diabetic patients, such as an increase in the levels of miR-192 and miR-193b, and a decrease in miR-126, and miRNA15a, among others [127,128,129].
A recent meta-analysis has found that 40 circulating miRNAs, including miR-21, miR-29a, miR-34a, miR-103, miR-107, miR-126, miR-132, miR-142-3p, miR-144 and miR-375, are significantly dysregulated in type 2 diabetes [130]. Another study demonstrates a decrease in miRNA levels; miR-20b, miR-21, miR-24, miR-15a, miR-126, miR-191, miR-197, miR-223, miR-320, and miR-486 in type 2 diabetes, and a modest increase in miR-28-3p [131].
These results strongly suggest the potential utility of miRNAs as diagnostic and follow-up biomarkers for therapeutic interventions in type 2I diabetes [132, 133]. Since a single miRNA can target multiple genes, and multiple miRNAs share common targets, miRNAs are particularly well suited for analyzing metabolic pathways and processes. In circulation, miRNAs can be found bound to protein complexes [134], HDL [135] or within EVs [136]. miRNA-containing EVs have been shown to participate in tumor progression and chemoresistance [137], as well as in the development of atherosclerosis [138], and diabetes [139], amongst other pathologies [140, 141].
Extracellular vesicular signaling in epigenetic programming in cardiovascular diseases
The development of diabetes involves alterations in insulin-sensitive tissues. This leads to a state of glucose intolerance or prediabetes, that eventually produces overt diabetes when pancreatic β cells are incapable of coping with an increased demand for insulin [142]. Communication between different tissues is essential for the maintenance of glucose homeostasis [143]. Cells can communicate with neighboring cells or with distant cells specifically through the secretion of EVs [144]. As previously mentioned, the term "EVs" describes a class of small membrane-enclosed vesicles (30–2000 nm) that contain a wide variety of messenger biomolecules, such as lipids, proteins, and nucleic acids, which are a representation of the metabolic and functional state of the cells of origin. Most cell types release these vesicles, which are able to cross biological barriers, and can therefore be found in a variety of fluids, including blood, urine, saliva, breast milk and cerebrospinal fluid, among others [145]. Cells can secrete different types of EVs, which are classified according to their subcellular origin and size [146]. The smallest EVs are known as exosomes (50–150 nm), which are generated within endosomes or multivesicular bodies (MVB) and are secreted when these compartments fuse with the plasma membrane. Other EVs are much larger, such as microvesicles (200–1000 nm) and apoptotic bodies (over 1000 nm), both of which are formed by sprouting from the plasma membrane [147]. EVs express their own surface molecules, which allow them to target specific recipient cells. Once bound to a target cell, EVs can induce signaling through receptor-ligand interaction or can be internalized by endocytosis and/or phagocytosis, or may fuse with the target cell's membrane to deliver its content, thereby modifying the physiological state of the recipient cell [148].
EVs can transfer proteins and nucleic acids (small and long non-coding RNAs) that can mediate epigenetic programming in recipient cells, including DNA methylation, histone modification, and posttranscriptional regulation of RNA [149, 150]. DNA methylation involves addition of methyl groups to the cytosine residues of DNA, converting them to 5-methylcytosine and this is mediated by DNA methyltransferase (DNMT) enzymes. DNA methylation leads to stable long-term gene silencing and demethylation leads to upregulation of gene transcription. EVs can transfer molecular factors that can mediate DNA methylation in target cells [151]. For example, EVs derived from leukemia cells contain breakpoint cluster region–Abelson leukemia gene human homolog 1 (BCR-ABL1) mRNA, which when transferred to normal cells upregulate the methyltransferases and global DNA hypermethylation resulting in malignant transformation [149]. sEVs derived from endothelial cells deliver miRNAs (miR-21-5p and miR-217) that target the DNA methyl transferase 1 (DNMT1) in recipient cells and suppress genes associated with cell proliferation conferring senescence phenotype in recipient cells [150]. The transfer of senescence signals has a key role in the development of age-related CVD and diabetes.
The posttranscriptional modification of histones, particularly acetylation and methylation can regulate gene transcription [151]. EVs can be involved in the transfer of factors that can mediate these histone modifications and regulate chromatin remodeling. For example, in ischemic heart disease, sEV-mediated transfer of Heat Shock Factor 1 (HSF1) interacts with the promotor region of miR-34 and alters the methylation profile of Histone 3 protein. This downregulates miR-34 expression and rescues the cardiomyocytes from apoptosis via HSF1/miR-34a/Heat shock protein (HSP)70 pathway [152]. Also, brain tumor cells secreting EVs carrying linker Histone H1 can bind DNA and regulate DNA methylation and histone modification and mediate tumorigenesis [153].
Long non-coding RNAs (lncRNA) consist of approximately 200 nucleotides and can regulate nuclear architecture and thereby modify gene transcription [154]. sEV-mediated transfer of lncRNA and their functional effects on gene expression in target cells have been reported in CVDs such as atherosclerosis and myocardial infarction. Given that lipid induced inflammation is a characteristic feature of CVD, cells exposed to LDL secrete sEVs containing lncRNA Growth arrest specific (GAS)5, and regulate apoptosis in target cells (macrophages and endothelial cells), and contribute to the pathology of atherosclerosis [155]. Further, sEVs containing lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) derived from endothelial cells activate nuclear factor erythroid 2-related factor (NRF2) signaling in dendritic cells, increasing their maturation and progression to an atherosclerotic state [156]. Also, MALAT1 containing sEVs from endothelial cells activate M2 macrophage polarization [157] and formation of neutrophil extracellular traps and contribute to the pathology of atherosclerosis [158]. In myocardial infarction, MALAT1 containing sEVs derived from cardiomyocytes attenuated miR-92a expression in target cells and help neovascularization [159]. Also, sEV mediated transfer of lncRNA H19 regulate the expression of miR-675 and activate pro-angiogenic factors leading to cardioprotective effects in myocardial infarction [160]. On the other hand, sEVs derived from hypoxic cardiomyocytes were enriched in ncRNA AK139128 which stimulated apoptosis and exacerbated the myocardial infarction in rat models [161].
LncRNAs are implicated in the pathophysiology of insulin resistant states such as diabetes. For example, transfer of lncRNA-p3134 via sEVs could positively regulate glucose-stimulated insulin secretion by promoting key regulators such as Pdx-1, MafA, GLUT2 and Tcf712, in beta cells and enrichment of this lncRNA in serum EVs is reported in type 2 diabetes compared to normoglycemic subjects [162]. This could be a compensatory mechanism of increasing beta cell function in an insulin resistant state. The protective mechanism of sEV transfer of lncRNA has been reported in diabetic complications as well [163]. In diabetic retinopathy, mesenchymal stem cell-derived EVs transfer lncRNA SNHG7 to retinal microvascular endothelial cells, which negatively regulate the miR-3aa-5p/X-box binding protein (XBP)1 axis. By targeting the miR-3aa-5p/XBP1 axis, sEV lncRNA SNHG7 inhibits high glucose induced endothelial mesenchymal transition and tube formation and impairs the pathogenesis of diabetic retinopathy [163]. Also, sEV transfer of lncRNA H19 from mesenchymal stem cells to fibroblast, target miR-152-3p/PTEN pathway. This negatively regulates the expression of miR-152-3p which in turn upregulates PTEN expression and leads to increased proliferation, and migration, and decreases apoptosis, which in turn improves diabetic wound healing [164]. As previously mentioned, ageing is a risk factor for CVD, and it has been reported that mesenchymal stem cell-derived sEVs prevent ageing induced cardiovascular dysfunction by transfer of lncRNA MALAT1. sEV mediated transfer of MALAT1 inhibits the NF-kB/Tumour necrosis factor (TNF)-α signaling pathway and impairs the ageing process [165]. In addition, circulating levels of sEV MALAT1 is significantly decreased in patients with type 2 diabetes [166].
Another subset of non-coding RNAs that has regulatory roles in gene transcription and protein translation are the circular RNAs, which are covalently closed RNAs and can act as miRNA sponges. Emerging evidence implicates the role of sEV associated circular RNA in CVD, and associated metabolic disorders. The circular RNA circRNA‑0006896 in serum sEVs was implicated in carotid plaque destabilization, a key event in ischemic heart disease. Transfer of sEV associated circRNA‑0006896 in endothelial cells resulted in decreased expression of miR-1264 and increased levels of DNMT1, leading to endothelial cell proliferation and migration and carotid plaque destabilization [167]. High glucose stimulated endothelial cells secrete sEVs containing circular RNA circRNA-0077930 which when delivered to vascular smooth muscle cells cause vascular senescence [168]. sEV circular RNA circ_DLGAP4 promote cell proliferation and fibrosis in diabetic kidney disease and exacerbates this diabetic complication by sponging miR-143 and targeting the ERBB3/NF-kB/Matrix metalloproteinase (MMP)-2 axis [169].
