Effects of Saturated Fatty Acids from Butter and Cheese on High‐density Lipoprotein (HDL)‐Mediated Cholesterol Efflux Capacity
Bibliographic record
Abstract
OBJECTIVE The association between dietary saturated fat (SFA) intake and cardiovascular disease risk remains controversial. Emerging data suggest that SFA from different sources may show different association with cardiovascular and cardiometabolic risk. The objective of this study was to examine how SFA from cheese (CHEESE) influences HDL‐mediated cholesterol efflux capacity (CEC), a key anti‐atherogenic property of HDL, compared with a low fat/high carbohydrate diet (CHO), a diet rich in SFA from butter (BUTTER) and diets either high in monounsaturated (MUFA) or polyunsaturated fatty acids (PUFA). METHODS A total of 46 men and women with abdominal obesity and with relatively low HDL cholesterol (HDL‐C) completed this randomized crossover controlled trial consisting of 5 predetermined isoenergetic diets of 4 weeks each (): 1) CHEESE, 2) BUTTER, 3) MUFA, 4) PUFA, 5) CHO. All foods were provided to participants during each experimental diet. CEC was assessed with radiolabelled J774 macrophages using apolipoprotein B depleted serum samples taken from participants at the end of each dietary phase. HDL lipid composition (gas chromatography) and HDL particle size distribution (polyacrylamide gradient gel electrophoresis) were also assessed. RESULTS All higher fat diets increased HDL‐C concentrations compared to CHO (all p<0.001). BUTTER and CHEESE increased low‐density lipoprotein cholesterol (LDL‐C) concentrations more than CHO, MUFA and PUFA diets, particularly among individuals with high baseline LDL‐C (all p<0.001). LDL‐C concentrations tended to be greater after BUTTER than after CHEESE (p=0.07). BUTTER, PUFA and CHEESE significantly increased large HDL2b levels compared to CHO (all p≤0.01). BUTTER also increased HDL2b levels more than MUFA (p=0.04). There was no difference in HDL‐mediated CEC between CHEESE and CHO (p=NS). BUTTER significantly increased HDL‐mediated CEC compared to CHEESE and CHO (both p<0.05). MUFA also increased CEC compared to CHEESE and CHO (both p<0.05). PUFA had no significant effect on HDL CEC compared to the others diets (p=NS). The increase in LDL‐C after BUTTER (vs. CHO) was significantly correlated with concurrent increase in CEC in men (r=0.44, p=0.04), but not in women (r=0.27, p=0.20). CONCLUSIONS These results provide evidence of a matrix effect modulating the effects of dairy SFA on HDL characteristics and function, with greater changes seen with SFA from butter than with SFA from cheese in men and women with abdominal obesity and with relatively low HDL‐C levels. The increase in HDL‐mediated CEC seen with SFA from butter parallels the increase in LDL‐C among men, but not among women. Additional studies assessing other HDL functions will provide further insights on how the food source may modify the effects of SFA on cardiometabolic risk. Support or Funding Information Dairy Research Cluster Initiative ( Dairy Farmers of Canada, Agriculture and Agri‐Food Canada, the Canadian Dairy Network and the Canadian Dairy Commission) and the National Dairy Council . Macronutrients (% energy), calcium, sodium and fibers content of experimental diets CHEESE BUTTER MUFA PUFA CHO Lipids (%) 32.0 32.0 32.0 32.0 25.0 SFA (%) 12.6 12.4 5.8 5.8 5.8 MUFA (%) 12.5 12.3 19.6 12.6 12.6 PUFA (%) 4.8 4.8 4.8 11.5 4.8 Carbohydrates (%) 51.9 52.0 51.9 51.9 58.9 Proteins (%) 16.0 16.0 16.0 16.0 16.0 Calcium (mg/2500 kcal) 1261 811 812 812 842 Sodium (mg/2500 kcal) 2482 2480 2479 2479 2485 Fibers (g/2500 kcal) 31 31 31 31 31
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".