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The ‘Obesity Paradox:’ a parsimonious explanation for relations among obesity, mortality rate, and aging?

OBJECTIVE: Current clinical guidelines and public health statements generically prescribe body mass index (BMI; [Formula: see text]) categories regardless of the individual’s situation (age, risk for diseases, etc.). However, regarding BMI and mortality rate (MR), two well-established observations a...

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Detalles Bibliográficos
Autores principales: Childers, Douglas K., Allison, David B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3186057/
https://www.ncbi.nlm.nih.gov/pubmed/20440298
http://dx.doi.org/10.1038/ijo.2010.71
Descripción
Sumario:OBJECTIVE: Current clinical guidelines and public health statements generically prescribe body mass index (BMI; [Formula: see text]) categories regardless of the individual’s situation (age, risk for diseases, etc.). However, regarding BMI and mortality rate (MR), two well-established observations are (1) there is a U-shaped (i.e., concave) association - people with intermediate BMIs tend to outlive people with higher or lower BMIs; and (2) the nadirs of these curves tend to increase monotonically with age. Multiple hypotheses have been advanced to explain either of these two observations. Here we introduce a new hypothesis that may explain both phenomena, by drawing on the so-called obesity paradox: the unexpected finding that obesity is often associated with increased survival time among people who have some serious injury or illness despite being associated with reduced survival time among the general population. RESULTS: We establish that the obesity paradox offers one potential explanation for two curious but consistently observed phenomena in the obesity field. CONCLUSION: Further research is needed to determine the extent to which the obesity paradox is actually an explanation for these phenomena, but if our hypothesis proves true the common practice of prescribing overweight patients to lower their BMI should currently be applied with caution. In addition, the statistical modeling technique employed here could be applied in such other areas involving survival analysis of disjoint subgroups, in order to explain possible interacting causal associations and to determine clinical practice.