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BACKGROUND: Sepsis is a heterogeneous syndrome. This study aimed to identify new sepsis sub-phenotypes using plasma cortisol trajectory. METHODS: This retrospective study included patients with sepsis admitted to the intensive care unit of Zhongshan Hospital Fudan University between March 2020 and July 2022. A group-based cortisol trajectory model was used to classify septic patients into different sub-phenotypes. The clinical characteristics, biomarkers, and outcomes were compared between sub-phenotypes. RESULTS: A total of 258 patients with sepsis were included, of whom 186 were male. Patients were divided into two trajectory groups: the lower-cortisol group (n = 217) exhibited consistently low and slowly declining cortisol levels, while the higher-cortisol group (n = 41) showed relatively higher levels in comparison. The 28-day mortality (65.9% vs.16.1%, P < 0.001) and 90-day mortality (65.9% vs. 19.8%, P < 0.001) of the higher-cortisol group were significantly higher than the lower-cortisol group. Multivariable Cox regression analysis showed that the trajectory sub-phenotype (HR = 5.292; 95% CI 2.218-12.626; P < 0.001), APACHE II (HR = 1.109; 95% CI 1.030-1.193; P = 0.006), SOFA (HR = 1.161; 95% CI 1.045-1.291; P = 0.006), and IL-1ß (HR = 1.001; 95% CI 1.000-1.002; P = 0.007) were independent risk factors for 28-day mortality. Besides, the trajectory sub-phenotype (HR = 4.571; 95% CI 1.980-10.551; P < 0.001), APACHE II (HR = 1.108; 95% CI 1.043-1.177; P = 0.001), SOFA (HR = 1.270; 95% CI 1.130-1.428; P < 0.001), and IL-1ß (HR = 1.001; 95% CI 1.000-1.001; P = 0.015) were also independent risk factors for 90-day mortality. CONCLUSION: This study identified two novel cortisol trajectory sub-phenotypes in patients with sepsis. The trajectories were associated with mortality, providing new insights into sepsis classification.

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BACKGROUND: In patients who experience out-of-hospital cardiac arrest (OHCA), it is important to assess the association of sub-phenotypes identified by latent class analysis (LCA) using pre-hospital prognostic factors and factors measurable immediately after hospital arrival with neurological outcomes at 30 days, which would aid in making treatment decisions. METHODS: This study retrospectively analyzed data obtained from the Japanese OHCA registry between June 2014 and December 2019. The registry included a complete set of data on adult patients with OHCA, which was used in the LCA. The association between the sub-phenotypes and 30-day survival with favorable neurological outcomes was investigated. Furthermore, adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by multivariate logistic regression analysis using in-hospital data as covariates. RESULTS: A total of, 22,261 adult patients who experienced OHCA were classified into three sub-phenotypes. The factor with the highest discriminative power upon patient's arrival was Glasgow Coma Scale followed by partial pressure of oxygen. Thirty-day survival with favorable neurological outcome as the primary outcome was evident in 66.0% participants in Group 1, 5.2% in Group 2, and 0.5% in Group 3. The 30-day survival rates were 80.6%, 11.8%, and 1.3% in groups 1, 2, and 3, respectively. Logistic regression analysis revealed that the ORs (95% CI) for 30-day survival with favorable neurological outcomes were 137.1 (99.4-192.2) for Group 1 and 4.59 (3.46-6.23) for Group 2 in comparison to Group 3. For 30-day survival, the ORs (95%CI) were 161.7 (124.2-212.1) for Group 1 and 5.78 (4.78-7.04) for Group 2, compared to Group 3. CONCLUSIONS: This study identified three sub-phenotypes based on the prognostic factors available immediately after hospital arrival that could predict neurological outcomes and be useful in determining the treatment strategy of patients experiencing OHCA upon their arrival at the hospital.

