{'fraction_non_alphanumeric': 0.03213119911904668, 'fraction_numerical': 0.012034451567231683, 'mean_word_length': 4.5933787945446545, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 19, 'https://': 2, 'lorem ipsum': 0, 'www.': 4, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 5, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Association between diabetes, metabolic syndrome and heart attack in US adults: a cross-sectional analysis using the Behavioral Risk Factor Surveillance System 2015

Objectives Diabetes mellitus (DM) and metabolic syndrome (MS) are both associated with heart attack. Evidence regarding which condition—MS or DM—is better associated with heart attack, however, is limited. The purpose of this study is to examine DM and MS, and their comparative associations with heart attack, using the 2015 Behavioral Risk Factor Surveillance System (BRFSS). Design Cross-sectional study. Methods A total of 332 008 subjects aged over 18 years were included in the analysis. All subjects were classified into four groups based on their DM and MS status: neither DM nor MS, DM without MS, MS without DM, and both DM and MS. A weighted hierarchical logistic regression was used to examine the difference between the four groups in their association with the risk of a heart attack. Results Differences in weighted frequency distributions of gender, age category (over 45 years or not), smoking status, education, race, physical activity and daily vegetable and fruit consumption were significantly different across the four groups (p<0.05). The weighted prevalence of heart attack was 5.2% for neither DM nor MS group, 8.5% for DM without MS group, 11.0% for MS without DM group and 16.1% for both DM and MS group. The weighted prevalence of heart attack in MS without DM group was significantly higher than that in the DM without MS group (p<0.01). After adjusting for confounding variables, DM without MS and MS without DM were both found to be independently associated with heart attack compared with those without DM nor MS (DM without MS, OR=2.09; MS without DM, OR=2.58, all p<0.01). Conclusion The BRFSS 2015 data indicated that MS without DM and DM without MS had comparable effects on heart attack, and the odds of risk are doubled than US adults with neither DM nor MS.

It would read more clearly and simply as: "The purpose of this study is to examine diabetes mellitus (DM) and metabolic syndrome (MS), and their comparative associations with heart attack, using the 2015 Behavioral Risk Factor Surveillance System (BRFSS)." Background 2. Page 4, Lines 7-10: Is there a reason you are using 2013 data, when this data is available from the CDC annually? At the very least, I would use 2015 to match with the BRFSS data. Methods 3. Page 5, Lines 39-48: Please describe how you handled missing values. It looks like you excluded missing values and analyzed as 'missing completely as random.' But there needs to be justification of this, and a description of where the missingness is coming from. It looks like roughly 25% of participants were excluded. 4. Page 7, Lines 4-18: Your description of 'MS alone' is not clear. The ATP-III definition of metabolic syndrome is 3 out of 5 of the following: abdominal obesity (based on waist circumference), triglycerides >=150, HDL <40 in men or HDL <50 in women, elevated BP >=130/85, and/or fasting glucose >=110. You describe the definition of MS as having "more than three components," but it is actually 'at least 3' or '3 out of 5' components. You should explicitly state each component of the definition and the survey item that was used as a surrogate. It's okay to use proxies-they just need to be explicitly identified and justified.
For instance, there needs to be justification for using BMI as a surrogate for central obesity, when ATP-III clearly defines this as abdominal waist circumference. I would imagine that there could be some reference to the WHO definition of metabolic syndrome, which does not use abdominal obesity but just obesity with BMI >30. But there needs to be some support for your rationale.
5. Page 7, Lines 4-18: The qualifier 'alone' is also quite ambiguous. I would also suggest using the language "MS without DM" and "DM without MS." DM alone suggests that they only have DM and nothing else, when they could actually have DM and dyslipidemia, but not qualify as having metabolic syndrome.
Results 6. Page 8, Lines 7-18: Are most of the DM without MS type I diabetes? Is type 1 included? I would imagine the BRFSS specifies between type I and type II? 7. Page 9, Lines 39-43: Given that you did not actually use central obesity, you need to describe this in your results as BMI>25, rather than using the language of 'central obesity.' Using a cutoff of 25 is also technically 'overweight' and not 'obesity.' So if you wanted to even just describe this as 'obesity,' you would need to analyze your sample based on a BMI>30 cutoff. Otherwise, describe the weighted prevalence of 'overweight' or 'BMI>25' as 67.2%. Discussion 8. Page 13, Lines 35-43: Please describe your rationale for using ROC, specificity, and sensitivity analyses, and explain what this adds to your study and interpretation of findings. 9. Page 13, Lines 46-51: It's not clear what you mean by, "The diagnosis of MS in this study was different from the original definition of MS. However, the association between MS and heart attack was consistent." I don't follow the logic of these two statements, how they connect, and why the association being consistent justifies the use of a different definition.
10. Page 14, Lines 3-10: There needs to be clearer language here. I do think changing your category from 'DM alone' to 'DM without MS' would help, since the presence of additional risk factors in your 'DM alone' group makes the nomenclature for this group contradictory. Immediately after, you also describe a study that uses 'DM alone' to suggest a group with only DM and no additional risk factors. This is highly confusing and took me multiple reads to understand what you were getting at. 11. Page 14, Lines 27-29: Technically, you did not base your diagnosis on the ATP-III definition. Your definition was adapted from the ATP-III definition, but you did not use the definition exactly. You again use the "more than three" language, but it's actually "at least three" components.
