Medication Adherence and Healthcare Costs in Chronically Ill Patients Using German Claims Data.

BACKGROUND AND OBJECTIVE: Despite the importance of medication adherence for chronically ill patients and the vast literature on its relationship to costs, this field suffers from methodological limitations. These are caused, amongst others, by the lack of generalizability of data sources, varying definitions of adherence, costs, and model specification. We aim to address this with different modeling approaches and to contribute evidence on the research question. METHODS: We extracted large cohorts of nine chronic diseases (n = 6747-402,898) from German claims data of stationary health insurances between 2012 and 2015 (t0-t3). Defined as the proportion of days covered by medication, we examined the relationship of adherence using several multiple regression models at baseline year t0 with annual total healthcare costs and four sub-categories. Models with concurrent, and differently time-lagged measurements of adherence and costs were compared. Exploratively, we applied non-linear models. RESULTS: Overall, we found a positive association between the proportion of days covered by medication and total costs, a weak association with outpatient costs, positive with pharmacy costs, and frequently negative with inpatient costs. There were major differences by disease and its severity but little between years, provided adherence and costs were not measured concurrently. The fit of linear models was mainly not inferior to that of non-linear models. CONCLUSIONS: The estimated effect on total costs differed from most other studies, which highlights concerns about generalizability, although effect estimates in sub-categories were as expected. Comparison of time lags indicates the importance of avoiding concurrent measurement. A non-linear relationship should be considered. These methodological approaches are valuable in future research on adherence and its consequences.

Identification of target groups and individuals for adherence interventions using tree-based prediction models.

Background: In chronically ill patients, medication adherence during implementation can be crucial for treatment success and can decrease health costs. In some populations, regression models do not show this relationship. We aim to estimate subgroup-specific and personalized effects to identify target groups for interventions. Methods: We defined three cohorts of patients with type 1 diabetes (n = 12,713), type 2 diabetes (n = 85,162) and hyperlipidemia (n = 117,485) from German claims data between 2012 and 2015. We estimated the association of adherence during implementation in the first year (proportion of days covered) and mean total costs in the three following years, controlled for sex, age, Charlson's Comorbidity Index, initial total costs, severity of the disease and surrogates for health behavior. We fitted three different types of models on training data: 1) linear regression models for the overall conditional associations between adherence and costs, 2) model-based trees to identify subgroups of patients with heterogeneous adherence effects, and 3) model-based random forests to estimate personalized adherence effects. To assess the performance of the latter, we conditionally re-estimated the personalized effects using test data, the fixed structure of the forests, and fixed effect estimates of the remaining covariates. Results: 1) our simple linear regression model estimated a positive adherence effect, that is an increase in total costs of 10.73 Euro per PDC-point and year for diabetes type 1, 3.92 Euro for diabetes type 2 and 1.92 Euro for hyperlipidemia (all p ≤ 0.001). 2) The model-based tree detected subgroups with negative estimated adherence effects for diabetes type 2 (-1.69 Euro, 24.4% of cohort) and hyperlipidemia (-0.11 Euro, 36.1% and -5.50 Euro, 5.3%). 3) Our model-based random forest estimated personalized adherence effects with a significant proportion (4.2%-24.1%) of negative effects (up to -8.31 Euro). The precision of these estimates was high for diabetes type 2 and hyperlipidemia patients. Discussion: Our approach shows that tree-based models can identify patients with different adherence effects and the precision of personalized effects is measurable. Identified patients can form target groups for adherence-promotion interventions. The method can also be applied to other outcomes such as hospitalization risk to maximize positive health effects of an intervention.

Identification of genetic elements in metabolism by high-throughput mouse phenotyping.

Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome.

Understanding gene functions and disease mechanisms: Phenotyping pipelines in the German Mouse Clinic.

Since decades, model organisms have provided an important approach for understanding the mechanistic basis of human diseases. The German Mouse Clinic (GMC) was the first phenotyping facility that established a collaboration-based platform for phenotype characterization of mouse lines. In order to address individual projects by a tailor-made phenotyping strategy, the GMC advanced in developing a series of pipelines with tests for the analysis of specific disease areas. For a general broad analysis, there is a screening pipeline that covers the key parameters for the most relevant disease areas. For hypothesis-driven phenotypic analyses, there are thirteen additional pipelines with focus on neurological and behavioral disorders, metabolic dysfunction, respiratory system malfunctions, immune-system disorders and imaging techniques. In this article, we give an overview of the pipelines and describe the scientific rationale behind the different test combinations.

