Genomic analysis of diet composition finds novel loci and associations with health and lifestyle.
Journal
Molecular psychiatry
ISSN: 1476-5578
Titre abrégé: Mol Psychiatry
Pays: England
ID NLM: 9607835
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
received:
14
10
2018
accepted:
20
02
2020
revised:
03
02
2020
pubmed:
13
5
2020
medline:
12
10
2021
entrez:
13
5
2020
Statut:
ppublish
Résumé
We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10
Identifiants
pubmed: 32393786
doi: 10.1038/s41380-020-0697-5
pii: 10.1038/s41380-020-0697-5
pmc: PMC7767645
mid: EMS86623
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2056-2069Subventions
Organisme : Medical Research Council
ID : MC_UU_12015/1
Pays : United Kingdom
Organisme : Intramural NIH HHS
ID : ZIA DK075068
Pays : United States
Organisme : NIA NIH HHS
ID : R56 AG042568
Pays : United States
Organisme : Medical Research Council
ID : MR/P014437/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00006/3
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_12015/5
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : P01 AG005842
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG012810
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG042568
Pays : United States
Organisme : NIA NIH HHS
ID : T32 AG000186
Pays : United States
Informations de copyright
© 2020. The Author(s).
Références
Mozaffarian D, Rosenberg I, Uauy R. History of modern nutrition science-implications for current research, dietary guidelines, and food policy. BMJ. 2018;361:k2392.
pubmed: 29899124
pmcid: 5998735
doi: 10.1136/bmj.k2392
Hall KD, Guo J. Obesity energetics: body weight regulation and the effects of diet composition. Gastroenterology. 2017;152:1718–27.
pubmed: 28193517
doi: 10.1053/j.gastro.2017.01.052
Buchholz AC, Schoeller DA. Is a calorie a calorie? Am J Clin Nutr. 2004;79:899S–906S.
Feinman RD, Fine EJ. A calorie is a calorie’ violates the second law of thermodynamics. Nutr J. 2004. https://doi.org/10.1186/1475-2891-3-9 .
Katz DL, Meller S. Can we say what diet is best for health? Annu Rev Public Health. 2014;35:83–103.
pubmed: 24641555
doi: 10.1146/annurev-publhealth-032013-182351
Atallah R, Filion KB, Wakil SM, Genest J, Joseph L, Poirier P, et al. Long-term effects of 4 popular diets on weight loss and cardiovascular risk factors: a systematic review of randomized controlled trials. Circ Cardiovasc Qual Outcomes. 2014;7:815–27.
pubmed: 25387778
doi: 10.1161/CIRCOUTCOMES.113.000723
Howard BV, Manson JAE, Stefanick ML, Beresford SA, Frank G, Jones B, et al. Low-fat dietary pattern and weight change over 7 years: The Women’s Health Initiative Dietary Modification Trial. J Am Med Assoc. 2006;295:39–49.
doi: 10.1001/jama.295.1.39
La Berge AF. How the ideology of low fat conquered America. J Hist Med Allied Sci. 2008;63:139–77.
pubmed: 18296750
doi: 10.1093/jhmas/jrn001
WHO. Information note about intake of sugars recommended in the WHO guideline for adults and children. 2015. http://www.who.int/nutrition/publications/guidelines/sugar_intake_information_note_en.pdf . Accessed 1 Mar 2018.
U.S. Department of Health and Human Services and U.S. Department of Agriculture. 2015-2020 Dietary Guidelines for Americans. 8th edn. 2015. http://health.gov/dietaryguidelines/2015/guidelines/ . Accessed 1 Mar 2018.
