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Variability of Thyroid Measurements from Ultrasound and Laboratory in a Repeated Measurements Study
(2020)
Background: Variability of measurements in medical research can be due to different sources. Quantification of measurement errors facilitates probabilistic sensitivity analyses in future research to minimize potential bias in epidemiological studies. We aimed to investigate the variation of thyroid-related outcomes derived from ultrasound (US) and laboratory analyses in a repeated measurements study. Subjects and Methods: Twenty-five volunteers (13 females, 12 males) aged 22–70 years were examined once a month over 1 year. US measurements included thyroid volume, goiter, and thyroid nodules. Laboratory measurements included urinary iodine concentrations and serum levels of thyroid-stimulating hormone (TSH), free triiodothyronine (fT3), free thyroxine (fT4), and thyroglobulin. Variations in continuous thyroid markers were assessed as coefficient of variation (CV) defined as mean of the individual CVs with bootstrapped confidence intervals and as intraclass correlation coefficients (ICCs). Variations in dichotomous thyroid markers were assessed by Cohen’s kappa. Results: CV was highest for urinary iodine concentrations (56.9%), followed by TSH (27.2%), thyroglobulin (18.2%), thyroid volume (10.5%), fT3 (8.1%), and fT4 (6.3%). The ICC was lowest for urinary iodine concentrations (0.42), followed by fT3 (0.55), TSH (0.64), fT4 (0.72), thyroid volume (0.87), and thyroglobulin (0.90). Cohen’s kappa values for the presence of goiter or thyroid nodules were 0.64 and 0.70, respectively. Conclusion: Our study provides measures of variation for thyroid outcomes, which can be used for probabilistic sensitivity analyses of epidemiological data. The low intraindividual variation of serum thyroglobulin in comparison to urinary iodine concentrations emphasizes the potential of thyroglobulin as marker for the iodine status of populations.
Objectives: An inverse relationship between education and cardiovascular risk has been described, however, the combined association of education, income, and neighborhood socioeconomic status with macrovascular disease is less clear. The aim of this study was to evaluate the association of educational level, equivalent household income and area deprivation with macrovascular disease in Germany.
Methods: Cross-sectional data from two representative German population-based studies, SHIP-TREND (n = 3,731) and KORA-F4 (n = 2,870), were analyzed. Multivariable logistic regression models were applied to estimate odds ratios and 95% confidence intervals for the association between socioeconomic determinants and macrovascular disease (defined as self-reported myocardial infarction or stroke).
Results: The study showed a higher odds of prevalent macrovascular disease in men with low and middle educational level compared to men with high education. Area deprivation and equivalent income were not related to myocardial infarction or stroke in any of the models.
Conclusion: Educational level, but not income or area deprivation, is significantly related to the macrovascular disease in men. Effective prevention of macrovascular disease should therefore start with investing in individual education.
Background
In non-randomized studies (NRSs) where a continuous outcome variable (e.g., depressive symptoms) is assessed at baseline and follow-up, it is common to observe imbalance of the baseline values between the treatment/exposure group and control group. This may bias the study and consequently a meta-analysis (MA) estimate. These estimates may differ across statistical methods used to deal with this issue. Analysis of individual participant data (IPD) allows standardization of methods across studies. We aimed to identify methods used in published IPD-MAs of NRSs for continuous outcomes, and to compare different methods to account for baseline values of outcome variables in IPD-MA of NRSs using two empirical examples from the Thyroid Studies Collaboration (TSC).
Methods
For the first aim we systematically searched in MEDLINE, EMBASE, and Cochrane from inception to February 2021 to identify published IPD-MAs of NRSs that adjusted for baseline outcome measures in the analysis of continuous outcomes. For the second aim, we applied analysis of covariance (ANCOVA), change score, propensity score and the naïve approach (ignores the baseline outcome data) in IPD-MA from NRSs on the association between subclinical hyperthyroidism and depressive symptoms and renal function. We estimated the study and meta-analytic mean difference (MD) and relative standard error (SE). We used both fixed- and random-effects MA.
Results
Ten of 18 (56%) of the included studies used the change score method, seven (39%) studies used ANCOVA and one the propensity score (5%). The study estimates were similar across the methods in studies in which groups were balanced at baseline with regard to outcome variables but differed in studies with baseline imbalance. In our empirical examples, ANCOVA and change score showed study results on the same direction, not the propensity score. In our applications, ANCOVA provided more precise estimates, both at study and meta-analytical level, in comparison to other methods. Heterogeneity was higher when change score was used as outcome, moderate for ANCOVA and null with the propensity score.
