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Aims/Introduction: A debate exists on the relation of adverse pregnancy outcomes with glycemic levels in early pregnancy. We aimed to investigate the association of maternal characteristics including post‐load glucose and first‐trimester HbA1c test results with adverse pregnancy outcomes in women without gestational diabetes mellitus. Materials and Methods: A dataset (January 2011 and September 2017) from a hospital prenatal clinic was explored to find the important predictors of adverse pregnancy outcomes using maternal characteristics and glucose assessments in mothers without gestational diabetes. We used two machine learning algorithms to capture nonlinearity in selecting important maternal characteristics and developed predictive models for each outcome. In total, 1,618 pregnant women were included in the analytic dataset with a mean (SD) age of 26.8 (3.5) years and gravida of 1.7 (0.9). Results: Important associations were detected between maternal features and primary cesarean section, fetal distress, premature rupture of membranes, macrosomia, small or large for gestational age, APGAR <7 at 1 or 5 min, hyperbilirubinemia, and poly‐ or oligo‐hydramnios. Overall, the predictive models showed good performance and large areas under the curves (0.732, 0.765, 0.646, 0.651, 0.730, 0.646, 0.684, 0.716, and 0.678, respectively). Specifically, they had high positive likelihood ratios. Conclusions: High glucose levels were associated with adverse pregnancy outcomes. Post‐load glucose was the most reliable test for predicting the outcomes. Overall, fasting blood sugar was of more predictive value than HbA1c. Our study showed that further research should account for the nonlinearity and interactions inherent in the data. [ABSTRACT FROM AUTHOR]

Introduction: Diabetic nephropathy (DN) is a major complication of diabetes Mellitus. Early detection and intervention of DN can slow its progression and improve patients' outcomes. Neutrophil gelatinase-associated lipocalin (NGAL) as a marker of tubular damage might become a useful biomarker for the evaluation of renal involvement in diabetic patients. We aimed to evaluate the serum and urine NGAL(s-NGAL and u-NGAL) in type 2 diabetic patients and its correlation with different stages of diabetic nephropathy.Methods: This cross-sectional study was designed on 198 subjects consisted of 50 controls and 148 type 2 diabetes patients (50 normoalbuminuric, 58 microalbuminuric, and 40 macroalbuminuric). The study was conducted with measuring s-NGAL and u-NGAL, albumin and spot urine creatinine were also measured.Results: A highly increased level of s-NGAL was detected in macroalbuminuric group compared with controls, normoalbuminurics and microalbuminurics (P < .01). Highly raised u-NGAL levels were observed in macroalbuminurics in comparison with controls (P < .01). ROC curve demonstrated the best sensitivity and specificity of s-NGAL/u-NGAL for the macroalbuminuric state (sensitivity, 26% and 60%; specificity, 98% and 72%; respectively), in which the best cut-off points for the detection of macroalbuminuric state for s-NGAL/u-NGAL were 300 ng/mL and 71.4 ng/mL, respectively.Conclusion: Serum and urine-NGAL are elevated in type 2 diabetic patients, with or without albuminuria, s-NGAL level clearly correlates with severity of renal damage caused by DN and u-NGAL increases in macroalbuminuric state. S-NGAL could be a useful, noninvasive, available and practical test for evaluation of diabetic renal involvement. We could suggest u-NGAL as a probable predictor of macroalbuminuria. [ABSTRACT FROM AUTHOR]