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Original Article

Metabolic biomarkers and gallstone disease – a population-based study

, , , &
Pages 1270-1277 | Received 02 Mar 2017, Accepted 02 Aug 2017, Published online: 11 Aug 2017
 

Abstract

Objectives: The objectives for this study were to examine the associations between metabolic biomarkers of obesity including insulin resistance, vascular dysfunction, systemic inflammation, genetic susceptibility and ultrasound proven gallstone disease or cholecystectomy in a population-based cross-sectional study.

Material and methods: A total of 2650 participants were included, of whom 422 had gallstone disease. Associations between selected metabolic biomarkers and gallstone disease were estimated by multivariable logistic regression models and expressed as odds ratio (OR) and 95% confidence interval (CI).

Results: Gallstone disease was associated with fasting glucose (OR 1.14, 95% CI [1.05;1.24]), fasting insulin (OR 1.03, 95% CI [1.01;1.05]), homeostasis model assessment insulin resistance (OR 1.18, 95% CI [1.02;1.36]), the metabolic syndrome (OR 1.51, 95% CI [1.16;1.96]), white blood cell count (OR 1.07, 95% CI [1.00;1.15]) and C-reactive protein (OR 1.03, 95% CI [1.01;1.05]). A tendency towards an association for soluble urokinase plasminogen activator receptor was also found (OR 1.08, 95% CI [0.99;1.18]). The MC4R(rs17782313) (OR 1.27, 95% CI [1.02;1.58]), MAP2K5(rs2241423) (OR 1.80, 95% CI [1.04;3.41]), NRXN3(rs10146997) (OR 1.26, 95% CI [1.01;1.57]), HHEX(rs1111875) (OR 1.29, 95% CI [1.03;1.62]), FAIM2(rs7138803) (OR 0.66, 95% CI [0.48;0.91]), and apolipoprotein E4 allele (OR 0.76, 95% CI [0.59;0.98]) were associated with gallstone disease. Urinary albumin was not associated with gallstone disease. The association between BMI and gallstone disease was explained by insulin resistance.

Conclusions: Biomarkers of insulin resistance, systemic inflammation and genetic obesity or type 2 diabetes risk alleles seem to be associated with gallstone disease. Future studies should explore temporal associations and genetic associations in other populations in order to clarify targets for prevention or intervention.

Acknowledgements

We would like to thank Anja Lykke Madsen for data management.

Disclosure statement

The authors report no conflicts of interest.

Additional information

Funding

The Faculty of Health and Medical Sciences
University of Copenhagen10.13039/501100001734
This work was supported by the University of Copenhagen, Faculty of Health and Medical Sciences.

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