Purpose The primary purpose of this study was to evaluate the validity of an interviewer-administered, 24-hour physical activity recall (PAR) compared to the SenseWear Armband (SWA) for estimation of energy expenditure (EE) and moderate-to-vigorous physical activity (MVPA) in a representative sample of adults. A secondary goal was to compare measurement errors for various demographic sub-groups (gender, age and weight status). Methods A sample of 1347 adults (20–71yrs; 786 females) wore an SWA for a single day and then completed a PAR recalling that previous day’s physical activity. The participants each performed two trials on two randomly selected days across a 2 year time span. The EE and MVPA values for each participant were averaged across the two days. Group-level and individual-level agreement were evaluated using 95% equivalence testing and mean absolute percent error (MAPE), respectively. Results were further examined for sub-groups by gender, age and body mass index (BMI). Results The PAR yielded equivalent estimates of EE (compared to the SWA) for almost all demographic subgroups but none of the comparisons for MVPA were equivalent. Smaller MAPE values were observed for EE (ranges from 10.3% to 15.0%) than for MVPA (ranges from 68.6% to 269.5%), across all comparisons. The PAR yielded underestimates of MVPA for younger, less obese people but overestimates for older, more obese people. Conclusions For EE measurement, the PAR demonstrated good agreement relative to the SWA. However, the use of PAR may result in biased estimates of MVPA both at the group and individual level in adults.

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography–MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

Background: The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. TEDDY has collected heterogenous data longitudinally to gain insights into the environmental and biological mechanisms driving the progression to persistent islet autoantibodies. Methods: We developed a machine learning model to predict imminent transition to the development of persistent islet autoantibodies based on timevarying metabolomics data integrated with time-invariant risk factors (eg, gestational age). The machine learning was initiated with 221 potential features (85 genetic, 5 environmental, 131 metabolomic) and an ensemble-based feature evaluation was utilized to identify a small set of predictive features that can be interrogated to better understand the pathogenesis leading up to persistent islet autoimmunity. Results: The final integrative machine learning model included 42 disparate features, returning a cross-validated receiver operating characteristic area under the curve (AUC) of 0.74 and an AUC of 0.65 on an independent validation dataset. The model identified a principal set of 20 time-invariant markers, including 18 genetic markers (16 single nucleotide polymorphisms [SNPs] and two HLA-DR genotypes) and two demographic markers (gestational age and exposure to a prebiotic formula). Integration with the metabolome identified 22 supplemental metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies. Conclusions: The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to three pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate, suggesting that these metabolic processes may play a role in triggering islet autoimmunity.

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