Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in Saudi Arabia.

The increasing incidence of type 1 diabetes (T1D) in children is a growing global concern. It is known that genetic and environmental factors contribute to childhood T1D. An optimal model to predict the development of T1D in children using Key Performance Indicators (KPIs) would aid medical practitioners in developing intervention plans. This paper for the first time has built a model to predict the risk of developing T1D and identify its significant KPIs in children aged (0-14) in Saudi Arabia. Machine learning methods, namely Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Artificial Neural Network have been utilised and compared for their relative performance. Analyses were performed in a population-based case-control study from three Saudi Arabian regions. The dataset (n = 1,142) contained demographic and socioeconomic status, genetic and disease history, nutrition history, obstetric history, and maternal characteristics. The comparison between case and control groups showed that most children (cases = 68% and controls = 88%) are from urban areas, 69% (cases) and 66% (control) were delivered after a full-term pregnancy and 31% of cases group were delivered by caesarean, which was higher than the controls (χ2 = 4.12, P-value = 0.042). Models were built using all available environmental and family history factors. The efficacy of models was evaluated using Area Under the Curve, Sensitivity, F Score and Precision. Full logistic regression outperformed other models with Accuracy = 0.77, Sensitivity, F Score and Precision of 0.70, and AUC = 0.83. The most significant KPIs were early exposure to cow's milk (OR = 2.92, P = 0.000), birth weight >4 Kg (OR = 3.11, P = 0.007), residency(rural) (OR = 3.74, P = 0.000), family history (first and second degree), and maternal age >25 years. The results presented here can assist healthcare providers in collecting and monitoring influential KPIs and developing intervention strategies to reduce the childhood T1D incidence rate in Saudi Arabia.

Predicting age at onset of type 1 diabetes in children using regression, artificial neural network and Random Forest: A case study in Saudi Arabia.

The rising incidence of type 1 diabetes (T1D) among children is an increasing concern globally. A reliable estimate of the age at onset of T1D in children would facilitate intervention plans for medical practitioners to reduce the problems with delayed diagnosis of T1D. This paper has utilised Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and Random Forest (RF) to model and predict the age at onset of T1D in children in Saudi Arabia (S.A.) which is ranked as the 7th for the highest number of T1D and 5th in the world for the incidence rate of T1D. De-identified data between (2010-2020) from three cities in S.A. were used to model and predict the age at onset of T1D. The best subset model selection criteria, coefficient of determination, and diagnostic tests were deployed to select the most significant variables. The efficacy of models for predicting the age at onset was assessed using multi-prediction accuracy measures. The average age at onset of T1D is 6.2 years and the most common age group for onset is (5-9) years. Most of the children in the sample (68%) are from urban areas of S.A., 75% were delivered after a full term pregnancy length and 31% were delivered through a cesarean section. The models of best fit were the MLR and RF models with R2 = (0.85 and 0.95), the root mean square error = (0.25 and 0.15) and mean absolute error = (0.19 and 0.11) respectively for logarithm of age at onset. This study for the first time has utilised MLR, ANN and RF models to predict the age at onset of T1D in children in S.A. These models can effectively aid health care providers to monitor and create intervention strategies to reduce the impact of T1D in children in S.A.

Personalised Medicine Possible With Real-Time Integration of Genomic and Clinical Data To Inform Clinical Decision-Making.

Despite widespread use of genomic sequencing in research, there are gaps in our understanding of the performance and provision of genomic sequencing in clinical practice. The Melbourne Genomics Health Alliance (the Alliance), has been established to determine the feasibility, performance and impact of using genomic sequencing as a diagnostic tool. The Alliance has partnered with BioGrid Australia to enable the linkage of genomic sequencing, clinical treatment and outcome data for this project. This integrated dataset of genetic, clinical and patient sourced information will be used by the Alliance to evaluate the potential diagnostic value of genomic sequencing in routine clinical practice. This project will allow the Alliance to provide recommendations to facilitate the integration of genomic sequencing into clinical practice to enable personalised disease treatment.

Patterns of care for metastatic renal cell carcinoma in Australia.

