Physiogenomic comparison of human fat loss in response to diets restrictive of carbohydrate or fat

Richard L Seip, Jeff S Volek, Andreas Windemuth, Mohan Kocherla, Maria Luz Fernandez, William J Kraemer, Gualberto Ruaño, Richard L Seip, Jeff S Volek, Andreas Windemuth, Mohan Kocherla, Maria Luz Fernandez, William J Kraemer, Gualberto Ruaño

Abstract

Background: Genetic factors that predict responses to diet may ultimately be used to individualize dietary recommendations. We used physiogenomics to explore associations among polymorphisms in candidate genes and changes in relative body fat (Delta%BF) to low fat and low carbohydrate diets.

Methods: We assessed Delta%BF using dual energy X-ray absorptiometry (DXA) in 93 healthy adults who consumed a low carbohydrate diet (carbohydrate ~12% total energy) (LC diet) and in 70, a low fat diet (fat ~25% total energy) (LF diet). Fifty-three single nucleotide polymorphisms (SNPs) selected from 28 candidate genes involved in food intake, energy homeostasis, and adipocyte regulation were ranked according to probability of association with the change in %BF using multiple linear regression.

Results: Dieting reduced %BF by 3.0 +/- 2.6% (absolute units) for LC and 1.9 +/- 1.6% for LF (p < 0.01). SNPs in nine genes were significantly associated with Delta%BF, with four significant after correction for multiple statistical testing: rs322695 near the retinoic acid receptor beta (RARB) (p < 0.005), rs2838549 in the hepatic phosphofructokinase (PFKL), and rs3100722 in the histamine N-methyl transferase (HNMT) genes (both p < 0.041) due to LF; and the rs5950584 SNP in the angiotensin receptor Type II (AGTR2) gene due to LC (p < 0.021).

Conclusion: Fat loss under LC and LF diet regimes appears to have distinct mechanisms, with PFKL and HNMT and RARB involved in fat restriction; and AGTR2 involved in carbohydrate restriction. These discoveries could provide clues to important physiologic mechanisms underlying the Delta%BF to low carbohydrate and low fat diets.

Figures

Figure 1
Figure 1
Distribution of baseline and change in percent body fat for LF (top) and LC (bottom) groups. The vertical axes (Frequency) indicates the number of patients observed within a given 10% interval up to 60% (baseline, left panels) or within a given 2% or 5% interval (change, right panels) on the horizontal axes. Genotyping was not completed in 3 LF subjects and 7 LC subjects.
Figure 2
Figure 2
Physiogenomic representation of the most significant genetic associations found in the low fat diet group. Individual patient genotypes (circles) of each SNP are overlaid on the distribution of Δ%BF (thin line). Each circle represents a patient, with the horizontal axis specifying the Δ%BF, and the vertical axis the carrier status for the minor allele: bottom, non-carriers; middle, single-carriers; top, double-carriers. A LOESS fit of the allele frequency (thick line) as a function of Δ%BF is shown. The ordinate is labeled for the marker frequency (thick line) of the SNP denoted at the top of each panel. The ordinate scale is the same in all three panels. The ordinate scales for the genotypes (circles) and Δ%BF distribution (thin line) are not shown. The abscissa is labeled for Δ%BF in each panel. The abscissa scale is the same in all three panels and applies identically to marker frequency, genotypes, and Δ%BF distribution.
Figure 3
Figure 3
Physiogenomic representation of the most significant genetic associations found in the low carbohydrate group. See Figure 2 legend for details regarding individual patient genotypes (circles), the distribution of Δ%BF (thin line), and the LOESS fit of the allele frequency (thick line) as a function of Δ%BF.

