2014 FASEB Posters
Coletta, A., M. Mardock, B. Lockard, M. Byrd, S.
Simbo, A. Jagim, J. Kresta, C. Baetge, Y. Jung, M. Koozehchian, D. Khanna, H.
Kyul, J. Oliver, M. Greenwood, C. Rasmussen and R. Kreider (2014). Effects of two popular weight loss programs
on changes in body composition and visceral adipose tissue in women (LB296). The FASEB Journal 28(1 Supplement).
The purpose of this study was to determine if
different types of exercise and diet interventions promote greater changes in
visceral adipose tissue (VAT). 54
sedentary women (34±8 yrs, BMI 35±6) were randomized to participate in the
Curves (C) or Weight Watchers (W) weight loss programs for 16-wks. Participants
in the C program followed a 1,200 kcal/d diet for 1-wk and 1,500 kcal/d diet
for 3 wks (30:45 CHO:PRO). Subjects then ingested 2,000 kcals/d for 2-wks
(45:30 CHO:PRO) and repeated this diet while participating in the Curves with
Zumba program 3-d-wk. Remaining subjects followed the W point-based diet
program, received weekly counseling, and were encouraged to exercise. DEXA body
composition with VAT was determined at 0 & 16 wks. MANOVA revealed a
significant time (p<0.001) and group x time (p=0.035) effect. Both groups
lost a similar amount of weight (W -6.3±4, C -4.8±4 kg, p=0.17), fat mass (W
-3.1±6, C -5.9±6 kg, p=0.13), and VAT mass (W -98±468, C -240±445 g, p=0.26).
Subjects in the C group experienced greater gains in FFM (W -3.3±5.9. C
+1.2±4.1 kg, p=0.002) and tended to lose more body fat (W -1.1±8, C -4.5±5.5 %,
p=0.07). Changes in VAT mass significantly correlated with changes in weight
(r=0.38), fat mass (r=0.73), FFM (r=-0.62), and body fat (r=0.64). Results
indicate that different types of diets can differentially affect changes in
body composition and VAT.
Grant Funding Source:
Curves International, Inc. (Waco, TX)
Simbo, S., A. Coletta, M. Mardock, B. Lockard, B.
Bryd, A. Jagim, J. Kresta, C. Baetge, Y. Jung, M. Koozehchian, D. Khanna, H.
Kyul, J. Oliver, M. Greenwood, C. Rasmussen and R. Kreider (2014). Effects of two popular weight loss programs
on changes in android and gynoid body composition in women (LB302). The FASEB Journal 28(1
Supplement).
The purpose of this study was to determine if
different diet and exercise programs differentially affect android and/or
gynoid body composition changes in sedentary women. 54 sedentary women (34±8 yrs, 35±6 kg/m2) were
randomized to participate in the Curves (C) or Weight Watchers (W) weight loss
programs for 16-wks. Participants in the C program followed a 1,200 kcal/d diet
for 1-wk and 1,500 kcal/d diet for 3 wks (30:45 CHO:PRO). Subjects then
ingested 2,000 kcals/d for 2-wks (45:30 CHO:PRO) and repeated this diet while
participating in the Curves with Zumba program 3-d-wk. Remaining subjects
followed the W point-based diet program, received weekly counseling, and were
encouraged to exercise. DEXA body composition with android (A) and gynoid (G)
measurements were analyzed at 0 and 16 wks. MANOVA revealed a significant time
(p<0.001) and group x time (p=0.004) effect. Both groups had similar changes
in A-FM (W -419±760, C -624±700 g, p=0.31), A-BF (W -2.1±10, C -4.9±6 %,
p=0.24), and G-FFM (W -639±1,853, C -9±1,247 g p=0.16). Differences were seen
in A-FFM (W -313±614, C 49±403 g, p=0.02), G-FM (W -395±1,657, C -1,398±1,728
g, p=0.03), G-BF (W -0.6±8, C -4.2±6 %, p=0.06), and the lean index to height
(W -2.4±4.6, C 0.5±1.5 kg/m2, p=0.005). Results indicate that different types
of diets can differentially affect changes in A and G body composition.
