Pregnenolone sulfate induces transcriptional and immunoregulatory effects on T cells.

peer reviewedPregnenolone sulfate is a steroid metabolite of the steroidogenesis precursor, pregnenolone, with similar functional properties, including immunosuppression. We recently reported an elevation in serum levels of pregnenolone sulfate in children with malaria, contributing to an immunosuppressed state. Yet, the molecular mechanisms in which this steroid exerts its immunoregulatory functions are lacking. In this study, we examined the effects of pregnenolone sulfate on T cell viability, proliferation and transcriptome. We observed a pregnenolone sulfate dose-dependent induction of T cell death and reduction in proliferation. RNA sequencing analysis of pregnenolone sulfate-treated T cells for 2 and 24 h revealed the downregulation of pro-inflammatory genes and the upregulation of the steroid nuclear receptor superfamily, NR4A, as early-response genes. We also report a strong activation of the integrated stress response mediated by the upregulation of EIF2AK3. These results contribute to the knowledge on transcriptional regulation driving the immunoregulatory effects of pregnenolone sulfate on T cells

Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities:a model development and validation study

Background: Type 2 diabetes disproportionately affects individuals of non-White ethnicity through a complex interaction of multiple factors. Therefore, early disease detection and prediction are essential and require tools that can be deployed on a large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes prevalence and incidence for multiple ethnicities.Methods: In this proof of principle analysis, logistic regression models to predict type 2 diabetes prevalence and incidence, using questionnaire-only variables reflecting health state and lifestyle, were trained on the White population of the UK Biobank (n = 472,696 total, aged 37–73 years, data collected 2006–2010) and validated in five other ethnicities (n = 29,811 total) and externally in Lifelines (n = 168,205 total, aged 0–93 years, collected between 2006 and 2013). In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Type 2 diabetes prevalence in the UK Biobank ranged between 6% in the White population to 23.3% in the South Asian population, while in Lifelines, the prevalence was 1.9%. Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC), and a detailed sensitivity analysis was conducted to assess potential clinical utility. We compared the questionnaire-only models to models containing physical measurements and biomarkers as well as to clinical non-laboratory type 2 diabetes risk tools and conducted a reclassification analysis.Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC = 0.901) and eight-year incidence (AUC = 0.873) in the White UK Biobank population. Both models replicated well in the Lifelines external validation, with AUCs of 0.917 and 0.817 for prevalence and incidence, respectively. Both models performed consistently well across different ethnicities, with AUCs of 0.855–0.894 for prevalence and 0.819–0.883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 additional cases. Model performance improved with the addition of blood biomarkers but not with the addition of physical measurements.Interpretation: Our findings suggest that easy-to-implement, questionnaire-based models could be used to predict prevalent and incident type 2 diabetes with high accuracy across several ethnicities, providing a highly scalable solution for population-wide risk stratification. Future work should determine the effectiveness of these models in identifying undiagnosed type 2 diabetes, validated in cohorts of different populations and ethnic representation.Funding: University Medical Center Groningen

Developing Effective Questionnaire-Based Prediction Models for Type 2 Diabetes for Several Ethnicities

Background: Type 2 diabetes disproportionately affects individuals of non-white ethnicity through a complex interaction of multiple factors. Early disease prediction and detection is therefore essential and requires tools that can be deployed at large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes for multiple ethnicities.Methods: Logistic regression models, using questionnaire-only features, were trained on the White population of the UK Biobank, and validated in five other ethnicities and externally in Lifelines. In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Predictive accuracy was assessed and a detailed sensitivity analysis was conducted to assess potential clinical utility. Furthermore, we compared the questionnaire algorithms to clinical non-laboratory type 2 diabetes risk tools.Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC=0·901) and eight-year incidence (AUC=0·873) in the White UK Biobank population. Both models replicate well in Lifelines, with AUCs of 0·917 and 0·817 for prevalence and incidence. Both models performed consistently well across ethnicities, with AUCs of 0·855 to 0·894 for prevalence and from 0·819 to 0·883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 type 2 diabetes cases. Model performance improved with the addition of blood biomarkers, but not with the addition of physical measurements.Interpretation: Easy-to-implement, questionnaire-based models can predict prevalent and incident type 2 diabetes with high accuracy across all ethnicities, providing a highly-scalable solution for population-wide risk stratification

Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities:a model development and validation study

