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Postdiagnosis weight change is associated with poorer survival in breast cancer survivors: A prospective population-based patient cohort study

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Abstract

More women are surviving after breast cancer due to early detection and modern treatment strategies. Body weight also influences survival. We aimed to characterize associations between postdiagnosis weight change and prognosis in postmenopausal long‐term breast cancer survivors. We used data from a prospective population‐based patient cohort study (MARIE) conducted in two geographical regions of Germany. Breast cancer patients diagnosed 50‐74 years of age with an incident invasive breast cancer or in situ tumour were recruited in 2002‐2005 and followed up until June 2015. Baseline weight was ascertained at an in‐person interview at recruitment and follow‐up weight was ascertained by telephone interview in 2009. Delayed entry Cox proportional hazards regression was used to assess associations between relative weight change and all‐cause mortality, breast cancer mortality, and recurrence‐free survival. In total, 2216 patients were included. Compared to weight maintenance (within 5%), weight loss >10% increased risk of all‐cause mortality (HR 2.50, 95%CI 1.61, 3.88), breast cancer mortality (HR 3.07, 95%CI 1.69, 5.60), and less so of recurrence‐free survival (HR 1.43, 95%CI 0.87, 2.36). Large weight gain of >10% also increased all‐cause mortality (HR 1.64, 95%CI 1.02, 2.62) and breast cancer mortality (HR 2.25, 95%CI 1.25, 4.04). Weight maintenance for up to 5 years in long‐term breast cancer survivors may help improve survival and prognosis. Postdiagnosis fluctuations in body weight of greater than 10% may lead to increased mortality. Survivors should be recommended to avoid large deviations in body weight from diagnosis onwards in order to maintain health and prolong life. This article is protected by copyright. All rights reserved.
CANCER EPIDEMIOLOGY
Postdiagnosis weight change is associated with poorer survival
in breast cancer survivors: A prospective population-based
patient cohort study
Audrey Y. Jung
1
| Anika Hüsing
1
| Sabine Behrens
1
| Julia Krzykalla
2
|
Nadia Obi
3
| Heiko Becher
3
| Jenny Chang-Claude
1,4
1
Division of Cancer Epidemiology, German
Cancer Research Center (DKFZ), Heidelberg,
Germany
2
Division of Biostatistics, German Cancer
Research Center (DKFZ), Heidelberg, Germany
3
Institute for Medical Biometry and
Epidemiology, University Medical Center
Hamburg-Eppendorf (UKE), Hamburg,
Germany
4
Cancer Epidemiology Group, University
Cancer Center Hamburg (UCCH), University
Medical Center Hamburg-Eppendorf (UKE),
Hamburg, Germany
Correspondence
Audrey Y. Jung, Division of Cancer
Epidemiology, German Cancer Research
Center (DKFZ), Im Neuenheimer Feld 581,
Heidelberg 69121, Germany.
Email: audrey.jung@dkfz.de
Funding information
Deutsche Krebshilfe e.V., Grant/Award
Numbers: 70112562, 70110826, 108419,
108253, 70-2892-BR I
Abstract
More women are surviving after breast cancer due to early detection and modern
treatment strategies. Body weight also influences survival. We aimed to characterize
associations between postdiagnosis weight change and prognosis in postmenopausal
long-term breast cancer survivors. We used data from a prospective population-
based patient cohort study (MARIE) conducted in two geographical regions of Ger-
many. Breast cancer patients diagnosed 50 to 74 years of age with an incident inva-
sive breast cancer or in situ tumor were recruited from 2002 to 2005 and followed
up until June 2015. Baseline weight was ascertained at an in-person interview at
recruitment and follow-up weight was ascertained by telephone interview in 2009.
Delayed entry Cox proportional hazards regression was used to assess associations
between relative weight change and all-cause mortality, breast cancer mortality, and
recurrence-free survival. In total, 2216 patients were included. Compared to weight
maintenance (within 5%), weight loss >10% increased risk of all-cause mortality (HR
2.50, 95% CI 1.61, 3.88), breast cancer mortality (HR 3.07, 95% CI 1.69, 5.60) and
less so of recurrence-free survival (HR 1.43, 95% CI 0.87, 2.36). Large weight gain of
>10% also increased all-cause mortality (HR 1.64, 95% CI 1.02, 2.62) and breast can-
cer mortality (HR 2.25, 95% CI 1.25, 4.04). Weight maintenance for up to 5 years in
long-term breast cancer survivors may help improve survival and prognosis. Post-
diagnosis fluctuations in body weight of greater than 10% may lead to increased mor-
tality. Survivors should be recommended to avoid large deviations in body weight
from diagnosis onwards to maintain health and prolong life.
KEYWORDS
breast cancer, postmenopausal, survival, weight change
Abbreviations: BMI, body mass index; CCI, Charlson comorbidity index; CI, confidence interval; HR, hazard ratio; HR, hormone receptor; ICD, International Classification of Diseases; IQR,
interquartile range; MARIE, Mammary Carcinoma Risk Factor Investigation; MET, metabolic equivalent of task.
Received: 4 March 2020 Revised: 4 June 2020 Accepted: 19 June 2020
DOI: 10.1002/ijc.33181
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
©2020 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.
Int. J. Cancer. 2020;110. wileyonlinelibrary.com/journal/ijc 1
1|INTRODUCTION
Improvements in early detection, and personalized and targeted treat-
ments have led to more women surviving after breast cancer.
1,2
Modi-
fiable factors such as body weight can also influence breast cancer
prognosis.
3
Results that include evidence from a large systematic
review and meta-analysis point toward increasing risk of total mortal-
ity, breast cancer mortality and risk of developing a second primary
breast cancer with obesity or excess body weight both before and
after breast cancer diagnosis.
3
Weight change is common during breast cancer. It may be a conse-
quence of breast cancer or a combination of breast cancer and antican-
cer treatments such as surgery, chemo, radiation and/or hormonal
therapies or combinations of these, which, while essential for improving
survival, contribute considerably to therapy-associated side effects.
4-6
There is accumulating evidence that weight gained in different
periods during the breast cancer trajectoryduring adulthood, from
prediagnosis to postdiagnosis breast cancer, from pretreatment to post-
treatment and during treatmentmay impact adversely on total mortal-
ity, although there was large heterogeneity between studies in meta-
analyses.
3,7
Similar but weaker patterns of association between weight
gain and breast cancer-specific mortality have also been observed, but
there are fewer investigations between associations with recurrence.
3,7
Although the bulk of weight change and prognosis research has focused
on weight gain, results of a systematic review and meta-analysis con-
ducted in six studieswith significant heterogeneityrevealed predia-
gnosis to postdiagnosis weight loss (highest vs lowest/stable) to be
strongly associated with all-cause mortality.
