Integrating image and gene-data with a semi-supervised attention model for prediction of KRAS gene mutation status in non-small cell lung cancer.

KRAS is a pathogenic gene frequently implicated in non-small cell lung cancer (NSCLC). However, biopsy as a diagnostic method has practical limitations. Therefore, it is important to accurately determine the mutation status of the KRAS gene non-invasively by combining NSCLC CT images and genetic data for early diagnosis and subsequent targeted therapy of patients. This paper proposes a Semi-supervised Multimodal Multiscale Attention Model (S2MMAM). S2MMAM comprises a Supervised Multilevel Fusion Segmentation Network (SMF-SN) and a Semi-supervised Multimodal Fusion Classification Network (S2MF-CN). S2MMAM facilitates the execution of the classification task by transferring the useful information captured in SMF-SN to the S2MF-CN to improve the model prediction accuracy. In SMF-SN, we propose a Triple Attention-guided Feature Aggregation module for obtaining segmentation features that incorporate high-level semantic abstract features and low-level semantic detail features. Segmentation features provide pre-guidance and key information expansion for S2MF-CN. S2MF-CN shares the encoder and decoder parameters of SMF-SN, which enables S2MF-CN to obtain rich classification features. S2MF-CN uses the proposed Intra and Inter Mutual Guidance Attention Fusion (I2MGAF) module to first guide segmentation and classification feature fusion to extract hidden multi-scale contextual information. I2MGAF then guides the multidimensional fusion of genetic data and CT image data to compensate for the lack of information in single modality data. S2MMAM achieved 83.27% AUC and 81.67% accuracy in predicting KRAS gene mutation status in NSCLC. This method uses medical image CT and genetic data to effectively improve the accuracy of predicting KRAS gene mutation status in NSCLC.

Residual neural network with mixed loss based on batch training technique for identification of EGFR mutation status in lung cancer.

Epidermal growth factor receptor (EGFR) is the key to targeted therapy with tyrosine kinase inhibitors in lung cancer. Traditional identification of EGFR mutation status requires biopsy and sequence testing, which may not be suitable for certain groups who cannot perform biopsy. In this paper, using easily accessible and non-invasive CT images, the residual neural network (ResNet) with mixed loss based on batch training technique is proposed for identification of EGFR mutation status in lung cancer. In this model, the ResNet is regarded as the baseline for feature extraction to avoid the gradient disappearance. Besides, a new mixed loss based on the batch similarity and the cross entropy is proposed to guide the network to better learn the model parameters. The proposed mixed loss utilizes the similarity among batch samples to evaluate the distribution of training data, which can reduce the similarity of different classes and the difference of the same classes. In the experiments, VGG16Net, DenseNet, ResNet18, ResNet34 and ResNet50 models with the mixed loss are trained on the public CT dataset with 155 patients including EGFR mutation status from TCIA. The trained networks are employed to the collected preoperative CT dataset with 56 patients from the cooperative hospital for validating the efficiency of the proposed models. Experimental results show that the proposed models are more appropriate and effective on the lung cancer dataset for identifying the EGFR mutation status. In these models, the ResNet34 with mixed loss is optimal (accuracy = 81.58%, AUC = 0.8861, sensitivity = 80.02%, specificity = 82.90%).

FDTrans: Frequency Domain Transformer Model for predicting subtypes of lung cancer using multimodal data.

