{"title":"Guide for Authors","authors":"","doi":"10.1002/cdt3.70029","DOIUrl":"https://doi.org/10.1002/cdt3.70029","url":null,"abstract":"","PeriodicalId":32096,"journal":{"name":"Chronic Diseases and Translational Medicine","volume":"11 4","pages":"323-330"},"PeriodicalIF":0.0,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cdt3.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abdirahman Omer Ali, Yusuf Abdi Hared, Hodo Abdi Abdillahi, Muhyadin Yusuf Dahir, Abdisalam Mahdi Hassan, Md. Moyazzem Hossain
Background
Chronic diseases represent a growing public health challenge globally, particularly in developing regions like Sub-Saharan Africa. Somaliland faces a dual burden of communicable and non-communicable diseases amidst post-conflict recovery, yet data on non-communicable disease (NCD) prevalence and determinants remain scarce. This study aimed to ascertain the prevalence and identify socio-demographic factors associated with self-reported chronic diseases among adults in Somaliland.
Methods
This cross-sectional study utilized data from the Somaliland Health and Demographic Survey (SLHDS) 2020. The final sample of 11,153 adults had a highly skewed age distribution (94.6% aged 35–44), a primary limitation of this analysis. The outcome was a self-reported physician diagnosis of any chronic disease. A multilevel mixed-effect logistic regression model was employed to identify significant determinants while adjusting for confounders.
Results
The analysis identified several factors associated with reporting a chronic disease. Higher odds were observed among female-headed households (adjusted odds ratio [AOR]: 1.26; 95% CI: 1.06–1.48) and divorced individuals (AOR: 1.92; 95% CI: 1.55–2.37). Conversely, lower odds were associated with higher education (AOR: 0.53; 95% CI: 0.34–0.82) and nomadic residence (AOR: 0.34; 95% CI: 0.22–0.51). Unexpectedly, lack of electricity and no savings were also associated with lower odds, likely reflecting diagnostic access bias and reverse causation.
Conclusion
Findings suggest that self-reported chronic diseases are associated with specific socio-demographic vulnerabilities in Somaliland. The results should be interpreted with extreme caution and viewed as hypothesis-generating at best. Future research using objective measures and representative sampling is urgently needed to validate these associations and accurately quantify the NCD burden.
{"title":"Prevalence and Associated Factors of Chronic Diseases Among Adults in Somaliland: A Multilevel Analysis From the 2020 SLHDS","authors":"Abdirahman Omer Ali, Yusuf Abdi Hared, Hodo Abdi Abdillahi, Muhyadin Yusuf Dahir, Abdisalam Mahdi Hassan, Md. Moyazzem Hossain","doi":"10.1002/cdt3.70027","DOIUrl":"https://doi.org/10.1002/cdt3.70027","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Chronic diseases represent a growing public health challenge globally, particularly in developing regions like Sub-Saharan Africa. Somaliland faces a dual burden of communicable and non-communicable diseases amidst post-conflict recovery, yet data on non-communicable disease (NCD) prevalence and determinants remain scarce. This study aimed to ascertain the prevalence and identify socio-demographic factors associated with self-reported chronic diseases among adults in Somaliland.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This cross-sectional study utilized data from the Somaliland Health and Demographic Survey (SLHDS) 2020. The final sample of 11,153 adults had a highly skewed age distribution (94.6% aged 35–44), a primary limitation of this analysis. The outcome was a self-reported physician diagnosis of any chronic disease. A multilevel mixed-effect logistic regression model was employed to identify significant determinants while adjusting for confounders.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The analysis identified several factors associated with reporting a chronic disease. Higher odds were observed among female-headed households (adjusted odds ratio [AOR]: 1.26; 95% CI: 1.06–1.48) and divorced individuals (AOR: 1.92; 95% CI: 1.55–2.37). Conversely, lower odds were associated with higher education (AOR: 0.53; 95% CI: 0.34–0.82) and nomadic residence (AOR: 0.34; 95% CI: 0.22–0.51). Unexpectedly, lack of electricity and no savings were also associated with lower odds, likely reflecting diagnostic access bias and reverse causation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Findings suggest that self-reported chronic diseases are associated with specific socio-demographic vulnerabilities in Somaliland. The results should be interpreted with extreme caution and viewed as hypothesis-generating at best. Future research using objective measures and representative sampling is urgently needed to validate these associations and accurately quantify the NCD burden.</p>\u0000 </section>\u0000 </div>","PeriodicalId":32096,"journal":{"name":"Chronic Diseases and Translational Medicine","volume":"11 4","pages":"306-315"},"PeriodicalIF":0.0,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cdt3.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Outi Kähkönen, Janne Engblom, Ying Lau, Eliza Lai-Yi Wong, Cindy Yue Tian, Annie Wai-Ling Cheung, Ho Hin Henry Chan, Anne Oikarinen
Background
University students are internationally recognized as a population at risk due to unhealthy lifestyle behaviors and elevated health risks. Supporting positive behavioral change requires understanding their intentions to adopt healthier habits. This study aimed to psychometrically evaluate and culturally validate the healthy lifestyle intention scale (HLIS) in three countries and regions.
