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Wang T, Beyene HB, Yi C, Cinel M, Mellett NA, Olshansky G, Meikle TG, Wu J, Dakic A, Watts GF, Hung J, Hui J, Beilby J, Blangero J, Kaddurah-Daouk R, Salim A, Moses EK, Shaw JE, Magliano DJ, Huynh K, Giles C, Meikle PJ. A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age. EBioMedicine 2024; 105:105199. [PMID: 38905750 PMCID: PMC11246009 DOI: 10.1016/j.ebiom.2024.105199] [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: 01/02/2024] [Revised: 05/30/2024] [Accepted: 05/30/2024] [Indexed: 06/23/2024] Open
Abstract
BACKGROUND Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health. METHODS Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status. FINDINGS Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have cardiovascular disease (OR = 2.13, 95% CI = 1.62-2.83), had a 2.01-fold increased risk of 12-year incident cardiovascular events (HR = 2.01, 95% CI = 1.45-2.57), and a 1.56-fold increased risk of 17-year all-cause mortality (HR = 1.56, 95% CI = 1.34-1.79), relative to the individuals in the bottom quintile group (Q1). Survival analysis further revealed that men in the Q5 group faced the challenge of reaching a median survival rate due to cardiovascular events more than six years earlier and reaching a median survival rate due to all-cause mortality more than four years earlier than men in the Q1 group. INTERPRETATION Our findings demonstrate that the mAge score captures age-related metabolic changes, predicts health outcomes, and has the potential to identify individuals at increased risk of metabolic diseases. FUNDING The specific funding of this article is provided in the acknowledgements section.
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Affiliation(s)
- Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia
| | - Habtamu B Beyene
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Changyu Yi
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Michelle Cinel
- Baker Heart and Diabetes Institute, Melbourne, Australia
| | | | | | - Thomas G Meikle
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Jingqin Wu
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | | | - Gerald F Watts
- School of Medicine, University of Western Australia, Perth, Australia; Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, Australia
| | - Joseph Hung
- School of Medicine, University of Western Australia, Perth, Australia
| | - Jennie Hui
- PathWest Laboratory Medicine of Western Australia, Nedlands, Western Australia, Australia; School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia; School of Biomedical Sciences, University of Western Australia, Australia
| | - John Beilby
- PathWest Laboratory Medicine of Western Australia, Nedlands, Western Australia, Australia; School of Biomedical Sciences, University of Western Australia, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioural Sciences, Duke University, Durham, NC, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, USA; Department of Medicine, Duke University, Durham, NC, USA
| | - Agus Salim
- Baker Heart and Diabetes Institute, Melbourne, Australia; Melbourne School of Population and Global Health School of Mathematics and Statistics, The University of Melbourne, Australia
| | - Eric K Moses
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | | | | | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia.
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Woolman M, Kiyota T, Belgadi SA, Fujita N, Fiorante A, Ramaswamy V, Daniels C, Rutka JT, McIntosh C, Munoz DG, Ginsberg HJ, Aman A, Zarrine-Afsar A. Lipidomic-Based Approach to 10 s Classification of Major Pediatric Brain Cancer Types with Picosecond Infrared Laser Mass Spectrometry. Anal Chem 2024; 96:1019-1028. [PMID: 38190738 PMCID: PMC10809247 DOI: 10.1021/acs.analchem.3c03156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024]
Abstract
Picosecond infrared laser mass spectrometry (PIRL-MS) is shown, through a retrospective patient tissue study, to differentiate medulloblastoma cancers from pilocytic astrocytoma and two molecular subtypes of ependymoma (PF-EPN-A, ST-EPN-RELA) using laser-extracted lipids profiled with PIRL-MS in 10 s of sampling and analysis time. The average sensitivity and specificity values for this classification, taking genomic profiling data as standard, were 96.41 and 99.54%, and this classification used many molecular features resolvable in 10 s PIRL-MS spectra. Data analysis and liquid chromatography coupled with tandem high-resolution mass spectrometry (LC-MS/MS) further allowed us to reduce the molecular feature list to only 18 metabolic lipid markers most strongly involved in this classification. The identified 'metabolite array' was comprised of a variety of phosphatidic and fatty acids, ceramides, and phosphatidylcholine/ethanolamine and could mediate the above-mentioned classification with average sensitivity and specificity values of 94.39 and 98.78%, respectively, at a 95% confidence in prediction probability threshold. Therefore, a rapid and accurate pathology classification of select pediatric brain cancer types from 10 s PIRL-MS analysis using known metabolic biomarkers can now be available to the neurosurgeon. Based on retrospective mining of 'survival' versus 'extent-of-resection' data, we further identified pediatric cancer types that may benefit from actionable 10 s PIRL-MS pathology feedback. In such cases, aggressiveness of the surgical resection can be optimized in a manner that is expected to benefit the patient's overall or progression-free survival. PIRL-MS is a promising tool to drive such personalized decision-making in the operating theater.
