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Cardioprotective pharmacotherapy in patients with type 2 diabetes across the Middle East and Africa: the PACT-MEA study

2023· article· en· W4388598156 on OpenAlexaff
Subodh Verma, Naji Alamuddin, Fatheya Alawadi, Hessa Alkandari, Wael Almahmeed, Samir H. Assaad‐Khalil, Jihad Haddad, Lise Lotte N. Husemoen, Landi Lombard, Rayaz A. Malik, Mutsotso S. Ngome, Hani Sabbour, Geeta Yadav, Sam Salek

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's Hospital
FundersNovo Nordisk
KeywordsMedicineType 2 diabetesMetforminPharmacotherapyInternal medicineDiabetes mellitusRenal functionAlbuminuriaBody mass indexObservational studyCreatininePopulationInsulinEndocrinologyEnvironmental health

Abstract

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Abstract Background There is limited information about the management of cardiovascular (CV) risk in patients with type 2 diabetes (T2D) in the Middle East and Africa. For patients with T2D and established atherosclerotic cardiovascular disease (eASCVD) or those at risk for ASCVD, global guidelines recommend the use of antidiabetic medications with proven cardiovascular and kidney benefit (glucagon-like peptide-1 receptor agonists [GLP-1 RAs] and sodium-glucose cotransporter-2 inhibitors [SGLT2is]), in addition to lipid lowering, blood pressure lowering, and antiplatelet therapy, independent of baseline HbA1c or metformin use. Purpose To determine the use of pharmacotherapy among a population of patients with T2D and eASCVD or high/very high ASCVD risk in seven countries across the Middle East and Africa. Methods Adult patients with T2D were enrolled in a cross-sectional, observational study in Bahrain, Egypt, Jordan, Kuwait, Qatar, South Africa, and United Arab Emirates. Pharmacotherapy data extracted from the medical charts of patients during a routine scheduled clinic visit in 2022 were analysed. Descriptive statistics were used to characterize patients with T2D and their glucose-lowering pharmacotherapy use by age, diabetes duration, body mass index, HbA1c, microvascular complications, estimated glomerular filtration rate (eGFR), and urinary albumin to creatinine ratio (UACR). Results Of the 3726 patients in the overall study sample (mean age, 58 ± 12; male, 53%), one in five had eASCVD (21%) and nearly all were classified as being at high (69%)/very high risk (30%, includes eASCVD), according to European Society of Cardiology (ESC) 2021 guidelines. About one-third (36%) of patients were taking SGLT2is (Table 1, range across countries: 20%-64%). Use of SGLT2is was similar across age and BMI but more patients with T2D for ≥10 years (40%) received these medications than those with T2D for <10 years (31%). More males than females were on SGLT2is (40% vs 32%). Use of SGLT2is was also higher among patients with HbA1c ≥7% (42% vs 33%, Table 2). SGLT2i use was similar by eGFR and UACR levels. Few (13%) of the overall sample of patients with T2D received GLP-1 RAs (Table 1, range across countries: 3%-25%), the use of which declined with age. More females than males were taking GLP1-RAs (16% vs 11%). More patients with obesity (BMI ≥30 kg/m2) received GLP-1 RAs than those without obesity (18% vs 8%); use was also higher among patients with eGFR ≥60 (14% vs 9%, Table 2). Conclusions Despite availability of cardio-renal therapy in each of the seven participating countries in the Middle East and Africa, few patients with T2D who had eASCVD or were at high/very high risk for ASCVD received SGLT2is or GLP-1 RAs, as recommended by guidelines. Active prioritization of cardio-renal protective therapies based on CV risk and renal target organ damage should be addressed given the high burden of disease and complications in the region.Table 1Table 2

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.064
GPT teacher head0.286
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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