Communications medicine最新文献

Background: Preterm birth remains a leading cause of neonatal morbidity and mortality. It is classified as spontaneous, characterized by the unexpected onset of labor, or medically indicated, resulting from obstetric intervention due to pregnancy complications. The mechanisms underlying each subtype are incompletely understood, and obesity further modulates preterm birth risk through unclear biological pathways. This study aims to identify second trimester maternal plasma proteomic signatures distinguishing spontaneous and medically-indicated preterm birth and to determine how body mass index modifies these profiles.

Methods: In 100 pregnant individuals (30 spontaneous preterm birth, 30 medically-indicated preterm birth, 40 uncomplicated term deliveries), second trimester plasma was profiled using 7 K SomaScan v4.1 aptamer-based proteomic assay. Multivariate modeling and pathway analyses identified protein signatures distinguishing preterm birth subtypes, and computational network modeling with in silico perturbation analysis defined protein intermediates linking body mass index and preterm birth subtypes.

Results: Here we show distinct proteomic signatures among spontaneous preterm birth, medically-indicated preterm birth, and term deliveries. Supervised modeling achieves clear separation and identifies key discriminatory proteins including SIGLEC6, DHFR, UBASH3A, and PHB2. Early pregnancy body mass index substantially contributes to proteomic variance and modifies preterm birth associated expression of inflammatory (PROK2, IL36A), vascular (F11R) and oxidative stress (GLX1) proteins. Network perturbation identifies FABP4, CRP, UBE2G2, and LRP8 as critical intermediates linking body mass index and preterm birth.

Conclusions: Distinct proteomic profiles characterize spontaneous and medically-indicated preterm birth. Body mass index emerges as a key modifier of these molecular signatures, offering insight into the obesity-associated pathways underlying preterm birth.

Background: Postoperative pancreatic fistula (POPF) is the major driver of postoperative morbidity after pancreatoduodenectomy (PD) and a healthcare issue. In patients with pancreatic tumors the occurrence of POPF could lead to a complete failure of the oncologic strategy by delaying or annihilating the delivery of the indicated adjuvant chemotherapy. However, current preoperative prediction models lack precision. This study aimed to determine the ability of a high dimensional analysis of the patient's peripheral immune system before PD to predict POPF.

Methods: Twenty-two patients in the prospective IMMUNOPANC trial (NCT03978702) underwent PD. Blood samples collected preoperatively were analyzed by combining single-cell mass cytometry and a sparse machine-learning pipeline, Stabl, to identify the most relevant POPF-predictive features. The logistic regression model output was evaluated using a five-fold cross-validation procedure.

Results: Eight (36%) patients experience POPF (grade B, n = 7; grade C, n = 1). The multivariable predictive model includes 11 features-six natural killer, three CD8⁺ T, and two CD4⁺ T lymphocyte cell clusters-revealing a preoperative POPF lymphocyte signature (Pancreatic Fistula Lymphocyte Signature, PFLS). The Stabl algorithm identifies a predictive model classifying POPF patients with high performance (area under the receiver operating characteristic curve=0.81, P = 2.04e-02).

Conclusions: In summary, preoperative circulating immune-cell composition can predict POPF in patients undergoing PD. The clinical application of the PFLS may enable the early identification of patients at high risk before pancreatic surgery, giving clinicians the opportunity to anticipate and mitigate POPF risk through tailored strategies in pre-, intra-, and post-operative settings.

Background: Persistent human-to-mosquito parasite transmission hinders malaria control in high burden settings. Understanding the human transmission reservoir can support the design of targeted interventions to reduce transmission.

Methods: In a year-long cohort study in rural Malawi, we used molecular methods to detect all Plasmodium falciparum (Pf) infections and those containing gametocytes, the parasite stage required for transmission, longitudinally at routine surveillance and sick visits. Using population-level analyses, we determined the demographic, temporal, and spatial clustering of infections containing gametocytes and gametocyte density, which predicts transmission.

