Is Height2.7 Appropriate for Indexation of Left Ventricular Mass in Healthy Adolescents? The Importance of Sex Differences.

BACKGROUND: Left ventricular mass (LVM) is an important predictor of cardiovascular risk. In adolescence, LVM is commonly indexed to height2.7, although some evidence suggests that this may not fully account for sex differences. METHODS: We investigated appropriate allometric scaling of LVM to height, total lean mass, and body surface area, in a UK birth cohort of 2039 healthy adolescents (17±1 years). Allometric relationships were determined by linear regression stratified by sex, following log transformation of x and y variables [log(y)=a+b×log(x)], b is the allometric exponent. RESULTS: Log (LVM) showed linear relationships with log(height) and log(lean mass). Biased estimates of slope resulted when the sexes were pooled. The exponents were lower than the conventional estimate of 2.7 for males (mean [95% CI]=1.66 [1.30-2.03]) and females (1.58 [1.27-1.90]). When LVM was indexed to lean mass, the exponent was 1.16 (1.05-1.26) for males and 1.07 (0.97-1.16) for females. When LVM was indexed to estimated body surface area, the exponent was 1.53 (1.40-1.66) for males and 1.34 (1.24-1.45) for females. CONCLUSIONS: Allometric exponents derived from pooled data, including men and women without adjustment for sex were biased, possibly due to sex differences in body composition. We suggest that when assessing LVM, clinicians should consider body size, body composition, sex, and age. Our observations may also have implications for the identification of young individuals with cardiac hypertrophy.

Workflow for Integrated Processing of Multicohort Untargeted 1H NMR Metabolomics Data in Large-Scale Metabolic Epidemiology.

Large-scale metabolomics studies involving thousands of samples present multiple challenges in data analysis, particularly when an untargeted platform is used. Studies with multiple cohorts and analysis platforms exacerbate existing problems such as peak alignment and normalization. Therefore, there is a need for robust processing pipelines that can ensure reliable data for statistical analysis. The COMBI-BIO project incorporates serum from ∼8000 individuals, in three cohorts, profiled by six assays in two phases using both 1H NMR and UPLC-MS. Here we present the COMBI-BIO NMR analysis pipeline and demonstrate its fitness for purpose using representative quality control (QC) samples. NMR spectra were first aligned and normalized. After eliminating interfering signals, outliers identified using Hotelling's T2 were removed and a cohort/phase adjustment was applied, resulting in two NMR data sets (CPMG and NOESY). Alignment of the NMR data was shown to increase the correlation-based alignment quality measure from 0.319 to 0.391 for CPMG and from 0.536 to 0.586 for NOESY, showing that the improvement was present across both large and small peaks. End-to-end quality assessment of the pipeline was achieved using Hotelling's T2 distributions. For CPMG spectra, the interquartile range decreased from 1.425 in raw QC data to 0.679 in processed spectra, while the corresponding change for NOESY spectra was from 0.795 to 0.636, indicating an improvement in precision following processing. PCA indicated that gross phase and cohort differences were no longer present. These results illustrate that the pipeline produces robust and reproducible data, successfully addressing the methodological challenges of this large multifaceted study.

The association of nonalcoholic fatty liver disease with central and peripheral blood pressure in adolescence: findings from a cross-sectional study.

OBJECTIVES: We aimed to determine the association of nonalcoholic fatty liver disease (NAFLD) with central and peripheral blood pressure (BP), in a general adolescent population and to examine whether associations are independent of adiposity. METHODS: Using cross-sectional data from a subsample (N = 1904) of a UK birth cohort, we assessed markers of NAFLD including ultrasound scan (USS) determined fatty liver, shear velocity (marker of liver fibrosis), alanine aminotransferase (ALT), aspartate aminotransferase (AST) and gamma-glutamyltransferase (GGT) at a mean age of 17.8 years. These were related to BP [central and peripheral SBP and DBP and mean arterial pressure (MAP)]. RESULTS: Fatty liver was positively associated with central and peripheral SBP, DBP and MAP in models adjusting for age, sex, social class, puberty and alcohol intake. These positive associations were attenuated to the null when fat mass was included. For example, in confounder-adjusted models, not including fat mass, mean central SBP was 3.74 mmHg [95% confidence interval (CI) 1.12 to 6.36] higher in adolescents with USS fatty liver than in those without; with additional adjustment for fat mass, the association attenuated to the null value (-0.37 mmHg; 95% CI -3.09 to 2.36). Similar patterns were found for associations of ALT and GGT with central and peripheral BP. There was no consistent evidence of associations of shear velocity or AST with BP measurements. Fatty liver was not consistently associated with central pulse pressure (PP), peripheral PP and Aix@75. CONCLUSION: NAFLD is not associated with higher central or peripheral BP in adolescents once confounding by adiposity is taken into account.

