Effect of Metformin on Metabolites and Relation With Myocardial Infarct Size and Left Ventricular Ejection Fraction After Myocardial Infarction

Technical Report: A Comprehensive Comparison between Different Quantification Versions of Nightingale Health's 1H-NMR Metabolomics Platform

1H-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of 1H-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (~28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean ρ > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman's correlations (ρ < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued.

Effects of Metformin on Ischemia/Reperfusion Injury: New Evidence and Mechanisms

The search for new drugs with the potential to ensure therapeutic success in the treatment of cardiovascular diseases has become an essential pathway to follow for health organizations and committees around the world. In June 2021, the World Health Organization listed cardiovascular diseases as one of the main causes of death worldwide, representing 32% of them. The most common is coronary artery disease, which causes the death of cardiomyocytes, the cells responsible for cardiac contractility, through ischemia and subsequent reperfusion, which leads to heart failure in the medium and short term. Metformin is one of the most-used drugs for the control of diabetes, which has shown effects beyond the control of hyperglycemia. Some of these effects are mediated by the regulation of cellular energy metabolism, inhibiting apoptosis, reduction of cell death through regulation of autophagy and reduction of mitochondrial dysfunction with further reduction of oxidative stress. This suggests that metformin may attenuate left ventricular dysfunction induced by myocardial ischemia; preclinical and clinical trials have shown promising results, particularly in the setting of acute myocardial infarction. This is a review of the molecular and pharmacological mechanisms of the cardioprotective effects of metformin during myocardial ischemia-reperfusion injury.

The function, mechanisms, and clinical applications of metformin: potential drug, unlimited potentials

Metformin has been used clinically for more than 60 years. As time goes by, more and more miraculous effects of metformin beyond the clinic have been discovered and discussed. In addition to the clinically approved hypoglycemic effect, it also has a positive metabolic regulation effect on the human body that cannot be ignored. Such as anti-cancer, anti-aging, brain repair, cardiovascular protection, gastrointestinal regulation, hair growth and inhibition of thyroid nodules, and other nonclinical effects. Metformin affects almost the entire body in the situation taking it over a long period, and the preventive effects of metformin in addition to treating diabetes are also beginning to be recommended in some guidelines. This review is mainly composed of four parts: the development history of metformin, the progress of clinical efficacy, the nonclinical efficacy of metformin, and the consideration and prospect of its application.

From Diabetes to Atherosclerosis: Potential of Metformin for Management of Cardiovascular Disease

Atherosclerosis is a common cause of cardiovascular disease, which, in turn, is often fatal. Today, we know a lot about the pathogenesis of atherosclerosis. However, the main knowledge is that the disease is extremely complicated. The development of atherosclerosis is associated with more than one molecular mechanism, each making a significant contribution. These mechanisms include endothelial dysfunction, inflammation, mitochondrial dysfunction, oxidative stress, and lipid metabolism disorders. This complexity inevitably leads to difficulties in treatment and prevention. One of the possible therapeutic options for atherosclerosis and its consequences may be metformin, which has already proven itself in the treatment of diabetes. Both diabetes and atherosclerosis are complex metabolic diseases, the pathogenesis of which involves many different mechanisms, including those common to both diseases. This makes metformin a suitable candidate for investigating its efficacy in cardiovascular disease. In this review, we highlight aspects such as the mechanisms of action and targets of metformin, in addition to summarizing the available data from clinical trials on the effective reduction of cardiovascular risks.

Pharmacokinetic-Pharmacometabolomic Approach in Early-Phase Clinical Trials: A Way Forward for Targeted Therapy in Type 2 Diabetes

