Association between diabetes and elevated circulating advanced oxidation protein products: a comprehensive systematic review and meta-analysis

Abstract

Background

Oxidative stress is a central feature of the metabolic disturbances observed in diabetes, often leading to cellular and molecular damage. Advanced oxidation protein products (AOPPs), formed during oxidative modification of plasma proteins, have been proposed as biomarkers of systemic oxidative stress. Despite increasing interest, the relationship between these markers and diabetes remains inconclusive. This study aimed to systematically evaluate and quantify differences in circulating AOPPs levels in diabetes.

Methods

A systematic review and meta-analysis was performed in accordance with PRISMA guidelines. Searches were conducted across PubMed, Scopus, Embase and Web of Science databases up to March 2025 to identify relevant observational studies involving adults aged 18 years or older. Studies were included if they reported circulating levels of AOPPs in individuals with type 1 diabetes (T1DM), type 2 diabetes (T2DM), or impaired fasting glucose (IFG) or impaired glucose tolerance (IGT), compared to non-diabetic controls. Data extraction and quality assessment were conducted independently by two reviewers, and a random-effects model was used for meta-analysis.

Results

Sixteen eligible articles with twenty individual studies were identified, encompassing diverse populations and diabetes subtypes. The meta-analysis demonstrated significantly elevated circulating AOPPs in both T₁DM (WMD ≈ 17.5 µmol/L) and T₂DM (WMD ≈ 24.68 µmol/L) compared with controls, though heterogeneity was high. Subgroup analyses confirmed the consistent direction of effect, with stronger associations in younger populations, studies with higher baseline AOPPs, and certain designs or regions. Meta-regression showed that examined moderators (continent, study design, baseline AOPPs, age, sample size, gender, study quality, presence of overweight/ obesity, hypertension, smoking and abnormal serum lipids) explained little to none of the variability. Sensitivity analyses indicated that results were robust. Publication bias assessment revealed funnel plot asymmetry for type 2 diabetes, but trim-and-fill analysis did not materially alter the pooled estimates, supporting the stability of the findings.

Conclusion

Elevated levels of AOPPs appear to be associated with diabetes, supporting the role of oxidative stress in its pathophysiology. Although pooled analyses demonstrated significantly higher circulating AOPP concentrations in patients with diabetes mellitus, substantial between-study heterogeneity and wide prediction intervals suggest that the magnitude and consistency of this association may vary across different populations and study settings. Also, the observational design of included studies limits the strength and generalizability of the pooled estimates. Therefore, causal inferences cannot be drawn, and further well-designed prospective research is warranted to clarify whether these biomarkers can reliably predict the onset or progression of diabetes.

PROSPERO registration number

CRD420251275798

Supplementary Information

The online version contains supplementary material available at 10.1186/s13098-026-02228-7.

Introduction

Diabetes mellitus is a major global health concern, affecting millions of individuals across all age groups. According to the International Diabetes Federation (IDF), the prevalence of diabetes has risen dramatically over the past few decades, with an estimated 537 million adults (20–79 years old) living with diabetes in 2021, a number expected to reach 783 million by 2045. This increasing burden is largely driven by lifestyle changes, urbanization, and rising obesity rates [1]. It is important to distinguish between the two main types of diabetes. Type 1 diabetes mellitus (T1DM), also known as insulin-dependent diabetes mellitus (IDDM), results from the destruction of pancreatic β-cells, leading to insufficient insulin production. In contrast, type 2 diabetes mellitus (T2DM), previously referred to as non-insulin-dependent diabetes mellitus (NIDDM), develops primarily due to insulin resistance in target tissues, often accompanied by a gradual decline in β-cell function. In both forms of diabetes, the progressive loss of β-cell mass and/or function impairs glucose regulation, ultimately leading to persistent hyperglycemia, the hallmark clinical feature of the disease [2, 3]. The role of hyperglycemia in the progression of diabetes and its complications is well established. However, oxidative stress further exacerbates the harmful effects of hyperglycemia, contributing to both diabetes development and its associated complications. Advanced oxidation protein products (AOPPs) are formed in response to chronic hyperglycemia, primarily as a result of oxidative stress-induced albumin degradation, leading to the production of di-tyrosine-containing protein derivatives. Due to their strong association with oxidative stress, AOPPs serve as important biomarkers in diabetes and its progression [4]. AOPPs are recognized as cardio-metabolic markers of protein oxidative modification, particularly affecting albumin. They are generated through reactions involving chlorinated oxidants, such as hypochlorous acid (HOCl) and chloramines, which interact with plasma proteins. The accumulation of AOPPs disrupts the balance between pro-oxidants and antioxidants, leading to excessive reactive oxygen species (ROS) production and subsequent tissue damage [5–8].

Numerous studies have identified elevated AOPPs levels as key indicators of various chronic non-communicable diseases, including cardiovascular disease [9–13], diabetes [5, 14–18], metabolic syndrome [19–21], and chronic kidney disease [22, 23]. These findings highlight the critical role of oxidative stress in the pathophysiology of diabetes and its complications, emphasizing the need for further research into targeted antioxidant therapies.

Nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) oxidase plays a crucial role in β-cell dysfunction and destruction through the Bcl-2/Bax apoptotic pathway, contributing to endothelial dysfunction in diabetes [24, 25]. Additionally, AOPPs facilitate the destruction of islet microvascular endothelial cells, partially via the receptor for advanced glycation end products (RAGEs) pathway, which is regulated by glucagon-like peptide-1 (GLP-1). These mechanisms highlight AOPPs’ involvement in the pathogenesis of diabetes [26]. AOPPs are also implicated in vascular complications associated with diabetes by promoting inflammatory responses. They stimulate the production of tumor necrosis factor (TNF)-α, interleukin (IL)−1, and IL-6, activate macrophages, and increase levels of monocyte chemoattractant protein (MCP)−1, thereby intensifying inflammation [27]. Furthermore, AOPPs elevate MCP-1 expression in vascular cells by activating the nuclear factor (NF)-κB pathway through ROS-mediated signaling, contributing to vascular damage in diabetes [28]. Several studies have examined the role of circulating AOPPs in type 1 and type 2 diabetes, as well as in individuals with impaired fasting glucose (IFG). In a study by Kalousová et al., [16], patients with T2DM exhibited significantly higher circulating AOPP levels compared to healthy controls. Similarly, Leoncini et al. reported that AOPPs concentrations were significantly elevated in individuals with both T1DM and T2DM compared to non-diabetic controls. These findings further support the role of AOPPs as key markers of oxidative stress and inflammation in diabetes progression [29].

