Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine.

The human microbiome plays an important role in human health and disease. Meta-omics analyses provide indispensable data for linking changes in microbiome composition and function to disease etiology. Yet, the lack of a mechanistic understanding of, e.g., microbiome-metabolome links hampers the translation of these findings into effective, novel therapeutics. Here, we propose metabolic modeling of microbial communities through constraint-based reconstruction and analysis (COBRA) as a complementary approach to meta-omics analyses. First, we highlight the importance of microbial metabolism in cardiometabolic diseases, inflammatory bowel disease, colorectal cancer, Alzheimer disease, and Parkinson disease. Next, we demonstrate that microbial community modeling can stratify patients and controls, mechanistically link microbes with fecal metabolites altered in disease, and identify host pathways affected by the microbiome. Finally, we outline our vision for COBRA modeling combined with meta-omics analyses and multivariate statistical analyses to inform and guide clinical trials, yield testable hypotheses, and ultimately propose novel dietary and therapeutic interventions.

Circulating metabolites modulated by diet are associated with depression.

Metabolome reflects the interplay of genome and exposome at molecular level and thus can provide deep insights into the pathogenesis of a complex disease like major depression. To identify metabolites associated with depression we performed a metabolome-wide association analysis in 13,596 participants from five European population-based cohorts characterized for depression, and circulating metabolites using ultra high-performance liquid chromatography/tandem accurate mass spectrometry (UHPLC/MS/MS) based Metabolon platform. We tested 806 metabolites covering a wide range of biochemical processes including those involved in lipid, amino-acid, energy, carbohydrate, xenobiotic and vitamin metabolism for their association with depression. In a conservative model adjusting for life style factors and cardiovascular and antidepressant medication use we identified 8 metabolites, including 6 novel, significantly associated with depression. In individuals with depression, increased levels of retinol (vitamin A), 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1) (lecithin) and mannitol/sorbitol and lower levels of hippurate, 4-hydroxycoumarin, 2-aminooctanoate (alpha-aminocaprylic acid), 10-undecenoate (11:1n1) (undecylenic acid), 1-linoleoyl-GPA (18:2) (lysophosphatidic acid; LPA 18:2) are observed. These metabolites are either directly food derived or are products of host and gut microbial metabolism of food-derived products. Our Mendelian randomization analysis suggests that low hippurate levels may be in the causal pathway leading towards depression. Our findings highlight putative actionable targets for depression prevention that are easily modifiable through diet interventions.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression.

Depression constitutes a leading cause of disability worldwide. Despite extensive research on its interaction with psychobiological factors, associated pathways are far from being elucidated. Metabolomics, assessing the final products of complex biochemical reactions, has emerged as a valuable tool for exploring molecular pathways. We conducted a metabolome-wide association analysis to investigate the link between the serum metabolome and depressed mood (DM) in 1411 participants of the KORA (Cooperative Health Research in the Augsburg Region) F4 study (discovery cohort). Serum metabolomics data comprised 353 unique metabolites measured by Metabolon. We identified 72 (5.1%) KORA participants with DM. Linear regression tests were conducted modeling each metabolite value by DM status, adjusted for age, sex, body-mass index, antihypertensive, cardiovascular, antidiabetic, and thyroid gland hormone drugs, corticoids and antidepressants. Sensitivity analyses were performed in subcohorts stratified for sex, suicidal ideation, and use of antidepressants. We replicated our results in an independent sample of 968 participants of the SHIP-Trend (Study of Health in Pomerania) study including 52 (5.4%) individuals with DM (replication cohort). We found significantly lower laurylcarnitine levels in KORA F4 participants with DM after multiple testing correction according to Benjamini/Hochberg. This finding was replicated in the independent SHIP-Trend study. Laurylcarnitine remained significantly associated (p value < 0.05) with depression in samples stratified for sex, suicidal ideation, and antidepressant medication. Decreased blood laurylcarnitine levels in depressed individuals may point to impaired fatty acid oxidation and/or mitochondrial function in depressive disorders, possibly representing a novel therapeutic target.

Personalized whole-body models integrate metabolism, physiology, and the gut microbiome.

Comprehensive molecular-level models of human metabolism have been generated on a cellular level. However, models of whole-body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ-specific information from literature and omics data to generate two sex-specific whole-body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole-body organ-resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter-organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host-microbiome co-metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.

