1
|
Schepp M, Freuer D, Wawro N, Peters A, Heier M, Teupser D, Meisinger C, Linseisen J. Association of the habitual dietary intake with the fatty liver index and effect modification by metabotypes in the population-based KORA-Fit study. Lipids Health Dis 2024; 23:99. [PMID: 38575962 PMCID: PMC10993479 DOI: 10.1186/s12944-024-02094-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/26/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND Non-alcoholic fatty liver disease (NAFLD) is an emerging threat for public health with diet being a major risk factor in disease development and progression. However, the effects of habitual food consumption on fatty liver are still inconclusive as well as the proposed role of the individuals' metabolic profiles. Therefore, the aim of our study is to examine the associations between diet and NAFLD with an emphasis on the influence of specific metabotypes in the general population. METHODS A total of 689 participants (304 men and 385 women) of the KORA-Fit (S4) survey, a follow-up study of the population-based KORA cohort study running in the Region of Augsburg, Germany, were included in this analysis. Dietary information was derived from repeated 24-h food lists and a food frequency questionnaire. The intake of energy and energy-providing nutrients were calculated using the national food composition database. The presence of fatty liver was quantified by the fatty liver index (FLI), and metabotypes were calculated using K-means clustering. Multivariable linear regression models were used for the analysis of habitual food groups and FLI; for the evaluation of macronutrients, energy substitution models were applied. RESULTS A higher consumption of nuts and whole grains, and a better diet quality (according to Alternate Healthy Eating Index and Mediterranean Diet Score) were associated with lower FLI values, while the intake of soft drinks, meat, fish and eggs were associated with a higher FLI. The isocaloric substitution of carbohydrates with polyunsaturated fatty acids was associated with a decreased FLI, while substitution with monounsaturated fatty acids and protein showed increased FLI. Statistically significant interactions with the metabotype were observed for most food groups. CONCLUSION The consumption of plant-based food groups, including nuts and whole grains, and diet quality, were associated with lower FLI values, whereas the intake of soft drinks and products of animal origin (meat, fish, eggs) were associated with a higher FLI. The observed statistically significant interactions with the metabotype for most food groups could help to develop targeted prevention strategies on a population-based level if confirmed in independent prospective studies.
Collapse
Affiliation(s)
- M Schepp
- University of Augsburg, University Hospital Augsburg, EpidemiologyAugsburg, Germany.
| | - D Freuer
- University of Augsburg, University Hospital Augsburg, EpidemiologyAugsburg, Germany
| | - N Wawro
- University of Augsburg, University Hospital Augsburg, EpidemiologyAugsburg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - A Peters
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany
| | - M Heier
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- KORA Study Centre, University Hospital Augsburg, Augsburg, Germany
| | - D Teupser
- Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany
| | - C Meisinger
- University of Augsburg, University Hospital Augsburg, EpidemiologyAugsburg, Germany
| | - J Linseisen
- University of Augsburg, University Hospital Augsburg, EpidemiologyAugsburg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| |
Collapse
|
2
|
Nogal A, Tettamanzi F, Dong Q, Louca P, Visconti A, Christiansen C, Breuninger T, Linseisen J, Grallert H, Wawro N, Asnicar F, Wong K, Baleanu AF, Michelotti GA, Segata N, Falchi M, Peters A, Franks PW, Bagnardi V, Spector TD, Bell JT, Gieger C, Valdes AM, Menni C. A Fecal Metabolite Signature of Impaired Fasting Glucose: Results From Two Independent Population-Based Cohorts. Diabetes 2023; 72:1870-1880. [PMID: 37699401 PMCID: PMC10658071 DOI: 10.2337/db23-0170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/30/2023] [Indexed: 09/14/2023]
Abstract
Prediabetes is a metabolic condition associated with gut microbiome composition, although mechanisms remain elusive. We searched for fecal metabolites, a readout of gut microbiome function, associated with impaired fasting glucose (IFG) in 142 individuals with IFG and 1,105 healthy individuals from the UK Adult Twin Registry (TwinsUK). We used the Cooperative Health Research in the Region of Augsburg (KORA) cohort (318 IFG individuals, 689 healthy individuals) to replicate our findings. We linearly combined eight IFG-positively associated metabolites (1-methylxantine, nicotinate, glucuronate, uridine, cholesterol, serine, caffeine, and protoporphyrin IX) into an IFG-metabolite score, which was significantly associated with higher odds ratios (ORs) for IFG (TwinsUK: OR 3.9 [95% CI 3.02-5.02], P < 0.0001, KORA: OR 1.3 [95% CI 1.16-1.52], P < 0.0001) and incident type 2 diabetes (T2D; TwinsUK: hazard ratio 4 [95% CI 1.97-8], P = 0.0002). Although these are host-produced metabolites, we found that the gut microbiome is strongly associated with their fecal levels (area under the curve >70%). Abundances of Faecalibacillus intestinalis, Dorea formicigenerans, Ruminococcus torques, and Dorea sp. AF24-7LB were positively associated with IFG, and such associations were partially mediated by 1-methylxanthine and nicotinate (variance accounted for mean 14.4% [SD 5.1], P < 0.05). Our results suggest that the gut microbiome is linked to prediabetes not only via the production of microbial metabolites but also by affecting intestinal absorption/excretion of host-produced metabolites and xenobiotics, which are correlated with the risk of IFG. Fecal metabolites enable modeling of another mechanism of gut microbiome effect on prediabetes and T2D onset. ARTICLE HIGHLIGHTS Prediabetes is a metabolic condition associated with gut microbiome composition, although mechanisms remain elusive. We investigated whether there is a fecal metabolite signature of impaired fasting glucose (IFG) and the possible underlying mechanisms of action. We identified a fecal metabolite signature of IFG associated with prevalent IFG in two independent cohorts and incident type 2 diabetes in a subanalysis. Although the signature consists of metabolites of nonmicrobial origin, it is strongly correlated with gut microbiome composition. Fecal metabolites enable modeling of another mechanism of gut microbiome effect on prediabetes by affecting intestinal absorption or excretion of host compounds and xenobiotics.
Collapse
Affiliation(s)
- Ana Nogal
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Francesca Tettamanzi
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
- Humanitas Clinical and Research Centre, IRCCS, Rozzano (Milan), Italy
| | - Qiuling Dong
- Institute of Epidemiology, Helmholtz Zentrum München, Research Unit of Molecular Epidemiology, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Panayiotis Louca
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Alessia Visconti
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Colette Christiansen
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
- School of Mathematics and Statistics, The Open University, Milton Keynes, U.K
| | - Taylor Breuninger
- Epidemiology, University Hospital Augsburg, University of Augsburg, Augsburg, Germany
| | - Jakob Linseisen
- Epidemiology, University Hospital Augsburg, University of Augsburg, Augsburg, Germany
- ZIEL-Institute for Food & Health, Technische Universität München, Freising, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology, Medical Faculty, Ludwig-Maximilian University Munich, Munich, Germany
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München, Research Unit of Molecular Epidemiology, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Nina Wawro
- Epidemiology, University Hospital Augsburg, University of Augsburg, Augsburg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Francesco Asnicar
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | | | - Andrei-Florin Baleanu
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | | | - Nicola Segata
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Mario Falchi
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Annette Peters
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Munich Heart Alliance, German Center for Cardiovascular Research (DZHK e.V., Partner-Site Munich), Munich, Germany
| | - Paul W. Franks
- Lund University Diabetes Center, Lund University, Malmö, Sweden
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Vincenzo Bagnardi
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
| | - Tim D. Spector
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Jordana T. Bell
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, Research Unit of Molecular Epidemiology, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Ana M. Valdes
- Academic Rheumatology Clinical Sciences Building, Nottingham City Hospital, University of Nottingham, U.K
| | - Cristina Menni
- Department of Twin Research, King’s College London, St Thomas’ Hospital Campus, London, U.K
| |
Collapse
|
3
|
Emmert-Fees KMF, Amies-Cull B, Wawro N, Linseisen J, Staudigel M, Peters A, Cobiac LJ, O’Flaherty M, Scarborough P, Kypridemos C, Laxy M. Projected health and economic impacts of sugar-sweetened beverage taxation in Germany: A cross-validation modelling study. PLoS Med 2023; 20:e1004311. [PMID: 37988392 PMCID: PMC10662751 DOI: 10.1371/journal.pmed.1004311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/13/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND Taxes on sugar-sweetened beverages (SSBs) have been implemented globally to reduce the burden of cardiometabolic diseases by disincentivizing consumption through increased prices (e.g., 1 peso/litre tax in Mexico) or incentivizing industry reformulation to reduce SSB sugar content (e.g., tiered structure of the United Kingdom [UK] Soft Drinks Industry Levy [SDIL]). In Germany, where no tax on SSBs is enacted, the health and economic impact of SSB taxation using the experience from internationally implemented tax designs has not been evaluated. The objective of this study was to estimate the health and economic impact of national SSBs taxation scenarios in Germany. METHODS AND FINDINGS In this modelling study, we evaluated a 20% ad valorem SSB tax with/without taxation of fruit juice (based on implemented SSB taxes and recommendations) and a tiered tax (based on the UK SDIL) in the German adult population aged 30 to 90 years from 2023 to 2043. We developed a microsimulation model (IMPACTNCD Germany) that captures the demographics, risk factor profile and epidemiology of type 2 diabetes, coronary heart disease (CHD) and stroke in the German population using the best available evidence and national data. For each scenario, we estimated changes in sugar consumption and associated weight change. Resulting cases of cardiometabolic disease prevented/postponed and related quality-adjusted life years (QALYs) and economic impacts from healthcare (medical costs) and societal (medical, patient time, and productivity costs) perspectives were estimated using national cost and health utility data. Additionally, we assessed structural uncertainty regarding direct, body mass index (BMI)-independent cardiometabolic effects of SSBs and cross-validated results with an independently developed cohort model (PRIMEtime). We found that SSB taxation could reduce sugar intake in the German adult population by 1 g/day (95%-uncertainty interval [0.05, 1.65]) for a 20% ad valorem tax on SSBs leading to reduced consumption through increased prices (pass-through of 82%) and 2.34 g/day (95%-UI [2.32, 2.36]) for a tiered tax on SSBs leading to 30% reduction in SSB sugar content via reformulation. Through reductions in obesity, type 2 diabetes, and cardiovascular disease (CVD), 106,000 (95%-UI [57,200, 153,200]) QALYs could be gained with a 20% ad valorem tax and 192,300 (95%-UI [130,100, 254,200]) QALYs with a tiered tax. Respectively, €9.6 billion (95%-UI [4.7, 15.3]) and €16.0 billion (95%-UI [8.1, 25.5]) costs could be saved from a societal perspective over 20 years. Impacts of the 20% ad valorem tax were larger when additionally taxing fruit juice (252,400 QALYs gained, 95%-UI [176,700, 325,800]; €11.8 billion costs saved, 95%-UI [€6.7, €17.9]), but impacts of all scenarios were reduced when excluding direct health effects of SSBs. Cross-validation with PRIMEtime showed similar results. Limitations include remaining uncertainties in the economic and epidemiological evidence and a lack of product-level data. CONCLUSIONS In this study, we found that SSB taxation in Germany could help to reduce the national burden of noncommunicable diseases and save a substantial amount of societal costs. A tiered tax designed to incentivize reformulation of SSBs towards less sugar might have a larger population-level health and economic impact than an ad valorem tax that incentivizes consumer behaviour change only through increased prices.
