- Research Paper
- Open Access
Effect of body fat distribution on the transcription response to dietary fat interventions
© The Author(s) 2009
Received: 20 March 2009
Accepted: 14 April 2009
Published: 30 April 2009
Combination of decreased energy expenditure and increased food intake results in fat accumulation either in the abdominal site (upper body obesity, UBO) or on the hips (lower body obesity, LBO). In this study, we used microarray gene expression profiling of adipose tissue biopsies to investigate the effect of body fat distribution on the physiological response to two dietary fat interventions. Mildly obese UBO and LBO male subjects (n = 12, waist-to-hip ratio range 0.93–1.12) were subjected to consumption of diets containing predominantly either long-chain fatty acids (PUFA) or medium-chain fatty acids (MCT). The results revealed (1) a large variation in transcription response to MCT and PUFA diets between UBO and LBO subjects, (2) higher sensitivity of UBO subjects to MCT/PUFA dietary intervention and (3) the upregulation of immune and apoptotic pathways and downregulation of metabolic pathways (oxidative, lipid, carbohydrate and amino acid metabolism) in UBO subjects when consuming MCT compared with PUFA diet. In conclusion, we report that despite the recommendation of MCT-based diet for improving obesity phenotype, this diet may have adverse effect on inflammatory and metabolic status of UBO subjects. The body fat distribution is, therefore, an important parameter to consider when providing personalized dietary recommendation.
The principal causes of the increasing obesity problem worldwide are sedentary lifestyles and an increased intake of high-fat, energy-dense diets. Combination of decreased energy expenditure and increased energy or food intake results in fat accumulation either in the abdominal site (upper body obesity, UBO) or on the hips (lower body obesity, LBO), also known as the apple- and pear-shape generalization. Handling of the consumed fatty acids is hypothesized to be different in men with UBO and LBO because of their fat disposition and the routing of the different fatty acids. Upper body obesity also seems to be related to increased metabolic risk .
It has been shown that the chain length of dietary fatty acids affects fat absorption, fat metabolism and hepatic gene expression profiles [4, 11, 13]. During the consumption of dietary fats, the first pass of the fatty acids differs: the long-chain fatty acids (PUFA) will be absorbed by the chylomicrons via the lymph and transported through the body to the periphery and the medium-chain fatty acids (MCT) will directly be transported as free fatty acids via the portal vein to the liver [3, 8]. As a result, PUFA might be disposed to the periphery (organs, muscles or subcutaneous fat) and MCT might easily be disposed centrally. Owing to their rapid breakdown and routing directly to the liver instead of being stored, diet rich in MCT has been advised to people suffering from obesity problems [10, 14].
In this study, we investigated how a difference in body fat distribution affects the physiological response to two dietary fat interventions, namely consumption of a spread containing predominantly PUFA and a spread containing predominantly MCT. We analyzed by microarray gene expression profiling the response of subcutaneous adipose tissue from upper body obese males and LBO males to these two different dietary fat interventions. The results suggest that physiological effects of dietary interventions are dependent on body fat distribution. In addition, this study shows that consumption of MCT-rich diet may have adverse effects for upper body obese males, due to the increased inflammation and decreased energy metabolism in adipose tissue.
Methods and materials
Study design and subjects
The study was conducted at TNO Quality of Life, where subjects were recruited from a pool of volunteers. After being informed about the study, both verbally and in writing, each subject signed the informed consent form. Twelve healthy male subjects aged between 30 and 60 years and with body mass index (BMI) between 27 and 35 kg/m2 were included in the study. Subjects were selected so that range of waist-hip ratio (WHR) was as high as possible, in the selected group WHR ranged from 0.93 to 1.12. Subjects with WHR <1 (n = 7) were considered LBO and subjects with WHR >1 (n = 5) upper body obese (UBO) .
