The DOI matches its Crossref record; 157 of the 157 references it cites were found in a registry or an index.
Aging and Imaging Assessment of Body Composition: From Fat to Facts
Open accessAbstract
The aging process is characterized by the chronic inflammatory status called "inflammaging", which shares major molecular and cellular features with the metabolism-induced inflammation called "metaflammation." Metaflammation is mainly driven by overnutrition and nutrient excess, but other contributing factors are metabolic modifications related to the specific body composition (BC) changes occurring with age. The aging process is indeed characterized by an increase in body total fat mass and a concomitant decrease in lean mass and bone density, that are independent from general and physiological fluctuations in weight and body mass index (BMI). Body adiposity is also re-distributed with age, resulting in a general increase in trunk fat (mainly abdominal fat) and a reduction in appendicular fat (mainly subcutaneous fat). Moreover, the accumulation of fat infiltration in organs such as liver and muscles also increases in elderly, while subcutaneous fat mass tends to decrease. These specific variations in BC are considered risk factors for the major age-related diseases, such as cardiovascular diseases, type 2 diabetes, sarcopenia and osteoporosis, and can predispose to disabilities. Thus, the maintenance of a balance rate of fat, muscle and bone is crucial to preserve metabolic homeostasis and a health status, positively contributing to a successful aging. For this reason, a detailed assessment of BC in elderly is critical and could be an additional preventive personalized strategy for age-related diseases. Despite BMI and other clinical measures, such as waist circumference measurement, waist-hip ratio, underwater weighing and bioelectrical impedance, are widely used as a surrogate measure for body adiposity, they barely reflect the distribution of body fat. Because of the great advantages offered by imaging tools in research and clinics, the attention of clinicians is now moving to powerful imaging techniques such as computed tomography, magnetic resonance imaging, dual-energy X-ray absorptiometry and ultrasound to obtain a more accurate estimation of BC. The aim of this review is to present the state of the art of the imaging techniques that are currently available to measure BC and that can be applied to the study of BC changes in the elderly, outlining advantages and disadvantages of each technique.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Sarcopenia: European consensus on definition and diagnosis
Cruz-Jentoft, A. J., Baeyens, J. P., Bauer, J. M., Boirie, Y., Cederholm, T., Landi, F., Martin, F. C., Michel, J.-P., Rolland, Y., Schneider, S. M., et al. (2010) · Age and Ageing · doi:10.1093/ageing/afq034 · Cited by 11,601
Inflammation and metabolic disorders
Hotamisligil, G. S. (2006) · Nature · doi:10.1038/nature05485 · Cited by 9,199
The metabolic phenotype in obesity: Fat mass, body fat distribution, and adipose tissue function
Goossens, G. H. (2017) · Obesity Facts · doi:10.1159/000471488 · Cited by 8,481
Adipose Expression of Tumor Necrosis Factor-α: Direct Role in Obesity-Linked Insulin Resistance
Hotamisligil, G. S., Shargill, N. S., & Spiegelman, B. M. (1993) · Science · doi:10.1126/science.7678183 · Cited by 7,518
Inflamm‐aging: An Evolutionary Perspective on Immunosenescence
FRANCESCHI, C., BONAFÈ, M., VALENSIN, S., OLIVIERI, F., DE LUCA, M., OTTAVIANI, E., & DE BENEDICTIS, G. (2000) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2000.tb06651.x · Cited by 5,358
Self-citationEpidemiology of Sarcopenia among the Elderly in New Mexico
Baumgartner, R. N., Koehler, K. M., Gallagher, D., Romero, L., Heymsfield, S. B., Ross, R. R., Garry, P. J., & Lindeman, R. D. (1998) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a009520 · Cited by 4,004
Flegal, K. M., Kit, B. K., Orpana, H., & Graubard, B. I. (2013) · JAMA · doi:10.1001/jama.2012.113905 · Cited by 3,998
Inflammaging: a new immune–metabolic viewpoint for age-related diseases
Franceschi, C., Garagnani, P., Parini, P., Giuliani, C., & Santoro, A. (2018) · Nature Reviews Endocrinology · doi:10.1038/s41574-018-0059-4 · Cited by 3,586
Self-citationGeroscience: Linking Aging to Chronic Disease
Kennedy, B. K., Berger, S. L., Brunet, A., Campisi, J., Cuervo, A. M., Epel, E. S., Franceschi, C., Lithgow, G. J., Morimoto, R. I., Pessin, J. E., et al. (2014) · Cell · doi:10.1016/j.cell.2014.10.039 · Cited by 2,764
Self-citationKanda, H. (2006) · Journal of Clinical Investigation · doi:10.1172/jci26498 · Cited by 2,682
Cited by
Immunosenescence and inflammaging in the aging process: age-related diseases or longevity?
Santoro, A., Bientinesi, E., & Monti, D. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101422 · Cited by 603
Self-citationTavella, T., Rampelli, S., Guidarelli, G., Bazzocchi, A., Gasperini, C., Pujos-Guillot, E., Comte, B., Barone, M., Biagi, E., Candela, M., et al. (2021) · Gut Microbes · doi:10.1080/19490976.2021.1880221 · Cited by 298
Self-citationAging Hallmarks and Progression and Age-Related Diseases: A Landscape View of Research Advancement
Tenchov, R., Sasso, J. M., Wang, X., & Zhou, Q. A. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.3c00531 · Cited by 276
Sarcopenic obesity in ageing: cardiovascular outcomes and mortality
Atkins, J. L., & Wannamathee, S. G. (2020) · British Journal of Nutrition · doi:10.1017/s0007114520002172 · Cited by 187
Quantitative imaging techniques for the assessment of osteoporosis and sarcopenia
Guerri, S., Mercatelli, D., Aparisi Gómez, M. P., Napoli, A., Battista, G., Guglielmi, G., & Bazzocchi, A. (2018) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims.2018.01.05 · Cited by 169
Self-citation
Metadata
- Authors
- Ponti, F., Santoro, A., Mercatelli, D., Gasperini, C., Conte, M., Martucci, M., Sangiorgi, L., Franceschi, C., & Bazzocchi, A.
- Venue
- Frontiers in Endocrinology
- Publisher
- Frontiers Media SA
- Published
- 2020-01-14
- Volume
- 10
- Pages
- 861-861
- ISSN
- 1664-2392 (ISSN-L)
- OpenAlex
- W2989961673
- Cited by
- 408
- References
- 157
- FWCI
- 23.51
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Aging and Imaging Assessment of Body Composition: From Fat to Facts. https://app.cochat.ai/papers/10.3389/fendo.2019.00861
- The paper
- Ponti, F., Santoro, A., Mercatelli, D., Gasperini, C., Conte, M., Martucci, M., Sangiorgi, L., Franceschi, C., & Bazzocchi, A. (2020). Aging and Imaging Assessment of Body Composition: From Fat to Facts. Frontiers in Endocrinology. https://doi.org/10.3389/fendo.2019.00861
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.