Pharmacometabolomics in early phase clinical trials demonstrate the metabolic profiles of a subject responding to a drug treatment in a controlled environment, whereas pharmacokinetics measure the drug plasma concentration in human circulation. Application of the personalized peak plasma concentration from pharmacokinetics in pharmacometabolomic studies provides insights into drugs’ pharmacological effects through dysregulation of metabolic pathways or pharmacodynamic biomarkers. This proof-of-concept study integrates personalized pharmacokinetic and pharmacometabolomic approaches to determine the predictive pharmacodynamic response of human metabolic pathways for type 2 diabetes. In this study, we use metformin as a model drug. Metformin is a first-line glucose-lowering agent; however, the variation of metabolites that potentially affect the efficacy and safety profile remains inconclusive. Seventeen healthy subjects were given a single dose of 1000 mg of metformin under fasting conditions. Fifteen sampling time-points were collected and analyzed using the validated bioanalytical LCMS method for metformin quantification in plasma. The individualized peak-concentration plasma samples determined from the pharmacokinetic parameters calculated using Matlab Simbiology were further analyzed with pre-dose plasma samples using an untargeted metabolomic approach. Pharmacometabolomic data processing and statistical analysis were performed using MetaboAnalyst with a functional meta-analysis peaks-to-pathway approach to identify dysregulated human metabolic pathways. The validated metformin calibration ranged from 80.4 to 2010 ng/mL for accuracy, precision, stability and others. The median and IQR for Cmax was 1248 (849–1391) ng/mL; AUC_0-infinity was 9510 (7314–10,411) ng·h/mL, and Tmax was 2.5 (2.5–3.0) h. The individualized Cmax pharmacokinetics guided the untargeted pharmacometabolomics of metformin, suggesting a series of provisional predictive human metabolic pathways, which include arginine and proline metabolism, branched-chain amino acid (BCAA) metabolism, glutathione metabolism and others that are associated with metformin’s pharmacological effects of increasing insulin sensitivity and lipid metabolism. Integration of pharmacokinetic and pharmacometabolomic approaches in early-phase clinical trials may pave a pathway for developing targeted therapy. This could further reduce variability in a controlled trial environment and aid in identifying surrogates for drug response pathways, increasing the prediction of responders for dose selection in phase II clinical trials.

Pharmacometabolomics by NMR in Oncology: A Systematic Review

Pharmacometabolomics (PMx) studies aim to predict individual differences in treatment response and in the development of adverse effects associated with specific drug treatments. Overall, these studies inform us about how individuals will respond to a drug treatment based on their metabolic profiles obtained before, during, or after the therapeutic intervention. In the era of precision medicine, metabolic profiles hold great potential to guide patient selection and stratification in clinical trials, with a focus on improving drug efficacy and safety. Metabolomics is closely related to the phenotype as alterations in metabolism reflect changes in the preceding cascade of genomics, transcriptomics, and proteomics changes, thus providing a significant advance over other omics approaches. Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical platforms in metabolomics studies. In fact, since the introduction of PMx studies in 2006, the number of NMR-based PMx studies has been continuously growing and has provided novel insights into the specific metabolic changes associated with different mechanisms of action and/or toxic effects. This review presents an up-to-date summary of NMR-based PMx studies performed over the last 10 years. Our main objective is to discuss the experimental approaches used for the characterization of the metabolic changes associated with specific therapeutic interventions, the most relevant results obtained so far, and some of the remaining challenges in this area.

Metformin intervention ameliorates AS in ApoE-/- mice through restoring gut dysbiosis and anti-inflammation

Atherosclerosis (AS) is closely associated with chronic low-grade inflammation and gut dysbiosis. Metformin (MET) presents pleiotropic benefits in the control of chronic metabolic diseases, but the impacts of MET intervention on gut microbiota and inflammation in AS remain largely unclear. In this study, ApoE-/- mice with a high-fat diet (HFD) were adopted to assess the MET treatment. After 12 weeks of MET intervention (100mg·kg-1·d-1), relevant indications were investigated. As indicated by the pathological measurements, the atherosclerotic lesion was alleviated with MET intervention. Moreover, parameters in AS including body weights (BWs), low-density lipoprotein (LDL), triglyceride (TG), total cholesterol (TC) and malondialdehyde (MDA) were elevated; whereas high-density lipoprotein (HDL) and total superoxide dismutase (T-SOD) levels were decreased, which could be reversed by MET intervention. Elevated pro-inflammatory interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α and lipopolysaccaride (LPS) in AS were decreased after MET administration. However, anti-inflammatory IL-10 showed no significant difference between AS group and AS+MET group. Consistently, accumulated macrophages in the aorta of AS were conversely lowered with MET treatment. The results of 16S rRNA sequencing and analysis displayed that the overall community of gut microbiota in AS was notably changed with MET treatment mainly through decreasing Firmicutes, Proteobacteria, Romboutsia, Firmicutes/Bacteroidetes, as well as increasing Akkermansia, Bacteroidetes, Bifidobacterium. Additionally, we found that microbiota-derived short-chain fatty acids (SCFAs) including acetic acid, propionic acid, butyric acid and valeric acid in AS were decreased, which were significantly up-regulated with MET intervention. Consistent with the attenuation of MET on gut dysbiosis, decreased intestinal tight junction protein zonula occludens-1 (ZO)-1 in AS was restored after MET supplementation. Correlation analysis showed close relationships among gut bacteria, microbial metabolites SCFAs and inflammation. Collectively, MET intervention ameliorates AS in ApoE-/- mice through restoring gut dysbiosis and anti-inflammation, thus can potentially serve as an inexpensive and effective intervention for the control of the atherosclerotic cardiovascular disease.

