Glycaemic Response to a Nut-Enriched Diet in Asian Chinese Adults with Normal or High Glycaemia: The Tū Ora RCT
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Diet Groups
2.3. Study Design
2.4. Anthropometry
2.5. Body Composition—DXA
2.6. Body Composition—MRI and MRS Organ Fat Imaging
2.7. Blood Sample Analyses
2.8. Compliance Assessment
2.9. Statistical Analysis
3. Results
3.1. Participant Flow and Characteristics at CID1/Baseline
3.2. Compliance to Treatment
3.3. Diet Subgroups: HP-NB vs. HC-CB
3.4. Glycaemia Subgroups: Normal vs. Impaired Fasting Glucose
3.5. Ectopic Fat Subgroups: Normal vs. High Pancreas and/or Liver Fat
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- International Diabetes Federation. IDF Diabetes Atlas, 10th ed.; International Diabetes Federation: Brussels, Belgium, 2021. [Google Scholar]
- Moreira, N.C.D.V.; Ceriello, A.; Basit, A.; Balde, N.; Mohan, V.; Gupta, R.; Misra, A.; Bhowmik, B.; Lee, M.K.; Zuo, H.; et al. Race/ethnicity and challenges for optimal insulin therapy. Diabetes Res. Clin. Pr. 2021, 175, 108823. [Google Scholar] [CrossRef] [PubMed]
- Rattarasarn, C. Dysregulated lipid storage and its relationship with insulin resistance and cardiovascular risk factors in non-obese Asian patients with type 2 diabetes. Adipocyte 2018, 7, 71–80. [Google Scholar] [CrossRef] [PubMed]
- Sattar, N.; Gill, J.M. Type 2 diabetes as a disease of ectopic fat? BMC Med. 2014, 12, 123. [Google Scholar] [CrossRef] [PubMed]
- Wulan, S.N.; Westerterp, K.R.; Plasqui, G. Ethnic differences in body composition and the associated metabolic profile: A comparative study between Asians and Caucasians. Maturitas 2010, 65, 315–319. [Google Scholar] [CrossRef] [PubMed]
- Liew, C.F.; Seah, E.S.; Yeo, K.P.; Lee, K.O.; Wise, S.D. Lean, nondiabetic Asian Indians have decreased insulin sensitivity and insulin clearance, and raised leptin compared to Caucasians and Chinese subjects. Int. J. Obes. 2003, 27, 784–789. [Google Scholar] [CrossRef] [PubMed]
- Sequeira, I.R.; Yip, W.; Lu, L.; Jiang, Y.; Murphy, R.; Plank, L.; Zhang, S.; Liu, H.; Chuang, C.L.; Vazhoor-Amarsingh, G.; et al. Visceral adiposity and glucoregulatory peptides are associated with susceptibility to Type 2 Diabetes: The TOFI_Asia Study. Obesity 2020, 28, 2368–2378. [Google Scholar] [CrossRef] [PubMed]
- Singh, R.G.; Yoon, H.D.; Poppitt, S.D.; Plank, L.D.; Petrov, M.S. Ectopic fat accumulation in the pancreas and its biomarkers: A systematic review and meta-analysis. Diabetes Metab. Res. Rev. 2017, 33, e2918. [Google Scholar] [CrossRef] [PubMed]
- Brownlee, M. Biochemistry and molecular cell biology of diabetic complications. Nature 2001, 414, 813–820. [Google Scholar] [CrossRef] [PubMed]
- Livesey, G.; Taylor, R.; Livesey, H.; Liu, S. Is there a dose-response relation of dietary glycemic load to risk of type 2 diabetes? Meta-analysis of prospective cohort studies. Am. J. Clin. Nutr. 2013, 97, 584–596. [Google Scholar] [CrossRef]
- Kataoka, M.; Venn, B.J.; Williams, S.M.; Te Morenga, L.A.; Heemels, I.M.; Mann, J.I. Glycaemic responses to glucose and rice in people of Chinese and European ethnicity. Diabet. Med. 2013, 30, e101–e107. [Google Scholar] [CrossRef]
- Tan, V.M.; Lee, Y.S.; Venkataraman, K.; Khoo, E.Y.; Tai, E.S.; Chong, Y.S.; Gluckman, P.; Leow, M.K.; Khoo, C.M. Ethnic differences in insulin sensitivity and beta-cell function among Asian men. Nutr. Diabetes 2015, 5, e173. [Google Scholar] [CrossRef] [PubMed]
