[en] [en] PURPOSE: The relationship between dietary patterns (DPs) and type 2 diabetes is well established, but the potential role of tissue-specific insulin resistance (IR) in this association remains unclear. This study aimed to derive DPs using reduced rank regression (RRR), incorporating hepatic IR index (HIRI) and muscle insulin sensitivity index (MISI) as response variables. We also examined whether these patterns are associated with insulin sensitivity and pancreatic β-cell function.
METHODS: We conducted a cross-sectional analysis of 700 adults with overweight or obesity participating in the screening phase of the PERSON study. Dietary intakes were assessed using a food frequency questionnaire. RRR was used to derive DPs based on HIRI and MISI. Associations with HOMA-IR, HOMA-β, Matsuda index and Disposition index were tested using multiple regression models adjusted for socio-demographic and lifestyle factors.
RESULTS: One DP was retained, explaining 13.7% of the variation in HIRI, 2.8% in MISI, and 8.2% of the combined variation. This DP was characterized by high intakes of unprocessed red meat, processed meat, fresh cream and whipped cream, and low intakes of fruits, vegetables, and tea. It was significantly associated with higher HOMA-IR (β-coefficient ± SE: 0.04 ± 0.02) and HOMA-β (0.05 ± 0.01), and lower Matsuda index (- 0.08 ± 0.02).
CONCLUSION: The identified DP was more strongly associated with hepatic than muscle IR. This finding highlights differential associations between diet and tissue-specific IR, and supports the relevance of considering tissue-specific insulin resistance phenotypes when investigating the relationship between diet, insulin resistance and type 2 diabetes risk. Trial registration The trial was registered at https://clinicaltrials.gov/study/NCT03708419 (identifier NCT03708419).
Disciplines :
Public health, health care sciences & services
Author, co-author :
Hoge, Axelle ; Université de Liège - ULiège > Santé publique : de la Biostatistique à la Promotion de la Santé
Donneau, Anne-Françoise ; Université de Liège - ULiège > Département des sciences de la santé publique
Dardenne, Nadia ; Université de Liège - ULiège > Département des sciences de la santé publique
Guillaume, Michèle ; Université de Liège - ULiège > Département des sciences de la santé publique > Santé publique : aspects spécifiques
Afman, Lydia A; Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
Feskens, Edith J M; Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
Goossens, Gijs H; Department of Human Biology, Institute of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University Medical Center+, Maastricht, The Netherlands
Blaak, Ellen E; Department of Human Biology, Institute of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University Medical Center+, Maastricht, The Netherlands. e.blaak@maastrichtuniversity.nl
Language :
English
Title :
Differential associations of diet with hepatic and muscle insulin resistance: insights from an dietary pattern analysis in the PERSON study.
Abel E O’Shea K Ramasamy R Insulin resistance: metabolic mechanisms and consequences in the heart Arterioscler Thromb Vasc Biol 2012 32 2068 2076 1:CAS:528:DC%2BC38Xht1SmurnF 10.1161/ATVBAHA.111.241984 22895668 3646067
Shanik M Xu Y Skrha J Insulin resistance and hyperinsulinemia: is hyperinsulinemia the cart or the horse? Diabetes Care 2008 10.2337/DC08-S264 18227495
