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Sodium-Glucose Transporter 2 Inhibitors

Sodium-Glucose Transporter 2 Inhibitors are a class of medications used to manage diabetes by reducing blood glucose levels.
These drugs work by blocking the sodium-glucose cotransporter 2 (SGLT2), a protein responsible for reabsorbing glucose in the kidneys.
By inhibiting SGLT2, more glucose is excreted through urine, leading to lowered blood sugar concentrations.
Sodium-Glucose Transporter 2 Inhibitors are an important theraputic option for patients with type 2 diabetes, as they can help improve glycemic control and reduce the risk of diabetes-related complications.
Reasearchers can optimize their SGLT2 Inhibitor studies using PubCompare.ai, an AI-driven platform that enhances reproducibility and accuracy by locating protocols from literature, preprints, and patents, and providing AI-driven comparisons to identify the best protocols and products.

Most cited protocols related to «Sodium-Glucose Transporter 2 Inhibitors»

1
Interactive Homeostatic Model of Glucose
Cited 14 times
The iHOMA2 model is shown in Fig. 1 as graphical (A), box diagrammatic (B), and mathematical (C), respectively. iHOMA2 is an integrated computer-based mathematical model of glucose and hormonal interaction under homeostatic conditions. The model, now available online at http://www.ihoma.co.uk, runs in real time with 24 operator-controlled variables (Table 1) and graphical output displays. The baseline characteristics were built from those used in the original HOMA2 model, with all of the dose-response variables now explicit. iHOMA2 runs interactively and exactly for each calculation. iHOMA2 in its default start-up setting gives identical readings to HOMA2 and can be used as a direct substitute for HOMA2 in this mode. The operator can modify each of the variables using an interactive sliding control display. The operator can control every aspect of the dose-response curve. For example, the β-cell characteristics are described by P1–P5, each of these being independently adjustable. This allows “what if” scenarios to be explored: “What would be the effect on glucose if Vmax of β-cell function were 50%?” “How might that be modified if the dose response curve were shifted to the left?” “What if autonomous insulin secretion continued at low blood glucose?” Similarly, the functions relating to the other organs and tissues involved in the glucose and hormonal compartments can be modified using sliding control displays. In the HOMA2 model, insulin sensitivity is treated as a whole-body effect, altering the liver and periphery to the same extent. In iHOMA2, this has been uncoupled and the insulin sensitivity of these organs and tissues can be modified independently. The ability to alter the 24 variables of iHOMA2 enables the modeling of known or surmised pathology and physiology and the effect of treatments both alone and in combination. The effects of the treatments on fasting glucose, insulin, β-cell function (%B), and insulin sensitivity (%S) are graphically represented in the model.
The model allows for analytical and predictive modes of use. The analytical mode allows insulin resistance and β-cell function to be read from the input of insulin and glucose in the basal state, while the predictive function shows the estimated and modeled insulin and glucose concentrations in the basal state when the β-cell function and insulin resistance parameters are set.
This article shows two detailed quantified scenarios to illustrate the interactive modalities. The first example shows that the effect of pioglitazones (thiazolidinediones) on insulin resistance can be partitioned between the liver and periphery. The second example illustrates the model’s use elucidating the effect of an SGLT2 inhibitor on glycemia. All analyses were performed using SPSS, version 19.0 (SPSS, Chicago, IL). Statistical comparisons were made using Z tests for skewness, Student independent samples t test for comparison of means, and F tests for assessment of fit of the model to the observed data (15 ).
Hill N.R., Levy J.C, & Matthews D.R. (2013). Expansion of the Homeostasis Model Assessment of β-Cell Function and Insulin Resistance to Enable Clinical Trial Outcome Modeling Through the Interactive Adjustment of Physiology and Treatment Effects: iHOMA2. Diabetes Care, 36(8), 2324-2330.
Publication 2013
Blood Glucose Glucose Homeostasis Human Body Insulin Insulin Resistance Insulin Secretion Insulin Sensitivity Liver Pancreatic beta Cells Physiology, Cell Sodium-Glucose Transporter 2 Inhibitors Student Thiazolidinediones Tissues
2
Evaluating SGLT2 Inhibitors in Heart Failure
Cited 5 times
Men and women aged 18 years or older with a diagnosis of heart failure for at least 2 months are eligible if they are in New York Heart Association functional class II or above, have a left ventricular ejection fraction documented to be ≤ 40% within the last 12 months, are optimally treated with pharmacological and device therapy for heart failure, and willing to provide written informed consent. In addition, patients must have a N‐terminal pro B‐type natriuretic peptide concentration ≥ 600 pg/mL or, if hospitalised for heart failure within the previous 12 months, ≥ 400 pg/mL. Patients with atrial fibrillation or atrial flutter must have a level ≥ 900 pg/mL, irrespective of history of heart failure hospitalization. Full details are provided in the online supplementary Appendix S1.
Key exclusion criteria include: recent treatment with or intolerance of a SGLT2 inhibitor, type 1 diabetes mellitus, symptoms of hypotension or systolic blood pressure < 95 mmHg, recent worsening heart failure or other cardiovascular events or procedures (or planned procedures), estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2 (or rapidly declining renal function) and other conditions likely to prevent patient participation in the trial or greatly limit life expectancy. A full list of exclusion criteria is provided in Table1.
McMurray J.J., DeMets D.L., Inzucchi S.E., Køber L., Kosiborod M.N., Langkilde A.M., Martinez F.A., Bengtsson O., Ponikowski P., Sabatine M.S., Sjöstrand M, & Solomon S.D. (2019). A trial to evaluate the effect of the sodium–glucose co‐transporter 2 inhibitor dapagliflozin on morbidity and mortality in patients with heart failure and reduced left ventricular ejection fraction (DAPA‐HF). European Journal of Heart Failure, 21(5), 665-675.
Publication 2019
Amino-terminal pro-brain natriuretic peptide Atrial Fibrillation Atrial Flutter Cardiovascular System Congestive Heart Failure Diabetes Mellitus, Insulin-Dependent Diagnosis Glomerular Filtration Rate Heart Hospitalization Kidney Medical Devices Patient Participation Patients Sodium-Glucose Transporter 2 Inhibitors Systolic Pressure Ventricular Ejection Fraction Woman
3
Cardiovascular Risk in SGLT2 Inhibitor Users
Cited 5 times
We used TriNetX, a global federated research network providing access to statistics on EMR (diagnoses, procedures, medications, laboratory values, genomic information). The analytics subset allowed the analysis of approximately 38 million patients in 35 large Health Care Organizations predominately in the United States. As a federated network, TriNetX received a waiver from Western IRB, since only aggregated counts, statistical summaries of de-identified information, and no protected health information is received. In addition, no study-specific activities are performed in retrospective analyses. Details of the network have been described elsewhere[6 -8 ]. All analyses were done in the TriNetX “Analytics” network using the browser-based real-time analytics features. At the time of the analysis in June 2018, we analyzed the EMR of 46909 patients in the network who had an instance of any SGLT2 inhibitor (empagliflozin, dapagliflozin or canagliflozin) any time within the past ten years in their electronic medical record. As a comparison group, we chose patients who had taken dipeptidyl peptidase (DPP) 4 inhibitors (linagliptin, alogliptin, sitagliptin or saxagliptin) during the same time, and found 189120 patients. Using a Bayesian statistical approach[9 ] on demographics and pre-existing (baseline) comorbidities of the two groups, we identified five potential confounding factors and built strata with the following criteria: age ≥ 60 years, presence of hypertension [International Classification of Diseases (ICD)10 code I10], presence of CKD (ICD10 code N18), co-medication with insulin, and co-medication with metformin. Separately analyzing strata allowed us to address potential bias in the federated data model without direct access to the individual data sets on the patient level.
Cardiovascular events were counted by selecting any stroke (ICD10 code I63) or myocardial infarction (ICD10 code I21) occurring during a three-year observation period after the first instance of the above mentioned medications in the patients’ records.