Conclusion
ANCOVA provided the most precise estimates at both study and meta-analytic level and thus seems preferable in the meta-analysis of IPD from non-randomized studies. For the studies that were well-balanced between groups, change score, and ANCOVA performed similarly.
(1) Background: Global incidence of type 1 diabetes (T1D) is rising and nearly half occurred in adults. However, it is unclear if certain early-life childhood T1D risk factors were also associated with adult-onset T1D. This study aimed to assess associations between birth order, delivery mode or daycare attendance and type 1 diabetes (T1D) risk in a population-based cohort and whether these were similar for childhood- and adult-onset T1D (cut-off age 15); (2) Methods: Data were obtained from the German National Cohort (NAKO Gesundheitsstudie) baseline assessment. Self-reported diabetes was classified as T1D if: diagnosis age ≤ 40 years and has been receiving insulin treatment since less than one year after diagnosis. Cox regression was applied for T1D risk analysis; (3) Results: Analyses included 101,411 participants (100 childhood- and 271 adult-onset T1D cases). Compared to “only-children”, HRs for second- or later-born individuals were 0.70 (95% CI = 0.50–0.96) and 0.65 (95% CI = 0.45–0.94), respectively, regardless of parental diabetes, migration background, birth year and perinatal factors. In further analyses, higher birth order reduced T1D risk in children and adults born in recent decades. Caesarean section and daycare attendance showed no clear associations with T1D risk; (4) Conclusions: Birth order should be considered in both children and adults’ T1D risk assessment for early detection.
The Study of Health in Pomerania (SHIP), a population-based study from a rural state in northeastern Germany with a relatively poor life expectancy, supplemented its comprehensive examination program in 2008 with whole-body MR imaging at 1.5 T (SHIP-MR). We reviewed more than 100 publications that used the SHIP-MR data and analyzed which sequences already produced fruitful scientific outputs and which manuscripts have been referenced frequently. Upon reviewing the publications about imaging sequences, those that used T1-weighted structured imaging of the brain and a gradient-echo sequence for R2* mapping obtained the highest scientific output; regarding specific body parts examined, most scientific publications focused on MR sequences involving the brain and the (upper) abdomen. We conclude that population-based MR imaging in cohort studies should define more precise goals when allocating imaging time. In addition, quality control measures might include recording the number and impact of published work, preferably on a bi-annual basis and starting 2 years after initiation of the study. Structured teaching courses may enhance the desired output in areas that appear underrepresented.
Background: It has not been investigated whether there are associations between urinary iodine (UI) excretion measurements some years apart, nor whether such an association remains after adjustment for nutritional habits. The aim of the present study was to investigate the relation between iodine-creatinine ratio (ICR) at two measuring points 5 years apart. Methods: Data from 2,659 individuals from the Study of Health in Pomerania were analyzed. Analysis of covariance and Poisson regressions were used to associate baseline with follow-up ICR. Results: Baseline ICR was associated with follow-up ICR. Particularly, baseline ICR >300 µg/g was related to an ICR >300 µg/g at follow-up (relative risk, RR: 2.20; p < 0.001). The association was stronger in males (RR: 2.64; p < 0.001) than in females (RR: 1.64; p = 0.007). In contrast, baseline ICR <100 µg/g was only associated with an ICR <100 µg/g at follow-up in males when considering unadjusted ICR. Conclusions: We detected only a weak correlation with respect to low ICR. Studies assessing iodine status in a population should take into account that an individual with a low UI excretion in one measurement is not necessarily permanently iodine deficient. On the other hand, current high ICR could have been predicted by high ICR 5 years ago.
The aim of the present study was to construct a biological age score reflecting one’s physiologic capability and aging condition with respect to tooth loss over 10 y. From the follow-up to the population-based Study of Health in Pomerania (i.e., SHIP-2), 2,049 participants were studied for their baseline biomarker measures 10 y before (i.e., in SHIP-0). Metabolic and periodontal data were regressed onto chronological age to construct a score designated as “biological age.” For either sex separately, the impact of this individualized score was used to predict tooth loss in the follow-up cohort in comparison with each participant’s chronological age. Outcome data after 10 y with respect to tooth loss, periodontitis, obesity, and inflammation were shown to be better for biologically younger subjects than as expected by their chronological age, whereas for the older subjects, data were worse. Especially for tooth loss, a striking increase was observed in subjects whose biological age at baseline appeared to be higher than their chronological age. Biological age produced significantly better tooth loss predictions than chronological age (P < 0.001). Areas under receiver operating characteristic curves for tooth loss of ≥3 teeth in men during follow-up were 0.811 and 0.745 for biological and chronological age, respectively. For women, these figures were 0.788 and 0.724. For total tooth loss, areas under the curve were 0.890 and 0.749 in men and 0.872 and 0.752 in women. Biological age combines various measures into a single score and allows identifying individuals at increased risk of tooth loss.