Mozaffarian D, Ludwig DS. The 2015 US Dietary Guidelines. JAMA. 2015;313:2421.
pubmed: 26103023
pmcid: 6129189
doi: 10.1001/jama.2015.5941
Johns DM, Oppenheimer GM. Was there ever really a “sugar conspiracy”? Science. 2018;359:747–50.
pubmed: 29449481
doi: 10.1126/science.aaq1618
Koletzko B, Demmelmair H, Grote V, Prell C, Weber M. High protein intake in young children and increased weight gain and obesity risk. Am J Clin Nutr. 2016;103:303–4.
pubmed: 26791192
doi: 10.3945/ajcn.115.128009
Sarris J, Logan AC, Akbaraly TN, Amminger GP, Balanzá-Martínez V, Freeman MP, et al. Nutritional medicine as mainstream in psychiatry. Lancet Psychiatry. 2015;2:271–4.
pubmed: 26359904
doi: 10.1016/S2215-0366(14)00051-0
Wade J, Milner J, Krondl M. Evidence for a physiological regulation of food selection and nutrient intake in twins. Am J Clin Nutr. 1981;34:143–7.
pubmed: 7193970
doi: 10.1093/ajcn/34.2.143
De Castro JM. Heritability of diurnal changes in food intake in free-living humans. Nutrition. 2001;17:713–20.
pubmed: 11527657
doi: 10.1016/S0899-9007(01)00611-6
Hasselbalch AL, Heitmann BL, Kyvik KO, Sørensen TIA. Studies of twins indicate that genetics influence dietary intake. J Nutr. 2008;138:2406–12.
pubmed: 19022965
doi: 10.3945/jn.108.087668
Martin LJ, Lee SY, Couch SC, Morrison J, Woo JG. Shared genetic contributions of fruit and vegetable consumption with BMI in families 20 y after sharing a household. Am J Clin Nutr. 2011;94:1138–43.
pubmed: 21831991
pmcid: 3173028
doi: 10.3945/ajcn.111.015461
Tanaka T, Ngwa JS, Van Rooij FJA, Zillikens MC, Wojczynski MK, Frazier-Wood AC, et al. Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake. Am J Clin Nutr. 2013;97:1395–402.
pubmed: 23636237
pmcid: 3652928
doi: 10.3945/ajcn.112.052183
Chu AY, Workalemahu T, Paynter NP, Rose LM, Giulianini F, Tanaka T, et al. Novel locus including FGF21 is associated with dietary macronutrient intake. Hum Mol Genet. 2013;22:1895–902.
pubmed: 23372041
pmcid: 3612009
doi: 10.1093/hmg/ddt032
Merino J, Dashti HS, Li SX, Sarnowski C, Justice AE, Graff M et al. Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium. Mol Psychiatry. 2019;24:1920–32.
pubmed: 29988085
doi: 10.1038/s41380-018-0079-4
Subar AF, Ziegler RG, Thompson FE, Johnson CC, Weissfeld JL, Reding D, et al. Is shorter always better? Relative importance of questionnaire length and cognitive ease on response rates and data quality for two dietary questionnaires. Am J Epidemiol. 2001;153:404–9.
pubmed: 11207159
doi: 10.1093/aje/153.4.404
Hewitt J, Walters M, Padmanabhan S, Dawson J. Cohort profile of the UK Biobank: diagnosis and characteristics of cerebrovascular disease. BMJ Open. 2016;6:e009161.
pubmed: 27006341
pmcid: 4809076
doi: 10.1136/bmjopen-2015-009161
Winkler TW, Day FR, Croteau-Chonka DC, Wood AR, Locke AE, Mägi R, et al. Quality control and conduct of genome-wide association meta-analyses. Nat Protoc. 2014;9:1192–212.
pubmed: 24762786
pmcid: 4083217
doi: 10.1038/nprot.2014.071
Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533:539–42.
pubmed: 27225129
pmcid: 4883595
doi: 10.1038/nature17671
de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: Generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11:e1004219.
pubmed: 25885710
pmcid: 4401657
doi: 10.1371/journal.pcbi.1004219
Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet. 2015;47:1228–35.
pubmed: 26414678
pmcid: 4626285
doi: 10.1038/ng.3404
Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.
pubmed: 21167468
pmcid: 3014363
doi: 10.1016/j.ajhg.2010.11.011
Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.
pubmed: 25642630
pmcid: 4495769
doi: 10.1038/ng.3211
Vilhjálmsson BJ, Yang J, Finucane HK, Gusev A, Lindström S, Ripke S, et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet. 2015;97:576–92.
pubmed: 26430803
pmcid: 4596916
doi: 10.1016/j.ajhg.2015.09.001
Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990;51:241–7.
pubmed: 2305711
doi: 10.1093/ajcn/51.2.241
Poslusna K, Ruprich J, De Vries JHM, Jakubikova M, Van ’t Veer P. Misreporting of energy and micronutrient intake estimated by food records and 24h recalls, control and adjustment methods in practice. Br J Nutr. 2009;101:S73–S85.
