Association of polygenic score and the involvement of cholinergic and glutamatergic pathways with lithium treatment response in patients with bipolar disorder.

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium's possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N = 2367) and replicated in the combined PsyCourse (N = 89) and BipoLife (N = 102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P < 0.05. Li+PGS was positively associated with lithium treatment response in the ConLi+Gen cohort, in both the categorical (P = 9.8 × 10-12, R2 = 1.9%) and continuous (P = 6.4 × 10-9, R2 = 2.6%) outcomes. Compared to bipolar patients in the 1st decile of the risk distribution, individuals in the 10th decile had 3.47-fold (95%CI: 2.22-5.47) higher odds of responding favorably to lithium. The results were replicated in the independent cohorts for the categorical treatment outcome (P = 3.9 × 10-4, R2 = 0.9%), but not for the continuous outcome (P = 0.13). Gene-based analyses revealed 36 candidate genes that are enriched in biological pathways controlled by glutamate and acetylcholine. Li+PGS may be useful in the development of pharmacogenomic testing strategies by enabling a classification of bipolar patients according to their response to treatment.

Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach.

BACKGROUND: Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment. AIMS: To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder. METHOD: This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework. RESULTS: The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data. CONCLUSIONS: Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Association of polygenic score for major depression with response to lithium in patients with bipolar disorder.

Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18-2.01) and European sample: OR = 1.75 (95% CI: 1.30-2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61-4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.

A vulnerable residential environment is associated with higher risk of mortality and early transition to permanent residential aged care for community dwelling older South Australians.

OBJECTIVES: This study examined the impact of the residential environment, measured by the Healthy Ageing/Vulnerable ENvironment (HAVEN) Index, on risk of mortality or entry into Permanent Residential Aged Care (PRAC). DESIGN: A retrospective cohort study using data from the Registry of Senior Australians (ROSA) was conducted. HAVEN Index values were matched to the ROSA by residential postcode. STUDY SETTING AND PARTICIPANTS: Older individuals living in metropolitan Adelaide and receiving their first eligibility assessment for aged care services between 2014 and 2016 (N = 16,944). MAIN OUTCOME MEASURE: Time to death and entry into PRAC were the main outcomes. RESULTS: A higher HAVEN Index value, which represents a favourable residential environment, was associated with a lower risk of mortality and delayed entry to PRAC. For every 0.1 unit increase in HAVEN Index value, the risk of mortality is 3% lower (adjusted hazard ratio [HR], 95% confidence interval [CI] = 0.97, 0.96-0.99) and the risk of entry to PRAC is 5% lower (adjusted subdistribution HR, 95%CI = 0.95, 0.94-0.97) in the first 2 years following aged care assessment. After 2 years, the HAVEN Index was not associated with the risk of transition to PRAC. CONCLUSION: Place-based health inequalities were identified in Australians seeking aged care services, demonstrating that a better understanding of local neighbourhoods may provide insight into addressing ageing inequalities. Spatial indexes, such as the HAVEN Index, are useful tools to identify areas where populations are more vulnerable to adverse health outcomes, informing responses to prioritise local improvements and health interventions to enable healthy ageing.

Bivariate genome-wide association analyses of the broad depression phenotype combined with major depressive disorder, bipolar disorder or schizophrenia reveal eight novel genetic loci for depression.

