Drug-Induced Diabetes Mellitus: Evidence for Statins and Other Drugs Affecting Glucose Metabolism

Impact of Physiological Fluctuations of Sex Hormones During the Menstrual Cycle on Glucose Metabolism and the Gut Microbiota

Diabetes mellitus is one of the most prevalent chronic diseases. Previous studies have shown differences in glucose metabolism between males and females. Moreover, difficulties in medication adherence have been reported in females with type 2 diabetes. These observations are believed to be caused by fluctuations in sex hormone concentrations during the menstrual cycle. Furthermore, gut microbiota is linked to female host metabolism and sex hormone production. Understanding the interactions between fluctuating hormone concentrations during the menstrual cycle, gut microbiota, and glucose metabolism in humans is significant because of the increasing prevalence of diabetes and the consequent need to expand preventive efforts. A literature search was performed to determine and summarize the existing evidence, deduce future research needs to maintain female health, and investigate the relationship between the physiological menstrual cycle and glucose metabolism. Studies from 1967 to 2020 have already examined the relationship between variations during the menstrual cycle and glucose metabolism in healthy female subjects using an oral-glucose tolerance test or intravenous glucose tolerance test. However, the overall number of studies is rather small and the results are contradictory, as some studies detected differences in glucose concentrations depending on the different cycle phases, whereas others did not. Some studies reported lower glucose levels in the follicular phase than in the luteal phase, whereas another study detected the opposite. Data on gut microbiota in relation to the menstrual cycle are limited. Conflicting results exist when examining the effect of hormonal contraceptives on the gut microbiota and changes in the course of the menstrual cycle. The results indicate that the menstrual cycle, especially fluctuating sex hormones, might impact the gut microbiota composition.The menstrual cycle may affect the gut microbiota composition and glucose metabolism. These results indicate that glucose tolerance may be the greatest in the follicular phase; however, further well-conducted studies are needed to support this assumption.

Drug-induced hyperglycemia and diabetes

BACKGROUND

Drug-induced hyperglycemia and diabetes have negative and potentially serious health consequences but can often be unnoticed.

METHODS

We reviewed the literature searching Medline database for articles addressing drug-induced hyperglycemia and diabetes up to January 31, 2023. We also selected drugs that could induce hyperglycemia or diabetes according official data from drug information databases Thériaque and Micromedex. For each selected drug or pharmacotherapeutic class, the mechanisms of action potentially involved were investigated. For drugs considered to be at risk of hyperglycemia or diabetes, disproportionality analyses were performed using data from the international pharmacovigilance database VigiBase. In order to detect new pharmacovigilance signals, additional disproportionality analyses were carried out for drug classes with more than 100 cases reported in VigiBase, but not found in the literature or official documents.

RESULTS

The main drug classes found to cause hyperglycemia are glucocorticoids, HMG-coA reductase inhibitors, thiazide diuretics, beta-blockers, antipsychotics, fluoroquinolones, antiretrovirals, antineoplastic agents and immunosuppressants. The main mechanisms involved are alterations in insulin secretion and sensitivity, direct cytotoxic effects on pancreatic cells and increases in glucose production. Pharmacovigilance signal were found for a majority of drugs or pharmacological classes identified as being at risk of diabetes or hyperglycemia. We identified new pharmacovigilance signals with drugs not known to be at risk according to the literature or official data: phosphodiesterase type 5 inhibitors, endothelin receptor antagonists, sodium oxybate, biphosphonates including alendronic acid, digoxin, sartans, linosipril, diltiazem, verapamil, and darbepoetin alpha. Further studies will be needed to confirm these signals.

CONCLUSIONS

The risks of induced hyperglycemia vary from one drug to another, and the underlying mechanisms are multiple and potentially complex. Clinicians need to be vigilant when using at-risk drugs in order to detect and manage these adverse drug reactions. However, it is to emphasize that the benefits of appropriately prescribed treatments most often outweigh their metabolic risks.

Okra ameliorates hyperglycaemia in pre-diabetic and type 2 diabetic patients: A systematic review and meta-analysis of the clinical evidence

Background: Despite the use of available pharmaceutical drugs, high rates of metabolic diseases and cardiovascular disorders are alarming. This calls for alternative therapies that can attenuate these complications. Therefore, we investigated the beneficial effects of okra on glycaemic control in pre-diabetes and type 2 diabetes mellitus (T2D).

Methods: MEDLINE and Scopus were searched for relevant studies. Collected data were analysed using RevMan and reported as mean difference and 95% confidence intervals (CI). Eight studies, including 331 patients with pre-diabetes or T2D, were eligible.

Results: Our findings showed that okra treatment reduced the levels of fasting blood glucose: mean difference (MD) = −14.63 mg/dL; 95% CI (-25.25, −4.00, p = 0.007); I2 = 33%, p = 0.17 compared to placebo. Glycated haemoglobin, however, did not differ significantly between the groups: MD = 0.01%; 95%CI (-0.51, 0.54, p = 0.96); I2 = 23%, p = 0.28.

