medRxiv : the preprint server for health sciences最新文献

Genome-wide association studies (GWAS) identified multiple loci for cardiovascular disease, but their relevance to individuals with chronic kidney disease (CKD), who are at higher risk of cardiovascular disease, is unknown. In this study, we performed GWAS analyses of coronary heart disease (CHD) or all-cause stroke in African (AFR) and European (EUR) American participants with CKD of the Chronic Renal Insufficiency Cohort (CRIC). Mixed- effect logistic regression models were race-stratified and adjusted for age, sex, site of recruitment, estimated glomerular filtration rate (eGFR), and principal components, followed by meta-analysis. We attempted replication in participants from two biobanks with biomarker or ICD-10 (International Classification of Diseases, 10th Revision) diagnostic codes for CKD. We assessed the association of single nucleotide variants (SNVs) at known CHD and stroke loci in CRIC and tested the genetic correlation among CRIC, a biobank-based cohort and published GWAS of cardiovascular disease. Among 3,588 CRIC participants, 1,203 had CHD and 535 had all-cause stroke. We identified six SNVs across three loci ( LINC02744 , AZIN1- AS1 , and ATP6V0A4 ) associated with all-cause stroke, and two intronic SNVs at the PPARG locus associated with CHD. However, SNV associations were not significant in replication studies. Published SNVs for CHD or stroke were not associated with cardiovascular outcomes in CRIC. When testing the genetic correlations between published GWAS and CRIC GWAS, they were significant for CHD (genetic correlations (rg) range of 0.39 to 0.51, p-value< 0.007). These findings suggest some differences in the genetic architecture of CHD and stroke among individuals with CKD compared to those from the general population, although large numbers of CKD participants are needed to assess if findings are related to participant selection and CKD severity, or non-traditional risk factors in people with CKD.

Mycobacterium tuberculosis ( Mtb ) is a major threat to global health and is responsible for over one million deaths each year. To stem the tide of cases and maximize opportunities for early interventions, there is an urgent need for affordable and simple means of tuberculosis diagnosis in under-resourced areas. We sought to develop a CRISPR-based isothermal assay coupled with a compatible, straightforward sample processing technique for point-of-care use. Here, we combine Recombinase Polymerase Amplification (RPA) with Cas13a and Cas12a, to create two parallelised one-pot assays that detect two conserved elements of Mtb ( IS6110 and IS1081 ) and an internal control targeting human DNA. These assays were shown to be compatible with lateral flow and can be readily lyophilized. Our finalized assay exhibited sensitivity over a wide range of bacterial loads (10 ⁵ to 10 ² CFU/mL) in sputum. The limit of detection (LoD) of the assay was determined to be 69.0 (51.0 - 86.9) CFU/mL for Mtb strain H37Rv spiked in sputum and 80.5 (59.4 - 101.6) CFU/mL for M. bovis BCG. Our assay showed no cross reactivity against a wide range of bacterial/fungal isolates. Clinical tests on 13 blinded sputum samples revealed 100% (6/6) sensitivity and 100% (7/7) specificity compared to culture. Our assay exhibited comparable sensitivity in clinical samples to the microbiological gold standard, TB culture, and to the nucleic acid state-of-the-art, GeneXpert MTB/RIF Ultra. This technology streamlines TB diagnosis from sample extraction to assay readout in a rapid and robust format, making it the first test to combine amplification and detection while being compatible with both lateral flow and lyophilization.

Objective: Sedentary behavior significantly increases the risk for chronic diseases and cognitive decline in aging, underscoring the need for effective interventions. Older adults exhibit a 'positivity effect', whereby processing of positive information is prioritized over negative information. In addition, self-affirmation was shown to reduce sedentary behavior in younger adults, but its effects in older adults remain unknown. This study tested a novel, technology-based intervention combining daily self-affirmation and gain-framed health messages to reduce sedentary behavior in older adults.

Methods: In a 6-week randomized controlled trial (NCT0431536), 48 cognitively unimpaired, sedentary older adults were randomized into two groups: The intervention group (mean age=70.0±5.4years) completed daily self-affirmation based on their highest-ranked value, followed by gain-framed health messages. The active control group (mean age=68.4±5.0years) performed self-affirmation on their lowest-ranked value, followed by loss-framed messages. This was a single-blinded clinical trial that incorporated a hybrid efficacy and implementation design. Thus, information about intervention feasibility was examined. In addition, baseline motivational traits, including reward sensitivity, were assessed as moderators of behavior change. The neural basis of self-affirmation and gain-framed health messaging was examined at baseline using a task-based, event-related fMRI paradigm across groups, after randomization at the outset of the intervention.

