Genetic, psychosocial and clinical factors associated with hippocampal volume in the general population

Abstract

The hippocampus--crucial for memory formation, recall and mood regulation--is involved in the pathophysiology of dementia and depressive disorders. Recent genome-wide association studies (GWAS) have identified five genetic loci associated with hippocampal volume (HV). Previous studies have described psychosocial and clinical factors (for example, smoking, type 2 diabetes and hypertension) to have an impact on HV. However, the interplay between genetic, psychosocial and clinical factors on the HV remains unclear. Still, it is likely that genetic variants and clinical or psychosocial factors jointly act in modifying HV; it might be possible they even interact. Knowledge of these factors might help to quantify ones individual risk of or rather resilience against HV loss. We investigated subjects (N=2463; 55.7% women; mean age 53 years) from the Study of Health in Pomerania (SHIP-2; SHIP-TREND-0) who underwent whole-body magnetic resonance imaging (MRI) and genotyping. HVs were estimated with FreeSurfer. For optimal nonlinear model fitting, we used regression analyses with restricted cubic splines. Genetic variants and associated psychosocial or clinical factors were jointly assessed for potential two-way interactions. We observed associations between HV and gender (P<0.0001), age (P<0.0001), body height (P<0.0001), education (P=0.0053), smoking (P=0.0058), diastolic blood pressure (P=0.0211), rs7294919 (P=0.0065), rs17178006 (P=0.0002), rs6581612 (P=0.0036), rs6741949 (P=0.0112) and rs7852872 (P=0.0451). In addition, we found three significant interactions: between rs7294919 and smoking (P=0.0473), rs7294919 and diastolic blood pressure (P=0.0447) and between rs7852872 and rs6581612 (P=0.0114). We suggest that these factors might have a role in the individual susceptibility to hippocampus-associated disorders.

Citation: Transl Psychiatry (2014) 4, e465; doi:10.1038/tp.2014.102 2014 Macmillan Publishers Limited All rights reserved 2158-3188/14

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ORIGINAL ARTICLE

Genetic, psychosocial and clinical factors associated with hippocampal volume in the general population

D Janowitz1, C Schwahn2, U Borchardt1,3, K Wittfeld3, A Schulz1, S Barnow4, R Biffar2, W Hoffmann3,5, M Habes5,6, G Homuth7, M Nauck8, K Hegenscheid9, M Lotze9, H Vlzke5, HJ Freyberger1,10, S Debette11,12,13,14 and HJ Grabe1,3,10

The hippocampuscrucial for memory formation, recall and mood regulationis involved in the pathophysiology of dementia and depressive disorders. Recent genome-wide association studies (GWAS) have identied ve genetic loci associated with hippocampal volume (HV). Previous studies have described psychosocial and clinical factors (for example, smoking, type 2 diabetes and hypertension) to have an impact on HV. However, the interplay between genetic, psychosocial and clinical factors on the HV remains unclear. Still, it is likely that genetic variants and clinical or psychosocial factors jointly act in modifying HV; it might be possible they even interact. Knowledge of these factors might help to quantify ones individual risk of or rather resilience against HV loss. We investigated subjects (N = 2463; 55.7% women; mean age 53 years) from the Study of Health in Pomerania (SHIP-2; SHIP-TREND-0) who underwent whole-body magnetic resonance imaging (MRI) and genotyping. HVs were estimated with FreeSurfer. For optimal nonlinear model tting, we used regression analyses with restricted cubic splines. Genetic variants and associated psychosocial or clinical factors were jointly assessed for potential two-way interactions. We observed associations between HV and gender (Po0.0001), age (Po0.0001), body height (Po0.0001), education (P = 0.0053), smoking (P = 0.0058), diastolic blood pressure (P = 0.0211), rs7294919 (P = 0.0065), rs17178006 (P = 0.0002), rs6581612 (P = 0.0036), rs6741949 (P = 0.0112) and rs7852872 (P = 0.0451). In addition, we found three signicant interactions: between rs7294919 and smoking (P = 0.0473), rs7294919 and diastolic blood pressure (P = 0.0447) and between rs7852872 and rs6581612 (P = 0.0114). We suggest that these factors might have

a role in the individual susceptibility to hippocampus-associated disorders.

Translational Psychiatry (2014) 4, e465; doi:10.1038/tp.2014.102; published online 14 October 2014

INTRODUCTIONThe hippocampusa core region in the limbic system

represents an important brain structure for memory1 and stress regulation.2 Decreased hippocampal volume (HV) is associated with executive dysfunction.3,4 In addition, HV and hippocampus-dependent memory measures (such as short-delay retention, long-delay retention and discriminability) are positively correlated.5 Patients with hippocampal degradation seem to have negative clinical outcome in depression.3,6 Cumulative factors lead to differences in the HV with increasing age. Therefore, it seems to be important to investigate factors inuencing HV changes.

A broad range of clinical and psychosocial determinants inuencing the HV and its function have been identied. Depression,7,8 alcohol consumption911 and smoking have been associated with decreased HVs.12 In addition, cardiometabolic factors have been associated with decreased HV: abdominal obesity,13,14 hypertension,15,16 type 2 diabetes1719 and elevated total cholesterol levels.12 In contrast, exercise intervention increased the HV and subsequently improved memory

function.20,21 High education buffered the age-related decrease in the HV.22

In twin studies, the heritability of HV has been estimated at 40 64%.23,24 Two genome-wide association studies (GWAS) have been performed to search for genetic markers related to HV.25,26

We have contributed with our samples (SHIP-2, SHIP-TREND-0) to these analyses, leading to the identication of the intergenic variant rs7294919,26 replicated by Bis et al.25 Rs7294919 is located on 12q24.22, a chromosomal region comprising the genes TESC, HRK and FBXW8. The biological mechanism how rs7294919 inuences on HV is currently unknown. However, all three neighboring genes could be involved. TESC has an important role in cell proliferation and differentiation for HV and brain development.26,27 Harakiri (HRK) is a regulator of apoptosis, which exhibits death-inducing activity in cells28,29 FBXW8 encodes a

member of the F-box protein family, which modies neuronal dendrite growth and elaboration.30 Furthermore, Bis et al.25 identied promising loci at 12q14 (WIF1 and MSRB3), 2q24 (within DPP4, dipeptidylpeptidase) and 9p33 (within ASTN2).

1Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany; 2Department of Prosthetic Dentistry, Gerostomatology and Dental Materials, Center of Oral Health, University Medicine Greifswald, Greifswald, Germany; 3German Center for Neurodegenerative Diseases DZNE, Site Rostock/ Greifswald, Germany; 4Institute of Clinical Psychology, University of Heidelberg, Heidelberg, Germany; 5Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; 6Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; 7Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany; 8Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany; 9Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany; 10Department of Psychiatry and Psychotherapy, HELIOS Hospital Stralsund, Stralsund, Germany; 11Department of Neurology, Boston University School of Medicine, Boston, MA, USA; 12Institut National de la Sant et de la Recherche Mdicale (INSERM), U740, Paris, France; 13University of Paris 7 Denis-Diderot, Paris, France and 14Department of Neurology, Lariboisire Hospital, Paris, France. Correspondence: Dr D Janowitz, Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Ellernholzstrasse 1-2, Greifswald 17475, Germany.

E-mail: mailto:[email protected]

Web End [email protected] Received 28 July 2014; accepted 13 August 2014

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2 333 subjects aged 30-93 years at the time of 10 years follow-up of SHIP (SHIP-2)

4 420 subjects aged 20-83 years at the time of baseline examination in TREND (TREND-0)

81 subjects

temporarily under quality control

172 subjects

1 189 women, 1 063 men

pre-final data set

2 181 women, 2 067 men

control

1 112 subjects

without MRI scanning

2 131 subjects

588 women, 552 men

with MRI scanning

1 084 women, 1 033 men

145 subjects

low MRI quality

213 subjects

control

529 women, 466 men

high MRI quality

1 001 women, 903 men

control

17+4 subjects

missing FS values or 2x low FS quality

17+16 subjects

control

525 women, 449 men

high FS quality in 1 hemispheres

997 women, 874 men

control

23 subjects

covariates missing not at random

17 subjects

n = 2 805: 1 504 women, 1 301 men aged 21-89 years used for multiple imputation

54 subjects

low FS quality in one hemisphere

132 subjects

490 women, 407men

high FS quality in both hemispheres

932 women, 790 men

n = 2 619: 1 422 women,1 197 men aged 21-89 years for prediction after imputation

132 subjects

covariates missing at random

24 subjects

418 women, 347 men

control

subjects for complete case analysis

917 women, 781 men

control

n=2 463: 1 335 women, 1 128 men aged 21-89 years for complete case analysis

Sample for SNP analysis excluding depression as covariate

15 subjects

without genotyping

1 051 subjects

514 women, 445 men

with genotyping

466 women, 354 men

n = 1 779: 980 women and 799 men aged 22-89 years with genotyping

56 subjects

low FS quality in one hemisphere

55 subjects

control

17 subjects

covariates missing not at random

5 subjects

control

n=1 646: 918 women and 728 men aged 22-89 years for SNP analysis after single imputation

3 subjects

covariates missing at random

2 subjects

479 women, 404 men

subjects for complete case analysis

435 women, 323 men

n = 1 641: 914 women and 727 men aged 22-89 years for SNP analysis via complete cases

Figure 1. Flow chart: inclusion and exclusion criteria for participants from the Study of Health in Pomerania (SHIP-2 and SHIP-TREND-0).

Those markers may be of relevance to the physiology of the hippocampus: Wingless-related integration site (WNT) inhibitory factor 1 is a protein that inhibits WNT proteins, which have an important role in the embryonic development, in the regulation of morphogenesis in the central nervous system and which can affect neuronal functions in adulthood.31,32 MSRB3 encodes

proteins that catalyze the reduction of methionine sulfoxide to methionine, thus preserving the biological activity of proteins after oxidative damage.33,34 DPP4 is a protein encoded by the

DPP4 gene. DPP4 inhibitors, as a new antidiabetic treatment approach, inhibit the degradation of Glucagon-like Peptide 1. The DPP-4 inhibitor moderated the impaired neuronal insulin receptor function and brain mitochondrial function caused by high-fat diet and showed improved cognitive behavior in an animal model.35

Astrotactin2 (ASTN2) is expressed in the brain among others in the

dendate gyrus of the hippocampus and, together with ASTN1, involved in neuronal migration.36

However, the interplay between genetic, psychosocial and clinical factors on the HV remains poorly understood. These factors might jointly act or interact in modifying HV. Knowledge of these factors and their interactions might help to quantify ones individual risk of or rather resilience against hippocampus-related diseases (for example, dementia) with HV loss and hippocampal loss function. It might improve our understanding of structural changes from this vulnerable brain tissue. We included the above mentioned, well-described and highly epidemiological relevant clinical and psychosocial factors to affect HV in our study.

The aim of our study was to explore direct associations of genetic candidate markers and potential psychosocial and clinical factors with HV in the general adult population. Moreover, we

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investigated putative genegene and geneenvironment interactions in relation to HVs.

Our aims were threefold:

(1) to explore clinical and psychosocial determinants of HV in large adult population-based samples (SHIP-2 and SHIP-TREND-0);

(2) to test associations between single-nucleotide polymorphisms (SNPs; known from GWAS25,26) and HV;

(3) to examine two-way interactions between those SNPs and psychosocial and clinical factors and interactions between these SNPs.

