Cohort profile: Korean Urban Rural Elderly (KURE) study, a prospective cohort on ageing and health in Korea

Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults

BACKGROUND

Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long-term mortality.

METHODS

The derivation set (n = 516; 75% train set, 25% internal test set) was constructed using age- and sex-stratified random sampling from two community-based cohorts. Data from participants in the individual health assessment programme (n = 380) were used as the external test set 1. Semi-automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi-layer perceptron (MLP)-based multi-label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary-level institution (n = 10 141).

RESULTS

The mean ages of the derivation and external sets were 62.8 and 59.7 years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9 kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi-level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT-parameter-based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P < 0.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5 years, women 79%; median follow-up 4.9 years), a total of 907 individuals (8.9%) died during follow-up. Among model-predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS + sarcopenia (aHR 1.65), osteoporosis + sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities.

CONCLUSIONS

A CT body composition-based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community-dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long-term mortality, independent of covariates.

A Prospective Sociocentric Study of 2 Entire Traditional Korean Villages: The Korean Social Life, Health, and Aging Project (KSHAP)

The Korean Social Life, Health, and Aging Project (KSHAP) was a multidisciplinary prospective study conducted in South Korea that measured various health biomarkers from blood, hair, and brain magnetic resonance imaging, and we examined their associations with sociocentric (global) social network data of older adults in 2 entire villages (or cohorts). Cohort K included participants aged 60 years or older, and cohort L included participants aged 65 years or older. We performed a baseline survey involving 814 of the 860 individuals (94.7% response rate) in cohort K in 2012 and 947 of the 1,043 individuals (90.8% response rate) in cohort L in 2017. We gathered longitudinal data for 5 waves in cohort K from 2011 to 2019 and 2 waves in cohort L from 2017 to 2022. Here, we describe for the first time the follow-up design of the KSHAP, the changes in social networks, and various biomarkers over a number of years. The data for cohort K are publicly available via the Korean Social Science Data Archive as well as the project website, and the data for cohort L will be shared soon.

A systematic review of automated segmentation of 3D computed-tomography scans for volumetric body composition analysis

Automated computed tomography (CT) scan segmentation (labelling of pixels according to tissue type) is now possible. This technique is being adapted to achieve three-dimensional (3D) segmentation of CT scans, opposed to single L3-slice alone. This systematic review evaluates feasibility and accuracy of automated segmentation of 3D CT scans for volumetric body composition (BC) analysis, as well as current limitations and pitfalls clinicians and researchers should be aware of. OVID Medline, Embase and grey literature databases up to October 2021 were searched. Original studies investigating automated skeletal muscle, visceral and subcutaneous AT segmentation from CT were included. Seven of the 92 studies met inclusion criteria. Variation existed in expertise and numbers of humans performing ground-truth segmentations used to train algorithms. There was heterogeneity in patient characteristics, pathology and CT phases that segmentation algorithms were developed upon. Reporting of anatomical CT coverage varied, with confusing terminology. Six studies covered volumetric regional slabs rather than the whole body. One study stated the use of whole-body CT, but it was not clear whether this truly meant head-to-fingertip-to-toe. Two studies used conventional computer algorithms. The latter five used deep learning (DL), an artificial intelligence technique where algorithms are similarly organized to brain neuronal pathways. Six of seven reported excellent segmentation performance (Dice similarity coefficients > 0.9 per tissue). Internal testing on unseen scans was performed for only four of seven algorithms, whilst only three were tested externally. Trained DL algorithms achieved full CT segmentation in 12 to 75 s versus 25 min for non-DL techniques. DL enables opportunistic, rapid and automated volumetric BC analysis of CT performed for clinical indications. However, most CT scans do not cover head-to-fingertip-to-toe; further research must validate using common CT regions to estimate true whole-body BC, with direct comparison to single lumbar slice. Due to successes of DL, we expect progressive numbers of algorithms to materialize in addition to the seven discussed in this paper. Researchers and clinicians in the field of BC must therefore be aware of pitfalls. High Dice similarity coefficients do not inform the degree to which BC tissues may be under- or overestimated and nor does it inform on algorithm precision. Consensus is needed to define accuracy and precision standards for ground-truth labelling. Creation of a large international, multicentre common CT dataset with BC ground-truth labels from multiple experts could be a robust solution.

Chest X-ray-based opportunistic screening of sarcopenia using deep learning

BACKGROUND

Early detection and management of sarcopenia is of clinical importance. We aimed to develop a chest X-ray-based deep learning model to predict presence of sarcopenia.

