Correlation analysis between cytokines’ profile,autoimmune antibodies and the duration of type 1 diabetes: A case control study in a specialized children’s centre in Riyadh

Abstract

Objective:

The aim of this study was to investigate the role of cytokines in children with T1D living in Saudi Arabia and their correlation with disease duration and autoimmune antibody markers.

Methods:

A case-control study was conducted in the endocrine clinic of King Abdullah Specialized Children’s Hospital in Riyadh. A total of 274 T1D and healthy control children were enrolled in the study. 5 mL of venous blood samples were collected in the morning after 9 to 12 h of fasting in BD Vacutainer® EDTA tubes and centrifuged at 250g for 15 min at. Plasma was then stored at −20°C for detection of anti-islet, anti-GAD antibodies (Abs), and C-peptide using commercial ELISA kits from Thermo Fisher Scientific. The levels of cytokines were measured using commercial sandwich ELISA kits from Abcam.

Results:

Median differences in cytokine levels (IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IL-6, IL-10, IL-13, IL-18, IL-21, IL-35, and IL-37) were significantly higher in T1D patients compared with healthy controls (p-value < .001). Spearman’s Rho correlation indicated that TNFα, IL-1β, IL-4, IL-10, IL-13, and IL-21 correlated significantly with T1D Abs (p-value = .01). HbA1C correlated negatively with IL-35 and IL-37, and positively with IL-18 (p-value = .01). Linear regression analysis showed a significant increase in anti-glutamic acid antibodies (GAD) in patients with >3 years of T1D duration.

Conclusion:

Autoantibodies remained positive at high levels in our patients over a 3-year duration of the disease and correlated with specific cytokines. The clear correlations with disease duration and profile of specific cytokines could be targets for future therapeutic interventions.

Keywords

type 1 diabetes mellitus cytokines T1D markers autoimmune biomarkers Saudi children

Introduction

Type 1 diabetes (T1D) is the third most common chronic disease in children.¹ A study conducted in Saudi Arabia in 2001–2007, found that the prevalence of T1D in children was 109.5 per 100,000, with an almost equal ratio between males to females.² In a recent national surveillance, the overall prevalence of diabetes was reported to be 10.84%, and the majority (77.2%) of T1D cases were documented in urban rather than rural areas (22.7%).³ T1D is characterized by hyperglycemia due to insulin deficiency, which leads to a series of complex metabolic aberrations.^1,4 T1D is associated with an inflammatory immune response and cellular penetration in the islets of Langerhans.⁵ Components of the Innate immunity include monocytes in the blood circulation, macrophage cells, dendritic cells (DC), and natural killer (NK) cells, all of which play important roles in the pathogenesis of T1D.⁶ The components of adaptive immunity also play a crucial role in the pathogenesis involving T-lymphocytes (CD4+ and CD8+) in patients with T1D.⁷ In general, the balance between the subsets of T-helper cells (Th1 and Th2) is crucial for the development pathogenesis of T1D.^8,9 CD4+, Th1, and Th2 are typically characterized by cytokine production and subsequent immune function modulated by these cytokines. Th1 cells produce interferon gamma (IFN-γ), tumor necrosis factor beta (TNF-β), interleukin-1 (namely; IL-1β), IL-2, and IL-6, which activate cell-mediated immune responses. Th2 cells, on the other hand, typically produce IL-4, IL-6, IL-10, and IL-13, which are responsible for activating humoral immunity. During immune homeostasis, a balance between Th1 and Th2 cells is achieved by the activating effect of cytokines on the differentiation and effector functions of the other cell type.¹⁰ It is common to classify cytokines according to their function rather than their origin.¹⁰ Type 1 cytokines include those produced by Th1 cells, with the addition of IL-12, which typically enhance cellular immunity while diminishing the humoral response.¹⁰ Type 2 cytokines include those produced by Th2 cells. They preferentially inhibit cell-mediated immune responses while activating humoral immunity.¹¹ Very few cytokines have exclusively proinflammatory or anti-inflammatory functions in the context of T1D.¹² Recent studies suggest that cytokines play an important role in the pathogenesis of diabetes.^13–15 The role of cytokines in the pathophysiology of T1D is often unclear and complex, especially when the duration of diabetes is factored in the equation.

