Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 18 entries
Sorted by: Best Match Show Resources per page
Exome-sequencing in a large population-based study reveals a rare Asn396Ser variant in the LIPG gene associated with depressive symptoms.

Molecular psychiatry

Amin N, Jovanova O, Adams HH, Dehghan A, Kavousi M, Vernooij MW, Peeters RP, de Vrij FM, van der Lee SJ, van Rooij JG, van Leeuwen EM, Chaker L, Demirkan A, Hofman A, Brouwer RW, Kraaij R, Willems van Dijk K, Hankemeier T, van Ijcken WF, Uitterlinden AG, Niessen WJ, Franco OH, Kushner SA, Ikram MA, Tiemeier H, van Duijn CM.
PMID: 27502476
Mol Psychiatry. 2017 Apr;22(4):634. doi: 10.1038/mp.2016.141. Epub 2016 Aug 09.

No abstract available.

Cite
Amin N, Jovanova O, Adams HH, et al. Exome-sequencing in a large population-based study reveals a rare Asn396Ser variant in the LIPG gene associated with depressive symptoms. Mol Psychiatry. 2016;22(4):634doi: 10.1038/mp.2016.141.
Amin, N., Jovanova, O., Adams, H. H., Dehghan, A., Kavousi, M., Vernooij, M. W., Peeters, R. P., de Vrij, F. M., van der Lee, S. J., van Rooij, J. G., van Leeuwen, E. M., Chaker, L., Demirkan, A., Hofman, A., Brouwer, R. W., Kraaij, R., Willems van Dijk, K., Hankemeier, T., van Ijcken, W. F., Uitterlinden, A. G., Niessen, W. J., Franco, O. H., Kushner, S. A., Ikram, M. A., Tiemeier, H., & van Duijn, C. M. (2017). Exome-sequencing in a large population-based study reveals a rare Asn396Ser variant in the LIPG gene associated with depressive symptoms. Molecular psychiatry, 22(4), 634. https://doi.org/10.1038/mp.2016.141
Amin, N, et al. "Exome-sequencing in a large population-based study reveals a rare Asn396Ser variant in the LIPG gene associated with depressive symptoms." Molecular psychiatry vol. 22,4 (2017): 634. doi: https://doi.org/10.1038/mp.2016.141
Amin N, Jovanova O, Adams HH, Dehghan A, Kavousi M, Vernooij MW, Peeters RP, de Vrij FM, van der Lee SJ, van Rooij JG, van Leeuwen EM, Chaker L, Demirkan A, Hofman A, Brouwer RW, Kraaij R, Willems van Dijk K, Hankemeier T, van Ijcken WF, Uitterlinden AG, Niessen WJ, Franco OH, Kushner SA, Ikram MA, Tiemeier H, van Duijn CM. Exome-sequencing in a large population-based study reveals a rare Asn396Ser variant in the LIPG gene associated with depressive symptoms. Mol Psychiatry. 2017 Apr;22(4):634. doi: 10.1038/mp.2016.141. Epub 2016 Aug 09. PMID: 27502476.
Copy Download .nbib Format: NLM
Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology.

