Circulating microRNA Related to Cardiometabolic Risk Factors for Metabolic Syndrome: A Systematic Review

MicroRNA regulates multiple pathways in inflammatory response, adipogenesis, and glucose and lipid metabolism, which are involved in metabolic syndrome (MetS). Thus, this systematic review aimed at synthesizing the evidence on the relationships between circulating microRNA and risk factors for MetS. The systematic review was registered in the PROSPERO database (CRD42020168100) and included 24 case-control studies evaluating microRNA expression in serum/plasma of individuals ≥5 years old. Most of the studies focused on 13 microRNAs with higher frequency and there were robust connections between miR-146a and miR-122 with risk factors for MetS, based on average weighted degree. In addition, there was an association of miR-222 with adiposity, lipid metabolism, glycemic metabolism, and chronic inflammation and an association of miR-126, miR-221, and miR-423 with adiposity, lipid, and glycemic metabolism. A major part of circulating microRNA was upregulated in individuals with risk factors for MetS, showing correlations with glycemic and lipid markers and body adiposity. Circulating microRNA showed distinct expression profiles according to the clinical condition of individuals, being particularly linked with increased body fat. However, the exploration of factors associated with variations in microRNA expression was limited by the variety of microRNAs investigated by risk factor in diverse studies identified in this systematic review.


Introduction
Dyslipidemia, high blood pressure, hyperglycemia, insulin resistance, and obesity are cardiometabolic risk factors associated with metabolic syndrome (MetS) and noncommunicable diseases (NCD) [1][2][3][4]. MetS is low-grade systemic inflammation, or meta inflammation, meaning it comprises a metabolically triggered inflammation process [2,4,5]. This condition is related to the expansion of white adipose tissue, consisting of hypertrophy and hyperplasia of adipocytes [2,4,5]. Exposure of adipocytes to oxidative stress and overexpression of inflammatory cytokines induce cellular responses, mediated by cellular kinases, including JNK and IKK, which are related to the inactivation of the insulin receptor substrate, resulting in impaired insulin action and sensitivity, an important risk factor for MetS [5].
MicroRNA corresponds to small non-coding RNA molecules (21-23 nucleotides) that regulate numerous processes related to metabolic diseases [6]. MicroRNA mediates gene expression through post-transcriptional mechanisms [6]. Circulating microRNAs are present in extracellular fluids (e.g., plasma) and tissues and may be secreted in vesicles (exosomes, microvesicles, and apoptotic bodies) or combined with proteins (argonauts-AGO, low-and high-density lipoproteins-LDL and HDL-and nucleophosmin 1-NPM1) [7,8]. These small RNA molecules are potential biomarkers for cardiometabolic diseases and may comprise markers for investigation of NCD early risk [9,10].

Eligibility Criteria
The present systematic review focused on cross-sectional studies investigating MetS factors (diabetes, obesity, dyslipidemia, hypertension, and/or dyslipidemia), published in full version and including case and control groups. Considering that individuals under 5 years old should experience important developmental milestones that potentially affect microRNA expression, only studies including individuals ≥5 years old were included, evaluating the relationship between microRNA levels in serum/plasma and biomarkers related to glycemic and/or lipid metabolism, inflammation, and/or anthropometric variables. Eligible studies should include a control group comprising individuals without the clinical condition under investigation in the case group (healthy vs. unhealthy group). In addition, studies should include only individuals with one disease or metabolic complication (i.e., studies including individuals with multiple conditions were excluded from the analysis).
In order to minimize wide variation in circulating microRNA expression, the following studies were considered ineligible: studies focusing on the analysis of microRNA in saliva/vesicles/blood cells or including individuals with clinical complications, such as cancer, kidney disease, thyroid dysfunction, AIDS, or acute inflammatory processes.

Study Selection and Data Extraction
Three researchers (PNBL, GBC, TBP) conducted the literature search and selection stages independently: first, studies identified in the search were screened by title and abstract; in sequence, full papers of studies selected in the first stage were analyzed to check eligibility (Supplementary Table S2). Any disagreement among researchers was resolved jointly and reviewed by a fourth researcher (MMR).
The Kappa coefficient proposed by Landis and Koch [28] was used to assess the agreement between researchers in the selection stages within a range from <0 to 1 in the following categories: <0 = no agreement; 0-0.20 = poor agreement; 0.21-0.40 = fair agreement; 0.41-0.60 = moderate agreement; 0.61-0.80 = substantial agreement; and 0.81-1.00 = almost perfect agreement.
Connections between microRNA and diseases identified in studies included in the systematic review were used to develop a complex network (graph) synthesizing the evidence obtained in the analysis. The complex network encompassed nodes of origin representing the microRNA investigated in the studies, nodes of destination representing the diseases studied, and connections between nodes (edges) representing studies that showed an association between microRNA expression and the diseases studied.
The sizes of nodes were assigned proportionally to the connections established (average degree) and the strength of connections was represented by the number of studies linking the microRNA expression and the diseases evaluated. The network was designed using the Fruchterman Reingold layout, which comprises a direct force algorithm representing nodes connected with higher intensity by proximity and presenting uniform distribution of network nodes to minimize intersections between arcs [42].

