Serum Lipidomic Analysis Reveals Biomarkers and Metabolic Pathways of Thyroid Dysfunction

Hypothyroidism, lipids, and lipidomics

PURPOSE

Hypothyroidism is a relatively common endocrine disorder and is well documented to be associated with lipid abnormalities.

METHODS

A narrative review was conducted of studies describing the alterations in the lipid profile accompanying both subclinical and overt hypothyroidism.

RESULTS

Lipid abnormalities are seen with TSH values in the upper end of the accepted reference range, as well as with subclinical and overt hypothyroidism. The degree of lipid derangement is generally proportional to the degree of TSH elevation. Other factors such as age, sex, and body mass index can also influence the pattern of the lipid abnormalities seen. The most robust finding with TSH elevation is increases in the low density lipoprotein cholesterol. Thyroid hormone treatment is efficacious in reversing the lipid abnormalities in both subclinical and overt hypothyroidism.

CONCLUSION

Given the association of lipid abnormalities with metabolic and cardiovascular disease, consideration of hypothyroidism as an important non-communicable disease may facilitate studies that test the hypothesis that thyroid hormone treatment to reverse hypothyroidism-associated lipid abnormalities may improve metabolic and cardiovascular outcomes.

Iodine-131 intervention in hyperthyroidism with hepatic insufficiency: Metabolomic evaluation

Hyperthyroidism, often accompanied by hepatic insufficiency (HI), poses significant clinical challenges, highlighting the necessity for identifying optimal treatment strategies and early diagnostic biomarkers to improve patient outcomes. This study aimed to determine the optimal iodine-131 (131I) intervention dose for alleviating hyperthyroidism with HI and to identify serum metabolic biomarkers for early diagnosis using UPLC-Q/TOF-MS technology. A mouse model for early 131I intervention was established to monitor changes in physiological response, body weight, fur condition, thyroid, and liver function. Metabolite identification was achieved through UPLC-Q/TOF-MS and further analyzed via MetaboAnalyst. Six biomarkers were identified and subjected to ROC analysis. Early intervention with 80 μCi 131I per gram of thyroid tissue effectively controlled hyperthyroidism and improved liver function. Metabolomics analysis uncovered 63 differentially abundant metabolites, six of which (L-kynurenine, Taurochenodesoxycholic acid, Glycocholic acid, Phytosphingosine, Tryptamine, and Betaine) were identified as early warning biomarkers. Post-intervention, these biomarkers progressively returned to normal levels. This study demonstrates the efficacy of UPLC-Q/TOF-MS in identifying metabolic biomarkers for early diagnosis of hyperthyroidism with HI and highlights the therapeutic potential of early 131I intervention in normalizing these biomarkers.

Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Preservatives are added as antimicrobial agents to extend the shelf life of meat. Adding preservatives to meat products can affect their flavor and nutrition. This review clarifies the effects of preservatives on metabolic pathways and network molecular transformations in meat products based on lipidomics, metabolomics and proteomics analyses. Preservatives change the nutrient content of meat products via altering ionic strength and pH to influence enzyme activity. Ionic strength in salt triggers muscle triglyceride hydrolysis by causing phosphorylation and lipid droplet splitting in adipose tissue hormone-sensitive lipase and triglyceride lipase. DisoLipPred exploiting deep recurrent networks and transfer learning can predict the lipid binding trend of each amino acid in the disordered region of input protein sequences, which could provide omics analyses of biomarkers metabolic pathways in meat products. While conventional meat quality assessment tools are unable to elucidate the intrinsic mechanisms and pathways of variables in the influences of preservatives on the quality of meat products, the promising application of omics techniques in food analysis and discovery through multimodal learning prediction algorithms of neural networks (e.g., deep neural network, convolutional neural network, artificial neural network) will drive the meat industry to develop new strategies for food spoilage prevention and control.

Associations of serum keratin 1 with thyroid function and immunity in Graves' disease

BACKGROUND

Autoimmune thyroid disease (AITD) can cause enormous health burdens; however, trustworthy biomarkers in identifying the onset and progression of AITD are limited. In this study, we attempted to discover new potential serum biomarkers to discriminate AITD using mass spectrometry (MS).