American journal of human genetics

Spracklen CN, Karaderi T, Yaghootkar H, Schurmann C, Fine RS, Kutalik Z, Preuss MH, Lu Y, Wittemans LBL, Adair LS, Allison M, Amin N, Auer PL, Bartz TM, Blüher M, Boehnke M, Borja JB, Bork-Jensen J, Broer L, Chasman DI, Chen YI, Chirstofidou P, Demirkan A, van Duijn CM, Feitosa MF, Garcia ME, Graff M, Grallert H, Grarup N, Guo X, Haesser J, Hansen T, Harris TB, Highland HM, Hong J, Ikram MA, Ingelsson E, Jackson R, Jousilahti P, Kähönen M, Kizer JR, Kovacs P, Kriebel J, Laakso M, Lange LA, Lehtimäki T, Li J, Li-Gao R, Lind L, Luan J, Lyytikäinen LP, MacGregor S, Mackey DA, Mahajan A, Mangino M, Männistö S, McCarthy MI, McKnight B, Medina-Gomez C, Meigs JB, Molnos S, Mook-Kanamori D, Morris AP, de Mutsert R, Nalls MA, Nedeljkovic I, North KE, Pennell CE, Pradhan AD, Province MA, Raitakari OT, Raulerson CK, Reiner AP, Ridker PM, Ripatti S, Roberston N, Rotter JI, Salomaa V, Sandoval-Zárate AA, Sitlani CM, Spector TD, Strauch K, Stumvoll M, Taylor KD, Thuesen B, Tönjes A, Uitterlinden AG, Venturini C, Walker M, Wang CA, Wang S, Wareham NJ, Willems SM, Willems van Dijk K, Wilson JG, Wu Y, Yao J, Young KL, Langenberg C, Frayling TM, Kilpeläinen TO, Lindgren CM, Loos RJF, Mohlke KL.
PMID: 31491410
Am J Hum Genet. 2019 Sep 05;105(3):670-671. doi: 10.1016/j.ajhg.2019.08.001.

No abstract available.

Cite
Spracklen CN, Karaderi T, Yaghootkar H, et al. Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology. Am J Hum Genet. 2019;105(3):670-671doi: 10.1016/j.ajhg.2019.08.001.
Spracklen, C. N., Karaderi, T., Yaghootkar, H., Schurmann, C., Fine, R. S., Kutalik, Z., Preuss, M. H., Lu, Y., Wittemans, L. B. L., Adair, L. S., Allison, M., Amin, N., Auer, P. L., Bartz, T. M., Blüher, M., Boehnke, M., Borja, J. B., Bork-Jensen, J., Broer, L., Chasman, D. I., Chen, Y. I., Chirstofidou, P., Demirkan, A., van Duijn, C. M., Feitosa, M. F., Garcia, M. E., Graff, M., Grallert, H., Grarup, N., Guo, X., Haesser, J., Hansen, T., Harris, T. B., Highland, H. M., Hong, J., Ikram, M. A., Ingelsson, E., Jackson, R., Jousilahti, P., Kähönen, M., Kizer, J. R., Kovacs, P., Kriebel, J., Laakso, M., Lange, L. A., Lehtimäki, T., Li, J., Li-Gao, R., Lind, L., Luan, J., Lyytikäinen, L. P., MacGregor, S., Mackey, D. A., Mahajan, A., Mangino, M., Männistö, S., McCarthy, M. I., McKnight, B., Medina-Gomez, C., Meigs, J. B., Molnos, S., Mook-Kanamori, D., Morris, A. P., de Mutsert, R., Nalls, M. A., Nedeljkovic, I., North, K. E., Pennell, C. E., Pradhan, A. D., Province, M. A., Raitakari, O. T., Raulerson, C. K., Reiner, A. P., Ridker, P. M., Ripatti, S., Roberston, N., Rotter, J. I., Salomaa, V., Sandoval-Zárate, A. A., Sitlani, C. M., Spector, T. D., Strauch, K., Stumvoll, M., Taylor, K. D., Thuesen, B., Tönjes, A., Uitterlinden, A. G., Venturini, C., Walker, M., Wang, C. A., Wang, S., Wareham, N. J., Willems, S. M., Willems van Dijk, K., Wilson, J. G., Wu, Y., Yao, J., Young, K. L., Langenberg, C., Frayling, T. M., Kilpeläinen, T. O., Lindgren, C. M., Loos, R. J. F., & Mohlke, K. L. (2019). Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology. American journal of human genetics, 105(3), 670-671. https://doi.org/10.1016/j.ajhg.2019.08.001
Spracklen, Cassandra N, et al. "Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology." American journal of human genetics vol. 105,3 (2019): 670-671. doi: https://doi.org/10.1016/j.ajhg.2019.08.001
Spracklen CN, Karaderi T, Yaghootkar H, Schurmann C, Fine RS, Kutalik Z, Preuss MH, Lu Y, Wittemans LBL, Adair LS, Allison M, Amin N, Auer PL, Bartz TM, Blüher M, Boehnke M, Borja JB, Bork-Jensen J, Broer L, Chasman DI, Chen YI, Chirstofidou P, Demirkan A, van Duijn CM, Feitosa MF, Garcia ME, Graff M, Grallert H, Grarup N, Guo X, Haesser J, Hansen T, Harris TB, Highland HM, Hong J, Ikram MA, Ingelsson E, Jackson R, Jousilahti P, Kähönen M, Kizer JR, Kovacs P, Kriebel J, Laakso M, Lange LA, Lehtimäki T, Li J, Li-Gao R, Lind L, Luan J, Lyytikäinen LP, MacGregor S, Mackey DA, Mahajan A, Mangino M, Männistö S, McCarthy MI, McKnight B, Medina-Gomez C, Meigs JB, Molnos S, Mook-Kanamori D, Morris AP, de Mutsert R, Nalls MA, Nedeljkovic I, North KE, Pennell CE, Pradhan AD, Province MA, Raitakari OT, Raulerson CK, Reiner AP, Ridker PM, Ripatti S, Roberston N, Rotter JI, Salomaa V, Sandoval-Zárate AA, Sitlani CM, Spector TD, Strauch K, Stumvoll M, Taylor KD, Thuesen B, Tönjes A, Uitterlinden AG, Venturini C, Walker M, Wang CA, Wang S, Wareham NJ, Willems SM, Willems van Dijk K, Wilson JG, Wu Y, Yao J, Young KL, Langenberg C, Frayling TM, Kilpeläinen TO, Lindgren CM, Loos RJF, Mohlke KL. Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology. Am J Hum Genet. 2019 Sep 05;105(3):670-671. doi: 10.1016/j.ajhg.2019.08.001. PMID: 31491410; PMCID: PMC6732522.
Copy Download .nbib Format: NLM
Author Correction: Heritability estimates for 361 blood metabolites across 40 genome-wide association studies.

