BACKGROUND/OBJECTIVES:To determine predictors of systolic and diastolic blood pressure (SBP/DBP), of heart rate, and of whole-grains and nuts consumption, on an individual level, using a series of N-of-1 studies. METHODS:Participants were enrolled in individual 24-wk N-of-1 studies, consisting of 8-wk observation, intervention, and follow-up periods. Participants completed personalized morning and evening questionnaires and took their own blood pressure daily. During the intervention period, participants received 3-4 portions of whole-grains and a portion of nuts daily. Fasted blood samples were collected every 4 weeks for analysis of lipids and plasma alkylresorcinol concentrations. Dynamic regression modeling was used to determine factors associated with changes in blood pressure, heart rate, and compliance with the intervention. RESULTS:12 participants completed their study, with 11 collecting enough data to permit analysis. Dynamic regression modeling identified variables significantly affecting individual blood pressure, heart rate, and consumption of whole-grains and nuts, including sleep quality, weekday, motivation to eat well, alcohol consumption, day of menstrual cycle, outside temperature, and physical activity. Consumption of whole-grains and nuts was associated with a significant (p < 0.01) decrease in SBP for one participant, a significant (p < 0.05) decrease in DBP for two participants, and a significant (p < 0.01) lowering in heart rate for five participants. CONCLUSIONS:The N-of-1 studies uniquely identified individual-level factors that were associated with changes in blood pressure and heart rate, and with compliance to consumption of whole-grains and nuts. N-of-1 studies are imperative to better understand heterogeneity of response and to develop more targeted, personalized and acceptable dietary advice. TRIAL REGISTRATION:https://clinicaltrials.gov/study/NCT04326686 . First registered 20 March 2020.

Background:Current knowledge on the quality of dietary intakes from plant-based meat (PBM)-containing diets is mainly based on theoretical modeling studies, whereas empirical evidence from actual consumption data is scarce. Objectives:This study aimed to investigate dietary intakes of PBM consumers compared with nonconsumers using real-life United Kingdom consumption data. Methods:Intakes of adults (18-65 y) from the National Diet and Nutrition Survey Rolling Program (2014-2019) in the United Kingdom were calculated based on a 4-d dietary diary. Nutrient intakes were expressed as percentages of nutrient-specific dietary reference values (DRVs), and Mann-Whitney U tests were performed to test for differences in food and nutrient intakes between PBM consumers (n = 101) and nonconsumers (n = 1845). Multiple regression analyses were conducted to assess PBM consumption as an independent predictor of nutrient intakes. Results:PBM consumers consumed more fruits, vegetables, and pulses (median [interquartile range]: 346 [234, 484] compared with 228 [121, 365] g/d, P < 0.001) and less animal meat compared with non-PBM consumers (17 [0, 105] compared with 125 [74, 185] g/d, P < 0.001). PBM consumers had intakes that were more in line with DRVs compared with non-PBM consumers, with higher intakes of fiber, vitamins E, A, C, and folate, calcium, magnesium, zinc, and copper (all P < 0.01). No differences were observed between PBM consumer groups in protein, saturated fatty acids, vitamins B12 and D, iron, potassium, iodine, and sodium intakes (all P > 0.05). Regression analyses confirmed that PBM consumption was independently associated with more favorable intakes of specific nutrients. Conclusions:PBM consumers had more favorable food and nutrient intakes than nonconsumers, indicating that PBM can be part of a healthier and more plant-based diet.

