Aim: To explore the correlation between new-onset diabetes (NOD), hypertension and blood pressure management among elderly individuals in China.
Materials and methods: A cohort analysis involved 1380 participants aged 60 years or older, initially free of diabetes in 2008, from the Chinese Longitudinal Healthy Longevity Survey. Follow-up assessments occurred every 2-3 years. The relationship between hypertension, blood pressure changes and NOD was analysed using multivariable-adjusted Cox regression.
Results: By 2018, 102 participants developed diabetes, while 1278 remained without diabetes. The cumulative diabetes prevalence increased from 3.1% at 3 years to 7.4% at 10 years. Hypertension prevalence increased from 20.9% at baseline to 41.0% at 10 years, with higher rates in those diagnosed with diabetes during follow-up. Multivariate analysis identified age, gender, baseline hypertension and systolic blood pressure (SBP) as independent predictors of NOD. Hypertension combined with overweight/obesity significantly increased the risk of NOD (hazard ratio [HR] 2.837; 95% confidence interval [CI], 1.680-4.792). We evaluated participants' blood pressure management levels in 2008 and 2011, then tracked the onset of diabetes from 2011 to 2018. Compared with participants with an average SBP below 120 mmHg in 2008 and 2011, those with SBP of 140 mmHg or higher had an 8-fold higher risk of developing NOD (adjusted HR8.492, 95% CI 2.048-35.217, P = .003), the highest risk group. Participants with SBP of 130-139.9 mmHg also had a significantly increased risk (adjusted HR 5.065, 95% CI 1.186-21.633, P = .029), while those with SBP of 120-129.9 mmHg showed no significant difference (HR 2.730, 95% CI 0.597-12.481, P = .195). Consistently high SBP (≥ 130 mmHg) further increased NOD risk (adjusted HR 3.464, 95% CI 1.464-8.196, P = .005).
Conclusions: Significant predictors of NOD included age, gender, baseline hypertension and blood pressure management. Maintaining SBP consistently below 130 mmHg may be an effective strategy to reduce the incidence of NOD in the general elderly population.
{"title":"Association of hypertension and long-term blood pressure changes with new-onset diabetes in the elderly: A 10-year cohort study.","authors":"Shanshan Li, Boyi Yang, Shasha Shang, Wei Jiang","doi":"10.1111/dom.15986","DOIUrl":"https://doi.org/10.1111/dom.15986","url":null,"abstract":"<p><strong>Aim: </strong>To explore the correlation between new-onset diabetes (NOD), hypertension and blood pressure management among elderly individuals in China.</p><p><strong>Materials and methods: </strong>A cohort analysis involved 1380 participants aged 60 years or older, initially free of diabetes in 2008, from the Chinese Longitudinal Healthy Longevity Survey. Follow-up assessments occurred every 2-3 years. The relationship between hypertension, blood pressure changes and NOD was analysed using multivariable-adjusted Cox regression.</p><p><strong>Results: </strong>By 2018, 102 participants developed diabetes, while 1278 remained without diabetes. The cumulative diabetes prevalence increased from 3.1% at 3 years to 7.4% at 10 years. Hypertension prevalence increased from 20.9% at baseline to 41.0% at 10 years, with higher rates in those diagnosed with diabetes during follow-up. Multivariate analysis identified age, gender, baseline hypertension and systolic blood pressure (SBP) as independent predictors of NOD. Hypertension combined with overweight/obesity significantly increased the risk of NOD (hazard ratio [HR] 2.837; 95% confidence interval [CI], 1.680-4.792). We evaluated participants' blood pressure management levels in 2008 and 2011, then tracked the onset of diabetes from 2011 to 2018. Compared with participants with an average SBP below 120 mmHg in 2008 and 2011, those with SBP of 140 mmHg or higher had an 8-fold higher risk of developing NOD (adjusted HR8.492, 95% CI 2.048-35.217, P = .003), the highest risk group. Participants with SBP of 130-139.9 mmHg also had a significantly increased risk (adjusted HR 5.065, 95% CI 1.186-21.633, P = .029), while those with SBP of 120-129.9 mmHg showed no significant difference (HR 2.730, 95% CI 0.597-12.481, P = .195). Consistently high SBP (≥ 130 mmHg) further increased NOD risk (adjusted HR 3.464, 95% CI 1.464-8.196, P = .005).</p><p><strong>Conclusions: </strong>Significant predictors of NOD included age, gender, baseline hypertension and blood pressure management. Maintaining SBP consistently below 130 mmHg may be an effective strategy to reduce the incidence of NOD in the general elderly population.</p>","PeriodicalId":158,"journal":{"name":"Diabetes, Obesity & Metabolism","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142337775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Piaopiao Li, Eliot Spector, Khalid Alkhuzam, Rahul Patel, William T Donahoo, Sarah Bost, Tianchen Lyu, Yonghui Wu, William Hogan, Mattia Prosperi, Brian E Dixon, Dana Dabelea, Levon H Utidjian, Tessa L Crume, Lorna Thorpe, Angela D Liese, Desmond A Schatz, Mark A Atkinson, Michael J Haller, Elizabeth A Shenkman, Yi Guo, Jiang Bian, Hui Shao
Aim: To develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents using electronic health records (EHRs) from the UF Health System.
Materials and methods: The CP algorithm was iteratively derived based on structured data from EHRs (UF Health System 2012-2020). We randomly selected 536 presumed cases among individuals aged <18 years who had (1) glycated haemoglobin levels ≥ 6.5%; or (2) fasting glucose levels ≥126 mg/dL; or (3) random plasma glucose levels ≥200 mg/dL; or (4) a diabetes-related diagnosis code from an inpatient or outpatient encounter; or (5) prescribed, administered, or dispensed diabetes-related medication. Four reviewers independently reviewed the patient charts to determine diabetes status and type.
Results: Presumed cases without type 1 (T1D) or type 2 diabetes (T2D) diagnosis codes were categorized as non-diabetes/other types of diabetes. The rest were categorized as T1D if the most recent diagnosis was T1D, or otherwise categorized as T2D if the most recent diagnosis was T2D. Next, we applied a list of diagnoses and procedures that can determine diabetes type (e.g., steroid use suggests induced diabetes) to correct misclassifications from Step 1. Among the 536 reviewed cases, 159 and 64 had T1D and T2D, respectively. The sensitivity, specificity, and positive predictive values of the CP algorithm were 94%, 98% and 96%, respectively, for T1D and 95%, 95% and 73% for T2D.
Conclusion: We developed a highly accurate EHR-based CP for diabetes in youth based on EHR data from UF Health. Consistent with prior studies, T2D was more difficult to identify using these methods.
{"title":"Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network.","authors":"Piaopiao Li, Eliot Spector, Khalid Alkhuzam, Rahul Patel, William T Donahoo, Sarah Bost, Tianchen Lyu, Yonghui Wu, William Hogan, Mattia Prosperi, Brian E Dixon, Dana Dabelea, Levon H Utidjian, Tessa L Crume, Lorna Thorpe, Angela D Liese, Desmond A Schatz, Mark A Atkinson, Michael J Haller, Elizabeth A Shenkman, Yi Guo, Jiang Bian, Hui Shao","doi":"10.1111/dom.15987","DOIUrl":"https://doi.org/10.1111/dom.15987","url":null,"abstract":"<p><strong>Aim: </strong>To develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents using electronic health records (EHRs) from the UF Health System.</p><p><strong>Materials and methods: </strong>The CP algorithm was iteratively derived based on structured data from EHRs (UF Health System 2012-2020). We randomly selected 536 presumed cases among individuals aged <18 years who had (1) glycated haemoglobin levels ≥ 6.5%; or (2) fasting glucose levels ≥126 mg/dL; or (3) random plasma glucose levels ≥200 mg/dL; or (4) a diabetes-related diagnosis code from an inpatient or outpatient encounter; or (5) prescribed, administered, or dispensed diabetes-related medication. Four reviewers independently reviewed the patient charts to determine diabetes status and type.</p><p><strong>Results: </strong>Presumed cases without type 1 (T1D) or type 2 diabetes (T2D) diagnosis codes were categorized as non-diabetes/other types of diabetes. The rest were categorized as T1D if the most recent diagnosis was T1D, or otherwise categorized as T2D if the most recent diagnosis was T2D. Next, we applied a list of diagnoses and procedures that can determine diabetes type (e.g., steroid use suggests induced diabetes) to correct misclassifications from Step 1. Among the 536 reviewed cases, 159 and 64 had T1D and T2D, respectively. The sensitivity, specificity, and positive predictive values of the CP algorithm were 94%, 98% and 96%, respectively, for T1D and 95%, 95% and 73% for T2D.</p><p><strong>Conclusion: </strong>We developed a highly accurate EHR-based CP for diabetes in youth based on EHR data from UF Health. Consistent with prior studies, T2D was more difficult to identify using these methods.</p>","PeriodicalId":158,"journal":{"name":"Diabetes, Obesity & Metabolism","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142337890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}