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. 2026 Apr 20;58(1):2654251. doi: 10.1080/07853890.2026.2654251

Comparative evaluation of oxidative stress biomarkers F2-isoprostanes and 8-OHdG in Parkinson’s disease and Type 2 Diabetes Mellitus: a systematic review and meta-analysis of human studies

Samwel Sylvester Msigwa a,b, Maiko Charles Mkwambe c, Qiu-Yan Yu d, Shi-Guo Zhu a,e, Jian-Hong Zhu f,✉, Xiong Zhang a,e,✉, Jian-Yong Wang a,e,✉
PMCID: PMC13097182  PMID: 42003321

Abstract

Background

Oxidative stress is central to type 2 diabetes mellitus (T2DM) and Parkinson’s disease (PD). However, the utility of biomarkers for lipid peroxidation (F2-isoprostanes) and DNA damage (8-OHdG) in the comorbidity of PD and T2DM remains unclear.

Methods

We conducted a systematic review and meta-analysis of 54 unique studies of human subjects aged ≥ 50 years (n = 7,521: 3,522 with T2DM, 722 with PD, and 3,277 controls), measuring biomarkers in serum, plasma, or leukocytes. Mixed-effects models quantified standardized differences (Hedges’ g) across subgroups.

Results

In T2DM, F2-isoprostanes (g = 1.60, 95% CI: 0.95–2.25) and 8-OHdG (g = 2.64, 95% CI: 2.13–3.14) were markedly elevated (p < 0.001). Stronger effects were observed in younger cohorts and serum/plasma samples, with complications like nephropathy exhibiting extreme oxidative stress (g = 5.24). In PD, 8-OHdG was moderately elevated (g = 0.78, 95% CI: 0.18–1.39; p = 0.011), particularly in randomized controlled trials and plasma samples, whereas F2-isoprostanes were not significantly elevated (g = 0.47, 95% CI: −0.43–1.38). High heterogeneity in T2DM (I2 > 90%) reflected methodological variability.

Conclusion

Distinct profiles – both markers elevated in T2DM but only 8-OHdG in PD – underscore 8-OHdG’s potential in PD-T2DM comorbidity. Future research should focus on standardized assays, multi-compartmental or multi-modal sampling, and longitudinal studies to clarify mechanisms and therapeutic targets.

Keywords: Oxidative stress, F2-isoprostanes, 8-OHdG, diabetes, Parkinson’s disease, comorbidity

1. Introduction

Parkinson’s disease (PD), the second most common neurodegenerative disorder, is characterized by the progressive loss of dopaminergic neurons in the substantia nigra, leading to motor impairments and non-motor symptoms [1–3]. By contrast, type 2 diabetes mellitus (T2DM), a metabolic disorder affecting over 500 million people worldwide, is marked by chronic hyperglycemia, insulin resistance, and systemic inflammation, often resulting in complications like nephropathy and retinopathy [4,5]. Despite their distinct clinical profiles, both conditions share a central pathogenic mechanism: oxidative stress, characterized by an imbalance between the production of reactive oxygen species (ROS) and antioxidant defenses [6–8]. Epidemiological evidence increasingly points to a bidirectional link between PD and T2DM, driven by shared pathways such as insulin resistance, mitochondrial dysfunction, neuroinflammation, and redox imbalance [9–11]. While T2DM is strongly associated with an increased risk of PD [12–15], the reverse association – from PD to T2DM – shows correlation but lacks clear causation [6,16].

In PD, oxidative stress significantly exacerbates neuronal damage through mechanisms such as mitochondrial dysfunction, impaired autophagy, and increased neuroinflammation, with dopaminergic neurons being particularly vulnerable [7,17]. Conversely, in T2DM, hyperglycemia disrupts mitochondrial function, leading to an overproduction of ROS [18], which in turn causes lipid peroxidation, DNA (deoxyribonucleic acid) damage, and pro-inflammatory signaling [18,19]. Despite differences in their primary manifestations, both conditions exhibit considerable overlap in oxidative stress pathways, suggesting that redox imbalance may be a critical link between PD and T2DM [10,11,20]. However, it is important to note that much of the evidence supporting shared oxidative mechanisms is derived from studies examining PD and T2DM independently rather than from populations with confirmed co-occurrence of both conditions. Consequently, the extent to which these overlapping pathways reflect true comorbidity at the individual level remains insufficiently characterized, particularly in older adults, where both diseases are most prevalent.

Blood-based biomarkers provide critical insights into oxidative damage, with F2-isoprostanes and 8-hydroxy-2′-deoxyguanosine (8-OHdG) being two of the most rigorously validated indicators [21,22]. F2-isoprostanes, stable byproducts of arachidonic acid peroxidation, are widely regarded as the gold standard for assessing lipid peroxidation, a process closely tied to metabolic dysregulation in T2DM [23]. Similarly, blood-based 8-OHdG quantifies cumulative oxidative DNA damage, implicated in both diabetic complications and the neurodegenerative progression of PD [10,24,25]. Notably, a recent large‐scale cohort of 2858 adults aged 18–65 showed that activation of the three major physiological stress systems (immune‑inflammatory, hypothalamic–pituitary–adrenal [HPA]‑axis, and autonomic nervous system) is positively associated with both plasma 8‑OHdG and F2‑isoprostane levels in a dose‑response fashion [26]. Although extensive research has examined these biomarkers separately in T2DM [27,28] and PD [29–31], showing consistent elevations in T2DM and variable results in PD, studies directly comparing their levels in patients with both conditions are scarce. Thus, while these biomarkers are well validated within each disease, their ability to capture shared oxidative signatures relevant to confirmed PD–T2DM comorbidity remains largely unexplored. Moreover, the evidence base is limited by methodological inconsistencies that hinder a clear understanding of the shared oxidative mechanisms in PD and T2DM. Variability in assay techniques, such as enzyme-linked immunosorbent assays (ELISA), gas chromatography-mass spectrometry (GC-MS), and high-performance liquid chromatography with electrochemical detection (HPLC-ECD), combined with differences in sample types (e.g. serum versus plasma), has led to conflicting findings [11,32–34]. These discrepancies impede cross-study comparisons and prevent definitive conclusions about oxidative stress in PD-T2DM comorbidity. This underscores the need for a systematic approach, such as a meta-analysis, to standardize biomarker assessments and identify reliable indicators of shared pathophysiology, paving the way for improved early diagnostic and therapeutic strategies targeting oxidative damage.

To tackle these challenges, this systematic review and meta-analysis synthesizes human studies on blood-based F2-isoprostanes and 8-OHdG in PD and T2DM, focusing on participants aged 50 years and above, a group with an elevated prevalence of both conditions. Our objectives are to (1) quantify and compare biomarker levels across both diseases using standardized effect sizes while assessing how methodological factors, such as assay type and sample source, affect variability and (2) assess whether convergent patterns of biomarker alterations across these diseases support the hypothesis of shared oxidative mechanisms relevant to PD–T2DM comorbidity, while acknowledging the current scarcity of direct evidence from cohorts with confirmed co-occurrence.

2. Methods

2.1. Study design

The systematic review protocol was developed in accordance with the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [35]. Before initiating data collection, the planned, systematic review and meta-analysis were registered with PROSPERO (Registration number: CRD42025633253).

