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

Aims: The EMPA-REG OUTCOME trial demonstrated that the sodium-glucose cotransporter-2 inhibitor (SGLT2) empagliflozin reduces the risk of cardiovascular (CV) and kidney outcomes in patients with type 2 diabetes. We previously developed the parameter response efficacy (PRE) score, which translates drug effects on multiple short-term risk markers into a predicted long-term treatment effect on clinical outcomes. The main objective of this study was to assess the accuracy of the PRE score in predicting the efficacy of empagliflozin in reducing the risk of CV and kidney outcomes. Methods: Short-term (baseline to 6-months) changes in glycated hemoglobin (HbA1c), systolic blood pressure (SBP), urinary-albumin-creatinine-ratio (UACR), hemoglobin, body weight, high-density-lipoprotein (HDL) cholesterol, low-density-lipoprotein (LDL) cholesterol, uric acid, and potassium were determined among 7020 patients with type 2 diabetes and established CV disease in the EMPA-REG OUTCOME trial. The beta-coefficients, derived from a Cox proportional hazards model in a pooled database consisting of 6355 patients with type 2 diabetes, were applied to the short-term risk markers in the EMPA-REG OUTCOME trial to predict the empagliflozin-induced impact on CV (defined as a composite of non-fatal myocardial infarction, non-fatal stroke, or CV death) and kidney (defined as a composite of doubling of serum creatinine or end-stage kidney disease) outcomes. Results: Empagliflozin compared to placebo reduced HbA1c (0.6%), SBP (4.2 mmHg), UACR (13.0%), body weight (2.1 kg), uric acid (20.4 μmol/L), and increased hemoglobin (6.6 g/L), LDL-cholesterol (0.1 mmol/L) and HDL-cholesterol (0.04 mmol/L) (all p<0.01). Integrating these effects in the PRE score resulted in a predicted relative risk reduction (RRR) for the CV outcome of 6.4% (95% CI 1.4–11.7), which was less than the observed 14.7% (95% CI 1.3–26.4%) RRR. For the kidney outcome, the PRE score predicted a RRR of 33.4% (95% CI 26.2–39.8); the observed RRR was 46.9% (95% CI 26.8–61.5). In a subgroup of 2,811 patients with UACR ≥30 mg/g at baseline, the PRE score predicted RRR was 40.8% (95% CI 31.2–49.1) vs. the observed RRR of 40.8% (95% CI 12.4–60.0) for the kidney outcome. Conclusions: Integrating multiple short-term risk marker changes in the PRE score underestimated the effect of empagliflozin on CV and kidney outcomes, suggesting that the currently used risk markers do not fully capture the effect of empagliflozin. In patients with increased albuminuria, the PRE score adequately predicted the effect of empagliflozin on kidney outcomes

A novel drug response score more accurately predicts renoprotective drug effects than existing renal risk scores

Background: Risk factor-based equations are used to predict risk of kidney disease progression in patients with type 2 diabetes order to guide treatment decisions. It is, however, unknown whether these models can also be used to predict the effects of drugs on clinical outcomes. Methods: The previously developed Parameter Response Efficacy (PRE) score, which integrates multiple short-term drug effects, was first compared with the existing risk scores, Kidney Failure Risk Equation (KFRE) and The Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) renal risk score, in its performance to predict end-stage renal disease (ESRD; KFRE) and doubling of serum creatinine or ESRD (ADVANCE). Second, changes in the risk scores were compared after 6 months' treatment to predict the long-term effects of losartan on these renal outcomes in patients with type 2 diabetes and chronic kidney disease. Results: The KFRE, ADVANCE and PRE scores showed similarly good performance in predicting renal risk. However, for prediction of the effect of losartan, the KFRE risk score predicted a relative risk change in the occurrence of ESRD of 3.1% [95% confidence interval (CI) -5 to 12], whereas the observed risk change was -28.8% (95% CI -42.0 to -11.5). For the composite endpoint of doubling of serum creatinine or ESRD, the ADVANCE score predicted a risk change of -12.4% (95% CI -17 to -7), which underestimated the observed risk change -21.8% (95% CI -34 to -6). The PRE score predicted renal risk changes that were close to the observed risk changes with losartan treatment [-24.0% (95% CI -30 to -17) and -22.6% (95% CI -23 to -16) for ESRD and the composite renal outcome, respectively]. Conclusion: A drug response score such as the PRE score may assist in improving clinical decision making and implement precision medicine strategies

