Cargando…

Prevalence, incidence and predictors of cardiovascular risk factors: longitudinal data from rural and urban South India and comparison with global data

INTRODUCTION: India has high mortality rates from cardiovascular disease (CVD). Understanding the trends and identifying modifiable determinants of CVD risk factors will guide preventive strategies and policy making. RESEARCH DESIGN AND METHODS: CVD risk factors (obesity, central obesity, and type 2...

Descripción completa

Detalles Bibliográficos
Autores principales: Vasan, Senthil K, Antonisamy, Belavendra, Gowri, Mahasampath, Selliah, Hepsy Y, Geethanjali, Finney S, Jebasingh, Felix S, Paul, Thomas V, Thomas, Nihal, Karpe, Fredrik, Johnson, Matthew, Osmond, Clive, Fall, Caroline H D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583064/
https://www.ncbi.nlm.nih.gov/pubmed/33093130
http://dx.doi.org/10.1136/bmjdrc-2020-001782
_version_ 1783599334791577600
author Vasan, Senthil K
Antonisamy, Belavendra
Gowri, Mahasampath
Selliah, Hepsy Y
Geethanjali, Finney S
Jebasingh, Felix S
Paul, Thomas V
Thomas, Nihal
Karpe, Fredrik
Johnson, Matthew
Osmond, Clive
Fall, Caroline H D
author_facet Vasan, Senthil K
Antonisamy, Belavendra
Gowri, Mahasampath
Selliah, Hepsy Y
Geethanjali, Finney S
Jebasingh, Felix S
Paul, Thomas V
Thomas, Nihal
Karpe, Fredrik
Johnson, Matthew
Osmond, Clive
Fall, Caroline H D
author_sort Vasan, Senthil K
collection PubMed
description INTRODUCTION: India has high mortality rates from cardiovascular disease (CVD). Understanding the trends and identifying modifiable determinants of CVD risk factors will guide preventive strategies and policy making. RESEARCH DESIGN AND METHODS: CVD risk factors (obesity, central obesity, and type 2 diabetes (T2D), hypertension, hypercholesterolemia and hypertriglyceridemia) prevalence and incidence were estimated in 962 (male 519) non-migrant adults from Vellore, South India, studied in: (1) 1998–2002 (mean age 28.2 years) and (2) 2013–2014 (mean age 41.7 years). Prevalence was compared with the Non-Communicable Disease Risk Collaboration (global) data. Incidence was compared with another Indian cohort from New Delhi Birth Cohort (NDBC). Regression analysis was used to test baseline predictors of incident CVD risk factors. RESULTS: The prevalence at 28 and 42 years was 17% (95% CI 14% to 19%) and 51% (95% CI 48% to 55%) for overweight/obesity, 19% (95% CI 17% to 22%) and 59% (95% CI 56% to 62%) for central obesity, 3% (95% CI 2% to 4%) and 16% (95% CI 14% to 19%) for T2D, 2% (95% CI 1% to 3%) and 19% (95% CI 17% to 22%) for hypertension and 15% (95% CI 13% to 18%) and 30% (95% CI 27% to 33%) for hypertriglyceridemia. The prevalence of T2D at baseline and follow-up and hypertension at follow-up was comparable with or exceeded that in high-income countries despite lower obesity rates. The incidence of most risk factors was lower in Vellore than in the NDBC. Waist circumference strongly predicted incident T2D, hypertension and hypertriglyceridemia. CONCLUSIONS: A high prevalence of CVD risk factors was evident at a young age among Indians compared with high and upper middle income countries, with rural rates catching up with urban estimates. Adiposity predicted higher incident CVD risk, but the prevalence of hypertension and T2D was higher given a relatively low obesity prevalence. Preventive efforts should target both rural and urban India and should start young.
format Online
Article
Text
id pubmed-7583064
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-75830642020-10-28 Prevalence, incidence and predictors of cardiovascular risk factors: longitudinal data from rural and urban South India and comparison with global data Vasan, Senthil K Antonisamy, Belavendra Gowri, Mahasampath Selliah, Hepsy Y Geethanjali, Finney S Jebasingh, Felix S Paul, Thomas V Thomas, Nihal Karpe, Fredrik Johnson, Matthew Osmond, Clive Fall, Caroline H D BMJ Open Diabetes Res Care Cardiovascular and Metabolic Risk INTRODUCTION: India has high mortality rates from cardiovascular disease (CVD). Understanding the trends and identifying modifiable determinants of CVD risk factors will guide preventive strategies and policy making. RESEARCH DESIGN AND METHODS: CVD risk factors (obesity, central obesity, and type 2 diabetes (T2D), hypertension, hypercholesterolemia and hypertriglyceridemia) prevalence