On the other hand miRNAs which are 19–24 nucleotides long can regulate gene expression at the post-transcriptional level by RNA-induced gene silencing and this can be considered an epigenetic mechanism under a broader definition of epigenetics [170]. The role of EVs in the transfer of miRNAs is well-established [171, 172], and EV-miRNAs play critical roles in the development and pathophysiology of CVD, including a contribution to the development of risk factors associated with CVD such as diabetes and obesity. Diabetic heart disease is characterized by injury to cardiac microvascular endothelial cells and cardiomyocyte dysfunction. During the initial course of diabetes, hyperglycemia can lead to endothelial dysfunction that contributes to the development of serious vascular complications [173]. There are reports which suggest that despite achieving good glycemic control, vascular damage persists due to induced changes in gene expression, a phenomenon called metabolic memory, which affects different cell types [174]. Currently, a series of studies have shown that miRNA containing EVs can regulate gene expression and modulate endothelial function, inflammation and cellular senescence, key phenomena in cardiovascular function [175]. miR-126 is the most widely studied miRNA in type 2 diabetes and reported to be associated with the maintenance of vascular function [176] and endothelial function [131]. High glucose levels have been shown to reduce miR-126 levels in the apoptotic bodies of endothelial cells. Likewise, the content of miR-126 is reduced in circulating vesicles in diabetic patients [131]. This finding is crucial, since miR-126 has been shown to play an important role in vascular endothelial repair [177]. A report using a cellular endothelial model demonstrated that hyperglycemia weakens potentially protective intercellular communication mechanisms by affecting the content of miRNA-126-3p and the function of endothelial microparticles [177]. Supporting these findings, EVs isolated from Goto-Kakizaki (GK) diabetic rat cardiomyocytes contained higher levels of miR-320 and lower levels of miR-126 compared to EVs obtained from healthy controls [178]. In particular, EVs which contained miRNA-320 lead to a decrease in migration, and proliferation capabilities, along with diminished angiogenesis in adjacent cardiac endothelial cells. Such events may happen through the negative interaction of these EVs with the recipient cells target genes, such as IGF-1, Hsp20 and Ets2, which damage the angiogenic function of these cells [178]. Overexpression of Hsp20 in diabetic mice significantly attenuates cardiac dysfunction and cardiovascular hypertrophy [179]. Interestingly, cardiomyocyte-derived sEVs obtained from mice which overexpress Hsp20, have high levels of p-Akt, Hsp20, survivin, and superoxide dismutase (SOD)1, and have a protective effect in vitro against cell death triggered by hyperglycemia [179].
García and collaborators demonstrated that under glucose starvation conditions, H9C2 cardiomyocytes increase sEV secretion and that their miRNA and protein cargo is dependent on glucose concentrations [180]. Under starvation conditions, EVs are enriched in glucose transporters and enzymes involved in glucose metabolism, which leads to an increased rate of glucose uptake and glycolysis in cardiac endothelial cells [180]. Additionally, another report describes that sEVs derived from cardiomyocytes alter the function of endothelial cells and stimulate angiogenesis, which is dependent on glucose concentration [181].
Furthermore, high glucose levels lead to an increase in the expression of mammalian sterile 20-like kinase 1 (Mst1) kinase in sEVs from diabetic cardiac endothelial cells and cardiomyocytes. In addition, an increase in Mst1 expression has been associated with diabetic heart disease due to its role as regulator of autophagy and apoptosis [182]. Interestingly, cardiomyocytes exposed to sEVs obtained from diabetic cardiac endothelial cells show reduced glucose uptake since Mst1 interrupts the translocation of GLUT4 to the membrane, thus affecting cardiomyocyte metabolism [182]. Further, several reports have shown that Mst1 contributes to the development of diabetic cardiomyopathy by inhibiting the expression of sirtuin 3 (Sirt3) [183], which plays a crucial role in mitochondrial homeostasis and confers a protective role against the onset and development of diabetic cardiomyopathy [184]. On the other hand, obesity is frequently associated with metabolic disease and cardiovascular risk. In an animal model, obesity has been shown to increase the levels of miR-122, miR-192, miR-27a-3p and miR-27b-3p when compared to the miRNA profile of plasma sEVs derived from lean animals. Interestingly, treatment of lean mice with sEVs isolated from obese mice induces glucose intolerance and insulin resistance. Furthermore, the administration of control sEVs transfected with obesity-associated miRNA mimics strongly induces glucose intolerance in lean mice and produces central obesity and hepatic steatosis [185], thus contributing to cardiovascular damage. Table 2 summarizes the key functional studies on EV signaling in CVD, and associated risk factors.
Potential role of EVs in epigenetic programming in GDM
Maternal glucose is the primary nutrient that sustains fetal growth and development. Hyperglycemia in the mother as a consequence of excessive insulin resistance and insufficient insulin production in GDM or undiagnosed type 2/type 1 diabetes results in increased placental transfer of glucose to the fetus. The prolonged exposure of fetal pancreas to high levels of glucose leads to hyperinsulinemia characterized by increased lipogenesis and fat storage in a sex dependent manner [186]. Short-term offspring consequences of GDM such as macrosomia and neonatal hypoglycemia are the result of excess supply of glucose from maternal to fetal circulation. Further, in-utero exposure to the altered metabolic milieu in GDM can lead to long-term offspring consequences such as increased insulin resistance, adiposity and cardiovascular risk [60, 61, 71, 72, 187,188,189], by influencing the epigenetic programming during fetal development.
The first observation of the influence of the in-utero environment on fetal risk of developing chronic diseases was made by Barker et al. [190]. This study reported that babies who were small for gestational age with large placentas had circulatory adaptation in fetal stage which predisposes them to a high risk of developing CVD in adulthood [43, 190]. In addition, human population studies have shown that maternal nutrient status can program the pancreatic beta cell function in fetus and modify the risk of developing metabolic disorders in adult life [191, 192]. Epigenetic dysregulation is a remarkable link between neonatal exposure to uterine environment and later adult health. For example, in individuals who were prenatally exposed to the Dutch famine during World War II, it was identified 60 years later that they had distinct epigenetic changes in the form of decreased DNA methylation of the Insulin-like growth factor (IGF)2 gene compared to their same sex sibling who were unexposed to maternal undernutrition [193]. Further, studies in animal models which manipulated the maternal diet during pregnancy, and analyzed the epigenetic modifications in the fetus, provided insight into the developmental origins of metabolic diseases [7, 194, 195]. For example, changes in dietary methyl supplements fed to pregnant mice alters DNA methylation in the fetus and leads to associated phenotypic changes [7, 194], indicating that maternal nutritional status influences the long-term health of the fetus. There are numerous studies that report epigenetic changes in placenta and cord blood [15, 35, 36, 108, 196,197,198] in association with GDM. However, the impact of GDM on the epigenetic landscape of offspring metabolic tissues such as liver, pancreas, adipose tissue, and skeletal muscle is unknown as this is unlikely to be feasible except in patients where there is a clinical need to obtain biopsies. Overall, this suggests that the altered placental nutrient transport and intrauterine conditions associated with GDM affects the offspring’s epigenome, and susceptibility to disease later in life.
The field of EVs is expanding rapidly, and there is continuous exploration of the novel roles of these nanovesicles in various biological processes [144]. EVs carry a wide array of biological molecules as proteins, nucleic acids and lipids which mediates changes in the gene expression and phenotypic response in recipient cells [144, 146]. EVs can be key mediators of epigenetic signals by transferring non-coding RNAs including miRNAs and long non-coding RNAs, which can modify proteins involved in DNA methylation and histone modifications [157, 158, 161, 163,164,165, 167,168,169, 199]. EVs are reported to be involved in mediating key biological processes which underlie the metabolic adaptations in healthy pregnancies, and those complicated by GDM [17, 26, 28,29,30,31]. The most studied EVs in pregnancy are the total circulating EVs, which originate from different maternal tissues [19, 26, 27, 30]. In addition, EVs derived from the placenta form a critical link between the maternal and fetal system and are hence studied for their involvement in physiological and pathological processes [19, 28, 29]. However, there is a lack of studies on EVs of fetal origin due to an inability to obtain fetal EVs, and hence there is a gap in the understanding of their effects on target tissues. Since EVs can act as mediators of epigenetic signals, the EVs originating from fetal tissues can potentially propagate the metabolic signals between fetal organs and mediate the epigenetic programming in response to altered in-utero metabolic exposure in GDM.
Another critical question is whether the maternal EVs can cross the placental barriers and transfer epigenetic signals to fetal cells. Although, placental EVs have been reported in the cord blood [19], the mechanism by which EVs from the maternal side of the placenta can cross the placental barrier to reach fetal blood is not clearly understood. Future studies in in-vitro settings and animal models should investigate whether- and how-the transfer of epigenetic modifying cargo will affect the susceptibility to future diseases, including CVD in offspring born to GDM mothers. Figure 2 shows EV-mediated transfer of epigenetic signals which can potentially lead to epigenetic reprogramming in recipient cells in a fetus exposed to GDM.
