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Enregistrement W2162163922 · doi:10.1093/jnci/djw126

RE: Serum Lipids, Lipoproteins, and Risk of Breast Cancer: A Nested Case-Control Study Using Multiple Time Points

2016· letter· en· W2162163922 sur OpenAlexaff
Lisa J. Martin, Ella Huszti, Philip W. Connelly, Cary Greenberg, Salomon Minkin, Norman F. Boyd

Notice bibliographique

RevueJNCI Journal of the National Cancer Institute · 2016
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer, Lipids, and Metabolism
Établissements canadiensPrincess Margaret Cancer CentreSt. Michael's HospitalUniversity of TorontoOntario Institute for Cancer Research
Organismes subventionnairesnon disponible
Mots-clésBreast cancerNested case-control studyMedicineBlood lipidsInternal medicineTriglycerideCancerOncologyApolipoprotein BHigh-density lipoproteinPercentileEndocrinologyCase-control studyHormone replacement therapy (female-to-male)CholesterolMathematics

Résumé

récupéré en direct d'OpenAlex

In a previous publication in the Journal, we showed that high-density lipoprotein-cholesterol (HDL-C) and apolipoprotein A1 (apoA1) levels were positively associated with breast cancer (BC) risk while non-HDL-C and apolipoprotein B (apoB) levels were negatively associated with BC risk (1). These associations were adjusted for most breast cancer risk factors but not for percent mammographic density (PMD) or alcohol intake as these variables needed additional data extraction (2). Here we report these associations adjusting for both PMD and alcohol intake. The methods used have been described in our earlier paper (1). This case-control study was nested within the cohort of the Canadian Diet and Breast Cancer Prevention Study, a multicenter randomized controlled trial designed to test whether a reduction in dietary fat intake would reduce the incidence of BC in women with extensive PMD. Subjects provided a nonfasting blood sample at entry to the trial and annually thereafter. We matched individually case subjects (n = 261) with two control subjects (n = 541) according to age (within one year), date of random assignment (within one year), study center, duration of follow-up (within six months), and the availability of blood samples. The dietary intervention did not have a statistically significant effect on BC incidence (3), and we combined the low-fat dietary intervention and comparison groups. PMD was measured in baseline mammograms using Cumulus software (4), and alcohol intake was assessed from food records collected from all subjects at intervals throughout the trial (average of 3.7 sets of three-day food records per subject). To take advantage of the multiple blood samples and to adjust for other variables that could change over time, we calculated up to three subaverages of serum lipid measurements for each woman depending on menopausal status at the time of blood collection (1). Subaverages of weight and alcohol (grams/day) were calculated in the same manner. We examined the association of serum lipid levels with risk of BC using generalized estimating equations analysis in which case-control status was the outcome variable and serum lipid levels (subaverages) were the independent variables. All P values are for two-sided statistical tests. A P value of less than .05 was considered statistically significant. Table 1 shows selected baseline characteristics of the case and control subjects, which differ slightly from those shown in our previous paper because of missing data on alcohol intake (n = 2) or unavailable mammograms (n = 33). Selected demographic characteristics, serum lipid variables, alcohol intake and percent mammographic density at baseline *P value for case compared with control subjects for two sample t tests for continuous variables and chi-square tests for categorical variables. For alcohol, two sample t tests and Wilcoxon test were used. All statistical tests were two-sided. ApoA1 = apolipoprotein A1; ApoB = apolipoprotein B; HRT = hormone replacement therapy; HDL-C = high-density lipoprotein cholesterol; IQR = interquartile range. †Random assignment group. ‡Among parous. §At least one first-degree relative diagnosed with breast cancer. ‖Calculated as the difference between total cholesterol and HDL-C. Selected demographic characteristics, serum lipid variables, alcohol intake and percent mammographic density at baseline *P value for case compared with control subjects for two sample t tests for continuous variables and chi-square tests for categorical variables. For alcohol, two sample t tests and Wilcoxon test were used. All statistical tests were two-sided. ApoA1 = apolipoprotein A1; ApoB = apolipoprotein B; HRT = hormone replacement therapy; HDL-C = high-density lipoprotein cholesterol; IQR = interquartile range. †Random assignment group. ‡Among parous. §At least one first-degree relative diagnosed with breast cancer. ‖Calculated as the difference between total cholesterol and HDL-C. Table 2 shows the associations of lipids and lipoproteins with risk of BC after adjustment for other risk factors shown in the table footnote, and before and after adjustment for PMD and alcohol. Alcohol intake (P = .01) and PMD (P = .004) were both positively associated with risk of BC. HDL-C, apoA1, and non-HDL-C were statistically significantly associated with BC risk before but not after adjustment for PMD and alcohol. ApoB was statistically significantly and inversely associated with BC risk before (P = .007) and after adjustment for both PMD and alcohol (P = .03). Association of serum lipids, alcohol intake, and baseline PMD with risk of breast cancer *Generalized estimating equations adjusted for random assignment group (intervention, comparison), parity at baseline (parous, nonparous), if smoked ever at baseline (yes, no), if had first-degree relatives with breast cancer at baseline (yes, no), study site, age at menarche (years), age at birth of first child (years), number of live births, subaverage weight (kg), subaverage age (years), date of random assignment, menopausal status, and HRT use (three categories). ApoA1 = apolipoprotein A1; ApoB = apolipoprotein B; HDL-C = high density lipoprotein cholesterol; PMD = percent mammographic density. †Subset with both alcohol and baseline PMD measurements available. ‡As in *, with addition of PMD. §As in *, with the addition of PMD and alcohol. Association of serum lipids, alcohol intake, and baseline PMD with risk of breast cancer *Generalized estimating equations adjusted for random assignment group (intervention, comparison), parity at baseline (parous, nonparous), if smoked ever at baseline (yes, no), if had first-degree relatives with breast cancer at baseline (yes, no), study site, age at menarche (years), age at birth of first child (years), number of live births, subaverage weight (kg), subaverage age (years), date of random assignment, menopausal status, and HRT use (three categories). ApoA1 = apolipoprotein A1; ApoB = apolipoprotein B; HDL-C = high density lipoprotein cholesterol; PMD = percent mammographic density. †Subset with both alcohol and baseline PMD measurements available. ‡As in *, with addition of PMD. §As in *, with the addition of PMD and alcohol. The previously reported inverse association of ApoB with BC risk is not because of confounding by alcohol or PMD. Further investigation of the relationship between serum lipids and BC risk, including the effects of long-term statin use, appears to be warranted (5). Clinical Trial registration: Clinicaltrials.gov. Identifier: NCT00148057.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,012
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,012
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0020,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,019
Tête enseignante GPT0,279
Écart entre enseignants0,260 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations80
Publié2016
Routes d'admission1
Résumé présentoui

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