Factors such as increasing mental pressure and poor living habits in modern society have led to an increase in the incidence of male reproductive diseases, including poor semen quality, testicular malignancy, and congenital developmental defects. The decline of male fertility deserves our attention. Resveratrol (3,4′, 5-trihydroxy-trans-Stilbene, 3,4′,5-trihydroxy), a polyphenol widely found in plant foods, is expected to enhance testicular function and promote breakthroughs in the treatment of diseases related to the male reproductive system. A large number of studies have shown that in male animals, resveratrol can enhance testicular function and spermatogenesis by activating SIRT1 expression and resist the damage of the testicular system by adverse factors. This article reviews the basic protective pathways of resveratrol against testicular and sperm damage, which involve oxidative stress, cell apoptosis, inflammatory damage, and mitochondrial function. The healthcare framework of predictive, preventive, and personalized medicine (PPPM/3PM) is by far the most beneficial for healthcare and is suitable for the management of chronic diseases. This review also summarizes the health benefits of resveratrol on male reproduction in the context of PPPM/3PM by comprehensively collecting and reviewing the available evidence, thus leading to a working hypothesis that resveratrol can personalize prevention and protection of male reproductive function. It provides a new perspective and direction for future research on the health effects of resveratrol in improving male reproductive function.
{"title":"Resveratrol: potential application in safeguarding testicular health","authors":"Xu Zhang, Ruhan Yi, Yun Liu, Jiaxuan Ma, Jiawei Xu, Qing Tian, Xinyu Yan, Shaopeng Wang, Guang Yang","doi":"10.1007/s13167-024-00377-1","DOIUrl":"https://doi.org/10.1007/s13167-024-00377-1","url":null,"abstract":"<p>Factors such as increasing mental pressure and poor living habits in modern society have led to an increase in the incidence of male reproductive diseases, including poor semen quality, testicular malignancy, and congenital developmental defects. The decline of male fertility deserves our attention. Resveratrol (3,4′, 5-trihydroxy-trans-Stilbene, 3,4′,5-trihydroxy), a polyphenol widely found in plant foods, is expected to enhance testicular function and promote breakthroughs in the treatment of diseases related to the male reproductive system. A large number of studies have shown that in male animals, resveratrol can enhance testicular function and spermatogenesis by activating SIRT1 expression and resist the damage of the testicular system by adverse factors. This article reviews the basic protective pathways of resveratrol against testicular and sperm damage, which involve oxidative stress, cell apoptosis, inflammatory damage, and mitochondrial function. The healthcare framework of predictive, preventive, and personalized medicine (PPPM/3PM) is by far the most beneficial for healthcare and is suitable for the management of chronic diseases. This review also summarizes the health benefits of resveratrol on male reproduction in the context of PPPM/3PM by comprehensively collecting and reviewing the available evidence, thus leading to a working hypothesis that resveratrol can personalize prevention and protection of male reproductive function. It provides a new perspective and direction for future research on the health effects of resveratrol in improving male reproductive function.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":"44 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1007/s13167-024-00378-0
Joon Yul Choi, Eoksoo Han, Tae Keun Yoo
<h3 data-test="abstract-sub-heading">Background</h3><p>Oculomics is an emerging medical field that focuses on the study of the eye to detect and understand systemic diseases. ChatGPT-4 is a highly advanced AI model with multimodal capabilities, allowing it to process text and statistical data. Osteoporosis is a chronic condition presenting asymptomatically but leading to fractures if untreated. Current diagnostic methods like dual X-ray absorptiometry (DXA) are costly and involve radiation exposure. This study aims to develop a cost-effective osteoporosis risk prediction tool using ophthalmological data and ChatGPT-4 based on oculomics, aligning with predictive, preventive, and personalized medicine (3PM) principles.</p><h3 data-test="abstract-sub-heading">Working hypothesis and methods</h3><p>We hypothesize that leveraging ophthalmological data (oculomics) combined with AI-driven regression models developed by ChatGPT-4 can significantly improve the predictive accuracy for osteoporosis risk. This integration will facilitate earlier detection, enable more effective preventive strategies, and support personalized treatment plans tailored to individual patients. We utilized DXA and ophthalmological data from the Korea National Health and Nutrition Examination Survey to develop and validate osteopenia and osteoporosis prediction models. Ophthalmological and demographic data were integrated into logistic regression analyses, facilitated by ChatGPT-4, to create prediction formulas. These models were then converted into calculator software through automated coding by ChatGPT-4.</p><h3 data-test="abstract-sub-heading">Results</h3><p>ChatGPT-4 automatically developed prediction models based on key predictors of osteoporosis and osteopenia included age, gender, weight, and specific ophthalmological conditions such as cataracts and early age-related macular degeneration, and successfully implemented a risk calculator tool. The oculomics-based models outperformed traditional methods, with area under the curve of the receiver operating characteristic values of 0.785 for osteopenia and 0.866 for osteoporosis in the validation set. The calculator demonstrated high sensitivity and specificity, providing a reliable tool for early osteoporosis screening.</p><h3 data-test="abstract-sub-heading">Conclusions and expert recommendations in the framework of 3PM</h3><p>This study illustrates the value of integrating ophthalmological data into multi-level diagnostics for osteoporosis, significantly improving the accuracy of health risk assessment and the identification of at-risk individuals. Aligned with the principles of 3PM, this approach fosters earlier detection and enables the development of individualized patient profiles, facilitating personalized and targeted treatment strategies. This study also highlights the potential of AI, specifically ChatGPT-4, in developing accessible, cost-effective, and radiation-free screening tools for advancing 3PM in clinical pract
