Detecting the Alteration of Vaspin, Insulin and Some Biochemical Parameters in Blood of Type2 Diabetic Patients

Zahraa Abdalelah Saleem and Khalid Shaalan Sahab^*

^*Correspondence: Khalid Shaalan Sahab, Department of Chemistry, College of Science, University of Diyala, Baquba, Diyala, Iraq, Email:

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Introduction

Diabetes mellitus is a syndrome categorized by rise blood glucose with a disruption in the metabolism of carbohydrates, fats and proteins causing from a defect in the secretion of the insulin or the action of insulin or both, which causes an imbalance in the use of glucose in tissues and the release of glucose by the liver [1]. Vaspin is a new adipokines that is mostly expressed in visceral white adipose tissue but also found in serum (Vaspin=visceral adipose tissue-derived serine protease inhibitor also known as Serpin A12). Vaspin is a protein belongs to the serpin family of serine protease inhibitors [2,3]. Obesity and insulin resistance increases visceral adipose vessel expression and serum serpins levels [3]. Some studies show that vaspin is a new diagnostic linking obesity with metabolic changes [4]. Adult humans have been discovered to have vascularized visceral and subcutaneous adipose tissue, with depotspecific regulation hypothesized to be controlled by body fat level or insulin sensitivity [5]. Vaspin is anti-inflammatory and anti-atherosclerotic, as well as improving insulin sensitivity. Circulating vascular levels of adipokines are low when compared to other adipokines [6]. Adipose tissue responds to nutritional, neuronal, and hormonal signals, as well as the release of adipokines, to regulate energy balance, nutrition, thermogenesis, immunity, and neuroendocrine function [7]. Adipose tissue is rapidly being recognized as a metabolically active endocrine organ in addition to being a fat-storage organ [8]. Expression of vaspin in humans was found in both subcutaneous and visceraladipse tissues of adults and this expression believed related to parameters of fat content (obesity), insulin resistance and metabolism of glucose. Also, beside subcutaneous and visceral adipose tissues, the expression of vaspin was found in the hypothalamus, skin, stomach, pancreas, liver, cerebrospinal fluid, placenta and ovaries [2,3,9- 11]. Vaspin in human blood ranging of 0.18 to 1.55 ng/ ml [12]. Studies showed that the circulating plasma levels of vaspin, are significantly higher in women [9,12,13] vaspin play an insulin-sensitizing role as part of mechanism against insulin resistance and also have anti-inflammatory effect. Up regulation of vaspin expression in adipose tissue and increased vaspin in plasma have a positive association to parameters of fat content, reduced insulin sensitivity (insulin resistance), and metabolic syndrome [14].

Namely Vaspin and glucose metabolism are linked, and vaspin could be viewed as a new link between obesity and related metabolic illnesses, including diabetes [15]. The evidence on serum vaspin levels in type 2 diabetes is mixed. According to [16] people with type 2 diabetes have greater vaspin levels and a favorable association between vaspin and postprandial blood glucose levels. Furthermore [17]. reported that continuous subcutaneous insulin infusion decreased serum vaspin concentrations while improving beta-cell function in type 2 diabetes. Other investigations [18]. reported no difference in vaspin levels between participants with and without glucose abnormalities, or found lower vaspin levels in the context of type 2 diabetes ,that reduced serum vaspin levels could be a risk factor for type 2 diabetes development [19,20].

Based on the evidence presented above, it is possible to conclude that vaspin plays a key role in the etiology of type 2 diabetes. According to several studies the compensatory capacity of vaspin secretion gradually decreases with the duration of diabetes or the onset of cardiovascular diseases and aggravation of atherosclerosis, resulting in a slow decrease in vaspin levels. , as evidenced by a number of research [21]. Therefore, the vaspin levels in type 2 diabetes are relatively conflicting.

The aims of this study were to detect the relationship between type2 DM and alteration of serum vaspin, insulin hormone and some biochemical parameters include urea, creatinine, lipid profile and CRP.

