bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.02.09.940460

Lower intake of animal-based products links to improved weight status, independent of depressive symptoms and personality in the general population

Medawar, E.·Enzenbach, C.·Roehr, S.·Villringer, A.·Riedel-Heller, S.·Witte, A. V.
View original source ↗Export metadata ↓

Abstract

BackgroundRestricting animal-based products from diet may exert beneficial effects on weight status, however whether this is also true for emotional health is unclear. Moreover, differential personality traits may underlie restrictive eating habits and therefore potentially confound diet-health associations. To systematically assess whether restrictive dietary intake of animal-based products relates to lower weight and higher depressive symptoms, and how this is linked to personality traits in the general population. MethodsCross-sectional data was taken from the baseline LIFE-Adult study collected from 2011-2014 in Leipzig, Germany (n = 8943). Main outcomes of interest were 12-month dietary frequency of animal-derived products measured using a Food Frequency Questionnaire (FFQ), body mass index (BMI) (kg/m2), and the Center of Epidemiological Studies Depression Scale (CES-D). Personality traits were assessed in a subsample of n = 7906 using the Five Factor Inventory (NEO-FFI). FindingsHigher restriction of animal-based product intake was associated with a lower BMI (age-, sex- and education-adjusted, n = 8943; {beta} = -.07, p < .001), but not depression score. Personality, i.e. lower extraversion (F (1,7897) = 9.8, p = .002), was related to frequency of animal product intake. Further, not diet but personality was significantly associated with depression, i.e. higher neuroticism ({beta} = .024), lower extraversion ({beta} = -.006), lower agreeableness ({beta} = -.001), lower conscientiousness ({beta} = -.007) and higher BMI ({beta} = .004) (all p < .001, overall model, R2 = .21). The beneficial association with lower weight seemed to be driven by the frequency of meat product intake and not secondary animal products. Likewise, the overall number of excluded food items from the individual diet was associated with a lower BMI (age-, sex- and education-adjusted, n = 8938, {beta} = -.15, p < .001) and additionally with lower depression scores ({beta} = -.004, t = -4.1, p < .001, R2 = .05, corrected for age, sex and education), also when additionally correcting for differences in personality traits ({beta} = -.003, t = -2.7, p = .007, R2 = .21). InterpretationHigher restriction of animal-based products in the diet was significantly associated with a lower BMI, but not with depressive symptoms scores in a large well-characterized population-based sample of adults. In addition, we found that certain personality traits related to restricting animal-based products - and that those traits, but not dietary habits, explained a considerable amount of variance in depressive symptoms. Upcoming longitudinal studies need to confirm these findings and to test the hypothesis if restricting animal-based products, esp. primary animal products ((processed) meat, wurst), conveys benefits on weights status, hinting to a beneficial relationship of animal-based restricted diets in regard to prevention and treatment of overweight and obesity. FundingWe thank all study participants. We very much appreciate the help of the physicians who performed the clinical examinations and data collection, in particular Ulrike Scharrer, Annett Wiedemann, Kerstin Wirkner and her team. This work was supported by LIFE--Leipzig Research Centre for Civilisation Diseases, University of Leipzig. LIFE is funded by means of the European Union, by means of the European Social Fund (ESF), by the European Regional Development Fund (ERDF), and by means of the Free State of Saxony within the framework of the excellence initiative. This work was supported by a scholarship (EM) by the German Federal Environmental Foundation and by the grants of the German Research Foundation contract grant number CRC 1052 "Obesity mechanisms" Project A1 (AV) and WI 3342/3-1 (AVW). The corresponding author had full access to all the data in the included in the analysis and had final responsibility for the decision to submit for publication. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=146 HEIGHT=200 SRC="FIGDIR/small/940460v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1f81d8org.highwire.dtl.DTLVardef@85bd35org.highwire.dtl.DTLVardef@48fd83org.highwire.dtl.DTLVardef@1fbc053_HPS_FORMAT_FIGEXP M_FIG C_FIG

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Medawar, E., Enzenbach, C., Roehr, S., Villringer, A., Riedel-Heller, S., Witte, A. V.. 2020-02-10. Lower intake of animal-based products links to improved weight status, independent of depressive symptoms and personality in the general population. https://doi.org/10.1101/2020.02.09.940460