- Venn, B.S.; Williams, S.M.; Mann, J.I. Comparison of postprandial glycaemia in Asians and Caucasians. Diabet. Med. 2010, 27, 1205–1208. [Google Scholar] [CrossRef] [PubMed]
- Hernández-Alonso, P.; Camacho-Barcia, L.; Bulló, M.; Salas-Salvadó, J. Nuts and dried fruits: An update of their beneficial effects on Type 2 Diabetes. Nutrients 2017, 9, 673. [Google Scholar] [CrossRef] [PubMed]
- Hou, Y.Y.; Ojo, O.; Wang, L.L.; Wang, Q.; Jiang, Q.; Shao, X.Y.; Wang, X.H. A randomized controlled trial to compare the effect of peanuts and almonds on the cardio-metabolic and inflammatory parameters in patients with Type 2 diabetes mellitus. Nutrients 2018, 10, 1565. [Google Scholar] [CrossRef] [PubMed]
- Luo, C.; Zhang, Y.; Ding, Y.; Shan, Z.; Chen, S.; Yu, M.; Hu, F.B.; Liu, L. Nut consumption and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality: A systematic review and meta-analysis. Am. J. Clin. Nutr. 2014, 100, 256–269. [Google Scholar] [CrossRef] [PubMed]
- Houston, L.; Probst, Y.C.; Singh, M.C.; Neale, E.P. Tree Nut and Peanut Consumption and Risk of Cardiovascular Disease. A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Adv. Nutr. Int. Rev. J. 2023, 14, 1029–1049. [Google Scholar] [CrossRef] [PubMed]
- Tan, S.Y.; Dhillon, J.; Mattes, R.D. A review of the effects of nuts on appetite, food intake, metabolism, and body weight. Am. J. Clin. Nutr. 2014, 100, 412S–422S. [Google Scholar] [CrossRef] [PubMed]
- Hernández-Alonso, P.; Salas-Salvadó, J.; Baldrich-Mora, M.; Juanola-Falgarona, M.; Bulló, M. Beneficial effect of pistachio consumption on glucose metabolism, insulin resistance, inflammation, and related metabolic risk markers: A randomized clinical trial. Diabetes Care 2014, 37, 3098–3105. [Google Scholar] [CrossRef] [PubMed]
- Kim, Y.; Keogh, J.B.; Clifton, P.M. Polyphenols and glycemic control. Nutrients 2016, 8, 17. [Google Scholar] [CrossRef]
- Augustin, L.S.A.; Kendall, C.W.C.; Jenkins, D.J.A.; Willett, W.C.; Astrup, A.; Barclay, A.W.; Björck, I.; Brand-Miller, J.C.; Brighenti, F.; Buyken, A.E.; et al. Glycemic index, glycemic load and glycemic response: An International Scientific Consensus Summit from the In-ternational Carbohydrate Quality Consortium (ICQC). Nutr. Metab. Cardiovasc. Dis. 2015, 25, 795–815. [Google Scholar] [CrossRef]
- Estruch, R.; Ros, E.; Salas-Salvadó, J.; Covas, M.-I.; Corella, D.; Arós, F.; Gómez-Gracia, E.; Ruiz-Gutiérrez, V.; Fiol, M.; Lapetra, J.; et al. Primary Prevention of Cardiovascular Disease with a Mediterranean Diet Supplemented with Extra-Virgin Olive Oil or Nuts. N. Engl. J. Med. 2018, 378, e34. [Google Scholar] [CrossRef] [PubMed]
- Gosal, H.; Kaur, H.; Ngassa, H.C.; A Elmenawi, K.; Anil, V.; Mohammed, L. The Significance of the Mediterranean Diet in the Management of Non-Alcoholic Fatty Liver Disease. A Syst. Review. Cureus 2021, 13, e15618. [Google Scholar] [CrossRef]
- Tindall, A.M.; Johnston, E.A.; Kris-Etherton, P.M.; Petersen, K.S. The effect of nuts on markers of glycemic control: A systematic review and meta-analysis of randomized controlled trials. Am. J. Clin. Nutr. 2019, 109, 297–314. [Google Scholar] [CrossRef] [PubMed]
- Ju, L.H.; Yu, D.M.; Xu, X.L. Status and variation trend of nut intake among Chinese residents, 2010-2012. Chinese J. Publ. Health 2017, 33, 916–918. [Google Scholar]
- Guasch-Ferré, M.; Li, J.; Hu, F.B.; Salas-Salvadó, J.; Tobias, D.K. Effects of walnut consumption on blood lipids and other cardiovascular risk factors: An updated meta-analysis and systematic review of controlled trials. Am. J. Clin. Nutr. 2018, 108, 174–187. [Google Scholar] [CrossRef] [PubMed]
