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Association between lipoprotein(a) and diabetic peripheral neuropathy in patients with type 2 diabetes: a meta-analysis

Diabetology & Metabolic Syndrome volume 17, Article number: 76 (2025) Cite this article

Abstract

Background

Diabetic peripheral neuropathy (DPN) is a common complication of type 2 diabetes (T2D). Lipoprotein(a) [Lp(a)], a known cardiovascular risk factor, has been hypothesized to influence the development of DPN. This meta-analysis aimed to investigate the relationship between Lp(a) levels and DPN in patients with T2D.

Methods

Following PRISMA 2020 guidelines, a systematic search of PubMed, Embase, Web of Science, Wanfang, and CNKI databases was performed up to October 12, 2024. Observational studies assessing blood Lp(a) levels in T2D patients with and without DPN or evaluating the association between Lp(a) and DPN risk were included. Data synthesis utilized a random-effects model to calculate standardized mean differences (SMDs) and odds ratios (ORs) with corresponding 95% confidence intervals (CIs).

Results

Eleven studies with 18,022 patients were included. Patients with DPN had significantly higher Lp(a) levels than those without DPN (SMD: 0.10, 95% CI: 0.02–0.19, p = 0.01; I² = 43%). High Lp(a) levels were associated with DPN (OR: 1.31, 95% CI: 1.07–1.60, p = 0.009; I² = 62%). Subgroup analyses according to study design, mean age of the patients, methods for measuring Lp(a) concentration, cutoff values of a high Lp(a), and study quality scores showed consistent results (p for subgroup difference all > 0.05). A high Lp(a) was associated with DPN in studies from Asian countries, but not in those from European countries (p for subgroup difference = 0.001).

Conclusion

Elevated Lp(a) levels are associated DPN in T2D patients, particularly in studies from Asian countries.

Introduction

Diabetic peripheral neuropathy (DPN) is one of the most common and debilitating complications of type 2 diabetes (T2D), affecting nearly half of all patients with long-standing diabetes [1,2,3]. Characterized by progressive nerve damage, DPN leads to sensory and motor dysfunction, culminating in pain, numbness, and an increased risk of foot ulcers and amputations [4]. Despite advancements in diabetes management, the global burden of DPN remains high, driven by the increasing prevalence of T2D [5, 6]. Current treatments primarily focus on glycemic control and symptomatic relief through pain management, but they offer limited efficacy in halting or reversing disease progression [5, 6]. As a result, DPN continues to impose significant healthcare costs and diminish quality of life for affected individuals. This underscores the critical need to identify novel risk factors that can inform prevention and intervention strategies [7].

Lipoprotein(a) [Lp(a)] is a distinct lipoprotein composed of low-density lipoprotein (LDL) and apolipoprotein(a), a glycoprotein with structural homology to plasminogen [8, 9]. Unlike LDL, the concentration of Lp(a) is largely genetically determined, with minimal modulation by diet or lifestyle [10]. Elevated Lp(a) levels have long been recognized as an independent risk factor for macrovascular complications, such as coronary artery disease and stroke, in both the general population and individuals with T2D [11, 12]. Emerging evidence suggests that Lp(a) may also play a role in microvascular complications of diabetes, including retinopathy, nephropathy, and potentially neuropathy [13]. The pathophysiological mechanisms linking Lp(a) to vascular damage include its pro-inflammatory and pro-thrombotic properties, as well as its interference with fibrinolysis through competition with plasminogen binding sites [14, 15]. These effects contribute to endothelial dysfunction and impaired blood flow, key features in the development of diabetic microangiopathy [16].

The association between Lp(a) and DPN is less well studied but biologically plausible. Chronic hyperglycemia in T2D leads to the accumulation of advanced glycation end products (AGEs) and oxidative stress, exacerbating vascular damage and nerve ischemia [17]. Elevated Lp(a) levels may amplify these effects by promoting low-grade inflammation and microvascular occlusion, creating an environment conducive to nerve injury [18, 19]. Preliminary studies have suggested that Lp(a) concentrations are higher in T2D patients with DPN compared to those without, but findings have been inconsistent [20,21,22,23,24,25,26,27,28,29,30]. Some studies have reported significant associations [24, 26], while others found no correlation [20,21,22,23, 25, 27,28,29,30], likely due to differences in study design, sample sizes, and population characteristics. Given the heterogeneity of these results, a comprehensive synthesis of available evidence is necessary to clarify the role of Lp(a) in DPN and determine its potential as a biomarker for identifying high-risk individuals. This meta-analysis aimed to systematically evaluate the association between blood Lp(a) levels and DPN in patients with T2D. Specifically, it sought to determine whether Lp(a) levels differ between T2D patients with and without DPN and whether elevated Lp(a) concentrations are associated with DPN in these patients.