Although a genetic basis of depression has been well established in twin studies, identification of genome-wide significant loci has been difficult. We hypothesized that bivariate analyses of findings from a meta-analysis of genome-wide association studies (meta-GWASs) of the broad depression phenotype with those from meta-GWASs of self-reported and recurrent major depressive disorder (MDD), bipolar disorder and schizophrenia would enhance statistical power to identify novel genetic loci for depression. LD score regression analyses were first used to estimate the genetic correlations of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia. Then, we performed four bivariate GWAS analyses. The genetic correlations (rg ± SE) of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia were 0.79 ± 0.07, 0.24 ± 0.08, 0.53 ± 0.09 and 0.57 ± 0.05, respectively. From a total of 20 independent genome-wide significant loci, 13 loci replicated of which 8 were novel for depression. These were MUC21 for the broad depression phenotype with self-reported MDD and ZNF804A, MIR3143, PSORS1C2, STK19, SPATA31D1, RTN1 and TCF4 for the broad depression phenotype with schizophrenia. Post-GWAS functional analyses of these loci revealed their potential biological involvement in psychiatric disorders. Our results emphasize the genetic similarities among different psychiatric disorders and indicate that cross-disorder analyses may be the best way forward to accelerate gene finding for depression, or psychiatric disorders in general.

Cortical surface area alterations shaped by genetic load for neuroticism.

Neuroticism has been shown to act as an important risk factor for major depressive disorder (MDD). Genetic and neuroimaging research has independently revealed biological correlates of neurotic personality including cortical alterations in brain regions of high relevance for affective disorders. Here we investigated the influence of a polygenic score for neuroticism (PGS) on cortical brain structure in a joint discovery sample of n = 746 healthy controls (HC) and n = 268 MDD patients. Findings were validated in an independent replication sample (n = 341 HC and n = 263 MDD). Subgroup analyses stratified for case-control status and analyses of associations between neurotic phenotype and cortical measures were carried out. PGS for neuroticism was significantly associated with a decreased cortical surface area of the inferior parietal cortex, the precuneus, the rostral cingulate cortex and the inferior frontal gyrus in the discovery sample. Similar associations between PGS and surface area of the inferior parietal cortex and the precuneus were demonstrated in the replication sample. Subgroup analyses revealed negative associations in the latter regions between PGS and surface area in both HC and MDD subjects. Neurotic phenotype was negatively correlated with surface area in similar cortical regions including the inferior parietal cortex and the precuneus. No significant associations between PGS and cortical thickness were detected. The morphometric overlap of associations between both PGS and neurotic phenotype in similar cortical regions closely related to internally focused cognition points to the potential relevance of genetically shaped cortical alterations in the development of neuroticism.

General practitioner conduct of clinical services representing comprehensive geriatric assessment is associated with lower risk of mortality in older Australians receiving home care packages.

OBJECTIVES: The purpose of this paper is to investigate the utilisation of general practice Medicare Benefit Schedule (MBS) services aligned to Comprehensive Geriatric Assessment (CGA) within 6 months of an aged care eligibility assessment and its effects on mortality and transition to permanent residential aged care (PRAC). DESIGN: Retrospective cohort study from the Historical Cohort of the Registry of Senior Australians. SETTING: Community. PARTICIPANTS: In total, 69,171 Individuals (aged 75+) receiving home care packages (HCPs) between 2011 and 2015. OUTCOME MEASURES: Mortality and transition to PRAC. RESULTS: The claims for a management plan with team care arrangement (TCA) within 3 months of the health assessment (i.e. CGA) was present in 5% and associated with 14% lower mortality (adjusted hazard ratio [aHR], 95%CI = 0.86, 0.80-0.93) compared to no claims, lower than that seen with partial CGA which was either health assessment claims only 7.0% (aHR, 95%CI = 0.93, 0.89-0.97) or management plan coupled with TCA claims only 9.0% (aHR, 95%CI = 0.91, 0.89-0.97). This pattern was seen in those frailer but not in those where the frailty index score was <0.21. Claims for management plans coupled with TCAs alone were associated with a 10% lower transition to PRAC (asHR, 95%CI = 0.90, 0.85-0.96) in those with FI score < 0.21 while this estimate was not significant in individuals with FI score ≥ 0.21. CONCLUSION: It appears the conduct of a combination of interventions considered to be components of the CGA by GPs was associated with a lower risk of mortality that no claims or partial conduct of CGA.

Utilisation of general practice health assessments around an aged care assessment is associated with lower mortality risk in older Australians.