12. Please include a discussion of missing data in your limitations section.
13. Overall, I think your discussion would benefit from a clearer description of what your study adds, compared to previous studies. Some of the studies cited seem to have more nuanced analyses, looking at specific components of MS and how they contribute to CVD risk. 14. Table 3: Please include sample size numbers for each model.
Thank you for the opportunity to review this manuscript.

REVIEWER
Guus de Waard Department of Cardiology, VU University Medical Center Amsterdam, The Netherlands.

GENERAL COMMENTS
In this paper, Guang-Ran and colleagues perform an statistical analysis using the BRFSS database, which is a health-related telephone survey performed in a large population of US adults. Their primary research question is to compare the risk of patients with diabetes mellitus, metabolic syndrome but no diabetes and patients with both diabetes and metabolic syndrome. Their findings are 1) that patients with both diabetes and metabolic syndrome are at highest risk, 2) patients with either diabetes or metabolic syndrome without diabetes have an elevated risk of myocardial infarction compared with patients without either, and 3) the difference in risk for heart attack between diabetes and metabolic syndrome without diabetes is comparable. 4. The use of cross-sectional data is problematic to address the research question. Patients who suffered from heart attack are more likely to suffer from death and therefore are underrepresented in this cross-sectional data. Furthermore, it could be possible that patients in any 4 of the groups is at increased risk for fatal heart attack, which can not be measured using the cross-sectional data. Please address.
Minor comments: 1. Abbreviations should be described in the abstract. 2. On p15, line 20 the authors state: 'Furthermore, when MS is combined with DM, the risk of heart attack will be increased by over 3.5 fold.' However, the authors find an OR of 3.451 in their adjusted model 4, and an OR of 6.787 for heart attack in the unadjusted model 1. Clarity on what over 3.5 fold relates to in this context is thus required. 3. The authors mix up hazard with odds on page 12, line 8. 4. Ethics approval should be discussed.

VERSION 1 -AUTHOR RESPONSE
Response to Reviewer(s)' Comments to Author: Reviewer: 1 Dear Prof. Elizabeth Tung, Thanks for your comments on our paper. We have revised our paper according to your comments.
Reviewer Name: Elizabeth Tung Institution and Country: University of Chicago, USA Please state any competing interests or state 'None declared': None declared Please leave your comments for the authors below This is a study on the relationship between DM, MS, and heart attack, using the 2015 BRFSS database. The authors report that having either DM or MS are associated with higher odds of heart attack, and having both DM and MS are associated with the highest odds of heart attack. Overall, I think this is an interesting paper. My primary concerns have to do with defining/clarifying independent measures, using precise language, and more clearly substantiating the importance of this study.
My suggestions below will hopefully serve to improve this manuscript. Abstract 1. Page 2, Line 5: The first sentence reads like it previously followed another sentence (particularly with the positioning of 'however'). Was an opening sentence deleted from this draft? Regardless, it should be re-written to stand alone. The word 'predictor' is problematic. This is a cross-sectional study and causal inference is limited; measures can only be defined as independent and dependent variables. Re: Thanks so much for your comments. We added one sentence to it and revised as follows "Diabetes mellitus (DM) and metabolic syndrome (MS) are both associated with heart attack. Evidence regarding which condition -MS or DM -is better associated with heart attack, however, is limited" .
It would read more clearly and simply as: "The purpose of this study is to examine diabetes mellitus (DM) and metabolic syndrome (MS), and their comparative associations with heart attack, using the 2015 Behavioral Risk Factor Surveillance System (BRFSS)." Re: Thanks so much for your comments. We have revised this sentence as follows in the abstract "The purpose of this study is to examine DM and MS, and their comparative associations with heart attack, using the 2015 Behavioral Risk Factor Surveillance System (BRFSS)".
Background 2. Page 4, Lines 7-10: Is there a reason you are using 2013 data, when this data is available from the CDC annually? At the very least, I would use 2015 to match with the BRFSS data. Re: Thanks so much for your comments. We have updated the CHD morbidity in 2015 and revised the sentence as follows "CHD alone caused approximately 1 of every 7 deaths in the U.S. with 366,801 deaths due to CHD in 2015". Accordingly, the first reference was changed to "Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation. 2018,20;137(12):e67-e492".
Methods 3. Page 5, Lines 39-48: Please describe how you handled missing values. It looks like you excluded missing values and analyzed as 'missing completely as random.' But there needs to be justification of this, and a description of where the missingness is coming from. It looks like roughly 25% of participants were excluded. Re: There were 441,456 subjects in the 2015 BRFSS survey data. Not all questions were answered by all the subjects in the BRFSS survey, thus there are some missing data in the BRFSS data set. Subjects with unknown responses or non-responses in the questions were excluded. After removing these subjects, 75.2% (332,008) subjects were included in the analysis. As each record in the 2015 BRFSS data was weighted using raking weighting methodology. Final weight was assigned to each respondent in this study. The way that the SAS software dealing with missing data, which exclude observations with missing values in the weighted hierarchical logistic regression models, is under the missing completely at random assumption. We have added this as a limitation in our discussion section.
