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3. Different Measures of Hyperglycemia Are Negatively Associated With Skin Microvascular Flowmotion: The Maastricht Study.

Author

Zhao, X., Schalkwijk, C., Kroon, A., Schram, M. T., Stehouwer, C., and Houben, A.

Subjects
TYPE 2 diabetes, TYPE 1 diabetes, CONTINUOUS glucose monitoring, BLOOD sugar, DIABETIC neuropathies
Abstract

Objective: Diabetes can lead to microvascular complications such as diabetic neuropathy, nephropathy, and retinopathy. Hyperglycemia may initiate microvascular function impairment early in the course of diabetes, even prior to its clinical establishment during the pre‐diabetes stage. Microvascular vasomotion, that is, the rhythmic arteriolar constriction and dilation, is an important function that regulates oxygen and nutrient delivery within the tissue and regulates peripheral resistance. Using laser Doppler flowmetry (LDF), vasomotion in skin microcirculation can be measured as flowmotion. Changes in flowmotion have been shown in individuals with obesity, and type 1 or type 2 diabetes mellitus. However, no data are available on associations between hyperglycemia and flowmotion in the general population. Our aim was to study whether measures of hyperglycemia were associated with different components of skin microvascular flowmotion (SMF) in a population‐based cohort (The Maastricht Study). Methods: Data from 7293 participants of The Maastricht Study were used. SMF was measured using LDF. Endothelial, neurogenic and myogenic component SMF power were used as dependent variables. We investigated the associations of glucose metabolism status (normal glucose metabolism, prediabetes, and type 2 diabetes mellitus), measures of hyperglycemia (fasting plasma glucose [FPG], 2‐h post‐load glucose [2 h‐PG], HbA1c, advanced glycation end‐products [AGEs] assessed as skin autofluorescence [SAF]), and indices of glucose variability (incremental glucose peak [IGP] and continuous glucose monitoring [CGM] ‐assessed as standard deviation [SD]) with each component of SMF power. We used linear regression analyses with adjustments for confounders, and trend analyses. Results: We observed consistent negative associations between HbA1c levels and all three (endothelial, neurogenic, and myogenic) skin microvascular flowmotion (SMF) powers in the additionally adjusted model. Similarly, in the conservative model, we found that multiple hyperglycemia metrics such as GMS trend, PreD, T2DM, FPG, 2 h‐PG, and HbA1c were consistently negatively associated with all three SMF powers. Conclusions: We showed that skin microvascular flowmotion is reduced in individuals with (pre)diabetes. In addition, different measures of hyperglycemia are negatively associated with skin microvascular flowmotion. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Association of ambient air pollution with cognitive functioning and markers of structural brain damage: The Maastricht study

Author

Soeterboek, J., Deckers, K., van Boxtel, M. P.J., Backes, W. H., Eussen, S. J.P.M., van Greevenbroek, M. M.J., Jansen, J. F.A., Koster, A., Schram, M. T., Stehouwer, C. D.A., Wesselius, A., Lakerveld, J., Bosma, H., Köhler, S., Soeterboek, J., Deckers, K., van Boxtel, M. P.J., Backes, W. H., Eussen, S. J.P.M., van Greevenbroek, M. M.J., Jansen, J. F.A., Koster, A., Schram, M. T., Stehouwer, C. D.A., Wesselius, A., Lakerveld, J., Bosma, H., and Köhler, S.

Published
2024

6. Different Measures of Hyperglycemia Are Negatively Associated With Skin Microvascular Flowmotion:The Maastricht Study

Author

Zhao, X., Schalkwijk, C., Kroon, A., Schram, M. T., Stehouwer, C., Houben, A., Zhao, X., Schalkwijk, C., Kroon, A., Schram, M. T., Stehouwer, C., and Houben, A.

