European Journal of Cardiovascular Medicine

MetS is a multifactorial, complex disorder that includes in its constellation of interconnected metabolic abnormalities: central obesity, insulin resistance, dyslipidemia, and hypertension. MetS prevalence has exponentially increased in recent decades and the increase is a reflection of the growing burden of obesity worldwide and sedentary lifestyles. MetS has been associated with a significantly increased risk of type 2 diabetes mellitus, cardiovascular disease, and all-cause mortality, hence its early diagnosis and management is considered a critical public health priority [1-2].

This has led to considering the pathophysiology of MetS as the complex interplay of genetic, environmental, and behavioral factors that are involved in chronic low-grade inflammation and dysregulated metabolic homeostasis. Thus, reliable biomarkers that can be useful for the early detection of risk stratification and therapeutic monitoring of MetS are in great demand. Among such emerging candidates, chemerin, which is an adipokine that plays pleiotropic roles in metabolic and inflammatory processes, has attracted maximum attention as a new potential biomarker of MetS [3-4]. Chemerin is a chemotactic protein produced and secreted mainly by adipose tissue, liver, and skin. Initially identified as an attractant to the immune cells, further studies have revealed an association of chemerin with the regulation of glucose metabolism, lipid homeostasis, and adipogenesis. High serum chemerin levels are also found in obese, T2DM, and CVD patients-and outcome of MetS. These findings thus suggest that chemerin could represent a bridge connecting adipose tissue dysfunction to systemic metabolic disturbances [5-6]]. Some of the mechanisms through which chemerin is involved in MetS include interactions with specific receptors, including expression of CMKLR1 chemerin receptor 1, localized on adipocytes, macrophages, and endothelial cells, where their stimulation may have effects on insulin sensitivity, inflammation, and vascular function. Also, chemerin expression is regulated by pro-inflammatory cytokines and metabolic stressors, hence can act as a marker and mediator for metabolic dysregulation [7-8].

The roles of chemerin in both metabolism and inflammation have been advanced as a biomarker for diagnosis and prognosis in MetS. Indeed, serum chemerin levels were found to positively correlate with different components of MetS, which include waist circumference, fasting glucose, triglycerides, and blood pressure. Of greatest importance is that it was documented that chemerin is involved in the prediction of the development of MetS and its secondary complications, hence its clinical utility [9].

This article will review the existing evidence regarding serum chemerin as a biomarker for MetS. We will outline its physiological and pathological functions, assess clinical studies concerning its potential in diagnosis and prognosis, and outline its possible application in treatment and early detection of MetS and its consciquences.

Pathophysiology of chemerin and its receptors

Chemerin act as a natural ligand. When chemerin act on Chemerin receptor (G protein coupled receptor) like CMKLR1 and GPR1, some signal transduction pathways are activated like mitogen activated protein kinase (MAPK), extracellular signal regulated kinases (ERK) and phosphatidylinositol 3 kinase. They play a critical role in human pathophysiology. That causes metabolic syndrome like dyslipidemia (proliferation and differentiation of adipocytes) , hypertension ( activate L type ca2† channel calcium influx and causes smooth muscle contraction ), hyper glycemia (PI3K-AKT) pathways (reduced glucose uptake and elevated hepatic glucose production) , insulin resistant (impaired insulin signaling pathway ) and central obesity. Other than this several biological systems are also affected

Cardiovascular disease: (Atheroschelerosis, Hypertension, Dilated cardiomyopathy,

                                           Endothelial cell proliferation and differentiation, angiogenesis)

Lung disease: (COPD, acute viral pneumonia)

Renal disease: (Diabetic nephropathy, lupus nephritis)

Liver disease: (Non alcoholic fatty liver and liver cirrhosis)

Gastrointestinal disease: (inflammatory bowel disease, gastric cancer, colorectal cancer)

Reproductive system disease: (polycystic ovarian syndrome)

Place of study: VIMSAR, BURLA, ODISHA

Research setting: Department of Biochemistry in collaboration with Department of General Medicine, VIMSAR, Burla

Period of Study: June 2023 to November 2023

Study Design: A cross -sectional analytical study

Sample Size: Total of 126 people between the age group of 35-65 years were enrolled in the study in to two groups. Participant with metabolic syndrome as cases and participant without metabolic syndrome as Control.

Statistical analysis:  SPSS software version 26, significant if P value < 0.05

Study population:

Cases in patients and out patients of PG department of Medicine VIMSAR. Selected as per ATP ІІІ guidelines – any three of the following

1.       Waist circumference: male > 102 cm and female >88 cm

2.       Raised TG >150 mg/dl

3.       Reduced HDL cholesterol <40 mg/ dl in males and < 50 mg/dl in females

4.       Raised BP: systolic BP (SBP) > 130mmHg or diastolic BP(DBP) >85 mmHg or on treatment

5.       Raised fasting plasma glucose > 100 mg/dl

Control: Age, Sex, status matched normal individuals among staff of VIMSAR.