Materials and Methods

Collection of samples

The study has been carried out atBaquba Teaching Hospital / Diyala Governorate in Iraq for the period from 1/10/2021 to 30/12/2021. Samples were collected, as (60) blood samples were collected from type2 diabetic patients after diagnosis by the specialist consultant doctor. The number of males was (29) and the number of females was (31) within age range between (40-70) years. Thirty (30) blood samples of apparently healthy people of both sexes were collected and used as a control group, where the number of males was (17) and a number of females (13) were within an age range between (19- 81) years and did not suffer from any chronic or acute disease at the time of collecting the samples. The samples were collected by drawing 5 ml of venous blood, using plastic medical syringes. The drawn blood was placed in gel tubes and let to clot for 15-30mins, then the sera were separated by the centrifuge device for (5) minutes at a rate of (3000 rpm).The serum for each sample was subjected to the following examinations: Insulin, Vaspin, Urea, Creatinine, Triglyceride (TG), Cholesterol, Highdensity lipoprotein (HDL), low density lipoprotein (LDL), Very low density lipoprotein (VLDL), C-reactive protein (CRP). Anthropometric measurements including age, weight and height were registered for each participant in study. The Body Mass Index (BMI) is determined using a formula that contains the basic equation of weight divided by the square of height.

Clinical laboratory analysis of groups

An ELISA kits of Shanghai company (China) were used to estimation the quantities of Vaspin and Insulin. These kits use enzyme-linked immune sorbent assay (ELISA) based on biotin double antibody sandwich technology. The FBG, lipid profile (TG, TC, and HDL), Urea, and Creatinine tests were measured by use commercial available laboratory kits of Mindray Company (China). HbA1c was measured by use commercial available laboratory kit of Human Company (Germany).

LDL-C is very difficult to isolated, therefore LDL is calculated according to the equation:

LDL-C=[Chol] - [HDLDirect]-{[TG]/5}.

VLDL-C concentration was estimated by using formula: VLDL-Cholesterol={[TG]/5}.

Ethical approval

Ethical Committees of Baquba Teaching Hospital, and College of Science/ University of Diyala gave their permission for this study. All volunteers in the study gave informed written permission before being included in the study, and the study was done according to the Helsinki Declaration.

Statistical analysis

The statistical package SPSS version 25.0 and Graph pad prism version 6 were employed to carry out these analyses. The numerical parameters (scale) were first tested for normality (Kolmogorov-Smirnov and Shapiro- Wilk test). Parameters that fit both tests (no significant difference) were given as mean ± standard deviation (SD), where student t test used to comparative between two groups and F test used to comparative more than two groups. The parameters that did not fit the normality tests (significant difference) were given as median and range, and significant difference between median was assessed by Mann-Whitney (for comparison between two groups). The other parameters (nominal and ordinal) were given as percentage frequencies, and significant differences between frequencies were assessed by Pearson-Chisquare test or two-tailed Fisher exact probability (p). The person correlation was employed to understand the correlation between certain parameters. Moreover, the multiple linear regressions was applied to predict the outcome of a response variable for several explanatory variables.

Results

Basic characteristics of study groups

The basic characteristics of the current study groups included gender, age groups, diseases, living, body mass index groups (BMI). The percentages of gender (males and females) for patients and healthy were (48.3% and 51.7%) and (56.7% and 43.3%) respectively. In sera of living, the percentage of City and Rural was (80% and 20%) for patients and (73.3% and 26.7%) for healthy. Statistical analysis show there is no significant difference in both gender and living (p>0.05).

In age groups, ≤ 20, 21-40 , 41-60, 61-80, >80 ,the percentages of patients and healthy were as follows; 0.0%, 5.0%, 55.0% ,40.0% ,0.0% and 3.3%, 73.3% ,20.0% ,0.0% ,3.3% respectively, the statistical analysis showed presence a significant difference between age groups (P<0.001). For BMI groups, the percentages of normal weight and obese in patients and healthy were as follows;38.3% ,61.7% and 73.3%, 26.7% respectively, the statistical analysis shows there is a significant difference between BMI groups (P<0.001). finally, for presence of diseases, the percentages for presence Blood pressure, Arthritis, Thyrodism, Dyslipidemia in patients and healthy were as follows;51.7%, 1.7%, 5.0%, 38.3%, 3.3% and 0.0%, 0.0%, 0.0%, 100.0%, 0.0% respectively, the statistical analysis shows there is a significant difference between diseases groups (P<0.001). Table 1 shows the above results.

Table 1: Comparative anthropometric features of participants in study groups.

Comparison of Age, BMI, and Waist parameters in study groups

The present study showed significant differences (p<0.05) between personal characters (age and BMI) of study groups. The mean value of age and BMI was high in patients (56.95 ± 8.77 and 29.12 ± 5.50) than healthy controls (35.13 ± 11.87 and 25.70 ± 3.69). The waist parameter not scored significant different between study groups (p>0.05) as shown in Table 2.

