This paper does what I have long been waiting for.
Takes some blood, just the serum, and do a comprehensive analysis of pretty much everything they can detect in that serum.
The subjects were divided into Control, Subclinical Hypothyroid and Hypothyroid groups.
They found clear differences between Control and the other two group. Somewhat less clear between Subclinical Hypothyroid and Hypothyroid.
Hands up if you are amazed by that! The authors do seem a little surprised.
I suggest that the definitions of Subclinical Hypothyroid and Hypothyroid are deeply questionable. Indeed, some of the results of this study might do a better job than do existing definitions.
Unsurprisingly to many here, T3 is slightly higher in Subclinical Hypothyroid and then drops in Hypothyroid. While T4 drops a bit in Subclinical Hypothyroid and much further in Hypothyroid. Which supports the idea that the body does what it can to protect T3 levels.
There is lots and lots more to read.
Maybe, in time, we'll see chasing blood levels of hormones as an exercise doomed to failure. If you can determine hypothyroidism by changes in metabolism, you could even take two people with identical thyroid hormone levels and see that one is hypothyroid, the other not. Thyroid hormone levels would still need to be tested - if only to double-check.
This study uses serum. Can they do the same with urine or saliva? What would those studies show?
Can they produce analysers that cost far, far less but which can identify and measure the most important compounds?
Plasma Metabolomics Reveals Systemic Metabolic Alterations of Subclinical and Clinical Hypothyroidism
Feifei Shao, Rui Li, Qian Guo, Rui Qin, Wenxiu Su, Huiyong Yin, Limin Tian
The Journal of Clinical Endocrinology & Metabolism, dgac555, doi.org/10.1210/clinem/dgac555
Published:
01 October 2022
Abstract
Context
Clinical hypothyroidism (CH) and subclinical hypothyroidism (SCH) have been linked to various metabolic comorbidities but the underlying metabolic alterations remain unclear. Metabolomics may provide metabolic insights into the pathophysiology of hypothyroidism.
Objective
We explored metabolic alterations in SCH and CH and identify potential metabolite biomarkers for the discrimination of SCH and CH from euthyroid individuals.
Methods
Plasma samples from a cohort of 126 human subjects, including 45 patients with CH, 41 patients with SCH, and 40 euthyroid controls, were analyzed by high-resolution mass spectrometry–based metabolomics. Data were processed by multivariate principal components analysis and orthogonal partial least squares discriminant analysis. Correlation analysis was performed by a Multivariate Linear Regression analysis. Unbiased Variable selection in R algorithm and 3 machine learning models were utilized to develop prediction models based on potential metabolite biomarkers.
Results
The plasma metabolomic patterns in SCH and CH groups were significantly different from those of control groups, while metabolite alterations between SCH and CH groups were dramatically similar. Pathway enrichment analysis found that SCH and CH had a significant impact on primary bile acid biosynthesis, steroid hormone biosynthesis, lysine degradation, tryptophan metabolism, and purine metabolism. Significant associations for 65 metabolites were found with levels of thyrotropin, free thyroxine, thyroid peroxidase antibody, or thyroglobulin antibody. We successfully selected and validated 17 metabolic biomarkers to differentiate 3 groups.
Conclusion
SCH and CH have significantly altered metabolic patterns associated with hypothyroidism, and metabolomics coupled with machine learning algorithms can be used to develop diagnostic models based on selected metabolites.
subclinical hypothyroidism, clinical hypothyroidism, metabolomics, thyrotropin, free thyroxine, biomarkers
Open access here:
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