Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index

Ping Wang, Claus Holst, Malene R Andersen, Arne Astrup, Freek G Bouwman, Sanne van Otterdijk, Will K W H Wodzig, Marleen A van Baak, Thomas M Larsen, Susan A Jebb, Anthony Kafatos, Andreas F H Pfeiffer, J Alfredo Martinez, Teodora Handjieva-Darlenska, Marie Kunesova, Wim H M Saris, Edwin C M Mariman, Ping Wang, Claus Holst, Malene R Andersen, Arne Astrup, Freek G Bouwman, Sanne van Otterdijk, Will K W H Wodzig, Marleen A van Baak, Thomas M Larsen, Susan A Jebb, Anthony Kafatos, Andreas F H Pfeiffer, J Alfredo Martinez, Teodora Handjieva-Darlenska, Marie Kunesova, Wim H M Saris, Edwin C M Mariman

Abstract

Background: Weight regain after weight loss is common. In the Diogenes dietary intervention study, high protein and low glycemic index (GI) diet improved weight maintenance.

Objective: To identify blood predictors for weight change after weight loss following the dietary intervention within the Diogenes study.

Design: Blood samples were collected at baseline and after 8-week low caloric diet-induced weight loss from 48 women who continued to lose weight and 48 women who regained weight during subsequent 6-month dietary intervention period with 4 diets varying in protein and GI levels. Thirty-one proteins and 3 steroid hormones were measured.

Results: Angiotensin I converting enzyme (ACE) was the most important predictor. Its greater reduction during the 8-week weight loss was related to continued weight loss during the subsequent 6 months, identified by both Logistic Regression and Random Forests analyses. The prediction power of ACE was influenced by immunoproteins, particularly fibrinogen. Leptin, luteinizing hormone and some immunoproteins showed interactions with dietary protein level, while interleukin 8 showed interaction with GI level on the prediction of weight maintenance. A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.

Conclusions: A selected panel of blood proteins/steroids can predict the weight change after weight loss. ACE may play an important role in weight maintenance. The interactions of blood factors with dietary components are important for personalized dietary advice after weight loss.

Registration: ClinicalTrials.gov NCT00390637.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. The predicting power of blood…
Figure 1. The predicting power of blood analytes for weight loss maintenance by Logistic Regression.
Volcano plot of the significance P-value versus odd ratio exp(B) of blood proteins/steroids for predicting continued weight loss during the 6-month maintenance period by logistic regression controlled for age and the fold change of weight. The symbols of the analytes are listed in Table 1. Suffix “_1”: concentration at CID1, “_2”: concentration at CID2, “_12”: the fold change of concentration CID2/CID1. The analytes are grouped as in Table 1 and marked in different shape/color.
Figure 2. Top 15 important predictors for…
Figure 2. Top 15 important predictors for weight loss maintenance identified by Random Forest.
A. The variables are ranked by the average of 10 runs on the mean decrease in classification accuracy (MDA) or by the mean decrease in classification Gini impurity (MDG). Suffix “_1”: concentration at CID1, “_2”: concentration at CID2, “_12”: fold change of the concentration (CID2/CID1). The symbol of blood analytes are listed in Table 1. B. Classification plot of continued weight-losers (red dots) and weight-regainers (blue triangles) during weight maintenance in pooled subjects (n = 96) by the top15 important variables.
Figure 3. The relation between weight maintenance…
Figure 3. The relation between weight maintenance score and the fold change of ACE during weight loss.
Boxplot shows the quartile range of weight maintenance score with outliers (in circle) across tertile of the fold change of ACE during weight loss, for subjects with low (≤9.6µmol/L, n = 48, blank bar) and with high (>9.6 µmol/L n = 47, grey bar) baseline fibrinogen level. The variation of weight maintenance score attributed to the fold change of ACE, p = 0.478 in low group and p = 0.014 in high group, was tested by one-way ANOVA controlled for age and the fold change of weight, and Bonferroni test for multiple comparisons. *T3 significantly different from T1 in high fibrinogen group, p = 0.013.
Figure 4. Predictors having interaction with dietary…
Figure 4. Predictors having interaction with dietary components for the outcome of weight maintenance.
Boxplots show the quartile range of the blood analytes without outliers for continued weight-losers (blank bar) and weight-regainers (grey bar) in each dietary group. The p-value above the chart is the significance of the interaction between dietary protein/GI and the concentration/change of the blood analyte with respect to the outcome of weight maintenance (weight-loss or -regain). The p-values under the chart is the significance of the prediction of the variable inside the subgroups. All were obtained by logistic regression (controlled for age and the fold change of weight). A. predictors having interaction with dietary protein levels. LP: low protein, HP: high protein. B. predictors having interaction with dietary glycemic index (GI) levels. LGI: low GI, HGI: high GI.

