Plasma metabolomic profiles of plant-based dietary indices reveal potential pathways for metabolic syndrome associations.

BACKGROUND AND AIMS: Plant-based dietary patterns have been associated with improved health outcomes. This study aims to describe the metabolomic fingerprints of plant-based diet indices (PDI) and examine their association with metabolic syndrome (MetS) and its components in a Danish population. METHODS: The MAX study comprised 676 participants (55% women, aged 18-67 y) from Copenhagen. Sociodemographic and dietary data were collected using questionnaires and three 24-h dietary recalls over one year (at baseline, and at 6 and 12 months). Mean dietary intakes were computed, as well as overall PDI, healthful (hPDI) and unhealthful (uPDI) scores, according to food groups for each plant-based index. Clinical variables were also collected at the same time points in a health examination that included complete blood tests. MetS was defined according to the International Diabetes Federation criteria. Plasma metabolites were measured using a targeted metabolomics approach. Metabolites associated with PDI were selected using random forest models and their relationships with PDIs and MetS were analyzed using generalized linear mixed models. RESULTS: The mean prevalence of MetS was 10.8%. High, compared to low, hPDI and uPDI scores were associated with a lower and higher odd of MetS, respectively [odds ratio (95%CI); hPDI: 0.56 (0.43-0.74); uPDI: 1.61 (1.26-2.05)]. Out of 411 quantified plasma metabolites, machine-learning metabolomics fingerprinting revealed 13 metabolites, including food and food-related microbial metabolites, like hypaphorine, indolepropionic acid and lignan-derived enterolactones. These metabolites were associated with all PDIs and were inversely correlated with MetS components (p < 0.05). Furthermore, they had an explainable contribution of 12% and 14% for the association between hPDI or uPDI, respectively, and MetS only among participants with overweight/obesity. CONCLUSIONS: Metabolites associated with PDIs were inversely associated with MetS and its components, and may partially explain the effects of plant-based diets on cardiometabolic risk factors.

POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis.

Metabolomics and proteomics, like other omics domains, usually face a data mining challenge in providing an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the most difficult challenges and it is critical in the subsequent biological interpretation of the results. Because of this, combined with the computational programming skills needed for this type of analysis, several bioinformatic tools aimed at simplifying metabolomics and proteomics data analysis have emerged. However, sometimes the analysis is still limited to a few hidebound statistical methods and to data sets with limited flexibility. POMAShiny is a web-based tool that provides a structured, flexible and user-friendly workflow for the visualization, exploration and statistical analysis of metabolomics and proteomics data. This tool integrates several statistical methods, some of them widely used in other types of omics, and it is based on the POMA R/Bioconductor package, which increases the reproducibility and flexibility of analyses outside the web environment. POMAShiny and POMA are both freely available at https://github.com/nutrimetabolomics/POMAShiny and https://github.com/nutrimetabolomics/POMA, respectively.

Exhaustion of mitochondrial and autophagic reserve may contribute to the development of LRRK2 G2019S -Parkinson's disease.

BACKGROUND: Mutations in leucine rich repeat kinase 2 (LRRK2) are the most common cause of familial Parkinson's disease (PD). Mitochondrial and autophagic dysfunction has been described as etiologic factors in different experimental models of PD. We aimed to study the role of mitochondria and autophagy in LRRK2 G2019S -mutation, and its relationship with the presence of PD-symptoms. METHODS: Fibroblasts from six non-manifesting LRRK2 G2019S -carriers (NM-LRRK2 G2019S ) and seven patients with LRRK2 G2019S -associated PD (PD-LRRK2 G2019S ) were compared to eight healthy controls (C). An exhaustive assessment of mitochondrial performance and autophagy was performed after 24-h exposure to standard (glucose) or mitochondrial-challenging environment (galactose), where mitochondrial and autophagy impairment may be heightened. RESULTS: A similar mitochondrial phenotype of NM-LRRK2 G2019S and controls, except for an early mitochondrial depolarization (54.14% increased, p = 0.04), was shown in glucose. In response to galactose, mitochondrial dynamics of NM-LRRK2 G2019S improved (- 17.54% circularity, p = 0.002 and + 42.53% form factor, p = 0.051), probably to maintain ATP levels over controls. A compromised bioenergetic function was suggested in PD-LRRK2 G2019S when compared to controls in glucose media. An inefficient response to galactose and worsened mitochondrial dynamics (- 37.7% mitochondrial elongation, p = 0.053) was shown, leading to increased oxidative stress. Autophagy initiation (SQTSM/P62) was upregulated in NM-LRRK2 G2019S when compared to controls (glucose + 118.4%, p = 0.014; galactose + 114.44%, p = 0.009,) and autophagosome formation increased in glucose media. Despite of elevated SQSTM1/P62 levels of PD-NM G2019S when compared to controls (glucose + 226.14%, p = 0.04; galactose + 78.5%, p = 0.02), autophagosome formation was deficient in PD-LRRK2 G2019S when compared to NM-LRRK2 G2019S (- 71.26%, p = 0.022). CONCLUSIONS: Enhanced mitochondrial performance of NM-LRRK2 G2019S in mitochondrial-challenging conditions and upregulation of autophagy suggests that an exhaustion of mitochondrial bioenergetic and autophagic reserve, may contribute to the development of PD in LRRK2 G2019S mutation carriers.