INTRODUCTION
Serum amyloid A (SAA) plays an important role in acute and chronic inflammation and is used as an indicator of inflammation in clinical laboratories. SAA is an acute-phase inflammatory protein that exists as an apolipoprotein in the high-density lipoproteins (HDL) fraction.[1,2] Clinically, SAA has a shorter half-life, greater variation, and greater sensitivity than C-reactive protein. In response to pro-inflammatory cytokines produced by macrophages and endothelial cells, SAA is primarily generated in the liver. SAA-1 and SAA-2 genes encode acute phase SAA protein isoforms, but SAA-3 is not expressed in humans and SAA-4 is constitutively produced but does not differ appreciably in the acute phase.[3,4] In response to an inflammatory stimulation, SAA proteins behave as apolipoproteins and become a substantial component of HDL. This procedure removes cholesterol and extracellular lipid deposits at the site of inflammation, although it has the potential to reduce HDL’s anti-inflammatory effectiveness.[5] Secondary amyloidosis also has chronically high SAA levels, resulting in the deposition of SAA-derived truncated fibrils (SAA proteins) throughout the body’s tissues.[6] Chronic inflammatory diseases such as rheumatoid arthritis and multiple sclerosis have been related to secondary amyloidosis. Furthermore, SAA has been identified as a potential biomarker for a variety of chronic disorders, including diabetes, cardiovascular disease (CVD), cancer, sickle cell disease, traumatic tissue injury, and infections.[7,8]
Even though SAA proteins are relatively small and their sequences and polymorphisms have been meticulously cataloged, their 3-dimensional structure (s) remain elusive. The generally low solubility of these proteins has posed a barrier to their complete characterization.[9] Despite lacking hydrophobic residues in their primary sequences, SAA 1, 2, and 4 have been found to have a known propensity to associate with HDL lipoproteins (they have been called apolipoproteins), which is consistent with their low aqueous solubility.[4] Thus, despite their small size, SAA proteins have proven difficult to achieve concentrations suitable for nuclear magnetic resonance study or crystallization.
Diabetes mellitus (DM) encompasses a variety of hyperglycemic disorders. According to the current classification, type 1 DM (T1DM) and Type 2 DM (T2DM) are the two major types. Historically, the distinction between the two types was based on age at onset, degree of cell dysfunction loss, degree of insulin resistance (IR), presence of diabetes-associated autoantibodies, and survival requirement of insulin treatment.[10,11] However, none of these characteristics definitively differentiate one type of diabetes from another, nor do they account for the entire spectrum of diabetes phenotypes. Commonly, T2DM is associated with a cluster of abnormal lipid profiles known to be atherogenic. The complications and long-term effects of diabetes include the progressive development of retinopathy, nephropathy, and neuropathy with microvascular diseases.[12] Diabetes-associated circulatory disturbances are attributed to macrovascular disorders such as atherosclerosis, which are recognized as a leading cause of mortality in diabetic populations.[13,14]
T2DM is primarily caused by lifestyle and genetic factors.[15] Several lifestyle factors are implicated in the development of T2DM. These include physical inactivity, a sedentary lifestyle, cigarette smoking, and excessive alcohol consumption.[16,17] Obesity has been linked to approximately 55% of cases of type 2 diabetes. It is believed that the increase in T2DM in children and adolescents between the 1960s and 2000s is attributable to the increase in childhood obesity.[12,18,19] Recent increases in the incidence of type 2 diabetes may be caused by environmental toxins.[20] Hyperglycemia is a hallmark of DM, a metabolic disease condition, and T2DM in particular is linked to a wide range of dyslipidemia patterns.[21] The atherogenic index of plasma (AIP) is a ratio of triglycerides (TG) to HDL cholesterol (HDL-C).[22,23] AIP is an excellent predictor of atherosclerosis and coronary heart disease risk (CHD). According to a number of studies, the visceral fat area in T2DM patients is associated with AIP. SAA as an acute phase protein is associated with atherosclerosis and T2DM.[24,25] Insufficient research has been conducted to determine the extent to which SAA may function as a defense mechanism in T2DM and its physiological roles in reducing atherosclerosis caused by a disorder in carbohydrate metabolism. This study is intended to close the existing knowledge gap by assessing the role of SAA as an acute phase protein and its correlation with atherogenic indices in T2DM.
METHODS
Experimental design
This is a cross-sectional comparative study. The study was conducted between February and July 2020. Random sampling was used to choose 60 diabetes patients (30 males and 40 females) aged 30–70 from the diabetic clinic at Federal Medical Centre, Owo. This study used a laboratory-based definition of diabetes in which fasting plasma glucose levels of >7.0 mmol/L were present on two or more separate occasions as the diagnostic threshold for the disease.[26] After receiving approval from the hospital’s ethics committee, a comprehensive questionnaire was used to collect the participants’ medical history and personal information. Thirty nondiabetic subjects (NS) attending the hospital’s outpatient family medicine clinic served as controls in this study. Therefore, all participants provided informed consent.
