medRxiv : the preprint server for health sciences最新文献

Increasingly, research suggests that aging is a coordinated multi-system decline in functioning that occurs at multiple biological levels. We developed and validated a transcriptomic (RNA-based) aging measure we call Transcriptomic Mortality-risk Age (TraMA) using RNA-seq data from the 2016 Health and Retirement Study using elastic net Cox regression analyses to predict 4-year mortality hazard. In a holdout test sample, TraMA was associated with earlier mortality, more chronic conditions, poorer cognitive functioning, and more limitations in activities of daily living. TraMA was also externally validated in the Long Life Family Study and several publicly available datasets. Results suggest that TraMA is a robust, portable RNAseq-based aging measure that is comparable, but independent from past biological aging measures (e.g., GrimAge). TraMA is likely to be of particular value to researchers interested in understanding the biological processes underlying health and aging, and for social, psychological, epidemiological, and demographic studies of health and aging.

The Alzheimer's Disease Sequencing Project (ADSP) is a national initiative to understand the genetic architecture of Alzheimer's Disease and Related Dementias (AD/ADRD) by sequencing whole genomes of affected participants and age-matched cognitive controls from diverse populations. The Genome Center for Alzheimer's Disease (GCAD) processed whole-genome sequencing data from 36,361 ADSP participants, including 35,014 genetically unique participants of which 45% are from non-European ancestry, across 17 cohorts in 14 countries in this fourth release (R4). This sequencing effort identified 387 million bi-allelic variants, 42 million short insertions/deletions, and 2.2 million structural variants. Annotations and quality control data are available for all variants and samples. Additionally, detailed phenotypes from 15,927 participants across 10 domains are also provided. A linkage disequilibrium panel was created using unrelated AD cases and controls. Researchers can access and analyze the genetic data via NIAGADS Data Sharing Service, the VariXam tool, or NIAGADS GenomicsDB.

Background: Brazil is considered an epicenter for emerging and re-emerging arboviruses that significantly impact public health. The mid-sized city of São José do Rio Preto (SJdRP) in northwestern São Paulo state is considered hyperendemic for arboviral diseases, with case numbers climbing each year. Only 45 cases of chikungunya (CHIKV) were reported in the city from 2015 to 2022, indicating cryptic circulation of this virus, but cases in the state increased notably in 2023. This study investigates the use of active entomological surveillance to detect new arbovirus introductions in specific areas like SJdRP.

Methodology/principal findings: We used molecular testing to investigate the presence of CHIKV in adult culicids collected monthly from various neighborhoods in SJdRP. Positive samples underwent whole-genome sequencing and phylogenetic analysis. Entomological surveillance successfully detected the early spread of CHIKV across SJdRP, revealing an infection rate of 6.67%, with the well-established vectors Aedes aegypti and Ae. albopictus as well as Culex sp. carrying the virus. The vector positivity rate increased from December 2023 to April 2024, which correlates with rising numbers of chikungunya fever cases reported in SJdRP during the same period. The resurgence of CHIKV in this region is attributed to several introduction events, mainly from the Southeast and North of Brazil, which facilitated establishment of the virus within the highly dense vector population and led to extensive spread and, in turn, a major CHIKV epidemic in this geographical area.

Conclusions/significance: Extensive circulation of CHIKV was documented within the human and vector population, marking the onset of the first major CHIKV epidemic in SJdRP and neighboring cities. Because multiple arboviruses co-circulate in several locations in Brazil, entomological surveillance, along with ongoing monitoring of patient samples, is a key to help health authorities to implement more effective measures to interrupt transmission cycles and mitigate new epidemic waves.

Background: The integration of genomic and geospatial data into infectious disease transmission analyses typically includes residential locations and excludes other activity spaces where transmission may occur (e.g. work, school, or social venues). The objective of this analysis was to explore residential as well as other activity spaces of tuberculosis (TB) outbreaks to identify potential geospatial 'hotspots' of transmission.

Methods: We analyzed data that included geospatial coordinates for residence and other activity spaces collected during 2012-2016 for the Kopanyo Study, a population-based study of TB transmission in Botswana. We included participants with results from whole genome sequencing conducted on archived samples from the original study. We used a spatial log-Gaussian Cox process model to detect core areas of increased activity spaces of individuals belonging to TB outbreaks (genotypic groups with ≤5 single-nucleotide polymorphisms), which we compared to ungrouped participants (those not in a genotypic group of any size).

Findings: We analyzed data collected from 636 participants, including 70 participants belonging to six outbreak groups with a combined total of 293 locations, and 566 ungrouped participants with a combined total of 2289 locations. Core areas of activity space for each outbreak group were geographically distinct, and we found evidence of localized transmission in four of six outbreaks. For most of the outbreaks, including activity space data led to the detection of larger areas of higher spatial intensity and more focal points compared to residential location alone.

