Mendelian randomization and pharmacovigilance analyses reveal the causal relationship between antidiabetic drugs and psychiatric disorders

doi:10.21203/rs.3.rs-5330829/v1

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Mendelian randomization and pharmacovigilance analyses reveal the causal relationship between antidiabetic drugs and psychiatric disorders

https://doi.org/10.21203/rs.3.rs-5330829/v1

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Background

Several studies have reported that antidiabetic drugs are associated with psychiatric disorders, but whether they have a causal relationship is unclear. The aim of this study was to explore the causal relationship between antidiabetic drugs and psychiatric disorders using two-sample Mendelian randomization (MR).

Methods

In this study, we used a two-sample Mendelian randomization approach to analyse the causal relationship between antidiabetic drugs and eight psychiatric disorders. The summary data for both exposure and outcomes were obtained from the Integrative Epidemiology Unit (IEU) online database. Inverse-variance-weighted (IVW) and wald ratio were used as the main analysis methods. Heterogeneity, horizontal pleiotropy test and leave-one-out method were used as sensitivity analyses. And finally we had a pharmacovigilance analysis.

Results

IVW method showed that sulfonylureas may be causally associated with the following three psychiatric disorders: bipolar disorder (OR = 2.55, 95% CI: 1.27–5.13, P = 0.008), anorexia nervosa (OR = 10.71, 95% CI: 1.20-95.54, P = 0.03), attention deficit hyperactivity disorder (ADHD) (OR = 5.21, 95% CI: 1.47–18.49, P = 0.01). Sulfonylureas pharmacovigilance analyses have also identified some signals of adverse effects in psychiatric disorders.

Conclusion

This study found some psychiatric adverse effects of sulfonylureas but more clinical trials and animal studies are needed to confirm this.

antidiabetic drugs

Mendelian randomization

psychiatric disorders

sulfonylureas

Psychiatric disorders are mental disorders caused by impaired brain functioning, mainly including schizophrenia, depression, bipolar disorder, and anxiety disorders (1, 2). In this case, the functional activity of the brain is disturbed, resulting in varying degrees of impairment of mental activities such as sensation, awareness, cognition, thinking and behavior (3, 4). Millions of people in the world reportedly suffer from psychiatric disorders, which has become a major public health challenge (5).

Diabetes is now affecting the lives and health of 500 million people worldwide (6). According to statistics, the cost of preventing and treating diabetes will exceed trillions of dollars in the next few years, which is a heavy burden on society (7, 8). Antidiabetic drugs such as sulfonylureas, glucagon-like peptide-1 (GLP-1) receptor agonists, sodium-glucose cotransporter-2 (SGLT-2) inhibitors, dipeptidyl peptidase 4 (DPP-4) inhibitors, and thiazolidinediones have become the mainstay of blood glucose control. As the number of people with diabetes grows by the day, the use of antidiabetic drugs is becoming more frequent. However, even though there is growing evidence that antidiabetic drugs affect psychiatric disorders, a consensus has not yet been reached. Several studies have demonstrated the anti-inflammatory and neuroprotective effects of GLP-1 receptor agonists, which holds promise for the treatment of neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease (9–11). Interestingly, a study from the FDA Adverse Event Reporting System (FAERS) database reported an association between GLP-1 receptor agonists and suicidal or self-injurious behaviors, which may be related to their potential psychiatric adverse effects (12). In addition, Peter's study found that long-term exposure to sulfonylureas was associated with the risk of hippocampal sclerosis of aging (HS-Aging) (13). These opposing views compel us to urgently elucidate the causal relationship between antidiabetic drugs and psychiatric disorders.

Mendelian randomization (MR) is an approach based on the laws of heredity that uses single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to explore causal relationships between exposure factors and outcomes (14). Since alleles are randomly assigned, MR is known as a natural randomized clinical trial that maximizes the exclusion of confounders (15).

In this study, we analyzed the relationship between antidiabetic drugs and psychiatric disorders based on MR and FARES. The possible neurological adverse effects of several antidiabetic drugs were elucidated.

Identification and validation of antidiabetic drug targets

In accordance with the guidelines issued by the World Health Organization (WHO), we included six common classes of antidiabetic drugs (including metformin, sulfonylureas, GLP-1 receptor agonists, SGLT-2 inhibitors, DPP-4 inhibitors, and thiazolidinediones). The DrugBank (https://go.drugbank.com/) and ChEMBL (https://www.ebi.ac.uk/chembl/) databases were then used to identify target genes for six classes of antidiabetic drugs. For drugs with multiple target genes, we usually choose the one that has been widely studied and reported. We labelled two target loci as “ABCC8/KCNJ11” if they overlapped with each other and had the same IVs. Here we identified a total of six classes of target gene information for antidiabetic drugs, including chromosomal locations and gene loci. Specific details have been shown in Table 1. Metformin was excluded because of inconsistent information on metformin target genes obtained from the two databases.