MATERIALS AND METHODS Participants

Data from the Study of Health in Pomerania (SHIP-2 and SHIP-TREND-0) were used.3739 SHIP-0 comprised adult German residents in northeastern Germany living in three cities and 29 communities, with a total population of 212 157. A two-stage stratied cluster sample (aged 2079 years at baseline) was randomly drawn from local registries. In all, 4308 Caucasian subjects participated at baseline SHIP-0 (19972001). The rst follow-up was conducted 5 years later (SHIP-1; N = 3300 subjects). The second follow-up examination (SHIP-2; N = 2333) was carried out about 10 years after baseline. In 2007, the LEGEND study was started (evaluation of lifetime diagnosis of depression in the SHIP-2 sample).37 Concurrent with SHIP-2, a new independent sample in the same area was drawn in 2008 and similar examinations were undertaken (SHIP-TREND-0; N = 4420).

For inclusion and exclusion criteria see ow chart (Figure 1).

Magnetic resonance imaging (MRI) scanning and segmentation of the hippocampusAll images were obtained from the same scanner (1.5 Tesla Magnetom

Avanto; Siemens Medical Solutions, Erlangen, Germany). We used the multiplanar reconstruction T1-weighted axial MRI sequence with the following parameters: 1900 ms repetition time, 3.4 ms echo time, ip angle = 15 and a voxel size of 1.0 1.0 1.0 mm. After le format conversion from DICOM to NIfTI, the whole preprocessing of the T1-weighted images and segmentation of the hippocampus was carried out with FreeSurfer v5.1.0 (Cambridge, MA, USA).

Interview (psychosocial factors) and clinical examination Sociodemographic factors and medical history were assessed by a computer-assisted face-to-face- interview. Smoking was dened as current smoking (occasional; 114 cigarette (s) per day; 15 cigarettes per day), former smoking (occasional; 114 cigarette (s) per day; 15 cigarettes per day) and never smoking. The highest level of education was assessed by interview and was divided into three groups of school education: 8 years, 10 years and 12 years. Leisure time physical activity (weekly sportive activity or exercise, for example, jogging, biking and swimming) was assessed by interview in three categories: no activity (neither in summer nor in winter), high activity (42 h per week as well in summer and winter)

and moderate activity (residual category). Alcohol consumption was calculated from the interview in g per day as a continuous variable. Having completed the interview, participants underwent medical examinations: including the measurement of height and weight to calculate the body mass index (continuous variable). Waist circumference was measured in cm (continuous variable). After a 5-min resting period, blood pressure was measured three times on the right arm of seated subjects using a digital blood pressure monitor (HEM-705CP, Omron, Tokyo, Japan), with each reading being followed by a further resting period of 3 min. Cuffs were applied according to the circumference of the participant's arm. The mean of the second and third measurements (mm Hg) was used for the analyses (continuous variables). All subjects were informed to bring in their packing containers of all medication they had taken during the last 7 days, as well as their drug prescription sheets. Every compound was recorded and categorized according to the Anatomical Therapeutic Chemical classication.40 We focused on antidepressants (ATC-code N06A*), antihypertensives (C02*, C03*, C07*, C08* and C09*), antidiabetic drugs (A10*) and lipid-lowering drugs (C10*).

Assessment of depressionThe lifetime diagnosis of depressive disorders was assessed with the

Munich-Composite International Diagnostic Interview (M-CIDI41) in SHIP-

LEGEND37 and in SHIP-TREND-0. The M-CIDI is a standardized, fully structured instrument for assessing psychiatric disorders over the lifespan according to DSM-IV criteria.

GenotypingSerum, EDTA, citrate plasma and DNA are stored at 80 C in a biobank. In

SHIP-0, 4081 individuals were successfully genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0 (Affymetrix, Santa Clara, CA, USA). The overall genotyping efciency of the GWA was 98.6%. Imputation of genotypes in the SHIP cohort was performed with the software IMPUTE v0.5.0 (Oxford, UK) against the HapMap II (CEU v22, Build 36; International HapMap Consortium) reference panel using 869 224 genotyped SNPs. The total number of SNPs after imputation and quality control was 2 748 910.

Genotyping of the SHIP-TREND-0 subjects (N = 986) was performed using the Illumina HumanOmni2.5-Quad (San Diego, CA, USA). DNA from whole blood was prepared using the Gentra Puregene Blood Kit (Qiagen, Hilden, Germany) according to the manufacturers protocol. Subsequent sample processing and array hybridization were performed as described by the manufacturer (Illumina) at the Helmholtz Zentrum Mnchen. The nal sample call rate was 99.51%. Imputation of genotypes in the SHIP-TREND-0 cohort was performed with the software IMPUTE v2.1.2.3 against the HapMap II (CEU v22, Build 36) reference panel. The total number of SNPs after imputation and quality control was 3 437 411. Allele frequencies were all in HardyWeinberg equilibrium P40.2 and imputation quality was

P40.90, except for rs17178006 (Supplementary eTable 1).

Purity and concentration of DNA was determined using a NanoDrop ND-1000 UV-Vis Spectrophotometer (Thermo Scientic, Waltham, MA, USA). The integrity of all DNA preparations was validated with electrophoresis using 0.8% agarose-1x TBE gels stained with ethidium bromide. Genotypes in SHIP-0 were determined using the Birdseed2 clustering algorithm. Genotypes in SHIP-TREND-0 were called with the GenCall algorithm of GenomeStudio Genotyping Module v1.0 (Illumina). Arrays with a call rate below 94%, duplicate samples as identied by estimated IBD as well as individuals with reported and genotyped gender mismatch were excluded. The genetic data analysis workow was created using the Software InforSense (London, UK). Genetic data were stored using the database Cach (InterSystems, Cambridge, MA, USA). HardyWeinberg equilibrium P-values are: rs7294919: P = 0.913; rs17178006: P = 0.844; rs6581612: P = 0.658; rs6741949: P = 0.215 and rs7852872: P = 0.4233.