The variability and inconsistencies among studies pose challenges in obtaining reliable conclusions about the relationship between diabetes and AOPPs. Findings across individual studies remain inconsistent, often limited by small sample sizes and heterogeneous methodologies. A comprehensive synthesis of the available evidence is therefore necessary to clarify the association between diabetes and AOPPs and to highlight their possible role in monitoring disease burden. To address this, the present meta-analysis consolidates findings from multiple studies to assess the association between circulating AOPPs and different types of diabetes in adults.

Methods

This study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (e.g. Supplementary Table 1) [30]. Additionally, the research protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO registration umber: CRD420251275798).

Search strategy and selection of studies

By March 2025, a systematic search of e-journals, including Embase, Scopus, and PubMed, and web of sciences yielded 1339 papers, without language restrictions. A comprehensive hand search of the reference lists did not identify any additional relevant articles. The search strategy is outlined in Supplementary Table 2. The retrieved articles were imported into EndNote for further processing. After removing 780 duplicate articles and excluding 520 articles based on title and abstract screening, a total of 16 articles which were extracted to twenty individual studies were included in the final analysis, as confirmed by three independent reviewers (Fig. 1).

Fig. 1.

Study flowchart

Inclusion and exclusion criteria

We selected cross-sectional and case-control studies with an observational design that investigated the relationship between circulating AOPPs and diabetes in adults. The extracted data included the mean ± standard deviation (SD) of circulating AOPPs levels in patients with diabetes compared to the control group. In the included studies, circulating AOPPs concentrations were expressed in µmol/L. Articles excluded from the analysis included case-series, case reports, experimental and in vitro studies, as well as interventional studies, review articles, letters to the editor, short communications, and abstracts from conferences or seminars. Table 1 outlines the study selection criteria based on the PECO model (Population, Exposure, Comparison, Outcome).

Table 1.

The PECO criteria used for the systematic review

PICO criteria	Description
Participants	Adult people
Exposure	Patients with T1DM and T2DM
Comparisons	Subjects without diabetes
Outcome	Circulating AOPPs
Study design	Cross-sectional and case-control studies

Data extraction and quality assessment of included studies

Using a standardized Excel spreadsheet, four researchers independently extracted data from the articles. The primary information gathered from each study included publication year, first author’s name, age range, country, total number of participants, study design, gender distribution, adjusted confounders, setting, methods for measuring circulating AOPPs, and the main findings of the investigation. The quality of the selected cross-sectional studies was evaluated using the Agency for Healthcare Research and Quality (AHRQ) checklist [31]. The studies were assessed for risk of bias, which was categorized into three groups: a score of 0 to 4 indicated high risk, 5 to 7 signified moderate risk, and 8 to 11 represented low risk of bias [32]. According to the results of AHRQ checklist (Table 2) all of the studies had moderate risk of bias. The methodological quality of the case-control studies was evaluated using Newcastle–Ottawa Scale (NOS) [33]. Each criterion received a score of 1 for “yes” responses and 0 for “no” or “unclear” responses. Based on the total score, studies were categorized as low quality (0–3), moderate quality [4–6], or high quality [7–9]. The scale assesses three major domains, including selection, comparability, and exposure/outcome. Table 3, the NOS checklist results, shows that six studies had moderate risk of bias and three studies had high quality score.

Table 2.

Agency for Healthcare Research and Quality (AHRQ) checklist to assess quality of the cross-sectional studies

Study	Source of information	Inclusion/ exclusion criteria	Time period stated	Consecutive subjects	Blinding of evaluators	Quality assurance described	Patient exclusions explained	Confounding assessed/ controlled	Missing data handling	Response rate reported	Follow-up described	Total score
ARHQ Methodology Checklist items for Cross-Sectional study
Kalousová M (16)	⊕	⊕	-	-	U	-	⊕	-	-	⊕	-	5
Gradinaru D (15)	⊕	⊕	⊕	⊕	U	U	⊕	⊕	-	⊕	-	7
Korkmaz GG (37)	⊕	⊕	-	⊕	U	U	⊕	⊕	-	⊕	-	6
Jakuš V (38)	⊕	⊕	-	⊕	U	U	⊕	-	-	⊕	-	5
Conti G (47)	⊕	⊕	-	⊕	U	U	⊕	⊕	-	⊕	-	6
Heidari G (5)	⊕	⊕	-	⊕	U	U	⊕	⊕	-	⊕	-	6
Lu Y (36)	⊕	⊕	-	⊕	U	U	⊕	⊕	-	⊕	-	6

The signs of “⊕ “, “-“and “U” denote “yes”, “no” and “unclear” respectively

Table 3.

