Relationships between body composition, anthropometrics, and standard lipid panels in a normative population

IntroductionMore than one third of adults in the United States (US) meet the clinical criteria for a diagnosis of metabolic syndrome, but often diagnosis is challenging due to healthcare access, costs and discomfort with the process and invasiveness associated with a standard medical examination. Less invasive and more accessible approaches to collecting biometric data may have utility in identifying individuals at risk of diagnoses, such as metabolic syndrome or dyslipidemia diagnoses. Body composition is one such source of biometric data that can be non-invasively acquired in a home or community setting that may provide insight into an individual's propensity for a metabolic syndrome diagnosis. Here we investigate possible associations between body composition, anthropometrics and lipid panels in a normative population.MethodsHealthy participants visited the Lab100 clinic location at a hospital setting in New York City and engaged in a wellness visit led by a nurse practitioner. Blood was analyzed at point-of-care using the Abbott Piccolo Xpress portable diagnostic analyzer (Abbott Laboratories, IL, USA) and produced direct measures of total cholesterol (TC), high density lipoprotein (HDL-C), low density lipoprotein (LDL-C), very-low density lipoprotein (VLDL-C), and triglycerides (TG). Body composition and anthropometric data were collected using two separate pieces of equipment during the same visit (Fit3D and InBody570). Regression analysis was performed to evaluate associations between all variables, after adjusting for age, sex, race, AUDIT-C total score (alcohol use), and current smoking status.ResultsData from 199 participants were included in the analysis. After adjusting for variables, percentage body fat (%BF) and visceral fat levels were significantly associated with every laboratory lipid value, while waist-to-hip ratio also showed some significant associations. The strongest associations were detected between %BF and VLDL-C cholesterol levels (t = 4.53, p = 0.0001) and Triglyceride levels (t = 4.51, p = 0.0001).DiscussionThis initial, exploratory analysis shows early feasibility in using body composition and anthropometric data, that can easily be acquired in community settings, to identify people with dyslipidemia in a normative population

Metabolic characteristics of keto-adapted ultra-endurance runners

Background: Many successful ultra-endurance athletes have switched from a high-carbohydrate to a low-carbohydrate diet, but they have not previously been studied to determine the extent of metabolic adaptations. Methods: Twenty elite ultra-marathoners and ironman distance triathletes performed a maximal graded exercise test and a 180 min submaximal run at 64% VO2max on a treadmill to determine metabolic responses. One group habitually consumed a traditional high-carbohydrate (HC: n = 10, %carbohydrate:protein:fat = 59:14:25) diet, and the other a low-carbohydrate (LC; n = 10, 10:19:70) diet for an average of 20 months (range 9 to 36 months). Results: Peak fat oxidation was 2.3-fold higher in the LC group (1.54 ± 0.18 vs 0.67 ± 0.14 g/min; P = 0.000) and it occurred at a higher percentage of VO2max (70.3 ± 6.3 vs 54.9 ± 7.8%; P = 0.000). Mean fat oxidation during submaximal exercise was 59% higher in the LC group (1.21 ± 0.02 vs 0.76 ± 0.11 g/min; P = 0.000) corresponding to a greater relative contribution of fat (88 ± 2 vs 56 ± 8%; P = 0.000). Despite these marked differences in fuel use between LC and HC athletes, there were no significant differences in resting muscle glycogen and the level of depletion after 180 min of running (− 64% from pre-exercise) and 120 min of recovery (− 36% from pre-exercise). Conclusion: Compared to highly trained ultra-endurance athletes consuming an HC diet, long-term keto-adaptation results in extraordinarily high rates of fat oxidation, whereas muscle glycogen utilization and repletion patterns during and after a 3 hour run are similar

The Effects of Energy Intake on Upper Respiratory Symptoms in Ultra-Endurance Triathletes