Halton TL, Hu FB. The effects of high protein diets on thermogenesis, satiety and weight loss: a critical review. J Am Coll Nutr. 2004;23:373–85.
pubmed: 15466943
doi: 10.1080/07315724.2004.10719381
Okbay A, Baselmans BML, De Neve J-E, Turley P, Nivard MG, Fontana MA, et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet. 2016;48:624–33.
pubmed: 27089181
pmcid: 4884152
doi: 10.1038/ng.3552
Karlsson Linnér R, Biroli P, Kong E, Meddens SFW, Wedow R, Fontana MA, et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat Genet. 2019;51:245–57.
pubmed: 30643258
doi: 10.1038/s41588-018-0309-3
Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–6.
Liu C-C, Kanekiyo T, Xu H, Bu G. Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nat Rev Neurol. 2013;9:106–18.
pubmed: 23296339
pmcid: 3726719
doi: 10.1038/nrneurol.2012.263
Ogawa Y, Kurosu H, Yamamoto M, Nandi A, Rosenblatt KP, Goetz R, et al. betaKlotho is required for metabolic activity of fibroblast growth factor 21. Proc Natl Acad Sci USA. 2007;104:7432–7.
pubmed: 17452648
pmcid: 1855074
doi: 10.1073/pnas.0701600104
Kurosu H, Choi M, Ogawa Y, Dickson AS, Goetz R, Eliseenkova AV, et al. Tissue-specific expression of βklotho and Fibroblast Growth Factor (FGF) receptor isoforms determines metabolic activity of FGF19 and FGF21. J Biol Chem. 2007;282:26687–95.
pubmed: 17623664
doi: 10.1074/jbc.M704165200
Schumann G, Liu C, O’Reilly P, Gao H, Song P, Xu B, et al. KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference. Proc Natl Acad Sci USA. 2016;113:14372–7.
pubmed: 27911795
pmcid: 5167198
doi: 10.1073/pnas.1611243113
Von Holstein-Rathlou S, Bondurant LD, Peltekian L, Naber MC, Yin TC, Claflin KE, et al. FGF21 mediates endocrine control of simple sugar intake and sweet taste preference by the liver. Cell Metab. 2016;23:335–43.
doi: 10.1016/j.cmet.2015.12.003
Talukdar S, Owen BM, Song P, Hernandez G, Zhang Y, Zhou Y, et al. FGF21 regulates sweet and alcohol preference. Cell Metab. 2016;23:344–9.
pubmed: 26724861
doi: 10.1016/j.cmet.2015.12.008
Adams AC, Gimeno RE. The sweetest thing: regulation of macronutrient preference by FGF21. Cell Metab. 2016;23:227–8.
pubmed: 26863484
doi: 10.1016/j.cmet.2016.01.013
Cornelis MC, Monda KL, Yu K, Paynter N, Azzato EM, Bennett SN, et al. Genome-wide meta-analysis identifies regions on 7p21 (AHR) and 15q24 (CYP1A2) as determinants of habitual caffeine consumption. PLoS Genet. 2011;7:e1002033.
pubmed: 21490707
pmcid: 3071630
doi: 10.1371/journal.pgen.1002033
Coffee and Caffeine Genetics Consortium C and CG, Cornelis MC, Byrne EM, Esko T, Nalls MA, Ganna A, et al. Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption. Mol Psychiatry. 2015;20:647–56.
doi: 10.1038/mp.2014.107
Chasman DI, Paré G, Mora S, Hopewell JC, Peloso G, Clarke R, et al. Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis. PLoS Genet. 2009;5:e1000730.
pubmed: 19936222
pmcid: 2777390
doi: 10.1371/journal.pgen.1000730
Vaxillaire M, Cavalcanti-proenc C, Tichet J, Marre M, Balkau B, Froguel P, et al. The common P446L polymorphism in GCKR inversely modulates fasting glucose and triglyceride levels and reduces type 2 diabetes risk in the DESIR prospective general French population. Diabetes. 2008;57:2253–7.
