Methods - Cambridge University Press
Supplementary Information TOC \o "1-5" \h \z \u Methods PAGEREF _Toc8669834 \h 4Exposure Definitions PAGEREF _Toc8669835 \h 4Covariate Measures PAGEREF _Toc8669836 \h 4Sociodemographic measures PAGEREF _Toc8669837 \h 4Local environment PAGEREF _Toc8669838 \h 4Lifestyle and physical measures PAGEREF _Toc8669839 \h 4Medical and family history PAGEREF _Toc8669840 \h 4Mental health and psychotropic medication PAGEREF _Toc8669841 \h 5Table 1Psychiatric and neurological diagnoses in hospital records PAGEREF _Toc8669842 \h 6Table 2Self-reported psychiatric and neurological diagnoses PAGEREF _Toc8669843 \h 8Table 3Self-reported psychotropic medications from UK Biobank data field 20003 PAGEREF _Toc8669844 \h 9Genome-wide polygenic scores PAGEREF _Toc8669845 \h 11Statistical Analysis PAGEREF _Toc8669846 \h 12Graphical Models PAGEREF _Toc8669847 \h 12Construction of the directed acyclic graphs PAGEREF _Toc8669848 \h 12Figure 1Original DAG used in the mania/BD analyses PAGEREF _Toc8669849 \h 13Table 4DAG constructs PAGEREF _Toc8669850 \h 14Shared ancestors of exposure and outcome PAGEREF _Toc8669851 \h 15Intermediates between exposure and outcome PAGEREF _Toc8669852 \h 16Other ancestors of outcome PAGEREF _Toc8669853 \h 17Testing the fit of the directed acyclic graphs PAGEREF _Toc8669854 \h 17Total Effects PAGEREF _Toc8669855 \h 17Mediation Analyses PAGEREF _Toc8669856 \h 18Sensitivity Analyses PAGEREF _Toc8669857 \h 18Conditional Exchangeability PAGEREF _Toc8669858 \h 18Missing Data PAGEREF _Toc8669859 \h 18Exposure Misclassification PAGEREF _Toc8669860 \h 18Equivalent Models PAGEREF _Toc8669861 \h 18Results PAGEREF _Toc8669862 \h 19Cognitive Impairment in Mania/BD PAGEREF _Toc8669863 \h 19Characteristics of the Sample PAGEREF _Toc8669864 \h 19Figure 2Mania/bipolar disorder analysis sample flowchart PAGEREF _Toc8669865 \h 19Table 5Summary of covariates in the mania/bipolar and comparison groups PAGEREF _Toc8669866 \h 21Evaluation of the Graphical Model PAGEREF _Toc8669867 \h 25Total Effects PAGEREF _Toc8669868 \h 25Figure 3Final DAG used in the mania/BD analyses PAGEREF _Toc8669869 \h 26Table 6Summary of covariates in matched mania/bipolar and comparison groups PAGEREF _Toc8669870 \h 27Figure 4Total effect of mania/bipolar disorder on prospective memory PAGEREF _Toc8669871 \h 28Figure 5Total effect of mania/bipolar disorder on reasoning PAGEREF _Toc8669872 \h 29Figure 6Total effect of mania/bipolar disorder on reaction time PAGEREF _Toc8669873 \h 30Figure 7Total effect of mania/bipolar disorder on numeric memory PAGEREF _Toc8669874 \h 31Mediation Analyses PAGEREF _Toc8669875 \h 32Table 7Tests of interactions between exposure and mediators in the mania/BD analyses PAGEREF _Toc8669876 \h 32Table 8Mediation of the effect of mania/bipolar disorder on cognitive outcome via cardiometabolic disease PAGEREF _Toc8669877 \h 33Table 9Mediation of the effect of mania/bipolar disorder on cognitive outcome via psychotropic medication PAGEREF _Toc8669878 \h 34Sensitivity Analyses PAGEREF _Toc8669879 \h 35Figure 8Comparison of missing data approaches in mania/bipolar disorder total effects analyses PAGEREF _Toc8669880 \h 36Table 10Mediation of the effect of mania/bipolar disorder on cognitive outcome via cardiometabolic disease, with missing data imputation PAGEREF _Toc8669881 \h 37Table 11Mediation of the effect of mania/bipolar disorder on cognitive outcome via psychotropic medication, with missing data imputation PAGEREF _Toc8669882 \h 38Cognitive Impairment in Major Depression PAGEREF _Toc8669883 \h 39Characteristics of the Sample PAGEREF _Toc8669884 \h 39Figure 9Major depression analysis sample flowchart PAGEREF _Toc8669885 \h 39Table 12Summary of covariates in the major depression and comparison groups PAGEREF _Toc8669886 \h 40Evaluation of the Graphical Model PAGEREF _Toc8669887 \h 44Total Effects PAGEREF _Toc8669888 \h 44Table 13Summary of covariates in matched major depression and comparison groups PAGEREF _Toc8669889 \h 45Figure 10Total effect of major depression on reasoning PAGEREF _Toc8669890 \h 46Figure 11Total effect of major depression on reaction time PAGEREF _Toc8669891 \h 47Figure 12Total effect of major depression on numeric memory PAGEREF _Toc8669892 \h 48Figure 13Total effect of major depression on prospective memory PAGEREF _Toc8669893 \h 49Mediation Analyses PAGEREF _Toc8669894 \h 50Table 14Tests of interactions between exposure and mediators in the major depression analyses PAGEREF _Toc8669895 \h 50Table 15Tests of interactions between exposure and intermediate confounders PAGEREF _Toc8669896 \h 51Table 16Mediation of the effect of major depression on cognitive outcome via cardiometabolic disease PAGEREF _Toc8669897 \h 52Table 17Mediation of the effect of major depression on cognitive outcome via psychotropic medication PAGEREF _Toc8669898 \h 53Sensitivity Analyses PAGEREF _Toc8669899 \h 54Figure 14Comparison of missing data approaches in major depression total effects analyses PAGEREF _Toc8669900 \h 55Table 18Mediation of the effect of major depression on cognitive outcome via cardiometabolic disease, with missing data imputation PAGEREF _Toc8669901 \h 56Table 19Mediation of the effect of major depression on cognitive outcome via psychotropic medication, with missing data imputation PAGEREF _Toc8669902 \h 57References PAGEREF _Toc8669903 \h 58MethodsExposure DefinitionsThe mood disorder exposures (mania/BD and major depression) were classified hierarchically into mutually exclusive groups within each information source. Using the self-reported diagnosis data, the hierarchy order was: mania/BD (‘mania/bipolar/manic depression’); major depression (‘depression’ or ‘post-natal depression’). Using the hospital ICD-10 codes, the order was: mania/BD (F30x or F31x); major depression (F32x or F33x). Using the mood questionnaire data, the hierarchy was as described by Smith et al.:PEVuZE5vdGU+PENpdGUgRXhjbHVkZUF1dGg9IjEiIEV4Y2x1ZGVZZWFyPSIxIj48QXV0aG9yPlNt
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ADDIN EN.CITE.DATA 1 mania/BD (BD type I and BD type II combined); major depression (single episode or recurrent). Owing to the limited detail in the self-reported diagnosis data and the mood questionnaire with regard to bipolar features, no distinction was made between single manic episode, bipolar disorder type I and bipolar disorder type II; the term ‘mania/BD’ is therefore used throughout.Covariate MeasuresSociodemographic measuresAge was truncated to whole years, and was centred at 55 (approximating the cohort mean age at baseline) in the analyses. Gender was self-reported as male or female. Ethnic background was self-reported as white, Asian/Asian British, black/black British, Chinese, mixed or other. Participants who had self-reported a white British background were further grouped by similarity of genetic ancestry based on a principal components analysis of the genotypic data (). Participants self-reported their birth country, and these were grouped according to whether or not English was an official/first language (UK, Isle of Man, Channel Islands, Gibraltar, Ireland, Australia, New Zealand, USA, Canada, Anguilla, Antigua & Barbuda, Aruba, Bahamas, Barbados, Bermuda). Self-reported data regarding participants’ highest educational qualification were dichotomized as university/college degree or not. Neighbourhood deprivation level was recorded by UK Biobank prior to baseline using the Townsend Index, ADDIN EN.CITE <EndNote><Cite><Author>Townsend</Author><Year>1987</Year><RecNum>16</RecNum><DisplayText><style face="superscript">2</style></DisplayText><record><rec-number>16</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1401462638">16</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Townsend, P.</author></authors></contributors><auth-address>TOWNSEND, P (reprint author), UNIV BRISTOL,SOCIAL POLICY,BRISTOL BS8 1TH,AVON,ENGLAND.</auth-address><titles><title>Deprivation</title><secondary-title>Journal of Social Policy</secondary-title></titles><periodical><full-title>Journal of Social Policy</full-title><abbr-1>J. Soc. Policy</abbr-1></periodical><pages>125-146</pages><volume>16</volume><dates><year>1987</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>0047-2794</isbn><accession-num>WOS:A1987J267500001</accession-num><work-type>Article</work-type><urls><related-urls><url><Go to ISI>://WOS:A1987J267500001</url></related-urls></urls><language>English</language></record></Cite></EndNote>2 and this was converted into quintiles in the whole cohort. Local environmentThe population density of each area of residence was classified categorically by UK Biobank, by combining participants’ residential postcodes with data generated from the 2001 census, using the GeoConvert tool provided by the UK Data Service Census Support (). Proximity to the nearest major road (traffic intensity >5,000 motor vehicles per 24 hours) was calculated by UK Biobank as the inverse distance (1/m) from the baseline address, using data for the year 2008 provided by the Department for Transport, and was converted to quintiles in the whole cohort. Neighbourhood air pollution data from a land use regression model and satellite-derived estimatesPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5WaWVubmVhdTwvQXV0aG9yPjxZZWFyPjIwMTM8L1llYXI+
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ADDIN EN.CITE.DATA 3 were linked by UK Biobank to participants’ baseline addresses; particulate matter of up to 10μm diameter (PM10) and nitrogen dioxide (NO2) were measured as annual average values in μg/m3 (for the years 2007 and 2005 respectively) and were converted to quintiles in the whole cohort. Other air pollution data were also available but these were measured in later years, thus post-dating the cognitive assessment date for most participants, and so were not analysed.Lifestyle and physical measuresTobacco smoking status (current, former or never) was classified by UK Biobank using self-reported data. Self-reported frequency of alcohol consumption was categorized as daily/almost daily, 3-4 times per week, 1-2 times per week, 1-3 times per month, special occasions only, former drinker, or never drinker. Sleeplessness/insomnia was self-reported as never/rarely, sometimes, or usually; if participants were unsure how to respond to this item, they were prompted to answer in relation to the past four weeks. Physical activity (walking, moderate and vigorous) in a typical week was recorded using self-reported items from the International Physical Activity Questionnaire short form, ADDIN EN.CITE <EndNote><Cite><Author>Booth</Author><Year>2000</Year><RecNum>11596</RecNum><DisplayText><style face="superscript">4</style></DisplayText><record><rec-number>11596</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1488900454">11596</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Booth, M.</author></authors></contributors><auth-address>New Childrens Hosp, NSW Ctr Advancement Adolescent Hlth, Parramatta, NSW 2124, Australia</auth-address><titles><title>Assessment of physical activity: An international perspective</title><secondary-title>Research Quarterly for Exercise and Sport</secondary-title></titles><periodical><full-title>Research Quarterly for Exercise and Sport</full-title><abbr-1>Res Q Exercise Sport</abbr-1></periodical><pages>S114-S120</pages><volume>71</volume><number>2</number><keywords><keyword>exercise</keyword><keyword>questionnaires</keyword><keyword>measurement</keyword><keyword>health</keyword><keyword>exercise</keyword></keywords><dates><year>2000</year><pub-dates><date>Jun</date></pub-dates></dates><isbn>0270-1367</isbn><accession-num>WOS:000087868000016</accession-num><urls><related-urls><url><Go to ISI>://WOS:000087868000016</url></related-urls></urls><language>English</language></record></Cite></EndNote>4 from which a single measure of total physical activity in metabolic equivalent of task (MET) hours per week was derived; this was converted into quintiles in the whole cohort. Body mass index (BMI; kg/m2) was calculated from measures of height and weight taken by UK Biobank staff, and was categorized as underweight (<18.5), normal (18.5 to 24.9), overweight (25.0 to 29.9), obese class I (30.0 to 34.9), obese class II (35.0 to 39.9), and obese class III (≥40.0).Medical and family historyParticipants were asked to self-report any illnesses previously diagnosed by a doctor. These data were also combined with hospital records by UK Biobank analysts to generate ‘adjudicated’ classifications of myocardial infarction () or stroke (). The lists of neurological or psychiatric conditions (apart from mood disorder or schizophrenia) are provided in Table 1 and Table 2 below. Mental health and psychotropic medicationFour questions were administered regarding frequency of depressive symptoms in the past two weeks: depressed mood or hopelessness; lack of interest or pleasure; tenseness or restlessness; and tiredness or low energy. These were based on items from the Patient Health Questionnaire. ADDIN EN.CITE <EndNote><Cite><Author>Kroenke</Author><Year>2001</Year><RecNum>11148</RecNum><DisplayText><style face="superscript">5</style></DisplayText><record><rec-number>11148</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1479813120">11148</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kroenke, K.</author><author>Spitzer, R. L.</author><author>Williams, J. B.</author></authors></contributors><auth-address>Regenstrief Institute for Health Care and Department of Medicine, Indiana University, Indianapolis 46202, USA. kkroenke@</auth-address><titles><title>The PHQ-9: validity of a brief depression severity measure</title><secondary-title>Journal of General Internal Medicine</secondary-title><alt-title>Journal of general internal medicine</alt-title></titles><periodical><full-title>Journal of General Internal Medicine</full-title><abbr-1>J. Gen. Intern. Med.</abbr-1><abbr-2>J Gen Intern Med</abbr-2></periodical><alt-periodical><full-title>Journal of General Internal Medicine</full-title><abbr-1>J. Gen. Intern. Med.</abbr-1><abbr-2>J Gen Intern Med</abbr-2></alt-periodical><pages>606-13</pages><volume>16</volume><number>9</number><keywords><keyword>Adult</keyword><keyword>Depression/*diagnosis</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Psychological Tests</keyword><keyword>Reproducibility of Results</keyword><keyword>*Severity of Illness Index</keyword><keyword>*Surveys and Questionnaires</keyword></keywords><dates><year>2001</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>0884-8734 (Print)
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ADDIN EN.CITE.DATA 7 and an overall dichotomous indicator was created to represent above-threshold responses on one or more of the five items. The list of psychotropic medications self-reported at baseline is shown in Table 3 below.Table 1Psychiatric and neurological diagnoses in hospital recordsICD-10 codeICD-10 descriptionA8x.xViral infections of the central nervous systemB22.0HIV disease resulting in encephalopathyB90.0Sequelae of central nervous system tuberculosisB94.1Sequelae of viral encephalitisC70.0Malignant neoplasm of meninges (cerebral)C71.xMalignant neoplasm of brainC72.8 Overlapping lesion of brain and other parts of central nervous systemC75.1Malignant neoplasm of pituitary glandC75.3Malignant neoplasm of pineal glandC79.3Secondary malignant neoplasm of brain and cerebral meningesD32.0Benign neoplasm of meninges (cerebral)D33.0Benign neoplasm of brain, supratentorialD33.1Benign neoplasm of brain, infratentorialD33.2Benign neoplasm of brain, unspecifiedD35.2 Benign neoplasm of pituitary glandD35.4Benign neoplasm of pineal glandD42.0Neoplasm of uncertain or unknown behaviour of meninges (cerebral)D43.0Neoplasm of uncertain or unknown behaviour of brain, supratentorialD43.1 Neoplasm of uncertain or unknown behaviour of brain, infratentorialD43.2Neoplasm of uncertain or unknown behaviour of brain, unspecifiedD44.3Neoplasm of uncertain or unknown behaviour of pituitary glandD44.5Neoplasm of uncertain or unknown behaviour of pineal glandFxx.xMental and behavioural disordersG0x.xInflammatory diseases of the central nervous systemG10Huntington diseaseG11.xHereditary ataxiaG12.2 Motor neuron diseaseG13.1Other systemic atrophy primarily affecting central nervous system in neoplastic disease (Paraneoplastic limbic encephalopathy)G2x.xExtrapyramidal and movement disordersG30.xAlzheimer diseaseG31.xOther degenerative diseases of nervous system, not elsewhere classifiedG32.8Other specified degenerative disorders of nervous system in diseases classified elsewhereG35Multiple sclerosisG36.xOther acute disseminated demyelinationG37.xOther demyelinating diseases of central nervous systemG4x.xEpisodic and paroxysmal disordersG8x.xCerebral palsy and other paralytic syndromesG90.3Multi-system degenerationG91.xHydrocephalusG92Toxic encephalopathyG93.xOther disorders of brainG94.xOther disorders of brain in diseases classified elsewhereG96.xOther disorders of central nervous systemG97.xPostprocedural disorders of nervous system, not elsewhere classifiedG98Other disorders of nervous system, not elsewhere classifiedH47.6Disorders