umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Causal inference in obesity research
Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Genetic & Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University Diabetes Center, Skåne University Hospital, Malmö; Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA.
2017 (English)In: Journal of Internal Medicine, ISSN 0954-6820, E-ISSN 1365-2796, Vol. 281, no 3, 222-232 p.Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
WILEY , 2017. Vol. 281, no 3, 222-232 p.
Keyword [en]
adiposity, Bayesian network analysis, genetics, lifestyle, Mendelian randomization
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:umu:diva-133703DOI: 10.1111/joim.12577ISI: 000394893800001PubMedID: 27933671OAI: oai:DiVA.org:umu-133703DiVA: diva2:1088989
Available from: 2017-04-18 Created: 2017-04-18 Last updated: 2017-04-18Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Franks, P. W.
By organisation
Medicine
In the same journal
Journal of Internal Medicine
Public Health, Global Health, Social Medicine and Epidemiology

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 8 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf