Chronological age and "bone age" are the same in most people, but sometimes they are different. For decades pediatricians have used bone age – which can be estimated from X-rays – as a tool to assess health and development in children (Creo and Schwenk, 2017). For physicians treating the elderly, improved methods for estimating bone age of older adults would be helpful when assessing the risk of osteoporotic fractures: this is important because osteoporosis is under-diagnosed, under-treated and under-appreciated as a factor that influences both life expectancy and quality of life. Now, in eLife, Thao Phuong Ho-Le, Tuan Nguyen and colleagues at the Garvan Institute of Medical Research in Sydney and other institutions in Australia and Viet Nam report that they have developed a model that can estimate bone age in older adults and provide improved estimates of the risks of subsequent osteoporotic fractures and death following an initial fracture (Ho-Le et al., 2021).
The data come from a well-established population-based study, the Dubbo Osteoporosis Epidemiology Study, which has been following around 3500 men and women in Dubbo, a city in south-west Australia, who were 60 or over in 1989. Ho-Le et al. developed a multi-state model to provide prediction estimates for fracture, refracture and death. In this model individuals can be in one of five states – no fracture, first fracture, second fracture, third fracture and death – and can transition through all five states, or move directly from any of the first four states to death. Ho-Le et al. report that, during the 20 year follow-up, the risk of a second fracture was higher in women (36%) than in men (22%), but the mortality risk was higher in men (41%) than women (25%). The risk of transitioning from any state to death was also much higher in men than women.
As mentioned above, chronological age and bone age are usually the same. But given a low bone mineral density coupled with other risk factors for fracture, the age of your bones can be greater than your chronological age. Physicians use a tool called the Fracture Risk Assessment tool (FRAX) to decide if a patient should receive treatment to protect against osteoporotic fractures: in general, if the probability of hip fracture over the next ten years is 3% or higher, or if the risk of a major osteoporotic fracture (that is, a fracture to the spine, forearm, hip or shoulder) is 20% or higher, treatment is recommended. While the 3% risk threshold for hip fracture prevention was deemed cost-effective when FRAX was developed (Tosteson et al., 2008), a patient might think: "But I have a 97% chance of not fracturing". However, if the physician could reply, "You may be 70, but you have the bones of an 80 year old", the patient may be more willing to consider treatment.
The results of this study are important for other reasons. Existing risk assessment tools do not take into account the increased chances of further fractures, let alone death (Rubin et al., 2013), but the model developed by Ho-Le et al. can estimate the 5 year individual probability of transitioning from no fracture to fracture or to death. For example, for a 70-year-old woman with low bone mineral density but no other risk factors, the probability of transitioning from no fracture to first fracture (10%) was similar to the risk of death (8.6%). However, once she experiences a first fracture, the risk of another fracture goes up dramatically (16.5%) and exceeds the risk of dying (10.4%). With this information, the patient may be more likely to consider treatment.
There are several unanswered questions and inherent limitations. Older folks fear institutionalization, so the possibility of transitioning to disability outcomes and assisted living could be added to the model. The Dubbo study is also a single cohort from one city, so the model needs to be validated in cohorts around the world. Moreover, since Dubbo residents are 98% white, the model needs to be tested in other race/ethnicities. Lastly, the model is only adjusted for comorbidities at baseline. It is highly likely that the participants developed other chronic diseases over the 20 year follow-up, but such diseases are not included in the model, so the risk of refracture and death may have been underestimated.
Screening for high-risk patients who may benefit from therapy is important because prevention of future fractures and their consequences is possible with the armamentarium of treatments that are available. Future pragmatic randomized clinical trials are needed to test whether screening in the community, using this type of multistate model, can increase treatment rates and ultimately reduce fractures and their consequences.
Risk assessment tools to identify women with increased risk of osteoporotic fracture: Complexity or simplicity? A systematic reviewJournal of Bone and Mineral Research 28:1701–1717.https://doi.org/10.1002/jbmr.1956
Cost-effective osteoporosis treatment thresholds: the United States perspectiveOsteoporosis International 19:437–447.https://doi.org/10.1007/s00198-007-0550-6
Type 2 diabetes mellitus (T2DM) is known to be associated with neurobiological and cognitive deficits; however, their extent, overlap with aging effects, and the effectiveness of existing treatments in the context of the brain are currently unknown.
