Having osteoporosis significantly increases the risk of having dementia, particularly when combined with smoking and diabetes, but regular exercise can reduce this risk. Implementing a national health screening program is crucial for early detection of these modifiable risk factors and prevention of dementia in aging populations.

To assess osteoporosis as a risk factor for dementia in the general population using nationwide dataset.

This study included 261,343 women aged 66 years who participated in the National Screening Program for Transitional Ages for Koreans and underwent their first medical checkup between January 2013 and December 2016. Participants were categorized into three groups based on bone mineral density (BMD): normal BMD, osteopenia, and osteoporosis. We investigated participants' demographic characteristics and chronic comorbidities that could affect dementia incidence. The hazard ratios (HRs) for dementia in individuals with osteoporosis and osteopenia were calculated and adjusted based on several risk factors.

Despite adjusting for demographic characteristics and chronic comorbidities, the risk of dementia was 1.18-fold higher in the osteoporosis group than in the normal BMD group. Regarding Alzheimer's disease and vascular dementia, the risk was 1.18- and 1.25-fold higher in the osteoporosis group, respectively. Notably, participants with osteoporosis who smoked had a 1.82-fold higher HR than that of nonsmoking participants with normal BMD; participants with diabetes mellitus (DM) had a 1.92-fold higher HR than that of participants with normal BMD without DM. The HR for participants with osteoporosis who exercised regularly was 1.00, whereas it was 1.18 for those who did not exercise.

This population-based cohort study demonstrates that having osteoporosis significantly increases dementia risk, which is amplified by smoking and diabetes but reduced by regular exercise.

Body composition analysis (BCA) is primarily used in the management of conditions such as obesity and endocrine disorders. However, its potential in providing nutritional guidance for patients with Alzheimer's disease (AD) remains relatively unexplored.

To explore the clinical efficacy of BCA-based dietary nutrition scheme on bone metabolism in AD patients.

This retrospective study included 96 patients with AD complicated by osteoporosis who were admitted to The Third Hospital of Quzhou between January 2023 and December 2024. Based on data from previous similar studies, the patients were randomly assigned to either a routine diet (RD) group (n = 48) or a personalized nutrition (PN) group (n = 48). The RD group received conventional dietary guidance, while the PN group received individualized diet intervention measures based on human BCA. The intervention period lasted for 12 weeks. Bone mineral density (BMD), body mass index (BMI), muscle mass, mineral content, osteocalcin, 25-hydroxyvitamin D, procollagen type I N-terminal propeptide (PINP), beta C-terminal telopeptide of type I collagen (β-CTX), and serum calcium were measured and compared between the two groups before and 12 weeks after the intervention.

No significant differences were observed between groups in terms of age, sex, height, BMI, or other baseline data (P > 0.05). In both groups, BMI did not show significant changes after the intervention (P > 0.05), whereas muscle mass and mineral content were significantly increased (P < 0.05). After the intervention, BMI in the PN group did not differ significantly from that of the RD group, but muscle mass and mineral content were significantly higher in the PN group (P < 0.05). After the intervention, a higher proportion of patients in the PN group had a T score > -1 compared to the RD group (P < 0.05). The mini-mental state examination (MMSE) score was similar in both groups before the intervention. However, 12 weeks after the intervention, the MMSE score in the PN group was significantly higher than that in the RD group (P < 0.05). In both groups, the MMSE score significantly increased 12 weeks post-intervention compared to pre-intervention levels (P < 0.05). Before the intervention, the levels of osteocalcin, serum calcium, PINP, β-CTX, and 25-hydroxyvitamin D were not significantly different between the two groups (P > 0.05). After 12 weeks of intervention, the PN group exhibited higher levels of osteocalcin, serum calcium, and 25-hydroxyvitamin D, as well as lower levels of PINP and β-CTX, compared to the RD group (P < 0.05). In both groups, osteocalcin, serum calcium, and 25-hydroxyvitamin D levels were significantly higher, while PINP and β-CTX levels were significantly lower after 12 weeks of intervention compared to baseline (P < 0.05).