OBJECTIVE: (i) to describe the general practitioner utilisation of health assessments, management plans, coordination of team care arrangements and medication review item numbers within 6 months of an aged care eligibility assessment for home care packages (HCP) and (ii) investigate the impact of health assessments on the risk of mortality and entry into permanent residential aged care (PRAC) of individuals accessing HCP. DESIGN AND SETTING: retrospective cohort study utilising data from the Registry of Senior Australians (ROSA) was conducted. SUBJECTS: 75,172 individuals aged ≥75 years who received HCP between 2011 and 2015. OUTCOME MEASURE: for objective 1: the use of comprehensive assessments (Medicare Benefits Schedule (MBS) items 705 or 707), management plans (MBS 721), coordination of team care arrangements (MBS 723), and medication reviews (MBS 900). For objective 2: time to death and entry into PRAC. RESULTS: of the 75,172 individuals, 28.2% (95% confidence interval (CI): 27.8-8.5%) had comprehensive assessments, 36.7% (95% CI: 36.3-37.0%) had management plans, 33.0% (95% CI: 32.7-33.3%) received coordination of team care arrangements and 5.4% (95% CI: 5.2-5.5%) had medication reviews. Individuals with a comprehensive assessment had a 5% lower risk of mortality (adjusted hazard ratio (aHR), 95% CI = 0.95, 0.92-0.98) but 5% higher risk of transition to PRAC (adjusted subdistribution HRs, 95% CI = 1.05, 1.02-1.08) compared to those who did not have these services. CONCLUSION: the utilisation of health assessments was associated with a lower risk of mortality. There is an opportunity for increased use of item numbers in frailer individuals.

Review and Consensus on Pharmacogenomic Testing in Psychiatry.

The implementation of pharmacogenomic (PGx) testing in psychiatry remains modest, in part due to divergent perceptions of the quality and completeness of the evidence base and diverse perspectives on the clinical utility of PGx testing among psychiatrists and other healthcare providers. Recognizing the current lack of consensus within the field, the International Society of Psychiatric Genetics assembled a group of experts to conduct a narrative synthesis of the PGx literature, prescribing guidelines, and product labels related to psychotropic medications as well as the key considerations and limitations related to the use of PGx testing in psychiatry. The group concluded that to inform medication selection and dosing of several commonly-used antidepressant and antipsychotic medications, current published evidence, prescribing guidelines, and product labels support the use of PGx testing for 2 cytochrome P450 genes (CYP2D6, CYP2C19). In addition, the evidence supports testing for human leukocyte antigen genes when using the mood stabilizers carbamazepine (HLA-A and HLA-B), oxcarbazepine (HLA-B), and phenytoin (CYP2C9, HLA-B). For valproate, screening for variants in certain genes (POLG, OTC, CSP1) is recommended when a mitochondrial disorder or a urea cycle disorder is suspected. Although barriers to implementing PGx testing remain to be fully resolved, the current trajectory of discovery and innovation in the field suggests these barriers will be overcome and testing will become an important tool in psychiatry.

Health Effects of Overweight and Obesity in 195 Countries over 25 Years.

BACKGROUND: Although the rising pandemic of obesity has received major attention in many countries, the effects of this attention on trends and the disease burden of obesity remain uncertain. METHODS: We analyzed data from 68.5 million persons to assess the trends in the prevalence of overweight and obesity among children and adults between 1980 and 2015. Using the Global Burden of Disease study data and methods, we also quantified the burden of disease related to high body-mass index (BMI), according to age, sex, cause, and BMI in 195 countries between 1990 and 2015. RESULTS: In 2015, a total of 107.7 million children and 603.7 million adults were obese. Since 1980, the prevalence of obesity has doubled in more than 70 countries and has continuously increased in most other countries. Although the prevalence of obesity among children has been lower than that among adults, the rate of increase in childhood obesity in many countries has been greater than the rate of increase in adult obesity. High BMI accounted for 4.0 million deaths globally, nearly 40% of which occurred in persons who were not obese. More than two thirds of deaths related to high BMI were due to cardiovascular disease. The disease burden related to high BMI has increased since 1990; however, the rate of this increase has been attenuated owing to decreases in underlying rates of death from cardiovascular disease. CONCLUSIONS: The rapid increase in the prevalence and disease burden of elevated BMI highlights the need for continued focus on surveillance of BMI and identification, implementation, and evaluation of evidence-based interventions to address this problem. (Funded by the Bill and Melinda Gates Foundation.).