Collapse
Affiliation(s)
- Karl M. F. Emmert-Fees
- Professorship of Public Health and Prevention, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health LMU Munich, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, Research Center for Environmental Health, Neuherberg, Germany
| | - Ben Amies-Cull
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- Oxford Health Biomedical Research Centre, National Institute of Health and Care Research, Oxford, United Kingdom
| | - Nina Wawro
- Institute of Epidemiology, Helmholtz Zentrum München, Research Center for Environmental Health, Neuherberg, Germany
| | - Jakob Linseisen
- Epidemiology, University of Augsburg, University Hospital Augsburg, Augsburg, Germany
| | - Matthias Staudigel
- TUM School of Management, Technical University of Munich, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, Research Center for Environmental Health, Neuherberg, Germany
| | - Linda J. Cobiac
- School of Medicine and Dentistry, Griffith University, Southport, Australia
| | - Martin O’Flaherty
- Department of Public Health, Policy & Systems, University of Liverpool, Liverpool, United Kingdom
| | - Peter Scarborough
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- Oxford Health Biomedical Research Centre, National Institute of Health and Care Research, Oxford, United Kingdom
| | - Chris Kypridemos
- Department of Public Health, Policy & Systems, University of Liverpool, Liverpool, United Kingdom
| | - Michael Laxy
- Professorship of Public Health and Prevention, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| |
Collapse
|
4
|
Ahn N, Wawro N, Baumeister SE, Nolde M, Gerlach R, Tauscher M, Günter A, Güntner F, Rückert-Eheberg IM, Meisinger C, Linseisen J. Time-Varying Use of Proton Pump Inhibitors and Cognitive Impairment and Dementia: A Real-World Analysis from Germany. Drugs Aging 2023:10.1007/s40266-023-01031-7. [PMID: 37178361 DOI: 10.1007/s40266-023-01031-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Cumulative evidence of dementia risk in patients taking proton pump inhibitors (PPIs) is still inconclusive, probably due to a variety of study designs. OBJECTIVE This study aimed to compare how the association between dementia risk and use of PPIs differs by different outcome and exposure definitions. METHODS We conceptualized a target trial using claims data with 7,696,127 individuals aged 40 years or older without previous dementia or mild cognitive impairment (MCI) from the Association of Statutory Health Insurance Physicians in Bavaria. Dementia was defined as either including or excluding MCI to compare how the results alter by different outcome definitions. We used weighted Cox models to estimate the PPI initiation effect on dementia risk and weighted pooled logistic regression to assess the effect of time-varying use versus non-use during 9 years of study period, including 1 year of wash-out period (2009-2018). The median follow-up time of PPI initiators and non-initiators was 5.4 and 5.8 years, respectively. We also evaluated the association between each PPI agent (omeprazole, pantoprazole, lansoprazole, esomeprazole, and combined use) and dementia risk. RESULTS A total of 105,220 (3.6%) PPI initiators and 74,697 (2.6%) non-initiators were diagnosed with dementia. Comparing PPI initiation with no initiation, the hazard ratio (HR) for dementia was 1.04 [95% confidence interval (CI) 1.03-1.05]. The HR for time-varying PPI use versus non-use was 1.85 (1.80-1.90). When MCI was included in the outcome, the number of outcomes increased to 121,922 in PPI initiators and 86,954 in non-initiators, but HRs remained similar, showing 1.04 (1.03-1.05) and 1.82 (1.77-1.86), respectively. Pantoprazole was the most frequently used PPI agent. Although the estimated HRs for the time-varying use effect of each PPI showed different ranges, all agents were associated with an increased dementia risk. A total of 105,220 (3.6%) PPI initiators and 74,697 (2.6%) non-initiators were diagnosed with dementia. Comparing PPI initiation with no initiation, the hazard ratio (HR) for dementia was 1.04 [95% confidence interval (CI) 1.03-1.05]. The HR for time-varying PPI use versus non-use was 1.85 (1.80-1.90). When MCI was included in the outcome, the number of outcomes increased to 121,922 in PPI initiators and 86,954 in non-initiators, but HRs remained similar, showing 1.04 (1.03-1.05) and 1.82 (1.77-1.86), respectively. Pantoprazole was the most frequently used PPI agent. Although the estimated HRs for the time-varying use effect of each PPI showed different ranges, all agents were associated with an increased dementia risk.A total of 105,220 (3.6%) PPI initiators and 74,697 (2.6%) non-initiators were diagnosed with dementia. Comparing PPI initiation with no initiation, the hazard ratio (HR) for dementia was 1.04 [95% confidence interval (CI) 1.03-1.05]. The HR for time-varying PPI use versus non-use was 1.85 (1.80-1.90). When MCI was included in the outcome, the number of outcomes increased to 121,922 in PPI initiators and 86,954 in non-initiators, but HRs remained similar, showing 1.04 (1.03-1.05) and 1.82 (1.77-1.86), respectively. Pantoprazole was the most frequently used PPI agent. Although the estimated HRs for the time-varying use effect of each PPI showed different ranges, all agents were associated with an increased dementia risk.A total of 105,220 (3.6%) PPI initiators and 74,697 (2.6%) non-initiators were diagnosed with dementia. Comparing PPI initiation with no initiation, the hazard ratio (HR) for dementia was 1.04 [95% confidence interval (CI) 1.03-1.05]. The HR for time-varying PPI use versus non-use was 1.85 (1.80-1.90). When MCI was included in the outcome, the number of outcomes increased to 121,922 in PPI initiators and 86,954 in non-initiators, but HRs remained similar, showing 1.04 (1.03-1.05) and 1.82 (1.77-1.86), respectively. Pantoprazole was the most frequently used PPI agent. Although the estimated HRs for the time-varying use effect of each PPI showed different ranges, all agents were associated with an increased dementia risk.A total of 105,220 (3.6%) PPI initiators and 74,697 (2.6%) non-initiators were diagnosed with dementia. Comparing PPI initiation with no initiation, the hazard ratio (HR) for dementia was 1.04 [95% confidence interval (CI) 1.03-1.05]. The HR for time-varying PPI use versus non-use was 1.85 (1.80-1.90). When MCI was included in the outcome, the number of outcomes increased to 121,922 in PPI initiators and 86,954 in non-initiators, but HRs remained similar, showing 1.04 (1.03-1.05) and 1.82 (1.77-1.86), respectively. Pantoprazole was the most frequently used PPI agent. Although the estimated HRs for the time-varying use effect of each PPI showed different ranges, all agents were associated with an increased dementia risk.A total of 105,220 (3.6%) PPI initiators and 74,697 (2.6%) non-initiators were diagnosed with dementia. Comparing PPI initiation with no initiation, the hazard ratio (HR) for dementia was 1.04 [95% confidence interval (CI) 1.03-1.05]. The HR for time-varying PPI use versus non-use was 1.85 (1.80-1.90). When MCI was included in the outcome, the number of outcomes increased to 121,922 in PPI initiators and 86,954 in non-initiators, but HRs remained similar, showing 1.04 (1.03-1.05) and 1.82 (1.77-1.86), respectively. Pantoprazole was the most frequently used PPI agent. Although the estimated HRs for the time-varying use effect of each PPI showed different ranges, all agents were associated with an increased dementia risk.A total of 105,220 (3.6%) PPI initiators and 74,697 (2.6%) non-initiators were diagnosed with dementia. Comparing PPI initiation with no initiation, the hazard ratio (HR) for dementia was 1.04 [95% confidence interval (CI) 1.03-1.05]. The HR for time-varying PPI use versus non-use was 1.85 (1.80-1.90). When MCI was included in the outcome, the number of outcomes increased to 121,922 in PPI initiators and 86,954 in non-initiators, but HRs remained similar, showing 1.04 (1.03-1.05) and 1.82 (1.77-1.86), respectively. Pantoprazole was the most frequently used PPI agent. Although the estimated HRs for the time-varying use effect of each PPI showed different ranges, all agents were associated with an increased dementia risk.A total of 105,220 (3.6%) PPI initiators and 74,697 (2.6%) non-initiators were diagnosed with dementia. Comparing PPI initiation with no initiation, the hazard ratio (HR) for dementia was 1.04 [95% confidence interval (CI) 1.03-1.05]. The HR for time-varying PPI use versus non-use was 1.85 (1.80-1.90). When MCI was included in the outcome, the number of outcomes increased to 121,922 in PPI initiators and 86,954 in non-initiators, but HRs remained similar, showing 1.04 (1.03-1.05) and 1.82 (1.77-1.86), respectively. Pantoprazole was the most frequently used PPI agent. Although the estimated HRs for the time-varying use effect of each PPI showed different ranges, all agents were associated with an increased dementia risk.A total of 105,220 (3.6%) PPI initiators and 74,697 (2.6%) non-initiators were diagnosed with dementia. Comparing PPI initiation with no initiation, the hazard ratio (HR) for dementia was 1.04 [95% confidence interval (CI) 1.03-1.05]. The HR for time-varying PPI use versus non-use was 1.85 (1.80-1.90). When MCI was included in the outcome, the number of outcomes increased to 121,922 in PPI initiators and 86,954 in non-initiators, but HRs remained similar, showing 1.04 (1.03-1.05) and 1.82 (1.77-1.86), respectively. Pantoprazole was the most frequently used PPI agent. Although the estimated HRs for the time-varying use effect of each PPI showed different ranges, all agents were associated with an increased dementia risk. CONCLUSION Our large study supports existing evidence that PPI use is related to an increased risk of dementia.
