PBMCs to Stress-Associated miR-18a-5p and miR-22-3p Ratios as New Indicators of Metabolic Syndrome

Purpose Metabolic syndrome (MetS) is associated with chronic stress. miR-18a-5p and miR-22-3p are two miRNAs which can target the glucocorticoid receptor. This study looked at the changes in metabolic parameters and the predictive value of the peripheral blood mononuclear cells (PBMCs) to stress-associated miRNA ratios as new indicators in subjects with and without MetS in southern China. Patients and Methods. There were 81 participants (39 with MetS and 42 without MetS) in this cross-sectional study. The potential miRNAs were filtrated in the GEO database. The expression of miR-18a-5p and miR-22-3p in PBMCs was evaluated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The risk of miRNA and PBMCs to stress-associated miRNA ratios contributing to the presence of MetS was estimated by univariate and multivariate logistic regression models. The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results MetS was positively correlated with cortisol, IL-6, lymphocyte to miR-18a-5p ratio (LT18R), lymphocyte to miR-22-3p ratio (LT22R), monocyte to miR-18a-5p ratio (MT18R), monocyte to miR-22-3p ratio (MT22R), PBMCs to miR-18a-5p ratio (PT18R), and PBMCs to miR-22-3p ratio (PT22R) and negatively associated with the expression levels of miR-18a-5p and miR-22-3p (P < 0.05). In addition, PT18R (odds ratio: 0.894; 95% CI: 0.823-0.966; P < 0.001) and PT22R (odds ratio: 0.809; 95% CI: 0.717-0.900; P < 0.001) were independent predictors of MetS, respectively. A receiver operating characteristic (ROC) curve analysis was performed to assess the value of the PT18R-PT22R (PMR) panel (odds ratio: 0.905; 95% CI: 0.838-0.971; P < 0.001) for predicting MetS. The area under the curve yielded a cut-off value of 0.608, with sensitivity of 74.4% and specificity of 95.2% (P < 0.001). Conclusion In summary, miR-18a-5p and miR-22-3p in PBMCs may be important biomarkers of stress reaction and may play a role in vulnerability to MetS. Besides, the inflammatory cells to the two miRNA ratios demonstrated high accuracy in the diagnosis of MetS.


Introduction
MetS, once identified as "IR syndrome," "syndrome X," "hypertriglyceridemia waist," and "the deadly quartet," was firstly globally defined as MetS in 1998 by the Diabetes Consultation Group of the World Health Organization. MetS is a combination of several characteristics, including obesity, glucose intolerance, IR, dyslipidaemia, microalbuminuria, hypertension, nonalcoholic fatty liver disease (NAFLD), the proinflammatory state, and oxidative stress, resulting in an increasing risk of T2DM, cardiovascular disease (CVD), fatty liver, and cancer [1][2][3]. Five risk factors with different critical cut-offs are used to identify individuals with the MetS: waist circumference, circulating levels of triglycerides and highdensity lipoprotein cholesterol (HDLC), fasting glucose, and blood pressure. IR also plays a pivotal role in the MetS [3,4]. Among the various risk factors of MetS, chronic stress has now emerged as contributors to the development of MetS. Psychological stress can affect health through complex interactions among neuroendocrine responses and energy homoeostasis [5]. Glucocorticoid (GC) (cortisol in human beings) is the critical matter responding to stress, which can cause central obesity, hypertension, hyperlipidaemia, and glucose intolerance. GC action is mediated by the glucocorticoid receptor (GR), a nuclear receptor that regulates physiological events through activation or repression of target genes involved in inflammation, gluconeogenesis, and adipocyte differentiation [6].
In recent years, miRNAs have arisen global interest in complex processes in health and diseases, including MetS and its components, which are used as noninvasive biomarkers for diagnosis [7]. miRNAs are small (19-23 nucleotides), single-stranded noncoding RNA molecules involved in posttranscriptional control of genetic expression of a large number of genes, acting as regulators of mRNA degradation and/or blocking protein translation of distinctive parts of target proteins via binding to their corresponding sequences within the 3′-untranslated regions (3′UTRs) of target mRNAs [7,8]. It has been manifested that miRNAs are stable in circulation because they are resistant to RNase's digestion. This shows great promise in becoming a novel diagnostic biomarker of MetS and its components [9]. Uchida and colleagues identified that miR-18a inhibited translation of GR mRNA in cultured neuronal cells and that high expression of miR-18a was present in Fischer 344 (F344) rats in the paraventricular nucleus [10]. Besides, using miRTarbase, we found that miR-22a-3p was predicted to target GR.
To our knowledge, few studies have investigated the expressions of miR-18a-5p and miR-22a-3p which can target GR in the MetS population. It has been suggested that the gene expression signature in PBMCs may provide an indicator of gene activation changes as differential response to stress in humans [11]. In this study, we analyzed the association between MetS and expression of miR-18a-5p and miR-22a-3p in PBMCs, which may unveil a new target for the prevention and treatment of stress-related disorders including MetS.

