Serum Levels of 1,5-Anhydroglucitol and Risk of Incident End-Stage Renal Disease

The progress of clinical research on the detection of 1,5-anhydroglucitol in diabetes and its complications

1,5-Anhydroglucitol (1,5-AG) is sensitive to short-term glucose fluctuations and postprandial hyperglycemia, which has great potential in the clinical application of diabetes as a nontraditional blood glucose monitoring indicator. A large number of studies have found that 1,5-AG can be used to screen for diabetes, manage diabetes, and predict the perils of diabetes complications (diabetic nephropathy, diabetic cardiovascular disease, diabetic retinopathy, diabetic pregnancy complications, diabetic peripheral neuropathy, etc.). Additionally, 1,5-AG and β cells are also associated with each other. As a noninvasive blood glucose monitoring indicator, salivary 1,5-AG has much more benefit for clinical application; however, it cannot be ignored that its detection methods are not perfect. Thus, a considerable stack of research is still needed to establish an accurate and simple enzyme assay for the detection of salivary 1,5-AG. More clinical studies will also be required in the future to confirm the normal reference range of 1,5-AG and its role in diabetes complications to further enhance the blood glucose monitoring system for diabetes.

Longitudinal Plasma Metabolome Patterns and Relation to Kidney Function and Proteinuria in Pediatric CKD

Key Points

Background

Understanding plasma metabolome patterns in relation to changing kidney function in pediatric CKD is important for continued research for identifying novel biomarkers, characterizing biochemical pathophysiology, and developing targeted interventions. There are a limited number of studies of longitudinal metabolomics and virtually none in pediatric CKD.

Methods

The CKD in Children study is a multi-institutional, prospective cohort that enrolled children aged 6 months to 16 years with eGFR 30–90 ml/min per 1.73 m2. Untargeted metabolomics profiling was performed on plasma samples from the baseline, 2-, and 4-year study visits. There were technologic updates in the metabolomic profiling platform used between the baseline and follow-up assays. Statistical approaches were adopted to avoid direct comparison of baseline and follow-up measurements. To identify metabolite associations with eGFR or urine protein-creatinine ratio (UPCR) among all three time points, we applied linear mixed-effects (LME) models. To identify metabolites associated with time, we applied LME models to the 2- and 4-year follow-up data. We applied linear regression analysis to examine associations between change in metabolite level over time (∆level) and change in eGFR (∆eGFR) and UPCR (∆UPCR). We reported significance on the basis of both the false discovery rate (FDR) <0.05 and P < 0.05.

Results

There were 1156 person-visits (N: baseline=626, 2-year=254, 4-year=276) included. There were 622 metabolites with standardized measurements at all three time points. In LME modeling, 406 and 343 metabolites associated with eGFR and UPCR at FDR <0.05, respectively. Among 530 follow-up person-visits, 158 metabolites showed differences over time at FDR <0.05. For participants with complete data at both follow-up visits (n=123), we report 35 metabolites with ∆level–∆eGFR associations significant at FDR <0.05. There were no metabolites with significant ∆level–∆UPCR associations at FDR <0.05. We report 16 metabolites with ∆level–∆UPCR associations at P < 0.05 and associations with UPCR in LME modeling at FDR <0.05.

Conclusions

We characterized longitudinal plasma metabolomic patterns associated with eGFR and UPCR in a large pediatric CKD population. Many of these metabolite signals have been associated with CKD progression, etiology, and proteinuria in previous CKD Biomarkers Consortium studies. There were also novel metabolite associations with eGFR and proteinuria detected.

Current updates on metabolites and its interlinked pathways as biomarkers for diabetic kidney disease: A systematic review

Diabetic kidney disease (DKD) is a major microvascular complication of diabetes mellitus (DM) that poses a serious risk as it can lead to end-stage renal disease (ESRD). DKD is linked to changes in the diversity, composition, and functionality of the microbiota present in the gastrointestinal tract. The interplay between the gut microbiota and the host organism is primarily facilitated by metabolites generated by microbial metabolic processes from both dietary substrates and endogenous host compounds. The production of numerous metabolites by the gut microbiota is a crucial factor in the pathogenesis of DKD. However, a comprehensive understanding of the precise mechanisms by which gut microbiota and its metabolites contribute to the onset and progression of DKD remains incomplete. This review will provide a summary of the current scenario of metabolites in DKD and the impact of these metabolites on DKD progression. We will discuss in detail the primary and gut-derived metabolites in DKD, and the mechanisms of the metabolites involved in DKD progression. Further, we will address the importance of metabolomics in helping identify potential DKD markers. Furthermore, the possible therapeutic interventions and research gaps will be highlighted.