The study was approved by the Medical Ethics Committee of Tilburg (METOPP) (18 April 2007) and conducted according to the current assembly (52nd) of the Declaration of Helsinki (Edinburgh, Scotland, October 2000) and the ICH Guidelines for Good Clinical Practice (ICH Topic E6, adopted 01-05-1996 and implemented 17-01-1997). The study was designed as a randomized, double-blind, cross-over trial and two treatments were supplied for 3 weeks with a wash-out period of 6 weeks in-between. The two dietary interventions consisted of consumption of a spread containing predominantly PUFA (71% long-chain fatty acids (mainly C18:2), 28% C16:0 and 1% fatty acids shorter than C16) and consumption of a spread containing predominantly MCT (65% C8:0 and C10:0, 29% C16:0 and 6% fatty acids longer than C18). In both the spreads, a similar percentage of palmitic acid (C16:0: 28–29%) is present, necessary to make a margarine spreadable.
The treatments were given in random order. Randomization was restricted for age, BMI and WHR. Each day, subjects consumed 60 g of the spread for 2.5 weeks; during the start of the intervention period subjects only consumed half the amount (so 30 g of test spread daily) to become habituated to the unusual fatty acids. The test spread replaced their normal spread and oil use. Two portions of 30 g of spread were consumed with each main meal; portions of 15 g were provided at the beginning of the intervention period. With the test spread provided, about 50% of the daily fat intake was of experimental origin. During the complete study period (84 days), no changes were seen in body weight or waist and hip circumferences. Also, the 6 weeks wash-out in-between both test periods did not affect these main characteristics of the study population.
Adipose tissue biopsies were taken at the end of the 3-week intervention.
Adipose tissue biopsies
On days 21 and 63, the last day of each supplementation period, biopsies of the subcutaneous fat tissue of the abdomen were taken as follows. The skin was disinfected with alcohol (70%) and after local anesthesia with lidocaine a fat biopsy was taken by needle aspiration. Part of the fat suspension was immediately frozen in liquid nitrogen and stored at –80°C for RNA isolation. Adipose tissue biopsies were not available from 1 subject, therefore, 11 subjects were included in the microarray analysis.
RNA isolation, labeling and hybridization
RNA was isolated using RNA-Bee (Campro Scientific, Veenendaal, The Netherlands) and glass beads according to the manufacturer’s instructions. The integrity of RNA obtained was examined by Agilent Lab-on-a-chip technology using the RNA 6000 Nano LabChip kit and a Bioanalyzer 2100 (both Agilent Technologies, Amstelveen, The Netherlands).
The isolated RNA samples were sent to ServiceXS BV (Leiden, The Netherlands) where they were processed according to Affymetrix protocols. Briefly, RNA concentration was determined by absorbence at 260 nm, and quality and integrity was verified using the Agilent 2100 Bioanalyzer (Agilent Technologies). Next, 2 μg of high quality total RNA was used with the Affymetrix Eukaryotic One-Cycle Target Labeling and Control reagents to generate biotin-labeled antisense cRNA. The quality of the cRNA was checked using the Agilent 2100 bioanalyzer.
The labeled cRNA was hybridized to the NuGO Affymetrix Human Genechip NuGO_Hs1a520180 (custom designed by the European Nutrigenomics Organisation NuGO, consisting of 23,941 probesets including 71 control probesets, for details see http://blog.bigcat.unimaas.nl/~martijn/NuGO/). Absolute gene expression values were calculated from the scanned array using the Affymetrix GCOS software. In total, study included 22 microarray hybridizations (two treatments of 11 subjects). The study design including one microarray per subject for each treatment was chosen to maximize the number of biological replicates (6 LBO and 5 UBO subjects) and, thus, power to detect the expression differences.
