MétaCan
Menu
← Back to cohort
Record W4404250948 · doi:10.1177/20543581241293199

Prevalence, Characteristics, and Outcomes of People With A High Body Mass Index Across the Kidney Disease Spectrum: A Population-Based Cohort Study

2024· article· en· W4404250948 on OpenAlexafffundabout
Gurleen Sahi, Jennifer Reid, Louise Moist, Michael Chiu, Amanda J. Vinson, Saverio Stranges, Kyla L. Naylor, Kristin K. Clemens

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsSt Joseph's Health CareNova Scotia Health AuthorityLondon Health Sciences CentreLawson Health Research InstituteWestern University
FundersSchulich School of Medicine and Dentistry, Western UniversityLondon Health Sciences CentreLawson Health Research Institute
KeywordsMedicineBody mass indexCohortKidney diseasePopulationDiseaseCohort studyDemographyGerontologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

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.

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)0.001
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 score0.581
Threshold uncertainty score0.842

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
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 head0.006
GPT teacher head0.269
Teacher spread0.263 · 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".

Quick stats

Citations8
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Kidney Health and Disease→Same topicChronic Kidney Disease and Diabetes→French-language works237,207→