Dispersion of Serum 1,5 Anhydroglucitol Values in patients with Type 2 Diabetes at goal of HbA1c

AIM

To investigate the relationship of 1,5 anhydroglucitol (1,5 AG) with HbA1c in patients with type 2 diabetes (T2D) with different ranges of glycemic control.

METHODS

One hundred outpatients with T2D ≥ 18 years old were studied. In addition, HbA1c, glycemia, 1,5 AG, lipids, albuminuria, estimated glomerular filtration rate, and clinical data were registered.

RESULTS

The patient's median age was 62.5 years, with a median of 10 years with T2D. Those with HbA1c <7 % had higher 1,5 AG than those with HbA1c ≥ 7 %, 16.8 ug/ml vs. 4.90 (p=0.00001).1,5 AG correlated inversely with HbA1c (r= -0.7910, p=0.00001), glycemia (r= -0.6307, p=0.00001), cholesterol (r= -0.2257, p= 0.0239), LDL-cholesterol (r= -0.2240 , p=0.0266), albuminuria (r= -0.3644, p=0.0002) and heart rate (r= -0.267 ,p=0.0072). Those on insulin therapy also had lower 1,5 AG (p=0.000). The scatter plot of 1,5 AG and HbA1c fitted a second-degree fractional polynomic regression model, with dispersion of 1 5 AG when HbA1c < 7.5%. An HbA1c ≥ 7.5 % predicted a 1,5 AG <10 ug/ml CONCLUSION: Dispersion of 1,5 AG values at HbA1c < 7.5 % indicates postprandial glucose excursions that may impair glucose control and increase the cardiovascular risk in these patients.

Serum Metabolites and Kidney Outcomes: The Atherosclerosis Risk in Communities Study

Rationale & Objective

Novel metabolite biomarkers of kidney failure with replacement therapy (KFRT) may help identify people at high risk for adverse kidney outcomes and implicated pathways may aid in developing targeted therapeutics.

Study Design

Prospective cohort.

Setting & Participants

The cohort included 3,799 Atherosclerosis Risk in Communities study participants with serum samples available for measurement at visit 1 (1987-1989).

Exposure

Baseline serum levels of 318 metabolites.

Outcomes

Incident KFRT, kidney failure (KFRT, estimated glomerular filtration rate <15 mL/min/1.73 m², or death from kidney disease).

Analytical Approach

Because metabolites are often intercorrelated and represent shared pathways, we used a high dimension reduction technique called Netboost to cluster metabolites. Longitudinal associations between clusters of metabolites and KFRT and kidney failure were estimated using a Cox proportional hazards model.

Results

Mean age of study participants was 53 years, 61% were African American, and 13% had diabetes. There were 160 KFRT cases and 357 kidney failure cases over a mean of 23 years. The 314 metabolites were grouped in 43 clusters. Four clusters were significantly associated with risk of KFRT and 6 were associated with kidney failure (including 3 shared clusters). The 3 shared clusters suggested potential pathways perturbed early in kidney disease: cluster 5 (15 metabolites involved in alanine, aspartate, and glutamate metabolism as well as 5-oxoproline and several gamma-glutamyl amino acids), cluster 26 (6 metabolites involved in sugar and inositol phosphate metabolism), and cluster 34 (21 metabolites involved in glycerophospholipid metabolism). Several individual metabolites were also significantly associated with both KFRT and kidney failure, including glucose and mannose, which were associated with higher risk of both outcomes, and 5-oxoproline, gamma-glutamyl amino acids, linoleoylglycerophosphocholine, 1,5-anhydroglucitol, which were associated with lower risk of both outcomes.

Limitations

Inability to determine if the metabolites cause or are a consequence of changes in kidney function.

Conclusions

We identified several clusters of metabolites reproducibly associated with development of KFRT. Future experimental studies are needed to validate our findings as well as continue unraveling metabolic pathways involved in kidney function decline.