BACKGROUND AND PURPOSE: Accurate identification of lung cancer subtypes in medical images is of great significance for the diagnosis and treatment of lung cancer. Despite substantial progress in existing methods, they remain challenging due to limited annotated datasets, large intra-class differences, and high inter-class similarities. METHODS: To address these challenges, we propose a Frequency Domain Transformer Model (FDTrans) to identify patients' lung cancer subtypes using the TCGA lung cancer dataset. We add a pre-processing process to transfer histopathological images to the frequency domain using a block-based discrete cosine transform and design a coordinate Coordinate-Spatial Attention Module (CSAM) to obtain critical detail information by reassigning weights to the location information and channel information of different frequency vectors. Then, a Cross-Domain Transformer Block (CDTB) is designed for Y, Cb, and Cr channel features, capturing the long-term dependencies and global contextual connections between different component features. At the same time, feature extraction is performed on the genomic data to obtain specific features. Finally, the image branch and the gene branch are fused, and the classification result is output through the fully connected layer. RESULTS: In 10-fold cross-validation, the method achieves an AUC of 93.16% and overall accuracy of 92.33%, which is better than similar current lung cancer subtypes classification detection methods. CONCLUSION: This method can help physicians diagnose the subtypes classification of lung cancer in patients and can benefit from both spatial and frequency domain information.

A progressive phased attention model fused histopathology image features and gene features for lung cancer staging prediction.

PURPOSE: Identifying the stage of lung cancer accurately from histopathology images and gene is very important for the diagnosis and treatment of lung cancer. Despite the substantial progress achieved by existing methods, it remains challenging due to large intra-class variances, and a high degree of inter-class similarities. METHODS: In this paper, we propose a phased Multimodal Multi-scale Attention Model (MMAM) that predicts lung cancer stages using histopathology image data and gene data. The model consists of two phases. In Phase1, we propose a Staining Difference Elimination Network (SDEN) to eliminate staining differences between different histopathology images, In Phase2, it utilizes the image feature extractor provided by Phase1 to extract image features, and sends the multi-scale image features together with gene features into our Adaptive Enhanced Attention Fusion (AEAF) module for multimodal multi-scale features fusion to enable prediction of lung cancer staging. RESULTS: We evaluated the proposed MMAM on the TCGA lung cancer dataset, and achieved 88.51% AUC and 88.17% accuracy on classification prediction of lung cancer stages I, II, III, and IV. CONCLUSION: The method can help doctors diagnose the stage of lung cancer patients and can benefit from multimodal data.

Weakly supervised label propagation algorithm classifies lung cancer imaging subtypes.

Aiming at the problems of long time, high cost, invasive sampling damage, and easy emergence of drug resistance in lung cancer gene detection, a reliable and non-invasive prognostic method is proposed. Under the guidance of weakly supervised learning, deep metric learning and graph clustering methods are used to learn higher-level abstract features in CT imaging features. The unlabeled data is dynamically updated through the k-nearest label update strategy, and the unlabeled data is transformed into weak label data and continue to update the process of strong label data to optimize the clustering results and establish a classification model for predicting new subtypes of lung cancer imaging. Five imaging subtypes are confirmed on the lung cancer dataset containing CT, clinical and genetic information downloaded from the TCIA lung cancer database. The successful establishment of the new model has a significant accuracy rate for subtype classification (ACC = 0.9793), and the use of CT sequence images, gene expression, DNA methylation and gene mutation data from the cooperative hospital in Shanxi Province proves the biomedical value of this method. The proposed method also can comprehensively evaluate intratumoral heterogeneity based on the correlation between the final lung CT imaging features and specific molecular subtypes.

DADFN: dynamic adaptive deep fusion network based on imaging genomics for prediction recurrence of lung cancer.

Objective. Recently, imaging genomics has increasingly shown great potential for predicting postoperative recurrence of lung cancer patients. However, prediction methods based on imaging genomics have some disadvantages such as small sample size, high-dimensional information redundancy and poor multimodal fusion efficiency. This study aim to develop a new fusion model to overcome these challenges.Approach. In this study, a dynamic adaptive deep fusion network (DADFN) model based on imaging genomics is proposed for predicting recurrence of lung cancer. In this model, the 3D spiral transformation is used to augment the dataset, which better retains the 3D spatial information of the tumor for deep feature extraction. The intersection of genes screened by LASSO, F-test and CHI-2 selection methods is used to eliminate redundant data and retain the most relevant gene features for the gene feature extraction. A dynamic adaptive fusion mechanism based on the cascade idea is proposed, and multiple different types of base classifiers are integrated in each layer, which can fully utilize the correlation and diversity between multimodal information to better fuse deep features, handcrafted features and gene features.Main results. The experimental results show that the DADFN model achieves good performance, and its accuracy and AUC are 0.884 and 0.863, respectively. This indicates that the model is effective in predicting lung cancer recurrence.Significance. The proposed model has the potential to help physicians to stratify the risk of lung cancer patients and can be used to identify patients who may benefit from a personalized treatment option.

Exploration of the prognostic signature reflecting tumor microenvironment of lung adenocarcinoma based on immunologically relevant genes.

Lung adenocarcinoma (LUAD) represents the major histological type of lung cancer with high mortality globally. Due to the heterogeneous nature, the same treatment strategy to various patients may result in different therapeutic responses. Hence, we aimed to elaborate an effective signature for predicting patient survival outcomes. The TCGA-LUAD cohort from the TCGA portal was used as a training dataset. The GSE26939 and GSE68465 cohorts from the GEO database were taken as validation datasets. All immunologically relevant genes were extracted from the ImmPort. The ESTIMATE algorithm was employed to explore LUAD microenvironment in the training dataset. Further, the DEGs were picked out based on the immune-associated genes reflecting different statuses in the immune context of TME. Univariate/multivariate Cox regression was performed to determine six prognosis- specific genes (PIK3CG, BTK, VEGFD, INHA, INSL4, and PTPRC) and established a risk predictive signature. The time-dependent ROC indicated that AUC values were all greater than 0.70 at 1-, 3-, and 5- year intervals. Corresponding RiskScore of each LUAD patient was calculated from the signature, and they were stratified into the high- and low-risk groups by the median value of RiskScore. K-M curves and Log-rank test demonstrated significant survival differences between the two groups (P < 0.05). Similar results were exhibited in the validation datasets. The RiskScore was incredibly relevant to clinicopathological factors like gender, AJCC stage, and T stage. Also, it can mirror the distribution state of 15 kinds of TIICs and have some predictive value for the sensitivity of therapeutic drugs.

Clinical relevance of different handgrip strength indexes and cardiovascular disease risk factors: A cross-sectional study in suburb-dwelling elderly Chinese.

BACKGROUND: Reduced muscle strength, as measured by handgrip strength (HS), has been associated with an increased risk of cardiovascular disease (CVD). The aim of this study was to examine the association between different HS indexes and CVD risk factors in elderly Chinese individuals. We also determine optimal cutoffs of HS indexes for predicting CVD risk factors. METHODS: Data were obtained from 603 men and 789 women aged ≥60 years (average age 66.8 ± 6.4 y). These study participants were recruited in the suburb area of Tianjin, China. An individual was considered a patient when they exhibited any one of three CVD risk factors: diabetes mellitus, hypertension and dyslipidemia. All participants were interviewed face-to-face. In addition, serum samples were collected from all participants, and all participants underwent measures of anthropometry and HS. RESULTS: The optimal cutoffs were 0.376 of HS/weight in men and 0.726 of HS/body fat mass in women for predicting diabetes mellitus. The adjusted odds ratios (ORs) of at least one CVD risk factor for those with low muscle strength identified by HS/body fat mass were 2.14 (95% confidence interval [CI]: 1.53, 3.44; p < 0.001) in men and 2.32 (95% CI: 1.60, 3.29; p < 0.001) in women. CONCLUSION: HS/body fat mass appear to be the index best associated with CVD risk factors except diabetes mellitus in men. The optimal cutoffs of HS indexes have the potential to identify elderly adults at risk of CVD.

Twelve-Month Incidence of Depressive Symptoms in Suburb-Dwelling Chinese Older Adults: Role of Sarcopenia.