4. Page 7, Lines 4-18: Your description of 'MS alone' is not clear. The ATP-III definition of metabolic syndrome is 3 out of 5 of the following: abdominal obesity (based on waist circumference), triglycerides >=150, HDL <40 in men or HDL <50 in women, elevated BP >=130/85, and/or fasting glucose >=110. You describe the definition of MS as having "more than three components," but it is actually 'at least 3' or '3 out of 5' components. You should explicitly state each component of the definition and the survey item that was used as a surrogate. It's okay to use proxies-they just need to be explicitly identified and justified. Re: Thanks so much for your comments. We have added the ATP-III definition of metabolic syndrome in the revised manuscript and added the reason we revised the definition of MS in this study. We revised as follows "The components of MS were abdominal obesity (waist circumference >40 inches in men or >35 inches in women), triglycerides ≥150 mg/dl, high density lipoprotein cholesterol <40 mg/dl in men or <50mg/dl in women, blood pressure ≥130/85 mmHg, and fasting glucose ≥110 mg/dl. As these were no data of waist circumference, blood pressure, fasting glucose and lipid profile. The diagnosis of MS was revised based on the questions in the BRFSS. The revised components of MS included diabetes, hypertension, BMI ≥ 25.0 kg/m2, and dyslipidemia. Respondents who had at least three components were regarded as having MS" .
For instance, there needs to be justification for using BMI as a surrogate for central obesity, when ATP-III clearly defines this as abdominal waist circumference. I would imagine that there could be some reference to the WHO definition of metabolic syndrome, which does not use abdominal obesity but just obesity with BMI >30. But there needs to be some support for your rationale.
Re: Thanks so much for your comments. As these were no data of waist circumference in the BRFSS survey. There are several definitions of MS. Only in the MS definition from the American College of Endocrinology, waist circumference a >40 inches for men, >35 inches for women and BMI >25kg/m2 were all recommended as a component of MS, which was stated in the discussion section. So we selected BMI ≥25.0 kg/m2 as a component of MS in our analysis. We have replaced central obesity with BMI ≥25.0kg/m2 in the revised manuscript.
5. Page 7, Lines 4-18: The qualifier 'alone' is also quite ambiguous. I would also suggest using the language "MS without DM" and "DM without MS." DM alone suggests that they only have DM and nothing else, when they could actually have DM and dyslipidemia, but not qualify as having metabolic syndrome. Re: Thanks so much for your comments. We have replaced MS alone and DM alone with MS without DM and DM without MS in the revised manuscript.
Results 6. Page 8, Lines 7-18: Are most of the DM without MS type I diabetes? Is type 1 included? I would imagine the BRFSS specifies between type I and type II? Re: In the BFRSS survey, diabetes related question is "ever told you have diabetes". There was no information on the type of diabetes.
7. Page 9, Lines 39-43: Given that you did not actually use central obesity, you need to describe this in your results as BMI>25, rather than using the language of 'central obesity.' Using a cutoff of 25 is also technically 'overweight' and not 'obesity.' So if you wanted to even just describe this as 'obesity,' you would need to analyze your sample based on a BMI>30 cutoff. Otherwise, describe the weighted prevalence of 'overweight' or 'BMI>25' as 67.2%. Re: Thanks so much for your comments. We have replaced central obesity with BMI ≥25.0 kg/m2 in the revised manuscript.
Discussion 8. Page 13, Lines 35-43: Please describe your rationale for using ROC, specificity, and sensitivity analyses, and explain what this adds to your study and interpretation of findings. Re: Besides odds ratios, ROC curve analyses were performed to compare the effects of MS without DM and DM without MS on heart attack. The predictive probability value of different DM and MS groups was calculated from the Logistic analysis. The ROC result further confirmed our results that "MS without DM and DM without MS may have similar effects on heart attack in the US adults.
9. Page 13, Lines 46-51: It's not clear what you mean by, "The diagnosis of MS in this study was different from the original definition of MS. However, the association between MS and heart attack was consistent." I don't follow the logic of these two statements, how they connect, and why the association being consistent justifies the use of a different definition. Re: Thanks so much for your comments. As the definition of MS in this manuscript was revised, however, we also found that MS was associated with heart attack. We added it at the end of the paragraph 2 in the Discussion section and revised as follows "In our analysis, the association between MS and heart attack was consistent. MS, regardless of its definition, was associated with heart attack".
10. Page 14, Lines 3-10: There needs to be clearer language here. I do think changing your category from 'DM alone' to 'DM without MS' would help, since the presence of additional risk factors in your 'DM alone' group makes the nomenclature for this group contradictory. Immediately after, you also describe a study that uses 'DM alone' to suggest a group with only DM and no additional risk factors. This is highly confusing and took me multiple reads to understand what you were getting at.
Re: Thanks so much for your comments. We have replaced "MS alone" and "DM alone" with "MS without DM" and "DM without MS" in the revised manuscript.