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Affiliation(s)
- Michael Woolman
- Princess
Margaret Cancer Centre, University Health
Network, 101 College
Street, Toronto, Ontario M5G 1L7, Canada
- Department
of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Taira Kiyota
- Ontario
Institute for Cancer Research (OICR), 661 University Ave Suite 510, Toronto, Ontario M5G 0A3, Canada
| | - Siham A. Belgadi
- Department
of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Naohide Fujita
- Arthur
and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, 686 Bay Street, Toronto, Ontario M5G 0A4, Canada
| | - Alexa Fiorante
- Princess
Margaret Cancer Centre, University Health
Network, 101 College
Street, Toronto, Ontario M5G 1L7, Canada
- Department
of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Vijay Ramaswamy
- Department
of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada
- Arthur
and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, 686 Bay Street, Toronto, Ontario M5G 0A4, Canada
| | - Craig Daniels
- Arthur
and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, 686 Bay Street, Toronto, Ontario M5G 0A4, Canada
| | - James T. Rutka
- Arthur
and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, 686 Bay Street, Toronto, Ontario M5G 0A4, Canada
- Department
of Surgery, University of Toronto, 149 College Street, Toronto, Ontario M5T 1P5, Canada
| | - Chris McIntosh
- Toronto
General Hospital Research Institute, University Health Network, 200 Elizabeth Street, Toronto, Ontario M5G-2C4, Canada
| | - David G. Munoz
- Keenan
Research Center for Biomedical Science & the Li Ka Shing Knowledge
Institute, St. Michael’s Hospital, 30 Bond Street, Toronto, Ontario M5B 1W8, Canada
- Department
of Laboratory Medicine and Pathobiology, University of Toronto, 1 King’s College Circle, Sixth Floor, Toronto,Ontario M5S 1A8, Canada
| | - Howard J. Ginsberg
- Department
of Surgery, University of Toronto, 149 College Street, Toronto, Ontario M5T 1P5, Canada
- Keenan
Research Center for Biomedical Science & the Li Ka Shing Knowledge
Institute, St. Michael’s Hospital, 30 Bond Street, Toronto, Ontario M5B 1W8, Canada
- Department
of Laboratory Medicine and Pathobiology, University of Toronto, 1 King’s College Circle, Sixth Floor, Toronto,Ontario M5S 1A8, Canada
| | - Ahmed Aman
- Ontario
Institute for Cancer Research (OICR), 661 University Ave Suite 510, Toronto, Ontario M5G 0A3, Canada
- Leslie
Dan, Faculty of Pharmacy, University of
Toronto, 144 College
Street, Toronto, Ontario M5S 3M2, Canada
| | - Arash Zarrine-Afsar
- Princess
Margaret Cancer Centre, University Health
Network, 101 College
Street, Toronto, Ontario M5G 1L7, Canada
- Department
of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario M5G 1L7, Canada
- Department
of Surgery, University of Toronto, 149 College Street, Toronto, Ontario M5T 1P5, Canada
- Keenan
Research Center for Biomedical Science & the Li Ka Shing Knowledge
Institute, St. Michael’s Hospital, 30 Bond Street, Toronto, Ontario M5B 1W8, Canada
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3
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He M, Hu S, Wang J, Wang J, Găman MA, Hariri Z, Tian Y. Effect of resistance training on lipid profile in postmenopausal women: A systematic review and meta-analysis of randomized controlled trials. Eur J Obstet Gynecol Reprod Biol 2023; 288:18-28. [PMID: 37421743 DOI: 10.1016/j.ejogrb.2023.06.023] [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: 02/21/2023] [Revised: 04/10/2023] [Accepted: 06/23/2023] [Indexed: 07/10/2023]
Abstract
OBJECTIVE Physical exercise decreases cardiovascular risk and can alter the lipid profile in postmenopausal women. Although it is believed that resistance training can potentially decrease serum lipid levels in postmenopausal females, the evidence remains inconclusive. The aim of this systematic review and meta-analysis of randomized controlled trials (RCTs) was to clarify the impact of resistance training on the lipid profile in postmenopausal women. METHODS Web of Science, Scopus, PubMed/Medline and Embase were searched. RCTs that evaluated the effect of resistance training on total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglyceride (TG) levels were included in this review. Effect size was estimated using the random effects model. Subgroup analyses based on age, duration of intervention, pre-enrolment serum lipid levels and body mass index were performed. RESULTS Data pooled from 19 RCTs revealed that resistance training can reduce TC [weighted mean difference (WMD) -11.47 mg/dl; p = 0.002], LDL-C (WMD -8.48 mg/dl; p = 0.01) and TG (WMD -6.61 mg/dl; p = 0.043) levels. TC levels decreased particularly in subjects aged < 60 years (WMD -10.77 mg/dl; p = 0.003), in RCTs lasting < 16 weeks (WMD -15.70 mg/dl; p = 0.048), and in subjects with hypercholesterolaemia (WMD -12.36 mg/dl; p = 0.001) or obesity (WMD -19.35 mg/dl; p = 0.006) before RCT enrolment. There was a significant decrease in LDL-C (WMD -14.38 mg/dl; p = 0.002) levels in patients with LDL-C ≥ 130 mg/dl before trial enrolment. Resistance training reduced HDL-C (WMD -2.97 mg/dl; p = 0.01) levels particularly in subjects with obesity. TG (WMD -10.71 mg/dl; p = 0.01) levels decreased particularly when the intervention lasted < 16 weeks. CONCLUSION Resistance training can decrease TC, LDL-C and TG levels in postmenopausal females. The impact of resistance training on HDL-C levels was small, and was only observed in individuals with obesity. The effect of resistance training on the lipid profile was more notable in short-term interventions and in postmenopausal women with dyslipidaemia or obesity before trial enrolment.