1,5-Anhydroglucitol Predicts Mortality in Patients with HBV-Related Acute-on-chronic Liver Failure

BACKGROUND AND AIMS

1,5-Anhydroglucitol (1,5AG) activity has been reported in chronic liver disease. Hepatitis B virus (HBV)-related acute-on-chronic liver failure (HBV-ACLF) patients have a high mortality. We aimed to discover the relationship between serum 1,5AG and the prognosis of HBV-ACLF.

METHODS

Serum 1,5AG levels were determined in 333 patients with HBV-ACLF, 300 without diabetes were allocated to derivation (n=206) and validation cohorts (n=94), and 33 were recruited to evaluate 1,5AG in those with diabetes. Forty patients with chronic hepatitis B, 40 with liver cirrhosis, and 40 healthy people were controls in the validation cohort.

RESULTS

In the derivation and validation cohorts, serum 1,5AG levels were significantly lower in nonsurvivors than in survivors. The AUC of 1,5AG for 28-day mortality was 0.811. In patients with diabetes, serum 1,5AG levels were also significantly lower in nonsurvivors than in survivors. In multivariate Cox regression analysis, serum 1,5AG levels were independently associated with 28-day mortality. A novel predictive model (ACTIG) based on 1,5AG, age, TB, cholesterol, and INR was derived to predict mortality. In ACTIG, the AUC for 28-day mortality was 0.914, which was superior to some prognostic score models. ACTIG was also comparable to those prognostic score models in predicting 6-month mortality. In mice with D-galactosamine/lipopolysaccharide-induced liver failure, 1,5AG levels were significantly reduced in serum and significantly increased in urine and liver tissue.

CONCLUSIONS

Serum 1,5AG levels are a promising predictor of short-term mortality in HBV-ACLF patients. The 1,5AG distribution changed in mice with D-galactosamine/ lipopolysaccharide-induced liver failure.

Assessment of glycemia in chronic kidney disease

Reliable assessment of glycemia is central to the management of diabetes. The kidneys play a vital role in maintaining glucose homeostasis through glucose filtration, reabsorption, consumption, and generation. This review article highlights the role of the kidneys in glucose metabolism and discusses the benefits, pitfalls, and evidence behind the glycemic markers in patients with chronic kidney disease. We specifically highlight the role of continuous glucose monitoring as an emerging minimally invasive technique for glycemic assessment.

Metabolomics-driven of relationships among kidney, bone marrow and bone of rats with postmenopausal osteoporosis

As a global public health problem, postmenopausal osteoporosis (PMOP) poses a great threat to old women's health. Bone is the target organ of PMOP, and the dynamic changes of bone marrow could affect the bone status. Kidney is the main organ regulating calcium and phosphorus homeostasis. Kidney, bone marrow and bone play crucial roles in PMOP, but the relationships of the three tissues in the disease have not been completely described. Here, metabolomics was employed to investigate the disease mechanism of PMOP from the perspectives of kidney, bone marrow and bone, and the relationships among the three tissues were also discussed. Six-month-old female Sprague-Dawley (SD) rats were randomly divided into ovariectomized (OVX) group (with bilateral ovariectomy) and sham group (with sham surgery). 13 weeks after surgery, gas chromatography-mass spectrometry (GC-MS) was performed to analyze the metabolic profiling of two groups. Multivariate statistical analysis revealed that the number of differential metabolites in kidney, bone marrow and bone between the two groups were 37, 16 and 17, respectively. The common differential metabolites of the three tissues were N-methyl-L-alanine. Kidney and bone marrow had common differential metabolites, including N-methyl-L-alanine, 2-hydroxybutyric acid, (R)-3-hydroxybutyric acid (β-hydroxybutyric acid, βHBA), urea and dodecanoic acid. There were three common differential metabolites between kidney and bone, including N-methyl-L-alanine, α-tocopherol and isofucostanol. The common differential metabolite of bone marrow and bone was N-methyl-L-alanine. Some common metabolic pathways were disturbed in multiple tissues of OVX rats, such as glycine, serine and threonine metabolism, purine metabolism, tryptophan metabolism, ubiquinone and other terpenoid-quinone biosynthesis and fatty acid biosynthesis. In conclusion, our study demonstrated that profound metabolic changes have taken place in the kidney, bone marrow and bone, involving common differential metabolites and metabolic pathways. The evaluation of differential metabolites strengthened the understanding of the kidney-bone axis and the metabolic relationships among the three tissues of OVX rats.