Methods
A cross-sectional methodological study was conducted in 2023 among university students in Finland (n = 753), Singapore (n = 750), and Hong Kong, China (n = 767). The HLIS was developed based on the Adherence to Treatment of Patients with Chronic Disease Instrument. Psychometric evaluation included Spearman's ρ, Cronbach's ɑ, and both exploratory and confirmatory factor analyses.
Results
Findings from all three countries and regions indicated that Spearman's ρ values ranged from weak to high across the dimensions of self-readiness (0.23–0.69), family support (0.42–0.78), peer support (0.48–0.80), and social media influence (0.48–0.80). Cronbach's ɑ values demonstrated acceptable internal consistency (0.41–0.92). Structural validity was supported by confirmatory factor analysis. While the Finnish model suggested a need for further refinement (comparative fit index [CFI] = 0.81, root mean square error of approximation [RMSEA] = 0.11), the models for Singapore (CFI = 0.92, RMSEA = 0.07) and Hong Kong, China (CFI = 0.92, RMSEA = 0.07) showed strong fit indices.
Conclusion
The current version of the HLIS demonstrates promising psychometric properties for assessing health-related behavioral intentions among university students in a cross-cultural context. Further validation with more diverse international samples is recommended.
{"title":"Cross-Cultural Validation and Psychometric Properties Test of the Healthy Lifestyle Intention Scale for University Students","authors":"Outi Kähkönen, Janne Engblom, Ying Lau, Eliza Lai-Yi Wong, Cindy Yue Tian, Annie Wai-Ling Cheung, Ho Hin Henry Chan, Anne Oikarinen","doi":"10.1002/cdt3.70023","DOIUrl":"https://doi.org/10.1002/cdt3.70023","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>University students are internationally recognized as a population at risk due to unhealthy lifestyle behaviors and elevated health risks. Supporting positive behavioral change requires understanding their intentions to adopt healthier habits. This study aimed to psychometrically evaluate and culturally validate the healthy lifestyle intention scale (HLIS) in three countries and regions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A cross-sectional methodological study was conducted in 2023 among university students in Finland (<i>n</i> = 753), Singapore (<i>n</i> = 750), and Hong Kong, China (<i>n</i> = 767). The HLIS was developed based on the Adherence to Treatment of Patients with Chronic Disease Instrument. Psychometric evaluation included Spearman's <i>ρ</i>, Cronbach's <i>ɑ</i>, and both exploratory and confirmatory factor analyses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Findings from all three countries and regions indicated that Spearman's <i>ρ</i> values ranged from weak to high across the dimensions of self-readiness (0.23–0.69), family support (0.42–0.78), peer support (0.48–0.80), and social media influence (0.48–0.80). Cronbach's <i>ɑ</i> values demonstrated acceptable internal consistency (0.41–0.92). Structural validity was supported by confirmatory factor analysis. While the Finnish model suggested a need for further refinement (comparative fit index [CFI] = 0.81, root mean square error of approximation [RMSEA] = 0.11), the models for Singapore (CFI = 0.92, RMSEA = 0.07) and Hong Kong, China (CFI = 0.92, RMSEA = 0.07) showed strong fit indices.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The current version of the HLIS demonstrates promising psychometric properties for assessing health-related behavioral intentions among university students in a cross-cultural context. Further validation with more diverse international samples is recommended.</p>\u0000 </section>\u0000 </div>","PeriodicalId":32096,"journal":{"name":"Chronic Diseases and Translational Medicine","volume":"11 4","pages":"293-305"},"PeriodicalIF":0.0,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cdt3.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shuang Li, Yuky Lam, Aaron Muth, Zhe-Sheng Chen, Shanzhi Wang
Gankyrin (PSMD10) is a 25 kDa oncogenic protein and regulatory subunit of the 26S proteasome, characterized by a sevenfold ankyrin repeat domain. Gankyrin is overexpressed in various malignancies, particularly gastrointestinal (GI) cancers. Gankyrin contributes to tumorigenesis by modulating key signaling pathways and engaging in oncogenic protein–protein interactions with tumor suppressors, including p53 and Rb, thereby promoting cell proliferation, metastasis, and resistance to treatment. Recent advances have shed light on the structural basis of gankyrin's molecular interactions, its potential as a diagnostic and prognostic biomarker, and emerging therapeutic strategies. Together, targeting gankyrin represents a promising strategy for precision oncology in GI cancers.