Newcastle Ottawa scale (NOS) for quality assessment of case-control studies

Authors (Year)	Selection				Comparability	Exposure			Final score
	Definition of cases	Representativeness of the cases	Selection of the controls	Definition of controls	Comparability of groups on the basis of the design or analysis	Ascertainment of exposure	Same method for Assessment of cases and controls	Non-response rate
Matteucci E/2000 [40]	*	*	*	*	*	*	*	-	7
Matteucci E./2004 [41]	*	*	*	*	*	*	*	-	7
Gil-del Valle L/2005 [42]	*	*	*	*	*	*	*	-	7
Pan, H. Z/2008 [43]	*	-	*	*	*	*	*	-	6
Pandey KB/2010 [44]	*	-	*	*	*	*	*	-	6
Tabak O/2011 [18]	*	-	*	*	*	*	*	-	6
Kar K/2014 [45]	*	-	*	*	-	*	*	-	5
D’Souza JM/2016 [14]	*	-	*	*	*	*	*	-	6
Piwowar A/2009 [46]	*	-	*	*	*	*	*	-	6

The signs of “* “and “-“denote positive and negative scores respectively

Statistical analysis

The data were analyzed using STATA version 13. Statistical significance was set at a P-value of less than 0.05. The weighted mean difference (WMD) with a 95% confidence interval (CI) was calculated to determine the unstandardized effect size using the mean and standard deviation (SD) of the variables. WMD was chosen because all studies reported AOPPs levels in the same units, enabling direct interpretation of differences between diabetic and control groups. To estimate the pooled WMD, we applied the DerSimonian and Laird random effects models. Cochran’s Q and I-squared (I²) tests were used to assess heterogeneity. If I² was less than 25%, no heterogeneity was found; if I² was between 25 and 50%, moderate heterogeneity was present, and if I² exceeded 50%, high heterogeneity was observed [34]. The random effects model was employed in cases of significant heterogeneity, indicated by a Q statistic P-value of less than 0.1 or an I² greater than 50% [35]. Subgroup analyses were conducted to explore potential sources of heterogeneity by stratifying studies according to predefined characteristics, including continent, study design, baseline AOPPs, age, sample size, gender, study quality, presence of overweight/obesity, hypertension, smoking and abnormal serum lipids. Alcohol intake was reported in only one study [36] and subgrouping was not possible. These analyses allowed us to examine whether pooled estimates differed across subgroups. We also, performed random-effects meta-regression analyses, in which study effect sizes were regressed on potential moderators (e.g., gender, geographic region, study design, baseline AOPPs, age and sample size). Although subgroup analyses were performed according to the reported presence or absence of obesity, hypertension, dyslipidemia and smoking status, formal meta-regression analyses for these variables were not feasible due to incomplete reporting across the included studies and the limited number of studies available within several subgroups. Meta-regression allowed us to examine whether these study-level characteristics explained part of the between-study heterogeneity. Prediction intervals were reported alongside confidence intervals to illustrate the expected range of effects in future studies. Sensitivity analyses were conducted to evaluate the robustness of the pooled effect estimates. We performed leave-one-out analyses, in which each study was sequentially omitted and the meta-analysis re-run to assess the influence of individual studies on the overall results. Begg’s Funnel plots, followed by Egger’s regression asymmetry tests, were utilized to evaluate potential publication bias. Trim and fill analysis was performed when publication bias was identified.

Results

Study characteristics

In this study, a total of twenty individual research studies extracted from 16 articles were included, and their characteristics are summarized in Table 4. Since only two studies examined the association between AOPPs and impaired fasting glucose (IFG), and only one study assessed the association with impaired glucose tolerance (IGT), the available evidence was insufficient. Therefore, the meta-analysis included only patients with type 1 and type 2 diabetes mellitus in two separate meta-analysis. When a single article reported separate results for independent populations (e.g., type 1 and type 2 diabetes), each population was entered as a distinct study in the meta-analysis. This approach preserved independence of effect sizes. In cases where multiple outcomes or overlapping subgroups were reported, estimates were either combined or the most relevant outcome was selected to avoid duplication.

Table 4.

Characteristics of studies included in the current systematic review and meta-analysis

Ref	First Author/year	Country	Journal	Study Pop.	Design	Age	Num.	Main finding	Consideration
[40]	Matteucci E/2000	Italy	Diab Care	T1DM	Case-control	30–60	60	Non-significantly higher circulating AOPPs in patients with T1DM versus control group
[41]	Matteucci E./2004	Italy	Clin Exp Immunol	T1DM	Case-control	18	77	Significantly higher circulating AOPPs in patients with T1DM versus control group (P < 0.001)
[16]	Kalousová M./2004	Prague	Prague Med Rep	T1DM & T2DM	Cross-sectional	40	54	Patients with T2DM had significantly higher circulating AOPPs compared with healthy controls (P < 0.005). While this was not significant for patients with T1DM	This study was performed in patients with T1DM and T2DM; therefore, the results are included as two independent researches in the meta-analysis
[42]	Gil-del Valle L/2005	Cuba	Pharmacol Res	T1DM	Case-control	48	80	Significantly higher circulating AOPPs in patients with T1DM versus control group (P < 0.01)	All of the studies were performed in combination of both genders except this study that was performed only among women
[43]	Pan, H. Z/2008	Italy	British J Ophtalm	T2DM	Case-control	20–60	115	Significantly higher circulating AOPPs in patients with T2DM compared with control group (P < 0.01)
[46]	Piwowar A/2009	Poland	British J Biomed Sci	T2DM	Case-control	54–55	82	Significantly higher circulating AOPPs in patients with T2DM compared with control group (P < 0.001)
[44]	Pandey KB/2010	India	Clin Biochem	T2DM	Case-control	58	62	Significantly higher circulating AOPPs in patients with T2DM compared with control group (P < 0.005)
[18]	Tabak O/2011	Turkey	Clin Invest Med	T2DM	Case-control	52–57	88	Significantly higher circulating AOPPs in patients with T2DM compared with control group (P = 0.001)
[15]	Gradinaru D/2013	Romania	J Proteomics	T2DM	Cross-sectional	60–75	178	Significantly higher circulating AOPPs in patients with T2DM compared with control group (P < 0.001)	Only the results for T2DM was included in the meta-analysis
[37]	Korkmaz GG/2013	Turkey	Scand J Clin Lab Invest	T2DM	Cross-sectional	30–60	110	Significantly higher circulating AOPPs in patients with IFG and T2DM compared with control group (P < 0.01)	Only the results for T2DM was included in the meta-analysis
[38]	Jakuš, V/2014	Slovak Republic	Physiol. Res	T2DM with or without complications	Cross-sectional	63 ± 9.6	101	Significantly higher circulating AOPPs in diabetic patients with or without complications (P < 0.05)	This study included two independent results for diabetic patients with or without complications
[45]	Kar K/2014	India	Sch J App Med Sci	T2DM	Case-control	40–60	97	Significantly higher circulating AOPPs in diabetic patients versus control
[14]	D’Souza JM/2016	India	Scand J Clin Lab Invest	T2DM with or without complications	Case-control	40–80	150	Significantly higher circulating AOPPs in diabetic patients with or without complications (P < 0.05 and P < 0.005 respectively)
[47]	Conti, G/2019	Italy	Medicina	T2DM	Cross-sectional	40–60	54	Non-significantly higher circulating AOPPs in patients with T2DM versus control group
[5]	Heidari, F/2020	Iran	Life Sci	T2DM	Cross-sectional	50	194	Significantly higher circulating AOPPs in patients with T2DM compared with control group (P < 0.05)
[36]	Lu Y/2021	China	Free Radical Biol Med	T2DM	Case-control	≥ 50	53	Non-significantly higher circulating AOPPs in patients with T2DM versus control group