Collapse
Affiliation(s)
- Nayeon Ahn
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-University Munich, Munich, Germany.
- Epidemiology, Medical Faculty, University of Augsburg, Augsburg, Germany.
| | - Nina Wawro
- Epidemiology, Medical Faculty, University of Augsburg, Augsburg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | | | - Michael Nolde
- Institute of Health Services Research in Dentistry, University of Münster, Münster, Germany
| | - Roman Gerlach
- Association of Statutory Health Insurance Physicians in Bavaria (Kassenärztliche Vereinigung Bayerns, KVB), Munich, Germany
| | - Martin Tauscher
- Association of Statutory Health Insurance Physicians in Bavaria (Kassenärztliche Vereinigung Bayerns, KVB), Munich, Germany
| | | | | | - Ina-Maria Rückert-Eheberg
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Christa Meisinger
- Epidemiology, Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Jakob Linseisen
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-University Munich, Munich, Germany
- Epidemiology, Medical Faculty, University of Augsburg, Augsburg, Germany
| |
Collapse
|
5
|
Hellbach F, Sinke L, Costeira R, Baumeister SE, Beekman M, Louca P, Leeming ER, Mompeo O, Berry S, Wilson R, Wawro N, Freuer D, Hauner H, Peters A, Winkelmann J, Koenig W, Meisinger C, Waldenberger M, Heijmans BT, Slagboom PE, Bell JT, Linseisen J. Pooled analysis of epigenome-wide association studies of food consumption in KORA, TwinsUK and LLS. Eur J Nutr 2023; 62:1357-1375. [PMID: 36571600 PMCID: PMC10030421 DOI: 10.1007/s00394-022-03074-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 12/12/2022] [Indexed: 12/27/2022]
Abstract
PURPOSE Examining epigenetic patterns is a crucial step in identifying molecular changes of disease pathophysiology, with DNA methylation as the most accessible epigenetic measure. Diet is suggested to affect metabolism and health via epigenetic modifications. Thus, our aim was to explore the association between food consumption and DNA methylation. METHODS Epigenome-wide association studies were conducted in three cohorts: KORA FF4, TwinsUK, and Leiden Longevity Study, and 37 dietary exposures were evaluated. Food group definition was harmonized across the three cohorts. DNA methylation was measured using Infinium MethylationEPIC BeadChip in KORA and Infinium HumanMethylation450 BeadChip in the Leiden study and the TwinsUK study. Overall, data from 2293 middle-aged men and women were included. A fixed-effects meta-analysis pooled study-specific estimates. The significance threshold was set at 0.05 for false-discovery rate-adjusted p values per food group. RESULTS We identified significant associations between the methylation level of CpG sites and the consumption of onions and garlic (2), nuts and seeds (18), milk (1), cream (11), plant oils (4), butter (13), and alcoholic beverages (27). The signals targeted genes of metabolic health relevance, for example, GLI1, RPTOR, and DIO1, among others. CONCLUSION This EWAS is unique with its focus on food groups that are part of a Western diet. Significant findings were mostly related to food groups with a high-fat content.
Collapse
Affiliation(s)
- Fabian Hellbach
- Institute for Medical Information Processing, Biometry, and Epidemiology, Medical Faculty, Ludwig-Maximilian University Munich, Marchioninistr. 15, 81377, Munich, Germany.
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany.
| | - Lucy Sinke
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - Ricardo Costeira
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, England, UK
| | - Sebastian-Edgar Baumeister
- Institute of Health Services Research in Dentistry, Medical Faculty, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - Panayiotis Louca
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, England, UK
| | - Emily R Leeming
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, England, UK
| | - Olatz Mompeo
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, England, UK
| | - Sarah Berry
- Department of Nutritional Sciences, King's College London, London, UK
| | - Rory Wilson
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Nina Wawro
- Institute for Medical Information Processing, Biometry, and Epidemiology, Medical Faculty, Ludwig-Maximilian University Munich, Marchioninistr. 15, 81377, Munich, Germany
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Dennis Freuer
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Hans Hauner
- Else Kröner-Fresenius-Center for Nutritional Medicine, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Georg-Brauchle-Ring 62, 80992, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD E.V.), Ingolstädter Landstr. 1, 85764, Munich-Neuherberg, Germany
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health (HmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Wolfgang Koenig
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstr. 8A & 9, 80336, Munich, Germany
- German Heart Centre Munich, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Helmholtzstr. 22, 89081, Ulm, Germany
| | - Christa Meisinger
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD E.V.), Ingolstädter Landstr. 1, 85764, Munich-Neuherberg, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstr. 8A & 9, 80336, Munich, Germany
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, England, UK
| | - Jakob Linseisen
- Institute for Medical Information Processing, Biometry, and Epidemiology, Medical Faculty, Ludwig-Maximilian University Munich, Marchioninistr. 15, 81377, Munich, Germany
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| |
Collapse
|
6
|
Dahal C, Wawro N, Meisinger C, Brandl B, Skurk T, Volkert D, Hauner H, Linseisen J. Evaluation of the metabotype concept after intervention with oral glucose tolerance test and dietary fiber-enriched food: An enable study. Nutr Metab Cardiovasc Dis 2022; 32:2399-2409. [PMID: 35850752 DOI: 10.1016/j.numecd.2022.06.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/19/2022] [Accepted: 06/10/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND AND AIMS Evidence suggests that people react differently to the same diet due to inter-individual differences. However, few studies have investigated variation in response to dietary interventions based on individuals' baseline metabolic characteristics. This study aims to examine the differential reaction of metabotype subgroups to an OGTT and a dietary fiber intervention. METHODS AND RESULTS We assigned 356 healthy participants of an OGTT sub-study and a 12-week dietary fiber intervention sub-study within the enable cluster to three metabotype subgroups previously identified in the KORA F4 study population. To explore the association between plasma glucose level and metabotype subgroups, we used linear mixed models adjusted for age, sex, and physical activity. Individuals in different metabotype subgroups showed differential responses to OGTT. Compared to the healthy metabotype (metabotype 1), participants in intermediate metabotype (metabotype 2) and unfavorable metabotype (metabotype 3) had significantly higher plasma glucose concentrations at 120 min after glucose bolus (β = 7.881, p = 0.005; β = 32.79, p < 0.001, respectively). Additionally, the linear regression model showed that the Area under the curve (AUC) of plasma glucose concentrations was significantly different across the metabotype subgroups. The associations between metabotype subgroups and metabolic parameters among fiber intervention participants remained insignificant in the multivariate-adjusted linear model. However, the metabotype 3 had the highest mean reduction in insulin, cholesterol parameters (TC, LDLc, and non-HDLc), and systolic and diastolic blood pressure at the end of the intervention period. CONCLUSION This study supports the use of the metabotype concept to identify metabolically similar subgroups and to develop targeted dietary interventions at the metabotype subgroup level for the primary prevention of diet-related diseases.
Collapse
Affiliation(s)
- Chetana Dahal
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Nina Wawro
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Beate Brandl
- ZIEL - Institute for Food & Health, Technical University of Munich, Freising, Germany
| | - Thomas Skurk
- Else Kröner-Fresenius-Center for Nutritional Medicine, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany; ZIEL - Institute for Food & Health, Technical University of Munich, Freising, Germany
| | - Dorothee Volkert
- Institute for Biomedicine of Aging, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nuremberg, Germany
| | - Hans Hauner
- Else Kröner-Fresenius-Center for Nutritional Medicine, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany; Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Georg-Brauchle-Ring 62, 80992 Munich, Germany
| | - Jakob Linseisen
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 München, Germany.
| |
Collapse
|
7
|
Breuninger TA, Wawro N, Freuer D, Reitmeier S, Artati A, Grallert H, Adamski J, Meisinger C, Peters A, Haller D, Linseisen J. Fecal Bile Acids and Neutral Sterols Are Associated with Latent Microbial Subgroups in the Human Gut. Metabolites 2022; 12:metabo12090846. [PMID: 36144250 PMCID: PMC9504437 DOI: 10.3390/metabo12090846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/31/2022] [Accepted: 09/05/2022] [Indexed: 11/28/2022] Open
Abstract
Bile acids, neutral sterols, and the gut microbiome are intricately intertwined and each affects human health and metabolism. However, much is still unknown about this relationship. This analysis included 1280 participants of the KORA FF4 study. Fecal metabolites (primary and secondary bile acids, plant and animal sterols) were analyzed using a metabolomics approach. Dirichlet regression models were used to evaluate associations between the metabolites and twenty microbial subgroups that were previously identified using latent Dirichlet allocation. Significant associations were identified between 12 of 17 primary and secondary bile acids and several of the microbial subgroups. Three subgroups showed largely positive significant associations with bile acids, and six subgroups showed mostly inverse associations with fecal bile acids. We identified a trend where microbial subgroups that were previously associated with “healthy” factors were here inversely associated with fecal bile acid levels. Conversely, subgroups that were previously associated with “unhealthy” factors were positively associated with fecal bile acid levels. These results indicate that further research is necessary regarding bile acids and microbiota composition, particularly in relation to metabolic health.
