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Wong THT, Mo JMY, Zhou M, Zhao JV, Schooling CM, He B, Luo S, Au Yeung SL. A two-sample Mendelian randomization study explores metabolic profiling of different glycemic traits. Commun Biol 2024; 7:293. [PMID: 38459184 PMCID: PMC10923832 DOI: 10.1038/s42003-024-05977-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024] Open
Abstract
We assessed the causal relation of four glycemic traits and type 2 diabetes liability with 167 metabolites using Mendelian randomization with various sensitivity analyses and a reverse Mendelian randomization analysis. We extracted instruments for fasting glucose, 2-h glucose, fasting insulin, and glycated hemoglobin from the Meta-Analyses of Glucose and Insulin-related traits Consortium (n = 200,622), and those for type 2 diabetes liability from a meta-analysis of multiple cohorts (148,726 cases, 965,732 controls) in Europeans. Outcome data were from summary statistics of 167 metabolites from the UK Biobank (n = 115,078). Fasting glucose and 2-h glucose were not associated with any metabolite. Higher glycated hemoglobin was associated with higher free cholesterol in small low-density lipoprotein. Type 2 diabetes liability and fasting insulin were inversely associated with apolipoprotein A1, total cholines, lipoprotein subfractions in high-density-lipoprotein and intermediate-density lipoproteins, and positively associated with aromatic amino acids. These findings indicate hyperglycemia-independent patterns and highlight the role of insulin in type 2 diabetes development. Further studies should evaluate these glycemic traits in type 2 diabetes diagnosis and clinical management.
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Affiliation(s)
- Tommy H T Wong
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jacky M Y Mo
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mingqi Zhou
- Department of Biological Chemistry, School of Medicine, University of California Irvine, Irvine, CA, USA
- Center for Epigenetics and Metabolism, University of California Irvine, Irvine, CA, USA
| | - Jie V Zhao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - C Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Baoting He
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Shan Luo
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| | - Shiu Lun Au Yeung
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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Alshuweishi Y, Basudan AM, Alfaifi M, Daghistani H, Alfhili MA. Association of the HALP Score with Dyslipidemia: A Large, Nationwide Retrospective Study. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:2002. [PMID: 38004051 PMCID: PMC10673399 DOI: 10.3390/medicina59112002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/07/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023]
Abstract
Background and Objectives: Dyslipidemia is a major risk factor for cardiovascular disease (CVD). The identification of new biomarkers that may enhance the risk assessment of lipid abnormalities is a promising approach in improving risk prediction of CVD. There is no information on the association of the hemoglobin, albumin, lymphocyte, and platelet (HALP) score with dyslipidemia. The aim of this study was to investigate the clinical utility of the HALP score in light of dyslipidemia. Materials and Methods: A retrospective analysis of 7192 subjects was initiated to assess the association between the HALP score and disturbed lipid markers. Medians were compared by Mann-Whitney U or Kruskal-Wallis tests and the diagnostic performance and risk assessment were calculated. Results: Median HALP score among all subjects was 53.3, with varying values between males and females. Notably, median HALP was significantly elevated in all forms of dyslipidemia and among males and females irrespective of age. The odds of having elevated HALP score values were significantly higher in all lipid abnormalities. Moreover, HALP score was significantly yet weakly correlated with lipid markers, while the highest diagnostic accuracy of the HALP score was observed with an elevated ratio of total cholesterol to high-density lipoprotein (TC/HDL) (area under the curve, AUC = 0.6411, p < 0.0001). The decision curve analysis (DCA) showed that the HALP score can reliably predict the presence of dyslipidemia. Conclusions: This study demonstrates that the HALP score is a novel, cost-effective index that is associated with a disturbed lipid profile. Further investigation of the nature of this association is needed.
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Affiliation(s)
- Yazeed Alshuweishi
- Chair of Medical and Molecular Genetics Research, Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi Arabia; (Y.A.)
| | - Ahmed M. Basudan
- Chair of Medical and Molecular Genetics Research, Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi Arabia; (Y.A.)
| | - Mohammed Alfaifi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
| | - Hussam Daghistani
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Department of Clinical Pathology, Al Borg Diagnostics, Jeddah 23437, Saudi Arabia
| | - Mohammad A. Alfhili
- Chair of Medical and Molecular Genetics Research, Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi Arabia; (Y.A.)
