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Record W4409658056 · doi:10.3390/jcm14092872

Cardiometabolic Risk in Chronic Spinal Cord Injury: A Systematic Review with Meta-Analysis and Temporal and Geographical Trends

2025· review· en· W4409658056 on OpenAlexaff
Gary J. Farkas, Lizeth J. Caldera, Daniel D. Hodgkiss, Jessica Mitchell, Maxwell A. Cusnier, Alex J. Cole, Scott G. Daniel, Matthew Farrow, Cameron M. Gee, Eric A. Kincaid-Sharp, Andrew Logan, David W. McMillan, Tom E. Nightingale, P. Sastre Portes, Francis T. Walson, Alyssa M. Volmrich, John Reynolds, Mark S. Nash, David R. Gater, Arthur Berg

Bibliographic record

VenueJournal of Clinical Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of British Columbia
FundersUniversity of MiamiLeonard M. Miller School of MedicineCraig H. Neilsen Foundation
KeywordsMedicineTetraplegiaParaplegiaSpinal cord injuryBody mass indexInternal medicineMeta-analysisPhysical therapyInsulin resistanceConfidence intervalInsulinSpinal cord

Abstract

fetched live from OpenAlex

Objectives: This systematic review with meta-analysis compared cardiometabolic syndrome (CMS) in adults with chronic (≥1 year) spinal cord injury (SCI) to non-SCI individuals (controls) and athletes, analyzing the effect of specific injury characteristics and exploring temporal and geographical trends. Methods: Ovid Medline, Embase, Cochrane, CINAHL, Scopus, and Web of Science were searched from inception to September 2024. Adults with chronic SCI were included based on observational and baseline data derived from experimental studies. Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields assessed quality. Weighted means with 95% bootstrapped confidence intervals (CI) were computed for risk stratification. Group differences were assessed using random effects meta-analysis, calculating weighted mean differences with 95% bootstrapped CI. Temporal and geographical trends were evaluated with linear regression based on sample-size-weighted distributions and relevant covariates. Results: Of 31,163 identified records, 471 studies were included (n ≤ 31,782 SCI participants). CMS was present in men with SCI, paraplegia, tetraplegia, and injuries above T6; men with complete SCI (AIS A); and men and women with motor-complete SCI (AIS A–B). Compared to controls, adults with SCI had a lower body mass index (BMI), higher total and visceral fat, and worse lipid and carbohydrate profiles, including increased insulin resistance (IR). Tetraplegia was associated with greater visceral fat, poorer glycemic control, and lower BMI, insulin sensitivity, high-density lipoprotein-cholesterol (HDL-C), and triglycerides than paraplegia. Motor-complete SCI had lower BMI, HDL-C, and fasting glucose than motor-incomplete injuries. Injuries above T6 had lower blood pressure and higher fasting insulin levels than those below T6. Athletes with SCI had a lower BMI, fat mass, and fasting glucose, and higher systolic blood pressure than non-athletes with SCI, but frequently presented with obesity and carbohydrate dysfunction. Temporal analysis revealed increasing obesity trends and improved systolic blood pressure, while other CMS risk factors remained unchanged. We also identified global variations in obesity, lipids, blood pressure, and carbohydrate patterns. Conclusions: With a large sample, we revealed a widespread cardiometabolic burden in chronic SCI, even among athletes. Specifically, obesity, IR, and hypoalphalipoproteinemia worsened with increasing injury severity, alongside rising obesity trends and geographic disparities in risk profiles. These patterns highlight the evolution of what was deemed an epidemic into a global cardiometabolic pandemic.

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)0.015
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.038
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0170.039
Bibliometrics0.0090.012
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.222
GPT teacher head0.563
Teacher spread0.341 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations8
Published2025
Admission routes1
Has abstractyes

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