Icd 10 for Family History of Breast Cancer

1. Inquiry
2. Investigating causal...
3. Investigating causal relations between sleep traits and hazard of breast cancer in women: mendelian randomisation report

Inquiry

Investigating causal relations between sleep traits and hazard of chest cancer in women: mendelian randomisation study

BMJ 2019; 365 doi: https://doi.org/10.1136/bmj.l2327 (Published 26 June 2019) Cite this every bit: BMJ 2019;365:l2327

Linked Editorial

Larks, owls, and breast cancer

1. Rebecca C Richmond , research swain12,
2. Emma L Anderson , research swain12,
3. Hassan Southward Dashti , postdoctoral researcher34,
4. Samuel East Jones , research beau5,
5. Jacqueline M Lane , postdoctoral researcher34,
6. Linn Beate Strand , associate professor6,
7. Ben Brumpton , research fellow67,
8. Martin One thousand Rutter , senior lecturer89,
9. Andrew R Wood , lecturer5,
10. Kurt Straif , scientist10,
11. Caroline L Relton , professor12,
12. Marcus Munafò , professor111,
13. Timothy M Frayling , professor5,
14. Richard M Martin , professor1212,
15. Richa Saxena , professor341314,
16. Michael Northward Weedon , associate professorv,
17. Debbie A Lawlor , professor1212,
18. George Davey Smith , professor1212

1. ^oneMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, Britain
2. ^twoPopulation Health Sciences, Bristol Medical School, Academy of Bristol, Bristol, UK
3. ³Middle for Genomic Medicine, Massachusetts General Infirmary, Harvard Medical School, Boston, MA, USA
4. ⁴Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, United states of america
5. ⁵Genetics of Complex Traits, University of Exeter Medical Schoolhouse, Exeter, Great britain
6. ⁶M.G. Jebsen Centre for Genetic Epidemiology, Department of Public Wellness and Nursing, Faculty of Medicine and Health sciences, Norwegian Academy of Scientific discipline and Technology, NTNU, Trondheim, Kingdom of norway
7. ^viiClinic of Thoracic and Occupational Medicine, St Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
8. ^viiiPartitioning of Endocrinology, Diabetes and Gastroenterology, Schoolhouse of Medical Sciences, Kinesthesia of Biological science, Medicine and Wellness, Academy of Manchester, Manchester, United kingdom
9. ⁹Manchester Diabetes Heart, Manchester University NHS Foundation Trust, Manchester Academic Health Scientific discipline Eye, Manchester, Manchester, UK
10. ¹⁰International Agency for Inquiry on Cancer, Lyon, France
11. ^xiSchool of Experimental Psychology, University of Bristol, Bristol, Britain
12. ¹²National Institute for Health Research (NIHR) Bristol Biomedical Research Heart, Academy Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK
13. ¹³Section of Anaesthesia, Critical Intendance and Pain Medicine, Massachusetts General Infirmary, Boston, MA, Usa
14. ¹⁴Division of Sleep and Circadian Disorders, Brigham and Women'southward Infirmary, Harvard Medical School, Boston, MA, United states of america

1. Correspondence to: R C Richmond, Office BS4, Oakfield House, Oakfield Grove, Bristol BS8 2BN, United kingdom of great britain and northern ireland Rebecca.richmond{at}bristol.ac.uk (or @beckyrichmond90 on Twitter)

• Accustomed 26 April 2019

Abstract

Objective To examine whether slumber traits accept a causal outcome on chance of breast cancer.

Design Mendelian randomisation study.

Setting Britain Biobank prospective cohort study and Breast Cancer Association Consortium (BCAC) case-control genome-broad association report.

Participants 156 848 women in the multivariable regression and one sample mendelian randomisation (MR) analysis in United kingdom Biobank (7784 with a breast cancer diagnosis) and 122 977 breast cancer cases and 105 974 controls from BCAC in the two sample MR assay.

Exposures Cocky reported chronotype (forenoon or evening preference), insomnia symptoms, and sleep duration in multivariable regression, and genetic variants robustly associated with these slumber traits.

Master upshot measure Breast cancer diagnosis.

Results In multivariable regression assay using UK Biobank data on breast cancer incidence, morning preference was inversely associated with breast cancer (take chances ratio 0.95, 95% confidence interval 0.93 to 0.98 per category increment), whereas there was little evidence for an association between sleep elapsing and indisposition symptoms. Using 341 single nucleotide polymorphisms (SNPs) associated with chronotype, 91 SNPs associated with slumber duration, and 57 SNPs associated with insomnia symptoms, one sample MR analysis in UK Biobank provided some supportive evidence for a protective result of morning preference on breast cancer gamble (0.85, 0.70, 1.03 per category increase) but imprecise estimates for sleep duration and insomnia symptoms. 2 sample MR using data from BCAC supported findings for a protective consequence of morning preference (changed variance weighted odds ratio 0.88, 95% confidence interval 0.82 to 0.93 per category increase) and adverse effect of increased sleep duration (1.19, i.02 to 1.39 per hour increase) on breast cancer hazard (both oestrogen receptor positive and oestrogen receptor negative), whereas evidence for insomnia symptoms was inconsistent. Results were largely robust to sensitivity analyses bookkeeping for horizontal pleiotropy.

Conclusions Findings showed consequent evidence for a protective outcome of morning time preference and suggestive evidence for an adverse effect of increased sleep duration on breast cancer risk.