Methods

This meta-analysis was conducted in accordance with the PRISMA 2020 guidelines [31, 32] and the Cochrane Handbook for Systematic Reviews [33], following their protocols for study design, data extraction, statistical evaluation, and interpretation of findings. The protocol of the meta-analysis has been registered in PROSPERO with the identifier CRD42024616829.

Database search

To identify studies relevant to this meta-analysis, we performed a comprehensive search of PubMed, Embase, Web of Science, Wanfang, and China National Knowledge Infrastructure (CNKI) using the following search terms: (1) “lipoprotein(a)” OR “Lp(a)” OR “Lp[a]” and (2) “diabetic neuropathy” OR “diabetic peripheral neuropathy” OR “neuropathy”. The search was restricted to studies involving human subjects, and only peer-reviewed articles published in English or Chinese were considered. The detailed search strategies for each database are provided in Supplemental Material 1. We also manually screened the reference lists of relevant original studies and review articles to identify additional eligible studies. The literature search covered all records up to October 12, 2024.

Inclusion and exclusion criteria

The inclusion criteria for eligible studies, structured according to the PICOS framework, were as follows:

Population (P): Adult patients (aged 18 years or older) with confirmed diagnosis of T2D.

Intervention/Exposure (I): Blood levels of Lp(a).

Comparator (C): Comparisons between T2D patients with and without DPN for the blood level of Lp(a), or the odds ratio (OR) for DPN compared between patients with high versus low concentration of Lp(a), based on cutoff values defined in the primary studies.

Outcome (O): The prevalence or the incidence of DPN, which was diagnosed in consistent with the criteria used among the primary studies.

Study Design (S): Observational studies, including cross-sectional studies, case-control studies, or cohort studies.

Studies were excluded if they were reviews, editorials, meta-analyses, preclinical studies, involved children or patients with type 1 diabetes, did not assess blood Lp(a) as an exposure, or did not report the outcome of DPN. If multiple studies included overlapping populations, the one with the largest sample size was selected for the meta-analysis.

Study quality assessment

The literature search, study identification, quality assessment, and data extraction were conducted independently by two authors to ensure thoroughness and objectivity, with any disagreements resolved through discussions with the corresponding author. We utilized the Newcastle–Ottawa Scale (NOS) [34] for quality assessment, which evaluates studies based on selection, comparability, and outcome assessment, assigning scores from 1 to 9, where 9 indicates the highest quality.

Data collection

Data extraction included key details such as study information (author, publication year, country, and design), patient characteristics (sample size, age, and sex), follow-up duration for cohort studies, methods for diagnosing DPN and the number of DPN cases, techniques for measuring blood Lp(a), reported outcomes, cutoff values for high Lp(a), and matched or adjusted variables used to account for potential confounders when estimating the association between Lp(a) and DPN risk.

