Association of Microalbuminuria with High HbA1C levels in Melanesian Adults with Diabetes of at-least 1 Year Duration.
Aglua, I.
Show abstract
BackgroundEvidence suggest a potential relationship between high or variable HbA1C levels and presence or rate of change of microalbuminuria. Disruption of the vascular endothelial glycocalyx has been linked to chronic hyperglycemia and microalbuminuria, suggesting a possible shared pathophysiological mechanism. AimTo 1) explore potential association between microalbuminuria and high HbA1C levels in Melanesian adults with diabetes mellitus, and 2) asses predictive value of a high HbA1C reading as an indicator of the presence or progression of microalbuminuria. MethodA cross-sectional study on 190 patients with either type 1 or 2 diabetes of at least 1-year duration done at a provincial hospital in Papua New Guinea in 2017. ResultA significant [P=0.0221], though weak [R2=0.028 <0.70], correlation between UACR and HbA1C [95% statistical confidence] was observed in univariate regression, which was marginally significant [P=0.056] after controlling for weight, systolic hypertension, duration of diabetes, gender and age in multivariate regression. ConclusionA significant [p=0.022], though weak, correlation between UACR and HbA1C levels was observed, which may support usefulness of HbA1C as a predictor for microalbuminuria and diabetic kidney disease. In settings without microalbuminuria testing, high HbA1c levels can be used as a proxy to indicate presence or progression of microalbuminuria, thus prompting timely interventions to prevent further progression of diabetic nephropathy.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Demographic and clinical profile of black patients with chronic kidney disease attending Charlotte Maxeke Johannesburg Academic Hospital (CMJAH) in Johannesburg, South Africa. 96%
- Prevalence and determinants of peripheral arterial disease in children with nephrotic syndrome 96%
- Kidney Damage and Associated Risk Factors in the Rural Eastern Cape, South Africa: A Cross-Sectional Study 95%
Similar papers in this journal
- Time In Range, as measured by continuous glucose monitor, as a predictor of microvascular complications in type 2 diabetes– A systemic review 96%
- Comprehensive validation of fasting- and oral glucose tolerance test-based indices of insulin secretion against gold-standard measures 93%
- Predictors of Arterial Stiffness in Adolescents and Adults with Type 1 Diabetes: A Cross-Sectional Study 92%
Similar papers in this journal
- An individualized risk prediction model for new-onset, progression and regression of chronic kidney disease in a retrospective cohort of patients with type 2 diabetes under primary care in Hong Kong 94%
- Risk Factors for Non-Communicable Diseases among Bangladeshi Adults: An Application of Generalized Linear Mixed Model on Multilevel Demographic and Health Survey Data 93%
- Cohort profile: The Nanjing Diabetes Cohort database – a population-based surveillance cohort 92%
Similar papers in this journal
- Glomerular spatial transcriptomics of IgA nephropathy according to the presence of mesangial proliferation 93%
- Maternal and cord-blood inflammatory markers and BDNF in diabetic vs non-diabetic pregnancies 92%
- Metformin Is Associated with Favorable Outcomes in Patients with COVID-19 and Type 2 Diabetes Mellitus 92%
Similar papers in this journal
- Subgroups of young type 2 diabetes in India reveal insulin deficiency as a major driver 93%
- Non-autoimmune, lean diabetes in young people from Assam, India highlights the role of undernutrition in its aetiology - PHEnotypingNOrthEastINDianYoung type 2 diabetes (PHENOEINDY-2) 93%
- Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict rapid progression of diabetic kidney disease 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.