2.2. Search strategy and eligibility criteria

A comprehensive electronic search was conducted across three databases – PubMed, Web of Science (WOS), and Scopus – to identify relevant human studies published up to December 20, 2024. The search terms encompassed both disease conditions and biomarkers. For participants, terms included ‘Parkinson’s Disease’, ‘Parkinson*’, ‘PD’, ‘Diabetes Mellitus’, ‘Diabetes’, and ‘DM’. For the intervention and outcomes, terms related to oxidative stress, mitochondrial dysfunction, and the biomarkers of interest were used, such as ‘F2-isoprostane’, ‘8-OHdG’, ‘8-iso-PGF2α’, ‘8-epi-PGF2α’, ‘15-F2t-isoprostane’, and ‘iPF2α-III’. Healthy control subjects were identified as ‘Control’ or ‘Healthy Control’. Boolean operators combined these terms, and the search was refined to exclude animal and in vitro studies. All retrieved references were exported to Covidence (Veritas Health Innovation, Melbourne, VIC, Australia) for deduplication and further screening. Duplicate records were initially removed using EndNote version X7 (Clarivate Analytics, Philadelphia, USA), followed by additional deduplication via Covidence. The eligibility criteria were as follows: Participants: Human subjects aged 50 years and above diagnosed with T2DM or PD, with control groups consisting of non-diseased individuals without significant comorbidities; Intervention(s): Measurement of blood-based F2-isoprostanes and/or 8-OHdG levels; Comparator(s): Healthy control subjects; Outcomes: Blood levels of F2-isoprostanes and 8-OHdG as markers of oxidative stress; Study Designs: Cross-sectional studies, case-control studies, cohort studies, and clinical trials; Exclusions: Non-English articles, reviews, editorials, studies without precise biomarker data for F2-isoprostanes or 8-OHdG, studies involving DM with infection (e.g. diabetic foot), and Type 1 Diabetes Mellitus (T1DM). Detailed search information is provided in the Supporting Information (Supplementary Tables S1–S3).

Importantly, PD and T2DM were treated as distinct disease cohorts throughout study selection, data extraction, and meta-analysis, with each condition analyzed separately against healthy control groups rather than as a combined or comorbid population.

2.3. Study selection

Two independent reviewers (SSM and MCM) screened titles and abstracts to assess eligibility. Full-text articles were retrieved for studies deemed potentially eligible for inclusion. Discrepancies were resolved through discussion or consultation with a third reviewer. Inclusion focused on human studies reporting blood-based measurements of F2-isoprostanes and 8-OHdG in patients with PD and/or T2DM compared with healthy controls.

2.4. Data extraction and synthesis

Two reviewers independently performed data extraction, and discrepancies were resolved through discussion or third-party adjudication. For each eligible study, the following variables were systematically extracted: author(s) and publication year, geographic location, study design, sample size (stratified by T2DM, PD, and control groups), mean age (± standard deviation [SD]), sample source (e.g. hospital or community-based cohorts), assay methodology, diagnostic criteria, and comparative outcomes (T2DM/PD vs. controls). Biochemical measurements were recorded as means and SDs, with exclusive use of baseline (pre-intervention) data to minimize confounding from therapeutic effects. For studies reporting non-parametric data (medians with interquartile ranges [IQRs] or ranges), validated conversion formulas were applied to approximate means and SDs: mean ≈ median + (lower bound + upper bound − 2 × median)/4 and SD ≈ (upper bound - lower bound)/4, as recommended by Hozo et al. [36] and Wan et al. [37] for meta-analytic standardization. For studies with multiple subgroups (e.g. diabetic patients with and without complications), subgroup means were combined using weighted averages, and pooled SDs were calculated according to standard meta-analytic procedures [38].

2.5. Critical appraisal of included studies

The methodological quality of the included studies was assessed using the appropriate Joanna Briggs Institute (JBI) critical appraisal tools for each study design: the checklist for analytical cross-sectional studies (n = 34), the checklist for case-control studies (n = 16), the checklist for cohort studies (n = 2), and the checklist for quasi-experimental studies (n = 1) [39]. For the randomized controlled trial (RCT) (n = 1), the risk of bias was assessed using the risk of bias 2 (ROB 2) tool [40]. The appraisal focused on reporting quality, external validity, and internal validity, including the assessment of bias and confounding. Quality assessments were conducted independently by SSM and MCM, with disagreements resolved through discussion.

2.6. Statistical analysis

All measurements were standardized to nanograms per milliliter (ng/mL) before analysis. Meta-analyses were conducted using standardized mean differences (Hedges’ g) and their 95% confidence intervals. Heterogeneity was assessed using the DerSimonian-Laird estimator. We performed comprehensive sensitivity analyses to evaluate the robustness of the pooled effect size estimates for F2-isoprostanes and 8-OHdG. First, we conducted a leave-one-out sensitivity analysis within the primary random-effects model to assess the influence of individual studies on the summary estimates by iteratively removing one study at a time and recalculating the pooled effect size. Second, we applied Duval and Tweedie’s trim-and-fill method within a random-effects framework to adjust for potential publication bias by imputing theoretically missing studies and estimating adjusted pooled effect sizes. Heterogeneity was quantified using I2 (the percentage of variation due to heterogeneity) and τ2 (the between-study variance) for each model, except for the trim-and-fill method, where heterogeneity statistics reflect those of the original random-effects model. All statistical analyses were done using Comprehensive Meta-Analysis (CMA) version 3.0 software and SPSS version 29.

3. Results

3.1. Search results

The initial database search yielded 2374 records, with 1201 identified in PubMed, 535 in Web of Science, and 638 in Scopus. Following deduplication – using EndNote (344 duplicates removed) and Covidence (an additional 104 duplicates eliminated) – the remaining unique records were subjected to title and abstract screening. Full-text articles were retrieved for studies meeting the eligibility criteria. The detailed search strategy is provided in the supplementary materials, and a PRISMA flow diagram summarizing the study selection process is included in Figure 1.

Figure 1.

PRISMA flow diagram showing study identification, screening, and inclusion process, starting with 2374 studies and ending with 54 included. This flowchart outlines the systematic review process for study selection in three phases: Identification, Screening, and Inclusion. It begins with 2374 studies identified from databases (PubMed: 1201, Scopus: 638, Web of Science: 535) and shows zero from other sources. After removing 448 duplicates (344 by EndNote, 104 by Covidence), 1926 studies were screened. Out of these, 267 were sought for retrieval, but 4 were not retrieved. In the eligibility assessment, 263 studies were reviewed, with 209 excluded for various reasons, leading to a final inclusion of 54 studies.

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PRISMA flow diagram illustrating the selection process of studies for this review.

All subsequent analyses were conducted as disease-specific comparisons of PD and T2DM cohorts relative to their respective control groups; no direct or comorbid comparisons between PD and T2DM populations were performed.

3.2. Study characteristics

This systematic review identified 18 studies examining F2-isoprostanes (13 in T2DM, 5 in PD; total n = 2807 participants [T2DM 1262, PD 177, Control 1368]) and 38 studies assessing 8-OHdG (30 in T2DM, 8 in PD; total n = 4719 participants [T2DM 2260, PD 545, Control 1914]). Two studies assessed both F2-isoprostanes and 8-OHdG, contributing to both analyses [41,42].

These investigations spanned diverse geographical regions, including North America, Europe, Asia, Africa, and the Middle East (Tables 1 and 2), with a quality index ranging from moderate to high (Supplementary Tables S4–S8).

