Function of fatty acids

Fatty acids play a number of critical roles in the homeostasis and structure of the cell, and the whole human body^{(Reference Brown and Marnett15)}. Firstly, they are the main constituents of all biological membranes built into sphingolipids, phospholipids, glycolipids, and lipoproteins. The fatty acid composition of these membranes can differ and influence the physical structure (‘fluidity’) of the membrane, which may impact both the functional properties and movement of membrane proteins^{(Reference Calder16)}. Secondly, various metabolites of fatty acids serve as essential intracellular and extracellular lipid mediators and hormones. Finally, they function as an energy source, stored in triacylglycerols^{(Reference Radzikowska, Rinaldi and Celebi Sozener17)}. Therefore, fatty acids have countless possibilities to influence cellular functions, by impacting its structure and metabolism, acting through surface proteins (G-protein-coupled receptors), intranuclear receptors or membrane transporters^{(Reference Brown and Marnett15)}.

SFA sources

SFA are a heterogeneous group of fatty acids which contain only carbon-to-carbon single bonds^{(Reference Astrup, Magkos and Bier5)}. As described above, one classification method for fatty acids is based on their carbon chain length, while another is based on function. The melting point of SFA increases as their chain length increases, for example those ≥ 10 carbon atoms are solid at room temperature^{(Reference Astrup, Magkos and Bier5)}. The main dietary sources of SFA are animal products, such as meat and dairy, but they are also found in tropical oils such as palm and coconut^{(Reference Radzikowska, Rinaldi and Celebi Sozener17)}. Although most fat food sources contain a mixture to some degree of all chain lengths, very generally, they differ depending on the specific food; i.e. SFA are mainly found in dairy fats, while medium and long-chain SFA are present in red meat, dairy fats, and plant oils^{(Reference Ratnayake and Galli18)}. Within these food sources, varying proportions of different SFA are present, along with other nutrients which can play a significant role in the observed physiological and biologic effects^{(Reference Astrup, Magkos and Bier5)}. SFA can also be produced within the body, from the precursor acetyl-CoA, derived from amino acid or carbohydrate metabolism^{(Reference Calder16)}.

Saturated fat and metabolic health

Research surrounding dietary fats and health, and in particular saturated fat, has centred around the harmful effects associated with a diet high in SFA^{(Reference Lawrence19)}. Current dietary recommendations were developed based on epidemiological studies which demonstrated a link between a high dietary intake of SFA and greater CVD incidence^{(Reference Keys20–Reference Appel, Champagne and Harsha22)}. However, in more recent years, new evidence has emerged which questions the link between SFA intake and CVD risk, which need to be considered^{(Reference Siri-Tarino, Sun and Hu9,Reference Astrup, Bertram and Bonjour23,Reference de Souza, Mente and Maroleanu24)} . A number of studies (Table 1) investigating the associations between saturated fat and cardiometabolic outcomes have reported little or no effect on all-cause or CVD mortality after reducing SFA intake^{(Reference Hooper, Martin and Jimoh11,Reference de Souza, Mente and Maroleanu24–Reference Mente, Dehghan and Rangarajan29)} . With regards to CVD events, however, a higher concentration of total SFA was associated with increasing risk of cardiometabolic diseases in a recent meta-analysis^{(Reference Li, Lei and Jiang27)}, and this was consistent with the Hooper et al. review^{(Reference Hooper, Martin and Jimoh11)} where a reduction in SFA was found to reduce CVD events, and to a greater extent with greater cholesterol reduction. One of the studies however, suggested an inverse linear relationship between SFA intake and stroke risk with a higher consumption of dietary SFA being associated with a lower risk of stroke^{(Reference Kang, Yang and Xiao26)}. While evidence of the biological mechanism behind this association remains unknown, some studies have reported an increase in HDL-C^{(Reference Micha and Mozaffarian30)} and a decrease in TAG and ApoB-to-ApoA1 ratio with a higher SFA intake^{(Reference Dehghan, Mente and Zhang31)}, and higher levels of circulating long-chain fatty acids (18:0, 20:0, 22:0, and 24:0) in particular, were associated with lower risk of atrial fibrillation^{(Reference Fretts, Mozaffarian and Siscovick32)}. These effects may play a role in reducing stroke risk.

Table 1.