Schematic illustration of the potential role of EVs in mediating epigenetic signals in fetal metabolic reprogramming in GDM. In GDM, excess nutrients are supplied to the fetus via placental transfer which leads to exposure of fetal cells to the altered metabolic milieu. The fetal cells respond to the altered uterine microenvironment by epigenetic alterations in metabolic genes. EVs can potentially mediate the transfer of epigenetic signals between fetal cells thus mediating the change in the epigenetic landscape, and susceptibility to future diseases
Conclusion and future directions
The development of metabolic disorders in life is dependent on genetic and epigenetic factors. The genetic factors are those that cause a change in the individual’s DNA sequence, whereas the epigenetic factors involve genetic control by factors other than alterations in DNA sequences. The effects of the external environment on genes and development of diseases involves epigenetic programming of the genome in response to the external cues. During pregnancy the changes in the maternal metabolism due to presence of metabolic disorders such as obesity, type 2 diabetes and GDM can have a crucial long-term impact on mothers and offspring. Available epidemiological data shows that the maternal intrauterine glycemic environment can epigenetically reprogram the fetus for the development of metabolic diseases in the future, including an increased risk of CVDs. Growing evidence reports the involvement of EVs of maternal and fetal origin as key players in the development of GDM, as well as the maternal and fetal pathology associated with GDM. Changes in the concentration, cargo, and bioactivity of total circulating and placental EVs have been described in GDM. However, there is no clear evidence showing direct involvement of EV signaling in the fetal programming for metabolic disease.
In general, EVs are reported to be key players in the pathology of metabolic disorders. EV-mediated transfer of molecules between cells is reported to play a critical role in mediating the metabolic, inflammatory and vascular events associated with the development and progression of CVD. Most importantly, emerging studies show that changes in the external environment lead to changes in the molecular cargo of EVs, and the altered molecular cargo, when transferred to recipient cells, could epigenetically program these cells to have altered metabolic phenotypes. The transfer of epigenetic signals between cells via EVs have been demonstrated in the context of CVD, and associated risk factors such as type 2 diabetes and obesity. Hence, understanding the effect of EV signaling during pregnancy, on fetal programming for the development of metabolic diseases such as CVD, is a fertile area for future investigation. Knowledge regarding the factors that contribute to fetal programming is imperative for the development of interventions to stop or slow down the development and progression of CVD and associated pathologies (Fig. 3).
Maternal factors during pregnancy modify cell signals via EVs, which contribute to fetal metabolic programming, thus leaving a transgenerational imprint that affects health during adult life
Availability of data and materials
Not applicable.
References
McClellan M, Brown N, Califf RM, Warner JJ. Call to action: urgent challenges in cardiovascular disease: a presidential advisory from the American Heart Association. Circulation. 2019;139(9):e44–54.
Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuniga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):122.
George MG, Tong X, Kuklina EV, Labarthe DR. Trends in stroke hospitalizations and associated risk factors among children and young adults, 1995–2008. Ann Neurol. 2011;70(5):713–21.
George MG, Tong X, Bowman BA. Prevalence of cardiovascular risk factors and strokes in younger adults. JAMA Neurol. 2017;74(6):695–703.
Andersson C, Vasan RS. Epidemiology of cardiovascular disease in young individuals. Nat Rev Cardiol. 2018;15(4):230–40.
Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–53.
Wolff GL, Kodell RL, Moore SR, Cooney CA. Maternal epigenetics and methyl supplements affect agouti gene expression in Avy/a mice. FASEB J. 1998;12(11):949–57.
Waterland RA, Jirtle RL. Transposable elements: targets for early nutritional effects on epigenetic gene regulation. Mol Cell Biol. 2003;23(15):5293–300.
Dolinoy DC, Weidman JR, Waterland RA, Jirtle RL. Maternal genistein alters coat color and protects Avy mouse offspring from obesity by modifying the fetal epigenome. Environ Health Perspect. 2006;114(4):567–72.
Anway MD, Cupp AS, Uzumcu M, Skinner MK. Epigenetic transgenerational actions of endocrine disruptors and male fertility. Science. 2005;308(5727):1466–9.
Yajnik CS. Early life origins of insulin resistance and type 2 diabetes in india and other Asian countries. J Nutr. 2004;134(1):205–10.
Wright CS, Rifas-Shiman SL, Rich-Edwards JW, Taveras EM, Gillman MW, Oken E. Intrauterine exposure to gestational diabetes, child adiposity, and blood pressure. Am J Hypertens. 2009;22(2):215–20.
Barbour LA, Hernandez TL. Maternal lipids and fetal overgrowth: making fat from Fat. Clin Ther. 2018;40(10):1638–47.
Elliott HR, Sharp GC, Relton CL, Lawlor DA. Epigenetics and gestational diabetes: a review of epigenetic epidemiology studies and their use to explore epigenetic mediation and improve prediction. Diabetologia. 2019;62(12):2171–8.
Howe CG, Cox B, Fore R, Jungius J, Kvist T, Lent S, Miles HE, Salas LA, Rifas-Shiman S, Starling AP, et al. Maternal gestational diabetes mellitus and newborn DNA methylation: findings From the Pregnancy and Childhood Epigenetics Consortium. Diabetes Care. 2020;43(1):98–105.
Alejandro EU, Mamerto TP, Chung G, Villavieja A, Gaus NL, Morgan E, Pineda-Cortel MRB. Gestational diabetes mellitus: a harbinger of the vicious cycle of diabetes. Int J Mol Sci. 2020;21(14):5003.
Adam S, Elfeky O, Kinhal V, Dutta S, Lai A, Jayabalan N, Nuzhat Z, Palma C, Rice GE, Salomon C. Review: fetal-maternal communication via extracellular vesicles—implications for complications of pregnancies. Placenta. 2017;54:83–8.
Théry C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, Antoniou A, Arab T, Archer F, Atkin-Smith GK, et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles. 2018;7(1):1535750.
Miranda J, Paules C, Nair S, Lai A, Palma C, Scholz-Romero K, Rice GE, Gratacos E, Crispi F, Salomon C. Placental exosomes profile in maternal and fetal circulation in intrauterine growth restriction—liquid biopsies to monitoring fetal growth. Placenta. 2018;64:34–43.
Czernek L, Duchler M. Exosomes as messengers between mother and fetus in pregnancy. Int J Mol Sci. 2020;21(12):4264.
Ying W, Riopel M, Bandyopadhyay G, Dong Y, Birmingham A, Seo JB, Ofrecio JM, Wollam J, Hernandez-Carretero A, Fu W, et al. Adipose tissue macrophage-derived exosomal miRNAs can modulate in vivo and in vitro insulin sensitivity. Cell. 2017;171(2):372-384.e312.
Ying W, Gao H, Dos Reis FCG, Bandyopadhyay G, Ofrecio JM, Luo Z, Ji Y, Jin Z, Ly C, Olefsky JM. MiR-690, an exosomal-derived miRNA from M2-polarized macrophages, improves insulin sensitivity in obese mice. Cell Metab. 2021;33(4):781-790.e785.
Castaño C, Kalko S, Novials A, Párrizas M. Obesity-associated exosomal miRNAs modulate glucose and lipid metabolism in mice. Proc Natl Acad Sci. 2018;115(48):12158–63.
Ji Y, Luo Z, Gao H, Dos Reis FCG, Bandyopadhyay G, Jin Z, Manda KA, Isaac R, Yang M, Fu W, et al. Hepatocyte-derived exosomes from early onset obese mice promote insulin sensitivity through miR-3075. Nat Metab. 2021;3(9):1163–74.
Whitham M, Parker BL, Friedrichsen M, Hingst JR, Hjorth M, Hughes WE, Egan CL, Cron L, Watt KI, Kuchel RP, et al. Extracellular vesicles provide a means for tissue crosstalk during exercise. Cell Metab. 2018;27(1):237-251.e234.
Salomon C, Scholz-Romero K, Sarker S, Sweeney E, Kobayashi M, Correa P, Longo S, Duncombe G, Mitchell MD, Rice GE, et al. Gestational diabetes mellitus is associated with changes in the concentration and bioactivity of placenta-derived exosomes in maternal circulation across gestation. Diabetes. 2016;65(3):598–609.
James-Allan LB, Rosario FJ, Barner K, Lai A, Guanzon D, McIntyre HD, Lappas M, Powell TL, Salomon C, Jansson T. Regulation of glucose homeostasis by small extracellular vesicles in normal pregnancy and in gestational diabetes. FASEB J. 2020;34(4):5724–39.
Rice GE, Scholz-Romero K, Sweeney E, Peiris H, Kobayashi M, Duncombe G, Mitchell MD, Salomon C. The effect of glucose on the release and bioactivity of exosomes from first trimester trophoblast cells. J Clin Endocrinol Metab. 2015;100(10):E1280-1288.
Nair S, Jayabalan N, Guanzon D, Palma C, Scholz-Romero K, Elfeky O, Zuniga F, Ormazabal V, Diaz E, Rice GE, et al. Human placental exosomes in gestational diabetes mellitus carry a specific set of miRNAs associated with skeletal muscle insulin sensitivity. Clin Sci. 2018;132(22):2451–67.
Nair S, Guanzon D, Jayabalan N, Lai A, Scholz-Romero K, Kalita de Croft P, Ormazabal V, Palma C, Diaz E, McCarthy EA, et al. Extracellular vesicle-associated miRNAs are an adaptive response to gestational diabetes mellitus. J Transl Med. 2021;19(1):360.