Results: Here we show that gametocytes are not randomly distributed among the population or among individuals with Pf infections; gametocytes are detected in only 23% of the population. Among all participants, school-age children have significantly higher incidence of gametocyte-containing infections and high-density gametocyte infections compared to other groups. The presence of school-age children is a key driver of gametocyte frequencies and densities within households, even after adjusting for Pf infection levels. Based on the total gametocyte abundance in the population, we estimate that clearing infections from asymptomatic school-age children in the rainy season would decrease gametocyte abundance by 67% in the population.

Conclusions: School-age children are the primary driver of ongoing Pf transmission in Malawi and interventions targeting school-age children are needed to effectively reduce Pf infection risk at a population level.

Background: Progesterone receptor antagonists such as mifepristone have emerged as candidates for breast cancer prevention, particularly in high-risk populations such as BRCA1/2 pathogenic variant carriers. However, their impact on endometrial safety remains insufficiently characterized, raising concerns about unopposed oestrogen stimulation in the setting of impaired DNA repair. This study reports secondary outcomes evaluating short-term endometrial effects of mifepristone in this high-risk population.

Methods: We previously conducted a randomized, double-blind, placebo-controlled trial (NCT01898312) involving 45 premenopausal women with BRCA1/2 pathogenic variants. Participants received mifepristone (50 mg every other day, n = 30) or a non-hormonal comparator (n = 15) for three months. Here we present secondary outcomes from the trial: Paired endometrial biopsies from a subset of 14 participants were analysed using transcriptomic, DNA methylation, and cell-type deconvolution methods. Statistical comparisons were performed using paired and unpaired Wilcoxon tests.

Results: Here we show that mifepristone induces amenorrhea in all treated participants without increasing epithelial cell proportions, the compartment most relevant to endometrial carcinogenesis. Multi-omics analyses reveal no molecular signatures consistent with oncogenic transformation. DNA methylation and gene expression indices associated with endometrial cancer remain stable after treatment, even after adjusting for age and cell composition.

Conclusions: Short-term mifepristone exposure does not produce molecular changes linked to endometrial carcinogenesis in BRCA1/2 pathogenic variant carriers. These findings provide important safety data for the future development of progesterone receptor modulators in cancer prevention. Long-term studies are needed to confirm these observations.

Background: Accurate health assessments in the Emergency Department are critical for guiding treatment decisions. However, these assessments are often heavily influenced by chronological age, which does not reflect the variability in physiological health among patients, particularly older adults. Biological age estimation based on biomarkers may better indicate a patient's true health status. However, its use in acute care Emergency Department settings is not established. This study introduces the concepts and corresponding estimates of Acute Biological Age and Acute Difference in Age in the Emergency Department setting and assesses their association with in-hospital care needs following acute conditions.

Methods: Using data from a prospective study of 6071 Emergency Department patients aged over 18, Acute Biological Age was calculated by converting 30-day mortality risk estimates from a machine-learning model incorporating fifteen biomarkers into biological age expressed in years. Acute Difference in Age was derived from the difference between Acute Biological Age and chronological age. Logistic regressions were used to analyze associations between these estimates and twenty predefined events requiring in-hospital care, with a detailed analysis conducted on nine specific events.

Results: Here, we show that Acute Biological Age and Acute Difference in Age are significantly associated with hospitalization risk. Each additional year of Acute Biological Age increases the odds of requiring intravenous treatments, in-hospital care, extended stays, and admission to intensive care, with odds ratios ranging from 1.03 to 1.05.

Conclusions: Acute Biological Age and Acute Difference in Age are promising metrics to identify high-risk Emergency Department patients and may improve resource allocation in acute care settings.

Background: Prior investigations of the impact of social determinants of health (SDH) on people with primary central nervous system cancers (PCNSC) have considered limited SDH-factors and pathologies. This study examines how the Social Vulnerability Index (SVI) influences disparities in outcome for people with PCNSC across the United States (US).

