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Hypoglycemic Agents

Hypoglycemic agents are a class of drugs used to manage blood sugar levels in individuals with diabetes mellitus.
These agents work by stimulating insulin production, inhibiting glucose absorption, or increasing glucose utilization, thereby helping to maintain normal blood glucose concentrations.
Subtypes include sulfonylureas, biguanides, and alpha-glucosidase inhibitors, among others.
Selecting the appropriate hypoglycemic agent requires consideration of individual patient factors and can be optimized through PubCompare.ai's AI-powered comparison platform, which scours the latest literature, preprints, and patents to provide evidnce-based guidance.
Experience the power of AI-driven comparisons today and make informed decisions for your hypoglycemic agent research and treatment.

Most cited protocols related to «Hypoglycemic Agents»

1
Optimized Fibrosis Scoring for Older NAFLD Patients
Cited 70 times
This study included consecutive patients with biopsy-proven NAFLD who attended specialist fatty liver clinics at the Freeman Hospital, Newcastle upon Tyne, UK; Addenbrooke's Hospital, Cambridge, UK; Antwerp University Hospital, Edegem, Belgium; and Pitié-Salpêtrière Hospital, Paris, France. These formed the initial ascertainment cohort. Liver biopsies were conducted as per routine clinical care for the investigation of abnormal liver function tests (raised ALT, AST, or gamma-glutamyl transferase) or to stage disease severity in patients with radiological evidence of fatty liver. Clinical and laboratory data were collected prospectively from the time of liver biopsy. Patients with evidence of other liver disease (autoimmune hepatitis, viral hepatitis, drug induced liver injury, hemochromatosis, cholestatic liver disease, or Wilson's disease) were excluded. In addition, subjects consuming excessive amounts of alcohol (alcohol intake >20 g/day for women; >30 g/day for men) at the time of biopsy or in the past were excluded. Patients with incomplete data to calculate all the non-invasive scores were excluded.
Relevant clinical details, including gender, age, weight, and height, were obtained at the time of biopsy. The body mass index was calculated by the formula: weight (kg)/height (m)2. Patients were identified as having diabetes if they had been diagnosed with diabetes according to the 2004 American Diabetes Association criteria or if they were taking an oral hypoglycemic drug or insulin (23 (link)).
Percutaneous liver biopsies were performed as per unit protocol at the sites and were assessed by an experienced local hepatopathologist. Patients with liver biopsies specimens <15 mm in length were excluded. Histological scoring was performed according to the non-alcoholic steatohepatitis (NASH) Clinical Research Network criteria (24 (link)). The NAFLD activity score was graded from 0 to 8, including scores for steatosis (0–3), lobular inflammation (0–3), and hepatocellular ballooning (0–2). NASH was defined as steatosis with hepatocyte ballooning and inflammation ± fibrosis (25 (link)). Fibrosis was staged from F0 to F4 (24 (link)). Patients with stage F3 or F4 fibrosis were considered to have advanced fibrosis.
The AST/ALT ratio, FIB-4, and NFS were calculated from blood tests taken at the time of liver biopsy as previously described (16 (link), 26 (link), 27 (link)). Details of the formulas and cutoffs for the tests under investigation are shown in Table 1. Previously published cutoffs were used to exclude and diagnose advanced fibrosis for each score (15 (link), 16 (link), 18 (link), 19 (link))
To validate new cutoffs for the NFS and FIB-4 score optimized for use in older patients (aged ≥65 years) that were derived in the initial ascertainment cohort, anonymized biochemical, histological, and anthropometric data were collected from a separate group of histologically characterized patients from the EPoS/EASL European NAFLD Registry. The “European NAFLD Registry” was established during the EU FP7 FLIP project (2010-) and is now maintained by the EU H2020 EPoS (Elucidating Pathways of Steatohepatitis) consortium to facilitate collaborative research into NAFLD. It is the largest multi-national registry of patients with histologically characterized NAFLD. These patients had data collected according to the same methodology as the main cohort.
All statistical analyses were performed using the SPSS software version 22.0 (SPSS, Chicago, IL). Continuous normally distributed variables were represented as mean ± s.d. Categorical and non-normal variables were summarized as median and range. Chi squared tests were used to determine the distribution of categorical variables between groups. To compare the means of normally distributed variables between groups, the Student's t-test or analysis of variance test was performed. To determine differences between groups for continuous non-normally distributed variables, medians were compared using the Mann–Whitney U-test. The diagnostic performance of the non-invasive tests was assessed by receiver operating characteristic (ROC) curve analysis. The area under the ROC (AUROC) was used as an index to compare the accuracy of tests. The sensitivity, specificity, positive predictive values (PPVs), negative predictive values (NPVs), positive likelihood ratios (LR +ve), and negative likelihood ratios (LR −ve) for relevant cutoffs were also displayed. In order to assess changes in sensitivity and specificity of the tests with age, plots of sensitivity and specificity in different age groups were displayed graphically. New cutoffs for the FIB-4 and NFS were derived for ≥65-year-old patients by taking the point on the ROC where the combined value of sensitivity and specificity was the highest. As the prevalence of advanced fibrosis can vary in different populations, the PPVs and NPVs for the new cutoffs were displayed at advanced fibrosis prevalence rates of 5, 10, 20, 30, and 40%. A P value of <0.05 was considered significant.
McPherson S., Hardy T., Dufour J.F., Petta S., Romero-Gomez M., Allison M., Oliveira C.P., Francque S., Van Gaal L., Schattenberg J.M., Tiniakos D., Burt A., Bugianesi E., Ratziu V., Day C.P, & Anstee Q.M. (2016). Age as a Confounding Factor for the Accurate Non-Invasive Diagnosis of Advanced NAFLD Fibrosis. The American Journal of Gastroenterology, 112(5), 740-751.
Publication 2016
Age Groups Autoimmune Chronic Hepatitis Biopsy Cholestasis Diabetes Mellitus Diagnosis Diet, Formula Drug-Induced Liver Disease Erythropoietin Ethanol Europeans Fatty Liver Fibrosis gamma-Glutamyl Transpeptidase Gender Hematologic Tests Hemochromatosis Hepatitis Viruses Hepatocyte Hepatolenticular Degeneration Hypersensitivity Hypoglycemic Agents Index, Body Mass Inflammation Insulin Liver Liver Diseases Liver Function Tests Non-alcoholic Fatty Liver Disease Nonalcoholic Steatohepatitis Patients Population Group Specialists Steatohepatitis Tests, Diagnostic Woman X-Rays, Diagnostic
2
Cardiac MRI Measurement of LV Mass and Volume
Cited 48 times

