Residual Standard Deviation Research Articles

Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes.

An informed estimate of subject-level variance is a key determinate for accurate estimation of the required sample size for clinical trials. Evaluating completed adult Type 2 diabetes studies submitted to the FDA for accuracy of the variance estimate at the planning stage provides insights to inform the sample size requirements for future studies. From the U.S. Food and Drug Administration (FDA) database of new drug applications containing 14,106 subjects from 26 phase 3 randomized studies submitted to the FDA in support of drug approvals in adult type 2 diabetes studies reviewed between 2013 and 2017, we obtained estimates of subject-level variance for the primary endpoint-change in glycated hemoglobin (HbA1c) from baseline to 6 months. In addition, we used nine additional studies to examine the impact of clinically meaningful covariates on residual standard deviation and sample size re-estimation. Our analyses show that reduced sample sizes can be used without interfering with the validity of efficacy results for adult type 2 diabetes drug trials. This finding has implications for future research involving the adult type 2 diabetes population, including the potential to reduce recruitment period length and improve the timeliness of results. Furthermore, our findings could be utilized in the design of future endocrinology clinical trials.

Comprehensive amino acids profiling: Grape varieties grown in California and in Tuscany, Italy.

Twenty-three amino acids and glutathione were profiled in 33 juices: Cabernet sauvignon, Cabernet franc, and Merlot grown in Bolgheri, Tuscany, Italy, and Napa Valley, CA, as well as Petit Verdot grown in Napa Valley, CA. The method was carried out by online pre-column derivatization followed by ultra-high performance liquid chromatography with fluorescence detection; residual standard deviation values for repeatability (n=20) were<5%. All amino acids could be successfully separated in these samples within 18 min. The most abundant amino acids, PRO, γ-aminobutyric acid, and ARG, varied by two-, two-, and three-fold, respectively, across all of the grape juices sampled. Comparisons between grape juices based on their geographic origin and variety were made with principal component analysis and hierarchical cluster analysis. Merlot juices from both regions were correlated differently by amino acids profile than Cabernet sauvignon and Cabernet franc juices. The amino acids profile in Merlot juices was more similar between most sites in Bolgheri. In contrast, Cabernet sauvignon and Cabernet franc juices had a higher similarity between vineyards in Bolgheri and vineyards in Napa Valley. Overall, most of the amino acids quantified in Cabernet franc juices and, to a lesser degree, Cabernet sauvignon juices from Bolgheri and Napa Valley vineyards were strongly correlated with each other.

Quantitative structure-property relationship for gas-chromatographic retention indices of volatile organic compounds in colored-quinoa seeds

This study outlines the development of a predictive in silico model for the retention index (RI) of 61 volatile organic compounds (VOCs) present in diverse colored-quinoa seeds. The analysis was performed using gas chromatography-ion mobility spectrometry (GC-IMS) with the FS-SE-54-CB-1 capillary column. The chemical information in the database was carefully curated, and the molecular geometries of the VOCs were properly optimized to calculate diverse molecular descriptors. The database was then divided into a training set (48 compounds) and a test set (13 molecules). For model development, unsupervised and supervised chemometric approaches were integrated to obtain a four-descriptor quantitative structure-property relationship (QSPR). The negligible differences in the coefficient of determination and residual standard deviation for the training set (R2 = 0.957, s = 34.16) and test set (R2 = 0.954, s = 35.46) indicate a stable and predictive model. The QSPR model was also subjected to cross-validation using diverse strategies, along with an applicability domain assessment. A thorough explanation of the mechanistic effects of these descriptors in predicting the RI is provided, ensuring the model accomplished all the criteria defined by the Organization for Economic Co-operation and Development. This model is simple and can assist chemists working on the analysis of VOCs in quinoa seeds using GC-IMS and to gain a deeper understanding of the molecular mechanisms involved in the retention index phenomenon. This QSPR model was also applied as a tool to predict the retention index of an external set of 89 new VOCs identified in quinoa seeds through GC-IMS analysis.

