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Fermín-Martínez CA, Bello-Chavolla OY, Paz-Cabrera CD, Ramírez-García D, Perezalonso-Espinosa J, Fernández-Chirino L, Vargas-Vázquez A, Díaz-Sánchez JP, Méndez-Labra PN, Núñez-Luna A, Basile-Alvarez MR, Sánchez-Castro P, Bragg F, Friedrichs LG, Aguilar-Ramírez D, Emberson JR, Berumen-Campos J, Kuri-Morales P, Tapia-Conyer R, Alegre-Díaz J, Seiglie JA, Antonio-Villa NE. Prediabetes as a risk factor for all-cause and cause-specific mortality: a prospective analysis of 115,919 adults without diabetes in Mexico City. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305840. [PMID: 38699295 PMCID: PMC11065040 DOI: 10.1101/2024.04.15.24305840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
BACKGROUND Prediabetes has been associated with increased all-cause and cardiovascular mortality. However, no large-scale studies have been conducted in Mexico or Latin America examining these associations. METHODS We analyzed data from 115,919 adults without diabetes (diagnosed or undiagnosed) aged 35-84 years who participated in the Mexico City Prospective Study between 1998 and 2004. Participants were followed until January 1st, 2021 for cause-specific mortality. We defined prediabetes according to the American Diabetes Association (ADA, HbA1c 5.7% to 6.4%) and the International Expert Committee (IEC, HbA1c 6.0-6.4%) definitions. Cox regression adjusted for confounders was used to estimate all-cause and cause-specific mortality rate ratios (RR) at ages 35-74 years associated with prediabetes. FINDINGS During 2,085,392 person-years of follow-up (median in survivors 19 years), there were 6,810 deaths at ages 35-74, including 1,742 from cardiovascular disease, 892 from renal disease and 108 from acute diabetic crises. Of 110,405 participants aged 35-74 years at recruitment, 28,852 (26%) had ADA-defined prediabetes and 7,203 (7%) had IEC-defined prediabetes. Compared with those without prediabetes, individuals with prediabetes had higher risk of all-cause mortality at ages 35-74 years (RR 1.13, 95% CI 1.07-1.19 for ADA-defined prediabetes and RR 1.28, 1.18-1.39 for IEC-defined prediabetes), as well as increased risk of cardiovascular mortality (RR 1.22 [1.10-1.35] and 1.42 [1.22-1.65], respectively), renal mortality (RR 1.35 [1.08-1.68] and 1.69 [1.24-2.31], respectively), and death from an acute diabetic crisis (RR 2.63 [1.76-3.94] and 3.43 [2.09-5.62], respectively). RRs were larger at younger than at older ages, and similar for men compared to women. The absolute excess risk associated with ADA and IEC-defined prediabetes at ages 35-74 accounted for6% and 3% of cardiovascular deaths respectively, 10% and 5% of renal deaths respectively, and 31% and 14% of acute diabetic deaths respectively. INTERPRETATION Prediabetes is a significant risk factor for all-cause, cardiovascular, renal, and acute diabetic deaths in Mexican adults. Identification and timely management of individuals with prediabetes for targeted risk reduction could contribute to reducing premature mortality from cardiometabolic causes in this population. FUNDING Wellcome Trust, the Mexican Health Ministry, the National Council of Science and Technology for Mexico, Cancer Research UK, British Heart Foundation, UK Medical Research Council. Instituto Nacional de Geriatría (Mexico City).
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Affiliation(s)
- Carlos A. Fermín-Martínez
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- MD/PhD (PECEM) Program, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | | | - César Daniel Paz-Cabrera
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- Especialidad en Medicina Preventiva, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | - Daniel Ramírez-García
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Jerónimo Perezalonso-Espinosa
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- MD/PhD (PECEM) Program, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Luisa Fernández-Chirino
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Arsenio Vargas-Vázquez
- MD/PhD (PECEM) Program, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Juan Pablo Díaz-Sánchez
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- MD/PhD (PECEM) Program, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Padme Nailea Méndez-Labra
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- MD/PhD (PECEM) Program, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Alejandra Núñez-Luna
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Martín Roberto Basile-Alvarez
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Paulina Sánchez-Castro
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Fiona Bragg
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Louisa Gnatiuc Friedrichs
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Diego Aguilar-Ramírez
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jonathan R. Emberson
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jaime Berumen-Campos
- Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Pablo Kuri-Morales
- Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey, Mexico
| | - Roberto Tapia-Conyer
- Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Jesus Alegre-Díaz
- Experimental Research Unit, Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico
| | - Jacqueline A. Seiglie
- Diabetes Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
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Olsen MT, Klarskov CK, Dungu AM, Hansen KB, Pedersen-Bjergaard U, Kristensen PL. Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review. J Diabetes Sci Technol 2024:19322968231221803. [PMID: 38179940 DOI: 10.1177/19322968231221803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
BACKGROUND Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages. METHODS A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163). RESULTS A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics. CONCLUSION This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.
