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2. Evidence for increased genetic risk load for major depression in patients assigned to electroconvulsive therapy

Author

Foo, J.C., Streit, F., Frank, J., Witt, S.H., Treutlein, J., Baune, B.T., Moebus, S., Jockel, K.H., Forstner, A.J., Nothen, M.M., Rietschel, M., Sartorius, A., Kranaster, L., Wray, N.R., Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E.M., Abdellaoui, A., Adams, M.J., Agerbo, E., Air, T.M., Andlauer, T.F.M., Bacanu, S.A., Baekvad-Hansen, M., Beekman, A.T.F., Bigdeli, T.B., Binder, E.B., Blackwood, D.H.R., Bryois, J., Buttenschon, H.N., Bybjerg-Grauholm, J., Cai, N., Castelao, E., Christensen, J.H., Clarke, T.K., Coleman, J.R.I., Colodro-Conde, L., Couvy-Duchesne, B., Craddock, N., Crawford, G.E., Davies, G., Deary, I.J., Degenhardt, F., Derks, E.M., Direk, N., Dolan, C.V., Dunn, E.C., Eley, T.C., Escott-Price, V., Kiadeh, F.F.H., Finucane, H.K., Gaspar, H.A., Gill, M., Goes, F.S., Gordon, S.D., Grove, J., Hall, L.S., Hansen, C.S., Hansen, T.F., Herms, S., Hickie, I.B., Hoffmann, P., Homuth, G., Horn, C., Hottenga, J.J., Hougaard, D.M., Ising, M., Jansen, R., Jones, I., Jones, L.A., Jorgenson, E., Knowles, J.A., Kohane, I.S., Kraft, J., Kretzschmar, W.W., Krogh, J., Kutalik, Z., Li, Y.H., Lind, P.A., MacIntyre, D.J., MacKinnon, D.F., Maier, R.M., Maier, W., Marchini, J., Mbarek, H., McGrath, P., McGuffin, P., Medland, S.E., Mehta, D., Middeldorp, C.M., Mihailov, E., Milaneschi, Y., Milani, L., Mondimore, F.M., Montgomery, G.W., Mostafavi, S., Mullins, N., Nauck, M., Ng, B., Nivard, M.G., Nyholt, D.R., O'Reilly, P.F., Oskarsson, H., Owen, M.J., Painter, J.N., Pedersen, C.B., Pedersen, M.G., Peterson, R.E., Pettersson, E., Peyrot, W.J., Pistis, G., Posthuma, D., Quiroz, J.A., Qvist, P., Rice, J.P., Riley, B.P., Rivera, M., Mirza, S.S., Schoevers, R., Schulte, E.C., Shen, L., Shi, J.X., Shyn, S.I., Sigurdsson, E., Sinnamon, G.C.B., Smit, J.H., Smith, D.J., Stefansson, H., Steinberg, S., Strohmaier, J., Tansey, K.E., Teismann, H., Teumer, A., Thompson, W., Thomson, P.A., Thorgeirsson, T.E., Traylor, M., Trubetskoy, V., Uitterlinden, A.G., Umbricht, D., Auwera, S. van der, Hemert, A.M. van, Viktorin, A., Visscher, P.M., Wang, Y., Webb, B.T., Weinsheimer, S.M., Wellmann, J., Willemsen, G., Wu, Y., Xi, H.L.S., Yang, J., Zhang, F.T., Arolt, V., Berger, K., Boomsma, D.I., Cichon, S., Dannlowski, U., Geus, E.J.C. de, DePaulo, J.R., Domenici, E., Domschke, K., Esko, T., Grabe, H.J., Hamilton, S.P., Hayward, C., Heath, A.C., Kendler, K.S., Kloiber, S., Lewis, G., Li, Q.Q.S., Lucae, S., Madden, P.A.F., Magnusson, P.K., Martin, N.G., McIntosh, A.M., Metspalu, A., Mors, O., Mortensen, P.B., Muller-Myhsok, B., Nordentoft, M., O'Donovan, M.C., Paciga, S.A., Pedersen, N.L., Penninx, B.W.J.H., Perlis, R.H., Porteous, D.J., Potash, J.B., Preisig, M., Schaefer, C., Schulze, T.G., Smoller, J.W., Stefansson, K., Tiemeier, H., Uher, R., Voelzke, H., Weissman, M.M., Werge, T., Lewis, C.M., Levinson, D.F., Breen, G., Borglum, A.D., Sullivan, P.F., and Major Depressive Disorder Worki

Subjects
treatment-resistance, polygenic risk scores, depression, major depression, electroconvulsive therapy
Abstract