OBJECTIVE: The objective of this study was to examine the incidence of new onset depressive symptoms and associated factors over a 1-year period in an older Chinese suburban population. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: The sample comprised 691 Chinese community-dwelling participants (304 men; mean age 67.5 ± 5.7 years) without depressive symptoms at baseline, recruited from Chadian of Tianjin, China. MEASURES: We had documented detailed information regarding sociodemographics, behavioral characteristics, and medical conditions. Sarcopenia was defined according to the Asian Working Group for Sarcopenia (AWGS) criteria. The outcome was new onset depressive symptoms at 1-year follow-up, defined as a score of ≥11 on the 30-item Geriatric Depression Scale. RESULTS: We found that 83 (12.0%) of the 691 participants without depressive symptoms at baseline had developed depressive symptoms. After multivariate adjustments, it was found that the incidence of new onset depressive symptoms was associated with sarcopenia, type 2 diabetes mellitus, and cardiovascular disease. People with a higher level of muscle mass and better sleep quality were significantly less likely to develop depressive symptoms than their counterparts. CONCLUSIONS/IMPLICATIONS: We found that the incidence of depressive symptoms increased with some chronic diseases, such as sarcopenia, type 2 diabetes mellitus, and cardiovascular disease. In addition, muscle mass was the most related protective factor among sarcopenia's 3 basic diagnosis components-muscle mass, muscle strength, and physical performance. Hence, maintaining enough muscle mass could be beneficial in the prevention of depressive symptoms for older adults.

The Relationship Between Sleep Duration, Falls, and Muscle Mass: A Cohort Study in an Elderly Chinese Population.

Epidemiological studies report that more than half of the people over the age of 65 years suffer from variable sleep problems. In this study, we conducted a cohort study to investigate the relationship between sleep duration, muscle mass, and function within a community-dwelling, elderly Chinese population. Our study population consisted of residents living in the township central hospital of suburban Tianjin, China. We measured muscle strength and walking speed. We divided sleep duration into the following four groups: <7 hours, 7-8 hours, >8-9 hours, and >9 hours. A total of 902 participants completed the 3-year follow-up. We observed a U-shaped relationship between sleep duration and fall risk. Compared with the 7-8-hour group, fall risk within the <7-hour group was 3.67 (2.59, 5.42) times higher and fall risk within the >9-hour group was 2.35 (1.29, 3.52) times higher. After adjustment, muscle mass declined by -6.82% (-11.27%, -3.83%) in the <7-hour group. In summary, we observed a U-shaped relationship between sleep duration and falls. Short sleep duration has a negative relationship with muscle mass decline in a community-dwelling, elderly Chinese population.

Risk factors for developing diabetes after 3 years among community-dwelling elderly with impaired fasting glucose.

BACKGROUND: The aim of the present study was to examine the risk factors for developing diabetes after 3 years in an elderly Chinese suburban population with impaired fasting glucose (IFG). METHODS: The study population comprised residents of the Hangu area of Tianjin, China, with IFG, aged ≥60 years, who joined the national free physical examination program (n = 328; mean [±SD] age 68.0 ±6.1 years; 48.2% men). Diabetes was defined by self-report of a physician's diagnosis or fasting plasma glucose (FPG) ≥7.0 mmol/L; IFG was defined as FPG ≥5.6 and < 7.0 mmol/L. Risk factors of incident diabetes at the 3-year follow-up were analyzed individually using logistic regression analysis. RESULTS: Between baseline and the 3-year follow-up, 56 subjects with IFG at baseline had developed diabetes. After multivariate adjustment for demographic and clinical factors, the incidence of diabetes increased with higher FPG (odds ratio [OR] 9.30, 95% confidence interval [CI] 2.84-30.48), but decreased with the grip strength/weight ratio (OR 0.88, 95% CI 0.82-0.94). Moreover, the combination of higher FPG and lower grip strength/weight was associated with a higher incidence of diabetes than higher FPG only or lower grip strength/weight (P < 0.05). CONCLUSION: The present study indicates that higher FPG and lower muscle strength are associated with glycemic deterioration among subjects with IFG after 3 years. The results suggest that not only glucose levels, but also physical performance may be useful markers of the risk of diabetes in this population.