METHODS

Data of participants who visited osteoporosis clinic at Severance Hospital, Seoul, South Korea, between January 2020 and June 2021 were used as derivation cohort as split to train, validation and test set (65:15:20). A community-based older adults cohort (KURE) was used as external test set. Sarcopenia was defined based on Asian Working Group 2019 guideline. A deep learning model was trained to predict appendicular lean mass (ALM), handgrip strength (HGS) and chair rise test performance from chest X-ray images; then the machine learning model (SARC-CXR score) was built using the age, sex, body mass index and chest X-ray predicted muscle parameters along with estimation uncertainty values.

RESULTS

Mean age of the derivation cohort (n = 926; women n = 700, 76%; sarcopenia n = 141, 15%) and the external test (n = 149; women n = 95, 64%; sarcopenia n = 18, 12%) cohort was 61.4 and 71.6 years, respectively. In the internal test set (a hold-out set, n = 189, from the derivation cohort) and the external test set (n = 149), the concordance correlation coefficient for ALM prediction was 0.80 and 0.76, with an average difference of 0.18 ± 2.71 and 0.21 ± 2.28, respectively. Gradient-weight class activation mapping for deep neural network models to predict ALM and HGS commonly showed highly weight pixel values at bilateral lung fields and part of the cardiac contour. SARC-CXR score showed good discriminatory performance for sarcopenia in both internal test set [area under the receiver-operating characteristics curve (AUROC) 0.813, area under the precision-recall curve (AUPRC) 0.380, sensitivity 0.844, specificity 0.739, F1-score 0.540] and external test set (AUROC 0.780, AUPRC 0.440, sensitivity 0.611, specificity 0.855, F1-score 0.458). Among SARC-CXR model features, predicted low ALM from chest X-ray was the most important predictor of sarcopenia based on SHapley Additive exPlanations values. Higher estimation uncertainty of HGS contributed to elevate the predicted risk of sarcopenia. In internal test set, SARC-CXR score showed better discriminatory performance than SARC-F score (AUROC 0.813 vs. 0.691, P = 0.029).

CONCLUSIONS

Chest X-ray-based deep leaning model improved detection of sarcopenia, which merits further investigation.

Low muscle mass, low muscle function, and sarcopenia in the urban and rural elderly

Health outcomes of the elderly vary between rural and urban areas. Sarcopenia is diagnosed as loss of muscle strength or impaired physical performance, namely "low muscle function" and low muscle mass. Outcomes of low muscle mass and low muscle function are not equal. This study aimed to investigate the prevalence of low muscle mass, low muscle function, and sarcopenia in rural and urban populations and to determine whether regional differences were associated with each of these components. Participants aged ≥ 69 years (n = 2354) were recruited from three urban districts and one rural district in Korea. Low muscle mass was defined by appendicular lean mass using bioelectrical impedance analysis. Low muscle function was defined by handgrip strength and 5-chair stand test. Sarcopenia was defined as low muscle mass plus low muscle function. The prevalence of low muscle function (53.7% vs. 72.8%), and sarcopenia (16.3% vs. 24.4%) were higher in the rural elderly population. Rural residence was associated with low muscle function (OR 1.63; 95% CI 1.13-2.37, P = 0.009), but not with low muscle mass (OR 0.58; 95% CI 0.22-1.54, P = 0.271) or with sarcopenia (OR 1.13; 95% CI 0.63-2.00, P = 0.683). Interventions to detect and improve low muscle function in rural elderly population are needed.

Chronological trends in patients undergoing cholecystectomy in Korea: a nationwide health insurance claims study

Purpose

The incidence of gallstone disease and cholecystectomy is increasing worldwide. The aim of this study was to determine trends in the incidence of cholecystectomy in Korea.

Methods

The National Health Insurance Services database was used to determine patterns in proportion of cholecystectomy and cholecystostomy in the total population of Korea from 2003 to 2017. The age-standardized rate (ASR) was calculated to compare the cholecystectomy and cholecystostomy according to changes in the population structure over time. The ASR was investigated according to patient age, sex, socioeconomic status, use of computed tomography, and type of hospital to identify trends.

Results

The ASR per 100,000 based on the 2010 population of cholecystectomy cases increased markedly from 67.7 to 211.4 between 2003 and 2017. The ASR was consistently higher in female than male (71.9 vs. 63.6 in 2003, 221.8 vs. 201.8 in 2017). Furthermore, the ASR for cholecystectomy increased with age, and surgery for gallstone disease was performed more often at a specialized center than at other medical facilities. The length of hospital stay of cholecystectomy decreased steadily from 10.6 days in 2003 to 6.9 days in 2017.