Dietary modelling studies suggest that replacing animal meat with plant-based meat (PBM) may enhance diet quality, yet empirical data on the diet quality of PBM consumers remains limited. This study aimed to fill this gap by investigating diet quality of PBM consumers versus non-PBM consumers using real-life consumption data of adults (18-69 y) from the Dutch National Food Consumption Survey (2019-2021). Nutrient intakes were expressed as percentages of dietary reference values (DRV), and probability of adequate nutrient intake was assessed using the PANDiet score. We used non-parametric Mann Whitney U tests to investigate differences in food and nutrient intakes and PANDiet scores between PBM consumers (n = 104) and non-PBM consumers (n = 1312). Multiple regression analyses were conducted to assess PBM consumption as independent predictor of dietary intakes beyond demographic and lifestyle factors. PBM consumers ate significantly more fruit (median (interquartile range) intake: 164 (104; 266) vs 114 (34; 203) g/d, p < 0.001), vegetables (175 (112; 296) vs 143 (87; 210) g/d, p = 0.002), and nuts & seeds (7 (0; 25) vs 0 (0; 15) g/d, p = 0.004), but less meat (0 (0; 38) vs 84 (45; 126) g/d, p < 0.001) than non-PBM consumers. The PANDiet score of PBM consumers was higher (67 (63; 71) vs 62 (58; 67), p < 0.001), reflecting more favorable intakes of fiber, saturated fat, linoleic acid, vitamins E and K1, folate equivalents, iron, magnesium and copper (all p < 0.002), while protein and sodium intakes did not differ (p > 0.05). Regression analyses confirmed that PBM consumption was independently associated with more favorable nutrient intakes. Overall, these empirical data show that Dutch PBM consumers had a better diet quality than non-PBM consumers and support the beneficial role of PBM replacing animal meat in a balanced and more plant-based diet.

Food biodiversity is receiving increased attention because of global food systems being a major contributor to biodiversity loss. Food biodiversity, defined as the diversity of plants, animals, and other organisms used for food, may benefit both human and planetary health. Despite this potential, little is known about the association between food biodiversity and the healthiness of human diets. This systematic scoping review presents an overview of the nexus between food biodiversity, diet quality, health outcomes, and environmental impact. Three search strategies were performed in Scopus and PubMed Central, to identify English articles published up until December 2024 on food biodiversity in relation to diet quality, health outcomes, and environmental impact. Eight studies reported on the association between food biodiversity and diet quality, and 4 on the association between food biodiversity and health outcomes. No studies reported on the association between food biodiversity and environmental impact. The studies quantified food biodiversity using Nutritional Functional Diversity, Dietary Species Richness (DSR), Simpson Diversity Index, Shannon Diversity Index, Berger-Parker Index, or a combination of these. One study compared the latter 4 metrics by calculating Hill numbers. Despite using different metrics, all studies showed significant positive associations between food biodiversity and nutritional adequacy, a reduced risk of total and cause-specific mortality, or a reduced risk of gastrointestinal cancers. One study reported a nonsignificant association between DSR and body fat percentage. In conclusion, limited available studies consistently find a positive association between food biodiversity, diet quality, and decreased health risks, highlighting the potential of food biodiversity to improve the healthiness of diets. Currently, DSR is proposed to be the most feasible metric to quantify food biodiversity. Future studies should focus on the added value of food biodiversity over dietary diversity in relation to human and planetary health, which is currently unclear.

To date, publications on the nutritional quality of plant-based meat mostly focus on a limited range of nutrients derived from on-pack nutrition labels. We aimed to systematically review analytical data on the intrinsic nutrient composition of plant-based meat and compare it with animal meat, benchmark it against nutrient requirements, and calculate nutrient density. When plant-based meats were fortified (e.g. with iron, vitamin B12), we did not include the values for these particular micronutrients in the analyses. Findings show that as compared to animal meat, plant-based meat is similarly high in protein, lower in energy and saturated fat and higher in sugar, carbohydrates and fiber. When benchmarked against requirements, plant-based meat can be a source of alpha-linolenic acid, vitamins B1, B2, B3, folate, E, K, calcium, magnesium, manganese, copper, iron, potassium, phosphorus, selenium and zinc. However, unlike plant-based meat, animal meat can be a source of vitamins B5, B6, B12 and D. Nutrient density was comparable between groups. In conclusion, plant-based meat can provide unique types and quantities of essential nutrients, resulting in a nutrient density comparable to animal meat, regardless of fortification. When product reformulation efforts to minimize nutrients to limit continue, plant-based meat fits in a healthy diet.

