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Thiazolidinediones

Thiazolidinediones are a class of antidiabetic drugs that work by activating peroxisome proliferator-activated receptor gamma (PPAR-γ), improving insulin sensitivity and glucose homeostasis.
They are commonly used to treat type 2 diabetes mellitus.
Thiazolidinediones include medications such as pioglitazone, rosiglitazone, and troglitazone.
These drugs have been associated with both beneficial and adverse effects, including improvements in glycemic control but also potential risks of weight gain, edema, and cardiovascular events.
Researchers studying thiazolidinediones can leverage PubCompare.ai to optimize their research by quickly identifying the best published protocols and products, enhacing reproducibility and accuaracy in their studies.

Most cited protocols related to «Thiazolidinediones»

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
Retrospective Cohort Study of Oral Hypoglycemic Usage
Cited 11 times
We assembled a cohort of patients who received pharmacy benefits through Horizon Blue Cross Blue Shield of New Jersey and who were prescribed an oral hypoglycemic agent between January 1, 2005, and December 31, 2005. We considered only those patients who had 1 or more inpatient or outpatient claims with a diagnosis of diabetes (International Classification of Diseases, Ninth Revision, Clinical Modification code 250.x) and were prescribed 2 or more classes of oral hypoglycemic medications during this period. While all oral hypoglycemics may be considered members of 1 therapeutic class, we defined classes on a mechanistic basis (ie, sulfonylurea, metformin hydrochloride, glitazones, acarbose, and meglitinides), as many patients take agents belonging to these different classes concurrently. Furthermore, this strategy allows for the generalization of our methods to patients using noninterchangeable medication classes (eg, statins, angiotensin-converting enzyme inhibitors, antiplatelets, and β-blockers after myocardial infarction).
We excluded patients who lost eligibility or did not fill any prescriptions or use any medical services in 2006. We defined an index date for each oral hypoglycemic class prescribed to each patient as the first prescription date for any member of the class during the accrual period.
We combined filled prescription data for patients in our cohort with complete paid claims data and eligibility files to create a relational database consisting of data for all filled prescriptions, procedures, inpatient and outpatient physician encounters, hospitalizations, and deaths for the patients in our cohort. Prescription information in the claims data included drug name, dosage, date dispensed, quantity dispensed, and days supplied. All traceable person-specific identifying factors were transformed into anonymous coded study numbers. The institutional review board of Brigham and Women's Hospital approved the study.
Choudhry N.K., Shrank W.H., Levin R.L., Lee J.L., Jan S.A., Brookhart M.A, & Solomon D.H. (2009). Measuring Concurrent Adherence to Multiple Related Medications. The American journal of managed care, 15(7), 457-464.
Publication 2009
Acarbose Angiotensin-Converting Enzyme Inhibitors Diabetes Mellitus Diagnosis Eligibility Determination Ethics Committees, Research Generalization, Psychological Hospitalization Hydroxymethylglutaryl-CoA Reductase Inhibitors Hypoglycemic Agents Inpatient meglitinide Metformin Hydrochloride Myocardial Infarction Outpatients Patients Pharmaceutical Preparations Physicians Sulfonylurea Compounds Therapeutics Thiazolidinediones
3
Isolation and Culture of Human Preadipocytes
Cited 9 times