Nature communications

Hagenbeek FA, Pool R, van Dongen J, Draisma HM, Jan Hottenga J, Willemsen G, Abdellaoui A, Fedko IO, den Braber A, Visser PJ, de Geus EJCN, Willems van Dijk K, Verhoeven A, Suchiman HE, Beekman M, Slagboom PE, van Duijn CM, Harms AC, Hankemeier T, Bartels M, Nivard MG, Boomsma DI.
PMID: 32235831
Nat Commun. 2020 Mar 31;11(1):1702. doi: 10.1038/s41467-020-15276-y.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cite
Hagenbeek FA, Pool R, van Dongen J, et al. Author Correction: Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. Nat Commun. 2020;11(1):1702doi: 10.1038/s41467-020-15276-y.
Hagenbeek, F. A., Pool, R., van Dongen, J., Draisma, H. M., Jan Hottenga, J., Willemsen, G., Abdellaoui, A., Fedko, I. O., den Braber, A., Visser, P. J., de Geus, E. J. C. N., Willems van Dijk, K., Verhoeven, A., Suchiman, H. E., Beekman, M., Slagboom, P. E., van Duijn, C. M., Harms, A. C., Hankemeier, T., Bartels, M., Nivard, M. G., & Boomsma, D. I. (2020). Author Correction: Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. Nature communications, 11(1), 1702. https://doi.org/10.1038/s41467-020-15276-y
Hagenbeek, Fiona A, et al. "Author Correction: Heritability estimates for 361 blood metabolites across 40 genome-wide association studies." Nature communications vol. 11,1 (2020): 1702. doi: https://doi.org/10.1038/s41467-020-15276-y
Hagenbeek FA, Pool R, van Dongen J, Draisma HM, Jan Hottenga J, Willemsen G, Abdellaoui A, Fedko IO, den Braber A, Visser PJ, de Geus EJCN, Willems van Dijk K, Verhoeven A, Suchiman HE, Beekman M, Slagboom PE, van Duijn CM, Harms AC, Hankemeier T, Bartels M, Nivard MG, Boomsma DI. Author Correction: Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. Nat Commun. 2020 Mar 31;11(1):1702. doi: 10.1038/s41467-020-15276-y. PMID: 32235831; PMCID: PMC7109051.
Copy Download .nbib Format: NLM
Classical risk factors for primary coronary artery disease from an aging perspective through Mendelian Randomization.