Electroconvulsive therapy (ECT) is the treatment of choice for severe and treatment-resistantdepression; disorder severity and unfavorable treatment outcomes are shown to be influencedby an increased genetic burden for major depression (MD). Here, we tested whether ECT assign-ment and response/nonresponse are associated with an increased genetic burden for majordepression (MD) using polygenic risk score (PRS), which summarize the contribution of disease-related common risk variants. Fifty-one psychiatric inpatients suffering from a major depressiveepisode underwent ECT. MD-PRS were calculated for these inpatients and a separatepopulation-based sample (n = 3,547 healthy; n = 426 self-reported depression) based on sum-mary statistics from the Psychiatric Genomics Consortium MDD-working group (Cases:n = 59,851; Controls: n = 113,154). MD-PRS explained a significant proportion of disease statusbetween ECT patients and healthy controls (p = .022, R2 = 1.173%); patients showed higherMD-PRS. MD-PRS in population-based depression self-reporters were intermediate betweenECT patients and controls (n.s.). Significant associations between MD-PRS and ECT response(50% reduction in Hamilton depression rating scale scores) were not observed. Our findings indi-cate that ECT cohorts show an increased genetic burden for MD and are consistent with thehypothesis that treatment-resistant MD patients represent a subgroup with an increased geneticrisk for MD. Larger samples are needed to better substantiate these findings.

Published
2019

3. Evidence for increased genetic risk load for major depression in patients assigned to electroconvulsive therapy

Author

Foo, JC, Streit, F, Frank, J, Witt, SH, Treutlein, J, Baune, BT, Moebus, S, Joeckel, K-H, Forstner, AJ, Noethen, MM, Rietschel, M, Sartorius, A, Kranaster, L, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JRI, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, CV, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, O'Reilly, PF, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, SI, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Berger, K, Boomsma, DI, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Breen, G, Borglum, AD, Sullivan, PF, Foo, JC, Streit, F, Frank, J, Witt, SH, Treutlein, J, Baune, BT, Moebus, S, Joeckel, K-H, Forstner, AJ, Noethen, MM, Rietschel, M, Sartorius, A, Kranaster, L, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JRI, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, CV, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, O'Reilly, PF, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, SI, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Berger, K, Boomsma, DI, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Breen, G, Borglum, AD, and Sullivan, PF

Abstract

Electroconvulsive therapy (ECT) is the treatment of choice for severe and treatment-resistant depression; disorder severity and unfavorable treatment outcomes are shown to be influenced by an increased genetic burden for major depression (MD). Here, we tested whether ECT assignment and response/nonresponse are associated with an increased genetic burden for major depression (MD) using polygenic risk score (PRS), which summarize the contribution of disease-related common risk variants. Fifty-one psychiatric inpatients suffering from a major depressive episode underwent ECT. MD-PRS were calculated for these inpatients and a separate population-based sample (n = 3,547 healthy; n = 426 self-reported depression) based on summary statistics from the Psychiatric Genomics Consortium MDD-working group (Cases: n = 59,851; Controls: n = 113,154). MD-PRS explained a significant proportion of disease status between ECT patients and healthy controls (p = .022, R2 = 1.173%); patients showed higher MD-PRS. MD-PRS in population-based depression self-reporters were intermediate between ECT patients and controls (n.s.). Significant associations between MD-PRS and ECT response (50% reduction in Hamilton depression rating scale scores) were not observed. Our findings indicate that ECT cohorts show an increased genetic burden for MD and are consistent with the hypothesis that treatment-resistant MD patients represent a subgroup with an increased genetic risk for MD. Larger samples are needed to better substantiate these findings.

Published
2019

11. Comprehensive metabolome characterization of leaves, internodes, and aerial roots of Vanilla planifolia by untargeted LC-MS and GC × GC-MS.

Author

Beer F, Weinert CH, Wellmann J, Hillebrand S, Ley JP, Soukup ST, and Kulling SE

Subjects
Chromatography, Liquid methods, Metabolomics methods, Liquid Chromatography-Mass Spectrometry, Plant Leaves metabolism, Plant Leaves chemistry, Plant Roots metabolism, Plant Roots chemistry, Gas Chromatography-Mass Spectrometry methods, Vanilla chemistry, Vanilla metabolism, Metabolome
Abstract

