The Causal Relationship Between Sleep and Obesity: Novel Insights and Therapeutic Target.

Sleep is a physiological part of human life under primarily neurobiologic regulation, and occupies between 20% and 40% of the day. The quality and quantity of sleep play an important role in the maintenance of health and wellbeing. Sleep disorders and insomnia are increasingly being diagnosed at all ages, affecting 25% to 30% of adults worldwide. Because sleep is involved with many physiologic systems, insufficient sleep duration and poor sleep quality have been associated with many adverse health outcomes. It is recognized as a public health concern in many countries. Numerous previous studies have revealed that sleep disorders are associated with the onset and progression of different diseases, including but not limited to cardiovascular disease, depression, mental disorders, cancer, Parkinson’s disease (PD), amyotrophic lateral sclerosis, and infectious diseases (1, 2).

A large body of epidemiologic evidence has linked insufficient sleep duration and quality to the risk of obesity, insulin resistance, and type 2 diabetes (3). Sleep restriction has been consistently shown to increase hunger, appetite, and food intake. Obstructive sleep apnea is the most prevalent type of obesity-related sleep disorder that leads to an increased risk for numerous chronic health conditions. In addition, the increased visceral adipose tissue might induce the secretion of inflammatory cytokines that could alter the sleep–wake rhythm. Although obesity could cause an alteration of sleep through several pathogenetic mechanisms, it has been reported that subjects suffering from sleep disorders are more prone to develop obesity. Experimental studies have shown that both decrease in the amount of sleep and impaired sleep quality could increase the risk of developing obesity. Sleep restriction also causes physiological, hormonal, and food behavioral changes that promote food intake, decrease in physical activity, and weight gain (4).

Although cumulative evidence shows obesity is associated with changes in sleep quality and quantity, data on the relationships between sleep and body composition are still limited. Previous cross-sectional studies could suggest that sleep disorders may contribute to body adiposity and sarcopenia in middle-aged and older humans (5). Sleep disturbances are also linked with lower fat-free mass. A previous randomized 2-period 2-condition crossover study indicates that sleep plays an important role in the preservation of human fat-free body mass during periods of reduced caloric intake. However, the relationship between location-specific fat-free mass changes and sleep disturbance remains uncertain. The observational nature of these studies does not allow for causal interpretation (5). The recent article by Chen et al (6), titled “The causal relationships between sleep-related phenotypes and body composition: A Mendelian randomized study” revealed the causal relationships between sleep-related phenotypes and body composition from different dimensions. In this study, the authors first estimated genetic correlations between common sleep-related phenotypes and anthropometric measures using linkage disequilibrium score regression. For location-specific anthropometric measures, the authors observed significant correlations between dozing and waist circumference, napping and waist circumference, insomnia and leg fat mass (right leg), and snoring and leg fat-free mass (right leg). In addition, both dozing and napping were also related to whole-body fat mass, while snoring was the only sleep-related phenotype correlated with whole-body fat-free mass. Subsequently, in the 2-sample Mendelian randomization analysis, the authors confirmed the correlation between snoring and whole-body fat-free mass and further revealed the relationship between snoring and leg fat-free mass (right leg) and trunk fat-free mass. They also detected bidirectional correlations between dozing and leg fat mass (right leg) and napping and arm fat mass (right arm).

Notably, in order to confirm the causal relationships detected in Mendelian randomization analysis, the authors conducted Bayesian colocalization analysis and identified rs143384 mapping on growth differentiation factor 5 (GDF5) and 6 overlapped 19 single nucleotide polymorphisms (eg, rs1421085, rs11642015) mapping on fat mass and obesity associated (FTO) genes. FTO variants confer higher risk for obesity. Previous study found that FTO genetic variant can influence the vulnerability of children to weight gain induced by short sleep duration (7). GDF5 was reported to regulate brown adipogenesis. When sleep loss leads to increased brown adipose tissue thermogenesis, pharmacological activation of brown adipose tissue may enhance sleep. Sleep regulation is closely related to appropriate brown adipose tissue thermogenic activity (8).

We understand there are some limitations as the authors mentioned in this study. For example, the individuals involved in the current data analyses were restricted to only “White British.” Therefore, for other ethnic groups the findings should be referred to with caution. Future researches are required to replicate these findings using independent samples of different ethnic background. However, to our knowledge, this is an innovative study to investigate the causal relationships between body composition and sleep-related phenotypes using a number of location-specific anthropometric measures other than body mass index. The authors found sleep-related phenotypes were associated with location-specific both fat mass and fat-free mass in different parts. This raises questions regarding the underlying biological mechanisms for this phenomenon. Another important finding was the related single nucleotide polymorphisms of sleep and obesity mapped on common genes, the role of which in circadian rhythms and sleep regulation in humans deserves further investigation. More importantly, these novel findings may add to the knowledge of the mechanism of sleep disturbances and obesity and provide potential therapeutic targets.