Statistical analysis

The primary objective of this meta-analysis was to compare the level of blood Lp(a)between T2D patients with and without DPN. In view of the various measurements and scales used for measuring Lp(a) among the included studies, standardized mean differences (SMDs) with corresponding 95% confidence intervals (CIs) were used to summarize the difference of blood Lp(a) levels [33]. The secondary objective focused on evaluating the association between Lp(a) concentration and DPN in patients with T2D, expressed as OR and 95% CI for patients categorized into groups based on high versus low serum Lp(a) levels. The OR values, along with their standard errors, were calculated from either 95% CIs or p-values and subsequently logarithmically transformed to stabilize variance for further analysis [33]. To assess heterogeneity across the studies included in the meta-analysis, we employed both the Cochrane Q test and I² statistics [35]. An I² value > 50% was considered indicative of significant statistical heterogeneity. To accommodate the variability of patient characteristics, methods for measuring Lp(a), cutoff of Lp(a) and definition of DPN among studies in our analysis, we utilized a random-effects model to pool the results [33]. Additionally, we conducted a sensitivity analysis by systematically excluding individual studies to determine the robustness and reliability of our findings. Subgroup analyses were performed to explore the influence of several key factors, such as study design, mean ages of the patients, methods for measuring blood Lp(a) concentration, NOS scores that reflect study quality, and countries of the studies (Asian or European). For defining these subgroups, we used the medians of continuous variables as cutoffs. To investigate potential publication bias, we assessed the symmetry of funnel plots visually and supplemented this analysis with Egger’s regression test [36]. All statistical analyses were performed using RevMan (Version 5.1; Cochrane Collaboration, Oxford, UK) and Stata software (version 12.0; Stata Corporation, College Station, TX, USA).

Results

Database search and study identification

The study inclusion process is depicted in Fig. 1. Initially, 233 potentially relevant records were identified from the five searched databases, with 68 records excluded due to duplication. A subsequent screening of titles and abstracts resulted in the exclusion of 140 studies, primarily because they did not align with the objectives of the meta-analysis. The full texts of the remaining 25 records were reviewed by two independent authors, leading to the exclusion of an additional 14 studies for various reasons, as detailed in Fig. 1. Ultimately, eleven observational studies were identified as suitable for inclusion in the quantitative analysis [20,21,22,23,24,25,26,27,28,29,30].

Fig. 1
figure 1

Flowchart of database search and study inclusion

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Overview of the study characteristics

Table 1 summarizes the overall characteristics of the studies included in the meta-analysis. Overall, two prospective cohort studies [27, 28], six cross-sectional studies [20,21,22, 24, 26, 29], and three case-control studies [23, 25, 30] were included. These studies were published from 2000 to 2024, and were conducted in China, Turkey, Italy, Iran, the Netherlands, and India, respectively. Among these studies, 18,022 adult patients with T2D were included, with the mean ages varying between 54.1 and 70.0 years, and the proportion of the males ranging from 39.7 to 60.4%. The mean follow-up duration or the two cohort studies [27, 28] were 7 and 5 years, respectively. The diagnosis of DPN was all based on clinical evaluation in the included studies, which generally involved symptom scores and neurologic examinations. The measuring of Lp(a) concentration was by the enzyme-linked immunosorbent assay (ELISA) in three studies [20,21,22], via the High-Performance Liquid Chromatography (HPLC) in two studies [23, 24], and via the immunoturbidimetry in five studies [25,26,27,28, 30]. Accordingly, 6,981 (38.7%) patients had DPN. The difference of Lp(a) concentration was reported in all of the included studies, while the OR for the association between a high Lp(a) concentration and the risk of DPN was reported in seven studies [20, 22, 25,26,27,28,29]. The cutoff values for defining a high blood Lp(a) varied from 26.8 to 50 mg/dL. Potential confounding factors, such as age, sex, duration of diabetes, and glycemic control etc. were adjusted to a varying degree among these studies. The NOS scores of the included studies were seven to nine, suggesting an overall moderate to good study quality (Table 2).

Table 1 Characteristics of the included observational studies
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Table 2 Study quality evaluation via the Newcastle-Ottawa Scale
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Difference of Lp(a) concentration at between patients with and without DPN

Overall, the pooled results of the 11 studies [20,21,22,23,24,25,26,27,28,29,30] showed a higher blood level of Lp(a) in T2D patients with DPN compared to those without DPN (SMD: 0.10, 95% CI: 0.02 to 0.19, p = 0.01; I² = 43%; Fig. 2A). Sensitivity analyses, conducted by sequentially excluding one dataset at a time, did not significantly alter the results (SMD range: 0.04 to 0.13, p all < 0.05). Further subgroup analyses yielded similar findings in cohort, cross-sectional and case-control studies (p for subgroup difference = 0.42, Fig. 2B), between patients with mean ages < 60 years or ≥ 60 years (p for subgroup difference = 0.50; Fig. 2C), in studies with Lp(a) measured with ELISA, HPLC, and immunoturbidimetry (p for subgroup difference = 0.57; Fig. 3A), and in studies with different NOS scores (p for subgroup difference = 0.21; Fig. 3B). Further subgroup analysis according to the country of the study showed a higher blood level of Lp(a) in patients with DPN than those without DPN in studies from Asian countries, but not in those from European countries (SMD: 0.14 versus 0.00; Fig. 3C). However, the difference between the subgroups were not statistically significant (p for subgroup difference = 0.13; Fig. 3C).

