Low serum amylase and obesity, diabetes and metabolic syndrome: A novel interpretation

For the last decade, low serum amylase(hypoamylasemia) has been reported in certain common cardiometabolic conditions such as obesity, diabetes(regardless of type), and metabolic syndrome, all of which appear to have a common etiology of insufficient insulin action due to insulin resistance and/or diminished insulin secretion. Some clinical studies have shown that salivary amylase may be preferentially decreased in obese individuals, whereas others have revealed that pancreatic amylase may be preferentially decreased in diabetic subjects with insulin dependence. Despite this accumulated evidence, the clinical relevance of serum, salivary, and pancreatic amylase and the underlying mechanisms have not been fully elucidated. In recent years, copy number variations(CNVs) in the salivary amylase gene(AMY1), which range more broadly than the pancreatic amylase gene(AMY2A and AMY2B), have been shown to be well correlated with salivary and serum amylase levels. In addition, low CNV of AMY1, indicating low salivary amylase, was associated with insulin resistance, obesity, low taste perception/satiety, and postprandial hyperglycemia through impaired insulin secretion at early cephalic phase. In most populations, insulin-dependent diabetes is less prevalent(minor contribution) compared with insulin-independent diabetes, and obesity is highly prevalent compared with low body weight. Therefore, obesity as a condition that elicits cardiometabolic diseases relating to insulin resistance(major contribution) may be a common determinant for low serum amylase in a general population. In this review, the novel interpretation of low serum, salivary, and pancreas amylase is discussed in terms of major contributions of obesity, diabetes, and metabolic syndrome.

Correlation of Liver Enzymes with Diabetes and Pre-diabetes in Middle-aged Rural Population in China

The survey aimed to explore the association of liver transaminases with the prevalence of type 2 diabetes mellitus（T2DM） and pre-diabetes（pre-DM） in the middle-aged rural population in China. A cross-sectional study was conducted in 10 800 middle-aged subjects who lived in rural area of central China. The 75-g oral glucose-tolerance test（OGTT） was performed. Participants were asked to complete physical examination and standard questionnaire. The serum liver transaminases（ALT and GGT）, Hb A1 C and serum lipids were measured. In middle-aged rural population, the prevalence of impaired fasting glucose（IFG）, impaired glucose tolerance（IGT）, impaired fasting glucose combined with impaired glucose tolerance（IFG＋IGT） and DM was 4.0%, 11.8%, 2.6% and 10.0%, respectively. Some measurements were higher in males than in females, such as waist hip ratio（WHR）, blood pressure, fasting blood glucose（FBG）, high density lipoprotein-cholesterol（HDL-C）, and liver enzymes（ALT and GGT）. Further, we found that elevated serum GGT and ALT levels were significantly positively correlated with the prevalence of DM, independent of central obesity, serum lipid and insulin resistance（IR） in both genders. However, the correlation of GGT and ALT with pre-DM was determined by genders and characteristics of liver enzymes. Higher serum GGT was indicative of IGT in both genders. The association of serum ALT with pre-DM was significant only in female IGT group. In conclusion, our present survey shows both serum GGT and ALT are positively associated with DM, independent of the cardiovascular risk factors in both genders.

An improvement of snow/cloud discrimination from machine learning using geostationary satellite data

Snow and cloud discrimination is a main factor contributing to errors in satellite-based snow cover.To address the error,satellite-based snow cover performs snow reclassification tests on the cloud pixels of the cloud mask,but the error still remains.Machine Learning(ML)has recently been applied to remote sensing to calculate satellite-based meteorological data,and its utility has been demonstrated.In this study,snow and cloud discrimination errors were analyzed for GK-2A/AMI snow cover,and ML models(Random Forest and Deep Neural Network)were applied to accurately distinguish snow and clouds.The ML-based snow reclassified was integrated with the GK-2A/AMI snow cover through post-processing.We used the S-NPP/VIIRS snow cover and ASOS in situ snow observation data,which are satellite-based snow cover and ground truth data,as validation data to evaluate whether the snow/cloud discrimination is improved.The ML-based integrated snow cover detected 33–53%more snow compared to the GK-2A/AMI snow cover.In terms of performance,the F1-score and overall accuracy of the GK-2A/AMI snow cover was 73.06%and 89.99%,respectively,and those of the integrated snow cover were 76.78–78.28%and 90.93–91.26%,respectively.