Jayabalan N, Lai A, Ormazabal V, Adam S, Guanzon D, Palma C, Scholz-Romero K, Lim R, Jansson T, McIntyre HD, et al. Adipose tissue exosomal proteomic profile reveals a role on placenta glucose metabolism in gestational diabetes mellitus. J Clin Endocrinol Metab. 2018;104(5):1735–52.
El Hajj N, Pliushch G, Schneider E, Dittrich M, Muller T, Korenkov M, Aretz M, Zechner U, Lehnen H, Haaf T. Metabolic programming of MEST DNA methylation by intrauterine exposure to gestational diabetes mellitus. Diabetes. 2013;62(4):1320–8.
Pinney SE, Joshi A, Yin V, Min SW, Rashid C, Condon DE, Wang PZ. Exposure to gestational diabetes enriches immune-related pathways in the transcriptome and methylome of human amniocytes. J Clin Endocrinol Metab. 2020;105(10):3250–64.
Haertle L, El Hajj N, Dittrich M, Muller T, Nanda I, Lehnen H, Haaf T. Epigenetic signatures of gestational diabetes mellitus on cord blood methylation. Clin Epigenet. 2017;9:28.
Houde AA, Guay SP, Desgagne V, Hivert MF, Baillargeon JP, St-Pierre J, Perron P, Gaudet D, Brisson D, Bouchard L. Adaptations of placental and cord blood ABCA1 DNA methylation profile to maternal metabolic status. Epigenetics. 2013;8(12):1289–302.
Finer S, Mathews C, Lowe R, Smart M, Hillman S, Foo L, Sinha A, Williams D, Rakyan VK, Hitman GA. Maternal gestational diabetes is associated with genome-wide DNA methylation variation in placenta and cord blood of exposed offspring. Hum Mol Genet. 2015;24(11):3021–9.
Emerging Risk Factors C, Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E, Ingelsson E, Lawlor DA, Selvin E, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–22.
Singh GM, Danaei G, Farzadfar F, Stevens GA, Woodward M, Wormser D, Kaptoge S, Whitlock G, Qiao Q, Lewington S, et al. The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis. PLoS ONE. 2013;8(7): e65174.
Einarson TR, Acs A, Ludwig C, Panton UH. Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007–2017. Cardiovasc Diabetol. 2018;17(1):83.
Fletcher B, Gulanick M, Lamendola C. Risk factors for type 2 diabetes mellitus. J Cardiovasc Nurs. 2002;16(2):17–23.
Andraweera P, Roberts CT, Leemaqz S, McCowan L, Myers J, Kenny LC, Walker J, Poston L, Dekker G, Consortium S. The duration of sexual relationship and its effects on adverse pregnancy outcomes. J Reprod Immunol. 2018;128:16–22.
Tam WH, Ma RCW, Ozaki R, Li AM, Chan MHM, Yuen LY, Lao TTH, Yang X, Ho CS, Tutino GE, et al. In utero exposure to maternal hyperglycemia increases childhood cardiometabolic risk in offspring. Diabetes Care. 2017;40(5):679–86.
Barker DJ. Fetal origins of coronary heart disease. BMJ. 1995;311(6998):171–4.
West NA, Crume TL, Maligie MA, Dabelea D. Cardiovascular risk factors in children exposed to maternal diabetes in utero. Diabetologia. 2011;54(3):504–7.
Lawlor DA, Lichtenstein P, Langstrom N. Association of maternal diabetes mellitus in pregnancy with offspring adiposity into early adulthood: sibling study in a prospective cohort of 280,866 men from 248,293 families. Circulation. 2011;123(3):258–65.
Reynolds RM, Allan KM, Raja EA, Bhattacharya S, McNeill G, Hannaford PC, Sarwar N, Lee AJ, Bhattacharya S, Norman JE. Maternal obesity during pregnancy and premature mortality from cardiovascular event in adult offspring: follow-up of 1 323 275 person years. BMJ. 2013;347: f4539.
American Diabetes A. Standards of medical care in diabetes-2015 abridged for primary care providers. Clin Diabetes. 2015;33(2):97–111.
American Diabetes Association. 2. Classification and diagnosis of diabetes. Diabetes Care. 2016;39(Suppl 1):S13-22.
Kanguru L, Bezawada N, Hussein J, Bell J. The burden of diabetes mellitus during pregnancy in low- and middle-income countries: a systematic review. Glob Health Action. 2014;7:23987.
Gibson KS, Waters TP, Catalano PM. Maternal weight gain in women who develop gestational diabetes mellitus. Obstet Gynecol. 2012;119(3):560–5.
Ruchat SM, Houde AA, Voisin G, St-Pierre J, Perron P, Baillargeon JP, Gaudet D, Hivert MF, Brisson D, Bouchard L. Gestational diabetes mellitus epigenetically affects genes predominantly involved in metabolic diseases. Epigenetics. 2013;8(9):935–43.
Ruchat SM, Mottola MF. The important role of physical activity in the prevention and management of gestational diabetes mellitus. Diabetes Metab Res Rev. 2013;29(5):334–46.
Tam WH, Ma RC, Yang X, Ko GT, Tong PC, Cockram CS, Sahota DS, Rogers MS, Chan JC. Glucose intolerance and cardiometabolic risk in children exposed to maternal gestational diabetes mellitus in utero. Pediatrics. 2008;122(6):1229–34.
Coles N, Patel BP, Birken C, Hanley AJ, Retnakaran R, Hamilton JK. Determinants of insulin resistance in children exposed to gestational diabetes in utero. Pediatr Diabetes. 2020;21(7):1150–8.
Oyen N, Diaz LJ, Leirgul E, Boyd HA, Priest J, Mathiesen ER, Quertermous T, Wohlfahrt J, Melbye M. Prepregnancy diabetes and offspring risk of congenital heart disease: a nationwide cohort study. Circulation. 2016;133(23):2243–53.
Boney CM, Verma A, Tucker R, Vohr BR. Metabolic syndrome in childhood: association with birth weight, maternal obesity, and gestational diabetes mellitus. Pediatrics. 2005;115(3):e290-296.
Catalano PM. The impact of gestational diabetes and maternal obesity on the mother and her offspring. J Dev Orig Health Dis. 2010;1(4):208–15.
Bateson P, Barker D, Clutton-Brock T, Deb D, D’Udine B, Foley RA, Gluckman P, Godfrey K, Kirkwood T, Lahr MM, et al. Developmental plasticity and human health. Nature. 2004;430(6998):419–21.
Drake AJ, Reynolds RM. Impact of maternal obesity on offspring obesity and cardiometabolic disease risk. Reproduction. 2010;140(3):387–98.
Kampmann FB, Thuesen ACB, Hjort L, Olsen SF, Pires SM, Tetens I, Grunnet LG. Exposure to gestational diabetes is a stronger predictor of dysmetabolic traits in children than size at birth. J Clin Endocrinol Metab. 2019;104(5):1766–76.
Lowe WL Jr, Scholtens DM, Lowe LP, Kuang A, Nodzenski M, Talbot O, Catalano PM, Linder B, Brickman WJ, Clayton P, et al. Association of gestational diabetes with maternal disorders of glucose metabolism and childhood adiposity. JAMA. 2018;320(10):1005–16.
Tam WH, Ma RC, Yang X, Li AM, Ko GT, Kong AP, Lao TT, Chan MH, Lam CW, Chan JC. Glucose intolerance and cardiometabolic risk in adolescents exposed to maternal gestational diabetes: a 15-year follow-up study. Diabetes Care. 2010;33(6):1382–4.
Malcolm JC, Lawson ML, Gaboury I, Lough G, Keely E. Glucose tolerance of offspring of mother with gestational diabetes mellitus in a low-risk population. Diabet Med. 2006;23(5):565–70.
Gao M, Cao S, Li N, Liu J, Lyu Y, Li J, Yang X. Risks of overweight in the offspring of women with gestational diabetes at different developmental stages: a meta-analysis with more than half a million offspring. Obes Rev. 2022;23(3): e13395.
Keely EJ, Malcolm JC, Hadjiyannakis S, Gaboury I, Lough G, Lawson ML. Prevalence of metabolic markers of insulin resistance in offspring of gestational diabetes pregnancies. Pediatr Diabetes. 2008;9(1):53–9.
Boerschmann H, Pfluger M, Henneberger L, Ziegler AG, Hummel S. Prevalence and predictors of overweight and insulin resistance in offspring of mothers with gestational diabetes mellitus. Diabetes Care. 2010;33(8):1845–9.
Egeland GM, Meltzer SJ. Following in mother’s footsteps? Mother-daughter risks for insulin resistance and cardiovascular disease 15 years after gestational diabetes. Diabet Med. 2010;27(3):257–65.
Gu Y, Lu J, Li W, Liu H, Wang L, Leng J, Li W, Zhang S, Wang S, Tuomilehto J, et al. Joint associations of maternal gestational diabetes and hypertensive disorders of pregnancy with overweight in offspring. Front Endocrinol. 2019;10:645.
