Epma Journal最新文献

Purpose: Vision loss in glaucoma is not only associated with elevated intraocular pressure and neurodegeneration, but vascular dysregulation (VD) is a major factor. To optimize therapy, an improved understanding of concepts of predictive, preventive, and personalized medicine (3PM) is needed which is based on a more detailed understanding of VD pathology. Specifically, to learn if the root cause of glaucomatous vision loss is of neuronal (degeneration) or vascular origin, we now studied neurovascular coupling (NVC) and vessel morphology and their relationship to vision loss in glaucoma.

Methods: In patients with primary open angle glaucoma (POAG) (n = 30) and healthy controls (n = 22), NVC was studied using dynamic vessel analyzer to quantify retinal vessel diameter before, during, and after flicker light stimulation to evaluate the dilation response following neuronal activation. Vessel features and dilation were then related to branch level and visual field impairment.

Results: Retinal arterial and venous vessels had significantly smaller diameters in patients with POAG in comparison to controls. However, both arterial and venous dilation reached normal values during neuronal activation despite their smaller diameters. This was largely independent of visual field depth and varied among patients.

Conclusions: Because dilation/constriction is normal, VD in POAG can be explained by chronic vasoconstriction which limits energy supply to retinal (and brain) neurons with subsequent hypo-metabolism ("silent" neurons) or neuronal cell death. We propose that the root cause of POAG is primarily of vascular and not neuronal origin. This understanding can help to better personalize POAG therapy of not only targeting eye pressure but also vasoconstriction to prevent low vision, slowing its progression and supporting recovery and restoration.

Trial registration: ClinicalTrials.gov, # NCT04037384 on July 3, 2019.

A form of genomic alteration called microsatellite instability (MSI) occurs in a class of tandem repeats (TRs) called microsatellites (MSs) or short tandem repeats (STRs) due to the failure of a post-replicative DNA mismatch repair (MMR) system. Traditionally, the strategies for determining MSI events have been low-throughput procedures that typically require assessment of tumours as well as healthy samples. On the other hand, recent large-scale pan-tumour studies have consistently highlighted the potential of massively parallel sequencing (MPS) on the MSI scale. As a result of recent innovations, minimally invasive methods show a high potential to be integrated into the clinical routine and delivery of adapted medical care to all patients. Along with advances in sequencing technologies and their ever-increasing cost-effectiveness, they may bring about a new era of Predictive, Preventive and Personalised Medicine (3PM). In this paper, we offered a comprehensive analysis of high-throughput strategies and computational tools for the calling and assessment of MSI events, including whole-genome, whole-exome and targeted sequencing approaches. We also discussed in detail the detection of MSI status by current MPS blood-based methods and we hypothesised how they may contribute to the shift from conventional medicine to predictive diagnosis, targeted prevention and personalised medical services. Increasing the efficacy of patient stratification based on MSI status is crucial for tailored decision-making. Contextually, this paper highlights drawbacks both at the technical level and those embedded deeper in cellular/molecular processes and future applications in routine clinical testing.

Purpose: This study assessed sleep quality in patients with burn scars and investigated risk factors of sleep disorders to guide clinical therapy. From the strategy of predictive, preventive, and personalized medicine (PPPM/3PM), we proposed that risk assessment based on clinical indicators could prompt primary prediction, targeted prevention, and personalized interventions to improve the management of sleep disorders present in patients with burn scars.

Methods: This retrospective study recruited patients with burn scars and healthy volunteers from the Shanghai Burn Treatment Center between 2017 and 2022. Relevant information and data, including demographic characteristics, scar evaluation, and sleep quality, were obtained through the hospital information system, classical scar scale, and self-report questionnaires. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and monitored using a cardiopulmonary-coupled electrocardiograph. Pain and pruritus were assessed using the visual analog scale (VAS). Scar appearance was assessed using the modified Vancouver scar scale (mVSS).

Results: The sample was comprised of 128 hypertrophic scar (HS) patients, with 61.7% males, a mean age of 41.1 ± 11.6 years, and burn area of 46.2 ± 27.9% total body surface area (TBSA). Patients with PSQI ≥ 7 accounted for 76.6%, and the global PSQI score was 9.4 ± 4.1. Objective sleep data showed that initial enter deep sleep time, light sleep time, awakening time, light sleep efficiency, and sleep apnea index were higher but deep sleep time, sleep efficiency, and deep sleep efficiency were lower in HS patients than that in healthy controls. Preliminary univariate analysis showed that age, hyperplasia time of scar, narrow airway, microstomia, VAS for pain and pruritus, and mVSS total (comprised of pigmentation, vascularity, height and pliability) were associated with the PSQI score (p < 0.1). Multivariable linear regression showed narrow airway, VAS for pain and pruritus, and mVSS specifically height, were the risk factors for PSQI score (p < 0.1).

Conclusions: This study model identified that narrow airway, pain, pruritus and scar appearance specifically height may provide excellent predictors for sleep disorders in HS patients. Our results provided a basis for the predictive diagnostics, targeted prevention, and individualized therapy of somnipathy predisposition and progression of HS patients in the setting of PPPM/3PM health care system, which contributed to a paradigm shift from reactive cure to advanced therapy.

