Elaine Holmes

Understanding individual variability to dietary interventions is emerging as an important consideration in dietary interventions. Prior research has demonstrated ranging success of interventions. For example, Gardner and colleagues (2007) compared 4 weight loss diets in participants over a 12-month period(1), noting the range of weight loss was between 3.1kg to 6.3kg depended on individual and diet. Song and colleagues (2023) examined post prandial glucose response (PPGR) to four different carbohydrate meals. Dependent on the meal, the PPGR varied significantly between individuals(2). As such, it is inappropriate to assume that there is one dietary pattern appropriate for all individuals. Understanding the driving factors behind individual variation to specific foods and dietary patterns will allow us to tailor interventions to create optimal health outcomes for each individual. The aim of our study is to examine individual responses to different diets promoted for health. In our study, we investigated the biological diversity in response to the same dietary inputs among 23 participants at risk of type 2 diabetes and chronic disease over a two-week period. All participants completed four days on three dietary interventions (Mediterranean, Australian and low carbohydrate diets). Urine, serum, plasma, and faecal samples were collected, alongside the use of continuous glucose monitoring data, to explore the metabolic and glycaemic responses. Our findings reveal significant individual differences in blood glucose levels and metabolic outcomes. When examining fasting blood glucose levels, the low carbohydrate and Australian diets were optimal for 8 participants each, while the Mediterranean diet was optimal for 7 participants. However, this did not always correlate with post prandial blood glucose level optimisation. While blood, urine and faecal samples are yet to be analysed, these are expected to provide further understanding of individual biological responses. These results underscore the limitations of a universal dietary approach for optimising glycaemic control and highlight the necessity of personalised dietary recommendations that consider individual metabolic profiles. Our study provides crucial insights for future advances in precision nutrition, suggesting that personalised nutrition plans could lead to more effective management and prevention of T2D.

Current nutritional rating systems, like the health star rating, help consumers understand the nutritional value of food and were designed in an effort to combat obesity. However, these systems have limitations, especially for edible oils, which vary widely in composition(1). Coupled with the lack of standardisation in ranking edible oils, there has been advocacy for the introduction of different nutritional scores for edible oils. This study aims to develop a simple and easy-to-use nutritional scoring index based on the composition of extra virgin olive oil (EVOO). The composition includes all fatty acid parameters and total polyphenol content, measurable by nuclear magnetic resonance (NMR) spectroscopy, thereby avoiding the need for multiple analytical platforms. The development of an EVOO nutritional score involved: i) establishing a unique consensus dietary reference index (DRIs) for each component and evaluating their impact on human health(2,3); and ii) computing Scoring Reference Values (SRVs) for each component, expressed as grams of component per 100 g of EVOO, based on the assumptions of a daily energy intake of 2000 kcal, with a fat intake of 35% of total caloric intake(2,3), and considering EVOO as the only source of fat. A nutritional score (0–100) was developed based on saturated fat (SFA), trans-unsaturated fat, oleic, linoleic, alpha-linolenic acids, and polyphenols. Components with more substantial effects/evidence on human health were given greater weight in the scoring. The developed index was subsequently applied to evaluate 314 EVOOs that passed the International Olive Oil Council (IOC) quality criteria. These oils were sourced from Australia (n = 94), Greece (n = 54), Italy (n = 54), Spain (n = 69), and Tunisia (n = 43) and analysed using 400 MHz NMR spectroscopy. Nutritional scores for all samples showed a mean of 62.3 (range 13 to 94), with Australian EVOOs exhibiting the highest mean score of 65, followed by Spain, Tunisia, Italy, and Greece. EVOOs were differentiated by their SFA content and the balance between polyunsaturated (PUFA) and monounsaturated fatty acids (MUFA). MUFA and PUFA were typically inversely related, except for two Australian oils that achieved high levels of both. This novel scoring index for EVOOs, grounded in health-related compositional parameters, facilitates the differentiation of EVOOs based on their nutritional value. This enables consumers to make informed choices regarding their oil selection. Given the rising prevalence of obesity and its associated morbidity, this tool is particularly significant. Additionally, the implementation of this nutritional index encourages producers to produce oils with superior nutritional profiles.

Diet is a key modifiable factor for improving suboptimal lipoprotein profiles and reducing cardiovascular disease (CVD) risk(1). Dietary patterns like the Dietary Approaches to Stop Hypertension (DASH) or the Mediterranean Diet, with varying macronutrient components, have shown positive effects on total cholesterol and low-density lipoproteins (LDL)(2). However, limited research exists on the impact of different healthy diets on lipoprotein subclass profiles, which are increasingly known to influence CVD risk. This study aims to compare the nuclear magnetic resonance (NMR)-measured 112 lipoprotein profiles across three healthy dietary patterns: a carbohydrate-rich diet (CARB), similar to the DASH diet; a protein-rich diet (PROT); and an unsaturated fat-rich diet (USFA), similar to the Mediterranean diet. Lipoprotein parameters were generated using the Bruker IVDr Lipoprotein Subclass Analysis (B.I.LISA) method(3). The lipoprotein subclasses included different molecular components of very low-density lipoprotein (VLDL, 0.950–1.006 kg/L), low-density lipoprotein (LDL, density 1.09–1.63 kg/L), intermediate-density lipoprotein (IDL, density 1.006–1.019 kg/L), and high-density lipoprotein (HDL, density 1.063–1.210 kg/L). The LDL subfraction was further divided into six density classes, and the HDL subfractions were divided into four different density classes. Plasma samples from a randomised cross-over intervention study involving 156 individuals who completed more than two dietary patterns were included for the NMR analysis (registered at www.clinicaltrials.gov as NCT00051350 and NCT03369535). The Friedman’s test with post-hoc analysis, corrected for multiple testing, showed that all healthy dietary patterns led to a reduction in overall lipoprotein subclasses known to be associated with atherogenic risk. This reduction included large and medium-sized LDL subclasses, all intermediate-density IDL subclasses, as well as total plasma cholesterol, triglycerides, apolipoprotein-B100, apo-B100/apo-A1 ratio, and LDL-cholesterol (p < 0.05). Additional variations in lipoprotein subclasses specific to each diet were also observed. The PROT diet showed a decrease in small-sized and dense LDL, large to medium VLDL subclasses, and large-sized HDL subclasses. Conversely, the CARB diet exhibited an increase in smaller-sized and denser LDL, along with a decrease in large-sized HDL and an increase in smaller-sized HDL subclasses. The USFA diet led to decreases in LDL and overall VLDL subclasses, while increasing LDL and HDL subclasses (p < 0.05). The impact of different healthy diets with differential effects on lipoproteins suggests the possibility of targeting the cholesterol status of individuals to optimise lipoprotein profiles and thereby reduce CVD risk. Preliminary exploratory analyses based on linear mixed-effect models coupled with a latent profile analysis, adjusted for cholesterol status, showed that individual lipoprotein responses to specific diets varied. Inter-individual variations in lipoprotein responses to healthy diets were evident. A small proportion of individuals only responded to specific diets, suggesting potential of personalised nutrition based on individual lipoprotein profiles. These observed variations highlight the complexity of individual responses to dietary interventions.