An ultra-sensitive and high-throughput trapping-micro-LC-MS method for quantification of circulating vitamin D metabolites and application in multiple sclerosis patients.

Quantitative analysis of the biologically-active metabolites of vitamin D (VitD), which are crucial in regulating various physiological and pathological processes, is important for clinical investigations. Liquid chromatography-tandem mass spectrometry (LC-MS) has been widely used for this purpose but existing LC-MS methods face challenges in achieving highly sensitive and accurate quantification of low-abundance VitD metabolites while maintaining high throughput and robustness. Here we developed a novel pipeline that combines a trapping-micro-LC-(T-µLC) with narrow-window-isolation selected-reaction monitoring MS(NWI-SRM) for ultra-sensitive, robust and high-throughput quantification of VitD metabolites in serum samples after derivatization. The selective-trapping and delivery approach efficiently removes matrix components, enabling high-capacity sample loading and enhancing sensitivity, throughput, and robustness. The NWI-SRM further improves the sensitivity by providing high selectivity. The lower limits of quantification (LOQs) achieved were markedly lower than any existing LC-MS methods: 1.0 pg/mL for 1,25(OH)2D3, 5.0 pg/mL for 24,25(OH)2D3, 30 pg/mL for both 25(OH)D2 and 25(OH)D3, all within a 9-min cycle. The method is applied to quantify VitD metabolites from 218 patients with multiple sclerosis. This study revealed negative correlations(r=- 0.44 to - 0.51) between the levels of 25(OH)D2 and all the three D3 metabolites in multiple sclerosis patients.

Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.

Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.