of visual cortexI6x.xCerebrovascular diseasesQ0x.xCongenital malformations of the nervous systemQ28.2Arteriovenous malformation of cerebral vesselsQ28.3Other malformations of cerebral vesselsQ9x.xChromosomal abnormalities, not elsewhere classifiedR41.xOther symptoms and signs involving cognitive functions and awarenessR90.0Intracranial space-occupying lesionR94.0Abnormal results of function studies of central nervous systemS02.0xFracture of vault of skullS02.1xFracture of base of skullS06.xIntracranial injuryS07.1Crushing injury of skullS09.7Multiple injuries of headT02.0xFractures involving head with neckT04.0Crushing injuries involving head with neckT06.0Injuries of brain and cranial nerves with injuries of nerves and spinal cord at neck levelT40.xPoisoning by narcotics and psychodysleptics [hallucinogens]T42.xPoisoning by antiepileptic, sedative-hypnotic and antiparkinsonism drugsT43.xPoisoning by psychotropic drugs, not elsewhere classifiedT51.xToxic effect of alcoholT58Toxic effect of carbon monoxideT90.2 Sequelae of fracture of skull and facial bonesT90.5Sequelae of intracranial injuryTable 2Self-reported psychiatric and neurological diagnosesUK Biobank data fieldDiagnosis6150 (touchscreen - vascular)Stroke20001 (interview - cancer)Brain cancer/primary malignant tumour“Meningeal cancer/malignant meningioma20002 (interview - non-cancer)Alcohol dependency“Anorexia/bulimia/other eating disorder“Anxiety/panic attacks“Benign/essential tremor“Brain haemorrhage“Brain/intracranial abscess“Cerebral aneurysm“Cerebral palsy“Chronic/degenerative neurological problem“Deliberate self-harm/suicide attempt“Dementia/Alzheimer's/cognitive impairment“Encephalitis“Epilepsy“Fracture skull/head“Head injury“Headaches (not migraine)“Infection of nervous system“Insomnia“Ischaemic stroke“Meningioma benign“Meningitis“Migraine“Motor neurone disease“Multiple sclerosis“Nervous breakdown“Neurological injury/trauma“Neuroma benign“Obsessive compulsive disorder (OCD)“Opioid dependency“Other demyelinating condition“Other neurological problem“Other substance abuse/dependency“Parkinson’s disease“Post-traumatic stress disorder“Psychological/psychiatric problem“Spina bifida“Stress“Stroke“Subarachnoid haemorrhage“Subdural haematoma“Transient ischaemic attackTable 3Self-reported psychotropic medications from UK Biobank data field 20003Mood stabilisersSelective serotonin reuptake inhibitorsOther antidepressantsTraditional antipsychoticsSecond generation antipsychoticsSedatives & hypnoticslithium productparoxetinemirtazapinechlorpromazinequetiapinediazepamPriadel (lithium)Seroxat (paroxetine)Zispin (mirtazapine)cpz - chlorpromazineSeroquel (quetiapine) diazepam productCamcolit (lithium)fluoxetineduloxetineLargactil (chlorpromazine) risperidoneValium tablet (diazepam)sodium valproateProzac (fluoxetine)Cymbalta (duloxetine)haloperidolRisperdal (risperidone) Valium syrup (diazepam)Epilim (sodium valproate)citalopramYentreve (duloxetine) Haldol (haloperidol)olanzapineValium supp (diazepam)Depakote (semisodium valproate)Cipramil (citalopram) venlafaxineSerenace (haloperidol) Zyprexa (olanzapine)temazepamvalproic acid escitalopramEfexor (venlafaxine)fluphenazine decanoatearipiprazoleNormison (temazepam)carbamazepine productCipralex (escitalopram)amitriptylinefluphenazineAbilify (aripiprazole) Euhypnos (temazepam)carbamazepinesertralineElavil (amitriptyline) Modecate (fluphenazine) amisulpridezopicloneTegretol (carbamazepine)Lustral (sertraline) Tryptizol (amitriptyline)Moditen tablet (fluphenazine) Solian (amisulpride)Zimovane (zopiclone) Teril (carbamazepine)fluvoxamineLentizol (amitriptyline) Moditen enanthate (fluphenazine)clozapine zaleplonTeril retard (carbamazepine)amitriptyline+perphenazineflupentixolClozaril (clozapine) Sonata (zaleplon)Timonil retard (carbamazepine)Triptafen (amitriptyline+perphenazine)Flupenthixol (flupentixol)zolpidemEpimaz (carbamazepine)amitriptyline+chlordiazepoxideDepixol (flupentixol)Stilnoct (zolpidem) lamotrigineLimbitrol 10 (amitriptyline+chlordiazepoxide)Fluanxol (flupentixol)nitrazepamLamictal (lamotrigine)Limbitrol-5 (amitriptyline+chlordiazepoxide)zuclopenthixolMogadon (nitrazepam)phenelzineClopixol (zuclopenthixol) Nitrados (nitrazepam)maoi - phenelzineloxapineRemnos (nitrazepam)Nardil (phenelzine)Loxapac (loxapine)Somnite (nitrazepam)moclobemidedroperidol Noctesed (nitrazepam)Manerix (moclobemide)Droleptan (droperidol)Surem (nitrazepam)imipraminetrifluoperazineUnisomnia (nitrazepam)Tofranil (imipramine)Stelazine (trifluoperazine) flunitrazepamtrimipraminethioridazineRohypnol (flunitrazepam)Surmontil (trimipramine)Melleril (thioridazine)triazolamdothiepinHalcion (triazolam)dosulepinProthiaden (dosulepin)Thaden (dosulepin)clomipramineAnafranil (clomipramine)lofepramineGamanil (lofepramine)Lomont (lofepramine)mianserinBolvidon (mianserin)Norval (mianserin)Genome-wide polygenic scoresParticipants provided a blood sample at the baseline assessment, and genotyping was carried out centrally by UK Biobank. Full details of the genotyping, imputation, and quality control processes used by UK Biobank are publicly available at and in Bycroft et al. ADDIN EN.CITE <EndNote><Cite ExcludeAuth="1" ExcludeYear="1"><Author>Bycroft</Author><Year>2017</Year><RecNum>11830</RecNum><DisplayText><style face="superscript">8</style></DisplayText><record><rec-number>11830</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1513880302">11830</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bycroft, C.</author><author>Freeman, C.</author><author>Petkova, D.</author><author>Band, G.</author><author>Elliott, L. T.</author><author>Sharp, K.</author><author>Motyer, A.</author><author>Vukcevic, D.</author><author>Delaneau, O.</author><author>O'Connell, J.</author><author>Cortes, A.</author><author>Welsh, S.</author><author>McVean, G.</author><author>Leslie, S.</author><author>Donnelly, P.</author><author>Marchini, J.</author></authors></contributors><titles><title>Genome-wide genetic data on ~500,000 UK Biobank participants</title><secondary-title>bioRXiv</secondary-title></titles><periodical><full-title>bioRXiv</full-title></periodical><edition>July 20, 2017</edition><dates><year>2017</year></dates><urls></urls><electronic-resource-num>10.1101/166298</electronic-resource-num></record></Cite></EndNote>8 Direct genotyping was performed using two custom Affymetrix arrays: approximately 50,000 participants were genotyped on the UK BiLEVE Axiom array, which was designed for the BiLEVE study of lung function (a partner study of UK Biobank), and the remainder were genotyped using the UK Biobank Axiom array. The two arrays are very similar, with over 95% common marker content. The arrays included more than 800,000 single nucleotide polymorphisms (SNP), chosen because of known or likely associations with a wide range of diseases and health-related phenotypes, as well as to provide good genome-wide coverage for imputation purposes in European populations across common (>5%) and low (1-5%) minor allele frequency (MAF) ranges. The directly genotyped data were imputed by UK Biobank to reference panels from the Haplotype Reference Consortium, UK10K Project and 1000 Genomes Project (Phase 3). Only the Haplotype Reference Consortium imputed data (approximately 40 million markers) were available at the time the present analyses were conducted, due to quality control problems with the UK10K and 1000 Genomes imputations. The cognitive genome-wide polygenic score (GPS) used summary statistics from a large genome-wide association study (GWAS) of years of education;PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Pa2JheTwvQXV0aG9yPjxZZWFyPjIwMTY8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 10 and current evidence suggests that a GPS based on the very large available GWAS of education has greater predictive power for observed cognitive ability than does a GPS based on smaller GWAS of cognitive ability itself. ADDIN EN.CITE <EndNote><Cite><Author>Plomin</Author><Year>2018</Year><RecNum>11903</RecNum><DisplayText><style face="superscript">11</style></DisplayText><record><rec-number>11903</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1518962807">11903</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Plomin, R.</author><author>von Stumm, S.</author></authors></contributors><auth-address>Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, UK.
Department of Psychological and Behavioural Science, London School of Economics and Political Science, Queens House, 55-56 Lincoln's Inn Fields, London WC2A 3LJ, UK.</auth-address><titles><title>The new genetics of intelligence</title><secondary-title>Nature Reviews Genetics</secondary-title></titles><periodical><full-title>Nature Reviews Genetics</full-title></periodical><pages>148-159</pages><volume>19</volume><number>3</number><dates><year>2018</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>1471-0064 (Electronic)
1471-0056 (Linking)</isbn><accession-num>29335645</accession-num><urls><related-urls><url> To minimize sample overlap with UK Biobank participants, the education GWAS authors provided summary statistics from analyses that did not include UK cohorts; participants from the 23andMe data resource were also omitted due to data-sharing restrictions. The BD GPS used summary statistics from the Psychiatric Genomics Consortium (PGC);PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Ta2xhcjwvQXV0aG9yPjxZZWFyPjIwMTE8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 13 the GWAS authors provided reanalysed statistics that excluded UK Biobank and 23andMe participants.The GPS were calculated using a bespoke script for R () and PLINK () software. They were generated from all available SNPs, applying the following quality control criteria: information score >0.8; Hardy-Weinberg equilibrium test P >1x10-6; MAF >0.01; linkage disequilibrium clumping R2 <0.1 using a 250kb window. GPS were created at various thresholds based on the P values in the source GWAS (5x10-8 to 0.9), and were weighted by the GWAS effect sizes at each SNP. The optimum GPS was chosen based on the magnitude of the variance explained (R2) in the relevant phenotype measures in the UK Biobank data. These analyses were conducted in unrelated UK Biobank participants of white British genetic ancestry, after standard quality control exclusions for sex mismatch, sex chromosome aneuploidy, and outlying values of heterozygosity and missingness (). Each GPS was first regressed on variables indicating the genotyping array and batch, UK Biobank assessment centre, and the first 20 genetic principal components. The residuals from these models were then used as the independent variable in models to predict the relevant UK Biobank phenotype.For each phenotype, the optimum GPS had a P threshold of 0.5. The R2 for the association between the optimum education/cognition GPS and having a degree in the UK Biobank cohort was 0.018, and was 0.013 for the raw reasoning test score. These results are similar to those previously reported in independent samples, e.g. R2 = 0.02 for both educational attainmentPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5kZSBaZWV1dzwvQXV0aG9yPjxZZWFyPjIwMTQ8L1llYXI+
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ADDIN EN.CITE.DATA 16 They are notably lower, however, than more recent analyses which made use of the full education GWAS results including UK participants, ADDIN EN.CITE <EndNote><Cite><Author>Selzam</Author><Year>2017</Year><RecNum>11818</RecNum><DisplayText><style face="superscript">17</style></DisplayText><record><rec-number>11818</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1507644701">11818</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Selzam, S.</author><author>Krapohl, E.</author><author>von Stumm, S.</author><author>O'Reilly, P. F.</author><author>Rimfeld, K.</author><author>Kovas, Y.</author><author>Dale, P. S.</author><author>Lee, J. J.</author><author>Plomin, R.</author></authors></contributors><auth-address>Kings Coll London, MRC Social Genet & Dev Psychiat Ctr, Inst Psychiat Psychol & Neurosci, 16 DeCrespigny Pk, London SE5 8AF, England
Goldsmiths Univ London, Dept Psychol, London, England
Tomsk State Univ, Lab Cognit Invest & Behav Genet, Tomsk, Russia
Univ New Mexico, Dept Speech & Hearing Sci, Albuquerque, NM 87131 USA
Univ Minnesota Twin Cities, Dept Psychol, Minneapolis, MN USA</auth-address><titles><title>Predicting educational achievement from DNA</title><secondary-title>Molecular Psychiatry</secondary-title><alt-title>Mol Psychiatr</alt-title></titles><periodical><full-title>Molecular Psychiatry</full-title></periodical><pages>267-272</pages><volume>22</volume><number>2</number><keywords><keyword>genome-wide association</keyword><keyword>genotype imputation</keyword><keyword>attainment</keyword><keyword>intelligence</keyword><keyword>traits</keyword><keyword>gwas</keyword></keywords><dates><year>2017</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>1359-4184</isbn><accession-num>WOS:000394102600016</accession-num><urls><related-urls><url><Go to ISI>://WOS:000394102600016</url></related-urls></urls><electronic-resource-num>10.1038/mp.2016.107</electronic-resource-num><language>English</language></record></Cite></EndNote>17 in which the R2 for educational attainment at age 16 was 0.091 and for general cognitive ability was 0.036. The R2 for the association between the optimum BD GPS and the mania/BD phenotype in the UK Biobank cohort was 0.01. This is lower than the variance explained in other independent samples, e.g. R2 = 0.024,PEVuZE5vdGU+PENpdGUgRXhjbHVkZUF1dGg9IjEiIEV4Y2x1ZGVZZWFyPSIxIj48QXV0aG9yPkFt
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ADDIN EN.CITE.DATA 18 although results may not be directly comparable due to different methods of calculating pseudo R2 in logistic regression models. The R2 for the optimum major depression GPS was 0.005. This is similar to that reported by the GWAS authors when using their core “anchor” cohort results alone to predict into independent samples, although R2 values of up to 0.02 were obtained when additional cohort results (including UK Biobank and 23andMe) were included in GWAS meta-analyses.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5XcmF5PC9BdXRob3I+PFllYXI+MjAxODwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA 13 Statistical AnalysisGraphical ModelsConstruction of the directed acyclic graphsThe DAG shown in Figure 1 below represents the original assumptions made about the causal relationship between mania/BD and cognitive function. The presence of an arrow represents the weak assumption of a causal relationship for at least one member of the population, and the absence of an arrow represents the strong assumption of no causal relationship for any member of the population. ADDIN EN.CITE <EndNote><Cite><Author>Elwert</Author><Year>2013</Year><RecNum>11622</RecNum><DisplayText><style face="superscript">19</style></DisplayText><record><rec-number>11622</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1500136487">11622</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>Elwert, F.</author></authors><secondary-authors><author>Morgan, S. L.</author></secondary-authors></contributors><titles><title>Graphical causal models</title><secondary-title>Handbook of Causal Analysis for Social Research</secondary-title></titles><pages>245-273</pages><section>13</section><dates><year>2013</year></dates><pub-location>New York</pub-location><publisher>Springer</publisher><urls></urls><electronic-resource-num>10.1007/978-94-007-6094-3</electronic-resource-num></record></Cite></EndNote>19 All nodes that are causally antecedent to a given node are known as ancestors; shared ancestors of the exposure and outcome are potential confounders, because they lie on non-causal ‘back-door’ paths. ADDIN EN.CITE <EndNote><Cite><Author>Pearl</Author><Year>2016</Year><RecNum>11620</RecNum><DisplayText><style face="superscript">20</style></DisplayText><record><rec-number>11620</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1500057165">11620</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Pearl, J.</author><author>Glymour, M.</author><author>Jewell, N. P.</author></authors></contributors><titles><title>Causal Inference in Statistics: A Primer</title></titles><pages>xvii, 136 pages</pages><keywords><keyword>Mathematical statistics.</keyword><keyword>Causation.</keyword><keyword>Probabilities.</keyword></keywords><dates><year>2016</year></dates><pub-location>Chichester, West Sussex</pub-location><publisher>Wiley</publisher><isbn>9781119186847 (pbk.)