Targeting GLP-1 signaling and barrier integrity with Leanskolin™, a standardized Coleus forskohlii extract: a Caco-2 cell-based study for metabolic health support
Sep 2026· BMC Complementary Medicine and Therapies· 0 citations
Diabetes Treatment and ManagementBiochemical Analysis and Sensing Techniques
Abstract
Glucagon-like peptide-1 (GLP-1) is a key incretin hormone involved in glucose regulation and metabolic health. This study reports, for the first time, the GLP-1 enhancing potential of a standardized
Coleus forskohlii
extract (Leanskolin™, ≥ 10% forskolin) using differentiated Caco-2 cells. MTT assay was performed to assess the cytotoxicity of Leanskolin (25–500 µg/mL) in Caco-2 cells. Later, the cells were treated with non-cytotoxic concentrations (25, 50, and 100 µg/mL) for 24 h. Further, in vitro Dipeptidyl peptidase-4 (DPP-4) enzyme inhibition assay was performed. At 100 µg/mL, Leanskolin significantly upregulated
Proglucagon
(3.33-fold,
p
< 0.001) and
GLP-1R
(3.32-fold,
p
< 0.01) gene expression, indicating stimulation of endogenous GLP-1 signaling. The extract also markedly inhibited cellular DPP-4 activity and its gene expression (
p
< 0.05 vs. control). In vitro DPP-4 inhibition assay revealed an IC₅₀ of 262.5 µg/mL of Leanskolin, suggesting potential to prolong GLP-1 bioavailability. Furthermore, Leanskolin modulated bitter taste receptor genes (
TAS2R38
,
TAS2R43
,
TAS2R14
) in a concentration-dependent manner (
p
< 0.05), implicating a role in nutrient sensing and enteroendocrine signaling. Leanskolin treatment (25–100 µg/mL, 24 h) did not alter TEER values but significantly reduced FITC-flux at 50 and 100 µg/mL (
p
< 0.01), indicating improved barrier integrity. Importantly, the extract enhanced the expression of tight junction (TJ) proteins (
p
< 0.05 vs. control). These findings highlight the multi-targeted potential of Leanskolin in promoting metabolic health via GLP-1 pathway activation, DPP-4 inhibition, and gut barrier support. This first report demonstrates integrated activity of this extract, offering promising insights into its therapeutic relevance for managing metabolic disorders.
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.· Journal of Systems and Softw...· 111 citations· ⚡8
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.· Journal of Systems and Softw...· 78 citations· ⚡6
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· SSE@SIGSOFT FSE· 56 citations· ⚡4
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· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
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 sequence constraints.
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.