A Screening Tool Using Five Risk Factors Was Developed for Fall-Risk Prediction in Chinese Community-Dwelling Elderly Individuals.

The objective of this study was to determine falls risk profiles to derive a falls risk prediction score and establish a simple and practical clinical screening tool for Chinese community-dwelling elderly individuals. This was a prospective cohort study (n = 619) among adults aged 60 years and older. Falls were ascertained at a 1-year follow-up appointment. Sociodemographic information, medical history, and physical performance data were collected. The mean age was 67.4 years; 57.7% were women. Female sex (odds ratios [ORs] 1.82; 95% confidence interval [95% CI] 1.17-2.82), diabetes (OR 2.13; 95% CI 1.13-3.98), a Timed Up and Go Test (TUGT) ≥10.49 seconds (OR 1.51; 95% CI 1.23-1.94), a history of falls (OR 3.15; 95% CI 1.72-5.79), and depression (Geriatric Depression Scale [GDS] ≥11, OR 2.51; 95% CI 1.36-4.63) were the strongest predictors. These predictors were used to establish a risk score. The area under the curve of the score was 0.748. From a clinical point of view, the most appropriate cutoff value was 7 (97.5% specificity, 70.7% positive predictive value, and 83.6% negative predictive value). For this cutoff, the fraction correctly classified was 82.5%. A cutoff score of 7 derived from a risk assessment tool using four risk factors (gender, falls history, diabetes, and depression) and the TUGT may be used in Chinese community-dwelling elderly individuals as an initial step to screen those at low risk for falls.

Muscle strength rather than muscle mass is associated with osteoporosis in older Chinese adults.

BACKGROUND: Assessment of the association of muscle strength and muscle mass with osteoporosis (OP) is of special interest as muscles are a potential target for interventions (i.e., strength training). METHODS: A cross-sectional descriptive study encompassing people aged ≥ 60 years (average age: 66.9 ± 6.2 years; men, n = 516; women, n = 652) in the Hangu area of Tianjin, China. The study populations were invited to participate in a comprehensive geriatric assessment. OS was identified by measuring the calcaneal using a quantitative ultrasound and a T score of less than -2.5. Muscle characteristics included grip strength and appendicular skeletal muscle mass (ASM). RESULTS: The prevalence of OS in this study was 61.6% (male 52.1%, female 69.1%). Grip strength was negatively related to OS and after adjusting for all other variables, higher grip strength was found to be associated with a lower OS risk (p = 0.023). ASM/height2 was not associated with OS (p = 0.205). CONCLUSION: Based on our study, muscle strength rather than muscle mass is negatively associated with OS in older people; thus, we should pay more attention to muscle strength training in the early stage of the OS.

Timed Up and Go Test can predict recurrent falls: a longitudinal study of the community-dwelling elderly in China.

PURPOSE: Falling is a major health problem in community-dwelling elderly individuals. The aim of the present study was to conduct a prospective investigation to evaluate the accuracy of the Timed Up and Go Test (TUGT), 4-meter walking test, and grip strength test to screen for the risk of falls and to determine a cutoff point to be used clinically. PATIENTS AND METHODS: This was a prospective study that included 541 participants. The fall data were obtained via face-to-face interview, and the date, site, and circumstances of any falls were recorded. TUGTs were recorded as part of a comprehensive geriatric assessment. We collected the same data at baseline and after follow-up via comprehensive geriatric assessment. RESULTS: The incidence of falls of our study subjects was 20.8%. The recurrent-fall group had a fall rate of 6.8% during the follow-up year. The standard area under the curve (AUC) of our screening tool was >0.70, and hence our tool can be used for clinical purposes. After adjusting for age and gender, the AUC of TUGT became 0.642, so it cannot be used as a predictive tool for measuring any types of falls. However, when recurrent falls were adjusted for age and gender, the TUGT's AUC improved to 0.733 and a score of 15.96 seconds is used as a cut-point to screen recurrent falls in community-dwelling elderly Chinese individuals. CONCLUSION: Future falls were best predicted by TUGT in recurrent fallers at baseline. A score of 15.96 seconds is used as a cut-point to screen recurrent falls in community-dwelling elderly Chinese individuals.