Objectives: Type 2 diabetes mellitus (T2DM), a major metabolic disorder, is expanding at a rapidly rising worldwide prevalence and has emerged as one of the most common chronic diseases. Suboptimal health status (SHS) is considered a reversible intermediate state between health and diagnosable disease. We hypothesized that the time frame between the onset of SHS and the clinical manifestation of T2DM is the operational area for the application of reliable risk assessment tools, such as immunoglobulin G (IgG) N-glycans. From the viewpoint of predictive, preventive, and personalized medicine (PPPM/3PM), the early detection of SHS and dynamic monitoring by glycan biomarkers could provide a window of opportunity for targeted prevention and personalized treatment of T2DM.

Methods: Case-control and nested case-control studies were performed and consisted of 138 and 308 participants, respectively. The IgG N-glycan profiles of all plasma samples were detected by an ultra-performance liquid chromatography instrument.

Results: After adjustment for confounders, 22, five, and three IgG N-glycan traits were significantly associated with T2DM in the case-control setting, baseline SHS, and baseline optimal health participants from the nested case-control setting, respectively. Adding the IgG N-glycans to the clinical trait models, the average area under the receiver operating characteristic curves (AUCs) of the combined models based on repeated 400 times fivefold cross-validation differentiating T2DM from healthy individuals were 0.807 in the case-control setting and 0.563, 0.645, and 0.604 in the pooled samples, baseline SHS, and baseline optimal health samples of nested case-control setting, respectively, which presented moderate discriminative ability and were generally better than models with either glycans or clinical features alone.

Conclusions: This study comprehensively illustrated that the observed altered IgG N-glycosylation, i.e., decreased galactosylation and fucosylation/sialylation without bisecting GlcNAc, as well as increased galactosylation and fucosylation/sialylation with bisecting GlcNAc, reflects a pro-inflammatory state of T2DM. SHS is an important window period of early intervention for individuals at risk for T2DM; glycomic biosignatures as dynamic biomarkers have the ability to identify populations at risk for T2DM early, and the combination of evidence could provide suggestive ideas and valuable insight for the PPPM of T2DM.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00311-3.

Currently colorectal cancer (CRC) is the third most prevalent cancer worldwide. Body mass index (BMI) is frequently used in CRC screening and risk assessment to quantitatively evaluate weight. However, the impact of BMI on clinical strategies for CRC has received little attention. Within the framework of the predictive, preventive, and personalized medicine (3PM/PPPM), we hypothesized that BMI stratification would affect the primary, secondary, and tertiary care options for CRC and we conducted a critical evidence-based review. BMI dynamically influences CRC outcomes, which helps avoiding adverse treatment effects. The outcome of surgical and radiation treatment is adversely affected by overweight (BMI ≥ 30) or underweight (BMI < 20). A number of interventions, such as enhanced recovery after surgery and robotic surgery, can be applied to CRC at all levels of BMI. BMI-controlling modalities such as exercise, diet control, nutritional therapy, and medications may be potentially beneficial for patients with CRC. Patients with overweight are advised to lose weight through diet, medication, and physical activity while patients suffering of underweight require more focus on nutrition. BMI assists patients with CRC in better managing their weight, which decreases the incidence of adverse prognostic events during treatment. BMI is accessible, noninvasive, and highly predictive of clinical outcomes in CRC. The cost-benefit of the PPPM paradigm in developing countries can be advanced, and the clinical benefit for patients can be improved with the promotion of BMI-based clinical strategy models for CRC.

Background: Currently, the rate of recurrence or metastasis (ROM) remains high in rectal cancer (RC) patients treated with the standard regimen. The potential of diffusion-weighted imaging (DWI) in predicting ROM risk has been reported, but the efficacy is insufficient.

Aims: This study investigated the potential of a new sequence called readout-segmented echo-planar imaging (RS-EPI) DWI in predicting the ROM risk of patients with RC using machine learning methods to achieve the principle of predictive, preventive, and personalized medicine (PPPM) application in RC treatment.

Methods: A total of 195 RC patients from two centres who directly received total mesorectal excision were retrospectively enrolled in our study. Machine learning methods, including recursive feature elimination (RFE), the synthetic minority oversampling technique (SMOTE), and the support vector machine (SVM) classifier, were used to construct models based on clinical-pathological factors (clinical model), radiomic features from RS-EPI DWI (radiomics model), and their combination (merged model). The Harrell concordance index (C-index) and the area under the time-dependent receiver operating characteristic curve (AUC) were calculated to evaluate the predictive performance at 1 year, 3 years, and 5 years. Kaplan‒Meier analysis was performed to evaluate the ability to stratify patients according to the risk of ROM.

Findings: The merged model performed well in predicting tumour ROM in patients with RC at 1 year, 3 years, and 5 years in both cohorts (AUC = 0.887/0.813/0.794; 0.819/0.795/0.783) and was significantly superior to the clinical model (AUC = 0.87 [95% CI: 0.80-0.93] vs. 0.71 [95% CI: 0.59-0.81], p = 0.009; C-index = 0.83 [95% CI: 0.76-0.90] vs. 0.68 [95% CI: 0.56-0.79], p = 0.002). It also had a significant ability to differentiate patients with a high and low risk of ROM (HR = 12.189 [95% CI: 4.976-29.853], p < 0.001; HR = 6.427 [95% CI: 2.265-13.036], p = 0.002).

Conclusion: Our developed merged model based on RS-EPI DWI accurately predicted and effectively stratified patients with RC according to the ROM risk at an early stage with an individualized profile, which may be able to assist physicians in individualizing the treatment protocols and promote a meaningful paradigm shift in RC treatment from traditional reactive medicine to PPPM.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00303-3.