Table 1

Target genes of antidiabetic drugs from DrugBank and ChEMBL databases
Drug class	Grug targets	Encoding genes		Gene location	Included
Drug class	Grug targets	Drugbank	ChEMBL	Gene location
Sulfonylureas	ATP-sensitive potassium channel	ABCC8	ABCC8	Chr11: 17414045–17498392	Yes
Sulfonylureas	ATP-sensitive potassium channel	KCNJ11	KCNJ11	Chr11: 17406795–17410893	Yes
Thiazolidinediones	Peroxisome proliferator-activated receptor gamma	PPARG	PPARG	Chr3: 12328867–12475843	Yes
Glucagon-like peptide-1 (GLP-1) analogues	Glucagon-like peptide 1 receptor	GLLPR	GLLPR	Chr6: 39016557–39059079	Yes
Sodium-glucose cotransporter (SGLT2) inhibitor	Sodium/glucose cotransporter 2	SLC5A2	SLC5A2	Chr16: 31494444–31502090	NO
Dipeptidyl peptidase-4 (DPP-IV) inhibitor	Dipeptidyl peptidase IV	DPP4	DPP4	Chr16:162848755–162930725	NO
Metformin	NA	PRKAB1	58 genes	NA	NO

Genetic instruments for drug target gene expression

Since we studied antidiabetic drugs, we used blood glucose as a biomarker. SNPs that were significantly associated with blood glucose and located within ± 100 kb of the target gene were selected as IVs (P < 5 × 10^− 8, MAF > 0.01). The threshold for r² was set at 0.3 in order to eliminate the linkage disequilibrium (LD). The strength of the genetic IVs was tested by calculating the F-value, and weak IVs with F-value < 10 would be excluded. Expressed as \(\:\text{F}\text{=}\frac{{R}^{2}\times\:(N-2)}{1-{R}^{2}}\), \(\:{R}^{2}=\frac{2\times\:{\beta\:}^{2}\times\:EAF\times\:\left(1-EAF\right)}{2\times\:{\beta\:}^{2}\times\:EAF\times\:\left(1-EAF\right)+2\times\:{SE}^{2}\times\:N\times\:EAF\times\:(1-EAF)}\). SNPs associated with blood glucose were provided by the Genome-Wide Association Study (GWAS) (ebi-a-GCST90025986, PMID: 34226706) from the IEU online database, this GWAS containing information on blood glucose of 400,458 European populations.

Positive control analysis and outcomes

The validity of IVs was confirmed by demonstrating the expected impact of antidiabetic drugs target genes on known outcomes. The intended effect of antidiabetic drugs is type 2 diabetes. In addition, thiazolidinediones improve insulin resistance, using the Insulin Resistance (IR) Index as a positive control. Sulfonylureas and GLP-1 receptor agonists promote insulin secretion, so peak insulin level was used as a positive control. Antidiabetic drugs have different effects on body weight, so body mass index (BMI), hip circumference, and waist circumference were chosen as positive controls. Here we have incorporated eight psychiatric disorders, including attention deficit hyperactivity disorder (ADHD), anorexia nervosa, anxiety disorder, autism spectrum disorder (ASD), bipolar disorder, major depressive disorder, obsessive compulsive disorder (OSB), schizophrenia。These genetic association data for positive controls and outcomes are available in the IEU database (https://gwas.mrcieu.ac.uk/).

Two sample MR analysis

Two sample MR method was used to analyze the causal relationship between antidiabetic drugs and psychiatric disorders, and the flow of the analysis was shown in Fig. 1. This approach must be based on three assumptions: (1) IVs are strongly correlated with exposure factors. (2) IVs can only have an effect on outcomes through exposure factors. (3) IVs are not associated with confounders. If multiple IVs were obtained, the inverse variance weighting (IVW) method was used (16). The IVW method is based on the fact that all SNPs are valid variables and independent of each other. IVW estimates the average effect of genetic variants on causality by means of a weighted linear regression model, and the results of IVW are unbiased if there is no horizontal pleiotropy (17). If only single IV was obtained, the Wald ratio test was used. The Wald ratio is the simplest method for MR analyses using single SNP as an IV without the necessity of heterogeneity and horizontal pleiotropy tests (18). IVW and MR Egger methods were used to detect heterogeneity of results. We concentrated on the Cochrane’s Q value, where a considerable heterogeneity is indicated if Q_P < 0.05 (19). To evaluate horizontal pleiotropy, MR Egger intercept method was employed. Significant horizontal pleiotropy is seen when P < 0.05 (20). Furthermore, the MR-PRESSO method was employed to identify any outliers (21). Finally, we applied the leave-one-out technique to a sensitivity analysis: One SNP at a time was eliminated, and the stability of the remaining SNPs was observed (22).

R version 4.3.2 was utilized to conduct all statistical analyses. Two-sample MR and MR-PRESSO packages were used.

Pharmacovigilance data analysis

In this study, data were acquired using Openvigil 2, an open, clean, and standardized pharmacovigilance data tool. The list of sulfonylureas (A10BB) was determined by the Anatomical Therapeutic Chemical (ATC) classification system. Reports of adverse events (AE) caused by sulfonylureas as the primary suspected drug were collected from the first quarter of 2004 to the second quarter of 2024. All reports were identified using the Medical Dictionary for Regulatory Activities (MedDRA) version 24.1 and focused on analyzing psychiatric disorders.

The potential adverse reaction signals of sulfonylureas were calculated using the algorithms including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayes Geometric Mean (EBGM). The four algorithms are demonstrated in Supplementary Table S1. A positive AE signal was identified when it met the thresholds for all four methods (ROR: n ≥ 3, lower limit of 95% CI > 1; PRR: χ2 ≥ 4, lower limit of 95% CI > 1; EBGM: EBGM05 (EBGM05 denotes the lower bound of 95% CI) > 2; BCPNN: IC025 (IC025 denotes the lower bound of 95% CI) > 0).

Genetic instruments for antidiabetic drugs

Using blood glucose as a biomarker, SNPs associated with blood glucose and located in the ± 100 kb range of the target genes were screened as IVs. Here we identified three IVs for sulfonylureas, one for thiazolidinediones and one for GLP-1 receptor agonists. Unfortunately, no validated IVs for SGLT-2 inhibitors and DPP-4 inhibitors were found. All of these IVs have F-value > 10, and other detailed information has been shown in Table 2.

Table 2

Instrumental variables from UK Biobank used in MR analysis
SNP	beta.exposure	eaf.exposure	Target gene	chr	se.exposure	pval.exposure
rs12225041	-0.01354	0.266813	ABCC8/KCNJ11	11	0.002513	4.20E-08
rs1330	0.012837	0.348369	ABCC8/KCNJ11	11	0.002281	7.30E-09
rs5215	-0.02034	0.639591	ABCC8/KCNJ11	11	0.002284	2.60E-19
rs17036326	-0.02024	0.120529	PPARG	3	0.003331	3.30E-09
rs9283906	-0.01458	0.765957	GLP1R	6	0.002572	4.60E-08

Positive control

Genetic variants of sulfonylureas and thiazolidinediones are associated with a decreased risk of type 2 diabetes: (OR = 0.08, 95% CI: 0.03–0.21, P = 7.68 x 10^− 8),

, (OR = 0.005, 95% CI: 0.001–0.02, P = 8.42 x 10^− 22). However, genetic variants of GLP-1 receptor agonists are not able to accurately predict the risk of type 2 diabetes: (OR = 0.99, 95% CI: 0.43–2.27, P = 0.98). Genetic variants of thiazolidinediones are associated with a decreased risk of insulin resistance: (OR = 0.45, 95% CI: 0.26–0.84, P = 0.006). Unfortunately, genetic variants for sulfonylureas do not accurately predict peak insulin levels: (OR = 5.51, 95% CI: 0.90-33.69, P = 0.06). In addition, genetic variants of thiazolidinediones and sulfonylureas were associated with a high risk of obesity and obesity phenotypes (waist and hip circumference). In comparison, genetic variants of GLP-1 receptor agonists cannot predict obesity and obesity phenotypes(Figure 2). In summary, with the exception of GLP-1 receptor agonists, the effects of the genetic variants of thiazolidinediones and sulfonylureas are largely consistent with the available evidence. This also confirms the validity of the IVs for these two classes of drugs.

