Causal inference in obesity research

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Franks, P. W.Umeå universitet,Umeå University,Lund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups,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,Skåne University Hospital,Medicin (author)

Causal inference in obesity research

Article/chapterEnglish2017

Publisher, publication year, extent ...

2016-12-08
Wiley,2017

Numbers

LIBRIS-ID:oai:lup.lub.lu.se:319cf612-99b0-4db0-884b-4ec6fd4ad5e7
https://lup.lub.lu.se/record/319cf612-99b0-4db0-884b-4ec6fd4ad5e7URI
https://doi.org/10.1111/joim.12577DOI
https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-133703URI

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Language:English
Summary in:English

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SwePubSwePub

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Subject category:for swepub-publicationtype
Subject category:ref swepub-contenttype

Notes

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.

Subject headings and genre

MEDICIN OCH HÄLSOVETENSKAP Hälsovetenskap Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi hsv//swe
MEDICAL AND HEALTH SCIENCES Health Sciences Public Health, Global Health, Social Medicine and Epidemiology hsv//eng
MEDICIN OCH HÄLSOVETENSKAP Medicinska och farmaceutiska grundvetenskaper Medicinsk genetik hsv//swe
MEDICAL AND HEALTH SCIENCES Basic Medicine Medical Genetics hsv//eng
Adiposity
Bayesian network analysis
Genetics
Lifestyle
Mendelian randomization
adiposity

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Atabaki-Pasdar, N.Lund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups,Skåne University Hospital(Swepub:lu)med-nha (author)
Genetisk och molekylär epidemiologiForskargrupper vid Lunds universitet (creator_code:org_t)

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In:Journal of Internal Medicine: Wiley281:3, s. 222-2320954-68201365-2796

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