Introduction

In 2007 the World Health Organisation's International Agency for Research on Cancer classified shift work that involves circadian disruption every bit beingness probably carcinogenic to humans.1 Disturbed sleep, exposure to light at nighttime, and exposure to other lifestyle factors have been proposed as possible underlying mechanisms.234 Although much of the literature on breast cancer risk has focused on the potentially adverse effects of dark shift piece of work and exposure to light at night, less investigation has been done into the potential adverse furnishings of slumber disruption and traits such equally chronotype (morning or evening preference), sleep duration, and insomnia.five

In a meta-analysis of 28 studies, potent testify suggested a positive clan betwixt circadian disruption and chest cancer risk (relative risk 1.14, 95% conviction interval 1.08 to 1.21). However, the association with short sleep elapsing (<7 hours a night) in seven contributing studies was much less conclusive (0.96, 0.86 to 1.06), and no dose-response association with sleep deficiency was observed.6 Findings from other meta-analyses have been conflicting, with ii showing no conclusive prove that sleep duration is associated with breast cancer risk78 and one showing testify of an adverse issue of increased sleep elapsing (>vii hours a night).9 Most studies in the meta-analyses, however, have been case-control designs, vulnerable to reverse causation, or cohort studies with a small number of cases. Fewer studies have investigated associations betwixt chronotype and indisposition with breast cancer risk. The Nurses' Health Study cohort of 72 517 women (1834 breast cancer cases) found no strong evidence of an association with chronotype,x and a prospective written report of 33 332 women (862 incident breast cancer cases) in the Nord-Trøndelag Health Study (HUNT) institute no potent evidence of an clan with individual insomnia symptoms, although at that place was evidence of some excess hazard amongst participants with multiple insomnia problems.11 Studies have tended to rely on self report of sleep exposures, significant associations could be biased past measurement error and by balance or unmeasured confounding, making causal inference challenging.

Mendelian randomisation (MR) uses genetic variants that are robustly associated with potentially modifiable take a chance factors to explore causal furnishings on outcomes.121314 This method is less susceptible to measurement error, confounding, and reverse causation than conventional multivariable regression approaches, provided certain assumptions are met. These are that the genetic variants are robustly associated with the exposure of involvement, are not associated with confounders of the exposure-outcome relation, and practise non influence the result through pathways other than the exposure of involvement. Genetic variants robustly associated with chronotype, sleep duration, and indisposition symptoms have recently been identified in big genome-wide association studies (GWAS) with sample sizes of around l 000 to more than i million.151617181920212223 Findings from those GWAS accept confirmed the role of several core circadian genes influencing sleep traits, and identified genetic variants with no previously known circadian role.24 These genetic variants have been used in ii sample MR and provided some prove that longer sleep has a causal event on schizophrenia risk,16 whereas being a "morn person" is causally associated with a reduced risk of schizophrenia and depression,xv and insomnia is causally associated with an increased take chances of type two diabetes, college trunk mass alphabetize (BMI), coronary heart affliction, and several psychiatric traits.1723 In our study we used MR to explore the causal effect of sleep traits on breast cancer risk.

Nosotros used genetic variants robustly associated with chronotype, sleep duration, and insomnia symptoms identified in iii contempo UK Biobank GWAS151617 to investigate whether these sleep traits accept a causal outcome on breast cancer gamble. To exercise this, we performed a one sample MR analysis using data from UK Biobank, from which estimates were compared with conventional observational multivariable regression results in the same study, equally well as a two sample MR analysis using data from the Breast Cancer Association Consortium (BCAC).25 Furthermore, we aimed to assess the extent to which findings were robust to potential pleiotropy and supported by genetic variants associated with accelerometer derived measures of chronotype (sleep midpoint timing of the least active v hours of the day), sleep duration, and sleep fragmentation (number of nocturnal slumber episodes).

Methods

Multivariable regression and one sample MR analysis

Study participants

Nosotros used information on women from the United kingdom of great britain and northern ireland Biobank, which recruited more than 500 000 participants (55% women) out of nine.ii million eligible adults anile between 40 and lxx years in the Great britain who were invited to participate (5.5% response rate).26 The study protocol is bachelor online (www.ukbiobank.ac.uk/wp-content/uploads/2011/11/UK-Biobank-Protocol.pdf) and more details are published elsewhere.27 At recruitment the participants gave informed consent to participate and exist followed-up. Overall, 503 317 participants consented to bring together the study cohort and visited an assessment centre. Data on sleep traits (chronotype, sleep elapsing, and insomnia symptoms), chest cancer status (prevalent and incident cases with up to nine years of follow-up), relevant confounding factors, and genetic variants are available in UK Biobank.

Sleep traits

At baseline cess, conducted in one of 22 UK Biobank cess centres between 2006 and 2010, participants completed a touchscreen questionnaire, which included questions nearly sociodemographic status, lifestyle and environment, early on life and family history, health and medical history, and psychosocial factors. Participants were asked about their chronotype (morning or evening preference), average slumber duration, and insomnia symptoms.

Chronotype (morn or evening preference) was assessed in the question "Do y'all consider yourself to be?" with i of 6 possible answers: "Definitely a 'morning' person," "More a 'morning time' than 'evening' person," "More an 'evening' than a 'morning' person," "Definitely an 'evening' person," "Do not know," or "Prefer non to respond." We derived a five level ordinal variable for chronotype where "Definitely a 'morning' person," "More than a 'morn' than 'evening' person," "More than an 'evening' than a 'morning' person," "Definitely an 'evening' person," "Do non know," or "Prefer not to reply" were coded as 2, one, −1, −2, 0, and missing, respectively. Slumber elapsing was assessed by asking: "About how many hours slumber exercise you get in every 24 hours? (please include naps)." The answer could merely contain integer values. Binary variables for short slumber duration (<7 hours v 7-8 hours) and long sleep duration (>viii hours v 7-8 hours) were also derived. To appraise insomnia symptoms, participants were asked: "Exercise you take trouble falling asleep at night or do you wake upwardly in the center of the night?" with responses "Never/rarely," "Sometimes," "Commonly," or "Prefer not to answer." Those who responded "Prefer not to answer" were set to missing. We derived a three level ordinal variable for insomnia symptoms where "Never/rarely," "Sometimes," and "Ordinarily" were coded as 0, 1, and 2, respectively.

Breast cancer

Participants were followed through tape linkage to the National Health Service cardinal registers, which provide data on cancer registrations, using ICD-9 and ICD-10 (international classification of diseases, ninth and 10th revisions, respectively) codes and cancer deaths. The endpoints in these analyses were starting time diagnosis of invasive breast cancer (ICD-ten C50, ICD-9 174), or chest cancer listed as the underlying crusade of death on the death certificate for women who died during follow-up but were non captured by the cancer registers. We excluded all women with any other cancer diagnosis from the analysis. At the time of analysis, mortality data were bachelor up to February 2016 and cancer registry data up to April 2015. Prevalent cases were divers as women with a diagnosis of breast cancer before engagement of recruitment to the UK Biobank. Incident cases were defined every bit women with a diagnosis of breast cancer or dying from it during follow-upwardly.

Confounders

We considered several factors to be potential confounders of the clan between sleep traits and breast cancer risk: educational activity, trunk mass index (BMI), alcohol intake, smoking, strenuous concrete activity, family history of breast cancer, age at menarche, parity, apply of oral contraceptives, menopause status, and hormone replacement therapy.

BMI was derived from weight and peak measured when participants attended the initial assessment centre, whereas information on other potential confounders was obtained from questionnaire responses completed at baseline (see methods in supplementary file). Additional information extracted from the initial cess visit included eye of initial assessment visit, age at recruitment derived from date of nascence, and date of attending assessment centre. Participants who were employed were also asked whether their current job involved night shifts: never/rarely, sometimes, commonly, or always.

Genetic variants

The full data release in Britain Biobank contains the cohort of successfully genotyped people (n=488 377). A total of 49 979 people were genotyped using the Britain BiLEVE genotyping chip and 438 398 using the UK Biobank axiom genotyping scrap. Pre-imputation quality command, phasing, and imputation of the UK Biobank genetic information have been described elsewhere.28

In the MR assay, we used a total of 341 single nucleotide polymorphisms (SNPs) associated with chronotype,15 91 SNPs associated with continuous sleep duration,xvi and 57 SNPs associated with insomnia symptoms17 (see supplementary file, tables 1-3). These genetic variants were derived from self report and confirmed with objective sleep cess and in independent cohorts.151617

Multivariable regression analysis

We carried out separate multivariable Cox regression between chronotype, insomnia symptoms, and slumber duration and incident breast cancer to investigate prospective associations between these sleep traits and to minimise the likelihood of reverse causality in observational associations. To minimise the role of confounding, we adjusted analyses for historic period, cess eye, and the top 40 genetic principal components (obtained from principal components analysis (PCA) to detect and quantify the genetic structure of populations). A second model additionally adjusted for education, BMI, alcohol intake, smoking, strenuous concrete activity, family history of breast cancer, age at menarche, parity, menopause status, utilize of oral contraceptives, and hormone replacement therapy.

One sample MR assay

For one sample MR, the genetic variants used were extracted genotypes from the UK Biobank imputation dataset (imputed to the Haplotype Reference Consortium reference panel), which performed extensive quality control including exclusion of nearly third caste or closer relatives from a genetic kinship analysis, too as those who were non classified as white British based on questionnaire and PCA29 (see methods in supplementary file). Unweighted allele scores were generated as the total number of slumber trait increasing alleles (morning preference alleles from chronotype) present in the genotype of each participant.

A two stage method was implemented to give a population average causal gamble ratio. The first stage model consisted of a regression of the sleep trait (chronotype, sleep duration, and insomnia symptoms) on the allele score and the 2nd phase model consisted of a Cox regression of breast cancer status on the fitted values from the start stage regression, with adjustment for age at recruitment, assessment centre, 40 genetic principal components, and genotyping bit in both stages.

Sensitivity analyses

To check the proportional hazards assumption, nosotros used Pearson correlations to exam Schoenfeld residuals from both multivariable Cox regression and ane sample MR Cox regression models for an association with follow-up time.

To assess the specificity of our findings to breast cancer, we performed multivariable regression and one sample MR analysis to assess the causal event of the sleep traits on other cancer diagnoses and on all cause mortality.

We too performed MR analysis using just those genetic variants that replicated at Bonferroni significance in a large contained dataset for chronotype15 (242 variants in 23andMe, n=240 098, highlighted in supplementary file, table ane) to evaluate the potential impact of winner's curse (ie, overestimation of genetic effects in the initial study), which can bias causal estimates in MR analysis. Given the relatively small-scale sample size of replication datasets for sleep duration (Accuse Consortium, n=47 180)16 and insomnia (HUNT, due north=62 533),17 few SNPs independently replicated at Bonferroni significance to serve as sufficiently potent instruments for this sensitivity analysis.

To test the MR assumption that genetic variants should not be associated with confounders of the exposure-outcome relation, we investigated associations betwixt the allele scores and potential confounders in Britain Biobank. We and so performed one sample MR analysis adjusted for whatever potential confounders found to be strongly associated with the allele scores (beyond a Bonferroni significance threshold of P<i.39×10⁻³) as a further sensitivity analysis.

Nosotros as well conducted both multivariable regression and one sample MR using all breast cancer cases (incident and prevalent) in a logistic regression analysis in UK Biobank, and performed sensitivity analysis removing participants who reported currently working night shifts (sometimes, usually, or e'er).

2 sample MR assay

We conducted a ii sample MR analysis of sleep traits on breast cancer take chances using female specific estimates of the associations between the genetic instruments and slumber traits identified in the corresponding GWAS151617 in United kingdom of great britain and northern ireland Biobank (sample 1) (see supplementary file, tables ane-3), and estimates of the associations between the genetic instruments and breast cancer from a large calibration GWAS of chest cancer (BCAC) (sample ii).