References

    1. Smyth S, Heron A. Diabetes and obesity: the twin epidemics. Nat Med. 2006;12:75–80.
    1. Wing RR, Phelan S. Long-term weight loss maintenance. Am J Clin Nutr. 2005;82:222S–225S.
    1. Moore CS, Lindroos AK, Kreutzer M, Larsen TM, Astrup A, et al. Dietary strategy to manipulate ad libitum macronutrient intake, and glycaemic index, across eight European countries in the Diogenes Study. Obes Rev. 2010;11:67–75.
    1. Larsen TM, Dalskov S, van Baak M, Jebb S, Kafatos A, et al. The Diet, Obesity and Genes (Diogenes) Dietary Study in eight European countries - a comprehensive design for long-term intervention. Obes Rev. 2010;11:76–91.
    1. Larsen TM, Dalskov S, van Baak MA, Jebb S, Papadaki A, et al. Diets with high or low protein content and glycemic index for weight-loss maintenance. N Engl J Med. 2010;363:2102–2113.
    1. Rankinen T, Zuberi A, Chagnon YC, Weisnagel SJ, Argyropoulos G, et al. The human obesity gene map: the 2005 update. Obesity (Silver Spring) 2006;14:529–644.
    1. Qi L, Cho YA. Gene-environment interaction and obesity. Nutr Rev. 2008;66:684–694.
    1. Vogels N, Mariman ECM, Bouwman FG, Kester ADM, Diepvens K, et al. Relation of weight maintenance and dietary restraint to peroxisome proliferator-activated receptor gamma 2, glucocorticoid receptor, and ciliary neurotrophic factor polymorphisms. Am J Clin Nutr. 2005;82:740–746.
    1. Phillips CM, Goumidi L, Bertrais S, Field MR, Peloso GM, et al. Dietary Saturated Fat Modulates the Association between STAT3 Polymorphisms and Abdominal Obesity in Adults. J Nutr. 2009;139:2011–2017.
    1. Pasman WJ, Saris WHM, Westerterp-Plantenga MS. Predictors of weight maintenance. Obes Res. 1999;7:43–50.
    1. Fabricatore AN, Wadden TA, Moore RH, Butryn ML, Heymsfield SB, et al. Predictors of attrition and weight loss success: Results from a randomized controlled trial. Behav Res Ther. 2009;47:685–691.
    1. Goyenechea E, Parra D, Crujeiras AB, Abete I, Martinez JA. A Nutrigenomic Inflammation-Related PBMC-Based Approach to Predict the Weight-Loss Regain in Obese Subjects. Ann Nutr Metab. 2009;54:43–51.
    1. Siroky D. Navigating Random Forests and related advances in algorithmic modeling. Statistics Surveys. 2009;3:147–163.
    1. Liaw A, Wiener M. Classification and Regression by randomForest. R news. 2002;2:18–22.
    1. R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2008.
    1. Xia J, Psychogios N, Young N, Wishart DS. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009;37:W652–660.
    1. Strobl C, Boulesteix AL, Zeileis A, Hothorn T. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics. 2007;8
    1. Eriksson U, Danilczyk U, Penninger JM. Just the Beginning: Novel Functions for Angiotensin-Converting Enzymes. Curr Biol. 2002;12:R745–R752.
    1. Brewster UC, Perazella MA. The renin-angiotensin-aldosterone system and the kidney: effects on kidney disease. Am J Med. 2004;116:263–272.
    1. Rigat B, Hubert C, Alhenc-Gelas F, Cambien F, Corvol P, et al. An insertion/deletion polymorphism in the angiotensin I-converting enzyme gene accounting for half the variance of serum enzyme levels. J Clin Invest. 1990;86:1343–1346.