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Publication 2008
Antihypertensive Agents Body Size Body Surface Area Dental Caries Diabetes Mellitus Diastole Endocardium Epistropheus Glucose Heart Heart Ventricle High Blood Pressures Hypoglycemic Agents Left Ventricles Males Myocardium Obesity Papillary Muscles Woman
3
Identification of Hypoglycemia in ED Records
Cited 35 times
Using the electronic medical records system at each site, we searched the following ICD-9-CM codes to identify possible visits for hypoglycemia: 250.3 (diabetes with other coma), 250.8 (diabetes with other specified manifestations) 251.0 (hypoglycemic coma), 251.1 (other specified hypoglycemia), 251.2 (hypoglycemia, unspecified), 270.3 (leucine-induced hypoglycemia), 775.0 (hypoglycemia in an infant born to a diabetic mother), 775.6 (neonatal hypoglycemia), and 962.3 (poisoning by insulins and antidiabetic agents).
Given the diversity of potential ICD-9-CM codes, we searched this broad range of codes and in all diagnosis fields (up to ten listed) in an attempt to capture all possible ED hypoglycemia visits. For admitted patients, we examined only ED-based codes, to avoid inclusion of incident hypoglycemia that occurred during inpatient hospitalization. In cases where multiple candidate codes were present, we recorded only the first-listed code. The exception to this was for the more ambiguous codes 250.3 and 250.8, for which we preferentially recorded any of the other candidate codes if present. We based this strategy on detailed examination of the ICD-9-CM coding manual [9 ], review of the experience from previously reported approaches [10 (link)-14 (link)], and discussion with coding experts.
The code 250.8 may be used for other specific diabetes-associated complications in addition to hypoglycemia, including: 259.8 (secondary diabetic glycogenosis), 272.7 (diabetic lipidosis), 707.xx (ulcers of the lower extremity), 709.3 (Oppenheim-Urbach syndrome), and 730.0–730.2, 731.8 (osteomyelitis). Based on discussion with coding experts, we determined that 681.xx (cellulitis of fingers/toes), 682.xx (other cellulitis), and 686.9x (local skin infection) may also be utilized as a co-diagnoses for 250.8, although not specifically mentioned in the manual. We prospectively proposed the coding algorithm displayed in Figure 1 and validated its accuracy through chart review.
We identified all ED visits with candidate ICD-9-CM codes between July 1, 2005 and June 30, 2006 at each site, and obtained written ED charts. For patients with multiple ED visits during the data collection period, we requested only the first visit to avoid overrepresentation by certain patients. Trained research staff abstracted all charts using a standardized form, and the research group met weekly to discuss data collection and resolve abstraction issues. Additionally, two reviewers independently abstracted 10% of charts to evaluate inter-rater agreement in data collection. To enhance the reliability of our chart review, we abstracted only charts with complete ED triage assessment, nursing notes, and emergency physician notes and considered all other charts incomplete.
Ginde A.A., Blanc P.G., Lieberman R.M, & Camargo CA J.r. (2008). Validation of ICD-9-CM coding algorithm for improved identification of hypoglycemia visits. BMC Endocrine Disorders, 8, 4.
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Publication 2008
Antidiabetics Cellulitis Childbirth Comatose Complications of Diabetes Mellitus Diabetes Mellitus Diabetic Comas Diagnosis Emergencies Fingers Glycogen Storage Disease Hospitalization Hypoglycemia Hypoglycemia, leucine-induced Hypoglycemic Agents Infant Infant, Newborn Inpatient Insulin Leg Ulcer Lipoidosis Mothers Osteomyelitis Patients Physicians Syndrome Toes
4
Estimating Diabetes Trends Globally
Cited 33 times

