Exploring the causal relationships between type 2 diabetes and neurological disorders using a Mendelian randomization strategy

Abstract

While there is ample evidence indicating an increased occurrence of general neurological conditions among individuals with diabetes, there has been limited exploration into the cause-and-effect connection between type 2 diabetes (T2D) and specific neurological disorders, including conditions like carpal tunnel syndrome and Bell’s palsy. We used Mendelian randomization (MR) approach to investigate the causal effects of T2D on 67 neurological diseases. We primarily utilized the inverse-variance weighted method for the analysis, and also employed the weighted median and MR-Egger methods in our study. To detect and correct potential outliers, MR-PRESSO analysis was used. Heterogeneity was assessed using Cochrane Q-values. The MR analyses found a possible relationship between T2D and a risk increase of 8 diseases at suggestive level of evidence (P < .05). Notably, among the positive findings that met the false discovery rate threshold, nerve, nerve root, and plexus disorders (odds ratio [OR] = 1.11; 95% confidence interval [CI] = 1.08–1.15); neurological diseases (OR = 1.05; 95% CI = 1.03–1.07) and carpal tunnel syndrome (OR = 1.10; 95% CI = 1.05–1.16) were identified. Our findings affirm a cause-and-effect association between T2D and certain neurological disorders.

1. Introduction

Neurological issues are one of the leading causes of death globally, following cardiovascular ailments.^[1–4] Over the 30 years preceding up to 2020, there was a 39% increase in mortality from neurological disorders and a 15% increase in disability-adjusted life years.^[2,5] The most prevalent contributors to disability-adjusted life years include stroke, migraine, Alzheimer disease, and meningitis.^[1,2,6–9] Likewise, diabetes affects around 10% of individuals globally, with type 2 diabetes (T2D) accounting for 90% of cases.^[10–12] Diabetes has numerous risk factors^[13–16] and is associated with a variety of complications.^[17–22] The brain is extremely glucose sensitive, with its levels influencing neuronal maintenance, neurogenesis, neurotransmitter modulation, and plasticity of synapses.^[23,24] It is anticipated that between 30% and 50% of diabetics may acquire neuropathy.^[25,26] Other studies also have discovered a relationship between neurological disorders and problems with blood glucose metabolism. For instance, based on other Mendelian randomization (MR) studies, T2D is independently associated with carpal tunnel syndrome (CTS).^[27] A case–control study, on the other hand, did not produce compelling evidence of an independent connection between T2D and CTS.^[28] Observational studies are susceptible to confounding variables and reverse causation, and the effect of T2D on specific type of neurologic illnesses remains an inconsistency.

MR using genetic variation as an instrumental variable (IVs) helps to clarify these associations.^[29,30] It efficiently reduces potential confounding and reverse causality interference by following the random allocation of alleles during conception and the absence of genotype modification by illnesses.^[31,32] With the causal mechanisms of T2D in causing neurological disorders still unknown, our study used a two-sample MR approach to assess the probability of neurological diseases in T2D patients. Our study may contribute to discovering novel strategies to prevent neurological complications in individuals with T2D.

2. Methods

With MR analysis, we assessed the causal relationship between T2D and 67 neurological disorders, utilizing information gathered through genome-wide association study (GWAS) of single nucleotide polymorphisms (SNPs) that were significantly related to T2D. The GWAS summary statistics pertaining to T2D was extracted from the study conducted by Anubha Mahajan et al,^[33] while data for the dependent variables was acquired from all 67 neurological diseases (further details are provided in Table S1, Supplemental Digital Content, http://links.lww.com/MD/N866 accessible in the public database).^[34,35]

We applied several criteria to select the IVs for our MR analyses, as described previously^[36,37]: (1) IVs are tightly connected with exposure on a genetic basis (P < 5 × 10^-8), (2) to eliminate linkage disequilibrium in IVs, we used R² < 0.001, a distance of 10 Mb, (3) along with a minor allele frequency criterion of 0.01. F-statistics were also employed to evaluate the IVs’ strength: values >10 were considered indicative of a lower probability of weak IVs.^[38]

The inverse-variance weighted method was the principal method used in this study, and we also employed the weighted median^[39] and MR-Egger^[40] methods in this study, similar to our previous works.^[41–43] To detect and eliminate any outliers, we employed MR-PRESSO analysis.^[44] Heterogeneity was evaluated through the examination of Cochrane Q-values. Moreover, using the leave-one-out method, we iteratively excluded individual SNPs to assess whether the results remain significant.^[45] The strength of causal effect was evaluated by analyzing odds ratios (OR) and 95% confidence intervals (95% CI). To account for multiple testing, a false discovery rate (FDR) threshold of 5% was applied. The significance threshold adjusted for FDR is presented in Figure 1. Indications of suggestive associations were taken into account when the P-value was <.05. The MR analyses were conducted with the two-sample MR software package in R (TwoSampleMR version 0.6.4).^[46,47]

Figure 1.

