MedComm - Future medicine最新文献

{"title":"Computational study unravels inhibitory potential of epicatechin gallate against inflammatory and pyroptosis-associated mediators in COVID-19","authors":"Prem Rajak,&nbsp;Abhratanu Ganguly","doi":"10.1002/mef2.52","DOIUrl":"10.1002/mef2.52","url":null,"abstract":"<p>Coronavirus disease-19 (COVID-19) is the global health emergency caused by SARS-CoV-2. Upon infection, antigenic determinants of the virus trigger massive production of proinflammatory/pyroptosis-associated proteins, resulting in cytokine storm, tissue damage, and multiorgan failure. Therefore, these proinflammatory/pyroptosis-associated mediators are promising therapeutic targets to combat COVID-19. Epicatechin gallate (ECG) is a polyphenol found in green tea. It has antioxidative and anti-inflammatory properties. Hence, in the present study, ECG was selected to explore its binding potential for inflammatory mediators such as interleukins, interferon-γ (IFNγ), and tumor necrosis factor-α (TNF-α), along with their native receptors. In addition, the interacting potential of ECG with pyroptosis-associated proteins, viz. caspases and BAX has also been investigated. Molecular docking analysis has revealed that ECG interacts with interleukins, IFNγ, TNF-α, cytokine receptors, caspase-1/4/11, and BAX with significant binding affinity. Several amino acid residues of these mediators were blocked by ECG through stable hydrogen bonds and hydrophobic contacts. ECG interacted with caspase-11, BAX, and TNF-R1 with better binding affinities. Therefore, the present in silico study indicates that ECG could be a potential drug to subvert cytokine storm and pyroptosis during COVID-19.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.52","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45414639","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":"Using ChatGPT in a clinical setting: A case report","authors":"Yongqin Ye,&nbsp;Shuvam Sarkar,&nbsp;Anand Bhaskar,&nbsp;Brian Tomlinson,&nbsp;Olivia Monteiro","doi":"10.1002/mef2.51","DOIUrl":"10.1002/mef2.51","url":null,"abstract":"<p>Large language models (LLMs) are rapidly becoming an important foundation model that has infiltrated our daily lives in many ways. The release of GPT-3 and GPT-4, a LLM that is capable of natural language processing (NLP) that has been trained on terabytes of text data through transfer learning to apply knowledge gained from a previous task to solve a different but related problem, immediately captured the attention of the medical field to investigate how LLMs can be used to process and interpret electronic health records and to streamline clinical writing.<span>¹</span> NLP models have traditionally been used mainly as diagnostic aids in healthcare. Its use generally requires supervised learning on manually labeled and training datasets with a huge involvement of time from healthcare professionals.<span>²</span> NLP models often lack precision, accuracy and mostly only accessible by the developers. Recent LLMs with their transformer and reinforcement learning with human feedback, have enabled better precision in text generation. The advancement of GPT-3 (Generative Pre-Trained Transformer, commonly known as ChatGPT) demonstrated that LLMs can rapidly adapt to new tasks resulting in better generalization. Also, ChatGPT has a simple interface, which has enabled broad adoption and use. Having such a versatile and user-friendly tool at our fingertips means that we can adapt to use LLMs for basic tasks such as generating clinical reports, providing clinical support, or to synthesize patient data from multiple sources.</p><p>We have used this case report as an opportunity to demonstrate the practicality of ChatGPT in basic writing tasks in a clinical context. This case report is obtained from two teaching videos uploaded by TTMedcastTraining Texas Tech University on YouTube. The two videos are of a patient called Jonathan who presented with bilateral knee pain with a history of sickle cell disease. One video is the bedside presentation of the patient by a medical intern, another is a group discussion of treatment plans for this patient. Since GPT-3 can only deal with text input, we have downloaded the transcript from each video. The transcripts sometimes contain people talking at the same time, filler words, mispronounced words, or incomplete sentences. Unaltered transcripts were submitted to ChatGPT separately for interpretation.