Human postprandial responses to food and potential for precision nutrition

[1]  O. Kwon,et al.  A Review of Recent Evidence from Meal-Based Diet Interventions and Clinical Biomarkers for Improvement of Glucose Regulation , 2020, Preventive nutrition and food science.

[2]  T. Spector,et al.  Personalised REsponses to DIetary Composition Trial (PREDICT): an intervention study to determine inter-individual differences in postprandial response to foods , 2020 .

[3]  M. Marco,et al.  The Role of the Gut Microbiome in Predicting Response to Diet and the Development of Precision Nutrition Models. Part II: Results. , 2019, Advances in nutrition.

[4]  T. Spector,et al.  TwinsUK: The UK Adult Twin Registry Update , 2019, Twin Research and Human Genetics.

[5]  T. Lehtimäki,et al.  Genetic predisposition to higher body fat yet lower cardiometabolic risk in children and adolescents , 2019, International Journal of Obesity.

[6]  L. G. Vu,et al.  Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 , 2019, The Lancet.

[7]  Soumeya Bekri,et al.  Paving the Way to Precision Nutrition Through Metabolomics , 2019, Front. Nutr..

[8]  P. Bork,et al.  Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation , 2019, Nature Medicine.

[9]  Jingyuan Fu,et al.  Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases , 2019, Nature Genetics.

[10]  M. Thoresen,et al.  Meals with Similar Fat Content from Different Dairy Products Induce Different Postprandial Triglyceride Responses in Healthy Adults: A Randomized Controlled Cross-Over Trial , 2019, The Journal of nutrition.

[11]  R. Gabbianelli Food and Public Health , 2018, Oxford Scholarship Online.

[12]  F. Atkinson,et al.  The physiologic and phenotypic significance of variation in human amylase gene copy number. , 2018, The American journal of clinical nutrition.

[13]  Amalio Telenti,et al.  The fecal metabolome as a functional readout of the gut microbiome , 2018, Nature Genetics.

[14]  John P. A. Ioannidis,et al.  Effect of Low-Fat vs Low-Carbohydrate Diet on 12-Month Weight Loss in Overweight Adults and the Association With Genotype Pattern or Insulin Secretion: The DIETFITS Randomized Clinical Trial , 2018, JAMA.

[15]  A I Pack,et al.  Estimating sleep parameters using an accelerometer without sleep diary , 2018, Scientific Reports.

[16]  J. Hirschhorn,et al.  Genetic Evidence That Carbohydrate-Stimulated Insulin Secretion Leads to Obesity. , 2018, Clinical chemistry.

[17]  E. Laurila,et al.  Genetic determinants of circulating GIP and GLP-1 concentrations. , 2017, JCI insight.

[18]  G. Mehrotra,et al.  Postprandial triglyceride responses and endothelial function in prediabetic first-degree relatives of patients with diabetes. , 2017, Journal of clinical lipidology.

[19]  J. Erdmann,et al.  Genome-wide association study in takotsubo syndrome - Preliminary results and future directions. , 2017, International journal of cardiology.

[20]  June-Goo Lee,et al.  Deep Learning in Medical Imaging: General Overview , 2017, Korean journal of radiology.

[21]  Pedro J. Ballester,et al.  Performance of machine-learning scoring functions in structure-based virtual screening , 2017, Scientific Reports.

[22]  Garcia-Perez Urintest für Ernährung , 2017, DMW - Deutsche Medizinische Wochenschrift.

[23]  Rachel Gibson,et al.  Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial , 2017, The lancet. Diabetes & endocrinology.

[24]  Erratum. Classification and diagnosis of diabetes. Sec. 2. In Standards of Medical Care in Diabetes–2016. Diabetes Care 2016;39(Suppl. 1):S13–S22 , 2016, Diabetes Care.

[25]  Vera Pawlowsky-Glahn,et al.  It's all relative: analyzing microbiome data as compositions. , 2016, Annals of epidemiology.

[26]  Paul J. McMurdie,et al.  DADA2: High resolution sample inference from Illumina amplicon data , 2016, Nature Methods.

[27]  John Salvatier,et al.  Probabilistic programming in Python using PyMC3 , 2016, PeerJ Comput. Sci..

[28]  M. Weickert,et al.  Impact of Diet Composition on Blood Glucose Regulation , 2016, Critical reviews in food science and nutrition.

[29]  E. Segal,et al.  Personalized Nutrition by Prediction of Glycemic Responses , 2015, Cell.

[30]  E. Nozaki,et al.  Different postprandial lipid metabolism and insulin resistance between non-diabetic patients with and without coronary artery disease. , 2015, Journal of cardiology.

[31]  I. Borecki,et al.  Genome-wide association study of triglyceride response to a high-fat meal among participants of the NHLBI Genetics of Lipid Lowering Drugs and Diet Network (GOLDN). , 2015, Metabolism: clinical and experimental.

