Dlovan Ali Jalal , Barna Vásárhelyi , Béla Blaha , Zoltán Tóth , Tamás Géza Szabó , Béla Gyarmati
{"title":"大型医院数据库中血红蛋白A1c水平、血脂、尿酸、C反应蛋白水平与年龄的相关性。","authors":"Dlovan Ali Jalal , Barna Vásárhelyi , Béla Blaha , Zoltán Tóth , Tamás Géza Szabó , Béla Gyarmati","doi":"10.1016/j.mcp.2023.101933","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><p>Hemoglobin A1c (HbA1c) is used to monitor glucose homeostasis and to identify risk for diabetes. As diabetic patients are frequently present with dyslipidaemia, low-grade inflammation and hyperuricemia, we tested whether HbA1c levels can be estimated having the information about lipid profile, uric acid (UA) and C-reactive protein (CRP) levels. We developed formulas to describe the association of these parameters with HbA1c levels.</p></div><div><h3>Methods</h3><p>Data of 9599 male and 10,817 female patients, measured between 2008 and 2018, were analysed. Patients represented a general hospital patient population with overrepresentation of those with elevated HbA1c over 5.6%. The impact of gender, age, CRP, lipid profile and UA levels on HbA1c % on HbA1c levels was tested with multiple linear regression model. The magnitude of effects of individual factors was used to develop formulas to describe the association between HbA1c and other cardiometabolic parameters. With these formulas we estimated median HbA1c values in each age in both gender and compared them to measured HbA1c levels.</p></div><div><h3>Results</h3><p>The developed formulas are as follow: HbA1c (estimated) in women = 0.752 + 0.237*log10(HDL/cholesterol) + 0.156*log10 (cholesterol) + 0.077*log10 (triglyceride) + 0.025*log10(CRP) +0.001*log10 (age) −0.026*log10(HDL/LDL) −0.063*log10 (uric acid)-0.075*log10 (LDL)-0.199*log10(HDL); HbA1c (estimated) in men = 1.146 + 0.08*log10 (triglyceride) + 0.046*log10(CRP) + 0.01*log10 (cholesterol) + 0.001*log10 (age) −0.014*log10(HDL)-0.018*log10(HDL/LDL)-0.025*log10(HDL/cholesterol) −0.068*log10 (LDL)-0.159*log10 (uric acid)</p><p>Between 20 and 70 years of age, estimated HbA1c matched perfectly to measured HbA1c in.</p></div><div><h3>Conclusion</h3><p>At population level, HbA1c levels can be estimated almost exactly based on lipid profile, CRP and uric acid levels in female patients between 20 and 70 years.</p></div>","PeriodicalId":49799,"journal":{"name":"Molecular and Cellular Probes","volume":"72 ","pages":"Article 101933"},"PeriodicalIF":2.3000,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Interrelationship of hemoglobin A1c level lipid profile, uric acid, C-reactive protein levels and age in a large hospital database\",\"authors\":\"Dlovan Ali Jalal , Barna Vásárhelyi , Béla Blaha , Zoltán Tóth , Tamás Géza Szabó , Béla Gyarmati\",\"doi\":\"10.1016/j.mcp.2023.101933\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><p>Hemoglobin A1c (HbA1c) is used to monitor glucose homeostasis and to identify risk for diabetes. As diabetic patients are frequently present with dyslipidaemia, low-grade inflammation and hyperuricemia, we tested whether HbA1c levels can be estimated having the information about lipid profile, uric acid (UA) and C-reactive protein (CRP) levels. We developed formulas to describe the association of these parameters with HbA1c levels.</p></div><div><h3>Methods</h3><p>Data of 9599 male and 10,817 female patients, measured between 2008 and 2018, were analysed. Patients represented a general hospital patient population with overrepresentation of those with elevated HbA1c over 5.6%. The impact of gender, age, CRP, lipid profile and UA levels on HbA1c % on HbA1c levels was tested with multiple linear regression model. The magnitude of effects of individual factors was used to develop formulas to describe the association between HbA1c and other cardiometabolic parameters. With these formulas we estimated median HbA1c values in each age in both gender and compared them to measured HbA1c levels.