Overview of studies investigating the effects of saturated fat on metabolic health

Recent research has also shown that although SFA generally increases lipid and lipoprotein levels, individual SFA may have differing effects on these plasma levels^{(Reference Kris-Etherton and Yu33)}. A systematic review conducted by Perna and Hewlings in 2022^{(Reference Perna and Hewlings34)}, investigated the effects of SFA chain lengths on CVD development, and found that overall, long-chain SFA (C12-18) were associated with increased risk for CVD while short and medium chain (C4-C10) may be associated with neutral or favourable effects. Researchers did note difficulty in distinguishing between individual SFA consumption due to most food sources containing a variation of SFA^{(Reference Perna and Hewlings34)}. In addition, researchers reported low intakes of C4-C10 across included studies, compared to longer chain fatty acids, e.g. C16 and C18. In one study, C18 was the only SFA linked to increased CVD risk^{(Reference Praagman, de Jonge and Kiefte-de Jong35)}. However, the main source of C18 in these studies was processed meat which may suggest the food source, rather than the individual SFA, is more responsible for this effect. The inconsistencies in findings are most likely influenced by shared food sources for SFA^{(Reference Praagman, de Jonge and Kiefte-de Jong35–Reference Zong, Li and Wanders37)}, making it difficult to draw definitive conclusions between SFA chain lengths and CVD risk. Therefore, with respect to public health and food based dietary guidelines, recommendations cannot be made for individual fatty acids, as this fails to consider the food source and the mixture of fatty acids within.

As different SFA have varying effects on blood lipid profiles, so too do their given food source. This has shown to be the case across not only food groups, but also between foods within the same food category, such as dairy products^{(Reference Timon, O’Connor and Bhargava38)}. These differences between foods that contain similar fat profiles are considered to be ‘food matrix’ effects, whereby the nutrients within the foods interact with the overall structure and may result in different health outcomes^{(Reference Timon, O’Connor and Bhargava38)}.

Saturated fat and dairy

Dairy is a significant contributor to SFA intake in the diet, both in Europe and the United States, accounting for approximately 20 % of population SFA intake^{(Reference Feeney, Barron and Dible39)}. Dairy foods vary considerably in their fat content, but in general, dairy fat itself contains ∼60 % SFA^{(Reference Thorning, Bertram and Bonjour40)}. For this reason, most current dietary guidelines tend to recommend consumption of fat-free or low-fat dairy products instead of full fat dairy. Although current recommendations surrounding SFA state that intake should not exceed 10 % of total energy, and be kept as low as possible, research is highlighting the importance of focusing on the food source (the food matrix) as opposed to individual nutrients^{(Reference Astrup, Geiker and Magkos41)}. As highlighted above, the difference in health effects between meat sources v dairy, with dairy derived fatty acids being associated with a lower risk of CHD when compared with meat^{(Reference Vissers, Rijksen and Boer42)}. Some research groups have expressed concern that if dietary guidelines are developed that focus on SFA content as opposed to source, nutrient-dense foods could be excluded, unintentionally resulting in lower levels of key micronutrients^{(Reference Astrup, Bertram and Bonjour23)}. Dairy foods, for example, are an important source of a variety of micronutrients in the Irish diet such as calcium and vitamin B12^{(Reference Feeney, Nugent and Mc Nulty43)}, but there has been some debate regarding the classification of ‘dairy foods’ in dietary guidelines, with the majority recommending milk, yoghurt and cheese (low fat varieties) and excluding butter and/or cream due to their contributions to saturated and trans-fat intakes in the diet^{(Reference Feeney, McKinley and Givens44)}. The lack of consistency across studies with regards to a universal definition of ‘high’ and ‘low’ fat makes it difficult to interpret data. In addition, categorising based on nutrient content alone (e.g. fat content), fails to consider the variability in other components and structures within the dairy food group^{(Reference Feeney, McKinley and Givens44)}. It is these differences in the dairy matrix which may play a role in the differences in health-related outcomes between dairy foods, for example cheese v butter. In terms of physical structure, butter is a water-in-oil emulsion and cheese is a fermented product, where the fat is present in milk fat globules in a solid matrix^{(Reference Astrup, Bertram and Bonjour23)}. A 2015 meta-analysis of RCT^{(Reference de Goede, Geleijnse and Ding45)} reported consistent favourable outcomes when hard cheese was compared with butter for TC (hard cheese: reduction of 0·28 mmol/l) and LDL-C (hard cheese: reduction of 0·22 mmol/l). Their results suggest that the associations observed between dairy foods and CVD risk is driven primarily by food type (cheese, yoghurt, milk) than fat content^{(Reference de Goede, Geleijnse and Ding45)}.

