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A double-blinded, randomized, parallel intervention to evaluate biomarker-based nutrition plans for weight loss: The PREVENTOMICS study.
Aldubayan, MA, Pigsborg, K, Gormsen, SMO, Serra, F, Palou, M, Galmés, S, Palou-March, A, Favari, C, Wetzels, M, Calleja, A, et al
Clinical nutrition (Edinburgh, Scotland). 2022;41(8):1834-1844
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Obesity, and particularly abdominal adiposity, is associated with various metabolic abnormalities. Diet has a vital role in preventing and managing obesity, but evidence from clinical studies demonstrates there is a great interindividual variability in response to the same dietary intervention, which likely indicates that no one diet is superior to another. The aim of this study was to examine the efficacy of the PREVENTOMICS (empowering consumers to PREVENT diet-related diseases through OMICS sciences) platform, incorporated in an e-commerce digital tool, for producing more favourable health outcomes over dietary plans based on general diet recommendations, in subjects with overweight or obesity and elevated waist circumference. This study is a 10-week randomised single-centre, parallel-group, double-blinded intervention study. Participants were allocated in a 1:1 ratio, stratified by cluster to either the intervention group (personalised plan) or the control group (generic recommendations). Results show that there isn’t any additional benefit of personalising dietary plans, over a generic approach, on the change in fat mass and body weight in individuals with overweight or obesity and elevated waist circumference. Accordingly, personalisation of the diet did not significantly improve health parameters beyond the changes induced by the control diet. Participants in both groups lost approximately 3 kg of body weight. Authors conclude that based on their findings evidence to translate personalised nutrition approaches into clinical practice is insufficient.
Abstract
BACKGROUND & AIMS Growing evidence suggests that biomarker-guided dietary interventions can optimize response to treatment. In this study, we evaluated the efficacy of the PREVENTOMCIS platform-which uses metabolomic and genetic information to classify individuals into different 'metabolic clusters' and create personalized dietary plans-for improving health outcomes in subjects with overweight or obesity. METHODS A 10-week parallel, double-blinded, randomized intervention was conducted in 100 adults (82 completers) aged 18-65 years, with body mass index ≥27 but <40 kg/m2, who were allocated into either a personalized diet group (n = 49) or a control diet group (n = 51). About 60% of all food was provided free-of-charge. No specific instruction to restrict energy intake was given. The primary outcome was change in fat mass from baseline, evaluated by dual energy X-ray absorptiometry. Other endpoints included body weight, waist circumference, lipid profile, glucose homeostasis markers, inflammatory markers, blood pressure, physical activity, stress and eating behavior. RESULTS There were significant main effects of time (P < 0.01), but no group main effects, or time-by-group interactions, for the change in fat mass (personalized: -2.1 [95% CI -2.9, -1.4] kg; control: -2.0 [95% CI -2.7, -1.3] kg) and body weight (personalized: -3.1 [95% CI -4.1, -2.1] kg; control: -3.3 [95% CI -4.2, -2.4] kg). The difference between groups in fat mass change was -0.1 kg (95% CI -1.2, 0.9 kg, P = 0.77). Both diets resulted in significant improvements in insulin resistance and lipid profile, but there were no significant differences between groups. CONCLUSION Personalized dietary plans did not result in greater benefits over a generic, but generally healthy diet, in this 10-week clinical trial. Further studies are required to establish the soundness of different precision nutrition approaches, and translate this science into clinically relevant dietary advice to reduce the burden of obesity and its comorbidities. CLINICAL TRIAL REGISTRY ClinicalTrials.gov registry (NCT04590989).
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Insulin resistance drives hepatic de novo lipogenesis in nonalcoholic fatty liver disease.