Grant Funding Source:
Supported by Curves International (Waco, TX)
Jung, Y., B. Lockard, C. Baetge, K. Levers, E.
Galvan, A. Jagim, S. Simbo, M. Byrd, J. Oliver, M. Koozehchian, R. Dalton, D.
Khanna, B. Sanchez, J. Kresta, K. Horrell, T. Leopold, M. Cho, S. Springer, A.
Rivera, C. Cerda, C. Chang, C. Rasmussen and R. Kreider (2014). Comparative effectiveness of popular diet
programs on changes in body composition and visceral adipose tissue in women
(LB297). The FASEB Journal
28(1 Supplement).
The purpose of this study was to determine if
different types of exercise and diet interventions promote greater changes in
visceral adipose tissue (VAT). 127 sedentary women (46±12 yr, 45.5±5% body fat,
35.1±5 kg/m2) were randomized to participate in a control group (C) or the Curves
Complete® program with online support (CC), Weight Watchers® Points Plus (WW),
Jenny Craig® (JC), or Nutrisystem® Advance Select™ (NS) weight loss programs
for 12-wks. DEXA body composition with VAT determination was obtained at 0
& 12 wks. MANOVA revealed significant (p=<0.001) time and group x time
(p=<0.001) effects. Participants in the CC group experienced greater loss in
fat mass (C -0.0±2.1; CC -4.6±3.7; WW -2.2±2.7; JC -3.5±3.3; NS -2.1±2.7 kg,
p<0.001), less loss in FFM (C 0.2±2.3; CC -0.3±2.7; WW -1.7±2.4; JC
-1.8±2.3; NS -1.8±2.4 kg, p=0.01), and greater reductions in percent body fat
(C -0.0±1.7; CC -3.1±2.5; WW -0.7±2.6; JC -1.4±2.5; NS -0.5±1.7 %,
p<<0.001). VAT mass, volume and area decreased over time (p=0.004) with
no significant differences among groups. Changes in VAT mass significantly
correlated with changes in fat mas (r=0.20) and body fat (r=0.22). Results
indicate that different types of diets can differentially affect changes in
body composition but promote proportional changes in VAT.
Grant Funding Source:
Supported by Curves International (Waco, TX)
Springer, S., B. Lockard, C.
Baetge, Y. Jung, K. Levers, E. Galvan, A. Jagim, S. Simbo, M. Byrd, J. Oliver,
M. Koozehchian, R. Dalton, D. Khanna, J. Kresta, B. Sanchez, K. Horrell, T.
Leopold, M. Cho, A. Rivera, C. Cerda, C. Chang, C. Rasmussen and R. Kreider
(2014). Comparative effectiveness
of popular diet programs on changes in android and gynoid body composition in
women (LB301). The FASEB
Journal 28(1 Supplement).
The purpose of this study was to determine if
different diet and exercise programs differentially affect android and/or
gynoid body composition changes in sedentary women. 127 sedentary women (46±12
yr, 45.5±5% body fat, BMI 35.1±5) were randomized to participate in a control
group (C) or the Curves Complete® program with online support (CC), Weight
Watchers® Points Plus (WW), Jenny Craig® (JC), or Nutrisystem® Advance Select™
(NS) weight loss programs for 12-wks. DEXA body composition with android (A)
and gynoid (G) measurements were obtained. MANOVA revealed a significant time
(p<0.001) and group x time (p=0.006) effects. Participants in the CC group
generally lost more A-FM (C -8±421; CC -479±462; WW -162±410; JC -275±406; NS
-237±456 g, p=0.01), A-BF (C -0.3±2.6; CC -2.9±3.6; WW -0.4±3.7; JC -1.3±3.2;
NS -1.2±2.5 %, p<0.04), G-FM (C 73±335; CC -823±658; WW -392±458; JC
-709±871; NS -430±631, p<0.001), and G-BF (C 0.0±2.1; CC -3.3±2.9; WW
-0.7±2.6; JC -1.6±2.4; NS -0.6±1.9 %, p<0.001) with less loss in G-FFM (C
112±476; CC -10±605; WW -319±559; JC -285±451; NS -335±540, p=0.01). No
significant differences were seen among groups in A/G ratio, trunk/leg body fat
ratio, or trunk/leg lean mass ratio. Results indicate that different types of
diets can differentially affect changes in A and G body composition.