Results: The intervention showed high adherence (0.92±0.08) and positive ease-of-use ratings. While the intervention did not significantly reduce sedentary behavior compared to the active control condition, increased reward sensitivity predicted reduced sedentary behavior across all participants. FMRI results showed increased ventral striatal activation in the intervention group, compared to the active control group during reading of gain-framed compared to neutral messages.

Conclusions: This study supports the feasibility of technology-based sedentary beahvior reduction interventions for older adults. While self-affirmation combined with gain-framed messaging did not significantly reduce sedentary behavior, gain-framed messages engaged the reward network, and reward sensitivity predicted future reduction in sedentary behavior.

Rapid health outcome data acquisition using existing questionnaires can greatly facilitate time-sensitive research during or after a wildfire event. We aimed to develop a readily available library of questionnaires for self-reported health outcomes to serve as a centralized platform for wildfire researchers seeking to quickly design instruments tailored to their research aims. In this paper, we describe the methodology used to identify relevant health questionnaires and compile them into a structured library. We first followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 checklist and performed a systematic literature review of prior research on wildfire exposure and health and used this to 1) identify health outcome categories associated with wildfire and smoke exposure and 2) extract questionnaires used for self-report of health outcomes related to wildfire exposure. We also performed a secondary search of existing questionnaire repositories to identify additional instruments relevant to health impacts of wildfire exposure. All questionnaires (n=100) were organized by eight health outcome categories (mental health = 60, respiratory health = 19, overall health = 17, sleep = 10, cardiovascular health = 4, allergy = 1, irritation (eye, throat, skin) = 2, and metabolic health = 1). The library (see supplementary information) will be accompanied by a decision-tree framework in development, which will assist future users in building new questionnaires that best fit their study population and research aims. Both the questionnaire library and the forthcoming decision-support framework will be publicly accessible to researchers, public health agencies, and other groups interested in rapid response data collection to characterize the impacts of wildfires. Additionally, this method of creating a wildfire exposure health questionnaire library may serve as a template for rapid collection of questionnaire-based data following other disasters.

Introduction: Social and behavioral determinants of health (SBDH) are increasingly recognized as essential for prognostication and informing targeted interventions. While medical notes contain rich SBDH details, these are unstructured and conventional extraction methods tend to be labor intensive, inaccurate, and/or unscalable. The emergence of large language models (LLMs) presents an opportunity to develop more effective approaches for extracting SBDH data.

Materials and methods: We developed the SBDH-Reader, an LLM-powered method to extract structured SBDH data from full-length medical notes through prompt engineering. Six SBDH categories were queried including: employment, housing, marital relationship, and substance use including alcohol, tobacco, and drug use. The development dataset included 7,225 notes from 6,382 patients in the MIMIC-III database. The method was then independently tested on 971 notes from 437 patients at UT Southwestern Medical Center (UTSW). We evaluated SBDH-Reader's performance using precision, recall, F1, and confusion matrix.

Results: When tested on the UTSW validation set, the GPT-4o-based SBDH-Reader achieved a macro-average F1 ranging from 0.85 to 0.98 across six SBDH categories. For clinically relevant adverse attributes, F1 ranged from 0.94 (employment) to 0.99 (tobacco use). When extracting any adverse attributes across all SBDH categories, the SBDH-Reader achieved an F1 of 0.96, recall of 0.97, and precision of 0.96 in this independent validation set.

Conclusion: A general-purpose LLM can accurately extract structured SBDH data through effective prompt engineering. The SBDH-Reader has the potential to serve as a scalable and effective method for collecting real-time, patient-level SBDH data to support clinical research and care.

