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Record W2009532387 · doi:10.5650/jos.54.617

The Mechanism by Which Flaxseed Oil Consumption Increases Bleeding Time in Patients with Type 2 Diabetes in Cape Breton, Nova Scotia, Canada is Independent of Lipoprotein(a) Concentration

2005· article· en· W2009532387 on OpenAlexaffabout
Douglas E. Barre, Odette Griscti, Kazimiera A. Mizier-Barre, Kevin Hafez

Bibliographic record

VenueJournal of Oleo Science · 2005
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsCape Breton University
Fundersnot available
KeywordsMedicineType 2 diabetesBleeding timePolyunsaturated fatty acidDiabetes mellitusMyocardial infarctionFish oilInternal medicineLipoprotein(a)Eicosapentaenoic acidPlateletLipoproteinIn vivoEndocrinologyApolipoprotein BFatty acidCholesterolPlatelet aggregationBiochemistryChemistryBiologyBiotechnology

Abstract

fetched live from OpenAlex

Platelet hyper-aggregation is a serious manifestation of type 2 diabetes and a precipitating factor in the most frequent cause of death in patients with type 2 diabetes-myocardial infarction. Consumption of flaxseed oil as a dietary supplement containing alpha-linolenic acid (ALA, 18:3 n-3) through its metabolism to eicosapentaenoic acid (EPA, 20:5 n-3) and subsequent production of antiaggregatory eicosanoids may reduce such aggregation in vivo. Lipoprotein(a) (Lp (a)) may also influence platelet aggregation in vivo. Furthermore, serum Lp(a) concentrations are increased and bleeding time is decreased in patients with type 2 diabetes, presenting an enhanced risk of myocardial infarction. It was hypothesized that Lp(a) and bleeding time would be correlated due to the considerable molecular homology between apolipoprotein(a) and plasminogen, which should decrease bleeding time. Bleeding time is an excellent measure of in vivo platelet aggregability. The purpose of this study was to determine, if as the result of flaxseed oil consumption, Lp(a) influences the mechanism of any change in bleeding time. A secondary purpose was to determine if gender differences exist in the response of bleeding time to Lp(a) in flaxseed oil consumers. Subjects (n = 40) were randomized to treatment with flaxseed oil (n = 20) or a safflower oil placebo (n = 20). Each of groups contained equal numbers of males (n = 10) and females (n= 10). Some subjects dropped from the study due to reasons not related to treatment side effects. Subjects came for three visits, each three months apart. On each visit, age, gender, and BMI were recorded, bleeding time was performed, and serum Lp(a) concentrations were determined. At the completion of visit 2, subjects were randomized to 1 g of oil per 10 kg body weight each day for three months. Compared with pretreatment measurements, there was a statistically significant increase in bleeding time in the flaxseed oil group among both males and females posttreatment. In contrast, there was no change in the safflower group regardless of gender. Males had a statistically shorter bleeding time pretreatment while males and females showed no difference posttreatment with flaxseed oil consumption. Pretreatment values for Lp(a) and bleeding time showed a nonsignificant correlation among males and a statistically significant correlation among females. A statistically significant correlation also held when all males and females in the study were combined though at a lower value than in females. Significant correlations were lost and/or maintained upon administration of flaxseed oil and safflower oil, respectively. It is also concluded that serum Lp(a) concentrations remain unchanged following flaxseed oil consumption; thus, at least in part diminishing the correlation of Lp(a) with bleeding time. These findings suggest that other factors such as EPA derived eicosanoids mediate the prolonged bleeding time in flaxseed oil consumers.

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 score0.503
Threshold uncertainty score0.987

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.250
Teacher spread0.241 · 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

Citations4
Published2005
Admission routes2
Has abstractyes

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