Sea surface wind retrieval from CFOSAT Rotating fan-beam scatterometer in ocean cyclones

ABSTRACT Rotating fan-beam scatterometer (RFSCAT) is a radar scatterometer system for sea surface wind vector measurement. Compared with other available scatterometers, RFSCAT can provide more combination of azimuth angles and incidence angles for a single WVC (wind vector cell), this observation mechanism is more conducive to the sea surface wind direction retrieval. In this paper, the NSCAT-4DS GMF (geophysical model function) with SST (sea surface temperature) correction, and the MSS (Multiple Solution Scheme) combination with a 2DVAR (2-dimensional variational analysis) are adopted to retrieve the sea surface winds from RFSCAT on the CFOSAT (Chinese-French Oceanography Satellite). The retrieved RFSCAT sea surface winds and ASCAT (advanced scatterometer) sea surface winds are compared and tested, and the feasibility of the RFSCAT measuring sea surface winds under high wind speeds is analysed. The results show that the RMSE of the RFSCAT sea surface wind speed using improved algorithm has decreased by 0.292 m s−1, the correlation coefficient has increased by 0.032, and the residual standard deviation has decreased by 0.194 m s−1. The RMSE of RFSCAT sea surface wind direction has decreased by 5.950°, the correlation coefficient has increased by 0.002, and the residual standard deviation has decreased by 5.567°. It is shown that the changes in winds RMSE using pre-improved and improved algorithms have statistical significance. In a word, the spaceborne Ku-band rotating fan-beam scatterometer can capture the winds structure of ocean cyclones. Although there may be high wind speeds saturation phenomena, the detecting sea surface winds at high wind speeds show preferable performance.

NASPrecision: Neural Architecture Search-Driven Multi-Stage Learning for surface roughness prediction in ultra-precision machining

Accurate surface roughness prediction is critical for ensuring high product quality, especially in sectors such as manufacturing, aerospace, and medical devices, where the smallest imperfections can compromise performance or safety. However, this is very challenging due to complex, non-linear interactions among variables, which is further exacerbated when working with limited and imbalanced datasets. Existing methods leveraging traditional machine learning algorithms require extensive domain knowledge for feature engineering and substantial human intervention for model selection. To address these issues, we propose a Neural Architecture Search (NAS)-Driven Multi-Stage Learning Framework, named NASPrecision. This innovative approach autonomously identifies the most suitable features and models for various surface roughness prediction tasks and significantly enhances the performance by multi-stage learning. Our framework operates in three stages: (1) architecture search stage, employing NAS to automatically identify the most effective model architecture; (2) initial training stage, where we train the neural network for initial predictions; (3) refinement stage, where a subsequent model is appended to refine and capture subtle variations overlooked by the initial training stage. In light of limited and imbalanced datasets, we adopt a generative data augmentation technique to balance and generate new data by learning the underlying data distribution. We perform extensive experiments on three distinct real-world datasets, each associated with a different machining technique, and compare with various machine learning algorithms. The experimental results underscore the superiority of our framework, which achieves an average improvement of 25.7%, 22.7%, and 39.1% in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Standard Deviation of Residuals (STDR), respectively. This significant performance enhancement not only confirms the robustness of our framework but also establishes it as a generic solution for accurate surface roughness prediction. The success of this approach can lead to improved production efficiency and product quality in critical industries while also reducing the need for extensive domain knowledge and human intervention.

Abstract P155: Elevated Long-Term Triglyceride-to-High Density Lipoprotein Cholesterol Ratio Variability From Childhood Is Linked To Higher Risk Of Early-Onset Hypertension: The Bogalusa Heart Study

