Lipid-anthropometric index optimization for insulin sensitivity estimation

Velásquez, Jesús; Wong, Sok Kuan; Encalada, Lorena; Herrera, Héctor; Severeyn, Erika

doi:10.1117/12.2209328

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Lipids play an important role in organism growth and development, energy storage, material transport, and the recognition and transduction of molecular signals (Belayneh et al, 2018 ). In the human body, lipids as important nutrients not only provide required nutrition and energy but also participate in immune regulation and inflammatory response, playing an important role in maintaining health (Velásquez et al, 2015 ). Lipid metabolism is closely related to disease/infection occurrence and development, and preliminary diagnosis and assessment can be made by analyzing lipid changes.…”

Section: Introductionmentioning

confidence: 99%

Lipidomic analyses of five Carya illinoinensis cultivars

Zhao

Wang

Tian

et al. 2023

Food Science & Nutrition

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Carya illinoinensis (Wangenh.) K. Koch, nuts are a renowned health food. However, there are many cultivars of this nut tree, and their mature kernel lipid composition has not been thoroughly studied. Therefore, we used liquid chromatography–mass spectrometry (LC–MS) to analyze the lipid composition of mature nuts of five C. illinoinensis cultivars. In the mature kernels of all cultivars, there were 58 lipid types which were mainly composed of glycerolipids (c. 65%) and phospholipids (>30%). Triacylglycerol (TG) accounted for the largest proportion of mature nuts of all cultivars, exceeding 50%; and diacylglycerol (DG), ceramide (Cer), phosphatidylcholine (PC), and phosphatidylethanolamine (PE) were also relatively high. Additionally, nuts contain fatty acids, mainly oleic, linoleic, and linolenic acids. Our research provides a new perspective for the processing and utilization of plant and edible oils, and for the use of C. illinoinensis kernels in the development of medicine and food science.

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Section: Introductionmentioning

confidence: 99%

Lipidomic analyses of five Carya illinoinensis cultivars

Zhao

Wang

Tian

et al. 2023

Food Science & Nutrition

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“…Machine learning techniques have been used to classify overweight, obesity, insulin resistance and metabolic syndrome [20,21]. Some studies have used support vector machines (SVM) and decision tree to differentiate individuals with and without metabolic syndrome from variables as waist circumference, waist to height ratio, body mass index, among others [20,22].…”

Section: Introductionmentioning

confidence: 99%

“…Some studies have used support vector machines (SVM) and decision tree to differentiate individuals with and without metabolic syndrome from variables as waist circumference, waist to height ratio, body mass index, among others [20,22]. The k-means algorithm has also been used to detect individuals with insulin resistance and overweight using as variables waist and hip circumferences [21,23].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Technique as Classifier of Impaired Body Fat Percentage

Cruz

Severeyn

Huerta

et al. 2021

Frontiers in Artificial Intelligence and Applications

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Excess weight and obesity are indicators of an unhealthy or harmful accumulation of fat that can be dangerous to health. Body mass index (BMI) refers to height-to-weight radio and is often used to identify overweight and obesity in adults. Although BMI is commonly used to diagnose obesity and overweight, it is ineffective in differentiating between high muscle mass and elevated body fat mass. Body fat percentage (BF%) is one of the best predictors of obesity because it quantifies adipose tissue. The Deurenberg equation is among the indirect methods to measure BF%; it uses BMI, age, and sex as parameters to calculate the BF%. Machine learning techniques demonstrated to be a good classifier of overweight, obesity, and diseases related to insulin resistance and metabolic syndrome. This study intends to evaluate anthropometric parameters as classifiers of BF% alteration using support vector machines and the Deurenberg equation for BF% estimation. The database used consisted of 1978 individuals with 24 different anthropometric measurements. The results suggest the SVM as a suitable technique for classifying individuals with normal and abnormal BF% values. Accuracy, F1 score, PPV, NPV, and sensitivity were above 0.8. Besides, the specificity value is below 0.7, which indicates that false positives may occur. As future work, this research intends to apply neural networks as a classification technique.

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