Human plasma is a biofluid that is high in information content, making it an excellent candidate for metabolomic studies. H NMR has been a popular technique to detect several dozen metabolites in blood plasma. In order for H NMR to become an automated, high-throughput method, challenges related to (1) the large signal from lipoproteins and (2) spectral overlap between different metabolites have to be addressed. Here diffusion-weighted H NMR is used to separate lipoprotein and metabolite signals based on their large difference in translational diffusion. The metabolite H NMR spectrum is then quantified through spectral fitting utilizing full prior knowledge on the metabolite spectral signatures. Extension of the scan time by 3 minutes or 15% per sample allowed the acquisition of a H NMR spectrum with high diffusion weighting. The metabolite H NMR spectra could reliably be modeled with 28 metabolites. Excellent correlation was found between results obtained with diffusion NMR and ultrafiltration. The combination of minimal sample preparation together with minimal user interaction during processing and quantification provides a metabolomics technique for automated, quantitative H NMR of human plasma.