[1]	S. Downer, S.A. Berkowitz, T.S. Harlan, D.L. Olstad, D. Mozaffarian. Food is medicine: actions to integrate food and nutrition into healthcare. BMJ, 369 (2020), Article m2482

[2]	M.E.J. Lean, A. Astrup, S.B. Roberts. Making progress on the global crisis of obesity and weight management. BMJ, 361 (2018), Article k2538

[3]	N.G. Forouhi, A. Misra, V. Mohan, R. Taylor, W. Yancy.Dietary and nutritional approaches for prevention and management of type 2 diabetes. BMJ, 361 (2018), Article k2234

[4]	E.W. Kjeldsen, J.Q. Thomassen, K.L. Rasmussen, B.G. Nordestgaard, A. Tybjærg-Hansen, R. Frikke-Schmidt. Impact of diet on ten-year absolute cardiovascular risk in a prospective cohort of 94 321 individuals: a tool for implementation of healthy diets. Lancet Reg Health Eur, 19 (2022), p. 100419

[5]	A. Afshin, P.J. Sur, K.A. Fay, L. Cornaby, G. Ferrara, J.S. Salama, et al. the GBD 2017 Diet Collaborators. Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the global burden of disease study 2017. Lancet, 393 (10184) (2019), pp. 1958-1972

[6]	C. Dong, X. Bu, J. Liu, L. Wei, A. Ma, T. Wang. Cardiovascular disease burden attributable to dietary risk factors from 1990 to 2019: a systematic analysis of the global burden of disease study. Nutr Metab Cardiovasc Dis, 32 (4) (2022), pp. 897-907

[7]	L. Qi. Nutrition for precision health: the time is now. Obesity, 30 (7) (2022), pp. 1335-1344

[8]	Y. Heianza, L. Qi. Gene-diet interaction and precision nutrition in obesity. Int J Mol Sci, 18 (4) (2017), p. 787

[9]	S. Dietrich, S. Jacobs, J.S. Zheng, K. Meidtner, L. Schwingshackl, M.B. Schulze. Gene-lifestyle interaction on risk of type 2 diabetes: a systematic review. Obes Rev, 20 (11) (2019), pp. 1557-1571

[10]	Z.M. Roa-Díaz, J. Teuscher, M. Gamba, M. Bundo, G. Grisotto, F. Wehrli, et al. Gene-diet interactions and cardiovascular diseases: a systematic review of observational and clinical trials. BMC Cardiovasc Disord, 22 (1) (2022), p. 377

[11]	B. Nogal, J.B. Blumberg, G. Blander, M. Jorge. Gut microbiota-informed precision nutrition in the generally healthy individual: are we there yet. Curr Dev Nutr, 5 (9) (2021), p. nzab107

[12]	J.B. Cole, R. Gabbianelli. Editorial: recent advances in nutrigenomics: making strides towards precision nutrition. Front Genet, 13 (2022), p. 997266

[13]	X. Li, L. Qi. Epigenetics in precision nutrition. J Pers Med, 12 (4) (2022), p. 533

[14]	D. Raubenheimer, S.J. Simpson. Nutritional ecology and human health. Annu Rev Nutr, 36 (1) (2016), pp. 603-626

[15]	K.J. Carpenter.A short history of nutritional science: part 2 (1885-1912). J Nutr, 133 (4) (2003), pp. 975-984

[16]	B.M. Popkin. Global nutrition dynamics: the world is shifting rapidly toward a diet linked with noncommunicable diseases. Am J Clin Nutr, 84 (2) (2006), pp. 289-298

[17]	T.G. Nutrition. What if Americans ate less saturated fat?. Science, 291 (5513) (2001), p. 2538

[18]	G.E. Fraser. A search for truth in dietary epidemiology. Am J Clin Nutr, 78 (3 Suppl) (2003), pp. 521S-S525

[19]	A. Yngve, L. Hambraeus, L. Lissner, L. Serra Majem, M.D. Vaz de Almeida, C. Berg, et al. The women’s health initiative. What is on trial: nutrition and chronic disease? Or misinterpreted science, media havoc and the sound of silence from peers?. Public Health Nutr, 9 (2) (2006), pp. 269-272

[20]	J. Stevens, D.R. Taber, D.M. Murray, D.S. Ward. Advances and controversies in the design of obesity prevention trials. Obesity, 15 (9) (2007), pp. 2163-2170

[21]	D.R. Jacobs Jr, M.D. Gross, L.C. Tapsell. Food synergy: an operational concept for understanding nutrition. Am J Clin Nutr, 89 (5) (2009), pp. 1543-1548S

