PGC-1αGenotype n EC Non-EC

G/G 319 50.8% 49.2%G/S 247 49.4% 50.6%S/S 52 32.7% 67.3%

Eating Competence is related to PPARβ/δ and PGC-1α genotypesBarbara A Lohse 1, John Vanden Heuvel 2, Jodi Stotts Krall 3, Jill Patterson 1, Kendra Kattelmann 4, Adrienne A White 5, Geoffrey Greene 6

1 Nutritional Sciences, 2 Molecular Toxicology, The Pennsylvania State University, University Park, PA, 3 University of Pittsburgh Medical Center, Pittsburgh, PA, 4 Health and Nutritional Sciences, South Dakota State University, Brookings, SD, 5 Food Science and Human Nutrition, University of Maine, Orono, ME, 6 Nutrition and Food Sciences, University of Rhode Island, Kingston, RI

Abstract: 2579 Program: 605.2

Poster Board: A305

The Three Factor Eating Questionnaire (TFEQ) has demonstrated genetic variation and association with Satter Eating Competence Inventory (ecSI) scores. We studied college students (n=661, 36% male, 91.5% non-Hispanic white, 26% overwt/obese) to identify genotype and eating competence (EC) relationships. PPARα L162A, PPARγ 12A, PPARβ/δ rs226766, and PGC-1α G482S genotype distributions were compared with TFEQ-R18 and ecSI scores; responses were gender-specific and paralleled published values. Results confirmed less emotional eating (EE) (P<.001), cognitive restraint (CR) (P<.001) and uncontrolled eating (UE) (P=.1) with EC (ecSI score ≥ 32). Subjects in the lowest ecSI tertile had significantly more CR (P<.001), EE (P<.001), and UE (P=.007). The homozygous G/G PPARβ/δ genotype tended(P=.06) toward less UE; females with the homozygous G/G PPARβ/δ genotype tended (P=.1) toward less CR than males. Homozygous A/A PPARγ genotype tended (P=.08) toward less CR and significantly more UE (P=.04). Students of homozygous S/S PGC-1α genotype (n=35) tended not to be eating competent(P=.05) and to be in the lowest ecSI tertile (P=.14). The PPARβ/δ genotype and gender interaction was significant (P=.04) with higher ecSI scores for males (35.3 ± 5.8 vs. 30.8 ± 4.9) who were homozygous G/G. These data support the hypothesis of a link between EC and PPARβ/δ and PGC-1α genotypes. Funded by NRI, USDA #2005-35215-154121541.

Why Study Eating Competence? EC is a bio-psychosocial model explaining intrapersonal approaches to eating and food-related behaviors. Competent eaters have positive attitudes about eating and enjoyment of food, food acceptance skills that enhance dietary variety, internal regulation skills to address energy balance, and skills and resources for managing mealtime structure, food selection, meal preparation. In addition, they self-permit choices of enjoyable foods and eat them in satisfying amounts, but enact self-discipline to provide regular and reliable meals and snacks planned to satisfy nutritional needs as well as food preferences. Many relationships between EC and health argue for biomedical research about this construct.

EC is associated with:•a lower body mass index, less dietary restraint,

disinhibited eating, and body dissatisfaction 1;•greater self-reported physical activity levels 1;•more favorable cardiovascular biomarkers, such

as higher HDL-cholesterol concentration, lower risk of high triglyceride and LDL-cholesterol levels 5,6 ;

•lower systolic and diastolic blood pressure 5;•higher nutrient intake and greater consumption

of fruit, legumes, cereal, olive oil and nuts and adherence to a Mediterranean diet. 6

NOTESThis research was approved by the Human Subjects Research Protection units at each University. Funding provided by National Research Initiative of the USDA CSREES grant # 2005-35215-154121541541 and NIH grant M01 RR10732. Appreciation to the General Clinical Research Center of The Pennsylvania State University.