Abstract

Objective: Diabetes can lead to microvascular complications such as diabetic neuropathy, nephropathy, and retinopathy. Hyperglycemia may initiate microvascular function impairment early in the course of diabetes, even prior to its clinical establishment during the pre-diabetes stage. Microvascular vasomotion, that is, the rhythmic arteriolar constriction and dilation, is an important function that regulates oxygen and nutrient delivery within the tissue and regulates peripheral resistance. Using laser Doppler flowmetry (LDF), vasomotion in skin microcirculation can be measured as flowmotion. Changes in flowmotion have been shown in individuals with obesity, and type 1 or type 2 diabetes mellitus. However, no data are available on associations between hyperglycemia and flowmotion in the general population. Our aim was to study whether measures of hyperglycemia were associated with different components of skin microvascular flowmotion (SMF) in a population-based cohort (The Maastricht Study). Methods: Data from 7293 participants of The Maastricht Study were used. SMF was measured using LDF. Endothelial, neurogenic and myogenic component SMF power were used as dependent variables. We investigated the associations of glucose metabolism status (normal glucose metabolism, prediabetes, and type 2 diabetes mellitus), measures of hyperglycemia (fasting plasma glucose [FPG], 2-h post-load glucose [2 h-PG], HbA1c, advanced glycation end-products [AGEs] assessed as skin autofluorescence [SAF]), and indices of glucose variability (incremental glucose peak [IGP] and continuous glucose monitoring [CGM] -assessed as standard deviation [SD]) with each component of SMF power. We used linear regression analyses with adjustments for confounders, and trend analyses. Results: We observed consistent negative associations between HbA1c levels and all three (endothelial, neurogenic, and myogenic) skin microvascular flowmotion (SMF) powers in the additionally adjusted model. Similarly, in th

Published
2024

7. The hypoxia-sensor carbonic anhydrase IX affects macrophage metabolism, but is not a suitable biomarker for human cardiovascular disease

Author

Demandt, J. A. F., Dubois, L. J., van Kuijk, K., Zaťovičová, M., Jin, H., Parkkila, S., van der Laan, S. W., Jelenska, L., Mees, B. M. E., Reutelingsperger, C. P. M., Cleutjens, K. B. J. M., van der Kallen, C. J. H., Schalkwijk, C. G., van Greevenbroek, M. M. J., Biessen, E. A. L., Pasterkamp, G., Pastoreková, S., Stehouwer, C. D. A., and Sluimer, J. C.

Published
2021
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8. The association between diabetes status, HbA1c, diabetes duration, microvascular disease, and bone quality of the distal radius and tibia as measured with high-resolution peripheral quantitative computed tomography—The Maastricht Study

Author

de Waard, E. A. C., de Jong, J. J. A., Koster, A., Savelberg, H. H. C. M., van Geel, T. A., Houben, A. J. H. M., Schram, M. T., Dagnelie, P. C., van der Kallen, C. J., Sep, S. J. S., Stehouwer, C. D. A., Schaper, N. C., Berendschot, T. T. J. M., Schouten, J. S. A. G., Geusens, P. P. M. M., and van den Bergh, J. P. W.

Published
2018
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12. In RA patients without prevalent CVD, incident CVD is mainly associated with traditional risk factors: A 20-year follow-up in the CARRÉ cohort study

Author

MS Geriatrie, Raadsen, R., Agca, R., Boers, M., van Halm, V. P., Peters, M. J.L., Smulders, Y., Beulens, J. W.J., Blom, M. T., Stehouwer, C. D.A., Voskuyl, A. E., Lems, W. F., Nurmohamed, M. T., MS Geriatrie, Raadsen, R., Agca, R., Boers, M., van Halm, V. P., Peters, M. J.L., Smulders, Y., Beulens, J. W.J., Blom, M. T., Stehouwer, C. D.A., Voskuyl, A. E., Lems, W. F., and Nurmohamed, M. T.