</p></div><div><h3>Results</h3><p>The developed formulas are as follow: HbA1c (estimated) in women = 0.752 + 0.237*log10(HDL/cholesterol) + 0.156*log10 (cholesterol) + 0.077*log10 (triglyceride) + 0.025*log10(CRP) +0.001*log10 (age) −0.026*log10(HDL/LDL) −0.063*log10 (uric acid)-0.075*log10 (LDL)-0.199*log10(HDL); HbA1c (estimated) in men = 1.146 + 0.08*log10 (triglyceride) + 0.046*log10(CRP) + 0.01*log10 (cholesterol) + 0.001*log10 (age) −0.014*log10(HDL)-0.018*log10(HDL/LDL)-0.025*log10(HDL/cholesterol) −0.068*log10 (LDL)-0.159*log10 (uric acid)</p><p>Between 20 and 70 years of age, estimated HbA1c matched perfectly to measured HbA1c in.</p></div><div><h3>Conclusion</h3><p>At population level, HbA1c levels can be estimated almost exactly based on lipid profile, CRP and uric acid levels in female patients between 20 and 70 years.</p></div>\",\"PeriodicalId\":49799,\"journal\":{\"name\":\"Molecular and Cellular Probes\",\"volume\":\"72 \",\"pages\":\"Article 101933\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2023-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular and Cellular Probes\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0890850823000427\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular and Cellular Probes","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0890850823000427","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Interrelationship of hemoglobin A1c level lipid profile, uric acid, C-reactive protein levels and age in a large hospital database
Introduction
Hemoglobin A1c (HbA1c) is used to monitor glucose homeostasis and to identify risk for diabetes. As diabetic patients are frequently present with dyslipidaemia, low-grade inflammation and hyperuricemia, we tested whether HbA1c levels can be estimated having the information about lipid profile, uric acid (UA) and C-reactive protein (CRP) levels. We developed formulas to describe the association of these parameters with HbA1c levels.
Methods
Data of 9599 male and 10,817 female patients, measured between 2008 and 2018, were analysed. Patients represented a general hospital patient population with overrepresentation of those with elevated HbA1c over 5.6%. The impact of gender, age, CRP, lipid profile and UA levels on HbA1c % on HbA1c levels was tested with multiple linear regression model. The magnitude of effects of individual factors was used to develop formulas to describe the association between HbA1c and other cardiometabolic parameters. With these formulas we estimated median HbA1c values in each age in both gender and compared them to measured HbA1c levels.
Results
The developed formulas are as follow: HbA1c (estimated) in women = 0.752 + 0.237*log10(HDL/cholesterol) + 0.156*log10 (cholesterol) + 0.077*log10 (triglyceride) + 0.025*log10(CRP) +0.001*log10 (age) −0.026*log10(HDL/LDL) −0.063*log10 (uric acid)-0.075*log10 (LDL)-0.199*log10(HDL); HbA1c (estimated) in men = 1.146 + 0.08*log10 (triglyceride) + 0.046*log10(CRP) + 0.01*log10 (cholesterol) + 0.001*log10 (age) −0.014*log10(HDL)-0.018*log10(HDL/LDL)-0.025*log10(HDL/cholesterol) −0.068*log10 (LDL)-0.159*log10 (uric acid)
Between 20 and 70 years of age, estimated HbA1c matched perfectly to measured HbA1c in.
Conclusion
At population level, HbA1c levels can be estimated almost exactly based on lipid profile, CRP and uric acid levels in female patients between 20 and 70 years.
期刊介绍:
MCP - Advancing biology through–omics and bioinformatic technologies wants to capture outcomes from the current revolution in molecular technologies and sciences. The journal has broadened its scope and embraces any high quality research papers, reviews and opinions in areas including, but not limited to, molecular biology, cell biology, biochemistry, immunology, physiology, epidemiology, ecology, virology, microbiology, parasitology, genetics, evolutionary biology, genomics (including metagenomics), bioinformatics, proteomics, metabolomics, glycomics, and lipidomics. Submissions with a technology-driven focus on understanding normal biological or disease processes as well as conceptual advances and paradigm shifts are particularly encouraged. The Editors welcome fundamental or applied research areas; pre-submission enquiries about advanced draft manuscripts are welcomed. Top quality research and manuscripts will be fast-tracked.