Methods: This population-based retrospective cohort study assessed adults with PCNSC between 1975-2017 from the Surveillance-Epidemiology-End Results database, categorized using the Central Brain-Tumor-Registry of the US (CBTRUS) classifications. SDH-vulnerability/SVI scores were assigned to patients' county-of-residence based on 15 SDH-factors of socioeconomic status (SES), proportions using a minority language (ML), household composition (HC), and infrastructure/housing-transportation (HT) characteristics, plus an overall composite of these four themes. Survival and logistic regressions were obtained for survival period and multimodal treatment receipt across all PCNSC-patients based on their SVI-scores/SDH-vulnerability.

Results: Across 116,373 PCNSC-patients (64,841 [55.7%] male; 92,476 [79.5%] non-Hispanic white race-ethnicity), increasing overall SDH-vulnerability is associated with relative mean survival period decreases between 22.12%-45.81% across 8/8 CBTRUS-classes, with the largest-magnitude effects among HC, HT, and SES-vulnerabilities. There are decreased odds of external beam radiation for 4/8 CBTRUS-classes (lowest-embryonal: OR, 0.87; 95%CI, 0.80-0.96) and surgery for 3/8 CBTRUS classes (lowest-oligodendroglial: OR 0.96; 95%CI 0.95-0.97). The largest-magnitude effects are among those using ML, followed by impact of HC and HT characteristics.

Conclusions: SDH-vulnerability associates with worse survival and treatment effects for PCNSC patients. Some specific SDH more strongly influence disparity associations, so resources could be focused more on reducing these disparity drivers.

Background: Maternal chronic kidney disease (CKD) is associated with an increased risk of adverse pregnancy outcomes. However, the overall risk of congenital malformations (CMs) in offspring of mothers with kidney disease, including CKD and end-stage kidney disease (ESKD), remains unclear.

Methods: In this nationwide cohort study, we analyzed National Health Insurance Service (NHIS) data from 2,680,092 women who gave birth between 2008 and 2017. Major CMs were identified using the International Classification of Diseases-10 (ICD-10) codes during the first 12 months after birth. A multivariable generalized estimating equation model was used to compare the risk of CMs between women with CKD or ESKD, including those on dialysis and post-kidney transplantation (KT), and healthy controls.

Results: Major CMs prevalence is 4.79% in offspring of healthy mothers, 5.29% in CKD mothers, and 9.65% in ESKD mothers, with congenital heart defects being the most common anomaly across all groups. After adjustment, mothers with kidney diseases show a higher risk of major CMs than healthy controls (adjusted odds ratio [aOR], 1.07; 95% confidence interval [CI], 1.03-1.11 in CKD; aOR, 1.71; 95% CI, 1.16-2.52 in ESKD, respectively). Among ESKD patients, KT recipients show an increased risk (aOR, 1.65; 95% CI, 1.06-2.59), but dialysis patients do not reach statistical significance (aOR, 2.02; 95% CI, 0.92-4.41).

Conclusions: Our findings suggest that neonates born to mothers with kidney diseases have an increased risk of CMs compared to those born to healthy mothers.

Background: Parkinson's disease is a progressive neurodegenerative disorder with both motor and non-motor symptoms. Mental and behavioural non-motor symptoms such as cognitive impairment, sleep disturbances, depression, and anxiety greatly affect quality of life but remain difficult to assess with traditional tools. Artificial intelligence has shown potential in healthcare, yet its role in evaluating these symptoms in Parkinson's disease remains under-reviewed. This systematic review aims to evaluate the performance of artificial intelligence tools in diagnosing, assessing, and managing these symptoms.

