Efficacy of a theory-based and setting-based intervention in promoting strength training among older adults in Hong Kong: A clustered randomized controlled trial.

The present study aimed to examine the efficacy of an intervention, based on the Health Belief Model (HBM) and social support, in promoting strength training (ST) among older adults. A two-arm clustered randomized controlled trial (RCT) was conducted among 235 older adults from eight elderly centers in Hong Kong. The intervention group engaged in a 6-month intervention comprising ST sessions, exercise consultations, social gatherings, and a buddy program, while the control group participated in social gatherings. Assessments were conducted at baseline (Month 0), post-intervention (Month 6), and 3-month follow-up (Month 9), with primary outcome being the prevalence of meeting the American College of Sports Medicine (ACSM) recommendations of ST. Results showed that the intervention group reported significantly higher prevalence of meeting ACSM recommendations for ST at both post-intervention and follow-up. Linear mixed models showed significant interaction effect between condition and time on perceived susceptibility of sarcopenia and muscle strength and significant condition effect on self-efficacy for ST, perceived severity of sarcopenia, perceived barriers of ST, and intention to perform ST. Findings suggest that the intervention, guided by HBM and social support, improves older adults' ST participation, muscle strength, perceptions on sarcopenia, and self-efficacy for ST, which offers great potential for broader application in other settings.

Trajectories of depressive symptom and its association with air pollution: evidence from the Mr. OS and Ms. OS Hong Kong cohort study.

BACKGROUND: Depression is a global health priority. Maintaining and delaying depressive symptoms in older adults is a key to healthy aging. This study aimed to identify depressive symptom trajectories, predictors and mortality, while also exploring the relationship between air quality and depressive symptoms in older adults in the Hong Kong community over 14 years. METHODS: This study is a longitudinal study in Hong Kong. The target population was community-dwelling older adults over age 65. Depressive symptoms were measured by the Geriatric Depression Scale (GDS-15). Group-based trajectory model was used to identify heterogeneity in longitudinal changes over 14 years and examine the associations between baseline variables and trajectories for different cohort members using multinomial logistic regression. The Kaplan-Meier method was employed to conduct survival analysis and explore the variations in survival probabilities over time among different trajectory group. Linear mixed model was used to explore the relationship between air quality and depressive symptoms. RESULTS: A total of 2828 older adults were included. Three different trajectories of depressive symptoms in older people were identified: relatively stable (15.4%), late increase (67.1%) and increase (17.5%). Female, more number of chronic diseases, poor cognitive function, and poor health-related quality of life (HRQOL) were significantly associated with other less favorable trajectories compared with participants with stable levels of depressive symptoms. The late increase group had a lower mortality rate than the relatively stable and increased groups. Lower baseline ambient air pollutant exposure to NO2 over 14 years was significantly associated with fewer depressive symptoms. CONCLUSIONS: In this study, we found that a late increase in depressive symptoms was the predominant trend in older Chinese people in Hong Kong. Poorer HRQOL was predictive of less favorable trajectories of depressive symptoms. Ambient air pollution was associated with depressive symptoms. This novel observation strengthens the epidemiological evidence of longitudinal changes in depressive symptoms and associations with late-life exposure to air pollution.

Health literacy mediates the relationships of cognitive and physical functions with health-related quality of life in older adults.

Background: Declining cognitive function (CF) and physical function (PF) relate to poorer health-related quality of life (HRQoL) in older adults. As health literacy (HL) facilitates health information utilization, it may mediate links between functionality and HRQoL appraisals. This study examined HL as an intermediary between joint CF and PF contributions and HRQoL in Hong Kong older adults. Methods: 490 older adults aged 50-80 years completed assessments from March to July 2021. Health Literacy Survey Questionnaire Short Form 12 questions (HLS-SF12), Montreal Cognitive Assessment (MoCA), Senior Fitness Test (SFT) and 12-Item Short-Form Health Survey version 2 (SF-12v2) were used to assess HL, CF, PF and HRQoL, respectively. Path analysis tested a model with HL mediating CF/PF predictors and HRQoL outcome. Results: Results for direct effects indicated that CF significantly associated with PF (ß = 0.115, SE = 0.012, p < 0.001), PF significantly connected to HL (ß = 0.101, SE = 0.022, p < 0.001), and HL significantly related to HRQoL (ß = 0.457, SE = 0.049, p < 0.001). Meanwhile, PF significantly linked to HRQoL directly (ß = 0.156, SE = 0.025, p < 0.001) as well as indirectly (ß = 0.046, 95% CI [0.028, 0.067]). Significant mediating effect of HL was found on the relationship of PF and HRQoL. Conclusion: Findings confirm CF and PF joint impacts on HL and HRQoL in older adults, elucidating HL's mediating role in translating functionality levels into HRQoL. Fostering enduring health knowledge access may thus buffer effects of age-related declines on well-being. Results can inform interventions leveraging this pathway to promote resilient trajectories.

