Background/objectives: We provide the rationale for and description of energy balance measures (i.e., energy intake and energy expenditure) in The Physiology Of the WEight Reduced State (POWERS) study which aims to understand the contribution of the many factors that influence weight regain following behavioral weight loss.
Methods: The primary dependent variable is weight regain over 1 year following a 7% or greater supervised weight loss. The balance between energy intake and expenditure is the primary determinant of weight regain. Healthy adults (target n = 205), aged 25- < 60 years, with body mass index (BMI) 30- < 40 kg/m2 are being recruited. Energy intake and expenditure phenotypes are measured prior to weight loss (baseline, BL), immediately following weight loss (T0), and then four (T4) and 12 months (T12) after weight loss. Weight stability is required before BL and T0 measurement periods. Weight change at T12 from T0 is the primary outcome variable. Energy intake is measured with serial doubly labeled water (DLW) measurements combined with dual x-ray absorptiometry (DXA) to assess changes in fat and lean mass; DLW is also used to measure twenty-four-hour energy expenditure (TEE). Components of TEE including resting energy expenditure (REE) and non-resting and activity energy expenditure (NREE and AEE), as well as skeletal muscle chemomechanical efficiency and grip strength are assessed. Self-reported dietary intake is assessed with interviewer-administered multiple-pass 24-hour food recalls.
Discussion: This manuscript describes the rationale for the methods chosen to assess energy balance and the analytical methods employed to normalize and express data in the setting of changes in body weight and composition immediately following behavioral weight loss and thereafter at 4- and 12-months post-weight loss.
Background: Central obesity measures, such as waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) have previously outperformed body mass index (BMI) in predicting health risks. BMI has been shown to underdiagnose obesity in older adults.
Methods: We used data from the Health Survey for England (2005-2021) for 120,024 individuals aged 11-89 years, born in 1919-2008. High-risk classifications for WC, WHR, WHtR, and BMI were defined using established thresholds (World Health Organisation and the UK National Institute for Health and Care Excellence). Age, period (changes over time), and cohort effects were assessed using logistic regression with grouped variables to address the identification problem inherent in age-period-cohort (APC) models.
Results: The prevalence of high-risk increased over time for all obesity measures. Central obesity measures showed a consistent linear increase with age until around 70 years of age. BMI exhibited an inverted U-shaped age trend. Obesity increased over time across all measures, while there was little evidence for a cohort effect. WHtR trends closely mirrored BMI at the population level but identified different high-risk individuals. The odds of high-risk WHtR increased with age, with odds ratios (OR) 4.91 (95% CI: 1.95-12.39) for females and 6.15 (95% CI: 2.24-16.89) for males by 85-89 years compared to 18-19 years. Period effects for WHtR showed ORs of 1.41 (95% CI: 1.16-1.72) for females and 1.25 (95% CI: 1.01-1.55) for males in 2019-2021 compared to 2005-2006.
Conclusions: Central obesity measures, particularly WHtR, could provide a more consistent reflection of age-related increases in obesity risk. The linear increase in high-risk with age for central obesity measures aligns better with known age-related increases in obesity-related comorbidities. Age plays a significant role in driving obesity trends meaning an aging population could leading to further increases in the prevalence of obesity.

