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      Plasma Steroid Profiling Between Patients With and Without Diabetes Mellitus in Nonfunctioning Adrenal Incidentalomas

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          Abstract

          Context

          Adrenal incidentalomas, including nonfunctioning adrenal incidentalomas (NFAI), are associated with a high prevalence of diabetes mellitus (DM). While NFAI is diagnosed by exclusion when no hormone excess exists, subtle cortisol secretion may exist and contribute to DM development. However, it alone cannot explain the increased risk, and whether other steroid metabolites are involved remains unclear.

          Purpose

          To investigate steroid metabolites associated with DM in patients with NFAI using plasma steroid profiles.

          Methods

          Using liquid chromatography-tandem mass spectrometry, 22 plasma steroid metabolites were measured in 68 patients with NFAI (31 men and 37 women). Data were adjusted for age before normalization.

          Results

          Discriminant analysis showed that plasma steroid profiles discriminated between patients with and without DM in men (n = 10 and = 21, respectively) but not women: 11β-hydroxytestosterone, an adrenal-derived 11-oxygenated androgen, contributed most to this discrimination and was higher in patients with DM than in those without DM (false discovery rate = .002). 11β-hydroxytestosterone was correlated positively with fasting plasma glucose (r = .507) and hemoglobin A1c (HbA1c) (r = .553) but negatively with homeostatic model assessment of β-cell function (HOMA2-B) (r = −.410). These correlations remained significant after adjusting for confounders, including serum cortisol after the 1-mg dexamethasone suppression test. Bayesian kernel machine regression analysis verified the association of 11β-hydroxytestosterone with HbA1c and HOMA2-B in men.

          Main Conclusion

          Plasma steroid profiles differed between those with and without DM in men with NFAI. 11β-hydroxytestosterone was associated with hyperglycemia and indicators related to pancreatic β-cell dysfunction, independently of cortisol.

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          Most cited references46

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          Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.

          The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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            MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

            Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC–MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca . Graphical Abstract From raw data to statistical and functional insights using MetaboAnalyst 5.0.
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              Global trends in diabetes complications: a review of current evidence

              In recent decades, large increases in diabetes prevalence have been demonstrated in virtually all regions of the world. The increase in the number of people with diabetes or with a longer duration of diabetes is likely to alter the disease profile in many populations around the globe, particularly due to a higher incidence of diabetes-specific complications, such as kidney failure and peripheral arterial disease. The epidemiology of other conditions frequently associated with diabetes, including infections and cardiovascular disease, may also change, with direct effects on quality of life, demands on health services and economic costs. The current understanding of the international burden of and variation in diabetes-related complications is poor. The available data suggest that rates of myocardial infarction, stroke and amputation are decreasing among people with diabetes, in parallel with declining mortality. However, these data predominantly come from studies in only a few high-income countries. Trends in other complications of diabetes, such as end-stage renal disease, retinopathy and cancer, are less well explored. In this review, we synthesise data from population-based studies on trends in diabetes complications, with the objectives of: (1) characterising recent and long-term trends in diabetes-related complications; (2) describing regional variation in the excess risk of complications, where possible; and (3) identifying and prioritising gaps for future surveillance and study.
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                Author and article information

                Contributors
                Journal
                J Endocr Soc
                J Endocr Soc
                jes
                Journal of the Endocrine Society
                Oxford University Press (US )
                2472-1972
                26 July 2024
                31 July 2024
                31 July 2024
                : 8
                : 9
                : bvae140
                Affiliations
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Division of Metabolomics, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University , Fukuoka 812-8582, Japan
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Division of Metabolomics, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University , Fukuoka 812-8582, Japan
                Division of Metabolomics, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University , Fukuoka 812-8582, Japan
                Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University , Fukuoka 812-8582, Japan
                Author notes
                Correspondence: Maki Yokomoto-Umakoshi, MD, PhD, Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. Email: umakoshi.maki.498@ 123456m.kyushu-u.ac.jp ; or Yoshihiro Ogawa, MD, PhD, Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. Email address: ogawa.yoshihiro.828@ 123456m.kyushu-u.ac.jp .

                Yui Nakano and Maki Yokomoto-Umakoshi contributed equally to this study.

                Author information
                https://orcid.org/0000-0003-0934-9148
                https://orcid.org/0000-0001-9881-9126
                https://orcid.org/0000-0002-0834-2836
                Article
                bvae140
                10.1210/jendso/bvae140
                11322837
                39145114
                7eb9e7a3-a70f-4041-b6a8-6fd69b664ff7
                © The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. See the journal About page for additional terms.

                History
                : 25 June 2024
                : 26 July 2024
                : 14 August 2024
                Page count
                Pages: 11
                Funding
                Funded by: JSPS KAKENHI;
                Award ID: JP23K15392
                Award ID: JP22H04993
                Award ID: JP22K08627
                Award ID: JP21K14472
                Award ID: JP22H05185
                Funded by: Takeda Science Foundation, DOI 10.13039/100007449;
                Funded by: Nakatomi Foundation, DOI 10.13039/100008731;
                Funded by: Japan Foundation for Applied Enzymology, DOI 10.13039/100008695;
                Funded by: The Uehara Memorial Foundation, DOI 10.13039/100008732;
                Funded by: JES Grant for Promising Investigator;
                Funded by: Secom Science and Technology Foundation, DOI 10.13039/501100004298;
                Funded by: Joint Research Program of Institute for Genetic Medicine, Hokkaido University;
                Funded by: AMED-BINDS;
                Award ID: JP24ama121055
                Funded by: JST A-STEP;
                Award ID: JPMJTR204J
                Funded by: JST-Moonshot;
                Award ID: JPMJMS2011-62
                Funded by: MEXT Cooperative Research Project Program;
                Funded by: Medical Research Center Initiative for High Depth Omics (to MIB);
                Award ID: JPMXP1323015486
                Categories
                Clinical Research Article
                AcademicSubjects/MED00250
                Jes/1

                steroid profiles,adrenal incidentalomas,diabetes mellitus,11-oxygenated androgens,11β-hydroxytestosterone

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