Unraveling the role of BMI and blood markers in the relationship between plant-based diets and osteoporosis: A prospective cohort study.

BACKGROUND: The potential adverse effects of plant-based diets on bone health have raised significant concern, while the prospective evidence is limited. This study aimed to evaluate the association between plant-based diet indexes and incident osteoporosis while exploring the underlying mechanisms involved in this relationship. METHODS: The analysis included 202,063 UK Biobank participants conducted between 2006 and 2022. Plant-based diet indexes (hPDI and uPDI) were calculated using the 24-h dietary questionnaire. Cox proportional risk regression and mediation analysis were used to explore the associations of plant-based diet indexes with osteoporosis, estimating the contribution of BMI and blood markers. RESULTS: We found the highest quintile for hPDI (HR = 1.16; 95% CI: 1.05 to 1.28) and uPDI (HR = 1.15; 95% CI: 1.05 to 1.26) were associated with an increased risk of osteoporosis. BMI was identified as an important mediator in the association between hPDI and osteoporosis, with mediation proportions of 46.17%. For blood markers, the mediating (suppressing) effects of C-reactive protein, alkaline phosphatase, and insulin-like growth factor-1 on the association between uPDI (hPDI) and osteoporosis were significant, ranging from 5.63%-16.87% (4.57%-6.22%). CONCLUSION: Adherence to a plant-based diet is associated with a higher risk of osteoporosis, with BMI and blood markers potentially contributing to this relationship. Notably, even a healthy plant-based diet necessitates attention to weight management to mitigate its impact on bone loss. These findings emphasize the importance of personalized dietary recommendations and lifestyle interventions to decrease the risk of osteoporosis.

Lipid metabolism mediates the association between body mass index change and bone mineral density: The Taizhou imaging study.

BACKGROUND: Limited research explores the impact of body mass index (BMI) change on osteoporosis, regarding the role of lipid metabolism. We aimed to cross-sectionally investigate these relationships in 820 Chinese participants aged 55-65 from the Taizhou Imaging Study. METHODS: We used the baseline data collected between 2013 and 2018. T-score was calculated by standardizing bone mineral density and was used for osteoporosis and osteopenia diagnosis. Multinomial logistic regression was used to examine the effect of BMI change on bone health status. Multivariable linear regression was employed to identify the metabolites corrected with BMI change and T-score. Exploratory factor analysis (EFA) and mediation analysis were conducted to ascertain the involvement of the metabolites. RESULTS: BMI increase served as a protective factor against osteoporosis (OR = 0.79[0.71-0.88], P-value<0.001) and osteopenia (OR = 0.88[0.82-0.95], P-value<0.001). Eighteen serum metabolites were associated with both BMI change and T-score. Specifically, high-density lipoprotein (HDL) substructures demonstrated negative correlations (ß = -0.08 to -0.06 and - 0.12 to -0.08, respectively), while very low-density lipoprotein (VLDL) substructions showed positive correlations (ß = 0.09 to 0.10 and 0.10 to 0.11, respectively). The two lipid factors (HDL and VLDL) extracted by EFA acted as mediators between BMI change and T-score (Prop. Mediated = 8.16% and 10.51%, all P-value<0.01). CONCLUSION: BMI gain among Chinese aged 55-65 is beneficial for reducing the risk of osteoporosis. The metabolism of HDL and VLDL partially mediates the effect of BMI change on bone loss. Our research offers novel insights into the prevention of osteoporosis, approached from the perspective of weight management and lipid metabolomics.

Predictive models of Alzheimer's disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal.

BACKGROUND: Mild cognitive impairment has received widespread attention as a high-risk population for Alzheimer's disease, and many studies have developed or validated predictive models to assess it. However, the performance of the model development remains unknown. OBJECTIVE: The objective of this review was to provide an overview of prediction models for the risk of Alzheimer's disease dementia in older adults with mild cognitive impairment. METHOD: PubMed, EMBASE, Web of Science, and MEDLINE were systematically searched up to October 19, 2023. We included cohort studies in which risk prediction models for Alzheimer's disease dementia in older adults with mild cognitive impairment were developed or validated. The Predictive Model Risk of Bias Assessment Tool (PROBAST) was employed to assess model bias and applicability. Random-effects models combined model AUCs and calculated (approximate) 95% prediction intervals for estimations. Heterogeneity across studies was evaluated using the I2 statistic, and subgroup analyses were conducted to investigate sources of heterogeneity. Additionally, funnel plot analysis was utilized to identify publication bias. RESULTS: The analysis included 16 studies involving 9290 participants. Frequency analysis of predictors showed that 14 appeared at least twice and more, with age, functional activities questionnaire, and Mini-mental State Examination scores of cognitive functioning being the most common predictors. From the studies, only two models were externally validated. Eleven studies ultimately used machine learning, and four used traditional modelling methods. However, we found that in many of the studies, there were problems with insufficient sample sizes, missing important methodological information, lack of model presentation, and all of the models were rated as having a high or unclear risk of bias. The average AUC of the 15 best-developed predictive models was 0.87 (95% CI: 0.83, 0.90). DISCUSSION: Most published predictive modelling studies are deficient in rigour, resulting in a high risk of bias. Upcoming research should concentrate on enhancing methodological rigour and conducting external validation of models predicting Alzheimer's disease dementia. We also emphasize the importance of following the scientific method and transparent reporting to improve the accuracy, generalizability and reproducibility of study results. REGISTRATION: This systematic review was registered in PROSPERO (Registration ID: CRD42023468780).