Circulating short and medium chain fatty acids are associated with normoalbuminuria in type 1 diabetes of long duration

A substantial number of subjects with Type 1 Diabetes (T1D) of long duration never develop albuminuria or renal function impairment, yet the underlying protective mechanisms remain unknown. Therefore, our study included 308 Joslin Kidney Study subjects who had T1D of long duration (median: 24 years), maintained normal renal function and had either normoalbuminuria or a broad range of albuminuria within the 2 years preceding the metabolomic determinations. Serum samples were subjected to global metabolomic profiling. 352 metabolites were detected in at least 80% of the study population. In the logistic analyses adjusted for multiple testing (Bonferroni corrected α = 0.000028), we identified 38 metabolites associated with persistent normoalbuminuria independently from clinical covariates. Protective metabolites were enriched in Medium Chain Fatty Acids (MCFAs) and in Short Chain Fatty Acids (SCFAs) and particularly involved odd-numbered and dicarboxylate Fatty Acids. One quartile change of nonanoate, the top protective MCFA, was associated with high odds of having persistent normoalbuminuria (OR (95% CI) 0.14 (0.09, 0.23); p < 10-12). Multivariable Random Forest analysis concordantly indicated to MCFAs as effective classifiers. Associations of the relevant Fatty Acids with albuminuria seemed to parallel associations with tubular biomarkers. Our findings suggest that MCFAs and SCFAs contribute to the metabolic processes underlying protection against albuminuria development in T1D that are independent from mechanisms associated with changes in renal function.

Reference intervals for serum 1,5-anhydroglucitol of a population with normal glucose tolerance in Jiangsu Province

BACKGROUND

Serum 1,5-anhydroglucitol (1,5-AG) is a new glycemic marker which can reflect glucose fluctuation over 3 to 7 days and is now increasingly used to monitor glucose control and to screen for diabetes. However, 1,5-AG has not been widely used in China due to lack of epidemiological support. Our study aims to establish the reference intervals for a population with normal glucose tolerance in Jiangsu Province and to explore the determinants of these intervals.

METHOD

The study enrolled 646 healthy adults aged 20 to 70 years in Jiangsu Province in 2018 after oral glucose tolerance test. 1,5-AG, fasting and 2-hour glucose, UA, liver enzyme, serum lipid, creatinine, and glycosylated hemoglobin were measured. We calculated reference intervals using the parametric method and examined the relationship between 1,5-AG and influence factors.

RESULTS

The average age of the participants was 50.5 ± 9.0 years, and 69.5% of them were females. The reference intervals were 15.8 to 52.6 μg/mL for males and 14.3 to 48.0 μg/mL for females. Among females, the reference intervals were 13.9 to 45.3 and 14.6 to 49.6 μg/mL for menopausal and postmenopausal females, respectively. Males showed higher 1,5-AG concentrations than females, and postmenopausal females had higher 1,5-AG than menopausal females. There was a positive correlation between uric acid and 1,5-AG in both genders. Positive correlation between 1,5-AG and age was only observed in females.

CONCLUSION

We established reference intervals for 1,5-AG in Jiangsu Province, and the level of 1,5-AG is affected by sex, uric acid, and age.

Plasma Endothelin-1 and Risk of Death and Hospitalization in Patients Undergoing Maintenance Hemodialysis

BACKGROUND AND OBJECTIVES

Endothelin-1 is a potent endothelium-derived vasoconstrictor peptide implicated in the pathogenesis of hypertension, congestive heart failure, and inflammation, all of which are critical pathophysiologic features of CKD.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

To test the hypothesis that plasma endothelin-1 levels are associated with increased risks of mortality and hospitalization in patients with chronic kidney failure, we measured plasma endothelin-1 levels in a prospective cohort of 794 individuals receiving maintenance hemodialysis. The primary outcomes were time to death and time to hospitalization.

