![]() Random survival forest (RSF) is a class of machine learning algorithms for survival analysis. Other suggest that raised variability in biomarkers reflects lifestyle changes, incomplete treatment adherence, pharmacotherapy prescribed, and generalized frailty. Although the exact pathways of pathogenesis by HbA1c and different lipid variability are unclear and appear to be divergent, the resulting chronic inflammation and endothelial dysfunction may have led to the presentation of systemic complications in diabetes. However, existing studies focused on cardiovascular events and mortality. Recently, HbA1c and lipid variability have attracted attention in its potential use for diabetic monitoring and risk stratification for adverse outcomes. Diabetic patients who are on insulin are more advanced in the disease life course, and as such are at a higher risk of complications and death. This raises the need for new parameters for monitoring diabetes, other than blood glucose, to improve the sensitivity towards the disease progression across different organ systems. ![]() Given the aging population, an increasing proportion of diabetic patients are elderly with multiple comorbidities, leading to a call for a more personalized and patient-centered approach in diabetic management over recent years. Diabetes mellitus can lead to a variety of complications affecting the cardiovascular, neurological, renal and other systems, placing significant burdens on healthcare systems globally. There is an increasing global prevalence of diabetes mellitus, with over 400 million people around the world currently suffering from the disease. The association between hypoglycemic frequency, baseline NLR, and both HbA1c and lipid variability implicate a role for inflammation in mediating adverse outcomes in diabetes, but this should be explored further in future studies. Raised variability in HbA1c and lipid parameters are associated with an elevated risk in both diabetic complications and all-cause mortality. Significant association was found between hypoglycemic frequency ( p < 0.0001), HbA1c ( p < 0.0001) and lipid variability against baseline neutrophil-lymphocyte ratio (NLR). Higher HbA1c and lipid variability measures were associated with increased risks of neurological, ophthalmological and renal complications, as well as incident dementia, osteoporosis, peripheral vascular disease, ischemic heart disease, atrial fibrillation and heart failure ( p < 0.05). HbA1c and lipid value and variability were significant predictors of all-cause mortality. The study consists of 25,186 patients (mean age = 63.0, interquartile range of age = 15.1 years, male = 50%). Secondary outcomes were diabetes-related complications. The primary outcome is all-cause mortality. Standard deviation (SD) and coefficient of variation were used to measure the variability of HbA1c, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglyceride. This retrospective cohort study consists of type 1 and type 2 diabetic patients who were prescribed insulin at outpatient clinics of Hong Kong public hospitals, from 1st January to 31st December 2009. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and variability of HbA1c and lipids for adverse outcomes. ![]() Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus.
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