AOPPs, advanced oxidation protein products; T₁DM, type 1 diabetes mellitus; T₂DM, type two diabetes mellitus. In all of the studies, measurement of AOPPs was performed by the method first described by Witko-Sarsat et al. as a modified spectrophotometry [39]

The study by Gradinaru et al. [15], reported results separately for IFG and T₂DM; therefore, only the results for T₂DM were included. Similarly, the study by Korkmaz et al. [37], presented results for patients with IGT, IFG, and T₂DM, but only the results for T₂DM patients were considered in the meta-analysis. The study by Kalousová et al. [16] involved both T₁DM and T₂DM patients, so it was represented by two independent datasets in the meta-analysis. Additionally, the study by Jakus et al. [38] included two distinct results for diabetic patients with or without complications. All studies included both genders. In all studies, the measurement of AOPPs was performed using the modified spectrophotometry method first described by Witko-Sarsat et al. [39]. Among the studies, nine studies had case-control design [14, 18, 40–46] and 6 studies had cross-sectional design [5, 15, 16, 36–38, 47]. Geographically, four studies were conducted in Italy [40, 41, 43, 47], two in Turkey [18, 37], one in Prague [16], one in Cuba [42], three in India [14, 44, 45], one in Romani [15], one in Slovak Republic [38], one in Poland [46], one in Iran [5] and one in China [36]. All studies showed that circulating AOPPs were significantly higher in individuals with diabetes (both T₁DM and T₂DM), except for three studies, which found no significant increase in circulating AOPPs in diabetic patients [36, 40, 47]. Following re-evaluation of the included studies, the study by Koçak et al. [48] was excluded from the quantitative synthesis because its AOPP measurement units and reporting methodology were not comparable with those of the remaining studies, potentially compromising the validity of the pooled estimates.

Results of meta-analysis

The Forest plots of the association between circulating AOPPs, T₁DM and T₂DM are represented in Fig. 2 (a, b). The analysis revealed that circulating AOPPs were 17.68 µmol/L higher in subjects with T₁DM and 24.58 µmol/L higher in subjects with T₂DM compared to control subjects (WMD: 17.534; CI 4.537, 30.531; P < 0.001; I²: 99.1 and WMD: 24.678, CI: 15.361, 33.994, P < 0.001; I²: 96.8 respectively). Details of results is represented in Table 5. Considerable heterogeneity was observed across the included studies. The 95% prediction intervals crossed the null value in both analyses, indicating variability in the direction and magnitude of the association across future populations. Given the high degree of heterogeneity in the studies, we conducted subgroup analyses and meta-regression tests separately for T₁DM and T₂DM to explore potential sources of this variation (Tables 7 and 9). Subgroup analyses for T₁DM (Table 7) showed that circulating AOPPs levels were consistently higher in patients compared with controls (overall WMD = 17.53 µmol/L, 95% CI: 4.54–30.53). Stronger associations were observed in cross-sectional studies, younger populations (< 40 years), and studies with higher baseline AOPPs levels (≥ 100 µmol/L). In contrast, case-control studies and older populations (≥ 40 years) yielded weaker or non-significant estimates. Although the magnitude of effect varied across subgroups, the overall direction remained stable, while substantial heterogeneity persisted in most categories. Subgroup analyses were additionally conducted according to the reported presence or absence of overweight/obesity, hypertension, and abnormal serum lipid profiles. Elevated circulating AOPP concentrations were generally observed across most subgroups; however, substantial heterogeneity remained in several analyses. Studies reporting overweight/obesity and hypertension showed significantly higher AOPP levels, whereas studies without these conditions demonstrated less consistent findings. Similar variability was observed across subgroups based on abnormal serum lipid status. Notably, a considerable number of studies did not report these clinical characteristics, and therefore the corresponding subgroup findings should be interpreted with caution due to the limited number of studies and incomplete reporting. Smoking status was not reported in the included studies and could not be evaluated.

Fig. 2.

Weighted mean difference (WMD) with a 95% confidence interval (CI) for the comparison of circulating advanced oxidation protein products (AOPPs) in (a) individuals with type 1 diabetes mellitus and (b) and type 2 diabetes mellitus compared with control group. I² indicates the level of heterogeneity

Table 5.