pubmed: 18556336
pmcid: 2494697
doi: 10.2337/db07-1807
Berthoud HR, Münzberg H, Richards BK, Morrison CD. Neural and metabolic regulation of macronutrient intake and selection. Proc Nutr Soc. 2012;71:390–400.
pubmed: 22617310
pmcid: 3617924
doi: 10.1017/S0029665112000559
Efeyan A, Comb WC, Sabatini DM. Nutrient-sensing mechanisms and pathways. Nature. 2015;517:302–10.
pubmed: 25592535
pmcid: 4313349
doi: 10.1038/nature14190
Whitfield JB, Martin NG. Aversive reactions and alcohol use in europeans. Alcohol Clin Exp Res. 1993;17:131–4.
pubmed: 8452193
doi: 10.1111/j.1530-0277.1993.tb00737.x
Harada S, Agarwal DP, Goedde HW, Tagaki S, Ishikawa B. Possible protective role against alcoholism for aldehyde dehydrogenase isozyme deficiency in Japan. Lancet (Lond, Engl). 1982;2:827.
doi: 10.1016/S0140-6736(82)92722-2
Potthoff MJ. A new frontier in FGF21 biology. Nat Rev Endocrinol. 2017;13:74–76.
pubmed: 27983736
doi: 10.1038/nrendo.2016.206
Finucane HK, Reshef YA, Anttila V, Slowikowski K, Gusev A, Byrnes A, et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat Genet. 2018;50:621–9.
pubmed: 29632380
pmcid: 5896795
doi: 10.1038/s41588-018-0081-4
Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh P-R, et al. Partitioning heritability by functional category using GWAS summary statistics. Nat Genet. 2015;47:1228–35.
pubmed: 26414678
pmcid: 4626285
doi: 10.1038/ng.3404
Fehrmann RSN, Karjalainen JM, Krajewska M, Westra H-J, Maloney D, Simeonov A, et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat Genet. 2015;47:115–25.
pubmed: 25581432
doi: 10.1038/ng.3173
Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2014;42:D472–7.
pubmed: 24243840
doi: 10.1093/nar/gkt1102
Bulik-Sullivan BK, Finucane HK, Anttila V, Gusev A, Day FR, Loh P-R, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47:1236–41.
pubmed: 26414676
pmcid: 4797329
doi: 10.1038/ng.3406
Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 2017;33:272–9.
pubmed: 27663502
doi: 10.1093/bioinformatics/btw613
Rietveld CA, Medland SE, Derringer J, Yang J, Esko T, Martin NW, et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science. 2013;340:1467–71.
pubmed: 23722424
pmcid: 3751588
doi: 10.1126/science.1235488
Hill WD, Hagenaars SP, Marioni RE, Harris SE, Liewald DCM, Davies G, et al. Molecular genetic contributions to social deprivation and household income in UK Biobank. Curr Biol. 2016;26:3083–9.
pubmed: 27818178
pmcid: 5130721
doi: 10.1016/j.cub.2016.09.035
Beaulac J, Kristjansson E, Cummins S. A systematic review of food deserts, 1966–2007. Prev Chronic Dis. 2009;6:A105.
pubmed: 19527577
pmcid: 2722409
Handbury Ilya Rahkovsky Molly Schnell J, Currie J, De Loecker J, Duranton G, Gyourko J, Kastl J et al. Is the focus on food deserts fruitless? Retail access and food purchases across the socioeconomic spectrum. NBER Work Pap. 2015. http://www.nber.org/papers/w21126 . Accessed 9 Jul 2018.
Adler NE, Boyce T, Chesney MA, Cohen S, Folkman S, Kahn RL, et al. Socioeconomic status and health. The challenge of the gradient. Am Psychol. 1994;49:15–24.
pubmed: 8122813
doi: 10.1037/0003-066X.49.1.15
Marmot MG, Wilkinson RG. Social determinants of health: The solid facts. 2nd edn. World Health Organization; 2003.