Background: It is unclear whether energy intake can impact the incidence of upper respiratory symptoms (URS). The purpose of this study was to examine if there are differences in energy intake between symptomatic (SYM) and asymptomatic (ASYM) groups of URS in Ironman-triathletes. Methods: Thirty-three subjects competing in the Lake Placid Ironman-triathlon (mean±SD; age,37±8y; height,178±8cm; mass,76.3±10.4kg; body fat,10.8±3.8%) were randomized into either the control (CON) or intervention (INT). INT consumed 4-commercial recovery drinks, two immediately post-race and two 3-hours post-race. Calorie and macronutrient intake were recorded pre-, during, and post-race. Subjects completed the Wisconsin URS Survey to assess URS over the next two weeks. Two analyses were done by comparing results between CON and INT, and when subjects were classified as either asymptomatic (ASYM=20) or symptomatic (SYM=13). Results: There were no differences in energy intake (p\u3e0.05) and URS (INT,32±38; CON,16±23; p=0.155). However, on the race day, SYM (9,044±2,598kcal) consumed less energy intake than ASYM (10,991±2497kcal) (p=0.044). Also, SYM consumed less energy the day before the race (p=0.031) and post-race (p=0.008). ASYM consumed greater carbohydrate the day before the race (p=0.032), fat the day of the race (p=0.006), carbohydrate post-race (p=0.08) and fat post-race (p=0.002). Conclusions: Overall energy intake was similar between CON and INT. However, when subjects were differentiated by URS, SYM consumed less calories the day before and day of the race versus ASYM

The Effects of Phytosterols on Lipoprotein Particle Size

Phytosterols (PS) have become a recent popular medication alternative for treatment of hypercholesterolemia and have proven effective. Whether PS decrease risk of cardiovascular disease through other mechanisms, such as lipoprotein particle size and inflammatory markers, remains unclear. PURPOSE: The primary aim of this study was to examine the effects of two forms of PS in milk on lipoprotein particle size, inflammatory markers and fat-soluble vitamins. METHODS: Twenty subjects (13 males, 7 females; age; 55±6.1 years, height; 169±10 cm, weight; 77.9±16.9 kg, BMI; 27.3) consumed 16oz of cow’s milk daily for 12 weeks. The three sequential four-week phases consisted of 2% cow’s milk, 2 grams/day of ‘unaided’ PS in skim milk, and 2 grams/day of ‘aided’ triglyceride recrystallized PS (TRP) in fat-free milk. Blood was taken after each phase for NMR lipoprotein particle size analysis, lipid panel, glucose, insulin, inflammatory markers, and fat-soluble vitamins. RESULTS: Subjects maintained body weight and composition, habitual diet, and physical activity throughout the twelve weeks (p\u3e0.05). Total LDL particle concentrations from NMR decreased to a greater extent with TRP (-15%) compared to unaided (-5%) PS (

Effects of Incremental Dietary Macronutrient Changes on Fat Oxidation and Body Composition

Despite evidence that low-carbohydrate diets (LCD) can improve metabolic syndrome (MetS) characteristics and risk for cardiovascular disease, concerns remain regarding the potential deleterious effects of higher fat intake. If higher fat intake (in the context of lower carbohydrate intake) is accompanied by higher fat oxidation, there would likely be more efficient fat loss and improved health parameters. Our aim was to examine how diets spanning a broad range of carbohydrate levels ranging from very low (\u3c50 g/day) to current dietary guidelines (~350 g/day) affect substrate oxidation patterns and changes in body composition within the same person while keeping caloric and protein intake constant. After an initial 3-wk run-in LCD, 16 adults with MetS (age 44.9 ± 9.9 yr, BMI 37.9 ± 6.3 kg/m2) were fed six sequential moderately hypocaloric 3-wk diets that progressively increased carbohydrate (CHO) (from 47 to 344 g/day) with concomitant decreases in total fat. Body composition was determined by dual-energy X-ray absorptiometry (DXA) and respiratory quotient (RQ), fat oxidation, and resting energy expenditure (REE) were determined by indirect calorimetry after each diet phase. Subjects lost significantly more fat mass (-2.32±1.53 kg) on the free-living LCD phase, but overall fat mass loss was variable between subjects and on average less than expected from the calculated caloric deficit (-8.3±4.5 vs 12.74±15.81 kg, respectively). There was a significant decrease in REE, but no significant change in relative REE (kcals/kg/day). Fat oxidation rates significantly increased when consuming diets with 7% CHO, but decreased to below baseline by the highest CHO phase. RQ decreased on the LCD (0.75±0.04), and increased linearly as CHO increased, up to 0.84 ±0.05. Body mass was significantly correlated with CHO consumption (r=0.49), insulin (r=0.34), fat consumption (r=-0.49) and ketones (r=0.46). These findings suggest that it is difficult to estimate weight loss within an individual, even with a constant caloric deficit, since individuals vary in their substrate oxidation response to reintroduction of dietary carbohydrate. Those who can maintain a higher fat oxidation to a greater extent as carbohydrates are increased (and fat decreased) may possess an enhanced ability for fat loss on higher carbohydrate diets

