Semantic Evaluation of Environmental and Nutrition Factors Impacting Female Reproductive Disorders

影响女性生殖疾病的环境和营养因素的语义评估

• 批准号: 10315771
• 负责人: Lauren E Chan
• 金额: $ 3.77万
• 依托单位: OREGON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-16 至 2023-12-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10315771
• 关键词: Semantic; Evaluation; Environmental; Nutrition; Factors

Chronic female reproductive disorders such as endometriosis, fibroids, and ovarian cysts can lead individuals to suffer from severe pelvic, abdominal, and/or back pain; heavy menstrual cycles; severe bloating; and reduced fertility. While some individuals with these disorders present with similar symptoms, others can appear asymptomatic until they experience infertility, or the disorder is identified during a medical procedure like tubal ligation. Identifying asymptomatic individuals or differentially diagnosing reproductive disorders for individuals with similar symptoms without pursuing expensive and invasive gold standard diagnostic imaging is difficult. Misdiagnosis and delays in diagnosis are common for reproductive disorders, fueling prolonged symptoms and delays in appropriate therapeutic intervention. Environmental exposures and nutrition have been hypothesized as modifiable risk factors for female reproductive disorders. As such, understanding these risk factors is essential for clinicians to provide recommendations for reproductive disorder prevention and diagnosis. Survey tools are commonly used to collect environmental and nutrition exposure information, but their limited computability makes it challenging to integrate these data with other biomedical data such as phenotypes or genotypes to evaluate reproductive disorder risk. The general objective of this research is to establish new methods for development of semantically computable survey data and their integration with other data modalities and subsequent machine learning for evaluating health risks using biomedical ontologies. The specific goal of this application is to apply these techniques to evaluate clinical data and exposures impacting female reproductive disorders and differentially classify endometriosis, fibroid, and ovarian cyst risk using semantically encoded versus unencoded data. This objective is in alignment with the NICHD Strategic Plan Research Theme of ‘promoting gynecological, andrological, and reproductive health’ through the utilization of ‘integrated genetic and phenotypic exposure data to understand the underlying mechanisms of conditions such as endometriosis, fibroids’. Specific Aim 1 will computationally model female reproductive disorder phenotypes, genotypes, and exposures, focusing on endometriosis, fibroids, and ovarian cysts. Specific Aim 2 will ontologically encode environmental exposures and evaluate reproductive disorder risk, supporting future reproductive research and clinical care improvements. The training plan outlined in this proposal will allow the Candidate to obtain essential training to: 1) Apply novel inference modeling methods for evaluating environmental exposure and nutrition factors influencing reproductive health, 2) Expand skills in data science techniques that can be applied to nutrition and other types of data integration in biomedical sciences, 3) Develop further expertise in nutrition and metabolism and their impact on human reproductive health, 4) Learn methods for clinical and translational requirements analysis and validation approaches, and 5) Develop leadership and career skills required to become a successful independent research scientist.

慢性女性生殖疾病，如子宫内膜异位症、子宫肌瘤和卵巢囊肿，可导致个人遭受严重的盆腔、腹部和/或背部疼痛；月经周期过多；严重腹胀；以及生育力下降。虽然一些患有这些疾病的人出现了类似的症状，但另一些人可能看起来没有症状，直到他们经历了不孕不育，或者在输卵管结扎术等医疗程序中发现了这种疾病。在不追求昂贵的侵入性黄金标准诊断成像的情况下，识别无症状的个体或鉴别诊断具有类似症状的个体的生殖障碍是困难的。对于生殖障碍来说，误诊和延误诊断是很常见的，这会导致症状延长和适当治疗干预的延误。环境暴露和营养一直被认为是女性生殖障碍的可改变的危险因素。因此，了解这些危险因素对于临床医生为生殖障碍的预防和诊断提供建议至关重要。调查工具通常用于收集环境和营养暴露信息，但其有限的计算能力使这些数据与其他生物医学数据(如表型或基因型)相结合以评估生殖障碍风险具有挑战性。这项研究的总体目标是建立新的方法来开发语义可计算的调查数据，并将其与其他数据形式和后续的机器学习相集成，以使用生物医学本体论来评估健康风险。这项应用的具体目标是应用这些技术来评估影响女性生殖障碍的临床数据和暴露，并使用语义编码和非编码数据来区分子宫内膜异位症、子宫肌瘤和卵巢囊肿的风险。这一目标与NICHD战略计划研究主题相一致，即通过利用综合遗传和表型暴露数据来了解子宫内膜异位症、子宫肌瘤等疾病的潜在机制，从而促进妇科、男科和生殖健康。具体目标1将对女性生殖障碍的表型、基因型和暴露进行计算建模，重点放在子宫内膜异位症、子宫肌瘤和卵巢囊肿上。具体目标2将对环境暴露进行本体论编码，并评估生殖障碍风险，支持未来的生殖研究和临床护理改进。本建议中概述的培训计划将使候选人能够获得必要的培训，以：1)应用新的推理建模方法来评估环境暴露和影响生殖健康的营养因素，2)扩大可应用于生物医学中营养和其他类型数据集成的数据科学技术的技能，3)进一步发展营养和新陈代谢及其对人类生殖健康的影响方面的专业知识，4)学习临床和翻译需求分析和验证方法的方法，以及5)发展成为一名成功的独立研究科学家所需的领导和职业技能。