Fitness Level is Associated with Sex-Specific Regional Fat Differences in Normal Weight Young Adults

1Department of Medicine, University of Minnesota Medical School, Minneapolis, MN 55455, USA 2School of Kinesiology, University of Minnesota, Minneapolis, MN 55455, USA 3Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, USA 4Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis, MN 55455, USA Journal of Endocrinology and Diabetes Open Access Research Article


Introduction
Body Mass Index (BMI) may misclassify people as "normal weight" because it does not detail the composition of tissue

Methods
Thirty-eight young adults (age 19-31 years) were recruited from the University of Minnesota-Twin Cities campus and surrounding metropolitan area.Participants were screened for Fitness level was assessed by indirect calorimetry (Medgraphics CPX-D metabolic cart, Medical Graphics Corporation, St. Paul, MN) using a maximal oxygen consumption (VO 2 peak) using a treadmill test to exhaustion.Based on current physical activity status using self-report from the short-form International Physical Activity Questionnaire (IPAQ), a validated physical activity questionnaire [23], VO 2 peak was measured in the sedentary (<30 min exercise/ week) group using the Bruce protocol and physically active participants (>5 days >45 min/day) completed a progressive incline protocol at a set speed with increasing 2.5% incline every 2 minutes.Two different protocols were used to ensure a valid maximal test was completed within 12 minutes for current level of physical activity.Following fitness testing, participants were grouped into high (HF) and low fitness (LF) group based on being above or below the median for VO 2 peak.Sixteen participants from each group (9 males/7 females) were matched on age (±2years), gender and BMI (±2 kg/m 2 ) and having a difference in VO 2 peak > 8 ml/kg/min.Six individuals (2M/4F) could not be matched and were excluded from the final analyses.The average BMI for the whole sample was 22.2 kg/m 2 (range 19.1-24.9kg/ m 2 ).Participants were excluded if they were currently using medications that affected lipid levels or glucose metabolism or consumed greater than 45% dietary fat as measured by a screening questionnaire [24], were pregnant, or experienced a recent weight change (>5 pounds within the last 3 months).At least one week after fitness testing, participants completed a full body composition scan using a Lunar Prodigy (GE Healthcare, Madison, WI) Dual Energy X-Ray Absorptiometry (DXA) and had insulin sensitivity measured by hyperinsulinemic-euglycemic clamp on separate days.Women were studied in the follicular phase of their menstrual cycle.This protocol was approved by the University of Minnesota Institutional Review Board and consent was obtained from each participant.

Body composition and maximal oxygen consumption
Total body composition was measured using DXA (Lunar Prodigy, General Electric Medical Systems, Madison, WI, USA) and analyzed using its encore TM software (platform version 13.6 rev.2).Participants were scanned using standard imaging and positioning protocols while fasted and hydrated.Subcutaneous fat (total, android and gynoid) and visceral fat were estimated using a method described previously for adults [15].The android region was defined with a caudal limit placed at the top of the iliac crest and its height set to 20% of the distance from the top of the iliac crest to the base of the skull [25].The gynoid region is located mid-pelvis to mid-thigh; the upper limit was set below the iliac crest a distance 1.5 times the height of the android region.The lower limit was set a distance of 2 times the height of the android region [25].Trunk fat is fat within the region defined from the base of the mandible and includes the chest, abdomen and pelvic triangle, but excludes the arms by a boundary bisects the shoulder joint.All scans were reviewed for accurate placement of the android box by the same technician.
Maximal Oxygen Consumption (VO 2 peak) was determined with a graded treadmill test until exhaustion.Expired oxygen and carbon dioxide concentrations and volumes were collected and analyzed using a Med Graphics CPX-D metabolic cart (MedGraphics CPX-D metabolic cart, Medical Graphics Corporation, St. Paul, MN).

Insulin sensitivity and measurement of blood markers
Insulin sensitivity was measured by the hyperinsulinemiceuglycemic clamp as previously described [26].Insulin was infused at a constant rate of 10.4 pmol/L/kg/min for 3 hours, and glucose was infused at a variable rate to maintain euglycemia.Insulin sensitivity (M) was expressed as the glucose infusion rate (mg/kg (FFM)/min of glucose) during the last 40 minutes of the clamp, with adjustment for fat free mass (M/FFM).Fasting blood samples were collected for glucose, and lipid levels including triglycerides, High-Density Lipoprotein Cholesterol (HCL-C), and Low-Density Lipoprotein Cholesterol (LDL-C).All assays were conducted with standard procedures at the Fairview Diagnostic Laboratories, Fairview-University Medical Center (Minneapolis, MN), Centers for Disease Control and Prevention-certified laboratory.

Statistical analysis
Normality of the data was evaluated using the Anderson-Darling test.A t-test was used to compare demographic, blood markers and insulin sensitivity between the high and low fitness groups with males and females combined (Two groups).Because of differences in fat accumulation between sexes, we stratified the HF and LF groups by sex (4 groups) for the body composition analyses.An analysis of variance (ANOVA) was used to compare total and regional body composition measurements and insulin sensitivity between HF and LF males and females (4 groups).A post-hoc analysis using Tukey honest significant difference compared the HF and LF groups stratified by sex.T-tests compared the average difference between male and female groups for a sex main effect.

