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Related Concept Videos

Obesity01:24

Obesity

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The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
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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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The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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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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Related Experiment Video

Updated: May 15, 2025

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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Association between eosinophil number and overweight status: a nonlinear, bidirectional study.

Xinghai Yue1, Chenchen Wang1, Xixu Zhang2

  • 1The Second Clinical College, Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, China.

Lipids in Health and Disease
|May 10, 2025
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Summary

Eosinophil counts show an inverted U-shaped link with overweight and obesity, suggesting a role in immune mechanisms related to body mass. This study analyzed NHANES data for overweight status and eosinophils.

Keywords:
Body mass indexEosinophilNHANESObesityOverweight

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Area of Science:

  • Immunology
  • Metabolic Health
  • Public Health Nutrition

Background:

  • The correlation between eosinophil levels and overweight/obesity is debated, with conflicting results from animal and human studies.
  • Existing research presents a positive correlation in clinical settings, contrasting with negative correlations suggested by animal models.
  • A potential nonlinear relationship between eosinophil count and overweight status necessitates further investigation.

Purpose of the Study:

  • To investigate the association between eosinophil number and overweight status using National Health and Nutrition Examination Survey (NHANES) data.
  • To explore the relationship between eosinophil count and related metabolic indicators such as body mass index (BMI), triglycerides, and High-Density Lipoprotein (HDL).
  • To determine if a nonlinear association exists between eosinophil levels and overweight/obesity.

Main Methods:

  • Utilized data from NHANES 1999-2018, encompassing laboratory measurements of eosinophil counts.
  • Defined overweight status as a body mass index (BMI) ≥ 25 kg/m².
  • Employed weighted logistic/linear regression, subgroup analyses, and restricted cubic splines (RCS) to analyze associations.

Main Results:

  • Included 77,217 participants; 39,111 were classified as overweight or obese (BMI ≥ 25).
  • Found a significant positive association between eosinophil number and overweight status (OR: 2.38, 95% CI: 1.81-3.12).
  • Observed positive correlations of eosinophil count with obesity, BMI, and triglycerides, and a negative correlation with HDL. Nonlinear analysis revealed an inverted U-shaped relationship.

Conclusions:

  • An inverted U-shaped relationship exists between eosinophil number and overweight status, obesity, and BMI.
  • Eosinophil count is significantly associated with metabolic markers including HDL and triglycerides.
  • These findings highlight eosinophils' potential role in overweight/obesity and offer insights into related immune mechanisms.