The risks of experiencing an event in each stratum were calculated by dividing the number of patients with an event (numerator) by the total number of patients with the respective medication in each stratum (denominator). The risk ratios for SGLT2 inhibitors vs the comparison group were calculated by dividing the risk for each SGLT2 stratum by the risk in each corresponding DPP4 stratum.
, & Stapff M.P. (2018). Using real world data to assess cardiovascular outcomes of two antidiabetic treatment classes. World Journal of Diabetes, 9(12), 252-257.
Publication 2018
alogliptin Canagliflozin Cardiovascular System Cerebrovascular Accident dapagliflozin Diagnosis Dipeptidyl-Peptidase IV Inhibitors DPP4 protein, human empagliflozin Genome High Blood Pressures Insulin Linagliptin Metformin Myocardial Infarction Patients Pharmaceutical Preparations saxagliptin Sitagliptin SLC5A2 protein, human Sodium-Glucose Transporter 2 Inhibitors
4
Genetic Proxies for SGLT2 Inhibition
Cited 4 times
The selection of genetic variants that were proxies of SGLT2 inhibition involved four steps (Fig. 1A). 1) Select genetic variants associated with mRNA expression level of SLC5A2 gene using data from Genotype-Tissue Expression (GTEx) (20 (link)) and eQTLGen Consortium (21 (link)) and the potential functional gene of SGLT2 inhibitors (Supplementary Table 1). 2) Estimate the association of each SLC5A2 variant with HbA1c level (an indicator of glucose-lowering effect via SGLT2 inhibition) and select variants that show regional-wide association with HbA1c using data from a subgroup of unrelated individuals of European ancestry without diabetes in the UK Biobank (n = 344,182) (association P value = 1 × 10−4) (Supplementary Table 1) (22 ). 3) Validate whether SLC5A2 and HbA1c share the same causal variant using the genetic colocalization approach. Colocalization is a bivariable genetic approach with application of a Bayesian model to estimate the posterior probability that the two traits, expression of SLC5A2 and circulating HbA1c level, share the same causal variant in the SLC5A2 region (19 (link)). Colocalization probability >70% between SLC5A2 expression and HbA1c was used as evidence of colocalization and noted as “colocalized.” The rest of the gene-disease associations were noted as “not colocalized” (Supplementary Table 1). And 4) after selection and validation, conduct a standard clumping process (using correlation among variants <0.8 as threshold to remove variants with very high correlation). To quantify the statistical power of the genetic variants, we estimated the strength of the genetic predictors of each tested exposure using F statistics. After selection and multiple validation steps, six genetic variants robustly associated with SGLT2 inhibition via HbA1c were selected as genetic predictors for the MR analysis (Supplementary Table 2).
Xu M., Zheng J., Hou T., Lin H., Wang T., Wang S., Lu J., Zhao Z., Li M., Xu Y., Ning G., Bi Y, & Wang W. (2022). SGLT2 Inhibition, Choline Metabolites, and Cardiometabolic Diseases: A Mediation Mendelian Randomization Study. Diabetes Care, 45(11), 2718-2728.
Publication 2022
Diabetes Mellitus Europeans Gene Expression Genes Genetic Diversity Genotype Glucose Hereditary Diseases Psychological Inhibition Reproduction RNA, Messenger SLC5A2 protein, human Sodium-Glucose Transporter 2 Inhibitors Tissues
5
CKD Risk Reduction Protocol
Cited 4 times
Adults ≥18 years of age were eligible, provided a local investigator judged that they neither required an SGLT-2 inhibitor nor that such treatment was inappropriate. Participants were also required to have CKD and to be at risk of progressive disease. This was established using local laboratory results from samples taken both ≥3 months before and at the time of the screening visit and defined as either an eGFR ≥20 but <45 mL/min/1.73 m2 or an eGFR ≥45 but <90 mL/min/1.73 m2 with a uACR ≥200 mg/g. All trial eGFR values are estimated using the Chronic Kidney Disease Epidemiology Collaboration creatinine formula available at the start of recruitment (i.e. adjustment for race was included) [39 (link)]. People with or without DM were eligible, although it was pre-specified that the trial should recruit at least one-third of its sample from each patient group. Participants were also required to be prescribed a clinically appropriate dose of single-agent RAS inhibitor, unless such treatment was either not tolerated or not indicated (as judged by a local investigator). Figure 2 provides all eligibility criteria. The only excluded primary renal diagnosis was polycystic kidney disease. The initial approved protocol (V1.4) excluded participants on immunosuppression or >10 mg prednisolone. Protocol V2.0 was implemented on 21 May 2021 and removed this exclusion unless they were on prednisolone >45 mg or had received intravenous immunosuppression in the last 3 months. Following a request from the sponsor, Protocol V2.0 also further excluded people with type 1 DM (i.e. no safety concern had been reported by the DMC).
