PEER REVIEW HISTORY ARTICLE DETAILS TITLE (PROVISIONAL)

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1 PEER REVIEW HISTORY BMJ Open publishes all reviews undertaken for accepted manuscripts. Reviewers are asked to complete a checklist review form ( and are provided with free text boxes to elaborate on their assessment. These free text comments are reproduced below. TITLE (PROVISIONAL) AUTHORS REVIEWER ARTICLE DETAILS Development and validation of an a priori risk model for extensive white matter lesions in people age 65 years or older: The Dijon MRI Study Tully, Phillip; Qchiqach, Sarah; Pereira, Edwige; Debette, Stephanie; Mazoyer, Bernard; Tzourio, Christophe VERSION 1 REVIEW Nils Richter University Hospital of Cologne Dept. of Neurology Cologne Germany 21-Aug-2017 The authors of this manuscript present a score based on clinical factors to assess the risk of extensive white matter lesions in adults ages years. The score was generated using data from a representative sample from the French general population and validated against a separate sample. The score has modest sensitivity and specificity, comparable to a CT scan, but a relatively high negative predictive value. The authors argue that the score may thus aid clinicians in deciding whether or not to conduct cerebral MRI in patients without overt neurological symptoms. In essence, the score indicates that a normotensive individual without cognitive or behavioral abnormalities is fairly unlikely to have extensive WML. In general this is an interesting study, drawing data from fairly large and representative samples and drawing attention to the important issue of blood pressure management as preventative measure. The authors aimed to make the score as simple as possible and thus reduced the number of factors they incorporated. However it might also have been interesting to know the effect of other common clinical phenomena such as smoking, diabetes or gait imbalance on likelihood of extensive WML. Unfortunately a lot of very relevant information regarding the methods and results is only reported in the online supplement. It would really be helpful to have this in the main manuscript to provide a clearer and more complete picture. Specific comments: - Page 11, line 8 What exactly does other validation refer to? Which sample is meant? What 8023 persons are being referred to? The samples listed before are much smaller.

2 REVIEWER - It would make sense to already list the six clinical factors, which constitute one of the main results of the analysis, in the results section. - For greater clarity please check whether abbreviations (for example IADL) are explained at first mention. - Page 35, e-table 2 Would it be possible to also provide standardized ßs for the multivariate analyses? Eric Jouvent University Paris 7 and APHP, France I know some of the co-authors and I have once co-authored a group position paper with C Tzourio and B Mazoyer. However, I have no competing interest regarding the present paper. 28-Aug-2017 In this paper, the authors sought to determine whether a simple score based on clinical data could help identify subjects without overt neurological symptoms who are at risk of having extensive white matter lesions (WML) on brain MRI. The paper clearly has many strengths. The approach is quite different from questions usually raised in the field, the results are obtained with a straightforward methodology from a very large cohort and thereafter validated on a second large cohort. Here are some comments that may help improve the paper: - Given that treatment of hypertension is not yet based on MRI, and that no study that I am aware of has yet demonstrated that care of hypertension and associated clinical events are improved by targeting WML rather than BP measures, I would suggest to be more cautious in the writing, particularly in the introduction "Identifying patients is of major importance", "to intensively manage and mitigate further risk for stroke", "guide more aggressive blood pressure lowering". "major interest to improve clinical decision making". - I would also recommend to more clearly focus the introduction on screening of large populations by general practitioners. Indeed, some of the "clinical predictors" could be per se a reason to go and see a neurologist (for instance gait disturbances, forgetfulness ), and thus the interest of such a screening method for neurologist would be lower. - in line, some aspects related to the predictors should also be discussed as potential limitations. While hypertension, forgetfulness, difficulties with calculations and psychotropic drug use might be detected in patients without overt neurological symptoms, this probably is different for IADL. Indeed, IADL are altered late during the course of most acquired neurologic disorders in the elderly. Thus, except possibly in certain rural areas, I wonder whether in real life it is actually plausible that subjects with altered IADL had never seen their gp or a neurologist before being potentially screened. Another question is whether an alternative version of the score could be set up without this particular variable. - I think that the interest of the secondary outcome (incident stroke) is low with respect to the primary outcome, and unnecessarily lengthen the paper. I would suggest to delete it. - WML delineation into deep and periventricular is not used in the paper, I would avoid getting into unnecessary details.