Parkinson's disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions.

BACKGROUND: Parkinson's disease (PD) is a systemic disease clinically defined by the degeneration of dopaminergic neurons in the brain. While alterations in the gut microbiome composition have been reported in PD, their functional consequences remain unclear. Herein, we addressed this question by an analysis of stool samples from the Luxembourg Parkinson's Study (n = 147 typical PD cases, n = 162 controls). RESULTS: All individuals underwent detailed clinical assessment, including neurological examinations and neuropsychological tests followed by self-reporting questionnaires. Stool samples from these individuals were first analysed by 16S rRNA gene sequencing. Second, we predicted the potential secretion for 129 microbial metabolites through personalised metabolic modelling using the microbiome data and genome-scale metabolic reconstructions of human gut microbes. Our key results include the following. Eight genera and seven species changed significantly in their relative abundances between PD patients and healthy controls. PD-associated microbial patterns statistically depended on sex, age, BMI, and constipation. Particularly, the relative abundances of Bilophila and Paraprevotella were significantly associated with the Hoehn and Yahr staging after controlling for the disease duration. Furthermore, personalised metabolic modelling of the gut microbiomes revealed PD-associated metabolic patterns in the predicted secretion potential of nine microbial metabolites in PD, including increased methionine and cysteinylglycine. The predicted microbial pantothenic acid production potential was linked to the presence of specific non-motor symptoms. CONCLUSION: Our results suggest that PD-associated alterations of the gut microbiome can translate into substantial functional differences affecting host metabolism and disease phenotype.

Early-Life Adversity Leaves Its Imprint on the Oral Microbiome for More Than 20 Years and Is Associated with Long-Term Immune Changes.

The early-life microbiome (ELM) interacts with the psychosocial environment, in particular during early-life adversity (ELA), defining life-long health trajectories. The ELM also plays a significant role in the maturation of the immune system. We hypothesised that, in this context, the resilience of the oral microbiomes, despite being composed of diverse and distinct communities, allows them to retain an imprint of the early environment. Using 16S amplicon sequencing on the EpiPath cohort, we demonstrate that ELA leaves an imprint on both the salivary and buccal oral microbiome 24 years after exposure to adversity. Furthermore, the changes in both communities were associated with increased activation, maturation, and senescence of both innate and adaptive immune cells, although the interaction was partly dependent on prior herpesviridae exposure and current smoking. Our data suggest the presence of multiple links between ELA, Immunosenescence, and cytotoxicity that occur through long-term changes in the microbiome.

Methanogenic granule growth and development is a continual process characterized by distinct morphological features.

Up-flow anaerobic bioreactors are widely applied for high-rate digestion of industrial wastewaters and rely on formation, and retention, of methanogenic granules, comprising of dense, fast-settling, microbial aggregates (approx. 0.5-4.0 mm in diameter). Granule formation (granulation) mechanisms have been reasonably well hypothesized and documented. However, this study used laboratory-scale bioreactors, inoculated with size-separated granular sludge to follow new granule formation, maturation, disintegration and re-formation. Temporal size profiles, volatile solids content, settling velocity, and ultrastructure of granules were determined from each of four bioreactors inoculated only with small granules, four with only large granules, and four with a full complement of naturally-size-distributed granules. Constrained granule size profiles shifted toward the natural distribution, which was associated with maximal bioreactor performance. Distinct morphological features characterized different granule sizes and biofilm development stages, including 'young', 'juvenile', 'mature' and 'old'. The findings offer opportunities toward optimizing management of high-rate, anaerobic digesters by shedding light on the rates of granule growth, the role of flocculent sludge in granulation and how shifting size distributions should be considered when setting upflow velocities.

Why do family dementia caregivers reject caregiver support services? Analyzing types of rejection and associated health-impairments in a cluster-randomized controlled intervention trial.