3
Collectively then, limited
evidence suggests that weight maintenance may be optimal for health
and survival following breast cancer. Sources of heterogeneity between
studies can be mainly attributed to differences in study designs that
affect timing and duration of weight measurements in relation to diag-
nosis and treatment. Indeed, time since diagnosis may influence the
associations between weight change and survival.
8
Risk of breast cancer and other chronic diseases increases with age.
Therefore, at diagnosis and during survivorship, many breast cancer sur-
vivors have other chronic comorbid conditions that also affect sur-
vival.
9,10
With this in mind, we aim to describe the associations
between weight change after diagnosis and subsequent prognosis,
including recurrence, in a population-based patient cohort of long-term
breast cancer survivors in Germany. We further explore whether these
associations differ by weight at recruitment, and number of com-
orbidities, and whether the rate at which weight changes also impacts
survival. To the best of our knowledge, this is the first study to evaluate
postdiagnosis weight change and prognosis within a European setting.
2|MATERIALS AND METHODS
2.1 |Study population
We used data from the MARIE (Mammary Carcinoma Risk Factor
Investigation) study,
11
a prospective population-based patient cohort
study that began as a case-control study conducted in two geographi-
cal regions of GermanyRhine-Neckar-Karlsruhe and Hamburg. Ger-
man-speaking breast cancer patients diagnosed between 50 and
74 years of age from January 1, 2001 to September 30, 2005 with an
incident histologically confirmed invasive (according to 10th revision
of the International Classification of Diseases (ICD-10-WHO; Stage I-
IV) or in situ tumor (Stage 0; ICD-10-D05) were recruited between
2002 and 2005. Potentially eligible subjects were identified based on
frequent monitoring of hospital admissions, surgery schedules, and
pathology records of 51 clinics serving these regions and through the
Hamburg Cancer Registry. In 2009, patients were re-interviewed
about weight and other exposures, and follow-up information about
endpoints was ascertained in 2009 and 2015.
12
The primary exposure of interest was weight change, so women
who completed baseline and follow-up interviews (n = 2542) were
included. After exclusion of patients who were premenopausal
(n = 148), had metastases at diagnosis (n = 22), tumors other than
breast cancer or nonmelanoma skin cancer before diagnosis (n = 138),
missing baseline (n = 2) or follow-up weight (n = 16), there were 2216
patients available for analyses of all-cause and breast cancer-specific
mortality (Figure 1). For recurrence-free survival, women who experi-
enced a recurrence prior to the follow-up were additionally excluded
leaving 2068 women for recurrence-free survival analyses.
2.2 |Assessment of weight and other exposures
At baseline interview (median 3.9 months after diagnosis), self-
reported current body weight in kg and height in cm were recalled in
an in-person interview. In 2009 (median 5.8 years after diagnosis),
self-reported current height and body weight were ascertained at the
follow-up telephone interview. Clinical and pathological characteris-
tics were obtained from hospital and pathology records. Information
on lifestyle, socioeconomic and demographic, comorbidities and other
pertinent protective and risk factors were collected from baseline and
follow-up interviews.
What's new?
Weight change, while common among breast cancer survi-
vors, may not be optimal for survival. Here, the authors offer
the first evaluation of the associations between post-diagno-
sis weight change and subsequent prognosis in postmeno-
pausal long-term breast cancer survivors within a European
setting. The results show that weight maintenance for up to
5 years after diagnosis is associated with better survival and
prognosis, while fluctuations of more than 10% of body
weight are associated with increased mortality. Encouraging
breast cancer survivors to maintain weight after diagnosis
may possibly help them to maintain a healthy and pro-
longed life.
2JUNG ET AL.
2.3 |Outcome assessment
Participant vital status was determined through population registries
of the study regions up until the end of June 2015, and all deaths
were verified by death certificates. Self-reported recurrences of the
primary breast cancer, second cancers and metastatic events were
identified during 2009 and 2015 telephone interviews with patients
and verified by clinical records or with treating physicians. For
patients who died, medical records were checked or treating physi-
cians were contacted. Primary outcomes were all-cause mortality
(death from any cause) and breast cancer-specific mortality (death
from breast cancer). Recurrence-free survival (including ipsilateral/
local/regional invasive recurrence, distant recurrence and metastases
occurring after primary diagnosis, death)
13
was a secondary outcome.
Participants without events of interest were censored at date of last
contact or June 30, 2015, whichever came first.
2.4 |Statistical analysis
Relative weight change was calculated using [(follow-up weight
baseline weight)/baseline weight] ×100. To assess weight change,
five categories were created: weight stable (weight change within 5%
from baseline to follow-up), moderate gain/loss (weight gain/loss of
5% to 10%), large gain/loss (weight gain/loss of >10%), respec-
tively.
7
These categories were selected for comparisons with other
studies
7
and considered to be clinically meaningful.
14
To estimate hazard ratios (HRs) and corresponding 95% confi-
dence intervals (CIs) for associations between weight change with all-
cause mortality, breast cancer mortality and recurrence-free survival,
delayed-entry Cox proportional hazards regression models from
follow-up interview until event of interest/censoring were con-
structed. Time-to-event started from date of diagnosis, and time-at-
risk started from the date of follow-up interview. Weight stable
women served as reference. The proportional hazards assumption
was examined using a weighted least-squares line fitted to the plots
of scaled Schoenfeld residuals.
15
No indication for violation of the
proportional hazards assumption was found. Follow-up time was cal-
culated using reverse Kaplan-Meier.
16
Subgroup analyses were defined a priori, and effect modification
was tested applying the likelihood ratio test to a model with the inter-
action term of the main exposure and potential modifier and a model
without the interaction term. For this purpose, five weight change cat-
egories were collapsed into three (weight stable (weight change within
5%), weight loss (weight change 5%), and weight gain (weight change
5%)). Associations between weight change and outcomes by baseline
BMI (18.5 to <25.0 kg/m
2
/25 kg/m
2
), and comorbidities using the
Charlson Comorbidity Index (CCI 0-1/2) were evaluated. The CCI
was built from 17 conditions
17,18
and adapted. The CCI at follow-up
was used, as new comorbidities may have been acquired after
baseline.
The rate of weight change (% weight change per 1 year) was also
assessed to facilitate comparisons between our results and those of
other studies, where there was large variation in the duration or win-
dow in which weight was gained or lost.
