Prevalence, Characteristics, and Outcomes of People With A High Body Mass Index Across the Kidney Disease Spectrum: A Population-Based Cohort Study
Notice bibliographique
Résumé
Background: Obesity has a major impact on health and health care, particularly in those with chronic kidney disease (CKD). Objective: The objective was to describe the prevalence, characteristics, and outcomes of people living with CKD and obesity (defined by a body mass index [BMI] ≥30 kg/m 2 ) in Canada. Design: Population-based cohort study using linked administrative health data (ICES). Patients: Adults aged 18 year and older with CKD G1-5D who had a height and weight recorded during a visit to an academic hospital in London Ontario Canada, between January 2010 and December 2019. Measures: CKD as defined by CKD 3A or higher. BMI as defined by weight kg/m 2 . Methods: As a primary interest, we described the percentage of patients with CKD across different BMI categories (<25 kg/m 2 , BMI 25-29.9 kg/m 2 , and BMI ≥30 kg/m 2 ), as well as their demographic and clinical profiles. As secondary interests, we followed patients until January 1, 2022 to summarize: (1) the percentage with CKD G3 who had kidney disease progression (50% decline from baseline estimated glomerular filtration rate [eGFR]) by BMI category, (2) the percentage with CKD G3-4 who developed kidney failure (initiation of maintenance dialysis or an eGFR of <15 mL/min/1.73 m 2 ) by BMI category, (3) the percentage with CKD G4-G5D who received a kidney transplant by BMI category, and (4) post-transplant outcomes in those transplanted over the study period, by BMI category. We performed similar analyses across CKD risk categories. Results: Of the 198 151 patients included, the percentage with obesity defined by a BMI ≥30 kg/m 2 increased from CKD G1 to CKD G4 (ie, 37% of those with CKD G1 had a BMI ≥30 kg/m 2 vs 40.9% of CKD G4). In CKD G5D and CKD T, the prevalence of high BMI appeared to drop (only ~38% had a BMI ≥30 kg/m 2 across groups). Across CKD categories, those with a BMI ≥30 kg/m 2 appeared to have more comorbidities, use more health care resources, and have more socioeconomic disparities than those with lower BMIs. Although secondary outcome events were limited, those with G3-4 with a BMI ≥30 kg/m 2 appeared to have a higher risk of CKD progression and those with CKD G5D with BMI ≥30 kg/m 2 were less likely to receive transplant over the study period. Interestingly those transplanted with a BMI ≥30 kg/m 2 appeared to have fewer post-transplant complications. We also observed an “obesity-paradox” in the risk of mortality, with high BMI appearing protective, particularly in the end stages of kidney disease. Limitations: We used BMI to capture obesity in this study but recognize its limitations as a measure of body composition. Secondary outcomes were descriptive and unadjusted due to small sample size and may have been subject to selection bias and confounding. Conclusions: Obesity defined by high BMI is highly prevalent in people with CKD, and patients have health, health care, and social disparity. Future studies to understand the impact of BMI on patients with CKD and how to individualize and manage BMI and obesity across the spectrum of CKD remain important.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».