The association between oral health and mild cognitive impairment in community-dwelling older adults.

Background: Older adults with cognitive impairment can experience poor oral health due to reduced self-care ability, yet the impact of various oral health indicators on the cognitive ability remains unclear. We investigated the relationship between oral health indicators and mild cognitive impairment (MCI) in older adults. Methods: A cross-sectional study of 234 older adults aged 65 years or over was performed form January to March 2023 at health screening departments of hospitals. This study used the Mini-mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Activities of Daily Living (ADL), Clinical Dementia Rating (CDR), and Hachinski Ischemic Score (HIS) to measure MCI. Two qualified dentists performed clinical oral examinations (number of teeth lost, dental caries, removable dentures, periodontitis). The other oral health status was measured by subjective assessment questionnaires, and the oral health-related quality of life (OHRQoL) was assessed by Geriatric Oral Health Assessment Index (GOHAI). Results: Of the 234 older adults, 166 had MCI and 68 had normal cognitive ability. The univariate analyses revealed that older adults with poor oral health indicators of dental caries, mastication ability, oral and maxillofacial pain, self-perceived oral health status and OHRQoL had lower cognitive levels. The stepwise logistic regression analysis observed that higher education level (OR = 0.06, 95%CI = 0.007, 0.567) and OHRQoL score (OR = 0.92, 95%CI = 0.878, 0.963) were negatively associated with the presence of MCI. The area under the ROC curve (AUC) of MCI was 0.675 (95% CI: 0.600, 0.749) with a low sensitivity of 41.6% and a moderate specificity of 86.8%. Conclusion: OHRQoL was found to be associated with MCI, implying that OHRQoL may be important in cognitive decline. The GOHAI scale can be used to more easily assess the oral health of older adults, which is important for the timely detection of poor oral status to delay cognitive decline.

Plasma proteomic profiles reveal proteins and three characteristic patterns associated with osteoporosis: A prospective cohort study.

INTRODUCTION: Exploration of plasma proteins associated with osteoporosis can offer insights into its pathological development, identify novel biomarkers for screening high-risk populations, and facilitate the discovery of effective therapeutic targets. OBJECTIVES: The present study aimed to identify potential proteins associated with osteoporosis and to explore the underlying mechanisms from a proteomic perspective. METHODS: The study included 42,325 participants without osteoporosis in the UK Biobank (UKB), of whom 1,477 developed osteoporosis during the follow-up. We used Cox regression and Mendelian randomization analysis to examine the association between plasma proteins and osteoporosis. Machine learning was utilized to explore proteins with strong predictive power for osteoporosis risk. RESULTS: Of 2,919 plasma proteins, we identified 134 significantly associated with osteoporosis, with sclerostin (SOST), adiponectin (ADIPOQ), and creatine kinase B-type (CKB) exhibiting strong associations. Twelve of these proteins showed significant associations with bone mineral density (BMD) T-score at the femoral neck, lumbar spine, and total body. Mendelian randomization further supported causal relationships between 17 plasma proteins and osteoporosis. Moreover, follitropin subunit beta (FSHB), SOST, and ADIPOQ demonstrated high importance in predictive modeling. Utilizing a predictive model built with 10 proteins, we achieved relatively accurate prediction of osteoporosis onset up to 5 years in advance (AUC = 0.803). Finally, we identified three osteoporosis-related protein modules associated with immunity, lipid metabolism, and follicle-stimulating hormone (FSH) regulation from a network perspective, elucidating their mediating roles between various risk factors (smoking, sleep, physical activity, polygenic risk score (PRS), and menopause) and osteoporosis. CONCLUSION: We identified several proteins associated with osteoporosis and highlighted the role of plasma proteins in influencing its progression through three primary pathways: immunity, lipid metabolism, and FSH regulation. This provides further insights into the distinct molecular patterns and pathogenesis of bone loss and may contribute to strengthening early diagnosis and long-term monitoring of the condition.