We characterized neurocognitive effects independently associated with T2DM and age in a large cohort of human subjects from the UK Biobank with cross-sectional neuroimaging and cognitive data. We then proceeded to evaluate the extent of overlap between the effects related to T2DM and age by applying correlation measures to the separately characterized neurocognitive changes. Our findings were complemented by meta-analyses of published reports with cognitive or neuroimaging measures for T2DM and healthy controls (HCs). We also evaluated in a cohort of T2DM-diagnosed individuals using UK Biobank how disease chronicity and metformin treatment interact with the identified neurocognitive effects.
The UK Biobank dataset included cognitive and neuroimaging data (N = 20,314), including 1012 T2DM and 19,302 HCs, aged between 50 and 80 years. Duration of T2DM ranged from 0 to 31 years (mean 8.5 ± 6.1 years); 498 were treated with metformin alone, while 352 were unmedicated. Our meta-analysis evaluated 34 cognitive studies (N = 22,231) and 60 neuroimaging studies: 30 of T2DM (N = 866) and 30 of aging (N = 1088). Compared to age, sex, education, and hypertension-matched HC, T2DM was associated with marked cognitive deficits, particularly in executive functioning and processing speed. Likewise, we found that the diagnosis of T2DM was significantly associated with gray matter atrophy, primarily within the ventral striatum, cerebellum, and putamen, with reorganization of brain activity (decreased in the caudate and premotor cortex and increased in the subgenual area, orbitofrontal cortex, brainstem, and posterior cingulate cortex). The structural and functional changes associated with T2DM show marked overlap with the effects correlating with age but appear earlier, with disease duration linked to more severe neurodegeneration. Metformin treatment status was not associated with improved neurocognitive outcomes.
The neurocognitive impact of T2DM suggests marked acceleration of normal brain aging. T2DM gray matter atrophy occurred approximately 26% ± 14% faster than seen with normal aging; disease duration was associated with increased neurodegeneration. Mechanistically, our results suggest a neurometabolic component to brain aging. Clinically, neuroimaging-based biomarkers may provide a valuable adjunctive measure of T2DM progression and treatment efficacy based on neurological effects.
The research described in this article was funded by the W. M. Keck Foundation (to LRMP), the White House Brain Research Through Advancing Innovative Technologies (BRAIN) Initiative (NSFNCS-FR 1926781 to LRMP), and the Baszucki Brain Research Fund (to LRMP). None of the funding sources played any role in the design of the experiments, data collection, analysis, interpretation of the results, the decision to publish, or any aspect relevant to the study. DJW reports serving on data monitoring committees for Novo Nordisk. None of the authors received funding or in-kind support from pharmaceutical and/or other companies to write this article.
Few studies have assessed the role of individual plasma cholesterol levels in the association between egg consumption and the risk of cardiovascular diseases. This research aims to simultaneously explore the associations of self-reported egg consumption with plasma metabolic markers and these markers with the risk of cardiovascular disease (CVD).
Totally 4778 participants (3401 CVD cases subdivided into subtypes and 1377 controls) aged 30–79 were selected based on the China Kadoorie Biobank. Targeted nuclear magnetic resonance was used to quantify 225 metabolites in baseline plasma samples. Linear regression was conducted to assess associations between self-reported egg consumption and metabolic markers, which were further compared with associations between metabolic markers and CVD risk.
Egg consumption was associated with 24 out of 225 markers, including positive associations for apolipoprotein A1, acetate, mean HDL diameter, and lipid profiles of very large and large HDL, and inverse associations for total cholesterol and cholesterol esters in small VLDL. Among these 24 markers, 14 were associated with CVD risk. In general, the associations of egg consumption with metabolic markers and of these markers with CVD risk showed opposite patterns.
In the Chinese population, egg consumption is associated with several metabolic markers, which may partially explain the protective effect of moderate egg consumption on CVD.
This work was supported by the National Natural Science Foundation of China (81973125, 81941018, 91846303, 91843302). The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants (2016YFC0900500, 2016YFC0900501, 2016YFC0900504, 2016YFC1303904) from the National Key R&D Program of China, National Natural Science Foundation of China (81390540, 81390541, 81390544), and Chinese Ministry of Science and Technology (2011BAI09B01). The funders had no role in the study design, data collection, data analysis and interpretation, writing of the report, or the decision to submit the article for publication.