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Ares-Blanco S, Polentinos-Castro E, Rodríguez-Cabrera F, Gullón P, Franco M, del Cura-González I. Inequalities in glycemic and multifactorial cardiovascular control of type 2 diabetes: The Heart Healthy Hoods study. Front Med (Lausanne) 2022; 9:966368. [PMID: 36569128 PMCID: PMC9769119 DOI: 10.3389/fmed.2022.966368] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/26/2022] [Indexed: 12/12/2022] Open
Abstract
Aim This study aimed to analyze glycemic control and multifactorial cardiovascular control targets in people with type 2 diabetes (T2DM) in primary care according to sex and socioeconomic status (SES). Materials and methods This is an observational, cross-sectional, and multicenter study. We analyzed all the patients with T2DMM aged between 40 and 75 years in Madrid city (113,265) through electronic health records from 01 August 2017 to 31 July 2018. SES was defined by an area-level socioeconomic index stratified by quintiles (1st quintile: more affluent). Outcomes Outcomes included glycemic control (HbA1c ≤ 7%), 3-factor cardiovascular control [HbA1c ≤ 7%, blood pressure (BP), < 140/90 mmHg, LDL < 100 mg/ml] and 4-factor control [HbA1c ≤ 7%, blood pressure (BP) < 140/90 mmHg, LDL < 100 mg/ml, and BMI < 30 kg/m2]. Multilevel logistic regression models analyzed factors associated with suboptimal glycemic control. Results In total 43.2% were women. Glycemic control was achieved by 63% of patients (women: 64.2% vs. men: 62.4%). Being more deprived was associated with suboptimal glycemic control (OR: 1.20, 95% CI: 1.10-1.32); however, sex was not related (OR: 0.97, 95% CI: 0.94-1.01). The optimal 3-factor control target was reached by 10.3% of patients (women: 9.3% vs. men: 11.2%), especially those in the 5th quintile of SES. The 4-factor control was achieved by 6.6% of the sample. In the 3-factor control target, being women was related to the suboptimal 3-factor control target (OR: 1.26, 95% CI: 1.19- 1.34) but only belonging to SES 4th quintile was related to the unachieved target (OR: 1.47, 95% CI: 1.04-2.07). Conclusion Suboptimal glycemic control was associated with being less affluent and suboptimal 3-factor control target was associated with being women.
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Affiliation(s)
- Sara Ares-Blanco
- Federica Montseny Health Centre, Gerencia Asistencial Atención Primaria, Servicio Madrileño de Salud, Madrid, Spain,Medical Specialties and Public Health, School of Health Sciences, University Rey Juan Carlos, Alcorcón, Spain,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain,*Correspondence: Sara Ares-Blanco,
| | - Elena Polentinos-Castro
- Medical Specialties and Public Health, School of Health Sciences, University Rey Juan Carlos, Alcorcón, Spain,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain,Primary Care Research Unit, Gerencia de Atención Primaria, Servicio Madrileño de Salud, Madrid, Spain,Health Services Research on Chronic Patients Network (REDISSEC and RICAPPS), Instituto de Salud Carlos III, Madrid, Spain
| | | | - Pedro Gullón
- Public Health and Epidemiology Research Group, Universidad de Alcalá, Alcala de Henares, Spain
| | - Manuel Franco
- Public Health and Epidemiology Research Group, Universidad de Alcalá, Alcala de Henares, Spain,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Isabel del Cura-González
- Medical Specialties and Public Health, School of Health Sciences, University Rey Juan Carlos, Alcorcón, Spain,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain,Primary Care Research Unit, Gerencia de Atención Primaria, Servicio Madrileño de Salud, Madrid, Spain,Health Services Research on Chronic Patients Network (REDISSEC and RICAPPS), Instituto de Salud Carlos III, Madrid, Spain
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Li L, Lip GYH, Li S, Adachi JD, Thabane L, Li G. Associations between glycated hemoglobin and the risks of incident cardiovascular diseases in patients with gout. Cardiovasc Diabetol 2022; 21:133. [PMID: 35841094 PMCID: PMC9284835 DOI: 10.1186/s12933-022-01567-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/08/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Evidence for the relationship between glycated hemoglobin (HbA1c) levels and risk of cardiovascular diseases (CVD) in patients with gout remained sparse and limited. This study aims to explore the associations between HbA1c levels and risks of incident CVD in patients with gout. METHODS We included patients with gout who had an HbA1c measurement at baseline from the UK Biobank. CVD events were identified from through medical and death records. We used multivariable Cox proportional hazards model with a restricted cubic spline to assess the potential non-linear effect of HbA1c on CVD risk. RESULTS We included a total of 6,685 patients (mean age 59.7; 8.1% females) with gout for analyses. During a mean follow-up of 7.3 years, there were 1,095 CVD events documented with an incidence of 2.26 events per 100 person-years (95% confidence interval [CI]: 2.13-2.40). A quasi J-shaped association between HbA1c and risk of CVD was observed, with the potentially lowest risk found at the HbA1c of approximately 5.0% (hazard ratio [HR] = 0.65, 95% CI: 0.53-0.81). When compared with the HbAlc level of 7%, a significantly decreased risk of CVD was found from 5.0 to 6.5%, while an increased risk was observed at 7.5% (HR = 1.05) and 8.0% (HR = 1.09). Subgroup analyses yielded similar results to the main findings in general. CONCLUSIONS Based on data from a nationwide, prospective, population-based cohort, we found a quasi J-shaped relationship between HbA1c and risk of CVD in patients with gout. More high-quality evidence is needed to further clarify the relationship between HbA1c and CVD risk in patients with gout.