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Affiliation(s)
- Mikkel Thor Olsen
- Department of Endocrinology and Nephrology, Copenhagen University Hospital-North Zealand, Hilleroed, Denmark
| | - Carina Kirstine Klarskov
- Department of Endocrinology and Nephrology, Copenhagen University Hospital-North Zealand, Hilleroed, Denmark
| | - Arnold Matovu Dungu
- Department of Pulmonary and Infectious Diseases, Copenhagen University Hospital-North Zealand, Hilleroed, Denmark
| | - Katrine Bagge Hansen
- Steno Diabetes Center Copenhagen, Copenhagen University Hospital-Herlev-Gentofte, Herlev, Denmark
| | - Ulrik Pedersen-Bjergaard
- Department of Endocrinology and Nephrology, Copenhagen University Hospital-North Zealand, Hilleroed, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Peter Lommer Kristensen
- Department of Endocrinology and Nephrology, Copenhagen University Hospital-North Zealand, Hilleroed, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Fermín-Martínez CA, Paz-Cabrera CD, Basile-Alvarez MR, Castro PS, Núñez-Luna A, Perezalonso-Espinosa J, Ramírez-García D, Antonio-Villa NE, Vargas-Vázquez A, Fernández-Chirino L, Carrillo-Herrera KB, Cabrera-Quintana LA, Rojas-Martínez R, Seiglie JA, Bello-Chavolla OY. Prevalence of prediabetes in Mexico: a retrospective analysis of nationally representative surveys spanning 2016-2022. LANCET REGIONAL HEALTH. AMERICAS 2023; 28:100640. [PMID: 38076414 PMCID: PMC10701418 DOI: 10.1016/j.lana.2023.100640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/13/2023] [Accepted: 11/13/2023] [Indexed: 12/30/2023]
Abstract
Background Characterizing prediabetes phenotypes may be useful in guiding diabetes prevention efforts; however, heterogeneous criteria to define prediabetes have led to inconsistent prevalence estimates, particularly in low- and middle-income countries. Here, we estimated trends in prediabetes prevalence in Mexico across different prediabetes definitions and their association with prevalent cardiometabolic conditions. Methods We conducted a serial cross-sectional analysis of National Health and Nutrition Surveys in Mexico (2016-2022), totalling 22 081 Mexican adults. After excluding individuals with diagnosed or undiagnosed diabetes, we defined prediabetes using ADA (impaired fasting glucose [IFG] 100-125 mg/dL and/or HbA1c 5.7-6.4%), WHO (IFG 110-125 mg/dL), and IEC criteria (HbA1c 6.0-6.4%). Prevalence trends of prediabetes over time were evaluated using weighted Poisson regression and its association with prevalent cardiometabolic conditions with weighted logistic regression. Findings The prevalence of prediabetes (either IFG or high HbA1c [ADA]) in Mexico was 20.9% in 2022. Despite an overall downward trend in prediabetes (RR 0.973, 95% CI 0.957-0.988), this was primarily driven by decreases in prediabetes by ADA-IFG (RR 0.898, 95% CI 0.880-0.917) and WHO-IFG criteria (RR 0.919, 95% CI 0.886-0.953), while prediabetes by ADA-HbA1c (RR 1.055, 95% CI 1.033-1.077) and IEC-HbA1C criteria (RR 1.085, 95% CI 1.045-1.126) increased over time. Prediabetes prevalence increased over time in adults >40 years, with central obesity, self-identified as indigenous or living in urban areas. For all definitions, prediabetes was associated with an increased risk of cardiometabolic conditions. Interpretation Prediabetes rates in Mexico from 2016 to 2022 varied based on defining criteria but consistently increased for HbA1c-based definitions and high-risk subgroups. Funding This research was supported by Instituto Nacional de Geriatría in Mexico. JAS was supported by NIH/NIDDK Grant# K23DK135798.