Quality Assessment of Studies
The quality of studies included in the systematic review was independently assessed by two researchers (PNBL and GBC) using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies from the National Institutes of Health [43], which is based on 14 criteria for assessment of the study quality. Studies were categorized as good (≥12), fair (5)(6)(7)(8)(9)(10)(11), or poor (<5) quality [43]. Any disagreements were resolved through discussion between the researchers and reviewed by a third researcher (MMR).
Overall, 1656 individuals were in the case group and 1152 individuals were in the control group. Two studies included children ≤10 years old [10,12], three studies included preadolescents/adolescents [13,14,19], and the remaining 19 studies included adults and older adults (Table 1).
Overall, 1656 individuals were in the case group and 1152 individuals were in the control group. Two studies included children ≤10 years old [10,12], three studies included preadolescents/adolescents [13,14,19], and the remaining 19 studies included adults and older adults (Table 1).
MicroRNAs were quantified in plasma (n = 12) or serum (n = 12) by RT-PCR and used different methods to control expression and normalize results ( Table 2). Four studies informed the methodology used to control hemolysis [9,14,37,38]. Age is presented as mean ± standard deviation or range. BMI Z-score: references [10,14]. NGT, Normal glucose tolerance; NW, Normal weight; T2D, type 2 diabetes; IR, Insulin resistance; HDL, high-density lipoprotein. * The study showed contradictory information regarding sample size, and did not report the number of M/F per group. ** The study did not report the number of M/F per group. & Cross-sectional arm. € Papers with the same sample. # Belgium, Cyprus, Estonia, Germany, Hungary, Italy, Spain, and Sweden.   Regarding the diseases of interest, 86 microRNAs were investigated in T2D, obesity, dyslipidemia, hypertension, and MetS. The disease most frequently investigated in the studies was T2D, including analysis of associations with 51 microRNAs, followed by obesity (35 microRNAs) and MetS with its risk factors (20 microRNAs). Considering the studies selected in the systematic review, a major part of the studies investigated the following microRNAs: miR-146a, miR-222, miR-126, miR-130b, miR-142, miR-423, miR-21, miR-532, miR-28, miR-122, miR-140, miR-143, and miR-486 (Figure 2), being related to three or more diseases.
Regarding the diseases of interest, 86 microRNAs were investigated in T2D, obesity, dyslipidemia, hypertension, and MetS. The disease most frequently investigated in the studies was T2D, including analysis of associations with 51 microRNAs, followed by obesity (35 microRNAs) and MetS with its risk factors (20 microRNAs). Considering the studies selected in the systematic review, a major part of the studies investigated the following microRNAs: miR-146a, miR-222, miR-126, miR-130b, miR-142, miR-423, miR-21, miR-532, miR-28, miR-122, miR-140, miR-143, and miR-486 (Figure 2), being related to three or more diseases. The network of studies included in the systematic review showed that there was a higher number of studies linking microRNA with adiposity, lipid metabolism, and glycemic metabolism (Figure 2). Based on higher average weighted degree, robust connections between miR-146a and miR-122 were identified in relation to the conditions studied and miR-222 in relation to adiposity, lipid metabolism, glycemic metabolism, and low-grade chronic inflammation. Adiposity, lipid metabolism, and glycemic metabolism were also intensely linked to miR-126, miR-221, and miR-423. Numerous microRNAs were unconnected to health conditions investigated in the systematic review ( Figure 2).
The average degree (1.791) and average weighted degree (3.988) of the graph showed that major part of the connections between nodes are still sparse and the low modularity (0.109) indicated the absence of robust structure in the network, i.e., there is a lack of groups of studies focusing on similar relationships between microRNA and the biomarkers evaluated in the systematic review. Thus, the absence of sufficient studies pairing similar microRNAs and biomarkers comprised an obstacle to perform a meta-analysis.
Specific aspects regarding microRNA expression according to risk factors and age groups are presented in subsequent sections (Table 2). Table 3 shows correlations between microRNA most frequently investigated in the selected studies and markers related to obesity, T2D, MetS, hypertension, and dyslipidemia. The network of studies included in the systematic review showed that there was a higher number of studies linking microRNA with adiposity, lipid metabolism, and glycemic metabolism ( Figure 2). Based on higher average weighted degree, robust connections between miR-146a and miR-122 were identified in relation to the conditions studied and miR-222 in relation to adiposity, lipid metabolism, glycemic metabolism, and low-grade chronic inflammation. Adiposity, lipid metabolism, and glycemic metabolism were also intensely linked to miR-126, miR-221, and miR-423. Numerous microRNAs were unconnected to health conditions investigated in the systematic review ( Figure 2).
The average degree (1.791) and average weighted degree (3.988) of the graph showed that major part of the connections between nodes are still sparse and the low modularity (0.109) indicated the absence of robust structure in the network, i.e., there is a lack of groups of studies focusing on similar relationships between microRNA and the biomarkers evaluated in the systematic review. Thus, the absence of sufficient studies pairing similar microRNAs and biomarkers comprised an obstacle to perform a meta-analysis.
Specific aspects regarding microRNA expression according to risk factors and age groups are presented in subsequent sections (Table 2). Table 3 shows correlations between microRNA most frequently investigated in the selected studies and markers related to obesity, T2D, MetS, hypertension, and dyslipidemia.

