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Dipeptidyl-Peptidase IV Inhibitors

Dipeptidyl-Peptidase IV Inhibitors are a class of medications that work by blocking the activity of the enzyme dipeptidyl peptidase-4 (DPP-4).
This enzyme is responsible for the breakdown of certain hormones, such as incretins, that play a key role in regulating blood sugar levels.
By inhibiting DPP-4, these medications help to increase the levels of these hormones, which can lead to improved glycemic control in individuals with type 2 diabetes.
Dipeptidyl-Peptidase IV Inhibitors are commonly used as a treatment option for diabetes, either alone or in combination with other antidiabetic agents.
This MeSH term provides a concise overview of this important class of medications and their mechanism of action.

Most cited protocols related to «Dipeptidyl-Peptidase IV Inhibitors»

1
National Health Insurance Claims Database Study of Type 2 Diabetes in Korea
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
2
Anagliptin vs Sitagliptin for LDL-C Reduction in Diabetes
This is a multicenter, randomized, open-label, parallel-group trial to determine the effectiveness of anagliptin versus sitagliptin on reduction in LDL-C in patients with type 2 diabetes and existing atherosclerotic vascular lesions. Patients with type 2 diabetes and existing atherosclerotic vascular lesions under treatment of statin therapy are included. The eligibility criteria are shown in Table 1.

Patient eligibility criteria

Inclusion criteriaPatients who fulfilled all of the following criteria were included
1High-risk (*) patients with type 2 diabetes who are undergoing diet therapy/exercise therapy or are using other hypoglycemic agents in conjunction with diet therapy/exercise therapy
2Patients who have been using statins for ≥8 weeks
3Patients with LDL-C ≥ 100 mg/gL in ≥ 1 of their previous three measurements after the use of statins
4Patients with HbA1c ≥ 6.0% and < 10.5% (if the investigational drug is added on, HbA1c ≥ 7.0% and < 10.5%)
5Patients aged ≥ 20 years at the time of consent
6Patients who provide written consent to participate in the trial of their own free will based on a sufficient understanding of the trial following an adequate explanation
Exclusion criteriaPatients who met any of the following criteria were excluded
1Patients with type 1 diabetes
2Patients with TG ≥ 400 mg/dL in a past fasting blood sample
3Women who are pregnant, potentially pregnant, or lactating
4Patients with severe infections, who are scheduled to undergo/have just undergone surgery, or who have serious trauma
5Patients with a serum creatinine level ≥ 2.4 mg/dL for men or ≥ 2.0 mg/dL for women
6Patients using GLP-1 receptor agonists
7Patients considered ineligible for any other reason by a study investigator
* High-riskDefined as the fulfillment of any one of the following criteria
1Stenotic lesions or plaques of ≥ 25% of the arterial diameter in past coronary angiography or CT
2Coronary artery calcification in past coronary CT
3Past history of acute coronary syndrome
4Past history of PCI or CABG
5Past history of stroke (ischemic cerebral infarction or cerebral hemorrhage)
6Past history of TIA
7Past history of peripheral artery disease (including aortic lesions)
8Past ankle-brachial index ≤ 0.9
9Presence of carotid artery plaque (including max IMT ≥ 1.1 mm) in past carotid duplex

CABG: coronary artery bypass surgery; CT: computed tomography; GLP-1: glucagon-like peptide-1; HbA1c: hemoglobin A1c; IMT: intima-media thickness; LDL-C: low-density lipoprotein cholesterol; TG: triglyceride; PCI: percutaneous coronary intervention; TIA: transient ischemic attack