Introduction: Untargeted metabolomics is a powerful tool that provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites, especially when combining different metabolomic platforms. Vanilla is one of the world's most popular flavors originating from cured pods of the orchid Vanilla planifolia. However, only a few studies have investigated the metabolome of V. planifolia, and no LC-MS or GC-MS metabolomics studies with respect to leaves have been performed., Objective: The aim of the study was to comprehensively characterize the metabolome of different organs (leaves, internodes, and aerial roots) of V. planifolia., Material and Methods: Characterization of the metabolome was achieved using two complementary platforms (GC × GC-MS, LC-QToF-MS), and metabolite identification was based on a comparison with in-house databases or curated external spectral libraries., Results: In total, 127 metabolites could be identified with high certainty (confidence level 1 or 2) including sugars, amino acids, fatty acids, organic acids, and amines/amides but also secondary metabolites such as vanillin-related metabolites, flavonoids, and terpenoids. Ninty-eight metabolites showed significantly different intensities between the plant organs. Most strikingly, aglycons of flavonoids and vanillin-related metabolites were elevated in aerial roots, whereas its O-glycoside forms tended to be higher in leaves and/or internodes. This suggests that the more bioactive aglycones may accumulate where preferably needed, e.g. for defense against pathogens., Conclusion: The results derived from the study substantially expand the knowledge regarding the vanilla metabolome forming a valuable basis for more targeted investigations in future studies, e.g. towards an optimization of vanilla plant cultivation., (© 2024 The Author(s). Phytochemical Analysis published by John Wiley & Sons Ltd.)

Published
2025
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12. Determinants of Single and Multiple Sensory Impairments in an Urban Population.

Author

Khil L, Wellmann J, and Berger K

Subjects
Adult, Aged, Cross-Sectional Studies, Diabetes Mellitus epidemiology, Female, Germany epidemiology, Humans, Male, Middle Aged, Prevalence, Risk Factors, Smoking epidemiology, Social Class, Urban Population, Sensation Disorders diagnosis, Sensation Disorders epidemiology
Abstract

Objective: To describe the co-occurrence pattern and determinants of auditory, olfactory, visual, and gustatory impairment across the life spectrum of adults., Study Design: Cross-sectional analysis., Setting: An urban population., Subjects and Method: In total, 1208 persons from the general adult population (age range, 25-74 years; 46.7% men) were included. Sensory impairments were assessed with validated tests. Alternating logistic regression was applied to characterize (1) the dependence of sensory impairments on selected independent variables and (2) the pairwise association between sensory impairments. The dependence of impairment grade (no to multisensory impairment) on the same set of independent variables was examined using ordinal logistic regression., Results: The prevalence of single sensory impairment was 38.8%, of dual 27.3%, and of multisensory impairment 7.5%. Auditory impairment was the most frequent impairment type (43.9%), followed by olfactory (21.5%), gustatory (20.3%), and visual impairment (14.1%). Besides age and sex, social status (odds ratio [OR], 2.61; 95% confidence interval [CI], 1.80-3.79), smoking status (OR, 1.45; 95% CI, 1.12-1.88), and diabetes (OR, 1.75; 95% CI, 1.16-2.63) were related to an elevated odds of moving from a lower into a higher impairment category., Conclusions: The presence of certain risk factors, such as a low social status, diabetes, and smoking, appears likely to increase the risk of multisensory impairment., (© American Academy of Otolaryngology—Head and Neck Surgery Foundation 2015.)

Published
2015
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13. The use of sliding time windows for the exploratory analysis of temporal effects of smoking histories on lung cancer risk.

Author

Hauptmann M, Lubin JH, Rosenberg P, Wellmann J, and Kreienbrock L

Subjects
Aged, Case-Control Studies, Female, Humans, Likelihood Functions, Male, Models, Statistical, Odds Ratio, Probability, Time Factors, Lung Neoplasms epidemiology, Risk, Smoking adverse effects
Abstract

To examine the time-dependent effects of exposure histories on disease we use sliding time windows as an exploratory alternative to the analysis of variables like time since last exposure and duration of exposure. The method fits a series of risk models which contain total cumulative exposure and an additional covariate for exposures received during fixed time intervals. Characteristics of the fitted models provide insight into the influence of exposure increments at different times on disease risk. A simulation study is performed to check the validity of the approach. We apply the method to data from a recent German case-control study on smoking and lung cancer risk with about 4300 lung cancer cases and a similiar number of controls. The sliding time window approach indicates that the amount of cigarettes smoked from two to 11 years before disease incidence is most predicitive of lung cancer incidence. Among different smoking profiles that result in the same lifelong cumulative number of cigarettes smoked, those with a concentration of smoked cigarettes within 20 years before interview bear substantially larger risk than others., (Copyright 2000 John Wiley & Sons, Ltd.)

Published
2000
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