    1. Engeli S, Bohnke J, Gorzelniak K, Janke J, Schling P, et al. Weight loss and the renin-angiotensin-aldosterone system. Hypertension. 2005;45:356–362.
    1. Harp JB, Henry SA, DiGirolamo M. Dietary weight loss decreases serum angiotensin-converting enzyme activity in obese adults. Obes Res. 2002;10:985–990.
    1. Goossens GH, Blaak EE, van Baak MA. Possible involvement of the adipose tissue renin-angiotensin system in the pathophysiology of obesity and obesity-related disorders. Obes Rev. 2003;4:43–55.
    1. Karlsson C, Lindell K, Ottosson M, Sjostrom L, Carlsson B, et al. Human adipose tissue expresses angiotensinogen and enzymes required for its conversion to angiotensin II. J Clin Endocrinol Metab. 1998;83:3925–3929.
    1. Skidgel RA, Erdos EG. Novel activity of human angiotensin I converting enzyme: release of the NH2- and COOH-terminal tripeptides from the luteinizing hormone-releasing hormone. Proc Natl Acad Sci U S A. 1985;82:1025–1029.
    1. Rouille Y, Martin S, Steiner DF. Differential processing of proglucagon by the subtilisin-like prohormone convertases PC2 and PC3 to generate either glucagon or glucagon-like peptide. J Biol Chem. 1995;270:26488–26496.
    1. Wulff BS, Johansen TE, Dalboge H, O'Hare MM, Schwartz TW. Processing of two homologous precursors, pro-neuropeptide Y and pro-pancreatic polypeptide, in transfected cell lines expressing different precursor convertases. J Biol Chem. 1993;268:13327–13335.
    1. McKinley MJ, Albiston AL, Allen AM, Mathai ML, May CN, et al. The brain renin-angiotensin system: location and physiological roles. Int J Biochem Cell Biol. 2003;35:901–918.
    1. Maubaret CG, Heads R, Dhamrait S, Montgomery H, Humphries SE. Down-regulation of ACE expression in huvecs using siRNA results in down-regulation of UCP2 expression: a potential mechanism of the clinical benefit of ACE inhibitors? Atherosclerosis. 2009;207:E10–E10.
    1. Mobbs CV. The “domino theory” of hunger: The hypothalamus is hot. Cell Metab. 2007;5:1–2.
    1. Gabay C, Kushner I. Acute-Phase Proteins and Other Systemic Responses to Inflammation. N Engl J Med. 1999;340:448–454.
    1. Festa A, D'Agostino R, Jr, Williams K, Karter AJ, Mayer-Davis EJ, et al. The relation of body fat mass and distribution to markers of chronic inflammation. Int J Obes Relat Metab Disord. 2001;25:1407–1415.
    1. Bouwman FG, Claessens M, van Baak MA, Noben JP, Wang P, et al. The Physiologic Effects of Caloric Restriction Are Reflected in the in Vivo Adipocyte-Enriched Proteome of Overweight/Obese Subjects. J Proteome Res. 2009;8:5532–5540.
    1. Calder PC. Fuel utilization by cells of the immune system. Proc Nutr Soc. 1995;54:65–82.
    1. Wolowczuk I, Verwaerde C, Viltart O, Delanoye A, Delacre M, et al. Feeding our immune system: impact on metabolism. Clin Dev Immunol. 2008;2008:639803.

Source: PubMed

Sponsors et collaborateurs

Les conditions médicales

Interventions en matière de drogue

3
S'abonner