Background: Early-onset hypertension is linked to higher risk of hypertensive organ damage compared to late-onset hypertension. Insulin resistance has been shown to be associated with greater risk of hypertension. Greater triglyceride-to-high density lipoprotein cholesterol ratio (TG/HDL-C), a surrogate marker of insulin resistance, has been linked to higher risk of hypertension. However, whether elevated long-term TG/HDL-C variability from childhood is associated with higher risk of early-onset hypertension is unclear. Methods: We studied 1271 participants of the Bogalusa Heart Study (age at enrollment: 9.9 ± 4.0 years, follow-up : 38.3 ± 3.8 years, age at last exam: 48.2 ± 5.2 years, 59.5% women, 34.4% Black participants) with ≥3 lipid measurements during follow-up. TG/HDL-C was obtained as TG (mg/dL) divided by HDL-C (mg/dL). Onset of hypertension was defined as blood pressure ≥140/90 mmHg or taking antihypertensive medications detected during follow-up. According to age at onset of hypertension, 4 subgroups were formed: &lt;35 years (early-onset hypertension), 35-44 years, ≥45 years, and people without hypertension. Long-term TG/HDL-C variability from childhood was calculated as the deviation from age predicted values (DEV) and residual SD (RSD). DEV and RSD were obtained from the growth curves derived from linear mixed effect models, as the mean and SD of the residuals of observed and predicted TG/HDL-C levels. Associations between TG/HDL-C variability and hypertension onset subgroups were evaluated using multinomial logistic regression models. People who did not have hypertension during follow-up were the reference group. Interactions by race and sex were performed. Results: Among the 1271 participants, 585 (46.0%) had hypertension at last exam; among the 585, 124 (21.2%) had early-onset hypertension. For 1 SD increase in DEV and RSD of TG/HDL-C, the odds of early-onset hypertension increased by 30.6% (95% CI 1.06, 1.61) and 28.8% (95% CI 1.06,1.57), compared to people without hypertension, after adjusting for race, sex, diabetes, obesity, and smoking status, education, and TG/HDL-C at last exam. No significant associations were observed between DEV and RSD of TG/HDL-C and other hypertension onset subgroups. No significant interactions were found between both DEV and RSD and race and sex. Conclusions: Our results suggest that individuals with higher long-term variability in insulin resistance may be at a greater risk of having early-onset hypertension.

Abstract P372: Greater Long-Term Non-High Density Lipoprotein Cholesterol Variability From Childhood Is Associated With Higher Risk Of Early-Onset Hypertension: The Bogalusa Heart Study

Background: Early-onset hypertension is associated with greater risk of cardiovascular mortality and hypertension associated organ damage, compared to late-onset hypertension. Non-high-density lipoprotein cholesterol (non-HDL-C) is known to better predict future cardiovascular event risk, compared to low-density lipoprotein cholesterol. Whether higher long-term non-HDL-C variability from childhood is associated with higher risk of early-onset hypertension is unclear. Methods: We included 1271 participants of the Bogalusa Heart Study (age at enrollment: 9.9 ± 4.0 years, follow-up: 38.3 ± 3.8 years, age at last exam: 48.2 ± 5.2 years, 59.5 % women, 34.4 % Black participants) with ≥3 lipid measurements during follow-up. Non-HDL-C was calculated as total cholesterol (mg/dL) - HDL-C (mg/dL). Onset of hypertension was defined as having blood pressure ≥140/90 mmHg or using antihypertensive medications during follow-up. Participants were categorized according to age at hypertension onset (years) into 4 subgroups: &lt;35 (early-onset), 35-44, ≥45, and people without hypertension. Long-term non-HDL-C variability was obtained from the deviation from age predicted values (DEV) and residual SD (RSD). DEV and RSD were calculated from growth curves derived from linear mixed effect models, as the mean and SD of the residuals of observed and predicted non-HDL-C levels. Associations between non-HDL-C variability and hypertension onset subgroups were analyzed using multinomial logistic regression models. Interactions by race and sex were performed. Results: For 1-SD increase in DEV and RSD of non-HDL-C, the odds of hypertension increased for all hypertension onset subgroups compared to people without hypertension, after adjusting for race, sex, diabetes, obesity, and smoking status, education, and non-HDL-C at last exam. The odds of hypertension was highest in the early-onset subgroup (Table). No significant interactions were found between both DEV and RSD and race and sex. Conclusions: Our findings suggest that individuals with higher long-term non-HDL-C variability may be at a greater risk of having early-onset hypertension.

Bayesian modeling of the Gompertz curve for meat quails growth data considering different error distributions

This study applied the Gompertz model to quail growth data, assuming symmetric and asymmetric homoscedastic and heteroscedastic error distributions (Normal, t-Student, Skew normal, and Skew t), undera Bayesian framework. Model selection criteria included the Bayesian Deviance Information Criterion (DIC) and the analysis of residual standard deviation (σ), as well as graphical assessment of the fit. For both homoscedastic error structures (males: DIC=7.186; σ=10.73) and (females: DIC=5.572; σ=11.88) as well as heteroscedastic structures (males: DIC=6.493; σ=0.795) and (females: DIC=4.405; σ=0.824), the best fits were obtained by considering the Skew t distribution for errors. In homoscedastic fits, significant residual asymmetry (λ) was observed only for female quails (CI(λ)=[-8.039;-0.340]), whereas in heteroscedastic fits, the parameter was not significant for both sexes. Additionally, heteroscedasticity (δ) captured in the fits was significant for both sexes (males: CI(δ)=[1.66;2.13] and females: CI(δ)=[1.80;2.26]). Understanding animal growth is crucial for optimizing management and feeding practices, reducing time and costs in production. In this case, the use of nonlinear models considering heteroscedastic and/or asymmetric residual structures contributes to greater accuracy in decision-making.