Laboratory workGlycated hemoglobin (HbA1c, measured in %) concentrations were determined with high-performance liquid chromatography (Bio-Rad Diamat, Munich, Germany). Skilled technical personnel performed all assays according to the manufacturers recommendations. In addition, the laboratory participates in ofcial quarterly German external prociency testing programs. HbA1c, glucose, triglycerides, total cholesterol, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) were meassured as dimensional scores. Triglyceride and HDL cholesterol concentrations were measured photometrically (Hitachi 704, Roche, Mannheim, Germany), whereas follow-up HDL concentrations were quantied with lipid electrophoresis (HELENA SAS-3 system, Helena 7 BioSciences Europe, Tyne & Wear, UK). Triglyceride and glucose concentrations were determined enzymatically using reagents from Roche Diagnostics (Hitachi 717, Roche Diagnostics).

To ensure comparability in the longitudinal HDL analyses, we used baseline HDL concentrations as the reference and calculated corrected follow-up HDL concentrations based on a previously published conversion formula (HDL_fu_corr = 80+(1.158*HDL_fu).42 Doing so, we found that the average HDL concentrations produced by the two methods were virtually identical, suggesting that the differences in HDL will be small within the range of practical relevance. Serum LDL was measured by applying a precipitation procedure using dextran sulphate (Immuno, Heidelberg, Germany) on an Epos 5060 (Eppendorf, Hamburg, Germany).

Statistical methodsAs recommended,43 we applied advanced statistical methods implemented in the `rms: Regression Modeling Strategies R package44 (including redundancy analysis with the `redun procedure, cluster analysis and

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FactorCompletecaseanalysis(n=2463)Imputation(n=2619)

EffectsestimatedConventionalestimationPenalizedestimationConventionalestimation

WomenMen199.725.3o0.0001170.020.2o0.0001205.924.3o0.0001

Age,years52.169.917.8290.725.0o0.0001291.418.1o0.0001288.224.4o0.0001

Bodyheight,cm17018212129.825.6o0.0001140.017.0o0.0001126.024.9o0.0001

Education0.00530.01200.0148

Education,10years8Years21.323.70.368414.720.30.470518.622.80.4144

Education,12years8Years65.624.50.007653.221.30.012557.323.80.0160

Cigarettesmoking0.00580.03290.0007

Ex-smoker,occasionalNever49.320.30.015141.718.50.024152.319.70.0079

EffectRobusts.e.P(factor)EffectRobusts.e.P(factor)EffectRobusts.e.P(factor)

Never67.424.60.006353.322.00.016067.423.40.0040

Never41.823.40.074834.220.10.089651.022.80.0256

Currentsmoker,occasionalNever16.941.70.685324.627.50.371119.441.40.6390

Never81.523.90.000765.421.10.001992.823.2o0.0001

Never37.528.10.182531.922.00.148143.827.20.1072

Alcoholconsumption,gperday4.022.318.39.724.10.68855.312.90.945616.623.30.5852

Majordepression,lifetimeNo3.119.00.86920.517.00.97655.218.70.7821

Physicalactivity0.14680.22560.1299

402hPerweek0hPerweek36.319.20.058829.717.40.087236.418.40.0484

42hPerweek0hPerweek19.722.60.384318.419.90.355721.221.80.3305

Waistcircumference,cm88.6106.017.45.617.40.26542.616.40.371710.117.00.1601

HbA1c,%5.26.00.824.714.60.137413.78.60.269534.314.50.0375

HDLcholesterol,mmoll1 1.421.980.5630.014.90.116620.314.90.395735.914.00.0368

LDLcholesterol,mmoll1 3.374.611.244.515.30.50895.413.40.52493.914.70.3934

Diastolicbloodpressure,mmHg77.591.013.538.213.70.021131.413.60.065435.513.00.0219

AntidepressantsNo60.530.90.050042.825.40.092652.529.80.0787

AntidiabeticsNo37.736.20.297122.830.40.452229.735.60.4053

Lipid-loweringdrugsNo16.327.10.547316.322.30.46476.226.10.8129

AntihypertensivemedicationNo26.217.40.133627.816.30.088635.717.00.0353

SHIP-2TREND-01.115.70.94491.017.00.94801.514.90.9221

Abbreviations:HDL,high-densitylipoprotein;LDL,low-densitylipoprotein.Pforjointtestincludingmajordepression,antidepressants:robust/penalized/imputed:0.1404/0.2308/0.2109.Pforjointtestincluding

HbA1c,antidiabetics:robust/penalized/imputed:0.0408/0.1859/0.0116.PforjointtestincludingHDL,LDL,lipid-loweringdrugs:robust/penalized/imputed:0.3037/0.5607/0.1501Pforjointtestincluding

diastolicbloodpressure,antihypertensivemedication:robust/penalized/imputed:0.0232/0.0429/0.0091.

Differenceor

reference

High(90th

percentile)

Low(median=50th

percentile)

Table1.Clinicalpredictionmodel

Ex-smoker,114cigarette

Ex-smoker,15cigarette

Currentsmoker,114cigarette

Currentsmoker,15cigarette

perday

perday

perday

perday

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Spearman correlations). Intracranial volume (ICV) was not considered to be a confounding factor because two of the three criteria for a confounding factor do not meet.45 First, the ICV includes the HV and is a consequence rather than a source of HV. Second, if an exposure affects the ICV, then the ICV is an intermediate step in the causal path between the exposure and the HV and, therefore, to be excluded from analysis.

