Did we get dietary saturated fats all wrong? The #HADLmodel provides a new understanding and an opportunity to get it right. THREAD👇👇👇
@simondankel @kariannesve

Increased dietary saturated fatty acids lead to increased cholesterol in lipoproteins, but we don’t know why. Enter the #HADLmodel, which explains changes in lipoprotein cholesterol as adaptive homeostatic adjustments that ensure optimal cell membrane fluidity and cell function.
We propose that circulating lipoproteins enable appropriate redistribution of cholesterol molecules between specific cells and tissues, to accomodate changes in dietary fatty acid supply, due to our omnivore nature and variable intake of fatty acids. #HADLmodel
Our #HADLmodel implies that circulating levels of LDL change for protective, not for pathological reasons; an SFA-induced raise in LDL cholesterol in healthy individuals is a normal response, while a lack of this needed response may reflect a deeper pathology in lipid handling.
Circulating lipoproteins may change for pathological reasons, when regulatory mechanisms become disrupted by pathogenic processes related e.g. to inflammatory processes. Diverging lipoprotein responses in healthy versus metabolically unhealthy individuals support this view.
Low grade inflammation can interfere with several fine-tuned signaling pathways necessary for homeostasis, including proper lipid handling. Altered circulating cholesterol levels may here reflect underlying pathogenic processes, unrelated to saturated fat intake. #HADLmodel
Dietary factors causing chronic low-grade inflammation, driven by diet-microbiome interactions, should receive more attention. The role of saturated fats in pathogenesis may be misconceived and greatly exaggerated. #HADLmodel
Is the #HADLmodel impossible? Is there more evidence to support the model? What else do we need to test in high-quality studies? Keep the discussion going - fair and factual. We need to improve the conversation on dietary fats. #publichealth #dietaryguidelines
@zoeharcombe @bigfatsurprise @DrAseemMalhotra @LDLSkeptic @ufferavnskov @malcolmken @LeventalLab @fedonlindberg @drmarkhyman @LorenCordain @chriskresser @ChrisMasterjohn @garytaubes @ProfTimNoakes @PeterAttiaMD @marionnestle @whsource @RobertLustigMD

More from Science

Hard agree. And if this is useful, let me share something that often gets omitted (not by @kakape).

Variants always emerge, & are not good or bad, but expected. The challenge is figuring out which variants are bad, and that can't be done with sequence alone.


You can't just look at a sequence and say, "Aha! A mutation in spike. This must be more transmissible or can evade antibody neutralization." Sure, we can use computational models to try and predict the functional consequence of a given mutation, but models are often wrong.

The virus acquires mutations randomly every time it replicates. Many mutations don't change the virus at all. Others may change it in a way that have no consequences for human transmission or disease. But you can't tell just looking at sequence alone.

In order to determine the functional impact of a mutation, you need to actually do experiments. You can look at some effects in cell culture, but to address questions relating to transmission or disease, you have to use animal models.

The reason people were concerned initially about B.1.1.7 is because of epidemiological evidence showing that it rapidly became dominant in one area. More rapidly that could be explained unless it had some kind of advantage that allowed it to outcompete other circulating variants.

You May Also Like