{"title":"Gankyrin-Protein Interactions in GI Cancers: A Novel Target of New Therapeutics","authors":"Shuang Li, Yuky Lam, Aaron Muth, Zhe-Sheng Chen, Shanzhi Wang","doi":"10.1002/cdt3.70025","DOIUrl":"https://doi.org/10.1002/cdt3.70025","url":null,"abstract":"<p>Gankyrin (PSMD10) is a 25 kDa oncogenic protein and regulatory subunit of the 26S proteasome, characterized by a sevenfold ankyrin repeat domain. Gankyrin is overexpressed in various malignancies, particularly gastrointestinal (GI) cancers. Gankyrin contributes to tumorigenesis by modulating key signaling pathways and engaging in oncogenic protein–protein interactions with tumor suppressors, including p53 and Rb, thereby promoting cell proliferation, metastasis, and resistance to treatment. Recent advances have shed light on the structural basis of gankyrin's molecular interactions, its potential as a diagnostic and prognostic biomarker, and emerging therapeutic strategies. Together, targeting gankyrin represents a promising strategy for precision oncology in GI cancers.</p>","PeriodicalId":32096,"journal":{"name":"Chronic Diseases and Translational Medicine","volume":"11 4","pages":"269-278"},"PeriodicalIF":0.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cdt3.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diet provides essential metals, which are required for the growth, development, and well-functioning of the body. Nonetheless, some natural and human activities add toxic heavy metals to the diet, consequently introducing them to our bodies, resulting in several disorders and death. The intestine, the metabolism and absorption site of metals, uptakes them through transporters, active processes, and passive processes. It harbors Enteric nervous system (ENS), connecting directly to central nervous system (CNS) and the micro-organisms acting as an endocrine organ, thus regulating the movement of metals into the host. Gut microbes mediate the uptake of essential metals and prohibit toxic metals. However, the imbalance in their concentration manifests into various neurodegenerative diseases via gut involving Firmicutes/Bacteriodetes ratio, Akkermansia, Bifidobacteria, Escherichia, Enterococcus, Bacteroides, and Clostridium. Most importantly, with population aging, the incidence of neurodegenerative disorders is rapidly flourishing, marking the need for novel theranostic approaches to overcome it. Several research have demonstrated the significance of gut microbial homeostasis and its influence on brain functions, often termed as gut–brain axis, necessary for the sustainment of overall health and well-being of the human body. This review presents the novel diagnostic potential of microbes for specific disorders and dietary metals in combination with the therapeutic approach. Furthermore, they propose its utilization as a theranostic tool to investigate the links between mentioned dietary metals and neurological illnesses via the gut.
{"title":"The Aging Gut–Brain Axis: Effects of Dietary Polyphenols and Metal Exposure","authors":"Luqi Cao, Saurabh Kumar Jha, Neha Gupta, Xiang Chen, Renuka Soni, Luoxi Yuan, Rashi Srivastava, Zhe-Sheng Chen","doi":"10.1002/cdt3.70026","DOIUrl":"https://doi.org/10.1002/cdt3.70026","url":null,"abstract":"<p>Diet provides essential metals, which are required for the growth, development, and well-functioning of the body. Nonetheless, some natural and human activities add toxic heavy metals to the diet, consequently introducing them to our bodies, resulting in several disorders and death. The intestine, the metabolism and absorption site of metals, uptakes them through transporters, active processes, and passive processes. It harbors Enteric nervous system (ENS), connecting directly to central nervous system (CNS) and the micro-organisms acting as an endocrine organ, thus regulating the movement of metals into the host. Gut microbes mediate the uptake of essential metals and prohibit toxic metals. However, the imbalance in their concentration manifests into various neurodegenerative diseases via gut involving <i>Firmicutes/Bacteriodetes</i> ratio, <i>Akkermansia, Bifidobacteria, Escherichia</i>, <i>Enterococcus</i>, <i>Bacteroides,</i> and <i>Clostridium</i>. Most importantly, with population aging, the incidence of neurodegenerative disorders is rapidly flourishing, marking the need for novel theranostic approaches to overcome it. Several research have demonstrated the significance of gut microbial homeostasis and its influence on brain functions, often termed as gut–brain axis, necessary for the sustainment of overall health and well-being of the human body. This review presents the novel diagnostic potential of microbes for specific disorders and dietary metals in combination with the therapeutic approach. Furthermore, they propose its utilization as a theranostic tool to investigate the links between mentioned dietary metals and neurological illnesses via the gut.</p>","PeriodicalId":32096,"journal":{"name":"Chronic Diseases and Translational Medicine","volume":"11 4","pages":"251-268"},"PeriodicalIF":0.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cdt3.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicotine has been associated with cognitive functions such as memory and attention, with serum neurofilament light chain (sNfL) as a biomarker for neurological diseases such as Alzheimer's disease (AD) and multiple sclerosis (MS). However, the associations between nicotine and its metabolites and sNfL levels remain underexplored. This study aims to investigate the associations of serum and urine levels of cotinine and trans-3’-hydroxycotinine (hydroxycotinine) with sNfL levels in a broad population.
Methods
Employing data from the National Health and Nutrition Examination Survey (NHANES) 2013–2014, this cross-sectional study applied multivariable linear regression models and restricted cubic splines to examine the links between cotinine, hydroxycotinine (both in serum and urine), and sNfL levels.
Results
A total of 2052 participants were included in the serum analysis (mean age, 46.8 years; SD, 15.3; weighted 52.1% women) and 661 participants in the urine analysis (weighted 49.5% women). sNfL levels were positively associated with both serum and urine concentrations of cotinine and hydroxycotinine. Adjusted analyses revealed increases in sNfL levels in association with these substances, noting nonlinear associations for serum and urine cotinine and hydroxycotinine with sNfL levels.
Conclusion
These findings demonstrate robust positive associations between nicotine metabolites and sNfL levels and identify novel U-shaped associations at lower exposure levels. The results raise the hypothesis that very low nicotine metabolite levels may be associated with lower axonal injury markers, warranting further longitudinal and mechanistic studies to clarify causality.
{"title":"Associations Between Nicotine Metabolites and Serum Neurofilament Light Chain Levels in the General Population","authors":"Qianqian Ji, Erhan Tang, Yueting Liao, Jialei Yang, Yunzhang Wang, Yiqiang Zhan","doi":"10.1002/cdt3.70024","DOIUrl":"https://doi.org/10.1002/cdt3.70024","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Nicotine has been associated with cognitive functions such as memory and attention, with serum neurofilament light chain (sNfL) as a biomarker for neurological diseases such as Alzheimer's disease (AD) and multiple sclerosis (MS). However, the associations between nicotine and its metabolites and sNfL levels remain underexplored. This study aims to investigate the associations of serum and urine levels of cotinine and trans-3’-hydroxycotinine (hydroxycotinine) with sNfL levels in a broad population.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Employing data from the National Health and Nutrition Examination Survey (NHANES) 2013–2014, this cross-sectional study applied multivariable linear regression models and restricted cubic splines to examine the links between cotinine, hydroxycotinine (both in serum and urine), and sNfL levels.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 2052 participants were included in the serum analysis (mean age, 46.8 years; SD, 15.3; weighted 52.1% women) and 661 participants in the urine analysis (weighted 49.5% women). sNfL levels were positively associated with both serum and urine concentrations of cotinine and hydroxycotinine. Adjusted analyses revealed increases in sNfL levels in association with these substances, noting nonlinear associations for serum and urine cotinine and hydroxycotinine with sNfL levels.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>These findings demonstrate robust positive associations between nicotine metabolites and sNfL levels and identify novel U-shaped associations at lower exposure levels. The results raise the hypothesis that very low nicotine metabolite levels may be associated with lower axonal injury markers, warranting further longitudinal and mechanistic studies to clarify causality.</p>\u0000 </section>\u0000 </div>","PeriodicalId":32096,"journal":{"name":"Chronic Diseases and Translational Medicine","volume":"11 4","pages":"284-292"},"PeriodicalIF":0.0,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cdt3.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>We read with interest the article by Lehrer et al. on an in silico molecular docking and molecular dynamics simulation study with Cx43 channels, which investigated whether insulin or metformin can dock within the Cx43 channel and effectively block it, thereby reducing the risk of amyotrophic lateral sclerosis (ALS) [<span>1</span>]. Based on data from the FDA Med Watch, it was found that the insulin heterodimer docks in the center of the Cx43 channel and effectively blocks it, and that this blockade is very stable, thereby achieving a protective effect on ALS [<span>1</span>]. It has been found that metformin also docks in the Cx43 channel, but due to the small size of the molecule, it does not impede the passage of toxic waste products from astrocytes [<span>1</span>]. The study is impressive, but some points should be discussed.</p><p>The first point is that the methods did not specify whether only patients with sporadic ALS (sALS) or also patients with familial ALS (fALS) were included in the study [<span>1</span>]. Since the underlying pathophysiology may vary between these two groups, it must be clearly defined whether both categories or only sALS patients were included in the study.</p><p>The second point is that there is little evidence that sALS is mono-causally attributable to the influx of toxic substances produced by astrocytes in sALS patients. Only in SOD1 mice, an animal model for ALS, is Cx43 known to be overexpressed [<span>2</span>]. Astrocytes from these mice exhibit enhanced gap junction coupling, increased hemichannel-mediated activity, and elevated intracellular calcium levels [<span>2</span>]. It was also found that knocking out Cx43 in the SOD1 mouse model slowed disease progression, protected motor neurons, and improved motor neuron survival [<span>3</span>]. There is also evidence that Cx43 expression is upregulated in postmortem tissues and cerebrospinal fluid from ALS patients [<span>4</span>]. Cx43 is also upregulated in pluripotent stem cell-derived astrocytes from fALS and sALS patients (hiPSC), and Cx43 hemichannels are enriched in the astrocyte membrane [<span>3</span>]. Pharmacological blockade of Cx43 channels with GAP-19 and tonabersat demonstrated neuroprotection of hiPSC and a reduction in astrocyte-mediated neuronal hyperexcitability [<span>3</span>]. Administration of tonabersat to SOD1 mice also resulted in motor neuron protection and reduction of reactive astrocyte and microcyte gliosis [<span>3</span>].</p><p>The third point is that the quality of MedWatch data is dependent on the feeding with data by those who recognize or not recognize a causal relation between the administration of metformin or insulin. Therefore, only events that are identified as beneficial or adverse effects will enter into the database, making the quality of the entries highly dependent on the vigilance of the clinically active doctors and their willingness to report their observations. Furthermore, it is questionable that a ben
我们饶有兴趣地阅读了Lehrer等人关于Cx43通道的硅分子对接和分子动力学模拟研究的文章，该研究研究了胰岛素或二甲双胍是否可以停靠在Cx43通道内并有效阻断它，从而降低肌萎缩侧索硬化（ALS）[1]的风险。根据FDA Med Watch的数据，我们发现胰岛素异二聚体停靠在Cx43通道的中心并有效阻断它，并且这种阻断非常稳定，从而达到对ALS[1]的保护作用。已经发现二甲双胍也停靠在Cx43通道上，但由于分子的小尺寸，它不会阻碍星形胶质细胞[1]的有毒废物的通过。这项研究令人印象深刻，但有几点值得讨论。第一点是，这些方法没有明确研究中是否只包括散发性ALS （sALS）患者，还是也包括家族性ALS （fALS）患者[b]。由于这两组之间的潜在病理生理可能不同，因此必须明确界定是否将这两类患者或仅将sALS患者纳入研究。第二点是，几乎没有证据表明sALS是由星形胶质细胞产生的有毒物质流入sALS患者的单原因原因。仅在SOD1小鼠（ALS的动物模型）中，已知Cx43过表达[2]。这些小鼠的星形胶质细胞表现出间隙连接偶联增强，半通道介导活性增加，细胞内钙水平升高。我们还发现，在SOD1小鼠模型中敲除Cx43可减缓疾病进展，保护运动神经元，并改善运动神经元存活[3]。也有证据表明，Cx43在ALS患者的死后组织和脑脊液中表达上调。Cx43在fALS和sALS患者（hiPSC）的多能干细胞来源的星形胶质细胞中也上调，并且Cx43半通道在星形胶质细胞膜[3]中富集。用GAP-19和托那伯沙阻断Cx43通道显示了hiPSC的神经保护作用和星形胶质细胞介导的神经元高兴奋性[3]的减少。托那伯沙对SOD1小鼠也有运动神经元保护作用，减少反应性星形胶质细胞和小细胞胶质增生[3]。第三点是，MedWatch数据的质量取决于那些认识到或不认识到二甲双胍或胰岛素管理之间存在因果关系的人提供的数据。因此，只有确定为有益或不良影响的事件才会进入数据库，使得条目的质量高度依赖于临床活跃医生的警惕性和他们报告观察结果的意愿。此外，通过二甲双胍或胰岛素产生的有益效果是否真的被认为是不良事件值得怀疑，这意味着这些效果相当被忽略，没有进入MedWatch数据库。第四点是作者没有定义“胰岛素”的含义。它们是指由β细胞产生的胰岛素还是用于治疗糖尿病的人工胰岛素？如果指的是身体自身的胰岛素，临床医生不太可能认识到与ALS的任何关系，因为糖尿病患者通常不会常规测量胰岛素。基于这些考虑，报告的模型反映现实是极不可能的，这就是为什么这些结果必须谨慎解释的原因。作者查阅了文献并撰写了手稿。作者没有什么可报道的。作者声明无利益冲突。支持本研究结果的数据可向通讯作者索取。由于隐私或道德限制，这些数据不会公开。
{"title":"Are Insulin and Metformin Really Protective on Amyotrophic Lateral Sclerosis by Blocking the Astrocytic Cx43 Channel?","authors":"Josef Finsterer","doi":"10.1002/cdt3.70022","DOIUrl":"https://doi.org/10.1002/cdt3.70022","url":null,"abstract":"<p>We read with interest the article by Lehrer et al. on an in silico molecular docking and molecular dynamics simulation study with Cx43 channels, which investigated whether insulin or metformin can dock within the Cx43 channel and effectively block it, thereby reducing the risk of amyotrophic lateral sclerosis (ALS) [<span>1</span>]. Based on data from the FDA Med Watch, it was found that the insulin heterodimer docks in the center of the Cx43 channel and effectively blocks it, and that this blockade is very stable, thereby achieving a protective effect on ALS [<span>1</span>]. It has been found that metformin also docks in the Cx43 channel, but due to the small size of the molecule, it does not impede the passage of toxic waste products from astrocytes [<span>1</span>]. The study is impressive, but some points should be discussed.</p><p>The first point is that the methods did not specify whether only patients with sporadic ALS (sALS) or also patients with familial ALS (fALS) were included in the study [<span>1</span>]. Since the underlying pathophysiology may vary between these two groups, it must be clearly defined whether both categories or only sALS patients were included in the study.</p><p>The second point is that there is little evidence that sALS is mono-causally attributable to the influx of toxic substances produced by astrocytes in sALS patients. Only in SOD1 mice, an animal model for ALS, is Cx43 known to be overexpressed [<span>2</span>]. Astrocytes from these mice exhibit enhanced gap junction coupling, increased hemichannel-mediated activity, and elevated intracellular calcium levels [<span>2</span>]. It was also found that knocking out Cx43 in the SOD1 mouse model slowed disease progression, protected motor neurons, and improved motor neuron survival [<span>3</span>]. There is also evidence that Cx43 expression is upregulated in postmortem tissues and cerebrospinal fluid from ALS patients [<span>4</span>]. Cx43 is also upregulated in pluripotent stem cell-derived astrocytes from fALS and sALS patients (hiPSC), and Cx43 hemichannels are enriched in the astrocyte membrane [<span>3</span>]. Pharmacological blockade of Cx43 channels with GAP-19 and tonabersat demonstrated neuroprotection of hiPSC and a reduction in astrocyte-mediated neuronal hyperexcitability [<span>3</span>]. Administration of tonabersat to SOD1 mice also resulted in motor neuron protection and reduction of reactive astrocyte and microcyte gliosis [<span>3</span>].</p><p>The third point is that the quality of MedWatch data is dependent on the feeding with data by those who recognize or not recognize a causal relation between the administration of metformin or insulin. Therefore, only events that are identified as beneficial or adverse effects will enter into the database, making the quality of the entries highly dependent on the vigilance of the clinically active doctors and their willingness to report their observations. Furthermore, it is questionable that a ben","PeriodicalId":32096,"journal":{"name":"Chronic Diseases and Translational Medicine","volume":"11 4","pages":"318-319"},"PeriodicalIF":0.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cdt3.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Guide for Authors","authors":"","doi":"10.1002/cdt3.70021","DOIUrl":"https://doi.org/10.1002/cdt3.70021","url":null,"abstract":"","PeriodicalId":32096,"journal":{"name":"Chronic Diseases and Translational Medicine","volume":"11 3","pages":"241-248"},"PeriodicalIF":0.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cdt3.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145038165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>The use of artificial intelligence (AI) to generate patient information has become increasingly common in medicine. In radiology, AI has demonstrated potential in improving patient education and care [<span>1</span>]. With the increased usage of AI and large language models (LLM) in medicine, it is important that their readability is evaluated. The Joint Commission states that the reading level of patient education material should be at or below a 5th grade reading level; thus, their outputs must meet established readability standards to support effective patient education [<span>2</span>]. In this study, we evaluated the readability of information generated by LLMs ChatGPT, Gemini, and Copilot on chronic diseases such as diabetes, cancer, and heart disease.</p><p>We evaluated the readability of AI-generated responses to common patient questions about diabetes, cancer, and heart disease. Using standardized prompts, we collected 12 outputs (4 per disease) from ChatGPT, Gemini, and Copilot. Readability was assessed using three validated formulas: Flesch-Kincaid Grade Level, Simple Measure of Gobbledygook (SMOG) Index, and Gunning Fog Index. A one-way analysis of variance (ANOVA) was performed to assess statistically significant differences in readability among the 3 LLMs for each readability formula.</p><p>Readability scores across the three LLMs exceeded recommended readability thresholds (Table 1). For the Flesch-Kincaid Grade Level, Gemini had the highest average score at 12.13, followed by Copilot at 11.87, and ChatGPT at 11.40. With the SMOG Index, Gemini again produced the highest score at 11.03, followed by Copilot at 10.75, and ChatGPT at 10.53. Gunning Fog also showed a consistent pattern, Gemini led with 12.97, followed by Copilot with 12.80, and ChatGPT was the lowest score with 12.10. ANOVA results showed no statistically significant differences in readability among the LLMs across the three formulas, with p-values of 0.604 for Flesch–Kincaid, 0.704 for SMOG, and 0.333 for Gunning Fog Index.</p><p>The scores produced by the reading level calculator correlate to the reading level associated with the text. Each LLM produced an average reading level greater than the 10th grade. Information generated by AI does not consider patient literacy levels, which can impact the care that patients receive. Systematic reviews have shown that patient education materials often fail to meet national readability recommendations, and the increased use of AI without consideration for literacy levels can further perpetuate challenges [<span>3</span>]. These results underscore the importance of selecting AI tools that address the suggested 5th–6th-grade readability levels for health education materials.</p><p>While our study focused on readability metrics using validated formulas (Flesch-Kincaid, SMOG, and Gunning Fog), we acknowledge that these tools do not fully capture patient comprehension. Readability scores provide an estimate of the expected reading
{"title":"Evaluating the Readability of AI-Generated Patient Information on Chronic Diseases","authors":"Faheed Shafau, Chase Wahl","doi":"10.1002/cdt3.70020","DOIUrl":"https://doi.org/10.1002/cdt3.70020","url":null,"abstract":"<p>The use of artificial intelligence (AI) to generate patient information has become increasingly common in medicine. In radiology, AI has demonstrated potential in improving patient education and care [<span>1</span>]. With the increased usage of AI and large language models (LLM) in medicine, it is important that their readability is evaluated. The Joint Commission states that the reading level of patient education material should be at or below a 5th grade reading level; thus, their outputs must meet established readability standards to support effective patient education [<span>2</span>]. In this study, we evaluated the readability of information generated by LLMs ChatGPT, Gemini, and Copilot on chronic diseases such as diabetes, cancer, and heart disease.</p><p>We evaluated the readability of AI-generated responses to common patient questions about diabetes, cancer, and heart disease. Using standardized prompts, we collected 12 outputs (4 per disease) from ChatGPT, Gemini, and Copilot. Readability was assessed using three validated formulas: Flesch-Kincaid Grade Level, Simple Measure of Gobbledygook (SMOG) Index, and Gunning Fog Index. A one-way analysis of variance (ANOVA) was performed to assess statistically significant differences in readability among the 3 LLMs for each readability formula.</p><p>Readability scores across the three LLMs exceeded recommended readability thresholds (Table 1). For the Flesch-Kincaid Grade Level, Gemini had the highest average score at 12.13, followed by Copilot at 11.87, and ChatGPT at 11.40. With the SMOG Index, Gemini again produced the highest score at 11.03, followed by Copilot at 10.75, and ChatGPT at 10.53. Gunning Fog also showed a consistent pattern, Gemini led with 12.97, followed by Copilot with 12.80, and ChatGPT was the lowest score with 12.10. ANOVA results showed no statistically significant differences in readability among the LLMs across the three formulas, with p-values of 0.604 for Flesch–Kincaid, 0.704 for SMOG, and 0.333 for Gunning Fog Index.</p><p>The scores produced by the reading level calculator correlate to the reading level associated with the text. Each LLM produced an average reading level greater than the 10th grade. Information generated by AI does not consider patient literacy levels, which can impact the care that patients receive. Systematic reviews have shown that patient education materials often fail to meet national readability recommendations, and the increased use of AI without consideration for literacy levels can further perpetuate challenges [<span>3</span>]. These results underscore the importance of selecting AI tools that address the suggested 5th–6th-grade readability levels for health education materials.</p><p>While our study focused on readability metrics using validated formulas (Flesch-Kincaid, SMOG, and Gunning Fog), we acknowledge that these tools do not fully capture patient comprehension. Readability scores provide an estimate of the expected reading","PeriodicalId":32096,"journal":{"name":"Chronic Diseases and Translational Medicine","volume":"11 4","pages":"316-317"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cdt3.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Naghmeh Ziaie, Mohammad Barary, Soheil Bakhshinasab, Romina Hamzehpour, Soheil Ebrahimpour
<p>Lin and colleagues leveraged the 32,609-participant Fu-CARE cohort to report a U-shaped association between the high-density-lipoprotein-cholesterol/low-density-lipoprotein-cholesterol (HDL-C/LDL-C) ratio and all-cause mortality, with the lowest risk at 0.30–0.50 [<span>1</span>]. Their large sample and sex-specific restricted-cubic-spline modeling deserve praise, but several methodological issues temper causal inference and translational value.</p><p><i>Short follow-up and event scarcity:</i> Median follow-up was 3.4 years (approx. 1100 deaths, 3.4% of the cohort). Cardiovascular epidemiology standards recommend ≥10 years to capture sufficient outcome variability and reduce reverse-causation from subclinical disease or frailty-related lipid changes [<span>2</span>]. Extending surveillance or performing landmark analyses that exclude deaths within the first 2 years could test the robustness of the U-shaped curve.</p><p><i>Single baseline lipid measurement:</i> HDL-C and LDL-C vary with diet, medication, and intercurrent illness. Repeated measures or time-updated covariates would mitigate regression-dilution bias and clarify whether extreme ratio categories represent chronic dyslipidemia or transient fluctuations [<span>3</span>].</p><p><i>Residual confounding:</i> Models adjusted for traditional factors but omitted socioeconomic status, alcohol quantity, dietary pattern, kidney function, inflammatory markers (high-sensitivity C-reactive protein, neutrophil-to-lymphocyte ratio), and medication classes (statins, SGLT-2 inhibitors, corticosteroids). Each independently influences both lipids and mortality [<span>4-6</span>]. Multiple-imputation and inverse-probability-weighted models incorporating these variables could diminish bias.</p><p><i>Data-driven categorization and lack of external validation:</i> Cut points (0.30 and 0.50) were derived from the same spline used to test association, risking overfitting. Internal bootstrap optimism-corrected C-statistics, or, ideally, application to an external cohort such as CHARLS or UK Biobank, would gauge generalizability [<span>7</span>].</p><p><i>Limited clinical utility metrics:</i> The authors reported hazard ratios but did not evaluate whether the ratio adds discrimination or net reclassification beyond LDL-C, HDL-C, and established risk scores (e.g., China-PAR). Decision-curve analysis could determine whether measuring HDL-C/LDL-C meaningfully improves risk stratification versus guideline-directed lipid panels [<span>8</span>].</p><p><i>Diabetes-specific findings require caution:</i> The null association in participants with type 2 diabetes mellitus (T2DM) may reflect inadequate power (T2DM subgroup deaths, <i>n</i> = 250) or differential statin intensity. Stratified analyses by baseline lipid-lowering therapy and glycemic control (hemoglobin A1c) would clarify effect modification [<span>9</span>].</p><p>In summary, the Fu-CARE data hint that maintaining an HDL-C/LDL-C ratio between 0.30 and 0.50
{"title":"Commentary on “HDL-C/LDL-C Ratio and All-Cause Mortality in Populations at High CVD Risk: A Prospective Observational Cohort Study”","authors":"Naghmeh Ziaie, Mohammad Barary, Soheil Bakhshinasab, Romina Hamzehpour, Soheil Ebrahimpour","doi":"10.1002/cdt3.70019","DOIUrl":"https://doi.org/10.1002/cdt3.70019","url":null,"abstract":"<p>Lin and colleagues leveraged the 32,609-participant Fu-CARE cohort to report a U-shaped association between the high-density-lipoprotein-cholesterol/low-density-lipoprotein-cholesterol (HDL-C/LDL-C) ratio and all-cause mortality, with the lowest risk at 0.30–0.50 [<span>1</span>]. Their large sample and sex-specific restricted-cubic-spline modeling deserve praise, but several methodological issues temper causal inference and translational value.</p><p><i>Short follow-up and event scarcity:</i> Median follow-up was 3.4 years (approx. 1100 deaths, 3.4% of the cohort). Cardiovascular epidemiology standards recommend ≥10 years to capture sufficient outcome variability and reduce reverse-causation from subclinical disease or frailty-related lipid changes [<span>2</span>]. Extending surveillance or performing landmark analyses that exclude deaths within the first 2 years could test the robustness of the U-shaped curve.</p><p><i>Single baseline lipid measurement:</i> HDL-C and LDL-C vary with diet, medication, and intercurrent illness. Repeated measures or time-updated covariates would mitigate regression-dilution bias and clarify whether extreme ratio categories represent chronic dyslipidemia or transient fluctuations [<span>3</span>].</p><p><i>Residual confounding:</i> Models adjusted for traditional factors but omitted socioeconomic status, alcohol quantity, dietary pattern, kidney function, inflammatory markers (high-sensitivity C-reactive protein, neutrophil-to-lymphocyte ratio), and medication classes (statins, SGLT-2 inhibitors, corticosteroids). Each independently influences both lipids and mortality [<span>4-6</span>]. Multiple-imputation and inverse-probability-weighted models incorporating these variables could diminish bias.</p><p><i>Data-driven categorization and lack of external validation:</i> Cut points (0.30 and 0.50) were derived from the same spline used to test association, risking overfitting. Internal bootstrap optimism-corrected C-statistics, or, ideally, application to an external cohort such as CHARLS or UK Biobank, would gauge generalizability [<span>7</span>].</p><p><i>Limited clinical utility metrics:</i> The authors reported hazard ratios but did not evaluate whether the ratio adds discrimination or net reclassification beyond LDL-C, HDL-C, and established risk scores (e.g., China-PAR). Decision-curve analysis could determine whether measuring HDL-C/LDL-C meaningfully improves risk stratification versus guideline-directed lipid panels [<span>8</span>].</p><p><i>Diabetes-specific findings require caution:</i> The null association in participants with type 2 diabetes mellitus (T2DM) may reflect inadequate power (T2DM subgroup deaths, <i>n</i> = 250) or differential statin intensity. Stratified analyses by baseline lipid-lowering therapy and glycemic control (hemoglobin A1c) would clarify effect modification [<span>9</span>].</p><p>In summary, the Fu-CARE data hint that maintaining an HDL-C/LDL-C ratio between 0.30 and 0.50 ","PeriodicalId":32096,"journal":{"name":"Chronic Diseases and Translational Medicine","volume":"11 4","pages":"320-321"},"PeriodicalIF":0.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cdt3.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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