Kaseva N, Vaarasmaki M, Sundvall J, Matinolli HM, Sipola M, Tikanmaki M, Heinonen K, Lano A, Wehkalampi K, Wolke D, et al. Gestational diabetes but not prepregnancy overweight predicts for cardiometabolic markers in offspring twenty years later. J Clin Endocrinol Metab. 2019;104(7):2785–95.
Holder T, Giannini C, Santoro N, Pierpont B, Shaw M, Duran E, Caprio S, Weiss R. A low disposition index in adolescent offspring of mothers with gestational diabetes: a risk marker for the development of impaired glucose tolerance in youth. Diabetologia. 2014;57(11):2413–20.
Lowe WL Jr, Scholtens DM, Kuang A, Linder B, Lawrence JM, Lebenthal Y, McCance D, Hamilton J, Nodzenski M, Talbot O, et al. hyperglycemia and adverse pregnancy outcome follow-up study (HAPO FUS): maternal gestational diabetes mellitus and childhood glucose metabolism. Diabetes Care. 2019;42(3):372–80.
Clausen TD, Mathiesen ER, Hansen T, Pedersen O, Jensen DM, Lauenborg J, Damm P. High prevalence of type 2 diabetes and pre-diabetes in adult offspring of women with gestational diabetes mellitus or type 1 diabetes: the role of intrauterine hyperglycemia. Diabetes Care. 2008;31(2):340–6.
Hyperglycemia and Adverse Pregnancy Outcomes. N Engl J Med. 2008;358(19):1991–2002.
Ritchie S, Connell J. The link between abdominal obesity, metabolic syndrome and cardiovascular disease. Nutr Metab Cardiovasc Dis. 2007;17(4):319–26.
Shi X, Wang D, Lin M, Su W, Wang L, Xiao F, Huang P, Liu W, Zeng J, Song H, et al. Maternal gestational diabetes mellitus and offspring’s body mass index from 1 to 4 years. Endocr Pract. 2020;26(6):619–26.
Hammoud NM, de Valk HW, Biesma DH, Visser GH. Intrauterine adiposity and BMI in 4- to 5-year-old offspring from diabetic pregnancies. Neonatology. 2017;111(2):177–81.
Patel BP, McLellan SS, Hanley AJ, Retnakaran R, Hamilton JK. Greater nutritional risk scores in 2-year-old children exposed to gestational diabetes mellitus in utero and their relationship to homeostasis model assessment for insulin resistance at age 5 years. Can J Diabetes. 2021;45(5):390–4.
Yu Y, Arah OA, Liew Z, Cnattingius S, Olsen J, Sorensen HT, Qin G, Li J. Maternal diabetes during pregnancy and early onset of cardiovascular disease in offspring: population based cohort study with 40 years of follow-up. BMJ. 2019;367: l6398.
Gomes D, von Kries R, Delius M, Mansmann U, Nast M, Stubert M, Langhammer L, Haas NA, Netz H, Obermeier V, et al. Late-pregnancy dysglycemia in obese pregnancies after negative testing for gestational diabetes and risk of future childhood overweight: An interim analysis from a longitudinal mother-child cohort study. PLoS Med. 2018;15(10): e1002681.
Pirkola J, Pouta A, Bloigu A, Hartikainen AL, Laitinen J, Jarvelin MR, Vaarasmaki M. Risks of overweight and abdominal obesity at age 16 years associated with prenatal exposures to maternal prepregnancy overweight and gestational diabetes mellitus. Diabetes Care. 2010;33(5):1115–21.
Pham MT, Brubaker K, Pruett K, Caughey AB. Risk of childhood obesity in the toddler offspring of mothers with gestational diabetes. Obstet Gynecol. 2013;121(5):976–82.
Hammoud NM, Visser GHA, van Rossem L, Biesma DH, Wit JM, de Valk HW. Long-term BMI and growth profiles in offspring of women with gestational diabetes. Diabetologia. 2018;61(5):1037–45.
Kaseva N, Vaarasmaki M, Matinolli HM, Sipola-Leppanen M, Tikanmaki M, Heinonen K, Lano A, Wolke D, Andersson S, Jarvelin MR, et al. Pre-pregnancy overweight or obesity and gestational diabetes as predictors of body composition in offspring twenty years later: evidence from two birth cohort studies. Int J Obes. 2018;42(4):872–9.
Mitanchez D, Yzydorczyk C, Simeoni U. What neonatal complications should the pediatrician be aware of in case of maternal gestational diabetes? World J Diabetes. 2015;6(5):734–43.
Sallam NA, Palmgren VAC, Singh RD, John CM, Thompson JA. Programming of vascular dysfunction in the intrauterine milieu of diabetic pregnancies. Int J Mol Sci. 2018;19(11):3665.
Gluckman PD, Hanson MA, Buklijas T. A conceptual framework for the developmental origins of health and disease. J Dev Orig Health Dis. 2010;1(1):6–18.
Godfrey KM, Gluckman PD, Hanson MA. Developmental origins of metabolic disease: life course and intergenerational perspectives. Trends Endocrinol Metab. 2010;21(4):199–205.
Dalfra MG, Burlina S, Del Vescovo GG, Lapolla A. Genetics and epigenetics: new insight on gestational diabetes mellitus. Front Endocrinol. 2020;11: 602477.
McCarthy MI. Genomics, type 2 diabetes, and obesity. N Engl J Med. 2010;363(24):2339–50.
Damm P, Houshmand-Oeregaard A, Kelstrup L, Lauenborg J, Mathiesen ER, Clausen TD. Gestational diabetes mellitus and long-term consequences for mother and offspring: a view from Denmark. Diabetologia. 2016;59(7):1396–9.
Pereira TJ, Moyce BL, Kereliuk SM, Dolinsky VW. Influence of maternal overnutrition and gestational diabetes on the programming of metabolic health outcomes in the offspring: experimental evidence. Biochem Cell Biol. 2015;93(5):438–51.
Jones PA, Takai D. The role of DNA methylation in mammalian epigenetics. Science. 2001;293(5532):1068–70.
Callinan PA, Feinberg AP. The emerging science of epigenomics. Hum Mol Genet. 2006;15:R95-101.
Cavalli G, Heard E. Advances in epigenetics link genetics to the environment and disease. Nature. 2019;571(7766):489–99.
Unnikrishnan A, Freeman WM, Jackson J, Wren JD, Porter H, Richardson A. The role of DNA methylation in epigenetics of aging. Pharmacol Ther. 2019;195:172–85.
Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo QM, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009;462(7271):315–22.
Barski A, Jothi R, Cuddapah S, Cui K, Roh TY, Schones DE, Zhao K. Chromatin poises miRNA- and protein-coding genes for expression. Genome Res. 2009;19(10):1742–51.
Sato F, Tsuchiya S, Meltzer SJ, Shimizu K. MicroRNAs and epigenetics. FEBS J. 2011;278(10):1598–609.
Lillycrop KA, Phillips ES, Jackson AA, Hanson MA, Burdge GC. Dietary protein restriction of pregnant rats induces and folic acid supplementation prevents epigenetic modification of hepatic gene expression in the offspring. J Nutr. 2005;135(6):1382–6.
McGowan PO, Sasaki A, D’Alessio AC, Dymov S, Labonte B, Szyf M, Turecki G, Meaney MJ. Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nat Neurosci. 2009;12(3):342–8.
Tammen SA, Friso S, Choi SW. Epigenetics: the link between nature and nurture. Mol Aspects Med. 2013;34(4):753–64.
Ruchat SM, Hivert MF, Bouchard L. Epigenetic programming of obesity and diabetes by in utero exposure to gestational diabetes mellitus. Nutr Rev. 2013;71(Suppl 1):S88-94.
Quilter CR, Cooper WN, Cliffe KM, Skinner BM, Prentice PM, Nelson L, Bauer J, Ong KK, Constancia M, Lowe WL, et al. Impact on offspring methylation patterns of maternal gestational diabetes mellitus and intrauterine growth restraint suggest common genes and pathways linked to subsequent type 2 diabetes risk. FASEB J. 2014;28(11):4868–79.
Kasuga Y, Kawai T, Miyakoshi K, Saisho Y, Tamagawa M, Hasegawa K, Ikenoue S, Ochiai D, Hida M, Tanaka M, et al. Epigenetic changes in neonates born to mothers with gestational diabetes mellitus may be associated with neonatal hypoglycaemia. Front Endocrinol. 2021;12: 690648.
Tobi EW, Juvinao-Quintero DL, Ronkainen J, Ott R, Alfano R, Canouil M, Geurtsen ML, Khamis A, Kupers LK, Lim IY, et al. Maternal glycemic dysregulation during pregnancy and neonatal blood DNA methylation: meta-analyses of epigenome-wide association studies. Diabetes Care. 2022;45(3):614–23.
Lesseur C, Armstrong DA, Paquette AG, Li Z, Padbury JF, Marsit CJ. Maternal obesity and gestational diabetes are associated with placental leptin DNA methylation. Am J Obstet Gynecol. 2014;211(6):654.
