Blood pressure (BP) is influenced by both genetics and diet. Dietary management of hypertension includes increasing potassium-rich foods while reducing sodium intakes. Dietary sodium and potassium intakes can be measured objectively using urinary sodium and urinary potassium, with lower urinary sodium to potassium ratios associated with lower BP(1). Understanding the interplay between diet and genetics may be useful in treating hypertension and determining which individuals may receive an outsized benefit from lowering their sodium potassium ratio. This study aims to investigate whether identifying genetic risk for hypertension could be utilised to identify individuals who may benefit most from lowering sodium intake and increasing potassium intake. UK Biobank cohort participants (n = 296,475) with data on genotype, BP and spot urinary sodium and potassium data were used. Diet quality was assessed using Oxford WebQ. Biologically directed genetic scores for BP were constructed for pathways related to sodium/potassium biology (pharmagenic enrichment scores [PES]), as well as traditional polygenic risk scores (PRS). A gene-by-environment effect between urinary electrolytes, diet quality and PRS on BP were tested. Genetic risk, diet quality and urinary electrolytes independently correlated with BP. Urinary sodium had larger BP increasing effects amongst individuals who had high genetic risk in sodium/potassium pathways than those with comparatively lower genetic risk. Higher diet quality had a small effect on reducing BP in baseline PRS models, but this did not remain significant in the full model. Polygenic scores for BP personalised to individual sodium/potassium biology (PES) could be used to identify individuals who may receive an outsized benefit from a personalised sodium/potassium dietary intervention. These findings may inform future precision and personalised dietary advice for the management of hypertension.

Diminished skeletal muscle strength and size, termed sarcopenia, contributes substantially to physical disability, falls, dependence and reduced quality of life among older people. Physical activity and nutrition are the cornerstones of sarcopenia prevention and treatment. The optimal daily protein intake required to preserve muscle mass and function among older adults is a topic of intense scientific debate. Older adults require protein intakes about 67 % higher than their younger counterparts to maximally stimulate postprandial muscle protein synthesis rates. In addition, evidence suggests a possible benefit of increasing protein intake above the population reference intake (0⋅83 g/kg/d) on lean mass and, when combined with exercise training, muscle strength. In addition to protein quantity, protein quality, the pattern of protein intake over the day and specific amino acids (i.e. leucine) represent key considerations. Long-chain n-3 PUFA (LC n-3 PUFA) supplementation has been shown to enhance muscle protein synthesis rates, increase muscle mass and function and augment adaptations to resistance training in older adults. Yet, these effects are not consistent across all studies. Emerging evidence indicates that an older person's dietary, phenotypic and behavioural characteristics may modulate the efficacy of protein and LC n-3 PUFA interventions for promoting improvements in muscle mass and function, highlighting the potential inadequacy of a ‘one-size-fits-all’ approach. The application of personalised or precision nutrition to sarcopenia represents an exciting and highly novel field of research with the potential to help resolve inconsistencies in the literature and improve the efficacy of dietary interventions for sarcopenia.

The overall aim of precision nutrition is to replace the ‘one size fits all’ approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more ‘precision’ dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene–diet or microbiota–diet interactions. Third, precision nutrition can be used for ‘personalised nutrition’ advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet–disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.

The precision nutrition paradigm is based on the premise that substantial variation exists between human subjects in terms of diet-related disease risk and response to dietary interventions. In terms of better defining, ‘the right diet for the right person at the right time’ may be more appropriate than ‘one-diet-fits-all’. This review will explore how systems biology and nutrigenomics approaches have advanced the precision nutrition paradigm. We will draw upon a number of elegant mechanistic studies that have enhanced our understanding with respect to the complex biology and inter-organ crosstalk, relating to inflammation and metabolism, that underpin cardio-metabolic health. Also, this review will explore the extent to which more targeted, precision nutrition approaches may attenuate adverse risk factors associated with cardio-metabolic disease. We will focus on the key characteristics or ‘metabotypes’ of high- v. low-risk individuals and response v. non-response to interventions, to generate greater insights with respect to risk stratification and therapeutic interventions to enhance disease prevention. The goal is to utilise systems biology to enhance understanding by underpinning more targeted nutritional approaches, which may improve efficacy of personalised nutrition interventions.