GeroScience

Jansen SA, Huiskens B, Trompet S, Jukema J, Mooijaart SP, Willems van Dijk K, van Heemst D, Noordam R.
PMID: 34932184
Geroscience. 2021 Dec 21; doi: 10.1007/s11357-021-00498-9. Epub 2021 Dec 21.

The significance of classical risk factors in coronary artery disease (CAD) remains unclear in older age due to possible changes in underlying disease pathologies. Therefore, we conducted Mendelian Randomization approaches to investigate the causal relationship between classical risk factors...

Cite
Jansen SA, Huiskens B, Trompet S, et al. Classical risk factors for primary coronary artery disease from an aging perspective through Mendelian Randomization. Geroscience. 2021;doi: 10.1007/s11357-021-00498-9.
Jansen, S. A., Huiskens, B., Trompet, S., Jukema, J., Mooijaart, S. P., Willems van Dijk, K., van Heemst, D., & Noordam, R. (2021). Classical risk factors for primary coronary artery disease from an aging perspective through Mendelian Randomization. GeroScience, . https://doi.org/10.1007/s11357-021-00498-9
Jansen, Swetta A, et al. "Classical risk factors for primary coronary artery disease from an aging perspective through Mendelian Randomization." GeroScience vol. (2021). doi: https://doi.org/10.1007/s11357-021-00498-9
Jansen SA, Huiskens B, Trompet S, Jukema J, Mooijaart SP, Willems van Dijk K, van Heemst D, Noordam R. Classical risk factors for primary coronary artery disease from an aging perspective through Mendelian Randomization. Geroscience. 2021 Dec 21; doi: 10.1007/s11357-021-00498-9. Epub 2021 Dec 21. PMID: 34932184.
Copy Download .nbib Format: NLM
The prevalence of metabolic syndrome and its association with body fat distribution in middle-aged individuals from Indonesia and the Netherlands: a cross-sectional analysis of two population-based studies.

Diabetology & metabolic syndrome

Sigit FS, Tahapary DL, Trompet S, Sartono E, Willems van Dijk K, Rosendaal FR, de Mutsert R.
PMID: 31921359
Diabetol Metab Syndr. 2020 Jan 07;12:2. doi: 10.1186/s13098-019-0503-1. eCollection 2020.

BACKGROUND: The prevalence of metabolic syndrome varies among populations with different ethnicities. Asian populations develop metabolic complications at lower amounts of adiposity than western populations. The role of abdominal obesity in the metabolic differences between the two populations is...

Cite
Sigit FS, Tahapary DL, Trompet S, et al. The prevalence of metabolic syndrome and its association with body fat distribution in middle-aged individuals from Indonesia and the Netherlands: a cross-sectional analysis of two population-based studies. Diabetol Metab Syndr. 2020;12:2doi: 10.1186/s13098-019-0503-1.
Sigit, F. S., Tahapary, D. L., Trompet, S., Sartono, E., Willems van Dijk, K., Rosendaal, F. R., & de Mutsert, R. (2020). The prevalence of metabolic syndrome and its association with body fat distribution in middle-aged individuals from Indonesia and the Netherlands: a cross-sectional analysis of two population-based studies. Diabetology & metabolic syndrome, 122. https://doi.org/10.1186/s13098-019-0503-1
Sigit, Fathimah S, et al. "The prevalence of metabolic syndrome and its association with body fat distribution in middle-aged individuals from Indonesia and the Netherlands: a cross-sectional analysis of two population-based studies." Diabetology & metabolic syndrome vol. 12 (2020): 2. doi: https://doi.org/10.1186/s13098-019-0503-1
Sigit FS, Tahapary DL, Trompet S, Sartono E, Willems van Dijk K, Rosendaal FR, de Mutsert R. The prevalence of metabolic syndrome and its association with body fat distribution in middle-aged individuals from Indonesia and the Netherlands: a cross-sectional analysis of two population-based studies. Diabetol Metab Syndr. 2020 Jan 07;12:2. doi: 10.1186/s13098-019-0503-1. eCollection 2020. PMID: 31921359; PMCID: PMC6947940.
Copy Download .nbib Format: NLM
Metabolomics dissection of depression heterogeneity and related cardiometabolic risk.