</isbn><accession-num>18779894</accession-num><call-num>QA276.A2 P43 2016</call-num><urls></urls></record></Cite></EndNote>20 Intermediate nodes were included in the DAG where these were of interest in the mediation analyses or were required as common parents of other pairs of nodes; these were not exhaustive, and so each individual arrow could in principle be shown in more detail as a chain of intermediate nodes that, for the purposes of the present study, are omitted or unknown. The node names, the constructs they are intended to represent, and the corresponding measures in UK Biobank, are listed in Table 4 below.Figure 1Original DAG used in the mania/BD analysesCardiomet, cardiometabolic disease; cog, cognitive; curr, current; edu, educational; eng_speak, English speaking birth country; famhx, family history; geno, genotype; hx, history; par, parental; PD, Parkinson’s disease; phys, physical; pre_IQ, premorbid intelligence; psych_meds, psychotropic medications; SES, socioeconomic status. Green node is the exposure and blue node is the outcome. Light nodes represent unmeasured constructs and darker nodes represent measured constructs.Table 4DAG constructsNode nameConstruct representedMeasurement in UK BiobankExposurebipolarLifetime history of mania or bipolar disorder (prior to cognitive assessment)Disorder exposure status (versus comparison group) Outcomecog_scorePerformance on cognitive assessment? Reasoning? Reaction time ? Numeric memory? Visuospatial memory? Prospective memoryShared ancestors of exposure and outcomeage AgeAge in yearsancestral_hx Various ancestral/migration factors that determine ethnicity, country of origin and family history (genetic and non-genetic)Unmeasuredbipolar_genoGenotype associated with bipolar disorderGenome-wide polygenic scorechild_adversity Adverse experiences in childhoodChildhood abuse and neglect (self-reported as ‘never true’ to ‘very often true’):When I was growing up…a) I felt lovedb) People in my family hit me so hard that it left me with bruises or marksc) I felt that someone in my family hated med) Someone molested me (sexually)e) There was someone to take me to the doctor if I needed itcog_geno Genotype associated with cognitive functionGenome-wide polygenic score (using education GWAS as proxy)edu_attain Educational attainmentHas a degree or not (self-reported)eng_speak Born in an English-speaking countryBorn in an English-speaking country (self-reported)ethnicity Ethnic background? Ethnic category (self-reported) ? Genetically-identified white British ancestryfamhx_depression Parent/sibling with depressionSelf-report of biological parent or sibling with ‘severe depression’gender GenderSelf-reported male or femalematernal_smoking Mother smoked around time of participant’s birthParticipant’s response to “Did your mother smoke regularly around the time when you were born?"other_famhx Other aspects of family history (non-genetic) and circumstances/environmentUnmeasuredpar_geno Genotype of parentsUnmeasuredpre_IQPremorbid intellectual abilityUnmeasuredIntermediates between exposure and outcomeadiposity Body fatBody mass indexadult_SES Socioeconomic status or deprivation in adulthoodTownsend index scoreair_pollution Airborne toxic particles/gasesNeighbourhood measures of:? Particulate matter? Nitrogen dioxidealcoholFrequency/amount of alcohol consumptionSelf-reported frequency of intakebrain_health Structural/functional brain stateUnmeasured (except for small subgroup)cardiometHistory of cardiometabolic disease? Self-reported history of angina, hypertension or diabetes (non-gestational)? Adjudicated history of myocardial infarction or strokecurr_moodMood state at time of cognitive assessmentPatient Health Questionnaire (four self-reported items)curr_psych_meds Psychotropic medication at time of cognitive assessmentOn any psychotropic medication (self-reported)past_mental_state Past psychiatric symptoms/illness course/duration/severity, over and above history of simply having exposure of interest or not Number of depressed/unenthusiastic episodes (self-reported on touchscreen or web)phys_activity Level of physical activityInternational Physical Activity Questionnaire (self-reported)phys_environ Physical aspects of the local environment? 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ADDIN EN.CITE.DATA 21 Age was also assumed to have an indirect effect on mania/BD status, via educational attainment. Gender differences in average cognitive performance have often been reported, although again the causal mechanisms are not well understood. ADDIN EN.CITE <EndNote><Cite><Author>Miller</Author><Year>2014</Year><RecNum>11875</RecNum><DisplayText><style face="superscript">22</style></DisplayText><record><rec-number>11875</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1518700761">11875</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Miller, D. I.</author><author>Halpern, D. F.</author></authors></contributors><auth-address>Northwestern Univ, Dept Psychol, Evanston, IL 60208 USA
Claremont Mckenna Coll, Dept Psychol, Claremont, CA 91711 USA</auth-address><titles><title>The new science of cognitive sex differences</title><secondary-title>Trends in Cognitive Sciences</secondary-title></titles><periodical><full-title>Trends in Cognitive Sciences</full-title></periodical><pages>37-45</pages><volume>18</volume><number>1</number><keywords><keyword>sex differences</keyword><keyword>cognitive abilities</keyword><keyword>hormones</keyword><keyword>culture</keyword><keyword>stereotypes</keyword><keyword>education</keyword><keyword>mental-rotation performance</keyword><keyword>congenital adrenal-hyperplasia</keyword><keyword>girls math performance</keyword><keyword>stereotype threat</keyword><keyword>gender-differences</keyword><keyword>mathematics performance</keyword><keyword>spatial abilities</keyword><keyword>verbal fluency</keyword><keyword>9-month-old infants</keyword><keyword>brain organization</keyword></keywords><dates><year>2014</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1364-6613</isbn><accession-num>WOS:000338397000007</accession-num><urls><related-urls><url><Go to ISI>://WOS:000338397000007</url></related-urls></urls><electronic-resource-num>10.1016/j.tics.2013.10.011</electronic-resource-num><language>English</language></record></Cite></EndNote>22 Gender was assumed not to affect mania/BD status,PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CbGFuY288L0F1dGhvcj48WWVhcj4yMDE3PC9ZZWFyPjxS
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ADDIN EN.CITE.DATA 25,26 These genotypes were depicted as descending from parental genotype (unmeasured). Parental genotype and other aspects of family history (unmeasured) were also assumed to affect parental behaviour (maternal smoking measure), childhood adversity, and family history of psychiatric and neurological conditions. A distal node representing ancestral history was conceptualised as giving rise to parental genotype and other aspects of family history, as well as ethnicity and English-speaking status. Ethnicity was assumed to be a possible antecedent of mania/BD, ADDIN EN.CITE <EndNote><Cite><Author>Lloyd</Author><Year>2005</Year><RecNum>11878</RecNum><DisplayText><style face="superscript">24</style></DisplayText><record><rec-number>11878</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1518716897">11878</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lloyd, T.</author><author>Kennedy, N.</author><author>Fearon, P.</author><author>Kirkbride, J.</author><author>Mallett, R.</author><author>Leff, J.</author><author>Holloway, J.</author><author>Harrison, G.</author><author>Dazzan, P.</author><author>Morgan, K.</author><author>Murray, R. M.</author><author>Jones, P. B.</author><author>AESOP Study Team,</author></authors></contributors><auth-address>Division of Psychiatry, University of Nottingham, Nottingham, UK. Tuhina.Lloyd@nottingham.ac.uk</auth-address><titles><title>Incidence of bipolar affective disorder in three UK cities: results from the AESOP study</title><secondary-title>British Journal of Psychiatry</secondary-title></titles><periodical><full-title>British Journal of Psychiatry</full-title><abbr-1>Br. J. Psychiatry</abbr-1></periodical><pages>126-31</pages><volume>186</volume><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Bipolar Disorder/*epidemiology/ethnology</keyword><keyword>England/epidemiology</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Incidence</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Risk Factors</keyword></keywords><dates><year>2005</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0007-1250 (Print)
0007-1250 (Linking)</isbn><accession-num>15684235</accession-num><urls><related-urls><url> and (through other nodes such as socioeconomic status) of cognitive performance. Being from a non-English-speaking country was assumed to influence educational attainment and childhood adversity (e.g. among individuals who had migrated at a young age), in turn influencing mania/BD status, and it was also assumed to affect cognitive performance. Maternal smoking was assumed to affect mania/BD, ADDIN EN.CITE <EndNote><Cite><Author>Talati</Author><Year>2013</Year><RecNum>11879</RecNum><DisplayText><style face="superscript">27</style></DisplayText><record><rec-number>11879</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1518718983">11879</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Talati, A.</author><author>Bao, Y.</author><author>Kaufman, J.</author><author>Shen, L.</author><author>Schaefer, C. A.</author><author>Brown, A. S.</author></authors></contributors><titles><title>Maternal smoking during pregnancy and bipolar disorder in offspring</title><secondary-title>American Journal of Psychiatry</secondary-title></titles><periodical><full-title>American Journal of Psychiatry</full-title></periodical><pages>1178-85</pages><volume>170</volume><number>10</number><keywords><keyword>Adult</keyword><keyword>Bipolar Disorder/*diagnosis/*psychology</keyword><keyword>Birth Weight</keyword><keyword>Cohort Studies</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Infant, Newborn</keyword><keyword>Internal-External Control</keyword><keyword>Male</keyword><keyword>Matched-Pair Analysis</keyword><keyword>Pregnancy</keyword><keyword>Prenatal Exposure Delayed Effects/*diagnosis/*psychology</keyword><keyword>Risk Factors</keyword><keyword>Smoking/*adverse effects</keyword><keyword>Tobacco Smoke Pollution/*adverse effects</keyword></keywords><dates><year>2013</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1535-7228 (Electronic)
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ADDIN EN.CITE.DATA 28 and of cognitive function via nodes such as educational attainment, socioeconomic status and health-related behaviours. Finally, family history of depression was depicted as a cause of mania/BD, ADDIN EN.CITE <EndNote><Cite><Author>Mortensen</Author><Year>2003</Year><RecNum>11881</RecNum><DisplayText><style face="superscript">29</style></DisplayText><record><rec-number>11881</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1518721974">11881</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Mortensen, P. B.</author><author>Pedersen, C. B.</author><author>Melbye, M.</author><author>Mors, O.</author><author>Ewald, H.</author></authors></contributors><auth-address>Aarhus Univ, Natl Ctr Register Based Res, DK-8000 Aarhus C, Denmark
Statens Serum Inst, Danish Epidemiol Sci Ctr, DK-2300 Copenhagen, Denmark
Univ Hosp, Hosp Psychiat, Inst Basic Psychiat Res, Aarhus, Denmark</auth-address><titles><title>Individual and familial risk factors for bipolar affective disorders in Denmark</title><secondary-title>Archives of General Psychiatry</secondary-title><alt-title>Arch Gen Psychiat</alt-title></titles><periodical><full-title>Archives of General Psychiatry</full-title><abbr-1>Arch. Gen. Psychiatry</abbr-1></periodical><pages>1209-1215</pages><volume>60</volume><number>12</number><keywords><keyword>influenza epidemics</keyword><keyword>adult schizophrenia</keyword><keyword>affective psychosis</keyword><keyword>prenatal exposure</keyword><keyword>birth</keyword><keyword>illness</keyword><keyword>season</keyword><keyword>pedigrees</keyword><keyword>genetics</keyword><keyword>symptoms</keyword></keywords><dates><year>2003</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0003-990x</isbn><accession-num>WOS:000187022200005</accession-num><urls><related-urls><url><Go to ISI>://WOS:000187022200005</url></related-urls></urls><electronic-resource-num>10.1001/archpsyc.60.12.1209</electronic-resource-num><language>English</language></record></Cite></EndNote>29 and of cognitive function via childhood adversity. Intermediates between exposure and outcomeLifetime history of mania/BD was assumed to affect multiple behaviour-related measures, namely physical activity, adiposity, alcohol consumption and smoking, ADDIN EN.CITE <EndNote><Cite><Author>Scott</Author><Year>2015</Year><RecNum>11882</RecNum><DisplayText><style face="superscript">30</style></DisplayText><record><rec-number>11882</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1518725479">11882</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Scott, E. M.</author><author>Hermens, D. F.</author><author>White, D.</author><author>Naismith, S. L.</author><author>GeHue, J.</author><author>Whitwell, B. G.</author><author>Glozier, N.</author><author>Hickie, I. B.</author></authors></contributors><auth-address>Univ Sydney, Brain & Mind Res Inst, Clin Res Unit, Camperdown, NSW, Australia</auth-address><titles><title>Body mass, cardiovascular risk and metabolic characteristics of young persons presenting for mental healthcare in Sydney, Australia</title><secondary-title>BMJ Open</secondary-title><alt-title>Bmj Open</alt-title></titles><periodical><full-title>Bmj Open</full-title></periodical><alt-periodical><full-title>Bmj Open</full-title></alt-periodical><pages>e007066</pages><volume>5</volume><number>3</number><keywords><keyword>critical period</keyword><keyword>psychotic disorders</keyword><keyword>early intervention</keyword><keyword>childhood obesity</keyword><keyword>major depression</keyword><keyword>disease</keyword><keyword>schizophrenia</keyword><keyword>prevention</keyword><keyword>abnormalities</keyword><keyword>1st-episode</keyword></keywords><dates><year>2015</year></dates><isbn>2044-6055</isbn><accession-num>WOS:000363458200070</accession-num><urls><related-urls><url><Go to ISI>://WOS:000363458200070</url></related-urls></urls><electronic-resource-num>10.1136/bmjopen-2014-007066</electronic-resource-num><language>English</language></record></Cite></EndNote>30 and these in turn were assumed to influence cognitive function,PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYXVtZ2FydDwvQXV0aG9yPjxZZWFyPjIwMTU8L1llYXI+
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ADDIN EN.CITE.DATA 34,35 It was assumed that mania/BD might affect socioeconomic status in adulthood (e.g. via impact on occupational functioning); ADDIN EN.CITE <EndNote><Cite><Author>Gitlin</Author><Year>2017</Year><RecNum>11765</RecNum><DisplayText><style face="superscript">36</style></DisplayText><record><rec-number>11765</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1504198050">11765</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Gitlin, M. J.</author><author>Miklowitz, David J.</author></authors></contributors><titles><title>The difficult lives of individuals with bipolar disorder: A review of functional outcomes and their implications for treatment</title><secondary-title>Journal of Affective Disorders</secondary-title></titles><periodical><full-title>Journal of Affective Disorders</full-title><abbr-1>J. Affect. Disord.</abbr-1></periodical><pages>147-154</pages><volume>209</volume><dates><year>2017</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0165-0327</isbn><accession-num>WOS:000393241400023</accession-num><urls><related-urls><url><Go to WoS>://MEDLINE:26689387</url></related-urls></urls><electronic-resource-num>10.1016/j.jad.2016.11.021</electronic-resource-num></record></Cite></EndNote>36 this in turn might influence performance on cognitive tests, ADDIN EN.CITE <EndNote><Cite><Author>Lyu</Author><Year>2016</Year><RecNum>11886</RecNum><DisplayText><style face="superscript">37</style></DisplayText><record><rec-number>11886</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1518782610">11886</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lyu, J.</author><author>Burr, J. A.</author></authors></contributors><auth-address>Korea Univ, Seoul, South Korea
Univ Massachusetts Boston, Boston, MA USA</auth-address><titles><title>Socioeconomic Status Across the Life Course and Cognitive Function Among Older Adults: An Examination of the Latency, Pathways, and Accumulation Hypotheses</title><secondary-title>Journal of Aging and Health</secondary-title><alt-title>J Aging Health</alt-title></titles><periodical><full-title>Journal of Aging and Health</full-title><abbr-1>J Aging Health</abbr-1></periodical><alt-periodical><full-title>Journal of Aging and Health</full-title><abbr-1>J Aging Health</abbr-1></alt-periodical><pages>40-67</pages><volume>28</volume><number>1</number><keywords><keyword>childhood ses</keyword><keyword>cognitive function</keyword><keyword>life course</keyword><keyword>health and retirement study</keyword><keyword>cumulative adversity</keyword><keyword>health inequalities</keyword><keyword>childhood</keyword><keyword>decline</keyword><keyword>position</keyword><keyword>reserve</keyword><keyword>trajectories</keyword><keyword>mortality</keyword><keyword>risk</keyword><keyword>epidemiology</keyword></keywords><dates><year>2016</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0898-2643</isbn><accession-num>WOS:000368574600003</accession-num><urls><related-urls><url><Go to ISI>://WOS:000368574600003</url></related-urls></urls><electronic-resource-num>10.1177/0898264315585504</electronic-resource-num><language>English</language></record></Cite></EndNote>37 although this relationship is complicated by shared genetic factors,PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NYXJpb25pPC9BdXRob3I+PFllYXI+MjAxNDwvWWVhcj48