Conclusion: this systematic review and meta-analysis found that okra treatment improves glycaemic control in patients with pre-diabetes or T2D. The findings suggest that okra may be used as a supplemental dietary nutrient, especially in pre-diabetic and T2D patients due to its potential to regulate hyperglycaemia.

Association of Fatty Liver Index with Incident Diabetes Risk in Patients Initiating Statin-Therapy: A 6-Year Retrospective Study

BACKGROUND

Statins are associated with new-onset type 2 diabetes (T2D), mainly in patients with metabolic syndrome (MetS). The fatty liver index (FLI) is used as a prognostic score for the diagnosis of non-alcoholic fatty liver disease (NAFLD), which is common in patients with MetS. We aimed to investigate the association of FLI with new-onset T2D in patients initiating statin therapy.

METHODS

A retrospective observational study including 1241 individuals with dyslipidemia and followed up for ≥3 years. Patients with T2D and those receiving lipid-lowering treatment at the baseline visit were excluded. Models with clinical and laboratory parameters were used to assess the association of FLI with incident T2D.

RESULTS

Among the 882 eligible subjects, 11% developed T2D during the follow-up (6 years; IQR: 4-10 years). After adjusting for sex, age and MetS parameters, a multivariate analysis revealed that age (HR:1.05; 95%CI: 1.01-1.09, p < 0.05), fasting plasma glucose (HR: 1.09; 95%CI: 1.06-1.13, p < 0.001) and FLI (HR: 1.02; 95%CI: 1.01-1.04, p < 0.01) were independently associated with T2D risk. The subjects with probable NAFLD (FLI ≥ 60) had a three-fold increased T2D risk compared with the subjects with FLI < 60 (HR: 3.14; 95%CI: 1.50-6.59, p = 0.001). A ROC curve analysis showed that FLI had a significant, although poor, predictive value for assessing T2D risk (C-Statistic: 0.67; 95%CI: 0.58-0.77, p = 0.001). Higher FLI values were associated with reduced T2D-free survival (log-rank = 15.46, p < 0.001).

CONCLUSIONS

FLI is significantly and independently associated with new-onset T2D risk in patients initiating statin therapy.

Quantifying the contribution of 31 risk factors to the increasing prevalence of diabetes among US adults, 2005-2018

Introduction

No study has comprehensively quantified the individual and collective contributions of various risk factors to the growing burden of diabetes in the United States.

Methods

This study aimed to determine the extent to which an increase in the prevalence of diabetes was related to concurrent changes in the distribution of diabetes-related risk factors among US adults (aged 20 years or above and not pregnant). Seven cycles of series of cross-sectional National Health and Nutrition Examination Survey data between 2005-2006 and 2017-2018 were included. The exposures were survey cycles and seven domains of risk factors, including genetic, demographic, social determinants of health, lifestyle, obesity, biological, and psychosocial domains. Using Poisson regressions, percent reduction in the β coefficient (the logarithm used to calculate the prevalence ratio for prevalence of diabetes in 2017-2018 vs. 2005-2006) was computed to assess the individual and collective contribution of the 31 prespecified risk factors and seven domains to the growing burden of diabetes.

Results

Of the 16,091 participants included, the unadjusted prevalence of diabetes increased from 12.2% in 2005-2006 to 17.1% in 2017-2018 [prevalence ratio: 1.40 (95% CI, 1.14-1.72)]. Individually, genetic domain [17.3% (95% CI, 5.4%-40.8%)], demographic domain [41.5% (95% CI, 24.4%-76.8%)], obesity domain [35.3% (95% CI, 15.8%-70.2%)], biological domain [46.2% (95% CI, 21.6%-79.1%)], and psychosocial domain [21.3% (95% CI, 9.5%-40.1%)] were significantly associated with a different percent reduction in β. After adjusting for all seven domains, the percent reduction in β was 97.3% (95% CI, 62.7%-164.8%).

Conclusion

The concurrently changing risk factors accounted for the increasing diabetes prevalence. However, the contribution of each risk factor domain varied. Findings may inform planning cost-effective and targeted public health programs for diabetes prevention.

Drug purchases prior to conception and the risk of gestational diabetes mellitus

OBJECTIVE

Some drugs have adverse effects on glucose metabolism, but it is unknown whether prescription drugs used prior to conception influence the future risk of gestational diabetes mellitus (GDM). Our study evaluated whether the purchase of prescription drugs 6 months prior to conception was associated with the occurrence of GDM.

METHODS

This cohort study enrolled women with a Finnish background who delivered between 2009 and 2015 in the city of Vantaa, Finland (N = 10,455). Data on maternal characteristics and prescription drug purchases were obtained from national health registers. The use of a unique personal identification number enabled us to combine the register data on an individual level.

RESULTS

Six months prior to conception, women who had pregnancies complicated by GDM purchased more prescription drugs than women without GDM (1.38 ± 2.04 vs. 1.11 ± 1.80). The GDM risk was higher in women with higher numbers of prescription purchases and those with more than three deliveries.

CONCLUSIONS

Multiparous women who purchase several prescription drugs should be given personalized counseling to prevent GDM.

Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness

The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included. We trained the model on 80% of the subjects and verified its predictive performance on the remaining 20%. Furthermore, the performances of several algorithms were compared, including logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Cox regression, and XGBoost Survival Embedding (XGBSE). The area under the receiver operating characteristic curve (AUROC) of the XGBoost model was the largest, followed by those of the decision tree, logistic regression, and random forest models. For the survival analysis, XGBSE yielded an AUROC exceeding 0.9 for the 2- to 9-year predictions and a C-index of 0.934, while the Cox regression achieved a C-index of 0.921. After lowering the threshold from 0.5 to 0.25, the sensitivity increased from 0.011 to 0.236 for the 2-year prediction model and from 0.607 to 0.994 for the 9-year prediction model, while the specificity showed negligible changes. We developed a high-performance diabetes prediction model that applied the XGBSE algorithm with threshold adjustment. We plan to use this prediction model in real clinical practice for diabetes prevention after simplifying and validating it externally.

Angiotensin-converting enzyme inhibitors versus angiotensin receptor blockers: New-onset diabetes mellitus stratified by statin use

WHAT IS KNOWN AND OBJECTIVES

Regardless of statin use, which is known to induce hyperglycaemia, comparative studies on the risk of new-onset diabetes mellitus (NODM) with angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) are needed. This study evaluated the effects of ACEIs and ARBs on NODM in the clinical setting.

METHODS

This retrospective cohort study utilized electronic medical record data from Seoul St. Mary's Hospital and Seoul National University Hospital from 2009 to 2012. Patients who were prescribed ACEIs or ARBs for the first time (irrespective of concomitant statin use) were followed up for 5 years.

RESULTS AND DISCUSSIONS

A total of 11,703 patients were included, 24.9% (n = 2916) were taking ACEIs and 75.1% (n = 9189) were taking ARBs. Patients on ACEIs had a significantly lower incidence of NODM both with statin use (HR = 0.13, p < 0.001) and without (HR = 0.15, p = 0.009) than patients on ARBs. Age ≥60 years (HR = 1.49, p = 0.010), BMI ≥25 (HR = 1.96, p < 0.010), use of calcium channel blockers (HR = 1.47, p = 0.010), and diuretics (HR = 1.48, p = 0.010) were risk factors for NODM with statin use.

WHAT IS NEW AND CONCLUSION

Patients taking ACEIs are less likely to develop NODM than patients taking ARBs, irrespective of statin use. Patients' conditions, including the risk of NODM, should be considered before prescribing ACEIs or ARBs. Future randomized clinical trials are needed to clarify further the relationship between ACEIs and ARBs and their effect on NODM.

Differences in the diabetogenic effect of statins in patients with prediabetes. The PRELIPID study

INTRODUCTION

Statins are used with the understanding that a slightly increased risk of diabetes is outweighed by their cardiovascular benefits. However, it may be necessary to reconsider whether statin therapy really increase this risk mainly in the population with prediabetes.

METHODS

A multicenter, cross-sectional, observational study was conducted to assess the relationship between statin therapy and glucose metabolism in 407 patients aged 63.1 years (11SD) diagnosed with dyslipidemia and prediabetes treated in specialized lipid clinics in Spain.

RESULTS

Significant differences were found in HbA1c values among treatment groups (p=0.015). Patients treated with pitavastatin (1-4mg/day) showed the lowest HbA1c levels, with significant differences compared to patients treated with atorvastatin 40-80mg/day (p=0.016) and simvastatin 10-40mg/day (p=0.036). By contrast, patients treated with atorvastatin 40-80mg/day showed the highest HbA1c levels compared to those receiving atorvastatin 10-20mg/day (p=0.003), pitavastatin 1-4mg/day (p=0.016), pravastatin 20-40mg/day (p=0.027), rosuvastatin 5-10mg/day (p=0.043), and no statin treatment (p=0.004). Patients treated with simvastatin 10-40mg/day also had higher values than those treated with atorvastatin 10-20mg/day (p=0.016) and pitavastatin 1-4mg/day (p=0.036) or with no statin treatment (p=0.018).

CONCLUSIONS

This study suggests that there are differences in the diabetogenic effect of statins. Simvastatin and high doses of atorvastatin may be associated with greater impairment in glucose metabolism than pitavastatin and other statins with less lipid-lowering potency such as pravastatin.

Brexpiprazole caused glycolipid metabolic disorder by inhibiting GLP1/GLP1R signaling in rats