</p><p>The workflow of using ChatGPT to generate the case report is summarized in Figure 1. We fed the transcript of Video 1 into ChatGPT and asked it to write a case report from it (Case Report 1). Then, we used the transcript of Video 2 to create Case Report 2. ChatGPT was asked to combine the two reports without summarizing and offer a diagnosis and a treatment plan. We also asked ChatGPT to write the final case report in the style for the New England Journal of Medicine. This process took around 1.5 h, including time the authors spent watching the videos. The full case report is found in Supportin","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.51","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47396186","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":"Biomarkers of ageing: Current state-of-art, challenges, and opportunities","authors":"Ruiye Chen,&nbsp;Yueye Wang,&nbsp;Shiran Zhang,&nbsp;Gabriella Bulloch,&nbsp;Junyao Zhang,&nbsp;Huan Liao,&nbsp;Xianwen Shang,&nbsp;Malcolm Clark,&nbsp;Qingsheng Peng,&nbsp;Zongyuan Ge,&nbsp;Ching-Yu Cheng,&nbsp;Yuanxu Gao,&nbsp;Mingguang He,&nbsp;Zhuoting Zhu","doi":"10.1002/mef2.50","DOIUrl":"10.1002/mef2.50","url":null,"abstract":"<p>Given the unprecedented phenomenon of population ageing, studies have increasing captured the heterogeneity within the ageing process. In this context, the concept of “biological age” has been introduced as an integrated measure reflecting the individualized ageing pace. Identifying reliable and robust biomarkers of age is critical for the accurate risk stratification of individuals and exploration into antiageing interventions. Numerous potential biomarkers of ageing have been proposed, spanning from molecular changes and imaging characteristics to clinical phenotypes. In this review, we will start off with a discussion of the development of ageing biomarkers, then we will provide a comprehensive summary of currently identified ageing biomarkers in humans, discuss the rationale behind each biomarker and highlight their accuracy and clinical value with a contemporary perspective. Additionally, we will discuss the challenges, potential applications, and future opportunities in this field. While research on ageing biomarkers has led to significant progress and applications, further investigations are still necessary. We anticipate that future breakthroughs in this field will involve exploring potential mechanisms, developing biomarkers by combining various data sources or employing new technologies, and validating the clinical value of existing and emerging biomarkers through comprehensive collaboration and longitudinal studies.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.50","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46740855","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":"Exploration of the link between COVID-19 and alcoholic hepatitis from the perspective of bioinformatics and systems biology","authors":"Tengda Huang,&nbsp;Bingxuan Yu,&nbsp;Xinyi Zhou,&nbsp;Hongyuan Pan,&nbsp;Ao Du,&nbsp;Jincheng Bai,&nbsp;Xiaoquan Li,&nbsp;Nan Jiang,&nbsp;Jinyi He,&nbsp;Kefei Yuan,&nbsp;Zhen Wang","doi":"10.1002/mef2.42","DOIUrl":"https://doi.org/10.1002/mef2.42","url":null,"abstract":"<p>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been suggested to purpose threats to health of mankind. Alcoholic hepatitis (AH) is a life-threatening acute and chronic liver failure that takes place in sufferers who drink excessively. During the epidemic, AH has an increasing incidence of severe illness and mortality. The intrinsic relationship of molecular pathogenesis, as well as common therapeutic strategies for two diseases are still poorly understood. The transcriptome of the COVID-19 and AH has been compared to obtain the altered genes and hub genes were screened out through protein–protein interaction (PPI) network analysis. Via gene ontology (GO), pathway enrichment, and transcription regulator analysis, a deeper appreciation of the interplay mechanism between hub genes were established. Finally, gene-disease and gene–drug analysis were displayed to instruct the clinical treatments. With 181 common differentially expressed genes (DEGs) of AH and COVID-19 were obtained, 10 hub genes were captured. Follow-up studies located that these 10 genes typically mediated the diseases occurrence by regulating the activities of the immune system. Other results suggest that the common pathways of the two ailments are enriched in regulating the function of immune cells and release of immune molecules. The top 10 drug candidates have been chosen primarily, some of which have been proved effective in treating AH sufferers infected with COVID-19. This study reveals the common pathogenesis of COVID-19 and AH and assist to discover necessary therapeutic targets to combat the ongoing pandemic induced via SARS-CoV-2 infection and acquire promising remedy strategies for the two diseases.