GWAS of chronotype (five level ordinal variable), sleep duration (continuous variable), and insomnia symptoms (three level ordinal variable) were performed among women of European ancestry (n=241 350 - 245 767) in the United kingdom Biobank. This was done using BOLT-LMM30 linear mixed models and an additive genetic model adjusted for historic period, sex, 10 genetic principal components, genotyping array, and genetic correlation matrix, as was washed previously.151617

The GWAS of breast cancer involved 122 977 women with the disease (oestrogen receptor positive and oestrogen receptor negative combined) and 105 974 controls of European ancestry from BCAC.25 BCAC summary information were based on imputation to the 1000 Genomes Project Phase 3 reference console. To explore potential heterogeneity by chest cancer subtype, nosotros also investigated the causal effect of the sleep traits on breast cancer stratified by oestrogen receptor status, using genetic clan information from 69 501 oestrogen positive and 21 468 oestrogen negative cases within BCAC.25

Two sample MR analyses were conducted using "TwoSampleMR," an R package for such analyses,31 which was first used to extract the SNPs being used to instrument the exposure (here the sleep trait of interest) from the outcome GWAS (here breast cancer in BCAC). If a SNP was unavailable in the chest cancer GWAS summary statistics, we identified proxy SNPs with a minimum linkage disequilibrium (LD) r²=0.viii. We then performed harmonisation of the management of furnishings between exposure and outcome associations, where palindromic SNPs were aligned when minor allele frequencies were less than 0.iii, or they were otherwise excluded. We then used an inverse variance weighted method to meta-analyse the SNP specific Wald estimates (SNP outcome gauge divided by SNP exposure judge) using random furnishings, to obtain an gauge for the causal effect of the sleep trait on breast cancer adventure.

Sensitivity analyses

The changed variance weighted random effects method will return an unbiased judge in the absenteeism of horizontal pleiotropy, or when horizontal pleiotropy is balanced.32 To business relationship for directional pleiotropy, nosotros compared results with 3 other MR methods, which each makes different assumptions about this: MR Egger,33 weighted median,34 and weighted mode,35 and therefore a consistent upshot beyond multiple methods strengthens causal evidence.

To further discover and right obtained causal estimates for potential violation of the MR assumptions,32 we performed RadialMR36 in the two sample analyses to place outliers with the most weight in the MR assay and the largest contribution to Cochran's Q statistic for heterogeneity, which may then be removed and the data reanalysed. Radial MR analysis was conducted using modified second guild weights and an α level of 0.05 divided by the number of SNPs existence used to musical instrument the exposure. For the outliers identified, nosotros also assessed their potential pleiotropic part by performing a phenome-wide association study (PheWAS) approach37 to investigate the associations between the SNPs and all bachelor traits in the MR-Base of operations PheWAS database (http://phewas.mrbase.org/).

To evaluate the potential bear upon of winner's curse, nosotros performed ii sample MR analysis using 242 genetic variants that replicated at Bonferroni significance in a large independent dataset for chronotype15 (23andMe, n=240 098, highlighted in supplementary file, table one). Nosotros besides carried out further MR analysis using robust adjusted profile scores, which provide an unbiased causal estimate in the presence of weak instruments.38

Given potential not-linear associations between sleep elapsing and chest cancer risk,ix we also used data on 27 SNPs associated with short sleep (<7 hours 5 seven-8 hours) and eight SNPs associated with long slumber (>8 hours v seven-8 hours)16 in two sample MR analysis (see supplementary file, tables 4 and v). Causal outcome estimates (ie, odds ratios for breast cancer) were rescaled to be interpreted for each doubling of genetic liability for short or long slumber, as recommended elsewhere.39

Finally, nosotros performed two sample MR using genetic variants robustly associated with accelerometer derived sleep traits in Britain Biobank, to be compared with causal estimates obtained using genetic variants associated with self reported traits. For this we used genetic variants identified in GWAS in relation to iii accelerometer based measures: timing of the to the lowest degree active five hours (L5 timing) (half-dozen SNPs), nocturnal slumber duration (11 SNPs), and number of nocturnal sleep episodes (21 SNPs) in upwardly to 85 205 participants, every bit previously described40 (see supplementary file, tables vi-eight). Also see the methods section in the supplementary file for more details about how accelerometer sleep traits were derived. Effect estimates represented an hour earlier L5 timing (correlated positively with and to be compared with the self reported chronotype measure of increased morning preference), an hr increase of nocturnal sleep duration (to be compared with self reported slumber elapsing), and a unit of measurement increase in the number of nocturnal slumber episodes (to be compared with self reported indisposition symptoms).

All analyses were conducted using Stata (version 15) and R (version 3.4.1).

Patient and public involvement

The electric current enquiry was not informed past patient and public involvement because it used secondary data. However, future inquiry following on from our findings should exist guided by patient and public opinions.

No patients were involved in setting the research question or the event measures, nor were they involved in developing plans for design or implementation of the study. No patients were asked to advise on interpretation or writing upwardly of results. The results of the research volition exist disseminated to study participants on asking, and to stakeholders and the broader public as relevant.

Results

Baseline characteristics

Of the 180 216 women in the United kingdom of great britain and northern ireland Biobank who had been successfully genotyped and passed the genetic quality command, and later excluding 23 368 who had a diagnosis of other types of cancer, 7784/156 868 (iv.nine%) had received an exclusive diagnosis of breast cancer. Of these, 5036/156 868 (3.2%) were defined equally prevalent cases and 2740/156 868 (1.7%) developed incident breast cancer over a median follow-up of 2.98 years.

Women with a chest cancer diagnosis (prevalent or incident) were more than probable to be older, have a higher BMI, be less physically agile, take had an before historic period at menarche, be postmenopausal, have ever used hormone replacement therapy, accept a family history of breast cancer, and be nulliparous. They were less likely to be never smokers, piece of work night shifts, and take ever used oral contraceptives (tabular array 1) compared with women without a chest cancer diagnosis. No strong difference was found in educational activity level between women with and without breast cancer, in line with previous findings,41 too as no clear difference in relation to booze intake.

