Evidence map›Paper›PMID 39379967›Full record

ArticleBMC medicine2024

The fetal origins of metabolic health: exploring the association between newborn biological age and metabolism hormones in childhood.

Zhenxian Jia, Feng Qiu, Yujie He, Huan Chen, Chenhui Yang, Hongxiu Liu, Tongzhang Zheng, Shunqing Xu, Shiqiong Wang, Yuanyuan Li

Abstract read
In one paragraph

Article in BMC medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Placental Pathophysiology and Developmental Programming of Adult Cardiometabolic Risk: A Narrative Review of Pregnancy Exposures.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026
    Review
  2. Review
  3. Article
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

10 authors.

Zhenxian JiaKey Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei, 430030, China.
Feng QiuKey Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei, 430030, China.
Yujie HeKey Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei, 430030, China.
Huan ChenKey Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei, 430030, China.
Chenhui YangKey Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei, 430030, China.
Hongxiu LiuKey Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei, 430030, China.
Tongzhang ZhengDepartment of Epidemiology, School of Public Health, Brown University, Providence, RI, 02912, USA.
Shunqing XuKey Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei, 430030, China. xus@hainanu.edu.cn.
Shiqiong WangInstitute of Maternal and Children Health, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, 430016, China. 2710048577@qq.com.
Yuanyuan LiKey Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei, 430030, China. liyuanyuan@hust.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTelomere length (TL), mitochondrial DNA copy number (mtDNAcn), and DNA methylation age (DNAmAge) are common aging biomarkers. However, research on the associations between these three markers at birth and subsequent metabolic status was limited. This study aimed to evaluate the association between TL, mtDNAcn, and DNAmAge in newborns and the variation in metabolic hormones of children at 3 years old.

methodsThis research involved 895 mother-child pairs from a birth cohort in China, with TL and mtDNAcn measured using quantitative real-time PCR, DNA methylation (DNAm) assessed using Infinium MethylationEPIC Beadchip, and DNAm age (DNAmAge) determined using Horvath's epigenetic clock. Insulin and leptin levels were measured via electrochemiluminescence assay. Multivariable adjusted linear regression and restricted cubic spline (RCS) analysis were utilized to examine the association between aging markers and metabolic hormones.

resultsThe linear regression analysis indicated the percentage change of metabolism hormones for per doubling of aging biomarkers alterations and found significant associations between DNAmAge and insulin levels (adjusted percent change (95% CI), - 13.22 (- 23.21 to - 1.94)), TL and leptin levels (adjusted percent change (95% CI), 15.32 (1.32 to 31.24)), and mtDNAcn and leptin levels (adjusted percent change (95% CI), - 14.13 (- 21.59 to - 5.95)). The RCS analysis revealed significant non-linear associations between TL (Ln transformed) and insulin (Ln transformed) (P = 0.024 for nonlinearity), as well as DNAmAge (Ln transformed) and leptin (Ln transformed) (P = 0.043 for nonlinearity). Specifically, for TL and insulin, a positive association was observed when TL (Ln transformed) was less than - 0.05, which transitioned to an inverse association when TL (Ln transformed) was greater than - 0.05. Regarding DNAmAge and leptin, there was a sharp decline when DNAmAge (Ln transformed) was less than - 1.35, followed by a plateau between - 1.35 and - 0.67 and then a further decline when DNAmAge (Ln transformed) was greater than - 0.67.

conclusionsIn this prospective birth cohort study, variation in metabolic hormones of children at 3 years old was associated with TL, mtDNAcn, and DNAmAge at birth. These findings suggested that TL, mtDNAcn, and DNAmAge might play a role in the biological programming of metabolic health from birth.

Indexed as

DNA MethylationInsulinLeptinAdultAgingBiomarkersBirth CohortChild, PreschoolChinaDNA, MitochondrialFemaleHumansInfant, NewbornMaleBiomarkersDNA, MitochondrialInsulinLeptinDNAm age (DNAmAge)InsulinLeptinMitochondria DNA copy number (mtDNAcn)Telomere length (TL)

Identifiers

PMID39379967
PMCPMC11462715

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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 OpenQuestion graph.