Associations between plasma sulfur amino acids and specific fat depots in two independent cohorts: CODAM and The Maastricht Study

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Abstract

Purpose

Sulfur amino acids (SAAs) have been associated with obesity and obesity-related metabolic diseases. We investigated whether plasma SAAs (methionine, total cysteine (tCys), total homocysteine, cystathionine and total glutathione) are related to specific fat depots.

Methods

We examined cross-sectional subsets from the CODAM cohort ( n = 470, 61.3% men, median [IQR]: 67 [61, 71] years) and The Maastricht Study (DMS; n = 371, 53.4% men, 63 [55, 68] years), enriched with (pre)diabetic individuals. SAAs were measured in fasting EDTA plasma with LC–MS/MS. Outcomes comprised BMI, skinfolds, waist circumference (WC), dual-energy X-ray absorptiometry (DXA, DMS), body composition, abdominal subcutaneous and visceral adipose tissues (CODAM: ultrasound, DMS: MRI) and liver fat (estimated, in CODAM, or MRI-derived, in DMS, liver fat percentage and fatty liver disease). Associations were examined with linear or logistic regressions adjusted for relevant confounders with z-standardized primary exposures and outcomes.

Results

Methionine was associated with all measures of liver fat, e.g ., fatty liver disease [CODAM: OR = 1.49 (95% CI 1.19, 1.88); DMS: OR = 1.51 (1.09, 2.14)], but not with other fat depots. tCys was associated with overall obesity, e.g., BMI [CODAM: β = 0.19 (0.09, 0.28); DMS: β = 0.24 (0.14, 0.34)]; peripheral adiposity, e.g., biceps and triceps skinfolds [CODAM: β = 0.15 (0.08, 0.23); DMS: β = 0.20 (0.12, 0.29)]; and central adiposity, e.g., WC [CODAM: β = 0.16 (0.08, 0.25); DMS: β = 0.17 (0.08, 0.27)]. Associations of tCys with VAT and liver fat were inconsistent. Other SAAs were not associated with body fat.

Conclusion

Plasma concentrations of methionine and tCys showed distinct associations with different fat depots, with similar strengths in the two cohorts.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00394-022-03041-4.

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Most cited references 77

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Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.

K.G.M.M. Alberti, P.Z. Zimmet (1998)

The classification of diabetes mellitus and the tests used for its diagnosis were brought into order by the National Diabetes Data Group of the USA and the second World Health Organization Expert Committee on Diabetes Mellitus in 1979 and 1980. Apart from minor modifications by WHO in 1985, little has been changed since that time. There is however considerable new knowledge regarding the aetiology of different forms of diabetes as well as more information on the predictive value of different blood glucose values for the complications of diabetes. A WHO Consultation has therefore taken place in parallel with a report by an American Diabetes Association Expert Committee to re-examine diagnostic criteria and classification. The present document includes the conclusions of the former and is intended for wide distribution and discussion before final proposals are submitted to WHO for approval. The main changes proposed are as follows. The diagnostic fasting plasma (blood) glucose value has been lowered to > or =7.0 mmol l(-1) (6.1 mmol l(-1)). Impaired Glucose Tolerance (IGT) is changed to allow for the new fasting level. A new category of Impaired Fasting Glycaemia (IFG) is proposed to encompass values which are above normal but below the diagnostic cut-off for diabetes (plasma > or =6.1 to or =5.6 to <6.1 mmol l(-1)). Gestational Diabetes Mellitus (GDM) now includes gestational impaired glucose tolerance as well as the previous GDM. The classification defines both process and stage of the disease. The processes include Type 1, autoimmune and non-autoimmune, with beta-cell destruction; Type 2 with varying degrees of insulin resistance and insulin hyposecretion; Gestational Diabetes Mellitus; and Other Types where the cause is known (e.g. MODY, endocrinopathies). It is anticipated that this group will expand as causes of Type 2 become known. Stages range from normoglycaemia to insulin required for survival. It is hoped that the new classification will allow better classification of individuals and lead to fewer therapeutic misjudgements.

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Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study.