A systemic view on the distribution of diet-derived methanol and hepatic acetone in mice.

Volatile organic compounds (VOCs) from breath can successfully be used to diagnose disease-specific pathological alterations in metabolism. However, the exact origin and underlying biochemical pathways that could be mapped to VOC signatures are mainly unknown. There is a knowledge gap regarding the contribution of tissues, organs, the gut microbiome, and exogenous factors to the 'sum signal' from breath samples. Animal models for human disease such as mutant mice provide the possibility to reproduce genetic predisposition to disease, thereby allowing in-depth analysis of metabolic and biochemical functions. We hypothesized that breath VOCs can be traced back to origins and organ-specific metabolic functions by combining breath concentrations with systemic levels detected in different organs and biological media (breath, blood, feces and urine). For this we fed C57Bl/6N mice a grain-based chow or a purified low-fat diet, thereby modifying the emission of methanol in breath whereas acetone levels were unaffected. We then measured headspace concentrations of both VOCs in ex vivo samples of several biological media. Cecum content especially was identified as a likely source of systemic methanol, whereas the liver showed highest acetone concentrations. Our findings are a first step to the systemic mapping of VOC patterns to metabolic functions in mice because differences between VOCs could be traced to different sources in the body. As a future aim, different levels of so-called omics technologies (genomics, proteomics, metabolomics, and breathomics) could be mapped to metabolic pathways in multiple tissues, deepening our understanding of VOC metabolism and possibly leading to early non-invasive biomarkers for human pathologies.

Diet-induced and mono-genetic obesity alter volatile organic compound signature in mice.

The prevalence of obesity is still rising in many countries, resulting in an increased risk of associated metabolic diseases. In this study we aimed to describe the volatile organic compound (VOC) patterns symptomatic for obesity. We analyzed high fat diet (HFD) induced obese and mono-genetic obese mice (global knock-in mutation in melanocortin-4 receptor MC4R-ki). The source strengths of 208 VOCs were analyzed in ad libitum fed mice and after overnight food restriction. Volatiles relevant for a random forest-based separation of obese mice were detected (26 in MC4R-ki, 22 in HFD mice). Eight volatiles were found to be important in both obesity models. Interestingly, by creating a partial correlation network of the volatile metabolites, the chemical and metabolic origins of several volatiles were identified. HFD-induced obese mice showed an elevation in the ketone body acetone and acrolein, a marker of lipid peroxidation, and several unidentified volatiles. In MC4R-ki mice, several yet-unidentified VOCs were found to be altered. Remarkably, the pheromone (methylthio)methanethiol was found to be reduced, linking metabolic dysfunction and reproduction. The signature of volatile metabolites can be instrumental in identifying and monitoring metabolic disease states, as shown in the screening of the two obese mouse models in this study. Our findings show the potential of breath gas analysis to non-invasively assess metabolic alterations for personalized diagnosis.

Online breath gas analysis in unrestrained mice by hs-PTR-MS.

The phenotyping of genetic mouse models for human disorders may greatly benefit from breath gas analysis as a noninvasive tool to identify metabolic alterations in mice. Phenotyping screens such as the German Mouse Clinic demand investigations in unrestrained mice. Therefore, we adapted a breath screen in which exhaled volatile organic compounds (VOCs) were online monitored by proton transfer reaction mass spectrometry (hs-PTR-MS). The source strength of VOCs was derived from the dynamics in the accumulation profile of exhaled VOCs of a single mouse in a respirometry chamber. A careful survey of the accumulation revealed alterations in the source strength due to confounders, e.g., urine and feces. Moreover changes in the source strength of humidity were triggered by changes in locomotor behavior as mice showed a typical behavioral pattern from activity to settling down in the course of subsequent accumulation profiles. We demonstrated that metabolic changes caused by a dietary intervention, e.g., after feeding a high-fat diet (HFD) a sample of 14 male mice, still resulted in a statistically significant shift in the source strength of exhaled VOCs. Applying a normalization which was derived from the distribution of the source strength of humidity and accounted for varying locomotor behaviors improved the shift. Hence, breath gas analysis may provide a noninvasive, fast access to monitor the metabolic adaptation of a mouse to alterations in energy balance due to overfeeding or fasting and dietary macronutrient composition as well as a high potential for systemic phenotyping of mouse mutants, intervention studies, and drug testing in mice.