Fig. 2
figure 2

Forest plots for the meta-analysis comparing blood Lp(a) concentration between T2D patients with and without DPN; A, forest plots for the overall meta-analysis; B, forest plots for the subgroup analysis according to study design; and C, forest plots for the subgroup analysis according to the mean ages of the patients

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Fig. 3
figure 3

Forest plots for the subgroup analyses comparing blood Lp(a) concentration between T2D patients with and without DPN; A, forest plots for the subgroup analysis according to the methods for measuring blood Lp(a); B, forest plots for the subgroup analysis according to the NOS scores; and C, forest plots for the subgroup analysis according to the region of the study countries

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Association between Lp(a) concentration and DPN

The meta-analysis of the seven studies [20, 22, 25,26,27,28,29] suggested that a high blood level of Lp(a) was associated with DPN in patients with T2D (OR: 1.31, 95% CI: 1.07 to 1.60, p = 0.009; I² = 62%; Fig. 4A). Sensitivity analyses, conducted by sequentially excluding one cohort at a time, yielded similar results (OR range: 1.24–1.39, p all < 0.05). Further subgroup analyses retrieved consistent results in cohort, cross-sectional and case-control studies (p for subgroup difference = 0.71, Fig. 4B), between patients with mean ages < 50 years or ≥ 50 years (p for subgroup difference = 0.84; Fig. 4C), in studies with Lp(a) measured with ELISA and immunoturbidimetry (p for subgroup difference = 0.77; Fig. 5A), in studies with cutoff values for a high Lp(a) ≤ or > 30 mg/dL (p for subgroup difference = 0.12; Fig. 5B), and in studies with different NOS scores (p for subgroup difference = 0.12; Fig. 3C). Interestingly, the subgroup analysis according to the study country showed that a high Lp(a) was associated with DPN in T2D patients in studies from Asian countries, but not in those from European countries (OR: 1.46 versus 0.98, p for subgroup difference = 0.001; Fig. 3D).

Fig. 4
figure 4

Forest plots for the meta-analysis of the association between a high Lp(a) concentration and DPN in patients with T2D; A, forest plots for the overall meta-analysis; B, forest plots for the subgroup analysis according to study design; and C, forest plots for the subgroup analysis according to the mean ages of the patients

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Fig. 5
figure 5

Forest plots for the subgroup analyses of the association between a high Lp(a) concentration and DPN in patients with T2D; A, forest plots for the subgroup analysis according to the methods for measuring blood Lp(a); B, forest plots for the subgroup analysis according to the cutoff values for defining a high Lp(a); C, forest plots for the subgroup analysis according to the NOS scores; and D, forest plots for the subgroup analysis according to the region of the study countries

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Publication bias

Upon visual inspection, the funnel plots for the meta-analyses of the difference in Lp(a) between patients with and without DPN, as well as the OR for the association between Lp(a) and DPN, appeared symmetrical, indicating a low likelihood of publication bias (Fig. 6A and B). Additionally, the results of Egger’s regression tests further supported this conclusion, suggesting a low risk of publication bias (p = 0.22 and 0.41, respectively).

Fig. 6
figure 6

Funnel plots for the meta-analyses; A, funnel plots for the meta-analysis comparing blood Lp(a) concentration between T2D patients with and without DPN; and B, funnel plots for the meta-analysis of the association between a high Lp(a) concentration and DPN in patients with T2D

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Discussion

The results of this meta-analysis revealed a potential association between elevated Lp(a) levels and DPN in patients with T2D. Patients with DPN were found to have higher circulating levels of Lp(a) compared to those without DPN, and elevated Lp(a) levels were significantly associated with DPN in patients with T2D. These findings provide compelling evidence supporting the role of Lp(a) as a potential biomarker for DPN in T2D patients and highlight the need for further research to validate and refine these observations.