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

Critical point network for drainage between rough surfaces

In this paper, we present a network method for computing two-phase flows between two rough surfaces with significant contact areas. Low-capillary number drainage is investigated here since one-phase flows have been previously investigated in other contributions. An invasion percolation algorithm is presented for modeling slow displacement of a wetting fluid by a non wetting one between two rough surfaces. Short-correlated Gaussian process is used to model random rough surfaces.The algorithm is based on a network description of the fracture aperture field. The network is constructed from the identification of critical points (saddles and maxima) of the aperture field. The invasion potential is determined from examining drainage process in a flat mini-channel. A direct comparison between numerical prediction and experimental visualizations on an identical geometry has been performed for one realization of an artificial fracture with a moderate fractional contact area of about 0.3. A good agreement is found between predictions and observations

Enkephalon - technological platform to support the diagnosis of alzheimer’s disease through the analysis of resonance images using data mining techniques

Dementia can be considered as a decrease in the cognitive function of the person. The main diseases that appear are Alzheimer and vascular dementia. Today, 47 million people live with dementia around the world. The estimated total cost of dementia worldwide is US $ 818 billion, and it will become a trilliondollar disease by 2019 The vast majority of people with dementia not received a diagnosis, so they are unable to access care and treatment. In Colombia, two out of every five people presented a mental disorder at some point in their lives and 90% of these have not accessed a health service. Here it´s proposed a technological platform so early detection of Alzheimer. This tool complements and validates the diagnosis made by the health professional, based on the application of Machine Learning techniques for the analysis of a dataset, constructed from magnetic resonance imaging, neuropsychological test and the result of a radiological test. A comparative analysis of quality metrics was made, evaluating the performance of different classifier methods: Random subspace, Decorate, BFTree, LMT, Ordinal class classifier, ADTree and Random forest. This allowed us to identify the technique with the highest prediction rate, that was implemented in ENKEPHALON platform

Approximate Ginzburg-Landau solution for the regular flux-line lattice. Circular cell method

A variational model is proposed to describe the magnetic properties of type-II superconductors in the entire field range between $H_{c1}$ and $H_{c2}$ for any values of the Ginzburg-Landau parameter $\kappa>1/\sqrt{2}$. The hexagonal unit cell of the triangular flux-line lattice is replaced by a circle of the same area, and the periodic solutions to the Ginzburg-Landau equations within this cell are approximated by rotationally symmetric solutions. The Ginzburg-Landau equations are solved by a trial function for the order parameter. The calculated spatial distributions of the order parameter and the magnetic field are compared with the corresponding distributions obtained by numerical solution of the Ginzburg-Landau equations. The comparison reveals good agreement with an accuracy of a few percent for all $\kappa$ values exceeding $\kappa \approx 1$. The model can be extended to anisotropic superconductors when the vortices are directed along one of the principal axes. The reversible magnetization curve is calculated and an analytical formula for the magnetization is proposed. At low fields, the theory reduces to the London approach at $\kappa \gg 1$, provided that the exact value of $H_{c1}$ is used. At high fields, our model reproduces the main features of the well-known Abrikosov theory. The magnetic field dependences of the reversible magnetization found numerically and by our variational method practically coincide. The model also refines the limits of some approximations which have been widely used. The calculated magnetization curves are in a good agreement with experimental data on high-T$_c$ superconductors.Comment: 8 pages, RevTex, 6 figures, submitted to Phys. Rev.

