Youth with obesity have an increased risk for cardiometabolic disease, but identifying those at highest risk remains a challenge. Four biomarkers that might serve this purpose are “by products” of clinical NMR LipoProfile® lipid testing: LPIR (Lipoprotein Insulin Resistance Index), GlycA (inflammation marker), BCAA (total branched-chain amino acids), and glycine. All are strongly related to insulin resistance and type 2 diabetes (T2DM) in adults (glycine inversely) and are independent of biological and methodological variations in insulin assays. However, their clinical utility in youth is unclear. We compared fasting levels of these biomarkers in 186 youth (42 lean normal glucose tolerant (NGT), 88 obese NGT, 23 with prediabetes (PreDM), and 33 with T2DM. All four biomarkers were associated with obesity and glycemia in youth. LPIR and GlycA were highest in youth with PreDM and T2DM, whereas glycine was lowest in youth with T2DM. While all four were correlated with HOMA-IR (Homeostatic Model Assessment for Insulin Resistance), LPIR had the strongest correlation (LPIR: r = 0.6; GlycA: r = 0.4, glycine: r = −0.4, BCAA: r = 0.2, all P < 0.01). All four markers correlated with HbA1c (LPIR, GlycA, BCAA: r ≥ 0.3 and glycine: r = −0.3, all P < 0.001). In multi-variable regression models, LPIR, GlycA, and glycine were independently associated with HOMA-IR (Adjusted R2 = 0.473, P < 0.001) and LPIR, glycine, and BCAA were independently associated with HbA1c (Adjusted R2 = 0.33, P < 0.001). An LPIR index of >44 was associated with elevated blood pressure, BMI, and dyslipidemia. Plasma NMR-derived markers were related to adverse markers of cardiometabolic risk in youth. LPIR, either alone or in combination with GlycA, should be explored as a non-insulin dependent predictive tool for development of insulin resistance and diabetes in youth.Clinical Trial RegistrationClinicaltrials.gov, identifier NCT:02960659

<div class="section abstract"><div class="htmlview paragraph">The aerodynamics of the front wing of modern race cars are critical to the performance of the vehicle. The Formula 1 line up represents the state of the art in this field as there are some very complex aerodynamic designs on display. It is strange, however, that there is no agreement on twist direction for the multiple wing sections of the front wing. This paper addresses this question by posing it as an optimization problem. The geometry of the wings has been simplified so that the twist of the upper sections could be studied in isolation. The whole assembly consisted of only two high lift surfaces. The forward wing remained fixed for the study, and twist of the secondary wing became the primary focus. Its geometry was generated by lofting a set of cross-sections at specified angles to create the surface. The resulting geometry was automatically meshed and then evaluated using CFD. This fully automated process was then used to find an ideal twist distribution of the secondary wing. The results show that a higher angle of attack at the tip of the wing produces superior aerodynamic performance. One of the advantages of this approach is that the final output of the process will be a CAD geometry, as opposed to a modified FEM. It avoids a manual step of putting the results back into CAD before being able to share the optimal geometry with other design teams. By removing a manual step, it makes it possible to integrate this method with other disciplines such as structural analysis and enable MDO.</div></div>