Adipose tissue was digested with collagenase as previously reported and cell size measured (19 (link)). The medium under the isolated adipocytes was collected and centrifuged for 10 min at 200g. The cell pellet was washed twice and the erythrocytes were lysed with 155 mmol/l NH4Cl for 5 min before seeding the cells in a 55-cm2 petri dish. After 3 days, when the cells had started to proliferate, the progenitor or inflammatory cells were isolated with magnetic immune separation. The remaining cells were then cultured at 37°C with Dulbecco's modified Eagle's medium (DMEM) and Ham's F12 (1:1) with 10% fetal bovine serum (FBS), 2 mmol/l glutamine, 100 units/ml penicillin, and 100 μg/ml streptavidin. After 2 weeks cells were trypsinized and any remaining inflammatory cells removed by magnetic immune separation of CD14- and CD45-positive cells (Miltenyi Biotech, Bergisch Gladbach, Germany). The remaining preadipocyte fraction was seeded (10,000 cells/cm2) and cultured in six-well plates (Nunc, Roskilde, Denmark). Cells were left untreated or grown in the presence of 5 ng/ml TNF-α, 20 ng/ml IL-6, 50 ng/ml resistin, or 10 ng/ml lipopolysaccharide for 10 days. In some experiments, 300 μmol/l oleic acid was added to the medium for 48 h after 10 days of culture.
Differentiation of the preadipocytes was induced with a differentiation cocktail consisting of 850 nmol/l insulin, 10 μmol/l dexamethasone, 0.5 mmol/l IBMX (isobutylmethylxanthine), 10 μmol/l pioglitazone, 33 μmol/l biotin, and 17 μmol/l pathenonate in DMEM/F12 supplemented with 3% FBS (vol/vol), 2 mmol/l glutamine, and antibiotics. After 3 days, the medium was changed to a medium containing only insulin, dexamethasone and pioglitazone in DMEM/F12, 10% FBS with glutamine, and antibiotics. (Human preadipocytes require the continuous presence of a thiazolidinedione to remain differentiated.) The cells were then left to differentiate for another 18 days with medium changed every other day. In some experiments 5 ng/ml TNF-α, 20 ng/ml IL-6, 50 ng/ml wingless-type MMTV (mouse mammary tumor virus) integration site family, member 3A (Wnt3a), or 50 ng/ml resistin was added to the differentiation medium.
Isakson P., Hammarstedt A., Gustafson B, & Smith U. (2009). Impaired Preadipocyte Differentiation in Human Abdominal Obesity: Role of Wnt, Tumor Necrosis Factor-α, and Inflammation. Diabetes, 58(7), 1550-1557.
Publication 2009
1-Methyl-3-isobutylxanthine Adipocytes Antibiotics Biotin Cells Collagenase Dexamethasone Erythrocytes Fetal Bovine Serum Glutamine Homo sapiens Hyperostosis, Diffuse Idiopathic Skeletal Inflammation Insulin Lipopolysaccharides Mouse mammary tumor virus Oleic Acid Penicillins Pioglitazone Resistin Streptavidin Thiazolidinediones Tissue, Adipose Tumor Necrosis Factor-alpha
4
National Health Insurance Claims Database Study of Type 2 Diabetes in Korea
Cited 8 times
The NHIS was initiated in 1963 in Korea according to the National Health Insurance Act, and all Korean citizens were mandated to participate in this program [12 ]. Currently, the Korean NHIS maintains and manages all databases of Korea’s health service utilization. The detailed structure and function of NHIS is described elsewhere [12 ].
In the present study, we used data from the NHIS-NSC 2002–2013, which were released by the Korean NHIS in 2014. The data comprise a nationally representative random sample of 1,025,340 individuals, which accounts for approximately 2.2 % of the entire population in 2002 [12 ]. The data were built by using probabilistic sampling to represent an individual’s total annual medical expenses within each of 1476 strata defined by age, sex, eligibility status (employed or self-employed), and income level (20 quantiles for each eligibility status and medical-aid beneficiary) combinations via proportional allocation from the 46,605,433 Korean residents in 2002 [12 , 13 (link)]. The NHIS-NSC is a semi-dynamically constructed cohort database; the cohort has been followed up to either the time of the participant’s disqualification from receiving health services due to death or emigration or until the end of the study period, whereas samples of newborn infants are included annually [12 , 13 (link)]. The database contains eligibility and demographic information regarding health insurance as well as data on medical aid beneficiaries, medical bill details, medical treatment, disease histories and prescriptions; such data were constructed after converting insurance claim information to the first day of medical treatment.