Collapse
Affiliation(s)
- Taylor A. Breuninger
- Chair of Epidemiology, University Hospital Augsburg, University of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany
- Correspondence:
| | - Nina Wawro
- Chair of Epidemiology, University Hospital Augsburg, University of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Dennis Freuer
- Chair of Epidemiology, University Hospital Augsburg, University of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany
| | - Sandra Reitmeier
- Chair of Nutrition and Immunology, Technische Universität München, Gregor-Mendel-Str. 2, 85354 Freising, Germany
- ZIEL—Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
| | - Anna Artati
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Metabolomics and Proteomics Core, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Harald Grallert
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
| | - Christa Meisinger
- Chair of Epidemiology, University Hospital Augsburg, University of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany
| | - Annette Peters
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Dirk Haller
- Chair of Nutrition and Immunology, Technische Universität München, Gregor-Mendel-Str. 2, 85354 Freising, Germany
- ZIEL—Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
| | - Jakob Linseisen
- Chair of Epidemiology, University Hospital Augsburg, University of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany
- ZIEL—Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
| |
Collapse
|
8
|
Hellbach F, Baumeister SE, Wilson R, Wawro N, Dahal C, Freuer D, Hauner H, Peters A, Winkelmann J, Schwettmann L, Rathmann W, Kronenberg F, Koenig W, Meisinger C, Waldenberger M, Linseisen J. Association between Usual Dietary Intake of Food Groups and DNA Methylation and Effect Modification by Metabotype in the KORA FF4 Cohort. Life (Basel) 2022; 12:life12071064. [PMID: 35888152 PMCID: PMC9318948 DOI: 10.3390/life12071064] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 11/17/2022] Open
Abstract
Associations between diet and DNA methylation may vary among subjects with different metabolic states, which can be captured by clustering populations in metabolically homogenous subgroups, called metabotypes. Our aim was to examine the relationship between habitual consumption of various food groups and DNA methylation as well as to test for effect modification by metabotype. A cross-sectional analysis of participants (median age 58 years) of the population-based prospective KORA FF4 study, habitual dietary intake was modeled based on repeated 24-h diet recalls and a food frequency questionnaire. DNA methylation was measured using the Infinium MethylationEPIC BeadChip providing data on >850,000 sites in this epigenome-wide association study (EWAS). Three metabotype clusters were identified using four standard clinical parameters and BMI. Regression models were used to associate diet and DNA methylation, and to test for effect modification. Few significant signals were identified in the basic analysis while many significant signals were observed in models including food group-metabotype interaction terms. Most findings refer to interactions of food intake with metabotype 3, which is the metabotype with the most unfavorable metabolic profile. This research highlights the importance of the metabolic characteristics of subjects when identifying associations between diet and white blood cell DNA methylation in EWAS.
Collapse
Affiliation(s)
- Fabian Hellbach
- Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilian University of Munich, Marchioninistr. 15, 81377 Munich, Germany; (N.W.); (J.L.)
- Epidemiology, Faculty of Medicine, University Hospital Augsburg, University of Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.D.); (D.F.); (C.M.)
- Correspondence: ; Tel.: +49-821-598-6473
| | - Sebastian-Edgar Baumeister
- Institute of Health Services Research in Dentistry, Medical Faculty, University of Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany;
| | - Rory Wilson
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (R.W.); (A.P.); (M.W.)
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Nina Wawro
- Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilian University of Munich, Marchioninistr. 15, 81377 Munich, Germany; (N.W.); (J.L.)
- Epidemiology, Faculty of Medicine, University Hospital Augsburg, University of Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.D.); (D.F.); (C.M.)
| | - Chetana Dahal
- Epidemiology, Faculty of Medicine, University Hospital Augsburg, University of Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.D.); (D.F.); (C.M.)
| | - Dennis Freuer
- Epidemiology, Faculty of Medicine, University Hospital Augsburg, University of Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.D.); (D.F.); (C.M.)
| | - Hans Hauner
- Else Kröner-Fresenius-Center for Nutritional Medicine, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany;
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Georg-Brauchle-Ring 62, 80992 Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (R.W.); (A.P.); (M.W.)
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf’m Hennekamp 65, 40225 Düsseldorf, Germany;
| | - Juliane Winkelmann
- Institute of Neurogenomic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany;
| | - Lars Schwettmann
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany;
- Department of Economics, Martin Luther University Halle-Wittenberg, 06099 Halle, Germany
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf’m Hennekamp 65, 40225 Düsseldorf, Germany;
| | - Florian Kronenberg
- Department of Genetics and Pharmacology, Institute of Genetic Epidemiology, Medical University of Innsbruck, Schöpfstr. 41, 6020 Innsbruck, Austria;
| | - Wolfgang Koenig
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstr. 8A & 9, 80336 Munich, Germany;
- German Heart Centre Munich, Technical University Munich, Lazarettstr. 36, 80636 Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Helmholtzstr. 22, 89081 Ulm, Germany
| | - Christa Meisinger
- Epidemiology, Faculty of Medicine, University Hospital Augsburg, University of Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.D.); (D.F.); (C.M.)
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (R.W.); (A.P.); (M.W.)
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf’m Hennekamp 65, 40225 Düsseldorf, Germany;
| | - Jakob Linseisen
- Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilian University of Munich, Marchioninistr. 15, 81377 Munich, Germany; (N.W.); (J.L.)
- Epidemiology, Faculty of Medicine, University Hospital Augsburg, University of Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.D.); (D.F.); (C.M.)
| |
Collapse
|
9
|
Reik A, Brandl B, Schauberger G, Wawro N, Linseisen J, Skurk T, Volkert D, Hauner H, Holzapfel C. Association between Habitual Diet and the Postprandial Glucose Response-An Enable Study. Mol Nutr Food Res 2022; 66:e2200110. [PMID: 35713029 DOI: 10.1002/mnfr.202200110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/13/2022] [Indexed: 11/05/2022]
Abstract
SCOPE It is inconclusive which factors influence inter-individual variations of postprandial glucose response (PPGR). This study investigates whether the habitual diet is associated with PPGR. METHODS AND RESULTS Data from healthy adults (young adults with 18-25 years, middle-aged adults with 40-65 years, and older adults with 75-85 years) is collected at baseline and during an oral glucose tolerance test (OGTT) collected. Habitual diet is assessed by a food frequency questionnaire and two 24-h food lists. Associations between habitual diet and glucose incremental area under the curve (iAUCmin ) are examined by regression models. The intake of cereals and cereal products is negatively associated with glucose iAUCmin (p = 0.002) in the total cohort (N = 459, 50% women, 55 ± 21 years, BMI 26 ± 5 kg m- 2 ). Up to 9% of the variance in the glycemic response is explained by the respective dietary parameters identified in the models of the specific age groups. CONCLUSION There are age-specific diet-related effects on PPGR. The usual intake of cereals and cereal products seems to play a greater role in PPGR in more than one age group. Further research is needed, to establish how diet can be optimized based on age and PPGR.
Collapse
Affiliation(s)
- Anna Reik
- Institute for Nutritional Medicine, School of Medicine, University Hospital Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Beate Brandl
- ZIEL - Institute for Food & Health, Technical University of Munich, Freising, Germany
| | - Gunther Schauberger
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - Nina Wawro
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.,Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, Augsburg, Germany
| | - Jakob Linseisen
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.,Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, Augsburg, Germany
| | - Thomas Skurk
- ZIEL - Institute for Food & Health, Technical University of Munich, Freising, Germany
| | - Dorothee Volkert
- Institute for Biomedicine of Aging, Friedrich-Alexander Universität Erlangen-Nürnberg, Nuremberg, Germany
| | - Hans Hauner
- Institute for Nutritional Medicine, School of Medicine, University Hospital Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Else Kröner-Fresenius-Center for Nutritional Medicine, Chair of Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Christina Holzapfel
- Institute for Nutritional Medicine, School of Medicine, University Hospital Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| |
Collapse
|
10
|
Pestoni G, Riedl A, Breuninger TA, Wawro N, Krieger JP, Meisinger C, Rathmann W, Thorand B, Harris C, Peters A, Rohrmann S, Linseisen J. Association between dietary patterns and prediabetes, undetected diabetes or clinically diagnosed diabetes: results from the KORA FF4 study. Eur J Nutr 2021; 60:2331-2341. [PMID: 33125578 PMCID: PMC8275503 DOI: 10.1007/s00394-020-02416-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 10/13/2020] [Indexed: 11/07/2022]
Abstract
PURPOSE Diet is one of the most important modifiable risk factors for the development of type 2 diabetes. Here, we aim to identify dietary patterns and to investigate their association with prediabetes, undetected diabetes and prevalent diabetes. METHODS The present study included 1305 participants of the cross-sectional population-based KORA FF4 study. Oral glucose tolerance test (OGTT) measurements together with a physician-confirmed diagnosis allowed for an accurate categorization of the participants according to their glucose tolerance status into normal glucose tolerance (n = 698), prediabetes (n = 459), undetected diabetes (n = 49), and prevalent diabetes (n = 99). Dietary patterns were identified through principal component analysis followed by hierarchical clustering. The association between dietary patterns and glucose tolerance status was investigated using multinomial logistic regression models. RESULTS A Prudent pattern, characterized by high consumption of vegetables, fruits, wholegrains and dairy products, and a Western pattern, characterized by high consumption of red and processed meat, alcoholic beverages, refined grains and sugar-sweetened beverages, were identified. Participants following the Western pattern had significantly higher chances of having prediabetes (odds ratio [OR] 1.92; 95% confidence interval [CI] 1.35, 2.73), undetected diabetes (OR 10.12; 95% CI 4.19, 24.43) or prevalent diabetes (OR 3.51; 95% CI 1.85, 6.67), compared to participants following the Prudent pattern. CONCLUSION To our knowledge, the present study is one of the few investigating the association between dietary patterns and prediabetes or undetected diabetes. The use of a reference group exclusively including participants with normal glucose tolerance might explain the strong associations observed in our study. These results suggest a very important role of dietary habits in the prevention of prediabetes and type 2 diabetes.