3 REVIEWER - I think that data definitions and results of the models are of primary importance and as such should be in the body of the manuscript rather than in supplementary material. I would suggest to include at least the "covariate data collection", e-table 1 and e-table 3 in the whole body of the manuscript. The flow chart, e table 2 and e table 4 could be left as supplementary material. - I may have missed the point, but I did not find the exact method used to determine the point scoring system from the models. It is stated on the e-table 3 title that is was "based on the regression coefficients". If not already described, this should be detailed, at least in the supplementary material. - rather than discussing the use of 1.5T, the use of T2 rather than FLAIR images should be discussed as a potential limitation regarding the translation of the study results into clinical practice. Michele Cavallari Brigham and Women's Hospital, Boston, MA, USA 30-Aug-2017 Tully et al. report on a the development of a predictive framework to stratify older individuals based on the severity of WML, and ultimately inform on the opportunity to perform MRI exam in older people at risk for WML and the associated cognitive-behavioral disability. Major Issues My main concern regards the take home message of the paper. Based on the presented results, in the absence of hypertension (as defined by either high DBP or hypertensive drug use), and neuropsychiatric symptoms and difficulties in IADL associated with WML, MRI would not be recommended, as it most likely would show a relatively low WML load. I doubt any geriatrician or neurologist would prescribe an MRI exam to an older individual in the described scenario, i.e. absence of cerebrovascular risk factors and neurological symptoms. Therefore, the clinical applicability of the presented results seems minimal to me. Another related issue regards the fact that the predictive framework is based on a collection of risk factors and correlates (potentially consequences) of WML pooled together. This is particularly relevant considering the aim of the study, i.e. to provide guidance to prescribe MRI exam to elderly people. In clinical practice, neuropsychological assessment is usually performed in subjects who report cognitive or psychiatric symptoms similar to those included in the predictive score. If the assessment shows evidence of cognitive impairment, then an MRI exam would be required anyway for differential diagnosis and prognostic purposes. Analysis of the predictive role of a different score based on risk factors only may provide clinically useful results. Minor issues Normalization of WML volume by WM volume may lead to inaccurate WML volume estimates (e.g., overestimation of WML in subjects with WM atrophy). Would the results be confirmed with no normalization or normalization by intracranial cavity volume? How does the primary outcome (dichotomized extwml load) relate to the severe Scheltens category? Is there a high association?

4 In other words, does the estimation of the dichotomized upper quartile of WML volume add information/power to the analysis with respect to the easier Scheltens score? Please list all the predictors included in the analysis in the Statistical Analysis section. Please report in the Results section which factors were significantly associated with extwml load. The authors discuss sex specific quartiles in the Discussion without defining them in the Methods. Some of the clinical predictors analyzed needs to be define in the Appendix (e.g., problems with balance and walking, forgetfulness and other cognitive symptoms). etables 1 and 2 should be incorporated in the main manuscript material, since they summarize the main results. VERSION 1 AUTHOR RESPONSE Reviewer: 1 Reviewer Name: Nils Richter Institution and Country: University Hospital of Cologne, Dept. of Neurology, Cologne, Germany Please state any competing interests or state None declared : None declared Please leave your comments for the authors below The authors of this manuscript present a score based on clinical factors to assess the risk of extensive white matter lesions in adults ages years. The score was generated using data from a representative sample from the French general population and validated against a separate sample. The score has modest sensitivity and specificity, comparable to a CT scan, but a relatively high negative predictive value. The authors argue that the score may thus aid clinicians in deciding whether or not to conduct cerebral MRI in patients without overt neurological symptoms. In essence, the score indicates that a normotensive individual without cognitive or behavioral abnormalities is fairly unlikely to have extensive WML. 1.1 In general this is an interesting study, drawing data from fairly large and representative samples and drawing attention to the important issue of blood pressure management as preventative measure. The authors aimed to make the score as simple as possible and thus reduced the number of factors they incorporated. However it might also have been interesting to know the effect of other common clinical phenomena such as smoking, diabetes or gait imbalance on likelihood of extensive WML. Response 1.1 As shown in e-table 1 common risk factors such as smoking, diabetes and difficulties with balance when walking were examined as candidate risk factors in univariate analysis. However these factors were not above the significance threshold for the multivariate predictive model after backward deletion. 1.2 Unfortunately a lot of very relevant information regarding the methods and results is only reported in the online supplement. It would really be helpful to have this in the main manuscript to provide a clearer and more complete picture.