Published
2023

13. Cognitive functioning in survivors of hematopoietic stem cell transplantation compared with a matched general population sample: The MOSA study

Author

Wauben, B., van der Poel, M. W. M., Greevenbroek, M. M. J., van Yperen, N. C., Schram, M. T., van Boxtel, M. P. J., Sastry, M., Simons, S. O., Stehouwer, C. D. A., Dagnelie, P. C., Wesselius, A., Schouten, H. C., Köhler, S., RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, Interne Geneeskunde, MUMC+: MA Hematologie (9), RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, MUMC+: HVC Pieken Maastricht Studie (9), Psychology 2, Section Neuropsychology, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, RS: FPN NPPP I, RS: NUTRIM - R3 - Respiratory & Age-related Health, Pulmonologie, MUMC+: MA Med Staf Spec Longziekten (9), MUMC+: MA Interne Geneeskunde (3), RS: CAPHRI - R5 - Optimising Patient Care, Epidemiologie, and Psychology 5

Abstract

BACKGROUND: Although cognitive problems can recover over time, a subgroup of hematopoietic stem cell transplantation (HCT) survivors experiences persistent cognitive problems in the long-term. Despite these implications, studies assessing cognitive functioning in HCT survivors are limited. OBJECTIVES: The aim of the present study was (1) to quantify the prevalence of cognitive impairment in patients treated with HCT who survived at least 2 years, and to compare these with a matched reference group representing the general population; (2) to identify potential determinants of cognitive functioning within the HCT survivor group. STUDY DESIGN: Within the single-centre Maastricht Observational study of late effects after Stem cell trAnsplantation (MOSA) cognitive performance was assessed by a neuropsychological test battery divided into three cognitive domains respectively memory, information processing speed, and executive function & attention. An overall cognition score was calculated as the average of the domain scores. A total of 115 HCT survivors were group-matched on a 1:4 ratio to the reference group by age, sex, and level of education. Regression analyses adjusted for different sets of covariates including demographic, health- and lifestyle-related factors were used to test for differences in cognition between HCT survivors and the reference group resembling the general population. A limited set of clinical characteristics (diagnosis, type of transplant, time since treatment, conditioning regimen with TBI and age at time of transplant) were assessed as potential determinants of neurocognitive dysfunction among HCT survivors. Cognitive impairment was defined as scores in the cognitive domains < -1.5 SD from what can be expected based on someone's age, sex, and education. RESULTS: The mean age at time of transplant was 50.2 (SD 11.2) years, and the mean number of years after transplant was 8.7 (SD 5.7) years. The majority of HCT survivors were treated with autologous HCT (N = 73; 64%). The prevalence of cognitive dysfunction in HCT survivors was 34.8%, and 21.3% in the reference group (p = .002.) When adjusted for age, sex, and level of education HCT survivors had a worse overall cognition score (b=-.35, 95%CI=-.55, -.16, p

Published
2023

14. A global initiative to deliver precision health in diabetes

Author

Cefalu, W. T., Franks, P. W., Rosenblum, N. D., Zaghloul, N. A., Florez, J. C., Giorgino, F., Ji, L., Ma, R. C. W., Mathieu, C., Misra, S., Ramirez, A. H., Roden, M., Scherer, P. E., Sheu, W. H. -H., Stehouwer, C. D. A., Woo, M., Pragnell, M., Anand, S. S., Carnethon, M., Chambers, J. C., Dennis, J. M., Gloyn, A. L., Herder, C., Holt, R. I. G., Manuel, D. G., Redondo, M. J., Tandon, N., Tsang, J. S., Udler, M. S., and Rich, S. S.

Abstract

A recent workshop brought together global leaders in diabetes to assess existing approaches to disease heterogeneity and to identify research gaps, with a goal of achieving precision diabetology for all patients globally.