Because of redundancy, systolic blood pressure, total cholesterol, triglycerides, glucose and body mass index were removed from further analysis. To avoid power loss and unjustied linearity assumptions, continuous variables were modeled as restricted cubic splines.43

To test interactions between genetic and psychosocial or clinical factors, we chose an approach in two steps. In the rst step we identied statistically signicant psychosocial or clinical factors in the sample with 2463 subjects. A statistically signicant psychosocial or clinical factor had to meet two conditions: (1) signicance (P 0.05) and (2) for factors that had to be controlled for medication, the related group of clinical predictors including medication had to be signicant in the corresponding joint test; a categorical variable was tested by joint test across categories (P 0.05).

(Specication of the related groups for joint test: included major depression and antidepressants; HbA1c and antidiabetics; HDL, LDL and lipid-lowering drugs; and diastolic blood pressure and antihypertensive medication.)

In the second step, interactions between genetic variants and clinical or psychosocial factors (signicant in the previous step) were modeled and analyzed in 1641 subjects with complete genetic and clinical data (data not shown).

We restricted interactions to be linear in continuous predictors. All twoway interactions that met the conditions were modeled in a single model. All factors from the rst step were left in the interaction model.43 Only

depression was excluded from this step because the proportion of missing values was higher than 5% (N = 116), and results in the rst step were not signicant.

Analyses and graphics were performed using the R software46 and the

STATA/MP software, release 12.1 (Stata, College Station, TX, USA).

Sensitivity analysisEffects of current and ex-smoking showed no statistical signicance (contrast between current and ex-smoking in occasional smokers:32.4 43.5 mm3, P = 0.4575; contrast between current and ex-smoking across three categories each 7.5 21.4, P = 0.7254). As a result, these six categories were collapsed for further analyses.

ImputationIn addition to the complete case analysis (N = 2463), we conducted an extra analysis for the clinical prediction model with imputed data (N = 2619) to validate the consistency of the associations with larger sample sizes (Table 1).

Imputation of missing data is recommended in general;47 therefore, two imputations were planned, one for the model without genetic data and one for the gene interaction model.

Additional 156 subjects revealed data `missing at random (for example, blood pressure, blood lipids; for details see Table 2). The overall proportion (SHIP-2/TREND combined) of missing data was 6% (SHIP-2 sample separate: 15%), which is acceptable for single imputation.43 SHIP-2 and TREND-0 data were separately imputed using chained equations. One hundred sixteen subjects did not participate in the psychiatric diagnostic interview (highest number of missing data in the diagnosis of lifetime depression); however, most of them took part in the CID screener from SHIP-0 and SHIP-1. Two items of the CID screener are also part of the depression interview and were used for recursive partitioning and imputation of depression. Observations with missing data of HV in one hemisphere were multiply-imputed (all variables were used); however, observations with missing outcome data dropped.47 Data not missing at random (for example, questions of alcohol consumption) were not imputed.

Given the insignicant P-value for depression in the second step of analysis, we decided to remove depression from the model, thereby avoiding a need for imputation in gene interaction analysis. Using imputed values sample data (N = 2619), however, HbA1c and HDL cholesterol additionally achieved signicant associations with HV (Table 1).

Table 2. Variables in clinical and genotype models

Characteristic Clinical prediction model Analysis of gene interactiona

Included (n = 263)

Excluded (n = 156)

(n = 1641)

3935.0 (430.4)

Women, N (%) 1335 (54.2) 87 (55.8) 914 (55.7) Age, mean (s.d.), years 52.1 (13.6) 55.8 (12.7)b 53.0 (13.1) Body height, mean (s.d.),cm

Mean hippocampal volume, mean (s.d.), mm3

3947.8 (423.7)

3892.5 (415.9)

170 (9.3) 169 (9.3) 170 (9.2)

Education, mean (s.d.)o10 Years 372 (15.1) 37 (23.9)c 253 (15.4)

10 Years 1382 (56.1) 83 (53.6) 924 (56.3) 410 Years 709 (28.8) 35 (22.6) 464 (28.3)

Cigarette smoking, N (%)Non-smoker 991 (40.2) 56 (38.6) 677 (41.3) Former smoker,occasional

389 (15.8) 13 (9.0) Former:

Former smoker, 114 cigarettes per day

206 (8.4) 18 (12.4) 624 (38.0)

Former smoker, 15 cigarettes per day

310 (12.6) 24 (16.6)

Current smoker, occasional

79 (3.2) 2 (1.4) Current:

Current smoker, 114 cigarettes per day

253 (10.3) 17 (11.7) 340 (20.7)

Current smoker, 15 cigarettes per day

235 (9.5) 15 (10.3)

Alcohol consumption, N (%)

0 g Per day 314 (12.8) 18 (11.5) 171 (10.4) 405 g Per day 1035 (42.0) 57 (36.5) 693 (42.2) 4510 g Per day 427 (17.3) 37 (23.7) 303 (18.5) 41020 g Per day 393 (16.0) 18 (11.5) 257 (15.7) 42030 g Per day 137 (5.6) 14 (9.0) 97 (5.9) 430 g Per day 157 (6.4) 12 (7.7) 120 (7.3) Major depression,lifetimed, N (%)

446 (18.1) 3 (15.0)e 254 (16.8)

Physical activity, N (%)

No 429 (17.4) 37 (23.9) 269 (16.4) 402 h Per week 1459 (59.2) 87 (56.1) 971 (59.2) 42 h Per week 575 (23.4) 31 (20.0) 401 (24.4) Waist circumference (cm),N (%)

88.9 (12.8) 90.5 (13.1)f 88.5 (12.6)

Body mass index (kg m21), N (%)

27.4 (4.4) 27.7 (4.3) 27.3 (4.3)

HbA1c (%) mean (s.d.) 5.3 (0.7) 5.4 (0.8) 5.3 (0.7) Glucose (mmol l1) 5.6 (1.3) 5.6 (1.5) 5.5 (1.1) Triglycerides (mmol l1),

mean (s.d.)