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

CoReg, tracking-based motion compensation for in vivo calcium imaging

In vivo calcium imaging enables the observation of neural activity with high spatial and temporal resolution. However, recordings are frequently affected by motion artifacts caused by animal movement, physiological processes, tissue drift, or instability of the imaging setup. These artifacts make signal extraction harder and reduce the consistency of downstream analyses. Current registration methods can correct rigid and non-rigid motion, but often estimate motion from individual frames or image patches and make limited use of temporal information. In addition, activity-driven fluorescence changes can make it difficult to distinguish changes in image appearance from actual motion. Here, we treat motion correction in calcium imaging as a point tracking problem and introduce CoReg, an open-source Python- based software tool with a graphical user interface. We benchmark CoReg against established registration methods using fluorescence imaging datasets from multiple species. CoReg performs competitively with or better than existing approaches across several settings. We further show that its performance is robust to the choice of tracked points, making the method straightforward to apply. CoReg includes implementations of both our method and the evaluated established methods. Our results show that tracking-based registration provides a simple and reliable approach for motion correction in vivo calcium imaging.

neuroscience↗
bioRxiv2026

A shared slowing axis and phenotype-specific rhythms link clinical EEG to FDG-PET dementia phenotypes

Early and accurate identification of neurodegenerative disorders has notable implications for treatment and quality of life. Whereas neuroimaging-based biomarkers can accurately identify neurodegenerative subtypes, EEG represents a low-cost alternative that could expand access to dementia care. Here, we investigated whether six data-driven EEG latent factors, derived by nonnegative tensor decomposition of clinical EEGs, differ systematically across FDG-PET-defined neurodegenerative phenotypes. In 455 patients with EEG and FDG-PET acquired within one year of each other, factor expression was compared across 14 diagnostic classes assigned by StateViewer, a validated artificial intelligence framework for FDG-PET interpretation. To separate disease-general severity from phenotype-specific topography, each factor was also regressed on a subject-level slowing index. Finally, we related the factors to regional FDG uptake and to neuropsychological performance (n = 98) and assessed their potential utility for single-subject classification. Four factors reflecting neurophysiological oscillations (posterior alpha, Rolandic beta, frontotemporal delta-theta and posterior theta-slow alpha) varied significantly across phenotypes, with attenuation of faster and augmentation of slower rhythms most pronounced in dementia with Lewy bodies (DLB), dysexecutive Alzheimer disease (AD) and posterior cortical atrophy, whereas factors dominated by eye-movement and muscle artifact showed no robust phenotypic association. Alpha and beta effects were largely attributable to overall slowing, whereas posterior theta-slow alpha became more phenotype-specific after adjustment (eta-squared 0.082 to 0.147), dissociating the Alzheimer disease spectrum from frontotemporal syndromes. The slowing index tracked with an FDG-PET pattern highly associated with DLB across the cohort, irrespective of assigned phenotype (r = 0.51, P < 0.001). Consistent with a model in which dementia phenotypes reflect altered hippocampal-neocortical oscillatory coupling (stochastic latent oscillatory diffusion), frontotemporal delta-theta tracked a limbocortical axis of metabolic pattern expression, rising with neocortical and falling with limbic-predominant involvement, independent of overall slowing and phenotype. Each factor mapped onto a distinct metabolic topography, with frontotemporal delta-theta resembling Alzheimer-type hypometabolism and posterior theta-slow alpha the medial occipital hypometabolism of DLB. In terms of neuropsychological performance, Rolandic beta was associated with executive functions, and frontotemporal delta-theta with memory, visuospatial and global performance. Using these four factors alone, an exploratory logistic regression classifier separated cognitively unimpaired participants from DLB, posterior cortical atrophy, typical AD, and dysexecutive AD (area under the curve > 0.8) but poorly discriminated frontotemporal dementia phenotypes canonically associated with 4-repeat tau. Thus, data-driven decomposition of clinical EEG separates oscillatory activity from artifact into factors that track metabolic phenotype, regional metabolism and cognition, capturing both overall slowing and phenotype-specific patterns. This work informs the further development of scalable, biology-informed EEG-based phenotyping of neurodegenerative disease.

neuroscience↗
bioRxiv2026

An application of fNIRS for the prediction of hearing thresholds in sleeping infants: a proof of principle

This study applied a two-component-response modelling approach to fNIRS recordings obtained from naturally sleeping infants presented with auditory stimuli at different sound intensities. We investigated whether the relationship between fNIRS response measures and stimulus intensity could be modelled to estimate individual auditory hearing thresholds. In the normal-hearing group, 75% of infants had threshold estimates for the sound /ba/ within the expected normal-hearing range, with an estimated mean equivalent normal loudness (ENL) of 10.72 dB HL (SD 8.64 dB HL). A case study further explored the potential application of this approach in an infant with hearing impairment, highlighting the importance of reliable responses at lower stimulus intensities and appropriate regression model selection. These findings provide preliminary evidence that cortical haemodynamic responses can be quantitatively related to sound intensity for estimating auditory sensitivity.

neuroscience↗