The distribution of P-values for the associations between T2D and 67 neurologic disorders in the MR analysis. The threshold line 1 showed the significance threshold adjusted for FDR. The threshold line 2 showed the significance threshold of P = .05. FDR = false discovery rate, MR = Mendelian randomization, T2D = type 2 diabetes.

3. Results

3.1. Results of the MR analysis

A total of 184 SNPs were chosen as IVs for T2D, with all these IVs having F-statistics topping 10 (refer Table S2, Supplemental Digital Content, http://links.lww.com/MD/N866). The inverse-variance weighted approach results suggested a possible causal relationship between T2D and 8 neurological disorders, such as “Carpal tunnel syndrome” (OR = 1.10; 95% CI: 1.05–1.16), “Bell’s palsy” (OR = 1.15; 95% CI: 1.05–1.27), “Lesion of sciatic nerve” (OR = 1.18; 95% CI: 1.04–1.35), “Transient ischemic attack” (OR = 1.05; 95% CI: 1.00–1.09), and “Cervical root disorders” (OR = 1.39; 95% CI: 1.00–1.94) (Figs. 1 and 2; Table S3, Supplemental Digital Content, http://links.lww.com/MD/N866). Furthermore, substantial associations between T2D and ailments such as “Nerve, nerve root, and plexus disorders,” “Neurological diseases,” and “Carpal tunnel syndrome” remained significant even after the FDR correction. Using the MR-Egger and weighted median techniques, the relationships between T2D and 8 out of 67 neurological disorders showed similar directions (see Fig. 2 and Table S3, Supplemental Digital Content, http://links.lww.com/MD/N866). Figure 3 shows a scatter plot illustrating the causative relationships between T2D and 8 neurological illnesses.

Figure 2.

Causal effects of T2D on 8 neurologic disorders in the MR analysis, showing odds ratios and corresponding 95% confidence intervals. IVW = inverse-variance weighted, MR = Mendelian randomization, T2D = type 2 diabetes, WM = weighted median.

Figure 3.

Scatter plot indicating the causal associations between T2D and 8 neurologic disorders. MR = Mendelian randomization, SNP = single nucleotide polymorphism, T2D = type 2 diabetes.

3.2. Results of the sensitivity analysis

The potential heterogeneity was examined (Fig. 4; Table S4, Supplemental Digital Content, http://links.lww.com/MD/N866), and the findings from Figure S1, Supplemental Digital Content, http://links.lww.com/MD/N866 suggest the removal of the majority of SNPs in the leave-one-out analysis had no significant effect on the outcomes. As shown in Table S5, Supplemental Digital Content, http://links.lww.com/MD/N866, the MR-Egger approach revealed no evidence of horizontal pleiotropy. Despite the MR-PRESSO study found several outliers, adjustments made little difference to the results (Table S6, Supplemental Digital Content, http://links.lww.com/MD/N866).

Figure 4.

Funnel plot of the analyses investigating the causal effects of T2D on 8 neurologic disorders, with each SNP functioning as an IV. IV = instrumental variable, MR = Mendelian randomization, SE = standard error, SNP = single nucleotide polymorphism, T2D = type 2 diabetes.

4. Discussion

We investigated the causative impact of T2D on 67 different neurologic disorders thorough MR analyses. Our data reveal that T2D is suggestively connected with an increased risk of 8 illnesses, such as “Bell’s palsy,” “Lesion of sciatic nerve” and “Cervical root disorders.” Despite FDR correction, genetic predisposition to T2D remained strongly related with an elevated risk of “nerve, nerve root, and plexus disorders,” “neurological diseases,” and “carpal tunnel syndrome.”

T2D was found to be strongly associated with a range of brain problems in a longitudinal investigation, including several psychiatric ailments and some neurological diseases (inflammatory brain diseases, epilepsy, and migraines).^[48] A cross-sectional study of 366 persons with T2D indicated that the prevalence of peripheral neuropathy was as high as 53.6%.^[49] Cervical root abnormalities, Bell’s palsy, sciatic nerve lesion, and CTS were found as types of peripheral neuropathy in our investigation. Furthermore, 39.3% (139 people) of 353 T2D patients had CTS within a year.^[50] The findings of our investigation are consistent with the results of previous studies.