Stringhini S, Carmeli C, Jokela M, Avendaño M, Muennig P, Guida F, et al. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1.7 million men and women. Lancet. 2017;389:1229–37.
pubmed: 28159391
pmcid: 5368415
doi: 10.1016/S0140-6736(16)32380-7
Townsend P. Deprivation. J Soc Policy. 1987;16:125.
doi: 10.1017/S0047279400020341
Reiner M, Niermann C, Jekauc D, Woll A. Long-term health benefits of physical activity—a systematic review of longitudinal studies. BMC Public Health. 2013;13:813.
pubmed: 24010994
pmcid: 3847225
doi: 10.1186/1471-2458-13-813
Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, et al. The physical activity guidelines for Americans. JAMA. 2018;320:2020–8.
pubmed: 30418471
doi: 10.1001/jama.2018.14854
Amani R. Is dietary pattern of schizophrenia patients different from healthy subjects? BMC Psychiatry. 2007;5:3–7.
Pelsser LM, Frankena K, Toorman J, Savelkoul HF, Dubois AE, Pereira RR, et al. Effects of a restricted elimination diet on the behaviour of children with attention-deficit hyperactivity disorder (INCA study): ā randomised controlled trial. Lancet. 2011;377:494–503.
pubmed: 21296237
doi: 10.1016/S0140-6736(10)62227-1
Campbell TC. A plant-based diet and animal protein: questioning dietary fat and considering animal protein as the main cause of heart disease. J Geriatr Cardiol. 2017;14:331–7.
pubmed: 28630612
pmcid: 5466939
Pimpin L, Jebb S, Johnson L, Wardle J, Ambrosini GL. Dietary protein intake is associated with body mass index and weight up to 5 y of age in a prospective cohort of twins. Am J Clin Nutr. 2016;103:389–97.
pubmed: 26718416
doi: 10.3945/ajcn.115.118612
Gunther AL, Remer T, Kroke A, Buyken AE. Early protein intake and later obesity risk: which protein sources at which time points throughout infancy and childhood are important for body mass index and body fat percentage at 7y of age? Am J Clin Nutr. 2007;86:2–9.
doi: 10.1093/ajcn/86.5.1765
Voortman T, Braun KVE, Kiefte-de JongJC, Jaddoe VWV, Franco OH, van den Hooven EH. Protein intake in early childhood and body composition at the age of 6 years: the Generation R Study. Int J Obes. 2016;40:1018–25.
doi: 10.1038/ijo.2016.29
Trichopoulou A, Gnardellis C, Benetou V, Lagiou P, Bamia C, Trichopoulos D. Lipid, protein and carbohydrate intake in relation to body mass index. Eur J Clin Nutr. 2002;56:37–43.
pubmed: 11840178
doi: 10.1038/sj.ejcn.1601286
Koletzko B, Von Kries R, Closa R, Escribano J, Scaglioni S, Giovannini M, et al. Lower protein in infant formula is associated with lower weight up to age 2 y: a randomized clinical trial. Am J Clin Nutr. 2009;89:1836–45.
pubmed: 19386747
doi: 10.3945/ajcn.2009.27113D
Solon-Biet SM, McMahon AC, Ballard JWO, Ruohonen K, Wu LE, Cogger VC, et al. The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice. Cell Metab. 2014;19:30.
doi: 10.1016/j.cmet.2014.02.009
Hörnell A, Lagström H, Lande B, Thorsdottir I. Protein intake from 0 to 18 years of age and its relation to health: a systematic literature review for the 5th Nordic Nutrition Recommendations. Food Nutr Res. 2013;57:21083.
doi: 10.3402/fnr.v57i0.21083
Van Nielen M, Feskens EJM, Mensink M, Sluijs I, Molina E, Amiano P, et al. Dietary protein intake and incidence of type 2 diabetes in Europe: The EPIC-InterAct case-cohort study. Diabetes Care. 2014;37:1854–62.
pubmed: 24722499
doi: 10.2337/dc13-2627
Weber M, Grote V, Closa-Monasterolo R, Escribano J, Langhendries J-P, Dain E, et al. Lower protein content in infant formula reduces BMI and obesity risk at school age: follow-up of a randomized trial. Am J Clin Nutr. 2014;99:1041–51.