Using blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification.

INTRODUCTION: Blood protein biomarkers demonstrate potential for Alzheimer's disease (AD) diagnosis. Limited studies examine the molecular changes in AD blood cells. METHODS: Bulk RNA-sequencing of blood cells was performed on AD patients of Chinese descent (n = 214 and 26 in the discovery and validation cohorts, respectively) with normal controls (n = 208 and 38 in the discovery and validation cohorts, respectively). Weighted gene co-expression network analysis (WGCNA) and deconvolution analysis identified AD-associated gene modules and blood cell types. Regression and unsupervised clustering analysis identified AD-associated genes, gene modules, cell types, and established AD classification models. RESULTS: WGCNA on differentially expressed genes revealed 15 gene modules, with 6 accurately classifying AD (areas under the receiver operating characteristics curve [auROCs] > 0.90). These modules stratified AD patients into subgroups with distinct disease states. Cell-type deconvolution analysis identified specific blood cell types potentially associated with AD pathogenesis. DISCUSSION: This study highlights the potential of blood transcriptome for AD diagnosis, patient stratification, and mechanistic studies. HIGHLIGHTS: We comprehensively analyze the blood transcriptomes of a well-characterized Alzheimer's disease cohort to identify genes, gene modules, pathways, and specific blood cells associated with the disease. Blood transcriptome analysis accurately classifies and stratifies patients with Alzheimer's disease, with some gene modules achieving classification accuracy comparable to that of the plasma ATN biomarkers. Immune-associated pathways and immune cells, such as neutrophils, have potential roles in the pathogenesis and progression of Alzheimer's disease.

Association of dietary protein intake, inflammation with muscle mass, physical performance and incident sarcopenia in Chinese community-dwelling older adults.

OBJECTIVES: Inflammation and impaired muscle synthesis are important factors of sarcopenia. Plant protein may reduce inflammation but may not be as efficient as animal protein in providing essential amino acids. We therefore examined the associations between dietary protein intake and changes in muscle mass and physical performance, incident sarcopenia, and the interaction effect of inflammation. DESIGN: Prospective cohort study. SETTING: The Mr. OS and Ms. OS (Hong Kong) cohort. PARTICIPANTS: A total of 2,811 sarcopenia-free participants and 569 sarcopenia participants aged ≥65 years were recruited from communities. MEASUREMENTS: Dietary protein intake was assessed using a validated food frequency questionnaire. Serum high-sensitivity C-reactive protein (hs-CRP) was measured. Linear regression examined the associations between dietary protein intake and 4-year changes in muscle mass and physical performance. Cox regression examined the association between dietary protein intake and incident sarcopenia. RESULTS: Higher plant protein intake, but not total and animal protein, was associated with less decline in muscle mass and gait speed among sarcopenia-free participants. Conversely, higher ratio of animal-to-plant protein was associated with reduced muscle mass loss among participants with sarcopenia. The highest tertile of plant protein intake was associated with lower incident sarcopenia risk (HR: 0.75, 95% CI: 0.57-0.98; P-trend = 0.034) compared to the lowest tertile. Notably, this association was observed among participants with higher serum hs-CRP levels (HR: 0.57, 95% CI: 0.34-0.95), but not in those with lower hs-CRP levels. CONCLUSION: Dietary animal and plant protein intake have differential associations with muscle mass and physical performance in older adults with and without sarcopenia. The role of plant protein in preventing sarcopenia involves modulation of inflammation.

A blood-based multi-pathway biomarker assay for early detection and staging of Alzheimer's disease across ethnic groups.