11. Page 14, Lines 27-29: Technically, you did not base your diagnosis on the ATP-III definition. Your definition was adapted from the ATP-III definition, but you did not use the definition exactly. You again use the "more than three" language, but it's actually "at least three" components. Re: Thanks so much for your comments. We have revised as follows "Respondents who had at least three components were regarded as having MS".
12. Please include a discussion of missing data in your limitations section. Re: Thanks so much for your comments. We added the discussion of missing data as follows "In this study, 24.8% subjects in the 2015 BRFSS data with unknown responses or non-responses in questions included in the study were excluded from the analysis under the assumption of missing completely at random, which might result in some bias of the results when the assumption is not valid".
13. Overall, I think your discussion would benefit from a clearer description of what your study adds, compared to previous studies. Some of the studies cited seem to have more nuanced analyses, looking at specific components of MS and how they contribute to CVD risk. Re: Thanks so much for your comments. We have revised the Discussion section.
14. Table 3: Please include sample size numbers for each model. Re: Thanks so much for your comments. We added the sample size numbers for each model in Table  3.
Reviewer: 2 Dear Prof. Guus de Waard, Thanks for your comments on our paper. We have revised our paper according to your comments. Please leave your comments for the authors below In this paper, Guang-Ran and colleagues perform an statistical analysis using the BRFSS database, which is a health-related telephone survey performed in a large population of US adults. Their primary research question is to compare the risk of patients with diabetes mellitus, metabolic syndrome but no diabetes and patients with both diabetes and metabolic syndrome. Their findings are 1) that patients with both diabetes and metabolic syndrome are at highest risk, 2) patients with either diabetes or metabolic syndrome without diabetes have an elevated risk of myocardial infarction compared with patients without either, and 3) the difference in risk for heart attack between diabetes and metabolic syndrome without diabetes is comparable.
Major comments: 1. The primary endpoint, defined as 'heart attack' seems particularly unclear. It could either mean myocardial infarction or cardiac arrest. Please clarify. Furthermore, it should probably contain non-fatal, since patients patients who experienced a fatal 'heart attack' can not be included in this study.
Re: Thanks so much for your comments. Our analysis used the Behavioral Risk Factor Surveillance System 2015. In the questionnaire of this survey, question about heart attack was "has a doctor, nurse, or other health professional ever told you that you had any of the following? ever told you had a heart attack, also called a myocardial infarction". We didn't revise the name of the endpoint. As fatal heart attack didn't included in this analysis, we added this as a limitation in the revised manuscript as follows "In the BRFSS survey the data of fatal heart attack are not included, which may also underestimate the actual prevalence of heart attack" .
2. It is unclear to what extent patients are actually aware of their prior medical history. It could very well be that numerous patients are unaware that they have a condition termed diabetes mellitus. A number of these patients could be included in the MS without DM group, whilst actually suffering from DM. Would it be possible for the authors to further specify this group, for example by redefining the DM group to those that take anti hyperglycemic agents? This would greatly strengthen the paper in my view.
Re: In the questionnaire of this survey, questions about respondents' past medical history were "has a doctor, nurse, or other health professional ever told you that you had any of the following? ever told you had a heart attack, also called a myocardial infarction, ever told you have diabetes, etc". We have revised this in the method section. People whose response were don't known/not sure or refuse to answer were not included in our analysis. In the 2015 BFRSS, there was no question about antihyperglycemic agents except insulin. So we defined diabetes by a yes answer to the question "has a doctor, nurse, or other health professional ever told you have diabetes".
3. The use of receiver operating characteristic curves is not described in the methods section. Furthermore, it is unclear how a categorical variable (no DM or MS, DM, MS w/o DM, or both) can be used in an ROC model which typically requires a continuous variable to predict a dichotomous outcome. Please clarify. Re: Thanks so much for your comments. ROC model was made based on the predictive probability value of each respondent from the logistic regression analysis. We revised the statistical analysis as follows "The predictive probability value of each respondent from the logistic regression analysis was calculated. Receivers operating characteristic (ROC) curve analyses, the sensitivity and the specificity of the predictive probability were performed to determine the association of different DM and MS groups with heart attack".
4. The use of cross-sectional data is problematic to address the research question. Patients who suffered from heart attack are more likely to suffer from death and therefore are underrepresented in this cross-sectional data. Furthermore, it could be possible that patients in any 4 of the groups is at increased risk for fatal heart attack, which can not be measured using the cross-sectional data. Please address. Re: Thanks so much for your comments. We revised the limitation as follows "In the BRFSS survey the data of fatal heart attack are not included, which may also underestimate the actual prevalence of heart attack" .
Minor comments: 1. Abbreviations should be described in the abstract. Re: Thanks so much for your comments. We added the description of abbreviations in the abstract.
2. On p15, line 20 the authors state: 'Furthermore, when MS is combined with DM, the risk of heart attack will be increased by over 3.5 fold.' However, the authors find an OR of 3.451 in their adjusted model 4, and an OR of 6.787 for heart attack in the unadjusted model 1. Clarity on what over 3.5 fold relates to in this context is thus required. Re: Sorry for this mistake. We have revised as follows "Furthermore, when MS is combined with DM, the risk of heart attack will be increased by over 3.4 fold".