5 Response 1.2 We have included more methodology, formerly in the supplement, and incorporated this into the main body of the manuscript. This includes a thorough description of how each candidate risk factor was measured, the univariate analysis, and the backward selection in multivariate analyses. We also elaborate on the exact scoring method and how points were allocated based on the Beta values (in the supplement). Such inclusions are consistent with Reviewer 2 see response to comment 2.6. Specific comments: 1.3 Page 11, line 8 What exactly does other validation refer to? Which sample is meant? What 8023 persons are being referred to? The samples listed before are much smaller. Response 1.3 The other validation is undertaken with the larger 3C cohort and we clarify on page 6-7 of the revised manuscript; The 3C study is a French multisite prospective cohort study investigating the determinants of dementia, coronary heart disease and stroke [24]. Commencing from 1999 to 2001, the recruitment of a French population sample was sought for individuals who were 65 years or older at baseline, registered in the electoral rolls in the Dijon, Bordeaux and Montpelier catchment area, and able to provide written informed consent. Briefly, 9294 non-institutionalized community dwelling adults aged 65 years were recruited and underwent extensive baseline examinations. Serial clinic visits were scheduled at approximately 2, 4, 7 and 10-year follow-up to assess cognitive function, depression, incident neurological diseases and comorbidities. Incident stroke and dementia was assessed in the greater 3C cohort of persons free from dementia and stroke at baseline (N = 8023). The development of the extwml score was obtained from a sub-sample of participants from the city of Dijon who underwent brain MRI and the MRI parameters are described elsewhere [25]. Briefly, the 3C Dijon MRI study is a prospective cohort designed to study the relationship between vascular risk factors and diseases and the risk of dementia. The current analyses of extwml concern only the individuals who were eligible for MRI, aged less than 80 years and were free from dementia or stroke. The cohort included a mix of persons with mild cognitive impairment and cognitively intact older persons. The study protocol has been approved by the Ethical Committee of the University Hospital of Kremlin-Bicêtre It would make sense to already list the six clinical factors, which constitute one of the main results of the analysis, in the results section. Response 1.4 The six factors are now described in the main body of the manuscript, page 13 of the revised manuscript under the sub-heading Multivariate analyses of extwml The final backward deletion model from multivariate analysis predicting extwml is presented in Table 2. The cardiovascular covariates associated with increased extwml risk were diastolic BP and antihypertensive drug use. Other factors that emerged in the model to increase risk for extwml were psychotropic drug use, dependence in IADL, forgetfulness and calculation difficulties. The Hosmer and Lemeshow test suggested an adequate model (P =.88) and the diagnostic accuracy was in the modest range (C = 0.63) (Figure 3). The percentage of concordant and discordant pairs was high (62.8% versus 37.1%) with a small relationship between the predictive model and extwml (Somer s D = 0.26).

6 This information is also supplemented with a table which comprises the final multivariate model, Table For greater clarity please check whether abbreviations (for example IADL) are explained at first mention. Response 1.5 The abbreviation for IADL now spelled out in full in the revised manuscript, and other abbreviations are spelled in full also where appropriate. 1.6 Page 35, e-table 2 Would it be possible to also provide standardized ßs for the multivariate analyses? Response 1.6 We believe the reviewer is referring to univariate analyses shown in e-table 1 (e-table 2 lists the standardized ß for multivariate analyses). The standardized ß values are now included in the revised manuscript for e-table 1. Reviewer: 2 Reviewer Name: Eric Jouvent Institution and Country: University Paris 7 and APHP, France Please state any competing interests or state None declared : I know some of the co-authors and I have once co-authored a group position paper with C Tzourio and B Mazoyer. However, I have no competing interest regarding the present paper. Please leave your comments for the authors below In this paper, the authors sought to determine whether a simple score based on clinical data could help identify subjects without overt neurological symptoms who are at risk of having extensive white matter lesions (WML) on brain MRI. The paper clearly has many strengths. The approach is quite different from questions usually raised in the field, the results are obtained with a straightforward methodology from a very large cohort and thereafter validated on a second large cohort. Here are some comments that may help improve the paper: 2.1 Given that treatment of hypertension is not yet based on MRI, and that no study that I am aware of has yet demonstrated that care of hypertension and associated clinical events are improved by targeting WML rather than BP measures, I would suggest to be more cautious in the writing, particularly in the introduction "Identifying patients is of major importance", "to intensively manage and mitigate further risk for stroke", "guide more aggressive blood pressure lowering". "major interest to improve clinical decision making". Response 2.1 We have revised the introduction rationale in accordance with Reviewer 2 s suggestion to emphasize that control of blood pressure and cardiovascular risk factors could mitigate stroke risk and progression of WML. We re-formulated the introduction to highlight that the presence of stroke risk factors might promote patient adherence to overall cardiovascular risk factor reduction, rather than the presence of WML alone. 2.2 I would also recommend to more clearly focus the introduction on screening of large populations by general practitioners. Indeed, some of the "clinical predictors" could be per se a reason to go and see a neurologist (for instance gait disturbances, forgetfulness ), and thus the interest of such a screening method for neurologist would be lower.