1.6 (1.1) 1.8 (1.4)c,g 1.6 (1.2)

Total cholesterol (mmol l1), mean (s.d.)

5.5 (1.1) 5.5 (1.1)g 5.5 (1.1)

HDL-cholesterol (mmol l1), mean (s.d.)

1.5 (0.4) 1.5 (0.4)g 1.5 (0.4)

LDL cholesterol (mmol l1), mean (s.d.)

3.4 (0.9) 3.3 (1.0)g 3.4 (0.9)

Diastolic blood pressure (mm Hg), mean (s.d.)

78.0 (9.9) 80.2

78.6 (10.0)

(10.4)c,h

Systolic blood pressure (mm Hg), mean (s.d.)

127.2(17.3)

131.8(19.9)b,h

127.7 (17.7)

MedicationAntidepressants, N (%) 138 (5.6) 11 (7.0) 90 (5.5) Antidiabetics, N (%) 95 (3.9) 13 (8.3)c 43 (2.6) Lipid-lowering drugs, N(%)

257 (10.4) 18 (11.5) 181 (11.0)

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Table. 2. (Continued )

Characteristic Clinical prediction model Analysis of gene interactiona

Included (n = 263)

Excluded (n = 156)

(n = 1641)

772 (31.3) 51 (32.7) 514 (31.3)

rs7294919 (TESC, HRK, FBXW8), N (%)CC 18 (1.1) CT 326 (19.9) TT 1297 (79.0)

rs17178006 (MSRB3), N (%)GG 21 (1.3) GT 284 (17.3) TT 1336 (81.4)

rs6581612 (WIF1), N (%)AA 908 (55.3) AC 613 (37.4) CC 120 (7.3)

rs6741949 (DPP4), N (%)CC 265 (16.2) CG 773 (47.1) GG 603 (36.8)

rs7852872 (ASTN2), N (%)CC 636 (38.8) CG 763 (46.5) GG 242 (14.8)

aExcluded major depression, lifetime diagnosis. bPo0.01. cPo0.05. dN = 1 510. eN = 20. fN = 156. gN = 153. hN = 152.

Antihypertensive medication, N (%)

Penalized regressionResults of penalized regression based on the `pentrace procedure were presented because `A shrinkage approach will often result in regression coefcient estimates that while biased are lower in the mean squared error and hence are more likely to be close to the true unknown parameter values.43

Mediator and pathway analysesFor two genesrs17178006 and rs6581612a mediator analysis (data not shown) was conducted. For the genegene interactionrs7852872 and rs6581612a pathway analyses was conducte48 (see supplement and discussion).

RESULTSBaseline characteristicsThe description of the sample is given in Table 2.

Associations of psychosocial and clinical factors with HV(N = 2463) Besides gender, age and body height, education, smoking and diastolic blood pressure were associated with HV (Table 1). Participants with high education had larger HV than participants with low education (P = 0.0076; Table 1). Current and former smokers revealed smaller HV than non-smokers (P = 0.0058; Table 1). Diastolic blood pressure was negatively associated with HV in participants having more than 80 mm Hg and slightly positively associated in participants having less than 75 mm Hg (P = 0.0211; Table 1). Although antidepressant medication was associated with smaller HV, the corresponding joint test (including depression) did not indicate an association.

We did not nd signicant associations for the following psychosocial and clinical factors: waist circumference, alcohol consumption, physical activity, lifetime major depression diagnosis, HDL cholesterol, LDL cholesterol and HbA1c (Table 1).

Associations of genetic variants with HV (N = 1641)

We conrmed signicant associations for genetic variants with HV (Table 3).

Interactions between clinical or psychosocial factors and genetic markers (N = 1641)

We identied two interactions between genetic markers und clinical factors. Rs7294919 modied both: the effects of smoking and diastolic blood pressure. In non-smokers, carriers of the C allele had larger HVs than non-smokers with TT genotype. In smokers, C allele carriers and non-carriers did not differ (Figure 2a; P for interaction = 0.0473). Moreover, participants with low diastolic blood pressure carrying the C allele had larger HVs than TT carriers (Figure 2b; P indication of interaction = 0.0447).

Interactions between genetic markers (N = 1641)

We found one genegene interaction: CG carriers of rs7852872 combined with CC genotype of rs6581612 had larger HVs than CG carriers combined with the A allele of rs6581612 (Figure 2c; P for interaction = 0.0114).

DISCUSSIONIn this adult population-based study, we conrmed genetic, psychosocial and clinical factors that are associated with HV: gender, age and body height, as well as education, smoking and diastolic blood pressure. As genetic factors, we found rs7294919 to modify effects of smoking and diastolic blood pressure. Moreover, we identied an interaction between genetic markers known as candidates of neural function.

The novelty of this study consists of the joint analysis of potential predictors within one model, allowing for nonlinear relationship between continuous predictors and HV in a large population-based sample.

Table 3. Genetic markers associated with hippocampal volume (comparison with Bis et al.22)

SNP SHIP/TREND N = 1641 Bis et al.22 SHIP/TREND (N = 1641) versus Bis et al.22

Allele coded Effect adjusted for sex and age Robust s.e. P- value Effect tested P-value

rs7294919 (TESC, HRK, FBXW8) T 52.7 19.4 0.0065 107.8 0.0045 rs17178006 (MSRB3) G 72.9 19.7 0.0002 123.8 0.0100 rs6581612 (WIF1) C 41.2 14.1 0.0036 63.3 0.1194 rs6741949 (DPP4) G 31.7 12.5 0.0112 52.8 0.0916 rs7852872 (ASTN2) C 26.6 13.3 0.0451 47.7 0.1126

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4200

rs7294919 (TESC)

CC/CT TT

4200

rs7294919 (TESC)