Cord serum metabolome and birth weight in patients with gestational diabetes treated with metformin, insulin, or diet alone

INTRODUCTION

Recent research has demonstrated the benefits of metformin treatment in gestational diabetes (GDM) on short-term pregnancy outcomes (including excessive fetal growth and pre-eclampsia), but its effects on fetal metabolism remain mostly unknown. Our aim was to study the effects of metformin treatment compared with insulin or diet on the cord serum metabolome and also to assess how these metabolites are related to birth weight (BW) in pregnancies complicated by GDM.

RESEARCH DESIGN AND METHODS

Cord serum samples were available from 113, 97, and 98 patients with GDM treated with diet, insulin, and metformin, respectively. A targeted metabolome was measured using nuclear magnetic resonance spectroscopy. The patients in the metformin and insulin groups had participated in a previous randomized trial (NCT01240785).

RESULTS

Cord serum alanine was elevated in the metformin group (0.53 mmol/L) compared with the insulin (0.45 mmol/L, p<0.001) and the diet groups (0.46 mmol/L, p<0.0001). All other measured metabolites were similar between the groups. The triglyceride (TG)-to-phosphoglyceride ratio, average very low-density lipoprotein particle diameter, docosahexaenoic acid, omega-3 fatty acids (FAs), and ratios of omega-3 and monounsaturated FA to total FA were inversely related to BW. The omega-6-to-total-FA and omega-6-to-omega-3-FA ratios were positively related to BW. Cholesterol in very large and large high-density lipoprotein (HDL) was positively (p<0.01) associated with BW when adjusted for maternal prepregnancy body mass index, gestational weight gain, glycated hemoglobin, and mode of delivery.

CONCLUSIONS

Metformin treatment in GDM leads to an increase in cord serum alanine. The possible long-term implications of elevated neonatal alanine in this context need to be evaluated in future studies. Although previous studies have shown that metformin increased maternal TG levels, the cord serum TG levels were not affected. Cord serum HDL cholesterol and several FA variables are related to the regulation of fetal growth in GDM. Moreover, these associations seem to be independent of maternal confounding factors.

TRIAL REGISTRATION NUMBER

NCT01240785.

Serum lipids and their association with birth weight in metformin and insulin treated patients with gestational diabetes

AIMS

To compare the effects of metformin and insulin treatment on maternal serum lipids in patients with gestational diabetes (GDM), and to analyse the associations between individual lipids and birth weight (BW).

METHODS

This is a secondary analysis of a randomized trial comparing metformin (n = 110) and insulin (n = 107) treatment of GDM. Fasting serum lipidome was measured at baseline (the time of diagnosis, mean 30 gestational weeks, gw) and at 36 gw using nuclear magnetic resonance spectroscopy.

RESULTS

Total and VLDL triglycerides, and VLDL cholesterol increased from baseline to 36 gw in both treatment groups. The rise in triglycerides was greater in the metformin treated patients (p < 0.01). Baseline total and VLDL triglycerides, VLDL cholesterol, and apolipoprotein B to A-1 ratio (apoB/apoA-1) associated positively with BW, more strongly in the metformin group. Among patients in the highest baseline VLDL cholesterol or apoB/apoA-1 quartile, those treated with insulin had lower BWs than those treated with metformin (p < 0.03).

CONCLUSION

Compared to insulin, metformin treatment of GDM led to higher maternal serum concentrations of triglyceride-rich lipoproteins. Especially triglycerides and cholesterol in VLDL were positively associated with BW. Women with high VLDL cholesterol or high apoB/apoA-1 may benefit from insulin treatment over metformin with respect to offspring BW.

Metformin and insulin treatment of gestational diabetes: effects on inflammatory markers and IGF-binding protein-1 - secondary analysis of a randomized controlled trial

Background

Gestational diabetes mellitus (GDM) is characterized by disturbed glucose metabolism and activation of low-grade inflammation. We studied whether metformin treatment has favorable or unfavorable effects on inflammatory markers and insulin-like growth factor-binding protein 1 (IGFBP-1) in GDM patients compared with insulin, and whether these markers associate with major maternal or fetal clinical outcomes.

