Harnessing chia seed polysaccharides for green synthesis of copper-based nanoparticles with antidiabetic and anti-inflammatory activities

Diabetes mellitus and chronic inflammation are global health issues, exacerbated by the incomplete control of metabolic-inflammatory pathways and the adverse effects of long-term pharmacotherapy. The development of multifunctional therapeutic nanomaterials via green nanotechnology has recently garnered attention. In this study, we synthesized copper-based nanoparticles (Cu NPs) using Salvia hispanica (chia) seed-derived polysaccharides and characterized their in vitro anti-inflammatory and antidiabetic activities, as well as in vivo antidiabetic effects in an alloxan-induced diabetic mouse model. Successful formation of polysaccharide-based Cu NPs was confirmed using UV-Visible, FTIR, XRD, and SEM analyses. Hemocompatibility assessment showed concentration-dependent hemolysis by Cu NPs (0.92 ± 0.06%, 5.71 ± 0.44%, and 8.42 ± 0.63% at the respective tested concentrations), indicating reduced hemocompatibility at higher concentrations. In the HRBC membrane stabilization assay, Cu NPs exhibited dose-dependent inhibition of hemolysis (45.83 ± 1.7% to 70.59 ± 2.1%), while heat hemolysis was reduced by 72.91 ± 1.5% at 1000 µg/mL. Enzyme inhibition studies showed significant inhibition of α-amylase (58.45 ± 0.7% to 87.20 ± 2.1%) and α-glucosidase (83.87 ± 1.6% at 1000 µg/mL) compared to acarbose. Glucose uptake in yeast cells increased to 59.87 ± 0.3% at 1000 µg/mL. In alloxan-induced diabetic mice, Cu NPs (150 mg/kg) reduced fasting blood glucose from 287 ± 4.2 to 131 ± 7.02 mg/dL over 30 days and improved lipid profiles (e.g., decreased total cholesterol to 85 ± 1.3 mg/dL and increased HDL to 39.8 ± 1.2 mg/dL). Histopathological analyses showed restoration of pancreatic and hepatic architecture. Collectively, chia seed polysaccharide-mediated copper-based nanoparticles exhibited anti-hyperglycemic, anti-inflammatory, and lipid-modulating activities under the investigated experimental conditions, supporting their further evaluation for diabetes-related applications.