CC/CT TT

Mean Hippocampal Volume, mm

Mean Hippocampal Volume, mm

4100

4100

4000

4000

3900

3900

3800

3800

3700

3700

3600

3600

60 70 80 90 100

Non-smoker Smoker

Smoking-nicotine

Diastolic Blood Pressure, mmHg

rs7852872 (ASTN2)

CG GG CC

4200

Mean Hippocampal Volume, mm

4100

4000

3900

3800

3700

3600

AA AC CC

rs6581612 (WIF1)

Figure 2. (a) The interaction between rs7294919 and smoking status for the mean hippocampal volume (mm3) is depicted. In this plot, an interaction becomes evident between carriers of the CC/CT genotype of rs7294919 and non-smoker for a larger hippocampal volume (P = 0.0473). Number of subjects for each combination of genotype and smoking status are given below: rs7294919CT/CC and non-smoker N = 140; rs7294919TT and non-smoker N = 537; rs7294919CT/CC and smoker N = 204; rs7294919TT and smoker N = 760. (b) The interaction between rs7294919 and diastolic blood pressure for the mean hippocampal volume (mm3) is depicted. In this plot, an interaction becomes evident between carriers of the CC/CT genotype of rs7294919 and lower diastolic blood pressure for a larger hippocampal volume (P = 0.0447). (c) Genegene interaction between rs7852872 (ASTN2) and rs6581612 (WIF1) is depicted for the mean hippocampal volume in mm3. This plot shows a considerable interaction for a larger hippocampal volume in CG-genotype carriers of rs7852872 and the CC-genotype carriers of rs6581612 (P = 0.0114). Number of subjects for each combination of genotype are given below: rs6581612AA and rs7852872CC: N = 350; rs6581612AA and rs7852872GG: N = 141; rs6581612AA and rs7852872CG: N = 417; rs6581612AC and rs7852872CC: N = 242; rs6581612AC and rs7852872GG: N = 290; rs6581612AC and rs7852872CG: N = 81; rs6581612CC and rs7852872CC: N = 44; rs6581612CC and rs7852872GG:N = 20; rs6581612CC and rs7852872CG; N = 56.

Associations of psychosocial and clinical factors with HVThe ndings for education are in line with a study in healthy elders49 and support the idea of an education-related biological and cognitive reserve (for review see Stern50); higher reserve could counterbalance aging or pathological effects. The result for smoking is consistent with observations from Durazzo et al.51,52

who found smaller HVs in smokers compared with non-smokers. Although several studies indicate that nicotine induces neuronal cell loss, the exact pathomechanism of how nicotine affects brain tissue in general and the hippocampus in particular remains unclear. There is evidence that smoking affects the hippocampus by decreasing the regional cerebral blood ow.53 Smoking was associated with atrophy compared with non-smoking in several brain regions in early stages of Alzheimers.51,54 In animal studies,

hippocampal neuronal damage after nicotine exposure has been demonstrated.55,56 Nicotine induced cell death in hippocampal progenitors.57 Diastolic blood pressure was associated with HV, which is in line with previous studies.5860 Hypertension is known

as a risk factor for Alzheimers, although causality might be difcult to determine because of high prevalence of hypertension and brain atrophy while aging.61 Hypertension increases the risk of atherosclerosis that might lead to decreased blood ow in the hippocampus.62 In animal models, blood pressure-related cerebral changes promoted neuronal death and brain atrophy.63,64 This is

consistent with Beauchet et al.60 who concluded in their systematic review and meta-analysis that brain volume reduction in hypertension cannot solely be explained by age and use of hypertensive drugs. Several psychosocial and clinical factors were not conrmed to inuence the HV in our study: waist circumference did not raise an association with HV. This result differs from other studies that could nd associations between overweight/ obese participants and smaller HV. In contrast to our study, their samples were not population-based or subjects were elderly.13,14

Effects of obesity on the HV may increase with age and in combination with other clinical determinants. Alcohol consumption was not associated with HV in contrast to the study from

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8 Agartz et al.9 who investigated 52 hospitalized alcohol-addicted patients.

The effect of physical activity in our study was not convincing. The rather crude assessment with low threshold (42 h per week)

of physical activity in our study may have missed protective effects in subjects with higher physical activity. Furthermore, socially desired answers have possibly led to an overestimation of sportive activities.

Previous studies about HVs and major depression are contradictory: although there is some evidence for decreased HVs or decreased subregions,6567 other studies did not nd associations between depression and decreased brain volumes.68,69 Although

we had data from clinical interviews (DSM-IV), we could not support any association for HV with depression. We neither analyzed severity or recurrence of depression nor measured hippocampal subregions. We controlled results for medication as effects from diseases on brain structures might be concealed or caused by the medication. Antidepressants have been demonstrated to induce hippocampal neuroneogenesis.7072 Antidepressantsindependent from the diagnosis of depressiondid have negative impact on HV in our study. This result appears counterintuitive. Although the intake of antidepressants may largely be associated with depressive disorders, they are also prescribed for patients suffering from pain, anxiety or sleep disturbances. Among those participants suffering from depression, the use of antidepressants may indicate as well chronicity, recurrence or severity of depression, which lead to decrease of HV. In addition, loss of HV or reduction of hippocampal cell proliferation might not be a direct effect of antidepressants. Antidepressants could also be understood as a proxy for distress by pain, depression or anxiety, which lead to hippocampal volume loss. Effects of the chronic diseases might not be completely removed from the neural plastic and proliferative effects of antidepressants.

We could not conrm that diabetes or elevated cholesterol levels were associated with smaller HV. This is contradictory to previous ndings of association of diabetes and total cholesterol levels and HV in a sample of older male subjects.12

Associations of genetic markers with HVWe conrmed not only rs7294919 but also the four loci discovered by Bis et al. (rs17178006, rs6741949, rs6581612 and rs7852872) as predictors for HV. These ndings are consistent with our hypotheses to conrm all genetic loci named above.

