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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Establishing causal relationships between sleep and adiposity traits using Mendelian randomization.

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Insomnia symptoms and napping are linked to increased adiposity, while higher body mass and hip circumference are associated with daytime sleepiness. Poor sleep and weight gain may form a detrimental health feedback loop.

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Area of Science:

  • Genetics
  • Epidemiology
  • Sleep Science

Background:

  • Sleep disturbances are increasingly recognized as potential contributors to metabolic dysfunction.
  • Adiposity, encompassing obesity and body fat distribution, is a major public health concern.
  • Understanding the bidirectional relationship between sleep and adiposity is crucial for developing effective health interventions.

Purpose of the Study:

  • To systematically investigate the causal direction between various sleep traits and adiposity metrics.
  • To utilize Mendelian randomization to assess genetic predispositions for sleep and adiposity.

Main Methods:

  • Two-sample Mendelian randomization analysis was employed.
  • Utilized large-scale genetic data from UK Biobank, 23andMe, GIANT, and EGG consortia.
  • Examined sleep traits including chronotype, insomnia, sleep duration, napping, and daytime sleepiness against adiposity measures like BMI, hip, waist, and waist-hip ratio.

Main Results:

  • Insomnia symptoms were causally associated with increased waist circumference, BMI, and waist-hip ratio.
  • Napping showed a causal link to increased waist-hip ratio.
  • Higher hip circumference, waist circumference, and adult BMI were associated with increased odds of daytime sleepiness.
  • Elevated childhood BMI was linked to reduced odds of napping.

Conclusions:

  • Evidence suggests a complex interplay where insomnia influences adiposity, and adiposity affects daytime sleepiness.
  • These findings highlight a potential feedback loop between poor sleep and weight gain, posing risks to overall health.
  • Further research into this bidirectional relationship is warranted to inform public health strategies.