Clinical Evidence of Exercise Benefits for Stroke.

Even though stroke is the third, not the first, most common cause of disability-adjusted life years in developed countries, it is one of the most expensive to treat. Part of the expense is due to secondary problems in the post-stroke period including: cognition, memory, attention span, pain, sensation loss, psychological issues, and problems with mobility and balance. Research has identified that exercise has both positive physical and psychosocial effects for post-stroke patients. Therefore, this scientific statement provides an overview on exercise rehabilitation for post-stroke patients.We will use systematic literature reviews, clinical and epidemiology reports, published morbidity and mortality studies, clinical and public health guidelines, patient files, and authoritative statements to support this overview.Evidence clearly supports the use of various kinds of exercise training (e.g., aerobic, strength, flexibility, neuromuscular, and traditional Chinese exercise) for stroke survivors. Aerobic exercise, the main form of cardiac rehabilitation, may play an important role in improving aerobic fitness, cardiovascular fitness, cognitive abilities, walking speed and endurance, balance, quality of life, mobility, and other health outcomes among stroke patients. Strength exercise, included in national stroke guidelines and recommended for general health promotion for stroke survivors, can lead to improvements in functionality, psychosocial aspects, and quality of life for post-stroke patients. Flexibility exercises can relieve muscle spasticity problems, improve motor function, range of motion, and prevent contractures. Stretching exercises can also prevent joint contractures, muscle shortening, decrease spasticity, reduce joint stiffness and improve a post-stroke patient's overall function. Neuromuscular exercises can improve activities of daily living (ADL) through coordination and balance activities. Traditional Chinese exercises are used to improve walking and balance ability as well as increase muscle strength, which is important for post-stroke patients.The present evidence strongly supports the power of exercise for post-stroke patients, which in this study combined aerobic exercises, strength training, flexibility exercises, neuromuscular exercises, and traditional Chinese exercises. This research can encourage post-stroke survivors to consider the importance of exercise in the rehabilitation process.

The increased risk of sarcopenia in patients with cardiovascular risk factors in Suburb-Dwelling older Chinese using the AWGS definition.

The aim of the present study is to investigate the relationship between sarcopenia and cardiovascular risk factors (CVRF) in the Chinese elderly. A total of 1611 elderly individuals aged ≥60 years were enrolled in this study. The well-established CVRF of diabetes, hypertensions, and dyslipidemia were assessed. Sarcopenia was defined according to the recommended algorithm of the Asian Working Group for Sarcopenia (AWGS). Multiple logistic regression analyses and the linear regressions were used to evaluate the components of CVRF and the number of CVRF of elderly patients with sarcopenia. After adjusting for potential confounders, CVRF was associated with a high prevalence of sarcopenia in elderly Chinese populations. Furthermore, diabetes and hypertension, but not dyslipidemia, were found to be significantly associated with sarcopenia. The OR and 95% CI for sarcopenia of the participants with 1, 2, and 3 features of CVRF were 2.27(1.14-4.48), 4.13(1.80-9.46), and 4.90(1.01-23.81), respectively. A linear increase in the prevalence of sarcopenia was found to be associated with the number of CVRF components in the elderly population (P values for the trends < 0.001). Knowledge of known CVRF, particularly diabetes and hypertension, may help predict the risk for sarcopenia in the elderly.

Combined Effect of Obesity and Mobility Limitation with Incidence of Type 2 Diabetes and Mortality in Chinese Elderly.