DeFronzo R Insulin resistance, lipotoxicity, type 2 diabetes and atherosclerosis: the missing links. The Claude Bernard Lecture 2009 Diabetologia 2010 53 1270 1287 1:CAS:528:DC%2BC3cXmsFyrs7w%3D 10.1007/S00125-010-1684-1 20361178 2877338
Penn L White M Lindström J Importance of Weight loss maintenance and risk prediction in the prevention of type 2 diabetes: analysis of European diabetes prevention study RCT PLoS ONE 2013 10.1371/journal.pone.0057143 24244303 3820682
Gannon MC, Nuttall FQ (2006) Control of blood glucose in type 2 diabetes without weight loss by modification of diet composition. Nutr Metabolism. https://doi.org/10.1186/1743-7075-3-16
Thomas T Pfeiffer A Foods for the prevention of diabetes: how do they work? Diab/Metab Res Rev 2012 28 25 49 10.1002/DMRR.1229
Schwingshackl L Hoffmann G Lampousi A et al. Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies Eur J Epidemiol 2017 32 363 375 10.1007/S10654-017-0246-Y 28397016 5506108
Deer J Koska J Ozias M Reaven P Dietary models of insulin resistance Metab Clin Exp 2015 64 163 171 1:CAS:528:DC%2BC2cXhvVamsrzP 10.1016/J.METABOL.2014.08.013 25441706
Hu FB Dietary pattern analysis: A new direction in nutritional epidemiology Curr Opin Lipidol 2002 13 3 9 1:CAS:528:DC%2BD38XitV2ms7k%3D 10.1097/00041433-200202000-00002 11790957
Medina-Remón A Kirwan R Lamuela-Raventós RM Estruch R Dietary patterns and the risk of obesity, type 2 diabetes mellitus, cardiovascular diseases, asthma, and neurodegenerative diseases Crit Rev Food Sci Nutr 2018 58 262 296 10.1080/10408398.2016.1158690 27127938
McNaughton SA Mishra GD Brunner EJ Dietary patterns, insulin resistance, and incidence of type 2 diabetes in the whitehall II study Diabetes Care 2008 31 1343 1348 10.2337/dc07-1946 18390803 2453656
Newby PK Tucker KL Empirically derived eating patterns using factor or cluster analysis: a review Nutr Rev 2004 62 177 203 1:STN:280:DC%2BD2czhtVehsg%3D%3D 10.1301/nr.2004.may.177-203 15212319
Jacobs DR Steffen LM Nutrients, foods, and dietary patterns as exposures in research: a framework for food synergy Am J Clin Nutr 2003 78 508S 513 1:CAS:528:DC%2BD3sXntFekt7g%3D 10.1093/ajcn/78.3.508S 12936941
Jacques PF Tucker KL Are dietary patterns useful for understanding the role of diet in chronic disease? Am J Clin Nutr 2001 73 1 2 1:CAS:528:DC%2BD3MXitVSlsg%3D%3D 10.1093/ajcn/73.1.1 11124739
Hoffmann K Schulze M Schienkiewitz A et al. Application of a new statistical method to derive dietary patterns in nutritional epidemiology Am J Epidemiol 2004 159 935 944 10.1093/AJE/KWH134 15128605
Schulze MB Hoffmann K Manson JAE et al. Dietary pattern, inflammation, and incidence of type 2 diabetes in women Am J Clin Nutr 2005 82 675 1:CAS:528:DC%2BD2MXhtVaksL3I 10.1093/AJCN.82.3.675 16155283 2563043
Jannasch F Kröger J Schulze M Dietary Patterns and Type 2 Diabetes: A Systematic Literature Review and Meta-Analysis of Prospective Studies J Nutr 2017 147 1174 1182 1:CAS:528:DC%2BC2sXhvFCht7bN 10.3945/JN.116.242552 28424256
Petersen MC Vatner DF Shulman GI Regulation of hepatic glucose metabolism in health and disease Nat Rev Endocrinol 2017 13 572 587 1:CAS:528:DC%2BC2sXht1Whsr3O 10.1038/nrendo.2017.80 28731034 5777172
Hodson L Karpe F Hyperinsulinaemia: does it tip the balance toward intrahepatic fat accumulation? Endocr Connect 2019 8 R157 R168 1:CAS:528:DC%2BB3cXjtF2kt78%3D 10.1530/EC-19-0350 31581129 6826170
Abdul-Ghani MA DeFronzo RA Pathogenesis of insulin resistance in skeletal muscle J Biomed Biotechnol 2010 2010 476279 1:CAS:528:DC%2BC3cXlvVCgt7w%3D 10.1155/2010/476279 20445742 2860140