Prediction of Physico-Chemical Parameters of Surface Waters Using Autoregressive Moving Average Models: A Case Study of Kis-Balaton Water Protection System, Hungary

In this work, the authors provide a case study of time series regression techniques for water quality forecasting. With the constant striving to achieve the Sustainable Development Goals (SDG), the need for sensitive and reliable water management tools has become critical. Continuous online surface water quality monitoring systems that record time series data about surface water parameters are essential for the supervision of water conditions and proper water management practices. The time series data obtained from these systems can be used to develop mathematical models for the prediction of the temporal evolution of water quality parameters. Using these mathematical models, predictions can be made about future trends in water quality to pinpoint irregular behaviours in measured data and identify the presence of anomalous events. We compared the performance of regression models with different structures for the forecasting of water parameters by utilizing a data set collected from the Kis-Balaton Water Protection System (KBWPS) wetland region of Hungary over an observation period of eleven months as a case study. In our study, autoregressive integrated moving average (ARIMA) regression models with different structures have been compared based on forecasting performance. Using the resulting models, trends of the oxygen saturation, pH level, electrical conductivity, and redox potential of the water could be accurately forecast (validation data residual standard deviation between 0.09 and 20.8) while in the case of turbidity, only averages of future values could be predicted (validation data residual standard deviation of 56.3).

Linearity Study of Color Measurement Methods in Digital Image Colorimetry

Color metrology is steadily developing in the interests of fundamental and applied science, improving industrial production processes. The author considers the problem of ensuring metrological traceability of color measurements under natural conditions.Digital image colorimetry methods demonstrate good linearity and precision in relatively small application ranges in studies of same type and mainly non-luminous objects. For complex scenes with a wide range of brightnesses, nonlinear functional color rendering models are used that are focused on visual perception, peak brightness of the monitor and reference viewing conditions, which complicates the processing of measurement results and reduces a confidence interval.The described method of measuring the chromaticity coordinates of an object by its digital image is based on tensor calculus and consists of multiple registration of an object and sets of reference samples with different exposure times, building calibration dependencies forming a multidimensional colorimetric scale in color space and determining chromaticity coordinates as points of intersection of color tensors with the plane of the color locus. In order to ensure accuracy and reliability of the measurement results in the software and hardware environments, a validation study of the performance indicators of this method was carried out, one of which is linearity. An experiment based on comparing the developed method with the reference measurement method showed the effectiveness of the developed wide-range measurement method with good linearity (correlation coefficient R=0.97… 0.99, residual relative standard deviation s0≈1–2%) when adjusting from the subjective factor and display device.The experimental materials may be useful to metrologists solving problems of determining the metrological traceability of the color of digital images in media. The article will be of interest to a wide range of readers with professional interests related to the colorimetry of digital images.

A Deep-Neural-Network-Based Prediction Model for Elastic Input Energy Spectra of Horizontal and Vertical Ground Motions

ABSTRACT Intensity measures based on energy have proven to be robust indicators of damage for a variety of structural types. This article presents a modified ground-motion model (GMM) incorporating a deep neural network to predict elastic input energy spectra for both horizontal and vertical ground motions, considering the pulselike ground motions. The newly developed model employs six predictor variables, that is, moment magnitude Mw, fault mechanism F, rupture distance Rrup, logarithmic rupture distance ln(Rrup), rupture directivity term Idir, and logarithmic shear-wave velocity ln(VS30) as inputs. A subset of records, sourced from the recently updated Next Generation Attenuation-West2 Project database constituted by 2745 ground motions from 97 earthquakes, have been employed in the development of the model. The performance of the developed model remains within the prescribed error range. In addition, the proposed model is compared against currently used GMMs. The predicted spectra obtained from the present study are in good agreement with those given by other literature, and the standard deviations of residuals have been reduced by ∼20% and are more stable. Observations from these results indicate that the newly proposed model generates improved predictions.