- Afshin, A.; Micha, R.; Khatibzadeh, S.; Mozaffarian, D. Consumption of nuts and legumes and risk of incident ischemic heart disease, stroke, and diabetes: A systematic review and meta-analysis. Am. J. Clin. Nutr. 2014, 100, 278–288. [Google Scholar] [CrossRef] [PubMed]
- Aune, D.; Keum, N.; Giovannucci, E.; Fadnes, L.T.; Boffetta, P.; Greenwood, D.C.; Tonstad, S.; Vatten, L.J.; Riboli, E.; Norat, T. Nut consumption and risk of cardiovascular disease, total cancer, all-cause and cause-specific mortality: A systematic review and dose-response meta-analysis of prospective studies. BMC Med. 2016, 14, 207. [Google Scholar] [CrossRef] [PubMed]
- He, Y.; Li, Y.; Yang, X.; Hemler, E.C.; Fang, Y.; Zhao, L.; Zhang, J.; Yang, Z.; Wang, Z.; He, L.; et al. The dietary transition and its association with cardiometabolic mortality among Chinese adults, 1982–2012: A cross-sectional population-based study. Lancet Diabetes Endocrinol. 2019, 7, 540–548. [Google Scholar] [CrossRef] [PubMed]
- Lu, L.W.; Silvestre, M.P.; Sequeira, I.R.; Plank, L.D.; Foster, M.; Middleditch, N.; Acevedo-Fani, A.; Hollingsworth, K.G.; Poppitt, S.D. A higher-protein nut-based snack product suppresses glycaemia and decreases glycaemic response to co-ingested carbohydrate in an overweight prediabetic Asian Chinese cohort: The Tū Ora postprandial, RCT. J. Nutr. Sci. 2021, 10, e30. [Google Scholar] [CrossRef]
- Brown, R.C.; Ware, L.; Gray, A.R.; Chisholm, A.; Tey, S.L. Snacking on almonds lowers glycaemia and energy intake compared to a popular high-carbohydrate snack food: An acute randomised crossover study. Int. J. Environ. Res. Public. Health 2021, 18, 10989. [Google Scholar] [CrossRef]
- Brown, R.C.; Ware, L.; Gray, A.R.; Tey, S.L.; Chisholm, A. Comparing the effects of consuming almonds or biscuits on body weight in habitual snackers: A 1-year randomized controlled trial. Am. J. Clin. Nutr. 2023, 118, 228–240. [Google Scholar] [CrossRef] [PubMed]
- Lindström, J.; Tuomilehto, J. The diabetes risk score: A practical tool to predict type 2 diabetes risk. Diabetes Care 2003, 26, 725–731. [Google Scholar] [CrossRef] [PubMed]
- American Diabetes Association (ADA). Diagnosis and classification of diabetes mellitus. Diabetes Care 2010, 33, S62–S69. [Google Scholar] [CrossRef] [PubMed]
- Food Standards Australia New Zealand: Short Guide for Industry to the Nutrient Profiling Scoring Criterion. 2016. Available online: https://www.foodstandards.gov.au/business/labelling/Short-guide-for-industry-to-the-NPSC (accessed on 30 April 2024).
- Philipsen, S.C. Validation of a Newly Developed Eating Habits Questionnaire for New Zealand Women. Master’s Thesis, Massey University, Auckland, New Zealand, 2015. Available online: https://mro.massey.ac.nz/server/api/core/bitstreams/c7b13842-457d-4de4-92e4-a8d1e794ac71/content (accessed on 2 April 2024).
- Ministry of Health. Eating and Activity Guidelines for New Zealand Adults; Ministry of Health: Wellington, New Zealand, 2015.
- Chinese Diabetes Society. China medical nutrition therapy guideline for diabetes. Chin. J. Diabetes Mellit. 2015, 7, 73–78. [Google Scholar]
- Park, S.S.; Lim, S.; Kim, H.; Kim, K.M. Comparison of Two DXA Systems, Hologic Horizon W and GE Lunar Prodigy, for Assessing Body Composition in Healthy Korean Adults. Endocrinol. Metab. 2021, 36, 1219–1231. [Google Scholar] [CrossRef] [PubMed]