Design, recruitment, and baseline characteristics of the EMPA-KIDNEY trial. (2022). Nephrology Dialysis Transplantation, 37(7), 1317-1329.
Publication 2022
Adult Chronic Kidney Diseases Creatinine Diagnosis EGFR protein, human Eligibility Determination Immunosuppression Kidney Patients Polycystic Kidney Diseases Prednisolone Safety Sodium-Glucose Transporter 2 Inhibitors

Most recents protocols related to «Sodium-Glucose Transporter 2 Inhibitors»

1
Covariate Adjustment and Propensity Scoring in SGLT2i Research
To mitigate risk of confounding, we assessed and adjusted for > 30 baseline covariates that were assessed in the 12-month period prior to and including the index date. These covariates included patient sociodemographics (e.g., age at medication initiation, biological sex, and race, calendar year), complications of diabetes (e.g., diabetic neuropathy, nephropathy, retinopathy), oral and injectable glucose lowering therapies (e.g., metformin, sulfonylureas, insulin), diagnosis of cardiovascular conditions (e.g., myocardial infarction, stroke, HF), and cardiovascular medication use (e.g., dispensing of β-blockers, loop diuretics, statins). Frailty status was ascertained using the claims based frailty index, and using a threshold of ≥ 0.25 to define frailty [23 (link)].
Propensity scores were estimated using a logistic regression that modelled the probability of initiating SGLT2i (exposure) versus a non-gliflozin medication (control) conditional on the baseline covariates. These propensity scores were then used to estimate stabilized inverse probability of treatment weights (IPTW) to account for imbalances in patient characteristics [24 (link)].
Gonzalez J., Bates B.A., Setoguchi S., Gerhard T, & Dave C.V. (2023). Cardiovascular outcomes with SGLT2 inhibitors versus DPP4 inhibitors and GLP-1 receptor agonists in patients with heart failure with reduced and preserved ejection fraction. Cardiovascular Diabetology, 22, 54.
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Publication 2023
Biopharmaceuticals Cardiovascular Agents Cardiovascular Diseases Cerebrovascular Accident Complications of Diabetes Mellitus Diabetic Neuropathies Diagnosis Glucose Hydroxymethylglutaryl-CoA Reductase Inhibitors Insulin Kidney Diseases Loop Diuretics Metformin Myocardial Infarction Patients Pharmaceutical Preparations Retinal Diseases Sodium-Glucose Transporter 2 Inhibitors Sulfonylurea Compounds
2
Evaluating IPTW for Confounding Control
We assessed the performance of propensity scores based IPTW to control for confounding by examining the distribution of baseline covariates prior and after IPT weighting, and using a threshold of 10% in standardized difference as a metric for a meaningful imbalance [25 (link)]. Using an as-treated approach, where patients were censored on treatment discontinuation or switching, we estimated the rates of the primary outcomes among patients using SGLT2i (exposure) or non-gliflozin medications (control) by calculating the number of events and incidence rates (IRs). Adjusted incidence-rate differences (RD) and hazard ratios (HR) along with their 95% confidence intervals (CIs) were modelled through weighted Cox and Poisson regressions respectively.
Sensitivity and secondary analyses were conducted to assess the robustness of the study findings. First, we examined several secondary outcomes including a composite of the two primary outcomes (i.e., HF, MI or stroke hospitalizations), as well as individually examined MI hospitalizations, stroke hospitalizations, and all-cause mortality. Second, we conducted sensitivity analyses varying exposure-related censoring criteria, where instead of censoring patients at the time of treatment switching or discontinuation, we carried the index exposure forward to mimic an intention-to-treat approach with a maximum follow up truncated to 2 years.
