Prediction of the Effects of Empagliflozin on Cardiovascular and Kidney Outcomes Based on Short-Term Changes in Multiple Risk Markers

Development and validation of Empagliflozin and Linagliptin simultaneous estimation in rat plasma using freezing lipid precipitation and SCX-SPE assisted HPLC-MS/MS method and its application in pharmacokinetic studies

A quick and sensitive liquid chromatography-mass spectrometry technique was designed, improved, and validated for simultaneous determination of Empagliflozin (EPG) and Linagliptin (LNG) using Empagliflozin-d4 (EPG-d4) and linagliptin-d4 (LNG-d4) as internal standards (IS) in rat plasma. Target analytes and the IS were extracted using freezing lipid precipitation (FLP) and optimized using the strong cation exchange solid phase extraction (SCX-SPE) method to achieve the maximum sample clean-up. In particular, when combined with SPE clean-up, FLP can efficiently eliminate the plasma sample's high lipid content. More than 84.14% of plasma lipids were rapidly removed during the FLP procedure, with minimal loss of EPG and LNG. We used LC-atmospheric chemical ionization (APCI)-mass spectrometry was employed to assess the efficiency of FLP in lipid removal. The SCX-SPE cartridges removed the remaining impurities from EPG and LNG, allowing for further purification. The samples were chromatographically separated using a Spherisorb RP/Cyano column by pumping a gradient mobile phase comprised of acetonitrile and 25 mM ammonium acetate buffer (pH 8.1) in positive ion mode at a flow rate of 0.8 mL/min. The selected reaction monitoring technique was performed using a Waters triple-stage quadrupole tandem mass spectrometer equipped with an electrospray ionization (ESI) source. The chromatographic separation was accomplished using a Waters Acquity® high-performance liquid chromatography (HPLC) system. Mass transition (m/z) of 451.15/71.12 for EPG, m/z 473.27/419.94 for LNG; m/z 455.19/71.12 for EPG-d4, and 477.27/423.94 for LNG-d4 was successfully achieved. This study successfully examined the concentration ranges of 25-1050 ng/mL for EPG and 0.35-15 ng/mL for LNG. The results showed that the linearity of EPG ranged from 25.14 to 985.26 ng/mL, while the linearity of LNG ranged from 0.59 to 14.86 ng/mL. The relative standard deviation (RSD) for both EPG and LNG, within and between days, were below 3.83%, indicating that they fall within acceptable limits. This novel approach demonstrated favourable outcomes in a pharmacokinetic study involving healthy rats, where EPG and LNG were co-administered. This study found that the co-administration of both drugs did not have a significant impact on their pharmacokinetic behavior, suggesting the absence of any drug-drug interactions.

Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study

(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases and should hold information about the optimal means of treatment and prevention. (2) Methods: We investigated the prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2, and CAD160 in a cohort of 5585 subjects, in a retrospective study. (3) Results: We have demonstrated a highly significant prediction of events, with an HR of 2.59, 1.71, and 4.12 for HF, CAD, and CKD, respectively. We applied in silico treatment, implementing on each patient's urinary profile changes to the classifiers corresponding to exactly defined peptide abundance changes, following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil, and exercise), as defined in previous studies. Applying the proteomic classifiers after the in silico treatment indicated the individual benefits of specific interventions on a personalized level. (4) Conclusions: The in silico evaluation may provide information on the future impact of specific drugs and interventions on endpoints, opening the door to a precision-based medicine approach. An investigation into the extent of the benefit of this approach in a prospective clinical trial is warranted.

Short-term anti-remodeling effects of gliflozins in diabetic patients with heart failure and reduced ejection fraction: an explainable artificial intelligence approach