Psychological medicine

Alshehri T, Mook-Kanamori DO, Willems van Dijk K, Dinga R, Penninx BWJH, Rosendaal FR, le Cessie S, Milaneschi Y.
PMID: 34078486
Psychol Med. 2021 Jun 03;1-10. doi: 10.1017/S0033291721001471. Epub 2021 Jun 03.

BACKGROUND: A recent hypothesis postulates the existence of an 'immune-metabolic depression' (IMD) dimension characterized by metabolic dysregulations. Combining data on metabolomics and depressive symptoms, we aimed to identify depressions associated with an increased risk of adverse metabolic alterations.METHOD: Clustering...

Cite
Alshehri T, Mook-Kanamori DO, Willems van Dijk K, et al. Metabolomics dissection of depression heterogeneity and related cardiometabolic risk. Psychol Med. 2021;1-10doi: 10.1017/S0033291721001471.
Alshehri, T., Mook-Kanamori, D. O., Willems van Dijk, K., Dinga, R., Penninx, B. W. J. H., Rosendaal, F. R., le Cessie, S., & Milaneschi, Y. (2021). Metabolomics dissection of depression heterogeneity and related cardiometabolic risk. Psychological medicine, 1-10. https://doi.org/10.1017/S0033291721001471
Alshehri, Tahani, et al. "Metabolomics dissection of depression heterogeneity and related cardiometabolic risk." Psychological medicine vol. (2021): 1-10. doi: https://doi.org/10.1017/S0033291721001471
Alshehri T, Mook-Kanamori DO, Willems van Dijk K, Dinga R, Penninx BWJH, Rosendaal FR, le Cessie S, Milaneschi Y. Metabolomics dissection of depression heterogeneity and related cardiometabolic risk. Psychol Med. 2021 Jun 03;1-10. doi: 10.1017/S0033291721001471. Epub 2021 Jun 03. PMID: 34078486.
Copy Download .nbib Format: NLM
Genetics of the human metabolome, what is next?.

Biochimica et biophysica acta

Dharuri H, Demirkan A, van Klinken JB, Mook-Kanamori DO, van Duijn CM, 't Hoen PA, Willems van Dijk K.
PMID: 24905732
Biochim Biophys Acta. 2014 Oct;1842(10):1923-1931. doi: 10.1016/j.bbadis.2014.05.030. Epub 2014 Jun 04.

Increases in throughput and decreases in costs have facilitated large scale metabolomics studies, the simultaneous measurement of large numbers of biochemical components in biological samples. Initial large scale studies focused on biomarker discovery for disease or disease progression and...