Periodontitis is one of the most prevalent oral diseases worldwide and is caused by multifactorial interactions between host and oral bacteria. Altered cellular metabolism of host and microbes releases a number of intermediary end products known as metabolites. There is an increasing interest in identifying metabolites from oral fluids such as saliva to widen the understanding of the complex pathogenesis of periodontitis. It is believed that some metabolites might serve as indicators toward early detection and screening of periodontitis and perhaps even for monitoring its prognosis in the future. Because contemporary periodontal screening methods are deficient, there is an urgent need for novel approaches in periodontal screening procedures. To this end, we associated oral parameters (clinical attachment level, periodontal probing depth, supragingival plaque, supragingival calculus, number of missing teeth, and removable denture) with a large set of salivary metabolites (n = 284) obtained by mass spectrometry among a subsample (n = 909) of nondiabetic participants from the Study of Health in Pomerania (SHIP-Trend-0). Linear regression analyses were performed in age-stratified groups and adjusted for potential confounders. A multifaceted image of associated metabolites (n = 107) was revealed with considerable differences according to age groups. In the young (20 to 39 y) and middle-aged (40 to 59 y) groups, metabolites were predominantly associated with periodontal variables, whereas among the older subjects (≥60 y), tooth loss was strongly associated with metabolite levels. Metabolites associated with periodontal variables were clearly linked to tissue destruction, host defense mechanisms, and bacterial metabolism. Across all age groups, the bacterial metabolite phenylacetate was significantly associated with periodontal variables. Our results revealed alterations of the salivary metabolome in association with age and oral health status. Among our comprehensive panel of metabolites, periodontitis was significantly associated with the bacterial metabolite phenylacetate, a promising substance for further biomarker research.
For the goal of individualized medicine, it is critical to have clinical phenotypes at hand which represent the individual pathophysiology. However, for most of the utilized phenotypes, two individuals with the same phenotype assignment may differ strongly in their underlying biological traits. In this paper, we propose a definition for individualization and a corresponding statistical operationalization, delivering thereby a statistical framework in which the usefulness of a variable in the meaningful differentiation of individuals with the same phenotype can be assessed. Based on this framework, we develop a statistical workflow to derive individualized phenotypes, demonstrating that under specific statistical constraints the prediction error of prediction scores contains information about hidden biological traits not represented in the modeled phenotype of interest, allowing thereby internal differentiation of individuals with the same assigned phenotypic manifestation. We applied our procedure to data of the population-based Study of Health in Pomerania to construct a refined definition of obesity, demonstrating the utility of the definition in prospective survival analyses. Summarizing, we propose a framework for the individualization of phenotypes aiding personalized medicine by shifting the focus in the assessment of prediction models from the model fit to the informational content of the prediction error.
Aims
Averaged measurements, but not the progression based on multiple assessments of carotid intima-media thickness, (cIMT) are predictive of cardiovascular disease (CVD) events in individuals. Whether this is true for conventional risk factors is unclear.
Methods and results
An individual participant meta-analysis was used to associate the annualised progression of systolic blood pressure, total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol with future cardiovascular disease risk in 13 prospective cohort studies of the PROG-IMT collaboration (n = 34,072). Follow-up data included information on a combined cardiovascular disease endpoint of myocardial infarction, stroke, or vascular death. In secondary analyses, annualised progression was replaced with average. Log hazard ratios per standard deviation difference were pooled across studies by a random effects meta-analysis. In primary analysis, the annualised progression of total cholesterol was marginally related to a higher cardiovascular disease risk (hazard ratio (HR) 1.04, 95% confidence interval (CI) 1.00 to 1.07). The annualised progression of systolic blood pressure, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol was not associated with future cardiovascular disease risk. In secondary analysis, average systolic blood pressure (HR 1.20 95% CI 1.11 to 1.29) and low-density lipoprotein cholesterol (HR 1.09, 95% CI 1.02 to 1.16) were related to a greater, while high-density lipoprotein cholesterol (HR 0.92, 95% CI 0.88 to 0.97) was related to a lower risk of future cardiovascular disease events.
Conclusion
Averaged measurements of systolic blood pressure, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol displayed significant linear relationships with the risk of future cardiovascular disease events. However, there was no clear association between the annualised progression of these conventional risk factors in individuals with the risk of future clinical endpoints.