RESULTS

The median plasma endothelin-1 level was 2.02 (interquartile range, 1.57-2.71) pg/ml. During a median follow-up period of 28 (interquartile range, 21-29) months, 253 individuals (32%) died and 643 individuals (81%) were hospitalized at least once. In multivariable models adjusted for demographic, clinical, and laboratory variables, individuals in the highest quartile of plasma endothelin-1 had a 2.44-fold higher risk of death (hazard ratio, 2.44; 95% confidence interval, 1.61 to 3.70) and a 1.54-fold higher risk of hospitalization (hazard ratio, 1.54; 95% confidence interval, 1.19 to 1.99) compared with individuals in the lowest quartile. The Harrell C-statistic of the fully adjusted model increased from 0.73 to 0.74 after addition of natural log-transformed plasma endothelin-1 (P<0.001) for all-cause mortality, and increased from 0.608 to 0.614 after addition of natural log-transformed plasma endothelin-1 (P=0.002) for hospitalization.

CONCLUSIONS

Higher plasma endothelin-1 is associated with adverse clinical events in patients receiving hemodialysis independent of previously described risk factors.

PODCAST

This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2020_05_15_CJN11130919.mp3.

Clinicopathological predictors for progression of chronic kidney disease in nephrosclerosis: a biopsy-based cohort study

BACKGROUND

Biopsy-based studies on nephrosclerosis are lacking and the clinicopathological predictors for progression of chronic kidney disease (CKD) are not well established.

METHODS

We retrospectively assessed 401 patients with biopsy-proven nephrosclerosis in Japan. Progression of CKD was defined as new-onset end-stage renal disease, decrease of estimated glomerular filtration rate (eGFR) by  ≥50% or doubling of serum creatinine, and the sub-distribution hazard ratio (SHR) with 95% confidence interval (CI) for CKD progression was determined for various clinical and histological characteristics in competing risks analysis. The incremental value of pathological information for predicting CKD progression was assessed by calculating Harrell's C-statistics, the Akaike information criterion (AIC), net reclassification improvement and integrated discrimination improvement.

RESULTS

During a median follow-up period of 5.3 years, 117 patients showed progression of CKD and 10 patients died before the defined kidney event. Multivariable sub-distribution hazards model identified serum albumin (SHR 0.48; 95% CI 0.35-0.67), hemoglobin A1c (SHR 0.71; 95% CI 0.54-0.94), eGFR (SHR 0.98; 95% CI 0.97-0.99), urinary albumin/creatinine ratio (UACR) (SHR 1.18; 95% CI 1.08-1.29), percentage of segmental/global glomerulosclerosis (%GS) (SHR 1.01; 95% CI 1.00-1.02) and interstitial fibrosis and tubular atrophy (IFTA) (SHR 1.52; 95% CI 1.20-1.92) as risk factors for CKD progression. The C-statistic of a model with only clinical variables was improved by adding %GS (0.790 versus 0.796, P < 0.01) and IFTA (0.790 versus 0.811, P < 0.01). The reclassification statistic was also improved after adding the biopsy data to the clinical data. The model including IFTA was superior, with the lowest AIC.

CONCLUSIONS

The study implies that in addition to the traditional markers of eGFR and UACR, we may explore the markers of serum albumin and hemoglobin A1c, which are widely available but not routinely measured in patients with nephrosclerosis, and the biopsy data, especially the data on the severity of interstitial damage, for the better prediction of CKD progression in patients with nephrosclerosis.

Response of 1,5-anhydroglucitol level to intensive glucose- and blood-pressure lowering interventions, and its associations with clinical outcomes in the ADVANCE trial

AIMS

To evaluate 1,5-anhydroglucitol (1,5-AG) according to clinical outcomes and assess the effects of glucose- and blood pressure-lowering interventions on change in 1,5-AG levels in people with type 2 diabetes.

METHODS

We measured 1,5-AG in 6826 stored samples at baseline and in a random subsample of 684 participants at the 1-year follow-up visit in the ADVANCE trial. We examined baseline 1,5-AG [< 39.7, 39.7-66.2, ≥ 66.2 μmol/L (<6, 6-10, ≥10 μg/mL)] and microvascular and macrovascular events and mortality using Cox regression models during 5 years of follow-up. Using an intention-to-treat approach, we examined 1-year change in 1,5-AG (mean and percent) in response to the glucose- and blood pressure-lowering interventions in the subsample.