Cite
Dharuri H, Demirkan A, van Klinken JB, et al. Genetics of the human metabolome, what is next?. Biochim Biophys Acta. 2014;1842(10):1923-1931doi: 10.1016/j.bbadis.2014.05.030.
Dharuri, H., Demirkan, A., van Klinken, J. B., Mook-Kanamori, D. O., van Duijn, C. M., 't Hoen, P. A., & Willems van Dijk, K. (2014). Genetics of the human metabolome, what is next?. Biochimica et biophysica acta, 1842(10), 1923-1931. https://doi.org/10.1016/j.bbadis.2014.05.030
Dharuri, Harish, et al. "Genetics of the human metabolome, what is next?." Biochimica et biophysica acta vol. 1842,10 (2014): 1923-1931. doi: https://doi.org/10.1016/j.bbadis.2014.05.030
Dharuri H, Demirkan A, van Klinken JB, Mook-Kanamori DO, van Duijn CM, 't Hoen PA, Willems van Dijk K. Genetics of the human metabolome, what is next?. Biochim Biophys Acta. 2014 Oct;1842(10):1923-1931. doi: 10.1016/j.bbadis.2014.05.030. Epub 2014 Jun 04. PMID: 24905732.
Copy Download .nbib Format: NLM
Genetic Studies of Metabolomics Change After a Liquid Meal Illuminate Novel Pathways for Glucose and Lipid Metabolism.

Diabetes

Li-Gao R, Hughes DA, van Klinken JB, de Mutsert R, Rosendaal FR, Mook-Kanamori DO, Timpson NJ, Willems van Dijk K.
PMID: 34610981
Diabetes. 2021 Dec;70(12):2932-2946. doi: 10.2337/db21-0397. Epub 2021 Oct 05.

Humans spend the greater part of the day in a postprandial state. However, the genetic basis of postprandial blood measures is relatively uncharted territory. We examined the genetics of variation in concentrations of postprandial metabolites (

Cite
Li-Gao R, Hughes DA, van Klinken JB, et al. Genetic Studies of Metabolomics Change After a Liquid Meal Illuminate Novel Pathways for Glucose and Lipid Metabolism. Diabetes. 2021;70(12):2932-2946doi: 10.2337/db21-0397.
Li-Gao, R., Hughes, D. A., van Klinken, J. B., de Mutsert, R., Rosendaal, F. R., Mook-Kanamori, D. O., Timpson, N. J., & Willems van Dijk, K. (2021). Genetic Studies of Metabolomics Change After a Liquid Meal Illuminate Novel Pathways for Glucose and Lipid Metabolism. Diabetes, 70(12), 2932-2946. https://doi.org/10.2337/db21-0397
Li-Gao, Ruifang, et al. "Genetic Studies of Metabolomics Change After a Liquid Meal Illuminate Novel Pathways for Glucose and Lipid Metabolism." Diabetes vol. 70,12 (2021): 2932-2946. doi: https://doi.org/10.2337/db21-0397
Li-Gao R, Hughes DA, van Klinken JB, de Mutsert R, Rosendaal FR, Mook-Kanamori DO, Timpson NJ, Willems van Dijk K. Genetic Studies of Metabolomics Change After a Liquid Meal Illuminate Novel Pathways for Glucose and Lipid Metabolism. Diabetes. 2021 Dec;70(12):2932-2946. doi: 10.2337/db21-0397. Epub 2021 Oct 05. PMID: 34610981.
Copy Download .nbib Format: NLM
A Workflow for Missing Values Imputation of Untargeted Metabolomics Data.

Metabolites

Faquih T, van Smeden M, Luo J, le Cessie S, Kastenmüller G, Krumsiek J, Noordam R, van Heemst D, Rosendaal FR, van Hylckama Vlieg A, Willems van Dijk K, Mook-Kanamori DO.
PMID: 33256233
Metabolites. 2020 Nov 26;10(12). doi: 10.3390/metabo10120486.

Metabolomics studies have seen a steady growth due to the development and implementation of affordable and high-quality metabolomics platforms. In large metabolite panels, measurement values are frequently missing and, if neglected or sub-optimally imputed, can cause biased study results....

