LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework.

Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.

Construct validity of the Visual Cognitive Assessment Test (VCAT)-a cross-cultural language-neutral cognitive screening tool.

BACKGROUND: The Visual Cognitive Assessment Test (VCAT) is a language-neutral cognitive screening tool designed for use in culturally diverse populations without the need for translations or adaptations. While it has been established to be language-neutral, the VCAT's construct validity has not been investigated. METHODS: 471 participants were recruited, comprising 233 healthy comparisons, 117 mild cognitive impairment (MCI), and 121 mild Alzheimer's disease (AD) patients. VCAT and domain-specific neuropsychological tests were administered in the same sitting. Construct validity was assessed by analyzing domain-specific associations between the VCAT and well-established cognitive assessments. Reliability (internal consistency) was measured by Cronbach's alpha. Diagnostic ability (area under the curve) and recommended cutoffs were determined by receiver operating characteristic (ROC) analysis. RESULTS: The VCAT and its subdomains demonstrated good construct validity in terms of both convergent and divergent validity and good internal consistency (α = .74). ROC analysis found that the VCAT was on par with the Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) at distinguishing between healthy comparisons, MCI, and mild AD. Consistent with previous studies, VCAT scores were not affected by language of administration or ethnicity in our cohort. Findings suggest the following cutoffs: Dementia 0-19, MCI 20-24, Normal 25-30. CONCLUSION: This study established the construct validity of the VCAT, which is vital to ensure its subdomains effectively measure the cognitive processes they were designed to. The VCAT is capable of detecting early cognitive impairments and allows for meaningful cross-cultural comparisons, especially useful for international collaborations and clinical trials, and for clinical use in diverse multiethnic populations.