Published
2024
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16. Correction for both common and rare cell types in blood is important to identify genes that correlate with age

Author

Pellegrino Coppola, D, Claringbould, A, Stutvoet, M, Heijmans, B, ‘t Hoen, P, van Meurs, J, Isaacs, A, Jansen, R, Pool, R, van Dongen, J, Hottenga, J, van Greevenbroek, M, Stehouwer, C, van der Kallen, C, Schalkwijk, C, Wijmenga, C, Zhernakova, S, Tigchelaar, E, Beekman, M, Deelen, J, van Heemst, D, Veldink, J, van den Berg, L, van Duijn, C, Hofman, B, Uitterlinden, A, Jhamai, P, Verbiest, M, Suchiman, H, Verkerk, M, van der Breggen, R, van Rooij, J, Lakenberg, N, Mei, H, van Iterson, M, van Galen, M, Bot, J, Zhernakova, D, van ‘t Hof, P, Deelen, P, Nooren, I, Vermaat, M, Luijk, R, Bonder, M, van Dijk, F, Arindrarto, W, Kielbasa, S, Swertz, M, van Zwet, E, Boomsma, D, Ikram, M, Slagboom, P, Westra, H, Franke, L, Pellegrino Coppola D., Claringbould A., Stutvoet M., Heijmans B. T., ‘t Hoen P. A. C., van Meurs J., Isaacs A., Jansen R., Pool R., van Dongen J., Hottenga J. J., van Greevenbroek M. M. J., Stehouwer C. D. A., van der Kallen C. J. H., Schalkwijk C. G., Wijmenga C., Zhernakova S., Tigchelaar E. F., Beekman M., Deelen J., van Heemst D., Veldink J. H., van den Berg L. H., van Duijn C. M., Hofman B. A., Uitterlinden A. G., Jhamai P. M., Verbiest M., Suchiman H. E. D., Verkerk M., van der Breggen R., van Rooij J., Lakenberg N., Mei H., van Iterson M., van Galen M., Bot J., Zhernakova D. V., van ‘t Hof P., Deelen P., Nooren I., Vermaat M., Luijk R., Bonder M. J., van Dijk F., Arindrarto W., Kielbasa S. M., Swertz M. A., van Zwet E. W., ‘t Hoen P. B., Boomsma D. I., Ikram M. A., Slagboom P. E., Westra H. J., Franke L., Pellegrino Coppola, D, Claringbould, A, Stutvoet, M, Heijmans, B, ‘t Hoen, P, van Meurs, J, Isaacs, A, Jansen, R, Pool, R, van Dongen, J, Hottenga, J, van Greevenbroek, M, Stehouwer, C, van der Kallen, C, Schalkwijk, C, Wijmenga, C, Zhernakova, S, Tigchelaar, E, Beekman, M, Deelen, J, van Heemst, D, Veldink, J, van den Berg, L, van Duijn, C, Hofman, B, Uitterlinden, A, Jhamai, P, Verbiest, M, Suchiman, H, Verkerk, M, van der Breggen, R, van Rooij, J, Lakenberg, N, Mei, H, van Iterson, M, van Galen, M, Bot, J, Zhernakova, D, van ‘t Hof, P, Deelen, P, Nooren, I, Vermaat, M, Luijk, R, Bonder, M, van Dijk, F, Arindrarto, W, Kielbasa, S, Swertz, M, van Zwet, E, Boomsma, D, Ikram, M, Slagboom, P, Westra, H, Franke, L, Pellegrino Coppola D., Claringbould A., Stutvoet M., Heijmans B. T., ‘t Hoen P. A. C., van Meurs J., Isaacs A., Jansen R., Pool R., van Dongen J., Hottenga J. J., van Greevenbroek M. M. J., Stehouwer C. D. A., van der Kallen C. J. H., Schalkwijk C. G., Wijmenga C., Zhernakova S., Tigchelaar E. F., Beekman M., Deelen J., van Heemst D., Veldink J. H., van den Berg L. H., van Duijn C. M., Hofman B. A., Uitterlinden A. G., Jhamai P. M., Verbiest M., Suchiman H. E. D., Verkerk M., van der Breggen R., van Rooij J., Lakenberg N., Mei H., van Iterson M., van Galen M., Bot J., Zhernakova D. V., van ‘t Hof P., Deelen P., Nooren I., Vermaat M., Luijk R., Bonder M. J., van Dijk F., Arindrarto W., Kielbasa S. M., Swertz M. A., van Zwet E. W., ‘t Hoen P. B., Boomsma D. I., Ikram M. A., Slagboom P. E., Westra H. J., and Franke L.