3. The authors mix up hazard with odds on page 12, line 8. Re: Sorry for this mistake. We have revised as follows "MS did not appear to be a greater odds for heart attack than DM from our analysis results".
4. Ethics approval should be discussed. Re: Thanks so much for your comments. The BRFSS data is publically available national survey data and can be directly downloaded from the Centers for Disease Control and Prevention website (https://www.cdc.gov/brfss/annual_data/annual_data.html) without any identifiable information included in the data. Thus, ethical approval was not required for this study. We have added the following statement in our revised manuscript "The 2015 BRFSS annual survey data does not include any identifiable information and is publically available from the Centers for Disease Control and Prevention website (https://www.cdc.gov/brfss/annual_data/annual_2015.html)."

GENERAL COMMENTS
My comments have been addressed appropriately.
Statistical review is needed however. I am not sure if the use of ROC curves using predicted probabilities is acceptable and would like to have this addressed by a statistician.

GENERAL COMMENTS
Thank you for asking me to review the revised version of this manuscript. It is clear that the authors have taken into consideration each of the reviewers comments, which the authors should be commended for.
In addition I have a few minor comments which could be addressed by the authors.
1. The authors state that they have replaced MS alone and DM alone with MS without DM and DM without MS in the revised manuscript based on the previous reviewers recommendations. However, the manuscript in particular the abstract still contains the wording "DM only group" and "MS only group". This should be checked throughout the manuscript and corrected.
2. In the "Measures" section, the authors have ethnicity (Hispanic, Latino/a, or Spanish origin or no), but in

GENERAL COMMENTS
As I am a statistician, I will restrict my comments to statistical methodology and general structure. As such I cannot comment on the whether the results are novel or of general interest. I've no doubt that other reviewers will do this.
I found the paper interesting and like how the results were presented. In particular, the way in which the authors allowed the reader compare risk levels in absolute terms, differences in percentages, as opposed to relying solely on relative measures (odds ratios). However, I also have many major concerns.
1. I must start by saying that at times the paper is very poorly written as such would need to be thoroughly proofread. This is not simply a question of correcting minor grammatical errors. I have not attempted to address this as part of this review, but the paper should not be published without major improvements.
2. The definition of MS used in the paper is worth noting.
"The revised components of MS included diabetes, hypertension, BMI ≥25.0 kg/m2, and dyslipidemia. Respondents who had at least three components were regarded as having MS. In this study, the "MS without DM" group means that respondents had the other three components of MS excluding diabetes".
This means that when comparing the MS but no DM to the DM but no MS you are in effect comparing subjects who are obese, are hypertensive, and have dyslipidemia to those with diabetes and missing one of the other three factors. Those with DM and MS have all four. The fundamental question then is why didn't the authors simply include each of the four risk factors in the model? They could then look at how the effect of hypertension, obesity, and dyslipidemia vary between diabetics and non-diabetics. The effect of each factor could be measured both in terms of relative effects (OR based on a logistic regression with interaction terms) and risk differences based on a comparison of marginal effects.
3. In the STROBE guidelines your reply to "If applicable, describe analytical methods taking account of sampling strategy" is "NA". This question is very important, and you need to do a better job explaining the statistical methods in the context of survey data analysis. This relates to my next point.
4. My major concern, in relation to the statistical analysis, relates to the weighting scheme used in the study. I was unable to locate the reference (reference 8) or follow the link. You should correct this as clearly the web-address is wrong. It refers to "wwwcdcgov". However, based on the reference under Ethics it's clear that the weighting scheme ('raking') aims to balance the demographics of the sample so that it resembles the demographic characteristics of populations, it is important that you describe the weighting scheme in more detail and not rely on a reference. Focus on explaining what raking achieves and why it is necessary. The fundamental question is this, how should the weighted percentages be interpreted? Typically, weights ensure that the sample estimates approximate population rates. However, it is not clear whether the weighted averages allow for difference between the four MS/DM groups in terms of age, gender etc. You need to make this clear. Given the differences that exist between the groups in terms of basic demographics, and the association between these demographics and the risk of a heart attack, it is essential when comparing groups that you allow for/adjust for these differences. I am not convinced that the weighting used in this paper does this.
I would suggest that you consider the following option.
(i) Carry out the logistic regression adjusting for appropriate risk factors -as done (ii) Estimate the marginal prevalence based on the results of the logistic regression.
Marginal estimates, for each of the 4 MS/ groups, could be obtained at the mean of the risk factors or alternatively using the (weighted) risk factor distribution in your sample. I recommend the latter. Both options are straightforward in SAS. I note that it may be that the weighted results in fact allow for difference in demographics between the four groups, but it is not clear to the reader whether this is the case. In fact, correctly applied, the weighted percentages could be seen as marginal rates.
5. The authors should be careful not to overstate results by focusing on statistical significance. For example, the authors state (page 9) that "The physical activity index was statistically significant between the DM without MS and MS without DM groups (48.2% vs 47.6%, p<0.001)". Although the stated result is correct the true message surely is that the levels of physical activity were very similar.