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Publication 2016
Biological Markers Diabetes Mellitus Diagnosis Glucose Hypoglycemic Agents Index, Body Mass Insulin Plasma Population Health Woman
5
Framingham Heart Study: Long-term CVD Outcomes
Cited 33 times
The Framingham Heart Study started in 1948 with the enrollment of the `original' cohort of 5209 individuals. In 1971, some 5,124 offspring of the original cohort and their spouses were enrolled into the Framingham Offspring Study 30 . Constant monitoring of CVD events and mortality has been carried out and was available through the end of 2007 for this investigation. Attendees of the first Offspring examination were eligible for this investigation if they were at least 20 and below 60 years of age (N=4828), free of CVD (N=4758) and cancer at baseline (N=4723), were not lost to follow-up (N=4680) and had complete risk factor profile yielding a final sample of 4506 individuals (2333 women, mean age 37). All participants gave written informed consent and the study protocol was approved by the Institutional Review Board of the Boston Medical Center.
A detailed physical examination, anthropometry, blood pressure determination, and phlebotomy for vascular risk factors were conducted at each Heart Study examination as described in D'Agostino et al.12 (link) Body mass index (BMI) was calculated as the weight in kilograms divided by the square of height in meters. Antihypertensive medication use was ascertained by the physician examiner at the Heart Study and based on self report. Serum total and high-density lipoprotein (HDL) cholesterol and triglycerides levels were determined using standardized enzymatic methods. Low-density lipoprotein (LDL) cholesterol was calculated using Friedwald's formula31 (link). Cigarette smoking in the year preceding the examination was ascertained by self-report. Diabetes was defined as fasting glucose at or above 126 mg/dL or use of insulin or oral hypoglycemic medications.
Participants were followed for a maximum of 35 years (median 32 years). We focused on a `hard' CVD as the primary outcome of interest and defined it as a composite of hard CHD (coronary death, myocardial infarction) and stroke (fatal and non-fatal). Full CVD defined as in D'Agostino et al.12 (link) (hard CHD plus coronary insufficiency and angina pectoris, stroke plus transient ischemic attack, intermittent claudication and congestive heart failure) was used as a secondary outcome. Medical histories, physical examinations at the study clinic, hospitalization records and communication with personal physicians were all used to obtain information about CVD events on follow up.
Pencina M.J., D'Agostino RB S.r., Larson M.G., Massaro J.M, & Vasan R.S. (2009). Predicting the Thirty-year Risk of Cardiovascular Disease: The Framingham Heart Study. Circulation, 119(24), 3078-3084.
Publication 2009
Angina Pectoris Antihypertensive Agents Blood Vessel Cardiac Death Cerebrovascular Accident Cholesterol Congestive Heart Failure Determination, Blood Pressure Diabetes Mellitus Enzymes Ethics Committees, Research Glucose Heart High Density Lipoprotein Cholesterol Hospitalization Hypoglycemic Agents Index, Body Mass Insulin Intermittent Claudication Low-Density Lipoproteins Malignant Neoplasms Myocardial Infarction Phlebotomy Physical Examination Physicians Serum Transient Ischemic Attack Triglycerides Woman

Most recents protocols related to «Hypoglycemic Agents»

