Increased glucose intolerance related to digoxin treatment in patients with type 2 diabetes mellitus

Spigset, Olav; Mjörndal, Tom

doi:10.1046/j.1365-2796.1999.00587.x

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…Digoxin was also tested in a phase I clinical trial for epilepsy, depressive disorder and type 2 diabetes. In type 2 diabetes patients, it increased glucose intolerance 78 . Digoxin augmented the effect of antiepileptic drugs in mice, increased the chances of depression after a myocardial infarction and was linked to increased risk of psychosis 79 – 81 .…”

Section: Resultsmentioning

confidence: 97%

Genomic insights into the comorbidity between type 2 diabetes and schizophrenia

Arruda,

Khandaker,

Morris

et al. 2024

Schizophr

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Multimorbidity represents an increasingly important public health challenge with far-reaching implications for health management and policy. Mental health and metabolic diseases have a well-established epidemiological association. In this study, we investigate the genetic intersection between type 2 diabetes and schizophrenia. We use Mendelian randomization to examine potential causal relationships between the two conditions and related endophenotypes. We report no compelling evidence that type 2 diabetes genetic liability potentially causally influences schizophrenia risk and vice versa. Our findings show that increased body mass index (BMI) has a protective effect against schizophrenia, in contrast to the well-known risk-increasing effect of BMI on type 2 diabetes risk. We identify evidence of colocalization of association signals for these two conditions at 11 genomic loci, six of which have opposing directions of effect for type 2 diabetes and schizophrenia. To elucidate these colocalizing signals, we integrate multi-omics data from bulk and single-cell gene expression studies, along with functional information. We identify putative effector genes and find that they are enriched for homeostasis and lipid-related pathways. We also highlight drug repurposing opportunities including N-methyl-D-aspartate (NMDA) receptor antagonists. Our findings provide insights into shared biological mechanisms for type 2 diabetes and schizophrenia, highlighting common factors that influence the risk of the two conditions in opposite directions and shedding light on the complex nature of this comorbidity.

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Section: Resultsmentioning

confidence: 97%

Genomic insights into the comorbidity between type 2 diabetes and schizophrenia

Arruda,

Khandaker,

Morris

et al. 2024

Schizophr

View full text Add to dashboard Cite

show abstract

“…Digoxin was also tested in a phase I clinical trial for epilepsy, depressive disorder and type 2 diabetes. In type 2 diabetes patients, it increased glucose intolerance 96 . Digoxin augmented the effect of antiepileptic drugs in mice, increased the chances of depression after a myocardial infarction and was linked to increased risk of psychosis 97-99 .…”

Section: Resultsmentioning

confidence: 97%

“…The majority of identified likely effector genes for the comorbidity between type 2 diabetes and schizophrenia have opposing direction of effect for the individual conditions. On the one hand, digoxin, a drug targeting ATP1A1 , has been shown to increase glucose tolerance in type 2 diabetes patients 96 , but on the other hand increase risk of psychosis 99,115 . To avoid complications, drugs targeting genes with opposing direction of effect for different diseases should be administered with caution and alongside with monitoring of comorbidity risk and progression.…”

Section: Discussionmentioning

confidence: 99%

Genomic insights into the comorbidity between type 2 diabetes and schizophrenia

Arruda,

Khandaker,

Morris

et al. 2023

Preprint

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Multimorbidity represents an increasingly important public health challenge with far-reaching implications for health management and policy. Mental health and metabolic diseases have a well-established epidemiological association. In this study, we investigate the genetic intersection between type 2 diabetes and schizophrenia. We use Mendelian randomization to examine potential causal relationships between the two conditions and related endophenotypes. We report no compelling evidence that type 2 diabetes genetic liability potentially causally influences schizophrenia risk and vice versa. Our findings show that increased body mass index (BMI) has a protective effect against schizophrenia, in contrast to the well-known risk-increasing effect of BMI on type 2 diabetes risk. We identify evidence of colocalization of association signals for these two conditions at 11 genomic loci, six of which have opposing directions of effect for type 2 diabetes and schizophrenia. To elucidate these colocalizing signals, we integrate multi-omics data from bulk and single-cell gene expression studies, along with functional information. We identify high-confidence effector genes and find that they are enriched for homeostasis and lipid-related pathways. We also highlight drug repurposing opportunities including N-methyl-D-aspartate (NMDA) receptor antagonists. Our findings provide insights into shared biological mechanisms for type 2 diabetes and schizophrenia, highlighting common factors that influence the risk of the two conditions in opposite directions and shedding light on the complex nature of this comorbidity.

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“…Digoxin has been shown to increase blood glucose levels in an animal model, 33 but blood glucose levels are unaffected in healthy adults. 34 Four case reports about a possible diabetogenic effect of digitalis glycosides have been published 35,36 and one observational study has reported this association. 37 At best, the current evidence can be classified as equivocal.…”

Section: Discussionmentioning

confidence: 99%

Identifying diabetogenic drugs using real world health care databases: A Danish and Australian symmetry analysis

Lund

Jensen

Pottegård

et al. 2023

Diabetes Obesity Metabolism

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Aims Drug‐induced diabetes is underreported in conventional drug safety monitoring and may contribute to the increasing incidence of type 2 diabetes. Therefore, we used routinely collected prescription data to screen all commonly used drugs for diabetogenic effects. Methods Leveraging the Danish nationwide health registries, we used a case‐only symmetry analysis design to evaluate all possible associations between drug initiation and subsequent diabetes. The study was conducted among individuals aged ≥40 years with a first‐ever prescription for any antidiabetic drug 1996‐2018 (n = 348 996). Sequence ratios (SRs) and 95% confidence intervals (CIs) were obtained for all possible drug class‐diabetes combinations. A lower bound of the 95% CI >1.00 was considered a signal. Signals generated in Denmark were replicated using the Services Australia, Pharmaceutical Benefits Scheme 10% data extract. Results Overall, 386 drug classes were investigated, of which 70 generated a signal. In total, 43 were classified as previously known based on the SIDER database or a literature review, for example, glucocorticoids (SR 1.67, 95% CI 1.62‐1.72) and β‐blockers (SR 1.20, 95% CI 1.16‐1.23). Of 27 new signals, three drug classes yielded a signal in both the Danish and Australian data source: digitalis glycosides (SR 2.15, 95% CI 2.04‐2.27, and SR 1.76, 95% CI 1.50‐2.08), macrolides (SR 1.20, 95% CI 1.16‐1.24, and SR 1.11, 95% CI 1.06‐1.16) and inhaled β2‐agonists combined with glucocorticoids (SR 1.35, 95% CI 1.28‐1.42, and SR 1.14, 95% CI 1.06‐1.22). Conclusion We identified 70 drug‐diabetes associations, of which 27 were classified as hitherto unknown. Further studies evaluating the hypotheses generated by this work are needed, particularly for the signal for digitalis glycosides.

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Increased glucose intolerance related to digoxin treatment in patients with type 2 diabetes mellitus

Cited by 14 publications

References 4 publications

Genomic insights into the comorbidity between type 2 diabetes and schizophrenia

Genomic insights into the comorbidity between type 2 diabetes and schizophrenia

Genomic insights into the comorbidity between type 2 diabetes and schizophrenia

Identifying diabetogenic drugs using real world health care databases: A Danish and Australian symmetry analysis

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