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.42","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50121601","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":"The inevitable transformation of medicine and research by large language models: The possibilities and pitfalls","authors":"Yuanxu Gao,&nbsp;Daniel T. Baptista-Hon,&nbsp;Kang Zhang","doi":"10.1002/mef2.49","DOIUrl":"10.1002/mef2.49","url":null,"abstract":"<p>Large language models (LLMs) often refer to artificial intelligence models that consist of extensive parameters and have the ability to understand and generate human-like language. They are typically developed in a self-supervised learning manner and are trained on large quantities of unlabeled text to learn patterns in language. LLMs were initially used in natural language processing (NLP), but they have since been extended to a variety of tasks like processing biological sequences and combining text with other modalities of data. LLMs have the potential to revolutionize the way we approach scientific research and medicine. For example, by leveraging their ability to understand and interpret vast quantities of text data, LLMs can provide insights and make predictions that would otherwise be impossible.</p><p>In the medical domain, LLMs can be used to analyze immense electronic health records and improve communication between healthcare professionals and patients. For example, LLMs can be used to automate triage, medical coding, and clinical documentation, which can help to improve the accuracy and efficiency of these processes. They can also be used to improve NLP in medical chatbots and virtual assistants, allowing patients to interact with healthcare services more efficiently and effectively. They can also be used to process medical records and patient data, enabling better diagnoses and more personalized treatments. They can also be used to analyze clinical trial data and identify trends that could lead to better outcomes. Finally, LLMs can also be used to answer medical questions and provide guidance to healthcare professionals, which can help to improve the quality of care. In the accompanying Review, Zheng et al.<span>¹</span> undertake a major effort to write a comprehensive review of this exciting and highly evolving field.</p><p>In research, LLMs can be used to search through diverse large datasets and identify patterns that would otherwise be difficult to detect. They can also be used to generate and test hypotheses and to summarize and analyze research papers. It is clear that LLMs will be transforming the way we communicate about medicine and research, and have the potential to revolutionize the field of healthcare.</p><p>The current state-of-the-art LLM is Generative Pre-trained Transformer 4 (GPT-4), developed by OpenAI, about which Technical details have not been made public yet.<span>²</span> Based on publicly available information, the number of parameters is comparable to its previous generation, GPT-3, which consists of 175 billion parameters. GPT-4 is a generative model, meaning it can generate human-like language and even create original content. Other notable LLMs include GPT-3, Bidirectional Encoder Representations from Transformers, and Text-to-Text Transfer Transformers, each with its unique strengths and capabilities. However, one example of an LLM developed specifically for the medical domain i","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.49","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47689815","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":"Accelerating the integration of ChatGPT and other large-scale AI models into biomedical research and healthcare","authors":"Ding-Qiao Wang,&nbsp;Long-Yu Feng,&nbsp;Jin-Guo Ye,&nbsp;Jin-Gen Zou,&nbsp;Ying-Feng Zheng","doi":"10.1002/mef2.43","DOIUrl":"10.1002/mef2.43","url":null,"abstract":"<p>Large-scale artificial intelligence (AI) models such as ChatGPT have the potential to improve performance on many benchmarks and real-world tasks. However, it is difficult to develop and maintain these models because of their