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      Metabolic profiling of polycystic ovary syndrome reveals interactions with abdominal obesity

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          Abstract

          Background:

          Polycystic ovary syndrome (PCOS) is a common reproductive disorder associated with metabolic disturbances including obesity, insulin resistance and diabetes mellitus. Here we investigate whether changes in the metabolic profile of PCOS women are driven by increased tendency to obesity or are specific features of PCOS related to increased testosterone levels.

          Design and methods:

          We conducted an NMR metabolomics association study of PCOS cases ( n=145) and controls ( n=687) nested in a population-based birth cohort ( n=3127). Subjects were 31 years old at examination. The main analyses were adjusted for waist circumference (WC) as a proxy measure of central obesity. Subsequently, metabolite concentrations were compared between cases and controls within pre-defined WC strata. In each stratum, additional metabolomics association analyses with testosterone levels were conducted separately among cases and controls.

          Results:

          Overall, women with PCOS showed more adverse metabolite profiles than the controls. Four lipid fractions in different subclasses of very low density lipoprotein (VLDL) were associated with PCOS, after adjusting for WC and correction for multiple testing ( P<0.002). In stratified analysis the PCOS women within large WC strata (⩾98 cm) had significantly lower high density lipoprotein (HDL) levels, Apo A1 and albumin values compared with the controls. Testosterone levels were significantly associated with VLDL and serum lipids in PCOS cases with large WC but not in the controls. The higher testosterone levels, adjusted for WC, associated adversely with insulin levels and HOMA IR in cases but not in the controls.

          Conclusions:

          Our findings show that both abdominal obesity and hyperandrogenism contribute to the dyslipidaemia and other metabolic traits of PCOS which all may negatively contribute to the long-term health of women with PCOS.

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          Most cited references52

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          Metabolite profiles and the risk of developing diabetes.

          Emerging technologies allow the high-throughput profiling of metabolic status from a blood specimen (metabolomics). We investigated whether metabolite profiles could predict the development of diabetes. Among 2,422 normoglycemic individuals followed for 12 years, 201 developed diabetes. Amino acids, amines and other polar metabolites were profiled in baseline specimens by liquid chromatography-tandem mass spectrometry (LC-MS). Cases and controls were matched for age, body mass index and fasting glucose. Five branched-chain and aromatic amino acids had highly significant associations with future diabetes: isoleucine, leucine, valine, tyrosine and phenylalanine. A combination of three amino acids predicted future diabetes (with a more than fivefold higher risk for individuals in top quartile). The results were replicated in an independent, prospective cohort. These findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment.
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            Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix.

            Correlated multiple testing is widely performed in genetic research, particularly in multilocus analyses of complex diseases. Failure to control appropriately for the effect of multiple testing will either result in a flood of false-positive claims or in true hits being overlooked. Cheverud proposed the idea of adjusting correlated tests as if they were independent, according to an 'effective number' (M(eff)) of independent tests. However, our experience has indicated that Cheverud's estimate of the Meff is overly large and will lead to excessively conservative results. We propose a more accurate estimate of the M(eff), and design M(eff)-based procedures to control the experiment-wise significant level and the false discovery rate. In an evaluation, based on both real and simulated data, the M(eff)-based procedures were able to control the error rate accurately and consequently resulted in a power increase, especially in multilocus analyses. The results confirm that the M(eff) is a useful concept in the error-rate control of correlated tests. With its efficiency and accuracy, the M(eff) method provides an alternative to computationally intensive methods such as the permutation test.
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              A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms.

              Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it might be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set.
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                Author and article information

                Journal
                Int J Obes (Lond)
                Int J Obes (Lond)
                International Journal of Obesity (2005)
                Nature Publishing Group
                0307-0565
                1476-5497
                September 2017
                26 May 2017
                27 June 2017
                : 41
                : 9
                : 1331-1340
                Affiliations
                [1 ]Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPE) Centre for Environment and Health, School of Public Health, Imperial College London , London, UK
                [2 ]Rheumatology Unit, Institute of Child Health, University College London , London, UK
                [3 ]South Australian Health and Medical Research Center , Adelaide, Australia
                [4 ]SAHMRI, School of Biological Sciences, University of Adelaide , Adelaide, Australia
                [5 ]Computational Medicine, Center for Life-Course Health Research, University of Oulu and Oulu University Hospital , Oulu, Finland
                [6 ]Department of Obstetrics and Gynecology, University Hospital of Oulu, Medical Research Center Oulu and PEDEGO Research Unit, University of Oulu , Oulu, Finland
                [7 ]Center for Life-Course Health Research, Northern Finland Cohort Center, Faculty of Medicine, University of Oulu , Oulu, Finland
                [8 ]Biocenter Oulu, University of Oulu , Oulu, Finland
                [9 ]NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland , Kuopio, Finland
                [10 ]Department of Statistical Science, University College London , London, UK
                [11 ]Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol , Bristol, UK
                [12 ]Unit of Primary Care, Oulu University Hospital , Oulu, Finland
                [13 ]Institute of Reproductive and Developmental Biology, Imperial College London , London, UK
                Author notes
                [* ]Department of Epidemiology and Biostatistics, School of Public Health, Imperial College , Room 156, Norfolk Place, London W2 1PG, UK. E-mail: m.jarvelin@ 123456imperial.ac.uk
                [* ]Institute of Reproductive and Developmental Biology, Imperial College London , Room 5009, Hammersmith Hospital, London W12 0NN, UK. E-mail: s.franks@ 123456imperial.ac.uk
                [14]

                These authors contributed equally to this work.

                Article
                ijo2017126
                10.1038/ijo.2017.126
                5578435
                28546543
                e8ce5b64-aa47-49b4-ab61-b5e96e186420
                Copyright © 2017 The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 03 October 2016
                : 21 March 2017
                : 26 March 2017
                Categories
                Original Article

                Nutrition & Dietetics
                Nutrition & Dietetics

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