From this cohort, we selected subjects recorded to have type 2 diabetes between 2002 and 2004. Type 2 diabetes was defined if anti-diabetic drugs were prescribed and the 10th revision of International Statistical Classification of Diseases, International Classification of Diseases (ICD)-10 codes E11 (non-insulin-dependent diabetes mellitus), E12 (malnutrition-related diabetes mellitus), E13 (other specified diabetes mellitus), or E14 (unspecified diabetes mellitus) was assigned as either principal or additional diagnosis. Antidiabetic drugs dispensed in the pharmacy during the study period in Korea consisted of six classes (i.e., sulfonylureas, biguanide, alpha-glucosidase inhibitor, thiazolidinediones, meglitinide and insulin) [14 (link)]. Incretin-based therapies (i.e. glucagon-like peptide -1 receptor agonists and dipeptidyl peptidase-4 inhibitors) were not introduced during the study period.
This diabetic cohort was followed up from the index date until the end of the study period (i.e., December 31, 2013), until the last year of qualification for those who were alive, or until the date of death for those who died. This study was approved by the NHIS inquiry commission. The personal privacy of each participant was protected by de-identification of the national insurance claims data for analysis. This study was also approved by the Institutional Review Board of the Asan Medical Center (IRB-No 2016-0149).
Kang Y.M., Kim Y.J., Park J.Y., Lee W.J, & Jung C.H. (2016). Mortality and causes of death in a national sample of type 2 diabetic patients in Korea from 2002 to 2013. Cardiovascular Diabetology, 15(1), 131.
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Publication 2016
agonists alpha-Glucosidase Inhibitors Antidiabetics Biguanide Diabetes Mellitus Diabetes Mellitus, Non-Insulin-Dependent Diagnosis Dipeptidyl-Peptidase IV Inhibitors Eligibility Determination Glucagon-Like Peptide-1 Receptor Health Insurance Incretins Infant, Newborn Insulin Koreans Malnutrition meglitinide National Health Insurance Pharmaceutical Preparations Prescriptions Sulfonylurea Compounds Thiazolidinediones
5
PCOS Metabolic Effects in Obese Adolescents
Cited 6 times
71 participants were enrolled; 41 with PCOS and 30 without. Inclusion criteria: female sex, obesity (BMI percentile >95%) and sedentary status (<3 hours of exercise/week; validated with both a 3 day activity recall and 7 day accelerometer use). Exclusion criteria: diabetes, alanine transferase (ALT) >80 IU/mL, BP >140/90 mmHg, hemoglobin <9mg/dl, serum creatinine >1.5 mg/dl, smoking, medications affecting IS (oral steroids, metformin, thiazolidinediones, atypical antipsychotics, hormonal contraceptives), antihypertensive medications, statins, pregnancy, and breastfeeding. PCOS was diagnosed per NIH criteria with adolescent adaptation: oligomenorrhea (<8 menses a year), clinical or biochemical signs of hyperandrogenism, and at least 2 years post-menarche (8 (link)). The study was approved by the University of Colorado AMC Institutional Review Board. Informed consent was obtained from all participants 18–20 years old, and parental consent and participant assent from all participants <18 years old.
Cree-Green M., Bergman B.C., Coe G.V., Newnes L., Baumgartner A.D., Bacon S., Sherzinger A., Pyle L, & Nadeau K.J. (2016). Hepatic Steatosis is Common in Adolescents with Obesity and PCOS and Relates to De Novo Lipogenesis but Insulin Resistance. Obesity (Silver Spring, Md.), 24(11), 2399-2406.
Publication 2016
Acclimatization Adolescent Alanine Antihypertensive Agents Antipsychotic Agents Contraceptive Agents Creatinine Diabetes Mellitus Ethics Committees, Research Females Hemoglobin Hydroxymethylglutaryl-CoA Reductase Inhibitors Hyperandrogenism Menarche Menstruation Mental Recall Metformin Obesity Oligomenorrhea Pharmaceutical Preparations Polycystic Ovary Syndrome Pregnancy Serum Steroids Thiazolidinediones Transferase