[22]	K.M. Davies, R.P. Heaney, R.R. Recker, J.M. Lappe, M.J. Barger-Lux, K. Rafferty, et al. Calcium intake and body weight. J Clin Endocrinol Metab, 85 (12) (2000), pp. 4635-4638

[23]	M. Jacqmain, E. Doucet, J.P. Després, C. Bouchard, A. Tremblay. Calcium intake, body composition, and lipoprotein-lipid concentrations in adults. Am J Clin Nutr, 77 (6) (2003), pp. 1448-1452

[24]	D. Han, X. Fang, D. Su, L. Huang, M. He, D. Zhao, et al. Dietary calcium intake and the risk of metabolic syndrome: a systematic review and meta-analysis. Sci Rep, 9 (2019), p. 19046

[25]	Z. Hajhashemy, P. Rouhani, P. Saneei. Dietary calcium intake in relation to type-2 diabetes and hyperglycemia in adults: a systematic review and dose-response meta-analysis of epidemiologic studies. Sci Rep, 12 (2022), p. 1050

[26]	J.Y. Hong, J.S. Lee, H.W. Woo, A.S. Om, C.K. Kwock, M.K. Kim. Meta-analysis of randomized controlled trials on calcium supplements and dairy products for changes in body weight and obesity indices. Int J Food Sci Nutr, 72 (5) (2021), pp. 615-631

[27]	W. Wei, W. Jiang, W. Gu, H. Wu, H. Jiang, G. Li, et al. The joint effect of energy reduction with calcium supplementation on the risk factors of type 2 diabetes in the overweight population: a two-year randomized controlled trial. Aging, 13 (4) (2021), pp. 5571-5584

[28]

C. Yang, X. Shi, H. Xia, X. Yang, H. Liu, D. Pan, et al. The evidence and controversy between dietary calcium intake and calcium supplementation and the risk of cardiovascular disease: a systematic review and meta-analysis of cohort studies and randomized controlled trials. J Am Coll Nutr, 39 (4) (2020), pp. 352-370

[29]	S.K. Myung, H.B. Kim, Y.J. Lee, Y.J. Choi, S.W. Oh. Calcium supplements and risk of cardiovascular disease: a meta-analysis of clinical trials. Nutrients, 13 (2) (2021), p. 368

[30]	E. Archer, G. Pavela, C.J. Lavie. The inadmissibility of what we eat in America and NHANES dietary data in nutrition and obesity research and the scientific formulation of national dietary guidelines. Mayo Clin Proc, 90 (7) (2015), pp. 911-926

[31]	J.P. Ioannidis. Implausible results in human nutrition research. BMJ, 347 (2013), Article f6698

[32]	E. Archer, S.N. Blair. Reply to LS Freedman et al.. Adv Nutr, 6 (4) (2015), pp. 489-490

[33]	E. Archer. The use of implausible data without caveats is misleading. Am J Clin Nutr, 106 (3) (2017), pp. 949-950

[34]	D.R. Jacobs Jr, L.C. Tapsell. Food synergy: the key to a healthy diet. Proc Nutr Soc, 72 (2) (2013), pp. 200-206

[35]	S.J. Simpson, D.G. Le Couteur, D.E. James, J. George, J.E. Gunton, S.M. Solon-Biet, et al. The geometric framework for nutrition as a tool in precision medicine. Nutr Healthy Aging, 4 (3) (2017), pp. 217-226

[36]	T. Peregrin. The new frontier of nutrition science: nutrigenomics. J Am Diet Assoc, 101 (11) (2001), p. 1306

[37]	D. Corella, J.M. Ordovas. Nutrigenomics in cardiovascular medicine. Circ Cardiovasc Genet, 2 (6) (2009), pp. 637-651

[38]	J.M. Ordovas, L.R. Ferguson, E.S. Tai, J.C. Mathers. Personalised nutrition and health. BMJ, 361 ( 2018), p. bmj.k2173

[39]	F.M. Sacks, G.A. Bray, V.J. Carey, S.R. Smith, D.H. Ryan, S.D. Anton, et al. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N Engl J Med, 360 (9) (2009), pp. 859-873

[40]

Q. Qi, G.A. Bray, S.R. Smith, F.B. Hu, F.M. Sacks, L. Qi. Insulin receptor substrate 1 gene variation modifies insulin resistance response to weight-loss diets in a 2-year randomized trial: the preventing overweight using novel dietary strategies (POUNDS LOST) trial. Circulation, 124 (5) (2011), pp. 563-571

[41]	X. Zhang, Q. Qi, C. Zhang, S.R. Smith, F.B. Hu, F.M. Sacks, et al. FTO genotype and 2-year change in body composition and fat distribution in response to weight-loss diets: the POUNDS LOST trial. Diabetes, 61 (11) (2012), pp. 3005-3011