Brexpiprazole (Bre) is a new multi-target antipsychotic drug (APD) approved by the US FDA in 2015, and shows good therapeutic potential. But it lacks assessments on the metabolic side effects, which obstructs the treatment of schizophrenia. Glucagon-like peptide 1 (GLP1), an incretin associated with insulin action and metabolism, is involved in the metabolic syndrome (MS) caused by most APDs. In this study, we examined the adverse effects of Bre on glycolipid metabolism in rats and determined whether GLP1 was involved in Bre-caused MS. In the first part of experiments, rats were orally administered Bre (0.5 mg· kg^-1· d^-1) for 28 days with aripiprazole (1.0 mg· kg^-1· d^-1) or olanzapine (1.0 mg· kg^-1· d^-1) as the controls. Compared to vehicle, Bre administration significantly increased the weight gain, serum lipid (TG, TC, LDL, FFA), and blood glucose levels accompanied by the hormonal (insulin, glucagon, GLP1) imbalance, and the impaired glucose tolerance and insulin sensitivity. Moreover, we demonstrated that Bre administration significantly decreased the protein and mRNA levels of GLP1 in pancreas and small intestine by suppressing CaMKIIα, AMPK, and β-catenin; Bre administration also caused islet dysfunction with decreased GLP1R, PI3K, IRβ expression in pancreas, and the interference of IRS1, PI3K, p-AKT, and GLUT4 expression in the liver and skeletal muscle that represented the insulin resistance. In the second part of experiments, rats were orally administered Bre (0.5 mg· kg^-1· d^-1) for 42 days. We showed that co-administration with the GLP1 receptor (GLP1R) agonist liraglutide (0.125 mg· kg^-1· d^-1, ip) could ameliorate Bre-caused metabolic abnormalities. Our results demonstrate that GLP1/GLP1R signaling is involved in Bre-induced glycolipid metabolic disorders and co-treatment with liraglutide is an effective intervention against those abnormal metabolisms.

Time-varying and dose-dependent effect of long-term statin use on risk of type 2 diabetes: a retrospective cohort study

Background

We evaluated the effect of statin use on new-onset type 2 diabetes among individuals without atherosclerotic cardiovascular disease (ASCVD) using nationally representative South Korean claims data (2002–2013, N = 1,016,820).

Methods

A total of 13,698 patients (statin users 5273, non-statin users 5273) aged 40–74 years, newly diagnosed with dyslipidemia but without any history of diabetes or ASCVD, were selected in 2005. We followed up the final sample until 2013 and evaluated the cumulative incidence of type 2 diabetes. We used extended Cox regression models to estimate the time-varying adjusted hazard ratios of statin use on new-onset type 2 diabetes. We performed further analyses based on the cumulative defined daily dose of statin received per year to evaluate the degree of risk compared to non-statin users.

Results

Over the mean follow-up period of 7.1 years, 3034 patients developed type 2 diabetes; the number of statin users exceeded that of non-users, demonstrating that statin use significantly increased the risk of new-onset type 2 diabetes. The risk of new-onset type 2 diabetes differed among statin users according to cDDD per year (adjusted HR = 1.31 [95% CI 1.18–1.46] for less than 30 cDDD per year; 1.58 [1.43–1.75] for 30–120 cDDD per year; 1.83 [1.62–2.08] for 120–180 cDDD per year; and 2.83 [2.51–3.19] for more than 180 cDDD per year). The diabetogenic effect of pitavastatin was not statistically significant, but the risk was the largest for atorvastatin. Long-term exposure (≥ 5 years) to statins was associated with a statistically significant increase in the risk of new onset type 2 diabetes in all statin subtypes explored, with the highest magnitude for simvastatin (HR = 1.916, 95% CI 1.647–2.228) followed by atorvastatin (HR = 1.830, 95% CI 1.487–2.252).

Conclusions

Statin use was significantly associated with an increased risk of new-onset type 2 diabetes. We also found a dose–response relationship in terms of statin use duration and dose maintenance. Periodic screening and monitoring for incident type 2 diabetes may be warranted in long-term statin users.

Medication Use and the Risk of Newly Diagnosed Diabetes in Patients with Epilepsy

Epilepsy is a common neurological disorder that affects millions of people worldwide. Patients with epilepsy generally require long-term antiepileptic therapy and many of them receive polypharmacy. Certain medications, including older-generation antiepileptic drugs, have been known to predispose patients to developing diabetes. Although data mining techniques have become widely used in healthcare, they have seldom been applied in this clinical problem. Here, the authors used association rule mining to discover drugs or drug combinations that may be associated with newly diagnosed diabetes. Their findings indicate in addition to the most common culprits such as phenytoin and valproic acid, prescriptions containing carbamazepine, oxcarbazepine, or lamotrigine may be related to the development of newly diagnosed diabetes. These mined rules are useful as guidance to both clinical practice and future research.

Modeling the cost effectiveness and budgetary impact of Polypills for secondary prevention of cardiovascular disease in the United States

BACKGROUND

There is underutilization of appropriate medications for secondary prevention of cardiovascular disease (CVD).

METHODS

Usual care (UC) was compared to polypill-based care with 3 versions using a validated micro-simulation model in the NHANES population with prior CVD. UC included individual prescription of up to 4 drug classes (antiplatelet agents, beta-blockers, renin-angiotensin-aldosterone inhibitors and statins). The polypills modeled were aspirin 81 mg, atenolol 50 mg, ramipril 5 mg, and either simvastatin 40 mg (Polypill I), atorvastatin 80 mg (Polypill II), or rosuvastatin 40 mg (Polypill III). Baseline medication use and adherence came from United Healthcare claims data.

