The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Peak bone mass from longitudinal data: Implications for the prevalence, pathophysiology, and diagnosis of osteoporosis
Open accessAbstract
We estimated peak bone mass (PBM) in 615 women and 527 men aged 16 to 40 years using longitudinal data from the Canadian Multicentre Osteoporosis Study (CaMos). Individual rates of change were averaged to find the mean rate of change for each baseline age. The age range for PBM was defined as the period during which bone mineral density (BMD) was stable. PBM was estimated via hierarchical models, weighted according to 2006 Canadian Census data. Lumbar spine PBM (1.046 ± 0.123 g/cm2) occurred at ages 33 to 40 years in women and at 19 to 33 years in men (1.066 ± 0.129 g/cm2). Total hip PBM (0.981 ± 0.122 g/cm2) occurred at ages 16 to 19 years in women and 19 to 21 years in men (1.093 ± 0.169 g/cm2). Analysis of Canadian geographic variation revealed that the levels of PBM and of mean BMD in those over age 65 sometimes were discordant, suggesting that PBM and subsequent rates of bone loss may be subject to different genetic and/or environmental influences. Based on our longitudinally estimated PBM values, the estimated Canadian prevalences of osteoporosis (T-score < –2.5) were 12.0% (L1–L4) and 9.1% (total hip) in women aged 50 years and older and 2.9% (L1–L4) and 0.9% (total hip) in men aged 50 years and older. These were higher than prevalences using cross-sectional PBM data. In summary, we found that the age at which PBM is achieved varies by sex and skeletal site, and different reference values for PBM lead to different estimates of the prevalence of osteoporosis. Furthermore, lack of concordance of PBM and BMD over age 65 suggests different determinants of PBM and subsequent bone loss. © 2010 American Society for Bone and Mineral Research
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
Updated Data on Proximal Femur Bone Mineral Levels of US Adults
Looker, A. C., Wahner, H. W., Dunn, W. L., Calvo, M. S., Harris, T. B., Heyse, S. P., Johnston Jr, C. C., & Lindsay, R. (1998) · Osteoporosis International · doi:10.1007/s001980050093 · Cited by 1,121 · cited 20×
“In men, within the same skeletal site, estimates of prevalence in CaMos were not appreciably different regardless of the methods and population used to determine PBM.”
MethodInfluential
Cited by
Weaver, C. M., Gordon, C. M., Janz, K. F., Kalkwarf, H. J., Lappe, J. M., Lewis, R., O’Karma, M., Wallace, T. C., & Zemel, B. S. (2016) · Osteoporosis International · doi:10.1007/s00198-015-3440-3 · Cited by 1,378
Estrogens and androgens in skeletal physiology and pathophysiology
Almeida, M., Laurent, M. R., Dubois, V., Claessens, F., O'Brien, C. A., Bouillon, R., Vanderschueren, D., & Manolagas, S. C. (2017) · Physiological Reviews · doi:10.1152/physrev.00033.2015 · Cited by 860
Factors influencing peak bone mass gain
Zhu, X., & Zheng, H. (2021) · Frontiers of Medicine · doi:10.1007/s11684-020-0748-y · Cited by 160
Golds, G., Houdek, D., & Arnason, T. (2017) · International Journal of Endocrinology · doi:10.1155/2017/4602129 · Cited by 150
How to manage osteoporosis before the age of 50.
Rozenberg, S., Bruyère, O., Bergmann, P., Cavalier, E., Gielen, E., Goemaere, S., Kaufman, J. M., Lapauw, B., Laurent, M. R., De Schepper, J., et al. (2020) · Maturitas · doi:10.1016/j.maturitas.2020.05.004 · Cited by 118
Metadata
- Authors
- Berger, C., Goltzman, D., Langsetmo, L., Joseph, L., Jackson, S., Kreiger, N., Tenenhouse, A., Davison, K. S., Josse, R. G., Prior, J. C., Hanley, D. A., & and the CaMos Research Group
- Venue
- Journal of Bone and Mineral Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-08-20
- Volume
- 25
- Issue
- 9
- Pages
- 1948-1957
- ISSN
- 0884-0431 (ISSN-L)
- 1523-4681
- OpenAlex
- W2058262707
- Cited by
- 286
- References
- 1
- FWCI
- 12.94
- Type
- journal article
- Language
- en
- Open access
- bronze
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: Peak bone mass from longitudinal data: Implications for the prevalence, pathophysiology, and diagnosis of osteoporosis. https://app.cochat.ai/papers/10.1002/jbmr.95
- The paper
- Berger, C., Goltzman, D., Langsetmo, L., Joseph, L., Jackson, S., Kreiger, N., Tenenhouse, A., Davison, K. S., Josse, R. G., Prior, J. C., Hanley, D. A., & and the CaMos Research Group. (2010). Peak bone mass from longitudinal data: Implications for the prevalence, pathophysiology, and diagnosis of osteoporosis. Journal of Bone and Mineral Research. https://doi.org/10.1002/jbmr.95
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.