Childhood maltreatment is associated with depression and cardiometabolic disease in adulthood. However, the relationships with these two diseases have so far only been evaluated in different samples and with different methodology. Thus, it remains unknown how the effect sizes magnitudes for depression and cardiometabolic disease compare with each other and whether childhood maltreatment is especially associated with the co-occurrence ("comorbidity") of depression and cardiometabolic disease. This pooled analysis examined the association of childhood maltreatment with depression, cardiometabolic disease, and their comorbidity in adulthood.We carried out an individual participant data meta-analysis on 13 international observational studies (N = 217,929). Childhood maltreatment comprised self-reports of physical, emotional, and/or sexual abuse before 18 years. Presence of depression was established with clinical interviews or validated symptom scales and presence of cardiometabolic disease with self-reported diagnoses. In included studies, binomial and multinomial logistic regressions estimated sociodemographic-adjusted associations of childhood maltreatment with depression, cardiometabolic disease, and their comorbidity. We then additionally adjusted these associations for lifestyle factors (smoking status, alcohol consumption, and physical activity). Finally, random-effects models were used to pool these estimates across studies and examined differences in associations across sex and maltreatment types.Childhood maltreatment was associated with progressively higher odds of cardiometabolic disease without depression (OR [95% CI] = 1.27 [1.18; 1.37]), depression without cardiometabolic disease (OR [95% CI] = 2.68 [2.39; 3.00]), and comorbidity between both conditions (OR [95% CI] = 3.04 [2.51; 3.68]) in adulthood. Post hoc analyses showed that the association with comorbidity was stronger than with either disease alone, and the association with depression was stronger than with cardiometabolic disease. Associations remained significant after additionally adjusting for lifestyle factors, and were present in both males and females, and for all maltreatment types.This meta-analysis revealed that adults with a history of childhood maltreatment suffer more often from depression and cardiometabolic disease than their non-exposed peers. These adults are also three times more likely to have comorbid depression and cardiometabolic disease. Childhood maltreatment may therefore be a clinically relevant indicator connecting poor mental and somatic health. Future research should investigate the potential benefits of early intervention in individuals with a history of maltreatment on their distal mental and somatic health (PROSPERO CRD42021239288). 

/pdf/depression-cardiometabolic-disease-and-their-co-occurrence-35kstf1f.pdf

Depression, cardiometabolic disease, and their co-occurrence after childhood maltreatment: an individual participant data meta-analysis including over 200,000 participants

Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways.We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed.We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism.Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology. 

/pdf/identification-of-candidate-metabolite-biomarkers-for-202s3kib.pdf

Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts

Objective Changes of the pancreaticobiliary ducts herald disease. Magnetic resonance cholangiopancreatography (MRCP) allows accurate duct visualisation. Data on reliable upper reference ranges are missing. Design Cross-sectional whole body MRI data from the population-based Study of Health in Pomerania were analysed. The width of the common bile duct (CBD) and the pancreatic duct (PD) was determined. We aimed to describe the distribution of physiological duct diameters on MRCP in a population of healthy subjects and to identify factors influencing duct size. Results After excluding pre-existing pancreaticobiliary conditions, CBD and PD diameters from 938 and 774 healthy individuals, respectively, showed a significant increase with age (p<0.0001) and exceeded the conventional upper reference limit of normal in 10.9% and 18.2%, respectively. Age-dependent upper reference limits of duct diameters were delineated with non-parametric quantile regression, defined as 95th percentile: for CBD up to 8 mm in subjects <65 years and up to 11 mm in subjects ≥65 years. For the PD reference diameters were up to 3 mm in subjects <65 years and up to 4 mm in subjects ≥65 years. Conclusions This is the first population-based study delineating age-adjusted upper reference limits of CBD and PD on MRCP. We showed that up to 18.2% of healthy volunteers would have needed diagnostic workup, if the conventional reference values were used. The utilisation of the adapted reference levels may help to avoid unnecessary investigations and thus to reduce healthcare expenditure and test-related adverse events.