Development and validation of a frailty index based on Australian Aged Care Assessment Program data.

OBJECTIVES: To develop and validate a frailty index, derived from aged care eligibility assessment data. DESIGN: Retrospective cohort study; analysis of the historical national cohort of the Registry of Senior Australians (ROSA). PARTICIPANTS: 903 996 non-Indigenous Australians aged 65 years or more, living in the community and assessed for subsidised aged care eligibility during 2003-2013. MAIN OUTCOME MEASURES: 44-item frailty index; summary statistics for frailty index score distribution; predictive validity with respect to mortality and entry into permanent residential aged care during the five years after assessment. RESULTS: The mean frailty index score during 2003-2013 was 0.20 (SD, 0.07; range, 0-0.41); the proportion of assessed older people with scores exceeding 0.20 increased from 32.1% in 2003-2005 to 75.0% in 2012-2013. The risks of death and entry into permanent residential aged care at one, three and five years increased with frailty index score level (at one year, high [over 0.35] v low scores [under 0.05]: hazard ratio for death, 5.99; 95% CI, 5.69-6.31; for entry into permanent residential aged care, 8.70; 95% CI, 8.32-9.11). The predictive validity (area under the receiver operating characteristic curve) of Cox proportional hazard models including age, sex, and frailty index score was 0.64 (95% CI, 0.63-0.64) for death and 0.63 (95% CI, 0.62-0.63) for entry into permanent residential aged care within one year of assessment. CONCLUSIONS: We used Australian aged care eligibility assessment program data to construct and validate a frailty index. It can be employed in aged care research in Australia, but its application to aged care planning requires further investigation.

The prevalence, trends and determinants of mental health disorders in older Australians living in permanent residential aged care: Implications for policy and quality of aged care services.

OBJECTIVE: Mental health disorders are a major health concern in older people and are associated with a higher risk of disability, frailty and early mortality. This study aimed to conduct a contemporary population-based assessment of the prevalence, trends and factors associated with mental health disorders in individuals who are living in permanent residential aged care (PRAC) in Australia. METHODS: A retrospective cross-sectional study was conducted using national data from the Registry of Senior Australians, a national cohort of older Australians who had aged care eligibility assessment and entered PRAC between 2008 and 2016. Stepwise multivariate logistic regression modeling was applied to identify factors associated with mental health disorders. RESULTS: Of 430,862 individuals included in this study, 57.8% had at least one mental health disorder. The prevalence of depression, phobia/anxiety and psychosis were as follows: 46.2% (95% confidence interval = [46.0%, 46.3%]), 14.9% (95% confidence interval = [14.8%, 15.0%]) and 9.7% (95% confidence interval = [9.6%, 9.8%]), respectively. The likelihood of having a mental health disorder was higher for those who were (adjusted odds ratio [95% confidence interval]) relatively younger, specifically for every 10-year increment in age, the odds of having mental health disorders was 44.0% lower (0.56, [0.55, 0.56]); female (1.33 [1.32, 1.35]); having increasing numbers of physical health comorbidities, 6-10 (1.26 [1.24, 1.29]) or 11-15 (1.48 [1.45, 1.51]) or more than 15 (1.64 [1.58, 1.71]) compared to people having less than five comorbidities; having limitations related to health care tasks (1.05 [1.04, 1.07]), meals (1.04 [1.02, 1.05]) or social and community participation (1.10 [1.08, 1.12]). CONCLUSION: The burden of mental health disorders in older Australians living in PRAC was high and individuals with these conditions tend to be younger, with several physical comorbidities and/or functional limitations. Understanding the profile of individuals with mental health disorders at entry into PRAC can be used as evidence for baseline resource allocation for this population and evaluation of future needs of mental health services.

