A study of Switzerland hospital employees, of mean age 38.1, had male mean BMI of 21.7 and female mean BMI of 21.6. That’s an extraordinarily weird, too low, non-representative sample. You should ignore their abstract.
They defined obesity, based on DEXA-measured body fat percentages, as 20% for men and 25% for women. That is ridiculous, lower thresholds than anyone else uses.
The ROC curve and bmi Cutoffs
They showed a graph of their ROC curve, so we can look at the ROC curve (below) and choose better thresholds ourselves.
The ROC curve shows the trade-offs between Sensitivity and Specificity. The article’s authors believed that a BMI of 20.5 was the optimum threshold to define obesity.
And they thought it gave a Sensitivity of 84% and Specificity of 60%. Can you believe it? A BMI of 20.5 to define obesity? What were they thinking?
[chat name="Hector" side="right"]Did they round up only skinny young nurses for their study population?
Why are the bmi cutoffs Nonsense?
That is nonsense, not only because their body fat percentage thresholds are too low, but also because choosing higher sensitivity over specificity, is not wise. This is because it will upset too many normal people who are mislabelled as obese.
The authors did mention, that if they had used higher body fat percentage cutoffs, 25% in men and 35% in women, then their optimum choice Body Mass Index cutoff would be 24 kg/m2. Unfortunately, the article didn’t show the actual data or ROC curve for these better body fat thresholds.
That is more like it. But still, the problem of their bias still exists. They presumably chose higher sensitivity over specificity again, so don’t use 24 kg/m2.
To remedy this, look at the graph above.
There is a 3 kg/m2 BMI difference between the pink and blue thresholds. Therefore, I think you should add 3 onto their value of 24 kg/m2, to give my recommendation of 27kg/m2 as the suitable BMI cutoff to define overweightness, for 38 year olds, adding 1 for men and subtracting 1 for women.