OBJECTIVE:We determined whether dietary species richness (DSR) (i) can be robustly measured using 4-day food intake data, (ii) is dependent on socio-demographic characteristics and (iii) is associated with diet quality. DESIGN:The National Diet and Nutrition Survey (NDNS) nutrient databank 2018-2019 was expanded to include FoodEx2 food classifications, ingredients, the number and identity of unique species, Nutrient Rich Food 8·3 (NRF 8·3) Index scores and greenhouse gas emissions. Four-day food intake data and socio-demographic variables were used to calculate diet quality and DSR on the food and diet level. SETTING:The United Kingdom (UK). PARTICIPANTS:Participants from NDNS 9-11 (2016-2019). RESULTS:Composite dishes had the highest DSR (median 8 (Q1 = 4, Q3 = 12)), followed by seasoning, sauces and condiments (median 7, (Q1 = 4, Q3 = 10)) and, grains and grain-based products (median 5, (Q1 = 2, Q3 = 7)). Median DSR over 4 days was 49 (Q1 = 43, Q3 = 56; range 14-92), with the first 2 days achieving 80 % of DSR measured over 4 days. DSR was significantly higher in those who were younger, those with a higher household income or those with a lower level of deprivation (all P < 0·001). Higher DSR was associated with a small but significant improvement in nutritional quality (P < 0·001). Also, adherence to dietary guidelines such as fibre, fruits and vegetables and fish was associated with significantly higher DSR (all P < 0·001). CONCLUSIONS:We successfully established DSR based on 4-day food intake data. We also identified opportunities to improve DSR by increasing the consumption of fruits, vegetables, fibre and fish.

Background: A shift to more plant-based consumption patterns may lower the protein adequacy of diets. Objectives: The objective of this study was to examine how replacing animal meat with plant-based meat alternatives impacts protein adequacy in the Dutch diet by considering protein quality data. Methods: Habitual total and utilizable protein intakes were calculated from meal-based food consumption data from 1633 participants aged 18 to 70 y of the Dutch National Food Consumption Survey 2012-2016. Utilizable protein intake was calculated as the sum of protein intake per meal adjusted for protein digestibility-corrected amino acid score and compared to the estimated average requirement for Dutch adults to calculate the percentage of the population with an adequate protein intake. In the modeling scenarios, all animal meat was replaced gram- for-gram with meat alternatives from various protein sources currently available on the Dutch market. Results: Replacing all meat with meat alternatives decreased the intake of animal protein from 59% to 36%, median total protein intake from 1.14 g/kg/d to 1.09 g/kg/d, median utilizable protein intake from 0.94 g/kg/d to 0.86 g/kg/d, and protein adequacy from 93% to 86%. Additional scenarios indicated that the protein adequacy was mostly impacted by total protein content, lysine content, and protein digestibility of the meat alternatives. Conclusions: This modeling study indicated that when all animal meat was replaced by plant-based meats, total and utilizable protein intake remained adequate for the majority (86%) of the Dutch adult population. Individuals relying primarily on plant-based protein should ensure a sufficient total protein intake from a variety of sources.

The manifestation of metabolic deteriorations that accompany overweight and obesity can differ greatly between individuals, giving rise to a highly heterogeneous population. This inter-individual variation can impede both the provision and assessment of nutritional interventions as multiple aspects of metabolic health should be considered at once. Here, we apply the Mixed Meal Model, a physiology-based computational model, to characterize an individual's metabolic health in silico. A population of 342 personalized models were generated using data for individuals with overweight and obesity from three independent intervention studies, demonstrating a strong relationship between the model-derived metric of insulin resistance (ρ = 0.67, p < 0.05) and the gold-standard hyperinsulinemic-euglycemic clamp. The model is also shown to quantify liver fat accumulation and β-cell functionality. Moreover, we show that personalized Mixed Meal Models can be used to evaluate the impact of a dietary intervention on multiple aspects of metabolic health at the individual level.

Plant-based meat substitutes replacing animal meat can potentially support the transition towards more sustainable diets. To enable the required transition, consumer acceptance of plant-based meat is essential. An important aspect of this is the feeling of satiety or being full after eating. This study determined the satiating capacity of both plant-based meat and animal meat in 60 adults under real-life in-home conditions. Participants consumed four fixed ready-to eat meals for lunch at home once per week. Two types of Indian curry with ‘chicken’ were investigated as well as two types of pasta Bolognese with ‘minced meat’. The two ‘chicken’ dishes and the two ‘minced meat’ dishes had the same recipe except for a gram-for-gram swap (125 g each) of either animal meat (chicken breast and minced meat) or plant-based (soy) meat. Results showed no difference in the satiating power of an animal meat dish and a plant-based meat dish when these were eaten as part of a full lunch meal at home. In addition, the meals did not result in energy nor macronutrient compensation during the rest of the day after consuming the meals. This occurred despite the caloric differences of the meals as a result of the real-life conditions (i.e., a lower energy content of the pasta with plant-based meat compared to the other meals). We conclude that meals with plant-based meat can be as satiating as meals with animal meat.