The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression.

Selective serotonin reuptake inhibitors (SSRIs) are first-line antidepressants for the treatment of major depressive disorder (MDD). However, treatment response during an initial therapeutic trial is often poor and is difficult to predict. Heterogeneity of response to SSRIs in depressed patients is partly driven by co-occurring somatic disorders such as coronary artery disease (CAD) and obesity. CAD and obesity may also be associated with metabolic side effects of SSRIs. In this study, we assessed the association of CAD and obesity with treatment response to SSRIs in patients with MDD using a polygenic score (PGS) approach. Additionally, we performed cross-trait meta-analyses to pinpoint genetic variants underpinnings the relationship of CAD and obesity with SSRIs treatment response. First, PGSs were calculated at different p value thresholds (PT) for obesity and CAD. Next, binary logistic regression was applied to evaluate the association of the PGSs to SSRIs treatment response in a discovery sample (ISPC, N = 865), and in a replication cohort (STAR*D, N = 1,878). Finally, a cross-trait GWAS meta-analysis was performed by combining summary statistics. We show that the PGSs for CAD and obesity were inversely associated with SSRIs treatment response. At the most significant thresholds, the PGS for CAD and body mass index accounted 1.3%, and 0.8% of the observed variability in treatment response to SSRIs, respectively. In the cross-trait meta-analyses, we identified (1) 14 genetic loci (including NEGR1, CADM2, PMAIP1, PARK2) that are associated with both obesity and SSRIs treatment response; (2) five genetic loci (LINC01412, PHACTR1, CDKN2B, ATXN2, KCNE2) with effects on CAD and SSRIs treatment response. Our findings implicate that the genetic variants of CAD and obesity are linked to SSRIs treatment response in MDD. A better SSRIs treatment response might be achieved through a stratified allocation of treatment for MDD patients with a genetic risk for obesity or CAD.

High burden of birthweight-lowering genetic variants in Africans and Asians.

BACKGROUND: Birthweight is an important predictor of infant morbidity and mortality, and is associated with cardiovascular diseases, obesity, and diabetes in childhood and adulthood. Birthweight and fetal growth show regional and population variations even under similar maternal conditions, and a large proportion of these differences are not explained by environmental factors. Whether and to what extent population genetic variations at key birthweight-associated loci account for the residual birthweight disparities not explained by environmental determinants is unknown. We hypothesized that the cumulative burden of genetic variants with a birthweight-lowering effect (GRB) is different among ancestrally diverse populations. METHODS: Genotype data were extracted from phase 3 of the 1000 Genomes Project for 2504 participants from 26 global populations grouped into five super-populations. GRB was calculated in offspring as the weighted sum of the number of birthweight-lowering genetic variants of 59 autosomal single-nucleotide polymorphisms associated with birthweight, and comparisons were made between Europeans and non-Europeans. RESULTS: GRB was significantly higher in Africans (mean difference 3.15; 95% confidence interval 2.64, 3.66), admixed Americans (3.02; 2.34, 3.70), East Asians (2.85; 2.29, 3.41), and South Asians (1.07; 0.49, 1.65) compared to Europeans. Birthweight-lowering genetic variants in Africans and East Asians were enriched for rare and frequency-fixed alleles (P < 0.001). African and Asian populations had the greatest deviation from the expectation of the common disease-common variant hyothesis. Compared to Europeans, the GRB of ancestral alleles was significantly higher and that of derived alleles was significantly lower in non-Europeans (P < 0.001). CONCLUSIONS: The burden of birthweight-lowering genetic variants is higher in Africans and East Asians. This finding is consistent with the high incidence of low birthweight in the two populations. The genetic variants we studied may not be causal and the extent to which they tag the causal variants in non-Europeans is unknown; however, our findings highlight that genetic variations contribute to population differences in birthweight.