Registration, randomization, and data collection are performed using an electronic data capture (EDC) system. Randomization is performed centrally through the EDC system with a stochastic minimization algorithm to balance treatment assignment within and across hospitals, HbA1c (≥ 8.0%, < 8.0%, use of DPP-4 inhibitors prior to trial registration, sex, body mass index (BMI) (≥ 25 kg/m2, < 25 kg/m2), and LDL-C (≥ 130 mg/dL, < 130 mg/dL).
Ueda S., Shimabukuro M., Arasaki O., Node K., Nomiyama T, & Morimoto T. (2018). Effect of Anagliptin and Sitagliptin on Low-Density Lipoprotein Cholesterol in Type 2 Diabetic Patients with Dyslipidemia and Cardiovascular Risk: Rationale and Study Design of the REASON Trial. Cardiovascular Drugs and Therapy, 32(1), 73-80.
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Publication 2018
anagliptin Aorta Arteries Artery, Coronary BLOOD Blood Vessel Calcinosis Carotid Arteries Carotid Artery Plaque Cerebral Hemorrhage Cerebral Infarction Cerebrovascular Accident Cholesterol, beta-Lipoprotein Coronary Angiography Coronary Artery Bypass Surgery Creatinine Diabetes Mellitus, Non-Insulin-Dependent Dipeptidyl-Peptidase IV Inhibitors Eligibility Determination Glucagon-Like Peptide-1 Receptor Glucagon-Like Peptide 1 Hemoglobin A, Glycosylated Hydroxymethylglutaryl-CoA Reductase Inhibitors Hypoglycemic Agents Index, Body Mass Indices, Ankle-Brachial Infection Investigational New Drugs LDL-1 Operative Surgical Procedures Patients Percutaneous Coronary Intervention Peripheral Arterial Diseases Satisfaction Senile Plaques Serum Sitagliptin Therapies, Exercise Therapy, Diet Transients Triglycerides Tunica Intima X-Ray Computed Tomography
3
Antidiabetic Drug Utilization in Korea
A diagnosis of type 2 diabetes based on ICD-10 codes included principal diagnosis and up to four additional accompanying diagnoses, in order of clinical significance in the current condition. Patients were classified as having type 2 diabetes when they had at least one service claim with a diagnosis of type 2 diabetes, either in outpatient or inpatient care, and were prescribed at least one antidiabetic drug anytime in a given year to exclude prediabetes or non-diabetic subjects.
Antidiabetic drugs dispensed in the pharmacy during the study period in Korea consisted of seven classes (i.e., SU, biguanide, alpha-glucosidase inhibitor, TZD, DPP-4 inhibitors, meglitinide, and insulin; Supplement Table S1). The GLP-1 agonist was introduced in Korea by the end of 2008; however, health insurance coverage was available only in November 2010 with strict conditions. Therefore, the GLP-1 agonist was not included in this analysis. We obtained the annual number of prescriptions for all antidiabetic drug classes, including insulin, during the study period.
For information on prescriptions, the name of the drug, date prescribed, days of supply, quantity dispensed, and price of each tablet or injection were collected. Insulin was not classified as intermediate-, short-, or long-acting forms, but was counted as one class of antidiabetic medication. If the patients took more than two different classes of antidiabetic drugs, either as a fixed-dose combination or different pills, they were defined as receiving combination therapy.[15 (link),16 (link)] We classified the medication as monotherapy, dual therapy, and triple therapy.
To investigate drug adherence, the medication possession ratio (MPR) was used and defined as a cumulative medication adherence of more than 80% (292 days) per year among patients with type 2 diabetes prescribed in a given year and included data only on antidiabetic drug prescriptions dispensed from pharmacies.[18 (link),19 (link)] For evaluation of medication costs, we used the database of pharmacy claims, and confined them to the cost of antidiabetic drugs.
Ko S.H., Kim D.J., Park J.H., Park C.Y., Jung C.H., Kwon H.S., Park J.Y., Song K.H., Han K., Lee K.U, & Ko K.S. (2016). Trends of antidiabetic drug use in adult type 2 diabetes in Korea in 2002–2013: Nationwide population-based cohort study. Medicine, 95(27), e4018.
Publication 2016
alpha-Glucosidase Inhibitors Antidiabetics Biguanide Combined Modality Therapy Contraceptives, Oral Diabetes Mellitus, Non-Insulin-Dependent Diagnosis Dietary Supplements Dipeptidyl-Peptidase IV Inhibitors Glucagon-Like Peptide 1 Hospitalization Insulin meglitinide Outpatients Patients Pharmaceutical Preparations Prescriptions States, Prediabetic Tablet Therapeutics
4
Determination of Glucose, Insulin, and Enterohormones in Mice