The food matrix

Research is now showing that the link between SFA and CVD is not as straightforward as initially shown, with an emerging role for differences in the food matrix^{(Reference Astrup, Bertram and Bonjour23)}. This matrix may be more influential in the effect on CVD than the absolute SFA content of the food^{(Reference Michas, Micha and Zampelas46)}. The importance of focusing on whole foods as opposed to individual nutrients, including saturated fat, has been increasingly highlighted because of the complex physical and nutritional structure of each food, and how these different matrices may impact nutrient digestion, absorption, and bioactivity, and subsequently the biological effects from the food^{(Reference Astrup, Geiker and Magkos41)}. Dairy products are a particular example of the food matrix resulting in different health outcomes from SFA-rich food^{(Reference Yuan, Singer and Pickering47,Reference Chen, Ahmed and Ha48)} . The main basis for the recommendation of low-fat dairy in the diet is that saturated fat raises LDL-C levels, which in turn increases one’s risk of CVD^{(Reference Drouin-Chartier, Brassard and Tessier-Grenier49)}. However, more recent human intervention studies have investigated the potential role of the matrix when examining the health effects of dairy. A randomised controlled trial (RCT) by Feeney et al. ^{(Reference Feeney, Barron and Dible39)} demonstrated the protective effect of a fermented dairy food matrix such as cheese on blood lipid concentrations. Their results show that cheese has a significant lowering effect on TC and LDL-C when compared with a deconstructed matrix of butter, protein, and calcium. This is consistent with other findings comparing cheese and butter^{(Reference Brassard, Tessier-Grenier and Allaire50–Reference Hjerpsted, Leedo and Tholstrup52)}, which also reported differential modifying effects on blood lipids when dairy fat was consumed as cheese v butter. These results suggest that even within dairy as a food group there is variability in its health effects, which may be due to the considerable differences in terms of structure and content^{(Reference Feeney, McKinley and Givens44)}. For example, while cheese has a high fat content, its composition is more comparable to that of yoghurt and milk than to butter. This is due to the protein, mineral, and milk fat globule membrane (MFGM) components^{(Reference Thorning, Bertram and Bonjour40)} (Table 2). High-fat dairy products are particularly rich in the MFGM, with the exclusion of butter, which loses most of the MFGM during processing, when the aqueous phase is released as buttermilk, which contains most of the MFGM^{(Reference Weaver53)}. This membrane protects fat globules by acting as an emulsifier and preventing enzymatic degradation^{(Reference Dewettinck, Rombaut and Thienpont54)}. Fermented dairy products such as yoghurt and cheese also contain bacteria which, during the fermentation process, can produce SCFA and bioactive peptides. These SCFA and bioactive peptides can impact the health promoting potential of the final fermented food products^{(Reference Leeuwendaal, Stanton and O’Toole55)}. SCFA have anti-inflammatory properties^{(Reference Smith, Howitt and Panikov56)} and have been linked to reduced risk of colon cancer^{(Reference Morrison, Mackay and Edwards57)}, while the most studied mechanism of bioactive peptides is their antihypertensive action, exhibited by the inhibition of the angiotensin-I-converting enzyme which regulates blood pressure^{(Reference Fernandez, Hudson and Korpela58)}. The overall nutrient structure of dairy foods, and the levels of these nutrients within, are impacted by the various processing steps (from milk to food product) and these differences may explain the different health outcomes associated with consumption^{(Reference Timon, O’Connor and Bhargava38)}.

Table 2.

Differences in bioactive components, fat structure and protein networks of common dairy products ^*

* Values reported as approximate amounts.

^† Differs depending on production method used.