BACKGROUND: Although there are a number of support services accessible for most family dementia caregivers, many caregivers reject available and affordable support. Previous research suggests that rejections of support services may result from insufficient fit of available services with caregivers' unmet needs and a lack of acknowledgement of caregivers' unmet needs and associated support services. The present study investigates (a) the number, proportion and types of caregivers' rejection on recommended tailored support, (b) socio-demographic and clinical determinants of caregiver's rejection of both people with dementia (PwD) and caregivers, and (c) caregivers' health-related variables related to caregivers' rejection. METHODS: Caregivers' rejection of tailored support services was identified based on a standardized, computerized unmet needs assessment conducted by dementia-specific qualified nurses. The present analysis is based on data of n = 226 dyads of caregivers and their community-dwelling PwD who participated in a general practitioner (GP)-based, cluster-randomized intervention trial. The trial was approved by the Ethical Committee of the Chamber of Physicians of Mecklenburg-Western Pomerania, registry number BB 20/11. Data analyses were conducted using Stata/IC 13.1. We conducted Welch's t-test, Pearson's product-moment correlation, and conditional negative binomial regression models with random effects for GP to account for over-dispersed count data. RESULTS: In sum, n = 505 unmet needs were identified and the same number of tailored recommendations were identified for n = 171 family dementia caregivers from the intervention group at baseline. For n = 55 family dementia caregivers not a single unmet need and recommendation were identified. A total of 17.6% (n = 89) of the recommendations were rejected by caregivers. Rejection rates of caregivers differed by type of recommendation. Whereas caregivers' rejection rate on recommendations concerning mental health (3.6%), physical health (2.5%), and social, legal, and financial affairs (0%) were low, caregivers' rejection rates concerning social integration (especially caregiver supporting groups) was high (71.7%). Thus, the rejections of family dementia caregivers are mainly linked to the delegation to caregiver supporting groups. Caregivers' rejections were mainly related to personal factors of caregivers (n = 66), service-related factors (n = 6), relational factors (n = 1), and other factors (n = 17). Furthermore, our results showed that the number of caregivers' rejections was associated with a higher functional status of the PwD and are mainly associated with the rejection of caregiver supporting groups. Thus, caregivers visit supporting groups more often when the PwD shows low abilities in activities of daily living. Importantly, this is independent of the status of cognition and depression of the PwD as well as the physical and mental health of the family dementia caregivers. CONCLUSIONS: Our results underline the importance of understanding factors that determine caregivers' rejection of support services. These need to be specifically addressed in tailored solutions for caregivers' support services. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01401582 (date: July 25, 2011, prospective registered).

Cost-effectiveness of a collaborative dementia care management-Results of a cluster-randomized controlled trial.

INTRODUCTION: The purpose of this study was to determine the cost-effectiveness of collaborative dementia care management (DCM). METHODS: The cost-effectiveness analysis was based on the data of 444 patients of a cluster-randomized, controlled trial, conceptualized to evaluate a collaborative DCM that aimed to optimize treatment and care in dementia. Health-care resource use, costs, quality-adjusted life years (QALYs), and incremental cost per QALY gained were measured over a 24-month time horizon. RESULTS: DCM increased QALYs (+0.05) and decreased costs (-569€) due to a lower hospitalization and a delayed institutionalization (7 months) compared with usual care. The probability of DCM being cost-effective was 88% at willingness-to-pay thresholds of 40,000€ per QALY gained and higher in patients living alone compared to those not living alone (96% vs. 26%). DISCUSSION: DCM is likely to be a cost-effective strategy in treating dementia and thus beneficial for public health-care payers and patients, especially for those living alone.

Interplay between RGS2 and childhood adversities in predicting anxiety and depressive disorders: Findings from a general population sample.

BACKGROUND: It remains unresolved whether childhood adversities interact with genetic variation in regulator of G-protein signaling 2 (RGS2) rs4606 in predicting various anxiety and depressive disorders and whether diagnostic specificity exists in these interactions. METHODS: The genotype of RGS2 rs4606 was determined for N = 2,263 adults with European ancestry from the Study of Health in Pomerania. Lifetime anxiety and depressive disorders according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, were assessed with the Munich Composite International Diagnostic Interview (DIA-X/M-CIDI). Childhood adversities were assessed with the Childhood Trauma Questionnaire (CTQ, when participants were aged 29-89). RESULTS: Logistic regressions adjusted for sex and age revealed that rs4606 interacted with total childhood adversity in predicting each diagnostic outcome except for panic disorder and generalized anxiety disorder, uncorrected and corrected for multiple testing (odds ratio [OR] = 1.06-1.16). That is, carriers of the GG (vs. CC/CG) genotype were at decreased risk for anxiety and/or depression in the presence of low, but at increased risk in the presence of high total childhood adversity. Respective gene-environment (G × E) interactions were found for (a) comorbid anxiety and depressive disorders (OR = 1.13), but neither pure anxiety nor pure depressive disorders and (b) pure/temporally primary anxiety disorders (OR = 1.07), but not pure/temporally primary depressive disorders. The G × E interaction remained associated with depressive disorders after introducing pure/temporally primary anxiety disorders as additional predictor (OR = 1.09). CONCLUSIONS: rs4606 alters the risk of developing a range of anxiety but also depressive disorders after childhood adversities. A complex risk pattern of genotype, environmental factors, and preexisting anxiety contributes to subsequent depression development.

