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Box plots of CSF metabolite measurements for CDR of 0 and CDR >0 (i.e., CDR 0.5 or 1). Level of significance is indicated as p value after Bonferroni correction. Values were log-transformed. Figure S2. Box plots of plasma metabolite measurements for CDR 0 and CDR >0 (i.e., CDR 0.5 or 1). Level of significance is indicated as p value after Bonferroni correction. Concentration values were log-transformed. Figure S3. CSF and plasma metabolites correlated with CSF p-tau181 (a) and CSF tau (b) in APOE ε4 carriers (n = 37). Concentration values were log-transformed. Each dot represents a subject. The black line represents the linear fit, and the gray shading represents its confidence interval. Figure S4. Box plots of plasma metabolite measurements in APOE ε4 carriers (n = 37) according to CSF p-tau181/Aβ1–42 ratio (i.e., “low” when p-tau181/Aβ1–42 is ≤0.0779 and “high” when p-tau181/Aβ1–42 is > 0.0779) for negative and positive CSF profiles of AD pathology, respectively. Concentration values were log10-transformed. Level of significance is indicated as p value after Bonferroni correction. Supplementary Methods. (PDF 831 kb)

Box plots of CSF metabolite measurements for CDR of 0 and CDR >0 (i.e., CDR 0.5 or 1). Level of significance is indicated as p value after Bonferroni correction. Values were log-transformed. Figure S2. Box plots of plasma metabolite measurements for CDR 0 and CDR >0 (i.e., CDR 0.5 or 1). Level of significance is indicated as p value after Bonferroni correction. Concentration values were log-transformed. Figure S3. CSF and plasma metabolites correlated with CSF p-tau181 (a) and CSF tau (b) in APOE ε4 carriers (n = 37). Concentration values were log-transformed. Each dot represents a subject. The black line represents the linear fit, and the gray shading represents its confidence interval. Figure S4. Box plots of plasma metabolite measurements in APOE ε4 carriers (n = 37) according to CSF p-tau181/Aβ1–42 ratio (i.e., “low” when p-tau181/Aβ1–42 is ≤0.0779 and “high” when p-tau181/Aβ1–42 is > 0.0779) for negative and positive CSF profiles of AD pathology, respectively. Concentration values were log10-transformed. Level of significance is indicated as p value after Bonferroni correction. Supplementary Methods. (PDF 831 kb)

Although the prevalence of main idiopathic forms of inflammatory bowel disease (IBD) has risen considerably over the last decades, their clinical features do not allow accurate prediction of prognosis, likelihood of disease progression, or response to specific therapy. Through a better understanding of the molecular pathways involved in IBD and the promise of more targeted therapies, the personalized approach to the management of IBD shows potential. To achieve this, there remains a significant need to better understand the disease process at cellular and molecular levels for any given individual with IBD. The complexity of biological functional networks behind the etiology of IBD highlights the need for their comprehensive analysis. In this, omics technologies can generate a systemic view of IBD pathogenesis on which to base novel, multiple pathway-integrated therapies. Omics sciences have just started to contribute here by generating gene, protein expression, metabolite data at global level and large scale, and more recently by offering new opportunities to explore gut functional ecology. In particular, there is much expectation regarding the putative role of the gut microbiome in IBD. No doubt it will provide additional insights and lead to the development of alternative, hopefully better, diagnostic, prognostic, and monitoring tools in the management of IBD. This review discusses perspectives of relevance to clinical translation with emphasis on gut microbial metabolic activities.