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Elisabeth Loder, MD, MPH
BMJ Editorial Team
Dear Prof. Loder,
We appreciate the comments provided by the reviewers of the BMJ for our manuscript,
“Trajectory of body shape in early and middle life and all-cause and cause-specific mortality:
results from two prospective US cohort studies”. The comments were quite useful and we thank
you for the opportunity to submit the paper for your continued consideration after revisions.
The revised manuscript with “track change” of the edits made from the previous article and the
specific responses to referees’ comments have been uploaded in accordance with your
specifications.
Thank you in advance for further consideration of our manuscript. Please feel free to contact me
with any questions. On behalf of my colleagues, I hope that this revised manuscript will now be
acceptable for publication in the BMJ.
Sincerely yours,
Mingyang Song, M.D., Sc.D.
Clinical and Translational Epidemiology Unit and Division of Gastroenterology,
Massachusetts General Hospital and Harvard Medical School,
55 Fruit St, Boston, MA 02114
2
Comments from the manuscript meeting committee
* Our statistician has a number of concerns that are detailed in his separate statistical report.
He will be looking at any revision, so please address his inquiries carefully.
* Despite the fact that results are not especially surprising, we thought the way the information
is presented might resonate with the public, who probably are more likely to remember body
shape than what they ate or weighed at a particular point in the past.
First, please revise your paper to respond to all of the comments by the reviewers. Their reports
are available at the end of this letter, below.
In your response please provide, point by point, your replies to the comments made by the
reviewers and the editors, explaining how you have dealt with them in the paper.
We appreciate the committee’s comments and the opportunity to re-submit this manuscript. We
have addressed the reviewers’ comments as detailed below in our point-by-point response.
Specifically, as per the statistician’s thoughtful comments, we have updated our methods of
rescaling BMI at age 50 by taking into account the BMI-somatotype relationship at younger age.
The data provided in the revised version and in this letter are from the analysis using the new
method. We believe that these changes have substantially improved the manuscript. We hope that
the revised manuscript will be suitable for publication and we welcome any additional feedback.
Response to Referee #1
Comments:
The manuscript describes a novel approach to assess the impact of adiposity on mortality.
Overall, I would give high priority for publication.
Point #1: The higher RRs in women compared to men are not restricted to the category of people
who started heavy, where women an average have higher BMI than men; for example in the
group lean-marked increase women have (roughly) twice excess risk compared to men for all
causes of death. One explanation is that the somatotypes seem to perform better in women than
in men (see ref. 20). Alternatively, the difference may reflect some heterogeneity in the biological
of adiposity early in life between sexes. In any case, more discussion on this issue would be
welcome.
We appreciate the reviewer’s comments. We agree that the better performance of somatotypes in
assessing body size in women than in men may have contributed to the gender difference
3
observed in the trajectory-mortality associations. Additionally, because trajectories were
constructed within each cohort separately and sex-specific reference groups were used in the
analysis, it is difficult to directly compare the results between women and men. Similar notions
apply to most previous studies that have used sex-specific percentiles to assess early-life BMI in
relation to adult mortality. Among studies that have used common cutoffs in both genders, there
is no strong evidence suggesting that obesity, whether in early life1 2
or in middle or late
adulthood,3-5
is differentially associated with mortality risk in women versus men. We have
added more discussion on the sex difference in the revised manuscript.
Discussion
Of note, the relative risk estimates were stronger in women than in men,
although strict comparison between the two sexes is difficult because the
trajectories were created within each cohort separately and sex-specific
reference groups were used in the analysis. Better performance of the
pictograms in assessing body shape in women (see Methods) may have
contributed to the stronger results than in men. Another explanation may be
related to the much higher BMI in the heavy-stable/increase group in women
than in men (e.g., mean BMI at age 50: 32.9 vs. 29.0 kg/m2, Table 1).
(DISCUSSION, Page 23)
Point #2: Similarly, the negative interaction with smoking deserves more discussion. Although it
is obvious that smoking may confound the association between BMI/somatotype and mortality
across categories, it is less clear whether confounding alone would explain the much smaller
effect compared to never smokers within BMI/somatotype categories, and why the confounding
effect would be stronger in women than in men.
We agree that the trajectory-mortality associations were strikingly stronger in never smokers than
in all subjects or in ever smokers. Similar difference has been reported in many previous
studies.6-9
For example, in a pooled study of 19 cohorts (n=1.46 million), compared to men with
a BMI of 22.5-24.9 kg/m2, the hazard ratios of all-cause mortality among all subjects versus
never smokers only were 1.03 and 1.17 for BMI of 27.5-29.9 kg/m2, 1.16 and 1.43 for BMI of
30.0-34.9 kg/m2, 1.44 and 1.95 for BMI of 35-39.9 kg/m
2, and 1.93 and 2.86 for BMI of 40-49.9
kg/m2, respectively.
8 Similar results were reported in women. Therefore, the strong effect
modification by smoking we observed in the current study is consistent with previous reports.
Moreover, compared to studies of BMI measured once at baseline or at a certain age, our study
may be more able to capture the confounding effect of smoking by assessing the body shape over
a long period of life, because smoking status can change at any time point in life and any
accompanied effect on body weight would have been incorporated into our analysis of all
subjects, which may have exaggerated the difference in the results from the restricted analysis
among never smokers.
4
Regarding the sex difference, we agree that the difference in trajectory-mortality associations
between never and ever smokers appeared somewhat more evident in women than in men. One
explanation may be related to the higher proportion of current smokers among women than
among men (14% vs. 7%) within our cohorts. Furthermore, there is some, albeit inconsistent,
evidence suggesting that a greater metabolic influence of smoking in women than men and
women tend to gain more weight after smoking cessation than men.10-12
Therefore, excluding
smokers from the analysis may have a larger impact on the trajectory-mortality associations in
women than men. We have added these discussions to the revised manuscript, as follows:
Discussion
Indeed, consistent with previous studies,5 13
we found that body-shape
trajectory was more strongly associated with mortality among never smokers
than among ever smokers. This difference appeared more striking in women
than in men, which may be due to the higher proportion of current smokers
among women than among men (14% vs. 7%) within our cohorts. Moreover,
some, albeit inconsistent, evidence suggests a greater metabolic influence of
smoking in women than men, and women tend to gain more weight after
smoking cessation than men.10-12
Therefore, excluding smokers from the
analysis may have a larger impact on the trajectory-mortality associations in
women than men.