Gijbels A Trouwborst I Jardon K The PERSonalized Glucose Optimization Through Nutritional Intervention (PERSON) Study: Rationale, Design and Preliminary Screening Results Front Nutr 2021 10.3389/FNUT.2021.694568 34277687 8278004
Abdul-Ghani M Matsuda M Balas B DeFronzo R Muscle and liver insulin resistance indexes derived from the oral glucose tolerance test Diabetes Care 2007 30 89 94 1:CAS:528:DC%2BD2sXhsFWqtb4%3D 10.2337/DC06-1519 17192339
O’Donovan SD Lenz M Goossens GH Improved quantification of muscle insulin sensitivity using oral glucose tolerance test data: the MISI Calculator Sci Rep 2019 10.1038/S41598-019-45858-W 31253846 6598992
DeFronzo R Simonson D Ferrannini E Hepatic and peripheral insulin resistance: a common feature of type 2 (non-insulin-dependent) and type 1 (insulin-dependent) diabetes mellitus Diabetologia 1982 23 313 319 1:STN:280:DyaL3s%2FlsVCluw%3D%3D 10.1007/BF00253736 6754515
Goldberg GR Black AE Jebb SA et al. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording - PubMed Eur J Clin Nutr 1991 45 569 581 1:STN:280:DyaK383ktFamsA%3D%3D 1810719
Black AE Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations Int J Obes 2000 2000 24 9 1119 1130 1:STN:280:DC%2BD3cvosFehsg%3D%3D 10.1038/sj.ijo.0801376
De Streppel MT Vries JH Meijboom S Relative validity of the food frequency questionnaire used to assess dietary intake in the Leiden Longevity Study Nutr J 2013 10.1186/1475-2891-12-75 23758629 3680188
The National Institute for Public Health and the Environment (RIVM) (2006) Dutch Food Composition Database (NEVO). https://www.rivm.nl/en/dutch-food-composition-database
Heidemann C Hoffmann K Spranger J et al. A dietary pattern protective against type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)- -Potsdam Study cohort Diabetologia 2005 48 1126 1134 1:STN:280:DC%2BD2MzhtFSlsw%3D%3D 10.1007/S00125-005-1743-1 15889235
Qian F Liu G Hu F et al. Association Between Plant-Based Dietary Patterns and Risk of Type 2 Diabetes: A Systematic Review and Meta-analysis JAMA Intern Med 2019 179 1335 1344 10.1001/JAMAINTERNMED.2019.2195 31329220 6646993
Ehrampoush E Nazari N Homayounfar R et al. Association between dietary patterns with insulin resistance in an Iranian population Clin Nutr ESPEN 2020 36 45 52 10.1016/J.CLNESP.2020.02.011 32220368
Pestoni G Riedl A Breuninger T et al. Association between dietary patterns and prediabetes, undetected diabetes or clinically diagnosed diabetes: results from the KORA FF4 study Eur J Nutr 2021 60 2331 2341 1:CAS:528:DC%2BB3cXitlWgt7fP 10.1007/S00394-020-02416-9 33125578
Kromhout D Spaaij C de Goede J Weggemans R The 2015 Dutch food-based dietary guidelines Eur J Clin Nutr 2016 70 869 878 1:CAS:528:DC%2BC28XmtVOitL0%3D 10.1038/EJCN.2016.52 27049034 5399142
Looman M Feskens EJM De Rijk M et al. Development and evaluation of the Dutch Healthy Diet index 2015 Public Health Nutr 2017 20 2289 2299 10.1017/S136898001700091X 28625202 10261559
Willett W Stampfer MJ Total energy intake: implications for epidemiologic analyses Am J Epidemiol 1986 124 17 27 1:STN:280:DyaL283jvFWruw%3D%3D 10.1093/oxfordjournals.aje.a114366 3521261
Alberti KG Zimmet PZ Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation Diabet Med 1998 15 199807 539 553 1:STN:280:DyaK1czkvFCrsA%3D%3D 10.1002/(SICI)1096-9136 9686693)15:7%3C539::AID-DIA668%3E3.0.CO;2-S
Matthews D Hosker J Rudenski A et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man Diabetologia 1985 28 412 419 1:CAS:528:DyaL2MXlslKnu7k%3D 10.1007/BF00280883 3899825
Matsuda M DeFronzo R Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp Diabetes Care 1999 22 1462 1470 1:STN:280:DyaK1MvgvVertQ%3D%3D 10.2337/DIACARE.22.9.1462 10480510
Jardon KM Canfora EE Goossens GH Blaak EE Dietary macronutrients and the gut microbiome: a precision nutrition approach to improve cardiometabolic health Gut 2022 71 1214 1226 10.1136/gutjnl-2020-323715 35135841 9120404
Bo T Gao L Yao Z et al. Hepatic selective insulin resistance at the intersection of insulin signaling and metabolic dysfunction-associated steatotic liver disease Cell Metab 2024 36 947 968 1:CAS:528:DC%2BB2cXhtVSltrnF 10.1016/j.cmet.2024.04.006 38718757
Batis C Mendez MA Gordon-Larsen P et al. Using both principal component analysis and reduced rank regression to study dietary patterns and diabetes in Chinese adults Public Health Nutr 2016 19 195 203 10.1017/S1368980014003103 26784586
Duan MJ Dekker LH Carrero JJ Navis G Blood lipids-related dietary patterns derived from reduced rank regression are associated with incident type 2 diabetes Clin Nutr 2021 40 4712 4719 1:CAS:528:DC%2BB3MXhvVKru7bM 10.1016/J.CLNU.2021.04.046 34237698
Jacobs S Kroeger J Schulze MB Dietary Patterns Derived by Reduced Rank Regression Are Inversely Associated with Type 2 Diabetes Risk across 5 Ethnic Groups in the Multiethnic Cohort Curr Developments Nutr 2017 10.3945/CDN.117.000620
Seah JYH Ong CN Koh WP et al. A Dietary Pattern Derived from Reduced Rank Regression and Fatty Acid Biomarkers Is Associated with Lower Risk of Type 2 Diabetes and Coronary Artery Disease in Chinese Adults J Nutr 2019 149 2001 2010 10.1093/JN/NXZ164 31386157 6825830
Brayner B Kaur G Keske MA Novel approach to investigate the association between type 2 diabetes risk and dietary fats in a dietary pattern context: a scoping review Front Nutr 2023 10.3389/FNUT.2023.1071855 37324743 10267339
Trouwborst I Bowser SM Goossens GH Blaak EE Ectopic Fat Accumulation in Distinct Insulin Resistant Phenotypes; Targets for Personalized Nutritional Interventions Front Nutr 2018 5 77 1:CAS:528:DC%2BC1MXitFKhurzJ 10.3389/fnut.2018.00077 30234122 6131567
Skytte MJ Samkani A Petersen AD et al. A carbohydrate-reduced high-protein diet improves HbA1c and liver fat content in weight stable participants with type 2 diabetes: a randomised controlled trial Diabetologia 2019 62 2066 2078 1:CAS:528:DC%2BC1MXhsVerur7E 10.1007/s00125-019-4956-4 31338545
Hodson L Rosqvist F Parry SA The influence of dietary fatty acids on liver fat content and metabolism Proc Nutr Soc 2020 79 30 41 1:CAS:528:DC%2BB3cXhs1enu7g%3D 10.1017/S0029665119000569 30942685
Gastaldelli A Cusi K Pettiti M et al. Relationship between hepatic/visceral fat and hepatic insulin resistance in nondiabetic and type 2 diabetic subjects Gastroenterology 2007 133 496 506 1:CAS:528:DC%2BD2sXhtVWqsr%2FO 10.1053/j.gastro.2007.04.068 17681171
Seppälä-Lindroos A Vehkavaara S Häkkinen A-M et al. Fat accumulation in the liver is associated with defects in insulin suppression of glucose production and serum free fatty acids independent of obesity in normal men J Clin Endocrinol Metab 2002 87 3023 3028 10.1210/jcem.87.7.8638 12107194
Buso ME Boshuizen HC Naomi ND et al. Relative validity of habitual sugar and low/no-calorie sweetener consumption assessed by food frequency questionnaire, multiple 24-h dietary recalls and urinary biomarkers: an observational study within the SWEET project Am J Clin Nutr 2024 119 546 559 1:CAS:528:DC%2BB2cXmvVejuw%3D%3D 10.1016/j.ajcnut.2023.11.019 38043866