Analysis and diagnosis of abnormal SLR validation results for BeiDou-3 SECM-B MEO C225 and C226 satellite orbits

Satellite laser ranging (SLR) is the only stand-alone and external tool for verifying the accuracy of microwave-based GNSS orbits. On February 2, 2023, the International Laser Ranging Service initiated the SLR global tracking campaign for the complete BeiDou-3 medium Earth orbit (MEO) constellation. This initiative provides an opportunity to unveil the potential issue for BeiDou-3 MEO orbit modeling. An examination of the SLR validation results of precise BeiDou-3 MEO orbits across different IGS/Multi-GNSS Experiment (MGEX) analysis centres (ACs) reveals that the SLR residual standard deviation for BeiDou-3 SECM-B MEOs C225 and C226 orbits reaches 18 cm. This figure is approximately four to nine times those for other BeiDou-3 MEOs. This contradicts the fact that the orbit quality of C225 and C226 is comparable to other satellites. By analyzing the correlation between SLR residuals and de-trend clock residuals, detector-specific bias performance, and the distribution characteristics of SLR residuals relative to satellite azimuth and nadir angles, we diagnose that abnormal SLR validation results for C225 and C226 originate from a large deviation in the official horizontal offsets for the laser retroreflector arrays (LRAs) in the satellite reference frame. LRA horizontal offsets for C225 and C226 are estimated based on SLR residuals. With our adjusted LRA offset estimates, the SLR residual metrics for C225 and C226 orbits (across various IGS/MGEX ACs) are comparable to the results obtained for other BeiDou-3 MEOs. Additionally, our adjustments enable the identification of their orbit modeling errors across different IGS/MGEX ACs according to the pattern of SLR residual variation. From the study, spacecraft manufacturers should be encouraged to conduct a comprehensive review of ground calibrations for BeiDou LRA offsets. This is crucial for optimizing BeiDou orbit model and enhancing its applications in high-precision geosciences.

Effect of systemic vascular resistance on the agreement between stroke volume by non-invasive pulse wave analysis and Doppler ultrasound in healthy volunteers.

Stroke volume can be estimated beat-to-beat and non-invasively by pulse wave analysis (PWA). However, its reliability has been questioned during marked alterations in systemic vascular resistance (SVR). We studied the effect of SVR on the agreement between stroke volume by PWA and Doppler ultrasound during reductions in stroke volume in healthy volunteers. In a previous study we simultaneously measured stroke volume by PWA (SVPWA) and suprasternal Doppler ultrasound (SVUS). We exposed 16 healthy volunteers to lower body negative pressure (LBNP) to reduce stroke volume in combination with isometric hand grip to elevate SVR. LBNP was increased by 20 mmHg every 6 minutes from 0 to 80 mmHg, or until hemodynamic decompensation. The agreement between SVPWA and SVUS was examined using Bland-Altman analysis with mixed regression. Within-subject limits of agreement (LOA) was calculated from the residual standard deviation. SVRUS was calculated from SVUS. We allowed for a sloped bias line by introducing the mean of the methods and SVRUS as explanatory variables to examine whether the agreement was dependent on the magnitude of stroke volume and SVRUS. Bias ± limits of agreement (LOA) was 27.0 ± 30.1 mL. The within-subject LOA was ±11.1 mL. The within-subject percentage error was 14.6%. The difference between methods decreased with higher means of the methods (-0.15 mL/mL, confidence interval (CI): -0.19 to -0.11, P<0.001). The difference between methods increased with higher SVRUS (0.60 mL/mmHg × min × L-1, 95% CI: 0.48 to 0.72, P<0.001). PWA overestimated stroke volume compared to Doppler ultrasound during reductions in stroke volume and elevated SVR in healthy volunteers. The agreement between SVPWA and SVUS decreased during increases in SVR. This is relevant in settings where a high level of reliability is required.

A contemporary look at the relationship between general cognitive ability and job performance.