Introduction: Sodium-glucose cotransporter type 2 inhibitors (SGLT2i), gliflozins, play an emerging role for the treatment of heart failure with reduced left ventricular ejection fraction (HFrEF). Nevertheless, the effects of SGLT2i on ventricular remodeling and function have not been completely understood yet. Explainable artificial intelligence represents an unprecedented explorative option to clinical research in this field. Based on echocardiographic evaluations, we identified some key clinical responses to gliflozins by employing a machine learning approach. Methods: Seventy-eight consecutive diabetic outpatients followed for HFrEF were enrolled in the study. Using a random forests classification, a single subject analysis was performed to define the profile of patients treated with gliflozins. An explainability analysis using Shapley values was used to outline clinical parameters that mostly improved after gliflozin therapy and machine learning runs highlighted specific variables predictive of gliflozin response. Results: The five-fold cross-validation analyses showed that gliflozins patients can be identified with a 0.70 ± 0.03% accuracy. The most relevant parameters distinguishing gliflozins patients were Right Ventricular S'-Velocity, Left Ventricular End Systolic Diameter and E/e' ratio. In addition, low Tricuspid Annular Plane Systolic Excursion values along with high Left Ventricular End Systolic Diameter and End Diastolic Volume values were associated to lower gliflozin efficacy in terms of anti-remodeling effects. Discussion: In conclusion, a machine learning analysis on a population of diabetic patients with HFrEF showed that SGLT2i treatment improved left ventricular remodeling, left ventricular diastolic and biventricular systolic function. This cardiovascular response may be predicted by routine echocardiographic parameters, with an explainable artificial intelligence approach, suggesting a lower efficacy in case of advanced stages of cardiac remodeling.

The importance of targeting multiple risk markers in patients with type 2 diabetes: A post-hoc study from the CANVAS programme

AIMS

To investigate the extent to which improvements in multiple cardiovascular risk markers are associated with a lower risk of cardiovascular and kidney outcomes in patients with type 2 diabetes and high cardiovascular risk participating in the CANVAS programme.

MATERIALS AND METHODS

Clinically relevant improvements in cardiovascular risk factors were defined as a reduction in glycated haemoglobin ≥1.0%, systolic blood pressure ≥10 mmHg, body weight ≥3 kg, urinary-albumin-creatinine ratio ≥30%, uric acid ≥0.5 mg/dl, and an increase in haemoglobin of ≥1.0 g/dl from baseline to week 26. Participants were categorized according to the number of improvements in cardiovascular risk markers: zero, one, two, three, or four or more risk marker improvements. The Cox proportional hazard regression adjusted for treatment assignment, demographic variables and laboratory measurements was performed to determine the association between the number of risk marker improvements and risk of a composite cardiovascular, heart failure or kidney outcomes.

RESULTS

We included 9487 (93.5%) participants with available data at baseline and week 26. After week 26, 566 composite cardiovascular, 370 heart failure/cardiovascular death and 153 composite kidney outcomes occurred. The multivariable adjusted hazard ratios associated with four or more improvements in risk markers versus no risk marker improvement were 0.67 (95% CI 0.48, 0.92), 0.58 (95% CI 0.39, 0.87) and 0.49 (95% CI 0.25, 0.96) for the three outcomes respectively. We observed a trend of decreased hazard ratios across subgroups of increasing number of risk marker improvements (p for trend = .008, .02 and .047, respectively).

CONCLUSIONS

In patients with type 2 diabetes, improvements in multiple risk markers were associated with a reduced risk of cardiovascular and kidney outcomes as compared with no risk marker improvement.

Renoprotective effects of empagliflozin in type 1 and type 2 models of diabetic nephropathy superimposed with hypertension

Diabetes, hypertension, and aging are major contributors to cardiovascular and chronic kidney disease (CKD). Sodium/glucose cotransporter 2 (SGLT2) inhibitors have become a preferred treatment for type II diabetic patients since they have cardiorenal protective effects. However, most elderly diabetic patients also have hypertension, and the effects of SGLT2 inhibitors have not been studied in hypertensive diabetic patients or animal models. The present study examined if controlling hyperglycemia with empagliflozin, or given in combination with lisinopril, slows the progression of renal injury in hypertensive diabetic rats. Studies were performed using hypertensive streptozotocin-induced type 1 diabetic Dahl salt-sensitive (STZ-SS) rats and in deoxycorticosterone-salt hypertensive type 2 diabetic nephropathy (T2DN) rats. Administration of empagliflozin alone or in combination with lisinopril reduced blood glucose, proteinuria, glomerular injury, and renal fibrosis in STZ-SS rats without altering renal blood flow (RBF) or glomerular filtration rate (GFR). Blood pressure and renal hypertrophy were also reduced in rats treated with empagliflozin and lisinopril. Administration of empagliflozin alone or in combination with lisinopril lowered blood glucose, glomerulosclerosis, and renal fibrosis but had no effect on blood pressure, kidney weight, or proteinuria in hypertensive T2DN rats. RBF was not altered in any of the treatment groups, and GFR was elevated in empagliflozin-treated hypertensive T2DN rats. These results indicate that empagliflozin is highly effective in controlling blood glucose levels and slows the progression of renal injury in both hypertensive type 1 and type 2 diabetic rats, especially when given in combination with lisinopril to lower blood pressure.