RESULTS

When compared to UC, there were annual reductions of 130,000 to 178,000 myocardial infarctions and 54,000 to 74,000 strokes using Polypill I and II, respectively. From a health sector perspective, in incremental analysis the ICERs for Polypill I and II were $20,073/QALY and $21,818/QALY respectively; Polypill III was dominated but had a similar cost-effectiveness ratio to Polypill II when compared directly to usual care. From a societal perspective, Polypill II was cost-saving and dominated all strategies. Over a 5-year period, those taking Polypill I and II compared to UC saved approximately $12 and $6 per-patient-per-year alive, respectively. Polypill II was the preferred strategy in 98% of runs at a willingness to pay of $50,000 in the probability sensitivity analysis.

CONCLUSIONS

Use of a polypill has a favorable cost profile for secondary CVD prevention in the United States. Reductions in CVD-related healthcare costs outweighed medication cost increases on a per-patient-per-year basis, suggesting that a polypill would be economically advantageous to both patients and payers.

Potential off-target effects of beta-blockers on gut hormone receptors: In silico study including GUT-DOCK-A web service for small-molecule docking

The prolonged use of many currently available drugs results in the severe side effect of the disruption of glucose metabolism leading to type 2 diabetes mellitus (T2DM. Gut hormone receptors including glucagon receptor (GCGR) and the incretin hormone receptors: glucagon-like peptide 1 receptor (GLP1R) and gastric inhibitory polypeptide receptor (GIPR) are important drug targets for the treatment of T2DM, as they play roles in the regulation of glucose and insulin levels and of food intake. In this study, we hypothesized that we could compensate for the negative influences of specific drugs on glucose metabolism by the positive incretin effect enhanced by the off-target interactions with incretin GPCR receptors. As a test case, we chose to examine beta-blockers because beta-adrenergic receptors and incretin receptors are expressed in a similar location, making off-target interactions possible. The binding affinity of drugs for incretin receptors was approximated by using two docking scoring functions of Autodock VINA (GUT-DOCK) and Glide (Schrodinger) and juxtaposing these values with the medical information on drug-induced T2DM. We observed that beta-blockers with the highest theoretical binding affinities for gut hormone receptors were reported as the least harmful to glucose homeostasis in clinical trials. Notably, a recently discovered beta-blocker compound 15 ([4-((2S)-3-(((S)-3-(3-bromophenyl)-1-(methylamino)-1-oxopropan-2-yl)amino)-2-(2-cyclohexyl-2-phenylacetamido)-3-oxopropyl)benzamide was among the top-scoring drugs, potentially supporting its use in the treatment of hypertension in diabetic patients. Our recently developed web service GUT-DOCK (gut-dock.miningmembrane.com) allows for the execution of similar studies for any drug-like molecule. Specifically, users can compute the binding affinities for various class B GPCRs, gut hormone receptors, VIPR1 and PAC1R.

Drug-induced diabetes type 2: In silico study involving class B GPCRs

A disturbance of glucose homeostasis leading to type 2 diabetes mellitus (T2DM) is one of the severe side effects that may occur during a prolonged use of many drugs currently available on the market. In this manuscript we describe the most common cases of drug-induced T2DM, discuss available pharmacotherapies and propose new ones. Among various pharmacotherapies of T2DM, incretin therapies have recently focused attention due to the newly determined crystal structure of incretin hormone receptor GLP1R. Incretin hormone receptors: GLP1R and GIPR together with the glucagon receptor GCGR regulate food intake and insulin and glucose secretion. Our study showed that incretin hormone receptors, named also gut hormone receptors as they are expressed in the gastrointestinal tract, could potentially act as unintended targets (off-targets) for orally administrated drugs. Such off-target interactions, depending on their effect on the receptor (stimulation or inhibition), could be beneficial, like in the case of incretin mimetics, or unwanted if they cause, e.g., decreased insulin secretion. In this in silico study we examined which well-known pharmaceuticals could potentially interact with gut hormone receptors in the off-target way. We observed that drugs with the strongest binding affinity for gut hormone receptors were also reported in the medical information resources as the least disturbing the glucose homeostasis among all drugs in their class. We suggested that those strongly binding molecules could potentially stimulate GIPR and GLP1R and/or inhibit GCGR which could lead to increased insulin secretion and decreased hepatic glucose production. Such positive effect on the glucose homeostasis could compensate for other, adverse effects of pharmacotherapy which lead to drug-induced T2DM. In addition, we also described several top hits as potential substitutes of peptidic incretin mimetics which were discovered in the drug repositioning screen using gut hormone receptors structures against the ZINC15 compounds subset.

Effect of statins on fasting glucose in non-diabetic individuals: nationwide population-based health examination in Korea

BACKGROUND

Increasing evidence suggest that statin therapy has a diabetogenic effect. Individual types of statin may have a different effect on glucose metabolism. Using the repeated nationwide population-based health screening data in Korea, we investigated the longitudinal changes in fasting glucose level of non-diabetic individuals by use of statins.

METHODS

From the National Health Screening Cohort, we included 379,865 non-diabetic individuals who had ≥ 2 health screening examinations with fasting blood glucose level measured in 2002-2013. Using the prescription records of statins in the database, we calculated the proportion of days covered (PDC) and average number of defined daily doses per day (anDDD) by statins. We constructed multivariate linear mixed models to evaluate the effects of statins on the changes in fasting glucose (Δglu).