59Fe-distribution in conditional ferritin-H-deleted mice.

The objective was to explore how ferritin-H deletion influences (59)Fe-distribution and excretion-kinetics in mice. Kinetics of (59)Fe-release from organs, whole-body excretion, and distribution-kinetics of intravenously injected (59)Fe trace amounts were compared in iron-deficient and iron-replete mice with (Fth(Δ/Δ)) and without (Fth(lox/lox)) conditional Mx-Cre-induced ferritin-H deletion. (59)Fe was released from spleen and liver beginning on day 2 and day 5 after ferritin-H deletion, respectively, but was not excreted from the body. Plasma-(59)Fe was cleared significantly faster in iron-deficient Fth(Δ/Δ)-mice than in iron-adequate Fth(lox/lox)-controls. (59)Fe-distribution showed a transient peak (e.g., in heart, kidney, muscle) in Fth(lox/lox) control mice, but not in ferritin-H-deleted Fth(Δ/Δ) mice 24 hours after (59)Fe injection. (59)Fe uptake into the liver and spleen was significantly lower in iron-deficient Fth(Δ/Δ) than in Fth(lox/lox) mice 24 hours and 7 days after injection, respectively, and rapidly appeared in circulating erythrocytes instead. The rate of (59)Fe release after ferritin-H deletion supports earlier data on ferritin turnover in mammals; released (59)Fe is not excreted from the body. Instead, (59)Fe is channeled into erythropoiesis and circulating erythrocytes significantly more extensively and faster. Along with a lack of transient interim (59)Fe storage (e.g., in the heart and kidney), this finding is evidence for ferritin-related iron storage-capacity affecting rate and extent of iron utilization.

Traditional medicinal plants used for the treatment of diabetes in rural and urban areas of Dhaka, Bangladesh--an ethnobotanical survey.

BACKGROUND: The usage of medicinal plants is traditionally rooted in Bangladesh and still an essential part of public healthcare. Recently, a dramatically increasing prevalence brought diabetes mellitus and its therapy to the focus of public health interests in Bangladesh. We conducted an ethnobotanical survey to identify the traditional medicinal plants being used to treat diabetes in Bangladesh and to critically assess their anti-diabetic potentials with focus on evidence-based criteria. METHODS: In an ethnobotanical survey in defined rural and urban areas 63 randomly chosen individuals (health professionals, diabetic patients), identified to use traditional medicinal plants to treat diabetes, were interviewed in a structured manner about their administration or use of plants for treating diabetes. RESULTS: In total 37 medicinal plants belonging to 25 families were reported as being used for the treatment of diabetes in Bangladesh. The most frequently mentioned plants were Coccinia indica, Azadirachta indica, Trigonella foenum-graecum, Syzygium cumini, Terminalia chebula, Ficus racemosa, Momordica charantia, Swietenia mahagoni. CONCLUSION: Traditional medicinal plants are commonly used in Bangladesh to treat diabetes. The available data regarding the anti-diabetic activity of the detected plants is not sufficient to adequately evaluate or recommend their use. Clinical intervention studies are required to provide evidence for a safe and effective use of the identified plants in the treatment of diabetes.

Innovations in phenotyping of mouse models in the German Mouse Clinic.

Under the label of the German Mouse Clinic (GMC), a concept has been developed and implemented that allows the better understanding of human diseases on the pathophysiological and molecular level. This includes better understanding of the crosstalk between different organs, pleiotropy of genes, and the systemic impact of envirotypes and drugs. In the GMC, experts from various fields of mouse genetics and physiology, in close collaboration with clinicians, work side by side under one roof. The GMC is an open-access platform for the scientific community by providing phenotypic analysis in bilateral collaborations ("bottom-up projects") and as a partner and driver in international large-scale biology projects ("top-down projects"). Furthermore, technology development is a major topic in the GMC. Innovative techniques for primary and secondary screens are developed and implemented into the phenotyping pipelines (e.g., detection of volatile organic compounds, VOCs).