Grant Funding Source:
Curves International, Inc. (Waco, TX)
O’Connor, A., K. Levers, E. Galvan, A. Coletta, R.
Dalton, Y. Jung, C. Goodenough, S. Simbo, C. Seesselberg, B. Bonin, M.
Koozehchian, B. Sanchez, N. Barringer, C. Rasmussen, M. Greenwood and R.
Kreider (2014). Analysis of the
validity of a carbohydrate intolerance questionnaire I (LB305). The FASEB Journal 28(1
Supplement).
The purpose of this study was to examine the
relationship of responses to a carbohydrate intolerance (CI) questionnaire to
body composition and markers of health. 108 women (31.6±13 yrs, 34.7±7% body
fat, BMI 25.3±4) donated fasting blood samples, completed a CI survey, had body
composition and health measures determined, and underwent a 75g, 2-h OGTT.
Pearson product correlations were performed to determine correlations to CI
questions. Being more than 25 lbs overweight correlated with OGTT G90 (r=0.22),
G120 (r=0.25), glucose AUMC (r=0.19), age (r=0.43), weight (r=0.57), waist
circumference (r=0.66), hip circumference (r=0.59), BMI (r=0.72), fat mass
(r=0.60), body fat % (r=0.51), and insomnia (r=0.30). Being overweight
throughout their life correlated with OGTT G30 (r=-0.28), glucose AUC
(r=-0.20), weight (r=0.47), waist (r=0.31) and hip circumference (r=0.49), BMI
(r=0.44), BMC (r=0.33), FFM (r=0.31), fat mass (r=0.42), body fat % (r=0.31),
and fatigue/exhaustion (r=-0.21). Being overweight since youth correlated with
OGTT G30 (r=-0.28), G60 (r=-0.24), G90 (r=-0.22), glucose AUC (r=-0.24), weight
(r=0.35), waist circumference (r=0.22), hip circumference (r=0.36), BMI
(r=0.32), BMC (r=0.32), FFM (r=0.25), fat mass (r=0.30), and body fat %
(r=0.22). Poor appetite and skipping meals correlated with the glucose/insulin
ratio (r=0.22), height (r=-0.22), and BMI (r=0.20). Results revealed that CI
questions moderately correlated with body composition, health markers, and/or
questions related to CI.
Grant Funding Source:
Curves International, Inc. (Waco, TX)
Levers, K., E. Galvan, A. Coletta, R. Dalton, Y.
Jung, A. O’Connor, C. Goodenough, S. Simbo, C. Seesselberg, B. Bonin, M.
Koozehchian, B. Sanchez, N. Barringer, C. Rasmussen, M. Greenwood and R.
Kreider (2014). Assessment of
factors related to carbohydrate intolerance I: OGTT glucose AUC (LB299). The FASEB Journal 28(1 Supplement).
The OGTT is a gold standard in assessing
carbohydrate intolerance (CI) and insulin resistance. However, the test is
costly and time consuming. This study examined whether a CI survey could
predict response to an OGTT. 108 women (31.6±13 yr, 34.7±7% body fat, 25.3±4
kg/m2) donated fasting blood samples, completed a CI inventory, had body
composition and health measures determined, and underwent a 75 g OGTT in which
glucose samples and perceptions of CI symptoms were obtained at 0, 30, 60, 90,
and 120 minutes. Pearson product correlations were performed to determine which
factors correlated with OGTT glucose AUC. Results revealed significant
correlations (p<0.05) in GAUC (264±46 mg/hr/dL) to G120 AUC (r=0.43),
glucose AUMC (r=0.97), Cmax (r=0.91), fasting insulin (r=0.26), HOMA (r=0.30),
height (r=-0.28), resting HR (r=0.19), BMC (r=-0.36), BMD (r=-0.32), FFM
(r=-0.28), being overweight since very young (r=0.27), and slowly gaining
weight after age 30. These findings indicate that OGTT GAUC is positively
correlated with G120, insulin, and HOMA. Further, shorter women who gained
weight as they got older with a higher resting HR and lower FFM, BMC, and BMD
were more related to GAUC during a OGTT.