Collapse
Affiliation(s)
- Giulia Pestoni
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Anna Riedl
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Augsburg, Germany
| | - Taylor A Breuninger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Augsburg, Germany
| | - Nina Wawro
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Augsburg, Germany
| | - Jean-Philippe Krieger
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Augsburg, Germany
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD E.V.), Neuherberg, Germany
| | - Barbara Thorand
- German Center for Diabetes Research (DZD E.V.), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Carla Harris
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Division of Metabolic and Nutritional Medicine, Dr. Von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
| | - Annette Peters
- German Center for Diabetes Research (DZD E.V.), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Sabine Rohrmann
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Jakob Linseisen
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
- Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Augsburg, Germany.
| |
Collapse
|
11
|
Askani E, Rospleszcz S, Rothenbacher T, Wawro N, Messmann H, De Cecco CN, von Krüchten R, Kulka C, Kiefer LS, Rathmann W, Peters A, Schlett CL, Bamberg F, Linseisen J, Storz C. Dietary habits and the presence and degree of asymptomatic diverticular disease by magnetic resonance imaging in a Western population: a population-based cohort study. Nutr Metab (Lond) 2021; 18:73. [PMID: 34271946 PMCID: PMC8283990 DOI: 10.1186/s12986-021-00599-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 07/03/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Despite the worldwide burden of diverticular disease, the connections between diverticular disease and dietary habits remain poorly understood, particularly in an asymptomatic representative sample. We investigated the association between asymptomatic diverticular disease as assessed by magnetic resonance imaging (MRI) and dietary habits in a Western study cohort. METHODS Participants from a cross-sectional sample of a population-based cohort study underwent whole-body 3T-MRI including an isotropic VIBE-Dixon sequence. The presence and extent of diverticular disease was assessed in blinded fashion. Habitual dietary intake was recorded using a blended approach, applying 24-h food lists and a food-frequency questionnaire. Traditional cardiometabolic risk factors were obtained by interviews and medical examination. Univariate and multivariate associations were calculated. RESULTS A total of 308 subjects were included in this analysis (56% male, 56.4 ± 9.1 years). 39.9% had any form of diverticular disease and 15.3% had advanced asymptomatic diverticular disease. After adjustment for age, sex and total energy intake a higher intake of fiber and vegetables was associated with a lower odds for asymptomatic diverticular disease (fiber: OR 0.68 95% CI [0.48, 0.95]; vegetables: OR 0.72 95% CI [0.53, 0.97]) and an increased intake of meat was associated with an approximately two-fold higher odds for advanced asymptomatic diverticular disease (OR 1.84 95% CI [1.13, 2.99]). However, after additional adjustment for body-mass-index (BMI), alcohol consumption, smoking behavior and physical activity only a high fiber and vegetables intake remained significantly associated with lower odds of asymptomatic diverticular disease. CONCLUSION Our results indicate that a high-fiber diet and increased intake of vegetables is associated with lower odds of having asymptomatic diverticular disease, independent of age, sex, total energy intake, BMI and other life-style factors.
Collapse
Affiliation(s)
- Esther Askani
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Freiburg, Germany
| | - Susanne Rospleszcz
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-University Munich, Munich, Germany.,Institute of Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Theresa Rothenbacher
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
| | - Nina Wawro
- Independent Research Group Clinical Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians University of Munich, UNIKA-T Augsburg, Augsburg, Germany
| | - Helmut Messmann
- Department of Internal Medicine III, Klinikum Augsburg, Augsburg, Germany
| | - Carlo N De Cecco
- Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, GA, USA
| | - Ricarda von Krüchten
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Freiburg, Germany
| | - Charlotte Kulka
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Freiburg, Germany
| | - Lena S Kiefer
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
| | - Wolfgang Rathmann
- Institute of Biometrics and Epidemiology, German Diabetes Center, Duesseldorf, Germany
| | - Annette Peters
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-University Munich, Munich, Germany.,Institute of Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Cardiovascular Disease Research (DZHK E.V.), Munich, Germany
| | - Christopher L Schlett
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Freiburg, Germany
| | - Jakob Linseisen
- Independent Research Group Clinical Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians University of Munich, UNIKA-T Augsburg, Augsburg, Germany
| | - Corinna Storz
- Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Str. 64, 79106, Freiburg, Germany.
| |
Collapse
|
12
|
Breuninger TA, Wawro N, Breuninger J, Reitmeier S, Clavel T, Six-Merker J, Pestoni G, Rohrmann S, Rathmann W, Peters A, Grallert H, Meisinger C, Haller D, Linseisen J. Associations between habitual diet, metabolic disease, and the gut microbiota using latent Dirichlet allocation. Microbiome 2021; 9:61. [PMID: 33726846 PMCID: PMC7967986 DOI: 10.1186/s40168-020-00969-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 12/06/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND The gut microbiome impacts human health through various mechanisms and is involved in the development of a range of non-communicable diseases. Diet is a well-known factor influencing microbe-host interaction in health and disease. However, very few findings are based on large-scale analysis using population-based studies. Our aim was to investigate the cross-sectional relationship between habitual dietary intake and gut microbiota structure in the Cooperative Health Research in the Region of Augsburg (KORA) FF4 study. RESULTS Fecal microbiota was analyzed using 16S rRNA gene amplicon sequencing. Latent Dirichlet allocation (LDA) was applied to samples from 1992 participants to identify 20 microbial subgroups within the study population. Each participant's gut microbiota was subsequently described by a unique composition of these 20 subgroups. Associations between habitual dietary intake, assessed via repeated 24-h food lists and a Food Frequency Questionnaire, and the 20 subgroups, as well as between prevalence of metabolic diseases/risk factors and the subgroups, were assessed with multivariate-adjusted Dirichlet regression models. After adjustment for multiple testing, eight of 20 microbial subgroups were significantly associated with habitual diet, while nine of 20 microbial subgroups were associated with the prevalence of one or more metabolic diseases/risk factors. Subgroups 5 (Faecalibacterium, Lachnospiracea incertae sedis, Gemmiger, Roseburia) and 14 (Coprococcus, Bacteroides, Faecalibacterium, Ruminococcus) were particularly strongly associated with diet. For example, participants with a high probability for subgroup 5 were characterized by a higher Alternate Healthy Eating Index and Mediterranean Diet Score and a higher intake of food items such as fruits, vegetables, legumes, and whole grains, while participants with prevalent type 2 diabetes mellitus were characterized by a lower probability for subgroup 5. CONCLUSIONS The associations between habitual diet, metabolic diseases, and microbial subgroups identified in this analysis not only expand upon current knowledge of diet-microbiota-disease relationships, but also indicate the possibility of certain microbial groups to be modulated by dietary intervention, with the potential of impacting human health. Additionally, LDA appears to be a powerful tool for interpreting latent structures of the human gut microbiota. However, the subgroups and associations observed in this analysis need to be replicated in further studies. Video abstract.
Collapse
Affiliation(s)
- Taylor A. Breuninger
- Independent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Neusässer Str. 47, 86156 Augsburg, Germany
| | - Nina Wawro
- Independent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Neusässer Str. 47, 86156 Augsburg, Germany
| | | | - Sandra Reitmeier
- Technische Universität München, Gregor-Mendel-Str. 2, 85354 Freising, Germany
- ZIEL - Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
| | - Thomas Clavel
- ZIEL - Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
- Functional Microbiome Research Group, Institute of Medical Microbiology, RWTH University Hospital, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Julia Six-Merker
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Giulia Pestoni
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland
| | - Sabine Rohrmann
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, Deutsches Diabetes-Zentrum (DDZ), Auf’m Hennekamp 65, 40225 Düsseldorf, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Christa Meisinger
- Independent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Neusässer Str. 47, 86156 Augsburg, Germany
| | - Dirk Haller
- Technische Universität München, Gregor-Mendel-Str. 2, 85354 Freising, Germany
- ZIEL - Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
| | - Jakob Linseisen
- Independent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Neusässer Str. 47, 86156 Augsburg, Germany
- ZIEL - Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354 Freising, Germany
| |
Collapse
|
13
|
Wawro N, Pestoni G, Riedl A, Breuninger TA, Peters A, Rathmann W, Koenig W, Huth C, Meisinger C, Rohrmann S, Linseisen J. Association of Dietary Patterns and Type-2 Diabetes Mellitus in Metabolically Homogeneous Subgroups in the KORA FF4 Study. Nutrients 2020; 12:nu12061684. [PMID: 32516903 PMCID: PMC7352280 DOI: 10.3390/nu12061684] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/02/2020] [Accepted: 06/03/2020] [Indexed: 12/16/2022] Open
Abstract
There is evidence that a change in lifestyle, especially physical activity and diet, can reduce the risk of developing type-2 diabetes mellitus (T2DM). However, the response to dietary changes varies among individuals due to differences in metabolic characteristics. Therefore, we investigated the association between dietary patterns and T2DM while taking into account these differences. For 1287 participants of the population-based KORA FF4 study (Cooperative Health Research in the Region of Augsburg), we identified three metabolically-homogenous subgroups (metabotypes) using 16 clinical markers. Based on usual dietary intake data, two diet quality scores, the Mediterranean Diet Score (MDS) and the Alternate Healthy Eating Index (AHEI), were calculated. We explored the associations between T2DM and diet quality scores. Multi-variable adjusted models, including metabotype subgroup, were fitted. In addition, analyses stratified by metabotype were carried out. We found significant interaction effects between metabotype and both diet quality scores (p < 0.05). In the analysis stratified by metabotype, significant negative associations between T2DM and both diet quality scores were detected only in the metabolically-unfavorable homogenous subgroup (Odds Ratio (OR) = 0.62, 95% confidence interval (CI) = 0.39-0.90 for AHEI and OR = 0.60, 95% CI = 0.40-0.96 for MDS). Prospective studies taking metabotype into account are needed to confirm our results, which allow for the tailoring of dietary recommendations in the prevention of T2DM.
Collapse
Affiliation(s)
- Nina Wawro
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
- Chair of Epidemiology, Ludwig-Maximilians-Universität München at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156 Augsburg, Germany
- Correspondence:
| | - Giulia Pestoni
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland;
| | - Anna Riedl
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
| | - Taylor A. Breuninger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
- Chair of Epidemiology, Ludwig-Maximilians-Universität München at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156 Augsburg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (A.P.); (C.H.)
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764 München-Neuherberg, Germany;
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764 München-Neuherberg, Germany;
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf’m Hennekamp 65, 40225 Düsseldorf, Germany
| | - Wolfgang Koenig
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstr. 8a & 9, 80336 Munich, Germany;
- Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636 Munich
- Institute of Epidemiology and Medical Biometry, University of Ulm, Helmholtzstr. 22, 89081 Ulm, Germany
| | - Cornelia Huth
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (A.P.); (C.H.)
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764 München-Neuherberg, Germany;
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
- Chair of Epidemiology, Ludwig-Maximilians-Universität München at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156 Augsburg, Germany
| | - Sabine Rohrmann
- Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland;
| | - Jakob Linseisen
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (G.P.); (A.R.); (T.A.B.); (C.M.); (J.L.)