The human BCA-based dietary nutrition regimen plays a crucial role in improving BMD and bone metabolism, with effects that surpass those of conventional nutrition strategies. The findings of this study provide strong evidence for the nutritional management of AD patients.

Alzheimer's disease (AD) and osteoporosis (OP) pose distinct but interconnected health challenges, both significantly impacting the aging population. AD, a neurodegenerative disorder characterized by memory impairment and cognitive decline, is primarily associated with the accumulation of abnormally folded amyloid beta (Aβ) peptides and neurofibrillary tangles in the brain. OP, a skeletal disorder marked by low bone mineral density, involves dysregulation of bone remodeling and is associated with an increased risk of fractures. Recent studies have revealed an intriguing link between AD and OP, highlighting shared pathological features indicative of common regulatory pathophysiological pathways. In this article, we elucidate the signaling mechanisms that regulate the pathology of AD and OP and offer insights into the intricate network of factors contributing to these conditions. We also examine the role of bone-derived factors in the progression of AD, underscoring the plausibility of bidirectional communication between the brain and the skeletal system. The presence of amyloid plaques in the brain of individuals with AD is akin to the accumulation of brain Aβ in vascular dementia, pointing towards the need for further investigation of shared molecular mechanisms. Moreover, we discuss the role of bone-derived microRNAs that may regulate the pathological progression of AD, providing a novel perspective on the role of skeletal factors in neurodegenerative diseases. The insights presented here should help researchers engaged in exploring innovative therapeutic approaches targeting both neurodegenerative and skeletal disorders in aging populations.

Mild cognitive impairment, dementia and osteoporosis are common diseases of ageing and, with the increasingly ageing global population, are increasing in prevalence. These conditions are closely associated, with shared risk factors, common underlying biological mechanisms and potential direct causal pathways. In this review, the epidemiological and mechanistic links between mild cognitive impairment, dementia and skeletal health are explored. Discussion will focus on how changes in brain and bone signalling can underly associations between these conditions, and will consider the molecular and cellular drivers in the context of inflammation and the gut microbiome. There is a complex interplay between nutritional changes, which may precede or follow the onset of mild cognitive impairment (MCI) or dementia, and bone health. Polypharmacy is common in patients with MCI or dementia, and there are difficult prescribing decisions to be made due to the elevated risk of falls associated with many drugs used for associated problems, which can consequently increase fracture risk. Some medications prescribed for cognitive impairment may directly impact bone health. In addition, patients may have difficulty remembering medication without assistance, meaning that osteoporosis drugs may be prescribed but not taken. Cognitive impairment may be improved or delayed by physical activity and exercise, and there is evidence for the additional benefits of physical activity on falls and fractures. Research gaps and priorities with the aim of reducing the burden of osteoporosis and fractures in people with MCI or dementia will also be discussed.

Observational studies have found associations between reproductive factors and bone density in women. However, the causal relationships are not well understood. This study aims to investigate whether various reproductive factors are causally related to bone density at different skeletal sites using both univariable and multivariable Mendelian randomization (MR) methods.

The study incorporated four reproductive factors, namely, age at menarche (AAM), age at first live birth (AFB), age at menopause (ANM), and age at last live birth (ALB), as well as five distinct skeletal sites, including bone mineral density (BMD), heel calcaneus BMD, ultradistal forearm bone mineral density (FA-BMD), lumbar spine bone mineral density (LS-BMD), and femoral neck bone mineral density (FN-BMD). Univariable two-sample MR and multivariable MR analyses were conducted using data from published genome-wide association studies (GWASs). A total of 150 single nucleotide polymorphisms (SNPs) associated with the four reproductive factors were extracted from GWAS databases. The primary statistical analysis method utilized in this study was the inverse variance weighted (IVW) method.

In the univariate MR analysis, we observed causal connections between four reproductive factors and bone density. Specifically, AAM had a significant impact on BMD and heel calcaneus BMD. Age at first live birth was negatively associated with FA-BMD. Age at last live birth showed a negative correlation with BMD and heel calcaneus BMD. ANM exhibited positive associations with BMD, heel calcaneus BMD, FA-BMD, and LS-BMD. Subsequently, we performed a multivariable MR analysis to examine the combined effects of multiple variables, which confirmed the persistence of associations between age at menopause and bone density at various sites. Additionally, we found a negative correlation between age at last live birth and heel calcaneus BMD.

