Journal of Personalized Medicine最新文献

Background: The high global prevalence of prediabetes requires its early identification. Amino acids (AAs) have emerged as potential predictors of prediabetes. This study investigates the association between amino acids and prediabetes in the Kazakh population.

Materials and methods: In this case-control study, serum AAs levels were measured using the Trace GC 1310 gas chromatography system coupled with the TSQ 8000 triple quadrupole mass spectrometer (Thermo Scientific, Austin, TX, USA) followed by silylation with the BSTFA + 1% TMCS derivatization method. Biochemical parameters, including total cholesterol, HDL-C, LDL-C, triglycerides, fasting glucose, HbA1c, and Creatinine, were assessed for each participant. Trained professionals conducted anthropometric and physical examinations (which included taking blood pressure and heart rate measurements) and family history collection.

Results: A total of 112 Kazakh individuals with prediabetes and 55 without prediabetes, aged 36-65 years, were included in the study. Only Alanine and valine showed a significant association with prediabetes risk among the 13 AAs analyzed. Our findings revealed an inverse relationship between Alanine and Valine and prediabetes in individuals of Kazakh ethnicity.

Conclusion: A lower serum level of Alanine and Valine may serve as a predictive biomarker for prediabetes in the Kazakh population.

Background: Few studies have compared the efficacy and safety of second-line biological therapies in ulcerative colitis (UC) patients with prior exposure to anti-tumor necrosis factor (TNF) therapy. We aim to compare the efficacy and safety between ustekinumab, vedolizumab, and tofacitinib, a current option as second-line biological therapy with different mechanisms in those patients.

Methods: This retrospective multi-center study was conducted across five institutions from 2011 to 2022. We enrolled patients with moderate to severe UC who failed anti-TNF therapy and subsequently received ustekinumab, vedolizumab, or tofacitinib as second-line biological therapy. The outcomes were analyzed for clinical response/remission and endoscopic improvement/remission rates after induction therapy, drug persistency, and adverse events.

Results: A total of 70 UC patients were included and grouped into ustekinumab (11 patients), vedolizumab (40 patients), and tofacitinib (19 patients) treatments. The clinical response/remission rates after induction therapy were similar between ustekinumab (90.9/81.8%), vedolizumab (92.5/65.0%), and tofacitinib (94.7/73.7%). There were no significant differences in the endoscopic improvement/remission rates between the three groups: 90.9/18.2% for ustekinumab, 72.5/12.5% for vedolizumab, and 84.2/26.3% for tofacitinib. Drug persistence was similar across the three agents (p = 0.130). Three patients of the tofacitinib group experienced adverse events (herpes zoster and hypertriglyceridemia).

Conclusions: Based on real-world data, second-line biological therapy with ustekinumab, vedolizumab, and tofacitinib showed comparable efficacy in patients with moderate to severe UC with prior exposure to anti-TNF therapy.

Background/objectives: Elexacaftor, tezacaftor, and ivacaftor (ETI) have significantly improved lung function in people with cystic fibrosis (pwCF). Despite exceptional improvements in most cases, treatment-related inter-subject variability and drug-drug interactions that complicate modulator therapy have been reported.

Methods: This retrospective analysis presents data on the serum concentration of ETI in our pwCF with full or reduced dosage from August 2021 to December 2023 via routine therapeutic drug monitoring (TDM). The data were compared with the maximum drug concentrations (Cmax) from the pharmaceutical company's summary of product characteristics.

Results: A total of 786 blood samples from 155 pwCF (41% female, 59% male) were analyzed. The examinations were divided into four groups: full dose within the given tmax (38.5% of all measurements), full dose outside the tmax (29%), reduced dose within the tmax (19.2%), and reduced dose outside the tmax (13.2%). In pwCF receiving the full dose and blood taken within the tmax, 45.3% of serum concentrations of elexacaftor, 51.1% of serum concentrations of ivacaftor, and 8.9% of serum concentrations of tezacaftor were found to be above the Cmax, respectively. For those on reduced doses within the tmax, 24.5% had a serum concentration of elexacaftor, 23.2% had a serum concentration of ivacaftor, and 2.5% had a serum concentration of tezacaftor above the Cmax, respectively.

Conclusions: Many pwCF under ETI therapy have Cmax values for elexacaftor and ivacaftor above the recommended range, even on reduced doses or before the tmax was reached. This highlights the value of a TDM program. Further pharmacokinetic studies are necessary.