3,7
To calculate rate of weight
change, percent weight change was divided by time between baseline
and follow-up in years. Five categories for rate of weight change were
constructed: weight maintenance (within 0.5% per year), slow weight
gain/loss (0.5% to 1.0% per year) and fast gain/loss (>1.0% per
year). Relative weight change was also modeled continuously using
absolute values in all women and also allowing for different slopes in
those who gained or lost 5% body weight. A model was fit using an
FIGURE 1 Flow chart of inclusion and exclusion criteria for participants of the MARIE study for analyses relating to changes in weight and
all-cause mortality, breast cancer mortality and recurrence-free survival
JUNG ET AL.3
TABLE 1 Characteristics of 2216 postmenopausal women diagnosed with a first primary breast cancer according to relative weight change
Relative weight change
Characteristic
Overall
n = 2216
Large loss
(>10%)
n = 111 (5.0)
Moderate loss
(5% to 10%)
n = 234 (10.6)
Weight stable
(within 5%)
n = 1421 (64.1)
Moderate
gain (5% to 10%)
n = 304 (13.7)
Large gain
(>10%)
n = 146 (6.6)
Age at diagnosis, years,
median (IQR)
62.9 (58.5-66.5) 64.2 (60.8-67.7) 63.4 (59.6-67.3) 63.0 (58.6-66.7) 61.8 (57.6-65.0) 61.6 (57.4-66.1)
Time between diagnosis
and recruitment,
months, median (IQR)
3.9 (0.4-12.6) 3.4 (0.4-16.5) 5.5 (0.5-16.9) 4.6 (0.4-13.3) 2.3 (0.4-8.1) 2.1 (0.4-9.6)
Absolute weight change,
kg, median (IQR)
0.0 (2.0-3.0) 10.0 (12.0-[8.0]) 5.0 (6.0-[4.0]) 0.0 (1.0-2.0) 5.0 (4.0-6.0) 10.0 (8.0-12.0)
Baseline height, m, median
(IQR)
1.65 (1.60-1.69) 1.65 (1.60-1.68) 1.64 (1.60-1.68) 1.65 (1.60-1.69) 1.64 (1.60-1.68) 1.65 (1.60-1.69)
Baseline BMI, kg/m
2
,
median (IQR)
25.2 (22.9-28.1) 27.5 (25.1-30.5) 25.8 (23.5-29.0) 24.9 (22.8-27.7) 25.3 (22.7-28.3) 24.5 (21.8-27.5)
Follow-up BMI, kg/m
2
,
median (IQR)
25.3 (22.9-28.3) 23.7 (21.6-26.2) 24.2 (22.1-27.2) 25.0 (22.8-27.7) 27.0 (24.3-30.5) 28.2 (24.8-31.6)
Follow-up waist-hip ratio,
median (IQR)
0.87 (0.83-0.90) 0.85 (0.82-0.90) 0.87 (0.83-0.89) 0.87 (0.83-0.90) 0.87 (0.83-0.91) 0.88 (0.83-0.92)
Follow-up leisure time
physical activity, MET-
hrs/week, median (IQR)
40.0 (22.6-66.0) 30.9 (15.4-52.2) 38.2 (22.7-59.2) 40.0 (23.8-67.9) 38.8 (24.0-64.5) 36.0 (20.0-62.0)
Follow-up alcohol intake,
g/day, median (IQR)
a
5.1 (1.9-13.6) 3.6 (1.2-12.5) 3.9 (1.4-12.4) 5.4 (2.0-13.4) 5.4 (2.2-16.8) 4.3 (1.6-14.3)
Follow-up smoking status,
n (%)
Never 1198 (54.1) 53 (47.7) 123 (52.6) 791 (55.7) 166 (54.6) 65 (44.5)
Former 811 (36.6) 42 (37.8) 83 (35.5) 498 (35.0) 123 (40.5) 65 (44.5)
Current 207 (9.3) 16 (14.4) 28 (12.0) 132 (9.3) 15 (4.9) 16 (11.0)
Education, n (%)
Low 1243 (56.1) 71 (64.0) 139 (59.4) 768 (54.0) 177 (58.2) 88 (60.3)
Medium 625 (28.2) 28 (25.2) 65 (27.8) 406 (28.6) 81 (26.6) 45 (30.8)
High 348 (15.7) 12 (10.8) 30 (12.8) 247 (17.4) 46 (15.1) 13 (8.9)
Follow-up self-perceived
health, n (%)
Excellent/very good 444 (20.0) 13 (11.7) 37 (15.8) 315 (22.2) 55 (18.1) 24 (16.5)
Good 1134 (51.2) 44 (39.6) 120 (51.3) 726 (51.1) 172 (56.6) 72 (49.3)
Not good/bad 635 (28.6) 54 (48.6) 77 (32.8) 379 (26.7) 76 (25.0) 49 (33.6)
Ever menopausal hormone
therapy, n (%)
1083 (48.9) 56 (50.5) 115 (49.1) 684 (48.1) 153 (50.3) 75 (51.4)
Mode of tumor detection
Self-detected by
palpation / secretion /
pain
1095 (49.4) 65 (58.6) 103 (44.0) 706 (49.7) 144 (47.4) 77 (52.7)
Routine examination /
mammography /
ultrasound
1115 (50.3) 45 (40.5) 129 (55.1) 714 (50.2) 158 (52.0) 69 (47.3)
Type of surgery, n (%)
Mastectomy 57 (2.6) 4 (3.6) 6 (2.6) 40 (2.8) 6 (2.0) 1 (0.7)
Mastectomy + axilla 520 (23.5) 30 (27.0) 61 (26.1) 312 (22.0) 78 (25.7) 39 (26.7)
Breast-conserving 190 (8.6) 6 (5.4) 17 (7.3) 130 (9.1) 25 (8.2) 12 (8.2)
therapy
4JUNG ET AL.
interaction term between weight change as a continuous variable and
a dummy for 5% weight gained or lost.
In addition, associations between baseline weight (per 5 kg
increase) as well as associations between follow-up weight (per
5 kg increase) and the three endpoints were investigated sepa-
rately. Associations between follow-up weight and cancer
outcomes were stratified by baseline BMI (normal/overweight and
obese).
All models included the prognostic factors tumor size, nodal sta-
tus, tumor grade, hormone receptor (HR) status, as well as mode of
tumor detection and were stratified by study center and age at diag-
nosis in 5-year categories to allow for variation in baseline hazard.