1
Cognitive Function Assessment in Transylvania
A cross-sectional quantitative survey was performed in Harghita County (Transylvania, Romania). The patients were recruited from three randomly chosen settlements (Joseni, Ciumani and Lăzarea) with the help of general practitioners of these villages. All of the patients were informed about the purpose of the study and the voluntary nature of the participation. Participants gave written informed consent before taking part in the study. The protocol of the study was approved by the Ethics Committee of the University of Babeș-Bolyai (RO) and by the Code of Deontology for the professions of psychologists, elaborated by the Romanian College of Psychologists. Cognitive functions were assessed independently for each participant by accredited clinical psychologists. Inclusion criteria were: 1) ages between 35 and 65 years [8 (link), 9 ], 2) diagnosed with T2D or hypertension or both (comorbid T2D and hypertension) according to the standards of the American Diabetes Association (2020) [10 (link)] and according to the standards of the American College of Cardiology and American Heart Association guidelines [11 ], respectively, and 3) diabetes and/or hypertension duration at least 5 years. From this study, patients were excluded with 1) any medical illnesses other than T2D, dyslipidemia, hypertension and obesity, 2) a history of hypoglycemic coma or complications of diabetes, 3) primary neurological condition as history of transient ischemic attacks, cerebrovascular stroke or epilepsy or psychiatric disease, 4) previous serious head injury, 5) any sensory or motor disorder that would preclude psychological testing (including blindness), 6) regular treatment with any medications known to have psychoactive effect, and 7) drug or alcohol abuse. The control group was made up of people who applied at the invitation of the municipality's management and did not have a chronic illness. Their health status was checked at the health clinic of the municipality.
The patients were divided into three groups: a group with T2D, a group with hypertension and a group with both diseases. The control group was specifically recruited from the settlement where the patients were registered.
Kálcza Jánosi K, & Lukács A. (2023). Independent and interactive effect of type 2 diabetes and hypertension on memory functions in middle aged adults. BMC Endocrine Disorders, 23, 59.
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Publication 2023
Abuse, Alcohol Blindness Cardiovascular System Cerebrovascular Accident Cognition Comatose Complications of Diabetes Mellitus Craniocerebral Trauma Diabetes Mellitus Disease, Chronic Dyslipidemias Epilepsy Ethics Committees General Practitioners High Blood Pressures Hypoglycemic Agents Mental Disorders Motor Disorders Nervous System Disorder Obesity Patients Pharmaceutical Preparations Pharmacotherapy Transient Ischemic Attack
2
Anthropometric Measurements and Biochemical Profiles
The measurements of anthropometric attributes and biochemical profiles have been described previously [23 (link)]. In brief, we used a digital system (BW-2200; NAGATA Scale Co. Ltd., Tainan, Taiwan) to measure the subject’s body weight and height. Waist circumference (WC) was measured at the level of mid-distance between the bottom of the rib cage and the top of the iliac crest. Hip circumference was the distance around the largest part of the subject’s hips. Blood pressure was measured three times, with an interval of 3 min, after 10 min of rest. The averages of repeated measurements of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were used for analyses. The fasting blood levels of total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (FTG), and glucose (FPG) were determined by an autoanalyzer (Toshiba TBA c16000; Toshiba Medical System, Holliston, MA, USA) with commercial kits (Denka Seiken, Tokyo, Japan).
We also used a structured questionnaire to collect personal histories of common diseases in adults and health behaviors. In the study, hypertension was defined as subjects who had physician-diagnosed hypertension or a history of taking antihypertensive medications. Hyperlipidemia was defined as subjects having been diagnosed with high blood lipids by a physician or having a history of taking lipid-lowering medications. DM was defined as FPG ≥ 126 mg/dL or the use of insulin or other hypoglycemic agents. Cigarette smoking and alcohol drinking were defined as having smoked cigarettes or drank alcohol-containing beverages at least 4 days per week during the past month before enrollment.
Wu T.W., Chou C.L., Cheng C.F., Lu S.X., Wu Y.J, & Wang L.Y. (2023). Associations of genetic markers of diabetes mellitus with carotid atherosclerosis: a community-based case–control study. Cardiovascular Diabetology, 22, 51.
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Publication 2023
Adult Alcoholic Beverages Antihypertensive Agents BLOOD Blood Pressure Body Weight Cholesterol Cholesterol, beta-Lipoprotein Coxa Fingers Glucose High Blood Pressures High Density Lipoprotein Cholesterol Hyperlipidemia Hypoglycemic Agents Iliac Crest Insulin Lipid A Lipids Pharmaceutical Preparations Physicians Pressure, Diastolic Rib Cage Systolic Pressure Triglycerides Waist Circumference
3
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
4
First-Episode Major Depressive Disorder Patients
Participants were recruited at the psychiatric outpatient department of the First Hospital of Shanxi Medical University from 2015 to 2017. Inclusion criteria were as follows: (1) fulfilling DSM-IV criteria for MDD, diagnosed by two trained psychiatrists using the Structured Clinical Interview for DSM-IV Disorders (SCID); (2) 17-item Hamilton Depression Scale (HAMD) score of more than 23; (3) age 18-60 years old, Han nationality; (4) no prior medication, including antidepressant, antipsychotic drugs, thyroid hormone therapy, hypoglycemic agents, antihypertensive and lipid-lowering drugs; and (5) depression symptoms were first-episode and the disease duration of no more than 24 months. Exclusion criteria included: (1) pregnant or breastfeeding women; (2) concurrent DSM-IV axis I disorder including bipolar disorder, schizophrenia, and schizoaffective or severe medical conditions; (3) substance use disorder except for tobacco; and (4) unwillingness to provide informed consent.
All participants provided written informed consent. This study was approved by the Institutional Review Board (IRB) of the First Hospital of Shanxi Medical University (No. 2016-Y27).
Peng P., Wang Q., Lang X., Liu T, & Zhang X.Y. (2023). Clinical symptoms, thyroid dysfunction, and metabolic disturbances in first-episode drug-naïve major depressive disorder patients with suicide attempts: A network perspective. Frontiers in Endocrinology, 14, 1136806.
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Publication 2023
Antidepressive Agents Antihypertensive Agents Antipsychotic Agents Bipolar Disorder Depressive Symptoms Epistropheus Ethics Committees, Research Hypoglycemic Agents Hypolipidemic Agents Outpatients Pharmaceutical Preparations Psychiatrist Schizophrenia Therapeutics Thyroid Hormones Tobacco Use Disorder Woman
5
Glycaemic Variability Quantification Protocol
The parameters of glycaemic variability were calculated by EasyGV software [17 (link)]. The parameters considered were: the glycaemic mean (GM), defined as the arithmetic mean of all blood glucose values measured by women; the glycaemic mean value (GMV), defined as weighted average of the glycaemic means divided by the number of measurements and compared to an ideal average blood glucose value [18 (link)]; the mean amplitude of glucose excursions (MAGE), which quantifies the main glycaemic variations [19 (link)]; the classical standard deviation (SD); the high blood glucose index (HBGI) and the low blood glucose index (LBGI), which indicate the frequency and the amplitudes of hyperglycaemic and hypoglycaemic events, respectively, and define the risks for patients to experience adverse glycaemic events [20 (link)]; the J-index, resulting from the means of glycaemic measures combined with the SDs of glycaemic values [21 (link)]; and the mean absolute glucose (MAG), as the sum of differences of consecutive glycaemic values divided by the total number of hours of observation [22 (link)]. All these parameters indicate data fluctuations, with higher values indicating higher variability.
Blasi I., Daolio J., Pugni V., Comitini G., Morciano M., Grassi G., Todros T., Gargano G, & Aguzzoli L. (2023). Correlations between parameters of glycaemic variability and foetal growth, neonatal hypoglycaemia and hyperbilirubinemia in women with gestational diabetes. PLOS ONE, 18(3), e0282895.
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Publication 2023
Blood Glucose Glucose Hyperglycemia Hypoglycemic Agents Patients Woman