(DISCUSSION, Page 24)
Point #3: Although the paper represents an elegant epidemiologic exercise, and might inform on
underlying biologic mechanisms, its contribution to clinical practice and public health is less
clear. How much does the analysis of lifespan trajectories add to a straightforward assessment of
current (i.e., adulthood) adiposity? RRs in the category lean-marked increase do not appear (at
least in men) to differ much from RRs among those who started heavy. Should a general
practitioner try to assess the trajectory of their patient (presumably with a large amount of
misclassification) rather than just measuring current BMI?
We appreciate the reviewer’s positive comments that our study “represents an elegant
epidemiologic exercise and might inform underlying biological mechanisms”. We agree that in
men the RR of all-cause mortality for the lean-marked increase group did not substantially differ
from that for the heavy-stable/increase group in men (among never smokers: 1.19 vs. 1.28).
However, considering that the lean-marked increase group includes over twice more participants
than the heavy-stable/increase group, dissecting the two groups is not trivial from a public health
point of view, and any moderate difference in the RRs can have a tremendous impact on
estimating the population risk of death attributable to each of the two trajectories. Regarding
whether assessing patients’ trajectory would produce significantly more information than just
measuring current BMI, we believe that it is definitely an interesting question that deserves a
5
formal assessment in future studies, by comparing clinically relevant measures (e.g., predictive
capability of the two instruments for mortality) and also taking into account the cost associated
with each tool. Nevertheless, given the advancement of technology, especially the growing use of
electronic medical records,14
assessment of individuals’ trajectory will conceivably have an
improved accuracy and may not be an overwhelming task for clinical practitioners to perform in
the future.
Response to Referee #2
Comments:
This study took advantage of the multiple body-shape data before mid-life obtained
retrospectively in conjunction with a BMI transformed body-shape data to investigate the
association between body-shape trajectories and mortality risk. A model-based modeling
approach was used to identify 5 distinct trajectories of body shape, i.e., lean-stable, lean-
moderate increase, lean-marked increase, medium-stable/increase, and heavy-stable/increase.
Authors found that the lean-stable had the lowest mortality risk and the consistently heavy body-
shape was associated with the highest mortality risk. In addition, this phenomenon was more
apparent in non-smokers than in smokers and for CVD than for other diseases. Significant
trajectory-smoking interactions were clearly demonstrated.
Major comments
This study has made use of a very unique set of body shape data collected retrospectively for
several time points before mid-life, which allowed for generating body-shape trajectories and
studying how these different trajectories were associated with mortality. Personally, I feel that
this type of study is what is needed to challenge the so called U-shape relation for BMI-mortality
which relies on BMI data on one time point.
We appreciate the reviewer’s comments, and agree that our trajectory analysis can better address
some of the methodological challenges currently encountered in assessing the influence of body
weight on long-term mortality. We have detailed as below our response to each of the reviewers’
comments.
Point #1: Title: The paper is about the trajectory of body shape “before mid-life” not “across
the lifespan”. Some modification should be made for the title and across the text.
We have modified the title and the text as per the reviewer’s suggestion.
Title: Trajectory of body shape in early and middle life and all-cause and
6
cause-specific mortality: results from two prospective US cohort studies
Abstract
Objective: To assess body-shape trajectories in early and middle life in
relation to risk of mortality
Conclusions: Using the trajectory approach, we found that heavy body shape
from age 5 up to 50, especially the increase in middle life, was associated with
higher mortality.
Introduction
Therefore, to extend our knowledge, we used a different, trajectory-based
approach to assess the relationship between body shape in early and middle
life and risk of all-cause and cause-specific mortality in two large US cohort
studies.
Discussion
To our knowledge, this is the first study to investigate adiposity throughout
early and middle life in relation to mortality.
In conclusion, we found that heavy body shape throughout early and middle
life, especially the increase in middle life, was associated with higher
mortality.
Point #2: Abstract and Discussion: This paper should stress not only the increased mortality risk
of heavy body shape, but also the lowest risk of the lean stable in the conclusion statement to
refute the previous viewpoint on increased risk at low BMI.
We have modified the abstract and discussion as per the reviewer’s suggestion.
Abstract
Conclusion:
Using the trajectory approach, we found that heavy body shape from age 5 up
to 50, especially the increase in middle life, was associated with higher
mortality. In contrast, individuals who maintained a stably lean body shape
had the lowest mortality. These results indicate the importance of weight
management across the lifespan.
Discussion
By comparing mortality risk between trajectory subgroups, we found that
participants who remained heavy from age 5 to 50 had the highest risk of
7
death, whereas those who maintained a stably lean body shape had the lowest
mortality. Compared with the latter group of individuals, even those who were
lean in childhood or adolescence but gained weight in middle life were at
higher risk of mortality.
In conclusion, we found that heavy body shape throughout early and middle
life, especially the increase in middle life, was associated with higher
mortality. In contrast, individuals who maintained a stably lean body shape
had the lowest mortality. These results indicate the health benefit of weight
management across the lifespan.
Point #3: Please make clear how the BMI data at age 50 is converted to body shape information
in the method section. Although authors pointed out the more details about the conversion are
provided in Supplementary Table 1, it is not clear by reading the Supplementary Table 1. Where
is the data coming from in Supplementary Table 1? Would you please provide the reference?