The relationship between general cognitive ability (GCA) and overall job performance has been a long-accepted fact in industrial and organizational psychology. However, the most prominent data on this relationship date back more than 50 years. This meta-analysis examines the relationship between GCA and overall job performance using studies from the current century. Results across 153 samples and a total sample size of 40,740 show a mean observed validity of .16, with a residual SD of .09. Correcting for unreliability in the criterion and correcting predictive studies for range restriction produces a mean corrected validity of .22 and a residual SD of .11. While this is a much smaller estimate than the .51 value offered by Schmidt and Hunter (1998), that value has been critiqued by Sackett et al. (2022), who offered a mean corrected validity of .31 based on integrating findings from prior meta-analyses of 20th century data. We obtain a lower value (.22) for 21st century data. We conclude that GCA is related to job performance, but our estimate of the magnitude of the relationship is lower than prior estimates. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Acoustic Emission Monitoring in Prestressed Concrete: A Comparative Study of Signal Attenuation from Wire Breaks and Rebound Hammer Impulses

Acoustic emission monitoring (AEM) has emerged as an effective technique for detecting wire breaks resulting from, e.g., stress corrosion cracking, and its application on prestressed concrete bridges is increasing. The success of this monitoring measure depends crucially on a carefully designed sensor layout. For this, the attenuation of elastic waves within the structure’s material is ideally determined in situ through object-related measurements (ORMs) with a reproducible signal source, typically a rebound hammer. This assumes that the attenuation coefficients derived from rebound hammer tests are comparable to those from wire breaks, thus allowing their results to be directly applied to wire break detection without further adjustments. This study challenges this assumption by analysing attenuation behaviour through an extensive dataset. Employing time-domain and frequency analysis, the research generates attenuation profiles from laboratory experiments and in situ measurements across various girders and bridge structures, extracting the slope and residual standard deviation (RSD). While generally validating this approach, the findings highlight differences in attenuation behaviour from among wire break signals and rebound hammer impulses, whereby the latter potentially underestimates the relevant attenuation of wire breaks by approximately 20%. Consequently, a transfer factor is proposed to adjust ORM results obtained with the rebound hammer for wire break scenarios. It consists of a scaling factor of 1.2 to modify the average attenuation coefficient and a constant term of ±1.0 dB/m to cover a 95% confidence interval, and thus, account for sample scattering. Moreover, the anisotropic attenuation behaviour across different structures was studied, showing that transverse attenuation consistently exceeds the longitudinal, significantly influenced by structural features such as voids. In prefabricated concrete bridges with in situ-cast concrete slabs, transverse signal transmission remains unhindered across multiple elements. Finally, the results provide a valuable reference for the design of sensor layouts in bridge monitoring, particularly benefiting scenarios where direct in situ experiences are lacking.

Comparison of the hemoglobin variability in non-chronic kidney disease or end-stage renal disease participants and patients with CKD and ESRD.

Hemoglobin (Hb) variability occurs frequently in hemodialysis (HD) patients during erythropoietin (EPO) therapy. Guidelines define a narrow target range for the anemia treatment in these patients that is difficult to adhere to in practice. Our aim was to evaluate whether the Hb variability in HD patients is higher compared to non-chronic kidney disease or end-stage renal disease (ESRD) participants and patients with CKD stage I or II. Monthly blood samples were assessed prospectively in 100 non-CKD or ESRD participants and 57 patients with CKD stage I or II, and retrospectively in 74 HD patients without changes in EPO or iron dose for 6 months. Variability was calculated and compared between the different groups. Hb variability was significantly higher in HD patients compared to the other groups, corresponding to results of previous studies. There were no significant differences between non-CKD or ESRD participants and patients with CKD stage I or II in terms of standard deviation (SD), residual SD, fluctuations across threshold, Hb cycling, and mean absolute change of Hb every 30 days (p>0.05), but a significant difference compared to HD patients (p<0.001). There were no significant differences between the groups in time in target and area under the curve (AUC) (p>0.05). Hb variability is a common phenomenon in all groups independently of the method used for assessment and even without EPO therapy. The target range is difficult to achieve for HD patients and should be reconsidered in the future to avoid unsettling both the patients and the staff.

A method to estimate endogenous losses of nitrogen and amino acids at the ileal level in growing rabbits

