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Cardiometabolic Profile after Pediatric Cancer Treatment: Insight into HDL Composition and Nutritional Intake

2019· article· en· W3176531580 on OpenAlexaffabout
Véronique Bélanger, Simon Saliou Diallo‐Blais, Carol‐Ann Robert, Juliette Sauve St‐Martin, Sabrina Beaulieu‐Gagnon, Simon Drouin, Laurence Bertout, Isabelle Bouchard, Caroline Laverdière, Daniel Sinnett, Valérie Marcil

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineDyslipidemiaMetabolic syndromeInternal medicineHyperlipidemiaCancerAnthropometryObesityEndocrinologyDiabetes mellitus

Abstract

fetched live from OpenAlex

Few studies have investigated the impact of pediatric cancer treatment on cardiometabolic health, although chemotherapy in children can cause rapid weight gain, transient hypertension, elevated blood glucose and hyperlipidemia. Besides, we have previously shown that young survivors of childhood leukemia are at increased risk of having the metabolic syndrome, dyslipidemia, hypertension and altered high‐density lipoprotein (HDL) composition. This study aims to examine the cardiometabolic profile of children after cancer treatment in relation to their diet, as well as HDL2 and HDL3 composition. Methods Patients were recruited at Sainte‐Justine University Health Center in Montreal as part of the VIE Program (Valorization, Implication, Education). Fasting blood samples were collected and we assessed participants' anthropometric, metabolic, and biochemical profiles. Three day food records were used to evaluate dietary intake. HDL2 and HDL3 fractions were isolated from plasma by ultracentrifugation, and total protein and lipids were measured by colorimetric reactions. Results To date, 63 patients (44.4% boys) were recruited. Mean age was 11.7 ± 5.6 years and 46.0% were treated for acute lymphoblastic leukemia. The time elapsed since the last treatment was 17.0 ± 9.7 months. We found that 25.4% of participants (n=16/63) had hypertension, 22.2% (n=14/63) were obese and 8.9% (n=5/56) were insulin resistant. One third of patients had dyslipidemia (n=21/63), a risk that was increased by age (OR: 1.31, 95%CI: 1.16–1.50, P<0.01). Patients over 10 years old had insufficient intakes of several nutrients (vitamins A and E, folate, magnesium, zinc). Compared to healthy children, the proportion of esterified cholesterol in HDL2 was lower (18.6 ± 1.2% vs. 11.9 ± 1.4%, P<0.01), but protein content was higher (41.1 ± 1.6% vs. 48.8 ± 1.0, P<0.01). The proportion of esterified cholesterol was also lower in HDL3 fractions of patients treated for cancer (12.6 ± 1.3% vs. 10.1 ± 0.7%, P<0.01). Conclusion These results indicate cardiometabolic disturbances and anomalies in HDL composition in children shortly after cancer treatment. They emphasize the need for nutritional interventions in this high‐risk population. Support or Funding Information This work is funded by The Foundation Centre de cancérologie Charles‐Bruneau and The Joy of Eating Better Foundation and the CHU Sainte‐Justine Foundation . This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.000
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.283
Teacher spread0.266 · 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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Citations2
Published2019
Admission routes2
Has abstractyes

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