6. The authors state that "Receivers operating characteristic (ROC) curve analyses were performed to determine the predictive probability value of different DM and MS groups in predicting heart attack". In fact, the authors calculate the AUROC in each of the four groups separately. They are comparing the diagnostic performance of the logistic regression model in the four subgroups. It is not clear why this is of any interest. In a given MS/DM group the OR associated with that group does not have any impact on the diagnostic performance of the model. Because no interaction terms were included in the model the weighting given to the other risk factors does not depend on which group you're in. So, how should we interpret differences in the AUC and why are the authors interested in comparing AUC in the four groups? The AUC results are not referred to in the abstract and I would drop these results.
7. The authors calculate the sensitivity and specificity of their model based on a cut-point of 0.5 in the predicted probability of a heart attack. Although software will often calculate this automatically it is of no use here. Nobody would ever use a cut-off of 0.5 (50%) when looking at heart attack.
8. OR should be rounded to two decimal places.
9. Formatting makes it impossible to read the tables.

VERSION 2 -AUTHOR RESPONSE
Reviewer: 2 Thank you for asking me to review the revised version of this manuscript. It is clear that the authors have taken into consideration each of the reviewers comments, which the authors should be commended for.
In addition I have a few minor comments which could be addressed by the authors.
1. The authors state that they have replaced MS alone and DM alone with MS without DM and DM without MS in the revised manuscript based on the previous reviewers recommendations. However, the manuscript in particular the abstract still contains the wording "DM only group" and "MS only group". This should be checked throughout the manuscript and corrected.
Re: Thanks so much for your comments. We have checked the manuscript and corrected. As I am a statistician, I will restrict my comments to statistical methodology and general structure. As such I cannot comment on the whether the results are novel or of general interest. I've no doubt that other reviewers will do this.
I found the paper interesting and like how the results were presented. In particular, the way in which the authors allowed the reader compare risk levels in absolute terms, differences in percentages, as opposed to relying solely on relative measures (odds ratios). However, I also have many major concerns.
1. I must start by saying that at times the paper is very poorly written as such would need to be thoroughly proofread. This is not simply a question of correcting minor grammatical errors. I have not attempted to address this as part of this review, but the paper should not be published without major improvements.
Re: Thanks so much for your comments. We have corrected grammatical errors in our resubmitted manuscript.
2. The definition of MS used in the paper is worth noting.
Respondents who had at least three components were regarded as having MS. In this study, the "MS without DM" group means that respondents had the other three components of MS excluding diabetes".
This means that when comparing the MS but no DM to the DM but no MS you are in effect comparing subjects who are obese, are hypertensive, and have dyslipidemia to those with diabetes and missing one of the other three factors. Those with DM and MS have all four. The fundamental question then is why didn't the authors simply include each of the four risk factors in the model? They could then look at how the effect of hypertension, obesity, and dyslipidemia vary between diabetics and non-diabetics. The effect of each factor could be measured both in terms of relative effects (OR based on a logistic regression with interaction terms) and risk differences based on a comparison of marginal effects.
Re: Thanks so much for your comments. It was confirmed that the effect of hypertension, obesity, and dyslipidemia in diabetes was higher than that in non-diabetes in previous studies. However, whether MS but no DM, DM but no MS had similar effect on heart attack was controversial.
3. In the STROBE guidelines your reply to "If applicable, describe analytical methods taking account of sampling strategy" is "NA". This question is very important, and you need to do a better job explaining the statistical methods in the context of survey data analysis. This relates to my next point.
Re: Thanks so much for your comments. We have corrected our answer to the question and added more details in the statistical methods section on how to take the complex survey sampling design in the BRFSS data into account in our data analysis.
4. My major concern, in relation to the statistical analysis, relates to the weighting scheme used in the study. I was unable to locate the reference (reference 8) or follow the link. You should correct this as clearly the web-address is wrong. It refers to "wwwcdcgov". However, based on the reference under Ethics it's clear that the weighting scheme ('raking') aims to balance the demographics of the sample so that it resembles the demographic characteristics of populations, it is important that you describe the weighting scheme in more detail and not rely on a reference. Focus on explaining what raking achieves and why it is necessary. The fundamental question is this, how should the weighted percentages be interpreted? Typically, weights ensure that the sample estimates approximate population rates. However, it is not clear whether the weighted averages allow for difference between the four MS/DM groups in terms of age, gender etc. You need to make this clear. Given the differences that exist between the groups in terms of basic demographics, and the association between these demographics and the risk of a heart attack, it is essential when comparing groups that you allow for/adjust for these differences. I am not convinced that the weighting used in this paper does this.
I would suggest that you consider the following option.
(i) Carry out the logistic regression adjusting for appropriate risk factors -as done (ii) Estimate the marginal prevalence based on the results of the logistic regression.
Marginal estimates, for each of the 4 MS/ groups, could be obtained at the mean of the risk factors or alternatively using the (weighted) risk factor distribution in your sample. I recommend the latter. Both options are straightforward in SAS. I note that it may be that the weighted results in fact allow for difference in demographics between the four groups, but it is not clear to the reader whether this is the case. In fact, correctly applied, the weighted percentages could be seen as marginal rates.