- Berglund, J.; Ahlström, H.; Johansson, L.; Kullberg, J. Two-point Dixon method with flexible ECHO times. Magn. Reson. Med. 2011, 65, 994–1004. [Google Scholar] [CrossRef] [PubMed]
- Schweitzer, L.; Geisler, C.; Pourhassan, M.; Braun, W.; Glüer, C.C.; Bosy-Westphal, A.; Müller, M.J. What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults? Am. J. Clin. Nutr. 2015, 102, 58–65. [Google Scholar] [CrossRef] [PubMed]
- Schneider, C.A.; Rasband, W.S.; Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 2012, 9, 671–675. [Google Scholar] [CrossRef] [PubMed]
- Al-Mrabeh, A.; Hollingsworth, K.G.; Steven, S.; Tiniakos, D.; Taylor, R. Quantification of intrapancreatic fat in type 2 diabetes by MRI. PLoS ONE 2017, 12, e0174660. [Google Scholar] [CrossRef]
- Bredella, M.A.; Ghomi, R.H.; Thomas, B.J.; Ouellette, H.A.; Sahani, D.V.; Miller, K.K.; Torriani, M. Breath-hold 1H-magnetic resonance spectroscopy for intrahepatic lipid quantification at 3 Tesla. J. Comput. Assist. Tomogr. 2010, 34, 372–376. [Google Scholar] [CrossRef]
- Crane, J.C.; Olson, M.P.; Nelson, S.J. SIVIC: Open-Source, Standards-Based Software for DICOM MR Spectroscopy Workflows. Int. J. Biomed. Imaging 2013, 2013, 169526. [Google Scholar] [CrossRef] [PubMed]
- Joblin-Mills, A.; Wu, Z.E.; Fraser, K.; Jones, B.; Yip, W.; Lim, J.J.; Lu, L.W.; Sequeira, I.R.; Poppitt, S.D. The impact of ethnicity and in-tra-pancreatic fat on the postprandial metabolome response to whey protein in overweight Asian Chinese and European Caucasian women with prediabetes. Front. Clin. Diab Healthc. 2022, 3, 980856. [Google Scholar] [CrossRef] [PubMed]
- Martin-Rodriguez, J.L.; Gonzalez-Cantero, J.; Gonzalez-Cantero, A.; Arrebola, J.P.; Gonzalez-Calvin, J.L. Diagnostic accuracy of serum alanine aminotransferase as biomarker for nonalcoholic fatty liver disease and insulin resistance in healthy subjects, using 3T MR spectroscopy. Medicine 2017, 96, e6770. [Google Scholar] [CrossRef] [PubMed]
- Matthews, D.R.; Hosker, J.P.; Rudenski, A.S.; Naylor, B.A.; Treacher, D.F.; Turner, R.C. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985, 28, 412–419. [Google Scholar] [CrossRef] [PubMed]
- Friedewald, W.T.; Levy, R.I.; Fredrickson, D.S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin. Chem. 1972, 18, 499–502. [Google Scholar] [CrossRef] [PubMed]
- Parnell, W.; Heath, A.-L.; Brown, R.; Walker, H.; Gray, A.; Blakey, C.; Fleming, L.; Turley, M.; Mackay, S.; Weerasekera, D. Methodology Report for the 2008/09 NZ Adult Nutrition Survey. Ministry of Health: Wellington, New Zealand. Available online: https://www.health.govt.nz/system/files/documents/publications/methodology-report.pdf (accessed on 1 December 2017).
- Fogh-Andersen, N.; Wimberley, P.D.; Thode, J.; Siggaard-Andersen, O. Direct reading glucose electrodes detect the molality of glucose in plasma and whole blood. Clin. Chim. Acta 1990, 189, 33–38. [Google Scholar] [CrossRef] [PubMed]
- Mohammadifard, N.; Salehi-Abargouei, A.; Salas-Salvadó, J.; Guasch-Ferré, M.; Humphries, K.; Sarrafzadegan, N. The effect of tree nut, peanut, and soy nut consumption on blood pressure: A systematic review and meta-analysis of randomized controlled clinical trials. Am. J. Clin. Nutr. 2015, 101, 966–982. [Google Scholar] [CrossRef]
- Ntzouvani, A.; Antonopoulou, S.; Nomikos, T. Effects of nut and seed consumption on markers of glucose metabolism in adults with prediabetes: A systematic review of randomised controlled trials. Br. J. Nutr. 2019, 122, 361–375. [Google Scholar] [CrossRef]