Pooled effect sizes, confidence intervals, and prediction intervals for the association between circulating AOPPs, T1DM, T2 DM

Disease	Num. studies included	Num. participants included	WMD	CI (95%)	Z	P	I2%	PI
T1 DM	5	317	17.534	4.537, 30.531	2.64	0.008	99.1	–11.83, 46,089
T2 DM	15	1536	24.678	15.361, 33.994	5.192	< 0.001	96.8	−9.97, 59.33

AOPPs, advanced oxidation protein products; T₁DM, type 1 diabetes mellitus; T₂DM, type 2 diabetes mellitus; CI, confidence interval; WMD, weighted mean difference; PI, prediction interval

Table 7.

cont. Subgroup analysis for the comparison of circulating AOPPs between patients with T1DM versus control group

Group	No. of studies	WMD (95% CI)	P within group	P between group	I2, %	P heterogeneity
Total	15	24.678 (15.361, 33.994)			96.8	< 0.001
Overweight/Obesity				< 0.001
Yes	1	24.000 (10.332, 37.668)	0.001		-	-
No	1	0.200 (−1.454, 1.854)	0.813		-	-
NR	3	19.508 (18.711, 20.306)	< 0.001		0.372	0.0
Hypertension
Yes	3	19.508 (18.711, 20.306)	0.349		91.3	0.001
No	2	11.093 (−12.147, 34.333)	< 0.001		0	0.372
Abnormal serum lipids
Yes	1	0.200 (−1.454, 1.854)	0.813	< 0.001	-
No	2	24.881 (9.068, 40.694)	0.002		29.2	0.235
NR	2	19.505 (18.707, 20.303)	< 0.001		0	0.519

AOPPs, advanced oxidation protein products; T₁DM, type 1 diabetes mellitus, NR, not reported; CI, confidence interval; WMD, weighted mean difference. Smoking was not reported in studies

Table 9.

cont. Subgroup analysis for the comparison of circulating AOPPs between patients with T2DM versus control group

Group	No. of studies	WMD (95% CI)	P within group	P between group	I2, %	P heterogeneity
Total	15	24.678 (15.361, 33.994)			96.8	< 0.001
Overweight/Obesity				< 0.001
Yes	9	20.055 (8.430, 31.680)	0.001		96.2	0.001
No	2	27.624 (19.298, 35.950)	< 0.001		55.7	0.133
NR	4	34.766 (−3.949, 73.482)	0.078		97.9	0.001
Hypertension				< 0.001
Yes	8	17.666 (7.804, 27.528)	< 0.001		95.3	< 0.001
No	3	36.247 (1.844, 70.649)	0.039		95.5	< 0.001
NR	4	24.421 (14.408, 34.433)	< 0.001		82.3	0.001
Smoking				< 0.001
Yes	7	18.817 (2.104, 35.530)	0.027		97.8	< 0.001
No	2	22.864 (13.112, 32.615)	< 0.001		51.8	0.150
NR	6	36.026 (17.118, 54.934)	< 0.001		93.6	< 0.001
Abnormal serum lipids				< 0.001
Yes	12	19.546 (11.197, 27.895)	< 0.001		96.0	< 0.001
No	1	33.700 (13.665, 53.735)	0.001		-	-
NR	2	56.545 (25.495, 87.595)	< 0.001		52.1	0.149

Table 6.

Subgroup analysis for the comparison of circulating AOPPs between patients with T1DM versus control group

Group	No. of studies	WMD (95% CI)	P within group	P between group	I2, %	P heterogeneity
Total	5	17.534 (4.537, 30.531)	0.008		99.1	< 0.001
Continent				< 0.001
Europe	4	17.474 (−0.126, 35.073)	0.052		88.2	< 0.001
America	1	19.490 (18.691, 20.289)	< 0.001		-	-
Design				< 0.001
Case- control	3	13.870 (−2.016, 29.757)	0.087		99.5	< 0.001
Cross-sectional	2	24.881 (9.068, 40.694)	0.002		29.2	0.235
Baseline AOPPs (µmol/L)				< 0.001
< 100	4	14.586 (0.441, 28.732)	0.043		99.3	< 0.001
≥ 100	1	33.700 (13.665, 53.735)	0.001		-	-
Age				< 0.001
< 40	3	24.301 (14.788, 33.814)	0.004		0	0.492
≥ 40	2	9.859 (−9.045, 28.763)	0.307		99.8	< 0.001
Sample size				< 0.001
< 60	2	24.881 (9.068, 40.694)	0.002		29.2	0.235
≥ 60	3	13.870 (−2.016, 29.757)	0.087		99.5	< 0.001
Gender (% Male)				< 0.001
> 50%	1	33.700 (13.665, 53.735)	0.001		-	-
≤ 50%	4	14.586 (0.441, 28.732)	0.043		99.3	< 0.001
Study quality
Score of 5	2	24.881 (9.068, 40.694)	0.002		29.2	0.235
Score of 7	3	13.870 (−2.016, 29.757)	0.087		99.5	< 0.001

Table 8.

Subgroup analysis for the comparison of circulating AOPPs between patients with T2DM versus control group

Group	No. of studies	WMD (95% CI)	P within group	P between group	I2, %	P heterogeneity
Total	15	24.678 15.361 33.994			96.8	< 0.001
Continent				< 0.001
Europe	8	26.048 11.770 40.327	< 0.001		91.7	< 0.001
Asia	7	23.755 (10.927, 36.582)	< 0.001		97.9	< 0.001
Design				< 0.001
Case- control	8	31.578 (18.362, 44.794)	< 0.001		98.2	< 0.001
Cross-sectional	7	15.915 (4.922, 26.907)	0.005		83.1	< 0.001
Baseline AOPPs (µmol/L)				< 0.001
< 100	5	15.952 (6.243, 25.661)	0.001		96.2	< 0.001
≥ 100	10	32.466 (14.852, 50.080)	< 0.001		95.6	< 0.001
Age				< 0.001
< 60	9	36.399 (21.637, 51.162)	0.004		96.2	0.001
≥ 60	6	10.621 (−0.685, 21.927)	0.066		94.2	< 0.001
Sample size				< 0.001
100 >	8	35.056 (20.644, 49.469)	< 0.001		98.0	< 0.001
≥ 100	7	15.162 (5.499, 24.826)	0.002		87.5	< 0.001
Gender (% Male)				< 0.001
> 50%	11	32.485 18.944 46.027	< 0.001		97.7	< 0.001
≤ 50%	4	6.170 −3.445 15.786	0.209		74.8	0.008
Study quality				< 0.001
Score of 5	4	23.754 −14.640 62.148	0.225		97.6	< 0.001
Score of 6	10	24.216 14.488 33.944	< 0.001		96.4	< 0.001
Score of 7	1	23.500 15.931 31.069	< 0.001		-	-

Subgroup analyses for T₂DM (Table 9) demonstrated that circulating AOPPs levels were consistently higher in patients with T₂DM compared to controls across all strata. Stronger associations were observed in European studies, case-control designs, younger populations (< 60 years), smaller sample sizes (< 100), and studies with higher baseline AOPPs levels (≥ 100 µmol/L). Although the magnitude of WMD varied between subgroups, the overall direction of effect remained stable, while substantial heterogeneity persisted within most categories.