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PURPOSE: Bipolar affective disorder (BP) causes major functional impairment and reduced quality of life not only for patients, but also for many close relatives. We aimed to investigate mRNA levels in BP patients to find differentially expressed genes linked to specific clinical course variants; assuming that several gene expression alterations might indicate vulnerability pathways for specific course and severity of the disease. MATERIALS: We searched for up- and down-regulated genes comparing patients with diagnosis of BP type I (BPI) vs type II (BPII), history of suicide attempts, psychotic symptoms, predominance of manic/hypomanic episodes, and history of numerous episodes and comorbidity of substance use disorders or anxiety disorders. RNA was extracted from peripheral blood mononuclear cells and analyzed with use of microarray slides. RESULTS: Differentially expressed genes (DEGs) were found in all disease characteristics compared. The lowest number of DEGs were revealed when comparing BPI and BPII patients (18 genes), and the highest number when comparing patients with and without psychotic symptoms (3223 genes). Down-regulated genes identified here with the use of the DAVID database were among others linked to cell migration, defense response, and inflammatory response. CONCLUSIONS: The most specific transcriptome profile was revealed in BP with psychotic symptoms. Differentially expressed genes in this variant include, among others, genes involved in inflammatory and immune processes. It might suggest the overlap of biological background between BP with a history of psychotic features and schizophrenia.

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Pediatric acute respiratory distress syndrome(PARDS)is a pulmonary inflammation syndrome caused by a variety of proinflammatory factors induced by many causes, which is mainly characterized by noncardiogenic pulmonary edema.The main pathophysiological feature is the destruction of the integrity of the alveolar capillary membrane, and the loss of the alveolar epithelial-endothelial barrier function.In the PARDS′s clinical practice, the mainstay of the treatment is supportive.Although there is still no clear definition and general consensus or guidelines, appropriate liquid therapy is an important part of non-ventilatory treatment measures.Proper fluid management strategy is helpful to improve pulmonary edema, maintain normal circulatory perfusion, prevent functional failure of important organs and improve the prognosis of patients.According to volume status, implementing the goal-oriented and phased differentiated fluid management strategy is significant for the therapy of PARDS patients.However, the effects of fluid strategy management according to PARDS phenotypes remain to be evaluated.

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Objective: The aim of this study was to identify clinical and laboratory phenotype distribution patterns and their usefulness as prognostic markers in COVID-19 patients admitted to the intensive care unit (ICU) at Tygerberg Hospital, Cape Town. Methods and results: A latent class analysis (LCA) model was applied in a prospective, observational cohort study. Data from 343 COVID-19 patients were analysed. Two distinct phenotypes (1 and 2) were identified, comprising 68.46% and 31.54% of patients, respectively. The phenotype 2 patients were characterized by increased coagulopathy markers (D-dimer, median value 1.73 ng/L vs 0.94 ng/L; p < 0.001), end-organ dysfunction (creatinine, median value 79 µmol/L vs 69.5 µmol/L; p < 0.003), under-perfusion markers (lactate, median value 1.60 mmol/L vs 1.20 mmol/L; p < 0.001), abnormal cardiac function markers (median N-terminal pro-brain natriuretic peptide (NT-proBNP) 314 pg/ml vs 63.5 pg/ml; p < 0.001 and median high-sensitivity cardiac troponin (Hs-TropT) 39 ng/L vs 12 ng/L; p < 0.001), and acute inflammatory syndrome (median neutrophil-to-lymphocyte ratio 15.08 vs 8.68; p < 0.001 and median monocyte value 0.68 × 109/L vs 0.45 × 109/L; p < 0.001). Conclusion: The identification of COVID-19 phenotypes and sub-phenotypes in ICU patients could help as a prognostic marker in the day-to-day management of COVID-19 patients admitted to the ICU.