- Chair of Epidemiology, Ludwig-Maximilians-Universität München at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156 Augsburg, Germany
| |
Collapse
|
14
|
Freuer D, Linseisen J, Waterboer T, Pessler F, Guzmán CA, Wawro N, Peters A, Meisinger C. Seropositivity of selected chronic infections and different measures of obesity. PLoS One 2020; 15:e0231974. [PMID: 32320435 PMCID: PMC7176148 DOI: 10.1371/journal.pone.0231974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 04/03/2020] [Indexed: 11/25/2022] Open
Abstract
The impact of sex-specific body fat distribution on the susceptibility to five chronic infections, helicobacter pylori and human herpesviruses 3 to 6 (i.e. varicella-zoster, Epstein-Barr, cytomegalo- and human herpesvirus 6), has not previously been examined. In the present study, seropositivity was determined via multiplex serology in serum samples of study participants collected in 2006/08 and 2013/14 during the follow-up examinations F4 (n = 3080) and FF4 (n = 2279) of the German population-based baseline KORA S4 survey. We quantified the severity of overall and abdominal obesity by body mass index, body adiposity index, waist circumference, waist-to-hip ratio, and waist-to-height ratio. Using sex-specific logistic spline-models, cross-sectional and longitudinal associations between obesity measures and seropositivity of the previously mentioned infections were investigated. Overall and abdominal fat content were significantly associated with seropositivity of varicella-zoster virus in both cross-sectional and longitudinal analyses among women. In addition, a non-significant inverse relationship with Epstein-Barr virus seroprevalence in both sexes and a trend towards a positive association with human herpesvirus 6 seropositivity in women were observed. Therefore, in women total body fat may be associated with VZV-seropositivity and may influence the reactivation of the varicella-zoster virus, independent of adipose tissue distribution.
Collapse
Affiliation(s)
- Dennis Freuer
- Chair of Epidemiology at UNIKA-T Augsburg, Ludwig-Maximilians Universität München, Augsburg, Germany
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
- * E-mail:
| | - Jakob Linseisen
- Chair of Epidemiology at UNIKA-T Augsburg, Ludwig-Maximilians Universität München, Augsburg, Germany
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
| | - Tim Waterboer
- Infections and Cancer Epidemiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Frank Pessler
- Research Group Biomarkers for Infectious Diseases, TWINCORE, Centre for Experimental and Clinical Infection Research, Hannover, Germany
- Department of Vaccinology and Applied Microbiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Centre for Individualised Infection Medicine, Hannover, Germany
| | - Carlos Alberto Guzmán
- Department of Vaccinology and Applied Microbiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Nina Wawro
- Chair of Epidemiology at UNIKA-T Augsburg, Ludwig-Maximilians Universität München, Augsburg, Germany
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
| | - Christa Meisinger
- Chair of Epidemiology at UNIKA-T Augsburg, Ludwig-Maximilians Universität München, Augsburg, Germany
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
| |
Collapse
|
15
|
Riedl A, Hillesheim E, Wawro N, Meisinger C, Peters A, Roden M, Kronenberg F, Herder C, Rathmann W, Völzke H, Reincke M, Koenig W, Wallaschofski H, Daniel H, Hauner H, Brennan L, Linseisen J. Evaluation of the Metabotype Concept Identified in an Irish Population in the German KORA Cohort Study. Mol Nutr Food Res 2020; 64:e1900918. [PMID: 32048458 DOI: 10.1002/mnfr.201900918] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 01/13/2020] [Indexed: 11/11/2022]
Abstract
SCOPE Previous work identified three metabolically homogeneous subgroups of individuals ("metabotypes") using k-means cluster analysis based on fasting serum levels of triacylglycerol, total cholesterol, HDL cholesterol, and glucose. The aim is to reproduce these findings and describe metabotype groups by dietary habits and by incident disease occurrence. METHODS AND RESULTS 1744 participants from the KORA F4 study and 2221 participants from the KORA FF4 study are assigned to the three metabotype clusters previously identified by minimizing the Euclidean distances. In both KORA studies, the assignment of participants results in three metabolically distinct clusters, with cluster 3 representing the group of participants with the most unfavorable metabolic characteristics. Individuals of cluster 3 are further characterized by the highest incident disease occurrence during follow-up; they also reveal the most unfavorable diet with significantly lowest intakes of vegetables, dairy products, and fibers, and highest intakes of total, red, and processed meat. CONCLUSION The three metabotypes originally identified in an Irish population are successfully reproduced. In addition to this validation approach, the observed differences in disease incidence across metabotypes represent an important new finding that strongly supports the metabotyping approach as a tool for risk stratification.
Collapse
Affiliation(s)
- Anna Riedl
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T, Neusässer Str. 47, 86156, Augsburg, Germany
| | - Elaine Hillesheim
- Institute of Food and Health, UCD School of Agriculture and Food Science, UCD, Stillorgan Rd, Belfield, Dublin, 4, Ireland
| | - Nina Wawro
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T, Neusässer Str. 47, 86156, Augsburg, Germany
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T, Neusässer Str. 47, 86156, Augsburg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Michael Roden
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany.,Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Schöpfstr. 41, 6020, Innsbruck, Austria
| | - Christian Herder
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany.,Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
| | - Henry Völzke
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstr. 8a & 9, 80336, Munich, Germany.,Institute for Community Medicine, University Medicine Greifswald, Walther-Rathenau-Str. 48, 17475, Greifswald, Germany
| | - Martin Reincke
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, Ludwig-Maximilians-Universität München, Ziemssenstr. 1, 80336, Munich, Germany
| | - Wolfgang Koenig
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstr. 8a & 9, 80336, Munich, Germany.,Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany.,Institute of Epidemiology and Medical Biometry, University of Ulm, Helmholtzstr. 22, 89081, Ulm, Germany
| | - Henri Wallaschofski
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Ferdinand-Sauerbruch-Str., 17489, Greifswald, Germany
| | - Hannelore Daniel
- Chair of Nutritional Physiology, Technical University of Munich, Gregor-Mendel-Str. 2, 85354, Freising-Weihenstephan, Germany
| | - Hans Hauner
- Else Kröner-Fresenius Centre for Nutritional Medicine, Technical University of Munich, Gregor-Mendel-Str. 2, 85354, Freising-Weihenstephan, Germany.,ZIEL - Institute for Food and Health, Technical University of Munich, Weihenstephaner Berg 1, 85354, Freising, Germany.,Institute of Nutritional Medicine, Klinikum rechts der Isar, Technical University of Munich, Georg-Brauchle-Ring 62, 80992, Munich, Germany
| | - Lorraine Brennan
- Institute of Food and Health, UCD School of Agriculture and Food Science, UCD, Stillorgan Rd, Belfield, Dublin, 4, Ireland
| | - Jakob Linseisen
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T, Neusässer Str. 47, 86156, Augsburg, Germany.,ZIEL - Institute for Food and Health, Technical University of Munich, Weihenstephaner Berg 1, 85354, Freising, Germany
| |
Collapse
|
16
|
Mitry P, Wawro N, Six-Merker J, Zoller D, Jourdan C, Meisinger C, Thierry S, Nöthlings U, Knüppel S, Boeing H, Linseisen J. Usual Dietary Intake Estimation Based on a Combination of Repeated 24-H Food Lists and a Food Frequency Questionnaire in the KORA FF4 Cross-Sectional Study. Front Nutr 2019; 6:145. [PMID: 31552261 PMCID: PMC6743021 DOI: 10.3389/fnut.2019.00145] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 08/23/2019] [Indexed: 11/23/2022] Open
Abstract
Background: Estimation of usual dietary intake poses a challenge in epidemiological studies. We applied a blended approach that combines the strengths provided by repeated 24-h food lists (24HFLs) and a food frequency questionnaire (FFQ). Methods: At least two web-based 24HFLs and one FFQ were completed by 821 participants in the KORA FF4 study. Consumption probabilities were estimated using logistic mixed models, adjusting for covariates and the FFQ data on consumption frequency. Intake amount of a consumed food item was predicted for each participant based on the results of the second Bavarian Food Consumption Survey (BVS II). By combining consumption probability and estimated consumption amount, the usual food intake for each participant was estimated. These results were compared to results obtained without considering FFQ information for consumption probability estimation, as well as to conventional FFQ data. Results: The results of the blended approach for food group intake were often higher than the FFQ-based results. Intraclass correlation coefficients between both methods ranged between 0.21 and 0.86. Comparison of both methods resulted in weighted kappa values based on quintiles ranging from fair (0.34) to excellent agreement (0.84). Omission of FFQ information in the consumption probability models distinctly affected the results at the group level, though individual intake data were slightly affected, for the most part. Conclusions: Usual dietary intake data based on the blended approach differs from the FFQ-based results both in absolute terms and in classification according to quintiles. The application of the blended approach has been demonstrated as a possible tool in nutritional epidemiology, as a comparison with published studies showed that the blended approach yields reasonable estimates. The inclusion of the FFQ information is valuable especially with regard to irregularly consumed foods. A validation study including biomarkers of dietary intake is warranted.