Table 1

Baseline characteristics of women who had and had not developed breast cancer by appointment of censoring in U.k. Biobank. Values are numbers (percentages) unless stated otherwise

Multivariable assay

In multivariable Cox regression analysis, an changed clan was observed between morning preference and risk of breast cancer, which remained similar in the fully adjusted model (hazard ratio 0.95, 95% confidence interval 0.93 to 0.98 per category increment) but at that place was no clear association betwixt sleep duration and insomnia symptoms with take a chance of breast cancer (table 2). The proportional hazards assumption held for all the multivariable Cox regression analyses (run into supplementary file, table 9). The inverse association with morning preference was non observed for other cancer diagnoses (take a chance ratio one.00, 95% confidence interval 0.99 to 1.02 per category increase) (see supplementary file, tabular array 10), although information technology was evident in multivariable Cox regression analysis of all cause mortality (0.95, 0.93 to 0.97 per category increase) (run into supplementary file, table 11). Associations with slumber duration and insomnia were also observed in relation to these other outcomes (come across supplementary file, tables x and 11).

Table 2

Multivariable and mendelian randomisation Cox regression assay for adventure of breast cancer associated with sleep traits

When incident and prevalent cases were combined and associations investigated in a logistic regression framework, testify was consequent for an inverse association betwixt morning preference and breast cancer risk (odds ratio 0.96, 95% conviction interval 0.94 to 0.98), as well a positive association between both sleep duration (1.02, one.00 to ane.05 per hour increase) and indisposition symptoms (1.11, i.07 to 1.xv per category increment) with chest cancer take chances, potentially reflecting reverse causation (see supplementary file, table 12). Cox regression estimates were like after excluding participants who reported working night shifts (meet supplementary file, table thirteen).

1 sample MR analysis

Among United kingdom of great britain and northern ireland Biobank female person participants, allele scores explained 2.iii% of the variance in chronotype, 0.7% of the variance in sleep elapsing, and 0.4% of the variance in insomnia symptoms (table 3). Some evidence suggested a protective effect of morning preference on breast cancer hazard (adventure ratio 0.85, 95% confidence interval 0.70 to 1.03 per category increase) and weaker bear witness for an adverse result of increased slumber duration (1.06, 0.70 to 1.59 per 60 minutes increase) and insomnia symptoms (ane.37, 0.59 to three.20 per category increase) (table two), albeit imprecisely estimated (wide conviction intervals). The proportional hazards assumption held for all the one sample MR Cox regression analyses (meet supplementary file, table 9). The protective result of morning preference was not supported by MR assay for other cancer diagnoses (1.05, 0.93 to one.17 per category increment) (see supplementary file, table 10) or all crusade mortality (1.fifteen, 0.97 to ane.35 per category increase) (see supplementary file, table 11), although prove suggested an agin result of indisposition on risk of other cancers (1.55, 0.94 to 2.55 per category increase) (see supplementary file, table 10).

Table iii

Allele scores for sleep traits in United kingdom Biobank

When using only the genetic variants that replicated in an independent dataset (242 variants in 23andMe) for chronotype, estimates of event on chest cancer gamble were like (0.89, 0.71 to i.12 per category increase); although with wider confidence intervals given that the replicated variants explained less of the variance in chronotype (i.6%) (see supplementary file, tabular array 14).

Although about of the confounding factors were not associated with the sleep trait allele scores in Great britain Biobank, afterward accounting for multiple testing the chronotype allele score was associated with parity and vigorous activeness; the sleep duration allele score was associated with age at menarche and BMI, and the insomnia allele score was associated with using hormone replacement therapy and historic period at menarche (see supplementary file, tabular array 15). Further sensitivity assay was undertaken adjusting for these potential confounders in the one sample MR analysis, and effect estimates were consequent (run into supplementary file, tabular array 16).

Findings of a protective effect of morning preference were supported in assay of all breast cancer cases (incident and prevalent) in logistic regression. Evidence for sleep duration and insomnia symptoms was weaker, although both had upshot estimates in the positive direction (see supplementary file, table 12). In analyses excluding women who reported working night shifts, findings were besides consistent with the main results from Cox regression (see supplementary file, table thirteen).

Two sample MR analysis

After harmonisation of the SNP furnishings in the two summary datasets (UK Biobank and BCAC), 305 SNPs were used to instrument chronotype, 82 SNPs were used to instrument sleep duration, and 50 SNPs were used to instrument insomnia symptoms. This included three proxy SNPs (r²≥0.8) for chronotype (rs376957969 for rs111867612, rs1871516 for rs4550782, and rs6583802 for rs61875203). Two sample MR supported the findings of a protective event of morning preference (inverse variance weighted odds ratio 0.88, 95% confidence interval 0.82 to 0.93 per category increase) (meet supplementary file, tabular array 17 and effigy 1) also as an adverse effect of increased slumber duration (ane.19, one.02, 1.39 per 60 minutes increase) on chest cancer gamble (see supplementary file, table 17 and figure two). Little testify for a causal effect of insomnia symptoms was observed (0.93, 0.49, 1.76 per category increase) (come across supplementary file, tabular array 17 and effigy three). Figure 1 shows the inverse variance weighted estimates for chronotype, sleep duration, and insomnia symptoms from two sample MR compared with multivariable and i sample MR approaches in UK Biobank. Findings were similar when stratified by oestrogen receptor positive and oestrogen receptor negative chest cancer (run into supplementary file, table 17).

Fig 1

Forest plot of multivariable and mendelian randomisation (MR) estimates for association between sleep traits and breast cancer adventure. Odds ratios are per category increase in chronotype (from definite evening, intermediate evening, neither, intermediate morning, definite morning), per hour increase in sleep duration, and per category increase in insomnia risk (from no, some, and frequent insomnia symptoms). Odds ratios rather than hazard ratios for incident breast cancer are shown for multivariable and 1 sample MR analysis to compare estimates across methods

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Effect estimates were broadly consistent between the changed variance weighted method and the pleiotropy robust methods applied (MR Egger, weighted median, and weighted fashion) in two sample MR (see supplementary file, table 17 and figures 1-3). Furthermore, the MR Egger test of directional pleiotropy was consistent with the nothing for all analyses (see supplementary file, tabular array xviii).