Abstract

Background Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. Results Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18-81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P <= 2.5x10(-6)). Moreover, 511 genes (similar to 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity. Conclusions We conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.

Published
2021

18. Individual and partnerʼs level of occupation and the association with HbA1c levels in people with Type 2 diabetes mellitus: the Dutch Diabetes Pearl cohort

Author

Rutte, A., Rauh, S. P., Schram, M. T., Nijpels, G., DeVries, J. H., Holleman, F., Pijl, H., Dekkers, O. M., Özcan, B., Sijbrands, E. J. G., Tack, C. J., Abbink, E. J., de Valk, H. W., Silvius, B., Wolffenbuttel, B. H. R., Stehouwer, C. D. A., Schaper, N. C., Dekker, J. M., Beulens, J. W., Elders, P. J. M., and Rutters, F.

Published
2017
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20. Deep phenotyping meets big data

Author

Lakerveld, J., Wagtendonk, A., Vaartjes, I., Karssenberg, D., Penninx, B., Beulens, J., Timmermans, E., Huisman, M., Kramer, S., van Wier, M., Boomsma, D., Willemsen, G., Schuengel, C., Oosterman, M., Stronks, K., Vermeulen, R., Koster, A., Stehouwer, C., van den Hurk, K., Koomen, E., de Mutsert, R., Have, M., Verschuren, M., Picavet, S., Beenackers, M., van Lenthe, F., Ikram, A., Jaddoe, V., Oldehinkel, T., de Jong, T., Mulder, S., Dotinga, A., Landdegradatie en aardobservatie, Landscape functioning, Geocomputation and Hydrology, Organizational Culture and Change, IRAS OH Epidemiology Chemical Agents, dIRAS RA-2, Afd Pharmacology, Urban Accessibility and Social Inclusion, Geography and Education, Leerstoel Aken, UU LEG Research USG Public Matters, Spatial Economics, Sociology, The Social Context of Aging (SoCA), Biological Psychology, APH - Mental Health, APH - Methodology, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Health Behaviors & Chronic Diseases, Clinical Child and Family Studies, LEARN! - Child rearing, APH - Aging & Later Life, RS: CAPHRI - R4 - Health Inequities and Societal Participation, Sociale Geneeskunde, MUMC+: Centrum voor Chronische Zieken (3), MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, MUMC+: MA Reumatologie (9), MUMC+: MA Nefrologie (9), MUMC+: MA Medische Oncologie (9), MUMC+: MA Hematologie (9), MUMC+: MA Maag Darm Lever (9), MUMC+: MA Endocrinologie (9), MUMC+: HVC Pieken Maastricht Studie (9), Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), Public Health, Epidemiology, Erasmus MC other, Pediatrics, Epidemiology and Data Science, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Societal Participation & Health, Otolaryngology / Head & Neck Surgery, APH - Quality of Care, APH - Digital Health, and Public and occupational health

Subjects
Earth science, Big data, 010501 environmental sciences, 01 natural sciences, Health informatics, DISEASE, Environmental data, Data science, Cohort Studies, 0302 clinical medicine, 030212 general & internal medicine, Netherlands, ASSOCIATIONS, Non-communicabledisease, Environmental exposure, Non-communicable disease, SDG 11 - Sustainable Cities and Communities, Exposome, Geography, Cohort, lcsh:R858-859.7, BURDEN, medicine.medical_specialty, General Computer Science, Health geography, GREEN, Environment, lcsh:Computer applications to medicine. Medical informatics, Exposure, 03 medical and health sciences, REGRESSION, medicine, Humans, 0105 earth and related environmental sciences, business.industry, Research, Public health, Prevention, Public Health, Environmental and Occupational Health, Environmental Exposure, AIR-POLLUTION, General Business, Management and Accounting, Earth Sciences, Upstream determinants, business, Cohorts, ENVIRONMENTS
Abstract