Sampling

Purposive sampling of cases fitting to our requirements.

Selection of cases

Inclusion criteria

Patient between the age group of 35-65 yrs with MetS screened as per the ATPІІІ  criteria of the NCEP.

Exclusion Criteria

Patient receiving insulin therapy/ Type 1 Diabetes mellitus

Patients taking thiazolidinediones, anti-inflammatory drugs, Angiotensin converting enzyme inhibitors, angiotensin receptor blockers.

Patients with Liver and renal failure

Patients who were critically ill.

Selection of Control

1.       Age, Sex and socioeconomic status matched normal individuals.

Intervention

Nil

Methodology

Clinical Evaluation

Generalised clinical assessments of all participants, including anthropometric measurement like height, weight, waist circumference were measured.

Measurement of BP: BP was measured by standard sphygmomanometer with right arm sitting position

Measurement of Body Mass Index:  Body mass index

BMI) was calculated by dividing the body weight in kilogram by the square of height in meter

Following biochemical investigations were carried out in both the study groups by standard methods.

Routine Blood Test

Fasting venous blood samples were collected from all participants after an overnight fast of at least 8 hours. Serum was separated by centrifugation and analysed

1.       Fasting blood sugar by GOD-POD method.

2.       Serum lipid profile

Total cholesterol by CHOD/PAP method

Triglyceride by GPO/PAP method

High density lipoprotein cholesterol by CHOD/ PAP direct method

Low density Lipoprotein cholesterol calculated by Friedwald formula

Serum Chemerin -Assayed by enzyme linked immunosorbent assay kit.

Data analysis

·         All the test results were compiled and analyzed statistically.

·         Variables such as age, weight, height, BMI, Waist circumference were summarized as Mean± standard deviation.

·         Variables such as Chemerin, Lipid profile, fasting blood sugar, BP, waist circumference were analyzed by t -test

·         Pearsons correlation coefficient was applied for data analysis.

P<0.05 will be considered to be statistically significant.

The findings of this study are presented in detail with differences in serum chemerin levels between individuals with and with out metabolic syndrome (MetS) and there  correlation with clinical and biochemical parameters.

In the cases with Mets 53.3% males and 46.7% were females. And of the controls was 43.7% males and 56.3% female and p value 0.312. The difference in the age was not significant (Table 1)

The mean BMI in the cases 31.48±4.32 kg/m2 was higher as compared to the controls 22.77±4.23kg/m2 with P value 0.001, the difference was statistically significant (Table 2 )

The mean SBP in the cases 150.53±17.62 was more as compared to the controls 119.87±6.28 with P value 0.001, the difference was statistically significant (Table 2).

The mean DBP in the cases 84.26±8.34 was higher compaired to the controls 75.44±3.03 with P value was 0.001 , the difference was statistically significant. (Table2)

The mean total cholesterol in cases 164.34±39.66  mg/dl was higher compared to the controls 148.83±34.5 mg/dl, P value was 0.004 , the difference was statisticallysignificant. (Table3)

The mean triglyceride (TG) in the cases282.38±74.79 mg/dl was higher compared to the controls 121.22±13.87mg/dl, P value 0.001, the difference was statistically significant (Table 3)

The mean HDL in the cases 24.43±1.41 mg/dl was lower compared to the controls 42.69±4.47, P value was 0.001, the difference was statistically significant .( Table 3)

The mean LDL in the cases 96.21±29.23 was higher compared to the controls 86.31±33.62, P value 0.002, the difference was statistically significant (Table3)

The mean fasting blood glucose (FBG) in the cases 153.31±26.50 was higher as compared to the controls 89.85±12.59, P value was 0.001. The difference in FBG was statistically  significant. (Table4)

The serum chemerin in cases 50.13±12.50 was higher as compared to the controls 25.21±12.95 , P value was 0.001. The difference was statistically significant. (Table4)

The comparison of the Lipid profile in the study group

The comparison of the serum chemerin and FBS in the study group

           p< 0.05 is statistically significant

Statistical Analysis

The statistical software was used for analysis. Continuous variables are expressed as mean ± SD, while categorical variables are presented in the form of frequency and percentages. For group comparisons, independent t-test was performed if the variables were normally distributed.  For studying the correlation between serum chemerin and components of MetS, Pearson's correlation is assessed.