Rs17178006 (MSRB3, methionine sulfoxide reductase B3) is a gene that encodes a protein involved in the reduction of methionine sulfoxide to methionine, preserving the biological activity of proteins after oxidative damage.33,34 Although patho-physiology is unclear, MSRB3 seems to be cell-protective in the hippocampus and may prevent apoptosis leading to HV loss.73,74

As rs17178006 (MSRB3) and rs6581612 (WIF1) were in low linkage disequilibrium (r2 = 0.2), we performed conditional analyses on their association on HV: both effects were attenuated but only the association with rs17178006 remained signicant. Although both genes are excellent candidates of neural function and protection, this nding implies a single locus or complex mediation effects (for example, through gene transcription modulation or protein networks). A pathway analysis revealed a putative connection between MSRB3 and WIF1 via leptin and APOB (via text mining; Supplementary Figure 1).

Rs6741949 (DPP) was not involved in interactions or pathways but in a genetic association. In animal models, DPP-4- inhibitors moderated brain mitochondrial function showed improved cognitive behavior35 and reduced deposits of amyloid in Alzheimer models.75,76

Interactions between clinical or psychosocial factors and genetic markers

Three interactions were associated with HV: the two interactions with rs7294919 are in line with both GWAS.25,26 C-allele carriers had larger HVs than non-carriers, but only in non-smokers or participants with low diastolic blood pressure. The intergenic variant rs7294919 comprises the genes TESC, HRK and FBXW8. TESC encodes a protein signaling complex77 and has an important role in cell proliferation and brain development.26,27 HRK is a

regulator of apoptosis,29,78 which exhibits death-inducing activity in cells.28,29 FBXW8 encodes a member of the F-box protein family, which modies neuronal dendrite growth and conguration.30

Interactions between genetic markersCarriers of CC genotype of rs6581612 (WIF1) and the CG genotype of rs7852872 (ASTN2) were associated with larger HVs. ASTN2 is expressed in the hippocampus and is involved in neuronal migration.36 WIF1 also known as WNT inhibitory factor 1 is a protein that is encoded by the WIF1 gene79,80 that inhibits WNT

proteins from triggering signals and affects neuronal functions.31,32 The WNT signaling pathway is part of the cellcell communication between neurons and astrocytes, which may be disturbed in neurogenenerative disorders.81 At the physiological level, an interaction between mechanisms of neuronal migration and cellcell communication seems conceivable.

Landmarks of this study provide: the joint analysis of potential factors affecting HV in a large general population sample, applying the same sampling methods and investigated in the same scanner with identical protocols. Modeling with restriced cubic splines made it possible to detect nonlinear associations with HV. The statistical model for interaction analysis comprised all main effects and interaction terms that lead to robust adjustment for all confounding effects and circumvent the problem of multiple testing.

Limitations of our study are: rst, our results may have a limited generalizability as distributions of risk/protective factors in different populations vary and lead to different associations. Second, selection bias because of MRI participation cannot be excluded. Third, unmeasured confounding and information bias could have inuenced our results. Fourth, we did not measure hippocampal subregions. Some studies demonstrated that volume loss may rather be a process affecting hippocampal subregions without showing signicant change in the whole hippocampus. Fifth, the cross-sectional nature of our study limits causal inference. Last, additional cognitive tests may be helpful to underlie the functional differences in HV.

This study conrmed the impact of education, diastolic blood pressure, smoking and genetic variants on HV. Complex interactions between these factors seem to have an important role in HV changes. We suggest composite scores for the individual estimate of HV and the biological reserve capacity.

CONFLICT OF INTEREST

The authors declare no conict of interest.

ACKNOWLEDGMENTS

The Study of Health in Pomerania (SHIP) is supported by the German Federal Ministry of Education and Research (grants 01ZZ9603, 01ZZ0103 and 01ZZ0403) and the German Research Foundation (DFG; GR 1912/5-1). Genome-wide data and MRI scans were supported by the Federal Ministry of Education and Research (grant 03ZIK012) and a joint grant from Siemens Healthcare, Erlangen, Germany, and the Federal State of MecklenburgWest Pomerania. The University of Greifswald is a member of the Center of Knowledge Interchange program of the Siemens AG. SHIP-TREND-0: The authors from SHIP are grateful to M. Stanke for the opportunity to use his Server Cluster for SNP Imputation. This cohort is a part of the Community Medicine Research net (CMR) of the University of Greifswald, which is funded by the German Federal

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Ministry of Education and Research and the German Ministry of Cultural Affairs, as well as by the Social Ministry of the Federal State of MecklenburgWest Pomerania. CMR encompasses several research projects that share data from the population-based SHIP (see URLs). The work is also supported by the Greifswald Approach to Individualized Medicine (GANI_MED) network funded by the Federal Ministry of Education and Research (grant 03IS2061A). MRI scans were supported by the Federal Ministry of Education and Research (grant 03ZIK012) and a joint grant from Siemens Healthcare and the Federal State of MecklenburgWest Pomerania. We thank all staff members and participants of the SHIP studies, as well as all of the genotyping staff for generating the SHIP SNP data set. We are especially grateful to Alexander Teumer PhD (Interfaculty Institute for Genetics and Functional Genomics; University Medicine Greifswald, Germany) for his critical comments and help to improve the paper. D. J. is supported by a scholarship from the Gerhard-Domagk programme of the University Medicine Greifswald. SD is a recipient of a Chaire dExcellence Grant from the Agence Nationale de la Recherche, in collaboration with INSERM Unit U708 and the University of Versailles Saint-Quentin en Yvelines. This study was supported by HELIOS- Kliniken GmbH grand-ID: 001283.

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