Subjects.
This study was conducted among workers who took annual physical examination for at least three consecutive years at the health examination center of The Third Affiliated Hospital, Southern Medical University, from January to April 2019. A total of 39 diagnosed MetS (31M : 8F; mean age 40:38 ± 9:38 yrs.) cases were recruited. 42 health individuals without any components of MetS (19M : 23F; mean age 32:87 ± 7:40 yrs.) were selected (baseline characteristics are shown in Table 1). The diagnosis of MetS was in accordance with the common criteria of the International Diabetes Federation and the American Heart Association/National Heart, Lung, and Blood Institute [12]. Patients with acute or chronic infectious or immunological diseases, obvious liver and kidney dysfunction, severe heart diseases, pregnancy, mental illness or drug abuse, gastrointestinal diseases (such as chronic gastrointestinal disorders, diarrhoea, biliary tract infection, and enteritis), and serious diseases of the blood or the endocrine systems were excluded from the study. A structured questionnaire was used to collect information on demographic data, environmental exposure, and medical histories. Current cigarette smokers were defined as those who smoked ≥1 cigarette/day. Alcohol use was defined as the intake of wine/beer/cider/spirits ≥ 1 time per week. Physical activity was defined as walking or riding 15 min/day, doing sports or physical exercise > 2 h/week, or lifting or carrying heavy objects at work daily [13]. This study was approved by the hospital ethical committee, and informed consent was obtained from each participant.

Anthropometric Measurement.
Anthropometric parameters including weight, height, waist circumference (WC), hip circumference (HC), and blood pressure were obtained using standard measurement. Body mass index (BMI) was calculated by dividing the weight (kg) by the squared value of height in metres.

Blood Sample Collection and RNA Extraction.
Following an overnight fast, 5 ml venous blood sample from each subject was collected using EDTA anticoagulant tubes and processed within three hours. A three-milliliter sample was immediately centrifuged to retrieve plasma. PBMCs were isolated from 2 ml whole blood by Ficoll-Hypaque density gradient centrifugation. Immediately after, total RNA was extracted from PBMCs by the standard protocol of the TRIzol reagent (Invitrogen, New York, USA). The purity of RNA was determined using a BioPhotometer plus Eppendorf nucleic acid protein analyzer (Hamburg, Germany), and the integrity was evaluated using agarose gel electrophoresis stained with ethidium bromide. All RNA used had OD260/OD280 ratio > 1:8, and electrophoresis showed integrity was acceptable. The plasma and RNAs were then stored at -80°C until assayed.