Precision feeding using real-time models to estimate daily tailored diets can potentially increase nutrient utilization efficiency. However, to improve the estimation of amino acid requirements for growing–finishing pigs, it is necessary to accurately estimate the real-time body protein (BP) mass. The aim of this study was to predict individual BP over time in order to obtain individual daily protein content of the gain (i.e., protein deposition/daily gain, PD/DG) to be integrated into a real-time model used for precision feeding. Two databases were used in this study: one for the development of the equations for the model and the other for model evaluation. For the equations, data from 79 barrows (25 to 144 kg BW) were used to estimate the parameters for a Gompertz function and a mixed linear-quadratic regression. Individual BP predictions obtained by dual X-ray absorptiometry were regressed as a function of BW. Individual pig BP estimates were obtained by linear-quadratic regression using the MIXED procedure of SAS, considering pig measurements repeated in time. Individual Gompertz curves were obtained using the NLMIXED procedure of SAS. Both procedures generate an average or a general model, which was assessed for accuracy with the database used to generate the equations. Coefficients of concordance and determination were both 0.99, and the RMSE was 0.21 kg for the linear-quadratic regression. The Gompertz curve coefficients of concordance and determination were both 0.99, and the RMSE was 0.36 kg. In sequence, the linear-quadratic regression and Gompertz curve were evaluated in an independent data set (488 observations; 21 to 126 kg BW). The linear-quadratic regression to predict BP mass was accurate (mean absolute percentage error (MAPE) = 2.5%; bias = 0.03); the Gompertz model performed worse (MAPE = 3.9%; bias = 0.04) than the linear-quadratic regression. When using the derivative of these equations to predict PD/DG, the linear-quadratic regression was more accurate (MAPE = 4.8%, bias = 0.17%) compared to the Gompertz (MAPE = 10.6%, bias = −0.99%) mainly due to the linear decrease in PD/DG in the observed data. Further analysis using individual pig data showed that the goodness of fit of PD/DG curve depends on the individual shape of the growth curve, with either the Gompertz or the linear-quadratic regression being more accurate for specific individuals. Therefore, both approaches are provided to allow end users to select the model that best fits their needs. The proposed update of the empirical component of the original model, using either linear-quadratic regression or the Gompertz function, is able to predict BP in real-time with good accuracy.

Significant differences in the estimation of amino acid requirements exist between the available factorial methods. This study aimed to compare current factorial models used to estimate the individual and population standardised ileal digestible (SID) lysine (Lys) requirements of growing pigs during a 26-day feeding phase. Individual daily feed intake and BW data from 40 high-performance pigs (25-kg initial BW) were smoothed by linear regression. Body weight gain was constant (regression slope not different from 0) for all the pigs. The CV of the SID Lys requirements ranged from 22% at the beginning of the trial to 8% at the end. The population Brazilian tables (BT-2017) and National Research Council (NRC-2012) SID Lys requirements for the average pig were 16% higher than the average requirement estimated by the individual precision-feeding model (IPF), but similar to the estimated for the population assuming that population requirements are those of the 80th-percentile pig of the population (IPF-80). Meaning that, the IPF-80, BT-2017, and NRC-2012 models would yield similar recommendations when pigs are group-fed in conventional multi-phase systems. Additionally, the IPF-80 estimates are independent of the phase length, whereas the BT-2017 and NRC-2012 models use average population values in the middle of the feeding phase for the calculations and therefore, conventional requirement estimations decrease as the length of the feeding phase increases. In conclusion, the BT-2017 and NRC-2012 methods were calibrated for maximum population responses, which explains why these methods yield higher values than those estimated for the average pig by the IPF model. This study shows the limitations of conventional factorial methods to estimate amino acid requirements for precision-feeding systems.

Rapid advances in ‘omics’ technologies have paved the way forward to an era where more ‘precise’ approaches – ‘precision’ nutrition – which leverage data on genetic variability alongside the traditional indices, have been put forth as the state-of-the-art solution to redress the effects of malnutrition across the life course. We purport that this inference is premature and that it is imperative to first review and critique the existing evidence from large-scale epidemiological findings. We set out to provide a critical evaluation of findings from genome-wide association studies (GWAS) in the roadmap to precision nutrition, focusing on GWAS of micronutrient disposition. We found that a large number of loci associated with biomarkers of micronutrient status have been identified. Mean estimates of heritability of micronutrient status ranged between 20 and 35 % for minerals, 56–59 % for water-soluble and 30–70 % for fat-soluble vitamins. With some exceptions, the majority of the identified genetic variants explained little of the overall variance in status for each micronutrient, ranging between 1·3 and 8 % (minerals), <0·1–12 % (water-soluble) and 1·7–2·3 % for (fat-soluble) vitamins. However, GWAS have provided some novel insight into mechanisms that underpin variability in micronutrient status. Our findings highlight obvious gaps that need to be addressed if the full scope of precision nutrition is ever to be realised, including research aimed at (i) dissecting the genetic basis of micronutrient deficiencies or ‘response’ to intake/supplementation (ii) identifying trans-ethnic and ethnic-specific effects (iii) identifying gene–nutrient interactions for the purpose of unravelling molecular ‘behaviour’ in a range of environmental contexts.