TABLE 1 (Continued)
Relative weight change
Characteristic
Overall
n = 2216
Large loss
(>10%)
n = 111 (5.0)
Moderate loss
(5% to 10%)
n = 234 (10.6)
Weight stable
(within 5%)
n = 1421 (64.1)
Moderate
gain (5% to 10%)
n = 304 (13.7)
Large gain
(>10%)
n = 146 (6.6)
Breast-conserving 1446 (65.3) 71 (64.0) 149 (63.7) 937 (66.0) 195 (64.1) 94 (64.4)
therapy + axilla
Tumor size, n (%)
<2 cm 1259 (56.8) 61 (55.0) 149 (63.7) 808 (56.9) 164 (53.9) 77 (52.7)
2-5 cm 679 (30.6) 37 (33.3) 60 (25.6) 432 (30.4) 97 (31.9) 53 (36.3)
>5 cm / growth into
chest wall
79 (3.6) 7 (6.3) 10 (4.3) 45 (3.2) 13 (4.3) 4 (2.7)
Neoadjuvant
chemotherapy-treated
carcinoma
b
69 (3.1) 2 (1.8) 4 (1.7) 45 (3.2) 14 (4.6) 4 (2.7)
In situ
a
127 (5.7) 3 (2.7) 10 (4.3) 90 (6.3) 16 (5.3) 8 (5.5)
Nodal status, n (%)
0 1436 (64.8) 72 (64.9) 150 (64.1) 934 (65.7) 188 (61.8) 92 (63.0)
1-3 436 (19.7) 25 (22.5) 56 (23.9) 265 (18.6) 56 (18.4) 34 (23.3)
>3 148 (6.7) 9 (8.1) 14 (6.0) 87 (6.1) 30 (9.9) 8 (5.5)
Tumor grade, n (%)
Low/moderate 1531 (69.1) 76 (68.5) 169 (72.2) 976 (68.7) 203 (66.8) 107 (73.3)
High 480 (21.7) 27 (24.3) 51 (21.8) 305 (21.5) 70 (23.0) 27 (18.5)
Hormone receptor status,
n (%)
ER+PR+ 1439 (65.0) 73 (65.8) 166 (70.9) 902 (63.5) 198 (65.1) 100 (68.5)
ER+PR/ERPR+ 322 (14.5) 25 (22.5) 30 (12.8) 209 (14.7) 36 (11.8) 22 (15.1)
ERPR259 (11.7) 8 (7.2) 24 (10.3) 175 (12.3) 40 (13.2) 12 (8.2)
Her2 status, n (%)
Her2 positive 365 (16.5) 19 (17.1) 40 (17.1) 212 (14.9) 62 (20.4) 32 (21.9)
Her2 negative 1549 (69.9) 78 (70.3) 170 (72.6) 1006 (70.8) 199 (65.5) 96 (65.8)
Chemotherapy, n (%) 1010 (45.6) 58 (52.3) 109 (46.6) 623 (43.8) 157 (51.6) 63 (43.2)
Radiation therapy, n (%) 1772 (80.0) 86 (77.5) 189 (80.8) 1141 (80.3) 245 (80.6) 111 (76.0)
Tamoxifen/aromatase
inhibitor use, n (%)
1792 (80.9) 100 (90.1) 198 (84.6) 1128 (79.4) 239 (78.6) 127 (87.0)
Follow-up Charlson Comorbidity Index, n (%)
0 1161 (52.4) 48 (43.2) 106 (45.3) 791 (55.7) 154 (50.7) 62 (42.5)
1 663 (29.9) 32 (28.8) 72 (30.8) 418 (29.4) 87 (28.6) 54 (37.0)
2 386 (17.4) 30 (27.0) 54 (23.1) 209 (14.7) 63 (20.7) 30 (20.5)
a
For patients with nonzero alcohol use (56.0%). Percentages of patients with nonzero alcohol use are 56.9% for maintenance, 53.8% for moderate increase,
56.9% large increase, 54.9% for moderate decrease and 52.2% for large decrease.
b
Applies also to nodal status, tumor grade, hormone receptor status and Her2 status.
JUNG ET AL.5
TABLE 2 Hazard ratios and corresponding 95% confidence intervals for the association between weight change and survival
Events/follow-up
time (years)
All-cause
mortality
HR (95% CI)
Events/
follow-up
time (years)
Breast
cancer-specific
mortality HR (95% CI)
Events/
follow-up
time (years)
Recurrence-free
survival HR
(95% CI)
Weight change from baseline
to follow-up
234/5.50 129/5.48 239/5.51
Large gain (>10%) 21/5.50 1.64 (1.02, 2.62) 15/5.48 2.24 (1.25, 4.02) 18/5.51 1.22 (0.74, 2.01)
Moderate gain (5% to 10%) 26/5.51 0.77 (0.50, 1.20) 17/5.49 0.81 (0.46, 1.42) 22/5.51 0.73 (0.46, 1.15)
Maintenance (within 5%) 127/5.50 1.00 (Ref.) 67/5.48 1.00 (Ref.) 147/5.51 1.00 (Ref.)
Moderate loss (5% to 10%) 32/5.50 1.42 (0.95, 2.13) 13/5.46 1.16 (0.62, 2.16) 33/5.51 1.28 (0.86, 1.89)
Large loss (>10%) 28/5.50 2.49 (1.61, 3.88) 17/5.45 3.09 (1.70, 5.62) 19/5.52 1.43 (0.86, 2.35)
Note: Adjusted for BMI at baseline, tumor size, nodal status, grade, mode of detection, HR status, time between baseline and follow-up 1, recurrences
between diagnosis and follow-up 1 and stratified by study center and age at diagnosis in 5-year age categories.
TABLE 3 Associations between postdiagnosis weight change and prognosis stratified by recruitment BMI, and comorbidities
Baseline BMI categories (WHO classification)
a
Events/follow-up
time (years)
Normal
(n = 1033)
Events/follow-up
time (years)
Overweight/obese
(n = 1144)
All-cause mortality
Weight gain (5%) 17/5.50 1.11 (0.62, 1.99) 28/5.51 0.94 (0.60, 1.48)
Maintenance (within 5%) 48/5.50 1.00 (Ref.) 78/5.50 1.00 (Ref.)
Weight loss (5%) 18/5.50 2.33 (1.30, 4.16) 42/5.50 1.70 (1.15, 2.53)
Breast cancer-specific mortality
Weight gain (5%) 14/5.49 1.76 (0.84, 3.69) 18/5.49 0.88 (0.48, 1.59)
Maintenance (within 5%) 23/5.48 1.00 (Ref.) 44/5.48 1.00 (Ref.)
Weight loss (5%) 8/5.45 2.17 (0.87, 5.42) 22/5.46 1.69 (0.97, 2.96)
Recurrence-free survival (n = 969) (n = 1062)
Weight gain (5%) 11/5.50 0.64 (0.33, 1.24) 27/5.52 0.98 (0.63, 1.53)
Maintenance (within 5%) 60/5.51 1.00 (Ref.) 86/5.52 1.00 (Ref.)
Weight loss (5%) 16/5.51 1.60 (0.91, 2.84) 36/5.52 1.29 (0.86, 1.95)
Charlson Comorbidity Index
b
Events/follow-up
time (years)
None to mild (0-1)
(n = 1811)
Events/follow-up
time (years)
Severe (2)
(n = 383)
All-cause mortality
Weight gain (5%) 32/5.50 1.08 (0.71, 1.62) 15/5.51 0.97 (0.47, 1.99)
Maintenance (within 5%) 102/5.50 1.00 (Ref.) 25/5.51 1.00 (Ref.)