Environmental exposures are increasingly investigated as possible drivers of health behaviours and disease outcomes. So-called exposome studies that aim to identify and better understand the effects of exposures on behaviours and disease risk across the life course require high-quality environmental exposure data. The Netherlands has a great variety of environmental data available, including high spatial and often temporal resolution information on urban infrastructure, physico-chemical exposures, presence and availability of community services, and others. Until recently, these environmental data were scattered and measured at varying spatial scales, impeding linkage to individual-level (cohort) data as they were not operationalised as personal exposures, that is, the exposure to a certain environmental characteristic specific for a person. Within the Geoscience and hEalth Cohort COnsortium (GECCO) and with support of the Global Geo Health Data Center (GGHDC), a platform has been set up in The Netherlands where environmental variables are centralised, operationalised as personal exposures, and used to enrich 23 cohort studies and provided to researchers upon request. We here present and detail a series of personal exposure data sets that are available within GECCO to date, covering personal exposures of all residents of The Netherlands (currently about 17 M) over the full land surface of the country, and discuss challenges and opportunities for its use now and in the near future.

Published
2020
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21. Genetics and Not Shared Environment Explains Familial Resemblance in Adult Metabolomics Data

Author

Pool, Rene, Hagenbeek, Fiona A., Hendriks, Anne M., van Dongen, Jenny, Willemsen, Gonneke, de Geus, Eco, van Dijk, Ko Willems, Verhoeven, Aswin, Suchiman, H. Eka, Beekman, Marian, Slagboom, P. Eline, Harms, Amy C., Hankemeier, Thomas, Boomsma, Dorret, I, Beekman, M., Suchiman, H. E. D., Amin, N., Beulens, J. W., van der Bom, J. A., Bomer, N., Demirkan, A., van Hilten, J. A., Meessen, J. M. T. A., Pool, R., Moed, M. H., Fu, J., Onderwater, G. L. J., Rutters, F., So-Osman, C., van der Flier, W. M., van der Heijden, A. A. W. A., van der Spek, A., Asselbergs, F. W., Boersma, E., Elders, P. M., Geleijnse, J. M., Ikram, M. A., Kloppenburg, M., Meulenbelt, I, Mooijaart, S. P., Nelissen, R. G. H. H., Netea, M. G., Penninx, B. W. J. H., Stehouwer, C. D. A., Teunissen, C. E., Terwindt, G. M., 't Hart, L. M., van den Maagdenberg, A. M. J. M., van der Harst, P., van der Horst, I. C. C., van der Kallen, C. J. H., van Greevenbroek, M. M. J., van Spil, W. E., Wijmenga, C., Zwinderman, A. H., Zhernikova, A., Jukema, J. W., Wolf, J. J. H. Barkey, Cats, D., Mei, H., Slofstra, M., Swertz, M., van den Akker, E. B., Deelen, J., Reinders, M. J. T., Boomsma, D., I, van Duijn, C. M., Slagboom, P. E., Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), MUMC+: MA Endocrinologie (9), MUMC+: MA Maag Darm Lever (9), MUMC+: MA Hematologie (9), MUMC+: MA Medische Oncologie (9), MUMC+: MA Nefrologie (9), MUMC+: MA Reumatologie (9), RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Molecular Neuroscience and Ageing Research (MOLAR), Cardiovascular Centre (CVC), Critical care, Anesthesiology, Peri-operative and Emergency medicine (CAPE), Center for Liver, Digestive and Metabolic Diseases (CLDM), Medical Microbiology and Infection Prevention, Graduate School, Tytgat Institute for Liver and Intestinal Research, AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, Obstetrics and Gynaecology, Cardiology, Public and occupational health, Gastroenterology and Hepatology, Paediatric Endocrinology, Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, APH - Mental Health, APH - Methodology, Epidemiology and Data Science, Obstetrics and gynaecology, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, Anatomy and neurosciences, Anesthesiology, General practice, Internal medicine, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Laboratory Medicine, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Neuroinfection & -inflammation, Amsterdam Reproduction & Development (AR&D), AMS - Musculoskeletal Health, AMS - Tissue Function & Regeneration, Rheumatology, Gastroenterology and hepatology, APH - Aging & Later Life, and APH - Digital Health

