Missing values are common in high-throughput mass spectrometry data. Two strategies are available to address missing values: (i) eliminate or impute the missing values and apply statistical methods that require complete data and (ii) use statistical methods that specifically account for missing values without imputation (imputation-free methods). This study reviews the effect of sample size and percentage of missing values on statistical inference for multiple methods under these two strategies. With increasing missingness, the ability of imputation and imputation-free methods to identify differentially and non-differentially regulated compounds in a two-group comparison study declined. Random forest and k-nearest neighbor imputation combined with a Wilcoxon test performed well in statistical testing for up to 50% missingness with little bias in estimating the effect size. Quantile regression imputation accompanied with a Wilcoxon test also had good statistical testing outcomes but substantially distorted the difference in means between groups. None of the imputation-free methods performed consistently better for statistical testing than imputation methods.

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.

Fragile X (FMR1) premutation is a common mutation that affects about 1 in 200 females and 1 in 450 males and can lead to the development of fragile-X-associated tremor/ataxia syndrome (FXTAS). Although there is no targeted, proven treatment for FXTAS, research suggests that sulforaphane, an antioxidant present in cruciferous vegetables, can enhance mitochondrial function and maintain redox balance in the dermal fibroblasts of individuals with FXTAS, potentially leading to improved cognitive function. In a 24-week open-label trial involving 15 adults aged 60–88 with FXTAS, 11 participants successfully completed the study, demonstrating the safety and tolerability of sulforaphane. Clinical outcomes and biomarkers were measured to elucidate the effects of sulforaphane. While there were nominal improvements in multiple clinical measures, they were not significantly different after correction for multiple comparisons. PBMC energetic measures showed that the level of citrate synthase was higher after sulforaphane treatment, resulting in lower ATP production. The ratio of complex I to complex II showed positive correlations with the MoCA and BDS scores. Several mitochondrial biomarkers showed increased activity and quantity and were correlated with clinical improvements.

This study contributes to a greater understanding of the utility of molecular biomarkers to identify clinical phenotypes of fragile X syndrome (FXS). Correlations of baseline clinical trial data (molecular measures—FMR1 mRNA, CYFIP1 mRNA, MMP9 and FMRP protein expression levels, nonverbal IQ, body mass index and weight, language level, NIH Toolbox, adaptive behavior rating, autism, and other mental health correlates) of 59 participants with FXS ages of 6–32 years are reported. FMR1 mRNA expression levels correlated positively with adaptive functioning levels, expressive language, and specific NIH Toolbox measures. The findings of a positive correlation of MMP-9 levels with obesity, CYFIP1 mRNA with mood and autistic symptoms, and FMR1 mRNA expression level with better cognitive, language, and adaptive functions indicate potential biomarkers for specific FXS phenotypes. These may be potential markers for future clinical trials for targeted treatments of FXS.

Fragile X syndrome (FXS) is the most common heritable cause of intellectual disability and autism spectrum disorder. The syndrome is often caused by greatly reduced or absent protein expression from the fragile X messenger ribonucleoprotein 1 (FMR1) gene due to expansion of a 5′-non-coding trinucleotide (CGG) element beyond 200 repeats (full mutation). To better understand the complex relationships among FMR1 allelotype, methylation status, mRNA expression, and FMR1 protein (FMRP) levels, FMRP was quantified in peripheral blood mononuclear cells for a large cohort of FXS (n = 154) and control (n = 139) individuals using time-resolved fluorescence resonance energy transfer. Considerable size and methylation mosaicism were observed among individuals with FXS, with FMRP detected only in the presence of such mosaicism. No sample with a minimum allele size greater than 273 CGG repeats had significant levels of FMRP. Additionally, an association was observed between FMR1 mRNA and FMRP levels in FXS samples, predominantly driven by those with the lowest FMRP values. This study underscores the complexity of FMR1 allelotypes and FMRP expression and prompts a reevaluation of FXS therapies aimed at reactivating large full mutation alleles that are likely not capable of producing sufficient FMRP to improve cognitive function.