For determination of d-glucose and insulin blood was collected from the tail. Concentrations of d-glucose were determined with an Ascensia CONTOUR Meter using Ascensia MICROFIL Test Strips (Bayer Vial GmbH, Leverkusen). Insulin levels were measured with the Insulin (Mouse) Ultrasensitive EIA from ALPCO Diagnostics (Salem, NH). For determination of enterohormones, mice were anesthetized with isoflurane and blood was collected after heart tap. For GLP-1 determination blood was immediately mixed with a dipeptidyl peptidase (DPP)-IV inhibitor yielding a final concentration of 100 μmol/L (Millipore Corporation, Billerica, MA). Active GLP-1 was determined by an ELISA kit supplied by Millipore Corporation (GLP-1(7–36) active, kit EGLP-35 K) (Fig. 5C). Total GLP-1 was determined by an ELISA kit provided by MesoScale Discovery (Gaithersburg, MD; kit K150JVC-4; Fig. 5D and Supplementary Fig. 5B). GIP was determined by an ELISA kit obtained from Millipore Corporation that is specific for both active GIP (1–42) and inactivated GIP (3–42) of rat and mouse (kit EZRMGIP-55 K).
Gorboulev V., Schürmann A., Vallon V., Kipp H., Jaschke A., Klessen D., Friedrich A., Scherneck S., Rieg T., Cunard R., Veyhl-Wichmann M., Srinivasan A., Balen D., Breljak D., Rexhepaj R., Parker H.E., Gribble F.M., Reimann F., Lang F., Wiese S., Sabolic I., Sendtner M, & Koepsell H. (2011). Na+-d-glucose Cotransporter SGLT1 is Pivotal for Intestinal Glucose Absorption and Glucose-Dependent Incretin Secretion. Diabetes, 61(1), 187-196.
Publication 2011
BLOOD Diagnosis Dipeptidyl-Peptidase IV Inhibitors Enzyme-Linked Immunosorbent Assay Glucagon-Like Peptide 1 Glucose Heart Insulin Isoflurane Mice, House Microfil Tail
5
Gpr119 Modulation of Colonic Ion Transport
Colonic mucosa from clinical specimens or from WT or knockout male mice (>15 weeks old) was voltage clamped at 0 mV in Ussing chambers, as described previously (Cox and Tough, 2002; Cox et al., 2001 ). Vectorial ion transport was measured continuously as Isc (μA/cm2), and all peptide additions were basolateral, as receptors are targeted to the basolateral epithelial domains. Gpr119 agonist (PSN632408, PSN375963, or OEA) additions were made to either the apical or basolateral reservoirs 15–20 min following VIP (10 nM). This is approximately the EC50 concentration of VIP in mouse mucosa (Cox et al., 2001 (link)) and an optimal secretory pretreatment for revealing subsequent Gαi-coupled epithelial responses in mouse mucosae.
Once stable Isc levels were achieved, mucosae were treated with the DPP-IV inhibitor (1 μM compound 3) (Lankas et al., 2005 (link)), neuronal activity was abolished with TTX (100 nM), or endogenous GLP-1 responses were inhibited with exendin(9-39) (1 μM). Treatment periods were 20–30 min prior to addition of the Y1 receptor antagonist BIBO3304 (BIBO; 300 nM) or the Y2 selective antagonist BIIE0246 (BIIE; 1 μM). A concentration of 10 μM PSN632408 was chosen as the Gpr119 stimulus, as it resulted in near maximal responses in mouse colon mucosa. Control experiments with Y agonists utilized concentrations that preferentially stimulated either Y1 receptors (10 nM Pro34PYY), Y2 receptors (30 nM PYY(3-36)), or Y4 receptors (30 nM rPP), as optimized in previous studies (Cox et al., 2001; Tough et al., 2006 ). Y agonist-induced reductions in Isc in epithelia are a result of Gαi-coupled attenuation of cAMP levels with consequent long-lasting decreases in Cl− ion secretion (Cox et al., 1988 (link)). For TRPV1 desensitization, two 1 μM additions of capsaicin (to both sides) were made, followed by VIP (10 nM) and then either apical PSN632408 or OEA (10 μM) at 10 min intervals.
In glucose sensitivity studies, tissues were bathed with KH buffer containing glucose (11.1 mM) on one side and mannitol (11.1 mM) in place of glucose on the other. Tolbutamide (1 mM) was used to block apical KATP channels, and changes in basal Isc levels and subsequent Gpr119 responses were recorded.
Cox H.M., Tough I.R., Woolston A.M., Zhang L., Nguyen A.D., Sainsbury A, & Herzog H. (2010). Peptide YY Is Critical for Acylethanolamine Receptor Gpr119-Induced Activation of Gastrointestinal Mucosal Responses. Cell Metabolism, 11(6), 532-542.
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Publication 2010
agonists BIBO 3304 BIIE 0246 Buffers Capsaicin Cardiac Arrest Cloning Vectors Colon Dipeptidyl-Peptidase IV Inhibitors Epithelium Glucagon-Like Peptide 1 Glucose Hypersensitivity Hyposensitization Therapies Ion Transport KATP Channels Males Mannitol Mice, Knockout Mucous Membrane Mus Neurons Peptides PPYR1 protein PSN 375963 PSN 632408 secretion Tissues Tolbutamide