References

    1. Volek J, Sharman M, Gomez A, Judelson D, Rubin M, Watson G, Sokmen B, Silvestre R, French D, Kraemer W. Comparison of energy-restricted very low-carbohydrate and low-fat diets on weight loss and body composition in overweight men and women. Nutr Metab (Lond) 2004;1:13. doi: 10.1186/1743-7075-1-13.
    1. Krauss RM, Blanche PJ, Rawlings RS, Fernstrom HS, Williams PT. Separate effects of reduced carbohydrate intake and weight loss on atherogenic dyslipidemia. Am J Clin Nutr. 2006;83:1025–1031.
    1. Gardner CD, Kiazand A, Alhassan S, Kim S, Stafford RS, Balise RR, Kraemer HC, King AC. Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women: the A TO Z Weight Loss Study: a randomized trial. JAMA. 2007;297:969–977. doi: 10.1001/jama.297.9.969.
    1. Foster GD, Wyatt HR, Hill JO, McGuckin BG, Brill C, Mohammed BS, Szapary PO, Rader DJ, Edman JS, Klein S. A randomized trial of a low-carbohydrate diet for obesity. N Engl J Med. 2003;348:2082–2090. doi: 10.1056/NEJMoa022207.
    1. Ruaño G, Windemuth A, Kocherla M, Holford T, Fernandez ML, Forsythe CE, Wood RJ, Kraemer WJ, Volek JS. Physiogenomic analysis of weight loss induced by dietary carbohydrate restriction. Nutr Metab (Lond) 2006;3:20. doi: 10.1186/1743-7075-3-20.
    1. Krieger JW, Sitren HS, Daniels MJ, Langkamp-Henken B. Effects of variation in protein and carbohydrate intake on body mass and composition during energy restriction: a meta-regression 1. Am J Clin Nutr. 2006;83:260–274.
    1. Harber MP, Schenk S, Barkan AL, Horowitz JF. Effects of dietary carbohydrate restriction with high protein intake on protein metabolism and the somatotropic axis. J Clin Endocrinol Metab. 2005;90:5175–5181. doi: 10.1210/jc.2005-0559.
    1. Kennedy AR, Pissios P, Otu HH, Xue B, Asakura K, Furukawa N, Marino FE, Liu FF, Kahn BB, Libermann TA, Maratos-Flier E. A High Fat, Ketogenic Diet, Induces a Unique Metabolic State in Mice. Am J Physiol Endocrinol Metab. 2007;292:E1724–E1739. doi: 10.1152/ajpendo.00717.2006.
    1. Kallio P, Kolehmainen M, Laaksonen DE, Kekalainen J, Salopuro T, Sivenius K, Pulkkinen L, Mykkanen HM, Niskanen L, Uusitupa M, Poutanen KS. Dietary carbohydrate modification induces alterations in gene expression in abdominal subcutaneous adipose tissue in persons with the metabolic syndrome: the FUNGENUT Study. Am J Clin Nutr. 2007;85:1417–1427.
    1. Eckel RH, Hernandez TL, Bell ML, Weil KM, Shepard TY, Grunwald GK, Sharp TA, Francis CC, Hill JO. Carbohydrate balance predicts weight and fat gain in adults. Am J Clin Nutr. 2006;83:803–808.
    1. Zurlo F, Lillioja S, Esposito-Del Puente A, Nyomba BL, Raz I, Saad MF, Swinburn BA, Knowler WC, Bogardus C, Ravussin E. Low ratio of fat to carbohydrate oxidation as predictor of weight gain: study of 24-h RQ. Am J Physiol. 1990;259:E650–E657.
    1. Thomas CD, Peters JC, Reed GW, Abumrad NN, Sun M, Hill JO. Nutrient balance and energy expenditure during ad libitum feeding of high-fat and high-carbohydrate diets in humans. Am J Clin Nutr. 1992;55:934–942.
    1. Rankinen T, Zuberi A, Chagnon YC, Weisnagel SJ, Argyropoulos G, Walts B, Perusse L, Bouchard C. The human obesity gene map: the 2005 update. Obesity (Silver Spring) 2006;14:529–644.