RESULTS

High PDC by statins had a significant positive effect on Δglu (coefficient for PDC 0.093 mmol/L, standard error 0.007, p < 0.001). anDDD by statins was also positively associated with Δglu (coefficient for anDDD 0.119 mmol/L, standard error 0.009, p < 0.001). Unlike statins, the PDC by fibrate and ezetimibe were not significantly associated with Δglu. There was no significant interaction effect on Δglu between time interval and statin. Considering individual types of statins, use of atorvastatin, rosuvastatin, pitavastatin, and simvastatin were significantly associated with increase of Δglu. Pravastatin, lovastatin, and fluvastatin were also positively associated with Δglu, but were not statistically significant.

CONCLUSIONS

More adherent and intensive use of statins was significantly associated with an increase in fasting glucose of non-diabetic individuals. In subgroup analysis of individual statins, use of atorvastatin, rosuvastatin, pitavastatin and simvastatin had significant association with increase in fasting glucose. Pravastatin, lovastatin, and fluvastatin had non-significant trend toward an increased fasting glucose. Our findings suggest the medication class effect of statins inducing hyperglycemia.

Actions of metformin and statins on lipid and glucose metabolism and possible benefit of combination therapy

Patients with diabetes type 2 have an increased risk for cardiovascular disease and commonly use combination therapy consisting of the anti-diabetic drug metformin and a cholesterol-lowering statin. However, both drugs act on glucose and lipid metabolism which could lead to adverse effects when used in combination as compared to monotherapy. In this review, the proposed molecular mechanisms of action of statin and metformin therapy in patients with diabetes and dyslipidemia are critically assessed, and a hypothesis for mechanisms underlying interactions between these drugs in combination therapy is developed.

Drug-induced hyperglycaemia and diabetes: pharmacogenomics perspectives

Drug-induced diabetes is widely reported in clinical conditions, and it is becoming a global issue because of its potential to increase the risk of severe cardiovascular complications. However, which drug mechanisms exert their diabetogenic effects and why the effects present significant inter-individual differences remain largely unknown. Pharmacogenomics, which is the study of how genomic variation influences drug responses, provides an explanation for individual differences in drug-induced diabetes. We highlight that pharmacogenomics can be involved in regulating the expression of genes in signaling pathways related to the pharmacokinetics or pharmacodynamics of drugs or the pathogenesis of diabetes, contributing to the differences in drug-induced glucose impairment. The pharmacogenomics studies of the major diabetogenic drugs are reviewed, including calcineurin inhibitors, antipsychotics, hormones, and antihypertensive drugs. We intend to elucidate the genetic basis of drug-induced diabetes and pave the way for the precise use of these drugs in the clinic.

Global aetiology and epidemiology of type 2 diabetes mellitus and its complications

Globally, the number of people with diabetes mellitus has quadrupled in the past three decades, and diabetes mellitus is the ninth major cause of death. About 1 in 11 adults worldwide now have diabetes mellitus, 90% of whom have type 2 diabetes mellitus (T2DM). Asia is a major area of the rapidly emerging T2DM global epidemic, with China and India the top two epicentres. Although genetic predisposition partly determines individual susceptibility to T2DM, an unhealthy diet and a sedentary lifestyle are important drivers of the current global epidemic; early developmental factors (such as intrauterine exposures) also have a role in susceptibility to T2DM later in life. Many cases of T2DM could be prevented with lifestyle changes, including maintaining a healthy body weight, consuming a healthy diet, staying physically active, not smoking and drinking alcohol in moderation. Most patients with T2DM have at least one complication, and cardiovascular complications are the leading cause of morbidity and mortality in these patients. This Review provides an updated view of the global epidemiology of T2DM, as well as dietary, lifestyle and other risk factors for T2DM and its complications.

Prevalence of Self-Reported Diabetes by Sexual Orientation: Results from the 2014 Behavioral Risk Factor Surveillance System

PURPOSE

This study aimed to compare the prevalence of self-reported diabetes and diabetes risk factors among adult sexual minority and heterosexual populations in the United States.

METHODS

Data from the 2014 Behavioral Risk Factor Surveillance System for 3776 lesbian, gay, and bisexual (LGB) adults and 142,852 heterosexual adults aged 18 years and older were used to estimate the prevalence of diabetes. Binomial logistic regression models were used to compare the odds of diabetes by sexual orientation.

RESULTS

Sexual minorities were younger and more racially diverse than heterosexuals. Gay men less often and lesbian and bisexual women more often reported a body mass index of 30 kg/m2 or higher than heterosexuals. Overall, 14.2% of bisexual men, 11.4% of gay men, and 10.8% of heterosexual men reported a lifetime diabetes diagnosis, as did 8.5% of lesbian women, 5.7% of bisexual women, and 10.2% of heterosexual women. After controlling for multiple factors, gay (odds ratio [OR] = 1.50; confidence interval [95% CI] = 1.09-2.07) and bisexual men [OR = 1.55; 95% CI = 1.00-2.07] were more likely to report a lifetime diabetes diagnosis than heterosexual men. Similar differences were not found for lesbian [OR = 1.22; 95% CI = 0.76-1.95] or bisexual women [OR = 0.88; 95% CI = 0.62-1.26].