Blood-based protein biomarkers may provide important insights into the long-term neuropathology of traumatic brain injury (TBI). This is urgently required to identify mechanistic processes underlying post-traumatic neurodegeneration (PTND); a progressive post-recovery clinical decline experienced by a portion of TBI survivors. The aim of this study was to examine change over time in protein levels in a chronic TBI cohort. We selected six markers (Aβ ₄₂ /Aβ ₄₀ , GFAP, NfL, BD-tau, p-tau231, and p-tau181) with known importance in acute TBI and/or other neurodegenerative conditions. We used a longitudinal design with two time points approximately 3.5 years apart on average (SD 1.34). Proteins were measured in plasma using the ultrasensitive Single molecule array technology for 63 participants with mild to severe chronic TBI (sustained ≥ 1 year ago; M 28 years; SD 16.3 since their first blow to the head) from the Late Effects of TBI study (48% female; current age M 52 years; SD 13.4). Multivariate linear mixed effect models with adjustments for multiple comparisons were performed to examine trajectories in proteins over time with age and age squared as covariates. A series of sensitivity analyses were conducted to account for outliers and to explore effects of key covariates: sex, APOE ε4 carrier status, medical comorbidities, age at first blow to the head, time since first blow to the head, and injury severity. Over an average of 3.5 years, there were significant reductions in plasma Aβ ₄₂ /Aβ ₄₀ (β = -0.004, SE = 0.001, t = -3.75, q = .001) and significant increases in plasma GFAP (β = 12.96, SE = 4.41, t = 2.94, q = .01). There were no significant changes in NFL, BD-tau, p-tau231, or p-tau181. Both plasma Aβ ₄₂ /Aβ ₄₀ and GFAP have been associated with brain amyloidosis, suggesting a role for Aβ mis-metabolism and aggregation in the long-term neuropathological consequences of TBI. These findings are hypothesis generating for future studies exploring the diverse biological mechanisms of PTND.

Objective: Myositis is a heterogeneous family of inflammatory myopathies. We sought to define the differential expression of cytokines, cytokine receptors, and immune checkpoint genes in muscle biopsies from patients with different forms of myositis in order to characterize patterns of inflammation in each.

Methods: Bulk RNA sequencing was performed on muscle biopsy samples from 669 patients, including 105 with dermatomyositis, 80 with immune-mediated necrotizing myopathy (IMNM), 65 with anti-synthetase syndrome, 53 with inclusion body myositis (IBM), 19 with anti-PM/Scl myositis, 310 with other inflammatory or genetic myopathies, and 37 controls with normal tissue (NT). Myositis clinical groups and autoantibody subgroups were analyzed separately. Expression data was analyzed for 338 genes encoding cytokines, cytokine receptors, and immune checkpoints. Myositis group-specific genes were identified from this list by finding genes that were specifically differentially expressed in one group compared to all samples and compared to NT (α<0.001).

Results: IBM patients had the most differentially overexpressed genes (71) among all clinical groups, including 37 that were IBM-specific. Among the top genes were several involved in type 1 inflammation, including CCL5 , CXCR3 , CCR5 , CXCL9 , and IFNG . Anti-Jo1 and anti-PM/Scl patients exhibited differential overexpression of a similar set of genes, while dermatomyositis patients exhibited differential overexpression of a different set of genes involved in type 1 inflammation. IMNM patients had the least number of differentially overexpressed genes with no predominant inflammatory pattern.

Conclusion: Each myositis clinical group and autoantibody subgroup had differentially overexpressed inflammatory mediators, including a strong type 1 inflammatory gene signature in IBM.

Key messages: Inclusion Body Myositis (IBM) muscle biopsies exhibit differential overexpression of a set of genes involved in type 1 inflammation. The CCL5 - CCR5 and XCL1 - XCL2 - XCR1 axes are specifically differentially overexpressed in IBM muscle and may contribute to T _C 1-mediated inflammation. Dermatomyositis, anti-Jo1 myositis, and anti-PM/Scl myositis muscle biopsies also exhibit overexpression of type 1 inflammatory genes, but to a lesser extent than IBM.

Colorectal cancer incidence rates vary geographically and have changed over time. Notably, in the past two decades, the incidence of early-onset colorectal cancer, affecting individuals under the age of 50 years, has doubled in many countries. The reasons for this increase are unknown. Here, we investigate whether mutational processes contribute to geographic and age-related differences by examining 981 colorectal cancer genomes from 11 countries. No major differences were found in microsatellite unstable cancers, but variations in mutation burden and signatures were observed in the 802 microsatellite-stable cases. Multiple signatures, most with unknown etiologies, exhibited varying prevalence in Argentina, Brazil, Colombia, Russia, and Thailand, indicating geographically diverse levels of mutagenic exposure. Signatures SBS88 and ID18, caused by the bacteria-produced mutagen colibactin, had higher mutation loads in countries with higher colorectal cancer incidence rates. SBS88 and ID18 were also enriched in early-onset colorectal cancers, being 3.3 times more common in individuals diagnosed before age 40 than in those over 70, and were imprinted early during colorectal cancer development. Colibactin exposure was further linked to APC driver mutations, with ID18 responsible for about 25% of APC driver indels in colibactin-positive cases. This study reveals geographic and age-related variations in colorectal cancer mutational processes, and suggests that early-life mutagenic exposure to colibactin-producing bacteria may contribute to the rising incidence of early-onset colorectal cancer.