UNASSIGNED: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. However, there is a lack of comprehensive data from low- and middle-income countries (LMICs) regarding factors influencing COPD outcomes, particularly in regions where biomass exposure is prevalent.
UNASSIGNED: The Factors Affecting Survival in Severe and Very Severe COPD Patients Admitted to Tertiary Centers of India (FAST) study aims to address this gap by evaluating factors impacting survival and exacerbation rates among COPD patients in LMICs like India, with a specific focus on biomass exposure, clinical phenotypes, and nutritional status in patients admitted to the Intensive Care Unit (ICU).
UNASSIGNED: The FAST study is an observational cohort study conducted in university teaching hospitals across India. The study aims to enroll 1000 COPD patients admitted to the ICU meeting specific inclusion criteria, with follow-up assessments conducted every 6 months over a 2-year period. Data collection includes demographic information, clinical manifestations, laboratory investigations, pulmonary function tests, medications, nutritional status, mental health, and health-related quality of life. Adjudication of exacerbations and mortality will also be undertaken. The FAST study seeks to provide crucial insights into COPD outcomes in LMICs, informing more precise management strategies and mitigating the burden of COPD in these settings. By evaluating factors such as biomass exposure, clinical phenotypes, and nutritional status, the study aims to address key knowledge gaps in COPD research.
UNASSIGNED: Arunachala S, Devapal S, Swamy DSN, Greeshma MV, Ul Hussain I, Siddaiah JB, et al. Factors Affecting Survival in Severe and Very Severe COPD after Admission in ICUs of Tertiary Care Centers of India (FAST COPD): Study Protocol for a Multicentric Cohort Study. Indian J Crit Care Med 2024;28(6):552-560.

OBJECTIVE: Obstructive sleep apnea (OSA) is a complex, heterogeneous disease. Categorizing the disorder into phenotypes can help us better understand its pathology and guide us toward more personalized treatment approaches. Nevertheless, most of the previous cluster analysis (CA) studies in OSA predominantly included middle-aged to older men and may not adequately represent the heterogeneity of OSA phenotypes in women. Our aim is to identify these phenotypes in women using an extensive, exclusively female cohort.
METHODS: Cross-sectional study of 1886 women diagnosed with OSA (apnea-hypopnea index >5 events/h) by PSG (polysomnography) and RP (respiratory polygraphy) at a tertiary hospital Sleep Unit. A CA was performed including general data, clinical variables, comorbidities and sleep study results.
RESULTS: Four phenotypic subtypes were identified: Cluster 1 \"Middle-aged paucisymptomatic women without cardiovascular risk factors\" (507 patients, 27 %); Cluster 2 \"Older paucisymptomatic women with established cardiovascular disease and severe OSA\" (228 patients, 12 %); Cluster 3 \"Middle-aged women with \"classic\" symptoms and cardiovascular risk factors\" (892 patients, 47 %), and Cluster 4 ″Middle-aged women with mood disorders, nonrestorative sleep and cardiovascular risk factors\" (259 patients, 14 %).
CONCLUSIONS: Conducting a CA exclusively within a female cohort reveals a heterogeneous presentation of OSA in women, similar to what has been previously reported in the literature for men. The \"classical\" presentation is notably the most prevalent, while the \"atypical\" presentation, which was previously more frequently associated with women, is less prevalent. Additionally, paucisymptomatic presentations, with or without associated comorbidities, are also present.