Most recents protocols related to «Dipeptidyl-Peptidase IV Inhibitors»

1
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
2
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
3
Diabetes Mellitus and Heart Failure

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Publication 2023
Blood Glucose Congenital Heart Defects Diabetes Mellitus Diabetic Retinopathy Diagnosis Dipeptidyl-Peptidase IV Inhibitors Glucose Hemoglobin, Glycosylated Human Body Hyperglycemia Japanese Oral Glucose Tolerance Test Patients Pericarditis, Constrictive Physicians Plasma Polydipsia Polyuria Valve Disease, Heart Xerostomia
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
Pneumonia Risk in Thiazolidinedione Users
We used propensity-score matching to optimize the relevant covariates between TZD users and nonusers (D’Agostino, 1998 (link)). The propensity score for each participant was estimated using non-parsimonious multivariable logistic regression, with TZD use as the dependent variable. We included 35 clinically related covariates as independent variables (Table 1). The nearest-neighbor algorithm was adopted to construct matched pairs, assuming the standardized mean difference (SMD) value <0.1 to be a negligible difference between the study and comparison cohorts.
We used crude and multivariable-adjusted Cox proportional hazards models to compare outcomes between TZD users and nonusers. The results were presented as hazard ratios (HRs) and 95% confidence intervals (CIs) for TZD users compared with nonusers. This study is based on the intention-to-treat hypothesis. To calculate the observed risks, we censored the participants until the date of respective outcomes, death, or at the end of follow-up on December 31, 2018, whichever came first. The Kaplan–Meier method and log-rank tests were used to compare the cumulative incidences of hospitalization for all-cause pneumonia, bacterial pneumonia, IMV, and death due to pneumonia during the follow-up time between TZD users and nonusers. We compared the risk of hospitalization for all-cause pneumonia among different subgroups of age, sex, comorbidities, medications (rosiglitazone, pioglitazone, and others) for clinical applicability of results. We also assessed the cumulative duration (<153, 153–549, ≧550 days) and dose (<2,940, 2,940–10,009, ≧10,110 mg) of pioglitazone for the risks of hospitalization for all-cause pneumonia, bacterial pneumonia, IMV, and death due to pneumonia compared with no-use of TZDs to explore the dose relationship. We performed a stratified analysis to see the effect of TZD vs. non-TZD in the risk of all-cause pneumonia stratified by the subgroups of metformin use vs. no-use, SU use vs. no-use, DPP-4 inhibitor use-vs. no-use trying to determine whether other hypoglycemic agents have effect on pneumonia risk; stratified by patient’s resident areas of the Northern, Central, Southern, and Eastern Taiwan trying to see whether the different environmental exposures have different effect on pneumonia risk.
A two-tailed value of p <0.05 was considered significant. SAS (version 9.4; SAS Institute, Cary, NC, United States) was used for statistical analysis.
Yen F.S., Wei J.C., Hung Y.T., Hsu C.Y., Hwu C.M, & Hsu C.C. (2023). Thiazolidinediones lower the risk of pneumonia in patients with type 2 diabetes. Frontiers in Microbiology, 14, 1118000.
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Publication 2023
Dipeptidyl-Peptidase IV Inhibitors Environmental Exposure Hospitalization Hypoglycemic Agents Metformin Patients Pharmaceutical Preparations Pioglitazone Pneumonia Pneumonia, Bacterial Rosiglitazone

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More about "Dipeptidyl-Peptidase IV Inhibitors"

Dipeptidyl-Peptidase IV (DPP-4) Inhibitors are a class of medications used to treat type 2 diabetes.
These drugs work by blocking the activity of the DPP-4 enzyme, which is responsible for breaking down incretins - hormones that play a key role in regulating blood sugar levels.
By inhibiting DPP-4, these medications help to increase the levels of these important hormones, leading to improved glycemic control in individuals with type 2 diabetes.
DPP-4 inhibitors, also known as gliptins, are commonly used as a standalone treatment or in combination with other antidiabetic agents.
Some common brand names of DPP-4 inhibitors include Sitagliptin (Januvia), Saxagliptin (Onglyza), Linagliptin (Tradjenta), and Alogliptin (Nesina).
These medications are generally well-tolerated and have a low risk of hypoglycemia (low blood sugar).
In addition to their use in diabetes management, DPP-4 inhibitors have also been investigated for their potential therapeutic effects in other conditions, such as cardiovascular disease, obesity, and Alzheimer's disease.
Reserach is ongoing to further explore the versatility and applications of this important class of drugs.
When conducting research on DPP-4 inhibitors, it's important to consider related terms and concepts, such as Aprotinin (a protease inhibitor), Protease inhibitor cocktails, and DPP4-010 (a specific DPP-4 inhibitor).
Additionally, the EGLP-35K assay kit can be used to screen for and evaluate the activity of DPP-4 inhibitors.
By incorporating these relevant terms and insights, researchers can optimize their search strategies and ensure they're accessing the most up-to-date and comprehensive information on this important class of medications.