[32]  K. Jablonski,et al.  Genetic Predisposition to Weight Loss and Regain With Lifestyle Intervention: Analyses From the Diabetes Prevention Program and the Look AHEAD Randomized Controlled Trials , 2015, Diabetes.

[33]  G. Steil,et al.  Impact of Fat, Protein, and Glycemic Index on Postprandial Glucose Control in Type 1 Diabetes: Implications for Intensive Diabetes Management in the Continuous Glucose Monitoring Era , 2015, Diabetes Care.

[34]  Pasi Soininen,et al.  Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. , 2015, Circulation. Cardiovascular genetics.

[35]  G. Bray,et al.  Effects of Weight Loss, Weight Cycling, and Weight Loss Maintenance on Diabetes Incidence and Change in Cardiometabolic Traits in the Diabetes Prevention Program , 2014, Diabetes Care.

[36]  Jennifer G. Robinson,et al.  2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines , 2014, Circulation.

[37]  Jennifer G. Robinson,et al.  2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines , 2014, Circulation.

[38]  Mark I. McCarthy,et al.  A Central Role for GRB10 in Regulation of Islet Function in Man , 2014, PLoS genetics.

[39]  David J. Parry-Smith,et al.  A new tool for converting food frequency questionnaire data into nutrient and food group values: FETA research methods and availability , 2014, BMJ Open.

[40]  G. Andrew,et al.  arm: Data Analysis Using Regression and Multilevel/Hierarchical Models , 2014 .

[41]  Jennifer G. Robinson,et al.  Reply: 2013 ACC/AHA guideline on the assessment of cardiovascular risk. , 2014, Journal of the American College of Cardiology.

[42]  M. Drazner,et al.  2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. , 2013, Journal of the American College of Cardiology.

[43]  Roy Taylor,et al.  Banting Memorial Lecture 2012 Reversing the twin cycles of Type 2 diabetes , 2013, Diabetic medicine : a journal of the British Diabetic Association.

[44]  Alireza Moayyeri,et al.  The UK Adult Twin Registry (TwinsUK Resource) , 2012, Twin Research and Human Genetics.

[45]  J. Holst,et al.  Impact of postprandial glycaemia on health and prevention of disease , 2012, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[46]  L. Mercado-Asis,et al.  Postprandial Peaking and Plateauing of Triglycerides and VLDL in Patients with Underlying Cardiovascular Diseases Despite Treatment , 2012, International journal of endocrinology and metabolism.

[47]  A. Minihane,et al.  Postprandial lipemia and cardiovascular disease risk: Interrelationships between dietary, physiological and genetic determinants. , 2012, Atherosclerosis.

[48]  B. Nordestgaard,et al.  Assessment and clinical relevance of non-fasting and postprandial triglycerides: an expert panel statement. , 2011, Current vascular pharmacology.

[49]  J. I. Pedersen,et al.  Nonfasting triglycerides and risk of cardiovascular death in men and women from the Norwegian Counties Study , 2010, European Journal of Epidemiology.

[50]  C. Törn,et al.  C-peptide in dried blood spots , 2010, Scandinavian journal of clinical and laboratory investigation.

[51]  J. Tuomilehto,et al.  Cardiovascular Disease Mortality in Europeans in Relation to Fasting and 2-h Plasma Glucose Levels Within a Normoglycemic Range , 2010, Diabetes Care.

[52]  Alex Doney,et al.  Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge , 2010, Nature Genetics.

[53]  Diabetes Prevention Program Research Group 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study , 2009, The Lancet.

[54]  P. Ridker,et al.  Fasting compared with nonfasting triglycerides and risk of cardiovascular events in women. , 2007, JAMA.

[55]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[56]  Michael Stumvoll,et al.  Type 2 diabetes: principles of pathogenesis and therapy , 2005, The Lancet.

[57]  S. Bailey,et al.  Children and adolescents. , 2004, Criminal behaviour and mental health : CBMH.

[58]  Shaun Purcell,et al.  Variance components models for gene-environment interaction in twin analysis. , 2002, Twin research : the official journal of the International Society for Twin Studies.

[59]  S. Fowler,et al.  Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. , 2002 .

[60]  E. Feskens,et al.  Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria. , 2001, Archives of internal medicine.

[61]  A. Astrup,et al.  Reproducibility, power and validity of visual analogue scales in assessment of appetite sensations in single test meal studies , 2000, International Journal of Obesity.

[62]  D. Lairon,et al.  Effects of graded amounts (0-50 g) of dietary fat on postprandial lipemia and lipoproteins in normolipidemic adults. , 1998, The American journal of clinical nutrition.

[63]  P. Knauf,et al.  Monthly publication in 1986 , 1985 .

[64]  S A Glantz,et al.  Multiple regression for physiological data analysis: the problem of multicollinearity. , 1985, The American journal of physiology.

[65]  BOULIN,et al.  [Classification and diagnosis of diabetes]. , 1953, Concours medical.