[42]	T. Tanaka, J. Shen, G.R. Abecasis, A. Kisialiou, J.M. Ordovas, J.M. Guralnik, et al. Genome-wide association study of plasma polyunsaturated fatty acids in the InCHIANTI study. PLoS Genet, 5 (1) (2009), p. e1000338

[43]	S. Liang, Y. Zhou, H. Wang, Y. Qian, D. Ma, W. Tian, et al. The effect of multiple single nucleotide polymorphisms in the folic acid pathway genes on homocysteine metabolism. BioMed Res Int, 2014 ( 2014), p. 560183

[44]	S. Tomei, P. Singh, R. Mathew, V. Mattei, M. Garand, M. Alwakeel, et al. The role of polymorphisms in vitamin D-related genes in response to vitamin D supplementation. Nutrients, 12 (9) (2020), p. 2608

[45]	L.K. Da Silva, S.K. Abe. Polymorphisms contributing to calcium status: a systematic review. Nutrients, 13 (8) (2021), p. 2488

[46]	A. Saint Pierre, E. Génin. How important are rare variants in common disease?. Brief Funct Genomics, 13 (5) (2014), pp. 353-361

[47]	L. Wang, H. Shen, H. Liu, G. Guo. Mixture SNPs effect on phenotype in genome-wide association studies. BMC Genomics, 16 (1) (2015), p. 3

[48]	T.J. Roseboom, J.H. van der Meulen, C. Osmond, D.J. Barker, A.C. Ravelli, J.M. Schroeder-Tanka, et al. Coronary heart disease after prenatal exposure to the Dutch famine, 1944-45. Heart, 84 (6) (2000), pp. 595-598

[49]	L.H. Lumey, M.D. Khalangot, A.M. Vaiserman. Association between type 2 diabetes and prenatal exposure to the Ukraine famine of 1932-33: a retrospective cohort study. Lancet Diabetes Endocrinol, 3 (10) (2015), pp. 787-794

[50]	R. Du, R. Zheng, Y. Xu, Y. Zhu, X. Yu, M. Li, et al. the REACTION Study Group. Early-life famine exposure and risk of cardiovascular diseases in later life: findings from the REACTION study. J Am Heart Assoc, 9 (7) (2020), p. e014175

[51]	A.M. Vaiserman. Early-life nutritional programming of type 2 diabetes: experimental and quasi-experimental evidence. Nutrients, 9 (3) (2017), p. 236

[52]	J. Zhou, J. Sheng, Y. Fan, X. Zhu, Q. Tao, K. Liu, et al. The effect of Chinese famine exposure in early life on dietary patterns and chronic diseases of adults. Public Health Nutr, 22 (4) (2019), pp. 603-613

[53]	K. Siddiqui, S.S. Joy, S.S. Nawaz. Impact of early life or intrauterine factors and socio-economic interaction on diabetes—an evidence on thrifty hypothesis. J Lifestyle Med, 9 (2) (2019), pp. 92-101

[54]	W. Chao, P.A. D’Amore. IGF2: epigenetic regulation and role in development and disease. Cytokine Growth Factor Rev, 19 (2) (2008), pp. 111-120

[55]	B.T. Heijmans, E.W. Tobi, A.D. Stein, H. Putter, G.J. Blauw, E.S. Susser, et al. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci USA, 105 (44) (2008), pp. 17046-17049

[56]	E.W. Tobi, J.J. Goeman, R. Monajemi, H. Gu, H. Putter, Y. Zhang, et al. DNA methylation signatures link prenatal famine exposure to growth and metabolism. Nat Commun, 5 (1) (2014), p. 5592

[57]	A. Salonen, L. Lahti, J. Salojärvi, G. Holtrop, K. Korpela, S.H. Duncan, et al. Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men. ISME J, 8 (11) (2014), pp. 2218-2230

[58]	D. Zeevi, T. Korem, N. Zmora, D. Israeli, D. Rothschild, A. Weinberger, et al. Personalized nutrition by prediction of glycemic responses. Cell, 163 (5) (2015), pp. 1079-1094

[59]	T. Korem, D. Zeevi, N. Zmora, O. Weissbrod, N. Bar, M. Lotan-Pompan, et al. Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses. Cell Metab, 25 (6) (2017), pp. 1243-1253

[60]	K. Korpela, H.J. Flint, A.M. Johnstone, J. Lappi, K. Poutanen, E. Dewulf, et al. Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals. PLoS One, 9 (3) (2014), p. e90702

[61]	Z. Jie, X. Yu, Y. Liu, L. Sun, P. Chen, Q. Ding, et al. The baseline gut microbiota directs dieting-induced weight loss trajectories. Gastroenterology, 160 (6) (2021), pp. 2029-2042

