Epidemiological studies demonstrate an association between migraine and chronic kidney disease (CKD), while the genetic basis underlying the phenotypic association has not been investigated. We... Show moreEpidemiological studies demonstrate an association between migraine and chronic kidney disease (CKD), while the genetic basis underlying the phenotypic association has not been investigated. We aimed to help avoid unnecessary interventions in individuals with migraine through the investigation of phenotypic and genetic relationships underlying migraine, CKD, and kidney function. We first evaluated phenotypic associations using observational data from UK Biobank (N = 255,896). We then investigated genetic relationships leveraging genomic data in European ancestry for migraine (N-case/N-control = 48,975/540,381), CKD (N-case/N-control = 41,395/439,303), and two traits of kidney function (estimated glomerular filtration rate [eGFR, N = 567,460] and urinary albumin-to-creatinine ratio [UACR, N = 547,361]). Observational analyses suggested no significant association of migraine with the risk of CKD (HR = 1.13, 95% CI = 0.85-1.50). While we did not find any global genetic correlation in general, we identified four specific genomic regions showing significant for migraine with eGFR. Cross-trait meta-analysis identified one candidate causal variant (rs1047891) underlying migraine, CKD, and kidney function. Transcriptome-wide association study detected 28 shared expression-trait associations between migraine and kidney function. Mendelian randomization analysis suggested no causal effect of migraine on CKD (OR = 1.03, 95% CI = 0.98-1.09; P = 0.28). Despite a putative causal effect of migraine on an increased level of UACR (log-scale-beta = 0.02, 95% CI = 0.01-0.04; P = 1.92 x 10(-3)), it attenuated to null when accounting for both correlated and uncorrelated pleiotropy. Our work does not find evidence supporting a causal association between migraine and CKD. However, our study highlights significant biological pleiotropy between migraine and kidney function. The value of a migraine prophylactic treatment for reducing future CKD in people with migraine is likely limited. Show less
Prognostic prediction of traumatic brain injury (TBI) in patients is crucial in clinical decision and health care policy making. This study aimed to develop and validate prediction models for in... Show morePrognostic prediction of traumatic brain injury (TBI) in patients is crucial in clinical decision and health care policy making. This study aimed to develop and validate prediction models for in-hospital mortality after severe traumatic brain injury (sTBI). We developed and validated logistic regression (LR), LASSO regression, and machine learning (ML) algorithms including support vector machines (SVM) and XGBoost models. Fifty-four candidate predictors were included. Model performance was expressed in terms of discrimination (C-statistic) and calibration (intercept and slope). For model development, 2804 patients with sTBI in the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China Registry study were included. External validation was performed in 1113 patients with sTBI in the CENTER-TBI European Registry study. XGBoost achieved high discrimination in mortality prediction, and it outperformed logistic and LASSO regression. The XGBoost model established in this study also outperformed prediction models currently available, including the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) core and International Mission for Prognosis and Analysis of Clinical Trials (CRASH) basic models. When including 54 variables, XGBoost and SVM reached C-statistics of 0.87 (95% confidence interval [CI]: 0.81-0.92) and 0.85 (95% CI: 0.79-0.90) at internal validation, and 0.88 (95% CI: 0.87-0.88) and 0.86 (95% CI: 0.85-0.87) at external validation, respectively. A simplified version of XGBoost and SVM using 26 variables selected by recursive feature elimination (RFE) reached C-statistics of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.91) at internal validation, and 0.87 (95% CI: 0.87-0.88) and 0.87 (95% CI: 0.86-0.87) at external validation, respectively. However, when the number of variables included decreased, the difference between ML and LR diminished. All the prediction models can be accessed via a web-based calculator. Glasgow Coma Scale (GCS) score, age, pupillary light reflex, Injury Severity Score (ISS) for brain region, and the presence of acute subdural hematoma were the five strongest predictors for mortality prediction. The study showed that ML techniques such as XGBoost may capture information hidden in demographic and clinical predictors of patients with sTBI and yield more precise predictions compared with LR approaches. Show less
Multi-ancestry genome-wide association analyses identify 124 risk loci for rheumatoid arthritis, of which 34 are novel. A polygenic risk score based on multi-ancestry data showed comparable... Show moreMulti-ancestry genome-wide association analyses identify 124 risk loci for rheumatoid arthritis, of which 34 are novel. A polygenic risk score based on multi-ancestry data showed comparable performance between populations of European and East Asian ancestries.Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 x 10(-8)), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA. Show less
Xie, M.; Liu, H.; Wan, S.; Lu, X.; Hong, D.; Du, Y.; ... ; Tian, Y. 2022
.Sensitive detection of weak acoustic signals at nanometer scale is challenging. Here, the authors present an acoustic detection system based on a single molecule as a probe, where frequency and... Show more.Sensitive detection of weak acoustic signals at nanometer scale is challenging. Here, the authors present an acoustic detection system based on a single molecule as a probe, where frequency and amplitude of acoustic vibrations can be extracted from its minute variations in distance to the surface of a plasmonic gold nanorod.Sensitive detection of local acoustic vibrations at the nanometer scale has promising potential applications involving miniaturized devices in many areas, such as geological exploration, military reconnaissance, and ultrasound imaging. However, sensitive detection of weak acoustic signals with high spatial resolution at room temperature has become a major challenge. Here, we report a nanometer-scale system for acoustic detection with a single molecule as a probe based on minute variations of its distance to the surface of a plasmonic gold nanorod. This system can extract the frequency and amplitude of acoustic vibrations with experimental and theoretical sensitivities of 10 pm Hz(-1/2) and 10 fm Hz(-1/2), respectively. This approach provides a strategy for the optical detection of acoustic waves based on molecular spectroscopy without electromagnetic interference. Moreover, such a small nano-acoustic detector with 40-nm size can be employed to monitor acoustic vibrations or read out the quantum states of nanomechanical devices. Show less