On 2016 Sep 29, Andrew Brown commented:

The ability to evaluate and compare this study is limited by missing methodoligical details about the outcome phenotype: various measurements of obesity and adiposity. Please excuse me if I missed the details somehow, and I hope the authors will consider adding details to better help the scientific community evaluate the authors' contribution to microbiome-obesity research.

The authors state that they evaluated three measures of abdominal adiposity, inlcuding subcutaneous fat mass (SFM). SFM is not a measurement of abdominal adiposity unless restricted to the abdomen. The methods do not make such a distinction, and instead only indicate the data were collected from DXA. The authors cite two articles in the methods with respect to adiposity phenotypes; neither describe how SFM was defined.

The authors state, "Visceral fat mass was calculated from one cross section of the whole body at L4–L5, the typical location of a CT slice;" no reference is provided to defend the reliability or appropriateness of such a method. For instance, some methods have used different lumbar positions and some people insist that DXA is inappropriate for visceral adipose tissue estimation.

The authors also dichotomize 'high' and 'low' phenotypes of adiposity measurements without explanation in Figure 2. The methods of dichotomizing also are not clear: "For each phenotype, individuals who were more than 1.5 standard deviations from the mean of the phenotype were assigned to high and low phenotype groups respectively." Does this mean those less than 1.5 SD below the mean were 'low' and those more than 1.5 SD were 'high'? This would eliminate a large portion of the sample (+/- 1.5 SD removed from the middle of the distribution would leave <15% of the sample if it was normally distributed). If, on the other hand, it was dichotomized on a single point value 1.5 SD above the mean (for instance), this has severe limitations because such cutoffs can be slid along the continuum to provide very different results. Thus, it is typically best to provide a theoretical basis for classification or to have a confirmation set (e.g., see Ivanescu AE, 2016).

It is also unclear if these dichotomized values were used throughout the rest of the manuscript (e.g., they appear to be in figure 4). This impairs the reader's ability to evaluate results and compare to new or old findings.

On 2016 Sep 29, Andrew Brown commented:

The ability to evaluate and compare this study is limited by missing methodoligical details about the outcome phenotype: various measurements of obesity and adiposity. Please excuse me if I missed the details somehow, and I hope the authors will consider adding details to better help the scientific community evaluate the authors' contribution to microbiome-obesity research.

The authors state that they evaluated three measures of abdominal adiposity, inlcuding subcutaneous fat mass (SFM). SFM is not a measurement of abdominal adiposity unless restricted to the abdomen. The methods do not make such a distinction, and instead only indicate the data were collected from DXA. The authors cite two articles in the methods with respect to adiposity phenotypes; neither describe how SFM was defined.

The authors state, "Visceral fat mass was calculated from one cross section of the whole body at L4–L5, the typical location of a CT slice;" no reference is provided to defend the reliability or appropriateness of such a method. For instance, some methods have used different lumbar positions and some people insist that DXA is inappropriate for visceral adipose tissue estimation.

The authors also dichotomize 'high' and 'low' phenotypes of adiposity measurements without explanation in Figure 2. The methods of dichotomizing also are not clear: "For each phenotype, individuals who were more than 1.5 standard deviations from the mean of the phenotype were assigned to high and low phenotype groups respectively." Does this mean those less than 1.5 SD below the mean were 'low' and those more than 1.5 SD were 'high'? This would eliminate a large portion of the sample (+/- 1.5 SD removed from the middle of the distribution would leave <15% of the sample if it was normally distributed). If, on the other hand, it was dichotomized on a single point value 1.5 SD above the mean (for instance), this has severe limitations because such cutoffs can be slid along the continuum to provide very different results. Thus, it is typically best to provide a theoretical basis for classification or to have a confirmation set (e.g., see Ivanescu AE, 2016).

It is also unclear if these dichotomized values were used throughout the rest of the manuscript (e.g., they appear to be in figure 4). This impairs the reader's ability to evaluate results and compare to new or old findings.