Autoimmune antibodies directed against islet cells of Langerhans are useful markers to confirm type 1 diabetes and predict diagnosis in non-diabetic subjects.^16,17 A 2016 study by Litwińczuk-Hajduk J. and associates showed that immunological markers have a potential role in patients with T1D and those with type 2 diabetes (T2D), increasing the risk of developing certain complications such as diabetic neuropathy in T2D patients.¹⁸ To our knowledge, the sustainability of these autoimmune markers in patients with T1D, their correlations with disease duration, and the profile of cytokines have not been extensively studied and reported. A recent study in Finland suggests that the elevated blood levels of granulocyte-macrophage colony stimulating factor (GM-CSF), IL-1β, and IFNγ were higher in T1D patients with two or more autoantibodies to T1D than in patients with single or without any autoantibodies in newly diagnosed T1D.¹⁸ A previous study by our group in T1D suggested that IFN-γ, TNF-α, IL-6, IL-1β, IL-4, and IL-10 were significantly higher in T1D children with deficient plasma levels of vitamin D [25(OH)D].¹²

Recently, the prevalence of type 1 diabetes has increased significantly in Saudi Arabia.¹⁹ Therefore, we aimed to investigate the role of cytokines in children with T1D living in Saudi Arabia and their correlation with disease duration and autoimmune antibody markers. This will hopefully take us a step further towards a better understanding of the possible mechanisms of pathogenesis and immune responses, as well as possible therapeutic interventions in our population.

Materials and methods

Study design and population

A case-control study was conducted in the pediatric endocrine outpatient clinic of King Abdullah Specialized Children’s Hospital (KASCH), Riyadh, Saudi Arabia. Retrospective data and prospective blood samples were collected during the study period (April 2017 to July 2019). This study included patients with T1D who attended KASCH and healthy (non-diabetic) children who were brought to the school clinic as part of the annual school health monitoring program. All participants met the following inclusion criteria: (a). They were free of infectious diseases, autoimmune diseases, asthma, eczema, and allergies; (b). They fasted for 9 to 12 h before blood collection samples. At the same time, control subjects from the school clinic were consecutively enrolled in the study. They had no type I or type 2 diabetes and no first-degree relative with T1D or autoimmune diseases. Patients in both groups of the study were gender and age matched. Demographic parameters such as sex, age, weight, and height were recorded for all participants.

Sample size calculation

The sample size was calculated using the sample calculation formula with a 95% confidence interval and a 5% margin of error, resulting in a required sample size of 274 patients.

Sample collection

Five ml of venous blood samples were collected in the morning after 9 to 12 h of fasting in BD Vacutainer® EDTA tubes. The collected blood samples were centrifuged at 250g for 15 min. Plasma was transferred to cryotubes and stored at −20°C until used for detection of cytokines and T1D autoimmune markers.^12,20 The white blood cell layer on the top of the red blood cells was collected in sterile cryotubes and frozen at −80°C for DNA extraction for further investigations.

Estimation of HbA1c and other T1D markers

HbA1c was determined using the ARCHITECT/AEROSET system (Abbott Diagnostics). The HbA1c percentage is the HbA1c/total hemoglobin (THb) ratio with a conversion factor to correlate the result with an NGSP-certified high performance liquid chromatography (HPLC) method.¹²

Detection of T1D autoantibody markers

According to the manufacturer’s instructions, antibodies (Abs) islet cells (anti-islet cell Abs) and anti-glutamic acid decarboxylase (anti-GAD-65 Abs) were analysed in plasma samples using one of several Thermo Fisher Scientific brands. Human anti-islet cell Abs was analysed using the ELISA (enzyme-linked immunosorbent assay) kit, from (Novus Biologicals™, Cat. No NBP2-60079), with a minimum detection limit of 0.1 U/mL. Anti-human glutamic acid decarboxylase autoantibodies (GAD-65 Ab) were determined using the ELISA kit from (Biotang™, HU9498) with a minimum detection limit of 0.3 U/mL. According to the manufacturer’s protocol, the sensitivity and specificity of the kit for the samples tested were reported to be greater than 96.0% sensitivity and 99.9% specificity for anti-islet, and about 87.5% sensitivity and 95.5% specificity for anti-GAD-65 Abs, with a minimum detection limit of 0.1 U/mL. For the detection of C-peptide, we used ELISA kits from Invitrogen™, BMS2191-2) with a detection limit of 0.01 U/mL. In accordance with the C-peptide kit manufacturer's guidelines, the sensitivity is 82.4%, and the specificity is 86.3%.

