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Elevated remnant lipoproteins may increase subclinical <scp>CVD</scp> risk in pre‐pubertal children with obesity: a case‐control study

2012· article· en· W2106453692 on OpenAlexafffund
Yijie Wang, Christine Pendlebury, MA Dodd, Katerina Maximova, Donna F. Vine, Mary Jetha, Geoff D.C. Ball, Spencer D. Proctor

Bibliographic record

VenuePediatric Obesity · 2012
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsProvincial Laboratory of Public HealthWomen and Children’s Health Research InstituteUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta Innovates - Health Solutions
KeywordsMedicineInternal medicineEndocrinologyObesityApolipoprotein BRisk factorDiabetes mellitusBody mass indexCholesterolTriglycerideLipoproteinChildhood obesityOverweight

Abstract

fetched live from OpenAlex

Summary What is already known about this subject Childhood obesity plays a fundamental role in the development of cardiovascular disease ( CVD ) and type 2 diabetes in adulthood. Clinical guidelines for the early management of CVD in children are poorly defined. Traditional cholesterol biomarkers such as low‐density lipoprotein cholesterol usually fall within the normal range in pre‐pubertal children with obesity. Remnant lipoproteins are overproduced by the intestine during obesity and type‐2 diabetes in adults and are an independent risk factor for CVD . What this study adds Pre‐pubertal children with obesity have elevated (3‐fold) remnant lipoprotein concentration (assessed as apolipoprotein B 48) relative to non‐obese controls, suggesting impaired metabolism of these atherogenic lipoproteins and potential increased CVD risk. Fasting apolipoprotein B48 is positively and significantly correlated with lipid biomarkers including triglyceride, total cholesterol and total cholesterol/high‐density lipoprotein cholesterol in pre‐pubertal children with obesity. Objectives Current clinical guidelines to assess paediatric cardiovascular disease ( CVD ) risk heavily rely on cholesterol parameters that are generally normal for obese children. Remnant lipoproteins have emerged as a critical CVD risk factor particularly in adults with normolipidemia. We assessed remnant lipoprotein concentration (measured by apolipoprotein [apo] B 48) and its relationship with other traditional CVD risk biomarkers in pre‐pubertal children with obesity. Methods Pre‐pubertal children ( n = 78) with obesity ( n = 39, 9.9 ± 0.3 years old) as well as sex‐matched normal‐weight controls ( n = 39, 9.8 ± 0.3 years) were assessed for anthropometry, blood pressure and fasting plasma biochemical parameters for remnant lipoprotein, lipid and glucose/insulin metabolism, and inflammatory status. Results Children with obesity had striking 2‐fold higher apo B 48‐containing remnant lipoproteins concentrations relative to normal‐weight peers; the magnitude of elevation in the remnant lipoproteins is comparable to the levels previously reported for adults with established CVD and type‐2 diabetes. Fasting apo B 48 was positively correlated with fasting triglyceride concentration in children with obesity ( r = 0.51, P &lt; 0.001) and their normal‐weight peers ( r = 0.45, P &lt; 0.01). Traditional CVD biomarkers including low‐density lipoprotein cholesterol showed no difference between groups and remained within the normal range for a paediatric population. Conclusion Elevated apo B 48‐containing remnant lipoprotein is a stronger biomarker for paediatric CVD risk compared to traditional cholesterol parameters and may be associated with early adaptation of the intestine during obesity. Further investigation of abnormalities associated with the secretion and/or clearance of atherogenic remnant lipoproteins during the postprandial state may yield insight into our understanding of and therapeutic targets for managing risk for CVD in children with obesity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.242
Teacher spread0.234 · 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 teacher head, not a consensus.

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

Citations15
Published2012
Admission routes2
Has abstractyes

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