/pdf/definition-of-age-dependent-reference-values-for-the-12lo2led.pdf

Definition of age-dependent reference values for the diameter of the common bile duct and pancreatic duct on MRCP: a population-based, cross-sectional cohort study

Existing neuroimaging studies have reported divergent structural alterations in insomnia disorder (ID). In the present study, we performed a large-scale coordinated meta-analysis by pooling structural brain measures from 1085 subjects (mean [SD] age 50.5 [13.9] years, 50.2% female, 17.4% with insomnia) across three international Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA)-Sleep cohorts. Two sites recruited patients with ID/controls: Freiburg (University of Freiburg Medical Center, Freiburg, Germany) 42/43 and KUMS (Kermanshah University of Medical Sciences, Kermanshah, Iran) 42/49, while the Study of Health in Pomerania (SHIP-Trend, University Medicine Greifswald, Greifswald, Germany) recruited population-based individuals with/without insomnia symptoms 75/662. The influence of insomnia on magnetic resonance imaging-based brain morphometry using an insomnia brain score was then assessed. Within each cohort, we used an ordinary least-squares linear regression to investigate the link between the individual regional cortical and subcortical volumes and the presence of insomnia symptoms. Then, we performed a fixed-effects meta-analysis across cohorts based on the first-level results. For the insomnia brain score, weighted logistic ridge regression was performed on one sample (Freiburg), which separated patients with ID from controls to train a model based on the segmentation measurements. Afterward, the insomnia brain scores were validated using the other two samples. The model was used to predict the log-odds of the subjects with insomnia given individual insomnia-related brain atrophy. After adjusting for multiple comparisons, we did not detect any significant associations between insomnia symptoms and cortical or subcortical volumes, nor could we identify a global insomnia-related brain atrophy pattern. Thus, we observed inconsistent brain morphology differences between individuals with and without insomnia across three independent cohorts. Further large-scale cross-sectional/longitudinal studies using both structural and functional neuroimaging are warranted to decipher the neurobiology of insomnia. 

Lack of structural brain alterations associated with insomnia: findings from the <scp>ENIGMA‐Sleep</scp> Working Group

Abstract For the ergonomic design of workplaces and products, a representative anthropometric dataset of the working-age population is needed. As body proportions are constantly changing and the latest publicly available dataset for Germany was published in 2004 (data collection period 1999–2002), the aim of this study was to create and publish an updated anthropometric dataset of the German working-age population. Within a regional epidemiological health study, 3D body scan data from 2313 subjects were collected and used to create an anthropometric dataset with a total of 39 ISO 7250-1 measures. To approximate the goal of generating representative values for Germany, the collected regional dataset was weighted with an algorithm, using values from a known nationally representative survey. Based on the weighted dataset, a gender stratified percentile table with values for the 5th, 50th, and 95th percentile was calculated. Practitioner summary: Body proportions are constantly changing and the latest publicly available anthropometric dataset for Germany was published in 2004. A new dataset was created, using 3D body scans from an epidemiological health study and a weighting algorithm. Ultimately, percentile tables with values for the 5th, 50th, and 95th percentile are published.

https://doi.org/10.1080/00140139.2022.2130440

Anthropometric dataset for the German working-age population using 3D body scans from a regional epidemiological health study and a weighting algorithm

... The Study of Health in Pomerania (SHIP) comprises the two independent cohorts SHIP-START (recruited between 1997 and 2001) and SHIP-TREND (recruited between 2008 to 2012), which were established to examine the health and disease status of the general adult population in Northeast Germany given that this region had the lowest life expectancy in Germany in the 1990s.1 The initial cohort was renamed SHIP-START to avoid confusion with designation of SHIP, as the whole project comprises different cohorts. Both cohorts serve the purpose of understanding the concepts of health and disease in their greatest possible complexity, rather than focusing on specific disease areas. Consequently, the examination programmes are the most comprehensive that have ever been applied to a general population sample worldwide; the baseline examinations of SHIP-TREND took ≤25 h. The second follow-up of SHIP-START (SHIP-START-2) and baseline SHIP-TREND-0 were the first studies worldwide to utilize whole-body magnetic resonance imaging (MRI) in a general population setting.2 

https://academic.oup.com/ije/article-pdf/51/6/e372/47882022/dyac034.pdf

Cohort Profile Update: The Study of Health in Pomerania (SHIP).

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Cohort Profile Update: The Study of Health in Pomerania (SHIP).