[62]

M.F. Hjorth, T. Blædel, L.Q. Bendtsen, J.K. Lorenzen, J.B. Holm, P. Kiilerich, et al. Prevotella-to-Bacteroides ratio predicts body weight and fat loss success on 24-week diets varying in macronutrient composition and dietary fiber: results from a post-hoc analysis. Int J Obes, 43 (1) (2019), pp. 149-157

[63]

L. Christensen, S. Vuholm, H.M. Roager, D.S. Nielsen, L. Krych, M. Kristensen, et al. Prevotella abundance predicts weight loss success in healthy, overweight adults consuming a whole-grain diet ad libitum: a post hoc analysis of a 6-wk randomized controlled trial. J Nutr, 149 (12) (2019), pp. 2174-2181

[64]	V. Kipnis, D. Midthune, L. Freedman, S. Bingham, N.E. Day, E. Riboli, et al. Bias in dietary-report instruments and its implications for nutritional epidemiology. Public Health Nutr, 5 (6A) (2002), pp. 915-923

[65]	B.M. Margetts, M. Nelson. Design concepts in nutritional epidemiology. Oxford University Press, New York City (1997)

[66]	B. Amoutzopoulos, P. Page, C. Roberts, M. Roe, J. Cade, T. Steer, et al. Portion size estimation in dietary assessment: a systematic review of existing tools, their strengths and limitations. Nutr Rev, 78 (11) (2020), pp. 885-900

[67]	A.K. Lindroos, J. Petrelius Sipinen, C. Axelsson, G. Nyberg, R. Landberg, P. Leanderson, et al. Use of a web-based dietary assessment tool (RiksmatenFlex) in Swedish adolescents: comparison and validation study. J Med Internet Res, 21 (10) (2019), p. e12572

[68]	U. Murai, R. Tajima, M. Matsumoto, Y. Sato, S. Horie, A. Fujiwara, et al. Validation of dietary intake estimated by web-based dietary assessment methods and usability using dietary records or 24-h dietary recalls: a scoping review. Nutrients, 15 (8) (2023), p. 1816

[69]	C. Höchsmann, C.K. Martin. Review of the validity and feasibility of image-assisted methods for dietary assessment. Int J Obes, 44 (12) (2020), pp. 2358-2371

[70]	K.V. Dalakleidi, M. Papadelli, I. Kapolos, K. Papadimitriou. Applying image-based food-recognition systems on dietary assessment: a systematic review. Adv Nutr, 13 (6) (2022), pp. 2590-2619

[71]	M. Sun, J.D. Fernstrom, W. Jia, S.A. Hackworth, N. Yao, Y. Li, et al. A wearable electronic system for objective dietary assessment. J Am Diet Assoc, 110 (1) (2010), pp. 45-47

[72]	V. Vijayan, J.P. Connolly, J. Condell, N. McKelvey, P. Gardiner. Review of wearable devices and data collection considerations for connected health. Sensors, 21 (16) (2021), p. 5589

[73]	M. Sun, W. Jia, G. Chen, M. Hou, J. Chen, Z.H. Mao. Improved wearable devices for dietary assessment using a new camera system. Sensors, 22 (20) (2022), p. 8006

[74]	S.A. Bingham. Biomarkers in nutritional epidemiology. Public Health Nutr, 5(6a 6A):821-27 (2002)

[75]	L.G. Rasmussen, H. Winning, F. Savorani, H. Toft, T.M. Larsen, L.O. Dragsted, et al. Assessment of the effect of high or low protein diet on the human urine metabolome as measured by NMR. Nutrients, 4 (2) (2012), pp. 112-131

[76]	C. Luceri, G. Caderni, M. Lodovici, M.T. Spagnesi, C. Monserrat, L. Lancioni, et al. Urinary excretion of sucrose and fructose as a predictor of sucrose intake in dietary intervention studies. Cancer Epidemiol Biomarkers Prev, 5 (3) (1996), pp. 167-171

[77]	N. Tasevska. Urinary sugars-a biomarker of total sugars intake. Nutrients, 7 (7) (2015), pp. 5816-5833

[78]	Q. Gao, G. Praticò, A. Scalbert, G. Vergères, M. Kolehmainen, C. Manach, et al. A scheme for a flexible classification of dietary and health biomarkers. Genes Nutr, 12 (1) (2017), p. 34

[79]	A.B. Ross. Present status and perspectives on the use of alkylresorcinols as biomarkers of wholegrain wheat and rye intake. J Nutr Metab, 2012 ( 2012), p. 462967

[80]	Ø. Midttun, A. Ulvik, O. Nygård, P.M. Ueland. Performance of plasma trigonelline as a marker of coffee consumption in an epidemiologic setting. Am J Clin Nutr, 107 (6) (2018), pp. 941-947