Assessment of subjective emotional valence and long-lasting impact of life events: development and psychometrics of the Stralsund Life Event List (SEL).

BACKGROUND: Life events (LEs) are associated with future physical and mental health. They are crucial for understanding the pathways to mental disorders as well as the interactions with biological parameters. However, deeper insight is needed into the complex interplay between the type of LE, its subjective evaluation and accompanying factors such as social support. The "Stralsund Life Event List" (SEL) was developed to facilitate this research. METHODS: The SEL is a standardized interview that assesses the time of occurrence and frequency of 81 LEs, their subjective emotional valence, the perceived social support during the LE experience and the impact of past LEs on present life. Data from 2265 subjects from the general population-based cohort study "Study of Health in Pomerania" (SHIP) were analysed. Based on the mean emotional valence ratings of the whole sample, LEs were categorized as "positive" or "negative". For verification, the SEL was related to lifetime major depressive disorder (MDD; Munich Composite International Diagnostic Interview), childhood trauma (Childhood Trauma Questionnaire), resilience (Resilience Scale) and subjective health (SF-12 Health Survey). RESULTS: The report of lifetime MDD was associated with more negative emotional valence ratings of negative LEs (OR = 2.96, p < 0.0001). Negative LEs (b = 0.071, p < 0.0001, ß = 0.25) and more negative emotional valence ratings of positive LEs (b = 3.74, p < 0.0001, ß = 0.11) were positively associated with childhood trauma. In contrast, more positive emotional valence ratings of positive LEs were associated with higher resilience (b = - 7.05, p < 0.0001, ß = 0.13), and a lower present impact of past negative LEs was associated with better subjective health (b = 2.79, p = 0.001, ß = 0.05). The internal consistency of the generated scores varied considerably, but the mean value was acceptable (averaged Cronbach's alpha > 0.75). CONCLUSIONS: The SEL is a valid instrument that enables the analysis of the number and frequency of LEs, their emotional valence, perceived social support and current impact on life on a global score and on an individual item level. Thus, we can recommend its use in research settings that require the assessment and analysis of the relationship between the occurrence and subjective evaluation of LEs as well as the complex balance between distressing and stabilizing life experiences.

Circulating metabolites and general cognitive ability and dementia: Evidence from 11 cohort studies.

INTRODUCTION: Identifying circulating metabolites that are associated with cognition and dementia may improve our understanding of the pathogenesis of dementia and provide crucial readouts for preventive and therapeutic interventions. METHODS: We studied 299 metabolites in relation to cognition (general cognitive ability) in two discovery cohorts (N total = 5658). Metabolites significantly associated with cognition after adjusting for multiple testing were replicated in four independent cohorts (N total = 6652), and the associations with dementia and Alzheimer's disease (N = 25,872) and lifestyle factors (N = 5168) were examined. RESULTS: We discovered and replicated 15 metabolites associated with cognition including subfractions of high-density lipoprotein, docosahexaenoic acid, ornithine, glutamine, and glycoprotein acetyls. These associations were independent of classical risk factors including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, glucose, and apolipoprotein E (APOE) genotypes. Six of the cognition-associated metabolites were related to the risk of dementia and lifestyle factors. DISCUSSION: Circulating metabolites were consistently associated with cognition, dementia, and lifestyle factors, opening new avenues for prevention of cognitive decline and dementia.

Genome-wide gene-environment interaction in depression: A systematic evaluation of candidate genes: The childhood trauma working-group of PGC-MDD.

