Open AccessPrevalence of adiposity‐based chronic disease in middle‐aged adults from Czech Republic: The Kardiovize study
Author(s) -
GonzalezRivas Juan P.,
Mechanick Jeffrey I.,
Hernandez José Pantaleón,
InfanteGarcia María M,
Pavlovska Iuliia,
MedinaInojosa José R.,
Kunzova Sarka,
NietoMartinez Ramfis,
Brož Jan,
Busetto Luca,
Maranhao Neto Geraldo A,
LopezJimenez Francisco,
Urbanová Jana,
Stokin Gorazd B
Publication year - 2021
Publication title -
obesity science and practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 14
ISSN - 2055-2238
DOI - 10.1002/osp4.496
Subject(s) - medicine , overweight , abdominal obesity , body mass index , obesity , metabolic syndrome , diabetes mellitus , stage (stratigraphy) , disease , endocrinology , paleontology , biology
Aims/Hypothesis The need for understanding obesity as a chronic disease, its stigmatization, and the lack of actionability related to it demands a new approach. The adiposity‐based chronic disease (ABCD) model is based on adiposity amount, distribution, and function, with a three stage complication‐centric rather than a body mass index (BMI)‐centric approach. The prevalence rates and associated risk factors are presented. Methods In total, 2159 participants were randomly selected from Czechia. ABCD was established as BMI ≥ 25 kg/m 2 or high body fat percent, or abdominal obesity and then categorized by their adiposity‐based complications: Stage 0: none; Stage 1: mild/moderate; Stage 2: severe. Results ABCD prevalence was 62.8%. Stage 0 was 2.3%; Stage 1 was 31.4%; Stage 2 was 29.1%. Comparing with other classifiers, participants in Stage 2 were more likely to have diabetes, hypertension, and metabolic syndrome than those with overweight, obesity, abdominal obesity, and increased fat mass. ABCD showed the highest sensitivity and specificity to detect participants with peripheral artery disease, increased intima media, and vascular disease. Conclusion/Interpretation The ABCD model provides a more sensitive approach that facilitates the early detection and stratification of participants at risk compared to traditional classifiers.