Neurons require glucose,^[51,52] and in diabetics, this requirement is increased fourfold.^[53,54] Because glucose uptake in neurons is independent of insulin, high glucose levels saturate hexokinase and shift more than 30% of glucose to the polyol pathway, resulting in increased NADPH consumption.^[55–58] It damages proteins via irreversible glycation, reduces glutathione peroxidase’s capacity for metabolism, and causes oxidative stress in neurons.^[56,59] Oxidative stress can lead to cell apoptosis,^[60,61] and studies demonstrate the involvement of oxidative stress in neurological conditions.^[62–66] Oxidative stress also activates mitogen-activated protein kinases like p38, c-Jun NH2-terminal kinase, and extracellular signal-regulated kinase, and the consequent over-phosphorylation of cells can induce neurodegenerative diseases.^[67–69]

High glucose-induced protein glycation has a significant influence on the nervous system.^[70,71] T2D causes a glycation process, which results in the production of advanced glycation end products (AGEs).^[72–74] The receptor for advanced glycation end products (RAGE)-mediated transmission system causes neuronal stress and activates several signaling pathways, affecting nerve cell activity.^[75,76] RAGE could interact and bind with AGEs.^[77–79] RAGE binding to AGEs increases endothelial permeability to macromolecules and stimulates the synthesis of vascular cell adhesion molecule-1, tissue factor, and interleukin-6,^[80–82] which play important role in inflammation.^[83–86] As consequence, detrimental impacts on protein structure and function ensue. Glycation is involved in amyotrophic lateral sclerosis, Parkinson disease, and Alzheimer disease.^[87–89]

The fibrous canal through which the median nerve passes can cause CTS, a compressive peripheral neuropathy resulting from nerve compression.^[90,91] Increased glucose levels make peripheral nerves more vulnerable to compression.^[71,92] Diabetes has been linked to decreased myelinated nerve fiber density in the posterior interosseous nerve, as well as thickening of the basal lamina of nerve endoneurial capillaries and decreased capillary density, making diabetic patients more susceptible to microvascular compression.^[93,94] Patients with CTS and diabetes who experience pathological ischemia–reperfusion (I/R) also demonstrate microvascular ischemia in nerve vasculature.^[50,95] Moreover, diabetes stimulates the production of profibrotic factors associated with sub-synovial connective tissue, such as vascular endothelial growth factor and interleukins, resulting in an increase in fibrosis of the sub-synovial connective tissue, which is a characteristic of CTS initiation and progression.^[96,97]

Regarding our other findings, there is currently a dearth of research on a link between T2D and cervical root disorders. Furthermore, additional observational or MR studies have shown that T2D is not associated with a rise in the risk of Bell’s palsy.^[98,99] However, our MR data suggest a probable causal connection between T2D and an increased frequency of cervical root issues and Bell’s palsy. Hence, we believe it is worthy of further investigation in the future.

According to our viewpoint, this is the first systematic MR study explaining the causal relationship between T2D and a variety of neurological diseases, providing more solid evidence of causation than observational studies. The impact of confounding factors may be considerably decreased by random allocation, and the genome stays unchanged by the disease, thereby reducing causal disruption. Moreover, the widespread availability of publicly known genetic associations enables the selection of appropriate genetic IVs, making MR an effective and affordable technique.^[100]

Our research, however, has certain limitations. Firstly, associations between exposures and outcomes may differ among populations. Since our findings are primarily derived from a European population, their applicability to other groups may be limited. Secondly, because we used GWAS summary data, we could not stratify the data by variables like age, which may have introduced population bias.

5. Conclusion

T2D has been identified as a risk factor for several types of neurological conditions, such as CTS. Understanding how T2D affects the nervous system may lead to the discovery of novel therapeutic targets, which reduces the risk of neurological illness development and progression.

Author contributions

Data curation: Yongfang Wei.

Formal analysis: Zhaoquan Wu.

Methodology: Mengling Zhang.

Writing – original draft: Yongfang Wei, Shuling Xu, Zhaoquan Wu, Mengling Zhang, Meihua Bao, Binsheng He.

Writing – review & editing: Meihua Bao, Binsheng He.

Supplementary Material

medi-103-e40412-s001.pdf ^{(445.7KB, pdf)}