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Secondary analyses of global datasets: do obesity and physical activity explain variation in diabetes risk across populations?.

International journal of obesity (2005)

Alkaf B, Blakemore AI, Järvelin MR, Lessan N.
PMID: 33574565
Int J Obes (Lond). 2021 May;45(5):944-956. doi: 10.1038/s41366-021-00764-y. Epub 2021 Feb 11.

Type 2 diabetes rates vary significantly across geographic regions. These differences are sometimes assumed to be entirely driven by differential distribution of environmental triggers, including obesity and insufficient physical activity (IPA). In this review, we discuss data which conflicts...

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Alkaf B, Blakemore AI, Järvelin MR, et al. Secondary analyses of global datasets: do obesity and physical activity explain variation in diabetes risk across populations?. Int J Obes (Lond). 2021;45(5):944-956doi: 10.1038/s41366-021-00764-y.
Alkaf, B., Blakemore, A. I., Järvelin, M. R., & Lessan, N. (2021). Secondary analyses of global datasets: do obesity and physical activity explain variation in diabetes risk across populations?. International journal of obesity (2005), 45(5), 944-956. https://doi.org/10.1038/s41366-021-00764-y
Alkaf, Budour, et al. "Secondary analyses of global datasets: do obesity and physical activity explain variation in diabetes risk across populations?." International journal of obesity (2005) vol. 45,5 (2021): 944-956. doi: https://doi.org/10.1038/s41366-021-00764-y
Alkaf B, Blakemore AI, Järvelin MR, Lessan N. Secondary analyses of global datasets: do obesity and physical activity explain variation in diabetes risk across populations?. Int J Obes (Lond). 2021 May;45(5):944-956. doi: 10.1038/s41366-021-00764-y. Epub 2021 Feb 11. PMID: 33574565; PMCID: PMC8081659.
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Free serum haemoglobin is associated with brain atrophy in secondary progressive multiple sclerosis.

Wellcome open research

Lewin A, Hamilton S, Witkover A, Langford P, Nicholas R, Chataway J, Bangham CRM.
PMID: 27996064
Wellcome Open Res. 2016 Nov 15;1:10. doi: 10.12688/wellcomeopenres.9967.2.

No abstract available.

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Lewin A, Hamilton S, Witkover A, et al. Free serum haemoglobin is associated with brain atrophy in secondary progressive multiple sclerosis. Wellcome Open Res. 2016;1:10doi: 10.12688/wellcomeopenres.9967.2.
Lewin, A., Hamilton, S., Witkover, A., Langford, P., Nicholas, R., Chataway, J., & Bangham, C. R. M. (2016). Free serum haemoglobin is associated with brain atrophy in secondary progressive multiple sclerosis. Wellcome open research, 110. https://doi.org/10.12688/wellcomeopenres.9967.2
Lewin, Alex, et al. "Free serum haemoglobin is associated with brain atrophy in secondary progressive multiple sclerosis." Wellcome open research vol. 1 (2016): 10. doi: https://doi.org/10.12688/wellcomeopenres.9967.2
Lewin A, Hamilton S, Witkover A, Langford P, Nicholas R, Chataway J, Bangham CRM. Free serum haemoglobin is associated with brain atrophy in secondary progressive multiple sclerosis. Wellcome Open Res. 2016 Nov 15;1:10. doi: 10.12688/wellcomeopenres.9967.2. PMID: 27996064; PMCID: PMC5159626.
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A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery.

Journal of the Royal Statistical Society. Series C, Applied statistics

Bottolo L, Banterle M, Richardson S, Ala-Korpela M, Järvelin MR, Lewin A.
PMID: 35001978
J R Stat Soc Ser C Appl Stat. 2021 Aug;70(4):886-908. doi: 10.1111/rssc.12490. Epub 2021 May 08.

Our work is motivated by the search for metabolite quantitative trait loci (QTL) in a cohort of more than 5000 people. There are 158 metabolites measured by NMR spectroscopy in the 31-year follow-up of the Northern Finland Birth Cohort...

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Bottolo L, Banterle M, Richardson S, et al. A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery. J R Stat Soc Ser C Appl Stat. 2021;70(4):886-908doi: 10.1111/rssc.12490.
Bottolo, L., Banterle, M., Richardson, S., Ala-Korpela, M., Järvelin, M. R., & Lewin, A. (2021). A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery. Journal of the Royal Statistical Society. Series C, Applied statistics, 70(4), 886-908. https://doi.org/10.1111/rssc.12490
Bottolo, Leonardo, et al. "A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery." Journal of the Royal Statistical Society. Series C, Applied statistics vol. 70,4 (2021): 886-908. doi: https://doi.org/10.1111/rssc.12490
Bottolo L, Banterle M, Richardson S, Ala-Korpela M, Järvelin MR, Lewin A. A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery. J R Stat Soc Ser C Appl Stat. 2021 Aug;70(4):886-908. doi: 10.1111/rssc.12490. Epub 2021 May 08. PMID: 35001978; PMCID: PMC7612194.
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