Apparent ileal digestibility can be corrected to give the true ileal digestibility of the nitrogen and amino acids provided by the diet, by determining the flow of endogenous nitrogen and amino acids (from desquamated epithelial cells of gastrointestinal mucosa, mucins and digestive enzymes). This flow of nitrogen and amino acids has been studied in adult rabbits fitted with a T-cannula, but remains unknown for growing rabbits. The aim of this work was to propose a method to estimate endogenous nitrogen and amino acid losses in the ileum of growing rabbits slaughtered at 64 d of age from 20:00 h. For this purpose, two experiments were carried out. The first was performed with 10 weaned rabbits fed with a diet with casein as the only source of protein (whose ileal digestibility is 100%) and labelled with ytterbium. This experiment allowed us to identify the relationship between the ileal flow of endogenous nitrogen (IFEN) and the dry matter intake in the last 24 h before slaughter (DMI), which fits the equation: IFEN (mg/d)=5.99 DMI (g/d) +133; (R2=0.778, residual standard deviation=138, P&lt;0.001, n=10). The second experiment was carried out with 36 rabbits fed the same diet but without ytterbium, with whose ileal content 9 pools were constituted to determine the amino acid profile of endogenous nitrogen, which was found to be rich in glutamic acid, serine, aspartic acid, glycine, valine and threonine (15.97±1.33; 8.00±0.80; 7.06±0.72; 6.24±0.77; 5.48±0.51 and 4.97±0.47 g/16 g of N, respectively) and poor in methionine and histidine (1.05±0.06 and 1.34±0.16 g/16 g of N, respectively). Knowing the DMI of a certain growing rabbit in the 24 h prior to slaughter, the combined use of the equation and the amino acid profile obtained makes it possible to estimate the ileal endogenous losses of each amino acid.

Abstract P271: Greater Long-Term Non-High Density Lipoprotein Cholesterol Variability From Childhood is Associated With Poorer Midlife Cognitive Function: The Bogalusa Heart Study

Background: Exposure to elevated plasma non-high density lipoprotein cholesterol (non-HDL-C) levels during adulthood has been associated with higher risk of cardiovascular events. However, whether long term non-HDL-C variability from childhood impacts midlife cognitive function (CF) is unclear. Hypothesis: We hypothesized that higher long-term non-HDL-C variability from childhood would be associated with worse midlife CF measures. Aim: We aimed to identify the effect of long term lipid variability from childhood on midlife CF. Methods: We studied 1,249 midlife participants of the Bogalusa Heart Study (age at baseline: mean 9.5 years+/- standard deviation(SD)3.5 years; age at midlife: mean 48.2 years+/- SD 5.2 years; 59.8 % women(747 of 1249), 34.3% Black(428 of 1249)) with ≥3 lipid measurements across follow up. Non-HDL-C levels were obtained as total cholesterol - HDL cholesterol. Visit-to visit non-HDL-C variability from childhood was estimated by the deviation from age predicted values (DEV), and residual SD (RSD). Non-HDL-C measurements were fitted in random-effects models stratified by race and sex, to produce individual growth curves. DEV and RSD were measured as the mean and SD of the residuals of observed and predicted non-HDL levels. Midlife CF was measured by 10 neuropsychological (NP) tests. NP test results were categorized into 3 distinct NP profiles (optimal, average, and mixed-low) using cluster analysis. Associations between non-HDL-C and NP profiles were analyzed using multinomial logistic regression models, adjusting for education status and the mean of non-HDL-C measurements from childhood to midlife. DEV and RSD were standardized to z-scores before conducting regression analysis. Interactions according to race and sex were performed. Results: The odds of having a mixed-low CF compared to optimal increased by 31.1% (95% CI 1.075, 1.599) for every 1 SD increase in DEV, and by 30.5% (95% CI 1.069, 1.591) for every 1 SD increase in RSD, respectively, adjusted for education status and mean non-HDL-C levels. Neither race or sex were found to modify the associations of DEV nor RSD on CF. Conclusions: Higher long-term non-HDL-C variability was linked to poorer midlife CF. Further studies are needed to identify the impact of long-term non-HDL-C variability in the association of cardiometabolic risk factors and midlife CF.

Abstract 45: Elevated Long-Term Triglyceride-Glucose Index Variability From Childhood is Linked to Poorer Midlife Cognitive Function: The Bogalusa Heart Study