Evidence for heterogeneity in causal effects for almost of the models (run into supplementary file, table 19) could still point potential violations of the MR assumptions. We used radial plots to aid in the detection of outlying variants. Radial MR assay identified six outliers for chronotype, three for sleep elapsing, and two for insomnia symptoms in both changed variance weighted and MR Egger (encounter supplementary file, table 20 and figures 4-vi). The pleiotropic effect of many of these outliers was indicated in a PheWAS of the SNPs on all existing traits in the MR-Base database (see supplementary file, effigy 7). With removal of outliers, inverse variance weighted and MR Egger effect estimates were largely unchanged (see supplementary file, table 21).

Outcome estimates for the causal issue of chronotype on breast cancer risk were consistent when using the 242 genetic variants associated with chronotype, which replicated at Bonferroni significance in 23andMe,15 indicating that winner's expletive is unlikely to have substantially biased effect estimates (see supplementary file, table 22). MR robust adjusted profile scores, which provide unbiased estimates in the presence of weak instruments, provided like causal estimates to the primary MR analysis (see supplementary file, table 23).

Findings of an adverse effect of increased sleep duration on breast cancer risk were supported using genetic variants specifically associated with short and long sleep elapsing, with bear witness for a protective effect of curt sleep elapsing on breast cancer (changed variance weighted odds ratio 0.92, 95% conviction interval 0.86 to 0.99 per doubling of genetic liability for short sleep elapsing) and adverse effect of long slumber duration (1.24, 0.96 to 1.60 per doubling of genetic liability for long slumber duration) (see supplementary file, table 24).

Finally, we performed two sample MR using genetic variants robustly associated with accelerometer derived sleep traits in United kingdom Biobank, to be compared with causal estimates obtained using genetic variants associated with cocky reported traits. Supplementary table 25 shows the genetic correlations betwixt these traits. Using genetic variants robustly associated with accelerometer derived sleep traits in UK Biobank, we found no clear show of clan with L5 timing measured objectively (1.04, 0.78 to ane.38 per hour decrease) (meet supplementary file, tabular array 26 and effigy 8). Nevertheless, an adverse effect of increased sleep duration was supported using estimates from objectively measured sleep duration (1.16, 1.02 to ane.32 per 60 minutes increase) (see supplementary file, table 26 and figure ix) and there was some prove for a causal effect of increased fragmentation on chest cancer adventure (i.fourteen, one.00 to 1.30 per sleep episode) (see supplementary file, table 26 and figure ten). Given the limited availability of SNPs being used to proxy for L5 timing to evaluate its causal role on breast cancer, and given the strong association found between chronotype and L5 timing (encounter supplementary file, table 25),xv we performed a further MR assay using the 305 chronotype variants with SNP exposure effect estimates taken from the GWAS of L5 timing, to besides evaluate the causal result of L5 timing (see supplementary file, table 27). This assay revealed some evidence for an association with L5 timing and risk of chest cancer in the inverse variance weighted analysis (0.86, 0.78 to 0.95), although this judge was non consistent across the pleiotropy robust methods, which were more consistent with the null.

Discussion

Mendelian randomisation (MR) uses genetic variation to investigate causal relations betwixt potentially modifiable hazard factors and health outcomes. In this report nosotros compared observational estimates from multivariable regression with those from MR analyses to brand inferences about the likely causal effects of three sleep traits on chest cancer risk.

In multivariable regression analysis using data on chest cancer incidence in the UK Biobank study, morning preference was inversely associated with chest cancer, whereas in that location was little prove for an association with sleep duration and insomnia. Using genetic variants associated with chronotype, sleep elapsing, and insomnia symptoms, one sample MR assay in Britain Biobank provided some bear witness for a protective effect of morning preference but imprecise estimates for sleep elapsing and insomnia. Findings for a protective effect of morning preference and agin upshot of increased sleep duration on breast cancer (both oestrogen receptor positive and oestrogen receptor negative) were supported by two sample MR using data from the Chest Cancer Association Consortium (BCAC), whereas there was inconsistent evidence for insomnia symptoms. Results were largely robust to sensitivity analyses accounting for horizontal pleiotropy.

Comparison with other studies

Previous studies have establish an enrichment of circadian pathway genetic variants in breast cancer.2542 Even so, these studies did not directly implicate modifiable sleep traits by which chance of breast cancer could be minimised and did not effort to carve up the effects of the genetic variants on breast cancer risk through cyclic disruption from pleiotropic pathways.

Findings of an adverse effect of evening preference on breast cancer run a risk in all analyses performed go some way to supporting hypotheses around carcinogenic light-at-night 4 and findings of increased risk among nighttime shift workers who might be exposed to artificial light at night.1 In particular, the specificity of the causal issue of chronotype on breast cancer, which was non observed in relation to other cancers or all cause mortality, is consequent with the hormonal mechanisms implicated in the light-at-night hypothesis. However, findings when using an objective measure of chronotype (the least agile five hours (L5 timing)) did not reveal the same adverse effect. Although this terminal analysis might exist limited by the number and strength of the genetic variants used to instrument L5 timing, the lack of consistency in estimates draws to question the mechanisms by which forenoon or evening preference (rather than actual activity) influences breast cancer risk. Further assay using the single nucleotide polymorphisms (SNPs) identified in relation to chronotype as instruments for L5 timing were consequent with a protective result of morning time preference, suggesting a protective effect of activity too equally reported preference, but as the pleiotropy robust tests were not consistent, more piece of work is needed to distinguish the causal effect of morning preference from activity—for example, with the employ of multivariable MR methods.43

Bear witness for an agin issue of increased sleep duration on breast cancer gamble contrasts with the observational findings in UK Biobank too equally much of the literature on circadian disruption and breast cancer risk,half dozen and unlike our findings for chronotype are not aligned with the light at night hypothesis. Notwithstanding, recent studies implicate longer sleep duration as a risk factor for breast cancer.9 Given previous reports of a J-shaped relation betwixt sleep duration and breast cancer hazard,9 besides as investigating sleep duration equally a continuous variable, nosotros likewise investigated the causal effects of both brusque and long sleep duration to investigate not-linear effects. In line with our principal findings, we constitute evidence for a protective effect of short sleep duration and adverse effect of long sleep duration on breast cancer hazard. Furthermore, using genetic variants associated with accelerometer derived nocturnal sleep duration, we establish evidence for an agin consequence of sleep elapsing with a similar magnitude of issue.