Background: Type 2 diabetes disproportionately affects individuals of non-White ethnicity through a complex interaction of multiple factors. Therefore, early disease detection and prediction are essential and require tools that can be deployed on a large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes prevalence and incidence for multiple ethnicities.Methods: In this proof of principle analysis, logistic regression models to predict type 2 diabetes prevalence and incidence, using questionnaire-only variables reflecting health state and lifestyle, were trained on the White population of the UK Biobank (n = 472,696 total, aged 37–73 years, data collected 2006–2010) and validated in five other ethnicities (n = 29,811 total) and externally in Lifelines (n = 168,205 total, aged 0–93 years, collected between 2006 and 2013). In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Type 2 diabetes prevalence in the UK Biobank ranged between 6% in the White population to 23.3% in the South Asian population, while in Lifelines, the prevalence was 1.9%. Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC), and a detailed sensitivity analysis was conducted to assess potential clinical utility. We compared the questionnaire-only models to models containing physical measurements and biomarkers as well as to clinical non-laboratory type 2 diabetes risk tools and conducted a reclassification analysis.Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC = 0.901) and eight-year incidence (AUC = 0.873) in the White UK Biobank population. Both models replicated well in the Lifelines external validation, with AUCs of 0.917 and 0.817 for prevalence and incidence, respectively. Both models performed consistently well across different ethnicities, with AUCs of 0.855–0.894 for prevalence and 0.819–0.883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 additional cases. Model performance improved with the addition of blood biomarkers but not with the addition of physical measurements.Interpretation: Our findings suggest that easy-to-implement, questionnaire-based models could be used to predict prevalent and incident type 2 diabetes with high accuracy across several ethnicities, providing a highly scalable solution for population-wide risk stratification. Future work should determine the effectiveness of these models in identifying undiagnosed type 2 diabetes, validated in cohorts of different populations and ethnic representation.Funding: University Medical Center Groningen

Saliva as an Alternative Specimen for Molecular COVID-19 Testing in Community Settings and Population-Based Screening.

PURPOSE: With the easing of restriction measures, repeated community-based sampling for tracking new COVID-19 infections is anticipated for the next 6 to 12 months. A non-invasive, self-collected specimen like saliva will be useful for such public health surveillance. Investigations on the use of saliva for SARS-CoV-2 RT-PCR have largely been among COVID-19 in-pa\tients and symptomatic ambulatory patients with limited work in a community-based screening setting. This study was carried out to address this paucity of data and reported discrepancies in diagnostic accuracy for saliva samples. PATIENTS AND METHODS: From 29th June to 14th July 2020, adults presenting for COVID-19 testing at a community-based screening facility in Dubai, United Arab Emirates were recruited. Clinical data, nasopharyngeal swab in universal transport media and drooling saliva in sterile containers were obtained. Reverse transcriptase PCR amplification of SARS-CoV-2 RdRp and N genes was used to detect the presence of the SARS-CoV-2 virus. RESULTS: Of the 401 participants, 35 (8.7%) had viral detection in at least one specimen type and the majority (n=20/35; 57.1%) were asymptomatic. Both swab and saliva were positive in 19 (54.2%) patients, while 7 (20.0%) patients had swab positive/saliva negative results. There were 9 (25.7%) patients with saliva positive/swab negative result and this included 5 asymptomatic COVID-19 patients undergoing repeat screening. Using the swab as the reference gold standard, the sensitivity and specificity of saliva were 73.1% (95% CI 52.2-88.4%) and 97.6% (95% CI 95.5-98.9%) while the positive and negative predictive values were 67.9% (95% CI 51.5-80.8%) and 98.1% (95% CI 96.5-99.0%), respectively. CONCLUSION: The findings suggest good diagnostic accuracy for saliva and feasibility of utilization of specimen without transport media for SARS-CoV-2 RT-PCR. Saliva represents a potential specimen of choice in community settings and population-based screening

Associations between Birth Weight and Adult Sleep Characteristics: A Cross-Sectional Analysis from the UAEHFS

Abnormal birth weight, particularly low birth weight (LBW), is known to have long-term adverse health consequences in adulthood, with disrupted sleep being suggested as a mediator or modifier of this link. We thus aimed to assess the associations between birth weight and self-reported adult sleep characteristics: sleep duration, difficulty waking up in the morning, daily nap frequency, sleep problems at night, snoring, daytime tiredness or sleepiness, and ever-stop breathing during sleep. This cross-sectional analysis used the United Arab Emirates Healthy Future Study data collected from February 2016 to March 2023 involving 2124 Emiratis aged 18–61 years. We performed a Poisson regression under unadjusted and age-sex-and-BMI-adjusted models to obtain the risk ratio and its 95% confidence interval for our analysis of the association between birth weight and each adult sleep characteristics, compared to individuals with normal birth weight (≥2.5 kg). Those with LBW had significantly a 17% increased risk of difficulty waking up in the morning, compared to those with normal birth weight. In addition, females with LBW history were also at an increased risk of reporting difficulty waking up in the morning. Studies with objective sleep assessments that include measurements of more confounding factors are recommended to confirm these risks