Table 1.

Summary of studies on F2-isoprostanes in Diabetes Mellitus and Parkinson’s disease.

Author (Year) Location Study design Sample size (DM/PD/Controls) Mean age (Years) Sample source Assay type Diagnostic criteria Results (DM vs Control) Results (PD vs Control)
Wang et al. (2024) [43] USA Cohort 132 / 0 / 571 DM: 73.9 ± 2.5, Controls: 73.6 ± 3.0 Plasma GC-MS Clinically diagnosed T2DM F2-Isoprostanes: DM − Control –
Kaliaperumal et al. (2022) [44] India Cross-sectional 60 / 0 / 60 DM: 59.18 ± 7.51, Controls: 62.57 ± 7.56 Serum ELISA Clinically diagnosed T2DM F2-Isoprostanes: DM ↑ Control –
Ma et al. (2021) [45] China Cross-sectional 66 / 0 / 53 DM: 58.33 ± 7.77, Controls: 55.42 ± 9.71 Plasma ELISA WHO criteria for T2DM F2-Isoprostanes: DM ↑ Control –
Morsi et al. (2018) [46] KSA Cross-sectional 58 / 0 / 20 DM: 53.31 ± 12.09, Controls: NR Serum ELISA WHO criteria for T2DM F2-Isoprostanes: DM ↑ Control –
Li et al. (2018) [47] China Cross-sectional 22 / 0 / 20 DM: 81.76 ± 9.42, Controls: 82.31 ± 10.27 Plasma ELISA Clinically diagnosed T2DM F2-Isoprostanes: DM ↑ Control –
Nakhjavani et al. (2014) [48] Iran Cross-sectional 45 / 0 / 45 DM: 55.18 ± 9.6, Controls: 52.7 ± 11.7 Serum ELISA Clinically diagnosed T2DM F2-Isoprostanes: DM ↑ Control –
Longo-Mbenza et al. (2014) [41] DR Congo Case-control 150 / 0 / 50 DM: 55.2 ± 13, Controls: 50.7 ± 13 Plasma ELISA ADA criteria for T2DM F2-Isoprostanes: DM ↑ Control –
Wu et al. (2014) [49] China Cross-sectional 462 / 0 / 160 DM: 52.59 ± 6.46, Controls: 51.81 ± 6.49 Serum ELISA WHO criteria for T2DM F2-Isoprostanes: DM ↑ Control –
Tabak et al. (2011) [42] Turkey Cross-sectional 69 / 0 / 19 DM: 57.41 ± 8.93, Controls: 52.60 ± 6.64 Serum ELISA WHO criteria for T2DM F2-Isoprostanes: DM ↑ Control –
Chao et al. (2010) [50] China Cross-sectional 118 / 0 / 74 DM: 56.2 ± 9.9, Controls: 52.3 ± 11.7 Plasma HPLC Clinically diagnosed T2DM F2-Isoprostanes: DM ↑ Control –
Calabrese et al. (2007) [51] Italy Cross-sectional 31 / 0 / 16 DM: 60, Controls: 59 Plasma HPLC Not stated T2DM F2-Isoprostanes: DM ↑ Control –
Feillet-Coudray et al. (2002) [52] France Cross-sectional 10 / 0 / 10 DM: 59 ± 7, Controls: 59 ± 6 Plasma EIA Not stated T2DM F2-Isoprostanes: DM − Control –
Gopaul et al. (1995) [53] UK Case-control 39 / 0 / 15 DM: 58.9 ± 2.3, Controls: 33.4 ± 3.1 Plasma GC-MS Not stated T2DM F2-Isoprostanes: DM ↑ Control –
Loffredo et al. (2020) [54] Italy Cross-sectional 0 / 8 / 64 PD: 70 ± 5, Controls: 72 ± 8 Serum ELISA NR – F2-Isoprostanes: PD ↑ Control
Seet et al. (2010) [55] Singapore Case-control 0 / 61 / 61 PD: 64 ± 11.2, Controls: 62.7 ± 5.2 Plasma GC-MS UK PD Society Criteria – F2-Isoprostanes: PD ↑ Control
Lee et al. (2009) [56] Singapore Case-control 0 / 25 / 47 PD: 55.3 ± 7.4, Controls: Age-matched Plasma GC-MS UK PD Society Criteria – F2-Isoprostanes: PD − Control
Connolly et al. (2008) [30] USA Case-control 0 / 36 / 30 PD: 69 ± 8.7, Controls: Age-matched Plasma GC-MS UK PD Society Criteria – F2-Isoprostanes: PD − Control
Irizarry et al. (2007) [57] USA Cross-sectional 0 / 47 / 48 PD: 68.2 ± 9.1, Controls: 71.4 ± 9.4 Plasma GC-MS NR – F2-Isoprostanes: PD − Control
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ADA: American Diabetes Association; DM: Diabetes Mellitus; DR Congo: Democratic Republic of the Congo; EIA: Enzyme Immunoassay; ELISA: Enzyme-Linked Immunosorbent Assay; GC-MS: Gas Chromatography-Mass Spectrometry; HPLC: High-Performance Liquid Chromatography; KSA: Kingdom of Saudi Arabia‌; NR: Not Reported; PD: Parkinson’s Disease; T2DM: Type 2 Diabetes Mellitus; UK: United Kingdom; USA: United States of America; WHO: World Health Organization; ↑: Indicates higher levels in the first group compared to the control group; −: Indicates no significant difference between the groups.

Table 2.

Summary of studies on 8-OHdG in Diabetes Mellitus and Parkinson’s disease.

Author (Year) Location Study design Sample size (DM/PD/Controls) Mean age (Years) Biomarkers Sample source Assay type (8-OHdG Only) Diagnostic criteria Results (Summary of DM vs Control on 8-OHdG) Results (Summary of PD vs Control on 8-OHdG)
Tajane et al. (2024) [58] India Case-control 50 / 0 / 50 DM: 53.08 ± 10.4, Controls: 42.98 ± 9.61 8-OHdG Serum ELISA WHO and ADA (2018) T2DM 8-OHdG: DM ↑ Control –
Lazutka et al. (2024) [59] Lithuania Interventional pre-post 8 / 0 / 16 DM: 53.2 ± 15.4, Controls: 44.6 ± 10.5 8-OHdG Plasma ELISA Not specified T2DM 8-OHdG: DM ↑ Control –
Corbacho Alonso et al. (2023) [60] Spain Cross-sectional 18 / 0 / 20 DM: 72 ± 9, Controls: 71 ± 9 8-OHdG Plasma ELISA Not specified T2DM 8-OHdG: DM − Control –
Nemtsova et al. (2022) [61] Ukraine Cohort 156 / 0 / 30 DM: 62.11 ± 0.64, Controls: age-matched 8-OHdG Serum ELISA Not specified T2DM 8-OHdG: DM ↑ Control –
Wang et al. (2022) [62] China Cross-sectional 94 / 0 / 100 DM: 65.8 ± 7.0, Controls: 64.4 ± 8.1 8-OHdG Serum ELISA Ophthalmology (3rd edition) criteria T2DM 8-OHdG: DM ↑ Control –
Attia et al. (2021) [63] Iraq Cross-sectional 45 / 0 / 45 DM: 54.44 ± 11.19, Controls: 49.21 ± 14.91 8-OHdG Serum ELISA Diagnosed clinically with T2DM 8-OHdG: DM ↑ Control –
Mahmoud et al. (2021) [64] Iraq Cross-sectional 297 / 0 / 188 DM: 52.9 ± 8.5, Controls: 47.0 ± 8.7 8-OHdG Serum ELISA ADA Criteria T2DM 8-OHdG: DM ↑ Control –
Abudawood et al. (2020) [65] KSA Cross-sectional 100 / 0 / 50 DM: 56.86 ± 14.52, Control: 49.77 ± 16.67 8-OHdG Serum ELISA ADA Criteria T2DM 8-OHdG: DM ↑ Control –
Goycheva et al. (2019) [66] Bulgaria Cross-sectional 93 / 0 / 94 DM: 64.2 ± 9.8, Controls: 56.6 ± 11.2 8-OHdG Serum ELISA WHO criteria for T2DM 8-OHdG: DM ↑ Control –
Nemtsova et al. (2018) [67] Ukraine Cross-sectional 126 / 0 / 20 DM: 57.8 ± 6.2, Controls: age-matched 8-OHdG Serum ELISA Ukrainian Ministry of Health orders T2DM 8-OHdG: DM ↑ Control –
Silva et al. (2018) [68] Brazil Cross-sectional 27 / 0 / 133 DM: 68.3 ± 5.60 Controls: age-matched 8-OHdG Serum ELISA Diagnosed clinically with T2DM 8-OHdG: DM ↑ Control –
Alaaraj et al. (2018) [69] Iraq Cross-sectional 51 / 0 / 31 DM: 51.00 ± 6.90, Controls: 49.00 ± 8.90 8-OHdG Serum ELISA Diagnosed clinically T2DM 8-OHdG: DM ↑ Control –
Mansour et al. (2018) [70] Egypt Cross-sectional 150 / 0 / 50 DM: 58.1 ± 4.4, Controls: 56 ± 3 8-OHdG Serum ELISA Diagnosed clinically with T2DM 8-OHdG: DM ↑ Control –
El Horany et al. (2017) [71] Egypt Cross-sectional 45 / 0 / 15 DM: 55.5 ± 3.8, Controls: 54.9 ± 5.1 8-OHdG Serum ELISA ADA criteria for T2DM 8-OHdG: DM ↑ Control –
Ye et al. (2016) [72] China Case-control 108 / 0 / 65 DM: 60 ± 17, Controls: 55 ± 11 8-OHdG Leukocyte ELISA WHO criteria T2DM 8-OHdG: DM ↑ Control –
Nasif et al. (2016) [73] KSA and Egypt Cross-sectional 17 / 0 / 50 DM: 50 ± 8, Controls: 27 ± 4 8-OHdG Serum ELISA ADA criteria for T2DM 8-OHdG: DM ↑ Control –
Sun et al. (2015) [74] China Case-control 28 / 0 / 65 DM: 50 ± 8, Controls: 27 ± 4 8-OHdG Serum ELISA WHO criteria T2DM 8-OHdG: DM ↑ Control –
Hasan & Mohieldein (2015) [75] KSA Case-control 60 / 0 / 60 DM: 56.54 ± 9.5, Controls: 45.66 ± 11.3 8-OHdG Serum ELISA Diagnosed clinically with T2DM 8-OHdG: DM ↑ Control –
Ravassa et al. (2015) [76] Spain Case-control 72 / 0 / 14 DM: 65.3 ± 8.6, Controls: 63.4 ± 8.7 8-OHdG Serum EIA ADA criteria for T2DM 8-OHdG: DM ↑ Control –
Longo-Mbenza et al. (2014) [41] DR Congo Case-control 150 / 0 / 50 DM: 55.2 ± 13, Controls: 50.7 ± 13 8-OHdG Serum ELISA ADA criteria for T2DM 8-OHdG: DM ↑ Control –
Mahfouz et al. (2012) [77] Egypt Cross-sectional 60 / 0 / 20 DM: 52.6 ± 1.44, Controls: 49.7 ± 1.87 8-OHdG Serum ELISA ADA criteria for T2DM 8-OHdG: DM ↑ Control –
Letonja et al. (2012) [78] Slovenia Cross-sectional 75 / 0 / 70 DM: 62.75 ± 9.77, Controls: 59.78 ± 9.16 8-OHdG Serum ELISA WHO criteria for T2DM 8-OHdG: DM ↑ Control –
Al Aubaidy & Jelinek (2011) [79] Australia Cross-sectional 35 / 0 / 119 DM: 69.9 ± 8.6, Controls: 66.4 ± 11.1 8-OHdG Serum ELISA Diagnosed clinically with T2DM 8-OHdG: DM ↑ Control –
Tabak et al. (2011) [42] Turkey Cross-sectional 69 / 0 / 19 DM: 57.41 ± 8.93, Controls: 52.60 ± 6.64 8-OHdG Plasma ELISA WHO criteria for T2DM 8-OHdG: DM − Control –
Chen et al. (2011) [80] China Case-control 66 / 0 / NR DM: 61.0 ± 16.3, Controls: NR 8-OHdG Peripheral blood leukocytes ELISA WHO criteria for T2DM 8-OHdG: DM ↑ Control –
Al Aubaidy & Jelinek (2010) [81] Australia Cross-sectional 35 / 0 / 98 DM: 70 ± 8.4, Controls: 66.2 ± 11.2 8-OHdG Serum ELISA ADA criteria for T2DM 8-OHdG: DM ↑ Control –
Pan et al. (2010) [82] China Case-control 77 / 0 / 40 DM: 51.80 ± 11.87, Controls: 52.38 ± 11.12 8-OHdG Serum ELISA ADA criteria for T2DM 8-OHdG: DM ↑ Control –
Pan et al. (2008) [83] China Cross-sectional 60 / 0 / 32 DM: 51.36 ± 12.66, Controls: 53.22 ± 11.64 8-OHdG Serum ELISA WHO criteria for T2DM 8-OHdG: DM ↑ Control –
Pan et al. (2007) [84] China Cross-sectional 47 / 0 / 25 DM: 58, Controls: 58 8-OHdG Serum ELISA WHO criteria for T2DM 8-OHdG: DM ↑ Control –
Shin et al. (2001) [85] Korea Cross-sectional 41 / 0 / 33 DM: 57.2 ± 2.1, Controls: 51.7 ± 1.4 8-OHdG Serum HPLC-ECD Diagnosed clinically with T2DM 8-OHdG: DM ↑ Control –
Tsai et al. (2025) [86] China RCT 0 / 41 / 20 PD: 64.85 ± 7.46, Controls: 67.00 ± 7.07 8-OHdG Serum ELISA Diagnosis confirmed by a movement disorders specialist – 8-OHdG: PD ↑ Control
Gmitterová et al. (2018) [87] Germany Case-control 0 / 44 / 32 PD: 65.30 ± 7.71, Controls: 65.25 ± 8.75 8-OHdG Serum ELISA MDS criteria for PD – 8-OHdG: PD – Control
Bolner et al. (2011) [88] Italy Case-control 0 / 13 / 13 All: 68 ± 12 8-OHdG Plasma HPLC-ED NR – 8-OHdG: PD ↑ Control
Chen et al. (2009) [24] China Case-control 0 / 211 / 135 PD: 67.3 ± 0.7, Controls: 68.4 ± 1.0 8-OHdG Leukocyte HPLC-ED GOG criteria for PD – 8-OHdG: PD ↑ Control
Bogdanov et al. (2008) [89] USA Case-control 0 / 66 / 25 PD: 66.0 ± 11.1, Controls: 61.5 ± 12.2 8-OHdG Plasma HPLC-ED Based on Pankratz et al. (2002) – 8-OHdG: PD ↑ Control
Dorszewska et al. (2007) [90] Poland Cross-sectional 0 / 98 / 50 PD: 60.8 ± 10.7, Controls: 44.6 ± 16.2 8-OHdG Leukocyte HPLC/ED/UV UK PD Society Criteria – 8-OHdG: PD – Control
Abe et al. (2003) [91] Japan Cross-sectional 0 / 24 / 15 PD: 63.3 ± 10.5, Controls: 62.3 ± 9.4 8-OHdG Serum HPLC-ED Koller’s criteria for PD – 8-OHdG: PD – Control
Kikuchi et al. (2002) [92] Japan Cross-sectional 0 / 48 / 22 PD: 65.75 ± 9.74, Controls: 60.54 ± 8.76 8-OHdG Serum ELISA Koller’s criteria for PD – 8-OHdG: PD ↑ Control
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ADA: American Diabetes Association; DM: Diabetes Mellitus; DR Congo: Democratic Republic of the Congo; EIA: Enzyme Immunoassay; ELISA: Enzyme-Linked Immunosorbent Assay; GOG: Gelb, Oliver, and Gilman; HPLC/ED/UV: High-Performance Liquid Chromatography with Electrochemical Detection and Ultraviolet Detection; HPLC-ECD: High-Performance Liquid Chromatography with Electrochemical Detection; HPLC-ED: High-Performance Liquid Chromatography with Electrochemical Detection; KSA: Kingdom of Saudi Arabia; MDS: Movement Disorder Society; NR: Not Reported; PD: Parkinson’s Disease; RCT: Randomized Controlled Trial; T2DM: Type 2 Diabetes Mellitus; UK: United Kingdom; USA: United States of America; WHO: World Health Organization; ↑: Indicates higher levels in the first group (DM or PD) compared to the control group; −: Indicates no significant difference between the groups.

