Evidence map›Paper›PMID 42092792›Full record

ArticleBMC medical imaging2026

Predicting MRI-derived total brain volume from DXA-derived head composition in middle-aged and older adults: WASEDA'S Health Study.

Toshiharu Tsutsui, Suguru Torii, Kumpei Tanisawa, Toru Takahashi, Kaori Usui, Nobuhiro Nakamura, Taishi Midorikawa, Kento Nakagawa, Reiji Ohkuma, Hiroaki Kumano and 5 more

Abstract read
In one paragraph

Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

15 authors.

Toshiharu TsutsuiFaculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Suguru ToriiFaculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan. shunto@waseda.jp.
Kumpei TanisawaFaculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Toru TakahashiCommunication Science Laboratories, NTT, Inc., Atsugi, Kanagawa, Japan.
Kaori UsuiInstitute of Applied Brain Sciences, Comprehensive Research Organization, Waseda University, Shinjuku, Tokyo, Japan.
Nobuhiro NakamuraFaculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Taishi MidorikawaCollege of Health and Welfare, J. F. Oberlin University, Tokyo, Japan.
Kento NakagawaFaculty of Business and Information Sciences, Jobu University, Isesaki, Gunma, Japan.
Reiji OhkumaGraduate School of Human Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Hiroaki KumanoFaculty of Human Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Kaori IshiiFaculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Katsuhiko SuzukiFaculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Shizuo SakamotoFaculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Mitsuru HiguchiFaculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Koichiro OkaFaculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan.

Funding

Japan Society for the Promotion of Science JP20H01775Japan Society for the Promotion of Science JP24K02832
6 · The paper itself

Abstract

backgroundTotal brain volume (TBV) derived from brain MRI is an important marker of brain structural health in middle-aged and older adults, but MRI is resource-intensive and not always feasible in largescale or repeated assessments. We examined whether dual-energy X-ray absorptiometry (DXA)-derived head composition measures can estimate MRI-derived TBV in middle-aged and older adults.

methodsThis study included 314 participants (≥ 40 years) who underwent whole-body DXA (head ROI manually defined using a sub-region tool) and 3T brain MRI within 1 year. MRI-derived TBV was defined as the sum of gray and white matter volumes. We developed multivariable linear regression models using either DXA-derived head lean-and-fat mass or head fat mass as the primary predictor. Nested models were fitted: Model 1 (predictor only), Model 2 (+ age and sex), and Model 3 (+ BMI). Apparent model performance was summarized using R² and RMSE, and internal validation was performed using 1,000 bootstrap resamples to obtain optimism-corrected performance estimates. Calibration was evaluated using calibration-in-the-large (CITL) and calibration slope. Agreement between observed and predicted TBV was assessed using Bland-Altman analysis. Sensitivity analyses additionally adjusted for the MRI-DXA measurement interval and evaluated sex-stratified performance.

resultsModel 3 was treated as the prespecified primary model because it was the fully adjusted model including clinically relevant covariates. In Model 3, both head lean-and-fat mass and head fat mass were positively associated with TBV, whereas age was negatively associated and male sex was associated with larger TBV. Across the nested models, optimism-corrected bootstrap validation showed broadly similar performance, with numerically slightly higher R² values and lower RMSE values for Model 3. Calibration was favorable in both predictor-based primary models (CITL approximately 0; calibration slope approximately 1.00). Bland-Altman analyses showed small mean bias with evidence of proportional bias across the TBV range. Bootstrap validation indicated stable performance. Sensitivity analyses yielded similar results after accounting for measurement interval and across sex strata.

conclusionsDXA-derived head composition measures can provide a practical approximation of MRI-derived TBV in middle-aged and older adults, with good calibration and stable internal validation performance.

Indexed as

Absorptiometry, PhotonBrainMagnetic Resonance ImagingAdultAgedBody CompositionFemaleHumansMaleMiddle AgedOrgan SizeBland–AltmanBody compositionBrain volumeCalibrationDual-energy X-ray absorptiometryMagnetic resonance imagingPrediction modelWASEDA’S Health Study

Identifiers

PMID42092792
PMCPMC13312767

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LicenceCC BY-NC-ND
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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.