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Lin L, Andersen MK, Stæger FF, Li Z, Hanghøj K, Linneberg A, Grarup N, Jørgensen ME, Hansen T, Moltke I, Albrechtsen A. Analysis of admixed Greenlandic siblings shows that the mean genotypic values for metabolic phenotypes differ between Inuit and Europeans. Genome Med 2024; 16:71. [PMID: 38778393 PMCID: PMC11112775 DOI: 10.1186/s13073-024-01326-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 03/28/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Disease prevalence and mean phenotype values differ between many populations, including Inuit and Europeans. Whether these differences are partly explained by genetic differences or solely due to differences in environmental exposures is still unknown, because estimates of the genetic contribution to these means, which we will here refer to as mean genotypic values, are easily confounded, and because studies across genetically diverse populations are lacking. METHODS Leveraging the unique genetic properties of the small, admixed and historically isolated Greenlandic population, we estimated the differences in mean genotypic value between Inuit and European genetic ancestry using an admixed sibling design. Analyses were performed across 26 metabolic phenotypes, in 1474 admixed sibling pairs present in a cohort of 5996 Greenlanders. RESULTS After FDR correction for multiple testing, we found significantly lower mean genotypic values in Inuit genetic ancestry compared to European genetic ancestry for body weight (effect size per percentage of Inuit genetic ancestry (se), -0.51 (0.16) kg/%), body mass index (-0.20 (0.06) kg/m2/%), fat percentage (-0.38 (0.13) %/%), waist circumference (-0.42 (0.16) cm/%), hip circumference (-0.38 (0.11) cm/%) and fasting serum insulin levels (-1.07 (0.51) pmol/l/%). The direction of the effects was consistent with the observed mean phenotype differences between Inuit and European genetic ancestry. No difference in mean genotypic value was observed for height, markers of glucose homeostasis, or circulating lipid levels. CONCLUSIONS We show that mean genotypic values for some metabolic phenotypes differ between two human populations using a method not easily confounded by possible differences in environmental exposures. Our study illustrates the importance of performing genetic studies in diverse populations.
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Affiliation(s)
- Long Lin
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Mette K Andersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark
| | - Frederik Filip Stæger
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Zilong Li
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Kristian Hanghøj
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Allan Linneberg
- Centre for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, The Capital Region of Denmark, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark
| | - Marit Eika Jørgensen
- Centre for Public Health in Greenland, National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark
- Clinical Research, Copenhagen University Hospital - Steno Diabetes Center Copenhagen, Herlev, Denmark
- Steno Diabetes Center Greenland, Nuuk, Greenland
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark.
| | - Ida Moltke
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.
| | - Anders Albrechtsen
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.
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Pohanka M. Glycated Hemoglobin and Methods for Its Point of Care Testing. BIOSENSORS 2021; 11:70. [PMID: 33806493 PMCID: PMC8000313 DOI: 10.3390/bios11030070] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 02/28/2021] [Accepted: 03/01/2021] [Indexed: 11/17/2022]
Abstract
Glycated hemoglobin (HbA1c) is a product of the spontaneous reaction between hemoglobin and elevated glucose levels in the blood. It is included among the so-called advanced glycation end products, of which is the most important for the clinical diagnosis of diabetes mellitus, and it can serve as an alternative to glycemia measurement. Compared to the diagnosis of diabetes mellitus by glycemia, the HbA1c level is less influenced by a short-term problem with diabetes compensation. Mass spectroscopy and chromatographic techniques are among the standard methods of HbA1c level measurement. Compared to glycemia measurement, there is lack of simple methods for diabetes mellitus diagnosis by means of the HbA1c assay using a point-of-care test. This review article is focused on the surveying of facts about HbA1c and its importance in diabetes mellitus diagnosis, and surveying standard methods and new methods suitable for the HbA1c assay under point-of-care conditions. Various bioassays and biosensors are mentioned and their specifications are discussed.
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Affiliation(s)
- Miroslav Pohanka
- Faculty of Military Health Sciences, University of Defense, Trebesska 1575, CZ-50001 Hradec Kralove, Czech Republic
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Gloaguen E, Bendelac N, Nicolino M, Julier C, Mathieu F. A systematic review of non-genetic predictors and genetic factors of glycated haemoglobin in type 1 diabetes one year after diagnosis. Diabetes Metab Res Rev 2018; 34:e3051. [PMID: 30063815 DOI: 10.1002/dmrr.3051] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 07/20/2018] [Accepted: 07/23/2018] [Indexed: 12/13/2022]
Abstract
Type 1 diabetes (T1D) results from autoimmune destruction of the pancreatic βcells. Although all T1D patients require daily administration of exogenous insulin, their insulin requirement to achieve good glycaemic control may vary significantly. Glycated haemoglobin (HbA1c) level represents a stable indicator of glycaemic control and is a reliable predictor of long-term complications of T1D. The purpose of this article is to systematically review the role of non-genetic predictors and genetic factors of HbA1c level in T1D patients after the first year of T1D, to exclude the honeymoon period. A total of 1974 articles published since January 2011 were identified and 78 were finally included in the analysis of non-genetic predictors. For genetic factors, a total of 277 articles were identified and 14 were included. The most significantly associated factors with HbA1c level are demographic (age, ethnicity, and socioeconomic status), personal (family characteristics, parental care, psychological traits...) and features related to T1D (duration of T1D, adherence to treatment …). Only a few studies have searched for genetic factors influencing HbA1c level, most of which focused on candidate genes using classical genetic statistical methods, with generally limited power and incomplete adjustment for confounding factors and multiple testing. Our review shows the complexity of explaining HbA1c level variations, which involves numerous correlated predictors. Overall, our review underlines the lack of studies investigating jointly genetic and non-genetic factors and their interactions to better understand factors influencing glycaemic control for T1D patients.
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Affiliation(s)
- Emilie Gloaguen
- Inserm UMRS-958, Paris, France
- Université Paris Diderot, Sorbonne Paris Cité, Paris, France
| | | | - Marc Nicolino
- Hôpital Femme-Mère-Enfant, Hospices Civils de Lyon, Bron, France
| | - Cécile Julier
- Inserm UMRS-958, Paris, France
- Université Paris Diderot, Sorbonne Paris Cité, Paris, France
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Andersen MK, Grarup N, Moltke I, Albrechtsen A, Hansen T. Genetic architecture of obesity and related metabolic traits — recent insights from isolated populations. Curr Opin Genet Dev 2018; 50:74-78. [DOI: 10.1016/j.gde.2018.02.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 02/11/2018] [Accepted: 02/15/2018] [Indexed: 12/29/2022]
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