As per the comments by Reviewer 3, we have revised our method of rescaling BMI at age 50 by
taking into account the BMI-somatotype relationship at younger age in a linear mixed effects
model. Detailed descriptions about the methods have been provided in the response to the Point
#1 of Reviewer 3 on pages 9-12.
Point #4: Line 31: Reference 24 was submitted in 2015. Please provide the journal information
was provided.
We have provided detailed citation for Reference 24 (the current Reference 21).
References
21. Song M, Willett WC, Hu FB, et al. Trajectory of body shape across the
lifespan and cancer risk. Int J Cancer 2015.
(REFERENCES, Page 31)
Point #5: Line 33 to line 42: It is not clear how authors calculated the mean BMI at different
ages in each trajectory group in Table 1, since body shape information was recalled, and BMI
was not measured for earlier life time points.
In the two cohorts, we have collected body weight data every two years since baseline by our
follow-up questionnaire. We used these data to calculate the BMI for ages 40 and 50 as shown in
Table 1. In addition, we asked participants to recall their body weight at adolescence (age 18 for
women and 21 for men) in 1980 in the Nurses’ Health Study and in 1986 in the Health
Professionals Follow-up Study, and included these data in Table 1. Because some participants
8
had already been older than 40 or 50 at baseline, or did not provide their weight at age 18 or 21,
the BMI data were not available for all the participants included in the analysis. We have noted
this in the footnote of Table 1. We have also provided more details in the methods to clarify this
point.
Table 1 Footnote b Data were not available in all participants because some participants had
already been older than 40 or 50 at baseline, or did not provide their body
weight at age 18 or 21.
(TABLE 1 FOOTNOTE, Page 13)
Methods
Body shape assessment
Height and body weight were queried on biennial follow-up questionnaires.
We used these data to calculate the BMI at age 50 and then converted it to the
same scale as somatotypes in younger ages. More details about this
conversion are provided in the Supplementary Methods. In addition, recalled
body weight at age 18 was inquired in 1980 in the NHS, and weight at age 21
was inquired in 1986 in the HPFS, as previously described.15
We used these
data to calculate the BMI at adolescence.
(METHODS, Page 8)
Point #6: Line 43-48: It is stated that “those in the lean-stable group were more physically
active, tended to use multivitamin, and consumed a healthier diet than those in the other
groups.” Statistical testing should be carried out.
Because of the large sample size, all the statistical tests yielded a P value of <0.001. We have
included these results in the table footnote.
Table 1 Footnote a All variables are standardized by age at baseline (1976 for women in the
Nurses’ Health Study and 1986 for men in the Health Professionals Follow-up
Study). Means are presented for continuous variables. Due to the large sample
size, P values for testing the difference across the trajectory groups were all
<0.001 for the variables listed in the table.
(TABLE 1 FOOTNOTE, Page 13)
Response to Referee #3
9
Comments:
The authors use two large cohorts to construct five shape trajectory groups from age 5 to 50, and
then compare mortality in the diffferent groups. I have some comments on the study design,
analysis and presentation.
Point #1: The key exposure here is the recalled somatotype at ages 5, 10, 20, 30 and 40, and the
corresponding rating at age 50 is inferred from the age 40 rating. The way this is done strikes me
as clunky, based on just the previous rating and ignoring the earlier ones. It also requires the age
40 rating to be present to estimate the age 50 rating. Since the purpose is to represent shape over
the life course it would surely be better to use all the available shape ratings and BMI at 40 to
impute the age 50 rating.
The description on page 39 implies, though does not state, that BMI was measured at ages 40
and 50: “…we assessed the average BMI from age 47 to 53 as the BMI for age 50. We then
divided BMI at these two ages into 9 categories”. Which two ages?
“The cutoff points for each category were calculated as the median BMI of this category at age
40 plus a constant to account for weight gain from age 40 to 50”. But why use the median as the
upper cut-off, which will misclassify half those in the group? Surely one needs cutoffs midway
between the group medians?
Also, using a single value of 1.5 kg/m2 for 10-year BMI gain ignores the fact that BMI is
increasing over time in some groups but not in others. The calculation needs to take into account
all the available information on individual trajectories.
We appreciate the reviewer’s thoughtful comments. First, we want to clarify that the somatotype
rating at age 50 is not inferred from the age 40 rating, but rather is based on the real BMI at age
50. What we did was just to find the cutoffs to rescale the BMI at age 50 into 9 groups, to be
consistent with the somatotype categorization in earlier ages.
Second, regarding how we assessed the BMI at age 40 and 50, we obtained the BMI data from
our biennial follow-up questionnaires, in which we queried participants about their current
weight every two years. To reduce random variation, we used the average BMI from age 37 to 43
to represent the BMI for age 40, and the BMI from age 47 to 53 as the BMI for age 50.
We agree that using the body shape ratings in earlier years will improve the rescaling accuracy
for BMI at age 50, that using medians as the cutoffs may have resulted in misclassification, and
that using a single value for the 10-year BMI gain ignores the fact that BMI is increasing over
time in some individuals but not in others. As per the reviewer’s comments, we have changed our
rescaling method. Instead of using the BMI data at age 40 only, we used all the time points at
which we had both BMI and somatotype data to run a linear mixed effects model, in which we
assumed there is a linear relationship between BMI and somatotype. To allow such relationship
to vary among individuals, we included a random intercept and a random slope in the model.
10
Therefore the model is specified as follows:
��� = �� + ���� + �� + ���� + ���
where ��� denotes the BMI for individual � at age and �� denotes the corresponding
somatotype, �� and �� specify the fixed intercept and slope, �� and �� specify the random,
subject-specific intercept and slope, and ��� represents the within-subject measurement error.
Furthermore, it is assumed that �~��0, ����, ���~��0, ��� , and that � and ��� are mutually
independent. We used an unstructured variance-covariance matrix (i.e., without making any
particular assumption about the covariance structure) for estimation.
