CHRONIC KIDNEY DISEASE INCIDENCE AND ITS ASSOCIATED RISK FACTORS IN THE EASTERN MEDITERRANEAN REGION: A SYSTEMATIC REVIEW AND META-ANALYSIS
Notice bibliographique
Résumé
In the last three decades, chronic kidney disease (CKD) has become the leading cause of morbidity and risen to become the world’s third fastest-growing cause of death, posing substantial societal, economic, and healthcare challenges. Yet, the epidemiological profile of new CKD cases in the Eastern Mediterranean Region (EMR) remains scarce, uncharted, and often fragmented across individual country-level studies with discrepancies in data on CKD burden. The factors influencing the natural course of CKD are complex and heterogeneous. However, the global data highlights age, diabetes, and hypertension as prime drivers, but EMR-specific factors are obscured by fragmented surveillance and scarce incidence reporting. This gap delays timely detection, early targeted preventive intervention, and evidence-based solution frameworks in policymaking in a region. Accordingly, the primary aim of this systematic review and meta-analysis is to quantify CKD incidence and synthesize its principal risk factors across EMR populations. Methods and Materials: Adhering to PRISMA 2020 guidelines and its search extension. Five bibliographic electronic databases (MEDLINE, EMBASE, CINAHL, PsycINFO, and EBSCO) were searched without language or date limits. Eligible studies were peer-reviewed observational studies (cohort and case-control) reporting CKD incidence and associated risk factors in any of the 22 EMR countries. Appraised methodological quality using the Newcastle–Ottawa Scale. The analytic approach integrates three main steps: (1) descriptive numeric summary, (2) narrative synthesis, and (3) meta-analysis. Incidence data were standardized to “cases per person-year”. Log-transformed incidence rates were pooled in a single-arm random-effects meta-analysis (REML), with inverse-variance weighting, and re-expressed as cases per 1,000 person-years. Cochran’s Q and I² were used to assess heterogeneity, while publication bias was assessed with funnel plots and Egger’s test (≥ 10 studies). Subgroup/meta-regression explored country-level and methodological moderators; sensitivity analyses (leave-one-out and fixed-effect models) tested robustness. Results: Nineteen EMR studies (total n = 66,494 participants) met inclusion criteria, and eleven of those had sufficient data for single-arm pooling. The overall pooled CKD incidence was 25 per 1,000 person-years (95% CI 19-32), with significant heterogeneity (Q = 1910.6, df = 10, P < 0.001; I² = 99.6 %). Country-specific estimates ranged from 23.1 in Iran to 41.0 in Tunisia per 1,000 person-years. However, meta-regression revealed that country, follow-up length, and sample size explained small variance. Narrative synthesis identified specific drivers of the disease incidence that are interconnected and overlapping, including age, female sex, low educational or marital status, diabetes, hypertension, general or central adiposity, dyslipidemia, high uric acid, and renal pathologies as the most consistently reported risk factors. Lifestyle contributors (tobacco use, high‑salt/processed diets, and inadequate or extreme physical activity) were also prominent, whereas coffee, lignan‑rich foods, and moderate exercise play a protective role. Furthermore, nephrotoxic medications and short endogenous estrogen exposure further elevated CKD risk. Conclusions: The overall pooled estimate rate is 25 incidents per 1,000 person-years. This incidence is comparable to the global average. Yet, certain nations within the region show significantly higher rates. The CKD incidence in the area is mainly driven by overlapping cardiometabolic risk factors (e.g., diabetes, hypertension), sociodemographic shifts, unhealthy lifestyle patterns (e.g., obesity and dietary risks), environmental exposure (e.g., pollution), and iatrogenic-related conditions (herbal medications). Calling for an urgent integrated solution approach combined with specific prevention strategies, such as early identification of high-risk groups, diabetes and hypertension control, emphasis on healthy lifestyle promotion, and constraint on nephrotoxic drug and herbal use. Improving surveillance systems and harmonized diagnostic criteria will be essential for tracking progress towards Sustainable Development Goal targets on non‑communicable diseases.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,019 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,016 | 0,029 |
| Bibliométrie | 0,008 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».