Biomedical subjects

Jinquan Li

Publications and source records attributed to Jinquan Li.

3 recordsLinked to original sources

PubMed2026

Genetic Evidence Links Sex Hormone-binding Globulin to Total Body Bone Mineral Density at Age 45-60 Years: A Two-sample Mendelian Randomization Study.

The menopausal transition and early postmenopause represent important periods for women's skeletal health, but the genetic relevance of metabolic, behavioral, and hormone-related factors to bone mineral density during midlife remains incompletely understood. This study used publicly available genome-wide association study summary statistics to examine associations between body mass index, 25-hydroxyvitamin D, sex hormone-binding globulin, high-density lipoprotein cholesterol, smoking initiation, and alcohol intake frequency and total body bone mineral density at ages 45-60 years. Exposure genome-wide association study summary statistics were derived from large European-ancestry populations and were not restricted to midlife women, whereas the outcome genome-wide association study captured an age-stratified total body bone mineral density phenotype at age 45-60 years. This age range overlaps with the menopausal transition and early postmenopause in women. Univariable, reverse, and multivariable Mendelian randomization analyses were performed, with inverse-variance weighting as the primary method and complementary sensitivity analyses used to assess heterogeneity, pleiotropy, and result stability. Genetically predicted higher sex hormone-binding globulin was associated with lower total body bone mineral density (β = -0.111, 95% CI: -0.170 to -0.051; P = 0.0003). Reverse Mendelian randomization did not support reverse causation from bone mineral density to sex hormone-binding globulin. Multivariable analyses suggested that this association persisted after adjustment for selected metabolic biomarkers. The other examined exposures did not show consistent evidence of association. These findings provide genetic evidence linking sex hormone-binding globulin to total-body bone mineral density at ages 45-60 years. Further prospective and predictive studies are needed to evaluate its clinical relevance beyond established bone health assessment tools.

Humans↗

PubMed2026

Journey Mapping of the Patient Experience from Diagnosis to End of Life in Lung Cancer: A Qualitative Meta-Synthesis.

OBJECTIVES: This study aimed to systematically synthesize the lived experiences and journey narratives of lung cancer patients across disease stages, and identify key tasks and pain points during the disease course through patient journey mapping, providing evidence for comprehensive disease management throughout the patient journey. METHODS: Ten databases, including PubMed, Embase, Web of Science, Scopus, PsycINFO, CINAHL, Cochrane Library, CNKI, Wanfang, and SinoMed, were systematically searched, with a search period from database inception to August 15, 2025. The JBI Critical Appraisal Tool for qualitative studies was used to evaluate the quality of studies, and the results were integrated using a meta-aggregative approach. RESULTS: Thirteen studies were included. Based on the patient journey mapping, the lung cancer patient journey comprises four potential stages: evaluation and diagnosis, initial treatment, maintenance therapy, and end-of-life. A total of 30 themes emerged within three dimensions: tasks, emotions, and pain points. Each dimension of each stage consists of 2-3 themes. CONCLUSION: The journey of lung cancer patients is protracted and complex, characterized by stage-specific needs and challenges. Future management strategies should be tailored to these distinct phases, providing precision supportive care to optimize treatment outcomes and enhance patients' quality of life. IMPLICATIONS FOR NURSING PRACTICE: This Patient Journey Map integrates routine clinical pathways with patients' lived experiences across each stage, revealing stage-specific challenges and providing targets for tailored nursing interventions. The framework promotes multidisciplinary, digitally enabled supportive care and indicates the importance of including patients' social circles to enhance patient-centered outcomes.

Humans↗

PubMed2025

Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.

Low-cost and high-throughput RNA sequencing data for barley RILs achieved GP performance comparable to or better than traditional SNP array datasets when combined with parental whole-genome sequencing SNP data. The field of genomic selection (GS) is advancing rapidly on many fronts including the utilization of multi-omics datasets with the goal of increasing prediction ability and becoming an integral part of an increasing number of breeding programs ensuring future food security. In this study, we used RNA sequencing (RNA-Seq) data to perform genomic prediction (GP) on three related barley RIL populations. We investigated the potential of increasing prediction ability by combining genomic and transcriptomic datasets, adding whole-genome sequencing (WGS) SNP data, functional annotation-based filtering, and empirical quality filtering. Our RNA-Seq data were generated cost-efficiently using small-footprint plant cultivation, high-throughput RNA extraction, and Library preparation miniaturization. We also examined sequencing depth reduction as an additional cost-saving measure. We used fivefold cross-validation to evaluate the prediction ability of the gene expression dataset, the RNA-Seq SNP dataset, and the consensus SNP dataset between the RNA-Seq and parental WGS data, resulting in prediction abilities between 0.73 and 0.78. The consensus SNP dataset performed best, with five out of eight traits performing significantly better compared to a 50K SNP array, which served as a benchmark. The advantage of the consensus SNP dataset was most prominent in the inter-population predictions, in which the training and validation sets originated from different RIL sub-populations. We were therefore able to not only show that RNA-Seq data alone are able to predict various complex traits in barley using RILs, but also that the performance can be further increased with WGS data for which the public availability will steadily increase.

Hordeum↗