Evidence map›Paper›PMID 38493245›Full record

ArticleJournal of endocrinological investigation2024

Metabolomic profiles of ovariectomized mice and their associations with body composition and frailty-related parameters in postmenopausal women.

S J Kim, Y Jo, S J Park, E Ji, J Y Lee, E Choi, J-Y Baek, I Y Jang, H-W Jung, K Kim and 3 more

Abstract read
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In one paragraph

Article in Journal of endocrinological investigation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 2 institutions in 1 country.

S J KimDepartment of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center,, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea.
Y Jo *Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea.
S J Park *Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
E JiAsan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
J Y LeeAsan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
E ChoiAsan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
J-Y BaekDivision of Geriatrics, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
I Y JangDivision of Geriatrics, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
H-W JungDivision of Geriatrics, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
K KimAsan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
D RyuDepartment of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. dryu@gist.ac.kr.ORCID http://orcid.org/0000-0001-5905-6760
H J YooDepartment of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center,, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea. yoohyunju@amc.seoul.kr.
B-J KimDivision of Endocrinology and Metabolism, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea. umkbj0825@amc.seoul.kr.ORCID http://orcid.org/0000-0001-8591-1759
Ulsan College · KRGwangju Institute of Science and Technology · KR

Funding

Asan Institute for Life Sciences, Asan Medical Center 2022IP0036National Research Foundation of Korea 2021R1C1C2006842
6 · The paper itself

Abstract

backgroundMenopause, a dramatical estrogen-deficient condition, is considered the most significant milestone in women's health. PURPOSE: To investigate the metabolite changes attributed to estrogen deficiency using random forest (RF)-based machine learning (ML) modeling strategy in ovariectomized (OVX) mice as well as determine the clinical relevance of selected metabolites in older women. METHODS AND

resultsUntargeted and targeted metabolomic analyses revealed that metabolites related to TCA cycle, sphingolipids, phospholipids, fatty acids, and amino acids, were significantly changed in the plasma and/or muscle of OVX mice. Subsequent ML classifiers based on RF algorithm selected alpha-ketoglutarate (AKG), arginine, carnosine, ceramide C24, phosphatidylcholine (PC) aa C36:6, and PC ae C42:3 in plasma as well as PC aa 34:1, PC aa C34:3, PC aa C36:5, PC aa C32:1, PC aa C36:2, and sphingosine in muscle as top featured metabolites that differentiate the OVX mice from the sham-operated group. When circulating levels of AKG, arginine, and carnosine, which showed the most significant changes in OVX mice blood, were measured in postmenopausal women, higher plasma AKG levels were associated with lower bone mass, weak grip strength, poor physical performance, and increased frailty risk.

conclusionsMetabolomics- and ML-based methods identified the key metabolites of blood and muscle that were significantly changed after ovariectomy in mice, and the clinical implication of several metabolites was investigated by looking at their correlation with body composition and frailty-related parameters in postmenopausal women. These findings provide crucial context for understanding the diverse physiological alterations caused by estrogen deficiency in women.

Indexed as

Body CompositionFrailtyMetabolomicsOvariectomyPostmenopauseAgedAnimalsBiomarkersFemaleHumansMachine LearningMetabolomeMiceMice, Inbred C57BLMiddle AgedBiomarkersAgingMachine learningMenopauseMetabolomicsOvariectomy

Identifiers

PMID38493245
OpenAlexW4392894850

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.