Adapted from Thorning et al. ^{(Reference Thorning, Bertram and Bonjour40)}

Traditional v newer risk markers of CVD

As the research linking SFA content to CVD risk becomes more nuanced, so too do the biological markers of CVD risk. Traditionally, epidemiological studies have focused on reduction of plasma cholesterol levels as clinically relevant markers of reduced CVD risk⁽⁵⁹⁾. It is evident that elevated LDL-C levels play a role in the development of CVD, and causality can be determined^{(Reference Ference, Ginsberg and Graham60)}. However individuals with LDL-C levels within the normal range can also develop atherosclerosis and CVD^{(Reference Raziani, Ebrahimi and Engelsen61)}. This variation in risk is thought to be due to the heterogeneity of these LDL particles with regards to their size, density and chemical composition^{(Reference Lakshmy, Dorairaj and Tarik62)}. Higher concentration of small, dense LDL-P is associated with an increased risk of CVD, independent of overall LDL-C levels^{(Reference Lamarche, Lemieux and Després63)}. These small LDL particles are considered to be more pro-atherogenic than large LDL particles due to their decreased affinity for the LDL receptor resulting in a prolonged retention time in the circulation. They can more easily enter and bind to the arterial wall, and are more susceptible to oxidation, which may enhance the uptake by macrophages^{(Reference Vekic, Zeljkovic and Cicero64,Reference Feingold65)} . Dreon et al. demonstrated a correlation between changes in dietary saturated fat and changes in concentration of the larger, more buoyant LDL particles, suggesting that the increases in LDL-C from saturated fat (especially C14:0 and C16:0) were owing to increases in the larger, less atherogenic particles^{(Reference Dreon, Fernstrom and Campos66)}. High-carbohydrate diets, which are associated with lower LDL-C, exhibit a positive correlation with pro-atherogenic small dense LDL-P in RCT^{(Reference Krauss and Dreon67–Reference Faghihnia, Tsimikas and Miller69)}. One such study by Krauss et al., looked at a group of 178 mildly obese and overweight men that consumed a 26, 39 or 54 % kcal from carbohydrate diet with 7–9 % energy intake as SFA (low-SFA diet) and one group that consumed a 26 % carbohydrate diet and 15 % energy intake as SFA (high-SFA diet) for 3 weeks. They found a linear relationship of increased carbohydrate intake with increased prevalence of small LDL particles. Those on the 26 % carbohydrate, low-SFA diet had reduced TG, ApoB, small LDL particles, and total HDL cholesterol compared to those on a 54 % carbohydrate, low-SFA diet. Those on the 26 % carbohydrate diet with high-SFA exhibited increased concentrations of medium- and large-sized LDL particles^{(Reference Krauss, Blanche and Rawlings70)}. Therefore, changes in both total LDL-C and LDL particle size can result from dietary change and must also be considered in the study of diet and CVD.

LDL-C levels, traditionally used within epidemiological studies examining the link between diet and disease, do not reflect levels of different LDL particle (LDL-P) sizes. Since many dietary recommendations focus solely on a reduction of total fat, or, saturated fat as a means of reducing CVD risk⁽⁴⁾, this may result in an overestimation of the beneficial effects of SFA reduction in such studies, by reliance on this change in LDL-C levels alone^{(Reference Astrup, Magkos and Bier5)}.

In the context of dairy fat and cheese, a study conducted by Raziani et al. in 2018 investigated the effects of full fat v reduced fat cheese on LDL particle size distribution, using NMR spectroscopy. They found that overall, LDL particle size distribution was not altered by full fat cheese when compared to reduced fat cheese. A recent, exploratory analysis of biological samples from an earlier study showing reductions in LDL-C following 6 weeks’ consumption of cheese v. reduced fat cheese v. butter^{(Reference Feeney, Barron and Dible39)} further investigated the role of the food matrix on lipoprotein particle size distribution^{(Reference Dunne, McGillicuddy and Gibney71)}. Results suggested a transition from small pro-atherogenic particles toward larger, more buoyant LDL particles following 6-weeks of dairy fat consumption (approx. 42 g/d). However, despite the fact that the changes in serum cholesterol levels were influenced by the food matrix, similar changes in LDL particle size were seen for the cheese, butter, and the reduced fat cheese groups^{(Reference Dunne, McGillicuddy and Gibney71)}. This indicated that there was a dominant role for the nutritional composition within the matrix in driving changes in LDL particle size, over the food matrix effect.

The lipoprotein balance between LDL and HDL particles has traditionally been assessed through measurement of their cholesterol content (LDL-C and HDL-C) as opposed to the number of particles^{(Reference Jeyarajah, Cromwell and Otvos72)}. Distinction between these is important, as the two are not equivalent; for example, two individuals could have the same LDL-C levels but have differing numbers of LDL particles and as a result, a different CVD risk (Fig. 1), i.e. the individual with the higher number of LDL particles would be at increased risk as this would suggest a higher number of small dense particles^{(Reference Otvos73)}. Analysis of LDL particle size should therefore be considered as a cornerstone biomarker within nutritional intervention studies for assessment of changes in CVD risk^{(Reference Cromwell, Otvos and Keyes6)}.