INTRODUCTION: Existing blood-based biomarkers for Alzheimer's disease (AD) mainly focus on its pathological features. However, studies on blood-based biomarkers associated with other biological processes for a comprehensive evaluation of AD status are limited. METHODS: We developed a blood-based, multiplex biomarker assay for AD that measures the levels of 21 proteins involved in multiple biological pathways. We evaluated the assay's performance for classifying AD and indicating AD-related endophenotypes in three independent cohorts from Chinese or European-descent populations. RESULTS: The 21-protein assay accurately classified AD (area under the receiver operating characteristic curve [AUC] = 0.9407 to 0.9867) and mild cognitive impairment (MCI; AUC = 0.8434 to 0.8945) while also indicating brain amyloid pathology. Moreover, the assay simultaneously evaluated the changes of five biological processes in individuals and revealed the ethnic-specific dysregulations of biological processes upon AD progression. DISCUSSION: This study demonstrated the utility of a blood-based, multi-pathway biomarker assay for early screening and staging of AD, providing insights for patient stratification and precision medicine. HIGHLIGHTS: The authors developed a blood-based biomarker assay for Alzheimer's disease. The 21-protein assay classifies AD/MCI and indicates brain amyloid pathology. The 21-protein assay can simultaneously assess activities of five biological processes. Ethnic-specific dysregulations of biological processes in AD were revealed.

East Asians' T-scores for the diagnosis of osteoporosis should be calculated using ethnicity- and gender-specific BMD reference ranges: justifications.

The 2013 ISCD consensus recommended a Caucasian female reference database for T-score calculation in men, which says "A uniform Caucasian (non-race adjusted) female reference database should be used to calculate T-scores for men of all ethnic groups." However, this statement was recommended for the US population, and no position was taken with respect to BMD reference data or ethnicity matching outside of the USA. In East Asia, currently, a Japanese BMD reference database is universally adopted in Japan for clinical DXA diagnosis, while both local BMD and Caucasian BMD reference databases are in use in Mainland China, South Korea, Taiwan, and Singapore. In this article, we argue that an ethnicity- and gender-specific BMD database should be used for T-score calculations for East Asians, and we list the justifications why we advocate so. Use of a Caucasian BMD reference database leads to systematically lower T-scores for East Asians and an overestimation of the prevalence of osteoporosis. Using a female BMD reference database to calculate T-scores for male patients leads to higher T-score values and an underestimation of the prevalence of osteoporosis. Epidemiological evidence does not support using a female BMD reference database to calculate T-scores for men. We also note that BMD reference databases collected in Asia should be critically evaluated for their quality.

Much lower prevalence and severity of spine degenerative changes among older Chinese women than among older Caucasian women and its implication for the interpretation of lumbar spine BMD T-score for Chinese women.

The prevalence and severity of spine degenerative changes have been noted to be lower among older Chinese women than among older Caucasian women. Spine degenerative changes associated with marginal osteophytosis, trabecular thickening, subchondral sclerosis, facet joint arthrosis, and disc space narrowing can all lead to artificially higher spine areal bone mineral density (BMD). The lower prevalence and severity of spine degeneration have important implications for the interpretation of spine areal BMD reading for Chinese women. With fewer contributions from spine degenerative changes, following natural aging, the declines of population group means of spine BMD and T-score are faster for Chinese women than for Caucasian women. While a cutpoint T-score ≤ -2.5 for defining spine densitometric osteoporosis is recommended for Caucasian women, for Chinese women the same cutpoint T-score of ≤ -2.5 inflates the estimated osteoporosis prevalence based on spine BMD measure. In addition to the use of an ethnicity-specific BMD reference database, a stricter cutpoint T-score for defining spine densitometric osteoporosis among older Chinese women should be applied.

Revision of the 1994 World Health Organization T-score definition of osteoporosis for use in older East Asian women and men to reconcile it with their lifetime risk of fragility fracture.

The 1994 WHO criterion of a T-score ≤ -2.5 for densitometric osteoporosis was chosen because it results in a prevalence commensurate with the observed lifetime risk of fragility fractures in Caucasian women aged ≥ 50 years. Due to the much lower risk of fragility fracture among East Asians, the application of the conventional WHO criterion to East Asians leads to an over inflated prevalence of osteoporosis, particularly for spine osteoporosis. According to statistical modeling and when a local BMD reference is used, we tentatively recommend the cutpoint values for T-score of femoral neck, total hip, and spine to be approximately -2.7, -2.6, and -3.7 for Hong Kong Chinese women. Using radiographic osteoporotic vertebral fracture as a surrogate clinical endpoint, we empirically demonstrated that a femoral neck T-score of -2.77 for Chinese women was equivalent to -2.60 for Italian women, a spine T-score of -3.75 for Chinese women was equivalent to -2.44 for Italian women, and for Chinese men a femoral neck T-score of -2.77 corresponded to spine T-score of -3.37. For older Chinese men, we tentatively recommend the cutpoint values for T-score of femoral neck, total hip, and spine to be approximately -2.7, -2.6, and -3.2. With the BMD reference published by IKi et al. applied, T-score of femoral neck, total hip, and spine of -2.75, -3.0, and -3.9 for Japanese women will be more in line with the WHO osteoporosis definition. The revised definition of osteoporosis cutpoint T-scores for East Asians will allow a more meaningful international comparison of disease burden.

Quantity and Variety of Fruit and Vegetable Intake with Changes in Measures of Adiposity among Community-Dwelling Chinese Older Adults.

BACKGROUND: The association between fruit and vegetable intake, considering both quantity and variety, and weight change among older adults remains controversial. We aimed to investigate the association between fruit and vegetable intake, variety, and changes in adiposity measures among community-dwelling Chinese older adults. METHODS: A total of 2944 participants aged ≥65 years from Hong Kong communities were included. Fruit and vegetable intake was estimated by a locally validated food frequency questionnaire. Adiposity measures (weight, body mass index (BMI), waist circumference, fat and lean mass) were assessed at baseline and the year four follow-up. Restricted cubic spline and logistic models were performed to estimate the associations between fruit and vegetable intake, variety, and changes in adiposity measures. RESULTS: A nonlinear association between vegetable intake and weight change was found, following a J-shape curve. Increased vegetable intake was associated with less increases in weight, BMI, and fat mass among those below median intakes. However, these associations became insignificant at higher intakes (all P-nonlinearity < 0.05). The ORs (95% CIs) for weight gain across the quartiles of vegetable intake were as follows: 1.00 (reference), 0.81 (0.56, 1.17), 0.55 (0.36, 0.83), and 0.88 (0.58, 1.33). Similar patterns were observed in overweight and normal weight participants, but not in those with low body weight. No associations with weight change were found for fruit intake or fruit and vegetable variety. CONCLUSIONS: Moderate vegetable intake was associated with less weight gain among community-dwelling Chinese older adults, but not in those with low body weight. No association was observed between fruit intake or variety with weight change.

Balance Confidence Modulates the Association of Gait Speed With Falls in Older Fallers: A Prospective Cohort Study.

OBJECTIVES: Self-perceived balance confidence (BC) and gait speed influence falls. Whether they modulate each other in fall prediction stays uncertain. This study examined whether and how BC modulated the association between gait speed and falls. DESIGN: Prospective observational cohort study. SETTING AND PARTICIPANTS: Older adults who were community-dwelling, ≥65 years old, able to walk for 10 meters independently, and had 1 or more falls in the past year were assessed at a research clinic. METHODS: Participants were followed up trimonthly for 12 months after the baseline. Optimal cutoff values for gait speed for prospective falls were identified by classification and regression tree analysis. Associations among gait speed, BC, and falls were estimated with negative binomial regression models. Subgroup analyses for high and low BC were performed. Covariates such as basic demographics, generic cognition, fall histories, and other physical functions were adjusted. RESULTS: During the follow-up period, 65 (14%) of the 461 included participants (median age 69.0 ± 10.0 years, range 60-92) reported 83 falls in total. In both the pooled and subgroup analyses for the low- and high-BC groups, the high-speed subgroup (≥1.30 m/s) showed an increased fall risk compared with the moderate-speed subgroup (≥0.81 and <1.30 m/s) [adjusted odds ratio (OR), 1.84-2.37; 95% CI, 1.26-3.09]. A statistically significant linear association between gait speed and falls was shown in the high-BC group. In the low-BC group, a u-shaped association was evident (adjusted OR, 2.19-2.44; 95% CI, 1.73-3.19) with elevated fall risks in both the high- and low-speed subgroups compared with the moderate-speed subgroup (adjusted OR, 1.84-3.29; 95% CI, 1.26-4.60). CONCLUSIONS AND IMPLICATIONS: BC modulated the association between gait speed and falls. There were linear and nonlinear associations between gait speed and falls in people with high and low BC, respectively. Clinicians and researchers should consider the effects of BC when predicting falls with gait speed.

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction.