While dietary intake has previously been related to various indices of poor sleep (e.g., short sleep duration, poor sleep quality), to date, few studies have examined chrononutrition from the perspectives of the relationship between dietary intake and social jet lag and temporal sleep variability. Moreover, recently it has been suggested that previous methods of measuring social jet lag have the potential to lead to large overestimations. Together, this precludes a clear understanding of the role of nutritional composition in the pathophysiology of poor sleep, via social jet lag and temporal sleep variability, or vice versa. The aim of the present study was to determine the relationships between nutrient intake and social jet lag (using a revised index, taking account of intention to sleep and sleep onset and offset difficulties), and temporal sleep variability. Using a cross-sectional survey, 657 healthy participants (mean age 26.7 ± 6.1 years), without sleep disorders, were recruited via an online platform and completed measures of weekly dietary intake, social jet lag, temporal sleep variability, stress/sleep reactivity and mood. Results showed limited associations between nutritional composition and social jet lag. However, levels of temporal sleep variability were predicted by consumption of polyunsaturated fats, sodium, chloride and total energy intake. The results suggest further examinations of specific nutrients are warranted in a first step to tailoring interventions to manage diet and temporal variabilities in sleep patterns.

There is a growing demand for plant-based protein-rich products for human consumption. During the production of plant-based protein-rich products, ingredients such as soy generally undergo several processing methods. However, little is known on the effect of processing methods on protein nutritional quality. To gain a better understanding of the effect of processing on the protein quality of soy, we performed a quantitative review of in-vivo and in-vitro studies that assessed the indispensable amino acid (IAA) composition and digestibility of varying soy products, to obtain digestibility indispensable amino acids scores (DIAAS) and protein digestibility corrected amino acid scores (PDCAAS). For all soy products combined, mean DIAAS was 84.5 ± 11.4 and mean PDCAAS was 85.6 ± 18.2. Data analyses showed different protein quality scores between soy product groups. DIAAS increased from tofu, soy flakes, soy hulls, soy flour, soy protein isolate, soybean, soybean meal, soy protein concentrate to soymilk with the highest DIAAS. In addition, we observed broad variations in protein quality scores within soy product groups, indicating that differences and variations in protein quality scores may also be attributed to various forms of post-processing (such as additional heat-treatment or moisture conditions), as well as study conditions. After excluding post-processed data points, for all soy products combined, mean DIAAS was 86.0 ± 10.8 and mean PDCAAS was 92.4 ± 11.9. This study confirms that the majority of soy products have high protein quality scores and we demonstrated that processing and post-processing conditions can increase or decrease protein quality. Additional experimental studies are needed to quantify to which extent processing and post-processing impact protein quality of plant-based protein-rich products relevant for human consumption.

The primary goal of this analysis was to use baseline and pre-intervention variables from a large dietary intervention study (the FINGEN study) to develop models to predict change in levels of plasma triglycerides (TG), and in the plasma long-chain polyunsaturated fatty acids eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA), after fish oil supplementation. A secondary goal was whether clustering of baseline and pre-intervention data could lead to identification of groups of participants who responded differentially. All statistical analyses were undertaken in R, with outcomes of interest kept on a continuous scale. Multiple imputation was conducted which generated 5 complete datasets. Variable selection methods (forward stepwise selection, backward stepwise selection, LASSO and the Boruta algorithm) were applied across each imputed dataset to generate models. Validation methods were applied to minimise model overfitting. Validation set root mean squared errors (RMSEs) were averaged across the 5 imputed datasets, with final model chosen corresponding to the lowest RMSE and therefore most accurate predictions on data not included in model development. The final model for predicting TG change contained the predictors pre-intervention TG and baseline fasting insulin and ApoB levels. For EPA + DHA change, these were pre-intervention EPA, DHA and baseline ApoE levels. Both models explained over 40% of variation in the outcome, generated using forward stepwise selection. Unsupervised analysis using baseline and pre-intervention data did not lead to significant differences in the outcomes between clusters. Our models successfully identified predictors of response for plasma triglyceride and EPA + DHA change upon intervention with fish oil. This analysis approach therefore offers opportunities as a tool for precision nutrition approaches, to determine those most likely to respond beneficially to dietary interventions. Biotechnology and Biological Sciences Research Council (BBSRC) UK and Unilever Foods Innovation Centre, Wageningen, The Netherlands: Collaborative Training Partnership (CTP) PhD.