Circulating microRNA Expression in Children with Obesity
Two studies [10,12] evaluated 475 children with normal weight (NW), overweight, and obesity (Table 1). Twenty-one microRNAs were assessed in serum and plasma, with fifteen upregulated and five downregulated in children with obesity ( Table 2). Two of them, miR-486 and miR-222, were consistently upregulated in children with obesity in both studies included. It is important to notice that participants did not present chronic or acute illnesses and, therefore, did not use medication. Although the study of Prats-Puig et al. [10] included children with higher mean age, children in the study were not under pubertal development and the statistical analysis was adjusted by age.

Circulating microRNA Expression in Preadolescents and Adolescents with Obesity and Metabolic Syndrome
Three studies included evaluated plasma microRNA expression in 597 preadolescents and adolescents [13,14,19] (Table 1). The absence of other diseases associated with obesity was confirmed in only one of the studies [13]. The pubertal development stage was not mentioned in the studies.

Circulating microRNA Expression in Adults with Obesity without Metabolic Diseases Associated
The evidence showed increased levels of miR-122 and miR-34a in obese compared to normal-weight adults, whilst three microRNAs (miR-126, miR-146a and miR-150) had reduced levels (Table 2) [24,30,31]. Furthermore, expression of miR-181b showed no differences in different groups of individuals [30]. The expression of miR-122 in adults showed similar patterns in relation to results reported among obese children and the opposite was observed in relation to miR-146a.
Regarding covariates, obese adults included in the studies did not present chronic or acute illness or major abnormalities. In addition, women were not menopausal, pregnant, or breastfeeding [20,31]. Age and gender were considered in the analysis presented by one study [31]. Hijmans et al. 2018b did not include smokers and participants using medication [30].
Individuals with hypertension presented reduced levels of miR-21, miR-126, and miR-146a, whilst miR-34a was increased in hypertensive individuals [21] ( Table 2). Three of the four microRNAs evaluated (miR-21, miR-126, and miR-146a) showed negative correlations with blood pressure values and only miR-34a showed positive correlation with systolic blood pressure [21]. There were no correlations between the expression of these microRNAs and other cardiometabolic biomarkers [21] (Tables 3 and S3).
Individuals with MetS had increased levels of miR-let-7g and miR-221 compared to individuals without MetS, expression that increased according to the presence of additional risk factors for MetS [9] ( Table 2). The authors also indicated that the difference in microRNA levels was greater in women [9]. The miR-let-7g was inversely related to HDL-c levels and blood pressure values [9] (Table S3).