    1. Moreno-Aliaga MJ, Santos JL, Marti A, Martinez JA. Does weight loss prognosis depend on genetic make-up? Obes Rev. 2005;6:155–168. doi: 10.1111/j.1467-789X.2005.00180.x.
    1. Sorensen TI, Boutin P, Taylor MA, Larsen LH, Verdich C, Petersen L, Holst C, Echwald SM, Dina C, Toubro S, Petersen M, Polak J, Clement K, Martinez JA, Langin D, Oppert JM, Stich V, Macdonald I, Arner P, Saris WH, Pedersen O, Astrup A, Froguel P. Genetic Polymorphisms and Weight Loss in Obesity: A Randomised Trial of Hypo-Energetic High- versus Low-Fat Diets. PLoS Clin Trials. 2006;1:e12. doi: 10.1371/journal.pctr.0010012.
    1. Ruaño G, Windemuth A, Holford TR. Physiogenomics: Integrating Systems Engineering and Nanotechnology for Personalized Medicine. In: Bronzino JD, editor. Tissue Engineering and Artificial Organs. 3rd. Vol. 28. Boca Raton, CRC: Taylor and Francis; 2006. pp. 28-1–28-9. (Bioengineering Engineering Handbook).
    1. Ruaño G, Thompson PD, Windemuth A, Smith A, Kocherla M, Holford TR, Seip R, Wu AH. Physiogenomic analysis links serum creatine kinase activities during statin therapy to vascular smooth muscle homeostasis. Pharmacogenomics. 2005;6:865–872. doi: 10.2217/14622416.6.8.865.
    1. Ruaño G, Goethe JW, Caley C, Woolley S, Holford TR, Kocherla M, Windemuth A, Leon J. Physiogenomic comparison of weight profiles of olanzapine- and risperidone-treated patients. Mol Psychiatry. 2007;12:474–482.
    1. Ruaño G, Thompson PD, Windemuth A, Seip RL, Dande A, Sarokin A, Kocherla M, Smith A, Holford TR, Wu AH. Physiogenomic Association of Statin-Related Myalgia to Serotonin Receptors. Muscle and Nerve. 2007;36:329–335. doi: 10.1002/mus.20871.
    1. Paradisi G, Smith L, Burtner C, Leaming R, Garvey WT, Hook G, Johnson A, Cronin J, Steinberg HO, Baron AD. Dual energy X-ray absorptiometry assessment of fat mass distribution and its association with the insulin resistance syndrome. Diabetes Care. 1999;22:1310–1317. doi: 10.2337/diacare.22.8.1310.
    1. Glickman SG, Marn CS, Supiano MA, Dengel DR. Validity and reliability of dual-energy X-ray absorptiometry for the assessment of abdominal adiposity. J Appl Physiol. 2004;97:509–514. doi: 10.1152/japplphysiol.01234.2003.
    1. Volek JS, Sharman MJ, Gomez AL, Scheett TP, Kraemer WJ. An isoenergetic very low carbohydrate diet improves serum HDL cholesterol and triacylglycerol concentrations, the total cholesterol to HDL cholesterol ratio and postprandial pipemic responses compared with a low fat diet in normal weight, normolipidemic women. J Nutr. 2003;133:2756–2761.
    1. Volek JS, Sharman MJ, Love DM, Avery NG, Gomez AL, Scheett TP, Kraemer WJ. Body composition and hormonal responses to a carbohydrate-restricted diet. Metabolism. 2002;51:864–870. doi: 10.1053/meta.2002.32037.
    1. Sharman MJ, Kraemer WJ, Love DM, Avery NG, Gomez AL, Scheett TP, Volek JS. A ketogenic diet favorably affects serum biomarkers for cardiovascular disease in normal-weight men. J Nutr. 2002;132:1879–1885.
    1. Wood RJ, Volek JS, Liu Y, Shachter NS, Contois JH, Fernandez ML. Carbohydrate restriction alters lipoprotein metabolism by modifying VLDL, LDL, and HDL subfraction distribution and size in overweight men. J Nutr. 2006;136:384–389.