Weight loss (5%) 37/5.50 1.48 (1.00, 2.20) 22/5.51 2.80 (1.46, 5.34)
Breast cancer-specific mortality
Weight gain (5%) 23/5.49 1.45 (0.87, 2.42) 9/5.48 1.39 (0.45, 4.26)
Maintenance (within 5%) 57/5.48 1.00 (Ref.) 10/5.47 1.00 (Ref.)
Weight loss (5%) 22/5.47 1.63 (0.95, 2.79) 7/5.40 2.56 (0.74, 8.90)
Recurrence-free survival (n = 1700) (n = 349)
Weight gain (5%) 30/5.51 0.91 (0.60, 1.36) 10/5.52 0.77 (0.35, 1.68)
Maintenance (within 5%) 117/5.51 1.00 (Ref.) 30/5.52 1.00 (Ref.)
Weight loss (5%) 31/5.51 1.08 (0.71, 1.62) 21/5.53 2.57 (1.33, 4.95)
a
Adjusted for tumor size, nodal status, grade, mode of detection, HR status, time between baseline and follow-up 1, recurrences between diagnosis and fol-
low-up 1 and stratified by study center and age at diagnosis in 5-year age categories.
b
Adjusted for BMI at baseline, tumor size, nodal status, grade, mode of detection, HR status, time between baseline and follow-up, recurrences between
diagnosis and follow-up and stratified by study center and age at diagnosis in 5-year age categories.
6JUNG ET AL.
The weight change model was additionally adjusted for BMI at base-
line, time between baseline and follow-up and occurrence of recur-
rence between diagnosis and follow-up (this covariate was not
included when recurrence-free survival was the outcome). Associa-
tions between both baseline and follow-up weight and cancer out-
comes were further adjusted for baseline height, menopausal
hormone therapy at diagnosis and chemotherapy. Potential con-
founding variables (ie, physical activity, waist-hip ratio, type of sur-
gery, radiation therapy, tamoxifen/aromatase inhibitor therapy,
comorbidities [CCI], smoking, alcohol, education, HER2 status, self-
perceived health) were determined a priori. Examining whether risk
estimates changed by at least 10%
19
when excluding one covariate at
a time using backward elimination yielded no changes in risk esti-
mates, so no potential confounding factors were included in the final
models. Categories for all variables can be seen in Table 1.
In sensitivity analyses, all analyses were repeated for all three out-
comes, excluding (1) women who developed a recurrence (ipsilateral,
local/regional, distant and metastatic recurrence or second tumor) by
the first follow-up interview (n = 117), and (2) women with in situ
tumors (n = 127).
For all analyses, complete-case analysis was performed, as the
proportion of missing was less than 5% for all variables. All tests of
statistical significance were two-sided and significance level was set
to 0.05. Analyses were conducted using the SAS statistical software
package (version 9.4).
3|RESULTS
Median age at breast cancer diagnosis for the 2216 postmenopausal
survivors included was 62.9 years. By June 30, 2015, a median fol-
low-up time of 5.5 years after the re-interview in 2009, 235 (10.6%)
women died, 130 (5.9%) of which were from breast cancer, and 363
(16.4%) women developed a recurrence (n = 117 of which occurred
between the initial diagnosis and 2009 follow-up interview). Median
(IQR) BMI at baseline and follow-up was 25.2 (22.9-28.1) and 25.2
(22.9-28.3) kg/m
2
, respectively.
Compared to weight stable women, women who lost >10% body
weight were at increased risk for all-cause mortality (HR (95% CI):
2.49 (1.61, 3.88)), breast cancer-specific mortality (HR (95% CI): 3.09
(1.70, 5.62)), and possibly poorer recurrence-free survival (HR (95%
CI): 1.43 (0.86, 2.35; Table 2). Weight gain >10% also increased risk of
all-cause mortality (HR (95% CI): 1.64 (1.02, 2.62)) and breast cancer-
specific mortality (HR (95% CI): 2.24 (1.25, 4.02)), and possibly sugges-
tive of poorer recurrence-free survival (HR (95% CI): 1.22 (0.74,
2.01)). Neither moderate gain nor moderate loss was associated with
any outcome.
In subgroup analysis stratified by baseline BMI, weight loss was
again associated with increased all-cause mortality in women with
normal baseline BMI (HR (95% CI): 2.33 (1.30, 4.16)) and women who
were overweight/obese (HR (95% CI): 1.70 (1.15, 2.53)) compared to
weight stable women (Table 3). Associations tended to be stronger for
those with normal baseline BMI than those who were overweight.
Associations between 5% weight gain and all-cause mortality as well
as recurrence-free survival were comparable in normal and over-
weight/obese women, while there could be suggestions for a differen-
tial association with breast cancer-specific mortality based on
direction of HRs (for normal-weight women 1.76 (0.84, 3.69) and for
overweight/obese women 0.88 (0.48, 1.59)). Stratified by the CCI,
associations were found in those with severe comorbidities (2),
whereby weight loss compared to weight maintenance increased all-
cause mortality (HR (95% CI): 2.80 (1.46, 5.34)) and recurrence-free
survival (HR (95% CI): 2.57 (1.33, 4.95); Table 3). Weight gain and loss
were associated with nonsignificant increased risk of breast cancer-
specific mortality irrespective of number of comorbidities.
When we evaluated the rate of weight change-survival associa-
tions, compared to weight maintenance, fast weight loss (>1.0% body
weight per year) was associated with an increased risk of all-cause
TABLE 4 Weight change and prognosis associations: rate of weight change (weight change per 1 year), and weight change as a continuous
variable
Total relative
weight change
(median (IQR)
Events/
follow-up
time (years)
All-cause
mortality
HR (95%CI)
Events/
follow-up
time (years)
Breast
cancer-specific
mortality
HR (95%CI)
Events/
follow-up
time (years)
Recurrence-free
survival
HR (95%CI)
Weight change per 1 year 234/5.50 129/5.48 239/5.51
Fast weight gain (>1.0%) 8.08 (6.58, 11.67) 49/5.50 1.23 (0.84, 1.79) 34/5.49 1.54 (0.93, 2.55) 41/5.51 0.95 (0.65, 1.39)
Slow weight gain (0.5-1.0%) 3.51 (2.99, 4.48) 27/5.51 1.04 (0.66, 1.64) 17/5.49 1.38 (0.75, 2.52) 32/5.52 0.88 (0.58, 1.35)
Maintenance (within 0.5%) 0.00 (1.27, 1.41) 70/5.50 1.00 (Ref.) 34/5.48 1.00 (Ref.) 86/5.51 1.00 (Ref.)