Impact of Nutrient Intake on Hydration Biomarkers Following Exercise and Rehydration Using a Clustering-Based Approach

We investigated the impact of nutrient intake on hydration biomarkers in cyclists before and after a 161 km ride, including one hour after a 650 mL water bolus consumed post-ride. To control for multicollinearity, we chose a clustering-based, machine learning statistical approach. Five hydration biomarkers (urine color, urine specific gravity, plasma osmolality, plasma copeptin, and body mass change) were configured as raw- and percent change. Linear regressions were used to test for associations between hydration markers and eight predictor terms derived from 19 nutrients merged into a reduced-dimensionality dataset through serial k-means clustering. Most predictor groups showed significant association with at least one hydration biomarker: (1) Glycemic Load + Carbohydrates + Sodium, (2) Protein + Fat + Zinc, (3) Magnesium + Calcium, (4) Pinitol, (5) Caffeine, (6) Fiber + Betaine, and (7) Water; potassium + three polyols, and mannitol + sorbitol showed no significant associations with any hydration biomarker. All five hydration biomarkers were associated with at least one nutrient predictor in at least one configuration. We conclude that in a real-life scenario, some nutrients may serve as mediators of body water, and urine-specific hydration biomarkers may be more responsive to nutrient intake than measures derived from plasma or body mass

Metabolic characteristics of keto-adapted ultra-endurance runners Metabolic characteristics of keto-adapted ultra-endurance runners

ABSTRACT Background: Many successful ultra-endurance athletes have switched from a high-carbohydrat

Cumulative change from baseline in (A) body mass and fat mass from dual-energy x-ray absorptiometry, (B) fasting lipoproteins, (C) insulin resistance determined from homeostatic model assessment (HOMA), and (D) blood pressure in 16 subjects who switched to a very low carbohydrate diet and then incrementally increased carbohydrate every 3 wk over six sequential phases (C1→C2→C3→C4→C5→C6).

<p>BL  =  baseline, FL  =  free-living low-carbohydrate diet). Significant differences from Baseline vs C1 were determined by dependent t-test and indicated by an asterisk. Differences from C1 to C6 were determined by repeated measures ANOVA and Fisher's LSD post hoc. Different letters at a time point indicate statistical significance.</p

Plasma fatty acid responses¹.

<p><i>¹Values are mean ± SD from 16. SFA  =  total saturated fatty acids; MUFA  =  total monounsaturated fatty acids.</i></p><p><i>²C1  =  lowest carbohydrate diet and C6  =  highest carbohydrate intake.</i></p><p><i>³3wk run-in diet phase before entering feeding portion of study.</i></p><p><i>⁴Dependent t-test (Baseline vs C1).</i></p><p>Plasma fatty acid responses^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113605#nt107" target="_blank">1</a>}.</p

Daily nutrient intakes at baseline (habitual diet) and during each dietary phase¹.

<p><i>¹Values are mean ± SD.</i></p><p><i>²Determined from 3-day diet records.</i></p><p>Daily nutrient intakes at baseline (habitual diet) and during each dietary phase^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113605#nt105" target="_blank">1</a>}.</p