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Clinical EHR data is naturally heterogeneous, where it contains abundant sub-phenotype. Such diversity creates challenges for outcome prediction using a machine learning model since it leads to high intra-class variance. To address this issue, we propose a supervised pre-training model with a unique embedded k-nearest-neighbor positive sampling strategy. We demonstrate the enhanced performance value of this framework theoretically and show that it yields highly competitive experimental results in predicting patient mortality in real-world COVID-19 EHR data with a total of over 7,000 patients admitted to a large, urban health system. Our method achieves a better AUROC prediction score of 0.872, which outperforms the alternative pre-training models and traditional machine learning methods. Additionally, our method performs much better when the training data size is small (345 training instances).

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The modulators of severe COVID-19 have emerged as the most intriguing features of SARS-CoV-2 pathogenesis. This is especially true as we are encountering variants of concern (VOC) with increased transmissibility and vaccination breakthroughs. Microbial co-infections are being investigated as one of the crucial factors for exacerbation of disease severity and complications of COVID-19. A key question remains whether early transcriptionally active microbial signature/s in COVID-19 patients can provide a window for future disease severity susceptibility and outcome? Using complementary metagenomics sequencing approaches, respiratory virus oligo panel (RVOP) and Holo-seq, our study highlights the possible functional role of nasopharyngeal early resident transcriptionally active microbes in modulating disease severity, within recovered patients with sub-phenotypes (mild, moderate, severe) and mortality. The integrative analysis combines patients' clinical parameters, SARS-CoV-2 phylogenetic analysis, microbial differential composition, and their functional role. The clinical sub-phenotypes analysis led to the identification of transcriptionally active bacterial species associated with disease severity. We found significant transcript abundance of Achromobacter xylosoxidans and Bacillus cereus in the mortality, Leptotrichia buccalis in the severe, Veillonella parvula in the moderate, and Actinomyces meyeri and Halomonas sp. in the mild COVID-19 patients. Additionally, the metabolic pathways, distinguishing the microbial functional signatures between the clinical sub-phenotypes, were also identified. We report a plausible mechanism wherein the increased transcriptionally active bacterial isolates might contribute to enhanced inflammatory response and co-infections that could modulate the disease severity in these groups. Current study provides an opportunity for potentially using these bacterial species for screening and identifying COVID-19 patient sub-groups with severe disease outcome and priority medical care. IMPORTANCE COVID-19 is invariably a disease of diverse clinical manifestation, with multiple facets involved in modulating the progression and outcome. In this regard, we investigated the role of transcriptionally active microbial co-infections as possible modulators of disease pathology in hospital admitted SARS-CoV-2 infected patients. Specifically, can there be early nasopharyngeal microbial signatures indicative of prospective disease severity? Based on disease severity symptoms, the patients were segregated into clinical sub-phenotypes: mild, moderate, severe (recovered), and mortality. We identified significant presence of transcriptionally active isolates, Achromobacter xylosoxidans and Bacillus cereus in the mortality patients. Importantly, the bacterial species might contribute toward enhancing the inflammatory responses as well as reported to be resistant to common antibiotic therapy, which together hold potential to alter the disease severity and outcome.

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BACKGROUND: Sickle cell anemia (SCA) patients exhibit sub-phenotypes associated to hemolysis and vaso-occlusion. The disease has a chronic inflammatory nature that has been also associated to alterations in the lipid profile. This study aims to analyze hematological and biochemical parameters to provide knowledge about the SCA sub-phenotypes previously described and suggest a dyslipidemic sub-phenotype. METHODS: A cross-sectional study was conducted from 2013 to 2014, and 99 SCA patients in steady state were enrolled. We assessed correlations and associations with hematological and biochemical data and investigated the co-inheritance of -α3.7Kb-thalassemia (-α3.7Kb-thal). Correlation analyses were performed using Spearman and Pearson coefficient. The median of quantitative variables between two groups was compared using t-test and Mann-Whitney. P-values <0.05 were considered statistically significant. RESULTS: We found significant association of high lactate dehydrogenase levels with decreased red blood cell count and hematocrit as well as high levels of total and indirect bilirubin. SCA patients with low nitric oxide metabolites had high total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol and reduced very low-density cholesterol, triglycerides, direct bilirubin level and reticulocyte counts. In SCA patients with high-density lipoprotein cholesterol greater than 40 mg/dL, we observed increased red blood cell count, hemoglobin, hematocrit, and fetal hemoglobin and decreased nitric oxide metabolites levels. The presence of -α3.7Kb-thal was associated with high red blood cell count and low mean corpuscular volume, mean corpuscular hemoglobin, platelet count and total and indirect bilirubin levels. CONCLUSIONS: Our results provide additional information about the association between biomarkers and co-inheritance of -α3.7Kb-thal in SCA, and suggest the role of dyslipidemia and nitric oxide metabolites in the characterization of this sub-phenotype.