    1. Leibowitz SF. Overconsumption of dietary fat and alcohol: Mechanisms involving lipids and hypothalamic peptides. Physiol Behav. 2007:513–521. doi: 10.1016/j.physbeh.2007.03.018.
    1. Shaw AM, Irani BG, Moore MC, Haskell-Luevano C, Millard WJ. Ghrelin-induced food intake and growth hormone secretion are altered in melanocortin 3 and 4 receptor knockout mice. Peptides. 2005;26:1720–1727. doi: 10.1016/j.peptides.2004.12.026.
    1. HNMT, histamine N-methyl transferase, function. 2007.
    1. LIPF, Gastic Lipase, function. 2007.
    1. Adrian TE, Ferri GL, Bacarese-Hamilton AJ, Fuessl HS, Polak JM, Bloom SR. Human distribution and release of a putative new gut hormone, peptide YY. Gastroenterology. 1985;89:1070–1077.
    1. Ingenhoven N, Beck-Sickinger AG. Molecular characterization of the ligand-receptor interaction of neuropeptide Y. Curr Med Chem. 1999;6:1055–1066.
    1. Farooqi S. Treating obesity: does antagonism of NPY fit the bill? Cell Metab. 2006;4:260–262. doi: 10.1016/j.cmet.2006.09.006.
    1. Erondu N, Wadden T, Gantz I, Musser B, Nguyen AM, Bays H, Bray G, O'Neil PM, Basdevant A, Kaufman KD, Heymsfield SB, Amatruda JM. Effect of NPY5R antagonist MK-0557 on weight regain after very-low-calorie diet-induced weight loss. Obesity (Silver Spring) 2007;15:895–905.
    1. Balthasar N. Genetic dissection of neuronal pathways controlling energy homeostasis. Obesity (Silver Spring) 2006;14 Suppl 5:222S–227S.
    1. Butler AA. The melanocortin system and energy balance. Peptides. 2006;27:281–290. doi: 10.1016/j.peptides.2005.02.029.
    1. Tso P, Liu M. Apolipoprotein A-IV, food intake, and obesity. Physiol Behav. 2004;83:631–643. doi: 10.1016/j.physbeh.2004.07.032.
    1. Salway JG. Metabolism at a glance. 2nd. Oxford, Blackwell Science, Ltd.; 1999.
    1. PFKL, phosphofructokinase, liver, function. 2007.
    1. PFKM, phosphofructokinase, muscle, function. 2007.
    1. Morgan PJ, Ross AW, Mercer JG, Barrett P. What can we learn from seasonal animals about the regulation of energy balance? Prog Brain Res. 2006;153:325–337.
    1. Redonnet A, Groubet R, Noel-Suberville C, Bonilla S, Martinez A, Higueret P. Exposure to an obesity-inducing diet early affects the pattern of expression of peroxisome proliferator, retinoic acid, and triiodothyronine nuclear receptors in the rat. Metabolism. 2001;50:1161–1167. doi: 10.1053/meta.2001.26759.
    1. Xiao CW, Mei J, Huang W, Wood C, L'abbe MR, Gilani GS, Cooke GM, Curran IH. Dietary soy protein isolate modifies hepatic retinoic acid receptor-beta proteins and inhibits their DNA binding activity in rats. J Nutr. 2007;137:1–6.
    1. Zorad S, Fickova M, Zelezna B, Macho L, Kral JG. The role of angiotensin II and its receptors in regulation of adipose tissue metabolism and cellularity. Gen Physiol Biophys. 1995;14:383–391.
    1. Storgaard H, Poulsen P, Ling C, Groop L, Vaag AA. Relationships of plasma adiponectin level and adiponectin receptors 1 and 2 gene expression to insulin sensitivity and glucose and fat metabolism in monozygotic and dizygotic twins. J Clin Endocrinol Metab. 2007;92:2835–2839. doi: 10.1210/jc.2006-1812.