Determination of cytokines

Plasma levels of IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IL-6, IL-10, IL-13, IL-18, IL-21, and IL-37 were determined using sandwich ELISA kits (cat. no. ab174443, ab46087, ab214025, ab270883, ab215089, ab178013, ab185986, ab100553, ab215539, ab119542, and ab213798, respectively) from (Abcam™, Cambridge, UK) according to the manufacturer's instructions. Plasma cytokine IL-35 was measured using the human high-sensitivity ELISA Kit (OKCD01365) from Aviva Systems Biology™ according to the manufacturer’s instructions. All cytokine levels were expressed in ng/ml. According to the manufacturer’s protocol for plasma cytokine kits, the sensitivity (>91.4%) and specificity (>94.3%) were at a detection limit of 0.01 ng/mL.

Statistical analysis

Data were entered and analysed using the statistical package IBM SPSS Statistics for Windows, version 26.0. Armonk, NY: IBM Corp. Categorical data were presented as number and percentage, and numerical data were presented median and (Lower Q1–upper Q3). Diabetes markers (HbA1c, anti-islet cells Abs, anti-GAD-65 Abs, and C-peptide) were performed using the Kruskal–Wallis H-test. Correlation between various autoimmune markers and C-peptide and cytokine levels in patients with T1D was performed using Spearman’s rho correlation).

Linear regression analysis

To investigate whether the duration of diabetes has a significant effect on diabetes autoimmune markers (anti-islet Abs, anti-GAD Abs, and C-peptide) and circulating cytokine (IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IL-6, IL-10, IL-13, IL-18, IL-21, IL-35, and IL-37) levels, we analysed these variables as additional explanatory variables using univariate general linear model analysis. All tests were performed with a significance level of 5%.

The normality of the residuals was assessed using the normal P-P plot of Regression Standardized Residual. Most of the dependent variables had acceptable p-p.

Calculation of ELISA cut-off value

The mean (x) and standard deviation (SD) of T1D markers (HbA1c, anti-islet cells, anti-GAD-65 Abs, and C-peptide) of the control groups were used to calculate the cut-off values by 3SD above the mean for independent validation of the ELISA kits. HbA1c ≥ 6.2, anti-islet cells ≥ 4.1, and anti-GAD-65 Abs ≥ 8.5 above the cut-off values were considered positive. However, C-peptide ≤ 2.2 below the cut-off value was considered positive. The accuracy for the ELISA kits was calculated in the table below:

Parameter	Cut-off value	Accuracy (%)
HbA1c	6.2	100
Islet Abs	4.1	99.2
GAD	8.5	99.2
C-peptide	0.4	100

Ethical considerations

This research project was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Institutional Reviewed Board (IRB) of King Abdulaziz Medical City (KAMC), Ministry of National Guard, Health Affairs (MNGHA), Riyadh, Saudi Arabia, located at King Abdullah International Medical Research Centre (KAIMRC), No. (SP16-237). Written informed consent was obtained from the children’s parents and their legal guardians before participation in the study.

Results

A total of 274 children were consecutively enrolled in the study and divided into two groups, Group I: Healthy school children (control group) (n = 153 [55.8%]; overall median age [Q1–Q3] was 10 [9–11] years, boys n = 87 [56.9%]) (Table 1).

Table 1.

Descriptive data for different study groups.

		Healthy		T1D		p-value
		N	%	N	%	p-value
Sex	Boy	87	56.9%	65	53.7%	.603
Sex	Girl	66	43.1%	56	46.3%	.603
Median (Q1–Q3)	Age/years	10 (9–11)		10 (8–12)		.173
	BMI-z score	0.24 (−0.80 to 1.15)		0.66 (−0.06 to 1.45)		.002
	HbA1C	5.00 (4.30–5.10)		9.60 (8.30–10.50)		<.001
	Islet abs	2.21 (1.78–2.71)		15.00 (5.18–19.56)		<.001
	GAD-65	2.97 (2.40–5.00)		35.30 (16.50–54.39)		<.001
	C-peptide	5.75 (3.16–7.94)		0.18 (0.15–0.24)		<.001
	Duration (months)	NA		49 (30–82)		NA

^aAll data are given as median (lower Q1) – (upper Q3) quartiles.