METHODS

In the biomarker study cohort, 20 patients with Graves' disease (GD), 20 patients with Hashimoto's thyroiditis (HT), and 20 healthy controls were enrolled for a liquid chromatographic-tandem MS assessment. A novel biomarker, keratin 1 (KRT1), was selected for further evaluation in the validation cohort, including 125 patients with GD, 34 patients with HT, and 77 controls. Relationships of serum KRT1 with AITD-related immunomodulatory cytokines were also analyzed using enzyme-linked immunosorbent assays (ELISAs).

RESULTS

In the MS analysis, KRT1 was the single marker overexpressed in GD, while it was underexpressed in HT. In the ELISA analysis of the validation cohort, KRT1 was consistently upregulated in GD, while it was not downregulated in HT. There were significant associations of KRT1 levels with thyroid function in GD, AITD, and overall subjects. Additionally, a significant association of KRT1 levels with thyroid-stimulating hormone receptor antibody (TSHRAb) levels was observed. Moreover, there were significant associations of KRT1 with osteopontin (OPN) and B-cell activating factor (BAFF) levels in GD.

CONCLUSIONS

Serum KRT1 levels were upregulated in GD and were associated with thyroid function and TSHRAb levels. Moreover, KRT1 was correlated with the BAFF and OPN levels in GD patients. Further molecular-based research to elucidate the role of KRT1 in the pathogenesis of AITD is needed.

Lipidomic Analysis Reveals Differences in the Extent of Remyelination in the Brain and Spinal Cord

During demyelination, lipid-rich myelin debris is released in the central nervous system (CNS) and must be phagocytosed and processed before new myelin can form. Although myelin comprises over 70% lipids, relatively little is known about how the CNS lipidome changes during demyelination and remyelination. In this study, we obtained a longitudinal lipidomic profile of the brain, spinal cord, and serum using a genetic mouse model of demyelination, known as Plp1-iCKO-Myrf. The mass spectrometry data is available at the Metabolomics Workbench, where it has been assigned Study ID ST002958. This model has distinct phases of demyelination and remyelination over the course of 24 weeks, in which loss of motor function peaks during demyelination. Using principal component analysis (PCA) and volcano plots, we have demonstrated that the brain and spinal cord have different remyelination capabilities and that this is reflected in different lipidomic profiles over time. We observed that plasmalogens (ether-linked phosphatidylserine and ether-linked phosphatidylcholine) were elevated specifically during the early stages of active demyelination. In addition, we identified lipids in the brain that were altered when mice were treated with a remyelinating drug, which may be CNS biomarkers of remyelination. The results of this study provide new insights into how the lipidome changes in response to demyelination, which will enable future studies to elucidate mechanisms of lipid regulation during demyelination and remyelination.

Lipidomic analysis reveals differences in the extent of remyelination in the brain and spinal cord

During demyelination, lipid-rich myelin debris is released in the central nervous system (CNS) and must be phagocytosed and processed before new myelin can form. Although myelin comprises over 70% lipids, relatively little is known about how the CNS lipidome changes during demyelination and remyelination. In this study, we obtained a longitudinal lipidomic profile of the brain, spinal cord, and serum using a genetic mouse model of demyelination, known as Plp1 -iCKO- Myrf mice. This model has distinct phases of demyelination and remyelination over the course of 24 weeks, in which loss of motor function peaks during demyelination. Using principal component analysis (PCA) and volcano plots, we have demonstrated that the brain and spinal cord have different remyelination capabilities and that this is reflected in different lipidomic profiles over time. We observed that plasmalogens (ether-linked phosphatidylserine and ether-linked phosphatidylcholine) were elevated specifically during the early stages of active demyelination. In addition, we identified lipids in the brain that were altered when mice were treated with a remyelinating drug, which may be CNS biomarkers of remyelination. The results of this study provide new insights into how the lipidome changes in response to demyelination, which will enable future studies to elucidate mechanisms of lipid regulation during demyelination and remyelination.