This study offers a fresh perspective on the prevention of osteoporosis in women, explicitly stating that reproductive factors such as early menopause and late childbirth play a significant predictive role in individual bone density decline. Therefore, when developing osteoporosis screening and management protocols, reproductive factors should be included for a more comprehensive guidance of clinical practice.

In females, Alzheimer's disease (AD) incidences increases as compared to males due to estrogen deficiency after menopause. Estrogen therapy is the mainstay therapy for menopause and associated complications. Estrogen, a hormone with multifaceted physiological functions, has been implicated in AD pathophysiology. Estrogen plays a crucial role in amyloid precursor protein (APP) processing and overall neuronal health by regulating various factors such as brain-derived neurotrophic factor (BDNF), intracellular calcium signalling, death domain-associated protein (Daxx) translocation, glutamatergic excitotoxicity, Voltage-Dependent Anion Channel, Insulin-Like Growth Factor 1 Receptor, estrogen-metabolising enzymes and apolipoprotein E (ApoE) protein polymorphisms. All these factors impact the physiology of postmenopausal women. Estrogen replacement therapies play an important treatment strategy to prevent AD after menopause. However, use of these therapies may lead to increased risks of breast cancer, venous thromboembolism and cardiovascular disease. Various therapeutic approaches have been used to mitigate the effects of estrogen on AD. These include hormone replacement therapy, Selective Estrogen Receptor Modulators (SERMs), Estrogen Receptor Beta (ERβ)-Selective Agonists, Transdermal Estrogen Delivery, Localised Estrogen Delivery, Combination Therapies, Estrogen Metabolism Modulation and Alternative Estrogenic Compounds like genistein from soy, a notable phytoestrogen from plant sources. However, mechanism via which these approaches modulate AD in postmenopausal women has not been explained earlier thoroughly. Present review will enlighten all the molecular mechanisms of estrogen and estrogen replacement therapies in AD. Along-with this, the association between estrogen, estrogen-metabolising enzymes and ApoE protein polymorphisms will also be discussed in postmenopausal AD.

Three review articles have been written that discuss the roles of the central and peripheral nervous systems in fracture healing. While content among the articles is overlapping, there is a key difference between them: the use of artificial intelligence (AI). In one paper, the first draft was written solely by humans. In the second paper, the first draft was written solely by AI using ChatGPT 4.0 (AI-only or AIO). In the third paper, the first draft was written using ChatGPT 4.0 but the literature references were supplied from the human-written paper (AI-assisted or AIA). This project was done to evaluate the capacity of AI to conduct scientific writing. Importantly, all manuscripts were fact checked and extensively edited by all co-authors rendering the final manuscript drafts significantly different from the first drafts.

Unsurprisingly, the use of AI decreased the time spent to write a review. The two AI-written reviews took less time to write than the human-written paper; however, the changes and editing required in all three manuscripts were extensive. The human-written paper was edited the most. On the other hand, the AI-only paper was the most inaccurate with inappropriate reference usage and the AI-assisted paper had the greatest incidence of plagiarism. These findings show that each style of writing presents its own unique set of challenges and advantages. While AI can theoretically write scientific reviews, from these findings, the extent of editing done subsequently, the inaccuracy of the claims it makes, and the plagiarism by AI are all factors to be considered and a primary reason why it may be several years into the future before AI can present itself as a viable alternative for traditional scientific writing.

There were two primary purposes to our reviews. First, to provide an update to the scientific community about the impacts of COVID-19 on musculoskeletal health. Second, was to determine the value of using a large language model, ChatGPT 4.0, in the process of writing a scientific review article. To accomplish these objectives, we originally set out to write three review articles on the topic using different methods to produce the initial drafts of the review articles. The first review article was written in the traditional manner by humans, the second was to be written exclusively using ChatGPT (AI-only or AIO), and the third approach was to input the outline and references selected by humans from approach 1 into ChatGPT, using the AI to assist in completing the writing (AI-assisted or AIA). All review articles were extensively fact-checked and edited by all co-authors leading to the final drafts of the manuscripts, which were significantly different from the initial drafts.