Allard C, Desgagne V, Patenaude J, Lacroix M, Guillemette L, Battista MC, Doyon M, Menard J, Ardilouze JL, Perron P, et al. Mendelian randomization supports causality between maternal hyperglycemia and epigenetic regulation of leptin gene in newborns. Epigenetics. 2015;10(4):342–51.
Bouchard L, Thibault S, Guay SP, Santure M, Monpetit A, St-Pierre J, Perron P, Brisson D. Leptin gene epigenetic adaptation to impaired glucose metabolism during pregnancy. Diabetes Care. 2010;33(11):2436–41.
Peng W, Chen Y, Luo X, Shan N, Lan X, Olson D, Zhang H, Ding YB, Qi HB. DNA methylation-associated repression of MEST/PEG1 expression contributes to the invasion of extravillous trophoblast cells. Placenta. 2016;46:92–101.
Himes KP, Young A, Koppes E, Stolz D, Barak Y, Sadovsky Y, Chaillet JR. Loss of inherited genomic imprints in mice leads to severe disruption in placental lipid metabolism. Placenta. 2015;36(4):389–96.
Takahashi M, Kamei Y, Ezaki O. Mest/Peg1 imprinted gene enlarges adipocytes and is a marker of adipocyte size. Am J Physiol Endocrinol Metab. 2005;288(1):E117-124.
Nikonova L, Koza RA, Mendoza T, Chao PM, Curley JP, Kozak LP. Mesoderm-specific transcript is associated with fat mass expansion in response to a positive energy balance. FASEB J. 2008;22(11):3925–37.
Joyce BT, Liu H, Wang L, Wang J, Zheng Y, Nannini D, Drong A, Shiau S, Li W, Leng J, et al. Novel epigenetic link between gestational diabetes mellitus and macrosomia. Epigenomics. 2021;13(15):1221–30.
Joyce C, Freeman L, Brewer HB Jr, Santamarina-Fojo S. Study of ABCA1 function in transgenic mice. Arterioscler Thromb Vasc Biol. 2003;23(6):965–71.
Zhu Z, Chen X, Xiao Y, Wen J, Chen J, Wang K, Chen G. Gestational diabetes mellitus alters DNA methylation profiles in pancreas of the offspring mice. J Diabetes Complicat. 2019;33(1):15–22.
Sobrevia L, Abarzua F, Nien JK, Salomon C, Westermeier F, Puebla C, Cifuentes F, Guzman-Gutierrez E, Leiva A, Casanello P. Review: differential placental macrovascular and microvascular endothelial dysfunction in gestational diabetes. Placenta. 2011;32(Suppl 2):S159-164.
Floris I, Descamps B, Vardeu A, Mitic T, Posadino AM, Shantikumar S, Sala-Newby G, Capobianco G, Mangialardi G, Howard L, et al. Gestational diabetes mellitus impairs fetal endothelial cell functions through a mechanism involving microRNA-101 and histone methyltransferase enhancer of zester homolog-2. Arterioscler Thromb Vasc Biol. 2015;35(3):664–74.
Owens GK, Kumar MS, Wamhoff BR. Molecular regulation of vascular smooth muscle cell differentiation in development and disease. Physiol Rev. 2004;84(3):767–801.
Bayol SA, Simbi BH, Stickland NC. A maternal cafeteria diet during gestation and lactation promotes adiposity and impairs skeletal muscle development and metabolism in rat offspring at weaning. J Physiol. 2005;567(Pt 3):951–61.
Liu N, Bezprozvannaya S, Shelton JM, Frisard MI, Hulver MW, McMillan RP, Wu Y, Voelker KA, Grange RW, Richardson JA, et al. Mice lacking microRNA 133a develop dynamin 2-dependent centronuclear myopathy. J Clin Invest. 2011;121(8):3258–68.
Mughal W, Nguyen L, Pustylnik S, da Silva Rosa SC, Piotrowski S, Chapman D, Du M, Alli NS, Grigull J, Halayko AJ, et al. A conserved MADS-box phosphorylation motif regulates differentiation and mitochondrial function in skeletal, cardiac, and smooth muscle cells. Cell Death Dis. 2015;6: e1944.
Zhang J, Ney PA. Role of BNIP3 and NIX in cell death, autophagy, and mitophagy. Cell Death Differ. 2009;16(7):939–46.
Houshmand-Oeregaard A, Schrolkamp M, Kelstrup L, Hansen NS, Hjort L, Thuesen ACB, Broholm C, Mathiesen ER, Clausen TD, Vaag A, et al. Increased expression of microRNA-15a and microRNA-15b in skeletal muscle from adult offspring of women with diabetes in pregnancy. Hum Mol Genet. 2018;27(10):1763–71.
Muhonen P, Holthofer H. Epigenetic and microRNA-mediated regulation in diabetes. Nephrol Dial Transplant. 2009;24(4):1088–96.
Chen X, Ba Y, Ma L, Cai X, Yin Y, Wang K, Guo J, Zhang Y, Chen J, Guo X, et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res. 2008;18(10):997–1006.
Parrizas M, Novials A. Circulating microRNAs as biomarkers for metabolic disease. Best Pract Res Clin Endocrinol Metab. 2016;30(5):591–601.
Parrizas M, Brugnara L, Esteban Y, Gonzalez-Franquesa A, Canivell S, Murillo S, Gordillo-Bastidas E, Cusso R, Cadefau JA, Garcia-Roves PM, et al. Circulating miR-192 and miR-193b are markers of prediabetes and are modulated by an exercise intervention. J Clin Endocrinol Metab. 2015;100(3):E407-415.
Liu Y, Gao G, Yang C, Zhou K, Shen B, Liang H, Jiang X. The role of circulating microRNA-126 (miR-126): a novel biomarker for screening prediabetes and newly diagnosed type 2 diabetes mellitus. Int J Mol Sci. 2014;15(6):10567–77.
Al-Kafaji G, Al-Mahroos G, Alsayed NA, Hasan ZA, Nawaz S, Bakhiet M. Peripheral blood microRNA-15a is a potential biomarker for type 2 diabetes mellitus and pre-diabetes. Mol Med Rep. 2015;12(5):7485–90.
Zhu H, Leung SW. Identification of microRNA biomarkers in type 2 diabetes: a meta-analysis of controlled profiling studies. Diabetologia. 2015;58(5):900–11.
Zampetaki A, Kiechl S, Drozdov I, Willeit P, Mayr U, Prokopi M, Mayr A, Weger S, Oberhollenzer F, Bonora E, et al. Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circ Res. 2010;107(6):810–7.
Kong L, Zhu J, Han W, Jiang X, Xu M, Zhao Y, Dong Q, Pang Z, Guan Q, Gao L, et al. Significance of serum microRNAs in pre-diabetes and newly diagnosed type 2 diabetes: a clinical study. Acta Diabetol. 2011;48(1):61–9.
Yan S, Wang T, Huang S, Di Y, Huang Y, Liu X, Luo Z, Han W, An B. Differential expression of microRNAs in plasma of patients with prediabetes and newly diagnosed type 2 diabetes. Acta Diabetol. 2016;53(5):693–702.
Arroyo JD, Chevillet JR, Kroh EM, Ruf IK, Pritchard CC, Gibson DF, Mitchell PS, Bennett CF, Pogosova-Agadjanyan EL, Stirewalt DL, et al. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc Natl Acad Sci USA. 2011;108(12):5003–8.
Vickers KC, Palmisano BT, Shoucri BM, Shamburek RD, Remaley AT. MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol. 2011;13(4):423–33.
Yoshioka Y, Katsuda T, Ochiya T. Extracellular vesicles and encapusulated miRNAs as emerging cancer biomarkers for novel liquid biopsy. Jpn J Clin Oncol. 2018;48(10):869–76.
Shen M, Dong C, Ruan X, Yan W, Cao M, Pizzo D, Wu X, Yang L, Liu L, Ren X, et al. Chemotherapy-induced extracellular vesicle miRNAs promote breast cancer stemness by targeting ONECUT2. Cancer Res. 2019;79(14):3608–21.
Nguyen MA, Karunakaran D, Geoffrion M, Cheng HS, Tandoc K, Perisic Matic L, Hedin U, Maegdefessel L, Fish JE, Rayner KJ. Extracellular vesicles secreted by atherogenic macrophages transfer microrna to inhibit cell migration. Arterioscler Thromb Vasc Biol. 2018;38(1):49–63.
Pardo F, Villalobos-Labra R, Sobrevia B, Toledo F, Sobrevia L. Extracellular vesicles in obesity and diabetes mellitus. Mol Aspects Med. 2018;60:81–91.
Nagano T, Katsurada M, Dokuni R, Hazama D, Kiriu T, Umezawa K, Kobayashi K, Nishimura Y. Crucial role of extracellular vesicles in bronchial asthma. Int J Mol Sci. 2019;20(10):2589.
Fu H, Hu D, Zhang L, Tang P. Role of extracellular vesicles in rheumatoid arthritis. Mol Immunol. 2018;93:125–32.