complexity and resource requirements. As a result, they are still inaccessible to healthcare industries and clinicians. This situation might soon be changed because of advancements in graphics processing unit (GPU) programming and parallel computing. More importantly, leveraging existing large-scale AIs such as GPT-4 and Med-PaLM and integrating them into multiagent models (e.g., Visual-ChatGPT) will facilitate real-world implementations. This review aims to raise awareness of the potential applications of these models in healthcare. We provide a general overview of several advanced large-scale AI models, including language models, vision-language models, graph learning models, language-conditioned multiagent models, and multimodal embodied models. We discuss their potential medical applications in addition to the challenges and future directions. Importantly, we stress the need to align these models with human values and goals, such as using reinforcement learning from human feedback, to ensure that they provide accurate and personalized insights that support human decision-making and improve healthcare outcomes.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.43","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46230747","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":"The distribution pattern of corneal volume in Chinese myopic patients from multiple centers","authors":"Changting Tang,&nbsp;Linyuan Qin,&nbsp;Wei Wang,&nbsp;Suqing Lu,&nbsp;Yinan Li,&nbsp;Ying Fang,&nbsp;Honghua Yu,&nbsp;Yijun Hu","doi":"10.1002/mef2.44","DOIUrl":"10.1002/mef2.44","url":null,"abstract":"<p>Corneal volume (CV) is a useful index for detecting forme fruste keratoconus from normal corneas. It can be used to evaluate the whole cornea, since it can measure corneal areas up to 10 mm in diameter. Thus, CV has become the clinicians' interest as a diagnostic tool of corneal ectatic disease and a measure of corneal integrity to determine suitability for refractive surgery. We conducted a cross-sectional study including 7893 myopic patients from five ophthalmic centers to investigate the distribution pattern of CV. Our study showed that distribution of CV-3, CV-5, and CV-7 mm were slightly positively skewed and the 2.5th to 97.5th percentiles were 3.6–4.4, 10.4–12.8, 22.5–27.5 mm³, respectively. Central corneal thickness (CCT) was significantly correlated with CV in all measurement regions. The correlation between CV and CCT showed an inconsistent trend with the increase of age. The correlation coefficient between CV and CCT did not change significantly with the increase of myopia degree in low to moderate myopia, but fluctuated significantly in high myopia (less than −6.0 diopters). According to our results, corneal volume follows a slightly positively skewed distribution pattern in myopic Chinese patients. The information is useful for screening refractive surgery candidates and assessing the risk of corneal refractive surgery.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.44","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43854442","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":"A multiparameter radiomic model for accurate prognostic prediction of glioma","authors":"Yan Li,&nbsp;Li Bao,&nbsp;Caiwei Yang,&nbsp;Zhenglong Deng,&nbsp;Xin Zhang,&nbsp;Pin Xu,&nbsp;Xiaorui Su,&nbsp;Fanxin Zeng,&nbsp;Mir Q. U. Mehrabi,&nbsp;Qiang Yue,&nbsp;Bin Song,&nbsp;Qiyong Gong,&nbsp;Su Lui,&nbsp;Min Wu","doi":"10.1002/mef2.41","DOIUrl":"10.1002/mef2.41","url":null,"abstract":"<p>An accurate prediction of prognosis is important for clinical treatments of glioma. In this study, a multiparameter radiomic model is proposed for accurate prognostic prediction of glioma. Three kinds of region of interest were extracted from preoperative postcontrast T1-weighted images and T2 fluid-attenuated inversion recovery images acquired from 140 glioma patients. Radiomics score (Radscore) was calculated and the conventional image features and clinical molecular characteristics that may be related to progression-free survival (PFS) were evaluated. Five uniparameter and various combinations of biparameter and multiparameter models based on above characteristics were built. The performance of these models was evaluated by concordance index (C index), and the nomogram of the multiparameter radiomic model was constructed. The results show that the proposed multiparameter radiomic model has a better prediction performance than other models. In the training and validation sets, the calibration curves of the multiparameter radiomic model for the 1-, 2-, and 3-year PFS probability demonstrate a high consistence between predictions and observations. In conclusion, this study demonstrates that the multiparameter radiomic model based on Radscore, conventional image features and clinical molecular characteristics can improve the prediction accuracy of glioma prognosis, which could be informative for individualized treatments.