Collapse
Affiliation(s)
- Patricia Mitry
- Institute of Epidemiology II, Helmholtz Zentrum Munich, German Research Center for Environmental Health (GmbH), Munich, Germany.,German Center for Diabetes Research (DZD e.V.), Düsseldorf, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany
| | - Nina Wawro
- Institute of Epidemiology II, Helmholtz Zentrum Munich, German Research Center for Environmental Health (GmbH), Munich, Germany.,German Center for Diabetes Research (DZD e.V.), Düsseldorf, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany
| | - Julia Six-Merker
- Institute of Epidemiology II, Helmholtz Zentrum Munich, German Research Center for Environmental Health (GmbH), Munich, Germany.,German Center for Diabetes Research (DZD e.V.), Düsseldorf, Germany
| | - Dorothee Zoller
- Institute of Epidemiology II, Helmholtz Zentrum Munich, German Research Center for Environmental Health (GmbH), Munich, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany
| | - Carolin Jourdan
- Institute of Epidemiology II, Helmholtz Zentrum Munich, German Research Center for Environmental Health (GmbH), Munich, Germany.,FGK Clinical Research GmbH, Munich, Germany
| | - Christa Meisinger
- Institute of Epidemiology II, Helmholtz Zentrum Munich, German Research Center for Environmental Health (GmbH), Munich, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany
| | - Sigrid Thierry
- Institute of Epidemiology II, Helmholtz Zentrum Munich, German Research Center for Environmental Health (GmbH), Munich, Germany
| | - Ute Nöthlings
- Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
| | - Sven Knüppel
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany
| | - Jakob Linseisen
- Institute of Epidemiology II, Helmholtz Zentrum Munich, German Research Center for Environmental Health (GmbH), Munich, Germany.,German Center for Diabetes Research (DZD e.V.), Düsseldorf, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany.,ZIEL Institute for Food and Health, Technical University of Munich, Freising, Germany
| |
Collapse
|
17
|
Breuninger TA, Wawro N, Meisinger C, Artati A, Adamski J, Peters A, Grallert H, Linseisen J. Associations between fecal bile acids, neutral sterols, and serum lipids in the KORA FF4 study. Atherosclerosis 2019; 288:1-8. [DOI: 10.1016/j.atherosclerosis.2019.06.911] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 04/17/2019] [Accepted: 06/21/2019] [Indexed: 01/06/2023]
|
18
|
Wawro N, Amann U, Butt J, Meisinger C, Akmatov MK, Pessler F, Peters A, Rathmann W, Kääb S, Waterboer T, Linseisen J. Helicobacter pylori Seropositivity: Prevalence, Associations, and the Impact on Incident Metabolic Diseases/Risk Factors in the Population-Based KORA Study. Front Public Health 2019; 7:96. [PMID: 31069210 PMCID: PMC6491664 DOI: 10.3389/fpubh.2019.00096] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 04/04/2019] [Indexed: 12/29/2022] Open
Abstract
Introduction: Helicobacter pylori (H. pylori) is a common infection and known risk factor for gastric cancer. We assessed cross-sectional and longitudinal associations to study the impact of H. pylori seropositivity on metabolic diseases. Methods: Helicobacter pylori seropositivity in serum samples of the KORA study was analyzed by multiplex serology. We calculated sex-specific prevalence of H. pylori seropositivity for the year 2007 based on the first follow-up survey (termed F4) of the KORA study S4. We identified factors associated with H. pylori seropositivity in the F4 survey. Further, we assessed relative risks of incident metabolic diseases/risk factors at the time of the second follow-up survey of S4 (termed FF4) and H. pylori seropositivity at the F4 survey as a determinant. Models were adjusted for age, sex, overweight status, physical activity, smoking status, education level, alcohol intake, and other metabolic diseases. Results: Based on 3,037 persons aged 32 to 82 years, the H. pylori prevalence for 2007 was 30.2% in men (n = 1,465) and 28.1% in women (n = 1,572). Increasing age, current smoking, low education and no alcohol intake were significantly associated with H. pylori seropositivity in the F4 survey. However, no association between H. pylori seropositivity and BMI, metabolic diseases (type 2 diabetes, hypertension and dyslipidemia, gout or increased uric acid) and gastrointestinal diseases (gastritis, inflammatory bowel disease, and gastric or duodenal ulcer) was observed. No significant associations between H. pylori seropositivity and one of the five investigated incident metabolic diseases/risk factors were detected in the longitudinal analysis. Conclusion: We identified associations between age, smoking, education and alcohol intake and H. pylori seropositivity but no impact of H. pylori seropositivity on incident metabolic diseases/risk factors.
Collapse
Affiliation(s)
- Nina Wawro
- German Research Center for Environmental Health (GmbH), Institute of Epidemiology II, Munich, Germany.,German Center for Diabetes Research (DZD e.V.), Munich, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany.,German Research Center for Environmental Health (GmbH), Independent Research Group Clinical Epidemiology, Munich, Germany
| | - Ute Amann
- German Research Center for Environmental Health (GmbH), Institute of Epidemiology II, Munich, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany
| | - Julia Butt
- Infections and Cancer Epidemiology, Infection, Inflammation and Cancer Program, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christa Meisinger
- German Research Center for Environmental Health (GmbH), Institute of Epidemiology II, Munich, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany
| | - Manas K Akmatov
- TWINCORE, Centre for Experimental and Clinical Infection Research, Hanover, Germany.,Helmholtz-Zentrum für Infektionsforschung (HZI), Braunschweig, Germany
| | - Frank Pessler
- TWINCORE, Centre for Experimental and Clinical Infection Research, Hanover, Germany.,Helmholtz-Zentrum für Infektionsforschung (HZI), Braunschweig, Germany
| | - Annette Peters
- German Research Center for Environmental Health (GmbH), Institute of Epidemiology II, Munich, Germany.,German Center for Diabetes Research (DZD e.V.), Munich, Germany
| | - Wolfgang Rathmann
- Deutsches Diabeteszentrum, Institute for Biometrics and Epidemiology, Düsseldorf, Germany
| | - Stefan Kääb
- Medizinische Klinik und Poliklinik I, Campus Grosshadern, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Tim Waterboer
- Infections and Cancer Epidemiology, Infection, Inflammation and Cancer Program, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Linseisen
- German Research Center for Environmental Health (GmbH), Institute of Epidemiology II, Munich, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany.,German Research Center for Environmental Health (GmbH), Independent Research Group Clinical Epidemiology, Munich, Germany
| |
Collapse
|
19
|
Riedl A, Wawro N, Gieger C, Meisinger C, Peters A, Roden M, Kronenberg F, Herder C, Rathmann W, Völzke H, Reincke M, Koenig W, Wallaschofski H, Hauner H, Daniel H, Linseisen J. Identification of Comprehensive Metabotypes Associated with Cardiometabolic Diseases in the Population-Based KORA Study. Mol Nutr Food Res 2018; 62:e1800117. [PMID: 29939495 DOI: 10.1002/mnfr.201800117] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 05/24/2018] [Indexed: 12/17/2022]
Abstract
SCOPE "Metabotyping" describes the grouping of metabolically similar individuals. We aimed to identify valid metabotypes in a large cohort for targeted dietary intervention, for example, for disease prevention. METHODS AND RESULTS We grouped 1729 adults aged 32-77 years of the German population-based KORA F4 study (2006-2008) using k-means cluster analysis based on 34 biochemical and anthropometric parameters. We identified three metabolically distinct clusters showing significantly different biochemical parameter concentrations. Cardiometabolic disease status was determined at baseline in the F4 study and at the 7 year follow-up termed FF4 (2013/2014) to compare disease prevalence and incidence between clusters. Cluster 3 showed the most unfavorable marker profile with the highest prevalence of cardiometabolic diseases. Also, disease incidence was higher in cluster 3 compared to clusters 2 and 1, respectively, for hypertension (41.2%/25.3%/18.2%), type 2 diabetes (28.3%/5.1%/2.0%), hyperuricemia/gout (10.8%/2.3%/0.7%), dyslipidemia (19.2%/18.3%/5.6%), all metabolic (54.5%/36.8%/19.7%), and all cardiovascular (6.3%/5.5%/2.3%) diseases together. CONCLUSION Cluster analysis based on an extensive set of biochemical and anthropometric parameters allows the identification of comprehensive metabotypes that were distinctly different in cardiometabolic disease occurrence. As a next step, targeted dietary strategies should be developed with the goal of preventing diseases, especially in cluster 3.
Collapse
Affiliation(s)
- Anna Riedl
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,German Center for Diabetes Research, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156, Augsburg, Germany
| | - Nina Wawro
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,German Center for Diabetes Research, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156, Augsburg, Germany
| | - Christian Gieger
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,German Center for Diabetes Research, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156, Augsburg, Germany
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,German Center for Diabetes Research, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Michael Roden
- German Center for Diabetes Research, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany.,Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Schöpfstr. 41, 6020, Innsbruck, Austria
| | - Christian Herder
- German Center for Diabetes Research, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
| | - Henry Völzke
- German Center for Diabetes Research, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,DZHK - German Centre for Cardiovascular Research, Partner Site Munich Heart Alliance, Pettenkoferstr. 8a & 9, 80336, Munich, Germany.,Institute for Community Medicine, University Medicine Greifswald, Walther-Rathenau-Str. 48, 17475, Greifswald, Germany
| | - Martin Reincke
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, Ludwig-Maximilians-Universität München, Ziemssenstr. 1, 81377, Munich, Germany
| | - Wolfgang Koenig
- DZHK - German Centre for Cardiovascular Research, Partner Site Munich Heart Alliance, Pettenkoferstr. 8a & 9, 80336, Munich, Germany.,Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany.,Department of Internal Medicine II-Cardiology, University of Ulm Medical Center, Albert-Einstein-Allee 23, 89081, Ulm, Germany
| | - Henri Wallaschofski
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Ferdinand-Sauerbruch-Str., 17489, Greifswald, Germany
| | - Hans Hauner
- Else Kröner-Fresenius Centre for Nutritional Medicine, Technical University of Munich, Gregor-Mendel-Str. 2, 85354, Freising-Weihenstephan, Germany.,ZIEL - Institute for Food and Health, Technical University of Munich, Weihenstephaner Berg 1, 85354, Freising, Germany.,Institute of Nutritional Medicine, Klinikum rechts der Isar, Technical University of Munich, Uptown München Campus D, Georg-Brauchle-Ring 60/62, 80992, Munich, Germany.,Technical University of Munich, Gregor-Mendel-Str. 2, 85354, Freising-Weihenstephan, Germany
| | - Hannelore Daniel
- Technical University of Munich, Gregor-Mendel-Str. 2, 85354, Freising-Weihenstephan, Germany
| | - Jakob Linseisen
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, at UNIKA-T (Universitäres Zentrum für Gesundheitswissenschaften am Klinikum Augsburg), Neusässer Str. 47, 86156, Augsburg, Germany.,ZIEL - Institute for Food and Health, Technical University of Munich, Weihenstephaner Berg 1, 85354, Freising, Germany
| |
Collapse
|
20
|
Mitry P, Wawro N, Rohrmann S, Giesbertz P, Daniel H, Linseisen J. Plasma concentrations of anserine, carnosine and pi-methylhistidine as biomarkers of habitual meat consumption. Eur J Clin Nutr 2018; 73:692-702. [DOI: 10.1038/s41430-018-0248-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 06/05/2018] [Accepted: 06/07/2018] [Indexed: 11/09/2022]
|
21
|
Wawro N, Kleiser C, Himmerich S, Gedrich K, Boeing H, Knueppel S, Linseisen J. Estimating Usual Intake in the 2nd Bavarian Food Consumption Survey: Comparison of the Results Derived by the National Cancer Institute Method and a Basic Individual Means Approach. Ann Nutr Metab 2017; 71:164-174. [PMID: 28930718 DOI: 10.1159/000481148] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 08/22/2017] [Indexed: 11/19/2022]
Abstract
BACKGROUND The valid estimation of the usual dietary intake remains a challenge till date. We applied the method suggested by the National Cancer Institute (NCI) to data from the 2nd Bavarian Food Consumption Survey (BVS II) and compared it to an individual means approach. METHODS Within the cross-sectional BVS II, 1,050 Bavarian residents aged 13-80 years participated in a personal interview and completed three 24-h dietary recalls by telephone interview. For the 13 main food groups and 23 subgroups the usual intake was calculated by (1) an individual means approach and (2) by the NCI method. RESULTS The distributions derived by the individual means approach are wider than those derived from the NCI approach. For a majority of food groups and subgroups, the proportion of participants who meet the dietary recommendations published by the German Nutrition Society is higher when the NCI approach is applied. The proportions of participants above or below recommended amounts differ greatly for "meat and meat products" and "cheese." CONCLUSION The mean intake at the groups level can easily be derived from the individual means approach. Since only the NCI method accounts for intra-personal variation, this method provides more valid intake estimates at the individual level and should be applied when, for example, individual intakes are compared with dietary recommendations.
