Developing count regression techniques for predicting the number of new type 2 diabetes cases in Saudi Arabia

by Faten Al-hussein, Laleh Tafakori, Mali Abdollahian, Khalid Al-Shali

Type 2 diabetes (T2D) is a chronic condition affecting millions globally. A robust predictive model to estimate the number of new cases of T2D can facilitate precise monitoring and effective intervention strategies. This study aims to predict the number of new T2D cases per month in Saudi Arabia and identify the Key Performance Indicators (KPIs) associated with T2D, using count regression models, Poisson Regression (PR), Negative Binomial Regression (NBR), Poisson Inverse Gaussian Regression (PIGR), and Bell Regression (BR). De-identified data from 1,000 patients with T2D in Saudi Arabia were used to develop the models. The performance of the full models, which include recommended Key Performance Indicators (KPIs), is compared using metrics such as the coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), 10-fold cross-validation (CV-10), Akaike information criterion (AIC), and Bayesian information criterion (BIC). The most significant KPIs identified by the full models were utilized to develop the reduced models. The full NBR model outperformed other models, achieving R² of 0.88, RMSE of 0.93, MAE of 0.69, CV-10 of 1.21, AIC = 873.23, and BIC = 880. The reduced NBR model, focusing solely on the five most influential variables (marital status, age, body mass index (BMI), total cholesterol (TC), and high-density lipoprotein (HDL)), with R² = 0.84, RMSE = 1.10, MAE = 0.86, CV-10 = 1.37, AIC = 899, and BIC = 910, also outperformed other reduced models. The Likelihood Ratio Test (LRT) did not show a significant difference between the full and reduced NBR models (p = 0.694), supporting the adequacy of the reduced model. The proposed reduced model, utilizing only five significant KPIs, can help healthcare providers develop effective, targeted strategies by monitoring a smaller number of KPIs to reduce the rising number of T2D cases in Saudi Arabia.

Anal HPV shedding assessed by self-sampling and multiplex real-time PCR among men who have sex with men in N’Djamena, Chad: a feasibility and acceptability study

by Donato Koyalta, Zita Aleyo Nodjikouambaye, Jonathan Muwonga Tukisadila, Hachim Djamal Abdoulaye Bargo, Suitombaye Noubaramadji Yamti, Amine Akouya, Ralph-Sydney Mboumba Bouassa, Laurent Belec

Background

High-risk (HR) human papillomavirus (HPV) infection remains a great concern in sub-Saharan Africa in men who have sex with men (MSM). The prevalence of anal shedding of HPV and associated risk factors was estimated for the first time in a cross-sectional observational study covering MSM living in N’Djamena, the capital city of Chad.

Methods

MSM were recruited from the community in 21 sites in neighborhoods of 5 districts randomly selected in N’Djamena by respondent-driven sampling (RDS) method. Anal Collector V-Veil UP2™ device was used for anal canal self-sampling. Manual silica-extracted DNA was subjected for HPV detection and genotyping using BMRT Human Papillomavirus Genotyping Real Time PCR assay (Jiangsu Bioperfectus Technologies Co., Ltd., Taizhou, China). HIV serostatus was assessed using two rapid tests in series.

Results

A total of 70 MSM (mean age: 29.9 years; range, 18–50) were included. The overall acceptability to practice veil-based anal self-sampling was 95.9%. The usability of the veil collector device was high (92.3%), with easy understandable instructions for use and correct placement in the anal canal. Satisfaction questionnaire reported high overall feeling, intimacy respect and lack of shame. The majority of MSM (44/70, 62.8%) showed anal shedding of HPV DNA, with HR-HPV frequently detected (38,70, 54.3%), including HPV-33 (30/70, 42.9%) HPV-68 (16/70, 22.9%), HPV-18 (4/70, 5.7%), HPV-35 (3/70, 4.3%), HPV-58 (2/70, 2.9%), and HPV-45 (1/70, 1.4%). The distribution of genotypes in HR-HPV DNA-positive MSM revealed that HPV-33 (30/70; 42.9%) was the predominant genotype, followed by the HPV-68 (16/70; 22.9%), HPV-18 (4/70; 5.7%), HPV-35 (3/70; 4.3%), HPV-58 (2/70; 2.9%), and HPV-45, HPV-51 and HPV-56 (each type, 1/70;1.4%).Among all HPV detected, only 42 HPV (36.8%) were covered by Gardasil-9^® vaccine, including the HR-HPV-33, −18, −58 and −45, and the low risk-HPV-6 (5.7%) and HPV-11 (1.4%). The majority of detected HPV were non-covered by Gardasil-9^® vaccine (63.1%). Overall HIV prevalence was 5.7%.

Conclusions

Taken together, these observations point the MSM population in N’Djamena as a very particular core group of HIV and HPV transmission. HIV prevalence was higher than that of general adult population, but limited to only one MSM of twenty. The RDS method of recruitment allowed to include MSM likely belonging to the same sexual network of HPV transmission leading to the selection of an atypical and specific profile of anal HPV distribution. The potential efficacy of HPV prophylactic vaccination in this population can be estimated at relatively weak.

Can physiological network mapping reveal pathophysiological insights into emerging diseases? Lessons from COVID-19

by Cindy Xinyu Ji, Majid Sorouri, Mohammad Abdollahi, Omalbanin Paknejad, Ali R. Mani

Network physiology is a multidisciplinary field that offers a comprehensive view of the complex interactions within the human body, emphasising the critical role of organ system connectivity in health and disease. This approach has the potential to provide pathophysiological insights into complex and emerging diseases. This study aims to evaluate the effectiveness of physiological network mapping in predicting outcomes for COVID-19 patients, using data from the first wave of the pandemic. Routine clinical and laboratory data from 202 patients with COVID-19 were retrospectively analysed. Twenty-one physiological variables representing various organ systems were used to construct organ network connectivity through correlation analysis. Parenclitic network analysis was also employed to measure deviations in individual patients’ organ system correlations from the reference physiological interactions observed in survivors. We observed distinct features in the correlation network maps of non-survivors compared to survivors. In non-survivors, there was a significant correlation between the level of consciousness and the liver enzyme cluster, a relationship not present in the survivor group. This relationship remained significant even after adjusting for age and degree of hypoxia. Additionally, a strong correlation along the BUN–potassium axis was identified in non-survivors, suggesting varying degrees of kidney damage and impaired potassium homeostasis in non-survivors. These findings highlight the potential of network physiology as a valuable tool for uncovering complex inter-organ interactions in emerging diseases, with applications that could support clinicians, researchers, and policymakers in future epidemics.