Joosen AMCP Kuhnle GGC Runswick SA Bingham SA Urinary sucrose and fructose as biomarkers of sugar consumption: comparison of normal weight and obese volunteers Int J Obes (Lond) 2008 32 1736 1740 1:CAS:528:DC%2BD1cXhtlyjtr%2FK 10.1038/ijo.2008.145 18725895
Slurink IAL den Braver NR Rutters F et al. Dairy product consumption and incident prediabetes in Dutch middle-aged adults: the Hoorn Studies prospective cohort Eur J Nutr 2022 61 183 196 1:CAS:528:DC%2BB3MXitVSksLfM 10.1007/s00394-021-02626-9 34245355
Eussen SJPM van Dongen MCJM Wijckmans N et al. Consumption of dairy foods in relation to impaired glucose metabolism and type 2 diabetes mellitus: the Maastricht Study Br J Nutr 2016 115 1453 1461 1:CAS:528:DC%2BC28XmvFKqsLc%3D 10.1017/S0007114516000313 26907098
Wagner S Girerd N Lemonnier C et al. Saturated fat from dairy sources and cardio-metabolic health: insights from the STANISLAS cohort Eur J Nutr 2025 64 267 1:CAS:528:DC%2BB2MXis1yqt7%2FE 10.1007/s00394-025-03763-1 40884570
Alvarez-Bueno C Cavero-Redondo I Martinez-Vizcaino V et al. Effects of Milk and Dairy Product Consumption on Type 2 Diabetes: Overview of Systematic Reviews and Meta-Analyses Adv Nutr 2019 10 S154 S163 10.1093/ADVANCES/NMY107 31089734 6518137
Berry SE Valdes AM Drew DA et al. Human Postprandial Responses to Food and Potential for Precision Nutrition Nat Med 2020 26 964 1:CAS:528:DC%2BB3cXhtFCms77M 10.1038/S41591-020-0934-0 32528151 8265154
Zeevi D Korem T Zmora N et al. Personalized Nutrition by Prediction of Glycemic Responses Cell 2015 163 1079 1094 1:CAS:528:DC%2BC2MXhvVyqtbvM 10.1016/J.CELL.2015.11.001 26590418
van der Kolk BW Vogelzangs N Jocken JWE et al. Plasma lipid profiling of tissue-specific insulin resistance in human obesity Int J Obes (Lond) 2019 43 989 998 1:CAS:528:DC%2BC1cXhslymtrnK 10.1038/s41366-018-0189-8 30242234
Vogelzangs N van der Kallen CJH van Greevenbroek MMJ et al. Metabolic profiling of tissue-specific insulin resistance in human obesity: results from the Diogenes study and the Maastricht Study Int J Obes 2020 44 1376 1386 1:CAS:528:DC%2BB3cXltl2ltb0%3D 10.1038/S41366-020-0565-Z
Van Der Kolk BW Kalafati M Adriaens M et al. Subcutaneous Adipose Tissue and Systemic Inflammation Are Associated With Peripheral but Not Hepatic Insulin Resistance in Humans Diabetes 2019 68 2247 2258 10.2337/DB19-0560 31492661
Trouwborst I Gijbels A Jardon KM et al. Cardiometabolic health improvements upon dietary intervention are driven by tissue-specific insulin resistance phenotype: A precision nutrition trial Cell Metabol 2023 35 71 83e5 1:CAS:528:DC%2BB3sXjtFKntw%3D%3D 10.1016/J.CMET.2022.12.002
Satija A Bhupathiraju SN Rimm EB Plant-based dietary patterns and incidence of type 2 diabetes in US men and women: results from three prospective cohort studies PLoS Med 2016 10.1371/JOURNAL.PMED.1002039 27299701 4907448
Chen Z Zuurmond MG van der Schaft N et al. Plant versus animal based diets and insulin resistance, prediabetes and type 2 diabetes: the Rotterdam Study Eur J Epidemiol 2018 33 883 893 1:CAS:528:DC%2BC1cXhtFGgsb%2FP 10.1007/S10654-018-0414-8/TABLES/2 29948369 6133017
Thomas MS Calle M Fernandez ML Healthy plant-based diets improve dyslipidemias, insulin resistance, and inflammation in metabolic syndrome. A narrative review. Advances in nutrition (Bethesda Md) 2023 14 44 54 10.1016/J.ADVNUT.2022.10.002