We modeled BMI instead of somatotype as the dependent variable because BMI is
approximately normally distributed, whereas somatotype is a discrete variable ranging from 1 to
9. As such, the conditional mean BMI for individual � at age can be written as:
�[���|��, ��] = �� + ���� + �� + ����
Based on the model output, we then calculated the somatotype rating at age 50 ��� as
��[�� |�!�,�"�]#$!#�!�$"%�"�
&. To keep the estimated somatotypes within the range of 1 to 9, for ��� that
was larger than 9, we rounded it as 9, and for ��� that was smaller than 1, we rounded it as 1.
There were 4 and 3 time points available for the linear mixed effects modeling in women and
men, respectively, as summarized below.
Time point Women
(Nurses’ Health Study)
Men
(Health Professionals Follow-up Study)
1 BMI: age 18
Somatotype: age 20
BMI: age 21
Somatotype: age 20
2 BMI: age 30
Somatotype: age 30
BMI: age 40
Somatotype: age 40
3 BMI: age 40
Somatotype: age 40
BMI: in 1988
Somatotype: in 1988
4 BMI: in 1988
Somatotype: in 1988
The details about anthropometric assessments at each time point are described below. It should
be noted that there may be missing data for each of the time points, but the missing data can be
accommodated by the linear mixed effects model.
In women (Nurses’ Health Study):
• Time point 1: in 1980 participants were asked to recall their body weight at age 18. We
then calculated their BMI, and linked it to their somatotype ratings at age 20.
• Time points 2 and 3: participants were queried about their current body weight in our
biennial follow-up questionnaires. We used these data to calculate their BMI at age 30
(the mean BMI from age 27 to 33) and 40 (the mean BMI from age 37 to 43), and then
linked it to their somatotype ratings at age 30 and 40.
11
• Time point 4: we calculated participants’ BMI in 1988 based on the follow-up
questionnaires, and then linked it to their somatotype ratings in 1988 when we also
queried about their current body shape.
In men (Health Professionals Follow-up Study):
• Time point 1: in 1986 participants were asked to recall their body weight at age 21. We
then calculated their BMI, and linked it to their somatotype ratings at age 20.
• Time point 2: participants were queried about their current body weight in our biennial
follow-up questionnaires. We used these data to calculate their BMI at age 40 (the mean
BMI from age 37 to 43), and then linked it to their somatotype ratings at age 40. We did
not include the BMI at age 30, because almost all participants were already at their 40s
when enrolled into the cohort and their BMI data at age 30 were not available.
• Time point 3: we calculated participants’ BMI in 1988 based on the follow-up
questionnaires, and then linked it to their somatotype ratings in 1988 when we also
queried about their current body shape.
Using these re-calculated somatotype ratings for age 50, we re-constructed the trajectory within
each cohort. As shown below, the plots look very similar to what we had before, except for the
slight increase of body shape levels in the “lean-stable” group. Such small increase may reflect
the natural, body fatness-independent change of body shape over time (e.g., although a person
may maintain the same BMI from childhood to middle adulthood, his/her body shape may
appear to increase during this time period).
Using these newly created trajectories, we assessed the relationship to mortality. Reassuringly
the results were quite similar to what we obtained before. For example, the HR (95% CI) for all-
cause mortality in the “heavy-stable/increase” group compared to the “lean-stable” group
changed from 1.48 (1.40-1.57) to 1.45 (1.38-1.54) in women, and from 1.20 (1.13-1.28) to 1.22
(1.14-1.29). Such similarity provides further support for the robustness of our findings. For the
reviewer’s convenience to compare the results, we have summarized the old and new main
findings in the table below.
Table. Hazard ratio of all-cause and cause-specific mortality according to trajectories of
body shape from age 5 to 50 among women in women and men using the old and new
rescaling method for BMI at age 50
Lean-stable
Lean-moderate
increase
Lean-marked
increase
Medium-
stable/increase
Heavy-
stable/increase
Women
All-cause
HR (95% CI), old 1 (reference) 1.04 (1.00-1.08) 1.12 (1.08-1.17) 1.03 (0.99-1.08) 1.48 (1.40-1.57)
HR (95% CI), new 1 (reference) 1.05 (1.01-1.09) 1.29 (1.23-1.35) 1.06 (1.02-1.10) 1.45 (1.38-1.54)
Cardiovascular disease
HR (95% CI), old 1 (reference) 1.07 (0.97-1.18) 1.31 (1.19-1.45) 1.04 (0.94-1.16) 2.05 (1.82-2.31)
HR (95% CI), new 1 (reference) 1.12 (1.03-1.21) 1.69 (1.53-1.86) 1.12 (1.01-1.23) 1.95 (1.74-2.19)
Coronary heart disease
HR (95% CI), old 1 (reference) 1.14 (1.01-1.28) 1.56 (1.38-1.75) 1.18 (1.03-1.34) 2.45 (2.12-2.82)
HR (95% CI), new 1 (reference) 1.13 (1.02-1.25) 1.96 (1.75-2.20) 1.16 (1.03-1.31) 2.15 (1.87-2.46)
12
Stroke
HR (95% CI), old 1 (reference) 0.97 (0.82-1.14) 0.90 (0.76-1.07) 0.82 (0.68-0.99) 1.35 (1.08-1.70)
HR (95% CI), new 1 (reference) 1.09 (0.95-1.27) 1.12 (0.92-1.36) 1.02 (0.86-1.21) 1.55 (1.24-1.94)
Cancer
HR (95% CI), old 1 (reference) 1.05 (0.98-1.13) 1.09 (1.01-1.17) 1.06 (0.98-1.15) 1.23 (1.11-1.36)