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2
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More about "Hypoglycemic Agents"

Hypoglycemic agents, also known as antidiabetic drugs or glucose-lowering medications, are a class of pharmaceutical compounds used to manage blood sugar (glucose) levels in individuals with diabetes mellitus.
These agents work by stimulating insulin production, inhibiting glucose absorption, or increasing glucose utilization, ultimately helping to maintain normal blood glucose concentrations.
The subtypes of hypoglycemic agents include sulfonylureas, biguanides, and alpha-glucosidase inhibitors, among others.
Selecting the appropriate hypoglycemic agent requires careful consideration of individual patient factors, such as their overall health, medication history, and response to treatment.
PubCompare.ai's AI-powered comparison platform can provide evidence-based guidance by scouring the latest literature, preprints, and patents to help optimize your hypoglycemic agent research and treatment decisions.
Related terms and abbreviations include SAS (Statistical Analysis System), STZ (streptozotocin), SPSS (Statistical Package for the Social Sciences), Humulin S (a brand of insulin), Cholesterol oxidase method (a laboratory technique), Actrapid (a brand of insulin), IPro2 (a continuous glucose monitoring system), and SAS software version 9.4.
These tools and techniques may be utilized in the study and management of hypoglycemic agents and diabetes mellitus.
Experience the power of AI-driven comparisons today and make informed decisions for your hypoglycemic agent research and treatment.