1. Lohse B, Satter E, Horacek T, Gebreselassie T, Oakland MJ. Measuring eating competence: Psychometric properties and validity of the ecSatter inventory. J Nutr Educ Behav, 2007; 39(5S):S154-S166.2. Krall JS, Lohse B. Validation of a measure of the Satter eating competence model with low-income females. Int J Behav Nutr Phys Act, accepted for publication.3. Tholin S, Rasmussen F, Tynelius P, Karlsson J. Genetics and environmental influences on eating behavior: the Swedish young male twins study. Am J Clin Nutr. 2005;81:564-569. 4. Vogels N, Mariman ECM, Bouwman FG, Kester ADM, Diepvens K, Westrterp-Plantenga MS. Relation of weight maintenance and dietary restraint to peroxisome proliferator-activated receptor γ2, glucocorticoid receptor, and ciliary neurotrophic factor polymorphisms. Am J Clin Nutr. 2006;82:740-746.5. Psota TL, Lohse B, West SG. Associations between eating competence and cardiovascular disease biomarkers. J Nutr Educ Behav. 2007; 39(5S):S171-S1786. Lohse B, Psota T, Estruch R, Zazpe I, Sorli JV, Salas-SalvadóJ, Serra M, Krall JS, Márquez F, Ros E on behalf of the PREDIMED study investigators. Eating Competence of Elderly Spanish Adults Is Associated with a Healthy Diet and a Favorable Cardiovascular Disease Risk Profile. J Nutr. 2010; 140:1322-1327.7. L. Andrulionyte, T. Kuulasmaa, J.L. Chiasson, M. Laakso, STOP-NIDDM Study Group, Single nucleotide polymorphisms of the peroxisome proliferator-activated receptor-alpha gene (PPARA) influence the conversion from impaired glucose tolerance to type 2 diabetes: the STOP-NIDDM trial. Diabetes. 2007;56:1181-1186.8. C. Thamer, J. Machann, N. Stefan, S.A. Schäfer, F. Machicao, H. Staiger, M. Laakso, M. Böttcher, C. Claussen, F. Schick, A. Fritsche, H.U. Haring, Variations in PPARD determine the change in body composition during lifestyle intervention: a whole-body magnetic resonance study. J Clin Endocrinol Metab. 2008;93:1497-1500.9. Gouda HN, Sagoo GS, Harding AH, Yates H, Sandhu MS, Higgins JP, The Association Between the Peroxisome Proliferator-Activated Receptor-{gamma}2 (PPARG2) Pro12Ala Gene Variant and Type 2 Diabetes Mellitus: A HuGE Review and Meta-Analysis. Am J Epidemiol. 2010;171:645-655.10. N. Stefan, C. Thamer, H. Staiger, F. Machicao, J. Machann, F. Schick, C. Venter, A. Niess, M. Laakso, A. Fritsche, H.U. Häring, Genetic variations in PPARD and PPARGC1A determine mitochondrial function and change in aerobic physical fitness and insulin sensitivity during lifestyle intervention. J Clin Endocrinol Metab. 2007;92:1827-1833.11. Greene GW, Schembre SM, White AA, Hoerr SL, Lohse B, Shoff S, Horacek T, Riebe D, Patterson J, Lohse B, Phillips BW, Kattelmann KK, Blissmer B. Identifying clusters of college students at elevated health risk based on eating and exercise behaviors and psychosocial determinants of body weight. J Amer Diet Assoc. 2011;111:394-400.12. Jaziri R, Lobbens S, Aubert R, Pean F, Lahmidi S, Vaxillaire M, et al. The PPARG pro12ala polymorphism is associated with a decreased risk of developing hyperglycemia over 6 years and combines with the effect of the APM1 G-11391A single nucleotide polymorphism: The data from an epidemiological study on the insulin resistance syndrome (DESIR) study. Diabetes. 2006;55:1157-1162.

Data Analyses. ecSI items were summed to a total score (possible range 0 - 48) and 4 skills subscales: Attitude (AT), Context (CS), Food Acceptance (FA), and Internal Regulation (IR). Possible scores are 0 - 15 for AT and CS and 0 - 9 for FA and IR subscales. Scores were grouped into high, medium, and low EC tertiles and dichotomized as EC (ecSI ≥ 32) or not EC (ecSI < 32). TFEQ items were summed to 3 scales: Cognitive Restraint (CR), Emotional Eating (EE), and Uncontrolled Eating (UE) with subscale score presented as a percentage ranging from low (0%) to high (100%). GHQ items were scored on a 4-point Likert scale from 0 (not at all) to 3 (much more than usual). Possible GHQ scores range from 0 to 36.

Weight status was defined by BMI using CDC criteria (i.e. underweight < 18.5, normal 18.5 - 24.9, overweight 25 -29.9, and obese ≥ 30). Survey findings, BMI, and genotypes were compared as appropriate with Chi square, t-tests, ANOVA, and GLM univariate analyses including gender in the model. P ≤ 0.05 was significant. Data were analyzed with SPSS v 18.0.0 (Somers, NY).

