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Genetically predicted amino acids related to neural regulation mediate the association between diabetes mellitus and postherpetic neuralgia: a Mendelian randomization study

Diabetology & Metabolic Syndrome volume 17, Article number: 104 (2025) Cite this article

Abstract

Background

Postherpetic neuralgia (PHN) and diabetes mellitus frequently coexist in clinical settings; however, the causal relationship between them remains unclear. Moreover, the potential mediating role of amino acids related to neural regulation in this association has not been fully explored yet.

Methods

Univariable Mendelian randomized (UVMR) was utilized to examine the causal relationship between various subtypes of diabetes mellitus and PHN, with the inverse variance weighted method as the main approach. Multivariable MR (MVMR) was conducted to assess the direct effect of diabetes mellitus, accounting for waist circumference, diabetic neuropathy/ulcers, and depression. Moreover, a two-step MR analysis was employed to investigate the mediating role of neurotransmitter-related amino acids in the association between diabetes mellitus and PHN.

Results

A significant statistical correlation was found between type 2 diabetes mellitus (T2DM) and PHN (odds ratio, OR: 1.23, 95% confidence interval, CI: 1.01–1.49, P = 0.036), while in type 1 diabetes mellitus or pregnancy diabetes mellitus, no evidence of the association with PHN was observed. MVMR analyses demonstrated that the effect of T2DM on PHN remained significant after adjusting for waist circumference, diabetic neuropathy/ulcers, and depression. Further mediation analysis revealed that phenylalanine accounted for 49.2% (95% CI: 22.7– 75.6%) of the total effect of T2DM on PHN.

Conclusion

The current study suggested that T2DM was associated with an increased risk of PHN, with phenylalanine playing a mediating role. These findings provided valuable insights for the screening and prevention of PHN in clinical practice.

Introduction

Herpes zoster (HZ), also known as shingles, is caused by the reactivation of the varicella-zoster virus [1]. A common complication of HZ is postherpetic neuralgia (PHN), which is characterized by persistent pain [2]. Without vaccination, approximately 30% of individuals have a lifetime risk of developing HZ infection. Among those contracting the infection, approximately 5–30% of patients subsequently experience PHN [3]. This condition can last for 90 days or more after the HZ rash has resolved [4], thus significantly affecting the patient’s quality of life and presenting a considerable social burden [5].

Several studies have suggested a link between diabetes mellitus and an increased susceptibility to both PHN and HZ [6, 7]. However, existing research is primarily confined to case-control and cohort studies [7, 8], which are prone to confounding variables, limited follow-up periods, and reverse causation. Moreover, only a few small-scale studies have investigated the effects of different subtypes of diabetes mellitus on this relationship [8].

The intermediary mechanisms of diabetes mellitus affecting HZ are not fully understood yet. Individuals with diabetes mellitus often exhibit a range of metabolic disturbances, including amino acids [9, 10]. In comparison, individuals with phenylketonuria suffer from disrupted phenylalanine metabolism due to a deficiency of phenylalanine carboxylase, which leads to further neurological abnormalities [11]. Glutamate and aspartic acid act as excitatory neurotransmitters, while glycine functions as an inhibitory neurotransmitter. Histidine, tryptophan, phenylalanine, and tyrosine play are involved in the synthesis and regulation of serotonin, catecholamines, histamine, and various other neurotransmitters [12]. These neurotransmitters are intricately involved in pain mechanisms, particularly in modulating the descending inhibitory system and peripheral hyperalgesia. Their influence can significantly affect the onset and progression of PHN [13]. Therefore, it was hypothesized that diabetes mellitus may cause disruptions in amino acid metabolism associated with neural regulation, contributing to the onset and progression of PHN.

Mendelian randomization (MR) utilizes extensive data from large-scale genome-wide association studies (GWASs) to determine the causal effects of exposures on particular diseases. Since genetic variations are randomly determined at conception and remain unaffected by environmental factors, MR effectively reduces the risk of confounding factors and reverse causation. The two-step MR approach, an extension of traditional MR, provides deeper insights into the role of mediating factors between risk factors and disease development [14]. Therefore, the current study employed a two-step MR method to prospectively examine the effects of various types of diabetes mellitus on the development of PHN and investigate the mediating role of amino acids involved in neural regulation.