Smith, GI, Shankaran, M, Yoshino, M, Schweitzer, GG, Chondronikola, M, Beals, JW, Okunade, AL, Patterson, BW, Nyangau, E, Field, T, et al
The Journal of clinical investigation. 2020;130(3):1453-1460
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Non-alcoholic fatty liver disease (NAFLD) is a common complication of obesity and is associated with multiorgan insulin resistance, dyslipidaemia and an increased risk of diabetes and coronary heart disease. The aims of this study were to (a) determine hepatic de novo lipogenesis (DNL) [the liver’s biochemical process of synthesising fatty acids] in 3 distinct cohorts, (b) determine the relationships among hepatic DNL and intrahepatic [within the liver] triglyceride (IHTG) content, and (c) determine the effect of moderate (10%) weight loss. This study is a cross-sectional study which included a total of 67 men and women (mean age: 39 ± 1 years; 14 men and 53 women). Results highlight the importance of DNL in the pathogenesis of hepatic steatosis [build up of fats in the liver] and suggest that increases in daily 24-hour plasma glucose and insulin concentrations are major drivers of increased DNL in individuals with obesity and NAFLD. Additionally, moderate (10%) weight loss caused a marked decrease in both hepatic DNL and IHTG content. Authors conclude that increases in circulating glucose and insulin promote hepatic DNL in individuals with NAFLD. Whereas an improvement in insulin sensitivity and a decrease in hepatic DNL, are potentially important contributors to the decline in IHTG content associated with moderate weight loss.
Abstract
BACKGROUNDAn increase in intrahepatic triglyceride (IHTG) is the hallmark feature of nonalcoholic fatty liver disease (NAFLD) and is decreased by weight loss. Hepatic de novo lipogenesis (DNL) contributes to steatosis in individuals with NAFLD. The physiological factors that stimulate hepatic DNL and the effect of weight loss on hepatic DNL are not clear.METHODSHepatic DNL, 24-hour integrated plasma insulin and glucose concentrations, and both liver and whole-body insulin sensitivity were determined in individuals who were lean (n = 14), obese with normal IHTG content (n = 26), or obese with NAFLD (n = 27). Hepatic DNL was assessed using the deuterated water method corrected for the potential confounding contribution of adipose tissue DNL. Liver and whole-body insulin sensitivity was assessed using the hyperinsulinemic-euglycemic clamp procedure in conjunction with glucose tracer infusion. Six subjects in the obese-NAFLD group were also evaluated before and after a diet-induced weight loss of 10%.RESULTSThe contribution of hepatic DNL to IHTG-palmitate was 11%, 19%, and 38% in the lean, obese, and obese-NAFLD groups, respectively. Hepatic DNL was inversely correlated with hepatic and whole-body insulin sensitivity, but directly correlated with 24-hour plasma glucose and insulin concentrations. Weight loss decreased IHTG content, in conjunction with a decrease in hepatic DNL and 24-hour plasma glucose and insulin concentrations.CONCLUSIONSThese data suggest hepatic DNL is an important regulator of IHTG content and that increases in circulating glucose and insulin stimulate hepatic DNL in individuals with NAFLD. Weight loss decreased IHTG content, at least in part, by decreasing hepatic DNL.TRIAL REGISTRATIONClinicalTrials.gov NCT02706262.FUNDINGThis study was supported by NIH grants DK56341 (Nutrition Obesity Research Center), DK20579 (Diabetes Research Center), DK52574 (Digestive Disease Research Center), and RR024992 (Clinical and Translational Science Award), and by grants from the Academy of Nutrition and Dietetics Foundation, the College of Natural Resources of UCB, and the Pershing Square Foundation.
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A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring.
Alpert, A, Pickman, Y, Leipold, M, Rosenberg-Hasson, Y, Ji, X, Gaujoux, R, Rabani, H, Starosvetsky, E, Kveler, K, Schaffert, S, et al
Nature medicine. 2019;25(3):487-495
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The human immune system changes with age, ultimately leading to a clinically evident, profound deterioration resulting in high morbidity and mortality rates attributed to infectious and chronic diseases. The aim of this study was to assess at high resolution the dynamics of older adults’ immune systems. The study uses multiple ‘omics’ technologies in a cohort of 135 adults (63 young adults and 72 older adults) of different ages who were sampled longitudinally over the course of 9 years to comprehensively capture population- and individual-level changes in the immune system over time. Results indicate that immune-cell frequencies changed at substantially different rates; some cell subsets show no directionality of change yet differ between young and old individuals, whereas other cell subsets continued changing (either increasing or decreasing) throughout the course of the study. Authors postulate that an individual’s immune age is a function of life history, namely environmental exposure coupled with genetic background. Thus, immune modulators may one day be identified that affect the position of an individual’s immune system along the immunological landscape.