Overall, we plant inconsistent evidence nigh the causal effect of insomnia symptoms on chest cancer hazard in multivariable and MR analyses. A previous written report of incident breast cancer in the Nord-Trøndelag Health Study (HUNT) revealed no strong evidence of an clan with individual insomnia symptoms,11 although people with multiple indisposition problems were found to be at increased take chances. In our analysis, indisposition was defined based on self report of either difficulty initiating slumber or waking in the night. Further work is therefore required to investigate individual symptoms of insomnia on breast cancer chance, and the potential cumulative event. Interestingly, MR analysis provided some evidence for adverse causal effect of accelerometer derived number of nocturnal slumber episodes on breast cancer risk.

Strengths and limitations of this study

Key strengths of the study are the integration of multiple approaches to assess the causal outcome of slumber traits on breast cancer, the inclusion of data from two large epidemiological resources—UK Biobank and BCAC—equally well every bit use of data derived from both self reported and objectively assessed measures of sleep. Furthermore, for MR assay nosotros used the largest number of SNPs identified in the genome-wide association studies (GWAS) literature, with full summary statistics available to obtain strong genetic instruments for MR analysis and to explore potential pleiotropic pathways.

The approaches of multivariable Cox regression of incident cases, multivariable logistic regression of prevalent and incident cases, one and two sample MR, each have different strengths and limitations in terms of key sources of bias (see supplementary file, table 28). In multivariable assay, attempts were made to mitigate key sources of bias, including confounding and reverse causation, with the utilise of multivariable Cox regression assay of incident cases of breast cancer and aligning for several hypothesised confounders. Nonetheless, rest or unmeasured confounding, selection bias, and measurement fault could too have distorted effect estimates. We used MR assay to minimise the likelihood of bias due to measurement error, misreckoning, and reverse causation. In addition, we conducted a serial of sensitivity analyses to assess the core assumptions that the genetic instruments are strongly associated with the exposures of interest, are not influenced by confounding factors, and exercise non directly influence the outcome other than through the exposure.

1 limitation of this report related to the cocky reported measures used in multivariable regression analyses and used to identify genetic variants for MR analysis. In particular, the measure of sleep duration might capture time spent napping and the any insomnia variable is actually a measure of insomnia symptoms and not necessarily clinical insomnia. However, both these measures have been validated with the use of objective measures from accelerometer data in the UK Biobank and concordance is good, especially for the effects of the genetic variants identified.151617

Another limitation relates to the selection of participants. Analysis in the two large epidemiological studies included hither (UK Biobank and BCAC) was restricted to women of European beginnings. Farther piece of work is required to investigate whether these findings translate to women in other beginnings groups. Although the Uk Biobank represents a large and well characterised epidemiological resource, information technology is not representative of the U.k. population attributable to low participation.27 As well as influencing the generalisability of findings, option into the study can lead to biased estimates of clan through "collider bias."44 To minimise the influence of this, nosotros besides used genetic data from a large instance-command written report of breast cancer (BCAC), and nosotros compared MR upshot estimates across these datasets.

In all MR analyses, SNP exposure estimates were obtained from the United kingdom Biobank as this has formed a major component of the GWAS of slumber traits conducted to appointment.151617202123 This could lead to winner'southward curse, when the magnitude of the event sizes for genetic variants identified within a discovery sample are likely to exist larger than in the overall population. In a one sample MR analysis, the affect of winner's expletive of the SNP exposure clan tin bias causal estimates towards the confounded observational guess, whereas in 2 sample MR, winner's curse can outcome in bias of the causal estimate towards the null. To minimise the impact of winner's curse in one sample MR assay we derived an additional allele score for chronotype composed of SNPs that replicated beyond a Bonferroni correction threshold in an independent study (23andMe).15 Similarly, for two sample MR assay, we used SNP exposure estimates from this replication assay in sensitivity analyses, and findings were consistent with the master analysis (meet supplementary file, tables fourteen and 22).

Nosotros were unable to apply the same approach to investigate the bear on of winner's curse in the sleep elapsing and insomnia assay owing to the relatively small-scale sample size of the replication datasets in those studies, pregnant genetic associations could exist imprecise. Although we are aware of a large GWAS for indisposition that was conducted using data from both UK Biobank and 23andMe, total summary data for the top SNPs in the replication assay are not freely bachelor.23 We used unweighted allele scores to minimise the contribution of potential weak instruments in the one sample MR analysis. We likewise applied a robust adjusted profile score method in the two sample MR analysis, which provides unbiased estimates in the presence of weak instruments, and this revealed similar causal estimates for chronotype, sleep duration, and insomnia every bit in the master analysis.

Although associations between the allele scores and confounders in Uk Biobank imply violation of the MR supposition that genetic variants should not exist associated with confounding factors, there are several explanations for these findings. Previous MR studies have identified causal furnishings of slumber traits on reproductive traits, body mass index, and activeness levels,15161723 suggesting that these factors might be mediators of the association betwixt slumber traits and chest cancer rather than confounders. Furthermore, some of the genetic variants associated with chronotype and indisposition take been plant to exist adiposity related loci,1516 implying potential pleiotropic pathways. All the same, we as well practical a series of pleiotropy robust MR methods and outlier detection to rigorously explore the possibility that findings of a causal effect of chronotype and sleep duration were not biased every bit a outcome of pleiotropy.

As well as attempting to mitigate key sources of bias for each epidemiological approach applied, nosotros too assessed the consistency in estimates between the approaches to provide the best inference about the causal effect of sleep traits on breast cancer. This is aligned with the practice of triangulation, which aims to obtain more reliable answers to research questions through the integration of results from different approaches, where each arroyo has different sources of potential bias that are unrelated to each other.4546 We as well compared estimates based on cocky reported slumber with the use of genetic variants associated with accelerometer derived measures of sleep,40 although we did not use female specific SNP estimates hither given the smaller number of participants in United kingdom of great britain and northern ireland Biobank with these data.

Implications of findings

Findings of a protective upshot of morning preference on breast cancer adventure add to other evidence from MR supporting a possible beneficial effect of morning preference on decreased gamble of schizophrenia and depression.15 Notwithstanding, whether it is the actual behaviour that poses the health risk or the preference for morn versus evening requires further evaluation. Further work is as well required to investigate the touch of cyclic misalignment, which can be determined by genetic risk, cocky reported chronotype, and objectively measured L5 timing. In addition, suggestive testify for a causal effect of increased sleep elapsing on breast cancer gamble should be investigated further.

Conclusions

In this study, both multivariable regression and MR analysis were used to provide potent show for a causal outcome of chronotype on chest cancer risk. Furthermore, some prove suggested a causal outcome of sleep elapsing on risk of breast cancer, although findings for these traits were less consequent across the dissimilar methods applied. However, the biological role of many of the genetic variants used to instrument these traits in MR and mechanistic pathways underlying the observed effects are not well understood. Previously reported pathways between sleep disruption and mammary oncogenesis include immunological, molecular, cellular, neuroendocrine, and metabolic processes.five Farther work to uncover these possible mediating processes is required. However, these findings take potential implications for influencing sleep habits of the full general population to improve wellness.

What is already known on this topic

• The Earth Health Arrangement'southward International Agency for Inquiry on Cancer classifies shift piece of work involving circadian disruption as probably carcinogenic to humans

• Much of the literature on breast cancer risk has focused on the potentially adverse effects of night shift work and exposure to light at night, and less into the potential agin furnishings of traits such equally chronotype (morning or evening preference), sleep elapsing, and insomnia

• Genetic variants robustly associated with chronotype, sleep duration, and indisposition symptoms have recently been identified in large genome-wide association studies

What this report adds

• This study found consistent evidence for a protective effect of morning preference and suggestive evidence for an agin consequence of increased slumber duration on chest cancer risk

• The bear witness for insomnia symptoms was inconclusive

• These findings take potential implications for influencing slumber habits of the general population to improve health

Acknowledgments

This research was conducted using the UK Biobank Resource under application numbers 9072, 6818, 15825, and 16391. We thank the participants and researchers from the Great britain Biobank who contributed or collected data; Ruth Mitchell, Gibran Hemani, Tom Dudding, and Lavinia Paternoster for conducting the quality control filtering of Britain Biobank data; and Wes Spiller, Jie Zheng, Gibran Hemani, Philip Haycock, and Kaitlin Wade for aid with data acquisition and statistical assay. This study was fabricated possible with the financial back up of Jonathan de Pass and Georgina de Pass.

Footnotes

• Contributors: RCR conceived the study and conducted the main analysis. HSD, SEJ, and JML conducted the female specific genome-wide association studies and assisted with sensitivity analyses. RCR, ELA, and GDS drafted the initial manuscript. All authors assisted with interpretation, commended on drafts of the manuscript, and approved the final version. RCR is the guarantor and attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

• Funding: The breast cancer genome-wide clan analyses were supported by the Authorities of Canada through Genome Canada and the Canadian Institutes of Wellness Research, the Ministère de l'Économie, de la Science et de l'Innovation du Québec through Genome Québec and grant PSR-SIIRI-701, the National Institutes of Health (U19 CA148065, X01HG007492), Cancer Research UK (C1287/A10118, C1287/A16563, C1287/A10710), and the Eu (HEALTH-F2-2009-223175 and H2020 633784 and 634935). All studies and funders are listed in Michailidou et al.25 RCR, ELA, BMB, CLR, RMM, MM, DAL, and GDS are members of the MRC Integrative Epidemiology Unit at the University of Bristol funded past the Medical Research Quango (grant Nos MM_UU_00011/one, MC_UU_00011/2, MC_UU_00011/5, MC_UU_00011/vi, and MC_UU_00011/seven). RCR is a de Pass VC research fellow at the University of Bristol. This written report was supported by the NIHR Biomedical Enquiry Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol. The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, National Institute for Wellness Research, or Section of Wellness and Social Care. This work was also supported by Cancer Research UK (grant No C18281/A19169) and the Economic and Social Research Council (grant No ES/N000498/1). SEJ is funded by the Medical Research Council (grant No MR/M005070/1). TMF is supported by the European Inquiry Council (grant No 323195:GLUCOSEGENES-FP7-IDEAS-ERC). MNW is supported by the Wellcome Trust Institutional Strategic Back up Honour (grant No WT097835MF).

• Competing interests: All authors take completed the ICMJE uniform disclosure form at http://www.icmje.org/coi_disclosure.pdf. MKR reports receiving enquiry funding from Novo Nordisk, consultancy fees from Novo Nordisk and Roche Diabetes Intendance, and modest owning of shares in GlaxoSmithKline, outside the submitted piece of work. DAL reports receiving inquiry support from Medtronic and Roche Diagnostics for research exterior the submitted piece of work. All other authors declare no support from any organisation for the submitted work, no fiscal relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.

• Ethical blessing: Britain Biobank has received ethical approving from the Britain National Health Service's National Enquiry Ethics Service (ref 11/ NW/0382).

• Data sharing: Scripts for the two sample mendelian randomisation analysis are available on GitHub at: https://github.com/rcrichmond/sleep_breastcancer_mr/. For statistical code relating to the individual level data analysis in Uk Biobank, please contact the corresponding author at rebecca.richmond@bristol.ac.uk.

• Transparency: The lead author (RCR) affirms that this manuscript is an honest, accurate, and transparent account of the written report being reported; that no important aspects of the study have been omitted; and that whatever discrepancies from the study as planned (and, if relevant, registered) have been explained.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC By 4.0) license, which permits others to distribute, remix, adapt and build upon this piece of work, for commercial use, provided the original work is properly cited. Run across: http://creativecommons.org/licenses/past/four.0/.

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Source: https://www.bmj.com/content/365/bmj.l2327

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