7 Response 2.2 We have revised the introduction rationale in accordance with Reviewer 2 s suggestion. For example, page 6 of the revised manuscript; No risk scores exist for extwml to stratify patients least likely to benefit from MRI, which could offer some guidance to general practitioners or at the population-level of two-stage screening. 2.3 In line, some aspects related to the predictors should also be discussed as potential limitations. While hypertension, forgetfulness, difficulties with calculations and psychotropic drug use might be detected in patients without overt neurological symptoms, this probably is different for IADL. Indeed, IADL are altered late during the course of most acquired neurologic disorders in the elderly. Thus, except possibly in certain rural areas, I wonder whether in real life it is actually plausible that subjects with altered IADL had never seen their gp or a neurologist before being potentially screened. Another question is whether an alternative version of the score could be set up without this particular variable. Response 2.3 We have added a statement in the limitations section regarding the lack of distinction between risk factors and consequences of WML. This is reported on page 17 of the revised manuscript; Indeed, some candidate variables such as memory difficulties may be consequences of WML rather than predictive risk factors, and our study was not designed to distinguish between such potential bi-directional associations. Removal of the IADL variable did not significantly change the AUC. We add such a sensitivity analysis on page 14 of the revised manuscript; Because of the possibility that observed difficulties with IADL might lead to geriatrician referral and brain imaging later during the course of neurological disorders, we performed a sensitivity analysis by excluding IADL from the scoring system. The AUC value was unchanged AUC = I think that the interest of the secondary outcome (incident stroke) is low with respect to the primary outcome, and unnecessarily lengthen the paper. I would suggest to delete it. Response 2.4 The prognostic risk for incident stroke was suggested by other reviewers in an earlier version of this paper. We have thus opted to include the incident stroke risk. 2.5 WML delineation into deep and periventricular is not used in the paper, I would avoid getting into unnecessary details. Response 2.5 The automated volumetry for WMH involved separate localization of deep and periventricular WMH. This methodological step is important to clarify the limit of WMH near the ventricle and therefore how the automated software might quantify WMH by comparison to visual ratings (e.g. Scheltens scale). 2.6 I think that data definitions and results of the models are of primary importance and as such should be in the body of the manuscript rather than in supplementary material. I would suggest to include at least the "covariate data collection", e-table 1 and e-table 3 in the whole body of the manuscript. The flow chart, e table 2 and e table 4 could be left as supplementary material. Response 2.6 Similar information was requested from Reviewer 1 regarding candidate risk factor definitions, univariate analyses, multivariate analyses and the results of the backward selection model see comment 1.2.