CONCLUSION

Sexual minorities may be at increased risk for diabetes than their heterosexual peers. This may be due partly to the chronic stressors associated with being a member of a marginalized population. Future research should explore the underlying causes and consequences of LGB diabetes disparities and elucidate best practices to improve diabetes screening and care for these vulnerable patient populations.

Activation of nuclear receptor PXR impairs glucose tolerance and dysregulates GLUT2 expression and subcellular localization in liver

Pregnane X receptor (PXR) is a nuclear receptor that senses chemical environment and is activated by numerous clinically used drugs and environmental contaminants. Previous studies have indicated that several drugs known to activate PXR appear to induce glucose intolerance. We now aimed to reveal the role of PXR in drug-induced glucose intolerance and characterize the mechanisms involved. We used PXR knockout mice model to investigate the significance of this nuclear receptor in the regulation of glucose tolerance. PXR ligand pregnenolone-16ɑ-carbonitrile (PCN) impaired glucose tolerance in the wildtype mice but not in the PXR knockout mice. Furthermore, DNA microarray and bioinformatics analysis of differentially expressed genes and glucose metabolism relevant pathways in PCN treated primary hepatocytes indicated that PXR regulates genes involved in glucose uptake. PCN decreased the expression of glucose transporter 2 (GLUT2) in mouse liver and in the wildtype mouse hepatocytes but not in the PXR knockout cells. Data mining of published chromatin immunoprecipitation-sequencing results indicate that Glut2 gene is a direct PXR target. Furthermore, PCN induced internalization of GLUT2 protein from the plasma membrane to the cytosol in the liver in vivo and repressed glucose uptake in the primary hepatocytes. Our results indicate that the activation of PXR impairs glucose tolerance and thus PXR represents a novel diabetogenic pathway. PXR activation dysregulates GLUT2 function by two different mechanisms. These findings may partly explain the diabetogenic effects of medications and environmental contaminants.

Medication use in long-term survivors from the MONICA/KORA Myocardial Infarction Registry

BACKGROUND

Prior studies reported high guideline adherence for secondary prevention medications (SPM) at hospital discharge in patients with acute myocardial infarction (AMI). Less is known about medication use in long-term AMI survivors.

METHODS

Of the 2077 registered persons with an AMI between 2000 and 2008 who responded to a postal follow-up survey in 2011, 1311 men and 356 women, aged between 34.4 and 84.9years, reported medication intake 7days prior to the survey. These study participants also had their current health condition and comorbidities assessed. Information regarding index AMI was selected from the population-based MONICA/KORA MI registry. Multivariable logistic regression models were conducted to identify factors associated with SPM use (all 4 drug classes).

RESULTS

The median time between index AMI and the follow-up survey was 6.1years (IQR: 3.9). At follow-up, a total of 10,422 medications were reported and polypharmacy was observed in 73.8%. Regarding SPM, the proportion of patients taking antiplatelet agents, beta-blockers, statins, and renin-angiotensin-aldosteron system blockers were 90.9%, 86.7%, 85.4%, and 79.3% respectively. Factors associated with SPM use were hypertension (odds ratio [OR] 1.48, p=0.006), SPM prescription at hospital discharge (OR 2.68, p<0.0001), revascularization therapy at index AMI (OR 2.46, p>0.0001), number of medications taken at follow-up (OR 1.48, p<0.0001), and several comorbidities such as lung disorders (OR 0.17; p<0.0001), depression (OR 0.53, p=0.001), neurological disorders (without stroke) (OR 0.34, p=0.002), and cancer (OR 0.45, p=0.005).

CONCLUSION

SPM use several years after AMI was high and associated with treatment at index AMI and patients' comorbidities.

Effects of background statin therapy on glycemic response and cardiovascular events following initiation of insulin therapy in type 2 diabetes: a large UK cohort study

Aim

Statins may increase the risk of new-onset diabetes and adversely affect glycaemic control, but their effects on the glycemic response and mortality outcomes following commencement of insulin therapy in patients with Type 2 Diabetes (T2D) are unclear.

Methods

A retrospective cohort study was conducted in 12,725 insulin initiators with T2D using The Health Improvement Network (THIN) UK database. Changes in HbA1c at 6, 12, 24 and 36 months, and the 5-year risk of mortality and (3-point) major adverse cardiovascular events (MACE), were compared between prior users (n = 10,682) and non-users (n = 2043) of statin therapy who were newly commenced on insulin treatment. Cox proportional hazard models were used to estimate the hazard ratios of the different outcomes.