Background: Insulin resistance has been linked to a higher risk of cardiovascular events and cognitive impairment in older adults. Recently, an elevated triglyceride-glucose (TyG) index, a noninvasive marker of insulin resistance, has been linked to a greater risk of cardiovascular disease. Whether long-term TyG index variability from childhood to midlife impacts midlife cognitive function (CF) is unclear. Hypothesis: We hypothesized that elevated TyG index variability from childhood to midlife is associated with poorer midlife CF. Aim: We aimed to identify the impact of elevated TyG index variability from childhood to midlife on midlife CF. Methods: We studied 1,100 midlife Bogalusa Heart Study participants (age at enrollment: mean 9.5 years +/- standard deviation(SD) 3.5 years; age at midlife: 48.1 +/- 5.2 years; 60.3% women(663 of 1100), 34% Black(374 of 1100)) with ≥ 3 lipid and glucose measurements across follow-up. Participants with Type 2 diabetes mellitus were excluded. TyG index levels were calculated as ln [(serum fasting triglyceride in mg/dL x serum fasting glucose in mg/dL)/2]. TyG index measures of each individual were fitted into random-effects models stratified by race and sex, and individual growth curves were obtained. Visit-to visit TyG index variability from childhood to midlife was calculated by the deviation from age-predicted values (DEV) and residual SD (RSD) from these models. Midlife CF was measured from 10 neuropsychological (NP) tests, which were categorized into 3 distinct NP profiles (optimal, average, and mixed-low) using cluster analysis. Associations between TyG index variability and NP profiles were analyzed using multinomial logistic regression models, adjusting for education status and the mean of TyG index levels from childhood to midlife. DEV and RSD were standardized to z-scores according to race and sex before regression analysis. Interactions according to race and sex were performed. Results: Every 1 SD increase in DEV increased the odds of having a mixed-low CF by 48.9% (95% CI 1.201, 1.847) and an average CF by 22.9% (95% CI 1.046, 1.443) compared to optimal CF, after adjusting for education status and mean TyG levels. Every 1 SD increase in RSD increased the odds of having a mixed-low CF by 51% (95% CI 1.217, 1.873) and an average CF by 25.4% (95% CI 1.068, 1.472) compared to optimal CF, adjusting for education status and mean TyG levels. Neither race nor sex modified the effects of DEV nor RSD on CF. Conclusions: Greater long-term TyG index variability was linked with poorer midlife CF. Further studies are needed to elucidate the brain effects of higher long-term variability in insulin resistance from childhood to midlife.

Abstract P270: Higher Long-Term Fasting Glucose Variability From Childhood is Associated With Poorer Cognitive Function in Midlife: The Bogalusa Heart Study

Background: Elevated fasting blood glucose(FBG) levels have been associated with poorer cognitive function. High FBG variability has also been associated with accelerated cognitive decline in late life. However, the impact of long term FBG variability from childhood on midlife cognitive function (CF) remains understudied. Hypothesis: We hypothesized that there would be an association of higher long-term FBG variability from childhood with poorer midlife CF measures. Aim: We aimed to identify the influence of higher long term FBG variability on midlife CF. Methods: We studied 1,005 midlife Bogalusa Heart Study participants (age at baseline: mean 9.4 years +/- SD 3.5 years; age at neuropsychological(NP) exam: mean 47.9 years+/- SD 5.2 years; 60.4% women(607 of 1005); 33.5 % Black(337 of 1005)) with ≥3 FBG measurements across follow up. Participants having type 2 diabetes mellitus(T2DM) were excluded. T2DM was defined as FBG ≥126 mg/dL or taking diabetes medication. Long-term visit-to visit FBG variability was measured as deviation from age predicted values (DEV) and residual SD (RSD). Using ≥3 FBG measurements, DEV and RSD were obtained from individual growth curves created by fitting random-effects models stratified by race and sex. Participants’ midlife CF was first measured with 10 NP tests, then were classified into 3 distinct NP profiles (optimal, average, and mixed-low) using cluster analysis. Multinomial logistic regression models were used to analyze the associations between long-term FBG variability parameters and NP profiles, adjusting for education status and mean of FBG measures from childhood to midlife . DEV and RSD were standardized to z-scores by race and sex before regression analysis. Tests for interaction according to race and sex were conducted. Results: After adjusting for the mean FBG level and education status, for a 1 SD increase in DEV, the odds of having a mixed-low CF increased by 57.3% (95% CI 1.244, 1.990) and the odds of an average CF increased by 36.9% (95% CI 1.144, 1.640) respectively, compared to optimal CF. After adjusting for the mean FBG level and education status, for a 1 SD increase in RSD, the odds of having a mixed-low CF increased by 56% (95% CI 1.232, 1.974) and the odds of an average CF increased by 37.7% (95% CI 1.147, 1.653), respectively, compared to optimal CF. No significant interactions between long-term FBG variability parameters and race and sex were found. Conclusions: Higher long-term FBG variability was associated with poorer midlife CF. Further studies are needed to identify the role of long-term FBG variability in the association of cardiometabolic risk factors and midlife CF.