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Kim H, Hong J, Ahn S, Lee W, Chun S, Min W. Association between measured or calculated small dense low-density lipoprotein cholesterol and oxidized low-density lipoprotein in subjects with or without type 2 diabetes mellitus. J Clin Lab Anal 2022; 37:e24807. [PMID: 36525335 PMCID: PMC9833976 DOI: 10.1002/jcla.24807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/31/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Small dense low-density lipoprotein (sdLDL) possesses atherogenic potential and is predicted to be susceptible to atherogenic modifications, which further increases its atherogenicity. However, studies on the association between measured or estimated sdLDL cholesterol (sdLDL-C) levels and atherogenic modification in diverse population groups are lacking. METHODS Surplus serum samples were collected from male subjects with type 2 diabetes mellitus (DM) under treatment (n = 300) and without DM (non-DM; n = 150). sdLDL and oxidized LDL (oxLDL) levels were measured using the Lipoprint LDL subfractions kit (Quantimetrix Corporation) and the Mercodia oxidized LDL competitive enzyme-linked immunosorbent assay kit (Mercodia), respectively. The estimated sdLDL-Cs were calculated from two relevant equations. The effects of sdLDL-C on oxLDL were assessed using multiple linear regression (MLR) models. RESULTS The mean (±SD) of measured sdLDL-C and oxLDL concentrations were 11.8 ± 10.0 mg/dl and 53.4 ± 14.2 U/L in the non-DM group and 0.20 ± 0.81 mg/dl and 46.0 ± 15.3 U/L in the DM group, respectively. The effects of measured sdLDL-Cs were significant (p = 0.031), whereas those of estimated sdLDL-Cs were not (p = 0.060, p = 0.116) in the non-DM group in the MLR models. The effects of sdLDL-Cs in the DM group were not significant. CONCLUSION In the general population, high level of sdLDL-C appeared to be associated with high level of oxLDL. The equation for estimating sdLDL-C developed from a general population should be applied with caution to a special population, such as patients with DM on treatment.
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Affiliation(s)
- Hyun‐Ki Kim
- Department of Laboratory MedicineUniversity of Ulsan College of Medicine, Ulsan University HospitalUlsanKorea
| | - Jinyoung Hong
- Department of Laboratory MedicineUniversity of Ulsan College of Medicine and Asan Medical CenterSeoulKorea
| | - Sunyoung Ahn
- Department of Laboratory MedicineDong In Medical CenterGangneungKorea
| | - Woochang Lee
- Department of Laboratory MedicineUniversity of Ulsan College of Medicine and Asan Medical CenterSeoulKorea
| | - Sail Chun
- Department of Laboratory MedicineUniversity of Ulsan College of Medicine and Asan Medical CenterSeoulKorea
| | - Won‐Ki Min
- Department of Laboratory MedicineUniversity of Ulsan College of Medicine and Asan Medical CenterSeoulKorea
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Tapio J, Vähänikkilä H, Kesäniemi YA, Ukkola O, Koivunen P. Higher hemoglobin levels are an independent risk factor for adverse metabolism and higher mortality in a 20-year follow-up. Sci Rep 2021; 11:19936. [PMID: 34620927 PMCID: PMC8497471 DOI: 10.1038/s41598-021-99217-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 09/21/2021] [Indexed: 12/22/2022] Open
Abstract
The aim of this study was to cross-sectionally and longitudinally examine whether higher hemoglobin (Hb) levels within the normal variation associate with key components of metabolic syndrome and total and cardiovascular mortality. The study included 967 Finnish subjects (age 40-59 years) followed for ≥ 20 years. The focus was on Hb levels, cardiovascular diseases (CVDs) and mortality rates. Higher Hb levels associated positively with key anthropometric and metabolic parameters at baseline. At the follow-up similar associations were seen in men. The highest Hb quartile showed higher leptin levels and lower adiponectin levels at baseline and follow-up (p < 0.05) and lower plasma ghrelin levels at baseline (p < 0.05). Higher baseline Hb levels associated independently with prevalence of type 2 diabetes at follow-up (p < 0.01). The highest Hb quartile associated with higher serum alanine aminotransferase levels (p < 0.001) and independently with increased risk for liver fat accumulation (OR 1.63 [1.03; 2.57]) at baseline. The highest Hb quartile showed increased risk for total (HR = 1.48 [1.01; 2.16]) and CVD-related mortality (HR = 2.08 [1.01; 4.29]). Higher Hb levels associated with an adverse metabolic profile, increased prevalence of key components of metabolic syndrome and higher risk for CVD-related and total mortality.
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Affiliation(s)
- Joona Tapio
- Biocenter Oulu and Faculty of Biochemistry and Molecular Medicine, Oulu Center for Cell-Matrix Research, University of Oulu, P.O. Box 5400, 90014, Oulu, Finland
| | - Hannu Vähänikkilä
- Northern Finland Birth Cohorts, Arctic Biobank, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, 90014, Oulu, Finland
| | - Y Antero Kesäniemi
- Medical Research Center Oulu, Faculty of Medicine, Oulu University Hospital and Research Unit of Internal Medicine, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
| | - Olavi Ukkola
- Medical Research Center Oulu, Faculty of Medicine, Oulu University Hospital and Research Unit of Internal Medicine, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland.
| | - Peppi Koivunen
- Biocenter Oulu and Faculty of Biochemistry and Molecular Medicine, Oulu Center for Cell-Matrix Research, University of Oulu, P.O. Box 5400, 90014, Oulu, Finland.