[81]	P. Quifer-Rada, M. Martínez-Huélamo, G. Chiva-Blanch, O. Jáuregui, R. Estruch, R.M. Lamuela-Raventós. Urinary isoxanthohumol is a specific and accurate biomarker of beer consumption. J Nutr, 144 (4) (2014), pp. 484-488

[82]	M. Bader, T. Lang, R. Lang, T. Hofmann. Synephrine as a specific marker for orange consumption. J Agric Food Chem, 65 (23) (2017), pp. 4853-4858

[83]	B.M. De Rooij, P.J. Boogaard, D.A. Rijksen, J.N. Commandeur, N.P. Vermeulen. Urinary excretion of N-acetyl-S-allyl-L-cysteine upon garlic consumption by human volunteers. Arch Toxicol, 70 (10) (1996), pp. 635-639

[84]	Y. Hövelmann, L. Lewin, K. Steinert, F. Hübner, H.U. Humpf. Mass spectrometry-based analysis of urinary biomarkers for dietary tomato intake. Mol Nutr Food Res, 64 (12) (2020), p. 2000011

[85]	P. Giesbertz, B. Brandl, Y.M. Lee, H. Hauner, H. Daniel,T. Skurk. Specificity, dose dependency, and kinetics of markers of chicken and beef intake using targeted quantitative LC-MS/MS: a human intervention trial. Mol Nutr Food Res, 64 (5) (2020), p. e1900921

[86]	K. Hanhineva, M.A. Lankinen, A. Pedret, U. Schwab, M. Kolehmainen, J. Paananen, et al. Nontargeted metabolite profiling discriminates diet-specific biomarkers for consumption of whole grains, fatty fish, and bilberries in a randomized controlled trial. J Nutr, 145 (1) (2015), pp. 7-17

[87]

K.A. Guertin, S.C. Moore, J.N. Sampson, W.Y. Huang, Q. Xiao, R.Z. Stolzenberg-Solomon, et al. Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations. Am J Clin Nutr, 100 (1) (2014), pp. 208-217

[88]	M. Serafini, R. Bugianesi, M. Salucci, E. Azzini, A. Raguzzini, G. Maiani. Effect of acute ingestion of fresh and stored lettuce (Lactuca sativa) on plasma total antioxidant capacity and antioxidant levels in human subjects. Br J Nutr, 88 (6) (2002), pp. 615-623

[89]	J. Cao, Y. Zhang, W. Chen, X. Zhao. The relationship between fasting plasma concentrations of selected flavonoids and their ordinary dietary intake. Br J Nutr, 103 (2) (2010), pp. 249-255

[90]	M.S. DuPont, A.J. Day, R.N. Bennett, F.A. Mellon, P.A. Kroon. Absorption of kaempferol from endive, a source of kaempferol-3-glucuronide, in humans. Eur J Clin Nutr, 58 (6) (2004), pp. 947-954

[91]	J.A. Matthew, M.R. Morgan, R. McNerney, H.W. Chan, D.T. Coxon. Determination of solanidine in human plasma by radioimmunoassay. Food Chem Toxicol, 21 (5) (1983), pp. 637-640

[92]	M. Boto-Ordóñez, M. Urpi-Sarda, M.I. Queipo-Ortuño, D. Corella, F.J. Tinahones, R. Estruch, et al. Microbial metabolomic fingerprinting in urine after regular dealcoholized red wine consumption in humans. J Agric Food Chem, 61 (38) (2013), pp. 9166-9175

[93]	R. Vázquez-Fresno, R. Llorach, F. Alcaro, M.Á. Rodríguez, M. Vinaixa, G. Chiva-Blanch, et al. 1H-NMR-based metabolomic analysis of the effect of moderate wine consumption on subjects with cardiovascular risk factors. Electrophoresis, 33 (15) (2012), pp. 2345-2354

[94]	L.H. Münger, A. Trimigno, G. Picone, C. Freiburghaus, G. Pimentel, K.J. Burton, et al. Identification of urinary food intake biomarkers for milk, cheese, and soy-based drink by untargeted GC-MS and NMR in healthy humans. J Proteome Res, 16 (9) (2017), pp. 3321-3335

[95]	A. Bouchard-Mercier, I. Rudkowska, S. Lemieux, P. Couture, M.C. Vohl. The metabolic signature associated with the Western dietary pattern: a cross-sectional study. Nutr J, 12 (1) (2013), p. 158

[96]

