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Goutman SA, Boss J, Iyer G, Habra H, Savelieff MG, Karnovsky A, Mukherjee B, Feldman EL. Body mass index associates with amyotrophic lateral sclerosis survival and metabolomic profiles. Muscle Nerve 2023; 67:208-216. [PMID: 36321729 PMCID: PMC9957813 DOI: 10.1002/mus.27744] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 10/25/2022] [Accepted: 10/29/2022] [Indexed: 11/07/2022]
Abstract
INTRODUCTION/AIMS Body mass index (BMI) is linked to amyotrophic lateral sclerosis (ALS) risk and prognosis, but additional research is needed. The aim of this study was to identify whether and when historical changes in BMI occurred in ALS participants, how these longer term trajectories associated with survival, and whether metabolomic profiles provided insight into potential mechanisms. METHODS ALS and control participants self-reported body height and weight 10 (reference) and 5 years earlier, and at study entry (diagnosis for ALS participants). Generalized estimating equations evaluated differences in BMI trajectories between cases and controls. ALS survival was evaluated by BMI trajectory group using accelerated failure time models. BMI trajectories and survival associations were explored using published metabolomic profiling and correlation networks. RESULTS Ten-year BMI trends differed between ALS and controls, with BMI loss in the 5 years before diagnosis despite BMI gains 10 to 5 years beforehand in both groups. An overall 10-year drop in BMI associated with a 27.1% decrease in ALS survival (P = .010). Metabolomic networks in ALS participants showed dysregulation in sphingomyelin, bile acid, and plasmalogen subpathways. DISCUSSION ALS participants lost weight in the 5-year period before enrollment. BMI trajectories had three distinct groups and the group with significant weight loss in the past 10 years had the worst survival. Participants with a high BMI and increase in weight in the 10 years before symptom onset also had shorter survival. Certain metabolomics profiles were associated with the BMI trajectories. Replicating these findings in prospective cohorts is warranted.
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Affiliation(s)
- Stephen A Goutman
- Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, Michigan, USA
| | - Jonathan Boss
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Gayatri Iyer
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Hani Habra
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Masha G Savelieff
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, Michigan, USA
| | - Alla Karnovsky
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Eva L Feldman
- Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, Michigan, USA
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Goutman SA, Guo K, Savelieff MG, Patterson A, Sakowski SA, Habra H, Karnovsky A, Hur J, Feldman EL. Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts. Brain 2022; 145:4425-4439. [PMID: 35088843 PMCID: PMC9762943 DOI: 10.1093/brain/awac025] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/22/2021] [Accepted: 01/05/2022] [Indexed: 11/12/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease lacking effective treatments. This is due, in part, to a complex and incompletely understood pathophysiology. To shed light, we conducted untargeted metabolomics on plasma from two independent cross-sectional ALS cohorts versus control participants to identify recurrent dysregulated metabolic pathways. Untargeted metabolomics was performed on plasma from two ALS cohorts (cohort 1, n = 125; cohort 2, n = 225) and healthy controls (cohort 1, n = 71; cohort 2, n = 104). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon, adjusted logistic regression and partial least squares-discriminant analysis, while group lasso explored sub-pathway level differences. Adjustment parameters included age, sex and body mass index. Metabolomics pathway enrichment analysis was performed on metabolites selected using the above methods. Additionally, we conducted a sex sensitivity analysis due to sex imbalance in the cohort 2 control arm. Finally, a data-driven approach, differential network enrichment analysis (DNEA), was performed on a combined dataset to further identify important ALS metabolic pathways. Cohort 2 ALS participants were slightly older than the controls (64.0 versus 62.0 years, P = 0.009). Cohort 2 controls were over-represented in females (68%, P < 0.001). The most concordant cohort 1 and 2 pathways centred heavily on lipid sub-pathways, including complex and signalling lipid species and metabolic intermediates. There were differences in sub-pathways that were enriched in ALS females versus males, including in lipid sub-pathways. Finally, DNEA of the merged metabolite dataset of both ALS and control cohorts identified nine significant subnetworks; three centred on lipids and two encompassed a range of sub-pathways. In our analysis, we saw consistent and important shared metabolic sub-pathways in both ALS cohorts, particularly in lipids, further supporting their importance as ALS pathomechanisms and therapeutics targets.
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Affiliation(s)
- Stephen A Goutman
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI, USA
| | - Kai Guo
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI, USA
| | - Masha G Savelieff
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI, USA
| | - Adam Patterson
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI, USA
| | - Stacey A Sakowski
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI, USA
| | - Hani Habra
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Alla Karnovsky
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota, Grand Forks, ND, USA
| | - Eva L Feldman
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI, USA
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Habra H, Kachman M, Padmanabhan V, Burant C, Karnovsky A, Meijer J. Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples. J Proteome Res 2022; 21:2936-2946. [PMID: 36367990 DOI: 10.1021/acs.jproteome.2c00371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.