and incidence were estimated in 962 (male 519) non-migrant adults from Vellore, South India, studied in: (1) 1998–2002 (mean age 28.2 years) and (2) 2013–2014 (mean age 41.7 years). Prevalence was compared with the Non-Communicable Disease Risk Collaboration (global) data. Incidence was compared with another Indian cohort from New Delhi Birth Cohort (NDBC). Regression analysis was used to test baseline predictors of incident CVD risk factors. RESULTS: The prevalence at 28 and 42 years was 17% (95% CI 14% to 19%) and 51% (95% CI 48% to 55%) for overweight/obesity, 19% (95% CI 17% to 22%) and 59% (95% CI 56% to 62%) for central obesity, 3% (95% CI 2% to 4%) and 16% (95% CI 14% to 19%) for T2D, 2% (95% CI 1% to 3%) and 19% (95% CI 17% to 22%) for hypertension and 15% (95% CI 13% to 18%) and 30% (95% CI 27% to 33%) for hypertriglyceridemia. The prevalence of T2D at baseline and follow-up and hypertension at follow-up was comparable with or exceeded that in high-income countries despite lower obesity rates. The incidence of most risk factors was lower in Vellore than in the NDBC. Waist circumference strongly predicted incident T2D, hypertension and hypertriglyceridemia. CONCLUSIONS: A high prevalence of CVD risk factors was evident at a young age among Indians compared with high and upper middle income countries, with rural rates catching up with urban estimates. Adiposity predicted higher incident CVD risk, but the prevalence of hypertension and T2D was higher given a relatively low obesity prevalence. Preventive efforts should target both rural and urban India and should start young. BMJ Publishing Group 2020-10-22 /pmc/articles/PMC7583064/ /pubmed/33093130 http://dx.doi.org/10.1136/bmjdrc-2020-001782 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Cardiovascular and Metabolic Risk
Vasan, Senthil K
Antonisamy, Belavendra
Gowri, Mahasampath
Selliah, Hepsy Y
Geethanjali, Finney S
Jebasingh, Felix S
Paul, Thomas V
Thomas, Nihal
Karpe, Fredrik
Johnson, Matthew
Osmond, Clive
Fall, Caroline H D
Prevalence, incidence and predictors of cardiovascular risk factors: longitudinal data from rural and urban South India and comparison with global data
title Prevalence, incidence and predictors of cardiovascular risk factors: longitudinal data from rural and urban South India and comparison with global data
title_full Prevalence, incidence and predictors of cardiovascular risk factors: longitudinal data from rural and urban South India and comparison with global data
title_fullStr Prevalence, incidence and predictors of cardiovascular risk factors: longitudinal data from rural and urban South India and comparison with global data
title_full_unstemmed Prevalence, incidence and predictors of cardiovascular risk factors: longitudinal data from rural and urban South India and comparison with global data
title_short Prevalence, incidence and predictors of cardiovascular risk factors: longitudinal data from rural and urban South India and comparison with global data
title_sort prevalence, incidence and predictors of cardiovascular risk factors: longitudinal data from rural and urban south india and comparison with global data
topic Cardiovascular and Metabolic Risk
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583064/
https://www.ncbi.nlm.nih.gov/pubmed/33093130
http://dx.doi.org/10.1136/bmjdrc-2020-001782
work_keys_str_mv AT vasansenthilk prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata
AT antonisamybelavendra prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata
AT gowrimahasampath prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata
AT selliahhepsyy prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata
AT geethanjalifinneys prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata
AT jebasinghfelixs prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata
AT paulthomasv prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata
AT thomasnihal prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata
AT karpefredrik prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata
AT johnsonmatthew prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata
AT osmondclive prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata
AT fallcarolinehd prevalenceincidenceandpredictorsofcardiovascularriskfactorslongitudinaldatafromruralandurbansouthindiaandcomparisonwithglobaldata