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Record W4390586313 · doi:10.1210/clinem/dgae004

DXA-based Fat Mass With Risk of Worsening Insulin Resistance in Adolescents: A 9-Year Temporal and Mediation Study

2024· article· en· W4390586313 on OpenAlexafffund
Andrew O. Agbaje, Christoph Saner, Jie Zhang, Mélanie Henderson, Tomi‐Pekka Tuomainen

Bibliographic record

VenueThe Journal of Clinical Endocrinology & Metabolism · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersJenny ja Antti Wihurin RahastoCanadian Society of Endocrinology and MetabolismPaulon SäätiöOrionin TutkimussäätiöSydäntutkimussäätiöBritish Heart FoundationWellcome TrustFoundation for Cardiovascular ResearchResearch Councils UKAlfred Kordelinin SäätiöAarne Koskelon SäätiöMedical Research CouncilYrjö Jahnssonin SäätiöSuomen KulttuurirahastoIda Montinin SäätiöPaavo Nurmen SäätiöUniversity of BristolWellcome
KeywordsInsulin resistanceMediationResistance (ecology)Internal medicineFat massMedicineEndocrinologyPsychologyInsulinBody mass indexBiologyPolitical science

Abstract

fetched live from OpenAlex

CONTEXT: Surrogate measures of childhood and adolescent obesity have impaired the understanding of the relationship of body composition with insulin resistance in the young population. OBJECTIVE: We aim to examine the longitudinal associations of directly measured total fat mass, trunk fat mass, and lean mass with the risk of hyperglycemia, hyperinsulinemia, and insulin resistance from ages 15 to 24 years, the mediation path through which lipids and inflammation influence insulin resistance, and whether increased fat mass temporally precede insulin resistance. METHODS: We studied 3160 adolescents from the Avon Longitudinal Study of Parents and Children (ALSPAC), UK birth cohort, who had complete dual-energy x-ray absorptiometry measure and fasting blood samples at age 15 years and repeated measures at ages 17- and 24-years clinic visit. Fasting glucose greater than 6.1 mmol/L, insulin greater than 11.78 mU/L, and homeostatic model assessment for insulin resistance (HOMA-IR) greater than or equal to the 75th percentile were categorized as hyperglycemia, hyperinsulinemia, and high insulin resistance, respectively. Longitudinal associations were examined with generalized logit-mixed-effect models, while mediation and temporal path analyses were examined using structural equation models, adjusting for cardiometabolic and lifestyle factors. RESULTS: Among 3160 participants (51% female), fat mass and lean mass increased linearly both in males and females, while glucose, insulin, and HOMA-IR had a U-shaped course from age 15 through 24 years. After full adjustment, each 1-kg cumulative increase in total fat mass (odds ratio 1.12 [95% CI, 1.11-1.13]) and trunk fat mass (1.21 [1.19-1.23]) from ages 15 through 24 years were associated with a progressively worsening risk of high insulin resistance as well as hyperglycemia and hyperinsulinemia. The association of increased total fat mass with increased insulin resistance was partly mediated by triglycerides (9% mediation). In the temporal path analysis, higher total fat mass at age 15 years was associated with higher insulin resistance at age 17 years, but not vice versa. Higher total fat mass at age 17 years was bidirectionally associated with higher insulin resistance at 24 years. CONCLUSION: Mid-adolescence may be an optimal time for interrupting the worsening fat mass-insulin resistance pathologic cycle and attenuating the risk of progressively worsening metabolic dysfunction before young adulthood.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.327
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations32
Published2024
Admission routes2
Has abstractyes

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