Table 2: Comparative age, BMI, and waist parameters between study.

Comparison of CRP parameter in study groups

The conducted study revealed significant difference (p<0.05) between patients and healthy according to CRP. The CRP scored highest positivity (81.7%) in patients compared to healthy that not scored positivity (0%) as shown in Table 3.

Table 3: The comparative CRP parameter between patients and healthy.

Comparison lipid profile parameters in studied groups

The present study show significant differences (p<0.05) between lipid profile (Cholesterol, Triglycerides, LDL, VLDL) parameters in study groups. The mean value of Cholesterol, Triglycerides, LDL, and VLDL were high in patients (201.93 ± 48.63, 193.17 ± 73.48, 121.43 ± 44.97 and 38.63 ± 14.70) respectively than healthy (140.07 ± 27.03, 141.60 ± 25.92, 69.05 ± 23.96, and 28.32 ± 5.18). The HDL parameter not scored significant different between study groups (p>0.05) as shown in Table 4.

Table 4: Comparative lipid profile parameters between study groups.

Comparison of urea, creatinine, glucose and HbA1C parameters in study groups

The results of present study show significant differences (p<0.05) between urea, glucose, and HbA1C parameters between study groups. The mean values of urea, glucose, and HbA1C were high in patients (34.55 ± 9.07, 187.90 ± 69.29, and 9.48 ± 2.24) respectively than healthy (29.37 ± 5.65, 86.93 ± 10.00, and 4.97 ± 0.47). The creatinine parameter not scored significant different between study groups (p >0.05) Table 5.

Table 5: Comparative urea, creatinine, glucose and HbA1C parameters in study groups.

Comparison of vaspin and insulin parameters in study groups

The results of study shown in Table 6 revealed significant differences (p<0.05) between vaspin in study groups. The mean value of vaspin was lower in patients (0.46 ± 0.22) than healthy (0.70 ± 0.33). The results of insulin parameter not scored significant different between study groups (p>0.05).

Table 6: Comparative vaspin and insulin parameter between study groups.

Correlation relationship between vaspin and insulin with other biochemical parameters

The results of relationship between vaspin and insulin with the anthropometries measurements and studied biochemical parameters have been not showed significant correlation with any parameter. The correlation of studied parameters is shown in Table 7 below.

Table 7: Correlation relationship between vaspin and insulin with another biochemical parameters were calculated by Pearson correlation.

Receiver Operator Characteristic (ROC) curve of parameters

The plotting ROC curve produces a measure for diagnostic power of the serological exams, expressed in one number in place of two, namely the area under the curve (AUC). What's more, the ROC curve and AUC permit easy comparison of diverse tests. Glucose, HbA1C, Cholesterol, LDL VLDL, triglycerides parameters showed a highest sensitivity (100%, 100%, 90%, 89%, 78% and 77%) respectively, compared to vaspin, Insulin, HDL, urea, creatinine, and CRP (42%, 57%, 50%, 69%, 55%, 62%) respectively, with high significant different (p<0.05) (Tables 4 to Table 7). Depending on specificity, LDL and vaspin parameters showed a highest specificity (70%, and 63%) respectively, compared to others parameters Cholesterol, Triglycerides, HDL, VLDL, Urea, Creatinine, HbA1C, CRP, and insulin (50%, 56%, 43%, 57%, 56%, 50%, 18%, 20%, 0%, and 50%) respectively , with high significant different (p<0.05) (Table 8). Vaspin (0.393 ± 0.066) was the lowest AUC and Insulin was with (0.551 ± 0.067) AUC.

Table 8: ROC curve, sensitivity, and specificity of all variables.

Discussion

According to the current results, there were no discernible differences between the study groups' genders (P>0.05). The findings support a study by [22] that found no gender-related differences existed between the study groups' healthy and patient gender participants, male and female participants, or between the two (p>0.05). Our findings concur with the study [23], which found no discernible differences in gender between the control group and the diabetic subjects.

According to age groups, the current study noted significant differences (P>0.05) between age categories for patients and healthy, with the age groups 41-60 and 61-80 year scoring highest percent for patients (55.0 percent and 40.00 percent, respectively), and the age groups 21-40 year scoring least percent for patients (5.0 percent ). Contrarily, the age group of 21 to 40 years scored the highest percentage of healthy individuals (73.3%), while the age group of 20 years or younger scored the lowest percentage (3.3%). These findings are consistent with a study conducted by [22] that found significant differences (p>0.05) by age group for both patients and the healthy. In another study, the average age of controls, who ranged in age from 18 to 73 years, was significantly different (P=0.001) from the mean age of diabetic subjects, who ranged in age from 19 to 86 years [24]. T2DM prevalence varied by age, gender, and living conditions, and it was higher in men than in women and in urban than in rural areas [25].