Most recents protocols related to «Thiazolidinediones»

1
Dulaglutide Effects on Blood Pressure in T2D
The analyses included six pivotal randomized, double-blind trials of dulaglutide 1.5 mg in participants with T2D that measured sitting SBP and DBP from vital sign data around the timeline of 6 months (week 24 to week 26). Five placebo-controlled studies were used to estimate the effects between dulaglutide 1.5 mg and placebo. AWARD-1 (NCT01064687), AWARD-5 (NCT00734474), AWARD-8 (NCT01769378), and AWARD-10 (NCT02597049) were phase 3, placebo-controlled trials which investigated the safety and glycemic efficacy of dulaglutide with various background glycemic therapies (Table 1). Ferdinand et al. (NCT01149421) was a phase 2, randomized, double-blind, placebo-controlled trial which evaluated BP and heart rate effects of dulaglutide vs. placebo in participants with T2D with and without hypertension and BP < 140/90 mmHg. In addition, AWARD-11 (NCT03495102) was a phase 3, non-placebo-controlled trial to evaluate safety and glycemic efficacy of dulaglutide 3.0 mg and 4.5 mg to dulaglutide 1.5 mg.

Study design for placebo-controlled trials included in the meta-analysis

ParametersAWARD-1AWARD-5AWARD-8AWARD-10AWARD-11Ferdinand et al
PhasePhase IIIPhase II/IIIPhase IIIPhase IIIPhase IIIPhase II
RandomizationRandomizedRandomizedRandomizedRandomizedRandomizedRandomized
BlindingBlindingDouble-blindDouble-blindDouble-blindDouble-blindDouble-blind
Primary EndpointA1cA1cA1cA1cA1c24-h SBP
Study Treatment Period52 weeks24 months24 weeks24 weeks52 weeks26 weeks
Last scheduled visit with PBO26 weeks6 months24 weeks24 weeks52 weeks (no PBO)26 weeks
Background therapy (Add-ons)Met + TZDMet monoSU monoSGLT2i with or without metforminMet monoStable OAM
Key inclusion/ exclusion criteria
 Age ≥ 18 years18–75 years ≥ 18 years ≥ 18 years ≥ 18 years ≥ 18 years
 T2D durationNA ≥ 6 monthsNANAfor ≥ 6 monthsNA
 A1c7.0–11.07.0–9.57.5–9.57.0–9.57.5–117–9.5
 BMI23–4525–40 ≤ 45 ≤ 45 ≥ 25NA
 MedicationStable OAMDiet & exercise / metformin and/or other OAMStable SUSGLT2i with or without metformin for ≥ 3 monthsStable metformin for ≥ 3 monthsOAM

BMI body mass index, NA not applicable for the study’s design, Met metformin, mono monotherapy, OAM oral antihyperglycemic medication, PBO placebo, SBP systolic blood pressure, SGLT2i sodium-glucose cotransporter-2 inhibitors, SU sulfonylurea, T2D type 2 diabetes, TZD thiazolidinediones