Quality control and normalization of microarray data was performed using R/BioConductor packages through the NuGO MadMax pipeline (https://madmax.bioinformatics.nl). Raw signal intensities were normalized using the GCRMA algorithm. The custom CDF file for NuGO_Hs1a520180 (based on Entrez Gene, version 10.0.0; available via http://nugo-r.bioinformatics.nl/NuGO_R.html) was used to re-annotate the probes to new probesets, remove poor quality probes and derive unique signal values for different probesets representing the same gene. This resulted in gene expression values for 16,242 genes with unique identifiers. The gene expression was required to have a value above 10 in at least 1 of 22 samples, resulting in a set of 9,716 genes considered as expressed in adipose tissue. Principal component analysis and analysis of overrepresented Gene Ontology “Biological process” categories were performed using GeneSpring GX 7.3.1 software (Agilent). A paired t test for treatment effect in all subjects and the UBO and LBO subgroups was performed in BRB ArrayTools (software for microarray data analysis developed by Dr. Richard Simon and Amy Peng Lam, http://linus.nci.nih.gov/BRB-ArrayTools.html), using p-value of 0.01 as a threshold for significance. The per-subject analysis of over- or under-represented pathways and gene sets upon consuming MCT versus PUFA spread was performed using T-profiler . As input values, log2 ratios of all 9,716 genes were used. Gene ontology (GO, www.geneontology.org), curated gene sets and motif gene sets (Molecular Signatures Database, http://www.broad.mit.edu/gsea/msigdb/index.jsp) and Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) pathway collections were used as source of queried gene sets. Pathways were considered significant if E-value (Bonferroni corrected) was smaller than 0.05. T-values resulting from T-profiler analysis were hierarchicaly clustered (Pearson correlation (uncentered), complete linkage) and visualized by HierarchicalClusteringViewer and HeatmapViewer modules within the GenePattern analysis suite [5, 6, 12]. The correlation between WHR and gene expression was analysed in SAS Enterprise guide 4.1 (SAS Institute Inc., Cary, USA). A Pearson correlation coefficient higher than 0.7 or lower then –0.7, associated with p-value smaller than 0.016 was used as threshold for significance. The gene expression data will be made available through ArrayExpress, a public repository for microarray data (www.ebi.ac.uk/arrayexpress/).
Overview of subject’s identification numbers, waist-to-hip ratios (WHR, sorted from lowest to highest) and body fat distribution grouping (either UBO or LBO)
The distinct transcriptional response of UBO and LBO subjects was supported by statistical analysis of differentially expressed genes between MCT- and PUFA-based dietary interventions. To investigate the overall effects of the MCT and PUFA diets, a paired (per subject) t test comparing two interventions (MCT vs. PUFA) was performed in (1) all subjects and (2) separately in UBO and LBO groups. Applying the statistical cutoff of p-value <0.01, 179 genes were identified as differentially expressed among all subjects, whereas 239 and 73 genes were identified as differentially expressed in UBO and LBO groups, respectively. Despite the decreased sample number when analysis was performed separately for UBO and LBO groups, we were able to identify a larger number of differentially expressed genes in the UBO group than when all subjects were analysed. This confirms the large heterogeneity of inter-subject responses to two dietary interventions observed by PCA and the dependence of this response on the subject’s body fat distribution. In addition, three times larger number of differentially expressed genes in UBO compared to LBO group shows that UBO subjects are more sensitive than LBO subjects to MCT versus PUFA dietary intervention.
Long and medium-chain fatty acids have different metabolic routing upon consumption: while long-chain fatty acids are considered to be disposed to the periphery, medium-chain fatty acids are rather disposed centrally. In addition, medium-chain fatty acids are more rapidly oxidized, increase energy expenditure after short-term feeding and attenuate weight gain after long-term feeding compared to long-chain fatty acids . These differences in metabolic handling form a basis for a belief that consumption of MCT (as opposed to PUFA) may be beneficial for improving obesity-associated disorders.
In this study, we investigated the effect of body fat distribution on the physiological response to dietary fat interventions based on MCT and long-chain fatty acids (PUFA). The microarray gene expression profiling of the subcutaneous adipose tissue revealed that (1) there is a large variation in the transcription response to MCT and PUFA diets between UBO and LBO subjects, (2) UBO subjects are more sensitive to MCT versus PUFA dietary intervention than the LBO subjects and (3) the regulation of immune and apoptotic response and metabolic activity (including oxidative, lipid, carbohydrate and amino acid metabolism) in response to MCT versus PUFA dietary intervention can be predicted by subject’s body fat distribution: immune and apoptotic response increase, while metabolic activity decreases with increasing WHR.