Cite
Faquih T, van Smeden M, Luo J, et al. A Workflow for Missing Values Imputation of Untargeted Metabolomics Data. Metabolites. 2020;10(12)doi: 10.3390/metabo10120486.
Faquih, T., van Smeden, M., Luo, J., le Cessie, S., Kastenmüller, G., Krumsiek, J., Noordam, R., van Heemst, D., Rosendaal, F. R., van Hylckama Vlieg, A., Willems van Dijk, K., & Mook-Kanamori, D. O. (2020). A Workflow for Missing Values Imputation of Untargeted Metabolomics Data. Metabolites, 10(12), . https://doi.org/10.3390/metabo10120486
Faquih, Tariq, et al. "A Workflow for Missing Values Imputation of Untargeted Metabolomics Data." Metabolites vol. 10,12 (2020). doi: https://doi.org/10.3390/metabo10120486
Faquih T, van Smeden M, Luo J, le Cessie S, Kastenmüller G, Krumsiek J, Noordam R, van Heemst D, Rosendaal FR, van Hylckama Vlieg A, Willems van Dijk K, Mook-Kanamori DO. A Workflow for Missing Values Imputation of Untargeted Metabolomics Data. Metabolites. 2020 Nov 26;10(12). doi: 10.3390/metabo10120486. PMID: 33256233; PMCID: PMC7761057.
Copy Download .nbib Format: NLM
Postprandial metabolite profiles associated with type 2 diabetes clearly stratify individuals with impaired fasting glucose.

Metabolomics : Official journal of the Metabolomic Society

Li-Gao R, de Mutsert R, Rensen PCN, van Klinken JB, Prehn C, Adamski J, van Hylckama Vlieg A, den Heijer M, le Cessie S, Rosendaal FR, Willems van Dijk K, Mook-Kanamori DO.
PMID: 29249917
Metabolomics. 2018;14(1):13. doi: 10.1007/s11306-017-1307-7. Epub 2017 Dec 12.

INTRODUCTION: Fasting metabolite profiles have been shown to distinguish type 2 diabetes (T2D) patients from normal glucose tolerance (NGT) individuals.OBJECTIVES: We investigated whether, besides fasting metabolite profiles, postprandial metabolite profiles associated with T2D can stratify individuals with impaired fasting...

Cite
Li-Gao R, de Mutsert R, Rensen PCN, et al. Postprandial metabolite profiles associated with type 2 diabetes clearly stratify individuals with impaired fasting glucose. Metabolomics. 2017;14(1):13doi: 10.1007/s11306-017-1307-7.
Li-Gao, R., de Mutsert, R., Rensen, P. C. N., van Klinken, J. B., Prehn, C., Adamski, J., van Hylckama Vlieg, A., den Heijer, M., le Cessie, S., Rosendaal, F. R., Willems van Dijk, K., & Mook-Kanamori, D. O. (2018). Postprandial metabolite profiles associated with type 2 diabetes clearly stratify individuals with impaired fasting glucose. Metabolomics : Official journal of the Metabolomic Society, 14(1), 13. https://doi.org/10.1007/s11306-017-1307-7
Li-Gao, Ruifang, et al. "Postprandial metabolite profiles associated with type 2 diabetes clearly stratify individuals with impaired fasting glucose." Metabolomics : Official journal of the Metabolomic Society vol. 14,1 (2018): 13. doi: https://doi.org/10.1007/s11306-017-1307-7
Li-Gao R, de Mutsert R, Rensen PCN, van Klinken JB, Prehn C, Adamski J, van Hylckama Vlieg A, den Heijer M, le Cessie S, Rosendaal FR, Willems van Dijk K, Mook-Kanamori DO. Postprandial metabolite profiles associated with type 2 diabetes clearly stratify individuals with impaired fasting glucose. Metabolomics. 2018;14(1):13. doi: 10.1007/s11306-017-1307-7. Epub 2017 Dec 12. PMID: 29249917; PMCID: PMC5727148.
Copy Download .nbib Format: NLM
Association of metabolic syndrome and electrocardiographic markers of subclinical cardiovascular disease.

Diabetology & metabolic syndrome

Elffers TW, de Mutsert R, Lamb HJ, Maan AC, Macfarlane PW, Willems van Dijk K, Rosendaal FR, Jukema JW, Trompet S.
PMID: 28539979
Diabetol Metab Syndr. 2017 May 22;9:40. doi: 10.1186/s13098-017-0238-9. eCollection 2017.