3.2.1. F2-isoprostanes

  • Study designs and sample sizes: Among T2DM studies, cross-sectional designs predominated (10/13), with sample sizes ranging from 10 to 462 participants (median: 66). PD studies, all employing case-control or cross-sectional designs, were smaller, with sample sizes ranging from 8 to 61 participants (median: 36).

  • Sample sources and assay types: In T2DM studies, plasma (8/13) and serum (5/13) were the primary sample matrices. Assay methods included enzyme-linked immunosorbent assay (ELISA; 8/13), gas chromatography-mass spectrometry (GC-MS; 2/13), high-performance liquid chromatography (HPLC; 2/13), and enzyme immunoassay (EIA; 1/13). For PD studies, plasma was the predominant sample source (4/5), with assays conducted via GC–MS (3/5) and ELISA (2/5).

  • Diagnostic criteria: Diagnostic criteria in T2DM studies included World Health Organization (WHO) guidelines (4/13), American Diabetes Association (ADA) standards (1/13), clinical diagnoses (5/13), or unspecified protocols (3/13). PD studies utilized the United Kingdom PD Society Brain Bank criteria (3/5), with two studies not specifying diagnostic standards.

3.2.2. 8-OhdG

  • Study designs and sample sizes: T2DM studies were predominantly cross-sectional (20/30), with sample sizes ranging from 8 to 297 participants (median: 60). PD studies, employing various designs including case-control and cross-sectional approaches, ranged from 13 to 211 participants (median: 44).

  • Sample sources and assay types: Serum (25/30) and plasma (3/30) were the primary sample sources in T2DM studies, with ELISA as the dominant assay method (28/30). In PD studies, sample sources included serum (4/8), plasma (2/8), and leukocyte DNA (2/8), with assays performed using ELISA (3/8) or HPLC-based methods (5/8).

  • Diagnostic criteria: T2DM studies applied WHO criteria (19/30), ADA criteria (8/30), clinical diagnoses (8/30), or unspecified criteria. PD studies utilized movement disorder specialist diagnoses (1/8), Movement Disorder Society criteria (1/8), or other diagnostic protocols.

Demographic characteristics varied across studies. T2DM cohorts exhibited a mean age range of 50–81.8 years. PD participants were generally older (mean age: 55.3–70 years), though sex-specific data were inconsistently provided. Methodological heterogeneity arose from differences in assay techniques (e.g. ELISA, GC-MS, HPLC-ECD, and sample matrices (e.g. serum, plasma, leukocytes), contributing to substantial between-study variability. These demographic differences were considered within disease-specific analyses rather than as inter-disease comparisons.

3.3. Systematic review and meta-analysis of F2-isoprostanes

The systematic review identified 18 studies on F2-isoprostanes, comprising 13 in T2DM and 5 in PD. Among the T2DM studies, 11 reported significantly elevated F2-isoprostane levels compared to controls, while two found no significant difference (Table 1). In PD cohorts, evaluated independently, findings were heterogeneous; two of five studies identified elevated levels, whereas three observed no significant difference. The type of assay appeared to influence these outcomes. In T2DM, studies using GC–MS yielded variable results (e.g. one study reported no difference, while another detected an elevation). These methodological patterns were assessed within each disease group rather than as direct inter-disease contrasts.

To quantify these observations, a meta-analysis was conducted on 15 of the 18 studies that provided sufficient data for analysis. The analysis revealed a significant overall increase in F2-isoprostane levels (Hedges’ g = 1.21, 95% CI: 0.68–1.74; p < 0.001), though this was accompanied by extreme heterogeneity (I2 ≈ 97%) (Figure 2A). Subgroup analysis demonstrated a significant effect in T2DM (g = 1.60, 95% CI: 0.95–2.25; p < 0.001; 11 studies), whereas no statistically significant pooled effect was observed within PD cohorts (g = 0.47, 95% CI: −0.43 to 1.38; p = 0.303; 4 studies). In PD, further subgroup analyses by study design, age group, sample source, and assay type did not yield significant results, with high heterogeneity observed in some subgroups (e.g. I2 = 95.3% for the 60–69 age group) (Table 3). Specifically, analyses by study design showed no significant differences for case-control (g = 0.78, 95% CI: −0.87 to 2.44; p = 0.35; 2 studies) or cross-sectional studies (g = 0.06, 95% CI: −0.37 to 0.49; p = 0.78; 2 studies). Similarly, stratification by age group indicated no significant effects for the 60–69 age group (g = 0.50, 95% CI: −0.65 to 1.64; p = 0.39; 3 studies) or the >70 age group (g = 0.41, 95% CI: −0.33 to 1.15; p = 0.28; 1 study). Subgroup analyses by sample source and assay type were also non-significant (Table 3). In T2DM, further stratification by sample source indicated more significant effects in plasma (g = 2.38, 95% CI: 1.15–3.62) compared to serum and in cohorts with a mean age under 60 years (g = 1.69, 95% CI: 1.05–2.34) compared to older cohorts (Table 3).

Figure 2.