These results demonstrate that the benefit of consumption of MCT-rich diet on obesity disorders strongly depends on body fat distribution. Although this diet may have positive effects for LBO subjects, increased inflammation and decreased energy metabolism suggest that MCT-rich diet has adverse effect on obesity-related complications of UBO subjects. The origin of these negative effects may be associated with hepatic fatty acid overload in UBO subjects. Since the UBO phenotype is usually associated with non-alcoholic fatty liver disease (NAFLD), transport of MCT directly to the liver by the portal vein may result in excessive hepatic fatty acid burden. This in turn would cause negative effects on obesity disorders on the systems level and provoke adverse changes that we detect in adipose tissue. The proposed mechanism is not plausible in LBO subjects because the particular risk of developing hepatic steatosis is specific to central obesity (UBO) .
The results obtained in this study highlight the importance of the personalized approach to understanding of a complex relationship between diet and physiology. The body fat distribution emerges as an important parameter to consider when assessing effects of long and medium chain dietary fats. Employment of a means to predict subject-specific effects of dietary interventions, such as gene expression signatures and biomarker levels, may be an essential component necessary to provide an appropriate dietary recommendation.
This study was financially supported by Dutch government through grant “Healthy nutrition” (Grant number 04003), Ministry of Health, Welfare and Sport, the Netherlands. Delegated sponsor of the study is BU Biosciences, TNO Quality of life, The Netherlands.
Authors declare not to have any conflict of interest.
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
- Biolo G, Toigo G, Guarnieri G (2001) Slower activation of insulin action in upper body obesity. Metabolism 50(1):19–23PubMedView ArticleGoogle Scholar
- Boorsma A, Foat BC, Vis D, Klis F, Bussemaker HJ (2005) T-profiler: scoring the activity of predefined groups of genes using gene expression data. Nucl Acids Res 33(Web Server issue):W592–W595PubMedView ArticleGoogle Scholar
- Cotter R, Taylor CA, Johnson R, Rowe WB (1987) A metabolic comparison of a pure long-chain triglyceride lipid emulsion (LCT) and various medium-chain triglyceride (MCT)–LCT combination emulsions in dogs. Am J Clin Nutr 45(5):927–939PubMedGoogle Scholar
- DeLany JP, Windhauser MM, Champagne CM, Bray GA (2000) Differential oxidation of individual dietary fatty acids in humans. Am J Clin Nutr 72(4):905–911PubMedGoogle Scholar
- de Hoon MJ, Imoto S, Nolan J, Miyano S (2004) Open source clustering software. Bioinformatics 20:1453–1454PubMedView ArticleGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95:14863–14868PubMedView ArticleGoogle Scholar
- Krotkiewski M, Björntorp P, Sjöström L, Smith U (1983) Impact of obesity on metabolism in men and women. Importance of regional adipose tissue distribution. J Clin Invest 72(3):1150–1162PubMedView ArticleGoogle Scholar
- Metges CC, Wolfram G (1991) Medium- and long-chain triglycerides labeled with 13C: a comparison of oxidation after oral or parenteral administration in humans. J Nutr 121(1):31–36PubMedGoogle Scholar
- Nugent C, Younossi ZM (2007) Evaluation and management of obesity-related nonalcoholic fatty liver disease. Nat Clin Practice Gastroenterol Hepatol 4(8):432–441View ArticleGoogle Scholar
- Papamandjaris AA, MacDougall DE, Jones PJ (1998) Medium chain fatty acid metabolism and energy expenditure: obesity treatment implications. Life Sci 62(14):1203–1215PubMedView ArticleGoogle Scholar
- Ramirez M, Amate L, Gil A (2001) Absorption and distribution of dietary fatty acids from different sources. Early Human Develop 65(Suppl 2):S95–S101View ArticleGoogle Scholar
- Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP (2006) GenePattern 2.0. Nat Genetics 38:500–501View ArticleGoogle Scholar
- Sanderson LM, De Groot PJ, Hooiveld GJEJ, Koppen A, Kalkhoven E, Müller M, Kersten S (2008) Effect of synthetic dietary triglycerides: a novel research paradigm for nutrigenomics. PLOS One, 3(2), art no e1681Google Scholar
- St-Onge MP, Jones PJ (2002) Physiological effects of medium-chain triglycerides: potential agents in the prevention of obesity. J Nutr 132(3):329–332PubMedGoogle Scholar