- Viguiliouk, E.; Kendall, C.W.; Blanco Mejia, S.; Cozma, A.I.; Ha, V.; Mirrahimi, A.; Jayalath, V.H.; Augustin, L.S.; Chiavaroli, L.; Leiter, L.A.; et al. Effect of tree nuts on glycemic control in diabetes: A systematic review and meta-analysis of randomized controlled dietary trials. PLoS ONE 2014, 9, e103376. [Google Scholar] [CrossRef]
- US Department of Agriculture (USDA). Composition of Foods Raw, Processed, Prepared USDA National Nutrient Database for Standard Reference, Release 23. Available online: https://www.ars.usda.gov/arsuserfiles/80400525/data/sr23/sr23_doc.pdf (accessed on 13 March 2022).
- Imamura, F.; Micha, R.; Wu, J.H.; de Oliveira Otto, M.C.; Otite, F.O.; Abioye, A.I.; Mozaffarian, D. Effects of saturated fat, polyunsaturated fat, monounsaturated fat, and carbohydrate on glucose-insulin homeostasis: A systematic review and meta-analysis of ran-domised controlled feeding trials. PLoS Med. 2016, 13, e1002087. [Google Scholar] [CrossRef]
- Njike, V.Y.; Ayettey, R.; Petraro, P.; Treu, J.A.; Katz, D.L. Walnut ingestion in adults at risk for diabetes: Effects on body composition, diet quality, and cardiac risk measures. BMJ Open Diabetes Res. Care 2015, 3, e000115. [Google Scholar] [CrossRef] [PubMed]
- Fernández-Rodríguez, R.; Martínez-Vizcaíno, V.; Garrido-Miguel, M.; A Martínez-Ortega, I.; Álvarez-Bueno, C.; Mesas, A.E. Nut consumption, body weight, and adiposity in patients with type 2 diabetes: A systematic review and meta-analysis of randomized controlled trials. Nutr. Rev. 2021, 80, 645–655. [Google Scholar] [CrossRef] [PubMed]
- Damasceno, N.R.; Pérez-Heras, A.; Serra, M.; Cofán, M.; Sala-Vila, A.; Salas-Salvadó, J.; Ros, E. Crossover study of diets enriched with virgin olive oil, walnuts or almonds. Effects on lipids and other cardiovascular risk markers. Nutr. Metab. Cardiovasc. Dis. 2011, 21, S14–S20. [Google Scholar] [CrossRef] [PubMed]
- Jenkins, D.J.; Kendall, C.W.; Marchie, A.; Josse, A.R.; Nguyen, T.H.; Faulkner, D.A.; Lapsley, K.G.; Singer, W. Effect of almonds on insulin secretion and insulin resistance in nondiabetic hyperlipidemic subjects: A randomized controlled crossover trial. Metabolism 2008, 57, 882–887. [Google Scholar] [CrossRef] [PubMed]
- Mukuddem-Petersen, J.; Stonehouse Oosthuizen, W.; Jerling, J.C.; Hanekom, S.M.; White, Z. Effects of a high walnut and high cashew nut diet on selected markers of the metabolic syndrome: A controlled feeding trial. Br. J. Nutr. 2007, 97, 1144–1153. [Google Scholar] [CrossRef] [PubMed]
- Wien, M.; Bleich, D.; Raghuwanshi, M.; Gould-Forgerite, S.; Gomes, J.; Monahan-Couch, L.; Oda, K. Almond consumption and car-diovascular risk factors in adults with prediabetes. J. Am. Coll. Nutr. 2010, 29, 189–197. [Google Scholar] [CrossRef] [PubMed]
- Wien, M.A.; Sabaté, J.M.; Iklé, D.N.; Cole, S.E.; Kandeel, F.R. Almonds vs complex carbohydrates in a weight reduction program. Int. J. Obes. Relat. Metab. Disord. 2003, 27, 1365–1372. [Google Scholar] [CrossRef] [PubMed]
- Abazarfard, Z.; Salehi, M.; Keshavarzi, S. The effect of almonds on anthropometric measurements and lipid profile in overweight and obese females in a weight reduction program: A randomized controlled clinical trial. J. Res. Med. Sci. 2014, 19, 457–464. [Google Scholar] [PubMed]
- Johnston, C.S.; Trier, C.M.; Fleming, K.R. The effect of peanut and grain bar preloads on postmeal satiety, glycemia, and weight loss in healthy individuals: An acute and a chronic randomized intervention trial. Nutr. J. 2013, 12, 35. [Google Scholar] [CrossRef]