Additional subgroup analyses based on reported presence or absence of overweight/obesity, hypertension, smoking, and abnormal serum lipid profiles in studies involving patients with T₂DM showed significantly elevated circulating AOPP levels in most subgroups, particularly among studies reporting overweight/obesity, hypertension, smoking, and abnormal serum lipid status. However, substantial heterogeneity persisted across several subgroup analyses. Moreover, a number of studies did not report these clinical characteristics, and some subgroup categories included only a limited number of studies. Therefore, these findings should be interpreted cautiously, particularly for the “not reported” subgroups and analyses with small sample sizes. Table 10 summarizes the results of meta-regression analyses for T₁DM and T₂DM. The Table presents Tau² values before and after inclusion of each moderator, together with p-values, confidence intervals, adjusted R² and residual I² values. Residual I² reflects the proportion of heterogeneity that remained unexplained after inclusion of each moderator. In the T₁DM analysis, age ≥ 40 years was significantly associated with heterogeneity (P = 0.049) and reduced the between-study variance (tau²) from 180.56 to 103.1. Other examined covariates were not statistically significant. In the T₂DM analysis, continent and sample size were significantly associated with heterogeneity (P = 0.046 and P = 0.005, respectively). However, substantial residual heterogeneity persisted across all models, and most evaluated covariates did not meaningfully reduce between-study variance, indicating that the observed heterogeneity was likely multifactorial and could not be fully explained by the available study-level characteristics.

Table 10.

Meta regression analysis for finding the possible sources of heterogeneity for the association between AOPPs and diabetes

	Tau2	P	95% CI	Adjusted R2	Residual I² (%)
T 1 DM
Estimate of between-study variance	180.5645
By design (Case -control versus others)	132.2	0.098	−8.534458, 58.89613	−1.86	99.30
By baseline AOPPs (≥ 100 versus others)	112.8	0.094	−4.442839, 32.92636	13.11	99.29
By age (≥ 40 versus others)	103.1	0.049	0.153137, 49.20802	20.59	99.29
By sample size (≥ 60 versus others)	132.2	0.098	−8.534458, 58.89613	−1.86	99.30
By males % (> 50 versus others)	112.8	0.308	−69.95464, 31.03817	13.11	99.29
By study quality (7 versus others)	392.3	0.071	−15.57079 210.7868	65.82	93.85
T2 DM
Estimate of between-study variance	290.1531
By continent (Europe versus others)	768.8	0.046	0.5196367 47.5273	−9.49	96.45
By design (Case -control versus others)	694.4	0.145	−6.610068 40.27861	−1.11	96.86
By baseline AOPPs (≥ 100 versus others)	698.0	0.166	−7.806949 40.78005	0.60	95.84
By age (≤ 60 versus others)	562	0.072	−55.31563 2.672303	19.97	95.63
By sample size (≤ 100 versus others)	955.6	0.005	15.742, 73.39976	11.64	96.65
By males % (50 < versus others)	601.3	0.113	−59.08971 7.079465	14.37	97.05
By study quality (≥ 6 versus others)	769.5	0.129	−7.937559 55.43851	−9.59	96.67

AOPPs, advanced oxidation protein products; T₁DM, type 1 diabetes mellitus; T₂DM, type two diabetes mellitus; CI, confidence interval; WMD, weighted mean difference; NR, not reported

AOPPs, advanced oxidation protein products; T₁DM, type one diabetes mellitus; T₂DM, type two diabetes mellitus; CI, confidence interval

The results of sensitivity analysis is represented in Table 11. Sensitivity analyses for T₁DM demonstrated that the overall direction of effect remained consistent across all leave-one-out scenarios, with weighted mean differences (WMDs) consistently indicating higher AOPPs levels in patients compared to controls. Notably, omission of one study substantially reduced heterogeneity (I² = 60.3%, τ² = 0), suggesting that this study contributed disproportionately to between-study variance [40]. In all other scenarios, residual heterogeneity remained high (I² > 99%). For type 2 diabetes, sequential omission of individual studies yielded pooled WMDs ranging from approximately 20 to 26 µmol/L, all statistically significant (p < 0.001). The direction of effect was stable, consistently showing elevated AOPPs levels in diabetic patients. However, heterogeneity remained persistently high (I² ≈ 94–97%) regardless of study omission, indicating that no single study was responsible for the observed variability.

Table 11.