HR (95% CI), new 1 (reference) 1.04 (0.98-1.11) 1.15 (1.06-1.25) 1.03 (0.96-1.11) 1.22 (1.10-1.34)
Other causes
HR (95% CI), old 1 (reference) 1.01 (0.95-1.07) 1.08 (1.02-1.15) 1.00 (0.94-1.07) 1.45 (1.34-1.57)
HR (95% CI), new 1 (reference) 1.03 (0.97-1.08) 1.24 (1.16-1.33) 1.06 (0.99-1.12) 1.44 (1.33-1.56)
Men
All-cause
HR (95% CI), old 1 (reference) 1.04 (0.98-1.10) 1.16 (1.10-1.22) 1.09 (1.03-1.14) 1.20 (1.13-1.28)
HR (95% CI), new 1 (reference) 1.04 (0.99-1.09) 1.18 (1.12-1.24) 1.05 (1.00-1.10) 1.22 (1.14-1.29)
Cardiovascular disease
HR (95% CI), old 1 (reference) 1.15 (1.04-1.27) 1.38 (1.26-1.51) 1.27 (1.16-1.40) 1.41 (1.26-1.58)
HR (95% CI), new 1 (reference) 1.14 (1.05-1.25) 1.38 (1.27-1.50) 1.17 (1.07-1.28) 1.42 (1.27-1.58)
Coronary heart disease
HR (95% CI), old 1 (reference) 1.20 (1.07-1.34) 1.44 (1.30-1.59) 1.30 (1.17-1.44) 1.48 (1.31-1.68)
HR (95% CI), new 1 (reference) 1.18 (1.07-1.30) 1.41 (1.28-1.55) 1.19 (1.08-1.31) 1.45 (1.29-1.64)
Stroke
HR (95% CI), old 1 (reference) 0.96 (0.76-1.21) 1.13 (0.92-1.39) 1.12 (0.91-1.39) 0.98 (0.74-1.29)
HR (95% CI), new 1 (reference) 1.00 (0.81-1.23) 1.24 (1.02-1.51) 1.02 (0.84-1.25) 1.17 (0.89-1.52)
Cancer
HR (95% CI), old 1 (reference) 0.96 (0.86-1.06) 1.01 (0.92-1.11) 1.00 (0.91-1.11) 1.08 (0.96-1.21)
HR (95% CI), new 1 (reference) 0.99 (0.90-1.08) 1.03 (0.94-1.12) 1.00 (0.91-1.09) 1.13 (1.00-1.26)
Other causes
HR (95% CI), old 1 (reference) 1.03 (0.94-1.12) 1.11 (1.03-1.21) 1.02 (0.94-1.11) 1.14 (1.03-1.26)
HR (95% CI), new 1 (reference) 1.00 (0.93-1.09) 1.14 (1.06-1.23) 0.99 (0.92-1.07) 1.13 (1.02-1.24)
We have updated the manuscript and all the tables and figures in the revised version using the
new method. All the numbers referred in this response letter are from the updated results. We
have also updated the method description for rescaling of the BMI at age 50 in the
Supplementary Materials, as shown below.
Supplementary Materials
Rescaling of BMI at age 50 years
To minimize random variation, we assessed the average BMI from age 37 to
43 to represent the BMI for age 40, and the average BMI from age 47 to 53 as
the BMI for age 50. We then rescaled the BMI at age 50 into 9 categories,
consistent with the grouping of somatotypes (ranging from 1 to 9) at younger
ages. The rescaling was conducted by using a linear mixed effects model, in
which we assumed there is a linear relationship between BMI and somatotype
at each age. We then used such relationship at younger ages to derive the
somatotype at age 50 from the corresponding BMI at age 50. To allow this
relationship to vary among individuals, we included a random intercept and a
random slope in the model, as specified below:
��� = �� + ���� + �� + ���� + ���
13
where ��� denotes the BMI for individual � at age and �� denotes the
corresponding somatotype, �� and �� specify the fixed intercept and slope, �� and �� specify the random, subject-specific intercept and slope, and ��� represents the within-subject measurement error. Furthermore, it is assumed
that �~��0, ����, ���~��0, ���, and that � and ��� are mutually independent.
We used an unstructured variance-covariance matrix (i.e., without making any
particular assumption about the covariance structure) for estimation.
We modeled BMI instead of somatotype as the dependent variable because
BMI is approximately normally distributed, whereas somatotype is a discrete
variable ranging from 1 to 9. Thus, the conditional mean BMI for individual � at age can be written as:
�[���|��, ��] = �� + ���� + �� + ���� Based on the model output, we then calculated the somatotype rating at age 50
��� as ��[�� |�!�,�"�]#$!#�!�$"%�"�
&. To keep the estimated somatotypes within the
range of 1 to 9, for ��� that was larger than 9, we rounded it as 9, and for ���
that was smaller than 1, we rounded it as 1.
There were 4 and 3 time points available for the linear mixed effects modeling
in women and men, respectively, as summarized below.
Time point Women
(Nurses’ Health Study)
Men
(Health Professionals Follow-up Study)
1 BMI: age 18
Somatotype: age 20
BMI: age 21
Somatotype: age 20
2 BMI: age 30
Somatotype: age 30
BMI: age 40
Somatotype: age 40
3 BMI: age 40
Somatotype: age 40
BMI: in 1988
Somatotype: in 1988
4 BMI: in 1988
Somatotype: in 1988
The details about anthropometric assessments at each time point are described
below. It should be noted that there may be missing data for each of the time
points, but the missing data can be accommodated by the linear mixed effects
model.
In women (Nurses’ Health Study):
• Time point 1: in 1980 participants were asked to recall their body weight
at age 18. We then calculated their BMI, and linked it to their somatotype
ratings at age 20.
• Time points 2 and 3: participants were queried about their current body
weight in our biennial follow-up questionnaires. We used these data to
calculate their BMI at age 30 (the mean BMI from age 27 to 33) and 40
14
(the mean BMI from age 37 to 43), and then linked it to their somatotype
ratings at age 30 and 40.
• Time point 4: we calculated participants’ BMI in 1988 based on the
follow-up questionnaires, and then linked it to their somatotype ratings in
1988 when we also queried about their current body shape.
