Back

Rapid point-of-care lipoprotein assays by benchtop NMR spectroscopy

Makinen, V.-P.; Tynkkynen, T.; Mantyselka, P.; Ala-Korpela, M.

2026-08-28 cardiovascular medicine
10.64898/2026.08.24.26361213 medRxiv
Show abstract

BACKGROUND: Circulating lipoprotein measures such as low-density lipoprotein (LDL) cholesterol and apolipoprotein B are causal biomarkers of cardiovascular risk. These can be quantified quickly and accurately by nuclear magnetic resonance (NMR) spectroscopy, but clinical translation has been slow. We investigated easy-to-operate and affordable benchtop NMR technology as a new means to quantify lipoprotein biomarkers in point-of-care settings. METHODS: Serum samples from 336 individuals were analysed with a benchtop NMR spectrometer operating at 80 MHz and a high-field NMR spectrometer operating at 600 MHz. Glucose, apolipoprotein A-I, apolipoprotein B, total triglycerides, total cholesterol, LDL cholesterol and high-density cholesterol were determined by standard biochemistry. Corresponding NMR-based measures were quantified by linear regression. The 80 MHz dataset included experiments with different scan settings to optimize measurement time (1,987 spectra in total). RESULTS: We identified 32 scans (2 min 8 s) as the minimum runtime for lipoprotein quantification. Total triglycerides and glucose were quantified with the highest accuracy (CV [≤]5.2%, R2 [≥]95%), while LDL cholesterol was more challenging (CV = 10.1%, R2 = 72%) and apolipoprotein B in between (CV = 7.4%, R2 = 74%). Epidemiological correlations between biochemistry assays and sex, obesity, glycemia and blood pressure were reproduced by the corresponding benchtop assays (P [≥]0.11 for difference). CONCLUSIONS: We developed a new lipoprotein quantification method and demonstrated its feasibility for standard lipoprotein analytics. The portability and cost-effectiveness of benchtop NMR make it an appealing choice for research and clinical settings where rapid and robust results on site are an advantage.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.