Multi-panel figure showing forest and funnel plots of Hedges's g values and confidence intervals for studies on PD and T2DM populations. The image features two panels. Panel A is a forest plot depicting Hedges's g values and 95% confidence intervals for studies categorized under PD (Parkinson's Disease) and T2DM (Type 2 Diabetes Mellitus). Blue squares represent individual study estimates, with horizontal lines indicating confidence intervals. The overall effect is illustrated by a red diamond at Hedges's g approximately 1.213. Panel B is a funnel plot illustrating standard errors against Hedges's g values, with separate color coding for PD (blue) and T2DM (purple). The distribution shows clusters and trends indicative of publication bias.

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Meta‑analysis and publication bias assessment of F2‑Isoprostane levels in T2DM and PD. (A) Forest plot showing pooled effect sizes and 95 % confidence intervals for F2‑isoprostane levels; comparisons between T2DM vs. controls, PD vs. controls, and the overall combined estimate. (B) Funnel plot displaying study effect sizes against standard errors for F2‑isoprostane levels in T2DM and PD; each point represents one study.

Table 3.

Subgroup analyses for F2-isoprostanes and 8-OHdG in T2DM and PD (mixed effects model).

Disease Biomarker Subgroup category Subgroup Number of studies Effect size (Hedges’ g [95% CI]) Heterogeneity (I²) P value
T2DM F2-Isoprostanes Age group <60 years 9 1.69 [1.05, 2.34] 95.8% <0.001
      ≥70 years 2 0.97 [−1.22, 3.15] 96.9% 0.385
    Sample source Plasma 6 2.38 [1.15, 3.62] 98.3% <0.001
      Serum 5 0.98 [0.48, 1.48] 88.7% <0.001
    Assay type ELISA 8 1.05 [0.67, 1.42] 98.3% <0.001
      GC-MS 2 4.95 [−5.08, 14.97] 88.7% 0.333
      HPLC 1* 2.98 [2.56, 3.40] 97.2% <0.001
    Study design Case-control 2 5.536 [−3.326, 14.398] 98.7% 0.221
      Cohort 1* −0.116 [−0.305, 0.073] 0.0% 0.230
      Cross-sectional 8 1.303 [0.730, 1.877] 94.2% <0.001
  8-OHdG Age group <60 years 18 3.01 [2.28, 3.74] 97.4% <0.001
      60–69 years 9 2.01 [1.38, 2.64] 95.1% <0.001
    DM complications Diabetic Nephropathy 2 5.24 [2.51, 7.98] 92.8% <0.001
      Diabetic Retinopathy 2 3.50 [3.13, 3.87] 0.0% <0.001
    Sample source Leukocyte DNA 1* 1.54 [1.19, 1.88] 0.0% <0.001
      Plasma 2 0.47 [0.04, 0.91] 0.0% 0.034
      Serum 24 2.88 [2.32, 3.43] 97.2% <0.001
    Assay type EIA (DNA/RNA) 1* 0.80 [0.22, 1.38] 0.0% 0.007
      ELISA 25 2.53 [2.03, 3.04] 96.8% <0.001
      HPLC-ECD 1* 7.69 [6.37, 9.01] 0.0% <0.001
    Study design Case-control 6 2.089 [1.239, 2.939] 94.6% <0.001
      Cohort 1* 2.752 [2.272, 3.231] 0.0% <0.001
      Cross-sectional 19 2.942 [2.290, 3.594] 97.5% <0.001
      Interventional 1* 0.430 [−0.416, 1.275] 0.0% 0.319
PD F2-Isoprostanes Study design Case-control 2 0.784 [−0.868, 2.436] 96.4% 0.352
      Cross-sectional 2 0.061 [−0.372, 0.493] 23.2% 0.783
    Age group 60–69 3 0.498 [−0.647, 1.643] 95.3% 0.394
      ≥70 1* 0.408 [−0.330, 1.146] 0.0% 0.279
    Sample source Plasma 3 0.498 [−0.647, 1.643] 95.3% 0.394
      Serum 1* 0.408 [−0.330, 1.146] 0.0% 0.279
    Assay type GC-MS 3 0.498 [−0.647, 1.643] 95.3% 0.394
      ELISA 1* 0.408 [−0.330, 1.146] 0.0% 0.279
  8-OHdG Study design Case-control 2 0.819 [−0.756, 2.394] 90.3% 0.308
      Cross-sectional 2 0.650 [−0.352, 1.652] 83.0% 0.204
      RCT 1* 1.138 [0.481, 1.794] 0.0% 0.001
    Age group 60–69a 5 0.784 [0.180, 1.389] 79.9% 0.011
    Sample source Serum 4 0.605 [−0.004, 1.215] 78.8% 0.052
      Plasma 1* 1.669 [0.797, 2.541] 0.0% <0.001
    Assay type ELISA 3 0.760 [−0.007, 1.528] 83.4% 0.052
      HPLC-ED 2 0.866 [−0.647, 2.379] 87.4% 0.262
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8-OHdG: 8-Hydroxy-2’-deoxyguanosine; CI: Confidence Interval; DM: Diabetes Mellitus; DNA: Deoxyribonucleic Acid; EIA: Enzyme Immunoassay; ELISA: Enzyme-Linked Immunosorbent Assay; GC-MS: Gas Chromatography-Mass Spectrometry; HPLC: High-Performance Liquid Chromatography; HPLC-ECD: High-Performance Liquid Chromatography with Electrochemical Detection; PD: Parkinson’s Disease; RCT: Randomized Controlled Trial; RNA: Ribonucleic Acid.

*

Indicates subgroups with only one study.

a

Indicates that all studies involved participants in the 60–69 age group.

3.4. Systematic review and meta-analysis of 8-OHdG

The systematic review identified 38 studies on 8-OHdG, comprising 30 in T2DM and 8 in PD. In T2DM, 28 of 30 studies demonstrated significantly elevated 8-OHdG levels compared to controls, with only two reporting no significant difference (Table 2). In PD cohorts, analyzed separately, five of eight studies demonstrated significant elevations, generally of smaller magnitude. Serum-based assays predominated in T2DM studies (25 of 30), and HPLC-based methods tended to yield larger effect sizes than ELISA.

Building on these findings, a meta-analysis was performed on 32 of the 38 studies that provided sufficient data. The pooled analysis revealed a significant increase in 8-OHdG levels (g = 1.88, 95% CI: 1.49–2.26; p < 0.001), with pronounced heterogeneity (I2 ≈ 97%) (Figure 3A). Subgroup analysis showed a strong effect in T2DM (g = 2.64, 95% CI: 2.13–3.14; p < 0.001; 27 studies) and a moderate increase in PD (g = 0.78, 95% CI: 0.18–1.39; p = 0.011; 5 studies). In PD, subgroup analyses by study design indicated a significant effect in randomized controlled trials (RCTs) (g = 1.14, 95% CI: 0.48 to 1.79; p = 0.001; 1 study) but not in case-control (g = 0.82, 95% CI: −0.76 to 2.39; p = 0.31; 2 studies) or cross-sectional studies (g = 0.65, 95% CI: −0.35 to 1.65; p = 0.20; 2 studies). All PD studies fell within the 60–69 age group, exhibiting a significant overall effect (g = 0.784, 95% CI: 0.180–1.389; p = 0.011; 5 studies). Further analyses by sample source revealed a significant elevation in plasma (g = 1.67, 95% CI: 0.80 to 2.54; p < 0.001; 1 study) but not in serum (g = 0.61, 95% CI: −0.004 to 1.215; p = 0.05; 4 studies). Subgroup analyses by assay type showed no significant effects for ELISA (g = 0.76, 95% CI: −0.01 to 1.53; p = 0.05; 3 studies) or HPLC-ED (g = 0.87, 95% CI: −0.65 to 2.38; p = 0.26; 2 studies) (Table 3). In T2DM, subgroup analyses identified more significant effects in serum samples (g = 2.88, 95% CI: 2.32–3.43) compared to other matrices and in cohorts with a mean age under 60 years (g = 3.01, 95% CI: 2.28–3.74) relative to older groups (Table 3). Additionally, studies focusing on diabetic complications reported substantial effect sizes for Diabetic Nephropathy (DN; g = 5.24, 95% CI: 2.51–7.98; p < 0.001; 2 studies) and Diabetic Retinopathy (DR; g = 3.50, 95% CI: 3.13–3.87; p < 0.001; 2 studies), though DN exhibited high heterogeneity (I2 = 92.8%) while DR showed none (I2 = 0.0%) (Table 3).