Body mass index (BMI) data revealed significant differences (P>0.05) between the patient and healthy groups, with the patient group scoring higher percentages within the obese weight BMI range (61.7%) than the healthy group, which scored higher percentages within the normal weight BMI range (73.3 percent ). The most common method for categorizing body weight is the Body Mass Index (BMI); BMI values greater than 30 kg/m² are regarded as obese [26,27]. Age and body mass index of the patients were strongly correlated with diabetes [23].According to our findings [28], DM patients have a high prevalence of obesity. Obesity management may aid in the treatment of type 2 diabetes and halt the development of the disease from prediabetes, according to reliable and strong evidence. In patients with type 2 diabetes who are also overweight or obese, modest and sustained weight loss has been shown to improve glycemic control and reduce the need for glucoselowering medications [29]. Different studies have found that those who are overweight and obese are more likely than those who are not to develop prediabetes and diabetes [30].

Numerous pathologies, including arthritis, arterial hypertension, Thyrodism, cardiovascular diseases, liver diseases, and obstructive sleep apnea, are linked to the development of obesity and diabetes mellitus [31-33]. Obesity is a significant independent risk factor that is also modifiable, and numerous epidemiological studies have demonstrated a progressive increase in the prevalence of type 2 diabetes (T2DM) in association with obesity [34]. When compared to healthy groups, the diseases associated with patients showed significant differences (P 0.05), with patients with blood pressure scoring the highest percentage (51.7%) and patients with arthritis scoring the lowest percentage (1.7%). (table 1). In comparison to the healthy, (non-diabetic control) T2DM patients, the mean blood pressure (BP) levels were higher (P 0.05), this results agree with results of previous study [35].

A significant portion of diabetic patients tested positive for CRP, while healthy individuals tested completely negative. Diabetes has been linked to high serum CRP levels, according to this study and other authors [36]. According to a study by Dongway, et al. [37], there was a high prevalence of obesity among diabetics along with elevated levels of CRP, and the CRP was significantly higher in the diabetic group (P 0.001). Hepatocytes are the main cells that synthesize and release C-reactive protein (CRP) [38]. Innate, non-specific physiological and biochemical responses to a number of path physiological conditions, such as tissue damage, infection, inflammation, and malignancy, can result in the production of CRP, an acute-phase protein [39]. Experiments showed that overt T2DM, insulin resistance, and hyperglycemia are all associated with CRP, a sensitive physiological marker of subclinical systemic inflammation [40]. Diabetes and systemic inflammation have a well-established relationship, which may be reflected in the level of CRP in the blood [41,42]. Other studies have shown that CRP is elevated before diabetes sets in, suggesting that it may be a precursor to inflammation in diabetics [43]. Obesity is linked to higher CRP levels, according to earlier research [44]. BMI and serum CRP levels in people with type 2 diabetes show a significant correlation [45]. It has been hypothesized that obesity and hyperglycemia cause oxidative stress [46,47], which leads to the production of free radicals in diabetic patients who may damage cell membranes, these free radicals are linked to an increase in CRP, a mediator of inflammation, in diabetic patients.

The current findings presented in table (4) are consistent with several studies, including those by Yadav et al., who discovered a statistically significant increase in the levels of serum total TC, TG, and LDL-c (p0.001), while serum HDL-c levels did not demonstrate a statistically significant difference between the two groups (p>0.05) when obese type-2 diabetic patients were compared to obese control subjects [48-50]. All lipid profile studies have consistently shown relationships between these factors and DMT2 in their results. Similar to Al Mansour's study, where the percentages of total cholesterol were nearly the same, the variability of the lipid panel components was observed [51]. According to Ahmad et al., study's obese T2DM patients had significantly higher BMI, total cholesterol, LDL, and triglyceride levels than did healthy participants [52]. In patients compared to controls, Tahir, et al. [53], findings of high levels of cholesterol, triglycerides, and LDL and low levels of HDL are consistent with our findings. Dyslipidemia, which is characterized by elevated plasma triglycerides, decreased HDL, and an increase in the quantity of small dense lowdensity lipoprotein (LDL) particles, is typically linked to Type 2 diabetes [54]. Increased triglyceride deposition in a variety of adipose tissues, including the heart, liver, pancreas, and skeletal muscle, is linked to obesity and type 2 diabetes mellitus [55]. In obese individuals with insulin resistance, many important enzymes involved in HDL metabolism are changed. In addition to insulin resistance and absolute or relative insulin deficiency, some of these changes are further developed in type 2 diabetes [56]. Increased VLDL production, at least in part because of increased fatty acid flux to the liver, is a significant metabolic trigger for lower HDL-C levels in obesity and insulin resistance [57].