Ferdinand K.C., Dunn J., Nicolay C., Sam F., Blue E.K, & Wang H. (2023). Weight-dependent and weight-independent effects of dulaglutide on blood pressure in patients with type 2 diabetes. Cardiovascular Diabetology, 22, 49.
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Publication 2023
Diabetes Mellitus, Non-Insulin-Dependent dulaglutide High Blood Pressures Hypoglycemic Agents Index, Body Mass inhibitors Metformin Pharmaceutical Preparations Placebos Rate, Heart Safety Signs, Vital SLC5A2 protein, human Sulfonylurea Compounds Systolic Pressure Therapeutics Thiazolidinediones TimeLine
2
Myocardial Fat Accumulation in Type 2 Diabetes
We searched the database of patients with type 2 diabetes who were referred for ECG-gated coronary computed tomography angiography (CCTA) examinations (Toshiba Aquilion CT scanner, Toshiba Medical, Tochigi, Japan; SOMATOM Definition or Force, Siemens Healthineers, Forchheim, Germany) for the first time and underwent abdominal CT scans (Toshiba Aquilion CT scanner, Toshiba Medical, Tochigi, Japan; Discovery CT750 HD, General Electric Healthcare, Chicago, IL, USA; SOMATOM Definition, Siemens Healthineers, Forchheim, Germany; GE Optima CT660, General Electric Healthcare, Chicago, IL, USA) within 1 year of CCTA at Osaka University Hospital or Sumitomo Hospital between January 2000 and March 2021. A total of 411 patients met these criteria. Among these patients, we excluded patients to avoid the influence of cardiac function or the myocardial CT value of pathological conditions other than myocardial fat accumulation. The excluded patients included those with heart failure with reduced ejection fraction (≤ 40%), those with valvular heart disease and those who had received past percutaneous coronary intervention (PCI) for coronary artery disease. Moreover, we excluded patients who had liver cirrhosis, renal failure (estimated glomerular filtration rate of < 30 mL/min/1.73 m2), malignant diseases and diseases requiring glucocorticoids for the treatment of other diseases. Furthermore, it is known that changes in X-ray tube voltage affect CT attenuation values [20 (link)]. Therefore, patients who underwent CCTA or abdominal CT examinations that were not performed at 120 kV were excluded. Using these criteria, 124 patients were finally included in our analyses. The flowchart for the recruitment of the patients is shown in Additional file 1: Fig S1. Among these 124 patients, the medications for diabetes at the time of CCTA were as follows: insulin for 42 patients, glucagon-like peptide-1 (GLP-1) receptor agonists for 9 patients, sulfonylureas for 31 patients, biguanides for 43 patients, dipeptidyl peptidase-4 inhibitors for 40 patients, α-glucosidase inhibitors for 26 patients, thiazolidinediones for 13 patients, glinides for 12 patients and sodium–glucose cotransporter 2 (SGLT2) inhibitors for 3 patients. The medications for dyslipidemia at the time of CCTA were as follows: statins for 69 patients, fibrates for 9 patients, ezetimibe for 5 patients, omega-3 fatty acids for 8 patients and probucol for 1 patient.
This study was approved by the Institutional Ethics Review Boards of Osaka University Hospital and Sumitomo Hospital and was carried out in accordance with the principles of the Declaration of Helsinki. The study was announced to the public on the websites of our department at Osaka University Hospital and Sumitomo Hospital, and all patients were allowed to participate or refuse to participate in the study.