The association between high Lp(a) levels and DPN can be explained by several plausible pathophysiological mechanisms. Lp(a) is known to exert pro-inflammatory and pro-thrombotic effects, primarily through its structural homology with plasminogen and its ability to promote oxidative stress [37]. In the context of diabetes, chronic hyperglycemia leads to endothelial dysfunction, oxidative damage, and advanced glycation end-product (AGE) accumulation, creating a milieu that predisposes patients to microvascular complications [38]. Elevated Lp(a) levels may exacerbate these processes by inhibiting fibrinolysis, increasing vascular inflammation, and impairing blood flow to peripheral nerves [39]. Reduced oxygen and nutrient delivery further compromise nerve function, ultimately leading to the progressive nerve damage characteristic of DPN [2, 40]. Moreover, the lipid-rich composition of Lp(a) contributes to vascular plaque formation and arterial stiffness, which could aggravate microvascular ischemia in diabetic patients [41, 42]. Finally, alternative explanations for the observed association, such as residual confounding and reverse causation, should also be considered. While most included studies adjusted for key confounders, unmeasured factors such as socioeconomic status, comorbid conditions, or medication use may still influence the association between Lp(a) and DPN [43]. Reverse causation is another possibility, as T2D-related metabolic changes could theoretically elevate Lp(a) levels, which may be more remarkable in patients with DPN [12]. Prospective cohort studies with long-term follow-up are needed to disentangle these complex relationships and strengthen causal inferences.

Subgroup analyses based on study design, mean age of patients, methods for measuring Lp(a) concentration, cutoff values for defining high Lp(a), and study quality scores demonstrated consistent results. These findings suggest that the association between elevated Lp(a) levels and DPN remains robust across varying study characteristics. The consistency across subgroups supports the generalizability of the results and indicates that the observed association is not significantly influenced by variations in study design, age of the patients, or methodological approaches. Interestingly, the subgroup analysis according to the study country showed a significant association between a high Lp(a) and DPN in studies from Asian countries, but not in those from European countries, which at least partly explained the heterogeneity. This finding highlights potential regional or ethnic differences in the relationship between Lp(a) and DPN. Possible explanations may include genetic variations affecting Lp(a) metabolism and its pro-inflammatory or pro-thrombotic effects, as well as differing baseline levels of Lp(a) among populations [44]. In addition, environmental factors, such as dietary habits and lifestyle, and variations in healthcare practices, including diagnostic criteria for DPN and approaches to diabetes management, may also contribute to this disparity [45]. However, the mechanisms underlying these regional differences remain speculative and warrant further investigation. Future studies should aim to collect and analyze ethnicity-specific data to better understand these variations and their clinical implications.

This meta-analysis has several notable strengths. To our knowledge, it is the first comprehensive synthesis of data on the association between Lp(a) and DPN, addressing an important gap in the literature. The extensive search strategy across five major databases ensured broad coverage and minimized the risk of missing relevant studies. By analyzing two distinct outcomes—mean differences in Lp(a) levels and the association between elevated Lp(a) and DPN—this study provides robust validation of the link between Lp(a) and DPN. The inclusion of subgroup analyses further enhances the interpretability and clinical relevance of the findings. Despite these strengths, several limitations warrant consideration. All included studies were observational, precluding definitive conclusions about causality. Future research employing Mendelian randomization could provide valuable insights into causality, as it leverages genetic variants as proxies for exposure to minimize confounding and reverse causation biases. Additionally, well-designed prospective cohort studies with standardized Lp(a) measurement protocols and adequate follow-up durations could help establish the temporal relationship between Lp(a) levels and the onset of DPN. Additionally, potential confounding factors, such as lipid-lowering therapy, may not have been fully accounted for in the primary studies, which could bias the results. Another important limitation of this meta-analysis is the variability in methods used to measure Lp(a) across the included studies, including ELISA, HPLC, and immunoturbidimetry. This inconsistency can lead to differences in Lp(a) quantification and complicate the comparability of results. Moreover, the lack of uniformity in reporting cutoff values for high Lp(a) levels further hinders the interpretation and application of findings [46]. Standardized measurement protocols and reference ranges for Lp(a) are urgently needed to ensure consistency across studies and improve the reliability of evidence linking Lp(a) to DPN. Clinically, standardized methods would facilitate the integration of Lp(a) as a biomarker for identifying high-risk individuals and monitoring disease progression. Future research should prioritize the development and adoption of standardized assays to advance both the scientific understanding and clinical utility of Lp(a) in diabetes-related complications. Finally, the diagnostic criteria for DPN also varied across the included studies, ranging from symptom-based assessments to objective neurologic examinations, which may affect the consistency and accuracy of the reported outcomes. Additionally, the quality of DPN diagnosis could be influenced by the resources and clinical practices in different regions, further contributing to variability.