{"title":"Application of ChatGPT-4 to oculomics: a cost-effective osteoporosis risk assessment to enhance management as a proof-of-principles model in 3PM","authors":"Joon Yul Choi, Eoksoo Han, Tae Keun Yoo","doi":"10.1007/s13167-024-00378-0","DOIUrl":"https://doi.org/10.1007/s13167-024-00378-0","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Oculomics is an emerging medical field that focuses on the study of the eye to detect and understand systemic diseases. ChatGPT-4 is a highly advanced AI model with multimodal capabilities, allowing it to process text and statistical data. Osteoporosis is a chronic condition presenting asymptomatically but leading to fractures if untreated. Current diagnostic methods like dual X-ray absorptiometry (DXA) are costly and involve radiation exposure. This study aims to develop a cost-effective osteoporosis risk prediction tool using ophthalmological data and ChatGPT-4 based on oculomics, aligning with predictive, preventive, and personalized medicine (3PM) principles.</p><h3 data-test=\"abstract-sub-heading\">Working hypothesis and methods</h3><p>We hypothesize that leveraging ophthalmological data (oculomics) combined with AI-driven regression models developed by ChatGPT-4 can significantly improve the predictive accuracy for osteoporosis risk. This integration will facilitate earlier detection, enable more effective preventive strategies, and support personalized treatment plans tailored to individual patients. We utilized DXA and ophthalmological data from the Korea National Health and Nutrition Examination Survey to develop and validate osteopenia and osteoporosis prediction models. Ophthalmological and demographic data were integrated into logistic regression analyses, facilitated by ChatGPT-4, to create prediction formulas. These models were then converted into calculator software through automated coding by ChatGPT-4.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>ChatGPT-4 automatically developed prediction models based on key predictors of osteoporosis and osteopenia included age, gender, weight, and specific ophthalmological conditions such as cataracts and early age-related macular degeneration, and successfully implemented a risk calculator tool. The oculomics-based models outperformed traditional methods, with area under the curve of the receiver operating characteristic values of 0.785 for osteopenia and 0.866 for osteoporosis in the validation set. The calculator demonstrated high sensitivity and specificity, providing a reliable tool for early osteoporosis screening.</p><h3 data-test=\"abstract-sub-heading\">Conclusions and expert recommendations in the framework of 3PM</h3><p>This study illustrates the value of integrating ophthalmological data into multi-level diagnostics for osteoporosis, significantly improving the accuracy of health risk assessment and the identification of at-risk individuals. Aligned with the principles of 3PM, this approach fosters earlier detection and enables the development of individualized patient profiles, facilitating personalized and targeted treatment strategies. This study also highlights the potential of AI, specifically ChatGPT-4, in developing accessible, cost-effective, and radiation-free screening tools for advancing 3PM in clinical pract","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":"18 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Suboptimal Health Status (SHS) is the physical state between health and disease. This study aimed to fill in the knowledge gap by investigating the prevalence of SHS and psychological symptoms among unpaid carers and to identify SHS-risk factors from the perspective of predictive, preventive and personalised medicine (PPPM).
Methods
A cross-sectional study was conducted among 368 participants who were enrolled from Australia, including 203 unpaid carers as cases and 165 individuals from the general population as controls. SHS scores were measured using SHSQ-25 (Suboptimal Health Status Questionnaire-25), whilst psychological symptoms were measured by DASS-21 (Depression, Anxiety and Stress Scale-21). Chi-square was used to measure SHS and psychological symptom prevalence. Spearman correlation analysis was utilised to identify the relationship between SHSQ-25 and DASS-21 scores. Logistic regression analysis was used for multivariate analysis.
Results
The prevalence of SHS in carers was 43.0% (98/203), significantly higher than the prevalence 12.7% (21/165) in the general population (p < 0.001). In addition, suboptimal health prevalence was higher in female carers (50.3%; 95/189) than females in the general population (12.4%; 18/145). Logistic regression showed that the caregiving role influenced SHS, with carers 6.4 times more likely to suffer from SHS than their non-caring counterparts (aOR = 6.400, 95% CI = 3.751–10.919).
Conclusions
Unpaid carers in Australia have a significantly higher prevalence of SHS than that in the general population and experience poorer health. The SHSQ-25 is a powerful tool that can be utilised to screen at-risk individuals to predict their risk of chronic disease development, an essential pillar for shifting the paradigm change from reactive medicine to that of predictive, preventive and personalised medicine (PPPM).
背景最佳健康状况（SHS）是介于健康与疾病之间的一种生理状态。本研究旨在通过调查无酬照护者中SHS和心理症状的发生率来填补知识空白，并从预测、预防和个性化医疗（PPPM）的角度来确定SHS的风险因素。方法本研究对来自澳大利亚的368名参与者进行了横断面研究，其中203名无酬照护者为病例，165名普通人群为对照。SHS评分采用SHSQ-25（Suboptimal Health Status Questionnaire-25）测量，心理症状采用DASS-21（Depression, Anxiety and Stress Scale-21）测量。SHS和心理症状患病率的测量采用了卡方检验法（Chi-square）。斯皮尔曼相关分析用于确定 SHSQ-25 和 DASS-21 分数之间的关系。结果护理人员的 SHS 患病率为 43.0%（98/203），明显高于普通人群的患病率 12.7%（21/165）（p <0.001）。此外，女性照顾者的亚健康患病率（50.3%；95/189）高于普通人群中的女性患病率（12.4%；18/145）。逻辑回归显示，照顾者的角色影响了SHS，照顾者患SHS的可能性是非照顾者的6.4倍（aOR = 6.400，95% CI = 3.751-10.919）。SHSQ-25是一种强大的工具，可用于筛查高危人群，预测他们患慢性疾病的风险，这也是将医学模式从反应性医学转变为预测性、预防性和个性化医学（PPPM）的重要支柱。
{"title":"The caregiving role influences Suboptimal Health Status and psychological symptoms in unpaid carers","authors":"Monique Garcia, Zheng Guo, Yulu Zheng, Zhiyuan Wu, Ethan Visser, Lois Balmer, Wei Wang","doi":"10.1007/s13167-024-00370-8","DOIUrl":"https://doi.org/10.1007/s13167-024-00370-8","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Suboptimal Health Status (SHS) is the physical state between health and disease. This study aimed to fill in the knowledge gap by investigating the prevalence of SHS and psychological symptoms among unpaid carers and to identify SHS-risk factors from the perspective of predictive, preventive and personalised medicine (PPPM).</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>A cross-sectional study was conducted among 368 participants who were enrolled from Australia, including 203 unpaid carers as cases and 165 individuals from the general population as controls. SHS scores were measured using SHSQ-25 (Suboptimal Health Status Questionnaire-25), whilst psychological symptoms were measured by DASS-21 (Depression, Anxiety and Stress Scale-21). Chi-square was used to measure SHS and psychological symptom prevalence. Spearman correlation analysis was utilised to identify the relationship between SHSQ-25 and DASS-21 scores. Logistic regression analysis was used for multivariate analysis.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The prevalence of SHS in carers was 43.0% (98/203), significantly higher than the prevalence 12.7% (21/165) in the general population (<i>p</i> < 0.001). In addition, suboptimal health prevalence was higher in female carers (50.3%; 95/189) than females in the general population (12.4%; 18/145). Logistic regression showed that the caregiving role influenced SHS, with carers 6.4 times more likely to suffer from SHS than their non-caring counterparts (aOR = 6.400, 95% CI = 3.751–10.919).</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>Unpaid carers in Australia have a significantly higher prevalence of SHS than that in the general population and experience poorer health. The SHSQ-25 is a powerful tool that can be utilised to screen at-risk individuals to predict their risk of chronic disease development, an essential pillar for shifting the paradigm change from reactive medicine to that of predictive, preventive and personalised medicine (PPPM).</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":"1402 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141867061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<h3 data-test="abstract-sub-heading">Background</h3><p>Glaucoma is the leading cause of irreversible blindness worldwide. Normal tension glaucoma (NTG) is a distinct subtype characterized by intraocular pressures (IOP) within the normal range (< 21 mm Hg). Due to its insidious onset and optic nerve damage, patients often present with advanced conditions upon diagnosis. NTG poses an additional challenge as it is difficult to identify with normal IOP, complicating its prediction, prevention, and treatment. Observational studies suggest a potential association between NTG and abnormal lipid metabolism, yet conclusive evidence establishing a direct causal relationship is lacking. This study aims to explore the causal link between serum lipids and NTG, while identifying lipid-related therapeutic targets. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of dyslipidemia in the development of NTG could provide a new strategy for primary prediction, targeted prevention, and personalized treatment of the disease.</p><h3 data-test="abstract-sub-heading">Working hypothesis and methods</h3><p>In our study, we hypothesized that individuals with dyslipidemia may be more susceptible to NTG due to a dysregulation of microvasculature in optic nerve head. To verify the working hypothesis, univariable Mendelian randomization (UVMR) and multivariable Mendelian randomization (MVMR) were utilized to estimate the causal effects of lipid traits on NTG. Drug target MR was used to explore possible target genes for NTG treatment. Genetic variants associated with lipid traits and variants of genes encoding seven lipid-related drug targets were extracted from the Global Lipids Genetics Consortium genome-wide association study (GWAS). GWAS data for NTG, primary open angle glaucoma (POAG), and suspected glaucoma (GLAUSUSP) were obtained from FinnGen Consortium. For apolipoproteins, we used summary statistics from a GWAS study by Kettunen et al. in 2016. For metabolic syndrome, summary statistics were extracted from UK Biobank participants. In the end, these findings could help identify individuals at risk of NTG by screening for lipid dyslipidemia, potentially leading to new targeted prevention and personalized treatment approaches.</p><h3 data-test="abstract-sub-heading">Results</h3><p>Genetically assessed high-density cholesterol (HDL) was negatively associated with NTG risk (inverse-variance weighted [IVW] model: OR per SD change of HDL level = 0.64; 95% CI, 0.49–0.85; <i>P</i> = 1.84 × 10<sup>−3</sup>), and the causal effect was independent of apolipoproteins and metabolic syndrome (IVW model: OR = 0.29; 95% CI, 0.14–0.60; <i>P</i> = 0.001 adjusted by ApoB and ApoA1; OR = 0.70; 95% CI, 0.52–0.95; <i>P</i> = 0.023 adjusted by BMI, HTN, and T2DM). Triglyceride (TG) was positively associated with NTG risk (IVW model: OR = 1.62; 95% CI, 1.15–2.29; <i>P</i> = 6.31 × 10<sup>−3</sup>), and the causal effect was independent of
{"title":"Genetic association of lipid traits and lipid-related drug targets with normal tension glaucoma: a Mendelian randomization study for predictive preventive and personalized medicine","authors":"Tianyi Kang, Yi Zhou, Cong Fan, Yue Zhang, Yu Yang, Jian Jiang","doi":"10.1007/s13167-024-00373-5","DOIUrl":"https://doi.org/10.1007/s13167-024-00373-5","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Glaucoma is the leading cause of irreversible blindness worldwide. Normal tension glaucoma (NTG) is a distinct subtype characterized by intraocular pressures (IOP) within the normal range (< 21 mm Hg). Due to its insidious onset and optic nerve damage, patients often present with advanced conditions upon diagnosis. NTG poses an additional challenge as it is difficult to identify with normal IOP, complicating its prediction, prevention, and treatment. Observational studies suggest a potential association between NTG and abnormal lipid metabolism, yet conclusive evidence establishing a direct causal relationship is lacking. This study aims to explore the causal link between serum lipids and NTG, while identifying lipid-related therapeutic targets. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of dyslipidemia in the development of NTG could provide a new strategy for primary prediction, targeted prevention, and personalized treatment of the disease.</p><h3 data-test=\"abstract-sub-heading\">Working hypothesis and methods</h3><p>In our study, we hypothesized that individuals with dyslipidemia may be more susceptible to NTG due to a dysregulation of microvasculature in optic nerve head. To verify the working hypothesis, univariable Mendelian randomization (UVMR) and multivariable Mendelian randomization (MVMR) were utilized to estimate the causal effects of lipid traits on NTG. Drug target MR was used to explore possible target genes for NTG treatment. Genetic variants associated with lipid traits and variants of genes encoding seven lipid-related drug targets were extracted from the Global Lipids Genetics Consortium genome-wide association study (GWAS). GWAS data for NTG, primary open angle glaucoma (POAG), and suspected glaucoma (GLAUSUSP) were obtained from FinnGen Consortium. For apolipoproteins, we used summary statistics from a GWAS study by Kettunen et al. in 2016. For metabolic syndrome, summary statistics were extracted from UK Biobank participants. In the end, these findings could help identify individuals at risk of NTG by screening for lipid dyslipidemia, potentially leading to new targeted prevention and personalized treatment approaches.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Genetically assessed high-density cholesterol (HDL) was negatively associated with NTG risk (inverse-variance weighted [IVW] model: OR per SD change of HDL level = 0.64; 95% CI, 0.49–0.85; <i>P</i> = 1.84 × 10<sup>−3</sup>), and the causal effect was independent of apolipoproteins and metabolic syndrome (IVW model: OR = 0.29; 95% CI, 0.14–0.60; <i>P</i> = 0.001 adjusted by ApoB and ApoA1; OR = 0.70; 95% CI, 0.52–0.95; <i>P</i> = 0.023 adjusted by BMI, HTN, and T2DM). Triglyceride (TG) was positively associated with NTG risk (IVW model: OR = 1.62; 95% CI, 1.15–2.29; <i>P</i> = 6.31 × 10<sup>−3</sup>), and the causal effect was independent of","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":"14 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ovarian cancer patients’ resistance to first-line treatment posed a significant challenge, with approximately 70% experiencing recurrence and developing strong resistance to first-line chemotherapies like paclitaxel.
Objectives
Within the framework of predictive, preventive, and personalized medicine (3PM), this study aimed to use artificial intelligence to find drug resistance characteristics at the single cell, and further construct the classification strategy and deep learning prognostic models based on these resistance traits, which can better facilitate and perform 3PM.
Methods
This study employed “Beyondcell,” an algorithm capable of predicting cellular drug responses, to calculate the similarity between the expression patterns of 21,937 cells from ovarian cancer samples and the signatures of 5201 drugs to identify drug-resistance cells. Drug resistance features were used to perform 10 multi-omics clustering on the TCGA training set to identify patient subgroups with differential drug responses. Concurrently, a deep learning prognostic model with KAN architecture which had a flexible activation function to better fit the model was constructed for this training set. The constructed patient subtype classifier and prognostic model were evaluated using three external validation sets from GEO: GSE17260, GSE26712, and GSE51088.
Results
This study identified that endothelial cells are resistant to paclitaxel, doxorubicin, and docetaxel, suggesting their potential as targets for cellular therapy in ovarian cancer patients. Based on drug resistance features, 10 multi-omics clustering identified four patient subtypes with differential responses to four chemotherapy drugs, in which subtype CS2 showed the highest drug sensitivity to all four drugs. The other subtypes also showed enrichment in different biological pathways and immune infiltration, allowing for targeted treatment based on their characteristics. Besides, this study applied the latest KAN architecture in artificial intelligence to replace the MLP structure in the DeepSurv prognostic model, finally demonstrating robust performance on patients’ prognosis prediction.
Conclusions
This study, by classifying patients and constructing prognostic models based on resistance characteristics to first-line drugs, has effectively applied multi-omics data into the realm of 3PM.
背景卵巢癌患者对一线治疗的耐药性是一项重大挑战，约70%的卵巢癌患者会出现复发，并对紫杉醇等一线化疗药物产生强烈的耐药性。目的在预测、预防和个性化医疗（3PM）的框架下，本研究旨在利用人工智能发现单细胞的耐药性特征，并根据这些耐药性特征进一步构建分类策略和深度学习预后模型，从而更好地促进和开展3PM。方法本研究采用了能够预测细胞药物反应的算法 "Beyondcell"，计算了21937个卵巢癌样本细胞的表达模式与5201种药物特征之间的相似性，从而识别出耐药细胞。利用耐药性特征对 TCGA 训练集进行了 10 次多组学聚类，以确定具有不同药物反应的患者亚群。同时，针对该训练集构建了一个具有 KAN 架构的深度学习预后模型，该模型具有灵活的激活函数，能更好地适应模型。所构建的患者亚型分类器和预后模型使用来自 GEO 的三个外部验证集进行了评估：结果这项研究发现内皮细胞对紫杉醇、多柔比星和多西他赛有耐药性，这表明它们有可能成为卵巢癌患者的细胞治疗靶点。根据耐药性特征，10个多组学聚类分析确定了对四种化疗药物反应不同的四种患者亚型，其中CS2亚型对所有四种药物的药物敏感性最高。其他亚型在不同生物通路和免疫浸润方面也表现出富集性，可根据其特点进行靶向治疗。此外，本研究还应用了人工智能领域最新的 KAN 架构，取代了 DeepSurv 预后模型中的 MLP 结构，最终在患者预后预测方面表现出了强劲的性能。 结论 本研究通过对患者进行分类，并根据一线药物的耐药特征构建预后模型，有效地将多组学数据应用到了 3PM 领域。
{"title":"Artificial intelligence in ovarian cancer drug resistance advanced 3PM approach: subtype classification and prognostic modeling","authors":"Cong Zhang, Jinxiang Yang, Siyu Chen, Lichang Sun, Kangjie Li, Guichuan Lai, Bin Peng, Xiaoni Zhong, Biao Xie","doi":"10.1007/s13167-024-00374-4","DOIUrl":"https://doi.org/10.1007/s13167-024-00374-4","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Ovarian cancer patients’ resistance to first-line treatment posed a significant challenge, with approximately 70% experiencing recurrence and developing strong resistance to first-line chemotherapies like paclitaxel.</p><h3 data-test=\"abstract-sub-heading\">Objectives</h3><p>Within the framework of predictive, preventive, and personalized medicine (3PM), this study aimed to use artificial intelligence to find drug resistance characteristics at the single cell, and further construct the classification strategy and deep learning prognostic models based on these resistance traits, which can better facilitate and perform 3PM.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This study employed “Beyondcell,” an algorithm capable of predicting cellular drug responses, to calculate the similarity between the expression patterns of 21,937 cells from ovarian cancer samples and the signatures of 5201 drugs to identify drug-resistance cells. Drug resistance features were used to perform 10 multi-omics clustering on the TCGA training set to identify patient subgroups with differential drug responses. Concurrently, a deep learning prognostic model with KAN architecture which had a flexible activation function to better fit the model was constructed for this training set. The constructed patient subtype classifier and prognostic model were evaluated using three external validation sets from GEO: GSE17260, GSE26712, and GSE51088.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>This study identified that endothelial cells are resistant to paclitaxel, doxorubicin, and docetaxel, suggesting their potential as targets for cellular therapy in ovarian cancer patients. Based on drug resistance features, 10 multi-omics clustering identified four patient subtypes with differential responses to four chemotherapy drugs, in which subtype CS2 showed the highest drug sensitivity to all four drugs. The other subtypes also showed enrichment in different biological pathways and immune infiltration, allowing for targeted treatment based on their characteristics. Besides, this study applied the latest KAN architecture in artificial intelligence to replace the MLP structure in the DeepSurv prognostic model, finally demonstrating robust performance on patients’ prognosis prediction.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>This study, by classifying patients and constructing prognostic models based on resistance characteristics to first-line drugs, has effectively applied multi-omics data into the realm of 3PM.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":"36 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Clinical data are essential for developing cloud platforms for intelligent diagnosis and treatment decision of diseases. However, cloud platforms for data sharing and exchange with clinicians are poorly suited. We aim to establish Eyecare-cloud, a platform which provide a novel method for clinical data and medical image sharing, to provide a convenient tool for clinicians.
Methods
In this study, we displayed the main functions of Eyecare-cloud that we established. Based on clinical data from the cloud platform, we analyzed the incidence trend of the most common infantile retinal diseases, such as retinopathy of prematurity (ROP), over the past 20 years, as well as the associated risk factors for ROP occurrence. Statistical analyses were performed using GraphPad Prism (V.8.0) and SPSS software (V.26.0).
Results
The Eyecare-cloud offers numerous advantages, including systematic archiving of patient information, one-click export data, simplifying data collection and management, eliminating the need for manual input of clinical information, reducing clinical data migration time, and lowering data management costs significantly. A total of 22,913 premature infants from Eyecare-cloud were included in the data analysis. Based on 20 years of premature infant screening data analysis, we found that the ROP incidence began to slowly decline starting in 2003 but showed a gradual increase trend again in 2016. The incidence of severe ROP remained relatively stable at a low level since 2010. The number of premature infants increased steadily before 2016 but decreased since then. ROP occurrence was significantly associated with male sex, lower gestational age, and lower birth weight (P < 0.001).
Conclusion
Eyecare-cloud provides clinicians and researchers with convenient tools for big data analysis, which helps alleviate clinical workloads and integrate research data. This cloud platform supports the principles of predictive, preventive, and personalized medicine (PPPM/3PM), empowering clinicians and researchers to deliver more precise, proactive, and patient-centered eye care.
{"title":"Eyecare-cloud: an innovative electronic medical record cloud platform for pediatric research and clinical care","authors":"Xinyu Zhao, Zhenquan Wu, Yaling Liu, Honglang Zhang, Yarou Hu, Duo Yuan, Xiayuan Luo, Mianying Zheng, Zhen Yu, Dahui Ma, Guoming Zhang","doi":"10.1007/s13167-024-00372-6","DOIUrl":"https://doi.org/10.1007/s13167-024-00372-6","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background and objectives</h3><p>Clinical data are essential for developing cloud platforms for intelligent diagnosis and treatment decision of diseases. However, cloud platforms for data sharing and exchange with clinicians are poorly suited. We aim to establish Eyecare-cloud, a platform which provide a novel method for clinical data and medical image sharing, to provide a convenient tool for clinicians.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>In this study, we displayed the main functions of Eyecare-cloud that we established. Based on clinical data from the cloud platform, we analyzed the incidence trend of the most common infantile retinal diseases, such as retinopathy of prematurity (ROP), over the past 20 years, as well as the associated risk factors for ROP occurrence. Statistical analyses were performed using GraphPad Prism (V.8.0) and SPSS software (V.26.0).</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The Eyecare-cloud offers numerous advantages, including systematic archiving of patient information, one-click export data, simplifying data collection and management, eliminating the need for manual input of clinical information, reducing clinical data migration time, and lowering data management costs significantly. A total of 22,913 premature infants from Eyecare-cloud were included in the data analysis. Based on 20 years of premature infant screening data analysis, we found that the ROP incidence began to slowly decline starting in 2003 but showed a gradual increase trend again in 2016. The incidence of severe ROP remained relatively stable at a low level since 2010. The number of premature infants increased steadily before 2016 but decreased since then. ROP occurrence was significantly associated with male sex, lower gestational age, and lower birth weight (<i>P</i> < 0.001).</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Eyecare-cloud provides clinicians and researchers with convenient tools for big data analysis, which helps alleviate clinical workloads and integrate research data. This cloud platform supports the principles of predictive, preventive, and personalized medicine (PPPM/3PM), empowering clinicians and researchers to deliver more precise, proactive, and patient-centered eye care.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":"20 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-28DOI: 10.1007/s13167-024-00368-2
Tamara Raschka, Zexin Li, Heiko Gaßner, Zacharias Kohl, Jelena Jukic, Franz Marxreiter, Holger Fröhlich
<h3 data-test="abstract-sub-heading">Background</h3><p>Huntington’s disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient’s quality of life. Despite this clear genetic course, high variability of HD patients’ symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care.</p><h3 data-test="abstract-sub-heading">Methods</h3><p>Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits.</p><h3 data-test="abstract-sub-heading">Results</h3><p>Results demonstrate two distinct subtypes, one large cluster (<i>n</i> = 7122) showing a relative stable disease progression and a second, smaller cluster (<i>n</i> = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients’ first visit only.</p><h3 data-test="abstract-sub-heading">Conclusion</h3><p>In summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients’ disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals’ treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. T
背景亨廷顿氏病（HD）是一种进行性神经退行性疾病，由亨廷顿基因中的 CAG 三核苷酸扩增引起。CAG重复的长度与疾病的发病成反比。HD 的特征是运动功能亢进、精神症状和认知障碍，这极大地影响了患者的生活质量。尽管有明确的遗传过程，但仍可观察到 HD 患者症状的高度变异性。目前对 HD 的临床诊断仅仅依赖于运动症状的出现，而忽略了疾病的其他重要方面。通过采用一种涵盖 HD 运动和非运动方面的更广泛的方法，预测、预防和个性化（3P）医学可提高诊断准确性并改善患者护理。方法首先将从 Enroll-HD 研究中收集的 HD 患者的多症状疾病轨迹按照共同的疾病时间尺度进行排列，以考虑疾病症状发作和诊断的异质性。然后，使用之前发布的变异深度嵌入与复发（VaDER）算法对对齐后的疾病轨迹进行聚类，并对由此产生的进展亚型进行临床特征描述。最后，我们学习了一个人工智能/ML 模型，以根据首次就诊数据或其他随访数据预测疾病进展亚型。结果结果显示了两种不同的亚型，一个大型群组（n = 7122）显示出相对稳定的疾病进展，而第二个较小的群组（n = 411）则显示出显著进展的疾病轨迹。这两种亚型的临床特征与 CAG 重复长度以及一些神经行为、精神和认知评分相关。事实上，认知障碍是两种亚型的主要区别。总之，本研究旨在实现从反应性医学到预防性和个性化医学的范式转变，表明非运动症状对于预测和分类每位患者的疾病进展模式至关重要，因为认知能力下降往往比运动能力下降更能反映 HD 的进展情况。在咨询和治疗定义时考虑到这些方面将使每个人的治疗个性化。为患者提供疾病进展的客观评估，从而为他们的 HD 生活提供一个视角，是提高他们生活质量的关键。通过对两种亚型的生物数据进行更多分析，有可能对这些亚型有更深入的了解，并发现疾病的潜在生物因素。这在很大程度上符合向 3P 医学转变的目标。
{"title":"Unraveling progression subtypes in people with Huntington’s disease","authors":"Tamara Raschka, Zexin Li, Heiko Gaßner, Zacharias Kohl, Jelena Jukic, Franz Marxreiter, Holger Fröhlich","doi":"10.1007/s13167-024-00368-2","DOIUrl":"https://doi.org/10.1007/s13167-024-00368-2","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Huntington’s disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient’s quality of life. Despite this clear genetic course, high variability of HD patients’ symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Results demonstrate two distinct subtypes, one large cluster (<i>n</i> = 7122) showing a relative stable disease progression and a second, smaller cluster (<i>n</i> = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients’ first visit only.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>In summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients’ disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals’ treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. T","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":"38 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141165624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-27DOI: 10.1007/s13167-024-00367-3
Wenqian Xu, Tianchuang Yang, Jinyuan Zhang, Heguo Li, Min Guo
A natural “medicine and food” plant, Rhodiola rosea (RR) is primarily made up of organic acids, phenolic compounds, sterols, glycosides, vitamins, lipids, proteins, amino acids, trace elements, and other physiologically active substances. In vitro, non-clinical and clinical studies confirmed that it exerts anti-inflammatory, antioxidant, and immune regulatory effects, balances the gut microbiota, and alleviates vascular circulatory disorders. RR can prolong life and has great application potential in preventing and treating suboptimal health, non-communicable diseases, and COVID-19. This narrative review discusses the effects of RR in preventing organ damage (such as the liver, lung, heart, brain, kidneys, intestines, and blood vessels) in non-communicable diseases from the perspective of predictive, preventive, and personalised medicine (PPPM/3PM). In conclusion, as an adaptogen, RR can provide personalised health strategies to improve the quality of life and overall health status.
{"title":"Rhodiola rosea: a review in the context of PPPM approach","authors":"Wenqian Xu, Tianchuang Yang, Jinyuan Zhang, Heguo Li, Min Guo","doi":"10.1007/s13167-024-00367-3","DOIUrl":"https://doi.org/10.1007/s13167-024-00367-3","url":null,"abstract":"<p>A natural “medicine and food” plant, <i>Rhodiola rosea</i> (RR) is primarily made up of organic acids, phenolic compounds, sterols, glycosides, vitamins, lipids, proteins, amino acids, trace elements, and other physiologically active substances. In vitro, non-clinical and clinical studies confirmed that it exerts anti-inflammatory, antioxidant, and immune regulatory effects, balances the gut microbiota, and alleviates vascular circulatory disorders. RR can prolong life and has great application potential in preventing and treating suboptimal health, non-communicable diseases, and COVID-19. This narrative review discusses the effects of RR in preventing organ damage (such as the liver, lung, heart, brain, kidneys, intestines, and blood vessels) in non-communicable diseases from the perspective of predictive, preventive, and personalised medicine (PPPM/3PM). In conclusion, as an adaptogen, RR can provide personalised health strategies to improve the quality of life and overall health status.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":"26 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141165461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-11DOI: 10.1007/s13167-024-00364-6
Ivica Smokovski, Nanette Steinle, Andrew Behnke, Sonu M. M. Bhaskar, Godfrey Grech, Kneginja Richter, Günter Niklewski, Colin Birkenbihl, Paolo Parini, Russell J. Andrews, Howard Bauchner, Olga Golubnitschaja
Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide.
Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs.
Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large.
DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs.
{"title":"Digital biomarkers: 3PM approach revolutionizing chronic disease management — EPMA 2024 position","authors":"Ivica Smokovski, Nanette Steinle, Andrew Behnke, Sonu M. M. Bhaskar, Godfrey Grech, Kneginja Richter, Günter Niklewski, Colin Birkenbihl, Paolo Parini, Russell J. Andrews, Howard Bauchner, Olga Golubnitschaja","doi":"10.1007/s13167-024-00364-6","DOIUrl":"https://doi.org/10.1007/s13167-024-00364-6","url":null,"abstract":"<p>Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide.</p><p>Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs.</p><p>Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large.</p><p>DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":"42 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140930545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-10DOI: 10.1007/s13167-024-00365-5
Lai Kun Tong, Yue Yi Li, Yong Bing Liu, Mu Rui Zheng, Guang Lei Fu, Mio Leng Au
<h3 data-test="abstract-sub-heading">Background</h3><p>Suboptimal health is identified as a reversible phase occurring before chronic diseases manifest, emphasizing the significance of early detection and intervention in predictive, preventive, and personalized medicine (PPPM/3PM). While the biological and genetic factors associated with suboptimal health have received considerable attention, the influence of social determinants of health (SDH) remains relatively understudied. By comprehensively understanding the SDH influencing suboptimal health, healthcare providers can tailor interventions to address individual needs, improving health outcomes and facilitating the transition to optimal well-being. This study aimed to identify distinct profiles within SDH indicators and examine their association with suboptimal health status.</p><h3 data-test="abstract-sub-heading">Method</h3><p>This cross-sectional study was conducted from June 16 to September 23, 2023, in five regions of China. Various SDH indicators, such as family health, economic status, eHealth literacy, mental disorder, social support, health behavior, and sleep quality, were examined in this study. Latent profile analysis was employed to identify distinct profiles based on these SDH indicators. Logistic regression analysis by profile was used to investigate the association between these profiles and suboptimal health status.</p><h3 data-test="abstract-sub-heading">Results</h3><p>The analysis included 4918 individuals. Latent profile analysis revealed three distinct profiles (prevalence): the Adversely Burdened Vulnerability Group (37.6%), the Adversity-Driven Struggle Group (11.7%), and the Advantaged Resilience Group (50.7%). These profiles exhibited significant differences in suboptimal health status (<i>p</i> < 0.001). The Adversely Burdened Vulnerability Group had the highest risk of suboptimal health, followed by the Adversity-Driven Struggle Group, while the Advantaged Resilience Group had the lowest risk.</p><h3 data-test="abstract-sub-heading">Conclusions and relevance</h3><p>Distinct profiles based on SDH indicators are associated with suboptimal health status. Healthcare providers should integrate SDH assessment into routine clinical practice to customize interventions and address specific needs. This study reveals that the group with the highest risk of suboptimal health stands out as the youngest among all the groups, underscoring the critical importance of early intervention and targeted prevention strategies within the framework of 3PM. Tailored interventions for the Adversely Burdened Vulnerability Group should focus on economic opportunities, healthcare access, healthy food options, and social support. Leveraging their higher eHealth literacy and resourcefulness, interventions empower the Adversity-Driven Struggle Group. By addressing healthcare utilization, substance use, and social support, targeted interventions effectively reduce suboptimal health risks and improv
{"title":"Social determinants of health and their relation to suboptimal health status in the context of 3PM: a latent profile analysis","authors":"Lai Kun Tong, Yue Yi Li, Yong Bing Liu, Mu Rui Zheng, Guang Lei Fu, Mio Leng Au","doi":"10.1007/s13167-024-00365-5","DOIUrl":"https://doi.org/10.1007/s13167-024-00365-5","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Suboptimal health is identified as a reversible phase occurring before chronic diseases manifest, emphasizing the significance of early detection and intervention in predictive, preventive, and personalized medicine (PPPM/3PM). While the biological and genetic factors associated with suboptimal health have received considerable attention, the influence of social determinants of health (SDH) remains relatively understudied. By comprehensively understanding the SDH influencing suboptimal health, healthcare providers can tailor interventions to address individual needs, improving health outcomes and facilitating the transition to optimal well-being. This study aimed to identify distinct profiles within SDH indicators and examine their association with suboptimal health status.</p><h3 data-test=\"abstract-sub-heading\">Method</h3><p>This cross-sectional study was conducted from June 16 to September 23, 2023, in five regions of China. Various SDH indicators, such as family health, economic status, eHealth literacy, mental disorder, social support, health behavior, and sleep quality, were examined in this study. Latent profile analysis was employed to identify distinct profiles based on these SDH indicators. Logistic regression analysis by profile was used to investigate the association between these profiles and suboptimal health status.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The analysis included 4918 individuals. Latent profile analysis revealed three distinct profiles (prevalence): the Adversely Burdened Vulnerability Group (37.6%), the Adversity-Driven Struggle Group (11.7%), and the Advantaged Resilience Group (50.7%). These profiles exhibited significant differences in suboptimal health status (<i>p</i> < 0.001). The Adversely Burdened Vulnerability Group had the highest risk of suboptimal health, followed by the Adversity-Driven Struggle Group, while the Advantaged Resilience Group had the lowest risk.</p><h3 data-test=\"abstract-sub-heading\">Conclusions and relevance</h3><p>Distinct profiles based on SDH indicators are associated with suboptimal health status. Healthcare providers should integrate SDH assessment into routine clinical practice to customize interventions and address specific needs. This study reveals that the group with the highest risk of suboptimal health stands out as the youngest among all the groups, underscoring the critical importance of early intervention and targeted prevention strategies within the framework of 3PM. Tailored interventions for the Adversely Burdened Vulnerability Group should focus on economic opportunities, healthcare access, healthy food options, and social support. Leveraging their higher eHealth literacy and resourcefulness, interventions empower the Adversity-Driven Struggle Group. By addressing healthcare utilization, substance use, and social support, targeted interventions effectively reduce suboptimal health risks and improv","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":"58 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140930205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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