{"title":"基于空腹和餐后血浆葡萄糖和胰岛素浓度的聚类分析","authors":"Miguel Altuve, Erika Severeyn","doi":"10.1007/s13410-024-01322-8","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Objective</h3><p>Plasma glucose and insulin concentrations are clinical markers used to diagnose metabolic diseases, particularly prediabetes and diabetes. In this paper, we conducted a cluster analysis using plasma glucose and insulin data collected during both fasting and 2-h postprandial periods.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Different clustering experiments were performed by changing the attributes, from one (fasting glucose) to four (fasting and postprandial glucose and insulin) attributes input to a k-means clustering algorithm. Based on the elbow and silhouette methods, three clusters were chosen to perform the clustering experiments. The Pearson correlation coefficient was utilized to evaluate the association between the levels of glucose and insulin within each created cluster.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Results show that one cluster comprised individuals with prediabetes, another cluster consisted of individuals with diabetes, while subjects without prediabetes and diabetes were assigned to a separate cluster. Despite not being used as an attribute, we observed varying age ranges among subjects in the three clusters. Furthermore, significant correlations were found between fasting and postprandial insulin levels, as well as between fasting and postprandial glucose levels, suggesting a consistent relationship between these variables, and highlighting their interdependence in the context of glucose metabolism.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The clustering analysis successfully differentiated individuals into distinct clusters based on their metabolic conditions, confirming that the approach effectively captured the underlying patterns in the plasma glucose and insulin data. Furthermore, despite not being a considered attribute, the varying age ranges observed within the clusters indicate that age may play a role in the development and progression of diabetes. Additionally, the fasting and postprandial associations in insulin and glucose levels exhibited greater strength in the cluster encompassing individuals with diabetes, where insulin production or action is compromised.</p>","PeriodicalId":50328,"journal":{"name":"International Journal of Diabetes in Developing Countries","volume":"81 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cluster analysis based on fasting and postprandial plasma glucose and insulin concentrations\",\"authors\":\"Miguel Altuve, Erika Severeyn\",\"doi\":\"10.1007/s13410-024-01322-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Objective</h3><p>Plasma glucose and insulin concentrations are clinical markers used to diagnose metabolic diseases, particularly prediabetes and diabetes. In this paper, we conducted a cluster analysis using plasma glucose and insulin data collected during both fasting and 2-h postprandial periods.</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>Different clustering experiments were performed by changing the attributes, from one (fasting glucose) to four (fasting and postprandial glucose and insulin) attributes input to a k-means clustering algorithm. Based on the elbow and silhouette methods, three clusters were chosen to perform the clustering experiments. The Pearson correlation coefficient was utilized to evaluate the association between the levels of glucose and insulin within each created cluster.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>Results show that one cluster comprised individuals with prediabetes, another cluster consisted of individuals with diabetes, while subjects without prediabetes and diabetes were assigned to a separate cluster. Despite not being used as an attribute, we observed varying age ranges among subjects in the three clusters. Furthermore, significant correlations were found between fasting and postprandial insulin levels, as well as between fasting and postprandial glucose levels, suggesting a consistent relationship between these variables, and highlighting their interdependence in the context of glucose metabolism.</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusion</h3><p>The clustering analysis successfully differentiated individuals into distinct clusters based on their metabolic conditions, confirming that the approach effectively captured the underlying patterns in the plasma glucose and insulin data. Furthermore, despite not being a considered attribute, the varying age ranges observed within the clusters indicate that age may play a role in the development and progression of diabetes. Additionally, the fasting and postprandial associations in insulin and glucose levels exhibited greater strength in the cluster encompassing individuals with diabetes, where insulin production or action is compromised.</p>\",\"PeriodicalId\":50328,\"journal\":{\"name\":\"International Journal of Diabetes in Developing Countries\",\"volume\":\"81 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Diabetes in Developing Countries\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s13410-024-01322-8\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Diabetes in Developing Countries","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s13410-024-01322-8","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Cluster analysis based on fasting and postprandial plasma glucose and insulin concentrations
Objective
Plasma glucose and insulin concentrations are clinical markers used to diagnose metabolic diseases, particularly prediabetes and diabetes. In this paper, we conducted a cluster analysis using plasma glucose and insulin data collected during both fasting and 2-h postprandial periods.
Methods
Different clustering experiments were performed by changing the attributes, from one (fasting glucose) to four (fasting and postprandial glucose and insulin) attributes input to a k-means clustering algorithm. Based on the elbow and silhouette methods, three clusters were chosen to perform the clustering experiments. The Pearson correlation coefficient was utilized to evaluate the association between the levels of glucose and insulin within each created cluster.
Results
Results show that one cluster comprised individuals with prediabetes, another cluster consisted of individuals with diabetes, while subjects without prediabetes and diabetes were assigned to a separate cluster. Despite not being used as an attribute, we observed varying age ranges among subjects in the three clusters. Furthermore, significant correlations were found between fasting and postprandial insulin levels, as well as between fasting and postprandial glucose levels, suggesting a consistent relationship between these variables, and highlighting their interdependence in the context of glucose metabolism.
Conclusion
The clustering analysis successfully differentiated individuals into distinct clusters based on their metabolic conditions, confirming that the approach effectively captured the underlying patterns in the plasma glucose and insulin data. Furthermore, despite not being a considered attribute, the varying age ranges observed within the clusters indicate that age may play a role in the development and progression of diabetes. Additionally, the fasting and postprandial associations in insulin and glucose levels exhibited greater strength in the cluster encompassing individuals with diabetes, where insulin production or action is compromised.
期刊介绍:
International Journal of Diabetes in Developing Countries is the official journal of Research Society for the Study of Diabetes in India. This is a peer reviewed journal and targets a readership consisting of clinicians, research workers, paramedical personnel, nutritionists and health care personnel working in the field of diabetes. Original research articles focusing on clinical and patient care issues including newer therapies and technologies as well as basic science issues in this field are considered for publication in the journal. Systematic reviews of interest to the above group of readers are also accepted.