    1. Huang ZH, Luque RM, Kineman RD, Mazzone T. Nutritional regulation of adipose tissue apolipoprotein E expression. Am J Physiol Endocrinol Metab. 2007;293:E203–E209. doi: 10.1152/ajpendo.00118.2007.
    1. Jiang XC, Moulin P, Quinet E, Goldberg IJ, Yacoub LK, Agellon LB, Compton D, Schnitzer-Polokoff R, Tall AR. Mammalian adipose tissue and muscle are major sources of lipid transfer protein mRNA. J Biol Chem. 1991;266:4631–4639.
    1. Drayna D, Jarnagin AS, McLean J, Henzel W, Kohr W, Fielding C, Lawn R. Cloning and sequencing of human cholesteryl ester transfer protein cDNA. Nature. 1987;327:632–634. doi: 10.1038/327632a0.
    1. Luo Y, Tall AR. Sterol upregulation of human CETP expression in vitro and in transgenic mice by an LXR element. J Clin Invest. 2000;105:513–520. doi: 10.1172/JCI8573.
    1. Wolf G. Insulin resistance and obesity: resistin, a hormone secreted by adipose tissue. Nutr Rev. 2004;62:389–394. doi: 10.1301/nr.2004.oct.389-394.
    1. Fan JB, Oliphant A, Shen R, Kermani BG, Garcia F, Gunderson KL, Hansen M, Steemers F, Butler SL, Deloukas P, Galver L, Hunt S, McBride C, Bibikova M, Rubano T, Chen J, Wickham E, Doucet D, Chang W, Campbell D, Zhang B, Kruglyak S, Bentley D, Haas J, Rigault P, Zhou L, Stuelpnagel J, Chee MS. Highly parallel SNP genotyping. Cold Spring Harb Symp Quant Biol. 2003;68:69–78. doi: 10.1101/sqb.2003.68.69.
    1. Oliphant A, Barker DL, Stuelpnagel JR, Chee MS. BeadArray technology: enabling an accurate, cost-effective approach to high-throughput genotyping. Biotechniques. 2002;Suppl:56–61.
    1. Dalgaard P. Introductory Statistics with R. New York, Springer Science + Business Media; 2002.
    1. Faraway JJ. Linear Models with R. Boca Raton, FL, Chapman & Hall/CRC; 2004. (CRC Texts in Statistical Science Series).
    1. Maindonald J, Braun J. Data Analysis and Graphics Using R: An Example-based Approach. 2nd. Cambridge, Cambridge University Press; 2007.
    1. Reiner A, Yekutieli D, Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics. 2003;19:368–375. doi: 10.1093/bioinformatics/btf877.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B. 1995;57:289–300.
    1. Benjamini Y, Hochberg Y. On the adaptive control of the false discovery rate in multiple testing with independent statistics. Journal of Educational and Behavioral Statistics. 2000;25:60–83.
    1. Cleveland WS, Devlin SJ. Locally weighted regression: an approach to regression analysis by local fitting. Journal of the Amercian Statistical Association. 2007;83:596–610. doi: 10.2307/2289282.
    1. Cleveland WS. Robust locally weighted regression and smoothing scatterplots. Journal of the Amercian Statistical Association. 2007;74:829–836. doi: 10.2307/2286407.
    1. Yang Q, Graham TE, Mody N, Preitner F, Peroni OD, Zabolotny JM, Kotani K, Quadro L, Kahn BB. Serum retinol binding protein 4 contributes to insulin resistance in obesity and type 2 diabetes. Nature. 2005;436:356–362. doi: 10.1038/nature03711.
    1. Mattick JS. The functional genomics of noncoding RNA. Science. 2005;309:1527–1528. doi: 10.1126/science.1117806.