Methods

Study overview

The current study was conducted using a two-step, two-sample MR framework to elucidate the causal effect of diabetes on PHN and explore the mediating role of amino acids related to neural regulation (Fig. 1). First, a two-sample MR was performed to assess the relationship between various subtypes of diabetes mellitus and PHN individually. Second, a two-step MR was conducted to determine the mediating effects of amino acids related to neural regulation between diabetes and PHN. The MR analysis adheres to three fundamental hypotheses: (1) the instrumental variables (IVs) must be strongly associated with the exposures; (2) the IVs should not be linked to any known or unknown confounders related to the exposures or outcomes; and (3) the IVs should influence the outcomes exclusively through the exposures. Reporting follows the Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization statement (Supplementary Table 1).

Fig. 1
figure 1

Overview of the study design in this study. T1DM, type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus; GDM, gestational diabetes mellitus; MVMR, multivariable Mendelian randomization; UKB, UK Biobank; CLSA, Canadian Longitudinal Study on Aging; DIAGRAM, DIAbetes Genetics Replication and Meta-analysis; ICD, International Classification of Diseases; IVW, inverse variance weighted; MR-PRESSO, MR-Pleiotropy Residual Sum and Outlier

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Data source

For the exposure phenotypes, various types of diabetes were examined, including type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), and gestational diabetes mellitus (GDM). T1DM is a chronic autoimmune disease characterized by insulin deficiency caused by destruction of pancreatic β-cells. The diagnosis of T1DM cases was based on the International Classification of Diseases, 10th Revision (ICD-10) code E10 or the 9th Revision (ICD-9) code 250.01. The data for T1DM was sourced from a GWAS, involving 9,266 cases and 15,574 controls [15]. For T2DM, diagnoses used the ICD-10 code E11 or the ICD-9 code 250.00. The genetic data for T2DM was obtained from the DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) consortium, which included 74,124 cases and 824,006 controls, with adjustments for sex and residual inflation [16]. GDM is defined as glucose intolerance that initially appears during pregnancy. GDM diagnoses were determined using the ICD-10 code O24.4 or the ICD-9 code 648.8. The data for GDM was derived from the United Kingdom Biobank (UKB), encompassing 113 cases and 247,427 controls [17].

Regarding the outcome phenotype, the genetic association data for single nucleotide polymorphisms (SNPs) with PHN was obtained from the FinnGen database (Round 10: https://www.finngen.fi/fi). This dataset included 356 cases and 360,538 controls [18]. The diagnosis of PHN was based on the ICD-10 code G53.0. Moreover, there was no sample overlap between the outcome and exposure datasets.

Based on the existing literature, several variables were identified as potential confounders in the relationship between T2DM and PHN. These included waist circumference, diabetic neuropathy/ulcers, and depression. These confounders were analysed using a multivariable MR (MVMR) model. The participants were instructed to wear light clothing, stand straight, and breathe normally. The measurement was taken horizontally around the abdomen at the midpoint between the top of the iliac crest and the lower edge of the 12th rib. This ensured that it was in contact with the skin without causing compression. The biological data for waist circumference was derived from 407,661 individuals in the UKB. Diabetes ulcer is also one of the common chronic complications of diabetes, which is the result of the combined effect of neuropathy and angiopathy caused by diabetes. Diabetes neuropathy is a frequent chronic complication of diabetes. This disease is marked by sensory abnormalities, pain, or numbness. Diabetes ulcer is another common complication. They arise from the combined effects of neuropathy and vascular issues caused by diabetes. The diabetic neuropathy/ulcers data, including 130 cases and 420,343 controls, was also derived from the UKB. Patients reported their previous diagnoses to interviewers. Moreover, depression-related data obtained from 500,199 individuals was obtained from the UKB and Psychiatric Genomics Consortium [19]. This data includes patients with broadly defined depression and major depression [21]. Individuals who have previously consulted a psychiatrist for depression, received a clinical diagnosis of depression, or undergone treatment for depression are classified as depressed patients [19,20,21].

