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.

Frequency of flash glucose monitoring and glucose metrics: real-world observational data from Saudi Arabia.

BACKGROUND: This real-world data study analyzed glucose metrics from FreeStyle Libre® flash glucose monitoring in relation to scanning frequency, time in range (TIR) and estimated A1c (eA1c) in Saudi Arabia. METHODS: Anonymized reader data were analyzed according to scanning frequency quartiles, eA1c categories (<7%,≥7%‒≤9% or>9%) and TIR categories (<50%,≥50%‒≤70% or>70%). Sensors, grouped by reader, were required to have≥120 h of operation. Differences in scanning frequency, eA1c, TIR, time in hypoglycemia and hyperglycemia, and glucose variability (standard deviation [SD] and coefficient of variation [CV]) were analyzed between groups. RESULTS: 6097 readers, 35,747 sensors, and 40 million automatic glucose measurements were analyzed. Patients in the highest scanning frequency quartile (Q4, mean 32.0 scans/day) had lower eA1c (8.47%), greater TIR (46.4%) and lower glucose variation (SD 75.0 mg/dL, CV 38.2%) compared to the lowest quartile (Q1, mean 5.2 scans/day; eA1c 9.77%, TIR 32.8%, SD 94.9 mg/dL, CV 41.3%). Lower eA1c and higher TIR were associated with greater scanning frequency, lower glucose variability and less time in hyperglycemia. CONCLUSIONS: Higher scanning frequency in flash glucose users from Saudi Arabia is associated with lower eA1c, higher TIR, lower glucose variability and less time in hypoglycemia or hyperglycemia.

Measuring the Impact of Flash Glucose Monitoring in a Pediatric Population in Saudi Arabia: A Retrospective Cohort Study.

INTRODUCTION: Measurement of glucose levels is the mainstay method of ensuring good glycemic control and preventing complications associated with uncontrolled diabetes. Continuous glucose monitoring enables easy and effective monitoring of interstitial glucose around the clock and hence improves glycemic control. OBJECTIVES: This study aimed to measure the effect of continuous glucose monitoring on glycated hemoglobin (HbA1c) at 3, 6, and 9 months following sensor insertion. METHODS: A retrospective cohort study of pediatric and adolescent type 1 diabetes mellitus patients randomly sampled from 32 Ministry of Health diabetes centers across Saudi Arabia was performed. Patients were subjected to flash glucose monitoring using the FreeStyle® Libre flash glucose monitoring system (Abbott Diabetes Care, Witney, UK), an intermittently scanned continuous glucose monitoring device approved by the Conformité Européenne in 2014. These patients were first-time users of any kind of continuous glucose monitoring system, aged 4-18 years, and received insulin via multiple dose injection or continuous subcutaneous insulin infusion for at least 6 months prior to study start. Patients were excluded if they had used flash glucose monitoring or other interstitial glucose monitoring systems in the past 3 months, were pregnant, or had existing hemoglobinopathies. The flash glucose monitoring sensor was attached to the back of the upper arm at the baseline visit. HbA1c (%) was measured at baseline and 3, 6, and 9 months. Patient demographics were collected from electronic health records. RESULTS: 1,307 patients were included, with a mean age of 11.1 years (standard deviation 3.6 years). Where specified, 51.4% were female. Mean HbA1c significantly reduced from baseline (10.8%) to 3 months (9.8%, p < 0.001), 6 months (9.2%, p < 0.001), and 9 months (9.1%, p < 0.001). For individuals with baseline HbA1c > 9%, mean HbA1c was significantly reduced from baseline (11.7%) to 3 months (10.3%, p < 0.001), 6 months (9.6%, p < 0.001), and 9 months (9.5%, p < 0.001). CONCLUSIONS: Flash glucose monitoring significantly reduced HbA1c levels at 3, 6, and 9 months following sensor insertion. This reduction was greatest in those patients with higher HbA1c at baseline (> 9%).

A Unique Genotype of Pseudohypoaldosteronism Type 1b in a Highly Consanguineous Population.

CONTEXT: Pseudohypoaldosteronism (PHA) is a condition in which serum aldosterone level is normal or elevated but its action is deficient. OBJECTIVE: This study describes the molecular genetics of PHA 1b in the highly consanguineous population of 2 Arabian Gulf countries, Saudi Arabia and Oman. METHODS: This study enrolled 22 patients from 13 unrelated families (2 families with 5 patients from Oman and 11 families with 17 patients from Saudi Arabia). All of these patients had presented within the first 10 days of life with nausea and vomiting, hyponatremia, hyperkalemia, and hypotension. We isolated DNA from peripheral blood and PCR-sequenced all exons and exon-intron boundaries of SCNN1A and, if negative, SCNN1B and SCNN1G using the Dideoxy Chain termination method. RESULTS: We found a total of 8 mutations in 13 families as follows: 6 mutations in SCNN1A, 1 in SCNN1B, and 1 in SCNN1G. All of these mutations were novel except one. SCNN1A mutations were: c.1496A>G, p.Q499R (novel) in 1 patient; c.1453C>T, p.Q485X (novel) in 1 patient; c.1322_1322delA, p.N441Tfs*41 (novel) in 2 patients of 1 family; c.876 + 2 delGAGT (novel) in 3 patients of 1 family; c.203_204 delTC, p.I68Tfs*76 (a known mutation) in 8 patients of 5 families; and whole SCNN1A gene deletion (novel) in 2 patients of 2 families. In addition, a nonsense SCNN1B mutation c.1694C>A, p.S565X (novel) was found in 3 siblings from 1 Omani family, and an SCNN1G deletion mutation c.527_528 delCA, p.T176Rfs*9 (novel) in 2 siblings from another Omani family. CONCLUSION: We characterized a unique genotype of PHA 1b with several novel gene structure-disrupting mutations in SCNN1A, SCNN1B, and SCNN1G in a highly consanguineous population.