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Affiliation(s)
- Likang Li
- Center for Clinical Epidemiology and Methodology (CCEM), Guangdong Second Provincial General Hospital, 510317, Guangzhou, China
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK.,Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Shuai Li
- Center for Clinical Epidemiology and Methodology (CCEM), Guangdong Second Provincial General Hospital, 510317, Guangzhou, China
| | | | - Lehana Thabane
- Department of Health Research Methods, Evidence, and Impact (HEI), McMaster University, 1280 Main St West, Hamilton, ON, L8S 4L8, Canada.,Centre for Evaluation of Medicines, St Joseph's Health Care, Hamilton, ON, Canada.,Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
| | - Guowei Li
- Center for Clinical Epidemiology and Methodology (CCEM), Guangdong Second Provincial General Hospital, 510317, Guangzhou, China. .,Department of Health Research Methods, Evidence, and Impact (HEI), McMaster University, 1280 Main St West, Hamilton, ON, L8S 4L8, Canada.
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Lee DY, Han K, Park S, Yu JH, Seo JA, Kim NH, Yoo HJ, Kim SG, Choi KM, Baik SH, Park YG, Kim NH. Glucose variability and the risks of stroke, myocardial infarction, and all-cause mortality in individuals with diabetes: retrospective cohort study. Cardiovasc Diabetol 2020; 19:144. [PMID: 32962711 PMCID: PMC7510288 DOI: 10.1186/s12933-020-01134-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 09/18/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Previous research regarding long-term glucose variability over several years which is an emerging indicator of glycemic control in diabetes showed several limitations. We investigated whether variability in long-term fasting plasma glucose (FG) can predict the development of stroke, myocardial infarction (MI), and all-cause mortality in patients with diabetes. METHODS This is a retrospective cohort study using the data provided by the Korean National Health Insurance Corporation. A total of 624,237 Koreans ≥ 20 years old with diabetes who had undergone health examinations at least twice from 2005 to 2008 and simultaneously more than once from 2009 to 2010 (baseline) without previous histories of stroke or MI. As a parameter of variability of FG, variability independent of mean (VIM) was calculated using FG levels measured at least three times during the 5 years until the baseline. Study endpoints were incident stroke, MI, and all-cause mortality through December 31, 2017. RESULTS During follow-up, 25,038 cases of stroke, 15,832 cases of MI, and 44,716 deaths were identified. As the quartile of FG VIM increased, the risk of clinical outcomes serially increased after adjustment for confounding factors including duration and medications of diabetes and the mean FG. Adjusted hazard ratios (95% confidence intervals) of FG VIM quartile 4 compared with quartile 1 were 1.20 (1.16-1.24), 1.20 (1.15-1.25), and 1.32 (1.29-1.36) for stroke, MI and all-cause mortality, respectively. The impact of FG variability was higher in the elderly and those with a longer duration of diabetes and lower FG levels. CONCLUSIONS In diabetes, long-term glucose variability showed a dose-response relationship with the risk of stroke, MI, and all-cause mortality in this nationwide observational study.
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Affiliation(s)
- Da Young Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Kyungdo Han
- Department of Biostatics, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - Sanghyun Park
- Department of Biostatics, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - Ji Hee Yu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Ji A Seo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Nam Hoon Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Hye Jin Yoo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sin Gon Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Kyung Mook Choi
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sei Hyun Baik
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Yong Gyu Park
- Department of Biostatics, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea.
| | - Nan Hee Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Danwon-gu, Ansan-si, Gyeonggi-do, 15355, Republic of Korea.
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Affiliation(s)
- Ji Cheol Bae
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea.
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