Group II: T1D children (n = 121 [44.2%]; overall median age [Q1–Q3] was 10 [8–12] years, boys n = 65 [53.7%]) (Table 1). The median BMI-z score was significantly higher in T1D compared to healthy controls (p-value = .002) (Table 1). In addition, the median values of HbA1c, anti-islet cell, and anti-GAD Abs were higher in children with T1D compared with the healthy group (p-value < .001) (Table 1). However, the median C-peptide level was significantly lower in T1D patients (p-value < .001) (Table 1).

Cytokine levels and correlation with various T1D markers

The median levels of cytokines (IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IL-6, IL-10, IL-13, IL-18, IL-21, IL-35, and IL-37) were significantly higher in T1D patients compared with healthy controls (p-value < .001) (Figure 1). In addition, Spearman Rho correlation between age and various diabetes autoimmune antibodies and cytokines in T1D patients indicated a positive correlation of higher levels of TNFα, IL-10, and IL-21 with age (p-value < .05, p-value = .01 and p-value = .01, respectively). IL-2 and IL-18 correlated negatively with age in the T1D groups (p-value = .01 and p-value < .05, respectively) (Table 2). HbA1c correlated negatively with high values of IL-35, (p-value = .01) IL-37, (p-value = .01), and IL-6 (p-value < .05). However, IL-18 correlated positively with the HbA1c value (p-value = .01) (Table 2).

Figure 1.

Levels of cytokines among the different study groups. All the cytokines were highly statistically significant in T1D compared to healthy controls p-value < .001.

Table 2.

Correlations between different autoimmune markers, and C-peptide and cytokine levels among patients with type 1 diabetes.

Spearman’s rho	Age/years	HgA1C	Anti-islet Abs	Anti-GAD Abs	C-peptide
IFN-γ	−0.06	0.12	0.15	0.12	−0.06
TNF-α	0.19^a	−0.05	0.61^b	0.57^b	0.04
IL-1β	0.17	−0.11	0.70^b	0.64^b	0.1
IL-2	−0.18^a	−0.04	−0.15	−0.16	0.02
IL-4	0.14	−0.03	0.30^b	0.33^b	0.1
IL-6	0.11	−0.18^a	0.01	0.03	0.14
IL-10	0.37^b	−0.02	0.99^b	0.97^b	−0.03
IL-13	0.17	−0.07	0.68^b	0.62^b	0.05
IL-18	−0.31^b	0.33^b	−0.1	−0.14	0.07
IL-21	0.35^b	0.01	1.00^b	0.97^b	−0.05
IL-35	−0.08	−0.79^b	−0.02	−0.07	0.57^b
IL-37	0.02	−0.82^b	−0.02	−0.03	0.56^b

^aCorrelation is significant at the 0.05 level (2-tailed).

^bCorrelation is significant at the 0.01 level (2-tailed).

Interestingly, TNFα, IL-1β, IL-4, IL-10, IL-13, and IL-21 correlated positively with anti-T1D autoantibodies such as anti-islet and anti-GAD antibodies (Abs) (p-value = .01) (Table 2). We have proposed an illustration to relate these results to the already known role of cytokines in the pathogenesis of T1D (Figure 2).

Figure 2.

Summarizing the main results for the current research article allied with the previous knowledge related to the role of cytokines among T1D patients. (a) Normal mechanisms for the normal secretion of the insulin from the β-cells. (b) Theoretical initiation of T cells attacks and destroying the β-cells and reduces the amount of secreted insulin due to the recognition of auto-reactive T cells to the self-antigen present over MHC and destroying the β-cells. IFNγ cytokine plays an important role in activating natural killer (NK) cells, macrophages and CD8+ T cells (cytotoxic T cells) for destroying the β-cells. (c) Development of humoral immune response by activating the B cells to release autoantibodies against (GAD and islet); those autoantibodies can positively have correlated with the duration of T1D pathogenesis.