BACKGROUND: The metabolic syndrome (MetS) and its components are well-established risk factors for cardiovascular diseases (CVD). It is inconclusive whether MetS and MetS score are associated with electrocardiographic markers of subclinical CVD, therefore we investigated this in a population...

Cite
Elffers TW, de Mutsert R, Lamb HJ, et al. Association of metabolic syndrome and electrocardiographic markers of subclinical cardiovascular disease. Diabetol Metab Syndr. 2017;9:40doi: 10.1186/s13098-017-0238-9.
Elffers, T. W., de Mutsert, R., Lamb, H. J., Maan, A. C., Macfarlane, P. W., Willems van Dijk, K., Rosendaal, F. R., Jukema, J. W., & Trompet, S. (2017). Association of metabolic syndrome and electrocardiographic markers of subclinical cardiovascular disease. Diabetology & metabolic syndrome, 940. https://doi.org/10.1186/s13098-017-0238-9
Elffers, Theodora W, et al. "Association of metabolic syndrome and electrocardiographic markers of subclinical cardiovascular disease." Diabetology & metabolic syndrome vol. 9 (2017): 40. doi: https://doi.org/10.1186/s13098-017-0238-9
Elffers TW, de Mutsert R, Lamb HJ, Maan AC, Macfarlane PW, Willems van Dijk K, Rosendaal FR, Jukema JW, Trompet S. Association of metabolic syndrome and electrocardiographic markers of subclinical cardiovascular disease. Diabetol Metab Syndr. 2017 May 22;9:40. doi: 10.1186/s13098-017-0238-9. eCollection 2017. PMID: 28539979; PMCID: PMC5441065.
Copy Download .nbib Format: NLM
Effects of low-stearate palm oil and high-stearate lard high-fat diets on rat liver lipid metabolism and glucose tolerance.

Nutrition & metabolism

Janssens S, Heemskerk MM, van den Berg SA, van Riel NA, Nicolay K, Willems van Dijk K, Prompers JJ.
PMID: 26691906
Nutr Metab (Lond). 2015 Dec 18;12:57. doi: 10.1186/s12986-015-0053-y. eCollection 2015.

BACKGROUND: Excess consumption of energy-dense, high-fat Western diets contributes to the development of obesity and obesity-related disorders, such as fatty liver disease. However, not only the quantity but also the composition of dietary fat may play a role in...

Cite
Janssens S, Heemskerk MM, van den Berg SA, et al. Effects of low-stearate palm oil and high-stearate lard high-fat diets on rat liver lipid metabolism and glucose tolerance. Nutr Metab (Lond). 2015;12:57doi: 10.1186/s12986-015-0053-y.
Janssens, S., Heemskerk, M. M., van den Berg, S. A., van Riel, N. A., Nicolay, K., Willems van Dijk, K., & Prompers, J. J. (2015). Effects of low-stearate palm oil and high-stearate lard high-fat diets on rat liver lipid metabolism and glucose tolerance. Nutrition & metabolism, 1257. https://doi.org/10.1186/s12986-015-0053-y
Janssens, Sharon, et al. "Effects of low-stearate palm oil and high-stearate lard high-fat diets on rat liver lipid metabolism and glucose tolerance." Nutrition & metabolism vol. 12 (2015): 57. doi: https://doi.org/10.1186/s12986-015-0053-y
Janssens S, Heemskerk MM, van den Berg SA, van Riel NA, Nicolay K, Willems van Dijk K, Prompers JJ. Effects of low-stearate palm oil and high-stearate lard high-fat diets on rat liver lipid metabolism and glucose tolerance. Nutr Metab (Lond). 2015 Dec 18;12:57. doi: 10.1186/s12986-015-0053-y. eCollection 2015. PMID: 26691906; PMCID: PMC4683731.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 18 entries