BACKGROUND: The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS: We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS: The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION: Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.

Polygenic diseases, such as Alzheimer's disease (AD), are those caused by the interplay between multiple genetic risk factors. Statistical models can be used to predict disease risk based on a person's genetic profile. However, there are limitations to existing methods, while emerging methods such as deep learning may improve risk prediction. Deep learning involves computer-based software learning from patterns in data to perform a certain task, e.g. predict disease risk. Here, we test whether deep learning models can help to predict AD risk. Our models not only outperformed existing methods in modeling AD risk, they also allow us to estimate an individual's risk of AD and determine the biological processes that may be involved in AD. With further testing and optimization, deep learning may be a useful tool to help accurately predict risk of AD and other diseases.

Conversion of osteoporotic-like vertebral fracture severity score to osteoporosis T-score equivalent status: A framework study for older Chinese men.

Objectives: To define what portion of older community men with what severity of radiographic osteoporotic-like vertebral fracture (OLVF) correspond to what low T-score status. Methods: There were 755 community Chinese men (age: 76.4 ± 6.7 years) with thoracic and lumbar spine radiographs, and hip and lumbar spine bone mineral density measures. For each vertebra in a subject, a score of 0, -0.5, -1, -1.5, -2, -2.5, and -3 was assigned for no OLVF or OLVF of <20%, ≥ 20-25%, ≥ 2 5%-1/3, ≥ 1/3-40%, ≥ 40%-2/3, and ≥ 2/3 vertebral height loss, respectively. OLVFss was defined as the summed score of vertebrae T4 to L5. OLVFss and T-scores were ranked from the smallest to the largest values. Results: OLVFss of -2, -2.5, -3, corresponded to a mean femoral neck T-score of -2.297 (range: -2.355∼-2.247), -2.494 (range: -2.637∼ -2.363), and -2.773 (range: -2.898∼-2.643), a mean hip T-score of-2.311 (range: -2.420∼-2.234), -2.572 (range: -2.708∼-2.432), -2.911 (range: -3.134∼-2.708), a mean lumbar spine T-score of -2.495 (range: -2.656∼-2.403), -2.931 (range: -3.255∼-2.664), and -3.369 (range: -3.525∼-3.258). The Pearson correlation value of OLVFss and T-score of femoral neck, hip and lumbar spine was r = 0.21, 0.26, and 0.22 (all P < 0.0001). Conclusions: A single severe grade radiological OLVF (≥ 40% height loss) or OLVFss ≤ -2.5 suggest the subject is osteoporotic, and a single collapse grade (≥ 2/3 height loss) OLVF or OLVFss ≤ -3 meets osteoporosis diagnosis criterion. The results highlight the difficulty of diagnosing osteoporotic vertebral fractures among Chinese older men.

Schmorl's nodes are associated with prevalent osteoporotic vertebral fracture and low bone mineral density: a population-based thoracic spine MRI study in older men and women.

Background: Schmorl's node (SN) corresponds to nucleus pulposus herniation into the vertebral spongy bone with thickened trabeculae around the formed node. We hypothesize that a pathway may exist that: osteoporosis → weakened endplate → SN development ↔ endplate fracture of an osteoporotic vertebra. Methods: For osteoporotic fractures in men (MrOS) and in women (MsOS) Hong Kong studies, at 14-year follow-up, thoracic spine magnetic resonance imaging (MRI) was sampled in 270 males (mean: 82.9±3.7 years) and 150 females (mean: 81.5±4.3 years). SN and Modic change were assessed as existed or not existed. For posterior disc protrusion, ligamentum flavum ossification, and spinal canal stenosis, semi-quantitative gradings were applied. For each vertebra in women, a score of 0, 0.5, 1, 1.5, 2, 2.5, 3 was assigned for no osteoporotic vertebral fracture (OVF) or OVF of <1/5, ≥1/5-1/4, ≥1/4-1/3, ≥1/3-2/5, ≥2/5-2/3, and ≥2/3 vertebral height loss, respectively, and a summed score was calculated by summing up the scores of vertebrae T1 to T12. For men, those of minimal grade were not considered as OVF and assigned a '0' score. Results: SN prevalence in women (55.5%) almost doubled that in men (25.9%). SN was statistically significantly correlated with lower bone mineral density (BMD) derived femoral neck T-score, while the other four spine degeneration changes were not statistically significantly correlated with the T-score. SN were statistically significantly correlated with OVF score. Subjects with SN were more likely to have OVF, with odds ratio for men of 4.32 [95% confidence interval (CI): 1.70-11.00, P=0.002] and odds ratio for women of 3.28 (95% CI: 1.23-8.74, P=0.018). Conclusions: Among older population, many features of SN parallel those of OVF.