Gene by environment (GxE) interaction studies have investigated the influence of a number of candidate genes and variants for major depressive disorder (MDD) on the association between childhood trauma and MDD. Most of these studies are hypothesis driven and investigate only a limited number of SNPs in relevant pathways using differing methodological approaches. Here (1) we identified 27 genes and 268 SNPs previously associated with MDD or with GxE interaction in MDD and (2) analyzed their impact on GxE in MDD using a common approach in 3944 subjects of European ancestry from the Psychiatric Genomics Consortium who had completed the Childhood Trauma Questionnaire. (3) We subsequently used the genome-wide SNP data for a genome-wide case-control GxE model and GxE case-only analyses testing for an enrichment of associated SNPs. No genome-wide significant hits and no consistency among the signals of the different analytic approaches could be observed. This is the largest study for systematic GxE interaction analysis in MDD in subjects of European ancestry to date. Most of the known candidate genes/variants could not be supported. Thus, their impact on GxE interaction in MDD may be questionable. Our results underscore the need for larger samples, more extensive assessment of environmental exposures, and greater efforts to investigate new methodological approaches in GxE models for MDD.

Predicting brain structure in population-based samples with biologically informed genetic scores for schizophrenia.

Schizophrenia is associated with brain structural abnormalities including gray and white matter volume reductions. Whether these alterations are caused by genetic risk variants for schizophrenia is unclear. Previous attempts to detect associations between polygenic factors for schizophrenia and structural brain phenotypes in healthy subjects have been negative or remain non-replicated. In this study, we used genetic risk scores that were based on the accumulated effect of selected risk variants for schizophrenia belonging to specific biological systems like synaptic function, neurodevelopment, calcium signaling, and glutamatergic neurotransmission. We hypothesized that this "biologically informed" approach would provide the missing link between genetic risk for schizophrenia and brain structural phenotypes. We applied whole-brain voxel-based morphometry (VBM) analyses in two population-based target samples and subsequent regions of interest (ROIs) analyses in an independent replication sample (total N = 2725). No consistent association between the genetic scores and brain volumes were observed in the investigated samples. These results suggest that in healthy subjects with a higher genetic risk for schizophrenia additional factors apart from common genetic variants (e.g., infection, trauma, rare genetic variants, or gene-gene interactions) are required to induce structural abnormalities of the brain. Further studies are recommended to test for possible gene-gene or gene-environment effects. © 2017 Wiley Periodicals, Inc.

Measuring Biological Age via Metabonomics: The Metabolic Age Score.

Chronological age is one of the most important risk factors for adverse clinical outcome. Still, two individuals at the same chronological age could have different biological aging states, leading to different individual risk profiles. Capturing this individual variance could constitute an even more powerful predictor enhancing prediction in age-related morbidity. Applying a nonlinear regression technique, we constructed a metabonomic measurement for biological age, the metabolic age score, based on urine data measured via (1)H NMR spectroscopy. We validated the score in two large independent population-based samples by revealing its significant associations with chronological age and age-related clinical phenotypes as well as its independent predictive value for survival over approximately 13 years of follow-up. Furthermore, the metabolic age score was prognostic for weight loss in a sample of individuals who underwent bariatric surgery. We conclude that the metabolic age score is an informative measurement of biological age with possible applications in personalized medicine.

Healthcare resource utilization and cost in dementia: are there differences between patients screened positive for dementia with and those without a formal diagnosis of dementia in primary care in Germany? - ERRATUM.

In the above mentioned article by Michalowsky et al., Johannes Hertel was mistakenly omitted from the authorship list. This error has been corrected in the print, PDF and HTML versions of the original article.

Healthcare resource utilization and cost in dementia: are there differences between patients screened positive for dementia with and those without a formal diagnosis of dementia in primary care in Germany?