Consent and ethical approval
Before participants in this study were asked to sign a consent form, they were provided with a thorough explanation of the research protocols. Under the registration number FMC/OW/380/VOL.LXXVI/99, the Health Research Ethics Committee of the Federal Medical Centre, Owo approved the study.
Inclusion and exclusion criteria
Inclusion criteria
For the study, diabetic participants were sought out between the ages of 30 and 70 years, and both sexes were included. The control group consisted of apparently healthy, nondiabetic individuals aged 30–70 without diabetes.
Exclusion criteria
Participants with preexisting diseases such as hypertension, HIV, hepatitis, or cancer; those under the age of 30 or above the age of 70 years; and nursing or pregnant women were all excluded from the study. The controls were subjected to the same exclusion criteria as the subjects.
Collection and storage of samples
Each subject’s blood was drawn by placing a tourniquet around the arm just above the elbow. After a 12-h fast, 6 ml of venous blood were drawn from each subject. For the measurement of all biochemical parameters, 4 ml of venous blood were dispensed into a plain bottle. After centrifuging the blood for 5 min at 1792 g, the serum was extracted and kept at − 20°C pending analysis. To confirm their diabetes status, 2 ml of extra venous blood were dispensed into a fluoride oxalate bottle for glucose estimation.
Analytical methods
Standard enzymatic methods were used to measure fasting plasma glucose, and serum levels of total cholesterol (TC), TG, and HDL-C using reagents provided by Randox Laboratories Ltd. (UK). The Friedwald equation[27] was used to compute low-density lipoprotein cholesterol (LDL-C). ELISA kits from Melsin Medical Company, USA, were used to measure amyloid A levels in the serum. All subjects had their height (in meters) measured with a stadiometer and their weight (in kilograms) measured subjects wearing light clothing and without shoes on a bodyweight weighing scale. Weight (in kilograms) was divided by the square of height (in meters) to determine body mass index (BMI) (m2).
Statistical analysis
The data were properly analyzed with the aid of a Statistical Package for Social Sciences version 23.0 (SPSS Inc., Chicago, IL, USA). To compare the groups, a one-way analysis of variance was used. Spearman correlation was employed to test the relationship between variables. Data were given as mean and standard deviation (SD) for all quantitative parameters (mean ± SD). The area under the receiver operating characteristic curve (AUROC) of each marker (fasting blood sugar [FBS] and SAA) was compared using pair-wise comparison on a sensitivity plot. The level of significance was determined using a 95% confidence interval, with P < 0.05 considered statistically significant.
RESULTS
Thirty naive diabetic subjects (NDS) (i.e. not currently taking diabetic medications), 30 diabetic subjects under treatment (DSUT), and 30 NS were examined. The diabetic group had a mean age of 55.09 ± 8.92 years, while the nondiabetic group had a mean age of 54.00 ± 10.20 years. The age and gender distribution of all participants are shown in Figure 1. In the diabetic and nondiabetic groups, there were 27 females and 33 males, and 16 females and 14 males, respectively. Overall, the NDS group comprised 33.3%, the DSUT group comprised 33.3%, and the NS group comprised 33.4%.
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Figure 1: Age and sex distribution of the subject population in percentage
Table 1 compares atherogenic indices, FBS, SAA, and BMI between diabetic (NDS and DSUT) and nondiabetic individuals. In comparison to the control group, the mean values of BMI, FBS, SAA, TC, HDL-C, and LDL-C were significantly higher in both NDS and DSUT (P < 0.05). In addition, posthoc testing reveals that the mean levels of BMI, FBS, SAA, TC, HDL-C, and LDL-C were significantly higher in the NDS group compared to the DSUT group (P < 0.05). Table 2 follows the same pattern, with significant differences in the mean values of BMI, FBS, SAA, TC, HDL-C, and LDL-C between obese diabetics, nonobese diabetics, and the control group (P < 0.05). In Table 3, BMI demonstrated a significant positive correlation with TG, HDL-C, and LDL-C in NDS, whereas SAA demonstrated a nonsignificant negative correlation with FBS and LDL-C. Furthermore, FBS only demonstrated a significant positive correlation with TG, whereas SAA continued to exhibit an insignificant inverse correlation with BMI and FBS [Table 4]. In addition, the diagnostic performance of SAA and FBS was evaluated. SAA had a significantly greater AUROC of 0.69 than FBS, which had an area of 0.49 [Figure 2].