Osteoporotic-like vertebral fracture with less than 20% height loss is associated with increased further vertebral fracture risk in older women: the MrOS and MsOS (Hong Kong) year-18 follow-up radiograph results.

Background: For osteoporotic fractures in men (MrOS) and in women (MsOS) (Hong Kong) baseline (BL) study, Chinese men and women ≥65 years were recruited during 2001 to 2003. This study presents the year-18 follow-up (FU) results. We were particularly interested in whether women with 'minimal' grade osteoporotic-like vertebral fracture (OLVF) of <20% height loss have an increased vertebral fracture (VF) risk than those without BL OLVF. Methods: At year-18 FU, spine radiography was performed on 144 males (mean: 87.4±3.1 years) and 156 females (mean: 87.0±3.2 years). OLVF classification included no OLVF (grade 0), and OLVFs with <20%, ≥20-25%, ≥25%-1/3, ≥1/3-40%, ≥40%-2/3, ≥2/3 height loss (grades 1-6). With an existing OLVF, a further height loss of ≥15% was an OLVF progression. A new incident OLVF was a change from grade 0 to ≥ grade 2 or to grade 1 with the appearance of endplate and/or cortex fracture (ECF) during FU. Both OLVF progression and incident OLVF were considered incident VF. Acquired short vertebra (aSV) was defined as with decreased vertebral anterior and middle heights, while without anterior wedging and bi-concave changes, and only those with at least two adjacent aSVs were recorded as aSV cases. Results: For subjects without BL OLVF, 12.5% of the males and 27.1% of the females had incident VF. For subjects with BL OLVF of ≥20% height loss, males' and females' incident VF rate were 20% and 66.7% respectively. Females subjects with BL minimal OLVF, while all without radiographic ECF, had an incident VF rate of 59.3% during the FU. For males with and without aSV, 11.8% and 15% have incident fracture of other vertebrae. For females with and without aSV, 35.3% and 34.5% have incident fracture of other vertebrae. Recovery from minimal or mild grades OLVF to normal shape was observed in a number of cases. Conclusions: OLVF with less than 20% height loss is associated with increased VF risk in older females, but not in older males. Acquired short vertebra (SV) is not associated with increased incident fracture risk for other vertebrae, both for females and males. OLVF among older subjects can repair and heal.

Speaker-turn aware diarization for speech-based cognitive assessments.

Introduction: Speaker diarization is an essential preprocessing step for diagnosing cognitive impairments from speech-based Montreal cognitive assessments (MoCA). Methods: This paper proposes three enhancements to the conventional speaker diarization methods for such assessments. The enhancements tackle the challenges of diarizing MoCA recordings on two fronts. First, multi-scale channel interdependence speaker embedding is used as the front-end speaker representation for overcoming the acoustic mismatch caused by far-field microphones. Specifically, a squeeze-and-excitation (SE) unit and channel-dependent attention are added to Res2Net blocks for multi-scale feature aggregation. Second, a sequence comparison approach with a holistic view of the whole conversation is applied to measure the similarity of short speech segments in the conversation, which results in a speaker-turn aware scoring matrix for the subsequent clustering step. Third, to further enhance the diarization performance, we propose incorporating a pairwise similarity measure so that the speaker-turn aware scoring matrix contains both local and global information across the segments. Results: Evaluations on an interactive MoCA dataset show that the proposed enhancements lead to a diarization system that outperforms the conventional x-vector/PLDA systems under language-, age-, and microphone-mismatch scenarios. Discussion: The results also show that the proposed enhancements can help hypothesize the speaker-turn timestamps, making the diarization method amendable to datasets without timestamp information.

Conversion of osteoporotic vertebral fracture severity score to osteoporosis T-score equivalent status: a framework and a comparative study of Hong Kong Chinese and Rome Caucasian older women.