Methods

This is a secondary analysis of a previous randomized controlled trial comparing metformin (n = 110) and insulin (n = 107) treatment of GDM. Fasting serum samples were collected at the time of diagnosis (baseline, mean 30 gestational weeks [gw]) and at 36 gw. Inflammatory markers serum high-sensitivity CRP (hsCRP), interleukin-6 (IL-6), matrix metalloproteinase-8 (MMP-8) and glycoprotein acetylation (GlycA) as well as three IGFBP-1 phosphoisoform concentrations were determined.

Results

In the metformin and insulin groups combined, hsCRP decreased (p = 0.01), whereas IL-6 (p = 0.002), GlycA (p < 0.0001) and all IGFBP-1 phosphoisoforms (p < 0.0001) increased from baseline to 36 gw. GlycA (p = 0.02) and non-phosphorylated IGFBP-1 (p = 0.008) increased more in patients treated with metformin than those treated with insulin. Inflammatory markers did not clearly associate with pregnancy outcomes but non-phosphorylated IGFBP-1 was inversely associated with gestational weight gain.

Conclusions

Metformin had beneficial effects on maternal serum IGFBP-1 concentrations compared to insulin, as increased IGFBP-1 related to lower total and late pregnancy maternal weight gain. GlycA increased more during metformin treatment compared to insulin. The significance of this observation needs to be more profoundly examined in further studies. There were no evident clinically relevant relations between inflammatory markers and pregnancy outcome measures.

Trial registration

The trial comparing metformin and insulin treatment was registered in ClinicalTrials.gov (NCT01240785) November 3, 2010. Retrospectively registered.

Metformin intervention prevents cardiac dysfunction in a murine model of adult congenital heart disease

OBJECTIVE

Congenital heart disease (CHD) is the most frequent birth defect worldwide. The number of adult patients with CHD, now referred to as ACHD, is increasing with improved surgical and treatment interventions. However the mechanisms whereby ACHD predisposes patients to heart dysfunction are still unclear. ACHD is strongly associated with metabolic syndrome, but how ACHD interacts with poor modern lifestyle choices and other comorbidities, such as hypertension, obesity, and diabetes, is mostly unknown.

METHODS

We used a newly characterized mouse genetic model of ACHD to investigate the consequences and the mechanisms associated with combined obesity and ACHD predisposition. Metformin intervention was used to further evaluate potential therapeutic amelioration of cardiac dysfunction in this model.

RESULTS

ACHD mice placed under metabolic stress (high fat diet) displayed decreased left ventricular ejection fraction. Comprehensive physiological, biochemical, and molecular analysis showed that ACHD hearts exhibited early changes in energy metabolism with increased glucose dependence as main cardiac energy source. These changes preceded cardiac dysfunction mediated by exposure to high fat diet and were associated with increased disease severity. Restoration of metabolic balance by metformin administration prevented the development of heart dysfunction in ACHD predisposed mice.

CONCLUSIONS

This study reveals that early metabolic impairment reinforces heart dysfunction in ACHD predisposed individuals and diet or pharmacological interventions can be used to modulate heart function and attenuate heart failure. Our study suggests that interactions between genetic and metabolic disturbances ultimately lead to the clinical presentation of heart failure in patients with ACHD. Early manipulation of energy metabolism may be an important avenue for intervention in ACHD patients to prevent or delay onset of heart failure and secondary comorbidities. These interactions raise the prospect for a translational reassessment of ACHD presentation in the clinic.

Amino acid profile in women with gestational diabetes mellitus treated with metformin or insulin

AIMS

We compared the effects of metformin and insulin treatments of gestational diabetes mellitus (GDM) on amino acid metabolism.

METHODS

217 pregnant women diagnosed with GDM were randomized to receive either metformin or insulin. ¹H nuclear magnetic spectroscopy was used to determine serum concentrations of alanine, glutamine, glycine, isoleucine, leucine, valine, histidine, phenylalanine, tyrosine, glucose and lactate at the time of diagnosis and at 36 gestational weeks (gw).

RESULTS

Majority of the amino acid concentrations increased from 30 to 36 gw. The rise in alanine (16% vs. 8%, p < 0.0001), isoleucine (11% vs. 5%, p = 0.035) and lactate (29% vs. 14% p = 0.015) was larger in the metformin group compared to insulin group. Baseline alanine, glycine, isoleucine, leucine, valine and tyrosine were positively related to slightly earlier delivery. Alanine at 36 gw was positively associated with birth weight and glutamine with gestational hypertension or preeclampsia. Lactate at 36 gw was not associated with any adverse outcome.