Grant Funding Source:
Curves International, Inc. (Waco, TX)
Goodenough, C., K. Levers, E. Galvan, A. Coletta,
R. Dalton, Y. Jung, A. O’Connor, S. Simbo, C. Seesselberg, B. Bonin, M.
Koozehchian, B. Sanchez, N. Barringer, C. Rasmussen, M. Greenwood and R.
Kreider (2014). Analysis of the
validity of a carbohydrate intolerance questionnaire II (LB304). The
FASEB Journal 28(1 Supplement).
The purpose of this study was to examine the
relationship of responses to a carbohydrate intolerance (CI) questionnaire to
body composition and markers of health. 108 women (31.6±13 yrs, 34.7±7% body
fat, BMI 25.3±4) donated fasting blood samples, completed a CI, had body
composition and health measures determined, and underwent a 75g, 2-h OGTT.
Having food cravings relieved with carbohydrate ingestion correlated with
irritability (r=0.24), nervousness (r=0.32), forgetfulness (r=0.31), mental
confusion (r=0.23), worrying (r=0.21), antisocial behavior (r=0.24), lack of
sex drive (r=0.27), leg cramps/blurred vision (r=0.25), cravings for sweets
(r=0.67), digestive disturbances (r=0.20), yawning (r=0.26), drowsiness (r=0.27),
and dizziness/shakiness (r=0.20). Must have foods correlated with worrying
(r=0.19), lack of sex drive (r=0.20), and food cravings (r=0.22). Having a
waistline larger than hips correlated with age (r=0.23), waist circumference
(r=0.28), lack of sex drive (r=0.21), and leg cramps/blurred vision (r=0.23).
Gaining weight after age 30 correlated with glucose AUMC (r=0.22), age
(r=0.64), DBP (r=0.24), waist circumference (r=0.37), BMI (r=0.21), fat mass
(r=0.25), body fat % (r=0.36), insomnia (r=0.21), lack of sex drive (r=0.32),
being more than 25 lbs overweight (r=0.20), and being overweight since youth
(r=0.26). Results revealed that CI questions moderately correlated with body
composition, health markers, and/or questions related to CI.
Grant Funding Source:
Curves International, Inc. (Waco, TX)
Galvan, E., K. Levers, A. Coletta, R. Dalton, Y.
Jung, A. O’Connor, C. Goodenough, S. Simbo, C. Seesselberg, B. Bonin, M.
Koozehchian, B. Sanchez, N. Barringer, C. Rasmussen, M. Greenwood and R.
Kreider (2014). Assessment of
factors related to carbohydrate intolerance II: OGTT glucose at 120 minutes
(LB298). The FASEB Journal
28(1 Supplement).
The OGTT is a gold standard in assessing
carbohydrate intolerance (CI) and insulin resistance. However, the test is
costly and time consuming. This study examined whether responses to a
carbohydrate intolerance survey (CI) could predict response to an OGTT. 108
women (31.6±13 yrs, 34.7±7% body fat, 25.3±4 kg/m2) donated fasting blood
samples, completed a CI inventory, had body composition and health measures
determined, and underwent a 75g, 2-hr OGTT. Pearson product correlations were
performed to determine which factors correlated with OGTT glucose at 120
minutes (G120). Results revealed significant correlations (p<0.05) in G120
(112±25 mg/dl) to glucose AUC (r=0.60), glucose AUMC (r=0.73), Cmax (r=0.34),
fasting insulin (r=0.34), HOMA (r=0.37), height (r=-0.33), resting HR (r=0.29),
BMC (r=-0.34), BMD (r=-0.23), FFM (r=-0.39), DEXA body fat (0.24), BIA body fat
(r=0.22), and perceptions of being more than 25 lbs overweight (r=0.25). These
findings indicate that OGTT G120 is positively correlated to OGTT glucose AUC,
fasting insulin, and HOMA. Further, that shorter women who perceive themselves
as more than 25 lbs overweight with a higher body fat and resting HR with lower
FFM, BMC, and BMD were more related to G120 during a OGTT.