Despite the pivotal role played by elevated circulating triglyceride levels in the pathophysiology of cardio-metabolic diseases many of the indices used to quantify metabolic health focus on deviations in glucose and insulin alone. We present the Mixed Meal Model, a computational model describing the systemic interplay between triglycerides, free fatty acids, glucose, and insulin. We show that the Mixed Meal Model can capture deviations in the post-meal excursions of plasma glucose, insulin, and triglyceride that are indicative of features of metabolic resilience; quantifying insulin resistance and liver fat; validated by comparison to gold-standard measures. We also demonstrate that the Mixed Meal Model is generalizable, applying it to meals with diverse macro-nutrient compositions. In this way, by coupling triglycerides to the glucose-insulin system the Mixed Meal Model provides a more holistic assessment of metabolic resilience from meal response data, quantifying pre-clinical metabolic deteriorations that drive disease development in overweight and obesity.

IntroductionSubstantial response heterogeneity is commonly seen in dietary intervention trials. In larger datasets, this variability can be exploited to identify predictors, for example genetic and/or phenotypic baseline characteristics, associated with response in an outcome of interest. ObjectiveUsing data from a placebo-controlled crossover study (the FINGEN study), supplementing with two doses of long chain n-3 polyunsaturated fatty acids (LC n-3 PUFAs), the primary goal of this analysis was to develop models to predict change in concentrations of plasma triglycerides (TG), and in the plasma phosphatidylcholine (PC) LC n-3 PUFAs eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA), after fish oil (FO) supplementation. A secondary goal was to establish if clustering of data prior to FO supplementation would lead to identification of groups of participants who responded differentially. MethodsTo generate models for the outcomes of interest, variable selection methods (forward and backward stepwise selection, LASSO and the Boruta algorithm) were applied to identify suitable predictors. The final model was chosen based on the lowest validation set root mean squared error (RMSE) after applying each method across multiple imputed datasets. Unsupervised clustering of data prior to FO supplementation was implemented using k-medoids and hierarchical clustering, with cluster membership compared with changes in plasma TG and plasma PC EPA + DHA. ResultsModels for predicting response showed a greater TG-lowering after 1.8 g/day EPA + DHA with lower pre-intervention levels of plasma insulin, LDL cholesterol, C20:3n-6 and saturated fat consumption, but higher pre-intervention levels of plasma TG, and serum IL-10 and VCAM-1. Models also showed greater increases in plasma PC EPA + DHA with age and female sex. There were no statistically significant differences in PC EPA + DHA and TG responses between baseline clusters. ConclusionOur models established new predictors of response in TG (plasma insulin, LDL cholesterol, C20:3n-6, saturated fat consumption, TG, IL-10 and VCAM-1) and in PC EPA + DHA (age and sex) upon intervention with fish oil. We demonstrate how application of statistical methods can provide new insights for precision nutrition, by predicting participants who are most likely to respond beneficially to nutritional interventions.

