DXA-Rectified Appendicular Lean Mass: Development of Ultrasound Prediction Models in Older Adults

Objective

Dual-energy X-ray absorptiometry (DXA)-derived appendicular lean soft tissue mass (aLM) is used to diagnose sarcopenia. However, DXA-derived aLM includes non-skeletal muscle components, such as fat-free component of adipose tissue fat cell. These components, if not accounted for, could falsely inflate the aLM in individuals with a high amount of adipose tissue mass. B-mode ultrasound accurately measures muscle size in older adults. We sought to develop regression-based prediction equations for estimating DXA-rectified appendicular lean tissue mass (i.e. DXA-derived aLM minus appendicular fat-free adipose tissue (aFFAT); abbreviated as aLM minus aFFAT) using B-mode ultrasound.

Design

Cross-sectional study.

Measurements

Three hundred and eighty-nine Japanese older adults (aged 60 to 79 years) volunteered in the study. aLM was measured using a DXA, and muscle thickness (MT) was measured using ultrasound at nine sites. An ordinary least-squares multiple linear regression model was used to predict aLM minus aFFAT from sex, age and varying muscle thicknesses multiplied by height. Based on previous studies, we chose to use 4 MT sites at the upper and lower extremities (4-site MT model) and a single site (1-site MT model) at the upper extremity to develop prediction models.

Results

The linear prediction models (4 site MT model; R² = 0.902, adjusted R² = 0.899, and 1-site MT model; R² = 0.868, adjusted R² = 0.866) were found to be stable and accurate for estimating aLM minus aFFAT. Bootstrapping (n=1000) resulted in optimism values of 0.0062 (4-site MT model) and 0.0036 (1-site MT model).

Conclusion

The results indicated that ultrasound MT combined with height, age and sex can be used to accurately estimate aLM minus aFFAT in older Japanese adults. Newly developed ultrasound prediction equations to estimate aLM minus aFFAT may be a valuable tool in population-based studies to assess age-related rectified lean tissue mass loss.

     

Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis?

Background

Identification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e.; sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards.

Methods

A total of 848 older inpatients (mean age, 83.0±7.2 years; 41.8% female) admitted to acute care medical wards in Angers University hospital (France) were included in this study using an observational prospective cohort design. Within 24 hours after admission of older inpatients, nurses performed a bedside clinical assessment. Participants were separated into non-fallers and fallers (i.e.; ≥1 fall during hospitalization stay). The analysis was conducted using three feed forward ANNs (multilayer perceptron [MLP], averaged neural network, and neuroevolution of augmenting topologies [NEAT]).

Results

Seventy-three (8.6%) participants fell at least once during their hospital stay. ANNs showed a high specificity, regardless of which ANN was used, and the highest value reported was with MLP (99.8%). In contrast, sensitivity was lower, with values ranging between 98.4 to 14.8%. MLP had the highest accuracy (99.7).

Conclusions

Performance criteria for fall prediction resulting from a bedside nursing assessment and an ANNs analysis was associated with a high specificity but a low sensitivity, suggesting that this combined approach should be used more as a diagnostic test than a screening test when considering older inpatients in acute care medical ward.

     

Childhood polybrominated diphenyl ether (PBDE) exposure and executive function in children in the HOME Study

Prenatal exposure to polybrominated diphenyl ethers (PBDEs) have been reported to impair executive function in children, but little is known whether childhood PBDE exposures play a role. Using the Health Outcomes and Measures of the Environment (HOME) Study, a prospective birth cohort in the greater Cincinnati area, we investigated the association between repeated measures of PBDEs during childhood and executive function at 8 years in 208 children and whether effect modification by child sex was present. We used child serum collected at 1, 2, 3, 5, and 8 years to measure PBDEs. The Behavior Rating Inventory of Executive Function was completed by parents to assess executive function at 8 years. We used multiple informant models to examine childhood PBDEs during several exposure windows. Null associations were observed between early childhood PBDEs and executive function. However, we observed significant adverse associations between a 10-fold increase in concurrent concentrations of BDE-28 (β = 4.6, 95% CI 0.5, 8.7) and BDE-153 (β = 4.8, 95% CI 0.8, 8.8) with behavioral regulation. In addition, PBDEs at 8 years were significantly associated with poorer emotional and impulse control. No associations were noted between childhood PBDEs and metacognition or global executive function. However, child sex significantly modified the associations, with significantly poorer executive function among males with higher concurrent BDE-153, and null associations in females. Our study findings suggest that concurrent PBDE exposures during childhood may be associated with poorer executive function, specifically behavior regulation. Males may also be more sensitive to adverse associations of concurrent PBDEs on executive function.     

Improving program targeting to combat early-life mortality by identifying high-risk births: an application to India

Background

It is widely recognized that there are multiple risk factors for early-life mortality. In practice most interventions to curb early-life mortality target births based on a single risk factor, such as poverty. However, most premature deaths are not from the targeted group. Thus interventions target many births that are at not at high risk and miss many births at high risk.

Methods

Using data from the second wave of Demographic and Health Surveys from India and a hierarchical Bayesian model, we estimate infant mortality risk for 73.320 infants in India as a function of 4 risk factors. We show how this information can be used to improve program targeting. We compare our novel approach against common programs that target groups based on a single risk factor.

Results

A conventional approach that targets mothers in the lowest quintile of income correctly identifies only 30% of infant deaths. By contrast, using four risk factors simultaneously we identify a group of births of the same size that includes 57% of all deaths. Using the 2012 census to translate these percentages into numbers, there were 25.642.200 births in 2012 and 4.4% died before the age of one. Our approach correctly identifies 643.106 of 1.128.257 infant deaths while poverty only identifies 338.477 infant deaths.

Conclusion

Our approach considerably improves program targeting by identifying more infant deaths than the usual approach that targets births based on a single risk factor. This leads to more efficient program targeting. This is particularly useful in developing countries, where resources are lacking and needs are high.

     

Comparison of Health and Risk Factors of Older,Working-age Australians,Italians and Italian-born Migrants to Australia,with Data from an Italian (PASSI), and an Australian (SAMSS) Risk Factor Surveillance System

Italian-born migrants (post-WWII) are the largest non-English-speaking background migrant group in South Australia. A cross-sectional, inter-country comparison using independent samples (40–69 years of age) from two (one in Australia, one in Italy) similar risk factor and chronic disease surveillance systems. None of the three groups (Italians, Australian-born and Italian-born Australians) had definitively worse health although the Italians had high rates for four of the seven risk factors reported (current high blood pressure, current high cholesterol, current smoking, eating less than five fruit and/or vegetables per day) than Australian-born and Italian-born Australians. Italian-born Australians had higher rates for insufficient physical activity, overweight/obese, poor self-reported health and diabetes. Australian respondents were more likely to report having two or more drinks of alcohol per day. Issues facing an ageing population require appropriate health care needs and an assessment of structural or cultural barriers to health services.