Clinically, these findings have significant implications. Identifying elevated Lp(a) levels as a possible biomarker for DPN could enable earlier identification of high-risk individuals, allowing for timely intervention to mitigate nerve damage. Routine measurement of Lp(a) levels could be incorporated into risk stratification models for patients with T2D, particularly in populations where elevated Lp(a) levels are more prevalent or strongly associated with DPN. Identifying individuals with high Lp(a) levels may enable earlier interventions, such as more aggressive glycemic control or lifestyle modifications, to mitigate the risk of DPN. Additionally, Lp(a) testing could complement other biomarkers and clinical parameters to provide a comprehensive risk profile for microvascular complications in diabetes. However, the integration of Lp(a) into clinical practice requires standardized measurement methods, consensus on clinically relevant cutoff values, and cost-effectiveness evaluations to ensure its practicality and utility in diverse healthcare settings. Future clinical guidelines should consider these factors to support the adoption of Lp(a) as part of personalized diabetes management strategies. Furthermore, if the causative relationship between Lp(a) and DPN could be validated in Mendelian Randomization or randomized controlled trials, Lp(a) may serve as a target for future therapeutic strategies aimed at reducing its pathogenic effects, particularly in diabetic patients with a predisposition to microvascular complications. However, translating these findings into practice requires the development of standardized assays for Lp(a) measurement, as well as clinical guidelines for interpreting its levels in the context of diabetes care. Future studies should focus on addressing the limitations of the current evidence base. Large-scale, prospective cohort studies are needed to establish a temporal relationship between Lp(a) levels and DPN onset, providing stronger evidence for causality. Additionally, mechanistic studies are essential to elucidate the precise molecular pathways linking Lp(a) to nerve damage, which could inform the development of targeted interventions. Research should also explore the interplay between Lp(a) and other risk factors, such as glycemic control, lipid profiles, and inflammatory markers, to better understand the multifactorial nature of DPN [47]. Finally, clinical trials investigating the efficacy of interventions aimed at lowering Lp(a) levels in reducing DPN risk or progression would be invaluable in advancing patient care.

Conclusions

In conclusion, this meta-analysis suggests a possible association between elevated Lp(a) levels and DPN in T2D patients, shedding light on a novel potential biomarker for this debilitating complication. While the findings are promising, they should be interpreted in the context of the study’s limitations. Further research is needed to establish causality, refine clinical applications, and explore therapeutic implications, ultimately improving the prevention and management of DPN in T2D patients.

Data availability

All data generated or analyzed during this study are included in this published article.

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Acknowledgements

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Funding

This study was supported by the Science and Technology Project Approved by Changzhou Health Commission (Major project: ZD202322).

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  1. Department of Endocrinology, Affiliated Hospital of Nanjing University of Chinese Medicine, Changzhou Hospital of Traditional Chinese Medicine, No.25 Heping North Road, Changzhou, 213003, China

    Li Sheng, Yiwen Yang & Yunqing Zhou

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  2. Yiwen Yang
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Contributions

Li Sheng and Yunqing Zhou designed the study. Li Sheng and Yiwen Yang performed database search, study identification, and study quality evaluation. Li Sheng, Yiwen Yang, and Yunqing Zhou performed statistical analysis and interpreted the results. Li Sheng drafted the manuscript. All authors revised the manuscript and approved the submission.

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Correspondence to Yunqing Zhou.

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Sheng, L., Yang, Y. & Zhou, Y. Association between lipoprotein(a) and diabetic peripheral neuropathy in patients with type 2 diabetes: a meta-analysis. Diabetol Metab Syndr 17, 76 (2025). https://doi.org/10.1186/s13098-025-01621-y

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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