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BACKGROUND: Digital diabetes prevention programs (dDPPs) are effective \"digital prescriptions\" but have high attrition rates and program noncompletion. To address this, we developed a personalized automatic messaging system (PAMS) that leverages SMS text messaging and data integration into clinical workflows to increase dDPP engagement via enhanced patient-provider communication. Preliminary data showed positive results. However, further investigation is needed to determine how to optimize the tailoring of support technology such as PAMS based on a user\'s preferences to boost their dDPP engagement.
OBJECTIVE: This study evaluates leveraging machine learning (ML) to develop digital engagement phenotypes of dDPP users and assess ML\'s accuracy in predicting engagement with dDPP activities. This research will be used in a PAMS optimization process to improve PAMS personalization by incorporating engagement prediction and digital phenotyping. This study aims (1) to prove the feasibility of using dDPP user-collected data to build an ML model that predicts engagement and contributes to identifying digital engagement phenotypes, (2) to describe methods for developing ML models with dDPP data sets and present preliminary results, and (3) to present preliminary data on user profiling based on ML model outputs.
METHODS: Using the gradient-boosted forest model, we predicted engagement in 4 dDPP individual activities (physical activity, lessons, social activity, and weigh-ins) and general activity (engagement in any activity) based on previous short- and long-term activity in the app. The area under the receiver operating characteristic curve, the area under the precision-recall curve, and the Brier score metrics determined the performance of the model. Shapley values reflected the feature importance of the models and determined what variables informed user profiling through latent profile analysis.
RESULTS: We developed 2 models using weekly and daily DPP data sets (328,821 and 704,242 records, respectively), which yielded predictive accuracies above 90%. Although both models were highly accurate, the daily model better fitted our research plan because it predicted daily changes in individual activities, which was crucial for creating the \"digital phenotypes.\" To better understand the variables contributing to the model predictor, we calculated the Shapley values for both models to identify the features with the highest contribution to model fit; engagement with any activity in the dDPP in the last 7 days had the most predictive power. We profiled users with latent profile analysis after 2 weeks of engagement (Bayesian information criterion=-3222.46) with the dDPP and identified 6 profiles of users, including those with high engagement, minimal engagement, and attrition.
CONCLUSIONS: Preliminary results demonstrate that applying ML methods with predicting power is an acceptable mechanism to tailor and optimize messaging interventions to support patient engagement and adherence to digital prescriptions. The results enable future optimization of our existing messaging platform and expansion of this methodology to other clinical domains.
BACKGROUND: ClinicalTrials.gov NCT04773834; https://www.clinicaltrials.gov/ct2/show/NCT04773834.
UNASSIGNED: RR2-10.2196/26750.

Background/Purpose: Polycystic ovary syndrome (PCOS) is an endocrine-metabolic disease most common in patients of childbearing age. This pathology is associated with clinical, metabolic, and reproductive complications. We evaluated the diversity of the vaginal microbiota (VM), the vaginal inflammatory reaction (VIR), the proinflammatory state, and the activation of polymorphonuclear neutrophils (PMN) with the production of neutrophil extracellular traps (NETs). Methods: Thirty-three patients who attended a consultation at the Hospital UTPL-Santa Inés, Loja, Ecuador, from May to August 2023 who were diagnosed with PCOS participated in this study. Blood samples, vaginal discharge, and a survey were obtained. Results: A high number of patients, 23/33 (69.7%), presented altered microbiota in clinical variables associated with PCOS phenotypes A and B, sexual partners (>2), and oligomenorrhoea. A significant statistical association was only observed for sexually transmitted infections at sampling (p = 0.023) and insulin (p = 0.002). All eight cases studied with VIR had PMN/NETotic activity. A high frequency of proinflammatory states was observed in all vaginal microbiota states. Conclusions: These results suggest that the PCOS could trigger a proinflammatory state in the vaginal epithelium independently of the state of the vaginal microbiota. Furthermore, the presence of NETs observed in the cases studied could decrease fertility in these PCOS patients.

UNASSIGNED: Pain is classified as nociceptive, neuropathic, or nociplastic. Neuropathic pain presents as variable phenotypes (characters) based on specific aetiology and pathophysiology. This study aimed to find out among cancer patients the incidence of different phenotypes of neuropathic pain and form specific phenotypic clusters based on the underlying neurophysiology and association of sensory profile with various organ systems - A prospective observational study.
UNASSIGNED: The Institutional Ethical Committee clearance (IEC code: 2020-49-MD-EXP-15) https://ctri.nic.in/Clinicaltrials/showallp.php?mid1=44886&EncHid=88651.15716&userName=CTRI/2020/09/027964 approval was obtained. After written and informed consent, patients of age group 18-80 years, registering in the pain and palliative outpatient department or radiotherapy department with complaints of pain and not taking any anti-neuropathic pain medications, were enrolled. They were assessed using Leeds assessment of neuropathic symptoms and signs (LANSS) pain score, and a score of >12 was eligible for assessment of neuropathic pain phenotypes.
UNASSIGNED: Out of 210 cancer patients complaining of pain, a neuropathic component with LANSS >12 was found in 73 (34.76%). The most predominant phenotypes, allodynia> tingling> pricking = burning, were found in 72.60%, 56.16%, and 43.84% of patients, respectively. Phenotypes were clustered into Nodes 1 and 2 based on clinically significant separation of phenotypes. Node 1 had neuropathic pain of spontaneous origin found predominantly in gastrointestinal tract (GIT) and genitourinary tract (GUT) cancers. Node 2 had stimulus-evoked negative and positive characters which occurred in head and neck, thoracic, and spinal metastatic cancers.
UNASSIGNED: Careful patient assessment reveals the incidence of neuropathic pain in 34.76%; allodynia and tingling astable the most prominent phenotypes. Broadly, sensory characters were clustered into spontaneous and stimulus-evoked sensations with GIT and GUT cancers presenting with Node 1 symptoms.