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ADDIN EN.CITE.DATA 41 Sleeplessness was also depicted as an intermediate between mania/BD status and cognitive performance. Although sleep disturbance can be a trigger for relapse in BD, it was placed temporally downstream of mania/BD status in this graph because it represented recent sleep patterns (in the four-week period preceding the UK Biobank cognitive assessment), whereas the mania/BD node represented lifetime status. Sleeplessness is an ongoing problem for many people with BD, ADDIN EN.CITE <EndNote><Cite><Author>Harvey</Author><Year>2005</Year><RecNum>11889</RecNum><DisplayText><style face="superscript">43</style></DisplayText><record><rec-number>11889</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1518801274">11889</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Harvey, A. G.</author><author>Schmidt, D. A.</author><author>Scarna, A.</author><author>Semler, C. N.</author><author>Goodwin, G. M.</author></authors></contributors><auth-address>Univ Oxford, Dept Expt Psychol, Oxford OX1 2JD, England
Univ Oxford, Dept Psychiat, Oxford OX1 2JD, England</auth-address><titles><title>Sleep-related functioning in euthymic patients with bipolar disorder, patients with insomnia, and subjects without sleep problems</title><secondary-title>American Journal of Psychiatry</secondary-title><alt-title>Am J Psychiat</alt-title></titles><periodical><full-title>American Journal of Psychiatry</full-title></periodical><pages>50-57</pages><volume>162</volume><number>1</number><keywords><keyword>dysfunctional beliefs</keyword><keyword>wrist activity</keyword><keyword>quality index</keyword><keyword>rating-scale</keyword><keyword>complaints</keyword><keyword>therapy</keyword><keyword>relapse</keyword><keyword>epidemiology</keyword><keyword>reliability</keyword><keyword>perception</keyword></keywords><dates><year>2005</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>0002-953x</isbn><accession-num>WOS:000226262000010</accession-num><urls><related-urls><url><Go to ISI>://WOS:000226262000010</url></related-urls></urls><electronic-resource-num>10.1176/appi.ajp.162.1.50</electronic-resource-num><language>English</language></record></Cite></EndNote>43 and it may affect cognitive performance.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Gb3J0aWVyLUJyb2NodTwvQXV0aG9yPjxZZWFyPjIwMTQ8
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ADDIN EN.CITE.DATA 44,45A node representing past mental state was included as an intermediate between mania/BD status and cognitive score. Although the past mental state node and the mania/BD exposure node both represent past states (i.e. lifetime experiences up to the time of the cognitive assessment), mania/BD was placed first in the temporal order depicted in the graph, on the grounds that having mania/BD influences the severity of the illness experience over time (measured here as number of depressed episodes). It was not a requirement in the exposure definition used here that a participant had to have experienced more than one affective episode to be classified in the mania/BD group, and so it was deemed more plausible that mania/BD status would influence the number of episodes, rather than the reverse. Similarly, current psychotropic medications at the time of the cognitive assessment were assumed to be a consequence of lifetime mania/BD status, and being on such medications did not contribute to the exposure classification used here. The temporal order of the past mental state and current psychotropic medication nodes was depicted such that the former influenced the latter, although it is likely that there is a reciprocal relationship between these variables over time (i.e. a graph for a longitudinal analysis might show mental state at time 1 influencing medication at time 2, and medication at time 2 influencing mental state at time 3). Given the nature of the present cross-sectional analysis, however, it was considered plausible that cumulative lifetime affective episodes (past mental state) would be an antecedent of current medication status. Both past mental state and current psychotropic medications were assumed to affect cognitive performance.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Cb3VybmU8L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxS
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ADDIN EN.CITE.DATA 41,46,47 A node representing brain health (i.e. as potentially measured by structural volume and integrity, and functional activation and connectivity) was depicted as intermediate between mania/BD status and cognitive performance. This assumes that changes occur in the brain as a consequence of mania/BD, as shown in longitudinal studies,PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MaW08L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxSZWNO
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ADDIN EN.CITE.DATA 48 although it may also be the case that some brain changes (e.g. reduced white matter tract integrity) are a marker of BD vulnerability that precedes illness onset, given that similar findings are evident in unaffected relatives of people with BD.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NaXNrb3dpYWs8L0F1dGhvcj48WWVhcj4yMDE3PC9ZZWFy
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ADDIN EN.CITE.DATA 21 It was not assumed that every causal path leading to cognitive outcome went through brain health, however, as other factors may be at play (such as confidence or test experience) that would affect performance on cognitive tests but are not necessarily mediated by brain structure or function. Since neuroimaging data were available only for a relatively small and non-representative sub-group (~2%) of the UK Biobank cohort, this node was tagged as unmeasured when planning the present analyses. Other ancestors of outcomeFamily history of neurodegenerative disease was assumed to be a potential additional cause of cognitive impairment, given that participants with this background may be at higher risk of cognitive decline arising from disease processes not necessarily captured by their own medical history data (e.g. individuals with unrecognised early-stage disease). This was represented in the graph by separate nodes for family history of dementia and of Parkinson’s disease, although this could have been depicted equivalently using one node, because they were considered to share the same antecedents and consequences. Family history of neurodegenerative disease was assumed not to be a causal antecedent of mania/BD status. Testing the fit of the directed acyclic graphsThe DAG was drawn using DAGitty software, ADDIN EN.CITE <EndNote><Cite><Author>Textor</Author><Year>2016</Year><RecNum>11625</RecNum><DisplayText><style face="superscript">50</style></DisplayText><record><rec-number>11625</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1500217698">11625</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Textor, J.</author><author>van der Zander, B.</author><author>Gilthorpe, M. S.</author><author>Liskiewicz, M.</author><author>Ellison, G. T. H.</author></authors></contributors><auth-address>Radboud Univ Nijmegen, Med Ctr, Dept Tumour Immunol, POB 9101, NL-6500 HB Nijmegen, Netherlands
Univ Lubeck, Inst Theoret Comp Sci, Lubeck, Germany
Univ Leeds, Leeds Inst Cardiovasc & Metab Med, Leeds, W Yorkshire, England
Univ Leeds, Leeds Inst Data Analyt, Leeds, W Yorkshire, England</auth-address><titles><title>Robust causal inference using directed acyclic graphs: the R package 'dagitty'</title><secondary-title>International Journal of Epidemiology</secondary-title><alt-title>Int J Epidemiol</alt-title></titles><periodical><full-title>International Journal of Epidemiology</full-title></periodical><alt-periodical><full-title>Int J Epidemiol</full-title></alt-periodical><pages>1887-1894</pages><volume>45</volume><number>6</number><dates><year>2016</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0300-5771</isbn><accession-num>WOS:000398261100024</accession-num><urls><related-urls><url><Go to ISI>://WOS:000398261100024</url></related-urls></urls><electronic-resource-num>10.1093/ije/dyw341</electronic-resource-num><language>English</language></record></Cite></EndNote>50 which automatically generated a list of all the testable independencies implied by its structure (ignoring any nodes that were tagged as being unmeasured). These were then tested in the dataset, by calculating partial correlation coefficients between each pair of nodes that were predicted to be independent, adjusting for other covariates if this was specified in the prediction. For example, a predicted conditional independency generated by DAGitty such as age ⊥ physical_environment | deprivation educational_attainmentwould be tested by calculating the partial correlation coefficient between age and a measure of the local physical environment, adjusted for the Townsend deprivation score and having a degree. These calculations were done using correlation or regression models, depending on the need to adjust for covariates. For simplicity, only continuous or dichotomous measures were used in the initial calculations. Where a node had more than one relevant available measure (e.g. physical_environment measured by population density or road proximity), the measure with the largest sample size was used, in order to minimize missing data bias.Total EffectsFor the purpose of comparison, estimation was conducted in several ways: Unadjusted regression model in all available participants; Unadjusted regression model only in participants who had complete data on all covariates that were to be used in the adjusted models; Multiple regression model adjusted for the minimum sufficient covariate set identified by DAGitty;Multiple regression model adjusted for the minimum sufficient set plus all other measured common antecedents of exposure and outcome; Multiple regression model adjusted for a propensity score created by regressing the mood disorder exposure on background covariates; Matched analyses (1:1 and 1:3) using the propensity score to form matched participant sets; Weighted regression model using inverse probability weights (IPW) derived from the propensity score; Doubly robust models (IPW-weighted regression with additional covariate adjustment or augmented weighting). Where models included age as a covariate, age squared was also entered, to account for possible curvilinear relationships. The propensity score model was specified in three ways, and the score that resulted in the best covariate balance (evaluated by comparing descriptive statistics for each covariate between the propensity score-matched samples) was taken forward into the total effects analyses listed above. This decision was based solely on covariate balance, without reference to the cognitive outcome data. The first propensity score model regressed the mood disorder exposure status variable on all ancestors of the exposure and all ancestors of the outcome that were not descended from the exposure.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Ccm9va2hhcnQ8L0F1dGhvcj48WWVhcj4yMDA2PC9ZZWFy
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ADDIN EN.CITE.DATA 51 The second propensity score model used the same predictor variables as the first, but also included all pairwise interaction terms. The third approach used boosted regression modelling (a machine learning method) to find the optimum prediction specification, ADDIN EN.CITE <EndNote><Cite><Author>Friedman</Author><Year>2001</Year><RecNum>11822</RecNum><DisplayText><style face="superscript">52</style></DisplayText><record><rec-number>11822</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1507666412">11822</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Friedman, J. H.</author></authors></contributors><auth-address>Stanford Univ, Dept Stat, Stanford, CA 94305 USA</auth-address><titles><title>Greedy function approximation: A gradient boosting machine</title><secondary-title>Annals of Statistics</secondary-title><alt-title>Ann Stat</alt-title></titles><periodical><full-title>Annals of Statistics</full-title><abbr-1>Ann Stat</abbr-1></periodical><alt-periodical><full-title>Annals of Statistics</full-title><abbr-1>Ann Stat</abbr-1></alt-periodical><pages>1189-1232</pages><volume>29</volume><number>5</number><keywords><keyword>function estimation</keyword><keyword>boosting</keyword><keyword>decision trees</keyword><keyword>robust nonparametric regression</keyword><keyword>regression</keyword><keyword>prediction</keyword></keywords><dates><year>2001</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>0090-5364</isbn><accession-num>WOS:000173361700001</accession-num><urls><related-urls><url><Go to ISI>://WOS:000173361700001</url></related-urls></urls><electronic-resource-num>10.1214/aos/1013203451</electronic-resource-num><language>English</language></record></Cite></EndNote>52 again using the same predictor variables as the other two models. The propensity score was also converted to an inverse probability weight, which was rescaled to sum to 1. ADDIN EN.CITE <EndNote><Cite><Author>Garrido</Author><Year>2014</Year><RecNum>11824</RecNum><DisplayText><style face="superscript">53</style></DisplayText><record><rec-number>11824</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1507669380">11824</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garrido, M. M.</author><author>Kelley, A. S.</author><author>Paris, J.</author><author>Roza, K.</author><author>Meier, D. E.</author><author>Morrison, R. S.</author><author>Aldridge, M. D.</author></authors></contributors><auth-address>James J Peters VA Med Ctr, GRECC, Bronx, NY 10468 USA
Icahn Sch Med Mt Sinai, Brookdale Dept Geriatr & Palliat Med, New York, NY 10029 USA
Icahn Sch Med Mt Sinai, Ctr Adv Palliat Care, New York, NY 10029 USA
Hertzberg Palliat Care Inst, Natl Palliat Care Res Ctr, New York, NY USA</auth-address><titles><title>Methods for Constructing and Assessing Propensity Scores</title><secondary-title>Health Services Research</secondary-title><alt-title>Health Serv Res</alt-title></titles><periodical><full-title>Health Services Research</full-title></periodical><alt-periodical><full-title>Health Serv Res</full-title></alt-periodical><pages>1701-1720</pages><volume>49</volume><number>5</number><keywords><keyword>observational data/quasi-experiments</keyword><keyword>administrative data uses</keyword><keyword>patient outcomes/function</keyword><keyword>variable selection</keyword><keyword>causal inference</keyword><keyword>balance</keyword><keyword>models</keyword><keyword>services</keyword><keyword>bias</keyword><keyword>care</keyword></keywords><dates><year>2014</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>0017-9124</isbn><accession-num>WOS:000343006400017</accession-num><urls><related-urls><url><Go to ISI>://WOS:000343006400017</url></related-urls></urls><electronic-resource-num>10.1111/1475-6773.12182</electronic-resource-num><language>English</language></record></Cite></EndNote>53 The analyses were conducted using Stata v15. ADDIN EN.CITE <EndNote><Cite><Author>StataCorp</Author><Year>2017</Year><RecNum>11823</RecNum><DisplayText><style face="superscript">54</style></DisplayText><record><rec-number>11823</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1507666718">11823</key></foreign-keys><ref-type name="Computer Program">9</ref-type><contributors><authors><author>StataCorp,</author></authors></contributors><titles><title>Stata Statistical Software: Release 15</title></titles><dates><year>2017</year></dates><pub-location>College Station, TX</pub-location><publisher>StataCorp LP</publisher><urls></urls></record></Cite></EndNote>54 The propensity scores were estimated using psmatch2 and boost. The total effects models were estimated using regress or logistic, psmatch2 and teffects, and results were reported as standardized mean differences with 95% confidence intervals (CI) calculated from robust standard errors. For the logistic regression models (prospective memory outcome measure), adjrr was used to convert the odds ratio (OR) estimates into risk differences. The matched models were performed with replacement and used a caliper set at 0.2 SD of the logit of the propensity score. ADDIN EN.CITE <EndNote><Cite><Author>Austin</Author><Year>2011</Year><RecNum>11641</RecNum><DisplayText><style face="superscript">55</style></DisplayText><record><rec-number>11641</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1500313747">11641</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Austin, P. C.