Kashiwagi-Takayama R., Kozawa J., Hosokawa Y., Kato S., Kawata S., Ozawa H., Mineo R., Ishibashi C., Baden M.Y., Iwamoto R., Saisho K., Fujita Y., Tamba S., Sugiyama T., Nishizawa H., Maeda N., Yamamoto K., Higashi M., Yamada Y., Sakata Y., Matsuzawa Y, & Shimomura I. (2023). Myocardial fat accumulation is associated with cardiac dysfunction in patients with type 2 diabetes, especially in elderly or female patients: a retrospective observational study. Cardiovascular Diabetology, 22, 48.
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Publication 2023
Abdomen agonists alpha-Glucosidase Inhibitors Biguanides Cardiomyopathies CAT SCANNERS X RAY Computed Tomography Angiography Congestive Heart Failure Coronary Artery Disease Diabetes Mellitus Diabetes Mellitus, Non-Insulin-Dependent Dipeptidyl-Peptidase IV Inhibitors Dyslipidemias Electricity Ezetimibe Fibrates Glomerular Filtration Rate Glucagon-Like Peptide-1 Receptor Glucocorticoids Heart Hydroxymethylglutaryl-CoA Reductase Inhibitors inhibitors Insulin Kidney Failure Liver Cirrhosis Myocardium Omega-3 Fatty Acids Patients Pharmaceutical Preparations Physical Examination Probucol Radiography SLC5A2 protein, human Sulfonylurea Compounds Thiazolidinediones Valve Disease, Heart X-Ray Computed Tomography
3
Diabetes Medications Comparative Effectiveness
Comparisons among the following interventions were included: insulin, metformin, sulfonylureas, thiazolidinediones (TZDs), active comparator drugs (ACDs), dipeptidyl peptidase-4 (DPP-4) inhibitors, Glucagon-like peptide-1 (GLP-1) analogues or agonists, sodium/glucose cotransporter 2 (SGLT-2) inhibitors, α-glucosidase inhibitors, meglitinides, or placebo. There are no restrictions on the combination formula such as whether to plus other agents to metformin or sulfonylureas. There are also no restrictions on different doses or frequency of the same agent. We classify all eligible drugs according to the above drug categories and because different drugs in the same category may have a variable effect, we include studies that compare drugs in a same category either. If a network meta-analysis included drugs of interest but also included drugs that were not of interest, or if multiple interventions included glycemic control by non-pharmacological methods, such studies would also be included. Interventions includes some drugs but not any drugs of interest within the list except for comparator drugs will not be included.
Chang Z., Xu J., Qin Y., Zheng Q., Zhao L., Wang Y, & Zhang Y. (2023). Safety and efficacy of anti-hyperglycemic agents in patients with type 2 diabetes mellitus (T2DM): Protocol for an overview of systematic reviews based on network meta-analysis. PLOS ONE, 18(3), e0282143.
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Publication 2023
agonists alpha-Glucosidase Inhibitors Dipeptidyl-Peptidase IV Inhibitors Glucagon-Like Peptide 1 Glycemic Control inhibitors Insulin meglitinide Metformin Pharmaceutical Preparations Placebos SLC5A2 protein, human Sulfonylurea Compounds Thiazolidinediones
4
Assessing Antidepressant Effects of Antidiabetic Drugs
By using the Oracle Empirica Signal software (Oracle Health Sciences, Austin, TX), we calculated disproportionality statistics produced by four signal detection methodologies, to assess the occurrence of therapy failure (cases) in depressed patients, in association with the exposure to at least one antidiabetic drug, defined as the following ATC Level 4 codes: A10BA Biguanides; A10BB Sulfonylureas; A10BG Thiazolidinediones; A10BH DPP4-inhibitors; A10BJ GLP-1 analogues; A10BK SGLT2 inhibitors (i.e., those agents for which preliminary evidence from literature supports our pharmacological hypothesis).
Three of these disproportionality scores, based on 2 × 2 disproportionality analysis, are well-established and currently used worldwide by several organisations for routine safety surveillance, i.e:

i) The Reporting Odds Ratio (ROR), defined as the ratio of the odds of the occurrence of therapy failure with antidiabetic drugs versus the occurrence of therapy failure without antidiabetic agents (van Manen et al., 2007 (link));

ii) The Proportional Reporting Ratio (PRR), comparing the frequency of occurrence of therapy failure in reports referring to antidiabetic agents with the frequency of occurrence of reports of therapy failure in reports that do not mention antidiabetic agents. (van Manen et al., 2007 (link)).

iii) The Empirical Bayesian Geometric Mean (EBGM) calculated using the Multi-item Gamma Poisson Shrinker (MGPS) Algorithm, using Bayesian shrinkage to improve the reliability of the disproportionality score (DuMouchel, 1999 (link)). We generated both the point estimates (EBGM) and their associated 90% confidence intervals labelled EB05–EB95.

Moreover, we used a more advanced regression-based methodology designed to produce disproportionality statistics with adjusted background rates; it can control masking and more extensive confounding effects by fitting separate Bayesian logistic regression models to each target AE and by automatically selecting predictors to be included in each regression model:

iv) The Regression-enhanced Empirical Bayesian Geometric Mean (ERAM) calculated using the Regression-Adjusted Gamma Poisson Shrinker (RGPS) Algorithm (DuMouchel and Harpaz, 2012 ). We generated the point estimates (ERAM) and their associated 90% confidentiality intervals labelled ER05–ER95.

With the aim to investigate the antidepressant effects of antidiabetic drugs, disproportionality signals were considered clinically meaningful if.

i) The upper limit of the 90% confidence interval (CI) of the ROR for cases (ROR95) is less than one;

ii) The PRR score is less than one and the corresponding p-value is less than 0.05;

iii) The upper limit of the 90% confidence interval of the EBGM for cases (EB95) is less than one;

iv) The upper limit of the 90% confidence interval of the ERAM for cases (ER95) is less than one.

Battini V., Van Manen R.P., Gringeri M., Mosini G., Guarnieri G., Bombelli A., Pozzi M., Nobile M., Radice S., Clementi E, & Carnovale C. (2023). The potential antidepressant effect of antidiabetic agents: New insights from a pharmacovigilance study based on data from the reporting system databases FAERS and VigiBase. Frontiers in Pharmacology, 14, 1128387.
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Publication 2023
Antidepressive Agents Antidiabetics austin Biguanides Dipeptidyl-Peptidase IV Inhibitors Gamma Rays Glucagon-Like Peptide 1 Patients Safety Signal Detection (Psychology) Sodium-Glucose Transporter 2 Inhibitors Sulfonylurea Compounds Therapeutics Thiazolidinediones
5
Type 2 Diabetes and Vascular Outcomes
This study obtained detailed medical information from the National Taiwan University Hospital‐Integrated Medical Database. We enrolled patients >50 years of age who were diagnosed with T2D (either prevalent or incident cases) at the National Taiwan University Hospital from January 1, 2014 to December 31, 2019. The index date for this cohort study was defined as the date of T2D diagnosis and established according to International Classification of Diseases (ICD) codes (ICD9: 250.XX, ICD10: E08.XX, E11.XX). Patients with any previous history of acute myocardial infarction (AMI), ischemic stroke, or lower‐extremity arterial disease, including PAD or CLI, were excluded from this study. Patients were followed from the index date (ie, the date of T2D diagnosis) until the occurrence of any study outcome, death, or December 31, 2019, whichever came first. The study protocol was approved by the institutional review board of National Taiwan University Hospital, and informed consent was waived because of the use of deidentified patient data.
Baseline characteristics, including body mass index and diagnoses of hypertension, hyperlipidemia, and coronary artery disease (CAD), were obtained from electronic health records. The estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease equation. Prescription medications were categorized into β‐blockers; calcium channel blockers; angiotensin‐converting enzyme inhibitors; angiotensin receptor blockers; diuretics; statins; anticoagulants, including direct oral anticoagulants and warfarin; and antidiabetic medications, including insulin, metformin, SGLT2 (sodium‐glucose co‐transporter‐2) inhibitor, DPP4 (dipeptidyl peptidase 4) inhibitor, sulfonylurea, repaglinide, acarbose, thiazolidinedione, and GLP‐1 (glucagon‐like peptide‐1) agonist.
The outcomes were the incidence of MALEs, defined as the first event of newly diagnosed PAD or newly diagnosed CLI, and the incidence of MACEs, defined as cardiovascular mortality, nonfatal myocardial infarction, or nonfatal ischemic stroke. Death events were evaluated by a central committee, and cardiac mortality was determined according to information in the electronic health records. The index dates for all outcomes were defined as the date of initial diagnosis. All medical records were reviewed until the last clinical visit or death.
Hsu J., Yang Y., Chuang S., Huang K., Lee J, & Lin L. (2023). Long‐Term Visit‐to‐Visit Glycemic Variability as a Predictor of Major Adverse Limb and Cardiovascular Events in Patients With Diabetes. Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 12(3), e025438.
Publication 2023
Acarbose Angiotensin-Converting Enzyme Inhibitors Angiotensin Receptor Antagonists Anticoagulants Antidiabetics Arteries Calcium Channel Blockers Cardiovascular System Coronary Artery Disease Diagnosis Diet Diuretics DPP4 protein, human Ethics Committees, Research Glomerular Filtration Rate Glucagon-Like Peptide 1 Heart High Blood Pressures Hydroxymethylglutaryl-CoA Reductase Inhibitors Hyperlipidemia Index, Body Mass Insulin Kidney Diseases Lower Extremity Males Metformin Myocardial Infarction Myristica fragrans Patients Prescription Drugs repaglinide SLC5A2 protein, human Sodium-Glucose Transporter 2 Stroke, Ischemic Sulfonylurea Compounds Thiazolidinediones Warfarin