In men (Health Professionals Follow-up Study):
• Time point 1: in 1986 participants were asked to recall their body weight
at age 21. We then calculated their BMI, and linked it to their somatotype
ratings at age 20.
• Time point 2: participants were queried about their current body weight
in our biennial follow-up questionnaires. We used these data to calculate
their BMI at age 40 (the mean BMI from age 37 to 43), and then linked it
to their somatotype ratings at age 40. We did not include the BMI at age
30, because almost all participants were already at their 40s when
enrolled into the cohort and their BMI data at age 30 were not available.
• Time point 3: we calculated participants’ BMI in 1988 based on the
follow-up questionnaires, and then linked it to their somatotype ratings in
1988 when we also queried about their current body shape.
Point #2: Related to this, Figure 1 shows that 28% of individuals were excluded as they had
“missing somatotype data for more than two different age points”, or put more simply, fewer
than 4 somatotypes. This serious data loss strikes me as unnecessary – Figure 1 shows that the
group trajectories are essentially linear (despite the cubic fit), which means that anyone with 2
or more somatotypes could reasonably be analysed. Even requiring a minimum of 3 ought to
reduce the dropout appreciably.
We appreciate the reviewer’s thoughtful comments. However, lowering the required number of
complete data points would not reduce the dropout appreciably, because most of the excluded
participants had missing somatotype data for 5 or 6 age points. This is understandable because all
the somatotype data from age 5 to 40 were collected at the same time, and the participant who
provided somatotype data for one age was likely to answer all the somatotype questions. The
following table shows the number of the excluded participants due to missing somatotype data
according to the number of missing data points.
Number of the age points that
have missing somatotype data Women Men
3 208 305
4 86 149
5 26,315 5,186
6 7,323 8,061
Total 33,932 13,701
Therefore, the gain in sample size will be minimal (208 in women, 305 in men) if we change the
15
exclusion criterion to missing somatotype data for more than three (instead of two) age points.
Point #3: A second data exclusion is for BMI < 18.5 kg/m2. I can see that this is meant to reduce
reverse causation, but since the whole purpose is to model BMI, it seems illogical to omit
individuals whose BMI is arbitrarily low. In any case the numbers involved are tiny and will
make no difference to the results.
Yes, we excluded participants with BMI of <18.5 kg/m2 to reduce reverse causation, because
these participants likely have some underlying chronic disease that may predispose them to early
death. As the reviewer pointed out, because of the small numbers involved, our results were
robust to this exclusion. For example, the HRs (95% CI) of all-cause mortality for the heavy-
stable/increase group in women were 1.45 (1.38-1.54) in the analysis including participants with
BMI of <18.5 kg/m2, and 1.48 (1.40-1.57) after excluding these participants. Since BMI <18.5
kg/m2 technically falls outside the range of normal BMI, we think that it is preferable to exclude
them.
Point #4: Related to point 6 above, the essential linearity of the group trajectories indicates that
a simple random-slope-random-intercept model would lead to broadly the same conclusions, and
the relative size of the slope and intercept random effects in predicting mortality would quantify
the importance of mean BMI versus BMI gain.
We agree that the essential linearity of body-shape measurements with age makes the random-
slope-random-intercept model a potential choice to assess subject-specific trajectory. Also, as the
reviewer pointed out, comparing the relative size of the slope and intercept random effects in
predicting mortality would give us some sense about the importance of mean BMI versus BMI
gain. However, we believe that the approach we employed has some unique advantages. First, it
avoids the difficulty in interpretation of the results obtained from statistical modeling of different
dimensions of adiposity (e.g., mean BMI versus BMI gain in the approach suggested by the
reviewer), because these measures are not biologically independent. For example, the mean BMI
at age 50 depends largely on how BMI changes over early years, and therefore it is difficult, if
not biologically meaningful, to separate their independent effects on mortality. In contrast,
instead of comparing different adiposity measures, our approach focuses on comparison of
different groups of individuals who have distinct body-shape trajectory profiles, and addresses a
more tangible and clinically relevant question: how does mortality differ among individuals with
different trajectories of body shape.
Another advantage of classifying individuals into distinct groups is that it provides an intuitive
way to probe into the population heterogeneity in the susceptibility of body shape change across
the lifespan, which does not only yield an easily-digested message for the public but also has
great implications for future research. For example, further studies examining the relative
16
contributions of individuals’ genetics and behaviors to their trajectory profiles will provide
critical insights into tailored prevention strategies. If it is found that, for instance, the heavy-
stable/increase group has a larger genetic component, whereas the lean-marked increase group is
more behaviorally oriented, public health strategies of behavioral change should then be targeted
towards the latter group. In addition, these approaches, for example identifying risk genes, are
much more feasible for mutually exclusive phenotypes than by using multivariable analysis,
where the meaning of a variable is conditional on other variables.
A potential limitation of our approach is information loss due to discrete grouping. However, the
good discrimination of our trajectory-building model (mean posterior probability of trajectory
assignment: 0.9) and well-tracked change in BMI across trajectories indicate that the trajectories
we identified can parsimoniously summarize, without a significant loss of information, the
predominant features of lifetime body shape in the study population. Additionally, it was
reassuring that we obtained similar results after excluding participants with suboptimal trajectory
assignment.
Point #5: Is there a way to superimpose on the group trajectories of Figure 1 the mean BMI
values appearing in Table 1? It would provide some validation of the group allocation.
We tried to add to Figure 1 all the mean BMI values in Table 1. But because the Y axis
encompasses the somatotype ratings for 6 different ages and across 5 different trajectory groups,
superimposing all the BMI data does not seem to work in Excel. Instead, we have added the
mean BMI values for age 50 only to the figures, as shown on the next page.