Third, as our primary definitions to identify HF subtypes prioritize positive predictive values at the possible cost of lowered sensitivity (i.e., under-detection of patients with HF), we also employed alternative-more sensitive-HF definitions to identify HFrEF and HFpEF patients. More specifically, we allowed patients to be included in the study if they had presence of relevant HF codes in (1) any position of the inpatient discharge diagnosis, or (2) any inpatient or outpatient diagnoses fields. Fourth, we conducted sensitivity analyses where we excluded patients with a recent hospitalization (i.e., 30-days prior to the index date). Finally, to assess impact of the study estimates across calendar time, we also estimated stratified results before and after 2016. Other eligibility criteria (e.g., no evidence of T1D) were similar for all cohorts. For all cohorts, pairwise comparisons, and sensitivity analyses, the propensity scores were re-estimated, and stabilized inverse probability of treatment weights were re-calculated. All analyses were performed using SAS 9.4 (SAS Institute Inc, Cary, NC).
Gonzalez J., Bates B.A., Setoguchi S., Gerhard T, & Dave C.V. (2023). Cardiovascular outcomes with SGLT2 inhibitors versus DPP4 inhibitors and GLP-1 receptor agonists in patients with heart failure with reduced and preserved ejection fraction. Cardiovascular Diabetology, 22, 54.
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Publication 2023
Cerebrovascular Accident Diagnosis Eligibility Determination Hospitalization Hypersensitivity Inpatient Outpatients Patient Discharge Patients Pharmaceutical Preparations Sodium-Glucose Transporter 2 Inhibitors
3
Cardiovascular Outcomes in Diabetes
Separately for each study outcome, patients began contributing to follow-up time on the day after cohort entry (i.e., medication initiation) up until the first occurrence of one of the following: end of pharmacy or health care eligibility, medication discontinuation defined as 60-day gap in treatment, medication switching (e.g., patients in SGLT2i arm initiating non-gliflozin therapy and vice versa), end of study data (December 2019), or the occurrence of the outcome.
The two primary outcomes of interest were (1) hospitalization for heart failure (HHF) (positive predictive value [PPV]: > 90%) [20 (link)], and (2) MI (PPV = 94%) or stroke (PPV = 85%) hospitalizations [21 (link), 22 (link)]. Analysis for each of the two primary outcomes was conducted independently of the other.
Gonzalez J., Bates B.A., Setoguchi S., Gerhard T, & Dave C.V. (2023). Cardiovascular outcomes with SGLT2 inhibitors versus DPP4 inhibitors and GLP-1 receptor agonists in patients with heart failure with reduced and preserved ejection fraction. Cardiovascular Diabetology, 22, 54.
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Publication 2023
Cerebrovascular Accident Congestive Heart Failure Eligibility Determination Hospitalization Patients Pharmaceutical Preparations Sodium-Glucose Transporter 2 Inhibitors Therapeutics
4
Comparative Effectiveness of SGLT2i in Heart Failure
Within the database, a separate cohort was created for each pairwise comparison of SGLT2i versus an alternative non-gliflozin class. Cohort membership required patients to be new users of the study medications of interest (defined as no use of the medications in the 365-day washout period preceding medication initiation), be older than 65 years of age at cohort entry and have no evidence of gestational or type 1 diabetes (T1D), cancer, end-stage renal disease, or human immunodeficiency virus infection. With the sole exception of heart failure phenotype (see below), all baseline covariates including eligibility criteria and patient characteristics were assessed in the 365 days prior to the date of medication initiation.
The study cohort was further restricted to patients with the presence of HHF with ICD codes corresponding to HFrEF (ICD-9: 428.2× or ICD-10: I50.2×) or HFpEF (ICD-9: 428.3 × or ICD-10: I50.3×) in either the first or second position of the inpatient discharge diagnosis using all available lookback. The positive predictive value for this approach for identifying patients with HFrEF is 72% and 90% using ejection fraction [EF] thresholds of ≤ 40% and ≤ 50%, respectively, and 92% for HFpEF for an EF threshold of > 50% [19 ]. Patients with evidence of both or neither HF subtypes were excluded from analyses.