Biochemical Analysis.
Fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), and HDLC were measured using standard laboratory methods. Low-density lipoprotein cholesterol (LDLC) was calculated using the Friedewald formula. Insulin (INS) and IL-6 were calculated by the high-pressure liquid chromatography method. Plasma cortisol was measured by commercial radio immunoassays. Plasma TNF-α concentrations were evaluated by an enzyme-linked immunosorbent assay using a microplate reader. The intra-assay and interassay coefficients of variation were <5.5% and <10.0% for these assays, respectively. The degree of IR was determined using the HOMA-IR which was calculated using the following formula: ½fasting insulin ðmIU/lÞ * fasting glucose ðmmol/lÞ/22:5.
2.5. miRNA Filtration. Gene Expression Omnibus (GEO), which is a database repository of high-throughput gene expression data and hybridization arrays, chips, and microarrays, was used to identify the differentially expressing miRNAs in PBMCs between the MetS group and healthy groups. The miRNA microarray data were collected from the GEO database, under the accession number GSE98896, which included the miRNA profiles of PBMC samples collected from 20 healthy children and 20 patients with the MetS group, as demonstrated on the previously published data [14]. The |Log Fold Change ðlogFCÞ|ðMetS group to control groupÞ > 1:3 and P value < 0.05 were taken as cut-off values for detection of upregulated or downregulated miRNAs in the two groups. 60 miRNAs were detected. Among these miRNAs, we chose two miRNAs, miR-18a-5p and miR-22-3p, whose logFC was 1.42 and 1.41, respectively.  Legend: * P value calculated with the independent sample test, and the data are expressed as the mean ± SEM. * * P value calculated with the nonparametric test, and the data are expressed as the median ± interquartile range. * * * P value calculated with the χ 2 test, and the data are expressed as the number and percentage. P′: P value calculated with binary logistic regression after adjusting for age and gender. Abbreviations: SUA: serum uric acid; WBC: white blood cell; Hb: hemoglobin; LC: lymphocyte count; MC: monocyte count; ALT: alanine aminotransferase; AST: aspartate transaminase; GGT: glutamyl transpeptidase; SCr: serum creatinine; eGFR: estimated glomerular filtration rate; PA: physical activity; UU: under university; UA: university and above; n: number.
3 BioMed Research International and 12.0 μl of nuclease-free water in a total volume of 20 μl. U6 was used as the internal reference of miRNAs. Reactions were performed in an ABI PRISM® 7500 Sequence Detection System (Applied Biosystems, Foster City, USA) for 60 min at 42°C, followed by heat inactivation of RT for 10 min at 85°C. qPCR was performed with 20 μl PCR reaction mixture that included 5 μl of cDNA, 10 μl of 2x SYBR Green qPCR Super-Mix (Invitrogen), 0.5 μl of forward primer (Qiagen), 0.5 μl of reverse primer, and 4 μl of nuclease-free water. Reactions were incubated in a 384-well optical plate at 95°C for 5 min., followed by 40 cycles at 95°C for 15 sec and 60°C for 32 sec. The specific generation of expected PCR product was confirmed by automated melting curve analysis. All samples were performed in triplicate (cDNA from the same PCR reaction but in separate wells). The expression levels of miRNAs were calculated using the 2 -ΔΔCt method ½ΔCt = mean Ct ðmiRNA of interestÞ-mean Ct ðU6Þ, ΔΔCt = ΔCtðΔ Ct of miRNA of interest in samples to be tested-ΔCt of U6 in reference samplesÞ. The upstream primer sequences were designed based on the miRNA sequences obtained from the miRBase database (Release 20.0) as follows: miR-18a-5p forward primer 5′-ACACTCCAGCTGGGTAAGGTGCAT CTAGTGCAGA-3′, miR-18a-5p reverse primer 5′-CTCA ACTGGTGTCGTGGA-3′; miR-22-3p forward primer 5′ -ACACTCCAGCTGGGAAGCTGCCAGTTGAAGAA-3′; miR-22-3p reverse primer 5 ′ -CTCAACTGGTGTCGTGGA-3 ′ .

Statistical
Analysis. Normality of data distribution was assessed using the Kolmogorov-Smirnov test. The independent sample test, Chi-squared test, and nonparametric test were used to compare differences of demographic and clinical parameters between two groups. Spearman's correlation coefficient was used to test the correlation between miRNA markers, inflammatory cells to miRNA ratios, and cortisol and other clinical variables. The odds ratios (ORs) and their 95% confidence intervals (CIs) were calculated to assess the risk of miRNAs and inflammatory cells to miRNA ratios contributing to the presence of MetS using both univariate and binary logistic regression models with or without adjustment for covariates. ROC analysis was used to assess the biomarker potential of each miRNA and inflammatory cells to miRNA ratios for MetS, and AUC was used as the diagnostic index. The diagnostic performance of the miRNA and ratio panels was further evaluated using the predicted probability of being diagnosed with MetS as a surrogate marker to construct the ROC curve. A P value of less than 0.05 was considered statistically significant. The reported P values were two-tailed in all calculations. All statistical analyses were performed using SPSS 20.0.