Interpretation: Geospatial analysis using activity space data (social gathering places as well as residence) may lead to improved understanding of areas of infectious disease transmission compared to using residential data alone.

Funding: This work was supported by funding from the National Institute of Allergy and Infectious Diseases R01AI097045, R01AI147336, and R01AI170204.

Up to 40% of elite athletes experience bone stress injuries (BSIs), with 20-30% facing reinjury. Early identification of runners at high risk of subsequent BSI could improve prevention strategies. However, the complex etiology and multifactorial risk factors of BSIs makes identifying predictive risk factors challenging. In a study of 30 female recreational athletes with tibial BSIs, 10 experienced additional BSIs over a 1-year period, prompting investigation of systemic biomarkers of subsequent BSIs using aptamer-based proteomic technology. We hypothesized that early proteomic signatures could discriminate runners who experienced subsequent BSIs. 1,500 proteins related to metabolic, immune, and bone healing pathways were examined. Using supervised machine learning and genetic programming methods, we analyzed serum protein signatures over the 1-year monitoring period. Models were also created with clinical metrics, including standard-of-care blood analysis, bone density measures, and health histories. Protein signatures collected within three weeks of BSI diagnosis achieved the greatest separation by sparse partial least squares discriminant analysis (sPLS-DA), clustering single and recurrent BSI individuals with a mean accuracy of 96 ± 0.02%. Genetic programming models independently verified the presence of candidate biomarkers, including fumarylacetoacetase, osteopontin, and trypsin-2, which significantly outperformed clinical metrics. Time-course differential expression analysis highlighted 112 differentially expressed proteins in individuals with additional BSIs. Gene set enrichment analysis mapped these proteins to pathways indicating increased fibrin clot formation and decreased immune signaling in recurrent BSI individuals. These findings provide new insights into biomarkers and dysregulated protein pathways associated with recurrent BSI and may lead to new preventative or therapeutic intervention strategies.

One sentence summary: Our study identified candidate serum biomarkers to predict subsequent bone stress injuries in female runners, offering new insights for clinical monitoring and interventions.

Background/objective: In a subset of participants from the CALERIE^™ Phase 2 study we evaluated the effects of 2y of ~25% Calorie Restriction (CR) diet on IgG N-glycosylation (GlycAge), plasma and complement C3 N-glycome as markers of aging and inflammaging.

Methods: Plasma samples from 26 participants in the CR group who completed the CALERIE2 trial and were deemed adherent to the intervention (~>10 % CR at 12 mo) were obtained from the NIA AgingResearchBiobank. Glycomic investigations using UPLC or LC-MS analyses were conducted on samples from baseline (BL), mid-intervention (12 mo) and post-intervention (24 mo), and changes resulting from the 2y CR intervention were examined. In addition, anthropometric, clinical, metabolic, DNA methylation (epigenetic) and skeletal muscle transcriptomic data were analyzed to identify aging-related changes that occurred in tandem with the N-glycome changes.

Results: Following the 2y CR intervention, IgG galactosylation was higher at 24mo compared to BL (p = 0.051), digalactosylation and GlycAge (the IgG-based surrogate for biological age) were not different between BL and 12mo or BL and 24mo, but increased between 12mo and 24mo (p = 0.016, 0.027 respectively). GlycAge was also positively associated with TNF-α and ICAM-1 (p=0.030, p=0.017 respectively). Plasma highly branched glycans were decreased by the 2y intervention (BL vs 24 mo: p=0.013), but both plasma and IgG bisecting GlcNAcs were increased (BL vs 24mo: p<0.001, p = 0.01 respectively). Furthermore, total complement C3 protein concentrations were reduced (BL vs 24mo: p <0.001), as were Man9 glycoforms (BL vs 24mo: p<0.001), and Man10 (which is glucosylated) C3 glycoforms (BL vs 24mo: p = 0.046).

Conclusions: 24-mos of CR was associated with several favorable, anti-aging, anti-inflammatory changes in the glycome: increased galactosylation, reduced branching glycans, and reduced GlycAge. These promising CR effects were accompanied by an increase in bisecting GlcNAc, a known pro-inflammatory biomarker. These intriguing findings linking CR, clinical, and glycomic changes may be anti-aging and inflammatory, and merit additional investigation.