Ultrasound measurements of brain structures differ between moderate-late preterm and full-term infants at term equivalent age

Background: Brain growth in moderate preterm (MP; gestational age (GA) 32(+0)-33(+6) weeks) and late preterm infants (LP; GA 34(+0)-36+6 weeks) may be impaired, even in the absence of brain injury.Aims: The aims of this study were to assess brain measurements of MP and LP infants, and to compare these with full-term infants (GA > 37 weeks) using linear cranial ultrasound (cUS) at term equivalent age (TEA).Study design: cUS data from two prospective cohorts were combined. Two investigators performed offline measurements on standard cUS planes. Eleven brain structures were compared between MP, LP and full-term infants using uni-and multivariable linear regression.Results were adjusted for postmenstrual age at cUS and corrected for multiple testing. Results: Brain measurements of 44 MP, 54 LP and 52 full-term infants were determined on cUS scans at TEA. Biparietal diameter and basal ganglia-insula width were smaller in MP (-9.1 mm and -1.7 mm, p < 0.001) and LP infants (-7.0 mm and -1.7 mm, p < 0.001) compared to full-term infants. Corpus callosum - fastigium length was larger in MP (+2.2 mm, p < 0.001) than in full-term infants. No significant differences were found between MP and LP infants.Conclusions: These findings suggest that brain growth in MP and LP infants differs from full-term infants. Whether these differences have clinical implications remains to be investigated.Research into fetal development and medicin

Tristetraprolin regulates interleukin‐6, which is correlated with tumor progression in patients with head and neck squamous cell carcinoma

BACKGROUND: Tumor‐derived cytokines play a significant role in the progression of head and neck squamous cell carcinoma (HNSCC). Targeting proteins, such as tristetraprolin (TTP), that regulate multiple inflammatory cytokines may inhibit the progression of HNSCC. However, TTP's role in cancer is poorly understood. The goal of the current study was to determine whether TTP regulates inflammatory cytokines in patients with HNSCC. METHODS: TTP messenger RNA (mRNA) and protein expression were determined by quantitative real‐time–polymerase chain reaction (Q‐RT‐PCR) and Western blot analysis, respectively. mRNA stability and cytokine secretion were evaluated by quantitative RT‐PCR and enzyme‐linked immunoadsorbent assay, respectively, after overexpression or knockdown of TTP in HNSCC. HNSCC tissue microarrays were immunostained for interleukin‐6 (IL‐6) and TTP. RESULTS: TTP expression in HNSCC cell lines was found to be inversely correlated with the secretion of IL‐6, vascular endothelial growth factor (VEGF), and prostaglandin E2 (PGE 2 ) . Knockdown of TTP increased mRNA stability and the secretion of cytokines. Conversely, overexpression of TTP in HNSCC cells led to decreased secretion of IL‐6, VEGF, and PGE 2 . Immunohistochemical staining of tissue microarrays for IL‐6 demonstrated that staining intensity is prognostic for poor disease‐specific survival ( P = .023), tumor recurrence and development of second primary tumors ( P = .014), and poor overall survival ( P = .019). CONCLUSIONS: The results of the current study demonstrated that down‐regulation of TTP in HNSCC enhances mRNA stability and promotes secretion of IL‐6, VEGF, and PGE 2 . Furthermore, high IL‐6 secretion in HNSCC tissue is a biomarker for poor prognosis. In as much as enhanced cytokine secretion is associated with poor prognosis, TTP may be a therapeutic target to reduce multiple cytokines concurrently in patients with HNSCC. Cancer 2011. © 2011 American Cancer Society. Tristetraprolin (TTP), a protein that decreases the stability of messenger RNA (mRNA) of cytokines and proinflammatory factors, is reduced in patients with head and neck squamous cell carcinoma with a corresponding increase in interleukin‐6 (IL‐6), vascular endothelial growth factor, and cyclooxygenase‐2 secretion. One of these tumor‐derived cytokines, IL‐6, is prognostic for poor disease‐specific survival, tumor recurrence, second primary lesions, and poor overall survival.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86915/1/25859_ftp.pd