MR analysis and sensitivity analysis

Genetic variants of thiazolidinediones and sulfonylureas are significantly associated with high risk bipolar disorder: (OR = 9.46, 95% CI: 02.49–36.01, P = 9.8 x 10^− 4), (OR = 2.55, 95% CI: 1.27–5.13, P = 0.008). Genetic variants of sulfonylureas are significantly associated with a high risk of ADHD and anorexia nervosa: (OR = 5.21, 95% CI: 1.47–18.49, P = 0.01), (OR = 10.71, 95% CI: 1.20-95.54, P = 0.03). Besides, no genetic variants of antidiabetic drugs have been found to be associated with other psychiatric disorders(Figure 3). According to the results of Cochran's Q test, no significant heterogeneity of SNPs for sulfonylureas was found (P > 0.05). The Egger_intercept method confirmed that there was no significant horizontal pleiotropy (P > 0.05), which suggests that the IVs were not affected by confounders (Supplementary Table S2 and Table S3). The leave-one-out method confirmed the stability of each SNP by excluding one SNP at a time (Supplementary Figure S1and S2).

Pharmacovigilance data analysis of sulfonylureas

A total of 7597 adverse events related to sulfonylureas were obtained and analyzed, with a disproportionate focus on psychiatric adverse event pt terms. Consequently, 17 positive signals were identified, with the three PTs with the highest number of reports being in a confusional state (n = 143, ROR (95%CI) = 2.78 (2.36–3.28), PRR (X²) = 2.75 (160.05), EBGM (EBGM05) = 2.75 (2.39), IC (IC025) = 0.69 (1.20)), mental status changes (n = 91, ROR (95%CI) = 9.73 (7.91–11.97), PRR (X²) = 9.62 (699.90), EBGM (EBGM05) = 9.57 (8.05), IC (IC025) = 0.31 (2.83)), and completed suicide (n = 82, ROR (95%CI) = 2.58 (2.08–3.21), PRR (X²) = 2.57 (78.53), EBGM (EBGM05) = 2.56 (2.14), IC (IC025) = 0.74 (1.01)). While perseveration (n = 4, ROR (95%CI) = 61.60 (22.70–167.15), PRR (X²) = 61.57 (229.82), EBGM (EBGM05) = 59.40 (25.77), IC (IC025) = 0.17 (0.91)) had the highest signal strength, it exhibited a wide confidence interval (Fig. 4).

Our study explored for the first time the causal relationship between antidiabetic drugs and psychiatric disorders using a two-sample MR approach. Using large-scale GWAS data, we found that sulfonylureas are associated with certain psychiatric disorders. In contrast, no such association was found for other antidiabetic drugs. We then performed pharmacovigilance analyses on the sulfonylureas and found that there were indeed some signals of adverse psychiatric reactions. Although similar outcomes were not obtained compared to MR, psychiatric adverse effects of sulfonylureas remain a concern.

Sulfonylureas are the first class of oral antidiabetic drugs to be used in the treatment of diabetes (23). Prior to this, some adverse effects of sulfonylureas have been reported by researchers. The most notable of these are hypoglycemia and cardiovascular adverse events (24–28). However, some studies have also reported neurological damage mediated by sulfonylureas. Liu et al. found that the administration of sulfonylureas to patients with type 2 diabetes mellitus may inhibit the neuroprotective effects of ATP-sensitive potassium (KATP), increasing the risk of stroke (29). Another study found that the use of sulfonylureas alone led to increased levels of N-terminal pro-Brain Natriuretic Peptide (NTproBNP) (30). Sulfonylureas induce the release of insulin from the β cells of the pancreas through the inhibition of KATP channel, thus achieving a hypoglycemic effect (26). This mechanism is not affected by the concentration of glucose in the blood. KATP is expressed in the brain and exerts a hypoxic protective effect at nerve endings. As KATP blockers, Sulfonylureas inhibit this protective effect (31). In addition, inhibition of KATP increases γ-aminobutyric acid (GABA) release (32). The latter is an inhibitory neurotransmitter. Thus sulfonylureas may increase the release of large amounts of GABA and have an inhibitory effect on the central nervous system (33).

These findings have significant implications for the assessment of drug side effects and the clinical use of drugs. ABCC8/KCNJ11-mediated sulfonylureas are causally associated with a variety of high-risk psychiatric disorders. Therefore, patients on long-term flavonoids should have their mental status assessed regularly, and patients with psychiatric disorders should avoid sulfonylureas.