Infant age inversely correlates with gut carriage of resistance genes, reflecting modifications in microbial carbohydrate metabolism during early life.

The infant gut microbiome is increasingly recognized as a reservoir of antibiotic resistance genes, yet the assembly of gut resistome in infants and its influencing factors remain largely unknown. We characterized resistome in 4132 metagenomes from 963 infants in six countries and 4285 resistance genes were observed. The inherent resistome pattern of healthy infants (N = 272) could be distinguished by two stages: a multicompound resistance phase (Months 0-7) and a tetracycline-mupirocin-ß-lactam-dominant phase (Months 8-14). Microbial taxonomy explained 40.7% of the gut resistome of healthy infants, with Escherichia (25.5%) harboring the most resistance genes. In a further analysis with all available infants (N = 963), we found age was the strongest influencer on the resistome and was negatively correlated with the overall resistance during the first 3 years (p < 0.001). Using a random-forest approach, a set of 34 resistance genes could be used to predict age (R 2 = 68.0%). Leveraging microbial host inference analyses, we inferred the age-dependent assembly of infant resistome was a result of shifts in the gut microbiome, primarily driven by changes in taxa that disproportionately harbor resistance genes across taxa (e.g., Escherichia coli more frequently harbored resistance genes than other taxa). We performed metagenomic functional profiling and metagenomic assembled genome analyses whose results indicate that the development of gut resistome was driven by changes in microbial carbohydrate metabolism, with an increasing need for carbohydrate-active enzymes from Bacteroidota and a decreasing need for Pseudomonadota during infancy. Importantly, we observed increased acquired resistance genes over time, which was related to increased horizontal gene transfer in the developing infant gut microbiome. In summary, infant age was negatively correlated with antimicrobial resistance gene levels, reflecting a composition shift in the gut microbiome, likely driven by the changing need for microbial carbohydrate metabolism during early life.

Investigating the link between gut microbiome and bone mineral density: The role of genetic factors.

Osteoporosis is a complex metabolic bone disease that severely undermines the quality of life and overall health of the elderly. While previous studies have established a close relationship between gut microbiome and host bone metabolism, the role of genetic factors has received less scrutiny. This research aims to identify potential taxa associated with various bone mineral density states, incorporating assessments of genetic factors. Fecal microbiome profiles from 605 individuals (334 females and 271 males) aged 55-65 from the Taizhou Imaging Study with osteopenia (n = 270, 170 women) or osteoporosis (n = 94, 85 women) or normal (n = 241, 79 women) were determined using shotgun metagenomic sequencing. The linear discriminant analysis was employed to identify differentially enriched taxa. Utilizing the Kyoto Encyclopedia of Genes and Genomes for annotation, functional pathway analysis was conducted to identify differentially metabolic pathways. Polygenic risk score for osteoporosis was estimated to represent genetic susceptibility to osteoporosis, followed by stratification and interaction analyses. Gut flora diversity did not show significant differences among various bone mineral groups. After multivariable adjustment, certain species, such as Clostridium leptum, Fusicatenibacter saccharivorans and Roseburia hominis, were enriched in osteoporosis patients. Statistically significant interactions between the polygenic risk score and taxa Roseburia faecis, Megasphaera elsdenii were observed (P for interaction = 0.005, 0.018, respectively). Stratified analyses revealed a significantly negative association between Roseburia faecis and bone mineral density in the low-genetic-risk group (ß = -0.045, P < 0.05), while Turicimonas muris was positively associated with bone mineral density in the high-genetic-risk group (ß = 4.177, P < 0.05) after multivariable adjustments. Functional predictions of the gut microbiome indicated an increase in pathways related to structural proteins in high-genetic-risk patients, while low-genetic-risk patients exhibited enrichment in enzyme-related pathways. This study emphasizes the association between gut microbes and bone mass, offering new insights into the interaction between genetic background and gut microbiome.

Unraveling the role of serum metabolites in the relationship between plant-based diets and bone health in community-dwelling older adults.