Collapse
Affiliation(s)
- Nina Wawro
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | | | | | | | | | | | | |
Collapse
|
22
|
Kleiser C, Wawro N, Stelmach-Mardas M, Boeing H, Gedrich K, Himmerich H, Linseisen J. Are sleep duration, midpoint of sleep and sleep quality associated with dietary intake among Bavarian adults? Eur J Clin Nutr 2017; 71:631-637. [DOI: 10.1038/ejcn.2016.264] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Revised: 09/30/2016] [Accepted: 11/06/2016] [Indexed: 12/16/2022]
|
23
|
Wawro N, Heinrich J, Thiering E, Kratzsch J, Schaaf B, Hoffmann B, Lehmann I, Bauer CP, Koletzko S, von Berg A, Berdel D, Linseisen J. Serum 25(OH)D concentrations and atopic diseases at age 10: results from the GINIplus and LISAplus birth cohort studies. BMC Pediatr 2014; 14:286. [PMID: 25421846 PMCID: PMC4251945 DOI: 10.1186/s12887-014-0286-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2014] [Accepted: 10/27/2014] [Indexed: 12/13/2022] Open
Abstract
Background Vitamin D is well recognized for its role in skeletal health and its involvement in the modulation of the immune system. In the literature, controversial results are reported for atopic diseases. Thus, we investigated the association between vitamin D status and the prevalence of atopic diseases. Methods Serum 25-hydroxy-vitamin D (25(OH)D) concentrations were measured in a sample of 2815 10-years old children from two German birth cohort studies. Self-reported physician-diagnosed eczema, hay fever or allergic rhinitis, and asthma were used as outcome variables as well as specific IgE positivity against common allergens. We applied logistic regression models, deriving adjusted odds ratio estimates (aOR) and 95% confidence intervals (CI). Results For asthma and hay fever or allergic rhinitis, no associations existed with serum 25(OH)D concentrations. We observed a significant positive relationship between serum 25(OH)D levels and eczema at age 10 (aOR = 1.09, CI = 1.01-1.17, per 10 nmol/l increase in serum 25(OH)D levels) and for the lifetime prevalence of eczema (aOR = 1.05, CI = 1.01-1.09). Specific IgE positivity for food allergens (aOR = 1.07, CI = 1.02-1.11) and aeroallergens (aOR = 1.05, CI = 1.01-1.08) at age 10, as well as lifetime prevalence, was significantly related to the vitamin D status. Conclusion In this study we found no indication that higher blood 25(OH)D levels are associated with decreased risk for any of the atopic outcomes in children. However, we observed a positive association of serum 25(OH)D concentrations with eczema and detectable specific IgE. Due to the given limitations of our study, the clinical relevance of these findings needs further clarification. Electronic supplementary material The online version of this article (doi:10.1186/s12887-014-0286-3) contains supplementary material, which is available to authorized users.
Collapse
|
24
|
Bammann K, Sioen I, Huybrechts I, Casajús JA, Vicente-Rodríguez G, Cuthill R, Konstabel K, Tubić B, Wawro N, Rayson M, Westerterp K, Mårild S, Pitsiladis YP, Reilly JJ, Moreno LA, De Henauw S. The IDEFICS validation study on field methods for assessing physical activity and body composition in children: design and data collection. Int J Obes (Lond) 2011; 35 Suppl 1:S79-87. [PMID: 21483426 DOI: 10.1038/ijo.2011.38] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE To describe the design, measurements and fieldwork of the IDEFICS (Identification and prevention of dietary- and lifestyle-induced health effects in children and infants) physical activity and body composition validation study, and to determine the potential and limitations of the data obtained. DESIGN Multicentre validation study. SUBJECTS A total of 98 children from four different European countries (age: 4-10 years). METHODS An 8-day measurement protocol was carried out in all children using a collaborative protocol. Reference methods were the doubly labelled water method for physical activity, and a three- and a four-compartment model for body composition. Investigated field methods were accelerometers, a physical activity questionnaire and various anthropometric measurements. RESULTS For the validation of physical activity field methods, it was possible to gather data from 83 to 89 children, laying the basis for age- and sex-specific results. The validation of body composition field methods is possible in 64-80 children and allows sex-specific analyses but has only limited statistical power in the youngest age group (<6 years). The amount of activity energy expenditure (AEE) varied between centres, sexes and age groups, with boys and older children having higher estimates of AEE. After normalisation of AEE by body weight, most group-specific differences diminished, except for country-specific differences. CONCLUSION The IDEFICS validation study will allow age- and sex-specific investigation of questions pertaining to the validity of several field methods of body composition and physical activity, using established reference methods in four different European countries. From the participant analyses it can be concluded that the compliance for the investigated field methods was higher than that for the reference methods used in this validation study.
Collapse
Affiliation(s)
- K Bammann
- Department of Biometry, Bremen Institute for Prevention Research and Social Medicine, University of Bremen, Germany.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
25
|
Abstract
Background Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied. Results The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction. Conclusions Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.
Collapse
Affiliation(s)
- Frauke Günther
- University of Bremen, Bremen Institute for Prevention Research and Social Medicine, Linzer Strasse 10, 28359 Bremen, Germany.
| | | | | |
Collapse
|
26
|
Abstract
BACKGROUND Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied. RESULTS The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction. CONCLUSIONS Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.
Collapse
Affiliation(s)
- Frauke Günther
- University of Bremen, Bremen Institute for Prevention Research and Social Medicine, Linzer Strasse 10, 28359 Bremen, Germany.
| | | | | |
Collapse
|
27
|
Wawro N, Bammann K, Pigeot I. Testing for association in the presence of population stratification: a simulation study comparing the S-TDT, STRAT and the GC. Biom J 2006; 48:420-34. [PMID: 16845906 DOI: 10.1002/bimj.200410214] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
A novel approach for association testing in the presence of population stratification has been introduced by Pritchard et al. (2000a) and Pritchard et al. (2000b). The structured association approach is a two-tiered procedure that first estimates the population structure and then tests the null hypothesis H0: 'no association within subpopulations' in the second step. A power comparison of the stratified test for association (STRAT) (Pritchard et al., 2000b) and the Transmission-Disequilibrium-Test (TDT) (Spielman and Ewens, 1993a) in a simulation framework showed superiority of STRAT if allele frequencies or associations between allele and disease differ strongly in subpopulations. In more homogeneous situations, the TDT had greater power than STRAT. However, the TDT, based on family trios,that uses population controls, needs 50% more genotyping compared to STRAT. The Sib-Transmission-Disequilibrium-Test (S-TDT) needs the same amount of genotyping since it relays in its minimal configuration on pairs of siblings. This raises the question how the S-TDT (Spielman and Ewens, 1998a) performs compared to the population based methods STRAT and Genomic Controls (GC). In this paper, we present a simulation study accounting for two different models of population stratification in different settings of allele frequencies and under different risk models. The results showed that under a discrete as well as under an admixed population model, STRAT strongly outperformed the S-TDT and the GC when different alleles were associated in different subpopulations. In contrast, the S-TDT had greater power than STRAT when the same allele was associated in both subpopulations. Here, the GC was sometimes even more powerful than the S-TDT, depending on the population model and the allele frequency differences. A general recommendation for the use of one of the tests can therefore not be given.
Collapse
Affiliation(s)
- Nina Wawro
- University of Bremen, Department of Mathematics and Computer Science, PO Box 33 04 40, D-28334 Bremen, Germany.
| | | | | |
Collapse
|
28
|
Bammann K, Wawro N. Die Einbeziehung genetischer Faktoren in Studien der Epidemiologie. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2006; 49:974-81. [PMID: 17013779 DOI: 10.1007/s00103-006-0042-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
During the last two decades, genetic epidemiology has been established in parallel to the area of classical epidemiology. This paper presents some essentials of the epidemiology of genetic factors. It begins with a discussion of complex diseases that are characterized by an involvement of several genes. The problems that are attached to modeling gene-gene and gene-environment interactions and their integration into causal pathways are elucidated and the role of genetic factors in the etiology of complex diseases is investigated. Classical and new epidemiological study designs that allow an integration of genetic data are introduced. The introduction of this data is partly motivated by the danger of bias due to genetic heterogeneity (population stratification) in classical designs. The problem of replication of study results is discussed and the concept of Mendelian randomization is presented.
Collapse
Affiliation(s)
- K Bammann
- Bremer Institut für Präventionsforschung und Sozialmedizin (BIPS), Linzer Strasse 10, 28359 Bremen.
| | | |
Collapse
|
29
|
Bammann K, Wawro N, Pigeot I. Genforschung: Chance oder Risiko für die Gesundheit? Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2006; 49:961-2. [PMID: 17028829 DOI: 10.1007/s00103-006-0054-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|