S. Galié, J. García-Gavilán, C. Papandreou, L. Camacho-Barcía, P. Arcelin, A. Palau-Galindo, et al. Effects of Mediterranean diet on plasma metabolites and their relationship with insulin resistance and gut microbiota composition in a crossover randomized clinical trial. Clin Nutr, 40 (6) (2021), pp. 3798-3806

[97]	G.C. Chen, J.C. Chai, J. Xing, J.Y. Moon, Z. Shan, B. Yu, et al. Healthful eating patterns, serum metabolite profile and risk of diabetes in a population-based prospective study of US Hispanics/Latinos. Diabetologia, 65 (7) (2022), pp. 1133-1144

[98]	A.E. McNamara, L. Brennan. Potential of food intake biomarkers in nutrition research. Proc Nutr Soc, 79 (4) (2020), pp. 1-11

[99]	P. Autier, M. Boniol, C. Pizot, P. Mullie. Vitamin D status and ill health: a systematic review. Lancet Diabetes Endocrinol, 2 (1) (2014), pp. 76-89

[100]

L. Schwingshackl, S. Balduzzi, J. Beyerbach, N. Bröckelmann, S.S. Werner, J. Zähringer, et al. Evaluating agreement between bodies of evidence from randomised controlled trials and cohort studies in nutrition research: meta-epidemiological study. BMJ, 374 (2021), Article n1864

[101]

S. Du, X. Wu, T. Han, W. Duan, L. Liu, J. Qi, et al. Dietary manganese and type 2 diabetes mellitus: two prospective cohort studies in China. Diabetologia, 61 (9) (2018), pp. 1985-1995

[102]

W. Jiang, X. Meng, W. Hou, X. Wu, Y. Wang, M. Wang, et al. Impact of overall diet quality on association between alcohol consumption and risk of hypertension: evidence from two national surveys with multiple ethnics. Eur J Clin Nutr, 75 (1) (2021), pp. 112-120

[103]

R. Varraso, S.E. Chiuve, T.T. Fung, R.G. Barr, F.B. Hu, W.C. Willett, et al. Alternate healthy eating index 2010 and risk of chronic obstructive pulmonary disease among US women and men: prospective study. BMJ, 350 (2015), Article h286

[104]

S. Onvani, F. Haghighatdoost, P.J. Surkan, B. Larijani, L. Azadbakht. Adherence to the healthy eating index and alternative healthy eating index dietary patterns and mortality from all causes, cardiovascular disease and cancer: a meta-analysis of observational studies. J Hum Nutr Diet, 30 (2) (2017), pp. 216-226

[105]

S. Soltani, T. Arablou, A. Jayedi, A. Salehi-Abargouei. Adherence to the dietary approaches to stop hypertension (DASH) diet in relation to all-cause and cause-specific mortality: a systematic review and dose-response meta-analysis of prospective cohort studies. Nutr J, 19 (1) (2020), p. 37

[106]

L.J. Dominguez, G. Di Bella, N. Veronese, M. Barbagallo. Impact of Mediterranean diet on chronic non-communicable diseases and longevity. Nutrients, 13 (6) (2021), p. 2028

[107]

E. Mattavelli, E. Olmastroni, D. Bonofiglio, A.L. Catapano, A. Baragetti, P. Magni. Adherence to the Mediterranean diet: impact of geographical location of the observations. Nutrients, 14 (10) (2022), p. 2040

[108]

A. Reyes-García, N. López-Olmedo, A. Basto-Abreu, T. Shamah-Levy, T. Barrientos-Gutierrez. Adherence to the DASH diet by hypertension status in Mexican men and women: a cross-sectional study. Prev Med Rep, 27 (2022), p. 101803

[109]

M.H. Tao, J.L. Liu, U.D.T. Nguyen.Trends in diet quality by race/ethnicity among adults in the United States for2011-2018. Nutrients, 14 (19) (2022), p. 4178

[110]

W. Wei, W. Jiang, T. Han, M. Tian, G. Ding, Y. Li, et al. The future of prevention and treatment of diabetes with nutrition in China. Cell Metab, 33 (10) (2021), pp. 1908-1910

[111]

J. Gao, X. Guo, W. Wei, R. Li, K. Hu, X. Liu, et al. The association of fried meat consumption with the gut microbiota and fecal metabolites and its impact on glucose homoeostasis, intestinal endotoxin levels, and systemic inflammation: a randomized controlled-feeding trial. Diabetes Care, 44 (9) (2021), pp. 1970-1979

[112]

M. Franzago, E. Alessandrelli, S. Notarangelo, L. Stuppia, E. Vitacolonna. Chrono-nutrition: circadian rhythm and personalized nutrition. Int J Mol Sci, 24 (3) (2023), p. 2571

[113]

P. Regmi, L.K. Heilbronn. Time-restricted eating: benefits, mechanisms, and challenges in translation. iScience, 23 (6) (2020), p. 101161