Figure 3.

Two panels: A forest plot (Panel A) showing Hedges's g values with confidence intervals for studies on PD and T2DM; a scatter plot (Panel B) of standard error versus Hedges's g for both populations. The figure contains two panels. Panel A features a forest plot summarizing Hedges's g values from various studies on Parkinson's Disease (PD) and Type 2 Diabetes Mellitus (T2DM). Each study is depicted as a horizontal line with blue squares indicating effect sizes and red diamonds for overall effect sizes. The plot also displays 95% confidence intervals, with key statistics for heterogeneity. Panel B presents a scatter plot where Hedges's g values (x-axis) are compared against standard errors (y-axis) for PD and T2DM, with color coding to distinguish between the two populations. Dashed lines indicate overall trends in the data.

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Meta‑analysis and publication bias assessment of 8‑OHdG levels in T2DM and PD. (A) Forest plot showing pooled effect sizes and 95 % confidence intervals for 8‑OHdG levels; T2DM vs. controls, PD vs. controls, and the overall combined estimate. (B) Funnel plot showing study effect sizes against standard errors for 8‑OHdG in T2DM and PD; each bullet denotes a distinct study.

3.5. Risk of bias and sensitivity analysis

Egger’s regression tests revealed significant small-study effects in T2DM cohorts, indicating potential publication bias (F2-isoprostanes: SEb = 10.10, p < 0.001; 8-OHdG: SEb = 13.15, p < 0.001), whereas no bias was detected in PD subgroups (Figures 2B and 3B). Sensitivity analyses confirmed the stability of pooled effect size estimates for both biomarkers (Supplementary Table S9). For F2-isoprostanes, the leave-one-out sensitivity analysis, conducted within a random-effects framework, indicated a stable pooled effect size with a representative estimate of 1.277 (95% CI: 0.746–1.809; I2 = 96.55%), while the fixed-effects model showed a lower estimate of 0.847 (95 giving a single estimate (95% CI: 0.754–0.940). The trim-and-fill method, adjusting for one imputed study, produced an effect size of 0.941 (95% CI: 0.371–1.510). For 8-OHdG, the leave-one-out sensitivity analysis within a random-effects framework resulted in a representative pooled effect size of 2.367 (95% CI: 1.908–2.825; I2 = 96.59%), compared to 1.547 (95% CI: 1.465–1.629) under fixed-effects assumptions. The trim-and-fill adjustment, imputing eight studies, yielded an effect size of 1.364 (95% CI: 0.845–1.883). High I2 values reflect substantial between-study heterogeneity, and trim-and-fill adjustments suggest modest publication bias. All bias and sensitivity assessments were conducted separately for PD and T2DM cohorts, reinforcing the robustness of disease-specific findings while supporting cautious interpretation.

4. Discussion

This systematic review and meta-analysis compiled evidence on the oxidative stress biomarkers F2-isoprostanes and 8-OHdG in T2DM and PD. Our findings demonstrate significant and consistent increases in both biomarkers in individuals with T2DM, with large effect sizes (Hedges’g ≈ 1.60 for F2-isoprostanes and g ≈ 2.64 for 8-OHdG) – in contrast to PD, where a moderate increase in 8-OHdG (g ≈ 0.78) was observed, and F2-isoprostanes exhibited only a modest, non-significant change (g ≈ 0.47). These divergent oxidative profiles highlight the differential contributions of lipid peroxidation and DNA damage under these conditions, providing a comparative framework to explore their potential comorbidity. Given that most included studies were observational, these findings should be interpreted as associations rather than evidence of causality; mechanistic interpretations are therefore inferential and hypothesis-generating.

4.1. Oxidative stress in T2DM

The significant increase in F2-isoprostanes and 8-OHdG levels in T2DM is consistent with prior evidence linking chronic hyperglycemia to elevated oxidative stress [18,93]. In T2DM, hyperglycemia-induced oxidative stress has been associated with lipid peroxidation and DNA oxidation, which are thought to contribute to the development of macrovascular and microvascular complications [18]. Subgroup analyses further indicate that T2DM patients with complications, such as diabetic nephropathy and retinopathy, tend to exhibit even more significant effect sizes, supporting an association between disease progression and oxidative burden. These findings align with prior meta-analyses that report elevated oxidative stress markers in metabolic disorders, supporting the use of these biomarkers for risk stratification and monitoring of T2DM, although their clinical application remains to be prospectively validated [94,95].

4.2. Oxidative stress in PD

In contrast to the pronounced systemic oxidative stress observed in T2DM, the pattern in PD appears more variable and compartmentalized. The moderate elevation in 8-OHdG is consistent with oxidative DNA damage being associated with dopaminergic neurodegeneration [29,96,97]. However, this relationship should be interpreted as associative rather than causal. The absence of a significant increase in F2-isoprostanes suggests that lipid peroxidation may be less prominent or more variable in PD. This variability could reflect the localized nature of lipid peroxidation in the central nervous system (CNS) or differences in disease stage among patients with PD and potentially other neurodegenerative diseases, warranting further investigation [7,98,99]. This discrepancy highlights the compartmentalized nature of oxidative stress in neurodegeneration, suggesting that blood-based biomarkers may not fully capture oxidative processes within the CNS [100,101].

Subgroup analyses of F2-isoprostanes in PD revealed no significant differences across study designs, age groups, sample sources, or assay types, highlighting the intricate nature of oxidative stress in this condition. For 8-OHdG, significant effects were observed in RCT and plasma samples but not in other study designs or sample sources. This suggests that methodological factors may influence biomarker detection and that these findings should be interpreted cautiously. This discrepancy highlights the compartmentalized nature of oxidative processes in neurodegeneration, suggesting that peripheral blood-based biomarkers may not accurately reflect CNS-specific oxidative damage [100,101]. Consequently, while oxidative stress is implicated in PD, its expression appears more heterogeneous than the systemic oxidative burden seen in T2DM. These distinct oxidative profiles may inform hypotheses regarding the interplay between T2DM and PD, although direct evidence from comorbid populations is currently lacking.

