Gut Microbiota Functional Traits, Blood pH, and Anti-GAD Antibodies Concur in the Clinical Characterization of T1D at Onset.
Federica Del ChiericoGiorgia ContaMaria Cristina MatteoliAlessandra FierabracciSofia ReddelGabriele MacariSimone GardiniValerio GuarrasiStefano Levi MorteraValeria MarzanoPamela VernocchiFabio SciubbaFederico MariniAnnalisa DeodatiNovella RapiniStefano CianfaraniAlfredo MiccheliLorenza PutignaniPublished in: International journal of molecular sciences (2022)
Alterations of gut microbiota have been identified before clinical manifestation of type 1 diabetes (T1D). To identify the associations amongst gut microbiome profile, metabolism and disease markers, the 16S rRNA-based microbiota profiling and 1 H-NMR metabolomic analysis were performed on stool samples of 52 T1D patients at onset, 17 T1D siblings and 57 healthy subjects (CTRL). Univariate, multivariate analyses and classification models were applied to clinical and -omic integrated datasets. In T1D patients and their siblings, Clostridiales and Dorea were increased and Dialister and Akkermansia were decreased compared to CTRL, while in T1D, Lachnospiraceae were higher and Collinsella was lower, compared to siblings and CTRL. Higher levels of isobutyrate, malonate, Clostridium , Enterobacteriaceae, Clostridiales, Bacteroidales, were associated to T1D compared to CTRL. Patients with higher anti-GAD levels showed low abundances of Roseburia , Faecalibacterium and Alistipes and those with normal blood pH and low serum HbA 1c levels showed high levels of purine and pyrimidine intermediates. We detected specific gut microbiota profiles linked to both T1D at the onset and to diabetes familiarity. The presence of specific microbial and metabolic profiles in gut linked to anti-GAD levels and to blood acidosis can be considered as predictive biomarker associated progression and severity of T1D.
Keyphrases
- end stage renal disease
- type diabetes
- cardiovascular disease
- intellectual disability
- chronic kidney disease
- multidrug resistant
- newly diagnosed
- machine learning
- pseudomonas aeruginosa
- magnetic resonance
- autism spectrum disorder
- single cell
- prognostic factors
- ejection fraction
- genome wide
- deep learning
- high resolution
- rna seq
- cystic fibrosis
- skeletal muscle
- mass spectrometry
- patient reported