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Dyslipidaemia and its management in patients with type 2 diabetes across the Middle East and Africa: the PACT-MEA study

2023· article· en· W4388600335 on OpenAlexaff
Hani Sabbour, Naji Alamuddin, Fatheya Alawadi, Hessa Alkandari, Wael Almahmeed, Samir H. Assaad‐Khalil, Jihad Haddad, Lise Lotte N. Husemoen, Landi Lombard, Rayaz A. Malik, M Ngome, Sam Salek, Geeta Yadav, Subodh Verma

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsSt. Michael's Hospital
FundersNovo Nordisk
KeywordsMedicineType 2 diabetesDiabetes mellitusInternal medicineObservational studyBody mass indexCreatinineAlbuminuriaEpidemiologyRenal functionEndocrinology

Abstract

fetched live from OpenAlex

Abstract Background There is a scarcity of high-quality epidemiological data on the clinical burden of type 2 diabetes (T2D) and cardiovascular risk (CV) in the Middle East and Africa due to under-representation of these regions in contemporary diabetes and cardiovascular trials. Multiple risk factors, including dyslipidaemia, increase CV risk in patients with T2D and established atherosclerotic cardiovascular disease (eASCVD) and those at risk for ASCVD. According to the European Society of Cardiology (ESC) 2021 Guidelines, target low-density lipoprotein cholesterol (LDL-C) for patients with T2D is <1.8 mmol/L (<1.4 mmol/L in patients with eASCVD or at very high risk). Purpose To establish the status of dyslipidaemia and its management in patients with T2D and eASCVD or high/very high ASCVD risk across the Middle East and Africa. Methods Adult patients with T2D participating in a cross-sectional, observational study in seven Middle East and African countries were included in the analysis. Laboratory and medication data were collected from medical charts of patients attending a routine health visit in 2022. Descriptive statistics were used to characterize the lipid profile and management of patients with T2D by age, diabetes duration, body mass index, HbA1c, microvascular complications, estimated glomerular filtration rate (eGFR), and urinary albumin to creatinine ratio (UACR). Results There were 3726 patients in the overall study sample (mean age, 58 ± 12; male, 53%), almost all of whom were at high (69%)/very high (30%, includes eASCVD) risk according to ESC 2021 guidelines. In patients for whom data was available in the medical record, median LDL-C (n=2525) was 2.2 mmol/L (IQR, 1.7 to 3.0), HDL-C (n=2453) was 1.1 mmol/L (IQR, 0.9 to 1.3), and triglycerides (n=2488) were 1.6 mmol/L (IQR, 1.2 to 2.3). Of the patients with high/very high ASCVD risk (n=2313), 30% met the ESC guideline-recommended target for LDL-C of <1.8 mmol/L and 16% met the target of <1.4mmol/L. Most patients were on statin therapy (77%, range across countries: 60%-87%, Table 1), with use increasing with age and duration of diabetes. More patients with T2D for ≥10 years were on high intensity statin therapy than those with T2D <10 years (37% vs 33%). A similar pattern was observed for patients with HbA1c levels ≥7% (37% vs 31%, Table 2). Most patients with nephropathy were on statins (87%); use increased with lower eGFR and higher UACR levels. Very few patients were on Ezetimibe (6.4%), fibrates (4.8%), fish oil (including icosapent ethyl) (1.4%), or proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors (0.5%). Conclusions Utilization of moderate intensity statin therapy (less than 50% LDL reduction) despite high and very high CV risk leads to failure to achieve cardioprotective LDL targets in most patients. Given the high burden of ASCVD in patients with T2D, prioritization of high intensity lipid lowering therapy is recommended.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 score0.011
Threshold uncertainty score0.022

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 science0.0010.001
Research integrity0.0010.001
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 head0.055
GPT teacher head0.268
Teacher spread0.212 · 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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Citations1
Published2023
Admission routes1
Has abstractyes

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