Abstract
Immune responses generally decline with age. However, the dynamics of this process at the individual level have not been characterized, hindering quantification of an individual's immune age. Here, we use multiple 'omics' technologies to capture population- and individual-level changes in the human immune system of 135 healthy adult individuals of different ages sampled longitudinally over a nine-year period. We observed high inter-individual variability in the rates of change of cellular frequencies that was dictated by their baseline values, allowing identification of steady-state levels toward which a cell subset converged and the ordered convergence of multiple cell subsets toward an older adult homeostasis. These data form a high-dimensional trajectory of immune aging (IMM-AGE) that describes a person's immune status better than chronological age. We show that the IMM-AGE score predicted all-cause mortality beyond well-established risk factors in the Framingham Heart Study, establishing its potential use in clinics for identification of patients at risk.
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A Randomized Study of the Effects of Additional Fruit and Nuts Consumption on Hepatic Fat Content, Cardiovascular Risk Factors and Basal Metabolic Rate.
Agebratt, C, Ström, E, Romu, T, Dahlqvist-Leinhard, O, Borga, M, Leandersson, P, Nystrom, FH
PloS one. 2016;11(1):e0147149
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Fruits and vegetables intake has been advocated to improve blood lipids profile and reduce risk of cardiovascular disease, diabetes and cancer. However, a low fat diet rich in fruits and vegetables has showed no effect on cardiovascular disease and cancer in a large randomized American trial. This might be due to the high sugar content in fruits, particularly fructose. The aim of this study was to compare the effects of adding either fruits or nuts to the diet of 30 healthy non-obese individuals on liver fat, metabolic rate and cardiovascular risk markers. Authors concluded that the trial only showed small effects on cardiovascular risk factors. Nevertheless, there was a significant change in lipoprotein (fats that transport fats in the blood) levels between the two groups, which tends to give an advantage to the consumption of nuts over fruits. They deduced that increased intake of fruits doesn’t negatively impact cardiovascular disease risk factors in healthy non-obese individuals. However, further research needs to evaluate the effects on obese and insulin-resistant participants.
Abstract
BACKGROUND Fruit has since long been advocated as a healthy source of many nutrients, however, the high content of sugars in fruit might be a concern. OBJECTIVES To study effects of an increased fruit intake compared with similar amount of extra calories from nuts in humans. METHODS Thirty healthy non-obese participants were randomized to either supplement the diet with fruits or nuts, each at +7 kcal/kg bodyweight/day for two months. Major endpoints were change of hepatic fat content (HFC, by magnetic resonance imaging, MRI), basal metabolic rate (BMR, with indirect calorimetry) and cardiovascular risk markers. RESULTS Weight gain was numerically similar in both groups although only statistically significant in the group randomized to nuts (fruit: from 22.15 ± 1.61 kg/m(2) to 22.30 ± 1.7 kg/m(2), p = 0.24 nuts: from 22.54 ± 2.26 kg/m(2) to 22.73 ± 2.28 kg/m(2), p = 0.045). On the other hand BMR increased in the nut group only (p = 0.028). Only the nut group reported a net increase of calories (from 2519 ± 721 kcal/day to 2763 ± 595 kcal/day, p = 0.035) according to 3-day food registrations. Despite an almost three-fold reported increased fructose-intake in the fruit group (from 9.1 ± 6.0 gram/day to 25.6 ± 9.6 gram/day, p<0.0001, nuts: from 12.4 ± 5.7 gram/day to 6.5 ± 5.3 gram/day, p = 0.007) there was no change of HFC. The numerical increase in fasting insulin was statistically significant only in the fruit group (from 7.73±3.1 mIE/L to 8.81±2.9 mIE/L, p = 0.018, nuts: from 7.29±2.9 mIE/L to 8.62±3.0 mIE/L, p = 0.14). Levels of vitamin C increased in both groups while α-tocopherol/cholesterol-ratio increased only in the fruit group. CONCLUSIONS Although BMR increased in the nut-group only this was not linked with differences in weight gain between groups which potentially could be explained by the lack of reported net caloric increase in the fruit group. In healthy non-obese individuals an increased fruit intake seems safe from cardiovascular risk perspective, including measurement of HFC by MRI. TRIAL REGISTRATION ClinicalTrials.gov NCT02227511.