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Affiliation(s)
- Hani Habra
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Maureen Kachman
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, Michigan 48105, United States
| | - Vasantha Padmanabhan
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, United States
- Department of Obstetrics & Gynecology, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Charles Burant
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, Michigan 48105, United States
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Jennifer Meijer
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
- Department of Medicine, Geisel School of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire 03756, United States
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LaBarre JL, Hirschfeld E, Soni T, Kachman M, Wigginton J, Duren W, Fleischman JY, Karnovsky A, Burant CF, Lee JM. Comparing the Fasting and Random-Fed Metabolome Response to an Oral Glucose Tolerance Test in Children and Adolescents: Implications of Sex, Obesity, and Insulin Resistance. Nutrients 2021; 13:nu13103365. [PMID: 34684365 PMCID: PMC8538092 DOI: 10.3390/nu13103365] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/12/2021] [Accepted: 09/23/2021] [Indexed: 11/25/2022] Open
Abstract
As the incidence of obesity and type 2 diabetes (T2D) is occurring at a younger age, studying adolescent nutrient metabolism can provide insights on the development of T2D. Metabolic challenges, including an oral glucose tolerance test (OGTT) can assess the effects of perturbations in nutrient metabolism. Here, we present alterations in the global metabolome in response to an OGTT, classifying the influence of obesity and insulin resistance (IR) in adolescents that arrived at the clinic fasted and in a random-fed state. Participants were recruited as lean (n = 55, aged 8–17 years, BMI percentile 5–85%) and overweight and obese (OVOB, n = 228, aged 8–17 years, BMI percentile ≥ 85%). Untargeted metabolomics profiled 246 annotated metabolites in plasma at t0 and t60 min during the OGTT. Our results suggest that obesity and IR influence the switch from fatty acid (FA) to glucose oxidation in response to the OGTT. Obesity was associated with a blunted decline of acylcarnitines and fatty acid oxidation intermediates. In females, metabolites from the Fasted and Random-Fed OGTT were associated with HOMA-IR, including diacylglycerols, leucine/isoleucine, acylcarnitines, and phosphocholines. Our results indicate that at an early age, obesity and IR may influence the metabolome dynamics in response to a glucose challenge.
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Affiliation(s)
- Jennifer L. LaBarre
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA;
- Department of Medicine, Dartmouth-Hitchcock Medical Center, Weight and Wellness Center, Lebanon, NH 03766, USA
- Correspondence: (J.L.L.); (J.M.L.)
| | - Emily Hirschfeld
- Susan B Meister Child Health Evaluation and Research Center, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Tanu Soni
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, MI 48109, USA; (T.S.); (M.K.); (J.W.); (W.D.)
| | - Maureen Kachman
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, MI 48109, USA; (T.S.); (M.K.); (J.W.); (W.D.)
| | - Janis Wigginton
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, MI 48109, USA; (T.S.); (M.K.); (J.W.); (W.D.)
| | - William Duren
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, MI 48109, USA; (T.S.); (M.K.); (J.W.); (W.D.)
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA;
| | - Johanna Y. Fleischman
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA;
| | - Charles F. Burant
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA;
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Joyce M. Lee
- Susan B Meister Child Health Evaluation and Research Center, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA;
- Division of Pediatric Endocrinology, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
- Correspondence: (J.L.L.); (J.M.L.)
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LaBarre JL, Singer K, Burant CF. Advantages of Studying the Metabolome in Response to Mixed-Macronutrient Challenges and Suggestions for Future Research Designs. J Nutr 2021; 151:2868-2881. [PMID: 34255076 PMCID: PMC8681069 DOI: 10.1093/jn/nxab223] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/26/2021] [Accepted: 06/15/2021] [Indexed: 12/22/2022] Open
Abstract
Evaluating the postprandial response to a dietary challenge containing all macronutrients-carbohydrates, lipids, and protein-may provide stronger insights of metabolic health than a fasted measurement. Metabolomic profiling deepens the understanding of the homeostatic and adaptive response to a dietary challenge by classifying multiple metabolic pathways and biomarkers. A total of 26 articles were identified that measure the human blood metabolome or lipidome response to a mixed-macronutrient challenge. Most studies were cross-sectional, exploring the baseline and postprandial response to the dietary challenge. Large variations in study designs were reported, including the macronutrient and caloric composition of the challenge and the delivery of the challenge as a liquid shake or a solid meal. Most studies utilized a targeted metabolomics platform, assessing only a particular metabolic pathway, however, several studies utilized global metabolomics and lipidomics assays demonstrating the expansive postprandial response of the metabolome. The postprandial response of individual amino acids was largely dependent on the amino acid composition of the test meal, with the exception of alanine and proline, 2 nonessential amino acids. Long-chain fatty acids and unsaturated long-chain acylcarnitines rapidly decreased in response to the dietary challenges, representing the switch from fat to carbohydrate oxidation. Studies were reviewed that assessed the metabolome response in the context of obesity and metabolic diseases, providing insight on how weight status and disease influence the ability to cope with a nutrient load and return to homeostasis. Results demonstrate that the flexibility to respond to a substrate load is influenced by obesity and metabolic disease and flexibility alterations will be evident in downstream metabolites of fat, carbohydrate, and protein metabolism. In response, we propose suggestions for standardization between studies with the potential of creating a study exploring the postprandial response to a multitude of challenges with a variety of macronutrients.
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Affiliation(s)
| | - Kanakadurga Singer
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
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