Effects of diabetes duration on the association of autoimmune markers in relation to cytokine levels

Using univariate linear regression analysis, islet and GAD were found to increase dramatically with T1D duration of greater than 36 months compared with a shorter duration of 13–24 months (the reference group used in the analysis) after diagnosis of T1D (p-value < .001) (Table 3). In addition, patients with disease duration of 25–36 months had shown higher islet cell positivity (B unstandardized coefficients = 0.8; 95% CI = (0.1 to 1.6) and p-value = .031) compared with the reference group (Table 3). Table 3 also shows the unstandardized coefficients (95% CI) adjusted with the different cytokine levels. It is known that the cytokines are correlated with each other, however, the aim of Table 3 was to assess the association of disease duration with diabetes markers adjusted with circulating cytokines. The variance inflation factor ranged from (2.2 to 16.3); this might affect the significance of these cytokines but not the significance of the disease duration.

Table 3.

Linear regression analysis of the duration of diabetes in association with autoimmune markers and C-peptide level adjusted with levels circulating cytokines’, age, sex, and BMI.

Dependent variable	HbA1c	Islet autoantibodies	GAD autoantibodies	C-peptide
Dependent variable	Bb (95% CI), p-value	B^b (95% CI), p-value	B^b (95% CI), p-value	B^b (95% CI), p-value
Intercept	11.63 (9.84 to 13.41), <.001	3.8 (1.8 to 5.7), <.001	10.88 (−0.3 to 22.0), .056	0.2 (0.1 to 0.3), .003
Duration^a
85+	−0.05 (−0.79 to 0.69), .896	16.4 (15.6 to 17.2), <.001	55.64 (51.0 to 60.3), <.001	0.0 (−0.1 to 0.1), .459
73–84	−0.36 (−1.3 to 0.58), .448	14.0 (12.9 to 15.0), <.001	34.41 (28.5 to 40.3), <.001	0.1 (0.01 to 0.1), .133
61–72	0.41 (−0.78 to 1.6), .494	13.9 (12.6 to 15.1), <.001	31.5 (19.1 to 33.9, <.001	0.01 (−0.1 to 0.1), .675
49–60	0.7 (−0.1 to 1.49), .086	12.0 (11.2 to 12.9), <.001	30.22 (25.3 to 35.2), <.001	0.1 (−0.1 to 0.4), .315
37–48	−0.19 (−0.98 to 0.59), .627	8.1 (7.3 to 9.0), <.001	16.32 (11.4 to 21.2), <.001	0.08 (0.01 to 0.1), .252
25–36	0.03 (−0.65 to 0.72), .926	0.8 (0.1 to 1.6), .031	3.30 (−1.0 to 7.6), .13	0.1 (0.01−0.2), .803
13–24 (ref)	0.0	0.0	0.00	0.0
Cytokines
IFN-γ	0.01 (−0.1 to 0.13), .837	0.01 (−0.1 to 0.2), .476	0.13 (−0.6 to 0.9), .714	0.1 (0.01 to 0.3), .495
TNF-α	0.04 (−0.05 to 0.12), .394	0.01 (−0.1 to 0.1), .562	0.01 (−0.5 to 0.5), .976	0.2 (0.01 to 0.4), .108
IL-1	−0.05 (−0.13 to 0.04), .284	0.1 (0.01 to 0.1), .261	0.06 (−0.5 to 0.6), .828	0.1 (0.01 to 0.2), .1
IL-2	0.17 (−0.4 to 0.8), .572	−0.03 (−0.38 to 0.32), .86	1.39 (−0.66 to 3.4), .182	−0.012 (−0.04 to 0.02), .39
IL-4	−0.01 (−0.05 to 0.03), .605	−0.006 (−0.03 to 0.02), .62	0.03 (−0.11 to 0.16), .68	0.001 (−0.001 to 0.003), .176
IL-6	−0.01 (−0.05 to 0.03), .658	0.01 (−0.1 to 0.01), 0.136	−0.03 (−0.3 to 0.2), 0.798	0.3 (0.02 to 0.5), 0.828
IL-10	−0.03 (−0.5 to 0.5), .921	0.3 (−0.003 to 0.61), .05	3.77 (1.2 to 5.53), <.001	0.009 (−0.015 to 0.033), .477
IL-13	−0.08 (0.15 to −0.003), .04	0.01 (−0.04 to 0.05), .71	0.09 (−0.16 to 0.33), .498	0.001 (−0.003 to 0.004), .734
IL-18	0.24 (0.132 to 0.34), <.001	0.03 (−0.04 to 0.09), .44	−0.28 (−0.65 to 0.09), .13	0.002 (−0.003 to 0.007), .538
IL-21	0.47 (0.238 to 1.17), .192	0.71 (0.29 to 1.13), .001	0.67 (−1.79 to 3.13) .59	−0.028 (−0.06 to 0.006), .101
IL-35	0.27 (0 to 0.53), .05	−0.1 (−0.4 to 0.2), .594	−0.04 (−1.7 to 1.6), .962	0.4 (−0.1 to 0.6), <.001
IL-37	−0.38 (−0.47 to −0.29), <.001	0.01 (−0.1 to 0.1), .898	0.10 (−0.5 to 0.7), .718	0.1 (0.01− 0.3), <.001
Age	0.02 (−0.03 to 0.63), .546	0.10 (0.01 to 0.20), .043	0.18 (−0.3 to 0.39), .09	−0.05 (−0.15 to 0.4), .28
Sex	−0.51 (−0.80 to −0.22), .001	−0.87 (−1.45 to −0.29), .004	−1.41 (−2.65 to −0.18), .025	−0.50 (−1.07 to 0.75), .09
BMI	0.06 (−0.02 to 0.14), .115	−0.06 (−0.21 to 0.10), .479	−0.03 (−0.36 to 0.3), .861	−0.02 (−0.17 to 0.13), .79

