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Certain leading medical organisations are considering alternatives to the Body Mass Index (BMI) as a predictor of the risk for non-communicable chronic disease (NCD) or death. Our objective was to evaluate the associations between various measures of body fat and the risk of incident NCDs or mortality, independent of inflammation.
Population-based prospective cohort study (the UK Biobank cohort).
The UK.
Adults (aged between 40 and 69 years) were accrued between March 2006 and October 2010 and followed until December 2022. There were 500 107 participants: the median age was 58 years (IQR 50–63) at baseline, 45.6% were male and 94.7% were white.
BMI, waist-to-hip ratio (WHR), body fat percentage measured by bioimpedance analysis (BIA; fatBIA), C-reactive protein (CRP) and various other measures of body fat obtained by dual-energy X-ray absorptiometry (DXA; including visceral adipose tissue (VAT)) and magnetic resonance imaging (MRI).
All-cause death, cardiovascular disease (heart failure, hypertension, myocardial infarction, pulmonary embolism and stroke), cancers (breast, colorectal, endometrial, oesophageal, kidney, ovarian, pancreatic and prostate), diabetes, asthma, gallbladder disease, chronic back pain and osteoarthritis.
The 5th and 95th percentiles for measures of body fat were BMI 20.5 (considered ‘healthy’) and 37.0 kg/m2 (considered ‘unhealthy’), WHR 0.71 and 0.94 and BIA 24.8% and 47.6% in females, and BMI 22.0 (considered ‘healthy’) and 35.4 kg/m2 (considered ‘unhealthy’), WHR 0.83 and 1.05 and BIA 15.5% and 34.7% in males. BMI was strongly correlated to fatBIA (0.85 in females and 0.80 in males) but less so with WHR (0.46 in females and 0.59 in males). All measures of body fat were positively associated with the incidence of NCDs, but only WHR remained positively associated with death after full adjustment (HR 95th percentile vs 5th percentile (95% CI): BMI 0.80 (0.76 to 0.84), WHR 1.21 (1.16 to 1.28) and BIA 0.80 (0.76 to 0.84) in females; BMI 0.89 (0.85 to 0.93), WHR 1.19 (1.14 to 1.24) and BIA 0.89 (0.85 to 0.92) in males). Simpler models that adjusted for age, sex, CRP, WHR and either BMI or fatBIA gave similar results. Associations between body fat and the incidence of NCDs after accounting for the competing risk of death were also similar.
BMI was strongly correlated with fatBIA, but WHR and visceral adipose tissue percentage were less so. All measures of body fat were associated with the incidence of NCDs, but only WHR was independently associated with mortality. These findings support the hypothesis that body fat may be protective against death and that the excess risk associated with higher WHR may be mediated by something other than body fat.
The purpose of this study was to explore nurses' perspectives on Machine Learning Clinical Decision Support (ML CDS) design, development, implementation, and adoption.
Qualitative descriptive study.
Nurses (n = 17) participated in semi-structured interviews. Data were transcribed, coded, and analyzed using Thematic analysis methods as described by Braun and Clarke.
Four major themes and 14 sub-themes highlight nurses' perspectives on autonomy in decision-making, the influence of prior experience in shaping their preferences for use of novel CDS tools, the need for clarity in why ML CDS is useful in improving practice/outcomes, and their desire to have nursing integrated in design and implementation of these tools.
This study provided insights into nurse perceptions regarding the utility and usability of ML CDS as well as the influence of previous experiences with technology and CDS, change management strategies needed at the time of implementation of ML CDS, the importance of nurse-perceived engagement in the development process, nurse information needs at the time of ML CDS deployment, and the perceived impact of ML CDS on nurse decision making autonomy.
This study contributes to the body of knowledge about the use of AI and machine learning (ML) in nursing practice. Through generation of insights drawn from nurses' perspectives, these findings can inform successful design and adoption of ML Clinical Decision Support.
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