Subjects
Male, Netherlands Twin Register (NTR), 0301 basic medicine, Shared environment, Metabolite, lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], Genome-wide association study, heritability, METABOLITES, Keywords: Classical twin design, chemistry.chemical_compound, 0302 clinical medicine, metabolite classes, Twins, Dizygotic, EPIDEMIOLOGY, Classical twin design, Genetics (clinical), Genetics, HERITABILITY, shared environment, Obstetrics and Gynecology, Phenotype, Quartile, Metabolome, Female, Adult, Dizygotic twin, POWER, TWIN, Environment, Biology, enrichment analysis, 03 medical and health sciences, All institutes and research themes of the Radboud University Medical Center, Metabolomics, AGE, Diseases in Twins, Humans, Family, GENOME-WIDE ASSOCIATION, Classical twin design [Keywords], Twins, Monozygotic, PROFILES, Heritability, Diet, Metabolomics data, 030104 developmental biology, chemistry, Pediatrics, Perinatology and Child Health, Gene-Environment Interaction, 030217 neurology & neurosurgery
Abstract

Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term ‘metabolomics’ refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented.

Published
2020
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31. Common carotid intima-media thickness does not add to Framingham risk score in individuals with diabetes mellitus: the USE-IMT initiative

Author

den Ruijter, H. M., Peters, S. A. E., Groenewegen, K. A., Anderson, T. J., Britton, A. R., Dekker, J. M., Engström, G., Eijkemans, M. J., Evans, G. W., de Graaf, J., Grobbee, D. E., Hedblad, B., Hofman, A., Holewijn, S., Ikeda, A., Kavousi, M., Kitagawa, K., Kitamura, A., Koffijberg, H., Ikram, M. A., Lonn, E. M., Lorenz, M. W., Mathiesen, E. B., Nijpels, G., Okazaki, S., O’Leary, D. H., Polak, J. F., Price, J. F., Robertson, C., Rembold, C. M., Rosvall, M., Rundek, T., Salonen, J. T., Sitzer, M., Stehouwer, C. D. A., Witteman, J. C., Moons, K. G., and Bots, M. L.

Published
2013
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41. Diabetes, prediabetes and cancer mortality

Author

Zhou, X. H., Qiao, Q., Zethelius, B., Pyörälä, K., Söderberg, S., Pajak, A., Stehouwer, C. D. A., Heine, R. J., Jousilahti, P., Ruotolo, G., Nilsson, P. M., Calori, G., Tuomilehto, J., and for the DECODE Study Group

Published
2010
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49. Betere zorg door anamnesestandaard

Author

Stehouwer, C D A, MUMC+: HVC Pieken Maastricht Studie (9), MUMC+: MA Interne Geneeskunde (3), Interne Geneeskunde, MUMC+: Centrum voor Chronische Zieken (3), MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), MUMC+: MA Endocrinologie (9), MUMC+: MA Maag Darm Lever (9), MUMC+: MA Hematologie (9), MUMC+: MA Medische Oncologie (9), MUMC+: MA Nefrologie (9), MUMC+: MA Reumatologie (9), and RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome

Subjects
Machine Learning, Humans, Medical History Taking/methods
Abstract

Use of machine learning has been proposed to improve the diagnostic performance of medical history taking, which would first have to be standardized. This commentary reviews theoretical, practical and ethical considerations with regard to this proposal.

