Cohort Profile: The LIFE-Adult-Study

Engel, Christoph; Wirkner, Kerstin; Zeynalova, Samira; Baber, Ronny; Binder, Hans; Ceglarek, Uta; Enzenbach, Cornelia; Fuchs, Michael; Hagendorff, Andreas; Henger, Sylvia; Hinz, Andreas; Rauscher, Franziska G.; Reusche, Matthias; Riedel‐Heller, Steffi G.; Röhr, Susanne; Sacher, Julia; Sander, Christian; Schroeter, Matthias L.; Tárnok, Attila; Treudler, Regina; Villringer, Arno; Wachter, Rolf; Witte, A. Veronica; Thiery, Joachim; Scholz, Markus; Loeffler, Markus; Ahnert, Peter; Bae, Yoon Ju; Baier, Daniel M.; Berg, M.; Berger, Thomas; Beutner, Frank; Beyer, Frauke; Brähler, Elmar; Büttner, Petra; Burkhardt, Ralph; Dittrich, J; Dogan-Sander, Ezgi; Elze, Tobias; Gaebler, Michael; Gielen, Stephan; Glaesmer, Heide; Hegerl, Ulrich; Hensch, Tilman; Hilbert, Anja; Hussenoeder, Felix S.; Husser, Daniela; Jawinski, Philippe; Jost, Lasse; Keil, Jan; Masouleh, Shahrzad Kharabian; Kiel, Alexander; Kirsten, Toralf; Kluge, Michael; Kocalevent, Rüya-Daniela; Kornej, Jelena; Kühnapfel, Andreas; Kumral, Deniz; Kynast, Jana; Lampe, Leonie; Liem, Franziskus; Löffler, Antje; Loeffler‐Wirth, Henry; Lorenz, Noah; Luck, Tobias; Margulies, Daniel S.; Massué, Mila; Melzer, Susanne; Netto, Jeffrey; Nüchter, Matthias; Polyakova, Maryna; Pott, Janne; Reinicke, Madlen; Reyes, Nigar; Rodriguez, Francisca S.; Schaare, H. Lina; Schönknecht, Peter; Simon, Jan C.; Spada, Janek; Speer, Ronald; Staníková, Daniela; Teren, Andrej; Ulke, Christine; Wichmann, Gunnar; Wicklein, Barbara; Willenberg, Anja; Wittekind, Dirk Alexander; Yahiaoui-Doktor, Maryam; Zachariae, Silke; Zhang, Rui; Zsido, Rachel G.; Zülke, Andrea

doi:10.1093/ije/dyac114

Cited by 30 publications

(16 citation statements)

References 60 publications

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“…Our age estimation models have previously achieved excellent prediction accuracies, with mean absolute errors (MAE) ranging between 3.09 and 3.37 years, and correlation coefficients between brain-predicted and chronological age ranging between r = 0.83 (R 2 = 0.68) and r = 0.86 (R 2 = 0.73) in the UK Biobank cohort (age range: 40-85 years, Jawinski, 2022). Prediction accuracies reached similar levels (combined gray and white matter: MAE = 3.56 years, r = 0.86) in an independent MRI sample of about 1,900 individuals (age range: 45-80 years) of the LIFE-Adult cohort (Loeffler et al, 2015;Engel et al, 2022).…”

Section: Age Estimation Modelsmentioning

confidence: 72%

Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II

Jawinski

Markett

Drewelies

et al. 2022

Front. Aging Neurosci.

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From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual’s biological and chronological age of the brain (“brain age gap”) can be assessed by applying machine learning techniques to Magnetic Resonance Imaging (MRI) data. Here, we examined the links between brain age gap and a broad range of cognitive, affective, socioeconomic, lifestyle, and physical health variables in up to 335 adults of the Berlin Aging Study II. Brain age gap was assessed using a validated prediction model that we previously trained on MRI scans of 32,634 UK Biobank individuals. Our statistical analyses revealed overall stronger evidence for a link between higher brain age gap and less favorable health characteristics than expected under the null hypothesis of no effect, with 80% of the tested associations showing hypothesis-consistent effect directions and 23% reaching nominal significance. The most compelling support was observed for a cluster covering both cognitive performance variables (episodic memory, working memory, fluid intelligence, digit symbol substitution test) and socioeconomic variables (years of education and household income). Furthermore, we observed higher brain age gap to be associated with heavy episodic drinking, higher blood pressure, and higher blood glucose. In sum, our results point toward multifaceted links between brain age gap and human health. Understanding differences in biological brain aging may therefore have broad implications for future informed interventions to preserve mental and physical health in old age.

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Section: Age Estimation Modelsmentioning

confidence: 72%

Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II

Jawinski

Markett

Drewelies

et al. 2022

Front. Aging Neurosci.

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“…The baseline sample consists of 10,000 Leipzig residents aged 18–79 years. Details of the LIFE-Adult-Study have been published elsewhere [ 29 , 30 ]. In this study, we analysed cross-sectional data of the first follow-up assessment, which included resilience for the first time and took place from 2017 to 2021 in Leipzig, Germany.…”

Section: Methodsmentioning

confidence: 99%

What Builds Resilience? Sociodemographic and Social Correlates in the Population-Based LIFE-Adult-Study

Weitzel

Glaesmer

Hinz

et al. 2022

IJERPH

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Resilience is closely related to mental health and well-being. Identifying risk groups with lower resilience and the variables associated with resilience informs preventive approaches. Previous research on resilience patterns in the general population is heterogeneous, and comprehensive large-scale studies are needed. The aim of our study is to examine sociodemographic and social correlates of resilience in a large population-based sample. We examined 4795 participants from the LIFE-Adult-Study. Assessments included resilience (RS-11), social support (ESSI), and social network (LSNS), as well as the sociodemographic variables age, gender, marital status, education, and occupation. The association of resilience with sociodemographic and social correlates was examined using linear regression analyses. Higher resilience was associated with female gender, married marital status, high education, and full-time occupation. Social support and social network were positively associated with resilience. Our results implicate that resilience is related to various sociodemographic variables. Social variables seem to be particularly important for resilience. We identified risk groups with lower resilience, which should be given special attention by public health policies, especially in times of crisis. Reducing loneliness and promoting social connectedness may be promising ways to build resilience in the general population.

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“…We included all individuals equal or over the age of 50 with available neuroimaging of LIFE (Engel et al, 2022), due to the accelerated volume shrinkage starting at about 50 years of age in the hippocampus (Fjell et al, 2013). To avoid reverse causation, we further excluded cognitive impairment or prior brain pathology such as history of stroke, neurodegenerative disease or brain tumor.…”

Section: Resultsmentioning

confidence: 99%

“…Longitudinal population-based neuroimaging studies now offer reliable sample sizes to gain knowledge on effect sizes and to disentangle correlation from causation to better understand the impact of social isolation on brain and cognitive aging. In this pre-registered analysis, we aimed to determine the relationship between social isolation, measured using the Lubben social network scale (LSNS-6, (Lubben et al, 2006)), and brain structure and cognitive functions, measured using freesurfer segmentations on advanced high-resolution MRI at 3 Tesla and neuropsychological testings, in a large well-characterized longitudinal sample of mid-to late-life individuals (n > 1900) from the Health Study of the Leipzig Research Centre for Civilization Diseases (LIFE) (Engel et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Social isolation is linked to declining grey matter structure and cognitive functions in the LIFE-Adult panel study

Lammer

Beyer

Luppa

et al. 2021

Preprint

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BackgroundSocial isolation is a risk factor for dementia, a devastating disease with a rapidly growing global prevalence. However, the link between social isolation and changes in brain structure and function is poorly understood, as studies are scarce in number, methodologically inconsistent and small in size. In this pre-registered analysis of a large population-based panel study, we aimed to determine the impact of social isolation on brain structures and cognitive functions central to age associated decline and dementia.Methods and findingsWe analysed data of 1992 cognitively healthy participants of the LIFE-Adult study at baseline (age range: 50-82 years) and of 1409 particpants at follow-up (average change in age: 5.89 years). We measured social isolation using the 30-point Lubben Social Network Scale (LSNS) and derived measures of grey matter structure from anatomical 3T MRIs. We employed covariate adjusted linear mixed models to test the associations of baseline social isolation and change in social isolation with hippocampal volume, cognitive functions (executive functions, memory, processing speed) and cortical thickness.We found stronger baseline social isolation to be significantly associated with smaller hippocampal volumes (β = −5.5 mm3/LSNS point(pt), FDR q = 0.004, BF = 14.6) and lower cognitive functions (all β < −0.014 SD/pt, FDR q < 0.003, BF > 49). Increases in social isolation over time were linked to hippocampal volume decline (β = −4.9 mm3/pt, FDR q = 0.01, BF = 2.9) and worse memory performance (β = −0.013 SD/pt, FDR q = 0.04, BF = 1.1). Furthermore, we detected a significant interaction of baseline social isolation with change in age on hippocampal volume (β = −0.556 mm3/pt*a, q = 0.04, BF = 0.5), indicating accelerated brain aging in more isolated individuals. Moreover, social isolation cross-sectionally and longitudinally correlated with lower cortical thickness in multiple clusters in the orbitofrontal cortex, precuneus and other areas (FDR q < 0.05).ConclusionsHere, we provide evidence that social isolation contributes to hippocampal and cortical atrophy and subtle cognitive decline in non-demented mid-to late-life adults. Importantly, within-subject effects of social isolation were similar to between-subject effects, indicating an opportunity for targeting social isolation to reduce dementia risk.

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Cohort Profile: The LIFE-Adult-Study

Cited by 30 publications

References 60 publications

Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II

Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II

What Builds Resilience? Sociodemographic and Social Correlates in the Population-Based LIFE-Adult-Study

Social isolation is linked to declining grey matter structure and cognitive functions in the LIFE-Adult panel study

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