Genome-wide association studies (GWASs) have identified tens of thousands of disease associated variants and provided critical insights into developing effective treatments. However, limited sample sizes have hindered the discovery of variants for uncommon and rare diseases. Here, we introduce KGWAS, a novel geometric deep learning method that leverages a massive functional knowledge graph across variants and genes to improve detection power in small-cohort GWASs significantly. KGWAS assesses the strength of a variant's association to disease based on the aggregate GWAS evidence across molecular elements interacting with the variant within the knowledge graph. Comprehensive simulations and replication experiments showed that, for small sample sizes ( N =1-10K), KGWAS identified up to 100% more statistically significant associations than state-of-the-art GWAS methods and achieved the same statistical power with up to 2.67× fewer samples. We applied KGWAS to 554 uncommon UK Biobank diseases ( N _case <5K) and identified 183 more associations (46.9% improvement) than the original GWAS, where the gain further increases to 79.8% for 141 rare diseases (N _case <300). The KGWAS-only discoveries are supported by abundant functional evidence, such as rs2155219 (on 11q13) associated with ulcerative colitis potentially via regulating LRRC32 expression in CD4+ regulatory T cells, and rs7312765 (on 12q12) associated with the rare disease myasthenia gravis potentially via regulating PPHLN1 expression in neuron-related cell types. Furthermore, KGWAS consistently improves downstream analyses such as identifying disease-specific network links for interpreting GWAS variants, identifying disease-associated genes, and identifying disease-relevant cell populations. Overall, KGWAS is a flexible and powerful AI model that integrates growing functional genomics data to discover novel variants, genes, cells, and networks, especially valuable for small cohort diseases.

Black women and men outpace other races for stimulant-involved overdose mortality despite lower lifetime use. Growth in mortality from prescription stimulant medications is increasing in tandem with prescribing patterns for these medications. We used Twitter to explore nonmedical prescription stimulant use (NMPSU) among Black women and men using emotion and sentiment analysis, and topic modeling. We applied the NRC Lexicon and VADER dictionary, and LDA topic modeling to examine feelings and themes in conversations about NMPSU by gender. We paid attention to the ability of natural language processing techniques to detect differences in emotion and sentiment among Black Twitter subscribers given increased mortality from stimulants. We found that, although emotion and sentiment outcomes match the directionality of emotions and sentiment observed (i.e., Black Twitter subscribers use more positive language in tweets), this belies limitations of NRC and VADER dictionaries to distinguish feelings for Black people. Even still, LDA topic models showcased the relevance of hip-hop, dependence on NMPSU, and recreational use as consequential to Black Twitter subscribers' discussions. However, gender shaped the relevance of these topics for each group. Greater attention needs to be paid to how Black women and men use social media to discuss important topics like drug use. Natural language processing methods and social media research should include larger proportions of Black, Hispanic/Latinx, and American Indian populations in development of emotion and sentiment lexicons, otherwise outcomes regarding NMPSU will not be generalizable to populations writ large due to cultural differences in communication about drug use online.

The COVID-19 pandemic has caused substantial worldwide disruptions in health, economy, and society, manifesting symptoms such as loss of smell (anosmia) and loss of taste (ageusia), that can result in prolonged sensory impairment. Establishing the host genetic etiology of anosmia and ageusia in COVID-19 will aid in the overall understanding of the sensorineural aspect of the disease and contribute to possible treatments or cures. By using human genome sequencing data from the University of Iowa (UI) COVID-19 cohort (N=187) and the National Institute of Health All of Us (AoU) Research Program COVID-19 cohort (N=947), we investigated the genetics of anosmia and/or ageusia by employing feature selection techniques to construct a novel variant and gene prioritization pipeline, utilizing machine learning methods for the classification of patients. Models were assessed using a permutation-based variable importance (PVI) strategy for final prioritization of candidate variants and genes. The highest held-out test set area under the receiver operating characteristic (AUROC) curve for models and datasets from the UI cohort was 0.735 and 0.798 for the variant and gene analysis respectively and for the AoU cohort was 0.687 for the variant analysis. Our analysis prioritized several novel and known candidate host genetic factors involved in immune response, neuronal signaling, and calcium signaling supporting previously proposed hypotheses for anosmia/ageusia in COVID-19.

Multiple Sclerosis (MS) is a chronic inflammatory and neurodegenerative disease affecting the brain and spinal cord. Genetic studies have identified many risk loci, that were thought to primarily impact immune cells and microglia. Here, we performed a multi-ancestry genome-wide association study with 20,831 MS and 729,220 control participants, identifying 236 susceptibility variants outside the Major Histocompatibility Complex, including four novel loci. We derived a polygenic score for MS and, optimized for European ancestry, it is informative for African-American and Latino participants. Integrating single-cell data from blood and brain tissue, we identified 76 genes affected by MS risk variants. Notably, while T cells showed the strongest enrichment, inhibitory neurons emerged as a key cell type, highlighting the importance of neuronal and glial dysfunction in MS susceptibility.