- Moreira Alves, R.D.; Boroni Moreira, A.P.; Macedo, V.S.; Bressan, J.; de Cássia Gonçalves Alfenas, R.; Mattes, R.; Brunoro Costa, N.M. High-oleic peanuts: New perspective to attenuate glucose homeostasis disruption and inflammation related obesity. Obesity 2014, 22, 1981–1988. [Google Scholar] [CrossRef]
- Wien, M.; Oda, K.; Sabaté, J. A randomized controlled trial to evaluate the effect of incorporating peanuts into an American Diabetes Association meal plan on the nutrient profile of the total diet and cardiometabolic parameters of adults with type 2 diabetes. Nutr. J. 2014, 13, 10. [Google Scholar] [CrossRef] [PubMed]
- Mohan, V.; Gayathri, R.; Jaacks, L.M.; Lakshmipriya, N.; Anjana, R.M.; Spiegelman, D.; Jeevan, R.G.; Balasubramaniam, K.K.; Shobana, S.; Jayanthan, M.; et al. Cashew nut consumption increases HDL-cholesterol and reduces systolic blood pressure in Asian Indi-ans with Type 2 Diabetes: A 12-week randomized controlled trial. J. Nutr. 2018, 148, 63–69. [Google Scholar] [CrossRef] [PubMed]
- Bamberger, C.; Rossmeier, A.; Lechner, K.; Wu, L.; Waldmann, E.; Stark, R.G.; Altenhofer, J.; Henze, K.; Parhofer, K.G. A Walnut-Enriched Diet Reduces Lipids in Healthy Caucasian Subjects, Independent of Recommended Macronutrient Replacement and Time Point of Consumption: A Prospective, Randomized, Controlled Trial. Nutrients 2017, 9, 1097. [Google Scholar] [CrossRef] [PubMed]
- Katz, D.L.; Davidhi, A.; Ma, Y.; Kavak, Y.; Bifulco, L.; Njike, V.Y. Effects of walnuts on endothelial function in overweight adults with visceral obesity: A randomized, controlled, crossover trial. J. Am. Coll. Nutr. 2012, 31, 415–423. [Google Scholar] [CrossRef]
- Riley, T.M.; Kris-Etherton, P.M.; Hart, T.L.; Petersen, K.S. Intake of pistachios as a nighttime snack has similar effects on short- and longer-term glycemic control compared with education to consume 1-2 carbohydrate exchanges in adults with prediabetes: A 12-wk randomized crossover trial. J. Nutr. 2024, 154, 1219–1231. [Google Scholar] [CrossRef]
HP-NB | HC-CB | |||||
---|---|---|---|---|---|---|
g | kJ | %en | g | kJ | %en | |
One bar/day | 50 | 1009 | - | 64 | 985 | - |
Total CHO | 23 | 270 | 27 | 40 | 647 | 64 |
available CHO | 12 | 205 | 20 | 38 | 635 | 63 |
starch | 7 | 119 | 12 | 28 | 475 | 47 |
sugars | 5 | 87 | 9 | 10 | 160 | 16 |
fibre | 10 | 65 | 6 | 2 | 12 | 1 |
Total protein | 8 | 130 | 13 | 4 | 74 | 7 |
Total fat | 16 | 608 | 60 | 7 | 264 | 26 |
SFA | 2 | 72 | 7 | 1 | 24 | 2 |
MUFA | 9 | 333 | 34 | 4 | 148 | 15 |
PUFA | 5 | 185 | 19 | 2 | 74 | 7 |
Nutrient Profile Score | −2 | 3 |
All | Diet | Glycaemia | 5 Ectopic Fat | |||||||
---|---|---|---|---|---|---|---|---|---|---|
HP-NB | HC-CB | p | Normo- Glycaemia | Pre- Diabetes | p | NEF | HEF | p | ||
n | 101 | 53 | 48 | 61 | 40 | 36 | 63 | |||
Age (y) | 46.3, 11.1 | 46.6, 10.4 | 45.9, 12.0 | 0.750 | 44.9, 10.8 | 48.5, 11.4 | 0.106 | 44.2, 11.4 | 47.7, 10.7 | 0.127 |
Body weight (kg) | 76.3, 14.8 | 77.6, 16 | 74.7, 13.3 | 0.323 | 74.5, 13.8 | 79.0, 16.0 | 0.138 | 70.8, 10.3 | 79.6, 16.2 | 0.004 |
BMI (kg/m2) | 27.4, 3.6 | 27.6, 3.7 | 27.2, 3.5 | 0.577 | 27.0, 3.1 | 28.1, 4.2 | 0.146 | 26.0, 2.6 | 28.3, 3.9 | 0.003 |
Waist circumf (cm) | 91.7, 10.8 | 92.4, 11.2 | 90.9, 10.4 | 0.482 | 89.2, 9.1 | 95.5, 12.1 | 0.004 | 86.5, 8.5 | 94.7, 11.0 | <0.0001 |
Hip circumf (cm) | 102.7, 8.0 | 103.2, 8.8 | 102.0, 7.0 | 0.453 | 102.3, 6.9 | 103.2, 9.4 | 0.581 | 100.3, 6.7 | 103.9, 8.5 | 0.031 |