Sensitivity analyses (Leave-One-Out) of pooled weighted mean differences in AOPPs levels for T1DM and T2DM

The leaved study	WMD	P	I2	P heterogeneity	tau²
T 1 DM
Matteucci E [40]	19.524 (18.727, 20.320)	< 0.001	60.3	0.495	0
Kalousová M [16]	17.556 (3.237, 31.875)	0.016	99.9	< 0.001	182.59
Matteucci E [41]	16.017 (1.471, 30.563)	0.031	99.3	< 0.001	183.11
Kalousová M [16]	14.586 (0.441, 28.732	0.043	99.3	< 0.001	180.93
Valle GL [42]	17.534 (4.537, 30.531	0.008	99.8	< 0.001	180.56
T 2 DM
Kalousová M [16]	24.125 (14.544, 33.705)	< 0.001	97.0	< 0.001	289.17
Pan HZ [43]	26.655 (15.231, 38.079)	< 0.001	97.1	< 0.001	418.51
Pandey KB [44]	24.606 (14.952, 34.260)	< 0.001	97.0	< 0.001	291.81
Tabak O [18]	24.596 (14.794, 34.398)	< 0.001	96.6	< 0.001	297.52
Korkmaz GG [37]	25.369 (15.644, 35.094)	< 0.001	97.0	< 0.001	297.57
Gradinaru D [15]	24.820 (15.012, 34.629)	< 0.001	96.9	< 0.001	298.75
Jakuš V [38]	27.107 (17.400, 36.814)	< 0.001	97.0	< 0.001	293.74
Kar K [45]	20.624 (12.574, 28.675)	< 0.001	95.4	< 0.001	190.56
Jakuš V [38]	26.383 (16.570, 36.196)	< 0.001	97.0	< 0.001	300.23
D’Souza JM [14]	25.938 (15.997, 35.880)	< 0.001	97.1	< 0.001	308.13
D’Souza JM [14]	24.055 (14.480, 33.630)	< 0.001	96.8	< 0.001	283.16
Piwowar A [46]	20.272 (11.308, 29.236)	< 0.001	96.6	< 0.001	252.29
Conti G [47]	24.512 (15.064, 33.960)	< 0.001	96.0	< 0.001	290.69
Heidari F [5]	24.582 (14.827, 34.337)	< 0.001	96.8	< 0.001	294.85
Lu Y [36]	26.763 (16.630, 36.896)	< 0.001	94.9	< 0.001	320.15

AOPPs, advanced oxidation protein products; T₁DM, type one diabetes mellitus; T₂DM, type two diabetes mellitus; CI, confidence interval; WMD, weighted mean difference

To assess publication bias, we examined Begg’s Funnel plot asymmetry and the result is illustrated in Fig. 3 (a, b). We also performed Egger’s tests for possible heterogeneity and the results of Egger for the association between circulating AOPPs, T₂DM and T₁DM were p = 0.004 and P = 0.759 respectively. Therefore, for the association of AOPPs and T₂DM trim and fill meta-analysis was performed and the results of Random WMD did not changed (Supplementary Fig. 1).

Fig. 3.

Begg’s funnel plot (showing pseudo 95% confidence intervals) displaying the weighted mean difference (WMD) against the standard error (SE) of WMD for the comparison of circulating advanced oxidation protein products (AOPPs) between (a) individuals with type one diabetes and (b) type two diabetes mellitus and control group

Discussion

Our meta-analysis demonstrated that circulating AOPPs are significantly elevated in both T₁DM and T₂DM compared with non-diabetic controls, supporting the involvement of oxidative protein damage in the pathophysiology of diabetes. Despite consistent directionality across all subgroup and sensitivity analyses, the magnitude of the pooled effects varied substantially, and heterogeneity remained high. These findings indicate a robust overall association between diabetes and increased AOPPs levels, while also highlighting variability across studies that warrants cautious interpretation.

Hyperglycemia is the primary trigger for the formation of AOPPs, and their concentration increases as serum glucose levels rise, further aggravating diabetes-related complications such as diabetic nephropathy and diabetic retinopathy [17, 18, 38, 43]. Hyperglycemia also impairs the production and availability of nitric oxide (NO), which contributes to endothelial dysfunction and elevated blood pressure in diabetic patients [15]. D’Souza JM et al. [14]. proposed that circulating AOPPs may be more reliable marker for tracking the progression and duration of diabetes. Elevated AOPPs levels are believed to play a crucial role in the depletion of the -SH group (e.g., thiol), a key component in chronic diseases. The increased ratio of AOPPs to thiol, termed “thiol stress,” may significantly contribute to the development of diseases associated with AOPPs, such as diabetes. This imbalance between ROS production and the antioxidant defense system, reflected by reduced -SH group levels and increased AOPPs and lipid peroxidation markers, is a hallmark of the pathophysiology of diabetes [41, 49, 50]. Conversely, the autoimmune nature of T1DM amplifies the negative effects of AOPPs. In T1DM, oxidative stress is more strongly linked to autoimmune processes [16]. It has also been suggested that fructosamine, a precursor to AOPPs, is elevated by oxidative stress and, in turn, contributes to the generation of oxidative stress. Fructosamine, which is formed through glycosylation of proteins, disrupts the function of antioxidant enzymes, impairing their ability to bind, transport, and maintain structure. Both fructosamine and AOPPs are found to be elevated in patients with diabetes [51]. Beyond serving as markers of oxidative stress, AOPPs are recognized as pro-inflammatory molecules that contribute to the inflammation seen in diabetes. They stimulate the activation of monocytes, promote the infiltration of macrophages, increase smooth muscle cell proliferation, and ultimately lead to insulin resistance [52].

Subgroup analyses showed that the association between circulating AOPPs and diabetes was consistently positive across all strata, but its magnitude varied according to population and study characteristics. Stronger effects in younger participants and in studies with higher baseline AOPPs likely reflect more active oxidative and inflammatory pathways early in the disease course, consistent with evidence that AOPPs stimulate ROS generation through NADPH oxidase activation and promote endothelial dysfunction [53, 54]. Studies with smaller sample sizes or specific designs (e.g., cross-sectional in T1DM and case-control in T2DM) also reported larger effects, a pattern aligned with known small-study effects and selective inclusion of patients with more severe metabolic disturbances [55]. Geographic differences, particularly stronger associations in European cohorts, may relate to variations in laboratory methods, genetic background, or dietary oxidative load, as suggested by regional studies on oxidative stress biomarkers [56].

