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Title: Shared exposure liability of type 2 diabetes and other chronic conditions in the UK Biobank
Abstract Aims

To investigate whether the cumulative exposure risks of incident T2D are shared with other common chronic diseases.

Research design and methods

We first establish and report the cross-sectional prevalence, cross-sectional co-prevalence, and incidence of seven T2D-associated chronic diseases [hypertension, atrial fibrillation, coronary artery disease, obesity, chronic obstructive pulmonary disease (COPD), and chronic kidney and liver diseases] in the UK Biobank. We use published weights of genetic variants and exposure variables to derive the T2D polygenic (PGS) and polyexposure (PXS) risk scores and test their associations to incident diseases.

Results

PXS was associated with higher levels of clinical risk factors including BMI, systolic blood pressure, blood glucose, triglycerides, and HbA1c in individuals without overt or diagnosed T2D. In addition to predicting incident T2D, PXS and PGS were significantly and positively associated with the incidence of all 7 other chronic diseases. There were 4% and 8% of individuals in the bottom deciles of PXS and PGS, respectively, who were prediabetic at baseline but had low risks of T2D and other chronic diseases. Compared to the remaining population, individuals in the top deciles of PGS and PXS had particularly high risks of developing chronic diseases. For instance, the hazard ratio of COPD and obesity for individuals in the top T2D PXS deciles was 2.82 (95% CI 2.39–3.35,P = 4.00 × 10−33) and 2.54 (95% CI 2.24–2.87,P = 9.86 × 10−50), respectively, compared to the remaining population. We also found that PXS and PGS were both significantly (P < 0.0001) and positively associated with the total number of incident diseases.

Conclusions

T2D shares polyexposure risks with other common chronic diseases. Individuals with an elevated genetic and non-genetic risk of T2D also have high risks of cardiovascular, liver, lung, and kidney diseases.

 
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NSF-PAR ID:
10366876
Author(s) / Creator(s):
;
Publisher / Repository:
Springer Science + Business Media
Date Published:
Journal Name:
Acta Diabetologica
Volume:
59
Issue:
6
ISSN:
1432-5233
Page Range / eLocation ID:
p. 851-860
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    To establish a polyexposure score (PXS) for type 2 diabetes (T2D) incorporating 12 nongenetic exposures and examine whether a PXS and/or a polygenic risk score (PGS) improves diabetes prediction beyond traditional clinical risk factors.

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    We identified 356,621 unrelated individuals from the UK Biobank of White British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 nongenetically ascertained exposure and lifestyle variables for the PXS in prospective T2D. We computed the clinical risk score (CRS) and PGS by taking a weighted sum of eight established clinical risk factors and >6 million single nucleotide polymorphisms, respectively.

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