17
18
Point #6: The hazard ratios in Tables 2 and subsequently are adjusted for a string of covariates
including lifestyle factors such as physical activity, alcohol consumption and dietary score. Are
these factors not on the causal pathway, and hence should not be adjusted for? The research
question involves the shape trajectories versus mortality, which must be due at least in part to
lifestyle differences – so why adjust for them if the interest is in the trajectories themselves?
It is a challenge to control for confounding in such studies as our current one of the exposure
across the lifespan, because some covariates can be both mediators and confounders for the
exposure-outcome relationship. Here we adjusted for several lifestyle factors because they may
influence individuals’ body-shape trajectory and are also important predictors for mortality. Not
adjusting for these factors may lead to confounding bias (i.e., the observed relationship between
body shape and mortality may be due to other lifestyle factors that are related to body shape).
However, as the reviewer pointed out, there can also be over-adjustment because individuals
with certain body shapes may be more likely to change their lifestyle that can in turn influence
their mortality risk. We tried to minimize such over-adjustment by using the cumulative average
lifestyle information collected throughout early and mid-life to age 50, because such long-term
habitual exposure is less susceptible to temporary lifestyle changes that were caused by body-
shape change at some time points during follow-up.
We also compared the crude and adjusted hazard ratios among never smokers. As shown in the
table below, the results were quite similar; indicating that neither confounding nor over-
adjustment had a substantial influence on our results, after we account for the confounding effect
by smoking.
19
Crude and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause and cause-
specific mortality according to trajectories of body shape from age 5 to 50 among never smokers of
women in the Nurses’ Health Study and men in the Health Professionals Follow-up Study
Lean-stable
Lean-moderate
increase
Lean-marked
increase
Medium-
stable/increase
Heavy-
stable/increase
Women
All-cause
Crude HR (95% CI) 1 (reference) 1.09 (1.03-1.16) 1.53 (1.43-1.65) 1.05 (0.98-1.13) 1.75 (1.60-1.92)
Adjusted HR (95% CI) 1 (reference) 1.08 (1.02-1.15) 1.43 (1.33-1.54) 1.04 (0.97-1.12) 1.63 (1.49-1.79)
Cardiovascular disease (CVD)
Crude HR (95% CI) 1 (reference) 1.35 (1.17-1.55) 2.16 (1.84-2.53) 1.23 (1.03-1.46) 2.77 (2.29-3.35)
Adjusted HR (95% CI) 1 (reference) 1.30 (1.13-1.50) 1.91 (1.62-2.24) 1.19 (1.00-1.42) 2.43 (2.00-2.94)
Coronary heart disease (CHD)
Crude HR (95% CI) 1 (reference) 1.40 (1.18-1.66) 2.49 (2.06-3.01) 1.30 (1.05-1.61) 3.11 (2.48-3.91)
Adjusted HR (95% CI) 1 (reference) 1.35 (1.13-1.60) 2.18 (1.80-2.64) 1.26 (1.02-1.56) 2.71 (2.15-3.41)
Stroke
Crude HR (95% CI) 1 (reference) 1.26 (0.99-1.60) 1.56 (1.17-2.10) 1.10 (0.81-1.49) 2.14 (1.51-3.04)
Adjusted HR (95% CI) 1 (reference) 1.23 (0.97-1.56) 1.40 (1.04-1.88) 1.07 (0.79-1.45) 1.90 (1.33-2.71)
Cancer
Crude HR (95% CI) 1 (reference) 1.04 (0.93-1.16) 1.26 (1.10-1.43) 1.03 (0.91-1.18) 1.44 (1.21-1.70)
Adjusted HR (95% CI) 1 (reference) 1.02 (0.92-1.14) 1.22 (1.07-1.40) 1.03 (0.91-1.18) 1.39 (1.17-1.66)
Other causes
Crude HR (95% CI) 1 (reference) 1.04 (0.96-1.14) 1.51 (1.37-1.67) 1.01 (0.91-1.12) 1.64 (1.44-1.86)
Adjusted HR (95% CI) 1 (reference) 1.04 (0.96-1.13) 1.41 (1.28-1.57) 0.99 (0.90-1.10) 1.54 (1.35-1.75)
Men
All-cause
Crude HR (95% CI) 1 (reference) 0.99 (0.91-1.07) 1.21 (1.12-1.31) 1.01 (0.94-1.10) 1.28 (1.16-1.43)
Adjusted HR (95% CI) 1 (reference) 0.98 (0.90-1.07) 1.19 (1.10-1.29) 1.01 (0.93-1.09) 1.29 (1.16-1.43)
Cardiovascular disease (CVD)
Crude HR (95% CI) 1 (reference) 1.07 (0.92-1.24) 1.51 (1.32-1.73) 1.11 (0.96-1.28) 1.66 (1.39-1.98)
Adjusted HR (95% CI) 1 (reference) 1.06 (0.91-1.23) 1.47 (1.28-1.68) 1.09 (0.95-1.26) 1.66 (1.39-1.98)
Coronary heart disease (CHD)
Crude HR (95% CI) 1 (reference) 1.18 (1.00-1.40) 1.60 (1.37-1.87) 1.13 (0.96-1.33) 1.82 (1.49-2.21)
Adjusted HR (95% CI) 1 (reference) 1.17 (0.99-1.38) 1.53 (1.31-1.79) 1.10 (0.94-1.30) 1.80 (1.48-2.20)
Stroke
Crude HR (95% CI) 1 (reference) 0.68 (0.48-0.96) 1.18 (0.88-1.59) 0.94 (0.69-1.28) 0.92 (0.58-1.44)
Adjusted HR (95% CI) 1 (reference) 0.68 (0.48-0.96) 1.19 (0.88-1.61) 0.95 (0.70-1.29) 0.95 (0.60-1.50)
Cancer
Crude HR (95% CI) 1 (reference) 1.07 (0.92-1.24) 0.97 (0.83-1.14) 0.91 (0.78-1.06) 1.10 (0.90-1.35)
Adjusted HR (95% CI) 1 (reference) 1.06 (0.91-1.23) 0.96 (0.82-1.13) 0.91 (0.78-1.06) 1.11 (0.90-1.36)
Other causes
Crude HR (95% CI) 1 (reference) 0.89 (0.78-1.01) 1.17 (1.03-1.32) 1.01 (0.90-1.14) 1.15 (0.97-1.36)
Adjusted HR (95% CI) 1 (reference) 0.89 (0.78-1.01) 1.17 (1.03-1.32) 1.02 (0.90-1.15) 1.17 (0.99-1.38)
In the multivariable model, we adjusted for age (continuous), height (continuous), race (non-white or white),
pack-years of smoking (0, 1-<6, 6-≤20, or >20), family history of cancer (yes or no), history of lower
gastrointestinal endoscopy (yes or no; for analysis of total cancer), multivitamin use (yes or no), regular
aspirin/NSAID use (yes or no), history of physical exam (yes and for screening, yes and for symptoms, or no),
mammography (women only, yes and for screening, yes and for symptoms, or no; for analysis of total cancer),
menopausal hormone therapy (women only, past use, current use, or no), prostate-specific antigen test (men
only, yes or no; for analysis of total cancer), physical activity (in quintiles), alcohol consumption (0-<0.5, 0.5-
<2, 2-<8, or ≥8 g/d), and AHEI dietary score (in quintiles).