Genetic Information

1. ecSI and TFEQ subscale relationships were confirmed.

ecSI Tertile Cognitive Restraint (CR)

Emotional Eating (EE)

Uncontrolled Eating (UE)

Low (n=217) 50.0 ± 20.4 a 45.8 ± 27.7 a 48.1 ± 19.1 a

Middle (n=214) 43.4 ± 19.2 b 38.1 ± 27.2 b 42.2 ± 19.0 b

High (n=206) 38.2 ± 21.2 c 34.8 ± 25.3 b 44.4 ± 19.9 a,b

Statistic F=18.0; P < .001 F=9.5; P < .001 F=5.0; P = .0072. Findings supported TFEQ associations with genotypes.•Mean UE tended to differ (F=2.8, P=.06) among PPAR β/δ genotypes: A/A 43.6 ± 19.6 (n=437); A/G 47.2 ± 18.6 (n=175); G/G

42.1 ± 22.4 (n=22).•Unlike PPARα L/L genotype in which male and female UE are similar (45.1 ± 18.8 vs 44.7 ± 19.4 respectively), PPARα L/V

genotype tended (F=3.3, P=.07) toward lower UE for females (n=32) than males (n=19) (38.4 ± 20.5 vs 49.5 ± 23.4). •Female PPARβ/δ G/G genotype (n=9) tended (F=2.1, P=.1) toward less CR than males (n=13) (37.2 ± 13.1 vs 43.21 ± 15.9); female

CR was higher than males for A/A ( 48.5 ± 20.3 vs 37.1 ± 20.9) and A/G genotypes (47.8 ± 19.3 vs 35.0 ± 21.2). •PPAR γ A/A (n=4) genotype had greater UE (58.3 ±26.8; F= 3.2, P=.04) than P/P (44.2 ± 19.0; n=550) or P/A (46.6 ± 20.9; n=106). •A trend (F=2.5, P=.08) was seen for lower CR (34.7 ±27.0) in PPAR γ A/A (n=4) genotype than P/P (43.6 ± 21.1; n=550) or P/A

(45.8 ± 19.6; n=106).

Competent eaters showed significantly less cognitive restraint (t= 4.8. P<.001) and emotional eating (t=4.2, P<.001) than non-EC students. Cognitive Restraint: EC was 40.0 ± 21.2; Non-EC was 47.8 ± 19.7Emotional Eating: EC was 35.1 ± 24.4; Non-EC was 44.1 ± 28.8

•We confirmed that Cognitive Restraint, Uncontrolled Eating, and Emotional Eating, as measured by the TFEQ, are lower in competent eaters.

•Our findings support a connection between cognitive eating behavior, as

measured by the TFEQ, and PPAR genotypes.

•Exploratory relationships found between ecSI, PGC-1α and PPARβ/δ in our small sample, encourage additional study to better translate the EC construct to nutrition education and intervention.

A B S T R A C T M E T H O D S F I N D I N G S A B O U T G E N O T Y P E R E L A T I O N S H I P S

C O N C L U S I O N S

Cognitive eating behaviors, as measured by the Three Factor Eating Questionnaire (TFEQ) have been shown to be related to EC 1,2 and to show genetic variation. 3,4

The purpose of this study was to explore the relationship between eating competence (EC) and PPAR genotype.

Eating Competence (EC)

TFEQ PPARs

?

S T U D Y P U R P O S E & B A C K G R O U N D

PPARα L162A (n=658)

PPARβ/δ rs226766 (n=634)

PPARγ P12A (n=661)

PGC-1α G482S (n=641)

L/L 92.1%

L/V 7.75%

V/V 0.15%

A/A 68.9% A/G 27.6% G/G 3.5%

P/P 83.4%

P/A 16.0%,

A/A 0.6%

G/G 51.8%

G/S 39.8%

S/S 8.4%

G/G subjects tended toward overweight/obesity (Chi Sq 9.1, P=.058); BMI was categorized as normal weight for 75.1% A/A, 78.9% A/G, and 54.5% G/G. G/G genotype females (n=9) tended (F=2.7, P=.07) to have a higher waist circumference than males (n=13) (83.4 ± 14.5 vs 80.7 ± 7.0 cm); this relationship was reversed for A/A and A/G genotypes.

Genotype Prevalence. Distribution was not gender-specific and, unless noted, not specific to BMI category.

Description of Subjects. Subjects (n=661) were mostly female (64%), freshmen or sophomores (70.2%), White (91.5%; Black 1.4%, Asian 3.7%, Hispanic 2%) and represented a variety of majors, e.g. biological sciences (12.9%), business (9.4%), education (6.2%, social sciences 6.8%, and agricultural sciences 3.0%). Mean age was 19.1 ± 1.1 y (range 18 - 24 y). BMI ranged from 18.5 - 46.2 with a mean of 23.6 ± 3.6; median BMI was 22.9 with nearly 26% classified as overweight or obese. Male BMI was significantly greater (t=4.0, P<.001); mean BMI 24.4 ± 3.6 vs 23.2 ± 3.5 for females.