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Affiliation(s)
- Carlos A. Fermín-Martínez
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- MD/PhD (PECEM) Program, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - César Daniel Paz-Cabrera
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- Especialidad en Medicina Preventiva, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | - Martín Roberto Basile-Alvarez
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Paulina Sánchez Castro
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Alejandra Núñez-Luna
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | | | - Daniel Ramírez-García
- Research Division, Instituto Nacional de Geriatría, Mexico City, Mexico
- Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | | | - Arsenio Vargas-Vázquez
- Especialidad en Medicina Preventiva, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | | | | | | | | | - Jacqueline A. Seiglie
- Diabetes Unit, Massachusetts General Hospital, Harvard Medical School, MA, USA
- Department of Medicine, Harvard Medical School, MA, USA
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Saruarov Y, Nuskabayeva G, Gencer MZ, Sadykova K, Zhunissova M, Tatykayeva U, Iskandirova E, Sarsenova G, Durmanova A, Gaipov A, Atageldiyeva K, Sarría-Santamera A. Associations of Clusters of Cardiovascular Risk Factors with Insulin Resistance and Β-Cell Functioning in a Working-Age Diabetic-Free Population in Kazakhstan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3918. [PMID: 36900929 PMCID: PMC10001384 DOI: 10.3390/ijerph20053918] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 02/14/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Cardiovascular risk factors aggregate in determined individuals. Patients with Type 2 diabetes mellitus (T2DM) have higher cardiovascular This study aimed to investigate insulinresistance (IR) and β-cell function using the homeostasis model assessment (HOMA) indexes in a general Kazakh population and determine the effect he effect that cardiovascular factors may have on those indexes. We conducted a cross-sectional study among employees of the Khoja Akhmet Yassawi International Kazakh-Turkish University (Turkistan, Kazakhstan) aged between 27 and 69 years. Sociodemographic variables, anthropometric measurements (body mass, height, waist circumference, hip circumference), and blood pressure were obtained. Fasting blood samples were collected to measure insulin, glucose, total cholesterol (TC), triglycerides (TG), and high- (HDL) andlow-density lipoprotein (LDL) levels. Oral glucose tolerance tests were performed. Hierarchical and K-means cluster analyses were obtained. The final sample was composed of 427 participants. Spearmen correlation analysis showed that cardiovascular parameters were statistically associated with HOMA-β (p < 0.001) and not with HOMA IR. Participants were aggregated into the three clusters where the cluster with a higher age and cardiovascular risk revealed deficient β-cell functioning, but not IR (p < 0.000 and p = 0.982). Common and easy to obtain biochemical and anthropometric measurements capturing relevant cardiovascular risk factors have been demonstrated to be associated with significant deficiency in insulin secretion. Although further longitudinal studies of the incidence of T2DM are needed, this study highlights that cardiovascular profiling has a significant role not just for risk stratification of patients for cardiovascular prevention but also for targeted vigilant glucose monitoring.
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Affiliation(s)
- Yerbolat Saruarov
- Department of Special Clinical Disciplines, Faculty of Medicine, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan 161200, Kazakhstan
| | - Gulnaz Nuskabayeva
- Department of Special Clinical Disciplines, Faculty of Medicine, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan 161200, Kazakhstan
| | - Mehmet Ziya Gencer
- Department of Special Clinical Disciplines, Faculty of Medicine, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan 161200, Kazakhstan
| | - Karlygash Sadykova
- Department of Special Clinical Disciplines, Faculty of Medicine, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan 161200, Kazakhstan
| | - Mira Zhunissova
- Department of Special Clinical Disciplines, Faculty of Medicine, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan 161200, Kazakhstan
| | - Ugilzhan Tatykayeva
- Department of Human Pathology and Physiology, Faculty of Dentistry, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan 161200, Kazakhstan
| | - Elmira Iskandirova
- Department of Therapy, Shymkent Medical Institute, Khoja Akhmet Yassawi International Kazakh-Turkish University, Shymkent 160019, Kazakhstan
| | - Gulmira Sarsenova
- Department of Therapy, Shymkent Medical Institute, Khoja Akhmet Yassawi International Kazakh-Turkish University, Shymkent 160019, Kazakhstan
| | - Aigul Durmanova
- Academic Department of Internal Medicine, University Medical Center, Astana 020000, Kazakhstan
| | - Abduzhappar Gaipov
- Academic Department of Internal Medicine, University Medical Center, Astana 020000, Kazakhstan
- Department of Medicine, Nazarbayev University School of Medicine, Astana 020000, Kazakhstan
| | - Kuralay Atageldiyeva
- Academic Department of Internal Medicine, University Medical Center, Astana 020000, Kazakhstan
- Department of Medicine, Nazarbayev University School of Medicine, Astana 020000, Kazakhstan
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