This article aims to examine the effect of mobility limitation and obesity on the risk of new incidence of type 2 diabetes mellitus (T2DM) and mortality. The design was a cohort study (n = 1075) among adults aged 60 years and older. Obesity was defined as body-mass index greater than or equal to 28 kg/m2. Mobility limitation was defined as participants scoring in the top 20% on the timed up and go test or in the slowest 20% for the 4-m walking test. The mean age of the study population was 67.4 ± 5.4 years (age range: 60-86 years), and 57.4% were women. Overall, 5.1% of women and 1.9% of men had both obesity and mobility limitations. During 3-year follow-up, the new incidence of T2DM was 2.98% and the adjusted risk of the new incidence of T2DM was progressively greater in obese subjects without mobility limitation, but not greater in the single mobility limitation subjects. The combination of mobility limitation and obesity (odds ratio = 10.3, 95% confidence interval = 2.25-70.13) has a significantly higher risk than obesity only or mobility limitation only. What is more, obesity with mobility limitation could be an independent predictor of 3-year mortality compared with the other subjects. We demonstrated the associations between obesity and mobility limitation and thus the increased risk of developing, with the combination over time, T2DM and mortality.

The association between sleep duration and physical performance in Chinese community-dwelling elderly.

BACKGROUND: Physical performance is an important healthy factor in elder people. Good living habits, which include sleep, can maintain physical strength and physical performance. The aim of the present study was to conduct a cross-sectional study to determine the association between total sleep duration and physical performance. METHODS: Our study population comprised residents of the township central hospital in the suburban of Tianjin, China. We measured muscle strength, walk speed and balance function by grip, 4-m walk test and timed up and go test (TUGT). We divided sleep duration into four groups <7h, 7-8h, >8-9h, >9h. RESULTS: A total 898 participants had completed data (392 men and 506 women, mean age 67.71 years). In man, adjusted sleep duration was associated with lower grip in > 9 h group, the mean value (95% CI) was 0.429 (0.409, 0.448), and longer TUGT time was also associated with long sleep duration, 10.46s (9.97 s, 10.95 s). In women, adjusted slower 4-m walk speed present an inverse U-shaped relation with sleep duration, by 0.93 m/s (0.86 m/s, 0.98 m/s), 0.97 m/s (0.96 m/s, 1.00 m/s), 0.97 m/s (0.95 m/s, 0.99 m/s) and 0.92 m/s (0.89 m/s, 0.96 m/s); longer TUGT time were associated with long sleep duration (> 9 h), by 11.23 s (10.70 s, 11.77 s). CONCLUSION: In Chinese community-dwelling elderly, lower muscle strength and lower balance function were associated with long sleep duration in men. Slower walk speed and lower balance function were associated with long sleep duration in women.

Clinical Relevance of Different Handgrip Strength Indexes and Mobility Limitation in the Elderly Adults.

BACKGROUND: More efficient clinical indexes are needed to identify older people most likely to present mobility impairments. The aim of the present study was to determine which handgrip strength (HS) indexes are clinically relevant to detect risk of mobility limitation in the elderly adults. In addition, we attempted to determine an optimal cutoff point for the most relevant index. METHODS: Data are from 469 men and 609 women aged 60 years and older recruited in the Hangu area of Tianjin, China. Participants scoring in the top 20% on the Timed Up and Go Test or in the slowest 20% for the 4-m walk test were defined as having mobility limitation. RESULTS: The prevalence of mobility limitation was 27.6% in women and 24.5% in men. The area under the receiver-operating characteristic curve for HS/body fat mass was 0.723 (95% confidence interval [CI] = 0.658-0.788) in men and for HS/weight was 0.684 (95% CI = 0.628-0.739) in women. These values were indicated higher levels of mobility limitation compared with HS and other relative HS indexes. The cutoffs of the most relevant index in men and women that effectively identified individuals at risk of mobility limitation were 1.884 and 0.281, respectively. CONCLUSION: HS/body fat mass and HS/weight appear to be the indices best associated with mobility limitation for men and women, respectively. Optimal cutoffs for clinically relevant index have the potential to identify elderly adults at risk of mobility limitation.