Sepsis is a heterogeneous syndrome and phenotypes have been proposed using clinical data. Less is known about the contribution of protein biomarkers to clinical sepsis phenotypes and their importance for treatment effects in randomized trials of resuscitation. The objective is to use both clinical and biomarker data in the Protocol-Based Care for Early Septic Shock (ProCESS) randomized trial to determine sepsis phenotypes and to test for heterogeneity of treatment effect by phenotype comparing usual care to protocolized early, goal-directed therapy(EGDT). In this secondary analysis of a subset of patients with biomarker sampling in the ProCESS trial (n = 543), we identified sepsis phenotypes prior to randomization using latent class analysis of 20 clinical and biomarker variables. Logistic regression was used to test for interaction between phenotype and treatment arm for 60-day inpatient mortality. Among 543 patients with severe sepsis or septic shock in the ProCESS trial, a 2-class model best fit the data (p = 0.01). Phenotype 1 (n = 66, 12%) had increased IL-6, ICAM, and total bilirubin and decreased platelets compared to phenotype 2 (n = 477, 88%, p < 0.01 for all). Phenotype 1 had greater 60-day inpatient mortality compared to Phenotype 2 (41% vs 16%; p < 0.01). Treatment with EGDT was associated with worse 60-day inpatient mortality compared to usual care (58% vs. 23%) in Phenotype 1 only (p-value for interaction = 0.05). The 60-day inpatient mortality was similar comparing EGDT to usual care in Phenotype 2 (16% vs. 17%). We identified 2 sepsis phenotypes using latent class analysis of clinical and protein biomarker data at randomization in the ProCESS trial. Phenotype 1 had increased inflammation, organ dysfunction and worse clinical outcomes compared to phenotype 2. Response to EGDT versus usual care differed by phenotype.

Pollen allergy poses a significant health and economic burden in Europe. Disease patterns are relatively homogeneous within Central and Northern European countries. However, no study broadly assessed the features of seasonal allergic rhinitis (SAR) across different Southern European countries with a standardized approach.
To describe sensitization profiles and clinical phenotypes of pollen allergic patients in nine Southern European cities with a uniform methodological approach.
Within the @IT.2020 multicenter observational study, pediatric and adult patients suffering from SAR were recruited in nine urban study centers located in seven countries. Clinical questionnaires, skin prick tests (SPT) and specific IgE (sIgE) tests with a customized multiplex assay (Euroimmun Labordiagnostika, Lübeck, Germany) were performed.
Three hundred forty-eight children (mean age 13.1 years, SD: 2.4 years) and 467 adults (mean age 35.7 years SD: 10.0 years) with a predominantly moderate to severe, persistent phenotype of SAR were recruited. Grass pollen major allergenic molecules (Phl p 1 and/or Phl p 5) ranged among the top three sensitizers in all study centers. Sensitization profiles were very heterogeneous, considering that patients in Rome were highly poly-sensitized (sIgE to 3.8 major allergenic molecules per patient), while mono-sensitization was prominent and heterogeneous in other cities, such as Marseille (sIgE to Cup a 1: n = 55/80, 68.8%) and Messina (sIgE to Par j 2: n = 47/82, 57.3%). Co-sensitization to perennial allergens, as well as allergic comorbidities also broadly varied between study centers.
In Southern European countries, pollen allergy is heterogeneous in terms of sensitization profiles and clinical manifestations. Despite the complexity, a unique molecular, multiplex, and customized in-vitro IgE test detected relevant sensitization in all study centers. Nevertheless, this geographical diversity in pollen allergic patients imposes localized clinical guidelines and study protocols for clinical trials of SAR in this climatically complex region.