Background The relation between dietary and circulating linoleic acid (18:2 n-6, LA), glucose metabolism and liver function is not yet clear. Associations of dietary and circulating LA with glucose metabolism and liver function markers were investigated. Methods Cross-sectional analyses in 633 black South Africans (aged > 30 years, 62% female, 51% urban) without type 2 diabetes at baseline of the Prospective Urban Rural Epidemiology study. A cultural-sensitive 145-item food-frequency questionnaire was used to collect dietary data, including LA (percentage of energy; en%). Blood samples were collected to measure circulating LA (% total fatty acids (FA); plasma phospholipids), plasma glucose, glycosylated hemoglobin (HbA1c), serum gamma-glutamyl transferase (GGT), alanine (ALT) and aspartate aminotransferase (AST). Associations per 1 standard deviation (SD) and in tertiles were analyzed using multivariable regression. Results Mean (±SD) dietary and circulating LA was 6.8 (±3.1) en% and 16.0 (±3.5) % total FA, respectively. Dietary and circulating LA were not associated with plasma glucose or HbA1c (β per 1 SD: − 0.005 to 0.010, P > 0.20). Higher dietary LA was generally associated with lower serum liver enzymes levels. One SD higher circulating LA was associated with 22% lower serum GGT (β (95% confidence interval): − 0.25 (− 0.31, − 0.18), P < 0.001), but only ≤9% lower for ALT and AST. Circulating LA and serum GGT associations differed by alcohol use and locality. Conclusion Dietary and circulating LA were inversely associated with markers of impaired liver function, but not with glucose metabolism. Alcohol use may play a role in the association between LA and liver function. Trial registration PURE North-West Province South Africa study described in this manuscript is part of the PURE study. The PURE study is registered in ClinicalTrials.gov (Identifier: NCT03225586 ; URL).

Background Circulating odd-chain fatty acids pentadecanoic (15:0) and heptadecanoic acid (17:0) are considered to reflect dairy intake. In cohort studies, higher circulating 15:0 and 17:0 were associated with lower type 2 diabetes risk. A recent randomized controlled trial in humans suggested that fiber intake also increased circulating 15:0 and 17:0, potentially resulting from fermentation by gut microbes. We examined the associations of dairy and fiber intake with circulating 15:0 and 17:0 in patients with a history of myocardial infarction (MI). Methods We performed cross-sectional analyses in a subsample of 869 Dutch post-MI patients of the Alpha Omega Cohort who had data on dietary intake and circulating fatty acids. Dietary intakes (g/d) were assessed using a 203-item food frequency questionnaire. Circulating 15:0 and 17:0 (as % of total fatty acids) were measured in plasma phospholipids (PL) and cholesteryl esters (CE). Spearman correlations ( r s ) were computed between intakes of total dairy, dairy fat, fiber, and circulating 15:0 and 17:0. Results Patients were on average 69 years old, 78% was male and 21% had diabetes. Total dairy intake comprised predominantly milk and yogurt (69%). Dairy fat was mainly derived from cheese (47%) and milk (15%), and fiber was mainly from grains (43%). Circulating 15:0 in PL was significantly correlated with total dairy and dairy fat intake (both r s = 0.19, p < 0.001), but not with dietary fiber intake ( r s = 0.05, p = 0.11). Circulating 17:0 in PL was correlated both with dairy intake ( r s = 0.14 for total dairy and 0.11 for dairy fat, p < 0.001), and fiber intake ( r s = 0.19, p < 0.001). Results in CE were roughly similar, except for a weaker correlation of CE 17:0 with fiber ( r s = 0.11, p = 0.001). Circulating 15:0 was highest in those with high dairy intake irrespective of fiber intake, while circulating 17:0 was highest in those with high dairy and fiber intake. Conclusions In our cohort of post-MI patients, circulating 15:0 was associated with dairy intake but not fiber intake, whereas circulating 17:0 was associated with both dairy and fiber intake. These data suggest that cardiometabolic health benefits previously attributed to 17:0 as a biomarker of dairy intake may partly be explained by fiber intake.

Background and aims: Population-based studies often use plasma fatty acids (FAs) as objective indicators of FA intake, especially for n-3 FA and linoleic acid (LA). The relation between dietary and circulating FA in cardiometabolic patients is largely unknown. We examined whether dietary n-3 FA and LA were reflected in plasma lipid pools in post-myocardial infarction (MI) patients. Methods and results: Patients in Alpha Omega Cohort filled out a 203-item food-frequency questionnaire from which eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), alpha-linolenic acid (ALA), and LA intake were calculated. Circulating individual FA (% total FA) were assessed in cholesteryl esters (CE; n = 4066), phospholipids (PL; n = 838), and additionally in total plasma for DHA and LA (n = 739). Spearman correlation coefficients (r(s)) were calculated for dietary vs. circulating FA. Circulating FA were also compared across dietary FA quintiles, overall and in subgroups by sex, obesity, diabetes, statin use, and high alcohol intake. Patients were on average 69 years old and 79% was male. Moderate correlations between dietary and circulating levels were observed for EPA (r(s)similar to 0.4 in CE and PL) and DHA (r(s)similar to 0.5 in CE and PL,similar to 0.4 in total plasma), but not for ALA (r(s)similar to 0.0). Weak correlations were observed for LA (r(s) 0.1 to 02). Plasma LA was significantly lower in statin users and in patients with a high alcohol intake. Conclusions: In post-MI patients, dietary EPA and DHA were well reflected in circulating levels. This was not the case for LA, which may partly be influenced by alcohol use and statins. (C) 2019 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