Results
3.1. Basic Characteristics of the Study Subjects. The demographic and clinical characteristics of the study participants are presented in Table 1. Among the metabolic characteristics, most of them (including FG, FINS, HOMA-IR, TG, HDL-C, and SUA) have been shown significant differences between two groups, except for LDL-C. Most of the demographic parameters, including weight, height, BMI, WC, HC, WHR, SBP, and DBP, were significantly higher in the MetS group than those in the control group (P < 0:05). We also found that white blood cell (WBC), lymphocyte count (LC), and monocyte count (MC) were significantly higher in the MetS group than those in the control group, whereas hemoglobin was significantly lower in the MetS group than those in the control group, even after adjusting for gender. As for the hepatic and renal function, there were significant differences of alanine aminotransferase, glutamyl transpeptidase, serum creatinine, and estimated glomerular filtration rate in two groups. There was no significant difference in the distribution of smoking and alcohol consumption (P > 0:05). Compared with those controls, subjects with MetS were more likely to be physically inactive (P < 0:05).

Comparison of miRNA Levels and Stress
Hormone between the Control and MetS Groups. The expression levels of miR-18a-5p and miR-22-3p in the PBMCs and plasma levels of stress hormones including cortisol, IL-6, and TNF-α of the two groups are listed in Table 2. The expression levels of miR-18a-5p and miR-22-3p in MetS patients were significantly lower than those in control individuals (P < 0:001) (Figure 1). Levels of plasma cortisol and IL-6 in the MetS group were significantly higher than those in the control group (P = 0:003 and P = 0:006, respectively); however, there were no significant differences of plasma TNF-α levels in the two groups (P = 0:872).

Risk of miRNA Expression Contributed to the Presence of
MetS. Univariate logistic regression analysis revealed that expression levels of miR-18a-5p and miR-22-3p were negatively associated with the presence of the MetS and control groups (P < 0:01) ( Table 3). These associations were also confirmed in binary logistic regression analysis after adjusting for age, gender, and physical activity and further for WC. With a unit decrease of miR-18a-5p and miR-22-3p levels, there were 0.022 (95% CI: 0.003-0.152, P < 0:001) and 0.168 (95% CI: 0.063-0.453, P = 0:001) times of greater risks of MetS, respectively. These results indicated that miR-18a-5p and miR-22-3p are both independent protective factors for MetS.   Figure 1: Comparison of microRNA expression in PBMCs among the MetS and control groups. Legend: the expression levels of miR-18a-5p in the MetS group and control group were 0:49 ± 0:31 and 1:05 ± 0:55 (median ± interquartile range), and P value was <0.001; the expression levels of miR-22-3p were 0:60 ± 0:44 and 1:18 ± 0:73 (median ± interquartile range), and P value was <0.001.  5 BioMed Research International miR-22-3p was negatively correlated with IL-6 (P = 0:009) and TNF-α (P = 0:013) in the total subjects and negatively correlated with TNF-α (P = 0:017) in the control group. We also found that LC was positively correlated with cortisol and that LC and MC were negatively and positively associated with HOMA-IR, respectively.
As shown in Table 6, miR-18a-5p and miR-22-3p expressions had a moderate and negative correlation with various MetS components (including BMI, WC, HC, WH, SBP, DBP, plasma FG, plasma TG, and plasma UA) and positive correlation with plasma HDLC.