The study was comprised of four pairwise comparison cohorts, which included patients with: (1a) HFrEF initiating SGLT2i versus DPP4i; (1b) HFrEF initiating SGLT2i versus GLP-1RA; (2a) HFpEF initiating SGLT2i or DPP4i; and (2b) HFpEF initiating SGLT2i or GLP-1RA. For SGLT2i versus DPP4i comparisons, patients using combination empagliflozin–linagliptin therapy were excluded from analysis. Further, individuals initiating SGLT2i and the comparator on the same day were also excluded from analyses. Patients meeting the inclusion and exclusion criteria could contribute to each cohort only once, but the same patient could be included in more than one cohort.
Gonzalez J., Bates B.A., Setoguchi S., Gerhard T, & Dave C.V. (2023). Cardiovascular outcomes with SGLT2 inhibitors versus DPP4 inhibitors and GLP-1 receptor agonists in patients with heart failure with reduced and preserved ejection fraction. Cardiovascular Diabetology, 22, 54.
Full text: Click here
Publication 2023
Combined Modality Therapy Congestive Heart Failure Diabetes Mellitus, Insulin-Dependent Diagnosis Eligibility Determination empagliflozin HIV Infections Inpatient Kidney Failure, Chronic Linagliptin Malignant Neoplasms Patient Discharge Patients Pharmaceutical Preparations Phenotype Pregnancy Sodium-Glucose Transporter 2 Inhibitors
5
Benefit-Risk Evaluation of SGLT2 Inhibitor Safety Issues
The rating-based conjoint analysis experiment was introduced by providing some basic information about a medicine for the treatment of T2DM. The medicine was presented as a hypothetical drug, without mentioning a specific medicine or class of medicines. However, the provided information regarding the medicine was based on real information regarding SGLT2 inhibitors and included a short summary of selected favourable and unfavourable effects. To obtain an indication of the responders’ benefit-risk evaluation of the drug, they were asked the question “How would you rate the benefit-risk balance of this drug?” (using a visual analogue scale (VAS) from 0 to 100).
Next, we presented various scenarios of safety issues described in terms of four characteristics, termed attributes. Each attribute had two or three alternatives, termed levels. The first attribute was the ADR, which could have three levels, namely DKA, amputation, or bone fracture. These ADRs have been associated with SGLT2 inhibitors and were described according to the definitions available in the EMA assessment reports of this drug class [24 –26 ]. We selected these ADRs because of the previously reported discrepancies in safety advisories among regulatory agencies worldwide [5 (link)]. The other three attributes were hypothetical for each scenario and had two levels each (Table 1): (1) source of information (i.e., spontaneous reports/epidemiological studies or clinical trials), (2) level of causality (possible or probable), and (3) frequency of the ADR (two times higher or three times higher than the risk with the standard of care, which was specified for each ADR). These attributes were selected because of their possible relevance at the time of assessing a safety issue, based on input from pharmacovigilance experts and information from regulatory guidelines [24 –29 ].

Attributes and attribute levels used in the rating-based conjoint experiment

AttributesLevels
ADRsDiabetic ketoacidosis—Serious complication caused by low insulin levels that leads to the accumulation of acidic ketone bodies in the blood. Patients may require hospitalization or treatment in an emergency department*
Amputations—Lower limb amputation (mostly affecting the toes)
Bone fracture—Bone fracture and decrease in bone mineral density. Bone fracture may occur when minor trauma. For example, when falling from standing height
Source of informationSpontaneous reports and/or epidemiological studies*
Clinical trials
Level of causalityPossible—the ADR happened within a reasonable time sequence to drug administration, but it could also be explained by concurrent disease or other drugs or chemicals*
Probable—the ADR happened within a reasonable time sequence to drug administration, and it is unlikely to be attributed to concurrent disease or other drugs or chemicals
Frequency of the ADRTwo times higher than with the standard of care (this was specified for each ADR)*
Three times higher than with the standard of care (this was specified for each ADR)

ADR adverse drug reaction

*Reference level

To obtain the minimum number of scenarios necessary to estimate all main effects and all possible interaction effects between the ADRs and the other attributes, we generated an orthogonal fractional factorial design for each ADR. This process resulted in a total of 12 scenarios, four per ADR, with differences in the level of at least one of the attributes. We created three blocks of scenarios based on the ADRs, and the order of the scenarios within each block was randomised. The order in which the blocks were presented in the survey was also randomised, and all participants were asked to assess the 12 scenarios.