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Glutamatergic neurotransmission has been shown to be dysregulated in bipolar disorder (BD), alcohol use disorder (AUD) and substance use disorder (SUD). Similarly, disruption in the hypothalamic-pituitary-adrenal (HPA)-axis has also been observed in these conditions. BD is often comorbid with AUD and SUD. The effects of the glutamatergic and HPA systems have not been extensively examined in individuals with BD-AUD and BD-SUD comorbidity. The aim of this investigation was to determine whether variants in the glutamatergic pathway and HPA-axis are associated with BD-AUD and BD-SUD comorbidity. The research cohort consisted of 498 individuals with BD type I from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). A subset of the cohort had comorbid current AUD and current SUD. A total of 1935 SNPs from both the glutamatergic and HPA pathways were selected from the STEP-BD genome-wide dataset. To identify population stratification, IBS clustering was performed using the program Plink 1.07. Single SNP association and gene-based association testing were conducted using logistic regression. A pathway analysis of glutamatergic and HPA genes was performed, after imputation using IMPUTE2. No single SNP was associated with BD-AUD or BD-SUD comorbidity after correction for multiple testing. However, from the gene-based analysis, the gene PRKCI was significantly associated with BD-AUD. The pathway analysis provided overall negative findings, although several genes including GRIN2B showed high percentage of associated SNPs for BD-AUD. Even though the glutamatergic and HPA pathways may not be involved in BD-AUD and BD-SUD comorbidity, PRKCI deserves further investigation in BD-AUD.

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Endometriosis is a heritable complex disorder that is influenced by multiple genetic and environmental factors. Identification of these genetic factors will aid a better understanding of the underlying biology of the disease. In this article, we describe different methods of studying genetic variation of endometriosis, summarize results from genetic studies performed to date and provide recommendations for future studies to uncover additional factors contributing to the heritable component of endometriosis.

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Oral clefts are common birth defects that have a major impact on the affected individual, their family and society. World-wide, the incidence of oral clefts is 1/700 live births, making them the most common craniofacial birth defects. The successful prediction of oral clefts may help identify sub-population at high risk, and promote new diagnostic and therapeutic strategies. Nevertheless, developing a clinically useful oral clefts risk prediction model remains a great challenge. Compelling evidences suggest the etiologies of oral clefts are highly heterogeneous, and the development of a risk prediction model with consideration of phenotypic heterogeneity may potentially improve the accuracy of a risk prediction model. In this study, we applied a previously developed statistical method to investigate the risk prediction on sub-phenotypes of oral clefts. Our results suggested subtypes of cleft lip (CL) and palate have similar genetic etiologies (AUC = 0.572) with subtypes of CL only (AUC = 0.589), while the subtypes of cleft palate only (CPO) have heterogeneous underlying mechanisms (AUCs for soft CPO and hard CPO are 0.617 and 0.623, respectively). This highlighted the potential that the hard and soft forms of CPO have their own mechanisms despite sharing some of the genetic risk factors. Comparing with conventional methods for risk prediction modeling, our method considers phenotypic heterogeneity of a disease, which potentially improves the accuracy for predicting each sub-phenotype of oral clefts.