To formulate precision nutrition prescriptions, it is essential to understand the relationship between dietary assessment, ethnicity and cardiometabolic disease. Erythrocyte (RBC) fatty acid (FA) profiles reflect long-term dietary fat intake but have not been rigorously compared with short-term FA intake in women of African ancestry who have paradoxically low triglycerides with high fat intake. We compared the type of dietary fat consumed (proportion of total fat) using food records with fasting RBC FA (mol%) determined by gas chromatography in 130 female, healthy, federal employees without diabetes (12% African Immigrants (AI), 47% African Americans (AA), 41% White Americans (WA)); mean age 43+10 y and BMI 30 + 6 kg/m2). Diet quality and home prepared meals were similar across ethnicities. AI reported the lowest energy intake. Regardless of absolute energy intake, macronutrient distributions were similar. Fat intake differed by ethnicity; WA had the highest saturated FA (SFA) and lowest eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) and omega-3 index (EPA + DHA), while AI and AA had similar SFA, EPA and DHA intake. AI women had the highest RBC DHA and omega-3 index. Associations between FA intake and RBC profiles were significant for DHA (r=0.3, p&lt;0.01), EPA (r=0.2, p&lt;0.05) and the omega-3 index (r=0.2, p&lt;0.01). Overall, RBC EPA and DHA were reliable biomarkers of dietary intake in women of African ancestry, consistent with data in predominantly white populations. These findings support the combined use of food records and RBC profiles for comprehensive dietary assessments. As RBC SFA and monounsaturated FA reflect both endogenous and exogenous sources, the discrepancies between intake and RBC profiles could reflect ethnic variations in nutrient metabolism. To enable precision nutrition prescriptions, exploration of population-specific biomarkers in concert with dietary assessment is needed. Disclosure A. B. Courville: None. S. T. Chung: None. S. Yang: None. L. Mabundo: None. C. K. Cravalho: None. S. Matta: None. A. Villalobos-perez: None. J. M. Dawson: None. A. H. Lichtenstein: None. A. E. Sumner: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases

Lipoprotein insulin resistance (LPIR) is an emerging biomarker of insulin resistance (IR), and a score of &gt;48 is a strong predictor of incident cardiometabolic disease disease in a predominantly European ancestry population. LPIR is derived from a composite score of nuclear magnetic resonance (NMR) lipoprotein (Lp) parameters: triglyceride-rich (TRLp), low density (LDLp), and high density (HDLp). Yet, there is a paucity of data in African ancestry population, in whom there is low-normal TRLp despite high rates of IR and diabetes. Therefore, we examined Lp profiles and LPIR in a large African ancestry cohort, stratified by sex to determine the relationship of LPIR with established markers of IR. This is a secondary analysis from 2 studies (The Africans in America and Federal Women’s Study) designed to evaluate the genetic, biological and socio-environmental factors of diabetes risk in those of African ancestry. All participants self-identified as healthy and lived in the DC metro area, n= 518: 87.7% African immigrant,12.3% African American; age 39±10 (20-65y); BMI 28.1±4.8 (18.2–45.2 kg/m2); 58% male; 31% with obesity, and 37% with abnormal glucose tolerance; mean±SD (range); median (25th-75th percentile). Fasting measures of IR (LPIR, triglyceride/HDL (TG/HDL) ratio and homeostasis model of insulin resistance (HOMA-IR)) were compared with the whole-body insulin sensitivity index (WBISI) obtained during a multi-sample 75g OGTT, using spearman correlations. Lp particle size and subclass concentrations were measured by the amplitudes of the lipid-methyl group signals (NMR LipoProfile®). Men had lower BMI (27.1±3.9 vs 29.3±5.6 kg/m2), fat mass (23.5±5.5 vs 37.9±6.8 %), insulin resistance (WBISI: 6.2 (3.7–10.1) vs 4.9 (3.2–8.6), HOMA-IR: 1.3 (0.7–2.0) vs 1.6 (0.9–2.4), TG/HDL: 1.4 (1.0–2.2) vs 1.1 (0.8–1.5)), all P&lt;0.001. LDLp (1226 (959–1531) vs 1239 (981–1553) nmol/L) and HDLp (17.6 (16.2–19) vs 17.5 (15.9–19.7) µmol/L) were similar by sex, P&gt;0.6, while small LDLp 734 (523–1039) vs 541 (370–805) nmol/L and TRLp 80.5 (52.2–116.4) vs 53.6 (28.7 -89.3) nmol/L were higher in men. The total mean LPIR score was 28.9±18.7 and was higher in men (34±19 vs. 23±17), P&lt;0.001. LPIR and TG/HDL ratio correlated with WBISI (r≥-0.40) and HOMA-IR (r≥0.40), P&lt;0.001 with no differences by sex. HOMA-IR correlated with WBISI (r=-0.95, P&lt;0.001). Overall, African ancestry individuals had high rates of abnormal glucose tolerance, obesity and LDLp but LPIR was 20 points lower than the established score for predicting cardiometabolic disease. It’s utility for detecting IR was modest but it may be an important adjunct for early cardiometabolic risk stratification in African ancestry populations in whom traditional screening methods have lower sensitivity.