Eating Competence. Overall (n=638), 49.2% were EC. ecSI scores ranged from 4 to 48 with a mean of 31.0 ± 6.9; median was 31. Subscale score means ± SD, range include: AT (n=656) 10.8 ± 3.2, 0 to 15; FA (n=657) 5.1 ± 2.3, 0 to 9; IR (n=652) 6.7 ± 1.8, 0 to 9; CS (n=652) 8.4 ± 3.0, 0 to 15. Male total ecSI scores were higher than females (32.6 ± 6.4 vs 30.2 ± 7.0; t=4.2, P< .001). AT subscale scores were greater (t=7.2, P<.001) for males (12.0 ± 2.7 vs 10.2 ± 3.5). More males than females were EC (55.4% vs 45.7%; Chi Sq 5.6, P=.02) and EC tertile distributions were gender-specific (Chi Sq 12.9, P=.002, i.e. 28.8% of the males were in the lowest tertile and 40.3% were in the highest tertile; for females 38.3% and 27.8% were in the lowest and highest tertiles respectively.

TFEQ. For all subscales, mean scores were below the 50th percentile (CR 43.9 ± 20.8, range 0 - 100; EE 39.4 ± 27.0, range 0 - 100; UE 44.7 ± 19.4, range 0 - 96; n=660 for all). Females (n=422) displayed more CR (t=6.7, P<.001) and more EE (t=8.3, P<.001) than males (n=238). (CR 47.8 ±19.8 vs 36.9 ± 20.9; EE 45.4 ± 27.3 vs 28.8 ± 23).

GHQ. Scores ranged from 2 to 31, mean 10.0 ± 3.8; n=645. Gender differences were significant (t=3.36, P=.001) with males expressing less emotional stress (9.3 ± 3.6 vs 10.4 ± 3.8).

Gene

Symbol

Function

SNP

Major/Minor Allele

Protein

Why Study?

Peroxisome proliferator-activated receptor α

PPARα

Ligand-activated transcription factor. Key regulator of lipid metabolism. PPARα is located primarily in the liver, adipose tissue, kidney, heart, skeletal muscle and large intestine where it is thought to regulate fatty acid synthesis and oxidation, gluconeogenesis,ketogenesis and lipoprotein assembly.

rs1800206 C/G L162A

G (162V) allele of rs1800206 increased the risk for diabetes by 1.9-fold (95% CI 1.05-3.58) and was associated with elevated levels of plasma glucose and insulin. 7

Peroxisome proliferator-activated receptor β/δ

PPARβ/δ

PPARβ/δ displays a ubiquitous expression pattern and is thought to regulate placental and gut development, fatty acid oxidation, adaptive thermogenesis, control of cell proliferation, differentiation and tissue repair.

rs2267668 A/G Noncoding (intronic)

SNPs in the PPARβ/δ i.e. rs1053049, rs6902123, and rs2267668, affect the improvement of mitochondrial function, aerobic physical fitness, and insulin sensitivity by lifestyle intervention. 8

Peroxisome proliferator-activated receptor γ

PPARγ

PPARγ is located primarily in the adipose tissue, lymphoid tissue, colon, liver and heart and regulates adipocyte differentiation and glucose homeostasis. PPARγ has been implicated in the pathology of numerous diseases including obesity, diabetes, atherosclerosis and cancer.

rs1801282 C/G P12AThere are reported associations of Metabolic Syndrome and surrogate measures of insulin resistance and SNP rs1801282. 9

Peroxisome proliferator-activated receptor γ coactivator 1

PGC-1α

Transcriptional coactivator for steroid receptors and nuclear receptors. Increases the transcriptional activity of PPARγ and thyroid hormone receptor on the uncoupling protein promoter. Can regulate key mitochondrial genes that contribute to the program of adaptive thermogenesis.

rs8192678 G/A G482S

Gly482Ser SNP in PPARGC1A have both independent and additive effects with PPARβ/δ rs2267668 on the effectiveness of aerobic exercise training to increase aerobic physical fitness and insulin sensitivity. 10

3. ecSI associations with genotypes.•For all genes, male ecSI scores were ~ 2.5 points higher than female scores (all P <.05). Mean ecSI scores did not differ

among PPARα, PPARβ/δ, and PPARγ genotypes. The PPARβ/δ genotype had higher ecSI scores for males (n=9) than females (n=12) who were homozygous G/G (35.3 ± 5.8 vs. 30.8 ± 4.9; P=.04).

•Genotype distribution did not differ significantly between EC and non-EC or among EC tertiles for PPARα, PPARβ/δ, and PPARγ.

•PGC-1α S/S genotype was less prevalent (Chi Sq 5.9, P=.05) in competent eaters. For all other genes, allelic distribution was uniform between EC and non-EC and among EC tertiles.

•Mean ecSI score tended (F=2.3, P=.1) to be lower for PGC-1α S/S than G/G or G/S genotypes (31.3 ± 6.8, 31.0 ± 7.0, 29.1 ± 7.3, respectively).