Additionally, the mediating role of metabolic features was explored, specifically focussing on seven amino acids associated with neural regulation. The GWAS summary statistics for serum glycine, histidine, phenylalanine, and tyrosine were obtained from Nightingale Health (biomarker quantification version 2020), encompassing data from 115,078 randomly selected participants in the UKB. Meanwhile, the GWAS data for plasma aspartate, glutamate, and tryptophan levels were sourced from metabolomics GWAS, involving 8,299 individuals from the Canadian Longitudinal Study on Aging cohort [22]. More detailed information is displayed in Supplementary Table 2.

Genetic variants selection criteria

The SNPs associated with the exposures were selected based on a significant threshold of P < 5 × 10− 8. In order to identify a set of independent SNPs, the SNPs within a 10,000-kb window were pruned using linkage disequilibrium (LD) with an R2 > 0.001. For exposures with a limited number of IVs at genome-wide significance, such as GDM, diabetic neuropathy/ulcer, and certain amino acids, the extracting cutoff was relaxed to P < 5 × 10− 6. The genetic information for these SNPs was then extracted from the outcome datasets, and any SNP that showed a strong correlation with the outcomes (P < 5 × 10− 5) was excluded. Then, the exposure and outcome data were harmonized to align the effect alleles and remove palindromic or incompatible SNPs. The F statistic was computed using Eq.: \(\:F={R}^{2}(n-2)/(1-{R}^{2}\)), where n was the sample size, and R2 was calculated as R2 = 2 × (1-MAF) ×MAF × β2 [23]. If the necessary information was missing, the formula F = (beta/se)2 was used [24]. SNPs with weak instrument bias, indicated by an F < 10, were excluded to reduce potential bias. Lastly, the Steiger test was used to eliminate SNPs with reverse causality. The remaining SNPs, after these rigorous filtering steps, were used for MR analysis (Supplementary Tables 3–4).

Statistical analysis

In the univariable MR analysis (UVMR), the random-effect inverse variance weighting (IVW) model was used as the main approach to elucidate the associations between diabetes and PHN. This approach yielded a main IVW effect (α), which was compared with the results from MR egger, weighted median, and weighted mode methods [25]. The Cochrane’s Q value was calculated to evaluate heterogeneity. A P-value derived from Cochrane’s Q greater than 0.05, along with an I2 statistic of less than 25% indicated no significant heterogeneity. For evaluating horizontal pleiotropy, the MR-Egger intercept was used, where P > 0.05 suggested the absence of horizontal pleiotropy [26]. If either heterogeneity or horizontal pleiotropy exceeded expectations, RadialMR was employed to identify and exclude outliers, followed by repeating the heterogeneity and pleiotropy assessments. Moreover, the leave-one-out method was employed to determine whether the pooled IVW estimates were influenced by specific single IVs. Finally, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) were used to validate the current findings, detect horizontal pleiotropy, and identify any outliers [27].

In order to confirm the significant causal relationships identified through UVMR analysis, MVMR analysis was conducted to adjust for potential confounding factors, including waist circumference, diabetic neuropathy/ulcers, and depression [28]. To address the issue of collinearity, we corrected for each confounding factor individually in separate analyses.

A two-step MR framework was then applied to investigate whether amino acids related to neural regulation mediated the association between diabetes and PHN. In the first step, the effect of diabetes on amino acids was assessed using the UVMR approach and calculated the main IVW effect (β1). In the second step, the effects of amino acids on PHN were initially evaluated using UVMR, and a False Discovery Rate (FDR) correction was applied to identify significant results. Subsequently, the MVMR approach was employed to eliminate the confounding bias of diabetes in the relationship between amino acids and PHN, and a main MV-IVW effect was obtained (β2). Finally, the proportion of mediating effects was determined by dividing the indirect effect divided by the total effect (β1 × β2/α) [29].

The data were analysed using the R Software 4.3.2 packages “TwoSampleMR” (version 0.5.10), “MendelianRandomization” (version 0.8.0), and “MRPRESSO” (1.0).

Results

Causal effects of diabetes mellitus on PHN

The results from the IVW analysis (odds ratio, OR: 1.23, 95% confidence interval, CI: 1.01–1.49, P = 0.036) (Fig. 2) and Weighted median method (OR: 1.43, 95% CI: 1.03–2.00, P = 0.034) (Fig. 2) indicated a suggestive causal effect of T2DM on PHN. The results of the “leave-one-out” test in the study demonstrated that there was no abnormal IV in this analysis that impacted the overall results (Supplementary Table 5). Cochrane’s Q test revealed no significant heterogeneity in the effect of T2DM on PHN (Cochran’s Q P value = 0.728). Moreover, the Egger intercept test showed that horizontal pleiotropy was not significant (Egger intercept P = 0.593). The MR-PRESSO analysis further supported the causal relationship between T2DM and PHN (OR: 1.23, 95% CI: 1.02–1.48, P = 0.032), with no evidence of horizontal pleiotropy (Global Test P = 0.727) and outliers.

