TY  - JOUR
A1  - Chaudry, Oliver
A1  - Grimm, Alexandra
A1  - Friedberger, Andreas
A1  - Kemmler, Wolfgang
A1  - Uder, Michael
A1  - Jakob, Franz
A1  - Quick, Harald H.
A1  - von Stengel, Simon
A1  - Engelke, Klaus
T1  - Magnetic Resonance Imaging and Bioelectrical Impedance Analysis to Assess Visceral and Abdominal Adipose Tissue
JF  - Obesity
N2  - Objective
This study aimed to compare a state‐of‐the‐art bioelectrical impedance analysis (BIA) device with two‐point Dixon magnetic resonance imaging (MRI) for the quantification of visceral adipose tissue (VAT) as a health‐related risk factor.


Methods
A total of 63 male participants were measured using a 3‐T MRI scanner and a segmental, multifrequency BIA device. MRI generated fat fraction (FF) maps, in which VAT volume, total abdominal adipose tissue volume, and FF of visceral and total abdominal compartments were quantified. BIA estimated body fat mass and VAT area.


Results
Coefficients of determination between abdominal (r\(^{2}\) = 0.75) and visceral compartments (r\(^{2}\) = 0.78) were similar for both groups, but slopes differed by a factor of two. The ratio of visceral to total abdominal FF was increased in older men compared with younger men. This difference was not detected with BIA. MRI and BIA measurements of the total abdominal volume correlated moderately (r\(^{2}\) = 0.31‐0.56), and visceral measurements correlated poorly (r\(^{2}\) = 0.13‐0.44).


Conclusions
Visceral BIA measurements agreed better with MRI measurements of the total abdomen than of the visceral compartment, indicating that BIA visceral fat area assessment cannot differentiate adipose tissue between visceral and abdominal compartments in young and older participants.
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-213591
VL  - 28
IS  - 2
SP  - 277
EP  - 283
ER  - 
TY  - JOUR
A1  - Biere, Silvia
A1  - Kranz, Thorsten M.
A1  - Matura, Silke
A1  - Petrova, Kristiyana
A1  - Streit, Fabian
A1  - Chiocchetti, Andreas G.
A1  - Grimm, Oliver
A1  - Brum, Murielle
A1  - Brunkhorst-Kanaan, Natalie
A1  - Oertel, Viola
A1  - Malyshau, Aliaksandr
A1  - Pfennig, Andrea
A1  - Bauer, Michael
A1  - Schulze, Thomas G.
A1  - Kittel-Schneider, Sarah
A1  - Reif, Andreas
T1  - Risk Stratification for Bipolar Disorder Using Polygenic Risk Scores Among Young High-Risk Adults
JF  - Frontiers in Psychiatry
N2  - Objective: 
Identifying high-risk groups with an increased genetic liability for bipolar disorder (BD) will provide insights into the etiology of BD and contribute to early detection of BD. We used the BD polygenic risk score (PRS) derived from BD genome-wide association studies (GWAS) to explore how such genetic risk manifests in young, high-risk adults. We postulated that BD-PRS would be associated with risk factors for BD.

Methods: 
A final sample of 185 young, high-risk German adults (aged 18–35 years) were grouped into three risk groups and compared to a healthy control group (n = 1,100). The risk groups comprised 117 cases with attention deficit hyperactivity disorder (ADHD), 45 with major depressive disorder (MDD), and 23 help-seeking adults with early recognition symptoms [ER: positive family history for BD, (sub)threshold affective symptomatology and/or mood swings, sleeping disorder]. BD-PRS was computed for each participant. Logistic regression models (controlling for sex, age, and the first five ancestry principal components) were used to assess associations of BD-PRS and the high-risk phenotypes.

Results: 
We observed an association between BD-PRS and combined risk group status (OR = 1.48, p < 0.001), ADHD diagnosis (OR = 1.32, p = 0.009), MDD diagnosis (OR = 1.96, p < 0.001), and ER group status (OR = 1.7, p = 0.025; not significant after correction for multiple testing) compared to healthy controls.

Conclusions: 
In the present study, increased genetic risk for BD was a significant predictor for MDD and ADHD status, but not for ER. These findings support an underlying shared risk for both MDD and BD as well as ADHD and BD. Improving our understanding of the underlying genetic architecture of these phenotypes may aid in early identification and risk stratification.
KW  - polygenic risk score
KW  - bipolar disorder
KW  - genetic phenotypes
KW  - depression
KW  - ADHD
KW  - early recognition
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-214976
VL  - 11
ER  -