Our study has several strengths. First, this is the first study to explore the causal relationship between antidiabetic drugs and psychiatric disorders using two-sample MR. Previously, there have been few reports on the risk of psychiatric disorders with antidiabetic drugs. Our study provides some insights into the use of antidiabetic drugs. Second, in this study, we set strict conditions to screen for exposure-related SNPs, which not only excluded the bias of weak IVs (F statistic value < 10) but also avoided the influence of confounders on the outcome. Third, we performed a positive control analysis to determine the validity of the selected IVs by comparing the genetic effects of drug target genes on known outcomes. Finally, we performed a series of sensitivity analyses, including heterogeneity, horizontal pleiotropy, and leave-one-out analyses, which ensured the robustness of the MR results.

However, our study has some shortcomings. First, although we analysed the genetic effects of three classes of glucose-lowering drugs (sulfonylureas, thiazolidinediones, and GLP-1 receptor agonists), no validated IVs were obtained for SGLT-2 inhibitors and DPP-4 inhibitors due to a lack of data. This has resulted in an inability to determine the causal relationship between the above drugs and psychiatric disorders. When more data become available, we will devise more rigorous methods to analyse the effects of these drugs on psychiatric disorders. Second, this study was designed to predict the effect of drugs on outcomes through known drug target genes; however, it cannot be ruled out whether drugs may have an effect on outcomes through genes other than the target gene. Thirdly, since the MR analysis results represent the effects of lifetime exposure to the drug, but in reality, drugs have effects over a short period of time. Therefore, our results may not be consistent with clinical data. Fourth, because the GWAS data are from a European population, the results of this study cannot be applied to other races.

This study provides some evidence for possible psychiatric disorders caused by sulfonylureas. However, more clinical trials and animal testing are needed in the future to confirm this finding.

Mendelian randomization

SNP

Single nucleotide polymorphism

IVW

Inverse variance weighted

Instrumental variable

Linkage disequilibrium

Confidence interval

Odds ratio

MAF

Minor allele frequency

GWAS

Genome-wide association study

IEU

Integrative Epidemiology Unit

ADHD

Attention deficit hyperactivity disorder

GLP-1

Glucagon-like peptide-1

SGLT-2

Sodium-glucose cotransporter-2

DPP-4

Dipeptidyl peptidase 4

FAERS

FDA Adverse Event Reporting System

HS-Aging

Hippocampal sclerosis of aging

WHO

World Health Organization

BMI

Body mass index

Insulin Resistance

ASD

Autism spectrum disorder

OSB

Obsessive compulsive disorder

ATC

Anatomical Therapeutic Chemical

Adverse events

MedDRA

Medical Dictionary for Regulatory Activities

ROR

Reporting odds ratio

PRR

Proportional reporting ratio

BCPNN

Bayesian Confidence Propagation Neural Network

EBGM

Empirical Bayes Geometric Mean

KATP

ATP-sensitive potassium

NTproBNP

N-terminal pro-Brain Natriuretic Peptide

GABA

γ-aminobutyric acid

Competing interests

The authors declare that they have no competing interests.

Funding

No funding

Author Contribution

Y.H, W.D contributed to the research design, statistical analysis, X.Y, L.H, K.T, Q.L contributed to the drafting and revision of the article. The final manuscript has been read and approved by all the listed authors.

Acknowledgements

We thank those who provided public data and those who acted in this study.

Availability of data and materials

All relevant data are within the manuscript and its additional files.

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No competing interests reported.

SupplementaryFigureS1.tif
Supplementary Figure S1 Results of the leave-one-out analysis
SupplementaryFigureS2.tif
Supplementary Figure S2 Scatterplot of Mendelian randomization analysis.
SupplementaryTable.docx
Supplementary Table S1 Four methods of disproportionality analysis. Supplementary Table S2 Horizontal pleiotropy between exposure and outcomes. Supplementary Table S3 The heterogeneity of instrumental variables from UK Biobank.

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Mendelian randomization and pharmacovigilance analyses reveal the causal relationship between antidiabetic drugs and psychiatric disorders

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Method

Identification and validation of antidiabetic drug targets

Genetic instruments for drug target gene expression

Positive control analysis and outcomes

Two sample MR analysis

Pharmacovigilance data analysis

Results

Genetic instruments for antidiabetic drugs

Positive control

MR analysis and sensitivity analysis

Pharmacovigilance data analysis of sulfonylureas

Discussion

Conclusion

Abbreviations

Declarations

Competing interests

Funding

Author Contribution

Acknowledgements

Availability of data and materials

References

Additional Declarations

Supplementary Files

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