TY - JOUR
T1 - Clustering of obesity-related characteristics
T2 - A latent class analysis from the Canadian Longitudinal Study on Aging
AU - Andreacchi, Alessandra T.
AU - Oz, Urun Erbas
AU - Bassim, Carol
AU - Griffith, Lauren E.
AU - Mayhew, Alexandra
AU - Pigeyre, Marie
AU - Stranges, Saverio
AU - Verschoor, Chris P.
AU - Anderson, Laura N.
N1 - Funding Information:
This work was supported by the Canadian Institutes of Health Research (CIHR) [grant number: ACD-162992 ]. Dr. Lauren Griffith is supported by the McLaughlin Foundation Professorship in Population and Public Health. The funding agencies had no role in the design of the study, collection and analysis of data, or the decision to publish.
Funding Information:
This work was supported by the Canadian Institutes of Health Research (CIHR) [grant number: ACD-162992]. Dr. Lauren Griffith is supported by the McLaughlin Foundation Professorship in Population and Public Health. The funding agencies had no role in the design of the study, collection and analysis of data, or the decision to publish. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland. Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) [grant number: LSA 94473] and the Canada Foundation for Innovation. This research has been conducted using the CLSA's dataset Baseline Tracking version 3.4 and Baseline Comprehensive Version 4.0, under Application Number 19CA004. The opinions expressed in this manuscript are the author's own and do not reflect the views of the Canadian Longitudinal Study on Aging. Data are available from the Canadian Longitudinal Study on Aging (www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data. The authors disclose that there are no competing financial interests.
Funding Information:
The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland. Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) [grant number: LSA 94473 ] and the Canada Foundation for Innovation. This research has been conducted using the CLSA's dataset Baseline Tracking version 3.4 and Baseline Comprehensive Version 4.0, under Application Number 19CA004.
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/12
Y1 - 2021/12
N2 - Measures of obesity, including body mass index (BMI) and waist circumference (WC), do not fully capture the complexity of obesity-related health risks. This study identified distinct classes of obesity-related characteristics and evaluated their associations with BMI, WC, and percent body fat (%BF) using cross-sectional data from 30,096 participants aged 45–85 in the Canadian Longitudinal Study on Aging (2011–2015). Sixteen obesity-related variables, including behavioural, metabolic, physical health, and mental health/social factors, were included in a latent class analysis to identify distinct classes of participants. Adjusted odds ratios (OR) were estimated from logistic regression for associations between each class and obesity defined by BMI, WC and %BF. Six latent classes were identified: “low-risk” (39.8%), “cardiovascular risk” (19.4%), “metabolic risk” (16.9%), “sleep and mental health risk” (12.1%), “multiple and complex risk” (6.7%), and “cardiometabolic risk” (5.1%). Compared to “low-risk”, all classes had increased odds of BMI-, WC- and %BF-defined obesity. For example, the “complex and multiple risk” class was associated with obesity by BMI (OR: 10.70, 95% confidence interval (CI): 9.51, 12.04), WC (OR: 9.21, 95% CI: 8,15, 10,41) and %BF (OR: 7.54, 95% CI: 6.21, 9.16). Distinct classes of obesity-related characteristics were identified and were strongly associated with obesity defined by multiple measures.
AB - Measures of obesity, including body mass index (BMI) and waist circumference (WC), do not fully capture the complexity of obesity-related health risks. This study identified distinct classes of obesity-related characteristics and evaluated their associations with BMI, WC, and percent body fat (%BF) using cross-sectional data from 30,096 participants aged 45–85 in the Canadian Longitudinal Study on Aging (2011–2015). Sixteen obesity-related variables, including behavioural, metabolic, physical health, and mental health/social factors, were included in a latent class analysis to identify distinct classes of participants. Adjusted odds ratios (OR) were estimated from logistic regression for associations between each class and obesity defined by BMI, WC and %BF. Six latent classes were identified: “low-risk” (39.8%), “cardiovascular risk” (19.4%), “metabolic risk” (16.9%), “sleep and mental health risk” (12.1%), “multiple and complex risk” (6.7%), and “cardiometabolic risk” (5.1%). Compared to “low-risk”, all classes had increased odds of BMI-, WC- and %BF-defined obesity. For example, the “complex and multiple risk” class was associated with obesity by BMI (OR: 10.70, 95% confidence interval (CI): 9.51, 12.04), WC (OR: 9.21, 95% CI: 8,15, 10,41) and %BF (OR: 7.54, 95% CI: 6.21, 9.16). Distinct classes of obesity-related characteristics were identified and were strongly associated with obesity defined by multiple measures.
KW - Anthropometric measure
KW - Canadian Longitudinal Study on Aging
KW - Latent class analysis
KW - Obesity
KW - Older adults
UR - https://www.scopus.com/pages/publications/85111246161
UR - https://www.ncbi.nlm.nih.gov/pubmed/34298025
U2 - 10.1016/j.ypmed.2021.106739
DO - 10.1016/j.ypmed.2021.106739
M3 - Article
C2 - 34298025
AN - SCOPUS:85111246161
SN - 0091-7435
VL - 153
SP - 106739
JO - Preventive Medicine
JF - Preventive Medicine
M1 - 106739
ER -