SBP (mmHg) | 117, 16 | 119, 17 | 116, 15 | 0.316 | 116, 16 | 119, 16 | 0.407 | 109, 15 | 122, 14 | <0.0001 |
DBP (mmHg) | 72, 13 | 75, 13 | 69, 11 | 0.027 | 71, 13 | 74, 12 | 0.216 | 68, 10 | 75, 13 | 0.008 |
Glycaemic markers | ||||||||||
1 Screen FPG (mmol/L) | 6.1, 0.4 | 6.1, 0.4 | 6.1, 0.4 | 0.473 | 6.0, 0.3 | 6.2, 0.4 | 0.004 | 6.1, 0.4 | 6.1, 0.3 | 0.366 |
FPG (mmol/L) | 5.5, 0.6 | 5.6, 0.6 | 5.5, 0.5 | 0.838 | 5.2, 0.3 | 6.1, 0.5 | <0.0001 | 5.4, 0.6 | 5.6, 0.5 | 0.206 |
HbA1c (mmol/mol) | 36.3, 10.5 | 36.1, 11.0 | 36.4, 10.1 | 0.902 | 32.7, 6.9 | 41.7, 12.6 | <0.0001 | 35.3, 10.9 | 36.5, 10.3 | 0.601 |
Insulin (uU/mL) | 13.2, 9.3 | 12.4, 8.4 | 14.1, 10.2 | 0.378 | 10.2, 6.9 | 17.9, 10.5 | <0.0001 | 10.3, 5.3 | 15.1, 10.6 | 0.013 |
HOMA-IR | 3.4, 2.6 | 3.2, 2.3 | 3.6, 2.8 | 0.443 | 2.4, 1.7 | 4.9, 3.0 | <0.0001 | 2.5, 1.4 | 3.9, 3.0 | 0.013 |
Lipid markers | ||||||||||
Total chol (mmol/L) | 4.9, 0.9 | 5.1, 0.7 | 4.7, 0.9 | 0.013 | 4.7, 0.8 | 5.2, 0.8 | 0.003 | 5.0, 0.9 | 4.9, 0.8 | 0.678 |
LDL-C (mmol/L) | 3.0, 0.7 | 3.2, 0.7 | 2.9, 0.7 | 0.029 | 2.9, 0.7 | 3.3, 0.7 | 0.017 | 3.0, 0.8 | 3.1, 0.7 | 0.907 |
HDL-C (mmol/L) | 1.2, 0.3 | 1.2, 0.3 | 1.2, 0.3 | 0.963 | 1.2, 0.3 | 1.1, 0.3 | 0.064 | 1.3, 0.3 | 1.1, 0.3 | 0.038 |
TG (mmol/L) | 1.6, 0.9 | 1.6, 0.9 | 1.5, 1 | 0.436 | 1.4, 0.7 | 1.9, 1.2 | 0.006 | 1.5, 1.1 | 1.6, 0.8 | 0.348 |
Inflammatory markers | ||||||||||
hsCRP (mg/L) | 13.3, 24.4 | 12.5, 23.1 | 14.1, 25.9 | 0.725 | 15.5, 30.0 | 9.9, 11.7 | 0.264 | 9.8, 22.7 | 15.6, 25.5 | 0.258 |
BDNF (ng/mL) | 836, 601 | 855, 716 | 815, 449 | 0.735 | 940, 665 | 678, 453 | 0.031 | 782, 428 | 862, 690 | 0.527 |
DKK-1 (ng/mL) | 17.1, 18.3 | 19.5, 24.0 | 14.5, 8.3 | 0.175 | 15.3, 8.9 | 19.8, 26.9 | 0.237 | 19.5, 27.3 | 16.0, 10.5 | 0.353 |
IL-6 (pg/mL) | 3.6, 5.2 | 4.2, 6.9 | 2.8, 1.8 | 0.177 | 2.9, 2.3 | 4.6, 7.7 | 0.116 | 3.4, 3.7 | 3.7, 5.9 | 0.843 |
S100beta (pg/mL) | 216, 163 | 230, 161 | 198, 165 | 0.352 | 199, 144 | 241, 187 | 0.218 | 226, 167 | 212, 163 | 0.700 |
2 DXA body composition | ||||||||||
Total FM (kg) | 25.8, 6.8 | 26.2, 6.8 | 25.3, 6.8 | 0.493 | 25.7, 6.1 | 25.8, 7.8 | 0.933 | 23.6, 5.1 | 27.1, 7.4 | 0.014 |
Total FFM (kg) | 50.5, 10.8 | 51.3, 11.3 | 49.5, 10.2 | 0.412 | 48.8, 10.6 | 52.9, 10.8 | 0.072 | 47.1, 9.1 | 52.7, 11.3 | 0.015 |
Total FM (%) | 33.7, 5.9 | 33.9, 5.3 | 33.5, 6.5 | 0.501 | 34.6, 5.8 | 32.4, 5.8 | 0.256 | 33.4, 5.6 | 33.8, 6.1 | 0.733 |
AbFM (%) | 40.6, 6.8 | 41.3, 6.1 | 39.9, 7.5 | 0.154 | 41.1, 7.3 | 39.9, 6.1 | 0.781 | 37.9, 7.1 | 42.1, 6.3 | 0.003 |
MRI/MRS imaging | ||||||||||
VAT:SAT ratio | 0.66, 0.29 | 0.69, 0.24 | 0.63, 0.33 | 0.328 | 0.58, 0.23 | 0.78, 0.31 | 0.002 | 0.55, 0.24 | 0.73, 0.29 | 0.001 |
3 Pancreas fat (%) | 4.8, 1.7 | 5.2, 1.7 | 4.4, 1.6 | 0.029 | 4.5, 1.6 | 5.3, 1.7 | 0.031 | 3.3, 0.8 | 5.7, 1.4 | <0.0001 |
4 Liver fat (%) | 5.2, 5.8 | 4.5, 4.4 | 6.0, 7.0 | 0.242 | 4.4, 5.1 | 6.4, 6.6 | 0.089 | 2.2, 1.6 | 7.0, 6.6 | <0.0001 |
HP-NB | HC-CB | |||
---|---|---|---|---|
Allocated to Diet Group | Completers | Allocated to Diet Group | Completers | |
n | 53 | 51 | 48 | 46 |
Total bars consumed | 58, 14 | 59, 12 | 60, 11 | 62, 10 |
Compliance (%) | 80, 19 | 82, 17 | 84, 16 | 85, 13 |
Variables | HP-NB | HC-CB | p | ||||
---|---|---|---|---|---|---|---|
Baseline | 12 Weeks | Baseline | 12 Weeks | Time | Diet | Diet*Time | |
n | 53 | 51 | 48 | 46 | |||
Body weight (kg) | 77.6, 2.2 | 77.0, 2.3 | 74.7, 1.9 | 74.8, 2.1 | 0.999 | 0.054 | 0.999 |
Body mass index, BMI (kg/m2) | 27.6, 0.5 | 27.3, 0.6 | 27.2, 0.5 | 27.3, 0.6 | 0.998 | 0.409 | 0.995 |