We explored how the severity of radiological osteoporotic vertebral fracture (OVF) can be converted to the equivalent T-score values. INTRODUCTION: To perform a study to define what portion of older community women with what severity of radiographic OVF correspond to what low T-score status. METHODS: There were age-matched 301 Italian community women and 301 Chinese community women (sub-group A, age, 73.6 ± 6.1 years). In addition, Chinese sub-groups B and C included 110 community women (age, 68.9 ± 5.5 years) and 101 community women (age: 82.2 ± 4.3 years), respectively. For each vertebra in women, a score of 0, - 0.5, - 1, - 1.5, - 2, - 2.5, and - 3 was assigned for no OVF or OVF of < 20%, ≥ 20 ~ 25%, ≥ 25% ~ 1/3, ≥ 1/3 ~ 40%, ≥ 40%-2/3, and ≥ 2/3 vertebral height loss, respectively, OVFss was defined as the summed score of vertebrae T4 to L5. OVFss and T-scores were ranked from the smallest to the largest values. RESULTS: For the Chinese total group (sub-groups A, B, and C together), OVFss = - 1 corresponded to lowest T-score (lowest T-score of lumbar spine, femoral neck, and total hip) of - 3.4 ~ - 3.2. OVFss ≤ - 1.5 corresponded to femoral neck T-score ≤ - 2.5. OVFss = -1.5 corresponded to a mean femoral neck T-score of - 3.0, - 2.6, and - 2.4, among Chinese sub-groups B, A, and C subjects, respectively. For Italians, all cases with OVFss ≤ - 1 had lowest T-score ≤ - 2.5. For cases with femoral neck T-score = - 2.5, 41.7% had OVFss = - 1.5, and 58.3% had OVFss = - 1. CONCLUSION: For older women, statistically OVFss ≤ - 1 suggests this subject is osteoporotic according to lowest T-score. If using femoral neck T-score, OVFss ≤ - 1.5 qualifies osteoporosis diagnosis.

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study.

BACKGROUND: There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typically involving expensive and sometimes invasive tests not commonly available outside highly specialised clinical settings. We aimed to develop a deep learning algorithm that could use retinal photographs alone, which is the most common method of non-invasive imaging the retina to detect Alzheimer's disease-dementia. METHODS: In this retrospective, multicentre case-control study, we trained, validated, and tested a deep learning algorithm to detect Alzheimer's disease-dementia from retinal photographs using retrospectively collected data from 11 studies that recruited patients with Alzheimer's disease-dementia and people without disease from different countries. Our main aim was to develop a bilateral model to detect Alzheimer's disease-dementia from retinal photographs alone. We designed and internally validated the bilateral deep learning model using retinal photographs from six studies. We used the EfficientNet-b2 network as the backbone of the model to extract features from the images. Integrated features from four retinal photographs (optic nerve head-centred and macula-centred fields from both eyes) for each individual were used to develop supervised deep learning models and equip the network with unsupervised domain adaptation technique, to address dataset discrepancy between the different studies. We tested the trained model using five other studies, three of which used PET as a biomarker of significant amyloid ß burden (testing the deep learning model between amyloid ß positive vs amyloid ß negative). FINDINGS: 12 949 retinal photographs from 648 patients with Alzheimer's disease and 3240 people without the disease were used to train, validate, and test the deep learning model. In the internal validation dataset, the deep learning model had 83·6% (SD 2·5) accuracy, 93·2% (SD 2·2) sensitivity, 82·0% (SD 3·1) specificity, and an area under the receiver operating characteristic curve (AUROC) of 0·93 (0·01) for detecting Alzheimer's disease-dementia. In the testing datasets, the bilateral deep learning model had accuracies ranging from 79·6% (SD 15·5) to 92·1% (11·4) and AUROCs ranging from 0·73 (SD 0·24) to 0·91 (0·10). In the datasets with data on PET, the model was able to differentiate between participants who were amyloid ß positive and those who were amyloid ß negative: accuracies ranged from 80·6 (SD 13·4%) to 89·3 (13·7%) and AUROC ranged from 0·68 (SD 0·24) to 0·86 (0·16). In subgroup analyses, the discriminative performance of the model was improved in patients with eye disease (accuracy 89·6% [SD 12·5%]) versus those without eye disease (71·7% [11·6%]) and patients with diabetes (81·9% [SD 20·3%]) versus those without the disease (72·4% [11·7%]). INTERPRETATION: A retinal photograph-based deep learning algorithm can detect Alzheimer's disease with good accuracy, showing its potential for screening Alzheimer's disease in a community setting. FUNDING: BrightFocus Foundation.