    1. Laudet V, Gronemeyer H. The nuclear receptor facts book. San Diego, Academic Press; 2002.
    1. Marshall H, Morrison A, Studer M, Popperl H, Krumlauf R. Retinoids and Hox genes. FASEB J. 1996;10:969–978.
    1. Loudig O, Babichuk C, White J, Abu-Abed S, Mueller C, Petkovich M. Cytochrome P450RAI(CYP26) promoter: a distinct composite retinoic acid response element underlies the complex regulation of retinoic acid metabolism. Mol Endocrinol. 2000;14:1483–1497. doi: 10.1210/me.14.9.1483.
    1. Shin DJ, Odom DP, Scribner KB, Ghoshal S, McGrane MM. Retinoid regulation of the phosphoenolpyruvate carboxykinase gene in liver. Mol Cell Endocrinol. 2002;195:39–54. doi: 10.1016/S0303-7207(02)00215-0.
    1. Shin DJ, McGrane MM. Vitamin A regulates genes involved in hepatic gluconeogenesis in mice: phosphoenolpyruvate carboxykinase, fructose-1,6-bisphosphatase and 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase. J Nutr. 1997;127:1274–1278.
    1. Lecklin A, Tuomisto L, MacDonald E. Metoprine, an inhibitor of histamine N-methyltransferase but not catechol-O-methyltransferase, suppresses feeding in sated and in food deprived rats. Methods Find Exp Clin Pharmacol. 1995;17:47–52.
    1. Brooks SP, Lampi BJ. Time course of enzyme changes after a switch from a high-fat to a low-fat diet. Comp Biochem Physiol B Biochem Mol Biol. 1997;118:359–365. doi: 10.1016/S0305-0491(97)00163-6.
    1. Liu J, Juo SH, Terwilliger JD, Grunn A, Tong X, Brito M, Loth JE, Kanyas K, Lerer B, Endicott J, Penchaszadeh G, Gilliam TC, Baron M. A follow-up linkage study supports evidence for a bipolar affective disorder locus on chromosome 21q22. Am J Med Genet. 2001;105:189–194. doi: 10.1002/ajmg.1195.
    1. Engeli S, Gorzelniak K, Kreutz R, Runkel N, Distler A, Sharma AM. Co-expression of renin-angiotensin system genes in human adipose tissue. J Hypertens. 1999;17:555–560. doi: 10.1097/00004872-199917040-00014.
    1. Yvan-Charvet L, Even P, Bloch-Faure M, Guerre-Millo M, Moustaid-Moussa N, Ferre P, Quignard-Boulange A. Deletion of the angiotensin type 2 receptor (AT2R) reduces adipose cell size and protects from diet-induced obesity and insulin resistance. Diabetes. 2005;54:991–999. doi: 10.2337/diabetes.54.4.991.
    1. Kuznetsova T, Staessen JA, Thijs L, Kunath C, Olszanecka A, Ryabikov A, Tikhonoff V, Stolarz K, Bianchi G, Casiglia E, Fagard R, Brand-Herrmann SM, Kawecka-Jaszcz K, Malyutina S, Nikitin Y, Brand E. Left ventricular mass in relation to genetic variation in angiotensin II receptors, renin system genes, and sodium excretion. Circulation. 2004;110:2644–2650. doi: 10.1161/.
    1. Zhang M, Ma H, Wang BS, Zhao YZ. Angiotensin II type 2 receptor gene polymorphisms and cardioprotective role in essential hypertension. Heart Vessels. 2006;21:95–101. doi: 10.1007/s00380-005-0865-1.
    1. Gannon MC, Nuttall FQ. Effect of feeding, fasting, and diabetes on liver glycogen synthase activity, protein, and mRNA in rats. Diabetologia. 1997;40:758–763. doi: 10.1007/s001250050746.
    1. Ruaño G. Quo Vadis Personalized Medicine? Personalized Medicine. 2004;1:1–7. doi: 10.1517/17410541.1.1.1.

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