Cargando…
Trends in Diabetes Subgroups and Their Risk for All-Cause, Cardiovascular Disease and Diabetes-Specific Mortality in the US: A Data-Driven Reproducible Machine Learning Approach
Background: Diabetes has been described as a heterogeneous entity which can be studied through data-driven subgroups (obesity related [MOD], severe-insulin deficient [SIID], severe-insulin resistant [SIRD] and age-related diabetes [MARD]). However, trends in prevalence and mortality risk are still u...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8089686/ http://dx.doi.org/10.1210/jendso/bvab048.863 |
_version_ | 1783687098423836672 |
---|---|
author | Antonio-Villa, Neftali Eduardo Fernández-Chirino, Luisa Vargas-Vazquez, Arsenio Bahena-López, Jessica Paola Bello-Chavolla, Omar Yaxmehen |
author_facet | Antonio-Villa, Neftali Eduardo Fernández-Chirino, Luisa Vargas-Vazquez, Arsenio Bahena-López, Jessica Paola Bello-Chavolla, Omar Yaxmehen |
author_sort | Antonio-Villa, Neftali Eduardo |
collection | PubMed |
description | Background: Diabetes has been described as a heterogeneous entity which can be studied through data-driven subgroups (obesity related [MOD], severe-insulin deficient [SIID], severe-insulin resistant [SIRD] and age-related diabetes [MARD]). However, trends in prevalence and mortality risk are still unclear. Aims: To analyze diabetes subgroup trends and to evaluate mortality risk in the US. Methods: Data and follow-up causes of mortality (all-cause, cardiovascular disease, and diabetes specific) was collected from NHANES cycles 1999–2018. Subgroup diabetes classification was performed using the self-normalizing neural networks algorithm using clinical parameters (HbA1c, time since diabetes diagnosis, HOMA2-IR, HOMA2-B, and BMI) proposed by Bello-Chavolla et al (https://bit.ly/3jSm1xv). Prevalence was estimated using sample weights. 2-year cycles were used as a continuous variable to evaluate the biannual change (BC) of the overall prevalence of diabetes and subgroups. Trends were stratified according to race. Cox-proportional and Fine-Gray semiparametric hazard regression models were used to evaluate mortality risk. Results: Data from 59,204 adult subjects was extracted for trend analysis. Follow-up information was obtained for 3,980 subjects. Diabetes prevalence in the US increased from 8.2% (95%CI 7.8–8.6) in 1999–2000 to 13.9% (95% CI 13.4–14.4) in 2017–2018 (BC 1.38%, 95% CI 1.20–1.56, p<0.001). Non-Hispanic Blacks had the largest increase in diabetes prevalence (BC: 1.40%, 95%CI 0.71–2.08, p=0.027), followed by Non-Hispanic Whites (BC: 1.36%, 95%CI 1.13–1.58, p<0.001), and Mexican Americans (BC: 1.33%, 95%CI 1.20–1.54, p<0.001). Regarding diabetes subgroups, MARD had the highest prevalence, with a moderate increase over time; however, MOD had the greatest increase over time (1.5%, [95%CI 1.2–1.8] to 4.5% [95%CI 4.0–5.0]; BC: 0.73% [95%CI 0.60–0.86], p<0.01). Both SIRD and SIID had non-significant increases in prevalence during the studied period. Non-Hispanic Blacks had an increase in the prevalence in MOD and SIID, Mexican Americans in MOD and SIRD, and non-Hispanic Whites in MOD and MARD. Compared with MOD, the risk for all-cause mortality was higher for MARD (HR 2.9 95% CI: 2.1–3.9), SIRD (HR 2.0 95% CI: 1.5–2.8), and SIID (HR 1.6 95% CI: 1.1–2.3). For CVD mortality, only MARD (HR 2.8 95% CI: 1.4–5.7) and SIRD (HR 2.5 95% CI: 1.2–5.3) displayed higher risk. For diabetes-specific mortality, only MARD (HR 2.2 95% CI: 1.3–3.7) was associated. Conclusion: There is an overall increase in diabetes prevalence and its subgroups from 1999 to 2018; MORD had the highest increase. The risk for all-cause, CVD and diabetes-specific mortality was different among subgroups. Our results supports the use of diabetes subgroups for a better understanding of diabetes and its complications. |
format | Online Article Text |
id | pubmed-8089686 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-80896862021-05-06 Trends in Diabetes Subgroups and Their Risk for All-Cause, Cardiovascular Disease and Diabetes-Specific Mortality in the US: A Data-Driven Reproducible Machine Learning Approach Antonio-Villa, Neftali Eduardo Fernández-Chirino, Luisa Vargas-Vazquez, Arsenio Bahena-López, Jessica Paola Bello-Chavolla, Omar Yaxmehen J Endocr Soc Diabetes Mellitus and Glucose Metabolism Background: Diabetes has been described as a heterogeneous entity which can be studied through data-driven subgroups (obesity related [MOD], severe-insulin deficient [SIID], severe-insulin resistant [SIRD] and age-related diabetes [MARD]). However, trends in prevalence and mortality risk are still unclear. Aims: To analyze diabetes subgroup trends and to evaluate mortality risk in the US. Methods: Data and follow-up causes of mortality (all-cause, cardiovascular disease, and diabetes specific) was collected from NHANES cycles 1999–2018. Subgroup diabetes classification was performed using the self-normalizing neural networks algorithm using clinical parameters (HbA1c, time since diabetes diagnosis, HOMA2-IR, HOMA2-B, and BMI) proposed by Bello-Chavolla et al (https://bit.ly/3jSm1xv). Prevalence was estimated using sample weights. 