A K FOTHERINGHAM1,2 , S M SOLON-BIET3, 4,9, A C MCMAHON3,5,9,W O BALLARD6, K RUOHONEN7, D J BORG1, 2 , D RAUBENHEIMER3,8, D G. LE COUTEUR3, 5, 9, S J SIMPSON3, 4, J M FORBES1,2
1Mater Research-UQ, Woolloongabba, QLD, Australia; 2University of Queensland, Faculty of Medicine, St Lucia, QLD, Australia; 3Charles Perkins Centre, University of Sydney, NSW, Australia; 4School of Life and Environmental Sciences, University of Sydney, NSW, Australia; 5 ANZAC Research Institute, Concord Hospital, University of Sydney, NSW, Australia; 6School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Australia; 7Cargill Aqua Nutrition, 4327, Sandnes, Norway; 8Faculty of Veterinary Science, University of Sydney, NSW, Australia; 9Centre for Education and Research on Aging, and Aging and Alzheimers Institute, Concord Hospital, University of Sydney, Sydney, NSW, Australia
Aim: To determine the lifetime effects of dietary macronutrient intake on markers of kidney ageing.
Background: The influence of dietary macronutrients consumed over a lifetime on kidney health has never been comprehensively examined. Here, we have used a novel geometric framework to assess long term effects of 25 macronutrient and caloric combinations on kidney function and structure.
Methods: C57BL/6J mice (♂/♀; N=4/group) were given 15 months ad-libitum access to 1 of 25 diets representing a spectrum of macronutrient combinations (protein, (5-60%) carbohydrate (20-75%) and fat (20-75%)), stratified by energy content (low, medium or high). Serum cystatin C (a surrogate for GFR) and risk factors for kidney disease (tubulointerstitial fibrosis, glomerulosclerosis, body composition, glucose tolerance, blood pressure, and lipids) were assessed. 3-dimensional models were used to visualise and quantify the impacts of macronutrients, as main effects and interactions using the mgcv package for R.
Results: Serum cystatin C, which ranged from 126 to 1006 ng/ml, was significantly influenced by dietary protein intake (P<.0001), whereby GFR increased with protein intake. TIF ranged from (0.5 to 9%) and was influenced by protein consumption in conjunction with either a high fat or high carbohydrate intake (P=0.0005, P= 0.016 respectively). Overall, lower protein intake in conjunction with higher consumption of fat and therefore calories resulted in a phenotype with the lowest GFR and the most structural damage as assessed by TIF and GSI. Macronutrient combinations which increased blood pressure, elevated cholesterol or triglycerides or resulted in glucose intolerance did not associate with adverse kidney outcomes.
Conclusions: Macronutrients, individually and in combination influence risk factors for CKD, providing a rationale to further explore these effects in both humans and animal models.