CONCLUSIONS

Compared to insulin metformin caused a greater increase in serum alanine, isoleucine and lactate concentrations. Although the observed differences in the metabolic variables were relatively small and not outright concerning, additional studies and follow-up data are required to ensure the safety of metformin use in pregnancy. The trial was registered in Clinicaltrials.gov, NCT01240785; http://clinicaltrials.gov/ct2/show/NCT01240785.

Blood Metabolomic Measures Associate With Present and Future Glycemic Control in Type 2 Diabetes

OBJECTIVE

We studied whether blood metabolomic measures in people with type 2 diabetes (T2D) are associated with insufficient glycemic control and whether this association is influenced differentially by various diabetes drugs. We then tested whether the same metabolomic profiles were associated with the initiation of insulin therapy.

METHODS

A total of 162 metabolomic measures were analyzed using a nuclear magnetic resonance-based method in people with T2D from four cohort studies (n = 2641) and one replication cohort (n = 395). Linear and logistic regression analyses with adjustment for potential confounders, followed by meta-analyses, were performed to analyze associations with hemoglobin A1c levels, six glucose-lowering drug categories, and insulin initiation during a 7-year follow-up period (n = 698).

RESULTS

After Bonferroni correction, 26 measures were associated with insufficient glycemic control (HbA1c >53 mmol/mol). The strongest association was with glutamine (OR, 0.66; 95% CI, 0.61 to 0.73; P = 7.6 × 10-19). In addition, compared with treatment-naive patients, 31 metabolomic measures were associated with glucose-lowering drug use (representing various metabolite categories; P ≤ 3.1 × 10-4 for all). In drug-stratified analyses, associations with insufficient glycemic control were only mildly affected by different glucose-lowering drugs. Five of the 26 metabolomic measures (apolipoprotein A1 and medium high-density lipoprotein subclasses) were also associated with insulin initiation during follow-up in both discovery and replication. The strongest association was observed for medium high-density lipoprotein cholesteryl ester (OR, 0.54; 95% CI, 0.42 to 0.71; P = 4.5 × 10-6).

CONCLUSION

Blood metabolomic measures were associated with present and future glycemic control and might thus provide relevant cues to identify those at increased risk of treatment failure.

Statin Effects on Metabolic Profiles: Data From the PREVEND IT (Prevention of Renal and Vascular End-stage Disease Intervention Trial)

BACKGROUND

Statins lower cholesterol by inhibiting HMG-CoA reductase, the rate-limiting enzyme of the metabolic pathway that produces cholesterol and other isoprenoids. Little is known about their effects on metabolite and lipoprotein subclass profiles. We, therefore, investigated the molecular changes associated with pravastatin treatment compared with placebo administration using a nuclear magnetic resonance-based metabolomics platform.

METHODS AND RESULTS

We performed metabolic profiling of 231 lipoprotein and metabolite measures in the PREVEND IT (Prevention of Renal and Vascular End-stage Disease Intervention Trial) study, a placebo-controlled randomized clinical trial designed to test the effects of pravastatin (40 mg once daily) on cardiovascular risk. Metabolic profiles were assessed at baseline and after 3 months of treatment. Pravastatin lowered low-density lipoprotein cholesterol (change in SD units [95% confidence interval]: -1.01 [-1.14, -0.88]), remnant cholesterol (change in SD units [95% confidence interval]: -1.03 [-1.17, -0.89]), and apolipoprotein B (change in SD units [95% confidence interval]: -0.98 [-1.11, -0.86]) with similar effect magnitudes. In addition, pravastatin globally lowered levels of lipoprotein subclasses, with the exception of high-density lipoprotein subclasses, which displayed a more heterogeneous response pattern. The lipid-lowering effect of pravastatin was accompanied by selective changes in lipid composition, particularly in the cholesterol content of very-low-density lipoproteinparticles. In addition, pravastatin reduced levels of several fatty acids but had limited effects on fatty acid ratios.

CONCLUSIONS

These randomized clinical trial data demonstrate the widespread effects of pravastatin treatment on lipoprotein subclass profiles and fatty acids.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifier: NCT03073018.

Linear regression and the normality assumption

OBJECTIVES

Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates.

STUDY DESIGN AND SETTING

Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient.

RESULTS

Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings.

CONCLUSION

Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations.