Association between dietary patterns and prediabetes, undetected diabetes or clinically diagnosed diabetes: results from the KORA FF4 study.
Dietary patterns
Glucose tolerance status
Prediabetes
Type 2 diabetes
Undetected diabetes
Western pattern
Journal
European journal of nutrition
ISSN: 1436-6215
Titre abrégé: Eur J Nutr
Pays: Germany
ID NLM: 100888704
Informations de publication
Date de publication:
Aug 2021
Aug 2021
Historique:
received:
25
03
2020
accepted:
13
10
2020
pubmed:
31
10
2020
medline:
15
7
2021
entrez:
30
10
2020
Statut:
ppublish
Résumé
Diet is one of the most important modifiable risk factors for the development of type 2 diabetes. Here, we aim to identify dietary patterns and to investigate their association with prediabetes, undetected diabetes and prevalent diabetes. The present study included 1305 participants of the cross-sectional population-based KORA FF4 study. Oral glucose tolerance test (OGTT) measurements together with a physician-confirmed diagnosis allowed for an accurate categorization of the participants according to their glucose tolerance status into normal glucose tolerance (n = 698), prediabetes (n = 459), undetected diabetes (n = 49), and prevalent diabetes (n = 99). Dietary patterns were identified through principal component analysis followed by hierarchical clustering. The association between dietary patterns and glucose tolerance status was investigated using multinomial logistic regression models. A Prudent pattern, characterized by high consumption of vegetables, fruits, wholegrains and dairy products, and a Western pattern, characterized by high consumption of red and processed meat, alcoholic beverages, refined grains and sugar-sweetened beverages, were identified. Participants following the Western pattern had significantly higher chances of having prediabetes (odds ratio [OR] 1.92; 95% confidence interval [CI] 1.35, 2.73), undetected diabetes (OR 10.12; 95% CI 4.19, 24.43) or prevalent diabetes (OR 3.51; 95% CI 1.85, 6.67), compared to participants following the Prudent pattern. To our knowledge, the present study is one of the few investigating the association between dietary patterns and prediabetes or undetected diabetes. The use of a reference group exclusively including participants with normal glucose tolerance might explain the strong associations observed in our study. These results suggest a very important role of dietary habits in the prevention of prediabetes and type 2 diabetes.
Identifiants
pubmed: 33125578
doi: 10.1007/s00394-020-02416-9
pii: 10.1007/s00394-020-02416-9
pmc: PMC8275503
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
2331-2341Subventions
Organisme : Bundesanstalt für Landwirtschaft und Ernährung
ID : 5.17.02ERN
Organisme : Bundesministerium für Bildung und Forschung
ID : FK 01EA1807E
Informations de copyright
© 2020. The Author(s).
Références
Cho NH, Shaw JE, Karuranga S et al (2018) IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract 138:271–281. https://doi.org/10.1016/j.diabres.2018.02.023
doi: 10.1016/j.diabres.2018.02.023
pubmed: 29496507
World Health Organization (WHO) (2016) Global report on diabetes. WHO, Geneva ((ISBN 978 92 4 156525 7))
Harris MI, Klein R, Welborn TA, Knuiman MW (1992) Onset of NIDDM occurs at least 4–7 years before clinical diagnosis. Diabetes Care 15:815–819
doi: 10.2337/diacare.15.7.815
Hu FB (2002) Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 13:3–9. https://doi.org/10.1097/00041433-200202000-00002
doi: 10.1097/00041433-200202000-00002
pubmed: 11790957
Ocké MC (2013) Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis. Proc Nutr Soc 72:191–199. https://doi.org/10.1017/S0029665113000013
doi: 10.1017/S0029665113000013
pubmed: 23360896
Malik VS, Fung TT, Van Dam RM et al (2012) Dietary patterns during adolescence and risk of type 2 diabetes in middle-aged women. Diabetes Care 35:12–18. https://doi.org/10.2337/dc11-0386
doi: 10.2337/dc11-0386
pubmed: 22074723
van Dam RM, Rimm EB, Willet WC et al (2002) Dietary patterns and risk for Type 2 diabetes Mellitus in US. Men Ann Intern Med 136:201–209
doi: 10.7326/0003-4819-136-3-200202050-00008
Fung TT, Schulze M, Manson JE et al (2004) Dietary patterns, meat intake, and the risk of Type 2 diabetes in women. Arch Intern Med 164:2235–2240
doi: 10.1001/archinte.164.20.2235
Heidemann C, Hoffmann K, Spranger J et al (2005) A dietary pattern protective against type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)—Potsdam Study cohort. Diabetologia 48:1126–1134. https://doi.org/10.1007/s00125-005-1743-1
doi: 10.1007/s00125-005-1743-1
pubmed: 15889235
Nettleton JA, Steffen LM, Ni H et al (2008) Dietary patterns and risk of incident Type 2 diabetes in the multi-ethnic study of atherosclerosis (MESA). Diabetes Care 31:1777–1782. https://doi.org/10.2337/dc08-0760
doi: 10.2337/dc08-0760
pubmed: 18544792
pmcid: 2518344
Schulze MB, Hoffmann K, Manson JE et al (2005) Dietary pattern, inflammation, and incidence of type 2 diabetes in women. Am J Clin Nutr 82:675–684. https://doi.org/10.1093/ajcn.82.3.675
doi: 10.1093/ajcn.82.3.675
pubmed: 16155283
Montonen J, Knekt P, Härkänen T et al (2005) Dietary patterns and the incidence of Type 2 diabetes. Am J Epidemiol 161:219–227. https://doi.org/10.1093/aje/kwi039
doi: 10.1093/aje/kwi039
pubmed: 15671254
McNaughton SA, Mishra GD, Brunner EJ (2008) Dietary patterns, insulin resistance, and incidence of type 2 diabetes in the Whitehall II study. Diabetes Care 31:1343–1348. https://doi.org/10.2337/dc07-1946
doi: 10.2337/dc07-1946
pubmed: 18390803
pmcid: 2453656
Gittelsohn J, Wolever TMS, Harris SB et al (1998) Specific patterns of food consumption and preparation are associated with diabetes and obesity in a native Canadian community. J Nutr 128:541–547. https://doi.org/10.1093/jn/128.3.541
doi: 10.1093/jn/128.3.541
pubmed: 9482761
Williams DEM, Prevost AT, Whichelow MJ et al (2000) A cross-sectional study of dietary patterns with glucose intolerance and other features of the metabolic syndrome. Br J Nutr 83:257–266
doi: 10.1017/S0007114500000337
Doostvandi T, Bahadoran Z, Mozaffari-khosravi H et al (2016) Food intake patterns are associated with the risk of impaired glucose and insulin homeostasis : a prospective approach in the Tehran Lipid and Glucose Study. Public Health Nutr 19:2467–2474. https://doi.org/10.1017/S1368980016000616
doi: 10.1017/S1368980016000616
pubmed: 27087273
Daniel CR, Prabhakaran D, Kapur K et al (2011) A cross-sectional investigation of regional patterns of diet and cardio-metabolic risk in India. Nutr J 10:12. https://doi.org/10.1186/1475-2891-10-12
doi: 10.1186/1475-2891-10-12
pubmed: 21276235
pmcid: 3042918
Bagheri F, Siassi F, Koohdani F et al (2016) Healthy and unhealthy dietary patterns are related to pre-diabetes: a case—control study. Br J Nutr 116:874–881. https://doi.org/10.1017/S0007114516002634
doi: 10.1017/S0007114516002634
pubmed: 27451983
Breuninger TA, Riedl A, Wawro N et al (2018) Differential associations between diet and prediabetes or diabetes in the KORA FF4 study. J Nutr Sci 7:1–12. https://doi.org/10.1017/jns.2018.25
doi: 10.1017/jns.2018.25
Lachat C, Hawwash D, Ocké MC et al (2016) Strengthening the reporting of observational studies in epidemiology—nutritional epidemiology (STROBE-nut): an extension of the STROBE statement. PLoS Med 13:1–15. https://doi.org/10.1371/journal.pmed.1002036
doi: 10.1371/journal.pmed.1002036
Holle R, Happich M, Löwel H, Wichmann HE (2005) KORA - A research platform for population based health research. Gesundheitswesen 67:S19–S25. https://doi.org/10.1055/s-2005-858235
doi: 10.1055/s-2005-858235
pubmed: 16032513
Kowall B, Rathmann W, Stang A et al (2017) Perceived risk of diabetes seriously underestimates actual diabetes risk: the KORA FF4 study. PLoS ONE 12:69–75. https://doi.org/10.1371/journal.pone.0171152
doi: 10.1371/journal.pone.0171152
American Diabetes Association (ADA) (2015) 2. Classification and diagnosis of diabetes. Diabetes Care 38:S8–S16. https://doi.org/10.2337/dc15-S005
doi: 10.2337/dc15-S005
Freese J, Feller S, Harttig U et al (2014) Development and evaluation of a short 24-h food list as part of a blended dietary assessment strategy in large-scale cohort studies. Eur J Clin Nutr 68:324–329. https://doi.org/10.1038/ejcn.2013.274
doi: 10.1038/ejcn.2013.274
pubmed: 24398637
Subar AF, Dodd KW, Guenther PM et al (2006) The food propensity questionnaire: concept, development, and validation for use as a covariate in a model to estimate usual food intake. J Am Diet Assoc 106:1556–1563. https://doi.org/10.1016/j.jada.2006.07.002
doi: 10.1016/j.jada.2006.07.002
pubmed: 17000188
Mitry P, Wawro N, Six-Merker J et al (2019) Usual dietary intake estimation based on a combination of repeated 24-hour food lists and a food frequency questionnaire in the KORA FF4 cross-sectional study. Front Nutr. https://doi.org/10.3389/FNUT.2019.00145
doi: 10.3389/FNUT.2019.00145
pubmed: 31552261
pmcid: 6743021
Slimani N, Deharveng G, Charrondière RU et al (1999) Structure of the standardized computerized 24-h diet recall interview used as reference method in the 22 centers participating in the EPIC project. Comput Methods Programs Biomed 58:251–266. https://doi.org/10.1016/S0169-2607(98)00088-1
doi: 10.1016/S0169-2607(98)00088-1
pubmed: 10094230
Riedl A, Wawro N, Gieger C et al (2019) Modifying effect of metabotype on diet–diabetes associations. Eur J Nutr. https://doi.org/10.1007/s00394-019-01988-5
doi: 10.1007/s00394-019-01988-5
pubmed: 31089867
pmcid: 7230059
Krieger J-P, Pestoni G, Cabaset S et al (2018) Dietary patterns and their sociodemographic and lifestyle determinants in switzerland: results from the national nutrition survey menuCH. Nutrients 11:62. https://doi.org/10.3390/nu11010062
doi: 10.3390/nu11010062
pmcid: 6356790
Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244
doi: 10.1080/01621459.1963.10500845
Rabel M, Meisinger C, Peters A et al (2017) The longitudinal association between change in physical activity, weight, and health-related quality of life: results from the population-based KORA S4/F4/FF4 cohort study. PLoS ONE 12:e0185205. https://doi.org/10.1371/journal.pone.0185205
doi: 10.1371/journal.pone.0185205
pubmed: 28953956
pmcid: 5617179
Stender M, Döring A, Hense HW et al (1991) Vergleich zweier Methoden zur Erhebung der körperlichen Aktivität. Sozial- und Präventivmedizin SPM 36:176–183. https://doi.org/10.1007/BF01352697
doi: 10.1007/BF01352697
Rathmann W, Haastert B, Icks A et al (2003) High prevalence of undiagnosed diabetes mellitus in Southern Germany: target populations for efficient screening. The KORA survey 2000. Diabetologia 46:182–189. https://doi.org/10.1007/s00125-002-1025-0
doi: 10.1007/s00125-002-1025-0
pubmed: 12627316
Vinther JL, Conklin AI, Wareham NJ, Monsivais P (2016) Marital transitions and associated changes in fruit and vegetable intake: findings from the population-based prospective EPIC-Norfolk cohort, UK. Soc Sci Med 157:120–126. https://doi.org/10.1016/j.socscimed.2016.04.004
doi: 10.1016/j.socscimed.2016.04.004
pubmed: 27082023
pmcid: 4857700
Ramezankhani A, Azizi F, Hadaegh F (2019) Associations of marital status with diabetes, hypertension, cardiovascular disease and all-cause mortality: a long term follow-up study. PLoS ONE 14:e0215593. https://doi.org/10.1371/journal.pone.0215593
doi: 10.1371/journal.pone.0215593
pubmed: 31009512
pmcid: 6476533
Lê S, Josse J, Husson F (2008) FactoMineR: an R package for multivariate analysis. J Stat Softw 25:1–18. https://doi.org/10.18637/jss.v025.i01
doi: 10.18637/jss.v025.i01
Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, Berlin
doi: 10.1007/978-0-387-21706-2
World Health Organisation (WHO) (2000) Obesity: preventing and managing the global epidemic: report of a WHO consultation. WHO Technical Report Series: 894. WHO, Geneva
Rathmann W, Kowall B, Tamayo T et al (2012) Hemoglobin A1c and glucose criteria identify different subjects as having type 2 diabetes in middle-aged and older populations: the KORA S4/F4 Study. Ann Med 44:170–177. https://doi.org/10.3109/07853890.2010.531759
doi: 10.3109/07853890.2010.531759
pubmed: 21091229
Van’t Riet E, Alssema M, Rijkelijkhuizen JM et al (2010) Relationship between A1C and glucose levels in the general Dutch population: the new Hoorn study. Diabetes Care 33:61–66. https://doi.org/10.2337/dc09-0677
doi: 10.2337/dc09-0677
Mooy JM, Grootenhuis PA, de Vries H et al (1996) Intra-individual variation of glucose, specific insulin and proinsulin concentrations measured by two oral glucose tolerance tests in a general Caucasian population: the Hoorn Study. Diabetologia 39:298–305. https://doi.org/10.1007/BF00418345
doi: 10.1007/BF00418345
pubmed: 8721775
Schulze MB, Manson JE, Willett WC, Hu FB (2003) Processed meat intake and incidence of Type 2 diabetes in younger and middle-aged women. Diabetologia 46:1465–1473. https://doi.org/10.1007/s00125-003-1220-7
doi: 10.1007/s00125-003-1220-7
pubmed: 14576980
Neuenschwander M, Ballon A, Weber KS et al (2019) Role of diet in type 2 diabetes incidence: umbrella review of meta-analyses of prospective observational studies. Br Med J 365:l2368. https://doi.org/10.1136/bmj.l2368
doi: 10.1136/bmj.l2368
Knott C, Bell S, Britton A (2015) Alcohol consumption and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of more than 1.9 million individuals from 38 observational studies. Diabetes Care 38:1804–1812. https://doi.org/10.2337/dc15-0710
doi: 10.2337/dc15-0710
pubmed: 26294775
Brunner EJ, Mosdøl A, Witte DR et al (2008) Dietary patterns and 15-years risks of major coronary events, diabetes, and mortality. Am J Clin Nutr 87:1414–1421
doi: 10.1093/ajcn/87.5.1414
Barbaresko J, Koch M, Schulze MB, Nöthlings U (2013) Dietary pattern analysis and biomarkers of low-grade inflammation: a systematic literature review. Nutr Rev 71:511–527. https://doi.org/10.1111/nure.12035
doi: 10.1111/nure.12035
pubmed: 23865797
Engeset D, Alsaker E, Ciampi A, Lund E (2005) Dietary patterns and lifestyle factors in the Norwegian EPIC cohort: the Norwegian Women and Cancer (NOWAC) study. Eur J Clin Nutr 59:675–684. https://doi.org/10.1038/sj.ejcn.1602129
doi: 10.1038/sj.ejcn.1602129
pubmed: 15785773
He Y, Ma G, Zhai F et al (2009) Dietary patterns and glucose tolerance abnormalities in Chinese adults. Diabetes Care 32:1972–1976. https://doi.org/10.2337/dc09-0714
doi: 10.2337/dc09-0714
pubmed: 19675202
pmcid: 2768212