Weir GC, Bonner-Weir S. Five stages of evolving beta-cell dysfunction during progression to diabetes. Diabetes. 2004;53(Suppl 3):S16-21.
DeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, Hu FB, Kahn CR, Raz I, Shulman GI, et al. Type 2 diabetes mellitus. Nat Rev Dis Primers. 2015;1:15019.
Tkach M, Thery C. Communication by extracellular vesicles: where we are and where we need to go. Cell. 2016;164(6):1226–32.
El Andaloussi S, Mager I, Breakefield XO, Wood MJ. Extracellular vesicles: biology and emerging therapeutic opportunities. Nat Rev Drug Discov. 2013;12(5):347–435.
Colombo M, Raposo G, Thery C. Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu Rev Cell Dev Biol. 2014;30:255–89.
Crescitelli R, Lasser C, Szabo TG, Kittel A, Eldh M, Dianzani I, Buzas EI, Lotvall J. Distinct RNA profiles in subpopulations of extracellular vesicles: apoptotic bodies, microvesicles and exosomes. J Extracell Vesicles. 2013;2:20677.
Raposo G, Stoorvogel W. Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol. 2013;200(4):373–83.
Zhu X, You Y, Li Q, Zeng C, Fu F, Guo A, Zhang H, Zou P, Zhong Z, Wang H, et al. BCR-ABL1-positive microvesicles transform normal hematopoietic transplants through genomic instability: implications for donor cell leukemia. Leukemia. 2014;28(8):1666–75.
Mensà E, Guescini M, Giuliani A, Bacalini MG, Ramini D, Corleone G, Ferracin M, Fulgenzi G, Graciotti L, Prattichizzo F, et al. Small extracellular vesicles deliver miR-21 and miR-217 as pro-senescence effectors to endothelial cells. J Extracell Vesicles. 2020;9(1):1725285.
Morgan HD, Santos F, Green K, Dean W, Reik W. Epigenetic reprogramming in mammals. Hum Mol Genet. 2005;14(suppl_1):e47–58.
Feng Y, Huang W, Meng W, Jegga AG, Wang Y, Cai W, Kim HW, Pasha Z, Wen Z, Rao F, et al. Heat shock improves Sca-1+ stem cell survival and directs ischemic cardiomyocytes toward a prosurvival phenotype via exosomal transfer: a critical role for HSF1/miR-34a/HSP70 pathway. Stem Cells. 2014;32(2):462–72.
Schiera G, Di Liegro CM, Saladino P, Pitti R, Savettieri G, Proia P, Di Liegro I. Oligodendroglioma cells synthesize the differentiation-specific linker histone H1˚ and release it into the extracellular environment through shed vesicles. Int J Oncol. 2013;43(6):1771–6.
Marchese FP, Raimondi I, Huarte M. The multidimensional mechanisms of long noncoding RNA function. Genome Biol. 2017;18(1):206.
Chen L, Yang W, Guo Y, Chen W, Zheng P, Zeng J, Tong W. Exosomal lncRNA GAS5 regulates the apoptosis of macrophages and vascular endothelial cells in atherosclerosis. PLoS ONE. 2017;12(9): e0185406.
Li H, Zhu X, Hu L, Li Q, Ma J, Yan J. Loss of exosomal MALAT1 from ox-LDL-treated vascular endothelial cells induces maturation of dendritic cells in atherosclerosis development. Cell Cycle. 2019;18(18):2255–67.
Huang C, Han J, Wu Y, Li S, Wang Q, Lin W, Zhu J. Exosomal MALAT1 derived from oxidized low-density lipoprotein-treated endothelial cells promotes M2 macrophage polarization. Mol Med Rep. 2018;18(1):509–15.
Gao H, Wang X, Lin C, An Z, Yu J, Cao H, Fan Y, Liang X. Exosomal MALAT1 derived from ox-LDL-treated endothelial cells induce neutrophil extracellular traps to aggravate atherosclerosis. Biol Chem. 2020;401(3):367–76.
Shyu KG, Wang BW, Fang WJ, Pan CM, Lin CM. Hyperbaric oxygen-induced long non-coding RNA MALAT1 exosomes suppress MicroRNA-92a expression in a rat model of acute myocardial infarction. J Cell Mol Med. 2020;24(22):12945–54.
Huang P, Wang L, Li Q, Tian X, Xu J, Xu J, Xiong Y, Chen G, Qian H, Jin C, et al. Atorvastatin enhances the therapeutic efficacy of mesenchymal stem cells-derived exosomes in acute myocardial infarction via up-regulating long non-coding RNA H19. Cardiovasc Res. 2019;116(2):353–67.
Wang L, Zhang J. Exosomal lncRNA AK139128 derived from hypoxic cardiomyocytes promotes apoptosis and inhibits cell proliferation in cardiac fibroblasts. Int J Nanomed. 2020;15:3363.
Ruan Y, Lin N, Ma Q, Chen R, Zhang Z, Wen W, Chen H, Sun J. Circulating LncRNAs analysis in patients with type 2 diabetes reveals novel genes influencing glucose metabolism and islet β-cell function. Cell Physiol Biochem. 2018;46(1):335–50.
Cao X, Xue LD, Di Y, Li T, Tian YJ, Song Y. MSC-derived exosomal lncRNA SNHG7 suppresses endothelial-mesenchymal transition and tube formation in diabetic retinopathy via miR-34a-5p/XBP1 axis. Life Sci. 2021;272: 119232.
Li B, Luan S, Chen J, Zhou Y, Wang T, Li Z, Fu Y, Zhai A, Bi C. The MSC-derived exosomal lncRNA H19 promotes wound healing in diabetic foot ulcers by upregulating PTEN via microRNA-152-3p. Mol Ther Nucleic Acids. 2020;19:814–26.
Zhu B, Zhang L, Liang C, Liu B, Pan X, Wang Y, Zhang Y, Zhang Y, Xie W, Yan B, et al. Stem cell-derived exosomes prevent aging-induced cardiac dysfunction through a novel exosome/lncRNA MALAT1/NF-κB/TNF-α signaling pathway. Oxid Med Cell Longev. 2019;2019:9739258.
Tello-Flores VA, Valladares-Salgado A, Ramírez-Vargas MA, Cruz M, del Moral-Hernández O, Cahua-Pablo JÁ, Ramírez M, Hernández-Sotelo D, Armenta-Solis A, Flores-Alfaro E. Altered levels of MALAT1 and H19 derived from serum or serum exosomes associated with type-2 diabetes. Non-coding RNA Res. 2020;5(2):71–6.
Wen Y, Chun Y, Lian ZQ, Yong ZW, Lan YM, Huan L, Xi CY, Juan LS, Qing ZW, Jia C. circRNA-0006896-miR1264-DNMT1 axis plays an important role in carotid plaque destabilization by regulating the behavior of endothelial cells in atherosclerosis. Mol Med Rep. 2021;23(5):1–13.
Wang S, Zhan J, Lin X, Wang Y, Wang Y, Liu Y. CircRNA-0077930 from hyperglycaemia-stimulated vascular endothelial cell exosomes regulates senescence in vascular smooth muscle cells. Cell Biochem Funct. 2020;38(8):1056–68.
Bai S, Xiong X, Tang B, Ji T, Li X, Qu X, Li W. Exosomal circ_DLGAP4 promotes diabetic kidney disease progression by sponging miR-143 and targeting ERBB3/NF-κB/MMP-2 axis. Cell Death Dis. 2020;11(11):1–13.
Pratt AJ, MacRae IJ. The RNA-induced silencing complex: a versatile gene-silencing machine. J Biol Chem. 2009;284(27):17897–901.
Mori MA, Ludwig RG, Garcia-Martin R, Brandão BB, Kahn CR. Extracellular miRNAs: from biomarkers to mediators of physiology and disease. Cell Metab. 2019;30(4):656–73.
Isaac R, Reis FCG, Ying W, Olefsky JM. Exosomes as mediators of intercellular crosstalk in metabolism. Cell Metab. 2021;33(9):1744–62.
Hink U, Li H, Mollnau H, Oelze M, Matheis E, Hartmann M, Skatchkov M, Thaiss F, Stahl RA, Warnholtz A, et al. Mechanisms underlying endothelial dysfunction in diabetes mellitus. Circ Res. 2001;88(2):E14-22.
Ceriello A. The emerging challenge in diabetes: the “metabolic memory.” Vascul Pharmacol. 2012;57(5–6):133–8.
Prattichizzo F, Giuliani A, De Nigris V, Pujadas G, Ceka A, La Sala L, Genovese S, Testa R, Procopio AD, Olivieri F, et al. Extracellular microRNAs and endothelial hyperglycaemic memory: a therapeutic opportunity? Diabetes Obes Metab. 2016;18(9):855–67.
Bijkerk R, van Solingen C, de Boer HC, van der Pol P, Khairoun M, de Bruin RG, van Oeveren-Rietdijk AM, Lievers E, Schlagwein N, van Gijlswijk DJ, et al. Hematopoietic microRNA-126 protects against renal ischemia/reperfusion injury by promoting vascular integrity. J Am Soc Nephrol. 2014;25(8):1710–22.