Top products related to «Thiazolidinediones»

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7
Triethylamine by Merck Group
Sourced in United States, Germany, United Kingdom, France, Italy, India, Spain, Switzerland, Poland, Canada, China, Sao Tome and Principe, Australia, Belgium, Singapore, Sweden, Netherlands, Czechia
Triethylamine is a clear, colorless liquid used as a laboratory reagent. It is a tertiary amine with the chemical formula (CH3CH2)3N. Triethylamine serves as a base and is commonly employed in organic synthesis reactions.
8
γh2ax by Cell Signaling Technology
Sourced in United States, United Kingdom, China, Germany, Canada
γH2AX is a histone variant that becomes phosphorylated in response to DNA double-strand breaks. It is a well-established marker for the detection and quantification of DNA damage.
9
Luciferase assay system by Promega
Sourced in United States, Germany, United Kingdom, Australia, Switzerland, Japan, China, France, Sweden, Italy, Spain, Austria, Finland
The Luciferase Assay System is a laboratory tool designed to measure the activity of the luciferase enzyme. Luciferase is an enzyme that catalyzes a bioluminescent reaction, producing light. The Luciferase Assay System provides the necessary reagents to quantify the level of luciferase activity in samples, enabling researchers to study biological processes and gene expression.
10
Pentobarbital sodium by Merck Group
Sourced in United States, Germany, China, Sao Tome and Principe, United Kingdom, Japan, Italy, Canada, Hungary, Macao
Pentobarbital sodium is a laboratory chemical compound. It is a barbiturate drug that acts as a central nervous system depressant. Pentobarbital sodium is commonly used in research and scientific applications.

More about "Thiazolidinediones"

Thiazolidinediones (TZDs) are a class of antidiabetic drugs that work by activating the peroxisome proliferator-activated receptor gamma (PPAR-γ), improving insulin sensitivity and glucose homeostasis.
They are commonly used to treat type 2 diabetes mellitus (T2DM).
Medications in this class include pioglitazone, rosiglitazone, and troglitazone.
TZDs have been associated with both beneficial and adverse effects.
On the positive side, they can improve glycemic control, but they have also been linked to potential risks like weight gain, edema, and cardiovascular events.
Researchers studying these drugs can leverage PubCompare.ai to optimize their research by quickly identifying the best published protocols and products, enhancing reproducibility and accuracy in their studies.
PubCompare.ai can help researchers working with TZDs locate relevant protocols from literature, preprints, and patents, and use AI-driven comparisons to identify the best protocols and products.
This can be particularly useful when working with related compounds and techniques, such as DMSO, pioglitazone hydrochloride, the AU5800 Platform, teneligliptin, triethylamine, γH2AX, and the Luciferase Assay System.
By utilizing PubCompare.ai, researchers can improve the reproducibility and accuracy of their TZD studies, leading to more reliable and impactful findings.
This can be especially helpful when using statistical software like SAS to analyze their data and draw conclusions about the efficacy and safety of these antidiabetic drugs.