Metabolic signatures of β-cell destruction in type 1 diabetes Original paper

Researched by:

  • Dr. Umar ID
    Dr. Umar

    User avatarClinical Pharmacist and Clinical Pharmacy Master’s candidate focused on antibiotic stewardship, AI-driven pharmacy practice, and research that strengthens safe and effective medication use. Experience spans digital health research with Bloomsbury Health (London), pharmacovigilance in patient support programs, and behavioral approaches to mental health care. Published work includes studies on antibiotic use and awareness, AI applications in medicine, postpartum depression management, and patient safety reporting. Developer of an AI-based clinical decision support system designed to enhance antimicrobial stewardship and optimize therapeutic outcomes.

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January 15, 2026

Researched by:

  • Dr. Umar ID
    Dr. Umar

    User avatarClinical Pharmacist and Clinical Pharmacy Master’s candidate focused on antibiotic stewardship, AI-driven pharmacy practice, and research that strengthens safe and effective medication use. Experience spans digital health research with Bloomsbury Health (London), pharmacovigilance in patient support programs, and behavioral approaches to mental health care. Published work includes studies on antibiotic use and awareness, AI applications in medicine, postpartum depression management, and patient safety reporting. Developer of an AI-based clinical decision support system designed to enhance antimicrobial stewardship and optimize therapeutic outcomes.

    Read More

Last Updated: 2026-01-15

Microbiome Signatures identifies and validates condition-specific microbiome shifts and interventions to accelerate clinical translation. Our multidisciplinary team supports clinicians, researchers, and innovators in turning microbiome science into actionable medicine.

Dr. Umar

Clinical Pharmacist and Clinical Pharmacy Master’s candidate focused on antibiotic stewardship, AI-driven pharmacy practice, and research that strengthens safe and effective medication use. Experience spans digital health research with Bloomsbury Health (London), pharmacovigilance in patient support programs, and behavioral approaches to mental health care. Published work includes studies on antibiotic use and awareness, AI applications in medicine, postpartum depression management, and patient safety reporting. Developer of an AI-based clinical decision support system designed to enhance antimicrobial stewardship and optimize therapeutic outcomes.

What was studied?

3-phenylpropionic acid biomarker in type 1 diabetes was studied as part of a plasma metabolomics effort to find stage-specific signatures of progressive β-cell destruction that could outperform (or complement) C-peptide and islet autoantibodies. In this cross-sectional observational study, investigators profiled fasting plasma using capillary electrophoresis–Fourier transform mass spectrometry and liquid chromatography–time-of-flight mass spectrometry, generating 737 metabolite peaks. They then used partial least squares (PLS) discriminant analysis to separate clinical stages of type 1 diabetes by residual endogenous insulin secretion and applied receiver operating characteristic (ROC) analyses to test candidate markers across all participants.

Who was studied?

Thirty-three Japanese participants were enrolled at a single university hospital: 23 with type 1 diabetes, seven with type 2 diabetes, and three healthy controls. Type 1 diabetes was subdivided into new-onset (n=6; sampled after diabetic ketoacidosis and 2–4 weeks of insulin therapy), microsecretors (n=9; fasting C-peptide >0.01 and <0.6 ng/mL at least 3 months after onset), and complete lack of endogenous insulin (n=8; fasting C-peptide below detection). Healthy controls had no personal/family autoimmune history and were medication-free; type 2 diabetes comparators were overweight with preserved C-peptide and no ketosis history.

Most important findings

PLS separated the three type 1 diabetes stages, implying that circulating small molecules track β-cell functional loss. The microbiome-relevant signal centered on phenylalanine metabolism: new-onset type 1 diabetes showed higher plasma 3-phenylpropionic acid (3-PPA) and lower phenylalanine than microsecretors, consistent with 3-PPA being an end-product of bacterial degradation of unabsorbed phenylalanine in the intestinal lumen and therefore a proxy for gut microbial activity. Oxidative-stress biology also emerged: hypotaurine (a taurine-pathway precursor that can be consumed under oxidative stress) was lower in new-onset disease, while taurine itself did not differ materially across the type 1 diabetes subgroups—suggesting acute consumption or altered synthesis at onset rather than a stable deficiency. For later-stage disease, 5-methylcytosine was higher in the complete-lack group, aligning with increased circulating DNA methylation fragments as a putative marker of cumulative cell destruction and/or immune-epigenetic remodeling. ROC analyses supported clinical discriminability: 3-PPA (AUC ~0.96) and hypotaurine (AUC ~0.88) identified new-onset type 1 diabetes, and a low phenylalanine/3-PPA ratio best captured new-onset versus other groups, while a high ratio helped identify microsecretors—useful for a “microbiome-metabolite balance” feature rather than a single metabolite alone.

Signature (plasma)Stage association / microbiome relevance
3-Phenylpropionic acid (3-PPA) ↑New-onset T1D; bacterial phenylalanine fermentation proxy
Hypotaurine ↓New-onset T1D; oxidative-stress/taurine-pathway consumption signal
Phenylalanine/3-PPA ratio ↑Microsecretor T1D; shifts away from microbiome-derived 3-PPA pattern
5-Methylcytosine ↑Complete lack of insulin; circulating methylated DNA fragmentation/epigenetic signal

Key implications

Clinically, these data propose a pragmatic staging concept: combine a gut-microbiome–linked metabolite (3-PPA), an oxidative-stress buffer marker (hypotaurine), and a composite ratio (phenylalanine/3-PPA) to flag early, high-intensity β-cell destruction when interventions to preserve residual function are most valuable. For microbiome-signatures databases, 3-PPA provides a mechanistically interpretable host–microbe co-metabolite that can be recorded as “phenylalanine fermentation product (gut bacteria) elevated in new-onset T1D,” while hypotaurine adds a parallel axis capturing redox stress at onset; together they suggest that microbiome activity and oxidative stress may be temporally coupled during autoimmune β-cell failure, even if this study cannot prove causality.

Citation

Noso S, Babaya N, Hiromine Y, Taketomo Y, Niwano F, Yoshida S, Ikegami H. Metabolic signatures of β-cell destruction in type 1 diabetes. J Diabetes Investig. 2023;14(1):48-57. doi:10.1111/jdi.13926

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