2-year cycles were used as a continuous variable to evaluate the biannual change (BC) of the overall prevalence of diabetes and subgroups. Trends were stratified according to race. Cox-proportional and Fine-Gray semiparametric hazard regression models were used to evaluate mortality risk. Results: Data from 59,204 adult subjects was extracted for trend analysis. Follow-up information was obtained for 3,980 subjects. Diabetes prevalence in the US increased from 8.2% (95%CI 7.8–8.6) in 1999–2000 to 13.9% (95% CI 13.4–14.4) in 2017–2018 (BC 1.38%, 95% CI 1.20–1.56, p<0.001). Non-Hispanic Blacks had the largest increase in diabetes prevalence (BC: 1.40%, 95%CI 0.71–2.08, p=0.027), followed by Non-Hispanic Whites (BC: 1.36%, 95%CI 1.13–1.58, p<0.001), and Mexican Americans (BC: 1.33%, 95%CI 1.20–1.54, p<0.001). Regarding diabetes subgroups, MARD had the highest prevalence, with a moderate increase over time; however, MOD had the greatest increase over time (1.5%, [95%CI 1.2–1.8] to 4.5% [95%CI 4.0–5.0]; BC: 0.73% [95%CI 0.60–0.86], p<0.01). Both SIRD and SIID had non-significant increases in prevalence during the studied period. Non-Hispanic Blacks had an increase in the prevalence in MOD and SIID, Mexican Americans in MOD and SIRD, and non-Hispanic Whites in MOD and MARD. Compared with MOD, the risk for all-cause mortality was higher for MARD (HR 2.9 95% CI: 2.1–3.9), SIRD (HR 2.0 95% CI: 1.5–2.8), and SIID (HR 1.6 95% CI: 1.1–2.3). For CVD mortality, only MARD (HR 2.8 95% CI: 1.4–5.7) and SIRD (HR 2.5 95% CI: 1.2–5.3) displayed higher risk. For diabetes-specific mortality, only MARD (HR 2.2 95% CI: 1.3–3.7) was associated. Conclusion: There is an overall increase in diabetes prevalence and its subgroups from 1999 to 2018; MORD had the highest increase. The risk for all-cause, CVD and diabetes-specific mortality was different among subgroups. Our results supports the use of diabetes subgroups for a better understanding of diabetes and its complications. Oxford University Press 2021-05-03 /pmc/articles/PMC8089686/ http://dx.doi.org/10.1210/jendso/bvab048.863 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the Endocrine Society. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Diabetes Mellitus and Glucose Metabolism Antonio-Villa, Neftali Eduardo Fernández-Chirino, Luisa Vargas-Vazquez, Arsenio Bahena-López, Jessica Paola Bello-Chavolla, Omar Yaxmehen Trends in Diabetes Subgroups and Their Risk for All-Cause, Cardiovascular Disease and Diabetes-Specific Mortality in the US: A Data-Driven Reproducible Machine Learning Approach |
title | Trends in Diabetes Subgroups and Their Risk for All-Cause, Cardiovascular Disease and Diabetes-Specific Mortality in the US: A Data-Driven Reproducible Machine Learning Approach |
title_full | Trends in Diabetes Subgroups and Their Risk for All-Cause, Cardiovascular Disease and Diabetes-Specific Mortality in the US: A Data-Driven Reproducible Machine Learning Approach |
title_fullStr | Trends in Diabetes Subgroups and Their Risk for All-Cause, Cardiovascular Disease and Diabetes-Specific Mortality in the US: A Data-Driven Reproducible Machine Learning Approach |
title_full_unstemmed | Trends in Diabetes Subgroups and Their Risk for All-Cause, Cardiovascular Disease and Diabetes-Specific Mortality in the US: A Data-Driven Reproducible Machine Learning Approach |
title_short | Trends in Diabetes Subgroups and Their Risk for All-Cause, Cardiovascular Disease and Diabetes-Specific Mortality in the US: A Data-Driven Reproducible Machine Learning Approach |
title_sort | trends in diabetes subgroups and their risk for all-cause, cardiovascular disease and diabetes-specific mortality in the us: a data-driven reproducible machine learning approach |
topic | Diabetes Mellitus and Glucose Metabolism |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8089686/ http://dx.doi.org/10.1210/jendso/bvab048.863 |
work_keys_str_mv | AT antoniovillaneftalieduardo trendsindiabetessubgroupsandtheirriskforallcausecardiovasculardiseaseanddiabetesspecificmortalityintheusadatadrivenreproduciblemachinelearningapproach AT fernandezchirinoluisa trendsindiabetessubgroupsandtheirriskforallcausecardiovasculardiseaseanddiabetesspecificmortalityintheusadatadrivenreproduciblemachinelearningapproach AT vargasvazquezarsenio trendsindiabetessubgroupsandtheirriskforallcausecardiovasculardiseaseanddiabetesspecificmortalityintheusadatadrivenreproduciblemachinelearningapproach AT bahenalopezjessicapaola trendsindiabetessubgroupsandtheirriskforallcausecardiovasculardiseaseanddiabetesspecificmortalityintheusadatadrivenreproduciblemachinelearningapproach AT bellochavollaomaryaxmehen trendsindiabetessubgroupsandtheirriskforallcausecardiovasculardiseaseanddiabetesspecificmortalityintheusadatadrivenreproduciblemachinelearningapproach |