Fig. 2
figure 2

Causal association between diabetes mellitus and postherpetic neuralgia. T1DM, type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus; GDM, gestational diabetes mellitus; OR, odds ratio; CI, confidence interval; SNPs, single nucleotide polymorphisms

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Using MVMR, the effects of T2DM on PHN remained significant after adjusting for waist circumference (OR: 1.25, 95% CI: 1.01–1.54, P = 0.036), diabetic neuropathy/ulcers (OR: 1.24, 95% CI: 1.01–1.51, P = 0.035), and depression (OR: 1.26, 95% CI: 1.02–1.56, P = 0.031). No heterogeneity or pleiotropy was observed in the MVMR models adjusted for waist circumference (Cochran’s Q P value = 0.768; Egger intercept P = 0.890), diabetic neuropathy/ulcers (Cochran’s Q P value = 0.594; Egger intercept P = 0.534), and depression (Cochran’s Q P value = 0.822; Egger intercept P = 0.808).

No causal association with PHN was identified for T1DM (IVW OR: 1.04, 95% CI: 0.97–1.13, P = 0.290) or GDM (IVW OR: 0.98, 95% CI: 0.92–1.06, P = 0.659) (Fig. 2). Other MR models, including weighted median, weighted mode, and MR Egger, showed similar results (Fig. 2). The leave-one-out method involved systematically eliminating one SNP successively (Supplementary Tables 6–7).

Causal effects of T2DM on amino acids related to neural regulation

IVW analysis revealed significant associations between T2DM and several amino acids, including glycine (OR = 0.97, 95% CI = 0.95–0.98, P < 0.001, PFDR< 0.001), histidine (OR = 1.02, 95% CI = 1.00–1.03, P = 0.027, PFDR = 0.047), phenylalanine (OR = 1.05, 95% CI = 1.04–1.06, P < 0.001, PFDR< 0.001), and tyrosine (OR = 1.04, 95% CI = 1.03–1.06, P < 0.001, PFDR< 0.001). These results were consistent across the other MR models (Fig. 3). However, heterogeneity was detected in the associations between T2DM and glycine, histidine and tyrosine (Supplementary Table 8). Moreover, horizontal pleiotropy was detected in the associations of T2DM with glycine, phenylalanine, and tyrosine (Supplementary Table 9). Upon removing the outliers using RadialMR, the issues of heterogeneity and horizontal pleiotropy were resolved (Cochrane’s Q P value = 0.995; Egger intercept P = 0.207) for phenylalanine. Furthermore, MR-PRESSO analysis no longer indicated any significant differences (Global Test P = 0.995), and the association between T2DM and phenylalanine remained significant (OR = 1.06, 95% CI = 1.04–1.07, P < 0.001).

Fig. 3
figure 3

Causal effects of type 2 diabetes mellitus on amino acids related to neural regulation. OR, odds ratio; CI, confidence interval; SNPs, single nucleotide polymorphisms

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Causal effect of phenylalanine on PHN

The UVMR analysis demonstrated a significant association between phenylalanine and PHN (OR = 5.27, 95% CI = 2.13–13.04, P < 0.001, PFDR< 0.001). No evidence of heterogeneity or horizontal pleiotropy was observed. Moreover, MVMR analysis revealed that phenylalanine independently affected the risk of PHN, even after adjusting for T2DM (β ± SE = 1.88 ± 0.47, P < 0.001) (Supplementary Table 10).

Mediating effect of phenylalanine

Based on the research findings, phenylalanine appeared to be a potential mediator. Using propagation error calculations, it was determined that phenylalanine accounted for 49.2% (95% CI: 22.7– 75.6%) of the overall effect of T2DM on PHN.