OBJECTIVE: Progressive supranuclear palsy (PSP) is a rare neurodegenerative disorder encompassing several phenotypes with various motor and cognitive deficits. We aimed to study motor and cognitive characteristics across PSP phenotypes and to assess the influence of apolipoprotein E (APOE) gene variants on PSP phenotypic expression.
METHODS: In this 20-year cross-sectional study, we retrospectively reviewed the charts of all patients classified as PSP patients and recategorized them according to phenotype using the Movement Disorder Society criteria (2017). Phenotypes were divided into three subgroups, Richardson\'s syndrome (PSP-RS), PSP-cortical (PSP with predominant frontal presentation [PSP-F] + PSP with predominant speech/language disorder [PSP-SL] + PSP with predominant corticobasal syndrome [PSP-CBS]) and PSP-subcortical (PSP with predominant parkinsonism [PSP-P] + PSP with progressive gait freezing [PSP-PGF] + PSP with predominant postural instability [PSP-PI] + PSP with predominant ocular motor dysfunction [PSP-OM] + PSP with cerebellar ataxia [PSP-C] + PSP with primary lateral sclerosis [PSP-PLS]), based on clinical presentation during the first 3 years after symptom onset, which defines the early disease stage. Clinical and neuropsychological assessment data were collected. Genotyping of APOE was performed using restriction fragment length polymorphism polymerase chain reaction and verified by Sanger sequencing.
RESULTS: We included 112 PSP patients comprising 10 phenotypes classified into 48 PSP-RS, 34 PSP-cortical (PSP-CBS, 17.6%; PSP-F, 9.4%; PSP-SL, 8.2%) and 30 PSP-subcortical (PSP-P, 11.6%; PSP-PI, 8%; PSP-OM, 2.7%; PSP-PGF, 1.8%; PSP-C, 1.8%; PSP-PLS, 0.9%) subgroups. PSP-RS patients were older at disease onset (p = 0.009) and had more akinetic-rigid and levodopa-resistant parkinsonism (p = 0.006), while PSP-cortical patients had more tremors and asymmetric and/or levodopa-responsive parkinsonism (p = 0.025). Cognitive domains were significantly less altered in the PSP-subcortical subgroup. Overall, PSP-APOEε4 carriers developed parkinsonism earlier (p = 0.038), had earlier oculomotor dysfunction (p = 0.052) and had more altered cognitive profiles. The APOEε4 allele was also associated with a younger age of parkinsonism onset in the PSP-RS phenotype group (p = 0.026).
CONCLUSIONS: This study demonstrated the wide phenotypic spectrum of PSP among Tunisians. Disease onset and akinetic-rigid and levodopa-resistant parkinsonism were the hallmarks of the PSP-RS phenotype, while milder cognitive impairment was characteristic of the PSP-subcortical subgroup. The APOEε4 allele was associated with earlier parkinsonism and oculomotor dysfunction and seemed to play a role in defining a more altered cognitive profile in PSP patients.

Asthma is a multifarious disease that manifests in various phenotypes. Among the various factors that contribute to the development of asthma, the gut microbiota has recently emerged as a compelling area of investigation. This study aims to investigate the causal relationships between gut microbiota and distinct asthma phenotypes.
The genome-wide association study (GWAS) summary statistics for 211 gut microbial taxa were used as study exposure. Five traits pertaining to various asthma phenotypes (asthma, allergic asthma, childhood asthma, suggestive for eosinophilic asthma and obesity-related asthma) were included as study outcome. We conducted Mendelian randomization (MR) analysis and sensitivity analysis for each bacterial taxa and asthma phenotypes.
We discovered a total of 58 associations that exhibited evidence of causality. Out of these, 4 associations remained significant even after applying multiple correction. An increased risk of asthma was causally associated with higher abundance of genus Holdemanella (OR = 1.11; CI: 1.05-1.17; p = 0.027), genus Oxalobacter (OR = 1.09; CI: 1.04-1.15; p = 0.025) and genus Butyricimonas (OR = 1.14; CI: 1.06-1.22; p = 0.027). Order NB1n was causally linked with an increased risk of obesity-related asthma (OR = 1.17; CI: 1.07-1.29; p = 0.015). There was limited overlap among the taxa that exhibited potential causal relationships with distinct asthma phenotypes.
Our research has provided genetic evidence that establishes multiple causal relationships between the gut microbiota and distinct asthma phenotypes, supporting the role of the gut microbiota in various asthma phenotypes. It is possible that different taxa play a role in the development of distinct asthma phenotypes. The causal relationships identified in this study require further investigation.