8 2.7 I may have missed the point, but I did not find the exact method used to determine the point scoring system from the models. It is stated on the e-table 3 title that is was "based on the regression coefficients". If not already described, this should be detailed, at least in the supplementary material. Response 2.7 We have included a broader description of the scoring method in the online supplement, and these methods are based on those described by Sullivan et al for the Framingham risk score. 2.8 Rather than discussing the use of 1.5T, the use of T2 rather than FLAIR images should be discussed as a potential limitation regarding the translation of the study results into clinical practice. Response 2.8 We have added a statement in the limitations section regarding the use of FLAIR imaging in other studies, page 17 revised manuscript; A related point is that identification and quantification of WMH is commonly performed using parallel imaging and fluid-attenuation inversion recovery (FLAIR) sequences. As parallel imaging and FLAIR sequences were not acquired in the 3C study, the current findings may translate less readily to clinical practices or healthcare systems utilizing such diagnostic methodologies. Reviewer: 3 Reviewer Name: Michele Cavallari Institution and Country: Brigham and Women's Hospital, Boston, MA, USA Please state any competing interests or state None declared : none Please leave your comments for the authors below Tully et al. report on a the development of a predictive framework to stratify older individuals based on the severity of WML, and ultimately inform on the opportunity to perform MRI exam in older people at risk for WML and the associated cognitive-behavioral disability. Major Issues 3.1 My main concern regards the take home message of the paper. Based on the presented results, in the absence of hypertension (as defined by either high DBP or hypertensive drug use), and neuropsychiatric symptoms and difficulties in IADL associated with WML, MRI would not be recommended, as it most likely would show a relatively low WML load. I doubt any geriatrician or neurologist would prescribe an MRI exam to an older individual in the described scenario, i.e. absence of cerebrovascular risk factors and neurological symptoms. Therefore, the clinical applicability of the presented results seems minimal to me. Response 3.1 As suggested by Reviewer 2 (see comment 2.1), the audience likely to benefit from our study findings include general physicians and persons responsible for two-stage population level screenings with MRI. The main clinical utility lies with the negative predictive value and thus avoiding unnecessary referral for MRI from primary care. 3.2 Another related issue regards the fact that the predictive framework is based on a collection of risk factors and correlates (potentially consequences) of WML pooled together. This is particularly relevant considering the aim of the study, i.e. to provide guidance to prescribe MRI exam to elderly people. In clinical practice, neuropsychological assessment is usually performed in subjects who report cognitive or psychiatric symptoms similar to those included in the predictive score. If the assessment shows evidence of cognitive impairment, then an MRI exam would be required anyway for differential diagnosis and prognostic purposes. Analysis of the predictive role of a different score based on risk factors only may provide clinically useful results.

9 Response 3.2 Indeed, the candidate variables can be considered as risk factors for, and consequences of, WML (also see Reviewer 2 comment 2.3). We make no distinction between these in our study. We have accordingly added a statement in the limitations section, page 17 of the revised manuscript; Indeed, some candidate variables such as memory difficulties may be consequences of WML rather than predictive risk factors, and our study was not designed to distinguish between such potential bi-directional associations. Though neuropsychological assessment offers valuable insight in cognitive functioning and possible brain disease without imaging, these necessitate specialist referral to geriatricians and neuropsychologists, which may require substantial waiting times or not be readily available in rural settings. We state this on page 5 Moreover, access to and utilization of MRI technology can be affected by factors such as rural location, patient health insurance, MRI contraindications, and the preference for cheaper neuroimaging techniques. Minor issues 3.3 Normalization of WML volume by WM volume may lead to inaccurate WML volume estimates (e.g., overestimation of WML in subjects with WM atrophy). Would the results be confirmed with no normalization or normalization by intracranial cavity volume? Response 3.3 In the present study, WML volume was normalized by the volume of the white matter mask in which they were detected rather than by the volume of the total white matter. The reason for so doing is that the entire brain was not entirely covered by the T2-acquisition protocol that was used. Also, due to variability in head size, the brain fraction within the field of view of the T2 acquisition differed between individuals. So raw WML volume or WML volume corrected by intracranial volume would be meaningless in the present case. The only way of comparing WML between individuals was indeed to consider the volume of WML detected within the volume of WM that was analyzed for each individual. 3.4 How does the primary outcome (dichotomized extwml load) relate to the severe Scheltens category? Is there a high association? In other words, does the estimation of the dichotomized upper quartile of WML volume add information/power to the analysis with respect to the easier Scheltens score? Response 3.5 A modified Scheltens score was only recorded in a sub-set of the EVA and 3C cohort during validation of the automated imaging processing to detect WML. As described in Maillard et al 2008, and in a test for quadratic trend the automated WMH quantification increased with Scheltens classification of mild moderate and severe. Unfortunately we do not have a Scheltens score for the entire cohort. With regards to association between extwml and Scheltens score, it is unclear whether this would increase validity because the Scheltens score is determined after a person has undergone MRI. Inclusion of post-mri variables would reduce face validity of a pre-imaging predictive score to explicitly determine whether a person should undergo MRI for extwml at all. See Zemek et al. JAMA. 2016;315(10): for an example of using a priori risk factors in risk prediction models to maintain face-validity. 3.6 Please list all the predictors included in the analysis in the Statistical Analysis section.

