Comparison of Body Mass Index with Body Fat Percentage in the Evaluation of Obesity in Chinese

Objective To evaluate the present Chinese body mass index （BMI） criteria with body fat percentage （BF%） in determining obesity in Chinese population. Methods A total of 4 907 subjects （age： 20-90 yrs） were enrolled in the baselirie survey of a longitudinal epidemiological study, and 2 638 of them were reevaluated in 5.5 years later. The Chinese BMI and WHO BF% were used to define obesity, respectively. Results The diagnostic agreement between the Chinese BMI and WHO BF% definitions for obesity was poor for both men （kappa： 0.210, 95% CI： 0.179-0.241） and women （kappa： 0.327, 95% CI： 0.296-0.358）. However, BMI had a good correlation with BF% both in men （r： 0.785, P〈0.01） and women （r： 0.864, P〈0.01）. The age and sex-adjusted relative risks （RR） for incidence of type 2 diabetes （T2DM） were significantly higher in subjects with intermediate BF% （BF%：20.1%-25% for men, 30.1%-35% for women） （RR： 2.35, 95% CI： 1.23-4.48） and high BF%（BF%〉25% for men and 〉35% for women）（RR： 2.89, 95% CI： 1.43-5.81）, or in subjects with high BMI （BMI≥ 28 kg/m2） （RR： 2.46, 95% CI： 1.31-4.63） when compared to those with low BF% （BF%≤20% for men ands〈30% for women） or low BMI （BMI〈24 kg/m^2） respectively. No difference in risk could be found in those with intermediate BMI （BMI： 24-27.9 kg/m^2） （RR： 1.44, 95% CI： 0.86-2.40）, as compared to those with low BMI （BMI〈24 kg/m^2）, whose BF% ranged widely from 7.8 to 50.3%. Conclusion BMI was correlated with BF%. Both BMI and BF% were associated with high risk for T2DM. However, BMI had its limitations in the interpretation of subjects with BMI between 24 and 27.9 kg/m^2.

Comparison of clinical characteristics in patients with coronavirus disease and influenza A in Guangzhou, China

BACKGROUND: This study aims to compare the epidemiological, clinical and laboratory characteristics between patients with coronavirus disease(COVID-19) and influenza A(H1N1), and to develop a differentiating model and a simple scoring system.METHODS: We retrospectively analyzed the data from patients with COVID-19 and H1N1. The logistic regression model based on clinical and laboratory characteristics was constructed to distinguish COVID-19 from H1N1. Scores were assigned to each of independent discrimination factors based on their odds ratios. The performance of the prediction model and scoring system was assessed. RESULTS: A total of 236 patients were recruited, including 20 COVID-19 patients and 216 H1N1 patients. Logistic regression revealed that age >34 years, temperature ≤37.5℃, no sputum or myalgia, lymphocyte ratio ≥20% and creatine kinase-myocardial band isoenzyme(CK-MB) >9.7 U/L were independent differentiating factors for COVID-19. The area under curves(AUCs) of the prediction model and scoring system in differentiating COVID-19 from H1N1 were 0.988 and 0.962, respectively. CONCLUSIONS: There are certain differences in clinical and laboratory features between patients with COVID-19 and H1N1. The simple scoring system may be a useful tool for the early identification of COVID-19 patients from H1N1 patients.