Discussion
The present study identified the relationship of expression levels of two stress-related miRNAs in PBMCs and the PBMCs to miR-18a-5p and miR-22-3p ratios between MetS patients and healthy controls in an occupational sample in Guangzhou. The decreased levels of miR-18a-5p and miR-22-3p were associated with the MetS presence, and they were both significant predictors for independence of MetS components. Besides, we have found that the expression levels of cortisol and IL-6 in MetS were significantly higher compared with healthy controls, and miR-18a-5p was negatively associated with cortisol. Our study also revealed that miR-18a-5p and miR-22-3p in PBMCs were potential markers for diagnosing MetS. But the main limitation of our study was the size of participants.
GR has two splice variants, named as GRα and GRβ. GRα is the classical receptor which is bound with GCs to regulate gene expression, and GRβ is served as an inhibitor of GRα, which can cause the GC resistance, providing a modest level of protection against the deleterious effects of GC exposure. Dexamethasone treatment and knockdown of GRα together with overexpression of GRβ had opposite effects on glucose, amino acid, and fatty acid levels. The former treatment can increase glucose, amino acids, cholesterol, and fatty acids [15]. Another study has found that expression of GRα in myoblast under basal conditions had positive associations with levels of insulin resistance, BMI, percent body fat, and blood pressure, which may be involved in mechanisms contributing to the pathogenesis of the MetS [16]. But in another study, GRα mRNA was negatively correlated with BMI and triglycerides and reduced in obesity subjects in the abdominal subcutaneous and omental depots, which is controversial with the two former observations [17]. miR-18a and miR-22-3p can both inhibit the GR mRNA. In our study, we found that the expression levels of these two miRNAs decreased   7 BioMed Research International in the MetS group compared with the healthy group, which can be explained by the studies above.
The activation of the GR-responsive gene was strongly impaired by the overexpression of miR-18a. miR-18a is expressed widely throughout the body, which might cause the GC resistance. Inversely, decreased expression of miR-18a may induce the presence of metabolic dysfunction [10]. Previous study has found that expression levels of miR-18a in PBMCs were positively correlated with cortisol, CRH, and IL-6, and chronic stress increased plasma cortisol, which subsequently decreased mRNA expression of GRα and increased the GRβ/GRα mRNA ratio. It has been also identified that   BioMed Research International the increased levels of miR-18a were associated with the T2DM presence [11]. Besides, it has been demonstrated that miR-18 could promote apoptosis of islet β-cells at least partially by inhibiting neuron navigator 1 expression and insulin production via suppression of the PI3K/AKT pathway [18]. In addition to the role in metabolic diseases, as an osteosarcoma-promoting miRNA, miR-18a-5p can also target the suppressor of cytokine signalling 5 in OS cells, which induced tumorigenesis [19]. Treated with chronic stress, the resilient rats (which were much less prone to the negative influence of stress than others) show a significantly decreased expression level of miR-18a-5p [20]. Our present study has found that the expression level of miR-18a-5p decreased in the MetS group, when compared with the control group, and was negatively associated with the stress hormone, cortisol. Based on our results and these earlier reports, the expression of miR-18a-5p is likely to be a protective factor of MetS. miR-22-3p (also mentioned as miR-22), an abundantly expressed hepatic miRNA, is elevated in the diabetic mouse liver. miR-22-3p inhibited transcription factor 7 via binding to the 3′untranslated region, which increased the expression of enzymes of the gluconeogenic pathway in HepG2 cells. Inversely, miR-22-3p antagonism improved glucose tolerance and insulin sensitivity [21]. Interestingly, miR-22 was downregulated, and its target gene IL6 receptor (IL6R) was upregulated in both diabetic serum and glucose-induced INS-1E cells. Further study has identified that miR-22 overexpression or IL6R inhibition significantly strengthened cell survival and suppressed cell apoptosis; the potential mechanism was that miR-22 overexpression or IL6R inhibition could activate the phosphorylation of the JAK/STAT signalling pathway. High IL6 concentration inhibits insulin secretion. Through combining with the IL6R on the membrane of target cells, IL6 can induce a series of biological reactions in different target cells to participate in glucose and lipid metabolism [22]. Our present study also found that miR-22 was downregulated in MetS patients, and it was negatively associated with the IL6 level.
As a window, PBMCs (mainly consisting of lymphocytes) can convert psychosocial stress into cellular dysfunction and finally contribute to the pathophysiology of lifestyle-related diseases such as DM, CVD, and atherosclerosis [11]. Chen et al. have conducted a cross-sectional study on 852 participants (n = 598 with MetS and n = 254 without MetS) in south China and found that the severity of MetS was significantly positively associated with the WBC count, neutrophil count, and total lymphocyte count [23]. These results have been also discovered in our present study. We have identified that LT18R, LT22R, MT18R, MT22R, PT18R, and PT22R can be good indicators for diagnosing MetS. It is the first time that we investigated the relationship between the two miR-NAs in PBMCs and the inflammatory cells to the two miRNA ratios and MetS at the population level. The reasonable diagnostic accuracy of miR-18a-5p, miR-22-3p, and the rates above indicates their clinical value in diagnosis of MetS. However, well-designed prospective studies with larger sample sizes are required to validate our findings. Besides, the expressions of these two miRNAs in circulation should be also detected, and the roles of these two miRNAs on GR and metabolic components need to be further identified.

Conclusion
Our findings suggest that miR-18a-5p and miR-22-3p in PBMCs may play a vital role in vulnerability to MetS by targeting GR. What is more, the inflammatory cells to the two miRNA ratios have demonstrated higher specificity and sensitivity in the diagnosis of MetS. These findings may unveil new targets for the prevention, diagnosis, and treatment of stress and inflammation-related disorders including MetS.

Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
The authors declare that they have no conflicts of interest.