Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data

Integrated multi-omics analysis of brain aging in female nonhuman primates reveals altered signaling pathways relevant to age-related disorders

The prefrontal cortex (PFC) has been implicated as a key brain region responsible for age-related cognitive decline. Little is known about aging-related molecular changes in PFC that may mediate these effects. To date, no studies have used untargeted discovery methods with integrated analyses to determine PFC molecular changes in healthy female primates. We quantified PFC changes associated with healthy aging in female baboons by integrating multiple omics data types (transcriptomics, proteomics, metabolomics) from samples across the adult age span. Our integrated omics approach using unbiased weighted gene co-expression network analysis to integrate data and treat age as a continuous variable, revealed highly interconnected known and novel pathways associated with PFC aging. We found Gamma-aminobutyric acid (GABA) tissue content associated with these signaling pathways, providing 1 potential biomarker to assess PFC changes with age. These highly coordinated pathway changes during aging may represent early steps for aging-related decline in PFC functions, such as learning and memory, and provide potential biomarkers to assess cognitive status in humans.

Improved GSimp: A Flexible Missing Value Imputation Method to Support Regulatory Bioequivalence Assessment

Missing values are not uncommon in in vivo bioequivalence (BE) studies and pose non-trivial challenges for BE assessment. Missing values typically appear as a mixture of different types, such as Missing Not at Random (MNAR) and Missing Completely at Random (MCAR), however, current data imputation methods were usually developed for a certain type of missing values (e.g., MNAR). Among them, an iterative Gibbs sampler-based left-censored missing value imputation approach (GSimp) was recently developed and showed superior performance over other methods in handling MNAR data. In this study, we introduce an improved GSimp ("Improved GSimp" thereafter) that offers flexibility in handling mixed types of missing data and better imputation accuracy to support BE assessment for studies with missing values. Simulations mimicking different missing value scenarios (e.g., mixture of different missing types and proportion of missing values) were conducted to compare performance of the Improved GSimp with other methods (e.g., original GSimp and half of minimal value). Normalized root mean square error (NRMSE) was used to evaluate imputation accuracy. Our results showed that the Improved GSimp always had the best accuracy in all simulated scenarios compared to other methods.

Maternal obesity alters offspring liver and skeletal muscle metabolism in early post-puberty despite maintaining a normal post-weaning dietary lifestyle

Maternal obesity (MO) during pregnancy is linked to increased and premature risk of age-related metabolic diseases in the offspring. However, the underlying molecular mechanisms still remain not fully understood. Using a well-established nonhuman primate model of MO, we analyzed tissue biopsies and plasma samples obtained from post-pubertal offspring (3-6.5 y) of MO mothers (n = 19) and from control animals born to mothers fed a standard diet (CON, n = 13). All offspring ate a healthy chow diet after weaning. Using untargeted gas chromatography-mass spectrometry metabolomics analysis, we quantified a total of 351 liver, 316 skeletal muscle, and 423 plasma metabolites. We identified 58 metabolites significantly altered in the liver and 46 in the skeletal muscle of MO offspring, with 8 metabolites shared between both tissues. Several metabolites were changed in opposite directions in males and females in both liver and skeletal muscle. Several tissue-specific and 4 shared metabolic pathways were identified from these dysregulated metabolites. Interestingly, none of the tissue-specific metabolic changes were reflected in plasma. Overall, our study describes characteristic metabolic perturbations in the liver and skeletal muscle in MO offspring, indicating that metabolic programming in utero persists postnatally, and revealing potential novel mechanisms that may contribute to age-related metabolic diseases later in life.

Imputation of Missing Values for Multi-Biospecimen Metabolomics Studies: Bias and Effects on Statistical Validity

The analysis of high-throughput metabolomics mass spectrometry data across multiple biological sample types (biospecimens) poses challenges due to missing data. During differential abundance analysis, dropping samples with missing values can lead to severe loss of data as well as biased results in group comparisons and effect size estimates. However, the imputation of missing data (the process of replacing missing data with estimated values such as a mean) may compromise the inherent intra-subject correlation of a metabolite across multiple biospecimens from the same subject, which in turn may compromise the efficacy of the statistical analysis of differential metabolites in biomarker discovery. We investigated imputation strategies when considering multiple biospecimens from the same subject. We compared a novel, but simple, approach that consists of combining the two biospecimen data matrices (rows and columns of subjects and metabolites) and imputes the two biospecimen data matrices together to an approach that imputes each biospecimen data matrix separately. We then compared the bias in the estimation of the intra-subject multi-specimen correlation and its effects on the validity of statistical significance tests between two approaches. The combined approach to multi-biospecimen studies has not been evaluated previously even though it is intuitive and easy to implement. We examine these two approaches for five imputation methods: random forest, k nearest neighbor, expectation-maximization with bootstrap, quantile regression, and half the minimum observed value. Combining the biospecimen data matrices for imputation did not greatly increase efficacy in conserving the correlation structure or improving accuracy in the statistical conclusions for most of the methods examined. Random forest tended to outperform the other methods in all performance metrics, except specificity.