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http://hdl.handle.net/11375/26699
Title: | Optimizing Body Mass Index Targets Using Genetics and Biomarkers |
Authors: | Khan, Irfan |
Advisor: | Paré, Guillaume |
Department: | Medical Sciences |
Keywords: | Obesity;Body Mass Index;Mortality;Cancer;Cardiovascular Disease;Respiratory Disease;Type 2 Diabetes;Bioinformatics;Biostatistics;Genome-Wide Association Studies;Polygenic Risk Scores;Mendelian Randomization;Genetic Epidemiology;Population Health;Genetics;Genomics;Cox Proportional Hazards Regression |
Publication Date: | 2021 |
Abstract: | Introduction/Background: Guidelines from the World Health Organization currently recommend targeting a body mass index (BMI) between 18.5 and 24.9 kg/m2 based on the lowest risk of mortality observed in epidemiological studies. However, these recommendations are based on population observations and do not take into account potential inter-individual differences. We hypothesized that genetic and non-genetic differences in adiposity, anthropometric, and metabolic measures result in inter-individual variation in the optimal BMI. Methods: Genetic variants associated with BMI as well as related adiposity, anthropometric, and metabolic phenotypes (e.g. triglyceride (TG)) were combined into polygenic risk scores (PRS), cumulative risk scores derived from the weighted contributions of each variant. 387,692 participants in the UK Biobank were split by quantiles of PRS or clinical biomarkers such as C-reactive protein (CRP), and alanine aminotransferase (ALT). The BMI linked with the lowest risk of all-cause and cause-specific mortality outcomes (“nadir value”) was then compared across quantiles (“Cox meta-regression model”). Our results were replicated using the non-linear mendelian randomization (NLMR) model to assess causality. Results: The nadir value for the BMI–all-cause mortality relationship differed across percentiles of BMI PRS, suggesting inter-individual variation in optimal BMI based on genetics (p = 0.005). There was a difference of 1.90 kg/m2 in predicted optimal BMI between individuals in the top and bottom 5th BMI PRS percentile. Individuals having above and below median TG (p = 1.29×10-4), CRP (p = 7.92 × 10-5), and ALT (p = 2.70 × 10-8) levels differed in nadir for this relationship. There was no difference in the computed nadir between the Cox meta-regression or NLMR models (p = 0.102). Conclusions: The impact of BMI on mortality is heterogenous due to individual genetic and clinical biomarker level differences. Although we cannot confirm that are results are causal, genetics and clinical biomarkers have potential use for making more tailored BMI recommendations for patients. |
URI: | http://hdl.handle.net/11375/26699 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Khan_Irfan_I_finalsubmission2021July_MSc.pdf | Irfan Khan - MSc. Thesis | 2.78 MB | Adobe PDF | View/Open |
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