Caroline S. Fox, Joseph Massaro, Udo Hoffmann … (2007)

Visceral adipose tissue (VAT) compartments may confer increased metabolic risk. The incremental utility of measuring both visceral and subcutaneous abdominal adipose tissue (SAT) in association with metabolic risk factors and underlying heritability has not been well described in a population-based setting. Participants (n=3001) were drawn from the Framingham Heart Study (48% women; mean age, 50 years), were free of clinical cardiovascular disease, and underwent multidetector computed tomography assessment of SAT and VAT volumes between 2002 and 2005. Metabolic risk factors were examined in relation to increments of SAT and VAT after multivariable adjustment. Heritability was calculated using variance-components analysis. Among both women and men, SAT and VAT were significantly associated with blood pressure, fasting plasma glucose, triglycerides, and high-density lipoprotein cholesterol and with increased odds of hypertension, impaired fasting glucose, diabetes mellitus, and metabolic syndrome (P range < 0.01). In women, relations between VAT and risk factors were consistently stronger than in men. However, VAT was more strongly correlated with most metabolic risk factors than was SAT. For example, among women and men, both SAT and VAT were associated with increased odds of metabolic syndrome. In women, the odds ratio (OR) of metabolic syndrome per 1-standard deviation increase in VAT (OR, 4.7) was stronger than that for SAT (OR, 3.0; P for difference between SAT and VAT < 0.0001); similar differences were noted for men (OR for VAT, 4.2; OR for SAT, 2.5). Furthermore, VAT but not SAT contributed significantly to risk factor variation after adjustment for body mass index and waist circumference (P < or = 0.01). Among overweight and obese individuals, the prevalence of hypertension, impaired fasting glucose, and metabolic syndrome increased linearly and significantly across increasing VAT quartiles. Heritability values for SAT and VAT were 57% and 36%, respectively. Although both SAT and VAT are correlated with metabolic risk factors, VAT remains more strongly associated with an adverse metabolic risk profile even after accounting for standard anthropometric indexes. Our findings are consistent with the hypothesized role of visceral fat as a unique, pathogenic fat depot. Measurement of VAT may provide a more complete understanding of metabolic risk associated with variation in fat distribution.

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A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.

A S Levey, J Bosch, J. B. Lewis … (1999)

Serum creatinine concentration is widely used as an index of renal function, but this concentration is affected by factors other than glomerular filtration rate (GFR). To develop an equation to predict GFR from serum creatinine concentration and other factors. Cross-sectional study of GFR, creatinine clearance, serum creatinine concentration, and demographic and clinical characteristics in patients with chronic renal disease. 1628 patients enrolled in the baseline period of the Modification of Diet in Renal Disease (MDRD) Study, of whom 1070 were randomly selected as the training sample; the remaining 558 patients constituted the validation sample. The prediction equation was developed by stepwise regression applied to the training sample. The equation was then tested and compared with other prediction equations in the validation sample. To simplify prediction of GFR, the equation included only demographic and serum variables. Independent factors associated with a lower GFR included a higher serum creatinine concentration, older age, female sex, nonblack ethnicity, higher serum urea nitrogen levels, and lower serum albumin levels (P < 0.001 for all factors). The multiple regression model explained 90.3% of the variance in the logarithm of GFR in the validation sample. Measured creatinine clearance overestimated GFR by 19%, and creatinine clearance predicted by the Cockcroft-Gault formula overestimated GFR by 16%. After adjustment for this overestimation, the percentage of variance of the logarithm of GFR predicted by measured creatinine clearance or the Cockcroft-Gault formula was 86.6% and 84.2%, respectively. The equation developed from the MDRD Study provided a more accurate estimate of GFR in our study group than measured creatinine clearance or other commonly used equations.

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Author and article information

Contributors

Elena C. Tore:

ORCID: http://orcid.org/0000-0002-9218-3849

e.tore@maastrichtuniversity.nl

Journal

Journal ID (nlm-ta): Eur J Nutr

Journal ID (iso-abbrev): Eur J Nutr

Title: European Journal of Nutrition

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 1436-6207

ISSN (Electronic): 1436-6215

Publication date (Electronic): 2 November 2022

Publication date PMC-release: 2 November 2022

Publication date (Print): 2023

Volume: 62

Issue: 2

Pages: 891-904

Affiliations

[1 ]GRID grid.5012.6, ISNI 0000 0001 0481 6099, Department of Internal Medicine, , Maastricht University, ; Maastricht, The Netherlands

[2 ]GRID grid.5012.6, ISNI 0000 0001 0481 6099, CARIM School for Cardiovascular Disease, , Maastricht University, ; Universiteitssingel 50, 6229 ER MD Maastricht, Postbus 616 6200, Maastricht, The Netherlands

[3 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Department of Pharmacology, , University of Oxford, ; Oxford, UK

[4 ]GRID grid.7155.6, ISNI 0000 0001 2260 6941, Department of Physiology, Faculty of Medicine, , University of Alexandria, ; Alexandria, Egypt

[5 ]GRID grid.412966.e, ISNI 0000 0004 0480 1382, Department of Radiology and Nuclear Medicine, , Maastricht University Medical Center, ; Maastricht, The Netherlands

[6 ]GRID grid.5012.6, ISNI 0000 0001 0481 6099, Department of Epidemiology, , Maastricht University, ; Maastricht, The Netherlands

