Changes in the lipidome in type 1 diabetes following low carbohydrate diet: Post-hoc analysis of a randomized crossover trial

Naba Al-Sari, Signe Schmidt, Tommi Suvitaival, Min Kim, Kajetan Trošt, Ajenthen G Ranjan, Merete B Christensen, Anne J Overgaard, Flemming Pociot, Kirsten Nørgaard, Cristina Legido-Quigley, Naba Al-Sari, Signe Schmidt, Tommi Suvitaival, Min Kim, Kajetan Trošt, Ajenthen G Ranjan, Merete B Christensen, Anne J Overgaard, Flemming Pociot, Kirsten Nørgaard, Cristina Legido-Quigley

Abstract

Aims: Lipid metabolism might be compromised in type 1 diabetes, and the understanding of lipid physiology is critically important. This study aimed to compare the change in plasma lipid concentrations during carbohydrate dietary changes in individuals with type 1 diabetes and identify links to early-stage dyslipidaemia. We hypothesized that (1) the lipidomic profiles after ingesting low or high carbohydrate diet for 12 weeks would be different; and (2) specific annotated lipid species could have significant associations with metabolic outcomes.

Methods: Ten adults with type 1 diabetes (mean ± SD: age 43.6 ± 13.8 years, diabetes duration 24.5 ± 13.4 years, BMI 24.9 ± 2.1 kg/m2, HbA1c 57.6 ± 2.6 mmol/mol) using insulin pumps participated in a randomized 2-period crossover study with a 12-week intervention period of low carbohydrate diet (< 100 g carbohydrates/day) or high carbohydrate diet (> 250 g carbohydrates/day), respectively, separated by a 12-week washout period. A large-scale non-targeted lipidomics was performed with mass spectrometry in fasting plasma samples obtained before and after each diet intervention. Longitudinal lipid levels were analysed using linear mixed-effects models.

Results: In total, 289 lipid species were identified from 14 major lipid classes. Comparing the two diets, 11 lipid species belonging to sphingomyelins, phosphatidylcholines and LPC(O-16:0) were changed. All the 11 lipid species were significantly elevated during low carbohydrate diet. Two lipid species were most differentiated between diets, namely SM(d36:1) (β ± SE: 1.44 ± 0.28, FDR = 0.010) and PC(P-36:4)/PC(O-36:5) (β ± SE: 1.34 ± 0.25, FDR = 0.009) species. Polyunsaturated PC(35:4) was inversely associated with BMI and positively associated with HDL cholesterol (p < .001).

Conclusion: Lipidome-wide outcome analysis of a randomized crossover trial of individuals with type 1 diabetes following a low carbohydrate diet showed an increase in sphingomyelins and phosphatidylcholines which are thought to reduce dyslipidaemia. The polyunsaturated phosphatidylcholine 35:4 was inversely associated with BMI and positively associated with HDL cholesterol (p < .001). Results from this study warrant for more investigation on the long-term effect of single lipid species in type 1 diabetes.

Trial registration: ClinicalTrials.gov NCT02888691.

Keywords: cardiovascular disease; dyslipidaemia; lipidomics; low carbohydrate diet; randomized trial; type 1 diabetes.

Conflict of interest statement

None of the investigators has personal interests in the conduct or the outcomes of the study.

© 2021 The Authors. Endocrinology, Diabetes & Metabolism published by John Wiley & Sons Ltd.

Figures

Figure 1
Figure 1
Jittered box plots of pre‐post lipid levels comparisons of PC(35:4) in each diet intervention and association with BMI and HDL cholesterol. PC(35:4): (β ± SE: 1.04 ± 0.28, p = .043) was elevated during LCD (a) and inversely associated with BMI (b) and positively associated with HDL cholesterol (c). HB: High carbohydrate diet at baseline, HF: High carbohydrate diet at follow‐up, LB: Low carbohydrate diet at baseline, LF: Low carbohydrate diet at follow‐up

