Scientific Reports · 2021 · 11 citations · 22 references
MedicineEndocrinologyPhysiologyCircadian RhythmsDiabetologyMetabolic RegulationBlood Glucose MonitoringContinuous Glucose MonitoringDiabetes MellitusGlycemic ControlGlycemic MonitoringInterindividual VariabilityCircadian OscillationFasting GlycemiaGlucose MonitoringGlucose Variability AssessmentGlycemic Variability MonitoringOscillatory DynamicsSex Differences In Diabetes
Abstract Daily glucose variability is higher in diabetic mellitus (DM) patients which has been related to the severity of the disease. However, it is unclear whether glycemic variability displays a specific pattern oscillation or if it is completely random. Thus, to determine glycemic variability pattern, we measured and analyzed continuous glucose monitoring (CGM) data, in control subjects and patients with DM type-1 (T1D). CGM data was assessed for 6 days (day: 08:00–20:00-h; and night: 20:00–08:00-h). Participants (n = 172; age = 18–80 years) were assigned to T1D (n = 144, females = 65) and Control (i.e., healthy; n = 28, females = 22) groups. Anthropometry, pharmacologic treatments, glycosylated hemoglobin (HbA1c) and years of evolution were determined. T1D females displayed a higher glycemia at 10:00–14:00-h vs. T1D males and Control females. DM patients displays mainly stationary oscillations (deterministic), with circadian rhythm characteristics. The glycemia oscillated between 2 and 6 days. The predictive model of glycemia showed that it is possible to predict hyper and hypoglycemia (R 2 = 0.94 and 0.98, respectively) in DM patients independent of their etiology. Our data showed that glycemic variability had a specific oscillation pattern with circadian characteristics, with episodes of hypoglycemia and hyperglycemia at day phases, which could help therapeutic action for this population.
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Predictive models for diabetes mellitus using machine learning techniques
Hang Lai, Huaxiong Huang, Karim Keshavjee et al. · BMC Endocrine Disorders · 2019 · 294 citations · Full text
Age at Menopause, Reproductive Life Span, and Type 2 Diabetes Risk
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