Re: Thanks for the suggestions. Our current analysis already adjusted all other potential risk factors and our analysis used the weighted risk factor distribution in the BRFSS sample. We have corrected the web-address of reference 8 in the reference section in the resubmitted manuscript.
5. The authors should be careful not to overstate results by focusing on statistical significance. For example, the authors state (page 9) that "The physical activity index was statistically significant between the DM without MS and MS without DM groups (48.2% vs 47.6%, p<0.001)". Although the stated result is correct the true message surely is that the levels of physical activity were very similar.
Re: Thanks so much for your comments. We have revised it in our resubmitted manuscript.
6. The authors state that "Receivers operating characteristic (ROC) curve analyses were performed to determine the predictive probability value of different DM and MS groups in predicting heart attack". In fact, the authors calculate the AUROC in each of the four groups separately. They are comparing the diagnostic performance of the logistic regression model in the four sub-groups. It is not clear why this is of any interest. In a given MS/DM group the OR associated with that group does not have any impact on the diagnostic performance of the model. Because no interaction terms were included in the model the weighting given to the other risk factors does not depend on which group you're in. So, how should we interpret differences in the AUC and why are the authors interested in comparing AUC in the four groups? The AUC results are not referred to in the abstract and I would drop these results.
Re: Thanks for the suggestions. We have dropped the AUC part in our resubmitted manuscript.
7. The authors calculate the sensitivity and specificity of their model based on a cut-point of 0.5 in the predicted probability of a heart attack. Although software will often calculate this automatically it is of no use here. Nobody would ever use a cut-off of 0.5 (50%) when looking at heart attack.
Re: We have dropped the predicted probability part in our resubmitted manuscript. Our paper focuses on association rather than prediction.
8. OR should be rounded to two decimal places.
Re: Thanks so much for your comments. OR in our resubmitted manuscript was rounded to two decimal places.
9. Formatting makes it impossible to read the tables.
Re: Thanks so much for your comments. We have revised these in our resubmitted manuscript.

GENERAL COMMENTS
1. Although some grammatical errors remain, the paper is much improved. I would still however recommend a final proof-read. For example Page 7: "As these were no data of waist circumference" should read "As there was no available data on waist circumference" Page 7: "Final weight was assigned to each respondent. All statistical analysis take the complex sampling design into account through incorporate the final weight in the data analysis: should read "Final weights were assigned to each respondent. All Statistical analyses and prevalence estimates have been weighted." Page 7: "Weighted hierarchial logistic regression was applied to investigate in greater depth". This should read "A weighted hierarchial logistic regression was used to examine the difference between the four groups in their association with the risk of a heart attack.
2. I remain uncertain as to what the results are really telling us. In my previous review I mentioned that The revised components of MS included diabetes, hypertension, BMI ≥25.0 kg/m2, and dyslipidaemia. Respondents who had at least three components were regarded as having MS. In this study, the "MS without DM" group means that respondents had the other three components of MS excluding diabetes".
This means that when comparing the MS but no DM to the DM but no MS you are in effect comparing subjects who are obese, are hypertensive, and have dyslipidaemia to those with diabetes and missing one of the other three factors. Those with DM and MS have all four. The fundamental question then is why didn't the authors simply include each of the four risk factors in the model? They could then look at how the effect of hypertension, obesity, and dyslipidaemia vary between diabetics and non-diabetics. For any comparison of the MS without DM and the DM without MS it is essential that the authors describe the prevalence of the other 3 components (obesity, hypertension, and dyslipidaemia) in the DM without MS group. 3. In your reply you state that it "was confirmed that the effect of hypertension, obesity, and dyslipidaemia in diabetes was higher than that in non-diabetes in previous studies. However, whether MS but no DM, DM but no MS had similar effect on heart attack was controversial". Are you referring to previous analysis of this data? If so then this should be stated clearly in the paper. If in fact the relative impact of the other risk factors is higher in diabetics then it would seem that this is a more meaningful result than comparing the relative impact of MS without DM and DM without MS.
4. The authors have included more details on 'raking' as suggested.
5. Although it's important to state that prevalence estimates are weighted, it is not necessary to state this repeatedly in the results section. I recommend that the authors state in the Statistical Methods section that all analysis and prevalence estimates have been weighted. In the results section they can then refer to prevalence as opposed to weighted prevalence.
In summary, the paper is nicely written and the results are clearly presented. The use of both relative and absolute measure is excellent. Although I don't have concerns with the statistical analysis, I do feel that the interpretation of the results is very difficult and the use of the "MS and not DM" and "DM and not MS" groupings disguises the true impact of the four components. Whereas each of the four components is clearly defined, the exact interpretation of "DM and not MS" is not clear without knowing the prevalence of each condition in this group. I am unsure as to how interesting the comparison of the "MS and not DM" and "DM and not MS" groups is, but I will let others decide on this.