Recent evidence supports our subgroup finding that younger individuals show stronger associations between diabetes and oxidative protein damage. Mechanistic reviews indicate that early-stage diabetes is characterized by more active oxidative and inflammatory pathways, including enhanced NADPH-oxidase–driven ROS production and endothelial dysfunction, processes that directly promote the formation of AOPPs [57]. These mechanisms are particularly pronounced in younger or early-onset patients, who typically exhibit higher metabolic stress before long-term treatment or chronic complications attenuate oxidative responses. Additional reviews on oxidative stress in diabetes further emphasize that hyperglycemia-induced ROS generation, mitochondrial dysfunction, and inflammatory signaling are more active in earlier disease stages, contributing to greater oxidative protein modification Together, these mechanistic insights support our observation that age modifies the magnitude of the AOPPs–diabetes association, with younger populations demonstrating more pronounced oxidative protein damage [58]. Figure 4 represents a hypothetical conceptual model summarizing potential mechanistic pathways through which AOPPs may contribute to diabetes-related oxidative and inflammatory processes. Although the pooled WMD indicated higher AOPPs levels in patients with diabetes compared to controls, substantial heterogeneity was observed across studies. Importantly, the direction of effect was consistent, with nearly all studies reporting elevated AOPPs in diabetic populations. This consistency in directionality strengthens the overall conclusion despite variability in the magnitude of effect sizes. Sensitivity analyses confirmed the robustness of the pooled estimates. In type 1 diabetes, the overall direction of effect remained consistent across all leave-one-out scenarios, although one study appeared to contribute substantially to heterogeneity [40]. In type 2 diabetes, the pooled estimates were stable, with WMD values consistently indicating elevated AOPPs levels compared to controls. However, residual heterogeneity remained high regardless of study omission, suggesting that variability is driven by broader methodological or population differences rather than by single influential studies. These findings reinforce the reliability of the observed association while underscoring the need for cautious interpretation given the persistent heterogeneity.

Fig. 4.

Hypothetical conceptual model illustrating the mechanisms underlying the association between advanced oxidation protein products (AOPPs) and diabetes. AOPPs may promote oxidative stress and inflammation through activation of reactive oxygen species (ROS)–generating pathways and downstream inflammatory mediators, including interleukins (IL), tumor necrosis factor-α (TNF-α), monocyte chemoattractant protein-1 (MCP-1), and NF-κβ signaling. These processes can further influence apoptotic pathways involving BCL2 and Bax, contributing to cellular dysfunction relevant to diabetic pathophysiology. Abbreviations: AOPPs, advanced oxidation protein products; ROS, reactive oxygen species; IL, interleukin; TNF-α, tumor necrosis factor-α; MCP-1, monocyte chemoattractant protein-1; BCL2, B-cell lymphoma 2; Bax, BCL2-associated X protein; NF-κβ, nuclear factor kappa β AOPPs, advanced oxidation protein products; T₁DM, type 1 diabetes mellitus; T₂DM, type two diabetes mellitus

It should be noted a considerable proportion of studies lacked detailed information regarding potential oxidative stress–related confounding factors, including obesity, hypertension, dyslipidemia, smoking status, and alcohol intake, which limited the ability to perform more comprehensive adjusted analyses and may have contributed to the substantial between-study heterogeneity. Therefore, the subgroup findings related to “not reported” categories should be interpreted cautiously. Also, meta-regression analyses did not fully explain the substantial heterogeneity observed across studies, as considerable residual between-study variance persisted after inclusion of the examined covariates. This finding suggests that the heterogeneity was likely influenced by multiple unmeasured methodological and clinical factors that could not be adequately evaluated using the available study-level data. Therefore, the pooled estimates should be interpreted cautiously.

A significant strength of this study is that, for the first time, we have provided a comprehensive overview of articles exploring the relationship between diabetes and circulating AOPPs in this systematic review and meta-analysis. One of the key contributions of this study is the potential for AOPPs to be used as a predictive biomarker for diabetic patients in clinical settings. More important than the exact magnitude is the consistent direction across studies, showing higher AOPPs levels in both type 1 and type 2 diabetes, which supports biological plausibility.

However, a limitation of this study is the scarcity of available data, particularly for T₁DM, which underscores the need for further research to reach definitive conclusions. Additionally, since the studies included were observational, establishing a cause-and-effect relationship is not feasible. Given the high heterogeneity, the pooled WMD should be interpreted with caution. Although elevated circulating AOPP concentrations were generally identified in patients with diabetes mellitus, considerable variability remained between studies even after subgroup and meta-regression analyses. This heterogeneity may be related to differences in patient characteristics, glycemic control, disease duration, assay methodologies, and the incomplete reporting of important oxidative stress–related factors such as obesity, hypertension, dyslipidemia, smoking status, and alcohol intake. In addition, because all included studies were observational in nature, a causal relationship between circulating AOPP levels and diabetes mellitus cannot be established. Therefore, the present findings should be considered as evidence of an association rather than causation, and further well-designed prospective studies are warranted to clarify the clinical significance of AOPPs in diabetes.

Conclusion

In conclusion, circulating AOPP levels are significantly higher in individuals with diabetes compared to those without, with this elevation observed in both T₁DM and T₂DM. The observational in design of included studies and the observed heterogeneity limits the strength and generalizability of the pooled estimates. Therefore, causal inferences cannot be drawn. Further research and clinical trials are necessary to better understand the specific role of AOPPs in the pathogenesis of diabetes and its potential implications for treatment strategies.

Author contributions

AAMAE was the supervisor, he was involved in hypothesis generation, data acquisition, validation and visualization of data, MSI and AAMAE were involved in systematic search, study screening and data extraction, and MFK, SF, MZI and SUDK were equally involved in data extraction and analysis. NK, VDS and AAMES were involved in data extraction, data screening and manuscript writing. The first draft of the manuscript was written by MSI and thoroughly revised by AAMAE. All of the authors were involved in data analysis and writing the manuscript and were aware about its submission.

Funding

None.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The current work has been registered in PROSPERO registration system with identification number: CRD420251275798. Consent to participate is not applicable.

Consent for publication

None.

Competing interests

The authors declare no competing interests.