Evidence map›Paper›PMID 39501285›Full record

ArticleBMC medicine2024

BMI trajectories from birth to young adulthood associate with distinct cardiometabolic profiles.

Gang Wang, Dang Wei, Simon Kebede Merid, Sandra Ekström, Susanna Klevebro, Natalia Hernandez-Pacheco, Sophia Björkander, Petter Ljungman, Inger Kull, Jochen M Schwenk and 2 more

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

12 authors.

Gang Wang *Division of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China. gang.wang@ki.se.
Dang Wei *Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Simon Kebede MeridDepartment of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, 118 83, Stockholm, Sweden.
Sandra EkströmDepartment of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, 118 83, Stockholm, Sweden.
Susanna KlevebroDepartment of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, 118 83, Stockholm, Sweden.
Natalia Hernandez-PachecoDepartment of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, 118 83, Stockholm, Sweden.
Sophia BjörkanderDepartment of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, 118 83, Stockholm, Sweden.
Petter LjungmanInstitute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Inger KullDepartment of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, 118 83, Stockholm, Sweden.
Jochen M SchwenkScience for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Anna Bergström *Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Erik Melén *Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, 118 83, Stockholm, Sweden.

Funding

European Research Council
6 · The paper itself

Abstract

backgroundNumerous studies have investigated links between body mass index (BMI) trajectories and cardiovascular risk, yet discrepancies in BMI measurement duration and timing of the cardiovascular-related outcome evaluation have led to inconsistent findings.

methodsWe included participants from the Swedish birth cohort (BAMSE) and applied latent class mixture modeling to identify BMI trajectories using data of multiple BMI measures (≥ 4 times) from birth until 24-year follow-up (n = 3204). Subsequently, we analyzed the associations of BMI trajectories with lipids (n = 1974), blood pressure (n = 2022), HbA1c (n = 941), and blood leukocytes (n = 1973) using linear regression. We also investigated the circulating levels of 92 inflammation-related proteins (n = 1866) across BMI trajectories.

resultsSix distinct BMI groups were identified, denoted as increasing-persistent high (n = 74; 2.3%), high-accelerated increasing (n = 209; 6.5%), increasing-accelerated resolving (n = 142; 4.4%), normal-above normal (n = 721; 22.5%), stable normal (n = 1608; 50.2%), and decreasing-persistent low (n = 450; 14.1%) BMI groups. The increasing-persistent high and high-accelerated increasing BMI groups had higher levels of total cholesterol [mean difference (95% confidence intervals): 0.30 (0.04-0.56) and 0.16 (0.02-0.31) mmol/L], triglyceride, low-density lipoprotein, hemoglobin A1C [3.61 (2.17-5.54) and 1.18 (0.40-1.98) mmol/mol], and low-density lipoprotein/high-density lipoprotein ratios, but a lower level of high-density lipoprotein than the stable normal BMI group. These two groups also had higher leukocyte cell counts and higher circulating levels of 28 inflammation-related proteins. No increased cardiometabolic markers were observed in the increasing-accelerated resolving BMI group.

conclusionsParticipants with persistently high or accelerated increasing BMI trajectories from birth to young adulthood have elevated levels of cardiometabolic risk markers at young adulthood than those with stable normal BMI. However, a raised BMI in childhood may not be inherently harmful to cardiometabolic health, provided it does not persist into adulthood.

Indexed as

Body Mass IndexAdolescentAdultBirth CohortBlood PressureCardiometabolic Risk FactorsCardiovascular DiseasesChildChild, PreschoolFemaleGlycated HemoglobinHumansInfantInfant, NewbornLipidsMaleGlycated HemoglobinLipidsBioimpedanceChildhoodHbA1cInflammationLipid

Identifiers

PMID39501285
PMCPMC11539615

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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.