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Romagnoli A, Savoia M, Papini G, Caprodossi A, Bartolini F. Adherence and persistence rates for antidiabetic treatments in type 2 diabetes: a real-world study in an Italian region. Eur J Hosp Pharm 2025:ejhpharm-2024-004383. [PMID: 40015721 DOI: 10.1136/ejhpharm-2024-004383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2024] [Accepted: 02/17/2025] [Indexed: 03/01/2025] Open
Abstract
OBJECTIVE Achieving glycaemic control is essential to avoid disease progression and diabetes-related complications. Non-adherence and discontinuity in diabetic therapy are major barriers to optimal glycaemic control. The aim of this study was to evaluate adherence, persistence and therapy switching over 1 year in real-life conditions in patients with type 2 diabetes within an Italian region. METHODS A retrospective, observational, non-interventional study was conducted, analysing patients treated with Anatomical Therapeutic Chemical (ATC) Classification A10B drugs dispensed by pharmacies under the local health authority (ASL) of the Umbria region from 1 January 2022 to 31 December 2023. Adherence was measured using the Proportion of Days Covered (PDC), while persistence was calculated as the duration between the start and end of therapy. RESULTS A total of 6928 patients with type 2 diabetes were analysed. After 1 year, the overall adherence rate was 0.78, with 58% (4017/6928) of patients having adherence greater than 0.80. The lowest adherence was observed in patients treated with metformin +dipeptidyl peptidase 4 (DPP4) inhibitors, with a mean adherence of 0.71 and 36% (142/395) of patients achieving adherence greater than 0.80. Conversely, the highest adherence was seen in patients on sodium-glucose co-transporter 2 (SGLT2) inhibitors, with a mean adherence of 0.91 and 97% (473/487) of patients achieving adherence greater than 0.80. Persistence data showed concerning results, with less than 10% of patients remaining on treatment for 1 year across all drug classes. Among patients initially treated with metformin (n=4427), there was a substantial loss to follow-up, with 3582 patients (81%) discontinuing treatment within the first year. CONCLUSIONS The 1 year data on adherence and persistence for antidiabetic drugs revealed concerning trends. These findings underscore the need for targeted interventions, involving clinicians and pharmacists, to improve adherence and persistence in patients with type 2 diabetes, ultimately ensuring better disease management and reducing long-term healthcare costs.
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Affiliation(s)
| | - Martina Savoia
- Pharmaceutical Assistance Department, Local Health Unit Umbria 2, Terni, Italy
| | - Gloria Papini
- Pharmaceutical Assistance Department, Local Health Unit Umbria 2, Terni, Italy
| | | | - Fausto Bartolini
- Pharmaceutical Assistance Department, Local Health Unit Umbria 2, Terni, Italy
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Mohammadi Jouabadi S, Peymani P, Nekouei Shahraki M, van Rooij JGJ, Broer L, Roks AJM, Stricker BH, Ahmadizar F. Effects and interaction of single nucleotide polymorphisms at the pharmacokinetic/pharmacodynamic site: insights from the Rotterdam study into metformin clinical response and dose titration. THE PHARMACOGENOMICS JOURNAL 2024; 24:31. [PMID: 39375343 DOI: 10.1038/s41397-024-00352-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 09/23/2024] [Accepted: 10/01/2024] [Indexed: 10/09/2024]
Abstract
Our study investigated the impact of genetic variations on metformin glycemic response in a cohort from the Rotterdam Study, comprising 14,926 individuals followed for up to 27 years. Among 1285 metformin users of European ancestry, using linear mixed models, we analyzed the association of single nucleotide polymorphisms (SNPs) and a Polygenic Risk Score (PRS) with glycemic response, measured by changes in metformin dosage or HbA1c levels. While individual genetic variants showed no significant association, rs622342 on SLC2A1 correlated with increased glycemic response only in metformin monotherapy patients (β = -2.09, P-value < 0.001). The collective effect of variants, as represented by PRS, weakly correlated with changes in metformin dosage (β = 0.023, P-value = 0.027). Synergistic interaction was observed between rs7124355 and rs8192675. Our findings suggest that while higher PRS correlates with increased metformin dosage, its modest effect size limits clinical utility, emphasizing the need for future research in diverse populations to refine genetic risk models.
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Affiliation(s)
- Soroush Mohammadi Jouabadi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.
- Division of Vascular Disease and Pharmacology, Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands.