For each scenario, the participants were asked three questions. The first question assessed their concern for the safety issue: “With this additional hypothetical information available, how concerned are you about this safety issue?” (VAS from 0 to 100). The next questions addressed their opinion on the need to communicate about the safety issue: “In your opinion, should the summary of product characteristics (SmPC) of the drug be updated?” (yes or no) and “In your opinion, should a direct healthcare professional communication (DHPC) be sent out?” (yes or no).
Roldan Munoz S., Postmus D., de Vries S.T., Gross-Martirosyan L., Bahri P., Hillege H, & Mol P.G. (2023). What Factors Make EU Regulators Want to Communicate Drug Safety Issues Related to SGLT2 Inhibitors? An Online Survey Study. Drug Safety, 46(3), 243-255.
Publication 2023
Acids Amputation BLOOD Bone and Bones Bone Density Drug Reaction, Adverse Fracture, Bone Health Personnel Hospitalization Insulin Ketone Bodies Ketosis Lower Extremity Patients Pharmaceutical Preparations Safety Sodium-Glucose Transporter 2 Inhibitors Toes Treatment, Emergency Visual Analog Pain Scale Wounds and Injuries

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Empagliflozin by Boehringer Ingelheim
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Empagliflozin is a laboratory equipment product used to measure and analyze various biological and chemical samples. It functions as a sodium-glucose co-transporter 2 (SGLT2) inhibitor, which plays a role in regulating glucose levels in the body.
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More about "Sodium-Glucose Transporter 2 Inhibitors"

Sodium-Glucose Transporter 2 (SGLT2) Inhibitors are a class of medications used to manage type 2 diabetes by lowering blood glucose levels.
These drugs work by blocking the SGLT2 protein, which is responsible for reabsorbing glucose in the kidneys.
By inhibiting SGLT2, more glucose is excreted through urine, leading to reduced blood sugar concentrations.
SGLT2 Inhibitors are an important therapeutic option for patients with type 2 diabetes, as they can help improve glycemic control and reduce the risk of diabetes-related complications.
Researchers can optimize their SGLT2 Inhibitor studies using PubCompare.ai, an AI-driven platform that enhances reproducibility and accuracy.
PubCompare.ai helps researchers locate protocols from literature, preprints, and patents, and provides AI-driven comparisons to identify the best protocols and products for their research.
For example, researchers can use SAS version 9.4, a powerful data analysis software, to analyze the effects of SGLT2 Inhibitors like Empagliflozin on glucose levels.
The μQuant spectrophotometer can be used to measure glucose concentrations, while the High-Capacity cDNA Reverse Transcription Kit can be utilized to study gene expression changes.
The Accu-Chek Performa glucose meter and the CyAn ADP cytometer can also be employed in SGLT2 Inhibitor studies.
Additionally, the natural compound Gurmarin, which inhibits the sweet taste receptor, can be investigated in combination with SGLT2 Inhibitors to potentially enhance their glucose-lowering effects.
Researchers can use JMP Pro 16, a powerful data visualization and analysis tool, to gain deeper insights into their SGLT2 Inhibitor research.
By leveraging the capabilities of these tools and the PubCompare.ai platform, researchers can streamline their SGLT2 Inhibitor studies, enhance reproducibility, and identify the most effective protocols and products for their research.
This can lead to a better understanding of the mechanisms and clinical applications of SGLT2 Inhibitors in the management of type 2 diabetes.