Circulating microRNA in Adults and Older Adults with Type 2 Diabetes
Twenty microRNAs showed increased levels and seventeen microRNAs showed reduced levels in individuals with T2D. Both positive and negative regulation were observed for miR-21, miR-24, miR-27a, miR-30d, miR-130b, and miR-222 (Table 2). It is important to emphasize that individuals with T2D were overweight and/or obese in the studies evaluated.
It should be noted that positive and negative correlations were observed for miR-130b and miR-222 in the studies evaluated (Table 3 and Table S3). There was a positive correlation between miR-130b and HbA1c in normoglycemic and newly diagnosed T2D adults without the use of medication [38]; however, miR-130b was negatively correlated with HbA1c in adults and older-adult men with T2D [25].
Adults and older adults with newly diagnosed T2D showed negative correlation between miR-222 and HbA1c [37], whilst adult and older-adult men with established T2D had positive correlations between miR-222 and HbA1c and FBG [25].
Increased levels of miR-130b, miR-423, and miR-532 were related to lower TG levels [25]. In contrast, increased levels of miR-140 and miR-142 were positively related to the increase in TG levels [25]. Furthermore, miR-423 was negatively correlated with HDL-c levels [38] and miR-21 showed negative correlation with total cholesterol (TC) and positive correlation with HDL-c [16,39]. On the other hand, miR-28 was positively correlated with TC and negatively correlated with LDL-c in individuals with newly diagnosed T2D without the use of medication [37] (Tables 3 and S3).
Only one study investigated the relationship between microRNA levels and inflammatory biomarkers in individuals with T2D [36]. The authors showed that miR-24 and miR-27a presented, respectively, negative and positive correlations with IL-8 levels. Furthermore, there was positive correlation between miR-34a and IL-6 level and positive correlations between miR-29b and miR-155 in relation to IL-12 levels (Table S3).
Time of T2D diagnosis may influence microRNA levels [25,37] and three studies evaluated individuals newly diagnosed with T2D who were not using antidiabetic drugs [33,37,38]. Nevertheless, some studies selected individuals with established T2D, continuous use of medication, and the use of insulin was not an exclusion factor for subjects [16,25,32,36,39]. Only one study considered that excessive alcohol consumption (>3 drinks/day) and smoking should be exclusion criteria [36].

Assessment of the Quality of Studies in the Systematic Review
In this systematic review, 24 studies presented 5 to 11 points, being categorized as fair quality [43]. The main criteria impacting the results of quality assessment were incomplete data on the population of study and the lack of sample size justification. In addition, the quality assessment tool is applicable in cross-sectional and cohort studies; therefore, some questions were not applicable to the studies included in the systematic review.