Results

Mean age of the cohort was 58.7 ± 14.0 years (51% male) and mean baseline HbA1c was 8.7 ± 1.8%. A greater initial reduction in HbA1c was observed following insulin initiation in the non-users of statins compared with the users, which was significant in the short term (−0.34% vs −0.26% at 6 months; mean diff = −0.09%, p = 0.004) but not in the long term: −0.31% versus −0.35% at 3 years (mean diff = 0.05%, p = 0.344). CV events (3-point MACE) were 878 versus 217 in statin users versus non-users (20.7 vs 30.9 per 1000 person-years; adjusted Hazard Ratio (aHR) 1.36 (95% CI 1.15–1.62; p < 0.0001). In a subgroup analysis of individual statins, HbA1c was higher throughout the study duration with all statins relative to non-users of statin therapy (p < 0.05). The aHRs for 3-point MACE for atorvastatin, simvastatin, rosuvastatin and pravastatin were 0.82 (95% CI 0.68–0.98), 0.67 (0.55–0.82), 0.56 (0.39–0.81) and 0.78 (0.60–1.01), respectively.

Conclusions

Following initiation of insulin therapy in patients with T2D in routine care, concurrent use of a statin was associated with less good glycaemic control in the short-term but a much lower risk of major adverse CV events.

Electronic supplementary material

The online version of this article (doi:10.1186/s12933-017-0587-6) contains supplementary material, which is available to authorized users.

In vitro inhibition of human erythrocyte hexokinase by various hyperglycemic drugs

Hemolysis is the red blood cell abnormality most often associated with adverse effect of drug therapy. Drug-induced or drug-associated hyperglycemia could decrease the activity of hexokinase. The aim of this study was to investigate the inhibitory effects of some commonly used drugs that have hyperglycemic side effect on the human erythrocyte hexokinase enzyme in vitro. Hexokinase was purified from human erythrocytes using sequential chromatography, with a specific activity of 0.96 ± 0.18 U/g hemoglobin, and assayed in the presence of selected drugs that have hyperglycemic side effect. The IC₅₀ were determined from the regression analysis graph. Correlation analysis showed that there was positive correlation between the hyperglycemic side effect of some of the tested drugs and decrease of hexokinase activity. This suggests that, at least in part, these drugs exert their hyperglycemic effect by inhibiting glucose phosphorylation by the hexokinase, which consequently causes the glucose accumulation.

Important differences in the durability of glycaemic response among second-line treatment options when added to metformin in type 2 diabetes: a retrospective cohort study

IMPORTANCE

There is limited information about the durability of glycaemic control when different oral glucose-lowering therapies (GLTs) are used as add-on treatments to metformin (MET) in patients with type 2 diabetes mellitus (T2DM).

OBJECTIVE

To compare time to treatment failure between different classes of oral GLT when used as second line (add-on) treatments to MET monotherapy at HbA1c ≥ 7.5%.

DESIGN, SETTING AND PARTICIPANTS

A retrospective cohort study on 20,070 patients who were newly treated with a sulphonylurea (SU), dipeptidyl-peptidase-4 (DPP-4) inhibitor or thiazolidinedione (TZD) following MET therapy failure (2007-2014). Patients' data was sourced from UK General Practices via The Health Improvement Network (THIN) database. The risk of dual therapy failure was compared between three treatment groups: MET + SU (reference group, n = 15,508), MET + DPP-4 inhibitor (n = 3,080) and MET + TZD (n = 1,482). Follow-up was until treatment substitution or intensification with a 3rd GLT, or for up to 5 years (totalling 46,430 person-years). Propensity score weighting and Cox proportional hazard regression analyses were employed.

MAIN OUTCOMES AND MEASURES

Risk of dual therapy failure was compared between treatment groups while adjusting for baseline covariates.

RESULTS

Unadjusted survival analysis showed the incidence of dual therapy failure at 1 year was 15% with SU, 23% with DPP-4 inhibitor and 8% with TZD. Corresponding failure rates at 2 years were 26, 38 and 12%, respectively. Adjusted multivariate models showed that, compared to the SU group, adding a DPP-4 inhibitor was associated with an increased risk of treatment failure (adjusted hazard ratio, aHR, 1.58; 95% CI: 1.48-1.68), while adding a TZD was associated with a reduced hazard (aHR, 0.45; 95% CI: 0.41-0.50). Baseline parameters associated with an increased hazard of intensification included HbA1c, diabetes duration, gender, smoking status and the use of statins.

CONCLUSIONS AND RELEVANCE

In routine clinical practice, adding a DPP-4 inhibitor to MET is associated with an increased, earlier requirement for treatment intensification compared to adding an SU or TZD. Adding a TZD to MET resulted in the most durable glycaemic response. Key messages The Agency for Healthcare Research and Quality has suggested that the durability of glycaemic response after treatment intensification is best investigated using well-designed long-term observational studies. In routine clinical practice, among patients with T2DM receiving a second line glucose lowering treatment as add-on to MET, the addition of a Thiazolidinediones is associated with the most durable glycaemic response, followed by a Sulfonylurea and then a DPP-4 inhibitor. Factors associated with earlier dual therapy failure included concomitant use of statin therapy, being female, a smoker, those with longer diabetes duration and higher baseline HbA1c levels. The addition of a Thiazolidinediones was associated with significant weight gain (1.8 kg, p < 0.001), while add-on DPP-4 inhibitor produced a significant weight reduction (-1.8 kg, p < 0.001). A very small reduction in body weight was observed with the SU (-0.2 kg, p < 0.001).