The term “cardiometabolic disease” describes a cluster of sub-clinical disorders that are shared by cardiovascular diseases and type 2 diabetes, including dyslipidaemia, and glucose intolerance. In... Show moreThe term “cardiometabolic disease” describes a cluster of sub-clinical disorders that are shared by cardiovascular diseases and type 2 diabetes, including dyslipidaemia, and glucose intolerance. In clinical settings, fasting measurement is still the gold standard for the diagnosis of hyperglycemia and dyslipidaemia. However, due to irregular meal intake, we spend the majority of our waking hours in a non-fasting state. The non-fasting state is a dynamic condition that is affected by many factors, including diet, lifestyle, physiological factors, pathological conditions, and genetics. Thus far, the genes and genetic loci that affect postprandial glucose and lipid metabolism have not been fully understood. By using the data from the Netherlands Epidemiology of Obesity study, we found 1) postprandial measures after a liquid mixed meal were as robust as fasting measures by repeated measures; 2) to stratify pre-diabetic individuals into high- and low-risk of developing to type 2 diabetes, the model performance by using postprandial metabolites was similar to the model performance using fasting metabolites; 3) the genetics of fasting and postprandial metabolite levels are highly overlapped. All the findings suggest that postprandial measures after a liquid meal are as reliable and clinically relevant as fasting measures for cardiometabolic disease research and diagnosis. Show less
The target of this work is to extend the canonical Evolution Strategies (ES) from traditional real-valued parameter optimization domain to mixed-integer parameter optimization domain. This is... Show moreThe target of this work is to extend the canonical Evolution Strategies (ES) from traditional real-valued parameter optimization domain to mixed-integer parameter optimization domain. This is necessary because there exist numerous practical optimization problems from industry in which the set of decision variables simultaneously involves continuous, integer and discrete variables. Furthermore, objective functions of this type of problems could be based on large-scale simulation models or the structure of the objective functions may be too complex to be modeled. From this perspective, optimization problems of this kind are classified into the black-box optimization category. For them, classic optimization techniques, which come from Mathematical Programming (MP) research field, cannot be easily applied, since they are based on the assumption that the search space can always be efficiently explored using a divide-and-conquer sche me. While our new proposed algorithm, the so-called Mixed-Integer Evolution Strategies (MIES), by contrast, is capable of yielding good solutions to these challenging black-box optimization problems by using specialized variation operators tailored for mixed-integer parameter classes. In this work not only did we study MIES intensively from a theoretical point of view, but also we develop the framework for applying MIES to the real-world optimization problem in the medical field. Show less