Table 1: Comparison of atherogenic indices, fasting blood sugar, serum amyloid A, and body mass index in diabetic subjects (naive diabetic subjects and diabetic subjects under treatment) and nondiabetic subjects
Table 2: Body mass index, fasting blood sugar, serum amyloid A, and atherogenic parameters in obese diabetic subjects, nonobesed diabetic subjects, and nondiabetic subjects
Table 3: Correlation of mean body mass index, fasting blood sugar, and serum amyloid A in Naïve diabetic subjects with atherogenic indices
Table 4: Correlation of mean body mass index, fasting blood sugar, and serum amyloid A in diabetic subjects under treatment with atherogenic indices
Figure 2: The ROC curve of blood levels of SAA and FBS as diagnostic tool in diabetic subjects. ROC: Receiver operating characteristic curve, SAA: Serum amyloid A, FBS: Fasting blood sugar
DISCUSSION
Numerous dyslipidemia patterns have been linked to DM, a metabolic disease characterized by hyperglycemia, and in particular T2DM.[21] TG and HDL-C levels are combined to form the atherogenic index of plasma (AIP).[22,23] There is a strong correlation between a high AIP and an increased risk of atherosclerosis and CHD. Having a larger amount of visceral fat is linked to an increased risk of AIP in type 2 diabetics, as shown by several studies.[24]
The mean values of BMI, FBS, TC, HDL-C, and LDL-C were significantly higher in both NDS and DSUT when compared to the NS group. High levels of TC, HDL-C, and LDL-C were documented in diabetic patients in previous studies, which corroborates these findings.[28,29] It has been reported that a high AIP increases the risk of T2DM, which may be a predisposing factor for atherosclerosis and CHD if not properly managed.[23] Lipid abnormalities in diabetic patients, commonly referred to as “diabetic dyslipidemia,” are typically characterized by high TC, high TG, low HDL-C, and an increased level of small dense LDL particles.[14,29] IR has been reported as the underlying cause of most metabolic complications of type 2 diabetes.[30] IR or deficiency affects the production of liver apolipoprotein and regulates the enzymatic activity of lipoprotein lipase and cholesterol ester transport protein, resulting in dyslipidemia in DM.[21,31] In addition, insulin deficiency diminishes the activity of hepatic lipase and several steps in the synthesis of biologically active lipoprotein lipase.[12,32,33] In light of these scientific findings, the dyslipidemia observed in this study could be due to IR, which is prevalent in obesity and T2DM.
In addition, diabetic patients had elevated levels of SAA when compared to controls. SAA activates the nuclear factor kappa B signaling pathway by stimulating the production of Interleukin-8 through the formyl peptide receptor-1. It has been demonstrated that the same pathway is a key mediator of inflammation associated with IR.[34] Similarly, based on BMI, our data further classified the diabetic subjects into two groups (ODS and NDS groups). There were statistically significant differences in the mean values of BMI, FBS, SAA, TC, HDL-C, and LDL-C between ODS, NDS, and NS groups. This is consistent with Suneetha,[35] who reported a strong correlation between DM and dyslipidemia, and can be attributed to the previously mentioned causes. In addition, SAA is an acute phase reactant, which plays a crucial role in acute and chronic inflammation. SAA, an acute-phase inflammatory protein, is present as an apolipoprotein in the HDL fraction because it is primarily produced in the liver in response to inflammatory cytokines produced by macrophages and endothelial cells.[1,2] Following an inflammatory stimulus in T2DM, this process eliminates cholesterol and extracellular lipid deposits at the inflammation site, but can reduce HDL’s anti-inflammatory capability.[5]
In this study, BMI demonstrated a significant positive correlation with TG, HDL-C, and LDL-C in NDM, whereas SAA demonstrated a nonsignificant negative correlation with FBS and LDL-C. This is consistent with previous research published in 2018 by Hari and Prerna, who discovered a correlation between BMI and lipid profile abnormalities. Obesity and dyslipidemia are prevalent risk factors among individuals with type 2 diabetes. Thus, as BMI rises, so do LDL-C levels, and vice versa.[34,36] In contrast, Hussain etal.[37] found a correlation between BMI and HDL-C and TG levels, but no correlation with LDL-C and TC levels. In this study, we also found that FBS had a statistically significant positive correlation with only TG, whereas SAA had an insignificant negative correlation with both BMI and FBS. This is consistent with a clinical report indicating that elevated FBS levels above the normal range are related to an increased risk of CVD.[38]
In addition, the diagnostic performance of SAA and FBS was evaluated. SAA exhibited a significantly greater AUROC than FBS. Type-2 diabetes is preceded by differential activation of innate immune system components.[39] SAA is a sensitive indicator of acute inflammatory state and correlates with inflammation severity; however, it is not specific to any disease. It has been reported that high levels of SAA in diabetic patients may influence the pathogenesis of T2DM.[29,40] As the measurement of SAA is widely available and reasonably priced, this finding is certainly of clinical significance. Utilizing SAA in conjunction with other laboratory tests to predict or monitor the treatment of diabetic patients would undoubtedly improve their treatment condition.
CONCLUSION
This study found a link between SAA, FBS, and lipid profile markers in diabetes individuals. This study reveals that SAA and lipid profile parameters have a role in type 2 diabetes etiology. Furthermore, SAA has been found as a superior potential indicator that could improve the diagnosis and management of diabetic patients.
Financial support and sponsorship
Self-sponsored.
Conflicts of interest
There are no conflicts of interest.
Acknowledgments
The authors would like to thank everyone who took part in the study and all of the medical staff at the diabetes clinic at the FMC, Owo.
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