Rapid increases in infant adiposity and overweight/obesity in childhood are associated with higher central and brachial blood pressure in early adulthood.

BACKGROUND: Small size at birth and greater BMI in childhood are associated with greater brachial blood pressure (BP) in later life. Aortic (central) BP differs from brachial BP and is more predictive of organ damage and cardiovascular events; the relationship between BMI in childhood and central BP is not known. METHODS: Using data from 3154 people from the Avon Longitudinal Study of Parents and Children, we assessed associations between repeated measures of BMI from birth to age 10 with central and brachial BP at age 17. RESULTS: Lower BMI at birth (thinness) was associated with greater central and brachial BP. No associations were seen between BMI in early childhood (<7 years) and later BP, but greater BMI from 7 to 10 years was associated with higher BP. Associations were similar for central and brachial SBP and for DBP, and were stronger in males compared with females. The highest BP was seen in participants who were low-birth-weight and overweight or obese at both the end of infancy (age 2) and at the time of BP assessment (age 17); mean central SBP was 104.2  mmHg (SD = 11.0) compared with 100.7 (SD = 10.5) in participants who were normal-birth-weight and overweight or obese at 2 and 17 years. CONCLUSION: Small size at birth followed by rapid adiposity gain in infancy and continued overweight/obesity are associated with greater BP in young adulthood. These findings emphasize the importance of maintenance of normal weight in childhood for the prevention of high BP.

Self-organizing map quality control index.

A new approach for process monitoring is described, the self-organizing map quality control (SOMQC) index. The basis of the method is that SOM maps are formed from normal operating condition (NOC) samples, using a leave-one-out approach. The distances (or dissimilarities) of the left out sample can be determined to all the units in the map, and the nth percentile measured distance of the left out sample is used to provide a null distribution of NOC distances which is generated using the Hodges-Lehmann method. The nth percentile distance of a test sample to a map generated from all NOC samples can be measured and compared to the null distribution at a given confidence level to determine whether the sample can be judged as out of control. The approach described in this paper is applied to online high-performance liquid chromatography (HPLC) measurements of a continuous pharmaceutical process and is compared to other established methods including Q and D statistics and support vector domain description. The SOMQC has advantages in that there is no requirement for multinormality in the NOC samples, or for linear models, or to perform principal components analysis (PCA) prior to the analysis with concomitant issues about choosing the number of PCs. It also provides information about which variables are important using component planes. The influence of extreme values in the background data set can also be tuned by choosing the distance percentile.

Multilevel simultaneous component analysis for fault detection in multicampaign process monitoring: application to on-line high performance liquid chromatography of a continuous process.

A continuous process is monitored by on-line HPLC in four separate campaigns, ranging in duration from 10 to 104 h. Methods are reported that allow the study of variation over all four campaigns using Multilevel Simultaneous Components Analysis, which separates out the within- and between-campaign variation. In order to obtain control charts, Q- and D-statistics are combined with a within-campaign submodel (Simultaneous Components Analysis) to obtain a single model that is based only on within-campaign variation over all four campaigns.

Dynamic analysis of on-line high-performance liquid chromatography for multivariate statistical process control.

A continuous process was studied over 83.32 h using on-line high-performance liquid chromatography, involving the acquisition of 252 chromatograms. A method for analysis of these data using multivariate statistical process control on peak tables, in real-time, is described. The normal operating condition (NOC) region of the process was identified using evolving principal components analysis to be between 5.77 and 8.13 h. 19 out of the 37 peaks detected throughout the process were found in the NOC region, the remainder representing undesirable contaminants found elsewhere in the process. A major challenge is to develop the peak table as the process evolves, which is dynamically updated as new peaks are detected after the NOC region: this approach involving an "unlocked" peak table is contrasted to an approach using a "locked" peak table where only peaks detected during the NOC region are included in the model. In addition, results are compared to those obtained using baseline corrected and aligned chromatograms, using a NOC region of 5.85-8.33h. D- and Q-charts were obtained. It is shown that the "unlocked" peak table detects out of control samples best and provides good diagnostic insight into problems with the process.