[114]

M.J. Wilkinson, E.N.C. Manoogian, A. Zadourian, H. Lo, S. Fakhouri, A. Shoghi, et al. Ten-hour time-restricted eating reduces weight, blood pressure, and atherogenic lipids in patients with metabolic syndrome. Cell Metab, 31 (1) (2020), pp. 92-104

[115]

K.L. Haganes, C.P. Silva, S.K. Eyjólfsdóttir, S. Steen, M. Grindberg, S. Lydersen, et al. Time-restricted eating and exercise training improve HbA1c and body composition in women with overweight/obesity: a randomized controlled trial. Cell Metab, 34 (10) (2022), pp. 1457-1471

[116]

D. Liu, Y. Huang, C. Huang, S. Yang, X. Wei, P. Zhang, et al. Calorie restriction with or without time-restricted eating in weight loss. N Engl J Med, 386 (16) (2022), pp. 1495-1504

[117]

R. Chamorro, C. Jouffe, H. Oster, N.H. Uhlenhaut, S.M. Meyhöfer. When should I eat: a circadian view on food intake and metabolic regulation. Acta Physiol (Oxf), 237 (3) (2023), p. e13936

[118]

T. Han, J. Gao, L. Wang, C. Li, L. Qi, C. Sun, et al. The association of energy and macronutrient intake at dinner versus breakfast with disease-specific and all-cause mortality among people with diabetes: the U.S. national health and nutrition examination survey, 2003-2014. Diabetes Care, 43 (7) (2020), pp. 1442-1448

[119]

W. Hou, J. Gao, W. Jiang, W. Wei, H. Wu, Y. Zhang, et al. Meal timing of subtypes of macronutrients consumption with cardiovascular diseases: NHANES, 2003 to 2016. J Clin Endocrinol Metab, 106 (7) (2021), pp. e2480-e2490

[120]

X. Ren, J. Gao, T. Han, C. Sun.Association between risk of type 2 diabetes and changes in energy intake at breakfast and dinner over 14 years: a latent class trajectory analysis from the China health and nutrition survey, 1997-2011. BMJ Open, 11 (7) (2021), p. e046183

[121]

X. Ren, X. Yang, H. Jiang, T. Han, C. Sun.The association of energy and macronutrient intake at dinner vs breakfast with the incidence of type 2 diabetes mellitus in a cohort study: the China health and nutrition survey, 1997-2011. J Diabetes, 13 (11) (2021), pp. 882-892

[122]

W. Wei, W. Jiang, J. Huang, J. Xu, X. Wang, X. Jiang, et al. Association of meal and snack patterns with mortality of all-cause, cardiovascular disease, and cancer: the US national health and nutrition examination survey, 2003 to 2014. J Am Heart Assoc, 10 (13) (2021), p. e020254

[123]

W. Hou, T. Han, X. Sun, Y. Chen, J. Xu, Y. Wang, et al. Relationship between carbohydrate intake (quantity, quality, and time eaten) and mortality (total, cardiovascular, and diabetes): assessment of 2003-2014 national health and nutrition examination survey participants. Diabetes Care, 45 (12) (2022), pp. 3024-3031

[124]

W. Jiang, Q. Song, J. Zhang, Y. Chen, H. Jiang, Y. Long, et al. The association of consumption time for food with cardiovascular disease and all-cause mortality among diabetic patients. J Clin Endocrinol Metab, 107 (7) (2022), pp. e3066-e3075

[125]

J. Qi, J. Gao, Y. Zhang, W. Hou, T. Han, C. Sun. The association of dietary fiber intake in three meals with all-cause and disease-specific mortality among adults: the U.S. national health and nutrition examination survey, 2003-2014. Nutrients, 14 (12) (2022), p. 2521

[126]

W. Gu, H. Wu, C. Hu, J. Xu, H. Jiang, Y. Long, et al. The association of dietary vitamin intake time across a day with cardiovascular disease and all-cause mortality. Front Cardiovasc Med, 9 (2022), p. 822209

[127]

X. Xu, W. Wei, J. Xu, J. Huang, L. Li, T. Han, et al. The association of minerals intake in three meals with cancer and all-cause mortality: the U.S. national health and nutrition examination survey, 2003-2014. BMC Cancer, 21 (1) (2021), p. 912

[128]

J. Licinio, C. Mantzoros, A.B. Negrão, G. Cizza, M.L. Wong, P.B. Bongiorno, et al. Human leptin levels are pulsatile and inversely related to pituitary-adrenal function. Nat Med, 3 (5) (1997), pp. 575-579

[129]