A dominant event determining the course of heart failure (HF) includes the disruption of the delicate sodium (Na⁺) and water balance leading to (Na⁺) and water retention and edema formation. Although incomplete decongestion adversely affects outcomes, it is unknown whether interventions directly targeting (Na⁺), such as strict dietary (Na⁺) restriction, intravenous hypertonic saline, and diuretics, reverse this effect. As a result, it is imperative to implement (Na⁺)-targeting interventions in selected HF patients with established congestion on top of quadruple therapy with angiotensin receptor neprilysin inhibitor, β-adrenergic receptor blocker, mineralocorticoid receptor antagonist, and sodium glucose cotransporter 2 inhibitor, which dramatically improves outcomes. The limited effectiveness of (Na⁺)-targeting treatments may be partly due to the fact that the current metrics of HF severity have a limited capacity of foreseeing and averting episodes of congestion and guiding (Na⁺)-targeting treatments, which often leads to dysnatremias, adversely affecting outcomes. Recent evidence suggests that spot urinary sodium measurements may be used as a guide to monitor (Na⁺)-targeting interventions both in chronic and acute HF. Further, the classical (2)-compartment model of (Na⁺) storage has been displaced by the (3)-compartment model emphasizing the non-osmotic accumulation of (Na⁺), chiefly in the skin. 23(Na⁺) magnetic resonance imaging (MRI) enables the accurate and reliable quantification of tissue (Na⁺). Another promising approach enabling tissue (Na⁺) monitoring is based on wearable devices employing ion-selective electrodes for electrolyte detection, including (Na⁺) and (Cl^-). Undoubtably, further studies using 23(Na⁺)-MRI technology and wearable sensors are required to learn more about the clinical significance of tissue (Na⁺) storage and (Na⁺)-related mechanisms of morbidity and mortality in HF.

"Nothing in life is to be feared, it is only to be understood [...].

Background: Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions.

Aim: This review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions.

Methods: A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included.

Results: The review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care.

Conclusions: Integrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.

Background: The use of microvascular grafts is the gold standard in oral and maxillofacial surgery for the reconstruction of soft tissue and bony and combined defects. Graft loss is one of the most serious complications in the field of reconstructive surgery. A comprehensive analysis of factors influencing this is, therefore, essential.

Methods: This hypothesis-generating study analyzed 251 patient cases of oral and maxillofacial surgery at the University Hospital Düsseldorf from 2016 to 2020 regarding patient- and therapy-specific parameters for their impact on graft survival.

Results: Statistically significant influencing factors were found among the 80 parameters examined: treatment with antiplatelet medication and a BMI ≥ 24.5 at the time of surgery had a positive influence on graft survival, while existing diabetes mellitus, atrial fibrillation, tracheostomy, and a longer operation time had a statistically relevant negative influence.

Conclusions: This work demonstrates the relevance of patient-specific risk stratification and the need for further research to develop a valid risk profile. Identifying high-risk patients with medium-sized defects, where alternatives to microvascular reconstruction are available, appears to be crucial for the clinical outcome.

In the original publication [...].

Background: Prostate cancer is the fourth most common cancer and eighth leading cause of cancer-related mortality worldwide. Its incidence is increasing in South Korea. This study aimed to investigate a predictive model for the 5-year survival probability of prostate cancer patients in a Korean primary care setting.

Method: This retrospective study used data from the nationwide insurance claims database. The main outcome was survival probability 5 years after the initial diagnosis of prostate cancer. Potential confounding factors such as age, body mass index (BMI), blood pressure, laboratory results, lifestyle behaviors, household income, and comorbidity index were considered. These variables were available in the national health check-up information. A Cox proportional hazards regression model was used to develop the predictive model. The predictive performance was calculated based on the mean area under the receiver operating characteristic curve (AUC) after 10-fold cross-validation.

Results: The mean 5-year survival probability was 82.0%. Age, fasting glucose and gamma-glutamyl transferase levels, current smoking, and multiple comorbidities were positively associated with mortality, whereas BMI, alkaline phosphatase levels, total cholesterol levels, alcohol intake, physical activity, and household income were inversely associated with mortality. The mean AUC after 10-fold cross-validation was 0.71.

Conclusions: The 5-year survival probability model showed a moderately good predictive performance. This may be useful in predicting the survival probability of prostate cancer patients in primary care settings. When interpreting these results, potential limitations, such as selection or healthy user biases, should be considered.

Background/objectives: Preeclampsia (PE) is a systemic disease that affects 4.6% of pregnancies. Despite the existence of a first-trimester screening for the prediction of preterm PE, no consensus exists regarding neither the right moment to end the pregnancy nor the appropriate variables to estimate the prognosis. The objective of this study was to obtain a prediction model for perinatal death in patients with preterm PE, useful for clinical practice.

Methods: Singleton pregnant women with PE and preterm delivery were included in an observational retrospective study. Multiple maternal and fetal variables were collected, and several multivariable logistic regression analyses were applied to construct models to predict perinatal death, selecting the most accurate and reproducible according to the highest area under the curve (AUC) and the lowest Akaike Information Criteria (AIC).

Results: A group of 148 pregnant women were included, and 18 perinatal deaths were registered. Univariable logistic regression selected as statistically significant variables the following: gestational age (GA) at admission, fetal sex, poor response to antihypertensive drugs, PlGF, umbilical artery (UA) pulsatility index (PI), cerebroplacental ratio (CPR), and absent/reversed ductus venosus (DV). The multivariable model, including all these parameters, presented an AUC of 0.95 and an AIC of 76.5. However, a model including only GA and fetal sex presented a similar accuracy with the highest simplicity (AUC 0.93, AIC 67.6). Finally, in fetuses with a similar GA, fetal death became dependent on PlGF and fetal sex, underlying the role of fetal sex in all circumstances.

Conclusions: Female fetal sex and low PlGF are notorious predictors of perinatal death in preterm PE, only surpassed by early GA at birth.