</author></authors></contributors><auth-address>Univ Toronto, Inst Clin Evaluat Sci, Dept Hlth Management Policy & Evaluat, Toronto, ON M4N 3M5, Canada</auth-address><titles><title>An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies</title><secondary-title>Multivariate Behavioral Research</secondary-title><alt-title>Multivar Behav Res</alt-title></titles><periodical><full-title>Multivariate Behavioral Research</full-title><abbr-1>Multivar Behav Res</abbr-1></periodical><alt-periodical><full-title>Multivariate Behavioral Research</full-title><abbr-1>Multivar Behav Res</abbr-1></alt-periodical><pages>399-424</pages><volume>46</volume><number>3</number><keywords><keyword>marginal structural models</keyword><keyword>causal inference</keyword><keyword>logistic-regression</keyword><keyword>untreated subjects</keyword><keyword>bias reduction</keyword><keyword>monte-carlo</keyword><keyword>adjustment</keyword><keyword>performance</keyword><keyword>selection</keyword><keyword>balance</keyword></keywords><dates><year>2011</year></dates><isbn>0027-3171</isbn><accession-num>WOS:000291533400002</accession-num><urls><related-urls><url><Go to ISI>://WOS:000291533400002</url></related-urls></urls><electronic-resource-num>10.1080/00273171.2011.568786</electronic-resource-num><language>English</language></record></Cite></EndNote>55 Mediation AnalysesThe covariate adjustment sets in these models were the minimum sufficient adjustment sets to block all confounding paths, as determined by DAGitty. All outcome and intermediate confounder variables were entered in continuous or binary form, as required by gformula.Sensitivity AnalysesConditional Exchangeability The assumption of conditional exchangeability implies that, within a matched pair of participants, each participant had equal odds of being exposed (‘treated’) and unexposed. Covariate balance checks allow this to be verified with respect to measured background factors, but cannot confirm that matched pairs are balanced (i.e. exchangeable) for unmeasured or unknown background variables. Sensitivity of treatment effect estimates or their P values to different potential magnitudes of departure from exchangeability can be evaluated quantitatively using ‘bounds’ methods developed by Rosenbaum. ADDIN EN.CITE <EndNote><Cite ExcludeAuth="1" ExcludeYear="1"><Author>Rosenbaum</Author><Year>2002</Year><RecNum>11675</RecNum><DisplayText><style face="superscript">56</style></DisplayText><record><rec-number>11675</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1500455309">11675</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Rosenbaum, P. R.</author></authors></contributors><titles><title>Observational Studies</title></titles><edition>2nd</edition><dates><year>2002</year></dates><pub-location>New York</pub-location><publisher>Springer</publisher><urls></urls></record></Cite></EndNote>56 Potential deviations from exchangeability are summarized in a parameter referred to as gamma (where Γ = 1 represents equal odds), and the value of gamma at which the effect estimate or P value crosses the null is ascertained using permutation methods. This was conducted following the propensity score matched models, using the Stata package rbounds. Missing DataBecause the estimation methods used here involved adjustment and/or propensity score estimation for a large number of covariates, results were potentially sensitive to selection bias or reduced power, arising from missing data. Multiple imputation with chained equations was implemented using the ice package in Stata, and the regression models for total effects were repeated on the imputed datasets (25 imputations) using the mi estimate function. The cognitive outcome variables were included in the imputation model specification, ADDIN EN.CITE <EndNote><Cite><Author>White</Author><Year>2011</Year><RecNum>11826</RecNum><DisplayText><style face="superscript">57</style></DisplayText><record><rec-number>11826</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1511701681">11826</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>White, I. R.</author><author>Royston, P.</author><author>Wood, A. M.</author></authors></contributors><auth-address>MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, U.K.. ian.white@mrc-bsu.cam.ac.uk.</auth-address><titles><title>Multiple imputation using chained equations: Issues and guidance for practice</title><secondary-title>Statistics in Medicine</secondary-title></titles><periodical><full-title>Statistics in Medicine</full-title><abbr-1>Stat Med</abbr-1></periodical><pages>377-99</pages><volume>30</volume><number>4</number><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Cardiovascular Diseases/epidemiology</keyword><keyword>Cholesterol/blood</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lipoproteins, HDL/blood</keyword><keyword>Mental Health/*statistics & numerical data</keyword><keyword>Middle Aged</keyword><keyword>*Models, Statistical</keyword><keyword>Multicenter Studies as Topic</keyword><keyword>Young Adult</keyword></keywords><dates><year>2011</year><pub-dates><date>Feb 20</date></pub-dates></dates><isbn>1097-0258 (Electronic)
0277-6715 (Linking)</isbn><accession-num>21225900</accession-num><urls><related-urls><url> but their original (unimputed) values were analysed in the outcome models. A chained equations imputation option was also implemented in gformula, to allow a comparison of the results of the mediation models using raw versus imputed mediator and covariate data. These methods assume missingness-at-random.Exposure Misclassification The effect of different hypothetical levels of exposure misclassification on cognitive outcome was assessed using the Stata package episens. The outcome was dichotomized as impaired (z-score ≤ -1.645, i.e. 5th percentile) or not. The range of assumed sensitivity and specificity values for correct exposure classification was entered as a trapezoidal function, by specifying minimum and maximum values around a narrower range of equally probable values (e.g. minimum 0.7 and maximum 1.0, around a peak interval of 0.8 to 0.9). Differential misclassification was assumed, on the grounds that cognitively impaired participants would be more likely to be misclassified on the self-reported exposure data. Equivalent ModelsThe final DAG that was used as the basis for the total effects and mediation models was analysed structurally in the DAGitty R package, to determine the number of alternative ways it could be drawn while retaining the same predicted conditional independencies. This ascertains whether there are other specifications of the model that would be statistically indistinguishable from the version that was analysed, i.e. an ‘equivalence class’. ADDIN EN.CITE <EndNote><Cite><Author>Textor</Author><Year>2016</Year><RecNum>11625</RecNum><DisplayText><style face="superscript">50</style></DisplayText><record><rec-number>11625</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1500217698">11625</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Textor, J.</author><author>van der Zander, B.</author><author>Gilthorpe, M. S.</author><author>Liskiewicz, M.</author><author>Ellison, G. T. H.</author></authors></contributors><auth-address>Radboud Univ Nijmegen, Med Ctr, Dept Tumour Immunol, POB 9101, NL-6500 HB Nijmegen, Netherlands
Univ Lubeck, Inst Theoret Comp Sci, Lubeck, Germany
Univ Leeds, Leeds Inst Cardiovasc & Metab Med, Leeds, W Yorkshire, England
Univ Leeds, Leeds Inst Data Analyt, Leeds, W Yorkshire, England</auth-address><titles><title>Robust causal inference using directed acyclic graphs: the R package 'dagitty'</title><secondary-title>International Journal of Epidemiology</secondary-title><alt-title>Int J Epidemiol</alt-title></titles><periodical><full-title>International Journal of Epidemiology</full-title></periodical><alt-periodical><full-title>Int J Epidemiol</full-title></alt-periodical><pages>1887-1894</pages><volume>45</volume><number>6</number><dates><year>2016</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0300-5771</isbn><accession-num>WOS:000398261100024</accession-num><urls><related-urls><url><Go to ISI>://WOS:000398261100024</url></related-urls></urls><electronic-resource-num>10.1093/ije/dyw341</electronic-resource-num><language>English</language></record></Cite></EndNote>50ResultsCognitive Impairment in Mania/BDCharacteristics of the SampleFigure 2 below shows a flowchart of exclusions leading to the final analysis sample. A large number of participants were excluded due to missing data in at least one exposure information source, which meant they could not be classified in the comparison group. Where genotyping data indicated relatedness (third degree or closer), one member of each related set was chosen at random for analysis. Ethnic ancestry exclusions were applied only in the adjusted models. Figure 2Mania/bipolar disorder analysis sample flowchartTable 5 summarizes the covariate data in the mania/BD and comparison groups. Missingness was more common in the mania/BD group, apart from on the number of depressed episodes. Owing to the low response rate on the web-based mental health questionnaire (N = 157,366; 31.3% of the whole cohort), the proportion of missing data was highest for the childhood trauma variable. Missingness was also common on the family medical history, maternal smoking, current depressive symptoms and physical activity variables. Table 5 indicates that the mania/BD group was younger on average than the comparison group, and had a higher proportion of women and of degree-holders. The participants with mania/BD also appeared to be more likely to live in urban and more deprived areas, and to be current smokers and former drinkers. The proportions with frequent sleeplessness, obesity, cardiometabolic disease, comorbid neurological/psychiatric conditions, family history of severe depression, current psychotropic medication, and history of childhood trauma were higher in the mania/BD group, and this group also reported more depressed episodes and a higher current depressive symptom score on average. The distribution of the education/cognition GPS score appeared to be somewhat different between the mania/BD and comparison groups, with both low (decile 1) and high (decile 10) GPS values being slightly over-represented in the mania/BD group. The distribution of the bipolar GPS score was skewed towards higher values in the mania/BD group. The subset of participants with complete covariate data appeared different from the full analysis sample, being on average younger, more highly educated and from less deprived areas, for example.Table 5Summary of covariates in the mania/bipolar and comparison groupsAll available dataComplete covariate dataaMania/BDComparisonMania/BDComparisonN2,709105,28450426,997Sociodemographic Age (years)bMean (SD)55.0 (8.1)57.0 (8.2)54.3 (7.5)56.3 (7.9)GenderbN (%) female1,437 (53.1)52,730 (50.1)277 (55.0)14,414 (53.4)Ethnic groupN (%) missingWhite, N (%)cAsian/Asian BritishBlack/Black BritishChineseMixed & other background18 (0.7)2,457 (91.3)74 (2.8)78 (2.9)4 (0.2)78 (2.9)411 (0.4)95,463 (91.0)3,697 (3.5)3,182 (3.0)474 (0.5)2,057 (2.0)0 (0.0)479 (95.0)16 (3.2)2 (0.4)1 (0.2)6 (1.2)73 (0.3)25,708 (95.5)459 (1.7)322 (1.2)99 (0.4)336 (1.3)White British genetic ancestryN (%) missingN (%)c104 (3.8)1,968 (75.6)3,419 (3.3)81,183 (79.7)0 (0.0)394 (78.2)0 (0.0)22,606 (83.7)English-speaking country of birthN (%) missingN (%)c6 (0.2)2,432 (90.0)149 (0.1)93,802 (89.2)0 (0.0)462 (91.7)0 (0.0)24,993 (92.6)Has a degree N (%) missingN (%)c29 (1.1)1,034 (38.6)1,045 (1.0)36,797 (35.3)0 (0.0)265 (52.6)0 (0.0)13,210 (48.9)Townsend quintiledN (%) missingQu1 (least deprived), N (%)cQu2Qu3Qu4Qu5 (most deprived)3 (0.1)334 (12.3)361 (13.3)461 (17.0)607 (22.4)943 (34.9)162 (0.2)18,097 (17.2)21,344 (20.3)21,794 (20.7)23,685 (22.5)20,202 (19.2)0 (0.0)78 (15.5)70 (13.9)93 (18.5)124 (24.6)139 (27.6)34 (0.1)5,162 (19.1)5,846 (21.7)5,924 (22.0)6,094 (22.6)3,937 (14.6)Local environmentHome area population densityeN (%) missingEngland/Wales urban, N (%)cEngland/Wales townEngland/Wales villageEngland/Wales hamlet/isolatedScotland large urbanScotland other urbanScotland small townScotland rural38 (1.4)2,311 (86.5)129 (4.8)88 (3.3)45 (1.7)80 (3.0)11 (0.4)5 (0.2)2 (0.1)968 (0.9)90,610 (86.9)6,603 (6.3)4,967 (4.8)2,136 (2.1)0 (0.0)0 (0.0)0 (0.0)0 (0.0)5 (1.0)433 (86.8)25 (5.0)20 (4.0)10 (2.0)9 (1.8)1 (0.2)1 (0.2)0 (0.00271 (1.0)22,766 (85.2)1,776 (6.7)1,533 (5.7)651 (2.4)0 (0.0)0 (0.0)0 (0.0)0 (0.0)Proximity to major road (1/m)N (%) missingMean (SD)65 (2.4)0.006 (0.021)1,299 (1.2)0.006 (0.013)9 (1.8)0.006 (0.010)348 (1.3)0.005 (0.011)Particulate matter ≤10μm (μg/m3)N (%) missingMean (SD)72 (2.7)22.9 (3.1)1,679 (1.6)22.8 (3.0)11 (2.2)23.2 (3.2)437 (1.6)22.8 (3.2)Nitrogen dioxide (μg/m3)N (%) missingMean (SD)65 (2.4)32.9 (11.0)1,299 (1.2)31.9 (10.6)9 (1.8)33.1 (11.7)348 (1.3)31.8 (11.0)Lifestyle and physical Smoking statusN (%) missingNever, N (%)cFormerCurrent15 (0.6)1,185 (44.0)922 (34.2)587 (21.8)380 (0.4)60,305 (57.5)35,436 (33.8)9,163 (8.7)1 (0.2)251 (49.9)175 (34.8)77 (15.3)47 (0.2)16,428 (61.0)8,885 (33.0)1,637 (6.1)Alcohol frequencyN (%) missingDaily/almost daily, N (%)c3-4 times per week1-2 times per week1-3 times per monthSpecial occasions onlyNever (former drinker)Never (not former drinker)12 (0.4)497 (18.4)451 (16.7)583 (21.6)312 (11.6)432 (16.0)261 (9.7)161 (6.0)80 (0.1)22,179 (21.1)24,718 (23.5)26,774 (25.5)11,397 (10.8)11,824 (11.2)3,186 (3.0)5,126 (4.9)1 (0.2)112 (22.3)98 (19.5)114 (22.7)63 (12.5)66 (13.1)36 (7.2)14 (2.8)4 (0.01)6,514 (24.1)7,176 (26.6)6,627 (24.6)2,872 (10.6)2,387 (8.8)620 (2.3)797 (3.0)SleeplessnessN (%) missingNever/rarely, N (%)cSometimesUsually2 (0.1)535 (19.8)1,199 (44.3)973 (35.9)79 (0.1)29,205 (27.8)50,293 (47.8)25,707 (24.4)0 (0.0)113 (22.4)211 (41.9)180 (35.7)14 (0.1)8, 019 (29.7)12,802 (47.4)6,162 (22.8)Physical activity (MET h/week)N (%) missingMedian (Q1, Q3) 256 (9.5)25.6 (11.6, 56.7)6,861 (6.5)29.8 (13.7, 60.1)31 (6.2)27.5 (12.3, 54.9)1,054 (3.9)29.1 (14.2, 55.7)Body mass indexN (%) missingUnderweight, N (%)cNormalOverweightObese class IObese class IIObese class III27 (1.0)16 (0.6)719 (26.8)1,043 (38.9)610 (22.7)205 (7.6)89 (3.3)741 (0.7)514 (0.5)34,893 (33.4)44,979 (43.0)17,804 (17.0)4,729 (4.5)1,624 (1.6)1 (0.2)3 (0.6)174 (34.6)210 (41.8)87 (17.3)20 (4.0)9 (1.8)90 (0.3)156 (0.6)10,751 (40.0)11,104 (41.3)3,750 (13.9)860 (3.2)286 (1.1)Medical and family historyCardiometabolic diseaseN (%) missingN (%)c11 (0.4)986 (36.6)178 (0.2)32,120 (30.6)0 (0.0)140 (27.8)22 (0.1)6,310 (23.4)Comorbid neurological or psychiatric conditionfN (%)814 (30.1)9,430 (9.0)120 (23.8)2,093 (7.8)Family history of dementiaN (%) missingN (%)c473 (17.5)384 (17.2)14,799 (14.1)15,755 (17.4)0 (0.0)83 (16.5)0 (0.0)4,661 (17.3)Family history of Parkinson’s diseaseN (%) missingN (%)c589 (21.8)107 (5.1)16,406 (15.6)4,250 (4.8)0 (0.0)18 (3.6)0 (0.0)1,276 (4.7)Family history of severe depressionN (%) missingN (%)c466 (17.2)874 (39.0)15,655 (14.9)10,503 (11.7)0 (0.0)170 (33.7)0 (0.0)3,086 (11.4)Maternal smoking around birthN (%) missingN (%)c390 (14.4)716 (30.9)13,420 (12.7)24,807 (27.0)0 (0.0)151 (30.0)0 (0.0)7,065 (26.2)Mental health Current depressive symptomsN (%) missingMean (SD) 265 (9.8)3.5 (3.2)8,758 (8.3)1.2 (1.7)27 (5.4)2.8 (2.9)1,223 (4.5)1.0 (1.4)Any psychotropic medicationN (%) missingN (%)c38 (1.4)1,457 (54.6)1,247 (1.2)2,647 (2.5)5 (1.0)221 (44.3)286 (1.1)503 (1.9)Number of depressed episodesN (%) missingMedian (Q1, Q3)127 (4.7)1 (0, 6)6,573 (6.2)0 (0, 1)23 (4.6)4 (2, 11)1,448 (5.4)0 (0, 1)Any childhood traumagN (%) missingN (%)c1,976 (72.9)476 (64.9)68,715 (65.3)16,015 (43.8)0 (0.0)317 (62.9)0 (0.0)11,240 (41.6)Genome-wide polygenic scoresEducation/cognition GPS deciledN (%) missingD1 (lowest), N (%)cD2D3D4D5D6D7D8D9D10 (highest)104 (3.8)291 (11.2)259 (9.9)281 (10.8)234 (9.0)245 (9.4)244 (9.4)251 (9.6)257 (9.9)246 (9.4)297 (11.4)3,419 (3.3)10,156 (10.0)10,188 (10.0)10,166 (10.0)10,213 (10.0)10,202 (10.0)10,203 (10.0)10,196 (10.0)10,190 (10.0)10,201 (10.0)10,150 (10.0)0 (0.0)36 (7.14)57 (11.3)51 (10.1)55 (10.9)51 (10.1)48 (9.5)42 (8.3)45 (8.9)54 (10.7)65 (12.9)0 (0.0)2,262 (8.4)2,439 (9.0)2,497 (9.3)2,619 (9.7)2,599 (9.6)2,666 (9.9)2,822 (10.5)2,878 (10.7)3,014 (11.2)3,201 (11.9)Bipolar disorder GPS deciledN (%) missingD1 (lowest), N (%)cD2D3D4D5D6D7D8D9D10 (highest)104 (3.8)199 (7.6)225 (8.6)232 (8.9)228 (8.8)228 (8.8)264 (10.1)274 (10.5)262 (10.1)306 (11.8)387 (14.9)3,419 (3.3)10,248 (10.1)10,222 (10.0)10,215 (10.0)10,219 (10.0)10,219 (10.0)10,183 (10.0)10,173 (10.0)10,185 (10.0)10,141 (10.0)10,060 (9.9)0 (0.0)39 (7.7)45 (8.9)47 (9.3)47 (9.3)36 (7.1)46 (9.1)49 (9.7)48 (9.5)63 (12.5)84 (16.7)0 (0.0)2,773 (10.3)2,698 (10.0)2,696 (10.0)2,654 (9.8)2,707 (10.0)2,638 (9.8)2,707 (10.0)2,706 (10.0)2,717 (10.1)2,701 (10.0)BD, bipolar disorder; D, decile; GPS, genome-wide polygenic score; MET, metabolic equivalent of task; Q, quartile; Qu, quintile; SD, standard deviation.a. Participants with complete data on all the covariates that were entered into the maximally-adjusted total effects models (age, gender, white British genetic ancestry, English-speaking country of birth, degree, comorbid neurological/psychiatric condition, family history of dementia, family history of Parkinson’s disease, family history of severe depression, maternal smoking around birth, childhood trauma, education/cognition GPS, bipolar disorder GPS).b. No missing data.c. Missing excluded from denominator.d. Based on data distribution in the whole UK Biobank cohort.e. Scottish psychiatric hospital records were unavailable, which meant no Scotland-based participants could be classified in the comparison group; therefore all locations for comparison participants are in England/Wales. f. Apart from mood disorder or schizophrenia; not possible to distinguish between missing data and self-report of no condition, therefore both classified as ‘No’.g. From the web-based questionnaire, which was completed by 157,366 (31.3%) of the cohort. Evaluation of the Graphical ModelThe original DAG shown in Figure 1 above postulated that educational attainment was a causal antecedent of mania/BD status, but a plausible alternative specification would show the arrow in reverse such that mania/BD causally influences educational attainment (for example, if illness onset occurs at a young age). The different predicted independencies implied by both specifications were tested. The results indicated poorer fit in the second specification, with a greater proportion of the partial correlation coefficients being above |0.1|. The first specification also showed poor fit involving certain nodes, particularly current psychotropic medication use. The reasons for poor fit (e.g. model misspecification, measurement error, selection bias) cannot be discerned from the data alone, but consideration was given to whether the graph should be modified by adding new nodes or paths. It was deemed plausible that paths should be added between educational attainment and current psychotropic medication, and between gender and current psychotropic medication (reflecting possible influences of, for example, knowledge or attitudes on likelihood of seeking or accepting treatment). A new node was also added, representing other psychiatric or neurological conditions (apart from mood disorder or schizophrenia); this was in line with previous results, ADDIN EN.CITE <EndNote><Cite><Author>Cullen</Author><Year>2017</Year><RecNum>11868</RecNum><DisplayText><style face="superscript">58</style></DisplayText><record><rec-number>11868</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1517763940">11868</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cullen, B.