[7 ]GRID grid.5012.6, ISNI 0000 0001 0481 6099, CAPHRI Care and Public Health Research Institute, Maastricht University, ; Maastricht, The Netherlands

[8 ]GRID grid.5012.6, ISNI 0000 0001 0481 6099, MHENS School for Mental Health and Neuroscience, Maastricht University, ; Maastricht, The Netherlands

[9 ]GRID grid.412966.e, ISNI 0000 0004 0480 1382, Central Diagnostic Laboratory, , Maastricht University Medical Center, ; Maastricht, The Netherlands

[10 ]GRID grid.5510.1, ISNI 0000 0004 1936 8921, Department of Nutrition, Institute of Basic Medical Sciences, , University of Oslo, ; Oslo, Norway

Author information

Elena C. Tore http://orcid.org/0000-0002-9218-3849

Amany K. Elshorbagy http://orcid.org/0000-0002-8624-860X

Frans C. H. Bakers http://orcid.org/0000-0002-2327-4712

Martijn C. G. J. Brouwers http://orcid.org/0000-0002-8229-3331

Pieter C. Dagnelie http://orcid.org/0000-0003-1767-8814

Simone J. P. M. Eussen http://orcid.org/0000-0003-0559-6838

Jacobus F. A. Jansen http://orcid.org/0000-0002-5271-8060

M. Eline Kooi http://orcid.org/0000-0001-7562-5724

Yvo H. A. M. Kusters http://orcid.org/0000-0002-9649-9605

Steven J. R. Meex http://orcid.org/0000-0001-8147-9006

Thomas Olsen http://orcid.org/0000-0003-1805-5221

Helga Refsum http://orcid.org/0000-0003-0382-5633

Kjetil Retterstøl http://orcid.org/0000-0002-1309-7556

Casper G. Schalkwijk http://orcid.org/0000-0003-0190-2690

Coen D. A. Stehouwer http://orcid.org/0000-0001-8752-3223

Kathrine J. Vinknes http://orcid.org/0000-0002-0756-5042

Marleen M. J. van Greevenbroek http://orcid.org/0000-0002-2989-1631

Article

Publisher ID: 3041

DOI: 10.1007/s00394-022-03041-4

PMC ID: 9941263

PubMed ID: 36322288

SO-VID: 86932b50-e963-4169-a2e8-c4208076fcb5

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 9 May 2022

Date accepted : 20 October 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;

Award ID: 940-35-034

Funded by: FundRef http://dx.doi.org/10.13039/501100003092, Diabetes Fonds;

Award ID: 98.901

Funded by: FundRef http://dx.doi.org/10.13039/501100008530, European Regional Development Fund;

Funded by: FundRef http://dx.doi.org/10.13039/100016244, Health Foundation Limburg;

Funded by: FundRef http://dx.doi.org/10.13039/501100003195, Ministerie van Economische Zaken;

Award ID: grant 31O.041

Funded by: Stichting the Weijerhorst

Funded by: Pearl String Initiative Diabetes

Funded by: Cardiovascular Center Maastricht

Funded by: FundRef http://dx.doi.org/10.13039/501100011097, CARIM School for Cardiovascular Diseases, Universiteit Maastricht;

Funded by: FundRef http://dx.doi.org/10.13039/501100019392, NUTRIM School of Nutrition and Translational Research in Metabolism;

Funded by: Stichting Annadal

Funded by: Province of Limburg

Funded by: FundRef http://dx.doi.org/10.13039/100005205, Janssen Research and Development;

Funded by: FundRef http://dx.doi.org/10.13039/501100000329, Novo Nordisk UK Research Foundation;

Funded by: FundRef http://dx.doi.org/10.13039/100004339, Sanofi;

Funded by: FundRef http://dx.doi.org/10.13039/100016036, Health~Holland;

Funded by: FundRef http://dx.doi.org/10.13039/100013279, Joint Programming Initiative A healthy diet for a healthy life;

Funded by: FundRef http://dx.doi.org/10.13039/501100007601, Horizon 2020;

Award ID: GA N° 727565

Award Recipient : Kjetil Retterstøl

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ScienceOpen disciplines: Nutrition & Dietetics

Keywords: sulfur amino acids,adiposity,regional fat distribution,liver fat

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ScienceOpen disciplines: Nutrition & Dietetics

Keywords: sulfur amino acids, adiposity, regional fat distribution, liver fat

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Associations between plasma sulfur amino acids and specific fat depots in two independent cohorts: CODAM and The Maastricht Study

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Abstract

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Supplementary Information

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Most cited references 77

Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.

Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study.

A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.

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