OBJECTIVE To study plasma and dietary linoleic acid (LA) in relation to type 2 diabetes risk in post–myocardial infarction (MI) patients. RESEARCH DESIGN AND METHODS We included 3,257 patients aged 60–80 years (80% male) with a median time since MI of 3.5 years from the Alpha Omega Cohort and who were initially free of type 2 diabetes. At baseline (2002–2006), plasma LA was measured in cholesteryl esters, and dietary LA was estimated with a 203-item food-frequency questionnaire. Incident type 2 diabetes was ascertained through self-reported physician diagnosis and medication use. Hazard ratios (with 95% CIs) were calculated by Cox regressions, in which dietary LA isocalorically replaced the sum of saturated (SFA) and trans fatty acids (TFA). RESULTS Mean ± SD circulating and dietary LA was 50.1 ± 4.9% and 5.9 ± 2.1% energy, respectively. Plasma and dietary LA were weakly correlated (Spearman r = 0.13, P < 0.001). During a median follow-up of 41 months, 171 patients developed type 2 diabetes. Plasma LA was inversely associated with type 2 diabetes risk (quintile [Q]5 vs. Q1: 0.44 [0.26, 0.75]; per 5%: 0.73 [0.62, 0.86]). Substitution of dietary LA for SFA+TFA showed no association with type 2 diabetes risk (Q5 vs. Q1: 0.78 [0.36, 1.72]; per 5% energy: 1.18 [0.59, 2.35]). Adjustment for markers of de novo lipogenesis attenuated plasma LA associations. CONCLUSIONS In our cohort of post-MI patients, plasma LA was inversely related to type 2 diabetes risk, whereas dietary LA was not related. Further research is needed to assess whether plasma LA indicates metabolic state rather than dietary LA in these patients.

The objective of this meta-analysis was to investigate the effects of plant-derived polyunsaturated fatty acids (PUFAs) on glucose metabolism and insulin resistance. Scopus and PubMed databases were searched until January 2018. Eligible studies were randomized controlled feeding trials that investigated the effects of a diet high in plant-derived PUFA as compared with saturated fatty acids (SFA) or carbohydrates and measured markers of glucose metabolism and insulin resistance as outcomes. Data from 13 relevant studies (19 comparisons of plant-derived PUFA with control) were retrieved. Plant-derived PUFA did not significantly affect fasting glucose (−0.01 mmol/L (95 % CI − 0.06 to 0.03 mmol/L)), but lowered fasting insulin by 2.6 pmol/L (−4.9 to −0.2 pmol/L) and homeostatic model assessment-insulin resistance (HOMA-IR) by 0.12 units (-0.23 to − 0.01 units). In dose–response analyses, a 5% increase in energy (En%) from PUFA significantly reduced insulin by 5.8 pmol/L (95% CI −10.2 to −1.3 pmol/L), but not glucose (change −0.07, 95% CI −0.17 to 0.04 mmol/L) and HOMA-IR (change − 0.24, 95% CI −0.56 to 0.07 units). In subgroup analyses, studies with higher PUFA dose (upper tertiles) reduced insulin (-6.7, –10.5 to −2.9 pmol/L) and HOMA-IR (-0.28, –0.45 to −0.12 units), but not glucose (−0.09, 95% CI −0.18 to 0.01 mmol/L), as compared with an isocaloric control. Subgroup analyses showed no differences in effects between SFA and carbohydrates as replacement nutrients (p interaction ≥0.05). Evidence from randomized controlled trials indicated that plant-derived PUFA as an isocaloric replacement for SFA or carbohydrates probably reduces fasting insulin and HOMA-IR in populations without diabetes.