</author><author>Smith, D. J.</author><author>Deary, I. J.</author><author>Evans, J. J.</author><author>Pell, J. P.</author></authors></contributors><auth-address>Univ Glasgow, Inst Hlth & Wellbeing, Glasgow, Lanark, Scotland
Univ Edinburgh, Ctr Cognit Ageing & Cognit Epidemiol, Edinburgh, Midlothian, Scotland</auth-address><titles><title>The 'cognitive footprint' of psychiatric and neurological conditions: cross-sectional study in the UK Biobank cohort</title><secondary-title>Acta Psychiatrica Scandinavica</secondary-title><alt-title>Acta Psychiat Scand</alt-title></titles><periodical><full-title>Acta Psychiatrica Scandinavica</full-title><abbr-1>Acta Psychiatr. Scand.</abbr-1></periodical><pages>593-605</pages><volume>135</volume><number>6</number><keywords><keyword>cognitive dysfunction</keyword><keyword>mood disorders</keyword><keyword>neurological disorders</keyword><keyword>prevalence</keyword><keyword>schizophrenia</keyword><keyword>major depressive disorder</keyword><keyword>bipolar disorder</keyword><keyword>multiple-sclerosis</keyword><keyword>impairment</keyword><keyword>prevalence</keyword><keyword>schizophrenia</keyword><keyword>metaanalysis</keyword><keyword>dysfunction</keyword><keyword>disease</keyword></keywords><dates><year>2017</year><pub-dates><date>Jun</date></pub-dates></dates><isbn>0001-690x</isbn><accession-num>WOS:000400993200011</accession-num><urls><related-urls><url><Go to ISI>://WOS:000400993200011</url></related-urls></urls><electronic-resource-num>10.1111/acps.12733</electronic-resource-num><language>English</language></record></Cite></EndNote>58 which had indicated different patterns of cognitive impairment in participants with and without psychiatric or neurological comorbidities. The implied independencies of this modified DAG were then tested, firstly with educational attainment specified as a causal antecedent of both mania/BD and other psychiatric/neurological conditions, and then with educational attainment specified as a causal consequence of both.The best fit was observed in the first version of the modified DAG, in which educational attainment was specified as a causal antecedent of both mania/BD and other psychiatric/neurological conditions. Twenty percent (28 of 138) of the partial correlation coefficients remained above |0.1|, but most of these were below |0.2| and the largest coefficient was |0.43|. The largest coefficients again involved the current psychotropic medication node. When evaluated further, however, regression models indicated wide variance in the estimated associations between the pairs of nodes, including the null in most instances (results not shown). No further modifications were made to the DAG; it was decided that the diagram broadly reflected the evidence-based assumptions drawn from the literature and expert knowledge, and additional data-driven modifications may have been misleading if they were in fact influenced by issues such as measurement error rather than model misspecification. The final DAG used in the analyses is shown in Figure 3 below.Total EffectsThe best propensity score model, in terms of covariate balance, was the first model with no interaction terms. This was used in all the outcome models that involved propensity score adjustment or matching, and was used as the basis for the inverse probability weights. Table 6 shows the degree of covariate balance, as illustrated within the matched samples used in the reaction time analyses. Figures 4 to 7 show the total effects results for prospective memory, reasoning, reaction time, and numeric memory.Figure 3Final DAG used in the mania/BD analysesCardiomet, cardiometabolic disease; cog, cognitive; curr, current; edu, educational; eng_speak, English speaking birth country; famhx, family history; geno, genotype; hx, history; other_neupsy, other comorbid neurological/psychiatric condition; par, parental; PD, Parkinson’s disease; phys, physical; pre_IQ, premorbid intelligence; psych_meds, psychotropic medications; SES, socioeconomic status. Green node is the exposure and blue node is the outcome. Light nodes represent unmeasured constructs and darker nodes represent measured constructs. This graph and the underlying code are publicly accessible online at mSTG_SM Table 6Summary of covariates in matched mania/bipolar and comparison groupsMania/BDComparisonMean (SD)Age (years)54.9 (7.4)55.1 (7.4)%Female gender52.952.0English-speaking country of birth97.798.3Has a degree 52.455.2Comorbid neurological or psychiatric condition25.225.1Family history of dementia18.617.5Family history of Parkinson’s disease3.64.3Family history of severe depression34.636.2Maternal smoking around birth33.134.5Any childhood trauma60.860.2Education/cognition GPSD1 (lowest)D2D3D4D5D6D7D8D9D10 (highest)7.59.910.210.710.49.28.78.711.513.27.18.68.012.39.910.37.810.011.114.9Bipolar disorder GPSD1 (lowest)D2D3D4D5D6D7D8D9D10 (highest)7.89.77.69.77.18.910.29.411.518.16.610.38.59.37.89.910.29.511.416.5BD, bipolar disorder; D, decile; GPS, genome-wide polygenic score; SD, standard deviation.These results are from the propensity-score matched samples used in the 1:3 matched model for the total effect of mania/BD on reaction time (mania/BD N = 392; comparison N = 1,081).Figure 4Total effect of mania/bipolar disorder on prospective memoryCI, confidence interval; IPW, inverse probability weighting; IPWRA, inverse probability weighting with regression adjustment; teffects, Stata teffects package. Estimates are proportions and can be interpreted as risk differences. No estimates are provided from propensity-score matched models as it was not possible to express these as risk differences.Figure 5Total effect of mania/bipolar disorder on reasoningCI, confidence interval; IPW, inverse probability weighting; IPWRA, inverse probability weighting with regression adjustment; teffects, Stata teffects package. Estimates are in z-score units and can be interpreted as standardized mean differences.Figure 6Total effect of mania/bipolar disorder on reaction timeCI, confidence interval; IPW, inverse probability weighting; IPWRA, inverse probability weighting with regression adjustment; teffects, Stata teffects package. Estimates are in z-score units and can be interpreted as standardized mean differences.Figure 7Total effect of mania/bipolar disorder on numeric memoryCI, confidence interval; IPW, inverse probability weighting; IPWRA, inverse probability weighting with regression adjustment; teffects, Stata teffects package. Estimates are in z-score units and can be interpreted as standardized mean differences.Mediation AnalysesMediation analyses were conducted to quantify the proportion of the total effect of mania/BD on each cognitive outcome that was accounted for by (1) cardiometabolic disease and (2) psychotropic medication. Effects via other intermediate nodes of interest (e.g. current depressive symptoms) could not be identified, because no covariate adjustment set could be found for the relevant exposure-mediator and/or mediator-outcome paths. In both sets of analyses, the indirect pathways were affected by intermediate confounding (i.e. the mediator-outcome path was confounded by at least one node that descended from mania/BD), and so the analyses were conducted under the identifying assumption of no interaction between mania/BD and either cardiometabolic disease or psychotropic medication.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5EZSBTdGF2b2xhPC9BdXRob3I+PFllYXI+MjAxNTwvWWVh
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ADDIN EN.CITE.DATA 59 This assumption was checked by conducting a regression model of each cognitive outcome on mania/BD exposure status, the mediator and all the covariates, including a product term for mania/BD * mediator; there was no evidence of interaction in any of the models (see Table 7 below). The mediation model estimates are interpreted as ‘randomized interventional analogs’ of the natural direct and indirect effects. ADDIN EN.CITE <EndNote><Cite><Author>VanderWeele</Author><Year>2014</Year><RecNum>11669</RecNum><DisplayText><style face="superscript">60</style></DisplayText><record><rec-number>11669</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1500442634">11669</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>VanderWeele, T. J.</author><author>Vansteelandt, S.</author><author>Robins, J. M.</author></authors></contributors><auth-address>Harvard Univ, Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA
Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA
Univ Ghent, Dept Appl Math Comp Sci & Stat, B-9000 Ghent, Belgium</auth-address><titles><title>Effect Decomposition in the Presence of an Exposure-Induced Mediator-Outcome Confounder</title><secondary-title>Epidemiology</secondary-title><alt-title>Epidemiology</alt-title></titles><periodical><full-title>Epidemiology</full-title><abbr-1>Epidemiology</abbr-1></periodical><alt-periodical><full-title>Epidemiology</full-title><abbr-1>Epidemiology</abbr-1></alt-periodical><pages>300-306</pages><volume>25</volume><number>2</number><keywords><keyword>sensitivity-analysis</keyword><keyword>causal inference</keyword><keyword>assumptions</keyword><keyword>mortality</keyword><keyword>models</keyword></keywords><dates><year>2014</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>1044-3983</isbn><accession-num>WOS:000331098600022</accession-num><urls><related-urls><url><Go to ISI>://WOS:000331098600022</url></related-urls></urls><electronic-resource-num>10.1097/Ede.0000000000000034</electronic-resource-num><language>English</language></record></Cite></EndNote>60Table 7Tests of interactions between exposure and mediators in the mania/BD analysesCoefficient for mania/BD * mediator95% CIPMediator: Cardiometabolic diseaseReasoninga0.029-0.207, 0.2640.812Reaction timea0.149-0.047, 0.3450.137Numeric memorya-0.124-0.652, 0.4030.644Visuospatial memorya0.239-0.019, 0.4970.070Prospective memoryb0.9910.475, 2.0710.982Mediator: Psychotropic medicationReasoninga0.005-0.255, 0.2640.973Reaction timea0.095-0.110, 0.3010.364Numeric memorya0.311-0.294, 0.9160.313Visuospatial memorya-0.045-0.295, 0.2050.725Prospective memoryb0.8830.435, 1.7960.732BD, bipolar disorder; CI, confidence interval.All models included mania/BD, the mediator and their product, as well as all the covariates entered into the gformula mediation models.a. Linear regression model with outcome measured in z-score units.b. Logistic regression model with outcome measured as correct or not; estimate expressed as odds ratio.Table 8Mediation of the effect of mania/bipolar disorder on cognitive outcome via cardiometabolic diseaseNEstimate95% CIaReasoningb 21,043TE-0.049-0.157, 0.059CDE-0.048-0.156, 0.061NDE-0.045-0.153, 0.063NIE-0.004-0.020, 0.013Reaction timeb 21,213TE0.014-0.082, 0.111CDE0.034-0.063, 0.132NDE0.007-0.090, 0.103NIE0.008-0.011, 0.026Numeric memoryb 6,396TE0.047-0.213, 0.306CDE0.021-0.237, 0.280NDE0.023-0.235, 0.281NIE0.024-0.008, 0.055Visuospatial memoryb 21,124TE-0.166-0.285, -0.047CDE-0.160-0.277, -0.044NDE-0.164-0.282, -0.046NIE-0.002-0.022, 0.017Prospective memoryc 21,139TE-0.033-0.078, 0.012CDE-0.038-0.083, 0.006NDE-0.038-0.083, 0.007NIE0.005-0.002, 0.012CDE, controlled direct effect when cardiometabolic disease = 0; CI, confidence interval; GPS, genome-wide polygenic score; NDE, natural direct effect; NIE, natural indirect effect; NO2, nitrogen dioxide; PM10, particulate matter of up to 10μm diameter; TE, total effect.Models were restricted to participants of white British genetic ancestry, and were adjusted for age, gender, educational attainment, English-speaking birth country, education/cognition GPS, bipolar disorder GPS, family history of dementia, family history of Parkinson’s disease, maternal smoking around birth, childhood trauma, other psychiatric/neurological conditions, deprivation, population density, road proximity, air pollution (PM10 and NO2), body mass index, alcohol frequency, smoking status, physical activity, and psychotropic medication.a. Normal-based, from bootstrapped standard error (1000 replicates). b. Estimate expressed as a standardized mean difference.c. Estimate expressed as a risk difference for the probability of being correct.Table 9Mediation of the effect of mania/bipolar disorder on cognitive outcome via psychotropic medicationNEstimate95% CIaReasoningb 21,339TE-0.065-0.176, 0.047CDE0.016-0.098, 0.130NDE-0.015-0.130, 0.099NIE-0.049-0.077, -0.021Reaction timeb 21,518TE-0.005-0.103, 0.094CDE0.073-0.036, 0.182NDE0.036-0.072, 0.144NIE-0.040-0.075, -0.005Numeric memoryb 6,547TE0.033-0.203, 0.270CDE0.058-0.181, 0.296NDE0.075-0.163, 0.312NIE-0.041-0.097, 0.015Visuospatial memoryb 21,424TE-0.194-0.311, -0.077CDE-0.098-0.220, 0.024NDE-0.140-0.263, -0.018NIE-0.054-0.094, -0.013Prospective memoryc 21,436TE-0.037-0.082, 0.009CDE-0.028-0.072, 0.016NDE-0.018-0.062, 0.026NIE-0.019-0.030, -0.007CDE, controlled direct effect when psychotropic medication = 0; CI, confidence interval; GPS, genome-wide polygenic score; NDE, natural direct effect; NIE, natural indirect effect; TE, total effect.Models were restricted to participants of white British genetic ancestry, and were adjusted for gender, educational attainment, English-speaking birth country, education/cognition GPS, bipolar disorder GPS, family history of dementia, family history of Parkinson’s disease, maternal smoking around birth, childhood trauma, other psychiatric/neurological conditions, deprivation, and lifetime number of episodes of depressed mood/anhedonia.a. Normal-based, from bootstrapped standard error (1000 replicates). b. Estimate expressed as a standardized mean difference.c. Estimate expressed as a risk difference for the probability of being correct.Sensitivity AnalysesRosenbaum bounds were calculated to check the sensitivity of the visuospatial memory total effects result to departures from exchangeability. The estimated effect crossed the null at a gamma value of 1.2, which corresponds to the probability of being in the exposed group being approximately 0.45 or 0.55, rather than 0.5 as would be the case if the groups were truly exchangeable. This indicates that the results would not be robust to an unmeasured confounder with even a weak association with group membership. There was evidence of missing data bias on several of the outcome measures: much of the attenuation towards the null seen in the total effects models occurred prior to any multivariable adjustment/matching, simply by restricting the sample to participants with complete covariate data. When the multiple linear regression models for total effects were repeated after multiple imputation of the covariate values, the results showed less attenuation (Figure 8 below). The effect size estimates in these reasoning, reaction time and numeric memory models were of small magnitude (point estimates approximately -0.10 to -0.15). The estimates in the visuospatial memory models remained similar regardless of which estimation method was used (approximate point estimate -0.19). When the mediation models were repeated with imputation of missing mediator and covariate values, the proportion of the total effect transmitted indirectly via cardiometabolic disease remained negligible (Table 10), whereas there was evidence of indirect effects via psychotropic medication for all the cognitive outcomes (Table 11).The results of the probabilistic analysis using episens indicated that the total effects estimate for visuospatial memory is likely to be sensitive to exposure misclassification. When dichotomized into impaired and unimpaired outcome categories, and assuming no exposure misclassification, the unadjusted relative risk of impairment was 1.70 in the mania/BD group (95% CI 1.45, 2.01). Assuming lower sensitivity to true mania/BD status among the cognitively impaired (sensitivity range 0.6 to 0.9) versus unimpaired participants (sensitivity range 0.7 to 1.0), the relative risk was estimated as 1.81 (0.22, 11.44). The DAGitty algorithm determined that there were six other DAGs that were equivalent to the final DAG used in the present analyses. None of these alternative configurations was causally plausible, owing to temporal order constraints (e.g. it is not possible for parental genotype to be causally influenced by offspring genotype). DAGitty also generated minimum sufficient covariate adjustment sets for the equivalent DAGs, for the total effect of mania/BD on cognitive outcome. The same minimum adjustment set was valid for the analysed DAG and for the six equivalent DAGs. This indicates that the multivariable analyses reported here remain valid, regardless of which model within the equivalence class is correct.Figure 8Comparison of missing data approaches in mania/bipolar disorder total effects analysesCI, confidence interval; MICE, multiple imputation with chained equations. Panels show: (a) reasoning, (b) reaction time, (c) numeric memory and (d) visuospatial memory. Prospective memory not shown as it was not possible to calculate risk differences.Table 10Mediation of the effect of mania/bipolar disorder on cognitive outcome via cardiometabolic disease, with missing data imputationNEstimate95% CIaReasoningb 80,698TE-0.092-0.158, -0.026CDE-0.087-0.152, -0.022NDE-0.085-0.151, -0.019NIE-0.007-0.017, 0.003Reaction timeb 82,648TE-0.094-0.150, -0.037CDE-0.091-0.148, -0.034NDE-0.091-0.148, -0.033NIE-0.003-0.014, 0.008Numeric memoryb 26,248TE-0.142-0.275, -0.008CDE-0.158-0.291, -0.024NDE-0.150-0.283, -0.018NIE0.008-0.011, 0.027Visuospatial memoryb 81,773TE-0.206-0.267, -0.145CDE-0.200-0.261, -0.139NDE-0.210-0.272, -0.149NIE0.005-0.007, 0.016Prospective memoryc 82,194TE-0.041-0.062, -0.021CDE-0.037-0.057, -0.017NDE-0.039-0.059, -0.018NIE-0.003-0.006, 0.001CDE, controlled direct effect when cardiometabolic disease = 0; CI, confidence interval; GPS, genome-wide polygenic score; NDE, natural direct effect; NIE, natural indirect effect; NO2, nitrogen dioxide; PM10, particulate matter of up to 10μm diameter; TE, total effect.Models were restricted to participants of white British genetic ancestry, and were adjusted for age, gender, educational attainment, English-speaking birth country, education/cognition GPS, bipolar disorder GPS, family history of dementia, family history of Parkinson’s disease, maternal smoking around birth, childhood trauma, other psychiatric/neurological conditions, deprivation, population density, road proximity, air pollution (PM10 and NO2), body mass index, alcohol frequency, smoking status, physical activity, and psychotropic medication. Missing mediator and covariate data were imputed via a single stochastic imputation using chained equations.a. Normal-based, from bootstrapped standard error (1000 replicates). b. Estimate expressed as a standardized mean difference.c. Estimate expressed as a risk difference for the probability of being correct.Table 11Mediation of the effect of mania/bipolar disorder on cognitive outcome via psychotropic medication, with missing data imputationNEstimate95% CIaReasoningb 80,698TE-0.095-0.162, -0.027CDE-0.034-0.103, 0.035NDE-0.034-0.103, 0.035NIE-0.061-0.078, -0.044Reaction timeb 82,648TE-0.080-0.136, -0.024CDE-0.034-0.095, 0.028NDE-0.024-0.085, 0.037NIE-0.056-0.078, -0.033Numeric memoryb 26,248TE-0.147-0.276, -0.018CDE-0.079-0.210, 0.051NDE-0.082-0.212, 0.049NIE-0.065-0.098, -0.033Visuospatial memoryb 81,773TE-0.196-0.255, -0.137CDE-0.147-0.210, -0.084NDE-0.146-0.209, -0.083NIE-0.050-0.076, -0.024Prospective memoryc 82,194TE-0.025-0.045, -0.005CDE-0.010-0.029, 0.010NDE-0.012-0.031, 0.007NIE-0.014-0.019, -0.008CDE, controlled direct effect when psychotropic medication = 0; CI, confidence interval; GPS, genome-wide polygenic score; NDE, natural direct effect; NIE, natural indirect effect; TE, total effect.Models were restricted to participants of white British genetic ancestry, and were adjusted for gender, educational attainment, English-speaking birth country, education/cognition GPS, bipolar disorder GPS, family history of dementia, family history of Parkinson’s disease, maternal smoking around birth, childhood trauma, other psychiatric/neurological conditions, deprivation, and lifetime number of episodes of depressed mood/anhedonia. Missing mediator and covariate data were imputed via a single stochastic imputation using chained equations.a. Normal-based, from bootstrapped standard error (1000 replicates). b. Estimate expressed as a standardized mean difference.c. Estimate expressed as a risk difference for the probability of being correct.Cognitive Impairment in Major DepressionCharacteristics of the SampleFigure 9 shows a flowchart of exclusions leading to the major depression analysis sample. As with the mania/BD analyses, a large number of participants were excluded due to missing data in at least one exposure information source, which meant they could not be classified in the comparison group. Figure 9Major depression analysis sample flowchartTable 12 describes the covariate data in both groups. As with the mania/BD analyses, the proportion of missing data was highest, by far, on the childhood trauma variable, and was also relatively high on the family history, current depressive symptoms and physical activity measures. The descriptive information indicated that the major depression group was younger, on average, than the comparison group, had a substantially higher proportion of women, and had higher proportions of current smokers, former drinkers, and participants living in deprived areas. The major depression group had higher proportions with frequent sleeplessness, obesity, cardiometabolic disease, comorbid neurological/psychiatric conditions, family history of severe depression, current psychotropic medication and history of childhood trauma, and they reported more depressed episodes and higher current depressive symptoms on average. The distribution of the major depression GPS score was skewed towards higher values in the major depression group. The subset of participants with complete covariate data appeared different from the full analysis sample across multiple measures, e.g. having a greater proportion of degree-holders and a smaller proportion from the most deprived areas. Table 12Summary of covariates in the major depression and comparison groupsAll available dataComplete covariate dataaMajor depressionComparisonMajor depressionComparisonN50,975102,93150,975102,931Sociodemographic Age (years)bMean (SD)55.6 (7.9)57.0 (8.2)55.6 (7.9)57.0 (8.2)GenderbN (%) female33,090 (64.9)51,463 (50.0)33,090 (64.9)51,463 (50.0)Ethnic groupN (%) missingWhite, N (%)cAsian/Asian BritishBlack/Black BritishChineseMixed & other background220 (0.4)48,345 (95.3)814 (1.6 )641 (1.3)69 (0.1)886 (1.8)401 (0.4)93,194 (90.9)3,663 (3.6)3,159 (3.1)473 (0.5)2,041 (2.0)220 (0.4)48,345 (95.3)814 (1.6 )641 (1.3)69 (0.1)886 (1.8)401 (0.4)93,194 (90.9)3,663 (3.6)3,159 (3.1)473 (0.5)2,041 (2.0)White British genetic ancestryN (%) missingN (%)c1,518 (3.0)41,509 (83.9)3,419 (3.3)79,097 (79.5)1,518 (3.0)41,509 (83.9)3,419 (3.3)79,097 (79.5)English-speaking country of birthN (%) missingN (%)c85 (0.2)47,741 (93.8)149 (0.2)91,524 (89.1)85 (0.2)47,741 (93.8)149 (0.2)91,524 (89.1)Has a degree N (%) missingN (%)c493 (1.0)16,713 (33.1)1,012 (1.0)36,211 (35.5)493 (1.0)16,713 (33.1)1,012 (1.0)36,211 (35.5)Townsend quintiledN (%) missingQu1 (least deprived), N (%)cQu2Qu3Qu4Qu5 (most deprived)93 (0.2)8,109 (15.9)9,124 (17.9)9,793 (19.3)11,261 (22.1)12,595 (24.8)159 (0.2)17,672 (17.2)20,807 (20.3)21,291 (20.7)23,193 (22.6)19,809 (19.3)93 (0.2)8,109 (15.9)9,124 (17.9)9,793 (19.3)11,261 (22.1)12,595 (24.8)159 (0.2)17,672 (17.2)20,807 (20.3)21,291 (20.7)23,193 (22.6)19,809 (19.3)Local environmentHome area population densityeN (%) missingEngland/Wales urban, N (%)cEngland/Wales townEngland/Wales villageEngland/Wales hamlet/isolatedScotland large urbanScotland other urbanScotland small townScotland rural596 (1.2)42,462 (84.3)3,239 (6.4)2,031 (4.0)842 (1.7)1,376 (2.7)300 (0.6)66 (0.1)63 (0.1)951 (0.9)88,651 (86.9)6,392 (6.3)4,853 (4.8)2,084 (2.0)0 (0.0)0 (0.0)0 (0.0)0 (0.0)596 (1.2)42,462 (84.3)3,239 (6.4)2,031 (4.0)842 (1.7)1,376 (2.7)300 (0.6)66 (0.1)63 (0.1)951 (0.9)88,651 (86.9)6,392 (6.3)4,853 (4.8)2,084 (2.0)0 (0.0)0 (0.0)0 (0.0)0 (0.0)Proximity to major road (1/m)N (%) missingMean (SD)772 (1.5)0.006 (0.014)1,290 (1.3)0.006 (0.013)772 (1.5)0.006 (0.014)1,290 (1.3)0.006 (0.013)Particulate matter ≤10μm (μg/m3)N (%) missingMean (SD)938 (1.8)22.4 (2.9)1,668 (1.6)22.8 (3.1)938 (1.8)22.4 (2.9)1,668 (1.6)22.8 (3.1)Nitrogen dioxide (μg/m3)N (%) missingMean (SD)772 (1.5)30.9 (10.1)1,290 (1.3)31.9 (10.6)772 (1.5)30.9 (10.1)1,290 (1.3)31.9 (10.6)Lifestyle and physical Smoking statusN (%) missingNever, N (%)cFormerCurrent170 (0.3)25,008 (49.2)18,320 (36.1)7,477 (14.7)368 (0.4)58,955 (57.5)34,658 (33.8)8,950 (8.7)170 (0.3)25,008 (49.2)18,320 (36.1)7,477 (14.7)368 (0.4)58,955 (57.5)34,658 (33.8)8,950 (8.7)Alcohol frequencyN (%) missingDaily/almost daily, N (%)c3-4 times per week1-2 times per week1-3 times per monthSpecial occasions onlyNever (former drinker)Never (not former drinker)100 (0.2)9,839 (19.3)10,085 (19.8)11,765 (23.1)6,344 (12.5)7,333 (14.4)3,189 (6.3)2,320 (4.6)80 (0.1)21,729 (21.1)24,196 (23.5)26,111 (25.4)11,118 (10.8)11,536 (11.2)3,096 (3.0)5,065 (4.9)100 (0.2)9,839 (19.3)10,085 (19.8)11,765 (23.1)6,344 (12.5)7,333 (14.4)3,189 (6.3)2,320 (4.6)80 (0.1)21,729 (21.1)24,196 (23.5)26,111 (25.4)11,118 (10.8)11,536 (11.2)3,096 (3.0)5,065 (4.9)SleeplessnessN (%) missingNever/rarely, N (%)cSometimesUsually58 (0.1)8,438 (16.6)23,090 (45.4)19,389 (38.1)79 (0.1)28,617 (27.8)49,193 (47.8)25,042 (24.4)58 (0.1)8,438 (16.6)23,090 (45.4)19,389 (38.1)79 (0.1)28,617 (27.8)49,193 (47.8)25,042 (24.4)Physical activity (MET h/week)N (%) missingMedian (Q1, Q3) 4,617 (9.1)26.2 (11.6, 55.9)6,690 (6.5)29.9 (13.8, 60.2)4,617 (9.1)26.2 (11.6, 55.9)6,690 (6.5)29.9 (13.8, 60.2)Body mass indexN (%) missingUnderweight, N (%)cNormalOverweightObese class IObese class IIObese class III354 (0.7)270 (0.5)15,166 (30.0)20,289 (40.1)9,781 (19.3)3,443 (6.8)1,672 (3.3)732 (0.7)496 (0.5)34,192 (33.5)43,942 (43.0)17,374 (17.0)4,588 (4.5)1,607 (1.6)354 (0.7)270 (0.5)15,166 (30.0)20,289 (40.1)9,781 (19.3)3,443 (6.8)1,672 (3.3)732 (0.7)496 (0.5)34,192 (33.5)43,942 (43.0)17,374 (17.0)4,588 (4.5)1,607 (1.6)Medical and family historyCardiometabolic diseaseN (%) missingN (%)c121 (0.2)17,459 (34.3)177 (0.2)31,361 (30.5)121 (0.2)17,459 (34.3)177 (0.2)31,361 (30.5)Comorbid neurological or psychiatric conditionfN (%)11,542 (22.6)9,155 (8.9)11,542 (22.6)9,155 (8.9)Family history of dementiaN (%) missingN (%)c7,815 (15.3)7,032 (16.3)14,476 (14.1)15,330 (17.3)7,815 (15.3)7,032 (16.3)14,476 (14.1)15,330 (17.3)Family history of Parkinson’s diseaseN (%) missingN (%)c9,456 (18.6)2,064 (5.0)16,090 (15.6)4,161 (4.8)9,456 (18.6)2,064 (5.0)16,090 (15.6)4,161 (4.8)Family history of severe depressionN (%) missingN (%)c7,871 (15.4)12,370 (28.7)15,347 (14.9)10,201 (11.7)7,871 (15.4)12,370 (28.7)15,347 (14.9)10,201 (11.7)Maternal smoking around birthN (%) missingN (%)c6,933 (13.6)14,521 (33.0)13,107 (12.7)24,125 (26.9)6,933 (13.6)14,521 (33.0)13,107 (12.7)24,125 (26.9)Mental health Current depressive symptomsN (%) missingMean (SD) 5,028 (9.9)3.1 (3.0)8,556 (8.3)1.2 (1.7)5,028 (9.9)3.1 (3.0)8,556 (8.3)1.2 (1.7)Any psychotropic medicationN (%) missingN (%)c860 (1.7)20,898 (41.7)1,194 (1.2)2,577 (2.5)860 (1.7)20,898 (41.7)1,194 (1.2)2,577 (2.5)Number of depressed episodesN (%) missingMedian (Q1, Q3)1,495 (2.9)1 (0, 4)6,451 (6.3)0 (0, 1)1,495 (2.9)1 (0, 4)6,451 (6.3)0 (0, 1)Any childhood traumagN (%) missingN (%)c34,801 (68.3)9,455 (58.5)67,160 (65.3)15,651 (43.8)34,801 (68.3)9,455 (58.5)67,160 (65.3)15,651 (43.8)Genome-wide polygenic scoresEducation/cognition GPS deciledN (%) missingD1 (lowest), N (%)cD2D3D4D5D6D7D8D9D10 (highest)1,518 (3.0)5,169 (10.5)5,089 (10.3)5,002 (10.1)4,931 (10.0)4,960 (10.0)4,890 (9.9)4,914 (9.9)4,830 (9.8)4,815 (9.7)4,857 (9.8)3,419 (3.3)9,728 (9.8)9,808 (9.9)9,895 (9.9)9,966 (10.0)9,937 (10.0)10,007 (10.1)9,983 (10.0)10,067 (10.1)10,082 (10.1)10,039 (10.1)1,518 (3.0)5,169 (10.5)5,089 (10.3)5,002 (10.1)4,931 (10.0)4,960 (10.0)4,890 (9.9)4,914 (9.9)4,830 (9.8)4,815 (9.7)4,857 (9.8)3,419 (3.3)9,728 (9.8)9,808 (9.9)9,895 (9.9)9,966 (10.0)9,937 (10.0)10,007 (10.1)9,983 (10.0)10,067 (10.1)10,082 (10.1)10,039 (10.1)Major depression GPS deciledN (%) missingD1 (lowest), N (%)cD2D3D4D5D6D7D8D9D10 (highest)1,518 (3.0)4,271 (8.6)4,645 (9.4)4,690 (9.5)4,693 (9.5)4,890 (9.9)4,887 (9.9)5,057 (10.2)5,247 (10.6)5,313 (10.7)5,764 (11.7)3,419 (3.3)10,626 (10.7)10,252 (10.3)10,207 (10.3)10,204 (10.3)10,007 (10.1)10,010 (10.1)9,840 (9.9)9,650 (9.7)9,584 (9.6)9,162 (9.2)1,518 (3.0)4,271 (8.6)4,645 (9.4)4,690 (9.5)4,693 (9.5)4,890 (9.9)4,887 (9.9)5,057 (10.2)5,247 (10.6)5,313 (10.7)5,764 (11.7)3,419 (3.3)10,626 (10.7)10,252 (10.3)10,207 (10.3)10,204 (10.3)10,007 (10.1)10,010 (10.1)9,840 (9.9)9,650 (9.7)9,584 (9.6)9,162 (9.2)D, decile; GPS, genome-wide polygenic score; MET, metabolic equivalent of task; Q, quartile; Qu, quintile; SD, standard deviation.a. Participants with complete data on all the covariates that were entered into the maximally-adjusted total effects models (age, gender, white British genetic ancestry, English-speaking country of birth, degree, comorbid neurological/psychiatric condition, family history of dementia, family history of Parkinson’s disease, family history of severe depression, maternal smoking around birth, childhood trauma, education/cognition GPS, major depression GPS).b. No missing data.c. Missing excluded from denominator.d. Based on data distribution in the whole UK Biobank cohort.e. Scottish psychiatric hospital records were unavailable, which meant no Scotland-based participants could be classified in the comparison group; therefore all locations for comparison participants are in England/Wales. f. Apart from mood disorder or schizophrenia; not possible to distinguish between missing data and self-report of no condition, therefore both classified as ‘No’.g. From the web-based questionnaire, which was completed by 157,366 (31.3%) of the cohort. Evaluation of the Graphical ModelThe different predicted independencies implied by the two specifications of the DAG (educational attainment as an antecedent, or a consequence, of major depression and other psychiatric/neurological conditions) were tested, and better fit was evident in the first specification. Fifteen percent (21 of 137) of the partial correlation coefficients were above |0.1|, but most of these were below |0.2| and the largest coefficient was |0.22|.Total EffectsThe best covariate balance was obtained using the first propensity score model with no interaction terms, as illustrated in Table 13. This was used in all the outcome models that involved propensity score adjustment or matching, or inverse probability weighting. Figures 10 to 13 show the total effects results for reasoning, reaction time, numeric memory and prospective memory.Table 13Summary of covariates in matched major depression and comparison groupsMajor depressionComparisonMean (SD)Age (years)55.1 (7.5)55.2 (7.5)%Female gender68.469.5English-speaking country of birth98.398.2Has a degree 46.046.5Comorbid neurological or psychiatric condition19.319.3Family history of dementia16.416.4Family history of Parkinson’s disease4.44.3Family history of severe depression25.224.7Maternal smoking around birth31.230.7Any childhood trauma54.854.7Education/cognition GPSD1 (lowest)D2D3D4D5D6D7D8D9D10 (highest)8.69.49.59.79.79.810.610.111.111.58.29.49.59.59.89.810.910.111.111.7Major depression GPSD1 (lowest)D2D3D4D5D6D7D8D9D10 (highest)8.69.69.99.09.89.810.210.610.811.78.89.79.88.810.09.310.610.710.911.4D, decile; GPS, genome-wide polygenic score; SD, standard deviation.These results are from the matched samples used in the 1:3 matched model for the total effect of major depression on reaction time (major depression N = 9,381; comparison N = 13,538).Figure 10Total effect of major depression on reasoningCI, confidence interval; IPW, inverse probability weighting; IPWRA, inverse probability weighting with regression adjustment; teffects, Stata teffects package. Estimates are in z-score units and can be interpreted as standardized mean differences.Figure 11Total effect of major depression on reaction timeCI, confidence interval; IPW, inverse probability weighting; IPWRA, inverse probability weighting with regression adjustment; teffects, Stata teffects package. Estimates are in z-score units and can be interpreted as standardized mean differences.Figure 12Total effect of major depression on numeric memoryCI, confidence interval; IPW, inverse probability weighting; IPWRA, inverse probability weighting with regression adjustment; teffects, Stata teffects package. Estimates are in z-score units and can be interpreted as standardized mean differences.Figure 13Total effect of major depression on prospective memoryCI, confidence interval; IPW, inverse probability weighting; IPWRA, inverse probability weighting with regression adjustment; teffects, Stata teffects package. Estimates are proportions and can be interpreted as risk differences. No estimates are provided from propensity-score matched models as it was not possible to express these as risk differences.Mediation AnalysesIn both sets of mediation analyses, the indirect pathways were affected by intermediate confounding, and so an identifying assumption was firstly made of no interaction between major depression and either cardiometabolic disease or psychotropic medication. ADDIN EN.CITE <EndNote><Cite><Author>Robins</Author><Year>1992</Year><RecNum>11668</RecNum><DisplayText><style face="superscript">61</style></DisplayText><record><rec-number>11668</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1500441059">11668</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Robins, J. M.