References

    1. Melendez‐Ramirez LY, Richards RJ, Cefalu WT. Complications of type 1 diabetes. Endocrinol Metab Clin North Am. 2010;39(3):625‐640.
    1. Ahola AJ, Forsblom C, Harjutsalo V, Groop P. Dietary carbohydrate intake and cardio‐metabolic risk factors in type 1 diabetes. Diabetes Res Clin Pract. 2019;155(2019):107818.
    1. Schofield JD, Liu Y, Rao‐Balakrishna P, Mlik RA, Soran H. diabetes dyslipidemia. Diabetes Ther. 2016;7(2):203‐219.
    1. Lopes SA, Fumani C, Jornayvaz FR, Philippe J, Gastaldi G. (2018) Dyslipidemia management in patients with type 1 diabetes. Rev Med Suisse 14(609), 1118‐1122.
    1. Vergès B. Dyslipidemia in type 1 diabetes: a masked danger. Trends Endocrinol Metab. 2020;31(6):422‐434.
    1. Homma TK, Endo CM, Saruhashi T, et al. Dyslipidemia in young patients with type 1 diabetes mellitus. Arch Endocrinol Metab. 2015;215‐219.
    1. Wu L, Parhofer KG. Diabetic dyslipidemia. Metabolism. 2014;63(12):1469‐1479.
    1. Schofield JD, Steiner R, Liu Y, et al. Ceramides, sphingolipids and deoxysphingolipids: Derailed carbohydrate and fatty acid metabolism in diabetes. Diabet Med. 2017;34(1):9.
    1. Alewijnse AE, Peters SLM. Sphingolipid signalling in the cardiovascular system: Good, bad or both? Eur J Pharmacol. 2008;585(2–3):292‐302.
    1. Jensen PN, Fretts AM, Hoofnagle AN, et al. Plasma ceramides and sphingomyelins in relation to atrial fibrillation risk: the cardiovascular health study. Am Heart J. 2020;9(4):1–13. 10.1161/JAHA.119.012853
    1. Yeboah J, McNamara C, Jiang XC, et al. Association of plasma sphingomyelin levels and incident coronary heart disease events in an adult population: Multi‐ethnic study of atherosclerosis. Arterioscler Thromb Vasc Biol. 2010;30(3):628‐633.
    1. Norris GH, Blesso CN. Dietary sphingolipids : potential for management of dyslipidemia and nonalcoholic fatty liver disease. Nutr Res Rev. 2017;75(4):274‐285.
    1. Stegemann C, Pechlaner R, Willeit P. Lipidomics profiling and risk of cardiovascular disease in the prospective population‐based bruneck study. J. Vasc Surg. 2014;60(2):532.
    1. Meikle PJ, Christopher MJ. Lipidomics is providing new insight into the metabolic syndrome and its sequelae. Curr Opin Lipidol. 2011;22(3):210‐215.
    1. Yang K, Han X. Lipidomics: Techniques, applications, and outcomes related to biomedical sciences. Trends Biochem Sci. 2017;41(11):954‐969.
    1. Loizides‐mangold U. On the future of mass‐spectrometry‐based lipidomics. FEBS J. 2013;280(12):2817‐2829.
    1. Ståhlman M, Pham HT, Adiels M, et al. Clinical dyslipidaemia is associated with changes in the lipid composition and inflammatory properties of apoprotein‐B‐containing lipoproteinjs from women with type 2 diabetes. Diabetologia. 2012;55:1156‐1166.
    1. Schmidt S, Christensen MB, Serifovski N, et al. Low versus high carbohydrate diet in type 1 diabetes : A 12‐week randomized open‐label crossover study. Diabetes Obes Metab. 2019;21(7):1680‐1688.
    1. Folch J, Lees M, Sloane GHS. A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem. 1957;226:497‐509.
    1. Tofte N, Suvitaival T, Ahonen L, et al. Lipidomic analysis reveals sphingomyelin and phosphatidylcholine species associated with renal impairment and all‐cause mortality in type 1 diabetes. Sci Rep. 2019;79:16398.
    1. Bowden JA, Heckert A, Ulmer CZ, et al. Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950‐Metabolites in Frozen Human Plasma. J Lipid Res. 2017;58:2275‐2288.
    1. Pluskal T, Castillo S, Villar‐Briones A, Orešič M. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry‐based molecular profile data. BMC Bioinformatics. 2010;11(1):395.
    1. The R Project for Statistical Computing . Available at: . Accessed: 1 Aug 2019
    1. Sas KM, Karnovsky A, Michailidis G, Pennathur S. Metabolomics and diabetes : analytical and computational approaches. Diabetes. 2015;64(3):718‐732.
    1. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003;100(16):9440‐9445.
    1. Lamichhane S, Ahonen L, Dyrlund TS, Siljander H. Data Descriptor : A longitudinal plasma lipidomics dataset from children who developed islet autoimmunity and type 1 diabetes. Sci Data. 2018;5:1‐9.
    1. Ohvo‐Rekilä H, Ramstedt B, Leppimäki P, Slotte JP. Cholesterol interactions with phospholipids in membranes. Prog Lipid Res. 2002;41(1):66‐97.
    1. Cohn JS, Kamili A, Wat E, Chung RWS, Tandy S. Dietary phospholipids and intestinal cholesterol absorption. Nutrients. 2010;2(2):116‐127.
    1. Shahin MH, Gong Y, Frye RF. Sphingolipid metabolic pathway impacts thiazide diuretics blood pressure response: Insights from genomics, metabolomics, and lipidomics. Am Heart J. 2018;7(1). 10.1161/JAHA.117.006656.1–6.
    1. Feinman RD, Pogozelski WK, Astrup A, et al. Dietary carbohydrate restriction as the first approach in diabetes management: Critical review and evidence base. Nutrition. 2015;31(1):1‐13.
    1. Fechner E, Smeets ETHC, Schrauwen P, Mensink R. The effect of different degrees of carbohydrate restriction and carbohydrate replacement on cardiometabolic risk markers in humans‐A systematic review and meta‐analysis. Nutrition. 2020;12(4):991.
    1. Holm LJ, Haupt‐Jorgensen M, Giacobini JD, et al. Fenofibrate increases very‐long‐chain sphingolipids and improves blood glucose homeostasis in NOD mice. Diabetologia. 2019;62:2262‐2272.
    1. Paquette M, Baass A. Predicting cardiovascular disease in familial hypercholesterolemia. Curr Opin Lipidol. 2018;29(4):299‐306.
    1. Sanin V, Koenig W. Therapy of hypercholesterolemia in primary prevention. Dtsch Med Wochenschr. 2019;144(5):322‐328.
    1. Illingworth DR. Management of hypercholesterolemia. Med Clin North Am. 2000;84(1):23‐42.
    1. Rysz J, Gluba‐Brzózka A, Rysz‐Górzynska M, Franczyk B. The role and function of HDL in patients with chronic kidney disease and the risk of cardiovascular disease. Int J Mol Sci. 2020;21:601.
    1. Alshehry ZH, Mundra PA, Barlow CK, et al. Plasma lipidomics profiles improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes mellitus. Circulation. 2016;134:1637‐1650.

Source: PubMed

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