E. Van Cauter, J.D. Blackman, D. Roland, J.P. Spire, S. Refetoff, K.S. Polonsky. Modulation of glucose regulation and insulin secretion by circadian rhythmicity and sleep. J Clin Invest, 88 (3) (1991), pp. 934-942

[130]

A.D. Biancolin, H. Jeong, K. Mak, Z. Yuan, P.L. Brubaker.Disrupted and elevated circadian secretion of glucagon-like peptide-1 in a murine model of type 2 diabetes. Endocrinology, 163 (9) (2022), p. bqac118

[131]

B. Zhou, Y. Zhang, F. Zhang, Y. Xia, J. Liu, R. Huang, et al. CLOCK/BMAL 1 regulates circadian change of mouse hepatic insulin sensitivity by SIRT1. Hepatology, 59 (6) (2014), pp. 2196-2206

[132]

B.M. Gabriel, A. Altıntaş, J.A.B. Smith, L. Sardon-Puig, X. Zhang, A.L. Basse, et al. Disrupted circadian oscillations in type 2 diabetes are linked to altered rhythmic mitochondrial metabolism in skeletal muscle. Sci Adv, 7 (43) (2021), p. eabi9654

[133]

Y. Tahara, M. Yamazaki, H. Sukigara, H. Motohashi, H. Sasaki, H. Miyakawa, et al. Gut microbiota-derived short chain fatty acids induce circadian clock entrainment in mouse peripheral tissue. Sci Rep, 8 (1) (2018), p. 1395

[134]

J. Cipolla-Neto, F.G.D. Amaral. Melatonin as a hormone: new physiological and clinical insights. Endocr Rev, 39 (6) (2018), pp. 990-1028

[135]

A.B. Nongonierma, R.J. FitzGerald. Milk proteins as a source of tryptophan-containing bioactive peptides. Food Funct, 6 (7) (2015), pp. 2115-2127

[136]

J. Vriend, R.J. Reiter. Melatonin feedback on clock genes: a theory involving the proteasome. J Pineal Res, 58 (1) (2015), pp. 1-11

[137]

H.A. Coleman, M. Tare, H.C. Parkington. Nonlinear effects of potassium channel blockers on endothelium-dependent hyperpolarization. Acta Physiol, 219 (1) (2017), pp. 324-334

[138]

C. Yuan, E. Fondell, A. Bhushan, A. Ascherio, O.I. Okereke, F. Grodstein, et al. Long-term intake of vegetables and fruits and subjective cognitive function in US men. Neurology, 92 (1) (2019), pp. e63-e75

[139]

A.M. Ahmad, M.T. Hopkins, W.D. Fraser, C.G. Ooi, B.H. Durham, J.P. Vora. Parathyroid hormone secretory pattern, circulating activity, and effect on bone turnover in adult growth hormone deficiency. Bone, 32 (2) (2003), pp. 170-179

[140]

P. Palombo, M. Moreno-Villanueva, A. Mangerich. Day and night variations in the repair of ionizing-radiation-induced DNA damage in mouse splenocytes. DNA Repair, 28 (2015), pp. 37-47

[141]

Y. Yang, O. Adebali, G. Wu, C.P. Selby, Y.Y. Chiou, N. Rashid, et al. Cisplatin-DNA adduct repair of transcribed genes is controlled by two circadian programs in mouse tissues. Proc Natl Acad Sci USA, 115 (21) (2018), pp. E4777-E4785

[142]

C.L. Partch, C.B. Green, J.S. Takahashi. Molecular architecture of the mammalian circadian clock. Trends Cell Biol, 24 (2) (2014), pp. 90-99

[143]

D. Barsasella, S. Gupta, S. Malwade, S.Y. Aminin, B. Tirmadi, et al. Predicting length of stay and mortality among hospitalized patients with type 2 diabetes mellitus and hypertension. Int J Med Inform, 154 (2021), p. 104569

[144]

A.K. Bonkhoff, C. Grefkes. Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain, 145 (2) (2022), pp. 457-475

[145]

N.H. Ho, H.J. Yang, J. Kim, D.P. Dao, H.R. Park, S. Pant. Predicting progression of Alzheimer’s disease using forward-to-backward bi-directional network with integrative imputation. Neural Netw, 150 (2022), pp. 422-439

[146]

D. Tjandra, R.Q. Migrino, B. Giordani, J. Wiens. Use of blood pressure measurements extracted from the electronic health record in predicting Alzheimer’s disease: a retrospective cohort study at two medical centers. Alzheimers Dement, 18 (11) (2022), pp. 2368-2372

[147]

I. Contreras, J. Vehi. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res, 20 (5) (2018), p. e10775

[148]

S. Ellahham. Artificial intelligence: the future for diabetes care. Am J Med, 133 (8) (2020), pp. 895-900