Slow weight loss (0.5-1.0%) 3.39 (4.23, 2.90) 32/5.50 1.36 (0.88, 2.09) 16/5.47 1.11 (0.59, 2.11) 27/5.51 1.06 (0.68, 1.64)
Fast weight loss (>1.0%) 8.33 (11.25, 6.25) 56/5.50 1.83 (1.27, 2.63) 28/5.46 1.82 (1.07, 3.10) 53/5.52 1.32 (0.93, 1.89)
Weight change per percent increase
Weight gain 5% 47/5.50 1.02 (1.00, 1.05) 32/5.49 1.04 (1.02, 1.07) 40/5.51 1.02 (0.99, 1.04)
Weight loss 5% 61/5.50 1.05 (1.03, 1.08) 30/5.45 1.06 (1.03, 1.10) 52/5.52 1.02 (1.00, 1.05)
Note: Adjusted for BMI at baseline, tumor size, nodal status, grade, mode of detection, HR status, recurrences between diagnosis and follow-up and
stratified by study center and age at diagnosis in 5-year age categories.
JUNG ET AL.7
(HR (95% CI): 1.83 (1.27, 2.63)) and breast cancer-specific (HR (95%
CI): 1.82 (1.07, 3.10)) mortality. There were also possible suggestions
that fast weight gain were associated with all-cause (HR (95% CI):
1.23 (0.84, 1.79)) and breast cancer-specific mortality (HR (95% CI):
1.54 (0.93, 2.55) but not recurrence-free survival (HR (95% CI): 0.95
(0.65, 1.39); Table 4). Risk patterns for slow and fast weight loss were
similar for recurrence-free survival. In women who had lost 5%
weight since baseline, per percent weight change was associated with
increased all-cause mortality (HR (95% CI): 1.05 (1.03, 1.08)) and
breast cancer-specific mortality (HR (95% CI): 1.06 (1.03, 1.10)). In
women who had gained 5% in weight since baseline, per percent
weight increase was also associated with breast cancer-specific mor-
tality (HR (95% CI): 1.04 (1.02, 1.07); Table 4).
Higher baseline weight was associated with increasing all-cause
mortality (per 5 kg HR 1.04 [95% CI 1.01, 1.07]) and possibly recur-
rence-free survival (per 5 kg HR 1.03 [95% CI 1.00, 1.06]) but not
breast cancer-specific mortality (per 5 kg HR 1.05 [95% CI 0.98,
1.13]). There was no evidence for associations between follow-up
weight and subsequent prognosis according to baseline BMI (data not
shown).
To better understand how various therapies may affect baseline
weight, we compared baseline weight and BMI in the whole popula-
tion against different subsets of the study population: in women who
never/before or during baseline/after baseline received aromatase
inhibitor therapy, tamoxifen, either aromatase or tamoxifen, chemo-
therapy, radiation therapy, mastectomy and breast-conserving ther-
apy. We did not find meaningful differences in weight or BMI at
recruitment between any of these subsets (data not shown).
4|DISCUSSION
Postdiagnosis weight change in relation to prognosis was evaluated in
2216 postmenopausal long-term breast cancer survivors in Germany.
Weight loss >10% of body weight was associated with poorer progno-
sis compared to weight maintenance. The increased mortality associ-
ated with weight loss was independent of baseline BMI, and more
pronounced in those with severe comorbidities, who were more likely
to be negatively impacted by weight loss. Per percent increments of
postdiagnosis weight from baseline to the follow-up was likewise
associated with poorer subsequent prognosis. That we see stronger
associations and a dose-response relationship with weight loss could
indicate that reservesare necessary for health and to possibly with-
stand metabolic challenges from breast cancer and its sequelae. In fur-
ther support that reservesmay be necessary after breast cancer, we
found evidence of a possible nonlinear dose-response relationships
between weight gain and survival. Moderate weight gain was associ-
ated with nonsignificant decreased risk for all three endpoints rather
than increased risk.
Several proposed biological mechanisms may underpin the associ-
ations between weight gain and survival. Obesity creates an
expanded, metabolically active adipose tissue as well as systemic
obesity-associated alterations that involve increased insulin and insu-
lin-like growth factors, elevated circulating estradiol and production of
proinflammatory cytokines and decreased sex hormone-binding glob-
ulin, all of which influences breast mammary tumor cells as well as the
breast tumor microenvironment to encourage breast tumor progres-
sion (reviewed in References 20 and 21). Neither intentional nor uni-
ntentional weight loss, which could potentially increase or decrease
mortality, were collected in our study and may bias our results,
although our results do demonstrate poorer survival with weight loss
independent of baseline weight, and associations were not modified
by self-perceived health (data not shown). Consequently, additional
mechanisms linking energy balance and imbalance following breast
cancer may be involved.
To date, three systematic reviews
3,7,22
and two meta-analyses
3,7
have examined weight change and prognosis after breast cancer. One
of these evaluated weight gain and found that only those who gained
>10% were at increased risk of all-cause mortality,
7
in line with our
results. There was no association with modest weight gain, and when
stratified by baseline BMI, there were no differences in all-cause mor-
tality. In the second systematic review and meta-analysis, weight
gained and weight lost at different time points were explored. Adult
weight gain (highest vs lowest/stable), and weight gain before and
12 months or more after diagnosis/treatment (highest vs lowest/sta-
ble) was associated with about 30% to 50% increased risk of all-cause
mortality. Only weight gained before and 12 months or more after
diagnosis/treatment (highest vs lowest/stable) was associated with
breast cancer-specific mortality (1.59 [1.05, 2.41]). Weight loss was
also examined in the second systematic review and meta-analysis with
overall relative risks of 2.33 (95% CI 1.42, 3.83) and 1.86 (95% CI
0.43, 7.89) for all-cause mortality and breast cancer-specific mortality,
respectively, with significant heterogeneity between studies.
3
In the
third systematic review, greater weight loss was associated with
higher all-cause mortality with HRs ranging from 1.40 to 4.75.
22
Definitions and categories for weight change are different across
studies with studies using various categorizations for absolute weight
change,
23,24
relative weight change
8,25
and BMI change,
26
which may
partially drive inconsistencies in results between studies. Different
time points where weight change is measured relative to diagnosis
and treatment may also partially explain observed differences in
results between studies. Indeed, associations of weight change on all-
cause mortality have been reported to be more pronounced during
the first 2 years after diagnosis (>5% gain HR (95% CI) 5.87 (0.89,
47.8)) than more than 2 years after diagnosis (HR (95%CI) 1.49 (0.85,
2.57)).
8
This could be due to a greater immediate consequence of the
breast cancer itself and its sequelae or a result of the rate of weight
change, as the rate for a given amount of weight change will be higher
within 2 years than over a longer period of time. Along with these
lines, our results indicate that gaining and losing weight quickly (faster
than >1.0% body weight per year) may be associated with poorer out-
comes than slow weight change. We suppose that a higher rate
implies faster weight change (and a lower rate implies slower weight
change).
8JUNG ET AL.