^aDuration of diabetes in months (maximum 89.6 months).

^bB represents unstandardized coefficients (95% CI) adjusted with different cytokines levels, age, sex, and BMI.

No significant difference was found for HbA1c and C-peptide in relation to the duration of T1D. A study assessing the decline of C-peptide in type T1D showed that there are two clear phases of C-peptide decline with an initial exponential fall over a 7-year period followed by a stable period where there is no longer a decline in the levels. This study may partially explain our results which demonstrate a lack of association found between C-peptide and diabetes duration. This is more so given that some patients were tested at the 7.47 years mark where the body would have stabilized and there is no longer a decline in the C-peptide levels.

Discussion

To better understand the pathogenesis of T1D, we investigated the role of different cytokines with their correlations between autoimmune antibody markers of Saudi children with T1D and the duration of the disease and adjusted this with the profile of their cytokines’ (IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IL-6, IL-10, IL-13, IL-18, IL-21, IL-35, and IL-37). As far as we know, this is the first study from Saudi Arabia to examine these correlations.

A previous report on Saudi children with T1D suggested that inflammatory cytokines were significantly higher in these patients compared to healthy controls, and revealed that cytokine levels (IFN-γ, TNF-α, IL-1β, IL-4, IL-6, IL-10, IL-13, IL-18, IL-21, IL-35, and IL-37) were significantly higher in T1D patients.¹²

Furthermore, IL-1β was significantly correlated with anti-islet and anti-GAD antibodies in our cohort of T1D patients. Many studies have shown that IL-1β is the most highly expressed cytokine gene in peripheral blood mononuclear cells (PBMC) of patients with T1D.²¹ There is also evidence of the involvement of IL-1β in the pathogenesis and complications of T1D.²²

No significant association was found between higher HbA1c levels and the levels of cytokine (IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IL-10, IL-13, and IL-21). The current study suggests that HbA1c in T1D patients is positively correlated with a high value of IL-18 compared to healthy controls. Similar results suggest that IL-18 significantly correlates with HbA1c in different studies,^23–25 which may be due to increased expression of IL-18 in diabetic patients, and IL-18 may be a contributing factor in the autoimmune destruction of pancreatic β-cells in patients with T1D. On the other hand, IL-18 has also been linked to other metabolic disorders such as dyslipidemia, hypertension, and insulin resistance.²⁶ However, the anti-inflammatory cytokines IL-35 and IL-37 showed a negative significant correlation with high HbA1c levels, suggesting that anti-inflammatory cytokines (IL-35 and IL-37) may play an important role in reducing the inflammatory process in T1D and contribute to increasing insulin sensitivity (Figure 2). A recent study in elderly diabetics suggests that IL-37 may be associated with increased insulin sensitivity by inhibiting dysbiosis of the gut microbiota.²⁷ IL-35 and IL-37 cytokines play a critical role as protective immunoregulatory cytokines that may suppress the development of autoimmune diseases see.²⁸ Consistent with this, higher levels of IL-35 and IL-37 in our cohort correlated positively with the C-peptide levels, which are also released when β cells release insulin in response to a rise in blood glucose. A recent study suggests that IL-37 enhances the suppressive activity of natural regulatory T cells (Treg).²⁹ Further studies could focus on the cytokines IL-35 and IL-37 as potential immunomodulators in the treatment of complications of T1D.