Waist circumference (cm) | 92.4, 1.5 | 91.7, 1.7 | 90.9, 1.5 | 89.9, 1 | 0.578 | 0.269 | 0.911 |
Hip circumference (cm) | 103.2, 1.2 | 101.6, 1.2 | 102.0, 1 | 101.8, 1.2 | 0.444 | 0.671 | 0.552 |
SBP (mmHg) | 119, 2 | 117, 2 | 116, 2 | 116, 2 | 0.710 | 0.429 | 0.485 |
DBP (mmHg) | 75, 2 | 68, 2 | 69, 2 | 65, 1 | 0.002 | 0.011 | 0.392 |
Fasting clinical markers | |||||||
FPG (mmol/L) | 5.6, 0.1 | 5.5, 0.1 | 5.5, 0.1 | 5.5, 0.1 | 0.509 | 0.822 | 0.991 |
HbA1c | 36.1, 1.5 | 40.2, 1.3 | 36.4, 1.6 | 39.5, 1.4 | 0.016 | 0.865 | 0.725 |
Insulin (µIU/mL) | 12.4, 1.1 | 15.3, 1.8 | 14.1, 1.5 | 14.8, 2.3 | 0.690 | 0.359 | 0.949 |
HOMA-IR | 3.2, 0.3 | 3.8, 0.5 | 3.6, 0.4 | 3.8, 0.7 | 0.711 | 0.350 | 0.991 |
Total cholesterol (mmol/L) | 5.1, 0.1 | 5.1, 0.1 | 4.7, 0.1 | 4.8, 0.1 | 0.803 | 0.002 | 0.835 |
LDL-C (mmol/L) | 3.2, 0.1 | 3.2, 0.1 | 2.9, 0.1 | 2.9, 0.1 | 0.866 | 0.017 | 0.741 |
HDL-C (mmol/L) | 1.2, 0.0 | 1.2, 0.0 | 1.2, 0.1 | 1.2, 0.1 | 0.573 | 0.694 | 0.648 |
TG (mmol/L) | 1.6, 0.1 | 2.0, 0.3 | 1.5, 0.1 | 1.5, 0.1 | 0.248 | 0.095 | 0.359 |
hsCRP (mg/L) | 12.5, 3.1 | 7.0, 1.3 | 14.1, 3.7 | 6.4, 1.3 | 0.014 | 0.844 | 0.654 |
BDNF (ng/mL) | 855, 98 | 741, 72 | 815, 65 | 666, 68 | 0.080 | 0.507 | 0.887 |
DKK-1 (ng/mL) | 19.5, 3.3 | 19.6, 3.3 | 14.5, 1.2 | 13.2, 1.2 | 0.830 | 0.027 | 0.763 |
IL-6 (pg/mL) | 4.2, 1.3 | 4.5, 1.1 | 2.8, 0.3 | 2.6, 0.3 | 0.786 | 0.037 | 0.795 |
S100beta (pg/mL) | 230, 26 | 220, 25 | 198, 27 | 168, 18 | 0.294 | 0.095 | 0.779 |
OGTT-glucose markers | |||||||
ΔCmax (mmol/L) | 4.7, 0.3 | 5.2, 0.4 | 4.6, 0.3 | 4.7, 0.3 | 0.431 | 0.481 | 0.651 |
iAUCglucose 0–120min (mmol/l*min) | 348, 30 | 385, 33 | 330, 29 | 357, 31 | 0.299 | 0.465 | 0.870 |
OGTT-insulin markers | |||||||
ΔCmax (µIU/mL) | 154.9, 13.3 | 156, 12.7 | 148.5, 11.8 | 159.9, 13.4 | 0.551 | 0.934 | 0.556 |
iAUCinsulin 0–120min (µIU/mL*min) | 10,942, 920 | 10,991, 894 | 10,134, 854 | 11,393, 1010 | 0.508 | 0.820 | 0.518 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sequeira-Bisson, I.R.; Lu, L.W.; Silvestre, M.P.; Plank, L.D.; Middleditch, N.; Acevedo-Fani, A.; Parry-Strong, A.; Hollingsworth, K.G.; Tups, A.; Miles-Chan, J.L.; et al. Glycaemic Response to a Nut-Enriched Diet in Asian Chinese Adults with Normal or High Glycaemia: The Tū Ora RCT. Nutrients 2024, 16, 2103. https://doi.org/10.3390/nu16132103
Sequeira-Bisson IR, Lu LW, Silvestre MP, Plank LD, Middleditch N, Acevedo-Fani A, Parry-Strong A, Hollingsworth KG, Tups A, Miles-Chan JL, et al. Glycaemic Response to a Nut-Enriched Diet in Asian Chinese Adults with Normal or High Glycaemia: The Tū Ora RCT. Nutrients. 2024; 16(13):2103. https://doi.org/10.3390/nu16132103
Chicago/Turabian StyleSequeira-Bisson, Ivana R., Louise W. Lu, Marta P. Silvestre, Lindsay D. Plank, Nikki Middleditch, Alejandra Acevedo-Fani, Amber Parry-Strong, Kieren G. Hollingsworth, Alexander Tups, Jennifer L. Miles-Chan, and et al. 2024. "Glycaemic Response to a Nut-Enriched Diet in Asian Chinese Adults with Normal or High Glycaemia: The Tū Ora RCT" Nutrients 16, no. 13: 2103. https://doi.org/10.3390/nu16132103
APA StyleSequeira-Bisson, I. R., Lu, L. W., Silvestre, M. P., Plank, L. D., Middleditch, N., Acevedo-Fani, A., Parry-Strong, A., Hollingsworth, K. G., Tups, A., Miles-Chan, J. L., Krebs, J. D., Foster, M., & Poppitt, S. D. (2024). Glycaemic Response to a Nut-Enriched Diet in Asian Chinese Adults with Normal or High Glycaemia: The Tū Ora RCT. Nutrients, 16(13), 2103. https://doi.org/10.3390/nu16132103