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Affiliation(s)
- Min He
- Departments of Ultrasound, West China Second University Hospital, Sichuan University/Key Laboratory of Obstetrics & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu, China
| | - Sha Hu
- Departments of Ultrasound, West China Second University Hospital, Sichuan University/Key Laboratory of Obstetrics & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu, China
| | - Jin Wang
- Departments of Ultrasound, West China Second University Hospital, Sichuan University/Key Laboratory of Obstetrics & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu, China
| | - Jing Wang
- Departments of Ultrasound, West China Second University Hospital, Sichuan University/Key Laboratory of Obstetrics & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu, China
| | - Mihnea-Alexandru Găman
- Faculty of Medicine, 'Carol Davila' University of Medicine and Pharmacy, Bucharest, Romania; Department of Haematology, Centre of Haematology and Bone Marrow Transplantation, Fundeni Clinical Institute, Bucharest, Romania
| | - Zahra Hariri
- Department of Clinical Nutrition and Dietetics, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Yu Tian
- Departments of Ultrasound, West China Second University Hospital, Sichuan University/Key Laboratory of Obstetrics & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu, China.
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Zhang W, Tian Z, Qi X, Chen P, Yang Q, Guan Q, Ye J, Yu C. Switching from high-fat diet to normal diet ameliorate BTB integrity and improve fertility potential in obese male mice. Sci Rep 2023; 13:14152. [PMID: 37644200 PMCID: PMC10465505 DOI: 10.1038/s41598-023-41291-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/24/2023] [Indexed: 08/31/2023] Open
Abstract
Obesity is a prominent risk factor for male infertility, and a high-fat diet is an important cause of obesity. Therefore, diet control can reduce body weight and regulate blood glucose and lipids, but it remains unclear whether it can improve male fertility and its mechanism. This study explores the effects of switching from a high-fat diet (HFD) to a normal diet (ND) on the fertility potential of obese male mice and its related mechanisms. In our study, male mice were separated into three groups: normal diet group (NN), continuous high-fat diet group (HH), and return to normal diet group (HN). The reproductive potential of mice was tested through cohabitation. Enzymatic methods and ELISA assays were used to measure metabolic indicators, follicle-stimulating hormone (FSH) levels and intratesticular testosterone levels. Transmission electron microscopy and immunofluorescence with biotin tracers assessed the integrity of the blood-testis barrier (BTB). Malondialdehyde (MDA), superoxide dismutase (SOD), and reactive oxygen species (ROS) were inspected for the assessment of oxidative stress. The expression and localization of BTB-related proteins were detected through the immunoblot and immunofluorescence. The mice in the high-fat diet group indicated increased body weight and epididymal fat weight, elevated serum TC, HDL, LDL, and glucose, decreased serum FSH, and dramatic lipid deposition in the testicular interstitium. Analysis of fertility potential revealed that the fertility rate of female mice and the number of pups per litter in the HH group were significantly reduced. After the fat intake was controlled by switching to a normal diet, body weight and epididymal fat weight were significantly reduced, serum glucose and lipid levels were lowered, serum FSH level was elevated and the deposition of interstitial lipids in the testicles was also decreased. Most significantly, the number of offspring of male mice returning to a normal diet was significantly increased. Following further mechanistic analysis, the mice in the sustained high-fat diet group had disrupted testicular BTB integrity, elevated levels of oxidative stress, and abnormal expression of BTB-related proteins, whereas the restoration of the normal diet significantly ameliorated the above indicators in the mice. Our study confirms diet control by switching from a high-fat diet to a normal diet can effectively reduce body weight, ameliorate testicular lipotoxicity and BTB integrity in male mice, and improve fertility potential, providing an effective treatment option for obese male infertility.