Fig. 1

Individuals with the same LDL cholesterol concentration (mmol/l) can differ in terms of their CVD risk. The risk scale, ranging from lower risk (left side of scale) to higher risk (right side of scale) shows the variation between individuals in terms of their CVD risk when considering the number and size of LDL particles, i.e. those with the lower number of LDL particles, larger in size are lower risk than those with the higher number of LDL particles, smaller in size.

Emerging nutrition modifiable biomarkers of CVD risk – HDL function and composition

While the causal link between LDL-C and CVD events has been well documented – the role of HDL-C in disease pathophysiology remains more controversial^{(Reference Asztalos, Horvath and Schaefer74)}. Increased levels of HDL-C are traditionally associated with reduced CVD risk^{(Reference Ballantyne, Herd and Ferlic75,Reference Nicholls, Tuzcu and Sipahi76)} but raising HDL-C levels pharmacologically has not significantly impacted disease outcome^{(Reference Tall and Rader77)} and similarly genetically defined high-HDL-C is not associated with reduced outcomes^{(Reference Wilkins, Ning and Stone78)}. However, low HDL-C remains a major risk factor associated with the metabolic syndrome^{(Reference Kaur, Pandey and Negi79)} and appears, in part, to be driven by an elevation in carbohydrate consumption^{(Reference Shin, Shin and Jk80)}. In more recent years there has been increasing evidence that measurement of the function of HDL particles (cholesterol efflux capacity (CEC)) is a better approach to assess CVD risk than measurement of static levels of HDL-C^{(Reference Khera, Cuchel and de la Llera-Moya81)}. HDL particles play a critical role in reverse cholesterol transport by stimulating the efflux of cholesterol from peripheral cells, including lipid laden macrophages in atherosclerotic lesions, and delivering acquired lipid back to the liver for excretion in the bile and the faeces^{(Reference Vyletelova, Novakova and Paskova82,Reference Rader and Hovingh83)} . A reduction in HDL-CEC is evident in acute inflammatory settings, independent of changes in HDL-C levels^{(Reference O’Reilly, Dillon and Guo84)} further demonstrating how measures of HDL-C fail to capture the functional properties of the particles. Another important determinant of HDL function is the size of the particles – small HDL-P can mediate CEC via ABCA1-dependent pathways^{(Reference Fazio and Pamir85)} while larger HDL-P mediate CEC via ABCG1/SR-BI dependent pathway^{(Reference Rubinow, Vaisar and Chao86)} and in general have a larger capacity to store acquired lipid. Changes in HDL-P size in response to a dietary intervention, may therefore modulate the particle’s cellular interactions and functional properties independent of changes in HDL-C. Finally, another key consideration on HDL-P is their composition. The HDL proteome in particular has long been considered a potentially key biomarker of CVD risk^{(Reference Reilly and Tall87)} that to date has not been exploited to its full potential. The HDL proteome is profoundly modulated in patients with CVD and with end-stage kidney disease, with increased association of pro-inflammatory proteins and reductions in anti-inflammatory proteins evident on the particles in the diseased state^{(Reference Bonizzi, Piuri and Corsi88–Reference Shao, Mathew and Thornock90)}. O’Reilly et al., have further demonstrated that the HDL proteome is remarkably sensitive to dietary fat composition within obesogenic diets in preclinical models^{(Reference O’Reilly, Dillon and Guo84)}. Increased association of pro-inflammatory proteins and reductions in anti-inflammatory proteins on HDL particles was evident in mice fed an SFA-enriched high-fat diet relative to mice consuming a MUFA-enriched high-fat diet^{(Reference O’Reilly, Dillon and Guo84)}. Both of these high-fat diets exerted similar increases in HDL-C levels again demonstrating how measurements of HDL-C fail to capture important information on the quality of the particles. Importantly the HDL proteome can mirror metabolic disturbances within the liver, acting as a novel systemic biomarker of hepatic dysmetabolism and chronic low-grade inflammation but within an easily accessible blood matrix^{(Reference Kajani, Curley and O’Reilly91)}. The HDL proteome is therefore an exciting future ‘immunometabolic’ biomarker for the nutrition field that may sensitively detect changes in hepatic metabolism and low-grade inflammation, and ultimately identify patients at high-risk of CVD. Importantly, reversal of these adverse changes in particle composition in response to dietary interventions, independent of changes in HDL-C, may provide important mechanistic insights on the health benefits of novel functional foods.