10 Response 3.6 All candidate variables are listed in the revised methods section, page 9-10; The risk factors tested included; age (per 1 year increase), marital status (married [reference], notmarried, separated or widowed), education (bachelor degree [reference], no or other education), tobacco smoking status (none [reference], former, current), body mass index ([BMI] < 25 kg/m² [reference], kg/m², 30 kg/m²), systolic BP in mmhg, diastolic BP in mmhg, antihypertensive drug use for hypertension, cardiovascular disease, acute coronary event (myocardial infarction), diabetes (self-report), fasting plasma glucose ([FPG], 6 mmol/l [reference], mmol/l, 7.2 mmol/l), hypercholesterolemia (self-report), total cholesterol ( 6.1 mmol/l [reference], 7.25 mmol/l), LDL cholesterol (mmol/l), HDL cholesterol (mmol/l), psychotropic drug use in the past month, depressive symptoms (total CES-D score 16), history of lifetime major depression (MINI diagnosis), MMSE score, Benton Visual Retention Test score, dependence in IADL (Lawton-Brody scale), problems with balance when walking (self-report), forgetfulness, (self-report), difficulties retaining new simple information (self-report), difficulties remembering old memories (self-report), difficulties with arithmetic calculations (self-report), difficulties with language or comprehension (self-report), difficulties with spatial orientation (e.g. in a city street). 3.7 Please report in the Results section which factors were significantly associated with extwml load. Response 3.7 We list the variables significantly associated with extwml in the revised manuscript, page 12 under the sub-heading Univariate analyses of extwml as; The univariate analysis of variables and extwml are reported in the online supplement (e-table 1). Variables significantly associated with extwml p <.05 included age, diastolic BP, antihypertensive drug use, psychotropic drug use, diabetes (self-reported), higher MMSE score, Benton Visual Retention Test score, dependence in IADL, calculation difficulties, forgetfulness, difficulties retaining new information and gait imbalance. Other candidate variables at p <.25 included systolic BP, hypercholesterolemia (self-reported), depressive symptoms on the CES-D, and difficulties with language comprehension. We also list the variables included in the final multivariable model in the revised manuscript under the sub-heading Multivariate analyses of extwml on page 13 as; The final backward deletion model from multivariate analysis predicting extwml is presented in Table 2. The cardiovascular covariates associated with increased extwml risk were diastolic BP and antihypertensive drug use. Other factors that emerged in the model to increase risk for extwml were psychotropic drug use, dependence in IADL, forgetfulness and calculation difficulties. The Hosmer and Lemeshow test suggested an adequate model (P =.88) and the diagnostic accuracy was in the modest range (C = 0.63) (Figure 3). The percentage of concordant and discordant pairs was high (62.8% versus 37.1%) with a small relationship between the predictive model and extwml (Somer s D = 0.26). 3.8 The authors discuss sex specific quartiles in the Discussion without defining them in the Methods. Response 3.8 The sex specific quartiles were described on page 8; The WML was defined as a binary variable; 0 = normal to low WML load (below sex specific upper quartile) and 1 = extwml (equal or above sex specific upper quartile; men [7.39 cm3], women [5.73 cm3]. 3.9 Some of the clinical predictors analyzed needs to be define in the Appendix (e.g., problems with balance and walking, forgetfulness and other cognitive symptoms).

11 Response 3.9 We have added a description and definition of the key covariates into the supplement. This includes the suggested variables; To complement the cognitive tests, a questionnaire was developed to determine cognitive and daily living problems, derived from the Katz scale. These self-report questions covered domains including; difficulties with balance when walking, memory recall problems (forgetfulness), memory difficulties retaining new simple information, memory recall difficulties of old memories, difficulties with simple arithmetic calculations, difficulties with language or comprehension, and difficulties with spatial orientation (e.g. in a city street). We have also added a description of the definition of lifetime history of major depression; A lifetime history of depression disorder was determined by the major depression and dysthymia modules of the Mini International Neuropsychiatric Interview. How depression disorder age of onset was determined in the 3C study is described elsewhere etables 1 and 2 should be incorporated in the main manuscript material, since they summarize the main results. Response 3.10 We have incorporated etables 2 (final multivariate model showing odds ratios) and etable 3 (extwml point scoring system) into the main body of the manuscript. We elected to include the point scoring system (as suggested by Reviewer 2.6) as this would be most useful to primary care physicians before referring for brain MRI. REVIEWER REVIEWER REVIEWER VERSION 2 REVIEW Nils Richter University Hospital of Cologne, Dept. of Neurology, Cologne, Germany 06-Oct-2017 The author's have adequately addressed my comments. Eric Jouvent Paris 7 University and APHP 15-Oct-2017 The authors adequately addressed by concerns. I have no further comments. Michele Cavallari Brigham and Women's Hospital, Boston, MA, USA 06-Nov-2017 The main critiques have not been addressed satisfactorily by the authors.