20
Point #7: Table 2 shows that for deaths due to stroke in women smokers (though not in men),
groups 3 and 4 are significantly protected relative to the lean-stable group. This is surely worth a
mention.
These protective associations did not show up any more in our updated results. Now the HRs
(95% CI) for stroke mortality in female smokers were 0.94 (0.72-1.23) for the “lean-marked
increase” group and 0.98 (0.80-1.22) for the “medium-stable/increase” group.
Point #8: The interaction in Figure 2B is said to be insignificant (P = 0.46), yet the two trends
look strikingly different, and much more so than in Figures 2A and 2C. Is it correct? It’s not
entirely clear what sort of interaction has been fitted – I assume it's comparing the linear trends
across shape groups in the two diabetes groups.
We did not assume a linear trend across trajectory groups. Instead, we used the likelihood ratio
test with 3 degrees of freedom to calculate the P value for interaction by treating the trajectory
group as a categorical variable (4 categories, with the medium-stable/increase group combined
with the lean-moderate increase group), and comparing the model with the product terms
between trajectory groups (3 indicator variables) and diabetic history (binary variable) to the
models without these terms. Although the hazard ratios (HRs) for CVD mortality were quite
different between diabetics and non-diabetics, they demonstrated more of an additive
relationship. In contrast, for all-cause and cancer mortality, there is more evident deviation from
a multiplicative relationship, which is what we are testing for (i.e., multiplicative interaction).
For example, for CVD mortality, the product of the HRs for the independent effects of history of
diabetes (i.e., the HR for the lean-stable group with diabetes) and heavy-stable/increase
trajectory (i.e., the HR for the heavy-stable/increase group without diabetes) was 1.29*1.97 =
2.54, which accounts for about 87% of the HR for the joint effect of the two factors (2.92). In
contrast, for cancer mortality, the product of the two HRs was 0.93*1.18=1.10, which accounts
for only half of the HR for the joint effect (2.08). Given that the interaction test in this case is to
assess deviation from multiplicative relationship (rather than additive interaction), it is not
surprising that the P value is not statistically significant for CVD mortality compared to that for
all-cause or cancer mortality.
As a sensitivity analysis, we also calculated the P value for interaction by comparing the linear
trends across trajectory groups in the two diabetes strata (the four trajectory groups from left to
right were ordered as 1, 2, 3, and 4). The results were similar to what we had: P=0.004 for all-
cause mortality, 0.46 for CVD mortality, and 0.001 for cancer mortality.
In addition, to see which trajectory group contributes most to the significant interaction, we also
examine the P value for interaction for each individual trajectory group (i.e., Wald test for each
of the product terms between trajectory group and diabetic history). As shown in the following
21
table, only the heavy-stable/increase group showed significant interaction for all-cause and
cancer mortality, indicating that this group dominates the overall interaction; whereas no
significant interaction was detected for CVD mortality.
Table. P value for the individual interaction test for each trajectory group with diabetic history
Lean/medium-
moderate increase
Lean-marked
increase
Heavy-
stable/increase
All-cause mortality 0.60 0.12 0.03
CVD mortality 0.40 0.22 0.69
Cancer mortality 0.97 0.28 0.003
We have updated the text and added these results to the figure legend, as follows.
Figure 2. Joint association of trajectories of body shape and history of
type 2 diabetes with risk of all-cause (A), cardiovascular (B), and cancer
(C) mortality among never smokers
Lean-moderate increase and medium-stable/increase groups were combined as
the “lean/medium-moderate increase” group due to small number of cases.
Multivariable Cox-proportional hazards model was used to calculate the
hazard ratio within each cohort after adjusting for the same set of covariates as
in Tables 2 and 3. Participants were categorized into 8 groups according to
trajectories and history of type 2 diabetes, with those in the lean-stable group
and without history of type 2 diabetes as the reference. Pooled results from the
two cohorts are shown here. The P values for interaction shown in the figures
were calculated from likelihood ratio test with three degrees of freedom by
comparing the model with the product terms between diabetic history (binary)
and the trajectory groups (indicator variables for the three non-reference
groups) to the model without these terms. We also calculated the P value for
interaction for each individual trajectory group by Wald test. For all-cause
mortality, the individual P value for interaction was 0. 60 for the
lean/medium-moderate increase group, 0.12 for the lean-marked increase
group, and 0.03 for the heavy-stable/increase group; the corresponding P
values for cardiovascular mortality were 0.40, 0.22, and 0.69; and for cancer
mortality were 0.97, 0.28, and 0.003.
(FIGURE LEGEND, Page 36)
The figures are shown on the next page.
22
23
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