Kelly OJ, Gilman JC, Ilich JZ. Utilizing dietary micronutrient ratios in nutritional research may be more informative than focusing on single nutrients. Nutrients. 2018;10(1):107. https://doi.org/10.3390/nu10010107.
Article
CAS
PubMed Central
Google Scholar
Moeller SM, Reedy J, Millen AE, Dixon LB, Newby PK, Tucker KL, et al. Dietary patterns: challenges and opportunities in dietary patterns research an experimental biology workshop, April 1, 2006. J Am Diet Assoc. 2007;107(7):1233–9. https://doi.org/10.1016/j.jada.2007.03.014.
Article
PubMed
Google Scholar
Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev. 2004;62(5):177–203. https://doi.org/10.1111/j.1753-4887.2004.tb00040.x.
Article
CAS
PubMed
Google Scholar
Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curropinlipidol. 2002;13(1):3–9.
CAS
Google Scholar
Solans M, Coenders G, Marcos-Gragera R, Castelló A, Gràcia-Lavedan E, Benavente Y, et al. Compositional analysis of dietary patterns. Stat Methods Med Res. 2018;28(9):2834–47. https://doi.org/10.1177/0962280218790110.
Article
PubMed
Google Scholar
Schulze MB, Martínez-González MA, Fung TT, Lichtenstein AH, Forouhi NG. Food based dietary patterns and chronic disease prevention. Bmj. 2018;361:k2396.
Article
Google Scholar
Jannasch F, Riordan F, Andersen LF, Schulze MB. Exploratory dietary patterns: a systematic review of methods applied in pan-European studies and of validation studies. Br J Nutr. 2018;120(6):601–11. https://doi.org/10.1017/S0007114518001800.
Article
CAS
PubMed
PubMed Central
Google Scholar
Michels KB, Schulze MB. Can dietary patterns help us detect diet-disease associations? Nutr Res Rev. 2005;18(2):241–8. https://doi.org/10.1079/NRR2005107.
Article
PubMed
Google Scholar
Ocké MC. Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis. Proc Nutr Soc. 2013;72(2):191–9. https://doi.org/10.1017/S0029665113000013.
Article
PubMed
Google Scholar
Gleason PM, Boushey CJ, Harris JE, Zoellner J. Publishing nutrition research: a review of multivariate techniques--part 3: data reduction methods. J Acad Nutr Diet. 2015;115(7):1072–82. https://doi.org/10.1016/j.jand.2015.03.011.
Article
PubMed
Google Scholar
Krebs-Smith SM, Pannucci TE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, et al. Update of the healthy eating index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591–602. https://doi.org/10.1016/j.jand.2018.05.021.
Article
PubMed
PubMed Central
Google Scholar
Trijsburg L, Talsma EF, de Vries JH, Kennedy G, Kuijsten A, Brouwer ID. Diet quality indices for research in low- and middle-income countries: a systematic review. Nutr Rev. 2019;77(8):515–40. https://doi.org/10.1093/nutrit/nuz017.
Article
PubMed Central
Google Scholar
Waijers PM, Feskens EJ, Ocke MC. A critical review of predefined diet quality scores. Br J Nutr. 2007;97(2):219–31. https://doi.org/10.1017/S0007114507250421.
Article
CAS
PubMed
Google Scholar
Haines PS, Siega-Riz AM, Popkin BM. The diet quality index revised: a measurement instrument for populations. J Am Diet Assoc. 1999;99(6):697–704. https://doi.org/10.1016/S0002-8223(99)00168-6.
Article
CAS
PubMed
Google Scholar
Chiuve SE, Fung TT, Rimm EB, Hu FB, Mccullough ML, Molin W, et al. Alternative dietary indices both strongly predict risk of chronic disease. J Nutr. 2012;142(6):1009–18. https://doi.org/10.3945/jn.111.157222.
Article
CAS
PubMed
PubMed Central
Google Scholar
Kosti RI, Panagiotakos DB, Mariolis A, Zampelas A, Athanasopoulos P, Tountas Y. The Diet-Lifestyle Index evaluating the quality of eating and lifestyle behaviours in relation to the prevalence of overweight/obesity in adolescents. Int J Food Sci Nutr. 2009;60(sup3):34–47.
Article
Google Scholar
Kant AK, Schatzkin A, Graubard BI, Schairer C. A prospective study of diet quality and mortality in women. Jama. 2000;283(16):2109–15. https://doi.org/10.1001/jama.283.16.2109.
Article
CAS
PubMed
Google Scholar
Stampfer MJ, Hu FB, Manson JE, Rimm EB, Willett WC. Primary prevention of coronary heart disease in women through diet and lifestyle. N Engl J Med. 2000;343(1):16–22. https://doi.org/10.1056/NEJM200007063430103.
Article
CAS
PubMed
Google Scholar
Hu FB, Manson JE, Stampfer MJ, Colditz G, ., Liu S, ., Solomon CG, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med 2001;345(11):790–797, DOI: https://doi.org/10.1056/NEJMoa010492.
Article
CAS
PubMed
Google Scholar
Kant A. Indexes of overall diet quality: a review. J Am Diet Assoc. 1996;96(8):785–91. https://doi.org/10.1016/S0002-8223(96)00217-9.
Article
CAS
PubMed
Google Scholar
Murphy SP, Davis MA, Neuhaus JM, Lein D. Dietary quality and survival among middle-aged and older adults in the NHANES I epidemiologic follow-up study. Nutr Res. 1996;16(10):1641–50. https://doi.org/10.1016/0271-5317(96)00183-2.
Article
Google Scholar
Nitin S, Steck SE, Hurley TG, Hussey JR, Hébert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689–96.
Article
Google Scholar
Martínez-González MA, Fernández-Jarne E, Serrano-Martínez M, Marti A, Martinez JA, Martín-Moreno JM. Mediterranean diet and reduction in the risk of a first acute myocardial infarction: an operational healthy dietary score. Eur J Nutr. 2002;41(4):153–60. https://doi.org/10.1007/s00394-002-0370-6.
Article
PubMed
Google Scholar
Monteagudo C, Mariscal-Arcas M, Rivas A, Lorenzo-Tovar ML, Tur JA, Olea-Serrano F. Proposal of a Mediterranean diet serving score. PLoS One. 2015;10(6):e0128594. https://doi.org/10.1371/journal.pone.0128594.
Article
CAS
PubMed
PubMed Central
Google Scholar
Osler M, Heitmann BL, Gerdes LU, Jørgensen LM, Schroll M. Dietary patterns and mortality in Danish men and women: a prospective observational study. Br J Nutr. 2001;85(2):219–25. https://doi.org/10.1079/BJN2000240.
Article
CAS
PubMed
Google Scholar
Patterson RE, Haines PS, Popkin BM. Diet quality index: capturing a multidimensional behavior. J Am Diet Assoc. 1994;94(1):57–64. https://doi.org/10.1016/0002-8223(94)92042-7.
Article
CAS
PubMed
Google Scholar
Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB. Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women. Arch Intern Med. 2008;168(7):713–20. https://doi.org/10.1001/archinte.168.7.713.
Article
PubMed
Google Scholar
Yuan YQ, Li F, Dong RH, Chen JS, He GS, Li SG, et al. The development of a Chinese healthy eating index and its application in the general population. Nutrients. 2017;9(9):977. https://doi.org/10.3390/nu9090977.
Article
CAS
PubMed Central
Google Scholar
Kuriyama N, Murakami K, Livingstone MBE, Okubo H, Kobayashi S, Suga H, et al. Development of a food-based diet quality score for Japanese: associations of the score with nutrient intakes in young, middle-aged and older Japanese women. J Nutr Sci. 2016;5:e41. https://doi.org/10.1017/jns.2016.36.
Article
PubMed
PubMed Central
Google Scholar
Custodio E, Kayikatire F, Fortin S, Thomas AC, Kameli Y, Nkunzimana T, et al. Minimum dietary diversity among women of reproductive age in urban Burkina Faso. Matern Child Nutr. 2020;16(2):e12897. https://doi.org/10.1111/mcn.12897.
Article
PubMed
Google Scholar
Miguel MA, Ana R, Celia M, Alicia G, Isabel C, Fatima OS. Proposal of a Mediterranean diet index for pregnant women. Br J Nutr. 2009;102(5):744–9.
Article
Google Scholar
Wong JE, Skidmore PML, Williams SM, Parnell WR. Healthy dietary habits score as an indicator of diet quality in New Zealand adolescents. J Nutr. 2014;144(6):937–42. https://doi.org/10.3945/jn.113.188375.
Article
CAS
PubMed
Google Scholar
Bork K, Cames C, Barigou S, Cournil A, Diallo A. A summary index of feeding practices is positively associated with height-for-age, but only marginally with linear growth, in rural Senegalese infants and toddlers. J Nutr. 2012;142(6):1116–22. https://doi.org/10.3945/jn.112.157602.
Article
CAS
PubMed
Google Scholar
De Jonge EA, Kiefte-de Jong JC, De Groot LC, Voortman T, Schoufour JD, Zillikens MC, et al. Development of a food group-based diet score and its association with bone mineral density in the elderly: the Rotterdam study. Nutrients. 2015;7(8):6974–90. https://doi.org/10.3390/nu7085317.
Article
CAS
PubMed
PubMed Central
Google Scholar
Fung TT, Rexrode KM, Mantzoros CS, Manson JE, Willett WC, Hu FB. Mediterranean diet and incidence of and mortality from coronary heart disease and stroke in women. Circulation. 2009;119(8):1093–100. https://doi.org/10.1161/CIRCULATIONAHA.108.816736.
Article
PubMed
PubMed Central
Google Scholar
George SM, Ballard-Barbash R, Manson JE, Reedy J, Shikany JM, Subar AF, et al. Comparing indices of diet quality with chronic disease mortality risk in postmenopausal women in the Women's Health Initiative observational study: evidence to inform national dietary guidance. Am J Epidemiol. 2014;180(6):616–25. https://doi.org/10.1093/aje/kwu173.
Article
PubMed
PubMed Central
Google Scholar
Harmon BE, Boushey CJ, Shvetsov YB, Reynolette E, Jill R, Wilkens LR, et al. Associations of key diet-quality indexes with mortality in the multiethnic cohort: the dietary patterns methods project. Am J Clin Nutr. 2015;101(3):587–97. https://doi.org/10.3945/ajcn.114.090688.
Article
CAS
PubMed
PubMed Central
Google Scholar
Jill R, Krebs-Smith SM, Miller PE, Liese AD, Kahle LL, Yikyung P, et al. Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults. J Nutr. 2014;144(6):881–9.
Article
Google Scholar
Francesco S, Claudio M, Rosanna A, Gian Franco G, Alessandro C. Mediterranean diet and health status: an updated meta-analysis and a proposal for a literature-based adherence score. Public Health Nutr. 2014;17(12):2769–82.
Article
Google Scholar
Schwingshackl L, Hoffmann G. Diet Quality as Assessed by the Healthy Eating Index, the Alternate Healthy Eating Index, the Dietary Approaches to Stop Hypertension Score, and Health Outcomes: A Systematic Review and Meta-Analysis of Cohort Studies. J Acad Nutr Dietetics. 2015;115(5):780–800 e5.
Article
Google Scholar
Sotos-Prieto M, Bhupathiraju SN, Mattei J, Fung TT, Li Y, Pan A, et al. Association of Changes in diet quality with Total and cause-specific mortality. N Engl J Med. 2017;377(2):143–53. https://doi.org/10.1056/NEJMoa1613502.
Article
PubMed
PubMed Central
Google Scholar
Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Food in the Anthropocene: the EAT–lancet commission on healthy diets from sustainable food systems. Lancet. 2019;393(10170):447–92. https://doi.org/10.1016/S0140-6736(18)31788-4.
Article
PubMed
Google Scholar
Baden MY, Liu G, Satija A, Li Y, Sun Q, Fung TT, et al. Changes in plant-based diet quality and Total and cause-specific mortality. Circulation. 2019;140(12):979–91. https://doi.org/10.1161/CIRCULATIONAHA.119.041014.
Article
PubMed
PubMed Central
Google Scholar
Satija A, Bhupathiraju SN, Rimm EB, Spiegelman D, Chiuve SE, Borgi L, et al. Plant-based dietary patterns and incidence of type 2 diabetes in US men and women: results from three prospective cohort studies. PLoS Med. 2016;13(6):e1002039. https://doi.org/10.1371/journal.pmed.1002039.
Article
PubMed
PubMed Central
Google Scholar
Satija A, Bhupathiraju SN, Spiegelman D, Chiuve SE, Manson JAE, Willett W, et al. Healthful and unhealthful plant-based diets and the risk of coronary HeartDisease in U.S. adults. J Am Coll Cardiol. 2017;70(4):411–22. https://doi.org/10.1016/j.jacc.2017.05.047.
Article
PubMed
PubMed Central
Google Scholar
Baden MY, Satija A, Hu FB, Huang T. Change in plant-based diet quality is associated with changes in plasma adiposity-associated biomarker concentrations in women. J Nutr. 2019;149(4):676–86. https://doi.org/10.1093/jn/nxy301.
Article
PubMed
PubMed Central
Google Scholar
Kim H, Caulfield LE, Rebholz CM. Healthy plant-based diets are associated with lower risk of all-cause mortality in US adults. J Nutr. 2018;148(4):624–31. https://doi.org/10.1093/jn/nxy019.
Article
PubMed
PubMed Central
Google Scholar
Golley RK, Smithers LG, Mittinty MN, Brazionis L, Emmett P, Northstone K, et al. An index measuring adherence to complementary feeding guidelines has convergent validity as a measure of infant diet quality. J Nutr. 2012;142(5):901–8. https://doi.org/10.3945/jn.111.154971.
Article
CAS
PubMed
Google Scholar
Vadiveloo M, Dixon LB, Mijanovich T, Elbel B, Parekh N. Development and evaluation of the US healthy food diversity index. Br J Nutr. 2014;112(9):1562–74. https://doi.org/10.1017/S0007114514002049.
Article
CAS
PubMed
Google Scholar
Krebs-Smith SM, Subar AF, Reedy J. Examining dietary patterns in relation to chronic disease: table. Circulation. 2015;132(9):790–3. https://doi.org/10.1161/CIRCULATIONAHA.115.018010.
Article
PubMed
Google Scholar
Canuto R, Camey S, Gigante DP, Menezes AMB, Olinto MTA. Focused principal component analysis: a graphical method for exploring dietary patterns. Cadernos de Saúde Pública. 2010;26(11):2149–56. https://doi.org/10.1590/S0102-311X2010001100016.
Article
PubMed
Google Scholar
Varraso R, Garcia-Aymerich J, Monier F, Le Moual N, De Batlle J, Miranda G, et al. Assessment of dietary patterns in nutritional epidemiology: principal component analysis compared with confirmatory factor analysis. Am J Clin Nutr. 2012;96(5):1079–92. https://doi.org/10.3945/ajcn.112.038109.
Article
CAS
PubMed
Google Scholar
Ryman TK, Boyer BB, Scarlett H, Jacques P, Diane OB, Kenneth T, et al. Characterising the reproducibility and reliability of dietary patterns among Yup'ik Alaska native people. Br J Nutr. 2015;113(4):634–43. https://doi.org/10.1017/S0007114514003596.
Article
CAS
PubMed
PubMed Central
Google Scholar
Newby PK, Weismayer C, Akesson A, Tucker KL, Wolk A. Long-term stability of food patterns identified by use of factor analysis among Swedish women. J Nutr. 2006;136(3):626–33. https://doi.org/10.1093/jn/136.3.626.
Article
CAS
PubMed
Google Scholar
Bédard A, Garcia-Aymerich J, Sanchez M, Le Moual N, Clavel-Chapelon F, Boutron-Ruault M-C, et al. Confirmatory factor analysis compared with principal component analysis to derive dietary patterns: a longitudinal study in adult women. J Nutr. 2015;145(7):1559–68. https://doi.org/10.3945/jn.114.204479.
Article
CAS
PubMed
Google Scholar
Hu F, Rimm E, Sa W, Feskanich D, Stampfer M, Ascherio A, et al. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr. 1999;69(2):243–9. https://doi.org/10.1093/ajcn/69.2.243.
Article
CAS
PubMed
Google Scholar
Murakami K, Shinozaki N, Fujiwara A, Yuan X, Hashimoto A, Fujihashi H, et al. A systematic review of principal component analysis-derived dietary patterns in Japanese adults: are major dietary patterns reproducible within a country? Adv Nutr. 2019;10(2):237–49. https://doi.org/10.1093/advances/nmy079.
Article
PubMed
PubMed Central
Google Scholar
Hong X, Ye Q, Wang Z, Yang H, Chen X, Zhou H, et al. Reproducibility and validity of dietary patterns identified using factor analysis among Chinese populations. Br J Nutr. 2016;116(5):842–52. https://doi.org/10.1017/S000711451600249X.
Article
CAS
PubMed
Google Scholar
Castelló A, Lope V, Vioque J, Santamariña C, Pedraz-Pingarrón C, Abad S, et al. Reproducibility of data-driven dietary patterns in two groups of adult Spanish women from different studies. Br J Nutr. 2016;116(4):734–42. https://doi.org/10.1017/S000711451600252X.
Article
CAS
PubMed
Google Scholar
Schulze MB, Kurt H, Anja K, Heiner B. Risk of hypertension among women in the EPIC-Potsdam study: comparison of relative risk estimates for exploratory and hypothesis-oriented dietary patterns. Am J Epidemiol. 2003;158(4):365–73. https://doi.org/10.1093/aje/kwg156.
Article
PubMed
Google Scholar
Martínez ME, Marshall JR, Sechrest L. Invited commentary: factor analysis and the search for objectivity. Am J Epidemiol. 1998;148(1):17–9. https://doi.org/10.1093/oxfordjournals.aje.a009552.
Article
PubMed
Google Scholar
Slattery ML, Edwards SL, Boucher KM, Anderson K, Caan BJ. Lifestyle and colon cancer: an assessment of factors associated with risk. Am J Epidemiol. 1999;150(8):869–77. https://doi.org/10.1093/oxfordjournals.aje.a010092.
Article
CAS
PubMed
Google Scholar
Greve B, Pigeot I, Huybrechts I, Pala V, Börnhorst C. A comparison of heuristic and model-based clustering methods for dietary pattern analysis. Public Health Nutr. 2015;19(02):255–64.
Article
Google Scholar
Lo Siou G, Yasui Y, Csizmadi I, McGregor SE, Robson PJ. Exploring statistical approaches to diminish subjectivity of cluster analysis to derive dietary patterns: the tomorrow project. Am J Epidemiol. 2011;173(8):956–67. https://doi.org/10.1093/aje/kwq458.
Article
PubMed
Google Scholar
Devlin UM, McNulty BA, Nugent AP, Gibney MJ. The use of cluster analysis to derive dietary patterns: methodological considerations, reproducibility, validity and the effect of energy mis-reporting. Proc Nutr Soc. 2012;71(4):599–609. https://doi.org/10.1017/S0029665112000729.
Article
PubMed
Google Scholar
Saxena A, Prasad M, Gupta A, Bharill N, Patel OP, Tiwari A, et al. A review of clustering techniques and developments. Neurocomputing. 2017;267:664–81. https://doi.org/10.1016/j.neucom.2017.06.053.
Article
Google Scholar
Milligan GW. A study of the Beta-flexible clustering method. Multivar Behav Res. 1989;24(2):163–76. https://doi.org/10.1207/s15327906mbr2402_2.
Article
CAS
Google Scholar
Xu SH, Qiao N, Huang JJ, Sun CM, Cui Y, Tian SS, et al. Gender differences in dietary patterns and their association with the prevalence of metabolic syndrome among Chinese: a cross-sectional study. Nutrients. 2016;8(4):180. https://doi.org/10.3390/nu8040180.
Article
CAS
PubMed
PubMed Central
Google Scholar
Wirfält E, Midthune D, Reedy J, Mitrou P, Flood A, Subar A, et al. Associations between food patterns defined by cluster analysis and colorectal cancer incidence in the NIH-AARP diet and health study. Eur J Clin Nutr. 2008;63:707–17.
Article
Google Scholar
He Y, Ma G, Zhai F, Li Y, Hu Y, Feskens EJ, et al. Dietary patterns and glucose tolerance abnormalities in Chinese adults. Diabetes Care. 2009;32(11):1972–6. https://doi.org/10.2337/dc09-0714.