Discussion

The current findings indicated that T2DM might increase the risk of PHN, whereas no such association was observed between T1DM and GDM. Moreover, T2DM was linked to higher levels of phenylalanine, histidine, and tyrosine, and lower levels of glycine. Elevated phenylalanine levels were associated with a higher risk of PHN, suggesting that phenylalanine might play a significant role in the mechanism through which diabetes mellitus increases the risk of PHN.

The previous studies have supported the correlation between T2DM and an increased risk of PHN, consistent with the current findings [8, 30]. However, the association between T1DM and PHN remains debated. Studies by Forbes HJ and Guignard AP reinforced the conclusion that T1DM did not increase PHN risk [30, 31]. However, a recent retrospective study reported a significant increase in PHN risk linked to T1DM, which contradicted the current study results [8]. Several factors might explain this discrepancy. First, T1DM is an autoimmune disease often associated with other autoimmune disorders, such as rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, and inflammatory bowel disease. These immune disorders have been linked to an elevated risk of HZ and PHN [6]; however, this connection was not considered in the retrospective study, potentially leading to an overestimation of TIDM’s impact. Second, both the current and retrospective studies had relatively small sample sizes, indicating that larger studies could yield more reliable results. Third, the retrospective study focussed on European populations, raising the possibility of bias due to racial differences.

Cross-sectional studies have consistently demonstrated elevated phenylalanine levels in patients with T2DM patients [32, 33]. In diabetic populations, phenylalanine levels were inversely related to is estimated glomerular filtration rate [34]. In individuals with chronic kidney disease, impaired phenylalanine hydroxylase activity disrupted the conversion of phenylalanine to tyrosine, resulting in increased phenylalanine and decreased tyrosine levels [35]. This metabolic dysfunction in diabetes contrasts with the normal population, highlighting a distinct pattern of phenylalanine metabolism. The metabolic difference also explains why phenylalanine might have analgesic effects in healthy individuals under certain conditions, while in those with diabetes, it could lead to neuropathic pain.

Both the gut microbiota and the neuroimmune play a key role in increasing the risk of PHN through phenylalanine. Previous research suggested that oral probiotics could alleviate neuroinflammation and enhance neurocognitive function by lowering phenylalanine levels, particularly in immunocompromised individuals [36]. This reduction is partially due to the alterations of gut microbiota induced by probiotics. The study also observed a decrease in CD4+ and CD8+ T cells. Moreover, amino acid metabolism has been shown to correlate with various T-cell activation markers related to inflammation and bacterial ectopia [37], suggesting that lower phenylalanine levels might be associated with a weakened immune response. Further evidence showed that elevated phenylalanine-activated genes involved in inflammation, extracellular matrix degradation, and sensory neuron nociceptors in the mouse brain [38, 39]. Regarding the connection between phenylalanine and neuroinflammation, the pro-inflammatory factors, such as interferon-γ might significantly contribute to this process by triggering the release of neopterin from dendritic cells and human monocyte-derived macrophages [40, 41]. Increased neopterin levels can, in turn, promote the production of reactive oxygen species, thereby exacerbating neuroinflammation [42]. The elevated phenylalanine levels in human induced pluripotent stem cells can significantly enhance the activity of both the nuclear factor-kB and p53 signalling pathways, resulting in lesions associated with the formation of myelin sheath [43]. The model preserving local brain tissue also showed that the high concentrations of phenylalanine activated microglia and significantly damaged the myelin sheath [44]. Moreover, phenylalanine increased biofilm, further amplifying the aforementioned phenomenon and accelerating the transmission of neural cell signals [45]. Therefore, it was hypothesized that elevated phenylalanine levels could be associated with changes in gut microbiota and dysregulated systemic immune response, exacerbating neuroinflammation by activating pro-inflammatory factors, increasing oxidative stress, and regulating other pathways. This unregulated immune response might impair peripheral neurons’ ability to inhibit pain signals [13], resulting in abnormal discharges, pain hypersensitivity, and the onset of PHN.