Our results suggest that the cytokines (TNF-α, IL-1β, IL-4, IL-10, IL-13, and IL-21) correlated positively with autoantibody markers of T1D (Islet and GAD). TNF-α and IL-1β are the most likely expressed cytokines to act synergistically during the inflammatory process involving the pancreatic islets of Langerhans. This leads to the activation of the apoptotic signalling pathway, an essential step in the loss of function of β-cells.³⁰

In general, it is proposed that the cytokines IL-4, IL-10, and IL-13 play an important role in activating the humoral immune responses by activating B cells to release autoantibodies against islet cells and GAD molecules.³¹

The current findings suggest that the diabetics have higher BMI-z compared to healthy controls. Obesity in patients with T1D can be challenging. To date, there are no specific guidelines to improve both, the glycemic control and weight management outcomes in these patients.³² In addition, these patients do not usually adhere to the dietary recommendations given to them by diabetes care teams. A recent study has shown that obesity affects a large number of patients with T1D during the course of the disease, with prevalence ranging from 2.8% to 37.1%.³³ As obesity is associated with insulin resistance, these patients usually require high doses of insulin and have a higher risk of developing cardiometabolic complications than T1D patients with normal or lean body mass.³³

Serum Islet, GAD-65 (glutamic acid decarboxylase) is commonly used to detect autoimmunity in diabetes. As expected and similar to a previous report from Saudi Arabia, our results indicate that T1D autoantibodies (Islet and GAD-65) were significantly higher in T1D compared to healthy controls.¹² Furthermore, Gürsoy et al. reported no difference between the case and control groups in GAD-65 antibodies, while our study found a significant difference between the groups in GAD.³⁴ This result was also previously noted by Basu et al., who indicated that T1D patients have a high prevalence of autoantibodies compared to healthy controls.³⁵ In general, we observed sustainability and even higher positivity of these autoimmune markers (ICA and anti-GAD Ab) in relation to the duration of T1D, over 36 months after diagnosis. This suggests the importance of these markers at diagnosis and later in the course of the disease. However, a recent large cohort study in the UK has reported a reduction in autoantibodies of the same patients when followed longitudinally.³⁶ Despite an overall reduction in autoantibody prevalence, number, and titer as a function of increasing diabetes duration, the degree of autoantibody loss was not influenced by the number or combination of autoantibodies present at onset as cytokines mainly play a role in the early stages of the disease, we understandably found few associations between their levels and high autoimmune markers in our study. This was especially true for IL-21 with ICA, IL-10 with GAD Abs and IL-4, IL-10, IL-18, and IL-21 with IA. Therefore, IL-10 and IL-21 appear to have a longer lasting effect in the pathogenesis of T1D compared to other cytokines. A further future study may compare a possible parallel increase in both GAD65 antigen, a major component of the ICA complex, and ICA levels after the initial diagnosis of T1D.

Similar to previous reports, C-peptide levels in children with T1D showed significantly lower levels compared to healthy controls throughout the period of our study as expected.³⁷ A recent study assessing the decline of C-peptide in type T1D showed that there are two clear phases of C-peptide decline with an initial exponential fall over a 7-year period followed by a stable period where there is no longer a decline in the levels.³⁸ This study may partially explain our results which demonstrate a lack of association found between C-peptide and diabetes duration. This is more so given that some patients were tested at the 7.47 years mark where the body would have stabilized and there is no longer a decline in the C-peptide levels.

Our patients had increased levels of IFN-γ, TNF-α, IL-1β, and IL-6, similar to previous reports.^18,39,40 Many inflammatory cytokines that attract T-cells can be activated and they contribute to the process of chronic inflammation, while other cytokines may play a different role in the pathogenesis of T1D.⁴¹ Several inflammatory cells produce proinflammatory cytokines that have been associated with pancreatic β-cells damage and the inflammatory response within the islets (insulates).⁴² Our results supported the above notion and suggested that anti-islet Abs may have a positive significant effect on the levels of cytokines (IFN-γ, TNF-α, IL-1β, and IL-10).