BACKGROUND: It is well-known that dementia is undiagnosed, resulting in the exclusion of patients without a formal diagnosis of dementia in many studies. Objectives of the present analyses were (1) to determine healthcare resource utilization and (2) costs of patients screened positive for dementia with a formal diagnosis and those without a formal diagnosis of dementia, and (3) to analyze the association between having received a formal dementia diagnosis and healthcare costs. METHOD: This analysis is based on 240 primary care patients who screened positive for dementia. Within the baseline assessment, individual data about the utilization of healthcare services were assessed. Costs were assessed from the perspective of insurance, solely including direct costs. Associations between dementia diagnosis and costs were evaluated using multiple linear regression models. RESULTS: Patients formally diagnosed with dementia were treated significantly more often by a neurologist, but less often by all other outpatient specialists, and received anti-dementia drugs and day care more often. Diagnosed patients underwent shorter and less frequent planned in-hospital treatments. Dementia diagnosis was significantly associated with higher costs of anti-dementia drug treatment, but significantly associated with less total medical care costs, which valuated to be € 5,123 compared, to € 5,565 for undiagnosed patients. We found no association between dementia diagnosis and costs of evidence-based non-medication treatment or total healthcare cost (€ 7,346 for diagnosed vs. € 6,838 for undiagnosed patients). CONCLUSION: There are no significant differences in total healthcare cost between diagnosed and undiagnosed patients. Dementia diagnosis is beneficial for receiving cost-intensive anti-dementia drug treatments, but is currently insufficient to ensure adequate non-medication treatment for community-dwelling patients.

Neuropsychiatric symptoms in people screened positive for dementia in primary care.

BACKGROUND: Neuropsychiatric symptoms are major determinants for caregiver distress and institutionalization in dementia. Little is known about the prevalence of neuropsychiatric symptoms and their association with use of medication, caregiver distress, and resource utilization in primary care. METHODS: We assessed frequency of neuropsychiatric symptoms in a sample retrieved from a primary care intervention study. Patients were screened for dementia by their primary care physicians. A study nurse assessed neuropsychiatric symptoms in 176 patients using the neuropsychiatric inventory (NPI) through face-to-face interviews by proxy during home visits. In addition, data on global cognition (MMSE), quality of life (QoL-AD), resource utilization in dementia (RUD), caregiver distress (BIS), and use of psychotropic medication in patients were obtained. We used linear mixed effect models taking into account the clustering of patients within general physician practices. RESULTS: Clinically relevant neuropsychiatric symptoms (NPI score ≥ 4) occurred in about 53% of the patients. Higher NPI scores were significantly associated with more severe cognitive impairment, higher caregiver distress, and higher utilization of caregiver resources by patients but not with a formal diagnosis of dementia from the primary care physician. Use of antipsychotics was associated with higher NPI scores, particularly in non-psychotic domains. CONCLUSIONS: Neuropsychiatric symptoms in a primary care cohort screened positive for dementia were associated with resource utilization and distress of caregivers. In contrast to guideline recommendations, the use of antipsychotics was associated with non-psychotic domains of behavioral symptoms. These findings underscore the relevance of neuropsychiatric symptoms for the design of future interventions in primary care.

Antidementia drug treatment in dementia networks in Germany: use rates and factors associated with treatment use.

BACKGROUND: Dementia networks in Germany constitute a specialised setting for integrated dementia care and have shown benefits on relevant outcomes, including those of drug treatment. National guidelines recommend treatment with acetylcholinesterase inhibitors (donepezil, galantamine, rivastigmine) or the N-Methyl-D-Aspartate antagonist (memantine) to reduce cognitive symptoms. However, prescription rates are lower than 30 % in general practises. This study aims to describe antidementia drug treatment and the factors that are associated with the treatment in different dementia networks across Germany. METHODS: We have analysed the socio-demographic, clinical and utilisation data from 560 patients with dementia (PWD), as well as data from their caregivers, in 13 selected dementia networks in Germany. The patients and caregivers were interviewed in their homes or in the network facilities. Multiple logistic regression models were fitted to evaluate the socio-demographic and clinical factors associated with the utilisation of antidementia drug treatment in the various networks. RESULTS: In all of the networks in the study, 52 % of the participants received an antidementia drug treatment. Factors associated with the utilisation of the antidementia drug treatment were: formal diagnosis (OR = 16.81, p < 0.001), association with a physician in the network (OR = 3.69, p < 0.001), higher number of comorbidities (OR = 0.88, p = 0.039), living alone (OR = 0.51, p = 0.032) and higher age (OR = 2.97, p = 0.002). CONCLUSION: Medical treatment of PWD with antidementia drugs in dementia networks in Germany is more frequent than in primary and nursing home care settings. Our findings also suggest that participants with a formal diagnosis and a physician in the network have increased rates of receiving antidementia drug treatments. These findings suggest that dementia networks focusing on medical treatment should support diagnostic procedures and incorporate physician specialists.