Conclusion

This study shows that the incidence of cholecystectomy and cholecystostomy has steadily increased over the years in Korea, with a trend toward older age and higher socioeconomic status in patients undergoing cholecystectomy. Increasing use of computed tomography investigations could be a primary cause for this trend. An integrated strategy is needed to manage the increase in older patients undergoing cholecystectomy and shorten their hospital stay with medical safety.

Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment

BACKGROUND & AIMS

Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural network applicable to whole-body CT images of PET-CT scan for the automatic volumetric segmentation of body composition.

METHODS

For model development, one hundred whole-body or torso ¹⁸F-fluorodeoxyglucose PET-CT scans of 100 patients were retrospectively included. Two radiologists semi-automatically labeled the following seven body components in every CT image slice, providing a total of 46,967 image slices from the 100 scans for training the 3D U-Net (training, 39,268 slices; tuning, 3116 slices; internal validation, 4583 slices): skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs with vessels, and central nervous system. The segmentation accuracy was assessed using reference masks from three external datasets: two Korean centers (4668 and 4796 image slices from 20 CT scans, each) and a French public dataset (3763 image slices from 24 CT scans). The 3D U-Net-driven values were clinically validated using bioelectrical impedance analysis (BIA) and by assessing the model's diagnostic performance for sarcopenia in a community-based elderly cohort (n = 522).

RESULTS

The 3D U-Net achieved accurate body composition segmentation with an average dice similarity coefficient of 96.5%-98.9% for all masks and 92.3%-99.3% for muscle, abdominal visceral fat, and subcutaneous fat in the validation datasets. The 3D U-Net-derived torso volume of skeletal muscle and fat tissue and the average area of those tissues in the waist were correlated with BIA-derived appendicular lean mass (correlation coefficients: 0.71 and 0.72, each) and fat mass (correlation coefficients: 0.95 and 0.93, each). The 3D U-Net-derived average areas of skeletal muscle and fat tissue in the waist were independently associated with sarcopenia (P < .001, each) with adjustment for age and sex, providing an area under the curve of 0.858 (95% CI, 0.815 to 0.901).

CONCLUSIONS

This deep neural network model enabled the automatic volumetric segmentation of body composition on whole-body CT images, potentially expanding adjunctive sarcopenia assessment on PET-CT scan and volumetric assessment of metabolism in whole-body muscle and fat tissues.

Predictors of Changes in Cognitive Function in Older Korean Adults: The 2006-2018 Korean Longitudinal Study of Aging

Cognitive decline with normal aging varies widely among individuals. This study aimed to investigate predictors of longitudinal changes in cognitive function in community-dwelling Korean adults aged 65 years and older. Data from 727 older adults who participated in the Korean Longitudinal Study of Aging (KLoSA) survey from 2006 (baseline) until 2018 (seventh wave) were used. Cognitive performance was assessed with the Korean Mini-Mental State Examination. The participants were retrospectively classified into normal cognition, mild cognitive impairment, and moderate/severe cognitive impairment. Education, income, religion, living area, alcohol intake, smoking, physical activity, handgrip strength, functional dependency, depression, comorbidity, medications, fall experience, and unintentional weight loss were included as covariates. A linear mixed regression analysis showed that a steeper decline in cognitive function over time was significantly associated with parameters of poor socio-economic status, health conditions, and unhealthy behaviors. Individuals with mild cognitive impairment or moderate/severe cognitive impairment were likely to have steeper cognitive declines compared with individuals with normal cognition. The current findings of the study showed that age-related cognitive decline was multifactorial in older Korean adults.

The diagnostic value of phase angle, an integrative bioelectrical marker, for identifying individuals with dysmobility syndrome: the Korean Urban-Rural Elderly study

Low phase angle, a non-invasive bioimpedance marker, is associated with elevated odds of dysmobility syndrome and its components. Phase angle (estimated cutoffs: < 4.8° in men; < 4.5° in women) can be used to detect dysmobility syndrome in community-dwelling older adults as a simple, integrative screening tool.

INTRODUCTION

Dysmobility syndrome uses a score-based approach to predict fracture risk that incorporates the concepts of osteoporosis, sarcopenia, and obesity. Low phase angle (PhA), a simple, non-invasive bioelectrical impedance marker, was associated with low lean mass, high fat mass, and poor muscle function. We aimed to investigate the association between PhA and dysmobility syndrome, with the exploration of the diagnostic cutoffs.

