Skip to content
#protein folding Open access

Before the Threshold: Hyperinsulinemia as a Candidate Modifiable Driver of β-Cell Exhaustion in Genetically Burdened, Normoglycemic Offspring of Patients With Type 2 Diabetes — A Translational Hypothesis and Prototype Risk Matrix for Trial Design

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)
Pancreatic function and diabetes

Abstract

Background. Type 2 diabetes mellitus (T2DM) is diagnosed at decompensation thresholds — fasting plasma glucose (FPG) ≥126 mg/dL or HbA1c ≥6.5% — that typically declare disease only after substantial β-cell loss. Normoglycemic offspring of parents with T2DM carry several-fold lifetime risk yet receive no structured surveillance. Hypothesis. In genetically burdened, normoglycemic individuals, chronic hyperinsulinemia may not be merely compensatory. Sustained secretory demand may itself injure the β cell through endoplasmic reticulum (ER) client overload with unfolded protein response dysregulation, mTORC1-mediated autophagy suppression, FoxO1 nuclear exclusion with identity erosion, and prohormone processing infidelity — a proposed feed-forward loop terminating in secretory failure. Framework. We present a prototype, pedigree-gated risk matrix in two configurations — an essential three-layer pathway (familial gatekeeper; triglyceride–glucose index and 1-hour oral glucose tolerance test glucose; clinical stigmata) and a full 0–10 score adding HOMA-IR and an optional fasting proinsulin:C-peptide module — with a provisional action threshold of ≥5.5. Every performance figure attached to this matrix is an explicit design specification for trial planning, not an observed result. Conclusion. The framework is deliberately hypothesis-generating: metformin as β-cell load reduction is positioned only within shared decision-making or the proposed BETA-SHIELD randomized trial; no component is ready for clinical adoption.

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.