Published
2021

50. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression

Author

Vosa, U. (Urmo), Claringbould, A. (Annique), Westra, H.-J. (Harm-Jan), Bonder, M. J. (Marc Jan), Deelen, P. (Patrick), Zeng, B. (Biao), Kirsten, H. (Holger), Saha, A. (Ashis), Kreuzhuber, R. (Roman), Yazar, S. (Seyhan), Brugge, H. (Harm), Oelen, R. (Roy), de Vries, D. H. (Dylan H.), van der Wijst, M. G. (Monique G. P.), Kasela, S. (Silva), Pervjakova, N. (Natalia), Alves, I. (Isabel), Fave, M.-J. (Marie-Julie), Agbessi, M. (Mawusse), Christiansen, M. W. (Mark W.), Jansen, R. (Rick), Seppala, I. (Ilkka), Tong, L. (Lin), Teumer, A. (Alexander), Schramm, K. (Katharina), Hemani, G. (Gibran), Verlouw, J. (Joost), Yaghootkar, H. (Hanieh), Flitman, R. S. (Reyhan Sonmez), Brown, A. (Andrew), Kukushkina, V. (Viktorija), Kalnapenkis, A. (Anette), Rueger, S. (Sina), Porcu, E. (Eleonora), Kronberg, J. (Jaanika), Kettunen, J. (Johannes), Lee, B. (Bernett), Zhang, F. (Futao), Qi, T. (Ting), Hernandez, J. A. (Jose Alquicira), Arindrarto, W. (Wibowo), Beutner, F. (Frank), Dmitrieva, J. (Julia), Elansary, M. (Mahmoud), Fairfax, B. P. (Benjamin P.), Georges, M. (Michel), Heijmans, B. T. (Bastiaan T.), Hewitt, A. W. (Alex W.), Kahonen, M. (Mika), Kim, Y. (Yungil), Knight, J. C. (Julian C.), Kovacs, P. (Peter), Krohn, K. (Knut), Li, S. (Shuang), Loeffler, M. (Markus), Marigorta, U. M. (Urko M.), Mei, H. (Hailang), Momozawa, Y. (Yukihide), Mueller-Nurasyid, M. (Martina), Nauck, M. (Matthias), Nivard, M. G. (Michel G.), Penninx, B. W. (Brenda W. J. H.), Pritchard, J. K. (Jonathan K.), Raitakari, O. T. (Olli T.), Rotzschke, O. (Olaf), Slagboom, E. P. (Eline P.), Stehouwer, C. D. (Coen D. A.), Stumvoll, M. (Michael), Sullivan, P. (Patrick), Thiery, J. (Joachim), Tonjes, A. (Anke), van Dongen, J. (Jenny), van Iterson, M. (Maarten), Veldink, J. H. (Jan H.), Voelker, U. (Uwe), Warmerdam, R. (Robert), Wijmenga, C. (Cisca), Swertz, M. (Morris), Andiappan, A. (Anand), Montgomery, G. W. (Grant W.), Ripatti, S. (Samuli), Perola, M. (Markus), Kutalik, Z. (Zoltan), Dermitzakis, E. (Emmanouil), Bergmann, S. (Sven), Frayling, T. (Timothy), van Meurs, J. (Joyce), Prokisch, H. (Holger), Ahsan, H. (Habibul), Pierce, B. L. (Brandon L.), Lehtimaki, T. (Terho), Boomsma, D. I. (Dorret, I), Psaty, B. M. (Bruce M.), Gharib, S. A. (Sina A.), Awadalla, P. (Philip), Milani, L. (Lili), Ouwehand, W. H. (Willem H.), Downes, K. (Kate), Stegle, O. (Oliver), Battle, A. (Alexis), Visscher, P. M. (Peter M.), Yang, J. (Jian), Scholz, M. (Markus), Powell, J. (Joseph), Gibson, G. (Greg), Esko, T. (Tonu), and Franke, L. (Lude)

Abstract

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.

Published
2021

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