</author><author>Greenland, S.</author></authors></contributors><titles><title>Identifiability and Exchangeability for Direct and Indirect Effects</title><secondary-title>Epidemiology</secondary-title><alt-title>Epidemiology</alt-title></titles><periodical><full-title>Epidemiology</full-title><abbr-1>Epidemiology</abbr-1></periodical><alt-periodical><full-title>Epidemiology</full-title><abbr-1>Epidemiology</abbr-1></alt-periodical><pages>143-155</pages><volume>3</volume><number>2</number><keywords><keyword>causality</keyword><keyword>causal modeling</keyword><keyword>epidemiologic methods</keyword><keyword>risk</keyword></keywords><dates><year>1992</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>1044-3983</isbn><accession-num>WOS:A1992HH93800013</accession-num><urls><related-urls><url><Go to ISI>://WOS:A1992HH93800013</url></related-urls></urls><electronic-resource-num>10.1097/00001648-199203000-00013</electronic-resource-num><language>English</language></record></Cite></EndNote>61 This assumption was checked by conducting a regression model of each cognitive outcome on major depression exposure status, the mediator and all the covariates, including a product term for major depression * mediator. There was no evidence of interaction in the cardiometabolic disease mediation models, but some evidence of interaction in the psychotropic medication models (Table 14 below). The alternative identifying assumption proposed by Peterson et al. ADDIN EN.CITE <EndNote><Cite ExcludeAuth="1" ExcludeYear="1"><Author>Petersen</Author><Year>2006</Year><RecNum>11843</RecNum><DisplayText><style face="superscript">62</style></DisplayText><record><rec-number>11843</rec-number><foreign-keys><key app="EN" db-id="xtsepwf2b5fswxerax7xrtdzt2rf2eawd0pr" timestamp="1515343259">11843</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Petersen, M. L.</author><author>Sinisi, S. E.</author><author>van der Laan, M. J.</author></authors></contributors><auth-address>Univ Calif Berkeley, Sch Publ Hlth, Div Biostat, Berkeley, CA 94720 USA</auth-address><titles><title>Estimation of direct causal effects</title><secondary-title>Epidemiology</secondary-title><alt-title>Epidemiology</alt-title></titles><periodical><full-title>Epidemiology</full-title><abbr-1>Epidemiology</abbr-1></periodical><alt-periodical><full-title>Epidemiology</full-title><abbr-1>Epidemiology</abbr-1></alt-periodical><pages>276-284</pages><volume>17</volume><number>3</number><keywords><keyword>apoptosis</keyword></keywords><dates><year>2006</year><pub-dates><date>May</date></pub-dates></dates><isbn>1044-3983</isbn><accession-num>WOS:000236926500013</accession-num><urls><related-urls><url><Go to ISI>://WOS:000236926500013</url></related-urls></urls><electronic-resource-num>10.1097/01.ede.0000208475.99429.2d</electronic-resource-num><language>English</language></record></Cite></EndNote>62 was therefore made for the psychotropic medication models; following De Stavola et al.PEVuZE5vdGU+PENpdGUgRXhjbHVkZUF1dGg9IjEiIEV4Y2x1ZGVZZWFyPSIxIj48QXV0aG9yPkRl
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ADDIN EN.CITE.DATA 59 this was checked by testing for interactions between major depression and each of the intermediate confounders (deprivation, and lifetime number of episodes of depressed mood/anhedonia), in regression models that included major depression, psychotropic medication and their product, along with all the other model covariates. There was little evidence of interaction between major depression and the intermediate confounders (Table 15), and so this identifying assumption was deemed reasonable and these mediation models were estimated with a product term included between major depression and psychotropic medication.Table 14Tests of interactions between exposure and mediators in the major depression analysesCoefficient for major depression * mediator95% CIPMediator: Cardiometabolic diseaseReasoninga0.029-0.027, 0.0850.304Reaction timea0.053-0.001, 0.1070.056Numeric memorya0.057-0.052, 0.1660.306Visuospatial memorya0.036-0.024, 0.0950.241Prospective memoryb1.1570.952, 1.4070.142Mediator: Psychotropic medicationReasoninga0.2440.135, 0.354<0.001Reaction timea0.080-0.025, 0.1850.134Numeric memorya0.2030.013, 0.3930.036Visuospatial memorya0.082-0.037, 0.2000.176Prospective memoryb1.4541.057, 2.0000.021CI, confidence interval.All models included major depression, the mediator and their product, as well as all the covariates entered into the gformula mediation models.a. Linear regression model with outcome measured in z-score units.b. Logistic regression model with outcome measured as correct or not; estimate expressed as odds ratio.Table 15Tests of interactions between exposure and intermediate confoundersCoefficient for major depression * deprivation95% CIPCoefficient for major depression * lifetime number of episodes of depressed mood/anhedonia95% CIPReasoninga0.018-0.030, 0.0660.458-0.020-0.074, 0.0350.481Reaction timea-0.017-0.064, 0.0300.4750.033-0.020, 0.0850.224Numeric memorya0.041-0.057, 0.1380.413-0.002-0.106, 0.1030.974Visuospatial memorya0.0560.005, 0.1080.033-0.023-0.081, 0.0350.433Prospective memoryb0.9890.836, 1.1710.9001.0000.834, 1.2120.996CI, confidence interval.Deprivation was entered as a dichotomous indicator for the two most deprived Townsend quintiles versus the three least deprived quintiles. Lifetime number of episodes of depressed mood/anhedonia was entered as a dichotomous indicator for ≥2 episodes versus <2 episodes. All models included the product terms indicated in the table above, as well as major depression, deprivation, lifetime number of episodes of depressed mood/anhedonia, psychotropic medication, major depression * psychotropic medication, and all the other covariates entered into the gformula mediation models.a. Linear regression model with outcome measured in z-score units.b. Logistic regression model with outcome measured as correct or not; estimate expressed as odds ratio.Table 16Mediation of the effect of major depression on cognitive outcome via cardiometabolic diseaseNEstimate95% CIaReasoningb 26,679TE-0.009-0.038, 0.019CDE-0.029-0.056, -0.001NDE0.005-0.023, 0.033NIE-0.014-0.028, 0.000Reaction timeb 29,422TE-0.015-0.042, 0.013CDE-0.006-0.036, 0.024NDE-0.009-0.037, 0.020NIE-0.006-0.021, 0.009Numeric memoryb 8,085TE-0.034-0.087, 0.019CDE-0.032-0.085, 0.021NDE-0.035-0.089, 0.018NIE0.001-0.027, 0.029Visuospatial memoryb 29,284TE-0.074-0.107, -0.042CDE-0.077-0.109, -0.046NDE-0.079-0.111, -0.047NIE0.005-0.012, 0.022Prospective memoryc 26,789TE-0.010-0.022, 0.001CDE-0.001-0.012, 0.010NDE-0.011-0.022, 0.000NIE0.001-0.012, 0.010CDE, controlled direct effect when cardiometabolic disease = 0; CI, confidence interval; GPS, genome-wide polygenic score; NDE, natural direct effect; NIE, natural indirect effect; NO2, nitrogen dioxide; PM10, particulate matter of up to 10μm diameter; TE, total effect.Models were restricted to participants of white British genetic ancestry, and were adjusted for age, gender, educational attainment, English-speaking birth country, education/cognition GPS, major depression GPS, family history of dementia, family history of Parkinson’s disease, maternal smoking around birth, childhood trauma, other psychiatric/neurological conditions, deprivation, population density, road proximity, air pollution (PM10 and NO2), body mass index, alcohol frequency, smoking status, physical activity, and psychotropic medication.a. Normal-based, from bootstrapped standard error (1000 replicates). b. Estimate expressed as a standardized mean difference.c. Estimate expressed as a risk difference for the probability of being correct.Table 17Mediation of the effect of major depression on cognitive outcome via psychotropic medicationNEstimate95% CIaReasoningb 27,263TE-0.009-0.037, 0.019CDE-0.011-0.040, 0.018NDE-0.010-0.039, 0.020NIE0.001-0.016, 0.017Reaction timeb 30,189TE0.002-0.026, 0.029CDE0.005-0.025, 0.036NDE0.026-0.004, 0.055NIE-0.024-0.042, -0.005Numeric memoryb 8,338TE-0.023-0.078, 0.032CDE-0.021-0.077, 0.035NDE-0.019-0.076, 0.038NIE-0.004-0.035, 0.028Visuospatial memoryb 30,038TE-0.058-0.088, -0.028CDE-0.066-0.100, -0.031NDE-0.039-0.073, -0.006NIE-0.019-0.040, 0.003Prospective memoryc 27,381TE0.001-0.011, 0.012CDE0.001-0.010, 0.013NDE0.001-0.011, 0.012NIE0.000-0.006, 0.006CDE, controlled direct effect when psychotropic medication = 0; CI, confidence interval; GPS, genome-wide polygenic score; NDE, natural direct effect; NIE, natural indirect effect; TE, total effect.Models were restricted to participants of white British genetic ancestry, and were adjusted for gender, educational attainment, English-speaking birth country, education/cognition GPS, major depression GPS, family history of dementia, family history of Parkinson’s disease, maternal smoking around birth, childhood trauma, other psychiatric/neurological conditions, deprivation, and lifetime number of episodes of depressed mood/anhedonia. All models included a product term for major depression * psychotropic medication.a. Normal-based, from bootstrapped standard error (1000 replicates). b. Estimate expressed as a standardized mean difference.c. Estimate expressed as a risk difference for the probability of being correct.Sensitivity AnalysesRosenbaum bounds were calculated to check the sensitivity of the visuospatial memory total effect result to departures from exchangeability. The estimated effect crossed the null at a gamma value of 1.07, i.e. the point where the probability of being in the exposed group is approximately 0.48 or 0.52. The results would therefore not be robust to an unmeasured confounder with even a very weak association with group membership. There was evidence of missing data bias, in that the unadjusted total effects estimates shifted (towards or away from the null) when the sample was restricted to participants with complete covariate data. When the multiple linear regression models for total effects were repeated using imputed covariate values, the estimates for reaction time indicated a very small detrimental effect (point estimate approximately -0.02 to -0.03) in the major depression group (Figure 14(b) below), which was not evident in the complete case analyses. The estimates for the other cognitive outcomes were similar between the complete case analyses and those using multiple imputation. When the mediation models were repeated with imputation of missing mediator and covariate values, there remained no evidence of indirect effects via cardiometabolic disease (Table 18). There was evidence of an indirect effect via psychotropic medication on visuospatial memory performance (Table 19), accounting for approximately 18% of the total effect.The results of the probabilistic analysis using episens indicated that the total effects estimate for visuospatial memory would be biased away from the null if there were differential misclassification of the exposure. When dichotomized into impaired and unimpaired outcome categories, and assuming no exposure misclassification, the unadjusted relative risk of impairment was 1.14 in the major depression group (95% CI 1.07, 1.20). Assuming lower sensitivity to true major depression status among the cognitively impaired (sensitivity range 0.6 to 0.9) versus unimpaired participants (sensitivity range 0.7 to 1.0), the relative risk was estimated as 1.37 (1.06, 1.75). DAGitty determined that there were six other DAGs that were equivalent to the DAG used in the analyses. None of these alternative configurations was causally plausible, owing to temporal order constraints. The same minimum adjustment set was valid for the analysed DAG and for the six equivalent DAGs, for estimating the total effect of major depression on cognitive outcome.Figure 14Comparison of missing data approaches in major depression total effects analysesCI, confidence interval; MICE, multiple imputation with chained equations. Panels show: (a) reasoning, (b) reaction time, (c) numeric memory and (d) visuospatial memory. Prospective memory not shown as it was not possible to calculate risk differences.Table 18Mediation of the effect of major depression on cognitive outcome via cardiometabolic disease, with missing data imputationNEstimate95% CIaReasoningb 102,643TE0.001-0.017, 0.019CDE-0.005-0.023, 0.013NDE-0.009-0.026, 0.009NIE0.009-0.023, 0.013Reaction timeb 119,830TE-0.024-0.042, -0.006CDE-0.022-0.039, -0.004NDE-0.032-0.050, -0.014NIE0.008-0.002, 0.019Numeric memoryb 33,250TE-0.006-0.039, 0.027CDE-0.013-0.047, 0.020NDE-0.006-0.039, 0.027NIE0.000-0.016, 0.017Visuospatial memoryb 118,363TE-0.072-0.091, -0.054CDE-0.074-0.093, -0.056NDE-0.072-0.091 -0.053NIE-0.001-0.011, 0.010Prospective memoryc 104,509TE-0.004-0.009, 0.001CDE-0.002-0.007, 0.003NDE-0.004-0.009, 0.002NIE-0.000-0.003, 0.002CDE, controlled direct effect when cardiometabolic disease = 0; CI, confidence interval; GPS, genome-wide polygenic score; NDE, natural direct effect; NIE, natural indirect effect; NO2, nitrogen dioxide; PM10, particulate matter of up to 10μm diameter; TE, total effect.Models were restricted to participants of white British genetic ancestry, and were adjusted for age, gender, educational attainment, English-speaking birth country, education/cognition GPS, major depression GPS, family history of dementia, family history of Parkinson’s disease, maternal smoking around birth, childhood trauma, other psychiatric/neurological conditions, deprivation, population density, road proximity, air pollution (PM10 and NO2), body mass index, alcohol frequency, smoking status, physical activity, and psychotropic medication. Missing mediator and covariate data were imputed via a single stochastic imputation using chained equations.a. Normal-based, from bootstrapped standard error (1000 replicates). b. Estimate expressed as a standardized mean difference.c. Estimate expressed as a risk difference for the probability of being correct.Table 19Mediation of the effect of major depression on cognitive outcome via psychotropic medication, with missing data imputationNEstimate95% CIaReasoningb 102,643TE-0.004-0.022, 0.014CDE0.003-0.016, 0.021NDE-0.010-0.028, 0.008NIE0.006-0.003, 0.015Reaction timeb 119,830TE-0.009-0.026, 0.008CDE0.011-0.009, 0.030NDE-0.008-0.026, 0.009NIE-0.001-0.011, 0.010Numeric memoryb 33,250TE-0.022-0.056, 0.011CDE-0.001-0.035, 0.034NDE-0.005-0.040, 0.030NIE-0.017-0.036, 0.002Visuospatial memoryb 118,363TE-0.068-0.087, -0.049CDE-0.035-0.057, -0.014NDE-0.056-0.074, -0.037NIE-0.012-0.023, -0.001Prospective memoryc 104,509TE0.002-0.003, 0.008CDE0.0070.001, 0.013NDE0.001-0.004, 0.006NIE0.001-0.002, 0.004CDE, controlled direct effect when psychotropic medication = 0; CI, confidence interval; GPS, genome-wide polygenic score; NDE, natural direct effect; NIE, natural indirect effect; TE, total effect.Models were restricted to participants of white British genetic ancestry, and were adjusted for gender, educational attainment, English-speaking birth country, education/cognition GPS, major depression GPS, family history of dementia, family history of Parkinson’s disease, maternal smoking around birth, childhood trauma, other psychiatric/neurological conditions, deprivation, and lifetime number of episodes of depressed mood/anhedonia. 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