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Affiliation(s)
- Wenjing Zhang
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging (Shandong First Medical University), Ministry of Education, Shandong, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, 250021, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China
| | - Zhenhua Tian
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging (Shandong First Medical University), Ministry of Education, Shandong, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, 250021, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China
| | - Xiangyu Qi
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging (Shandong First Medical University), Ministry of Education, Shandong, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, 250021, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China
| | - Pengcheng Chen
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging (Shandong First Medical University), Ministry of Education, Shandong, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, 250021, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China
| | - Qian Yang
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging (Shandong First Medical University), Ministry of Education, Shandong, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, 250021, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China
| | - Qingbo Guan
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging (Shandong First Medical University), Ministry of Education, Shandong, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, 250021, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China
| | - Jifeng Ye
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China.
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging (Shandong First Medical University), Ministry of Education, Shandong, China.
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, 250021, Shandong, China.
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China.
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China.
- Department of Endocrinology and Metabolism, The Second People's Hospital of Liaocheng, Shandong, 252601, China.
| | - Chunxiao Yu
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China.
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging (Shandong First Medical University), Ministry of Education, Shandong, China.
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, 250021, Shandong, China.
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China.
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, 250021, Shandong, China.
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Brain N-Glycosylation and Lipidomic Profile Changes Induced by a High-Fat Diet in Dyslipidemic Hamsters. Int J Mol Sci 2023; 24:ijms24032883. [PMID: 36769208 PMCID: PMC9918045 DOI: 10.3390/ijms24032883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/25/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
The consumption of diets rich in saturated fats is known to be associated with higher mortality. The adoption of healthy habits, for instance adhering to a Mediterranean diet, has proved to exert a preventive effect towards cardiovascular diseases and dyslipidemia. Little is known about how a suboptimal diet can affect brain function, structure, and the mechanisms involved. The aims of this study were to examine how a high-fat diet can alter the brain N-glycan and lipid profile in male Golden Syrian hamsters and to evaluate the potential of a Mediterranean-like diet to reverse this situation. During twelve weeks, hamsters were fed a normal fat diet (CTRL group), a high-fat diet (HFD group), and a high-fat diet followed by a Mediterranean-like diet (MED group). Out of seventy-two identified N-glycans, fourteen were significant (p < 0.05) between HFD and CTRL groups, nine between MED and CTRL groups, and one between MED and HFD groups. Moreover, forty-nine lipids were altered between HFD and CTRL groups, seven between MED and CTRL groups, and five between MED and HFD groups. Our results suggest that brain N-glycan composition in high-fat diet-fed hamsters can produce events comparable to those found in some neurodegenerative diseases, and may promote brain ageing.
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Katz L, Tata A, Woolman M, Zarrine-Afsar A. Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns. Metabolites 2021; 11:metabo11100660. [PMID: 34677375 PMCID: PMC8537725 DOI: 10.3390/metabo11100660] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/18/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023] Open
Abstract
Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.
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Affiliation(s)
- Lauren Katz
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7, Canada; (L.K.); (M.W.)
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada
| | - Alessandra Tata
- Laboratorio di Chimica Sperimentale, Istituto Zooprofilattico delle Venezie, Viale Fiume 78, 36100 Vicenza, Italy;
| | - Michael Woolman
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7, Canada; (L.K.); (M.W.)
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada
| | - Arash Zarrine-Afsar
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7, Canada; (L.K.); (M.W.)
- Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON M5G 1P5, Canada
- Department of Surgery, University of Toronto, 149 College Street, Toronto, ON M5T 1P5, Canada
- Keenan Research Center for Biomedical Science & the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 30 Bond Street, Toronto, ON M5B 1W8, Canada
- Correspondence: ; Tel.: +1-416-581-8473
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