The potential adverse effects of the plant-based dietary pattern on bone health have received widespread attention. However, the biological mechanisms underlying the adverse effects of plant-based diets on bone health remain incompletely understood. The objective of this study was to identify potential biomarkers between plant-based diets and bone loss utilizing metabolomic techniques in the Taizhou Imaging Study (TIS) (N = 788). Plant-based diet indexes (overall plant-based diet index (PDI), healthy plant-based diet index (hPDI), and unhealthy plant-based diet index (uPDI)) were calculated using the food frequency questionnaire, and bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. A multinomial logistic regression was used to explore the associations of plant-based diet indexes with bone loss. Furthermore, mediation analysis and exploratory factor analysis (EFA) were performed to explore the mediated effects of metabolites on the association of plant-based diets with BMD T-score. Our results showed that higher hPDI and uPDI were positively associated with bone loss. Moreover, nineteen metabolites were significantly associated with BMD T-score, among them, seven metabolites were associated with uPDI. Except for cholesterol esters in VLDL-1, the remaining six metabolites significantly mediated the negative association between uPDI and BMD T-score. Interestingly, we observed that the same six metabolites mediated the positive association between fresh fruit and BMD T-score. Collectively, our results support the deleterious effects of plant-based diets on bone health and discover the potential mediation effect of metabolites on the association of plant-based diets with bone loss. The findings offer valuable insights that could optimize dietary recommendations and interventions, contributing to alleviate the potential adverse effects associated with plant-based diets.

Higher remnant cholesterol is associated with an increased risk of amnestic mild cognitive impairment: a community-based cross-sectional study.

Background and aims: Amnestic mild cognitive impairment (aMCI) is the most common subtype of MCI, which carries a significantly high risk of transitioning to Alzheimer's disease. Recently, increasing attention has been given to remnant cholesterol (RC), a non-traditional and previously overlooked risk factor. The aim of this study was to explore the association between plasma RC levels and aMCI. Methods: Data were obtained from Brain Health Cognitive Management Team in Wuhan (https://hbtcm.66nao.com/admin/). A total of 1,007 community-dwelling elders were recruited for this project. Based on ten tools including general demographic data, cognitive screening and some exclusion scales, these participants were divided into the aMCI (n = 401) and normal cognitive groups (n = 606). Physical examinations were conducted on all participants, with clinical indicators such as blood pressure, blood sugar, and blood lipids collected. Results: The aMCI group had significantly higher RC levels compared to the normal cognitive group (0.64 ± 0.431 vs. 0.52 ± 0.447 mmol/L, p < 0.05). Binary logistics regression revealed that occupation (P<0.001, OR = 0.533, 95%CI: 0.423-0.673) and RC (p = 0.014, OR = 1.477, 95% CI:1.081-2.018) were associated factors for aMCI. Partial correlation analysis, after controlling for occupation, showed a significant negative correlation between RC levels and MoCA scores (r = 0.059, p = 0.046), as well as Naming scores (r = 0.070, p = 0.026). ROC curve analysis demonstrated that RC levels had an independent predictive efficacy in predicting aMCI (AUC = 0.580, 95%CI: 0.544 ~ 0.615, P < 0.001). Conclusion: Higher RC levels were identified as an independent indicator for aMCI, particularly in the naming cognitive domain among older individuals. Further longitudinal studies are necessary to validate the predictive efficacy of RC.

Biopanning and characterization of peptides with Fe3O4 nanoparticles-binding capability via phage display random peptide library technique.

Functionalization of inorganic nanoparticles (NPs) play an important role in biomedical applications. A proper functionalization of NPs can improve biocompatibility, avoid a loss of bioactivity, and further endow NPs with unique performances. Modification with vairous specific binding biomolecules from random biological libraries has been explored. In this work, two 7-mer peptides with sequences of HYIDFRW and TVNFKLY were selected from a phage display random peptide library by using ferromagnetic NPs as targets, and were verified to display strong binding affinity to Fe3O4 NPs. Fourier transform infrared spectrometry, fluorescence microscopy, thermal analysis and X-ray photoelectron spectroscopy confirmed the presence of peptides on the surface of Fe3O4 NPs. Sequence analyses revealed that the probable binding mechanism between the peptide and Fe3O4 NPs might be driven by Pearson hard acid-hard base specific interaction and hydrogen bonds, accompanied with hydrophilic interactions and non-specific electrostatic attractions. The cell viability assay indicated a good cytocompatibility of peptide-bound Fe3O4 NPs. Furthermore, TVNFKLY peptide and an ovarian tumor cell A2780 specific binding peptide (QQTNWSL) were conjugated to afford a liner 14-mer peptide (QQTNWSLTVNFKLY). The binding and targeting studies showed that 14-mer peptide was able to retain both the strong binding ability to Fe3O4 NPs and the specific binding ability to A2780 cells. The results suggested that the Fe3O4-binding peptides would be of great potential in the functionalization of Fe3O4 NPs for the tumor-targeted drug delivery and magnetic hyperthermia.