Results
Table 1 presents the demographic and clinical data by fitness level (sexes combined).The HF and LF groups were matched by age, gender, and BMI.The HF group had significantly lower percent body fat (%BF) and higher VO 2 peak than the LF group (-6.8% P=0.01, +9.5ml/kg/min P<0.001).The HF group also had higher insulin sensitivity (+3.5 mg/kg (FFM)/min P=0.002), as measured by the hyperinsulinemic-euglycemic clamp, than the LF group.Table 2 presents the body composition characteristics and insulin sensitivity comparisons for fitness level and sex.Higher fitness was associated with significantly lower %BF, trunk fat, android fat and subcutaneous abdominal fat (P<0.05 for all).Additionally higher fitness was associated with higher total lean mass, leg lean mass and insulin sensitivity in males (P<0.05 for all).In females, higher fitness was associated with lower %BF and lower leg fat.However, there were no other differences in regional fat or lean mass as well as insulin sensitivity between HF and LF females (-6.7%, P=0.001; -2.7kg, P<0.001; 2.5 mg/ kg(FFM)/min, P=0.40).Interestingly in females, there was no difference in total lean mass, trunk lean mass or leg lean mass (P=0.59,P=0.17, P=0.99).In both males and females there was no differences in VAT (P>0.05 for all).

Discussion
The purpose of this study was to compare total and regional body composition and insulin sensitivity between HF and LF young adults with normal BMI.Interestingly, total fat was not different between HF and LF males and females; however, %BF was significantly lower in HF males and females compared to LF males and females.There was an effect of sex on total and Copyright: © 2015 Bosch et al. regional composition differences.HF males had higher total lean mass and lower regional fat as most locations compared to LF males.In contrast HF females only had significantly lower leg fat compared to LF females.Contrary to our hypotheses there was no significant difference in VAT mass between fitness levels.Sex also influenced insulin sensitivity differences between HF and LF individuals.HF males were more insulin sensitive than LF males but the same was not observed for females.These results suggest that higher fitness influences the proportion of lean and fat mass and those regional differences may be sex specific.
Overall, these results in a younger population are consistent with previous research in older and heavier adults [14][15][16][17][18].However, this study observed differences between males and females between regional fat and lean mass between young adults with different fitness levels.These differences could be important in future interventional studies.While crosssectional in design, these results suggest that higher fitness has a sex specific effect on regional fat mass and lean mass.These differences may explain why higher insulin sensitivity was only observed in males with higher fitness.The lack of difference in VAT mass may be explained by recently identified %BF thresholds for VAT accumulation in males and females [27,28]; both males and females were below or near these thresholds and would not have started accumulating VAT.However, LF males and females had significantly higher %BF, longitudinal studies are needed to ascertain if this puts LF individuals at greater risk for future cardiometabolic complications (since they are closer to the VAT accumulation threshold).Interestingly, other depots have been associated with changes in insulin sensitivity [29] and may explain the sex differences in insulin sensitivity between HF and LF males and females.Training intensity or training volume differences between HF males and females may explain the regional lean mass differences between males and females.However, this study suggests that improved fitness results in sexspecific differences in regional body composition.
Generally, males store more fat in the abdominal region and females store fat in the lower body.This could explain why HF males and females had difference patterns of regional fat compared to their LF groups.These results suggest that in males, higher fitness (usually associated with higher physical activity levels) is associated with lower %BF, lower regional fat and higher insulin sensitivity.In females however, higher fitness was associated with lower %BF and lower leg fat but no difference in insulin sensitivity or lean mass differences.A previous study observed no change in leg fat mass following a six-month intervention using resistance training [29], suggesting that different training modalities may result in differential changes in regional body composition.While fitness level was not associated with any difference in standard clinical blood markers (ex. lipids, cholesterol, etc.) insulin was significantly higher in the LF group (Table 1: males and females combined).Additionally it has been observed that higher baseline fitness levels are associated with a lower incidence of future cardiovascular disease and prediabetes/diabetes [20][21][22].Future studies should examine the role of %BF over time as it may be an influencing factor.
The strengths of this study were the use of gold standard measurements for assessing body composition, fitness level and insulin sensitivity [30].The primary weakness was the small sample size, which may have limited the statistical power to detect differences between groups, however this was a pilot study aimed at identifying regional fat and lean mass differences between HF and LF young adults with normal BMI.Additionally, the small sample size limited our ability to control of other factors that affect body composition (ex.occupation etc.).These results have identified significant %BF differences in males and females with normal BMI that may influence future cardiometabolic risk.Differences in %BF may explain the metabolically unhealthy normal-weight individuals as well as those with obesity who are metabolically healthy [31].

Conclusion
This study highlights that even in young adults classified with normal BMI, having a higher fitness level is associated with a more favorable body composition.Sex influences regional fat differences between HF and LF males and females, but does not have any effect on VAT mass.Higher %BF during young adulthood, observed in both LF males and females, may influence future metabolic risk and VAT accumulation but has no association with cardiovascular risk factors in young adults with normal BMI.

Table 1 :
Baseline characteristics of trained and sedentary young adults mean (+sd).

Table 2 :
Comparison of participants by activity level and gender mean (+SD).Groups that do not share a letter within the same row are significantly different at P=0.05 adjusted for multiple comparisons and small sample size.
A/G ratio is ratio of android fat over gynoid fat Subq is the subcutaneous android (abdominal) fat depot Mean pair differences compares the average difference between each pair between males and females (ex.Average difference for percent fat between male and female pairs) Differences in Normal Weight Young Adults.J Endocrinol Diab 2(3): 5. DOI: http://dx.doi.org/10.15226/2374-6890/2/3/00122FitnessLevel