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.41","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47169859","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":"Posterior corneal surface and anterior chamber changes after Small incision lenticule extraction and Femtosecond laser-assisted laser in-situ keratomileusis","authors":"Lijie Xie,&nbsp;Shanqing Zhu,&nbsp;Guanrong Wu,&nbsp;Changting Tang,&nbsp;Yunxiang Guo,&nbsp;Honghua Yu,&nbsp;Yijun Hu","doi":"10.1002/mef2.40","DOIUrl":"10.1002/mef2.40","url":null,"abstract":"<p>We aimed to evaluate the posterior corneal and anterior chamber changes following small incision lenticule extraction (SMILE) and femtosecond laser-assisted laser in situ keratomileusis (FS-LASIK). This retrospective study included 93 eyes for SMILE and 228 eyes for FS-LASIK. Posterior corneal curvature, anterior chamber biometrics, and posterior corneal aberrations preoperatively and at 1 week and 3 months after surgery were evaluated. The correlation between surgical-related factors and posterior corneal morphology changes, posterior corneal morphology changes, and posterior corneal aberration alterations were determined. There was an increase in mean posterior corneal radius (PCC R) and flat posterior corneal radius (PCC R1) and decrease in anterior chamber depth and anterior chamber volume in the SMILE and FS-LASIK group (all <i>p</i> &lt; 0.05). A significant difference was observed in posterior corneal higher-order aberrations before and after the FS-LASIK (<i>p</i> = 0.006). A negative correlation was found between alterations of PCC R and changes of posterior corneal spherical aberrations in the SMILE (<i>r</i> = −0.289) and FS-LASIK group (<i>r</i> = −0.383). In conclusion, posterior corneal and anterior chamber biometrics are changed after SMILE and FS-LASIK. Innovatively, we find that changes in posterior corneal morphology are correlated with posterior corneal aberration alterations.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.40","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43643689","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":"Anti-HIV drugs reduce risk of prediabetes and progression to type 2 diabetes in HIV-infected patients","authors":"Joseph Magagnoli,&nbsp;Felipe Pereira,&nbsp;Siddharth Narendran,&nbsp;Peirong Huang,&nbsp;Tammy Cummings,&nbsp;James W. Hardin,&nbsp;Joseph Nguyen,&nbsp;S. Scott Sutton,&nbsp;Jayakrishna Ambati","doi":"10.1002/mef2.37","DOIUrl":"10.1002/mef2.37","url":null,"abstract":"<p>The aim of this study was to investigate whether the use of nucleoside reverse transcriptase inhibitors (NRTIs) impacts the incidence of prediabetes or type 2 diabetes mellitus (T2DM) or the progression from prediabetes to T2DM in people living with HIV (PLWH). We conducted a retrospective cohort study using the US Veterans Health Administration database among adult patients with an HIV diagnosis from the year 2000 until 2021 to determine the incidence of prediabetes and further progression to T2DM among NRTI exposed and unexposed patients. A multistate model was used to evaluate progression from normoglycemia to prediabetes and then to T2DM, and covariate adjustment with the Cox proportional hazards model was used to estimate the hazard ratios (HRs). Among 32,240 veterans diagnosed with HIV, prediabetes and T2DM were observed among 20.2% and 20.7% of patients, respectively. Among those diagnosed with prediabetes, 31.8% progressed to T2DM. Patients exposed to NRTIs at any time (86.6%), had a reduced risk of prediabetes [HR: 0.50 (95% confidence interval, CI: 0.47–0.53)] and among prediabetics, a lower risk of progression to T2DM [HR: 0.73 (95% CI: 0.63–0.85)] when compared to patients who never used NRTIs. In summary, NRTIs may reduce the risk of developing prediabetes and the progression from prediabetes to T2DM in PLWH.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.37","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10239593","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}