Furthermore, large weight losses of >10% were associated with
poorer recurrence-free survival, which was similar to results from one
study.
27
These results were not supported by another study that
assessed BMI change stratified by smoking status,
26
which may possi-
bly account for the heterogeneous findings. Recurrence rather than
recurrence-free survival was the endpoint of interest in these two
studies.
26,27
Results from a meta-analysis of three studies did not indi-
cate an association between weight gain and recurrence.
7
Similarly,
we observed no association between weight gain and recurrence-free
survival. An additional challenge of summarizing associations with
recurrence is inconsistent definitions of recurrence; we have used
those defined by Hudis.
13
Of all studies identified in the systematic lit-
erature reviews and included in the meta-analyses on postdiagnosis
weight change and prognosis, none were European. Yet we are aware
of three studies from Europe, two of which assessed weight gain dur-
ing adulthood and total and breast cancer-specific mortality
28,29
and
one that examined weight change during chemotherapy and total
mortality and disease-free survival.
30
We are not aware of any studies
from Europe evaluating weight loss in breast cancer survivors. Weight
is a result of myriad components involving environmental factors
along the life course (eg, physical activity, smoking, nutrition) and
genetics. Given that lifestyles in Europeand Germany specifically
are distinct and not like those in the United States and Asia, studies
investigating these associations in European countries are essential to
further delineate these complex relationships between weight and
prognosis.
A limitation of our study is that weight directly before surgery
was not collected. Because weight can be affected by external influ-
ences at any point along with the course of treatment and afterward,
baseline weight may not represent true weight before, at, or immedi-
ately after diagnosis. Another limitation is use of self-reported expo-
sure information. Those who have low BMI may overestimate true
BMI and those who have high BMI may tend to underestimate true
BMI; older respondents may also underestimate BMI.
31,32
In this anal-
ysis, however, relationships between weight change and cancer end-
points were evaluated, so systematic underestimation or
overestimation of weight at both time points in the same direction
may be less likely to bias results than if evaluating weight alone.
Weight underestimation and overestimation by overweight and
underweight women, respectively, to normal weight would also dilute
observed associations, thus true associations could likely be stronger.
Also, though comorbidities used to generate the CCI were self-
reported and unverified, use of patient questionnaires to ascertain
comorbidities has been shown to be reliable.
33
Reverse causation is
possible if women loss or gain weight because they are sicker than
those who are weight stable. Although tumor size, nodal status and
grade were similar between weight change groups, women severely
impacted by comorbidities were apparently more likely to have poorer
prognosis from weight loss than women who had no or mild
comorbidities.
Study strengths include longitudinal follow-up data from breast
cancer survivors over more than 10 years. Most studies on weight
gain or loss have examined weight change before (as prediagnosis or
usual weight) and 12 months or more after diagnosis or treatment
3,7
with time between measurements a median 1.5 years,
7
during which
time patients may still be undergoing treatment or may still be
experiencing the effects of treatment. Duration between the two
weight measurements in our study was median 5.1 years, so weight
change due to acute effects of different treatment regimens could
possibly have been circumvented. Weight at follow-up may better
indicate long-term weight, when acute sequelae of breast cancer or
behavioral changes related to initial diagnosis or treatment may have
abated or plateaued.
34
Information on numerous important potential
confounding factors were collected and tested in our comparison
models. In the current analysis, we only included women for whom
we had both weight measuresat baseline (between 2002 and 2005)
and follow-up (2009)so these women may reflect a healthier subset
of women, as they survived at least 5 years after the initial breast can-
cer. To the best of our knowledge, this is the largest analysis to eluci-
date associations between postdiagnosis weight change and
prognosis in a European population. Specifically, our results further
contribute to the literature by demonstrating evidence for the first
time in our study sample that weight loss, particularly weight that is
lost quickly, may be associated with higher risk of recurrence in breast
cancer survivors.
5|CONCLUSIONS
In conclusion, our results are the first within a European setting to
underline the importance of weight maintenance for up to 5 years
after diagnosis in long-term breast cancer survivors for the benefit of
survival and prognosis after diagnosis. Survivors should strive to avoid
large changes in weight, especially in a short period of time, from diag-
nosis onwards to maintain health and prolong life.
ACKNOWLEDGEMENTS
We are grateful to all the MARIE study participants. We thank U.
Eilber for her most valuable technical assistance and data manage-
ment. We express our deep gratitude to Dieter Flesch-Janys for his
invaluable contributions to the MARIE projects throughout all these
years as PI of the Hamburg study region and close collaborator. This
work was supported by the Deutsche Krebshilfe e.V. (grant numbers
70-2892-BR I, 108253 and 108419, 70110826 and 70112562).
CONFLICT OF INTEREST
Authors declare that they have no conflicts of interest.
DATA AVAILABILITY STATEMENT
The data can be made available upon reasonable request to the princi-
pal investigator(s) (J. C.-C. and H. B.).
ETHICS STATEMENT
The study was approved by the ethics committees of the University
of Heidelberg, the State of Rhineland-Palatinate and the Hamburg
Medical Council, and was conducted in accordance with the
JUNG ET AL.9
Declaration of Helsinki. All study participants provided informed writ-
ten consent.
ORCID
Audrey Y. Jung https://orcid.org/0000-0003-0875-6673
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How to cite this article: Jung AY, Hüsing A, Behrens S, et al.
Postdiagnosis weight change is associated with poorer survival
in breast cancer survivors: A prospective population-based
patient cohort study. Int. J. Cancer. 2020;110. https://doi.
org/10.1002/ijc.33181
10 JUNG ET AL.
... A few previous studies have examined the association, but the results were extremely inconsistent. For instance, some studies have showed that weight loss after diagnosis was associated with a worse survival [8][9][10][11][12][13][14], while others have revealed inverse associations [15,16], or null findings [17,18]. These inconsistencies may be attributed to different timing of the weight measurements; in each of the studies, weight was measured only at one point in time after diagnosis. ...
... In contrast, weight change at 2 years or over 2 years after diagnosis might better represent long-term weight change, which was closely associated with breast cancer prognosis. We further found that the association between weight loss and disease progression was more pronounced in postmenopausal women than pre-menopausal subjects, which was supported by previous findings: Caan et.al showed that weight loss was related to a higher overall mortality by using the data of 71.7% post-menopausal breast cancer patients [13]; Audrey et.al reported that post-menopausal breast cancer patients who lost weight after diagnosis had a significantly higher risk of all-cause mortality and breast cancerspecific mortality [10]. Circulating inflammatory cytokines increase with age [27], and anti-inflammatory ovarian 17b-estradiol is markedly decreased after menopause [28], indicating that interior milieu of post-menopausal women is more pro-inflammatory than that of pre-menopausal women. ...