RESULTS

Low 1,5-AG level [<39.7 μmol/L vs ≥ 66.2 μmol/L (<6 μg/mL vs ≥10 μg/mL)] was associated with microvascular events (hazard ratio 1.28, 95% confidence interval 1.03-1.60) after adjustment for risk factors and baseline glycated haemoglobin (HbA1c); however, the associations for macrovascular events and mortality were not independent of HbA1c. The glucose-lowering intervention was associated with a significant 1-year increase in 1,5-AG (vs standard control) of 6.69 μmol/L (SE 2.52) [1.01 μg/mL (SE 0.38)], corresponding to an 8.26% (SE 0.10%) increase from baseline. We also observed an increase in 1,5-AG of similar magnitude in response to the blood pressure intervention independent of the glucose-lowering effect.

CONCLUSIONS

Our results suggest that 1,5-AG is a marker of risk in adults with type 2 diabetes, but only for microvascular events independently of HbA1c. We found that 1,5-AG was improved (increased) in response to an intensive glucose-lowering intervention, although the independent effect of the blood pressure-lowering intervention on 1,5-AG suggests potential non-glycaemic influences.

Serum Metabolomic Alterations Associated with Proteinuria in CKD

BACKGROUND AND OBJECTIVES

Data are scarce on blood metabolite associations with proteinuria, a strong risk factor for adverse kidney outcomes. We sought to investigate associations of proteinuria with serum metabolites identified using untargeted profiling in populations with CKD.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Using stored serum samples from the African American Study of Kidney Disease and Hypertension (AASK; n=962) and the Modification of Diet in Renal Disease (MDRD) study (n=620), two rigorously conducted clinical trials with per-protocol measures of 24-hour proteinuria and GFR, we evaluated cross-sectional associations between urine protein-to-creatinine ratio and 637 known, nondrug metabolites, adjusting for key clinical covariables. Metabolites significantly associated with proteinuria were tested for associations with CKD progression.

RESULTS

In the AASK and the MDRD study, respectively, the median urine protein-to-creatinine ratio was 80 (interquartile range [IQR], 28-359) and 188 (IQR, 54-894) mg/g, mean age was 56 and 52 years, 39% and 38% were women, 100% and 7% were black, and median measured GFR was 48 (IQR, 35-57) and 28 (IQR, 18-39) ml/min per 1.73 m2. Linear regression identified 66 serum metabolites associated with proteinuria in one or both studies after Bonferroni correction (P<7.8×10-5), 58 of which were statistically significant in a meta-analysis (P<7.8×10-4). The metabolites with the lowest P values (P<10-27) were 4-hydroxychlorthalonil and 1,5-anhydroglucitol; all six quantified metabolites in the phosphatidylethanolamine pathway were also significant. Of the 58 metabolites associated with proteinuria, four were associated with ESKD in both the AASK and the MDRD study.

CONCLUSIONS

We identified 58 serum metabolites with cross-sectional associations with proteinuria, some of which were also associated with CKD progression.

PODCAST

This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2019_02_07_CJASNPodcast_19_03_.mp3.

Analysis of risk factors of metabolic syndrome using a structural equation model: a cohort study

PURPOSE

We aimed to use a structural equation model (SEM) to determine the interrelations between various risk factors, including latent variables, involved in the development of metabolic syndrome(MetS).

METHODS

This study used data derived from the MJ Longitudinal Health Check-up Population Database for participants aged 20 to 70 years, who were asymptomatic for MetS at enrollment and were followed up for 5 years. A SEM was applied to investigate the attributions of MetS and the interrelations between different risk factors.

RESULTS

Socioeconomic status (SES), living habits, components of metabolic syndrome (COMetS), and blood pressure had a diverse impact on the onset of MetS, directly and (or) indirectly. When investigating the latent risk factors and the interrelations between different risk factors. The standardized total effect (the sum of the direct and indirect effects, β_t) of SES, living habits, blood pressure and COMetS on the onset of MetS was 0.084, -0.179, 0.154, and 0.353, respectively. SES, as a distal risk factor, directly influenced living habits, blood pressure, and COMetS with standardized regression coefficients (β_r) of -0.079 (P < 0.001), 0.200 (P < 0.001), and -0.163 (P < 0.001) respectively. Unfavorable living habits exerted an inverse effect on blood pressure and COMetS (β_r = -0.101, P < 0.001; β_r = -0.463, P < 0.001), which was an important path way for developing MetS.

CONCLUSIONS

These results demonstrate that individuals with a higher level of SES are susceptible to high blood pressure and are at increased risk for MetS. Additionally, there is a decrease in exercise and an increase in smoking and consumption of alcohol corresponded to an increase in metabolic risk factors.