Background: To evaluate the association of statins and co-morbidities with new onset type 2 diabetes mellitus (T2DM) in patients 65 years and older.
Methods: This retrospective study used de-identified administrative healthcare claims and enrolment data from a Medicare Advantage Prescription Drug (MAPD) health plan offered by a large multistate healthcare company. The plan covered >2.4 million individuals, of whom >1.7 million individuals were ≥65 years. Of these, 265 554 individuals had continuous MAPD enrolment January 2008 to December 2015. The unadjusted model assessed demographic, pharmacy and T2DM co-morbidities as covariates. Significant variables (P < .05) in the unadjusted model were then included in the adjusted model. The adjusted model used Cox proportional hazards to evaluate covariate effects. Matched propensity score analysis was used to analyse the association of statins and T2DM onset.
Results: The cumulative rate of diagnosed T2DM onset in the study cohort was 4.82% (4314/89 390). Annualised incidence of T2DM diagnosis was 0.82%, 0.88%, 1.04% and 2.09% in 2012, 2013, 2014 and 2015, respectively. T2DM onset was associated with male sex, non-white (African American or Hispanic ethnicity), statin use, hypertension, hyperlipidaemia, heart failure, lower limb ulceration, atherosclerosis, other retinopathy, angina pectoris, poor vision and blindness and absence ischaemic heart disease (IHD). Matched propensity score analysis showed that statin use was significantly associated with T2DM onset (Odds Ratio = 1.26, 95% Confidence Interval: 1.12-1.41, P < .0001) in the adjusted model.
Conclusions: Analyses indicated that statin usage was associated with new onset T2DM after adjusting for covariates.
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http://dx.doi.org/10.1002/dmrr.3310 | DOI Listing |
Diabetes Ther
January 2025
First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical, University & Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Institute of Nephrology, Jinan, China.
Introduction: More than half of diabetes patients are Asians, and their tolerance to antidiabetic drugs may differ from that of non-Asians. Oral semaglutide has recently gained attention for its advantages in glycemic and body weight control. However, its effects across different ethnic groups remain unknown.
View Article and Find Full Text PDFExpert Opin Drug Saf
January 2025
Department of Endocrinology, Guang'anmen Hospital of China Academy of Chinese Medical Sciences, Beijing, China.
Background: Fulminant type 1 diabetes mellitus (FT1DM) is a severe subtype of type 1 diabetes characterized by rapid onset, metabolic disturbances, and irreversible insulin secretion failure. Recent studies have suggested associations between FT1DM and certain medications, warranting further investigation.
Objectives: This study aims to analyze drugs associated with an increased risk of FT1DM using the Food and Drug Administration Adverse Event Reporting System (FAERS) database.
Clin Trials
January 2025
Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Background: Implementation and hybrid effectiveness-implementation trials aspire to speed the translation of science into practice by generating crucial evidence for improving the uptake of effective health interventions. By design, they pose unique recruitment and retention challenges due to their aims, units of analysis, and sampling plans, which typically require many clinical sites (i.e.
View Article and Find Full Text PDFJ Clin Med
January 2025
Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy.
Sepsis is one of the leading causes of mortality in hospital settings, and early diagnosis is a crucial challenge to improve clinical outcomes. Artificial intelligence (AI) is emerging as a valuable resource to address this challenge, with numerous investigations exploring its application to predict and diagnose sepsis early, as well as personalizing its treatment. Machine learning (ML) models are able to use clinical data collected from hospital Electronic Health Records or continuous monitoring to predict patients at risk of sepsis hours before the onset of symptoms.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Medical Sciences, University of Turin, 10126 Turin, Italy.
The characterization of patients with inflammatory bowel disease (IBD) and type 2 diabetes mellitus (T2DM) as a new group has not been well detailed. This study aimed to evaluate the impact of T2DM on IBD progression and analyze the prevalence of steatotic liver disease and liver damage in these patients. Through a retrospective case-control study, we compared severe IBD occurrence in patients with both IBD-T2DM (cases) versus those with IBD alone (controls).
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