Discussion
The systematic review synthesized the evidence on circulating microRNAs related to risk factors for MetS in individuals ≥5 years old. Eleven of the thirteen microRNAs most frequently investigated were associated with lipid, anthropometric, glycemic, and inflammatory variables in individuals in different life stages. Overweight/obesity was often observed in the studies included in the systematic review, which suggests that metabolic alterations caused by the total (BMI) and central (waist circumference) adiposity may be responsible for the change in circulating microRNA levels [10,[12][13][14]31].
The miR-130b, a potential biomarker for obesity, was related to lipid, glycemic, and inflammatory metabolism, suggesting that miR-130b may be associated with impaired metabolic control [9]. The miR-130b is secreted by adipose tissue and mediates the metabolic regulatory action of TGF-β, which acts on body energy homeostasis [9,44]. Other mechanisms related to body-weight gain are the JAK-STAT and MAPK pathways, in which the action of miR-140 is observed [45]. There was no evidence of significant associations of miR-21 with body adiposity in humans; however, in vitro studies showed that miR-21 was involved both in TGF-β pathway and adipocyte differentiation [46][47][48].
Indeed, miR-122 showed robust connections with risk factors for MetS investigated in the systematic review, in accordance with predicted target genes, highlighting its participation in lipid oxidation and hepatic synthesis of fatty acid and cholesterol [23,52]. In addition, considering the correlation with adiponectin levels, which regulate the production of TNFα and IL-6 [53], it may play an important role in inflammatory processes. Similarly, miR-126 had altered expression in obesity and may modulate CCL2 (chemokine ligand 2) through genes that encode ETS1, MAX, NFKB1, RELB, and STAT6 proteins [54][55][56][57][58]. The miR-126 has been consistently associated with T2D in the literature [59] and has been shown to regulate vascular integrity and angiogenesis [59] through Notch1 inhibitor delta-like 1 homolog (Dlk1) [60] and the argonaute-2 (Ago2)/Mex3a complex [61]. The interaction of miR-486 and miR-142 in the forkhead box O1 transcription factor inhibition was also identified [10] and participation with other microRNAs in the phosphatase and tensin homolog protein (PTEN) pathway and consequent activation of the PI3K/Akt [62,63].
The miR-146a showed increased levels in obese children [12], conversely to individuals with overweight/obesity in different age groups [13,30], thus, demonstrating that age may be an important factor in the evaluation of circulating microRNAs. Although the direct relationship between miR-146a and aging has not been demonstrated in the studies analyzed in the systematic review, some microRNAs may regulate cellular senescence at the post-transcriptional level. For example, in human mammary epithelial cells, miR-130b repressed p21 expression [64]. In addition, a previous study identified microRNAs (miR-142-5p, miR-222) related to the aging process through different cellular damage pathways in human serum samples [65].
Different patterns of miR-130b and miR-222 were observed in individuals with obesity, isolated or associated with T2D [25,37,38]. Increased levels of miR-130b were associated with long-term glycemic alterations (HbA1c) in adults with obesity and newly diagnosed T2D [38]. On the other hand, miR-130b levels showed negative correlations with glycemic biomarkers in adults and older adults with obesity and established T2D [25]. Similarly, miR-222 was positively correlated with elevated glycemic biomarkers in adults or older adults with T2D and inversely correlated in newly diagnosed individuals [37].
A potential explanation for the differences observed in the studies may be the initial compensatory mechanisms that precede pancreatic failure, marked by increases in insulin synthesis and release by pancreatic beta cells to re-establish glycemic homeostasis [45]. Thus, microRNA involved in beta cell mass control, insulin secretion, and signaling mechanisms respond to the glycemic imbalance conditions [66][67][68].
The expression levels of miR-221 were increased in individuals with MetS, being proportional to the number of risk factors for MetS [9]. Based on predicted target genes, miR-221 was related to inflammatory response, cell signaling, and insulin metabolism, presenting complementary action in relation to miR-222, since they are homologous mi-croRNAs [29,52].
Challenges remain for the use of circulating microRNAs as biomarkers for MetS, considering that a single microRNA may be regulated by multiple factors. An important aspect to be discussed is the potential influence of medical treatments on the results ( Table 1). The use of antidiabetic agents by individuals with T2D may influence the expression of circulating microRNA [25,[69][70][71]. Results of one study included in the systematic review showed that metformin altered the plasma expression of miR-140, miR-142, and miR-222 in individuals with T2D [25]. Additionally, antihypertensives, another class of drugs largely utilized by individuals with MetS, have been associated with microRNA expression in previous studies [72,73].
Furthermore, sex may influence gene expression and microRNA regulation under different physiological conditions, due to genes linked to the X chromosome and action of sexual hormones [74][75][76]. Potential functional variants in the genome have been identified that may justify differential gene expression between sexes [76] and sexual dimorphism observed in some diseases [74]. It has been suggested that sex steroid hormones (e.g., estrogen) may regulate ribonucleases Drosha and Dicer and the expression of argonaut proteins, thus, indicating their role in post-transcriptional processing of microRNA [75]. However, there was an absence of evidence on differences in circulating microRNA levels due to sex in the systematic review [13,14,16,22,29,31,33,36,39].
Although a considerable number of microRNAs were assessed in studies included in the systematic review, studies were marked by high heterogeneity, with few studies identified that evaluated similar circulating microRNAs in association with the same clinical conditions.
The studies included in the systematic review adopted several strategies for normalization of microRNA expression, ranging from the use of synthetic spike-in or identification of relatively stable endogenous circulating oligonucleotides to applying an average of cycle thresholds. Some inconsistencies identified in studies screened in the systematic review might be explained by the absence of a standardized normalization method [77].
Moreover, nutritional aspects may influence the expression of circulating microRNAs, in addition to clinical and lifestyle characteristics considered in the systematic review [77] and, thus, should be considered in future studies due to their role as modifiable risk factors for the development of NCD.

Conclusions
Circulating microRNAs were mainly related to adiposity, lipid metabolism, and glycemic metabolism, showing distinct expression profiles according to the clinical condition of individuals. We highlighted the connections between miR-122, miR-126, miR-146a, miR-221, miR-222, and miR-423 expressions and risk factors for MetS. In addition, excess body fat was often observed in studies included in the systematic review, potentially playing a key role in circulating microRNA dysregulation.
Although there were numerous studies identified in the literature, the high heterogeneity of studies investigating the association between microRNA and MetS risk factors prevented further exploration of factors responsible for variations in microRNA expression. Therefore, further studies are required to allow for the identification of potential associations between circulating microRNAs and risk factors for MetS.
Supplementary Materials: The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/metabo12111044/s1, Table S1: Key terms used in database searches; Table S2: Papers excluded after full reading and reasons for exclusion; Table S3: Associations between microRNA and risk factors for metabolic syndrome.