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Record 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 on OpenAlexaff
Lisa J. Martin, Ella Huszti, Philip W. Connelly, Cary Greenberg, Salomon Minkin, Norman F. Boyd

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2016
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsPrincess Margaret Cancer CentreSt. Michael's HospitalUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsBreast cancerNested case-control studyMedicineBlood lipidsInternal medicineTriglycerideCancerOncologyApolipoprotein BHigh-density lipoproteinPercentileEndocrinologyCase-control studyHormone replacement therapy (female-to-male)CholesterolMathematics

Abstract

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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.

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)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.019
GPT teacher head0.279
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreCommentary

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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Citations80
Published2016
Admission routes1
Has abstractyes

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