Grant Funding Source:
Supported by Curves International (Waco, TX)
Koozehchian, M., K. Levers, E. Galvan, A. Coletta,
R. Dalton, Y. Jung, A. OConnor, C. Goodenough, S. Simbo, C. Seesselberg, B.
Bonin, B. Sanchez, N. Barringer, C. Rasmussen, M. Greenwood and R. Kreider
(2014). Analysis of the validity of
a carbohydrate intolerance questionnaire III (LB303). The FASEB Journal 28(1 Supplement).
The purpose of this study was to examine the
relationship of responses to a carbohydrate intolerance (CI) questionnaire to
body composition and markers of health. 108 women (31.6±13 yrs, 34.7±7% body
fat, BMI 25.3±4) donated fasting blood samples, had body composition and health
measures determined, and underwent a 75g, 2-h OGTT. Being less than 25 lbs
overweight correlated with age (r=0.19) and DBP (r=0.27). Getting hungry at
meals correlated with BMI (r=0.23), antisocial behavior (r=0.37) and having a
poor appetite/skipping meals (r=0.39). Having few food cravings correlated with
waist circumference (r=-0.20), mental confusion (r=0.23), being 25 lbs
overweight (r=-0.27), having temporary food cravings (r=-0.23), and must have
foods (r=-0.30). Consistent eating habits correlated with RHR (r=-0.22), FFM
(r=-0.19), being overweight throughout life (r=-0.20), and skipping meals
(r=-0.20). Eating 3 times/d correlated with OGTT glucose AUC (r=-0.91),
antisocial behavior (r=-0.19), being overemotional (r=-0.21), leg
cramps/blurred vision (r=-0.27), drowsiness (r=-0.19), and skipping meals
(r=-0.43). Maintaining weight gain correlated with waist (r=0.21) and hip
(r=0.20) circumference, fat mass (r=0.21), body fat (r=0.22), headaches
(r=0.20), mental confusion (r=0.28), leg cramps/blurred vision (r=0.28), and
yawning (r=0.26). Results revealed that CI questions moderately correlated with
body composition, health markers, and/or questions related to CI.
Grant Funding Source:
Curves International, Inc. (Waco, TX)
Dalton, R., K. Levers, E. Galvan, A. Coletta, Y.
Jung, A. O’Connor, C. Goodenough, S. Simbo, C. Seesselberg, B. Bonin, M.
Koozehchian, B. Sanchez, N. Barringer, C. Rasmussen, M. Greenwood and R.
Kreider (2014). Assessment of
factors related to carbohydrate intolerance III: Fasting HOMA (LB300). The FASEB Journal 28(1
Supplement).
The fasting homeostatic model assessment (HOMA) is
a gold standard in assessing insulin resistance. However, the test is costly
and time consuming. This study examined whether responses to a carbohydrate
intolerance survey (CI) correlate to HOMA, an OGTT, body composition and/or
markers of health. 108 women (31.6±13 yrs, 34.7±7% body fat, 25.3±4 kg/m2)
donated fasting blood samples, completed a CI inventory, had body composition
and health measures determined, and underwent a 75g, 2-hr OGTT. Pearson product
correlations were performed to determine which factors correlated with HOMA.
Results revealed significant correlations (p<0.05) in HOMA (1.51±1.1) to
G120 (r=0.37), glucose AUC (r=0.30), glucose AUMC (r=0.32), Cmax (r=0.24),
fasting insulin (r=0.99), G/I ratio (r=-0.49), height (r=-0.27), waist
circumference (r=0.26), BMI (r=0.21), BMC (r=-0.21), BMD (r=-0.27), DEXA body
fat (r=0.28), and BIA body fat (r=0.23). However, HOMA did not significantly
correlate to any question on the CI or symptoms during the OGTT. Results
indicate that HOMA is positively correlated to OGTT glucose values, fasting
insulin, the G/I ratio, waist circumference, BMI, and %BF and negatively
correlated with height, BMC, and BMD but not related to CI questionnaire items
or CI symptoms during an OGTT.
Grant Funding Source:
Curves International, Inc. (Waco, TX)