Repurposing SGLT-2 Inhibitors to Target Aging: Available Evidence and Molecular Mechanisms

Caloric restriction promotes longevity in multiple animal models. Compounds modulating nutrient-sensing pathways have been suggested to reproduce part of the beneficial effect of caloric restriction on aging. However, none of the commonly studied caloric restriction mimetics actually produce a decrease in calories. Sodium-glucose cotransporter 2 inhibitors (SGLT2-i) are a class of drugs which lower glucose by promoting its elimination through urine, thus inducing a net loss of calories. This effect promotes a metabolic shift at the systemic level, fostering ketones and fatty acids utilization as glucose-alternative substrates, and is accompanied by a modulation of major nutrient-sensing pathways held to drive aging, e.g., mTOR and the inflammasome, overall resembling major features of caloric restriction. In addition, preliminary experimental data suggest that SGLT-2i might also have intrinsic activities independent of their systemic effects, such as the inhibition of cellular senescence. Consistently, evidence from both preclinical and clinical studies have also suggested a marked ability of SGLT-2i to ameliorate low-grade inflammation in humans, a relevant driver of aging commonly referred to as inflammaging. Considering also the amount of data from clinical trials, observational studies, and meta-analyses suggesting a tangible effect on age-related outcomes, such as cardiovascular diseases, heart failure, kidney disease, and all-cause mortality also in patients without diabetes, here we propose a framework where at least part of the benefit provided by SGLT-2i is mediated by their ability to blunt the drivers of aging. To support this postulate, we synthesize available data relative to the effect of this class on: 1- animal models of healthspan and lifespan; 2- selected molecular pillars of aging in preclinical models; 3- biomarkers of aging and especially inflammaging in humans; and 4- COVID-19-related outcomes. The burden of evidence might prompt the design of studies testing the potential employment of this class as anti-aging drugs.

Prediction of the Effects of Liraglutide on Kidney and Cardiovascular Outcomes Based on Short-Term Changes in Multiple Risk Markers

Aims: The LEADER trial demonstrated that the glucagon-like peptide-1 receptor agonist (GLP1-RA) liraglutide reduces kidney and cardiovascular (CV) risk in patients with type 2 diabetes. We previously developed a Parameter Response Efficacy (PRE) score that translates multiple short-term risk marker changes, from baseline to first available follow-up measurement, into a predicted long-term drug effect on clinical outcomes. The objective of this study was to assess the accuracy of the PRE score in predicting the efficacy of liraglutide in reducing the risk of kidney and CV outcomes.

Methods: Short-term changes in glycated hemoglobin (HbA1c), systolic blood pressure (BP), urinary-albumin-creatinine-ratio (UACR), hemoglobin, body weight, high-density-lipoprotein (HDL) cholesterol, low-density-lipoprotein (LDL) cholesterol, and potassium were monitored in the LEADER trial. Associations between risk markers and kidney or CV outcomes were established using a multivariable Cox proportional hazards model in a separate pooled database of 6,355 patients with type 2 diabetes. The regression coefficients were then applied to the short-term risk markers in the LEADER trial to predict the effects of liraglutide on kidney (defined as a composite of doubling of serum creatinine or end-stage kidney disease) and CV (defined as a composite of non-fatal myocardial infarction, non-fatal stroke, and CV death) outcomes.

Results: Liraglutide compared to placebo reduced HbA1c (1.4%), systolic BP (3.0 mmHg), UACR (13.2%), body weight (2.3 kg), hemoglobin (2.6 g/L), and increased HDL-cholesterol (0.01 mmol/L) (all p-values <0.01). Integrating multiple risk marker changes in the PRE score resulted in a predicted relative risk reduction (RRR) of 16.2% (95% CI 13.7–18.6) on kidney outcomes which was close to the observed RRR of 15.5% (95% CI -9.0–34.6). For the CV outcome, the PRE score predicted a 7.6% (95% CI 6.8–8.3) RRR, which was less than the observed 13.2% (95% CI 3.2–22.2) RRR.

Conclusion: Integrating multiple short-term risk markers using the PRE score adequately predicted the effect of liraglutide on the composite kidney outcome. However, the PRE score underestimated the effect of liraglutide for the composite CV outcome, suggesting that the risk markers included in the PRE score do not fully capture the CV benefit of liraglutide.