Methods: Five databases (Medline, Embase, Scopus, Web of Science and PubMed) were searched up to June 2024 for peer-reviewed studies applying artificial intelligence to mental or behavioural symptoms in adults with Parkinson's disease. Studies published before 2010 or lacking artificial-intelligence technologies were excluded. Study quality and risk of bias were assessed using QUADAS-2. Extracted data include study objectives, data sources, algorithms, best model, and diagnostic performance (accuracy, sensitivity, specificity). The study received no external financial support.

Results: Here we show sixteen studies examine cognitive impairment and seven examine sleep disorders. However, only three studies focus on depression and one on anxiety, revealing a research gap. No meta-analysis was performed due to heterogeneity.

Conclusions: Artificial intelligence shows promise for assessing mental and behavioural symptoms in Parkinson's disease, particularly cognitive and sleep disorders. Multimodal models demonstrate higher accuracy than single-source models, though external validation is necessary. The limited studies on depression and anxiety reflect existing diagnostic challenges and data limitations. Future research should refine diagnostic tools and expand multimodal approaches to these symptoms.

Background: Vascular properties of the retina are indicative of both ocular and systemic cardio- and cerebrovascular health. However, the specific relationships between retinal and non-retinal vascular phenotypes have not been systematically investigated in large samples. This study aims to compare cross-organ phenotypic and genetic relationships between vascular characteristics across different body sites.

Methods: We compared vascular image-derived phenotypes from the brain, carotid artery, aorta, and retina, using UK Biobank sample sizes ranging from 18,808 to 68,740 participants. We examined phenotypic and genetic correlations, as well as common associated genes and pathways.

Results: Here we show that white matter hyperintensities are positively correlated with carotid intima-media thickness (r = 0.03), lumen diameter (r = 0.14), and aortic cross-sectional areas (r = 0.09), but negatively correlated with aortic distensibilities (r ≤ -0.05). Arterial retinal vascular density shows negative correlations with white matter hyperintensities (r = -0.04), intima-media thickness (r = -0.04), lumen diameter (r = -0.06), and aortic areas (r = -0.05), while positively correlating with aortic distensibilities (r = 0.04). Significant correlations also persist after correcting for hypertension.

Conclusions: Our findings shed light on the complex interplay between vascular morphology across different organs, revealing both shared and distinct genetic underpinnings. Retinal vascular features reflect broader systemic vascular morphology and offer an accessible window into cardio- and cerebrovascular health.

Background: The population distribution of age-related functional impairments (ARFIs) and their associations with mortality and life expectancy (LE) among Chinese adults remain poorly understood.

Methods: We included 12,906 participants (mean age: 62.6 years) from the China Health and Retirement Longitudinal Study. Visual impairment, hearing impairment, cognitive impairment, sleep disorder, depressive symptoms, and disability in activities of daily living (ADL) were assessed. Cox proportional hazards models were used to estimate the associations of ARFIs with all-cause mortality. Life expectancy at age 50 was estimated by the presence and number of key ARFIs.

Results: The six ARFIs exhibit distinct distributions by ages and provinces across China. During the 9-year follow-up, ADL disability, cognitive impairment, and depressive symptoms are independently associated with 64% (95% confidence interval [CI]: 48%-80%), 41% (21%-64%), and 20% (8%-33%) higher risks of mortality, corresponding to LE losses of 4.45, 3.08, and 1.59 years at the age of 50 years. A greater number of key ARFIs is associated with higher mortality risk in a dose-response manner (hazard ratios [95% CI] vs. none: 1.23 [1.11-1.36] for one, 1.42 [1.26-1.61] for two, and 1.86 [1.47-2.36] for three) and greater LE loss (1.63 [1.35-1.90] years for one, 3.37 [3.02-3.71] for two, and 4.96 [4.22-5.71] for three, with three ARFIs accounting for 17% of total LE).

Conclusions: The study highlights the important roles of co-existing ARFIs in mortality and LE loss. Integrated prevention strategies and management systems for multiple functional impairments are warranted in the context of rapid population aging.