Article
PubMed
PubMed Central
Google Scholar
Wirfalt E, Midthune D, Reedy J, Mitrou P, Flood A, Subar AF, et al. Associations between food patterns defined by cluster analysis and colorectal cancer incidence in the NIH-AARP diet and health study. Eur J Clin Nutr. 2009;63(6):707–17. https://doi.org/10.1038/ejcn.2008.40.
Article
CAS
PubMed
Google Scholar
Sauvageot N, Schritz A, Leite S, Alkerwi A, Stranges S, Zannad F, et al. Stability-based validation of dietary patterns obtained by cluster analysis. Nutr J. 2017;16(1):4. https://doi.org/10.1186/s12937-017-0226-9.
Article
PubMed
PubMed Central
Google Scholar
Fahey MT, Thane CW, Bramwell GD, Coward WA. Conditional Gaussian mixture modelling for dietary pattern analysis. J R Stat Soc Ser A (Statistics in Society). 2007;170(1):149–66. https://doi.org/10.1111/j.1467-985X.2006.00452.x.
Article
Google Scholar
Fahey MT, Ferrari P, Slimani N, Vermunt JK, White IR, Hoffmann K, et al. Identifying dietary patterns using a normal mixture model: application to the EPIC study. J Epidemiol Community Health. 2012;66(1):89–94. https://doi.org/10.1136/jech.2009.103408.
Article
PubMed
Google Scholar
Thorpe MG, Milte CM, Crawford D, McNaughton SA. A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians. Int J Behav Nutr Phys Act. 2016;13(1):1–14.
Article
Google Scholar
Sotres-Alvarez D, Herring AH, Siega-Riz AM. Latent class analysis is useful to classify pregnant women into dietary patterns. J Nutr. 2010;140(12):2253–9. https://doi.org/10.3945/jn.110.124909.
Article
CAS
PubMed
PubMed Central
Google Scholar
Gaio AR, Costa JP, Santos AC, Ramos E, Lopes C. A restricted mixture model for dietary pattern analysis in small samples. Stat Med. 2012;31(19):2137–50. https://doi.org/10.1002/sim.5336.
Article
Google Scholar
Fraley C, Raftery AE. How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J. 1998;41(8):578–88. https://doi.org/10.1093/comjnl/41.8.578.
Article
Google Scholar
Gorst-Rasmussen A, Dahm CC, Dethlefsen C, Scheike T, Overvad K. Exploring dietary patterns by using the treelet transform. Am J Epidemiol. 2011;173(10):1097–104. https://doi.org/10.1093/aje/kwr060.
Article
PubMed
Google Scholar
Lee AB, Nadler B, Wasserman L. Treelets--an adaptive multi-scale basis for sparse unordered data. Ann Appl Stat. 2008;2(2):435–71.
Google Scholar
Imamura F, Jacques PF. Invited commentary: dietary pattern analysis. Am J Epidemiol. 2011;173(10):1105–10. https://doi.org/10.1093/aje/kwr063.
Article
PubMed
Google Scholar
Assi N, Moskal A, Slimani N, Viallon V, Chajes V, Freisling H, et al. A treelet transform analysis to relate nutrient patterns to the risk of hormonal receptor-defined breast cancer in the European prospective investigation into Cancer and nutrition (EPIC). Public Health Nutr. 2015;19(02):242–54.
Article
Google Scholar
Schoenaker DA, Dobson AJ, Soedamah-Muthu SS, Mishra GD. Factor analysis is more appropriate to identify overall dietary patterns associated with diabetes when compared with Treelet transform analysis. J Nutr. 2013;143(3):392–8. https://doi.org/10.3945/jn.112.169011.
Article
CAS
PubMed
Google Scholar
Weikert C, Schulze MB. Evaluating dietary pattern the role of reduced rank regression. Curr Opin Clin Nutr Metab Care. 2016;19(5):341–6. https://doi.org/10.1097/MCO.0000000000000308.
Article
CAS
PubMed
Google Scholar
Hoffmann K. Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol. 2004;159(10):935–44. https://doi.org/10.1093/aje/kwh134.
Article
PubMed
Google Scholar
Hoffmann K, Zyriax BC, Boeing H, Windler E. A dietary pattern derived to explain biomarker variation is strongly associated with the risk of coronary artery disease. Am J Clin Nutr. 2004;80(3):633–40. https://doi.org/10.1093/ajcn/80.3.633.
Article
CAS
PubMed
Google Scholar
DiBello JR, Kraft P, McGarvey ST, Goldberg R, Campos H, Baylin A. Comparison of 3 methods for identifying dietary patterns associated with risk of disease. Am J Epidemiol. 2008;168(12):1433–43. https://doi.org/10.1093/aje/kwn274.
Article
PubMed
PubMed Central
Google Scholar
Melaku YA, Gill TK, Taylor AW, Adams R, Shi Z. A comparison of principal component analysis, partial least-squares and reduced-rank regressions in the identification of dietary patterns associated with bone mass in ageing Australians. Eur J Nutr. 2018;57(5):1969–83. https://doi.org/10.1007/s00394-017-1478-z.
Article
PubMed
Google Scholar
Van Dam RM. New approaches to the study of dietary patterns. Br J Nutr. 2005;93(05):573.
Article
Google Scholar
Kroke A. Re: "application of a new statistical method to derive dietary patterns in nutritional epidemiology". Am J Epidemiol. 2004;160(11):1132–3. https://doi.org/10.1093/aje/kwh329.
Article
PubMed
Google Scholar
Yang TC, Aucott LS, Duthie GG, Macdonald HM. An application of partial least squares for identifying dietary patterns in bone health. Arch Osteoporos. 2017;12(1):63. https://doi.org/10.1007/s11657-017-0355-y.
Article
PubMed
PubMed Central
Google Scholar
Linden A, Yarnold PR. Using data mining techniques to characterize participation in observational studies. J Eval Clin Pract. 2016;22(6):835–43. https://doi.org/10.1111/jep.12625.
Article
PubMed
Google Scholar
Lazarou C, Karaolis M, Matalas A-L, Panagiotakos DB. Dietary patterns analysis using data mining method. An application to data from the CYKIDS study. Comput Methods Prog Biomed. 2012;108(2):706–14. https://doi.org/10.1016/j.cmpb.2011.12.011.
Article
Google Scholar
Hearty AP, Gibney MJ. Analysis of meal patterns with the use of supervised data mining techniques--artificial neural networks and decision trees. Am J Clin Nutr. 2008;88(6):1632–42. https://doi.org/10.3945/ajcn.2008.26619.
Article
CAS
PubMed
Google Scholar
Easton JF, Roman Sicilia H, Stephens CR. Classification of diagnostic subcategories for obesity and diabetes based on eating patterns. Nutr Dietetics. 2019;76(1):104–9. https://doi.org/10.1111/1747-0080.12495.
Article
Google Scholar
Panaretos D, Koloverou E, Dimopoulos AC, Kouli G-M, Vamvakari M, Tzavelas G, et al. A comparison of statistical and machine-learning techniques in evaluating the association between dietary patterns and 10-year cardiometabolic risk (2002–2012): the ATTICA study. Br J Nutr. 2018;120(03):326–34. https://doi.org/10.1017/S0007114518001150.
Article
CAS
PubMed
Google Scholar
Quinlan JR. C4.5: programs for machine learning. San Francisco: Morgan Kaufmann Publishers Inc; 1993.
Google Scholar
Biesbroek S, van der AD BMC, Beulens JW, Verschuren WM, van der Schouw YT, et al. Identifying cardiovascular risk factor-related dietary patterns with reduced rank regression and random forest in the EPIC-NL cohort. Am J Clin Nutr. 2015;102(1):146–54. https://doi.org/10.3945/ajcn.114.092288.
Article
CAS
PubMed
Google Scholar
Ziegler A, Maccluer JW, Almasy L. Brief review of regression-based and machine learning methods in genetic epidemiology: the genetic analysis workshop 17 experience. Genet Epidemiol. 2011;35(S1):S5–S11.
Article
Google Scholar
Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B Methodol. 1996;58(1):267–88.
Google Scholar
Zhang F, Tapera TM, Gou J. Application of a new dietary pattern analysis method in nutritional epidemiology. BMC Med Res Methodol. 2018;18(1):119. https://doi.org/10.1186/s12874-018-0585-8.
Article
PubMed
PubMed Central
Google Scholar
Leite MLC, Prinelli F. A compositional data perspective on studying the associations between macronutrient balances and diseases. Eur J Clin Nutr. 2017;71(12):1365–9. https://doi.org/10.1038/ejcn.2017.126.
Article
CAS
PubMed
Google Scholar
Leite MLC. Applying compositional data methodology to nutritional epidemiology. Stat Methods Med Res. 2016;25(6):3057–65. https://doi.org/10.1177/0962280214560047.
Article
PubMed
Google Scholar
Aitchison J. The statistical analysis of compositional data. J R Stat Soc Ser B Methodol. 1982;44:139–60.
Google Scholar
Aitchison J. Principal component analysis of compositional data. Biometrika. 1983;70(1):57–65. https://doi.org/10.1093/biomet/70.1.57.
Article
Google Scholar
Bruno F, Greco F, Ventrucci M. Spatio-temporal regression on compositional covariates: modeling vegetation in a gypsum outcrop. Environ Ecol Stat. 2015;22(3):445–63. https://doi.org/10.1007/s10651-014-0305-4.
Article
Google Scholar
Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barceló-Vidal C. Isometric Logratio transformations for compositional data analysis. Math Geol. 2003;35(3):279–300. https://doi.org/10.1023/A:1023818214614.
Article
Google Scholar
Egozcue JJ, Pawlowsky-Glahn V. Groups of parts and their balances in compositional data analysis. Math Geol. 2006;37(7):795–828.
Article
Google Scholar
Martín-Fernández JA, Pawlowsky-Glahn V, Egozcue JJ, Tolosona-Delgado R. Advances in principal balances for compositional data. Math Geosci. 2017;50(3):273–98.
Article
Google Scholar
Pawlowsky-Glahn V, Egozcue JJ. Exploring compositional data with the CoDa-Dendrogram. Aust Stat Soc. 2011;40(1):103–13.
Google Scholar
Palarea-Albaladejo J, Martín-Fernández JA. A modified EM alr-algorithm for replacing rounded zeros in compositional data sets. Comput Geosci. 2008;34(8):902–17. https://doi.org/10.1016/j.cageo.2007.09.015.
Article
Google Scholar
Mozaffarian D. Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review. Circulation. 2016;133(2):187–225. https://doi.org/10.1161/CIRCULATIONAHA.115.018585.
Article
CAS
PubMed
PubMed Central
Google Scholar
Schulze MB, Hoffmann K. Methodological approaches to study dietary patterns in relation to risk of coronary heart disease and stroke. Br J Nutr. 2007;95(5):860–9.
Article
Google Scholar
Corrêa Leite ML. Compositional data analysis as an alternative paradigm for nutritional studies. Clin Nutr ESPEN. 2019;33:207–12. https://doi.org/10.1016/j.clnesp.2019.05.011.
Article
PubMed
Google Scholar
Arnold KF, Berrie L, Tennant PWG, Gilthorpe MS. A causal inference perspective on the analysis of compositional data. Int J Epidemiol. 2020;49(4):1307-13.
Godichon-Baggioni A, Maugis-Rabusseau C, Rau A. Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data. J Appl Stat. 2019;46(1):47–65. https://doi.org/10.1080/02664763.2018.1454894.
Article
Google Scholar
Dumuid D, Pedišić Ž, Stanford TE, Martín-Fernández JA, Hron K, Maher CA, et al. The compositional isotemporal substitution model: a method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat Methods Med Res. 2019;28(3):846–57. https://doi.org/10.1177/0962280217737805.
Article
PubMed
Google Scholar
Dumuid D, Stanford TE, Martin-Fernández J-A, Pedišić Ž, Maher CA, Lewis LK, et al. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res. 2018;27(12):3726–38. https://doi.org/10.1177/0962280217710835.
Article
PubMed
Google Scholar
Zhang S, Midthune D, Guenther PM, Krebs-Smith SM, Kipnis V, Dodd KW, et al. A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment. Ann Appl Stat. 2011;5(2B):1456–87. https://doi.org/10.1214/10-AOAS446.
Article
PubMed
PubMed Central
Google Scholar
Brennan L, Hu FB. Metabolomics-based dietary biomarkers in nutritional epidemiology-current status and future opportunities. Mol Nutr Food Res. 2019;63(1):e1701064. https://doi.org/10.1002/mnfr.201701064.
Article
CAS
PubMed
Google Scholar