Causal inference in obesity research
Franks, P. W. LU and Atabaki-Pasdar, N. LU
(2017)
In Journal of Internal Medicine
281(3).
p.222-232
- Abstract
Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease... (More)
Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease prediction, although not dependent on knowing the causes, is nevertheless enhanced by such knowledge. Here, we provide an overview of some of the barriers to causal inference in obesity research and discuss analytical approaches, such as Mendelian randomization, that can help to overcome these obstacles. In a systematic review of the literature in this field, we found: (i) probable causal relationships between adiposity and bone health/disease, cancers (colorectal, lung and kidney cancers), cardiometabolic traits (blood pressure, fasting insulin, inflammatory markers and lipids), uric acid concentrations, coronary heart disease and venous thrombosis (in the presence of pulmonary embolism), (ii) possible causal relationships between adiposity and gray matter volume, depression and common mental disorders, oesophageal cancer, macroalbuminuria, end-stage renal disease, diabetic kidney disease, nuclear cataract and gall stone disease, and (iii) no evidence for causal relationships between adiposity and Alzheimer's disease, pancreatic cancer, venous thrombosis (in the absence of pulmonary embolism), liver function and periodontitis.
(Less)
- author
- Franks, P. W.
LU
and Atabaki-Pasdar, N.
LU
- organization
- publishing date
- 2017
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Adiposity, Bayesian network analysis, Genetics, Lifestyle, Mendelian randomization
- in
- Journal of Internal Medicine
- volume
- 281
- issue
- 3
- pages
- 222 - 232
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:85006823519
- pmid:27933671
- wos:000394893800001
- ISSN
- 0954-6820
- DOI
- 10.1111/joim.12577
- language
- English
- LU publication?
- yes
- id
- 319cf612-99b0-4db0-884b-4ec6fd4ad5e7
- date added to LUP
- 2017-01-19 11:19:08
- date last changed
- 2024-06-02 00:24:24
@article{319cf612-99b0-4db0-884b-4ec6fd4ad5e7,
abstract = {{<p>Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease prediction, although not dependent on knowing the causes, is nevertheless enhanced by such knowledge. Here, we provide an overview of some of the barriers to causal inference in obesity research and discuss analytical approaches, such as Mendelian randomization, that can help to overcome these obstacles. In a systematic review of the literature in this field, we found: (i) probable causal relationships between adiposity and bone health/disease, cancers (colorectal, lung and kidney cancers), cardiometabolic traits (blood pressure, fasting insulin, inflammatory markers and lipids), uric acid concentrations, coronary heart disease and venous thrombosis (in the presence of pulmonary embolism), (ii) possible causal relationships between adiposity and gray matter volume, depression and common mental disorders, oesophageal cancer, macroalbuminuria, end-stage renal disease, diabetic kidney disease, nuclear cataract and gall stone disease, and (iii) no evidence for causal relationships between adiposity and Alzheimer's disease, pancreatic cancer, venous thrombosis (in the absence of pulmonary embolism), liver function and periodontitis.</p>}},
author = {{Franks, P. W. and Atabaki-Pasdar, N.}},
issn = {{0954-6820}},
keywords = {{Adiposity; Bayesian network analysis; Genetics; Lifestyle; Mendelian randomization}},
language = {{eng}},
number = {{3}},
pages = {{222--232}},
publisher = {{Wiley-Blackwell}},
series = {{Journal of Internal Medicine}},
title = {{Causal inference in obesity research}},
url = {{http://dx.doi.org/10.1111/joim.12577}},
doi = {{10.1111/joim.12577}},
volume = {{281}},
year = {{2017}},
}