The findings in table (4) above are consistent with a number of earlier studies, including one by AL-karkhi, et al. [58] who found that urea, glucose, and HbA1C parameters were significantly higher in DM2T patients than in controls (p 0.05). Although creatinine did not significantly differ between study groups (p>0.05), these findings support our findings. Nedyalkova, et al. found that DM2T patients had higher percentages of HbA1C and creatinine than the control group [59].

The best indicators of the progression, diagnosis, and establishment of dietary restrictions in the renal disease in type 2 DM are serum creatinine and urea [60]. When blood glucose levels rise, urea and creatinine levels change, which suggests reduced kidney function in diabetic patients.

Chao, et al. [61] findings of higher HbA1c levels in patients compared to controls are consistent with our findings. HbA1c is frequently used to manage blood glucose in people with diabetes [62]. The relationship between HbA1c and the red blood cell life cycle is well established. According to recent research, iron deficiency anemia has an impact on the HbA1c results and is correlated with the degree of anemia [63]. The American Diabetes Association (ADA) advises using the HbA1c level as an index for the diagnosis of diabetes because it can show blood glucose levels from two to three months ago [64].

The increased glucose concentrations are known as hyperglycemic conditions. It may reflect the -cell's inability to produce sufficient insulin to maintain normal blood glucose levels, which is the root cause of DMT2 development. Visceral obesity may have a negative impact on glycemic control by increasing gluconeogenesis and insulin resistance [65]. As a result, maintaining a regular body weight and developing healthy eating habits are important components of the DMT2 management strategy [66].

The current findings revealed decreased vaspin levels and increased insulin levels in DM2T patients; these findings concur with those of a Czech, study [67] that found increased insulin levels in DM2T patients who were obese. Similar to our findings, Sathyaseelan, et al. study 's demonstrated that obese Type 2 DM patients had lower fasting serum vaspin concentrations (0.430.22 ng/ mL) than controls (0.830.29 ng/mL), and this difference was statistically highly significant (p=0.0001) [68]. The current findings conflict with those of Blüheret, al. [69] who found that obese adults had significantly higher levels of circulating vaspin.

One of the adipokines is vaspin. It blocks the proteases that cause carotid plaque development and rupture as well as insulin resistance [70]. Given these beneficial effects of vaspin, it is found that obese and T2DM patients have high levels of the substance as a compensatory measure to stop the chain of events that leads to atherosclerosis and insulin resistance. However, several studies revealed that T2DM patients with microvascular complications had lower serum vaspin levels than T2DM patients without microvascular complications [71]. Additionally, it was discovered that T2DM patients with carotid plaque had lower levels of vaspin than those without carotid plaque.

According to Ye, et al. [72], subjects with type 2 diabetes had higher vaspin levels, and there was a direct correlation between the two. Along with improving beta-cell function in type 2 diabetes, Li, et al. [17] also described a lowering effect of continuous subcutaneous insulin infusion on serum vaspin concentrations. Other research either found no difference in vaspin levels between subjects with and without glucose abnormalities [19] or found that type 2 diabetes was associated with a decrease in vaspin levels [21]. There is some controversy surrounding the vaspin levels in type 2 diabetes.

Evidence from obese and diabetic Otsuka Long-Evans Tokushima rats (OLEFT) suggests that vaspin improves glucose tolerance and insulin action, and that its levels significantly fell as diabetes got worse [73]. The rise in insulin resistance has a positive correlation with changes in vaspin levels. The aforementioned information suggests that vaspin is crucial to the pathogenesis of type 2 diabetes [74].

Conclusion

Data findings implicated decrease plasma vaspin levels in T2DM patients. Vaspin levels were significantly lowest in patients of T2DM and had the lowest AUC and sensitivity in ROC curve. Vaspin is not correlated with any studied parameter. Therefore, the decreasing significantly in serum vaspin level may be a probable indicator of the worsening of T2DM.

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