| | - Payam Peymani
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- College of pharmacy, University of Manitoba, Winnipeg, MB, Canada
| | - Mitra Nekouei Shahraki
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jeroen G J van Rooij
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Linda Broer
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Anton J M Roks
- Division of Vascular Disease and Pharmacology, Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Fariba Ahmadizar
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Data Science and Biostatistics, Julius Global Health, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Gillies MB, Bharat C, Camacho X, Daniels B, Hall K, Kelly TL, Kelty E, Lin J, Litchfield M, Lopez D, Noghrehchi F, Raubenheimer J, Varney B, Pratt N. Medicine utilization studies in Australian individual-level dispensing data: A blinded, multi-center replicated analysis. Pharmacoepidemiol Drug Saf 2024; 33:e5776. [PMID: 38479400 DOI: 10.1002/pds.5776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 11/05/2024]
Abstract
PURPOSE Medicine dispensing data require extensive preparation when used for research and decisions during this process may lead to results that do not replicate between independent studies. We conducted an experiment to examine the impact of these decisions on results of a study measuring discontinuation, intensification, and switching in a cohort of patients initiating metformin. METHODS Four Australian sites independently developed a HARmonized Protocol template to Enhance Reproducibility (HARPER) protocol and executed their analyses using the Australian Pharmaceutical Benefits Scheme 10% sample dataset. Each site calculated cohort size and demographics and measured treatment events including discontinuation, switch to another diabetes medicine, and intensification (addition of another diabetes medicine). Time to event and hazard ratios for associations between cohort characteristics and each event were also calculated. Concordance was assessed by measuring deviations from the calculated median of each value across the sites. RESULTS Good agreement was found across sites for the number of initiators (median: 53 127, range: 51 848-55 273), gender (56.9% female, range: 56.8%-57.1%) and age group. Each site employed different methods for estimating days supply and used different operational definitions for the treatment events. Consequently, poor agreement was found for incidence of discontinuation (median 55%, range: 34%-67%), switching (median 3.5%, range: 1%-7%), intensification (median 8%, range: 5%-12%), time to event estimates and hazard ratios. CONCLUSIONS Differences in analytical decisions when deriving exposure from dispensing data affect replicability. Detailed analytical protocols, such as HARPER, are critical for transparency of operational definitions and interpretations of key study parameters.
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Affiliation(s)
- Malcolm B Gillies
- Medicines Intelligence Research Program, School of Population Health, UNSW Sydney, Kensington, Australia
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
| | - Chrianna Bharat
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
- National Drug and Alcohol Research Centre, UNSW Sydney, Kensington, Australia
| | - Ximena Camacho
- Medicines Intelligence Research Program, School of Population Health, UNSW Sydney, Kensington, Australia
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
| | - Benjamin Daniels
- Medicines Intelligence Research Program, School of Population Health, UNSW Sydney, Kensington, Australia
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
| | - Kelly Hall
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Thu-Lan Kelly
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Erin Kelty
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
- School of Population and Global Health, University of Western Australia, Perth, Australia
| | - Jialing Lin
- Medicines Intelligence Research Program, School of Population Health, UNSW Sydney, Kensington, Australia
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
| | - Melisa Litchfield
- Medicines Intelligence Research Program, School of Population Health, UNSW Sydney, Kensington, Australia
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
| | - Derrick Lopez
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
- School of Population and Global Health, University of Western Australia, Perth, Australia
| | - Firouzeh Noghrehchi
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
- The University of Sydney, Faculty of Medicine and Health, School of Medical Sciences, Biomedical Informatics and Digital Health, Translational Australian Clinical Toxicology Research Group, Australia
| | - Jacques Raubenheimer
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
- The University of Sydney, Faculty of Medicine and Health, School of Medical Sciences, Biomedical Informatics and Digital Health, Translational Australian Clinical Toxicology Research Group, Australia
| | - Bianca Varney
- Medicines Intelligence Research Program, School of Population Health, UNSW Sydney, Kensington, Australia
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
| | - Nicole Pratt
- Methods and Data Working Group, NHMRC Centre of Research Excellence in Medicines Intelligence, University of South Australia, University of New South Wales, University of Sydney, University of Western Australia, Australia
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia
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Tan GSQ, Morton JI, Wood S, Trevaskis NL, Magliano DJ, Windsor J, Shaw JE, Ilomäki J. COX2 inhibitor use and type 2 diabetes treatment intensification: A registry-based cohort study. Diabetes Res Clin Pract 2024; 207:111082. [PMID: 38160735 DOI: 10.1016/j.diabres.2023.111082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/22/2023] [Accepted: 12/25/2023] [Indexed: 01/03/2024]
Abstract
AIM This study examined the association between cyclooxygenase-2 inhibitor (COX2i) use and diabetes progression in people with type 2 diabetes. METHODS We conducted a nation-wide cohort study using an Australian diabetes registry linked to medication dispensing data. We assessed time to diabetes treatment intensification among new users of COX2i compared to mild opioids. Inverse probability of treatment-weighted Cox regression models were used to adjust for age, sex, time since diabetes diagnosis, comorbidities, and socio-economic disadvantage. We conducted several sensitivity analyses, including per-protocol analyses and comparing use of any NSAID to mild opioids. RESULTS There were 8,071 new users of COX2i and 7,623 of mild opioids with 4,168 diabetes treatment intensifications over a median follow-up of 1.6 years. Use of COX2i was associated with decreased risk of treatment intensification when compared to mild opioids (HR 0.91, 95 %CI 0.85-0.96). The results were not significant in the per-protocol analyses. Use of any NSAID was associated with a lower risk of treatment intensification compared to mild opioids (HR 0.90, 95 %CI 0.85-0.96). CONCLUSIONS Treatment with COX2i may be associated with a modest decreased risk of diabetes treatment intensification compared to mild opioids. Future clinical studies are required to confirm whether COX2 inhibition has clinically significant benefits for glycaemic control.
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Affiliation(s)
- George S Q Tan
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
| | - Jedidiah I Morton
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Stephen Wood
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia
| | - Natalie L Trevaskis
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Faculty of Pharmacy and Pharmaceutical, Sciences, Monash University, Melbourne, Victoria, Australia
| | - Dianna J Magliano
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - John Windsor
- Surgical and Translational Research Centre, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - Jenni Ilomäki
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia.
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