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Auvinen J, Tapio J, Karhunen V, Kettunen J, Serpi R, Dimova EY, Gill D, Soininen P, Tammelin T, Mykkänen J, Puukka K, Kähönen M, Raitoharju E, Lehtimäki T, Ala-Korpela M, Raitakari OT, Keinänen-Kiukaanniemi S, Järvelin MR, Koivunen P. Systematic evaluation of the association between hemoglobin levels and metabolic profile implicates beneficial effects of hypoxia. SCIENCE ADVANCES 2021; 7:7/29/eabi4822. [PMID: 34261659 PMCID: PMC8279517 DOI: 10.1126/sciadv.abi4822] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 06/01/2021] [Indexed: 05/22/2023]
Abstract
Activation of the hypoxia-inducible factor (HIF) pathway reprograms energy metabolism. Hemoglobin (Hb) is the main carrier of oxygen. Using its normal variation as a surrogate measure for hypoxia, we explored whether lower Hb levels could lead to healthier metabolic profiles in mice and humans (n = 7175) and used Mendelian randomization (MR) to evaluate potential causality (n = 173,480). The results showed evidence for lower Hb levels being associated with lower body mass index, better glucose tolerance and other metabolic profiles, lower inflammatory load, and blood pressure. Expression of the key HIF target genes SLC2A4 and Slc2a1 in skeletal muscle and adipose tissue, respectively, associated with systolic blood pressure in MR analyses and body weight, liver weight, and adiposity in mice. Last, manipulation of murine Hb levels mediated changes to key metabolic parameters. In conclusion, low-end normal Hb levels may be favorable for metabolic health involving mild chronic activation of the HIF response.
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Affiliation(s)
- Juha Auvinen
- Faculty of Medicine, Center for Life Course Health Research, University of Oulu, 90014 Oulu, Finland
- Medical Research Center, Oulu University Hospital and University of Oulu, 90220 Oulu, Finland
| | - Joona Tapio
- Biocenter Oulu, 90014 Oulu, Finland
- Faculty of Biochemistry and Molecular Medicine, Oulu Center for Cell-Matrix Research, University of Oulu, 90014 Oulu, Finland
| | - Ville Karhunen
- Faculty of Medicine, Center for Life Course Health Research, University of Oulu, 90014 Oulu, Finland
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, W2 1PG London, UK
- Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
| | - Johannes Kettunen
- Faculty of Medicine, Center for Life Course Health Research, University of Oulu, 90014 Oulu, Finland
- Biocenter Oulu, 90014 Oulu, Finland
- Computational Medicine, Faculty of Medicine, University of Oulu, 90014 Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, 70210 Kuopio, Finland
| | - Raisa Serpi
- Biocenter Oulu, 90014 Oulu, Finland
- Faculty of Biochemistry and Molecular Medicine, Oulu Center for Cell-Matrix Research, University of Oulu, 90014 Oulu, Finland
| | - Elitsa Y Dimova
- Biocenter Oulu, 90014 Oulu, Finland
- Faculty of Biochemistry and Molecular Medicine, Oulu Center for Cell-Matrix Research, University of Oulu, 90014 Oulu, Finland
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, W2 1PG London, UK
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George's, University of London, London, UK
| | - Pasi Soininen
- Computational Medicine, Faculty of Medicine, University of Oulu, 90014 Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, 70210 Kuopio, Finland
| | - Tuija Tammelin
- LIKES Research Center for Physical Activity and Health, 40700 Jyväskylä, Finland
| | - Juha Mykkänen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Katri Puukka
- NordLab Oulu, Medical Research Center Oulu, Oulu University Hospital and Department of Clinical Chemistry, University of Oulu, 90014 Oulu, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital and Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Emma Raitoharju
- Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Health Technology, Finnish Cardiovascular Research Center - Tampere, Tampere University, Tampere, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Health Technology, Finnish Cardiovascular Research Center - Tampere, Tampere University, Tampere, Finland
| | - Mika Ala-Korpela
- Faculty of Medicine, Center for Life Course Health Research, University of Oulu, 90014 Oulu, Finland
- Biocenter Oulu, 90014 Oulu, Finland
- Computational Medicine, Faculty of Medicine, University of Oulu, 90014 Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, 70210 Kuopio, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, 20520 Turku, Finland
| | - Sirkka Keinänen-Kiukaanniemi
- Faculty of Medicine, Center for Life Course Health Research, University of Oulu, 90014 Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, 90220 Oulu, Finland
| | - Marjo-Riitta Järvelin
- Faculty of Medicine, Center for Life Course Health Research, University of Oulu, 90014 Oulu, Finland.
- Biocenter Oulu, 90014 Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, 90220 Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK
| | - Peppi Koivunen
- Biocenter Oulu, 90014 Oulu, Finland.
- Faculty of Biochemistry and Molecular Medicine, Oulu Center for Cell-Matrix Research, University of Oulu, 90014 Oulu, Finland
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