Jansen F, Yang X, Hoelscher M, Cattelan A, Schmitz T, Proebsting S, Wenzel D, Vosen S, Franklin BS, Fleischmann BK, et al. Endothelial microparticle-mediated transfer of MicroRNA-126 promotes vascular endothelial cell repair via SPRED1 and is abrogated in glucose-damaged endothelial microparticles. Circulation. 2013;128(18):2026–38.
Wang X, Huang W, Liu G, Cai W, Millard RW, Wang Y, Chang J, Peng T, Fan GC. Cardiomyocytes mediate anti-angiogenesis in type 2 diabetic rats through the exosomal transfer of miR-320 into endothelial cells. J Mol Cell Cardiol. 2014;74:139–50.
Wang X, Gu H, Huang W, Peng J, Li Y, Yang L, Qin D, Essandoh K, Wang Y, Peng T, et al. Hsp20-mediated activation of exosome biogenesis in cardiomyocytes improves cardiac function and angiogenesis in diabetic mice. Diabetes. 2016;65(10):3111–28.
Garcia NA, Moncayo-Arlandi J, Sepulveda P, Diez-Juan A. Cardiomyocyte exosomes regulate glycolytic flux in endothelium by direct transfer of GLUT transporters and glycolytic enzymes. Cardiovasc Res. 2016;109(3):397–408.
Garcia NA, Ontoria-Oviedo I, Gonzalez-King H, Diez-Juan A, Sepulveda P. Glucose starvation in cardiomyocytes enhances exosome secretion and promotes angiogenesis in endothelial cells. PLoS ONE. 2015;10(9): e0138849.
Hu J, Wang S, Xiong Z, Cheng Z, Yang Z, Lin J, Wang T, Feng X, Gao E, Wang H, et al. Exosomal Mst1 transfer from cardiac microvascular endothelial cells to cardiomyocytes deteriorates diabetic cardiomyopathy. Biochim Biophys Acta Mol Basis Dis. 2018;1864(11):3639–49.
Wang S, Zhao Z, Fan Y, Zhang M, Feng X, Lin J, Hu J, Cheng Z, Sun C, Liu T, et al. Mst1 inhibits Sirt3 expression and contributes to diabetic cardiomyopathy through inhibiting Parkin-dependent mitophagy. Biochim Biophys Acta Mol Basis Dis. 2019;1865(7):1905–14.
Yu W, Gao B, Li N, Wang J, Qiu C, Zhang G, Liu M, Zhang R, Li C, Ji G, et al. Sirt3 deficiency exacerbates diabetic cardiac dysfunction: role of Foxo3A-Parkin-mediated mitophagy. Biochim Biophys Acta Mol Basis Dis. 2017;1863(8):1973–83.
Castano C, Kalko S, Novials A, Parrizas M. Obesity-associated exosomal miRNAs modulate glucose and lipid metabolism in mice. Proc Natl Acad Sci USA. 2018;115(48):12158–63.
Eder M, Csapo B, Wadsack C, Haas J, Catalano PM, Desoye G, van Poppel MN. Sex differences in the association of cord blood insulin with subcutaneous adipose tissue in neonates. Int J Obes. 2016;40(3):538–42.
Grunnet LG, Hansen S, Hjort L, Madsen CM, Kampmann FB, Thuesen ACB, Granstrømi C, Strøm M, Maslova E, Frikke-Schmidt R, et al. Adiposity, dysmetabolic traits, and earlier onset of female puberty in adolescent offspring of women with gestational diabetes mellitus: a clinical study within the danish national birth cohort. Diabetes Care. 2017;40(12):1746–55.
Silverman BL, Rizzo T, Green OC, Cho NH, Winter RJ, Ogata ES, Richards GE, Metzger BE. Long-term prospective evaluation of offspring of diabetic mothers. Diabetes. 1991;40(Suppl 2):121–5.
Dabelea D, Hanson RL, Lindsay RS, Pettitt DJ, Imperatore G, Gabir MM, Roumain J, Bennett PH, Knowler WC. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships. Diabetes. 2000;49(12):2208–11.
Barker DJ, Bull AR, Osmond C, Simmonds SJ. Fetal and placental size and risk of hypertension in adult life. BMJ. 1990;301(6746):259–62.
Hales CN, Barker D. Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. Int J Epidemiol. 2013;42(5):1215–22.
Nielsen JH, Haase TN, Jaksch C, Nalla A, Søstrup B, Nalla AA, Larsen L, Rasmussen M, Dalgaard LT, Gaarn LW. Impact of fetal and neonatal environment on beta cell function and development of diabetes. Acta Obstet Gynecol Scand. 2014;93(11):1109–22.
Heijmans BT, Tobi EW, Stein AD, Putter H, Blauw GJ, Susser ES, Slagboom PE, Lumey LH. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci. 2008;105(44):17046–9.
Cooney CA, Dave AA, Wolff GL. Maternal methyl supplements in mice affect epigenetic variation and DNA methylation of offspring. J Nutr. 2002;132(8 Suppl):2393s–400s.
Kaminen-Ahola N, Ahola A, Maga M, Mallitt KA, Fahey P, Cox TC, Whitelaw E, Chong S. Maternal ethanol consumption alters the epigenotype and the phenotype of offspring in a mouse model. PLoS Genet. 2010;6(1): e1000811.
Xie X, Gao H, Zeng W, Chen S, Feng L, Deng D, Qiao F-Y, Liao L, McCormick K, Ning Q. Placental DNA methylation of peroxisome-proliferator-activated receptor-γ co-activator-1α promoter is associated with maternal gestational glucose level. Clin Sci. 2015;129(4):385–94.
Cardenas A, Gagné-Ouellet V, Allard C, Brisson D, Perron P, Bouchard L, Hivert M-F. Placental DNA methylation adaptation to maternal glycemic response in pregnancy. Diabetes. 2018;67(8):1673–83.
Yan J, Su R, Zhang W, Wei Y, Wang C, Lin L, Feng H, Yang H. Epigenetic alteration of Rho guanine nucleotide exchange Factor 11 (ARHGEF11) in cord blood samples in macrosomia exposed to intrauterine hyperglycemia. J Matern Fetal Neonatal Med. 2021;34(3):422–31.
Ren XS, Tong Y, Qiu Y, Ye C, Wu N, Xiong XQ, Wang JJ, Han Y, Zhou YB, Zhang F, et al. MiR155-5p in adventitial fibroblasts-derived extracellular vesicles inhibits vascular smooth muscle cell proliferation via suppressing angiotensin-converting enzyme expression. J Extracell Vesicles. 2020;9(1):1698795.
Nehring I, Chmitorz A, Reulen H, von Kries R, Ensenauer R. Gestational diabetes predicts the risk of childhood overweight and abdominal circumference independent of maternal obesity. Diabet Med. 2013;30(12):1449–56.
Lee H, Jang HC, Park HK, Cho NH. Early manifestation of cardiovascular disease risk factors in offspring of mothers with previous history of gestational diabetes mellitus. Diabetes Res Clin Pract. 2007;78(2):238–45.
Perng W, Hockett CW, Sauder KA, Dabelea D. In utero exposure to gestational diabetes mellitus and cardiovascular risk factors in youth: A longitudinal analysis in the EPOCH cohort. Pediatr Obes. 2020;15(5): e12611.
Kelstrup L, Clausen TD, Mathiesen ER, Hansen T, Holst JJ, Damm P. Incretin and glucagon levels in adult offspring exposed to maternal diabetes in pregnancy. J Clin Endocrinol Metab. 2015;100(5):1967–75.
Li J, Tan M, Xiang Q, Zhou Z, Yan H. Thrombin-activated platelet-derived exosomes regulate endothelial cell expression of ICAM-1 via microRNA-223 during the thrombosis-inflammation response. Thromb Res. 2017;154:96–105.
Feng Y, Huang W, Wani M, Yu X, Ashraf M. Ischemic preconditioning potentiates the protective effect of stem cells through secretion of exosomes by targeting Mecp2 via miR-22. PLoS ONE. 2014;9(2): e88685.
Li S, Gao Y, Liu Y, Li J, Yang X, Hu R, Liu J, Zhang Y, Zuo K, Li K. Myofibroblast-derived Exosomes contribute to development of a susceptible substrate for atrial fibrillation. Cardiology. 2020;145(5):324–32.
Li J, Xue H, Li T, Chu X, Xin D, Xiong Y, Qiu W, Gao X, Qian M, Xu J, et al. Exosomes derived from mesenchymal stem cells attenuate the progression of atherosclerosis in ApoE(-/-) mice via miR-let7 mediated infiltration and polarization of M2 macrophage. Biochem Biophys Res Commun. 2019;510(4):565–72.
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This study was supported by the National Health and Medical Research Council (NHMRC 1195451), and Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT 11190522).
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Ormazabal, V., Nair, S., Carrión, F. et al. The link between gestational diabetes and cardiovascular diseases: potential role of extracellular vesicles. Cardiovasc Diabetol 21, 174 (2022). https://doi.org/10.1186/s12933-022-01597-3
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DOI: https://doi.org/10.1186/s12933-022-01597-3