Multiple linear regression analysis was used to evaluate independent predictors of serum chemerin levels after adjusting for potential confounders such as age, sex, and BMI. ROC curve analysis was used to assess the diagnostic utility of serum chemerin in identifying MetS. All analyses were considered statistically significant at a p-value of less than 0.05.

Table 2. Correlation between Serum Chemerin Levels and MetS Components

Diagnostic Utility of Serum Chemerin

Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of serum chemerin for MetS. The area under the ROC curve was 0.89 (95% CI: 0.85–0.93, p < 0.001), suggesting excellent diagnostic accuracy (Figure 2). A serum chemerin cutoff value of 250 pg/mL was determined with a sensitivity of 88% and specificity of 82%.

This study does show a tremendous increase in the serum chemerin levels among those with MetS compared to normal controls, with its potential utility as a MetS biomarker. In addition, the correlation of serum chemerin levels strongly with MetS components such as waist circumference, fasting glucose, triglycerides, and blood pressure also promotes its role in the pathogenesis of this syndrome. These results complement and extend from previous studies looking into the multidimensional roles of chemerin in metabolic and inflammatory disorders.

Chemerin, a chemotactic protein released mainly by adipose tissue, is central in linking metabolic dysfunction and inflammation. As described before by Helfer and Wu (2018), chemerin acts as a mediator and also as a marker of metabolic dysfunction. This is often seen in conditions of obesity and its complications. Our results support these observations, with patients with MetS, driven by central obesity, having serum chemerin levels significantly higher. This would make chemerin an indicator of the chronic low-grade inflammatory state observed in MetS, a notion consistent with known interactions with immune cells and pro-inflammatory pathways [10]. Finally, the positive correlation of chemerin with markers of adiposity, such as waist circumference, indicates its function in adipose tissue metabolism and function. Goralski et al. (2007) first identified chemerin as an adipokine regulating adipogenesis and adipocyte metabolism. This study builds on their findings by demonstrating that chemerin not only regulates adipocyte function but also serves as a systemic marker of metabolic disturbances. This finding is consistent with that reported by Rourke et al. (2014) that has proven the involvement of chemerin and its receptor GPR1 in glucose homeostasis. Moreover, a significant positive correlation between chemerin levels with fasting glucose and indicates the contribution of chemerin in the disturbances of glucose metabolism associated with MetS [12, 13].

The diagnostic utility of serum chemerin in identifying MetS is another key finding of this study. The ROC analysis showed an area under the curve (AUC) of 0.89, indicating excellent discriminatory power. These results are in agreement with Bozaoglu et al. (2007), who first reported elevated chemerin levels in individuals with MetS and proposed its utility as a biomarker for this condition.  which exhibits high sensitivity and specificity, thereby further establishing the clinical relevance of chemerin measurement in MetS diagnosis [14].

An additional insight provided by the stepwise increase in chemerin levels with an increase in the number of MetS components is its association with the severity of the syndrome. This observation agrees with that by Reverchon et al. (2015), who suggested that chemerin plays a crucial role in potentiating inflammatory responses in metabolic tissues. The progressive rise in chemerin levels with increasing metabolic derangements supports its role as both an indicator and effector of systemic metabolic dysfunction [11].

This study points out chemerin as a promising biomarker for MetS, but some limitations must be acknowledged. For example, the cross-sectional design does not allow for any causal inferences regarding the relationship between chemerin and MetS. Longitudinal studies are needed to establish whether elevated chemerin levels predict the onset of MetS or its complications. In addition, though the study controlled for major confounders, the influence of unmeasured variables, such as diet and physical activity, cannot be entirely excluded. Studies here confirmed previous research that has established chemerin to be central in the pathophysiology of MetS. Elevations in serum chemerin levels only reflect the inflammatory and metabolic derangements characteristic of MetS but also hold considerable potential for diagnosis. Future studies should aim at clearly illustrating how chemerin mechanisms may trigger metabolic pathways and consider its application in clinical practice for early detection and monitoring of therapy in MetS. Addition of chemerin in the regular clinical evaluation could open up new avenues for better, more personalized management strategies in at-risk populations toward metabolic disorders.

This study establishes serum chemerin as a crucial biomarker of metabolic syndrome, showing increased concentrations in patients with MetS and strong correlations with the core metabolic and inflammatory elements of the syndrome, such as adiposity, glucose dysregulation, lipid abnormalities, and blood pressure. The high sensitivity and specificity of serum chemerin suggest that it could be useful in the early identification and risk stratification of MetS. These findings not only confirm the role of chemerin in the pathophysiology of MetS but also open up the possibility of its integration into clinical practice as a tool for improved diagnosis and management of metabolic disorders. Further longitudinal studies are required to elucidate its causal role and therapeutic implications in metabolic dysfunction.

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