Article
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Purpose Results of the associations between weight change after breast cancer diagnosis and prognosis were inconsistent. The modification effects of menopausal status and endocrine therapy on the associations remain poorly understood. Methods A total of 2016 breast cancer patients were recruited between October 2008 and January 2018 and followed up until December 31, 2019 in Guangzhou. Multivariate Cox models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (95% CIs) for progression-free survival (PFS) in association with weight change after diagnosis. Results Weight loss at 2 years (HR = 1.34, 95% CI 0.87–2.06) or more than 2 years (HR = 1.95, 95% CI 1.22–3.10) after diagnosis increased risk of breast cancer progression. The adverse effect of weight loss was significantly more pronounced in post-menopausal than pre-menopausal women, particularly for weight loss at 2 years after diagnosis, with the HRs and 95% CIs of 2.41 (1.25–4.63) and 0.90 (0.49–1.64), respectively. Weight gain tended to reduce the risk of disease progression among patients with endocrine therapy but not for those with non-endocrine therapy; the significant interaction between weight gain at 2 years after diagnosis and endocrine therapy was observed (Pinteraction = 0.042). Conclusion Our finding suggested that weight loss was detrimental to breast cancer prognosis, particularly for post-menopausal women, while weight gain may be a potential beneficial indicator for the patients with endocrine therapy but not for those with non-endocrine therapy.
... We propose several possible explanations for our unexpected null findings. First, the distribution of weight change after breast cancer diagnosis in our sample was inconsistent compared to prior studies [24][25][26][27][28]. In our sample 64.4% of women were weight stable (± 5% of body weight), 16.7% gained more than 5% of body weight and 18.9% lost more than 5% of body weight. ...
... Our findings build on previous evidence from the WHI, where there was similar distribution of weight change among women in the OS cohort to our sample of breast cancer survivors [29]. Prior reports found weight gain frequencies of 40-70% after a breast cancer diagnosis [24][25][26][27], with greater weight gain seen among postmenopausal women [24][25][26], those diagnosed with advanced stage breast cancer [25], and those who received chemotherapy [24,26,28]. In our study, results from post-hoc analyses showed no differences in post-diagnosis weight change were seen among early and advanced stage diagnoses. ...
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PurposeShort and long sleep duration and poor sleep quality are risk factors for weight gain and cancer mortality. The purpose of this study is to investigate the relationship between sleep and weight change among postmenopausal breast cancer survivors.Methods Women participating in the Women’s Health Initiative who were diagnosed with incident breast cancer between year one and year three were included. Self-reported sleep duration was categorized as ≤ 5 h (short), 6 h, 7–8 h (optimal), and ≥ 9 h (long). Self-reported sleep quality was categorized as poor, average, and above average. Post-diagnosis weight change was the difference of weight closest to, but preceding diagnosis, and year 3 weight. We used linear regression to evaluate sleep duration and sleep quality associations with post-diagnosis weight change adjusted for potential confounders.ResultsAmong 1156 participants, 63% were weight stable after diagnosis; average weight gain post cancer diagnosis was 3.2 kg. Six percent of women reported sleeping ≤ 5 h, 26% reported 6 h, 64% reported 7–8 h, and 4% reported ≥ 9 h. There were no differences in adjusted estimates of weight change among participants with short duration (0.37 kg; 95% CI − 0.88, 1.63), or long duration (− 0.56 kg; 95% CI − 2.03, 0.90) compared to optimal duration, nor was there a difference among poor quality (− 0.51 kg; 95% CI − 1.42, 0.41) compared to above average quality.Conclusion Among postmenopausal breast cancer survivors, sleep duration and quality were not associated with weight change after breast cancer diagnosis. Future studies should consider capturing change in adiposity and to expand beyond self-reported sleep.
... We propose several possible explanations for our unexpected null ndings. First, the distribution of weight change after breast cancer diagnosis in our sample was inconsistent compared to prior studies [24][25][26][27][28]. In our sample 64.4% of women were weight stable (±5% of body weight), 16.7% gained more than 5% of body weight and 18.9% lost more than 5% of body weight. ...
... where there was similar distribution of weight change among women in the OS cohort to our sample of breast cancer survivors [29]. Prior reports found weight gain frequencies of 40-70% after a breast cancer diagnosis [24][25][26][27], with greater weight gain seen among postmenopausal women [24][25][26], those diagnosed with advanced stage breast cancer [25], and those who received chemotherapy [24,26,28]. In our study, results from post-hoc analyses showed no differences in post-diagnosis weight change were seen among early and advanced stage diagnoses. ...
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Purpose: Short and long sleep duration and poor sleep quality are risk factors for weight gain and cancer mortality. The purpose of this study is to investigate the relationship between sleep and weight change among postmenopausal breast cancer survivors. Methods: Women participating in the Women’s Health Initiative who were diagnosed with incident breast cancer between year 1 and year 3 were included. Self-reported sleep duration was categorized as ≤5 hours (short), 6 hours, 7-8 hours (optimal), and ≥9 hours (long). Self-reported sleep quality was categorized as poor, average, and above average. Post-diagnosis weight change was the difference of weight closest to, but preceding diagnosis, and year 3 weight. We used linear regression to evaluate sleep duration and sleep quality associations with post-diagnosis weight change adjusted for potential confounders. Results: Among 1,156 participants, 63% were weight stable after diagnosis; average weight gain post cancer diagnosis was 3.2 kg. Six percent of women reported sleeping ≤5 hours, 26% reported 6 hours, 64% reported 7-8 hours, and 4% reported ≥9 hours. There were no differences in adjusted estimates of weight change among participants with short duration (0.37kg; 95%CI -0.88, 1.63), or long duration (-0.56kg; 95% CI -2.03, 0.90) compared to optimal duration, nor was there a difference among poor quality (-0.51kg; 95% CI -1.42, 0.41) compared to above average quality. Conclusion: Among postmenopausal breast cancer survivors, sleep duration and quality were not associated with weight change after breast cancer diagnosis. Future studies should consider capturing change in adiposity and to expand beyond self-reported sleep.
... Breast cancer patients were unique in that they were more likely to gain weight following treatment than lose weight. Jung et al., (2020) documented that, there is an adverse relationship between weight change and prognosis for breast cancer patients and consequently patients likely to gain weight and are in need for nutritional advice on how to avoid gaining weight. This highlights a shift in the relationship between cancer and weight. ...
... Bei Patienten mit einer Brustkrebsdiagnose war die Mortalität bei denjenigen Frauenam geringsten,die überdenNachbeobachtungszeitraum von 5 Jahren ihr Gewicht stabil halten konnten [8]. Bei den Frauen, die entweder 10 % des Ausgangsgewichts zugenommen oder abgenommen hatten, sank die Überlebenswahrscheinlichkeit. ...
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Objectives: Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. Methods: ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD- 10, codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Results: Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. Conclusions: These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.