4.3. Implications for PD-T2DM comorbidity

The differing oxidative profiles in T2DM and PD raise hypotheses about potential mechanistic overlaps between these conditions. Epidemiological studies have consistently suggested that T2DM increases the risk of developing PD and may accelerate its progression [12–14,20]. Shared pathogenic features – including mitochondrial dysfunction, insulin resistance, and neuroinflammation – may be linked to elevated ROS levels, although direct mechanistic connections in comorbid patients remain to be established. This pro-oxidative environment has been associated with lipid peroxidation of cellular membranes, as indicated by increased levels of F2-isoprostanes, and induces oxidative damage to DNA, as reflected by elevated 8-OHdG levels. Taken together, these parallel findings suggest a potential overlap in oxidative pathways; however, this inference is indirect and remains to be validated in explicitly comorbid populations [9–11,102].

The consistent elevation of 8-OHdG in both T2DM and PD, particularly its significant increase in PD RCTs and plasma samples, supports the hypothesis that oxidative DNA damage may represent a convergent feature of these conditions, warranting further investigation in explicitly comorbid populations [103,104]. Additionally, the divergent findings for F2-isoprostanes across PD subgroups underscore the need to investigate whether these biomarkers can serve as early indicators of PD-T2DM comorbidity, although this remains speculative. If validated in longitudinal studies, early alterations in these markers could potentially inform risk assessment or therapeutic timing, but prospective clinical studies are required. Emerging evidence suggests that antidiabetic agents add clinical relevance to these hypotheses, yet direct applications remain to be established [20]. Future studies should focus on comorbid populations to determine whether systemic oxidative stress in T2DM exacerbates PD-related neurodegeneration, or vice versa, thereby guiding the development of targeted therapeutic approaches.

4.4. Methodological considerations and future directions

The high heterogeneity (I2 ≈ 97%) observed across studies reflects variability in assay methods (e.g. ELISA, GC-MS, HPLC-ECD), sample sources (serum vs. plasma), and patient demographics. These methodological inconsistencies complicate direct comparisons of oxidative stress measurements, highlighting the pressing need for standardized protocols and suggesting that caution is warranted when interpreting pooled effect sizes. The lack of significant differences in F2-isoprostanes across PD subgroups (e.g. study design, age, sample source, assay type) and the selective significance of 8-OHdG in RCTs and plasma samples further illustrate how methodological factors may obscure biomarker detection.

Future research should employ multi-compartment sampling, including cerebrospinal fluid, to capture both systemic and CNS-specific oxidative stress, thereby addressing whether peripheral markers accurately reflect brain oxidative damage. A multi-modal approach integrating biochemical, neuroimaging, clinical, and electrophysiological data is also essential for a holistic understanding of PD-T2DM comorbidity. Such an approach could confirm whether early alterations in F2-isoprostanes and 8-OHdG predict subsequent neurodegenerative processes. Refining assay techniques – balancing cost-effective methods like ELISA with precise methodologies like GC-MS – is crucial for elucidating the subtle dynamics of oxidative damage in PD, particularly in the context of T2DM comorbidity. Longitudinal studies are further required to clarify the temporal patterns of redox dysregulation and validate these biomarkers as early predictors of PD-T2DM comorbidity.

4.5. Limitations

Despite the strengths of this meta-analysis, several limitations must be acknowledged. First, the predominance of cross-sectional studies precludes causal inferences, meaning that observed associations should not be interpreted as evidence of mechanistic links. The underrepresentation of PD studies – especially those reporting F2 isoprostanes – limits the statistical power for detecting subgroup differences. Second, no data are available to characterize how F2-isoprostanes and 8-OHdG vary across different disease stages, genders, or clinical phenotypes of PD. This prevents any assessment of biomarker trajectories over disease progression. Third, variability in diagnostic criteria and assay methodologies introduces additional confounding, and potential publication bias in T2DM studies may inflate effect sizes. Nonetheless, the consistency of T2DM findings and the absence of significant bias in PD data strengthen the reliability of our conclusions.

An additional limitation is the restriction of included studies to participants aged ≥50 years. Aging is independently associated with increased oxidative stress and may have amplified biomarker levels irrespective of disease status. Consequently, these findings may not generalize to younger populations with T2DM or PD, in whom oxidative stress dynamics could differ. Future studies should examine age-stratified cohorts to clarify whether biomarker patterns are disease-specific across the lifespan.

Finally, while this review highlights potential clinical and translational relevance of F2-isoprostanes and 8-OHdG, these applications remain speculative and hypothesis-generating until validated in prospective or comorbid cohorts.

5. Conclusion

This comparative analysis demonstrates that oxidative stress biomarkers – particularly 8-OHdG – are consistently elevated in T2DM, while PD exhibits a more modest oxidative signature. Given that the studies included are predominantly observational, these findings reflect associations rather than mechanistic causation. The potential of F2-isoprostanes and 8-OHdG as early indicators of PD-T2DM comorbidity remains hypothetical and requires further investigation in explicitly comorbid populations. Any overlap in oxidative pathways, including features related to mitochondrial dysfunction and insulin resistance, should be interpreted as an indirect observation rather than a confirmed causal link. Future studies should prioritize comorbid populations with standardized assays, multi-compartment sampling, and multi-modal longitudinal designs.

Additionally, research should consider age-stratified cohorts as the current evidence is largely restricted to participants aged ≥50 years, which may limit generalizability to younger populations. Such approaches will be essential to clarify whether systemic oxidative stress is associated with bidirectional pathology and to evaluate the prospective utility of these biomarkers in guiding targeted, personalized interventions.

Supplementary Material

Supplementary material.docx
IANN_A_2654251_SM2180.docx (115.8KB, docx)
PRISMA_2020_checklist.docx
IANN_A_2654251_SM2179.docx (269.9KB, docx)

Acknowledgements

Not applicable. All authors have read and approved the final version of the manuscript.

Funding Statement

This study was supported by funding from the Zhejiang Provincial Natural Science Foundation (LZ25H090005 and ZCLZ25H2502) and the National Natural Science Foundation of China (82271282 and 82471617).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Open scholarship

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This article has earned the Center for Open Science badges for Open Data, Open Materials and Preregistered+. The data and materials are openly accessible at https://doi.org/10.6084/m9.figshare.30356386, https://doi.org/10.6084/m9.figshare.30356386 and https://www.crd.york.ac.uk/PROSPERO/view/CRD42025633253

Data availability statement

The PRISMA checklist for this article is deposited in Figshare (Title: PRISMA_2020_checklist for Comparative Evaluation of Oxidative Stress Biomarkers F2-Isoprostanes and 8-OHdG in Parkinson’s Disease and Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis of Human Studies; Doi:10.6084/m9.figshare.30356386; License: CC0) [105]. The data that support the findings of this study are included in the article/Supplementary material. Further inquiries can be directed to the corresponding authors.

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Associated Data

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Supplementary Materials

Supplementary material.docx
IANN_A_2654251_SM2180.docx (115.8KB, docx)
PRISMA_2020_checklist.docx
IANN_A_2654251_SM2179.docx (269.9KB, docx)

Data Availability Statement

The PRISMA checklist for this article is deposited in Figshare (Title: PRISMA_2020_checklist for Comparative Evaluation of Oxidative Stress Biomarkers F2-Isoprostanes and 8-OHdG in Parkinson’s Disease and Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis of Human Studies; Doi:10.6084/m9.figshare.30356386; License: CC0) [105]. The data that support the findings of this study are included in the article/Supplementary material. Further inquiries can be directed to the corresponding authors.

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