VERSION 3 -AUTHOR RESPONSE
Reviewer: 4 Reviewer Name: Tony Fitzgerald Institution and Country: University College Cork Please state any competing interests or state 'None declared': None declared Please leave your comments for the authors below 1. Although some grammatical errors remain, the paper is much improved. I would still however recommend a final proof-read. For example Page 7: "As these were no data of waist circumference" should read "As there was no available data on waist circumference" Page 7: "Final weight was assigned to each respondent. All statistical analysis take the complex sampling design into account through incorporate the final weight in the data analysis: should read "Final weights were assigned to each respondent. All Statistical analyses and prevalence estimates have been weighted." Page 7: "Weighted hierarchial logistic regression was applied to investigate in greater depth". This should read "A weighted hierarchial logistic regression was used to examine the difference between the four groups in their association with the risk of a heart attack. Re: Thanks so much for your comments. We have corrected these grammatical errors in our resubmitted manuscript.
2. I remain uncertain as to what the results are really telling us. In my previous review I mentioned that The revised components of MS included diabetes, hypertension, BMI ≥25.0 kg/m2, and dyslipidaemia.
Respondents who had at least three components were regarded as having MS. In this study, the "MS without DM" group means that respondents had the other three components of MS excluding diabetes".
This means that when comparing the MS but no DM to the DM but no MS you are in effect comparing subjects who are obese, are hypertensive, and have dyslipidaemia to those with diabetes and missing one of the other three factors. Those with DM and MS have all four. The fundamental question then is why didn't the authors simply include each of the four risk factors in the model? They could then look at how the effect of hypertension, obesity, and dyslipidaemia vary between diabetics and nondiabetics. For any comparison of the MS without DM and the DM without MS it is essential that the authors describe the prevalence of the other 3 components (obesity, hypertension, and dyslipidaemia) in the DM without MS group. Re: Thanks so much for your comments. Diabetes itself is a risk factor for heart attack. It is confirmed that the effect of hypertension, obesity, and dyslipidemia in diabetes was higher than that in nondiabetes in other published studies. However, whether MS but no DM, DM but no MS had similar effect on heart attack was controversial. In our study, DM without MS is defined as diabetic people without or with only one of the other three factors of MS. It was mentioned in the Result and Discussion section about the prevalence of the other 3 components (obesity, hypertension, and dyslipidaemia) in the DM without MS group as follows "In the DM without MS group, 83% respondents had one component of MS other than DM, with 17% people having no other components of MS besides DM." "In our analysis, the weighted prevalence of hypertension, dyslipidemia and overweight in DM without MS group was 13.9%, 12.2% and 56.8%, respectively.".
3. In your reply you state that it "was confirmed that the effect of hypertension, obesity, and dyslipidaemia in diabetes was higher than that in non-diabetes in previous studies. However, whether MS but no DM, DM but no MS had similar effect on heart attack was controversial". Are you referring to previous analysis of this data? If so then this should be stated clearly in the paper. If in fact the relative impact of the other risk factors is higher in diabetics then it would seem that this is a more meaningful result than comparing the relative impact of MS without DM and DM without MS. Re: Thanks so much for your comments. It is confirmed that the effect of hypertension, obesity, and dyslipidemia in diabetes was higher than that in non-diabetes in other published studies, not previous analysis of this data.
4. The authors have included more details on 'raking' as suggested. Re: Thanks so much for your comments.
5. Although it's important to state that prevalence estimates are weighted, it is not necessary to state this repeatedly in the results section. I recommend that the authors state in the Statistical Methods section that all analysis and prevalence estimates have been weighted. In the results section they can then refer to prevalence as opposed to weighted prevalence. Re: Thanks so much for your comments. We have added "All statistical analyses and prevalence estimates have been weighted." in the Statistical Methods section. And we replaced the weighted prevalence with the prevalence in the Results section.
In summary, the paper is nicely written and the results are clearly presented. The use of both relative and absolute measure is excellent. Although I don't have concerns with the statistical analysis, I do feel that the interpretation of the results is very difficult and the use of the "MS and not DM" and "DM and not MS" groupings disguises the true impact of the four components. Whereas each of the four components is clearly defined, the exact interpretation of "DM and not MS" is not clear without knowing the prevalence of each condition in this group. I am unsure as to how interesting the comparison of the "MS and not DM" and "DM and not MS" groups is, but I will let others decide on this. Re: Thanks so much for your comments. Diabetes itself is a risk factor for heart attack. It is confirmed that the effect of hypertension, obesity, and dyslipidemia in diabetes was higher than that in nondiabetes in other published studies. However, whether MS but no DM, DM but no MS had similar effect on heart attack was controversial. In our study, DM without MS is defined as diabetic people without or with only one of the other three factors of MS. It was mentioned in the Result and Discussion section about the prevalence of the other 3 components (obesity, hypertension, and dyslipidaemia) in the DM without MS group as follows "In the DM without MS group, 83% respondents had one component of MS other than DM, with 17% people having no other components of MS besides DM." "In our analysis, the weighted prevalence of hypertension, dyslipidemia and overweight in DM without MS group was 13.9%, 12.2% and 56.8%, respectively.".

REVIEWER
Tony Fitzgerald University College Cork Ireland REVIEW RETURNED 18-Aug-2019

GENERAL COMMENTS
First of all, I would like to apologise for delaying the review process.