pubmed: 24622805
doi: 10.3945/ajcn.113.064071
Patro-Gołąb B, Zalewski BM, Kołodziej M, Kouwenhoven S, Poston L, Godfrey KM, et al. Nutritional interventions or exposures in infants and children aged up to 3 years and their effects on subsequent risk of overweight, obesity and body fat: a systematic review of systematic reviews. Obes Rev. 2016;17:1245–57.
pubmed: 27749991
pmcid: 5325317
doi: 10.1111/obr.12476
Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9:311–26.
pubmed: 19356713
pmcid: 3640280
doi: 10.1016/j.cmet.2009.02.002
Lynch CJ, Adams SH. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol. 2014;10:723–36.
pubmed: 25287287
pmcid: 4424797
doi: 10.1038/nrendo.2014.171
Fontana L, Cummings NE, Arriola SI, Alexander CM, Kimple ME, Lamming Correspondence DW, et al. Decreased consumption of branched-chain amino acids improves metabolic health. Cell Rep. 2016;16:520–30.
pubmed: 27346343
pmcid: 4947548
doi: 10.1016/j.celrep.2016.05.092
Levine ME, Suarez JA, Brandhorst S, Balasubramanian P, Cheng CW, Madia F, et al. Low protein intake is associated with a major reduction in IGF-1, cancer, and overall mortality in the 65 and younger but not older population. Cell Metab. 2014;19:407–17.
pubmed: 24606898
pmcid: 3988204
doi: 10.1016/j.cmet.2014.02.006
Seidelmann SB, Claggett B, Cheng S, Henglin M, Shah A, Steffen LM et al. Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis. Lancet Public Heal. 2018;3:E419–E428.
doi: 10.1016/S2468-2667(18)30135-X
Reid M, Hammersley R. Sugars and obesity: meta-analysis establishes the strength of the correlation, not the cause. Nutr Bull. 2014;39:153–6.
doi: 10.1111/nbu.12085
Te Morenga L, Mallard S, Mann J. Dietary sugars and body weight: systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ. 2012;346:e7492.
doi: 10.1136/bmj.e7492
Khan TA, Sievenpiper JL. Controversies about sugars: results from systematic reviews and meta-analyses on obesity, cardiometabolic disease and diabetes. Eur J Nutr. 2016;55:25–43.
pubmed: 27900447
pmcid: 5174149
doi: 10.1007/s00394-016-1345-3
Tappy L, Mittendorfer B. Fructose toxicity: is the science ready for public health actions? Curr Opin Clin Nutr Metab Care. 2012;15:357–61.
pubmed: 22617566
pmcid: 3695375
doi: 10.1097/MCO.0b013e328354727e
Lustig RH, Schmidt LA, Brindis CD. The toxic truth about sugar. Nature. 2012;482:27–29.
pubmed: 22297952
doi: 10.1038/482027a
Egli L, Lecoultre V, Cros J, Rosset R, Marques AS, Schneiter P, et al. Exercise performed immediately after fructose ingestion enhances fructose oxidation and suppresses fructose storage. Am J Clin Nutr. 2016;103:348–55.
pubmed: 26702120
doi: 10.3945/ajcn.115.116988
Bidwell AJ, Fairchild TJ, Redmond J, Wang L, Keslacy S, Kanaley JA. Physical activity offsets the negative effects of a high-fructose diet. Med Sci Sports Exerc. 2014;46:2091–8.
pubmed: 24848492
pmcid: 4199877
doi: 10.1249/MSS.0000000000000343
Tappy L, Rosset R. Fructose metabolism from a functional perspective: Implications for athletes. Sport Med. 2017;47:23–32.
doi: 10.1007/s40279-017-0692-4
Rowlands DS, Houltham S, Musa-Veloso K, Brown F, Paulionis L, Bailey D. Fructose–glucose composite carbohydrates and endurance performance: Critical review and future perspectives. Sport Med. 2015;45:1561–76.
doi: 10.1007/s40279-015-0381-0
Vergnaud A-C, Norat T, Mouw T, Romaguera D, May AM, Bueno-de-Mesquita HB, et al. Macronutrient composition of the diet and prospective weight change in participants of the EPIC-PANACEA study. PLoS ONE. 2013;8:e57300.
pubmed: 23472080
pmcid: 3589445
doi: 10.1371/journal.pone.0057300