Although inflammatory cytokines are typically high in the initial and destructive phase of beta cells, they could also correlate with episodic hyperglycemia during the course of T1D. In a previous study in California, plasma concentrations of IL-1α, IL-4, and IL-6 were found to be elevated during hyperglycaemia in children with T1D, and these elevations persisted for hours after correction of hyperglycaemia.⁴³ Previously, we published similar results showing that T1D patients had significantly higher levels of serum cytokines such as IFN-γ, TNF-α, IL-6, IL-1β, IL-4, and IL-10 than age and gender matched healthy controls.^12,20 A recent study by our group suggests that hyperglycaemia in patients with T1D upregulates inflammatory pathways and increases the expiration of inflammatory/and proinflammatory cytokines such as IL-1 family, IL-6, IL-8, and TNFα.⁴⁴

In general, higher production of some cytokines, such as IFN-γ, TNF-α, IL-1β, IL-4, IL-6, and IL-18, can activate phagocytic cells (e.g., neutrophils and macrophage) and also trigger the inflammatory processes. Hyperglycemia may also increase vascular permeability by enhancing the kallikrein smooth muscles response, thus increasing the risk of complications in patients with T1D.^45,46 In addition, hypertrophic adipose tissue (lipohypertrophy) resulting from repeated insulin injections at the same site decreases nitric oxide by producing cytokines such as TNF-α, which activates NFκB and impairs eNOS expression.⁴⁷ We did not investigate these clinical/immunoregulatory associations between episodic hyperglycemia and/or lipohypertrophy and cytokine release in this study.

Limitations

The authors acknowledge that incorporating genetic and clinical data would have enriched the study. Unfortunately, due to the dynamic nature of the patient cohort at the tertiary center, it was challenging to obtain comprehensive genetic and detailed clinical follow-up information for our study groups. We will certainly take this into account for future research endeavors.

Conclusion

Autoimmune antibody biomarkers for T1D were persistently positive in our cohort. Their levels have remained raised at 3 years duration of the disease showing clear correlations with specific cytokines. Identifying the cytokine profile and describing these complex correlations of immune markers with disease duration would potentially provide better opportunities for therapeutic intervention.

Footnotes

Acknowledgements

The authors would like to thank all the participating children and families for their support and the staff of KASCH and KAMC for their valuable contributions. The authors would like to thank the staff of King Abdullah International Medical Research Center (KAIMRC) for their continuous support and cooperation. The authors thank all the staff of the Department of Basic Medical Sciences, College of Medicine for their excellent technical support and willingness to respond to any call for help. The authors thank Professor Ibrahim Al Alwan for always having time and willingness to answer my questions with so much patience and knowledge, which really added a lot to this manuscript. The authors would like to thank Dr Amjad M. Ahmed, KAMC for his help, support, and cooperation in calculation of ELISA cut-off values.

Author contributions

Conceptualization: M.A., A.B., and A.N.; sample collection and clinical follow−up: M.A., A.B., A.A., A.AL., M.A.A., T.A., and F.A.; methodology done by students: A.A., A.AL., T.A., AN.A., and FI.A; validation: M.N. and A.N.; software: E.M., M.N., and A.N.; formal analysis, E.M., A.B., and A.N.; investigation: A.A., A.AL., T.A., AN.A., F.A., FI.A., M.N., and A.N.; resources, M.A., A.B., and I.A.; data correction, E.M., A.B., M.A., I.A., and A.N.; statistical analysis: E.M.; writing—original draft preparation: M.D., A.B., A.A., A.AL., T.A., M.A.A., AN.A., F.A., FI.A., and A.N.; writing—review and editing: M.A., A.B., and A.N.; visualization: M.A., A.B., and A.N.; supervision: M.A and A.N.; project administration: M.A.; funding acquisition—this research received no external funding. All authors have read and agreed to the published version of the manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by King Abdullah International Medical Research Center, No. SP16-237. This study is part of a student’s research project at College of Medicine, KSAU-HS.

Informed consent

Written informed consent was obtained from all subjects before enrollment in the study.

ORCID iDs

Anas Alotaibi

Faisal Alotaibi

Amre Nasr

Data availability statement

The are not publicly available because the Regional Committee for Medical and Health Research Ethics does not permit public release of the data.

Appendix

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