Unfortunately, during this process, it became clear that approach 2 was not feasible for a very recent topic like COVID-19 as at the time, ChatGPT 4.0 had a cutoff date of September 2021 and all articles published after this date had to be provided to ChatGPT, making approaches 2 and 3 virtually identical. Therefore, only two approaches and two review articles were written (human and AI-assisted). Here we found that the human-only approach took less time to complete than the AI-assisted approach. This was largely due to the number of hours required to fact-check and edit the AI-assisted manuscript. Of note, the AI-assisted approach resulted in inaccurate attributions of references (about 20%) and had a higher similarity index suggesting an increased risk of plagiarism. The main aim of this project was to determine whether the use of AI could improve the process of writing a scientific review article. Based on our experience, with the current state of technology, it would not be advised to solely use AI to write a scientific review article, especially on a recent topic.

This Comment represents three review articles on the relationship between Alzheimer's disease, osteoporosis, and fracture in an exploration of the benefits that AI can provide in scientific writing. The first drafts of the articles were written (1) entirely by humans; (2) entirely by ChatGPT 4.0 (AI-only or AIO); and (3) by humans and ChatGPT 4.0 whereby humans selected literature references, but ChatGPT 4.0 completed the writing (AI-assisted or AIA). Importantly, each review article was edited and carefully checked for accuracy by all co-authors resulting in a final manuscript which was significantly different from the original draft.

The human-written article took the most time from start to finish, the AI-only article took the least time, and the AI-assisted article fell between the two. When comparing first drafts to final drafts, the AI-only and AI-assisted articles had higher percentages of different text than the human article. The AI-only paper had a higher percentage of incorrect references in the first draft than the AI-assisted paper. The first draft of the AI-assisted article had a higher similarity score than the other two articles when examined by plagiarism identification software. This writing experiment used time tracking, human editing, and comparison software to examine the benefits and risks of using AI to assist in scientific writing. It showed that while AI may reduce total writing time, hallucinations and plagiarism were prevalent issues with this method and human editing was still necessary to ensure accuracy.

With the recent explosion in the use of artificial intelligence (AI) and specifically ChatGPT, we sought to determine whether ChatGPT could be used to assist in writing credible, peer-reviewed, scientific review articles. We also sought to assess, in a scientific study, the advantages and limitations of using ChatGPT for this purpose. To accomplish this, 3 topics of importance in musculoskeletal research were selected: (1) the intersection of Alzheimer's disease and bone; (2) the neural regulation of fracture healing; and (3) COVID-19 and musculoskeletal health. For each of these topics, 3 approaches to write manuscript drafts were undertaken: (1) human only; (2) ChatGPT only (AI-only); and (3) combination approach of #1 and #2 (AI-assisted). Articles were extensively fact checked and edited to ensure scientific quality, resulting in final manuscripts that were significantly different from the original drafts. Numerous parameters were measured throughout the process to quantitate advantages and disadvantages of approaches.

Overall, use of AI decreased the time spent to write the review article, but required more extensive fact checking. With the AI-only approach, up to 70% of the references cited were found to be inaccurate. Interestingly, the AI-assisted approach resulted in the highest similarity indices suggesting a higher likelihood of plagiarism. Finally, although the technology is rapidly changing, at the time of study, ChatGPT 4.0 had a cutoff date of September 2021 rendering identification of recent articles impossible. Therefore, all literature published past the cutoff date was manually provided to ChatGPT, rendering approaches #2 and #3 identical for contemporary citations. As a result, for the COVID-19 and musculoskeletal health topic, approach #2 was abandoned midstream due to the extensive overlap with approach #3. The main objective of this scientific study was to see whether AI could be used in a scientifically appropriate manner to improve the scientific writing process. Indeed, AI reduced the time for writing but had significant inaccuracies. The latter necessitates that AI cannot currently be used alone but could be used with careful oversight by humans to assist in writing scientific review articles.