OBJECTIVE: To examine the patterns of care and outcomes for metastatic renal cell carcinoma (mRCC) in Australia, where there are limited reimbursed treatment options. In particular, we aim to explore prescribing patterns for first-line systemic treatment, the practice of an initial watchful-waiting approach, and the use of systemic treatments in elderly patients. SUBJECTS/PATIENTS AND METHODS: Patients with mRCC undergoing treatment between 2006 and 2012 were identified from four academic hospitals in Victoria and Australian Capital Territory. Demographic, clinicopathological, treatment, and survival data were recorded by chart review. Descriptive statistics were used to report findings. Survival was estimated by the Kaplan-Meier method and compared using the log-rank test. The study was supported by a grant from Pfizer Australia. RESULTS: Our study identified 212 patients with mRCC for analysis. Patients were predominantly of clear cell histology (75%), Eastern Cooperative Oncology Group performance status <2 (67%) and with favourable/intermediate Memorial Sloan-Kettering Cancer Center risk (68%). The median age at diagnosis was 61 years. In all, 163 (77%) patients received first-line systemic therapy, while 49 (23%) received best supportive care (BSC). The most frequently used first-line treatment was sunitinib (125 patients, 77%). Patients who received sunitinib had a median overall survival (OS) of 27.6 months. In all, 43% of patients who received sunitinib underwent a watchful-waiting period of >90 days before initiating treatment; these patients had a median OS of 56.3 months. Elderly patients (50 patients aged ≥70 years) were more likely to receive BSC alone than younger patients (46% vs 16%, P < 0.001). Of those who received systemic therapy, elderly patients were also more likely to have upfront dose reductions (30% vs 8%, P = 0.03). CONCLUSION: Our study of patients with mRCC treated in Australian centres showed that sunitinib was the most commonly prescribed systemic treatment between 2006 and 2012, associated with survival outcomes similar to pivotal studies. We also found that an initial watchful-waiting approach is commonly adopted without apparent detriment to survival. And finally, we found that age has an impact on the prescribing of systemic therapy.

An analysis of DNA methylation in human adipose tissue reveals differential modification of obesity genes before and after gastric bypass and weight loss.

BACKGROUND: Environmental factors can influence obesity by epigenetic mechanisms. Adipose tissue plays a key role in obesity-related metabolic dysfunction, and gastric bypass provides a model to investigate obesity and weight loss in humans. RESULTS: Here, we investigate DNA methylation in adipose tissue from obese women before and after gastric bypass and significant weight loss. In total, 485,577 CpG sites were profiled in matched, before and after weight loss, subcutaneous and omental adipose tissue. A paired analysis revealed significant differential methylation in omental and subcutaneous adipose tissue. A greater proportion of CpGs are hypermethylated before weight loss and increased methylation is observed in the 3' untranslated region and gene bodies relative to promoter regions. Differential methylation is found within genes associated with obesity, epigenetic regulation and development, such as CETP, FOXP2, HDAC4, DNMT3B, KCNQ1 and HOX clusters. We identify robust correlations between changes in methylation and clinical trait, including associations between fasting glucose and HDAC4, SLC37A3 and DENND1C in subcutaneous adipose. Genes investigated with differential promoter methylation all show significantly different levels of mRNA before and after gastric bypass. CONCLUSIONS: This is the first study reporting global DNA methylation profiling of adipose tissue before and after gastric bypass and associated weight loss. It provides a strong basis for future work and offers additional evidence for the role of DNA methylation of adipose tissue in obesity.

Benzylpiperazine: a drug of abuse?

N-benzylpiperazine (BZP) is the active ingredient in recreational 'party' or 'p.e.p.' pills, which are used to provide a stimulant, euphoric effect akin to that of methylenedioxymethamphetamine (MDMA, 'ecstasy'). BZP predominantly affects dopamine neurotransmission in a similar fashion to known 'drugs of abuse', such as methamphetamine and cocaine, which strongly suggests BZP has abuse liability. BZP is illegal in many countries including the United States of America and Australia, yet it remains legal in the United Kingdom, Canada and New Zealand. There has been little research, to date, on the neurological consequences of high dose or chronic exposure of BZP. Here we provide a comprehensive review of the information currently available on BZP and suggest a need for further research into the mechanisms of action, long-term effects and potentially addictive properties of BZP.