Mitochondrial dysfunction and cell apoptosis might play a crucial role in the modulation of PHN by phenylalanine. Both in vivo and in vitro studies showed that elevated phenylalanine levels could disrupt mitochondrial function in different brain regions of rodents, affecting key processes, such as bioenergy production and cellular apoptosis [46]. Moreover, animal studies also supported the association between high phenylalanine levels and oxidative stress, which manifests in lipid peroxidation, impaired antioxidant defences, increased reactive oxygen species production, and damage to proteins and DNA [47, 48]. Previous studies showed that phenylalanine promoted the translocation of B-cell lymphoma 2 -associated X protein /Bak from the cytoplasm to mitochondria, and triggered the release of cytochrome c, indicating that cell apoptosis was induced via the mitochondrial pathway (intrinsic pathway) [49]. Simultaneously, other studies showed that phenylalanine could also mediate apoptosis through the extrinsic pathway [50]. In models of spinal cord nerve ligation, several apoptosis-related genes were found to be upregulated in the dorsal root ganglia, further supporting the link between cell apoptosis and neuropathic pain [51].

Moreover, phenylalanine acts as a precursor for the synthesis of dopamine and norepinephrine. Elevated phenylalanine levels were associated with increased concentrations of these neurotransmitters [52], which could disrupt the balance of the descending inhibitory pathway, potentially contributing to the development of neuropathic pain.

The current study introduced several innovations. First, the current research investigated the causal relationship between diabetes mellitus and PHN from a genetic perspective and included subgroup analysis to deepen our understanding. Second, the association between seven neurotransmitter-related amino acids and these conditions was explored, identifying phenylalanine as a key mediator. Lastly, MVMR was employed to validate the findings and ensure their reliability. However, there were certain limitations to the current study. First, the sample sizes for some phenotypes were relatively small, and larger sample sizes in the future might enhance the robustness of the current findings. Second, despite our efforts, eliminating heterogeneity and horizontal pleiotropy remains challenging. Lastly, the current study primarily involved the European population, which might limit the generalizability of the results to other populations.

Conclusions

The current research examined the causal relationships between three subtypes of diabetes mellitus and PHN, revealing a significant causal link between T2DM and PHN. Moreover, results indicated that phenylalanine might play a critical mediating role in this relationship. By elucidating these connections, the current study aimed to provide valuable insights that could guide the development of targeted interventions for the more effective management of PHN.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

HZ:

Herpes zoster

PHN:

Postherpetic neuralgia

MR:

Mendelian randomization

GWASs:

Genome-wide association studies

IVs:

Instrumental variables

T1DM:

Type 1 diabetes mellitus

T2DM:

Type 2 diabetes mellitus

DIAGRAM:

DIAbetes Genetics Replication and Meta-analysis

UKB:

United Kingdom Biobank

SNPs:

Single nucleotide polymorphisms

MVMR:

Multivariable mendelian randomization

UVMR:

Univariable mendelian randomization

IVW:

Inverse variance weighting

MR-PRESSO:

Mendelian randomization -Pleiotropy residual sum and outlier

FDR:

False Discovery Rate

OR:

Odds ratio

CI:

Confidence interval

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Acknowledgements

We want to acknowledge the participants and investigators of the DIAGRAM consortium, the GWAS Catalog, the FinnGen study, the United Kingdom Biobank, Nightingale Health and the Canadian Longitudinal Study on Aging cohort.

Funding

This work was supported by the National Natural Science Foundation of China (82171217, 81771181, 81571065), the Beijing Natural Science Foundation (7202053), and the Beijing Friendship Hospital Zhongzi Project (YYZZ202332).

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Authors and Affiliations

  1. Department of Anaesthesiology, Beijing Friendship Hospital, Capital Medical University, No. 95, Yong’ an Road, Beijing, 100050, China

    Haoning Lan, Zhangran Ai, Songchao Xu, Huili Li, Zhong Feng, Ruijuan Guo & Yun Wang

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Contributions

Haoning Lan contributed to conceptualization and original draft; Zhangran Ai and Songchao Xu contributed to investigation and methodology; Huili Li contributed to software and funding acquisition; Zhong Feng contributed to Supervision; Validation; Ruijuan Guo contributed to formal analysis and visualization; Yun Wang contributed to conceptualization and funding acquisition. All authors have reviewed and approved the final manuscript.

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Correspondence to Yun Wang.

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Lan, H., Ai, Z., Xu, S. et al. Genetically predicted amino acids related to neural regulation mediate the association between diabetes mellitus and postherpetic neuralgia: a Mendelian randomization study. Diabetol Metab Syndr 17, 104 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01672-1

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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