METHODS

In a community-dwelling Korean older adult cohort, dysmobility syndrome was defined as the presence of ≥ 3 of the following components: osteoporosis, low lean mass, falls in the preceding year, low grip strength, high fat mass, and poor timed up and go performance.

RESULTS

Among the 1825 participants (mean age 71.6, women 66.7%), subjects were classified into sex-stratified PhA tertiles. The prevalence of dysmobility syndrome increased from the highest PhA tertile group to the lowest (15.50 to 2.45% in men; 33.41 to 12.25% in women, P for trend < 0.001). The mean PhA values decreased as the dysmobility score increased (5.33° to 4.65° in men; 4.76° to 4.39° in women, P for trend < 0.001). Low PhA (cutoff: < 4.8° in men; < 4.5° in women) was associated with twofold elevated odds of dysmobility syndrome after adjusting for age, sex, and conventional risk factors. Low PhA improved the identification of individuals with dysmobility syndrome when added to the conventional risk model (area under the curve, 0.73 to 0.75, P = 0.002).

CONCLUSION

Low PhA was associated with dysmobility syndrome and its components, independent of age, sex, body mass index, nutritional status, and inflammation.

Defining an international cut-off of two-legged countermovement jump power for sarcopenia and dysmobility syndrome

We aimed to establish jump power cut-offs for the composite outcome of either sarcopenia (EWGSOP2) or dysmobility syndrome using Asian and Caucasian cohorts. Estimated cut-offs were sex specific (women: < 19.0 W/kg; men: < 23.8 W/kg) but not ethnicity specific. Jump power has potential to be used in definitions of poor musculoskeletal health.

PURPOSE

Weight-corrected jump power measured during a countermovement jump may be a useful tool to identify individuals with poor musculoskeletal health, but no cut-off values exist. We aimed to establish jump power cut-offs for detecting individuals with either sarcopenia or dysmobility syndrome.

METHODS

Age- and sex-matched community-dwelling older adults from two cohorts (University of Wisconsin-Madison [UW], Korean Urban Rural Elderly cohort [KURE], 1:2) were analyzed. Jump power cut-offs for the composite outcome of either sarcopenia defined by EWGSOP2 or dysmobility syndrome were determined.

RESULTS

The UW (n = 95) and KURE (n = 190) cohorts were similar in age (mean 75 years) and sex distribution (68% women). Jump power was similar between KURE and UW women (19.7 vs. 18.6 W/kg, p = 0.096) and slightly higher in KURE than UW in men (26.9 vs. 24.8 W/kg, p = 0.050). In UW and KURE, the prevalence of sarcopenia (7.4% in both), dysmobility syndrome (31.6% and 27.9%), or composite of either sarcopenia or dysmobility syndrome (32.6% and 28.4%) were comparable. Low jump power cut-offs for the composite outcome differed by sex but not by ethnicity (< 19.0 W/kg in women; < 23.8 W/kg in men). Low jump power was associated with elevated odds of sarcopenia (adjusted odds ratio [aOR] 4.07), dysmobility syndrome (aOR 4.32), or the composite of sarcopenia or dysmobility syndrome (aOR 4.67, p < 0.01 for all) independent of age, sex, height, and ethnicity.

CONCLUSION

Sex-specific jump power cut-offs were found to detect the presence of either sarcopenia or dysmobility syndrome in older adults independent of Asian or Caucasian ethnicity.

Social Activities and Health-Related Quality of Life in Rural Older Adults in South Korea: A 4-Year Longitudinal Analysis

During later life, inadequate social interactions may be associated with worse quality of life in older adults. Rural older adults are prone to developing unhealthy lifestyles related to social activities, which can lead to a poorer quality of life than that enjoyed by older adults living in urban areas. This study aimed to describe longitudinal changes in social activity participation and health-related quality of life among rural older adults, exploring potential associations with changes to in-person social activity over four years. We used prospective community-based cohort data from the Korean Social Life, Health, and Aging Project (KSHAP) collected between December 2011 and January 2016. The sample included 525 older adults who completed the measure of health-related quality of life. Our results showed a significant change in health-related quality of life according to changes in participation in meeting with friends. Even though an individual's participation in other social activities did not show significant differences in health-related quality of life, our findings imply that in-person social activities may be an important resource to encourage participation in physical activities and to develop other positive outcomes, such as a sense of belonging or satisfaction with later life, among rural older adults.