Familial Determinants of Resting Metabolic Rate: A Parent-Adult Child Comparative Study

Resting metabolic rate (RMR) represents the largest component of total daily energy expenditure and is a key determinant of energy balance, weight regulation, and metabolic health. While factors such as fat-free mass, fat mass, age, and sex explain a substantial portion of interindividual variability in RMR, evidence from family and genetic studies suggests a significant hereditary component.

Classic family-based studies have reported moderate parent-offspring correlations in RMR, with heritability estimates ranging from approximately 30% to 50%. More recent genetic and epidemiological research supports these findings, indicating that a meaningful proportion of variability in metabolic rate is influenced by genetic and familial factors. However, most direct parent-child calorimetry studies were conducted decades ago, and contemporary data using standardized methodologies in adult populations remain limited.

This study is designed as a cross-sectional observational family study to evaluate the relationship between RMR in biological parents and their adult children (≥18 years). Studying adult offspring is important because metabolic rate stabilizes after growth and development, allowing for more accurate assessment of underlying physiological and familial patterns.

Participants will include families consisting of one or both biological parents (aged 40-60 years) and at least one adult child. All participants will undergo RMR measurement using a validated indirect calorimetry system under standardized laboratory conditions, including overnight fasting, avoidance of recent exercise, and controlled environmental conditions. Body composition (including fat-free mass and fat mass) will be assessed using bioelectrical impedance analysis under standardized conditions.

The primary outcome is the correlation between parent and offspring RMR. Secondary analyses will evaluate whether these associations persist after adjustment for body composition, age, and sex, and will explore potential differences between same-sex and opposite-sex parent-child pairs. Statistical analyses will include correlation coefficients, multivariable regression models, and approaches that account for clustering within families.