Evidence map›Paper›PMID 36615821›Full record

ArticleNutrients2022

Cardiometabolic Traits in Adult Twins: Heritability and BMI Impact with Age.

Xuanming Hong, Zhiyu Wu, Weihua Cao, Jun Lv, Canqing Yu, Tao Huang, Dianjianyi Sun, Chunxiao Liao, Yuanjie Pang, Zengchang Pang and 6 more

Open access · goldAbstract readTwin Study
In one paragraph

Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
4.6field-weighted citation impact, top 5% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

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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

16 authors at 6 institutions in 1 country.

Xuanming HongDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Zhiyu WuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Weihua CaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Jun LvDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Canqing YuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.ORCID 0000-0002-0019-0014
Tao HuangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Dianjianyi SunDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Chunxiao LiaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Yuanjie PangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Zengchang PangQingdao Center for Disease Control and Prevention, Qingdao 266033, China.
Liming CongZhejiang Center for Disease Control and Prevention, Hangzhou 310051, China.
Hua WangJiangsu Center for Disease Control and Prevention, Nanjing 210008, China.
Xianping WuSichuan Center for Disease Control and Prevention, Chengdu 610041, China.
Yu LiuHeilongjiang Center for Disease Control and Prevention, Harbin 150090, China.
Wenjing GaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Liming LiPeking University Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing 100191, China.
Peking University · CNHeilongjiang Center for Tuberculosis Control and Prevention · CNJiangsu Provincial Center for Disease Control and Prevention · CNQingdao Municipal Center for Disease Control and Prevention · CNSichuan Center for Disease Control and Prevention · CNZhejiang Center for Disease Control and Prevention · CN

Funding

National Natural Science Foundation of China 81573223National Natural Science Foundation of China 81973126National Natural Science Foundation of China 82073633Special Fund for Health scientific research in public welfare 201002007Special Fund for Health scientific research in public welfare 201502006
6 · The paper itself

Abstract

Background: The prevalence of obesity and cardiometabolic diseases continues to rise globally and obesity is a significant risk factor for cardiometabolic diseases. However, to our knowledge, evidence of the relative roles of genes and the environment underlying obesity and cardiometabolic disease traits and the correlations between them are still lacking, as is how they change with age. Method: Data were obtained from the Chinese National Twin Registry (CNTR). A total of 1421 twin pairs were included. Univariate structural equation models (SEMs) were performed to evaluate the heritability of BMI and cardiometabolic traits, which included blood hemoglobin A1c (HbA1c), fasting blood glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C). Bivariate SEMs were used to assess the genetic/environmental correlations between them. The study population was divided into three groups for analysis: ≤50, 51−60, and >60 years old to assess the changes in heritability and genetic/environmental correlations with ageing. Results: Univariate SEMs showed a high heritability of BMI (72%) and cardiometabolic traits, which ranged from 30% (HbA1c) to 69% (HDL-C). With age increasing, the heritability of all phenotypes has different degrees of declining trends. Among these, BMI, SBP, and DBP presented significant monotonous declining trends. The bivariate SEMs indicated that BMI correlated with all cardiometabolic traits. The genetic correlations were estimated to range from 0.14 (BMI and LDL-C) to 0.39 (BMI and DBP), while the environmental correlations ranged from 0.13 (BMI and TC/LDL-C) to 0.31 (BMI and TG). The genetic contributions underlying the correlations between BMI and SBP and DBP, TC, TG, and HDL-C showed a progressive decrease as age groups increased. In contrast, environmental correlations displayed a significant increasing trend for HbA1c, SBP, and DBP. Conclusions: The findings suggest that genetic and environmental factors have essential effects on BMI and all cardiometabolic traits. However, as age groups increased, genetic influences presented varying degrees of decrement for BMI and most cardiometabolic traits, suggesting the increasing importance of environments. Genetic factors played a consistently larger role than environmental factors in the phenotypic correlations between BMI and cardiometabolic traits. Nevertheless, the relative magnitudes of genetic and environmental factors may change over time.

Indexed as

Cardiovascular DiseasesObesityBlood PressureBody Mass IndexCholesterol, HDLCholesterol, LDLGlycated HemoglobinHumansPhenotypeRisk FactorsTriglyceridesCholesterol, HDLCholesterol, LDLGlycated HemoglobinTriglyceridesblood pressurebody mass indexglycemic indexlipidstwin study

Identifiers

PMID36615821
PMCPMC9824881
OpenAlexW4313361020

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.