Bivariate Genome-Wide Association Study of Depressive Symptoms With Type 2 Diabetes and Quantitative Glycemic Traits.

OBJECTIVE: Shared genetic background may explain phenotypic associations between depression and Type 2 diabetes (T2D). We aimed to study, on a genome-wide level, if genetic correlation and pleiotropic loci exist between depressive symptoms and T2D or glycemic traits. METHODS: We estimated single-nucleotide polymorphism (SNP)-based heritability and analyzed genetic correlation between depressive symptoms and T2D and glycemic traits with the linkage disequilibrium score regression by combining summary statistics of previously conducted meta-analyses for depressive symptoms by CHARGE consortium (N = 51,258), T2D by DIAGRAM consortium (N = 34,840 patients and 114,981 controls), fasting glucose, fasting insulin, and homeostatic model assessment of ß-cell function and insulin resistance by MAGIC consortium (N = 58,074). Finally, we investigated pleiotropic loci using a bivariate genome-wide association study approach with summary statistics from genome-wide association study meta-analyses and reported loci with genome-wide significant bivariate association p value (p < 5 × 10). Biological annotation and function of significant pleiotropic SNPs were assessed in several databases. RESULTS: The SNP-based heritability ranged from 0.04 to 0.10 in each individual trait. In the linkage disequilibrium score regression analyses, depressive symptoms showed no significant genetic correlation with T2D or glycemic traits (p > 0.37). However, we identified pleiotropic genetic variations for depressive symptoms and T2D (in the IGF2BP2, CDKAL1, CDKN2B-AS, and PLEKHA1 genes), and fasting glucose (in the MADD, CDKN2B-AS, PEX16, and MTNR1B genes). CONCLUSIONS: We found no significant overall genetic correlations between depressive symptoms, T2D, or glycemic traits suggesting major differences in underlying biology of these traits. However, several potential pleiotropic loci were identified between depressive symptoms, T2D, and fasting glucose, suggesting that previously established phenotypic associations may be partly explained by genetic variation in these specific loci.

Burden of disease attributable to suboptimal diet, metabolic risks and low physical activity in Ethiopia and comparison with Eastern sub-Saharan African countries, 1990-2015: findings from the Global Burden of Disease Study 2015.

BACKGROUND: Twelve of the 17 Sustainable Development Goals (SDGs) are related to malnutrition (both under- and overnutrition), other behavioral, and metabolic risk factors. However, comparative evidence on the impact of behavioral and metabolic risk factors on disease burden is limited in sub-Saharan Africa (SSA), including Ethiopia. Using data from the Global Burden of Disease (GBD) Study, we assessed mortality and disability-adjusted life years (DALYs) attributable to child and maternal undernutrition (CMU), dietary risks, metabolic risks and low physical activity for Ethiopia. The results were compared with 14 other Eastern SSA countries. METHODS: Databases from GBD 2015, that consist of data from 1990 to 2015, were used. A comparative risk assessment approach was utilized to estimate the burden of disease attributable to CMU, dietary risks, metabolic risks and low physical activity. Exposure levels of the risk factors were estimated using spatiotemporal Gaussian process regression (ST-GPR) and Bayesian meta-regression models. RESULTS: In 2015, there were 58,783 [95% uncertainty interval (UI): 43,653-76,020] or 8.9% [95% UI: 6.1-12.5] estimated all-cause deaths attributable to CMU, 66,269 [95% UI: 39,367-106,512] or 9.7% [95% UI: 7.4-12.3] to dietary risks, 105,057 [95% UI: 66,167-157,071] or 15.4% [95% UI: 12.8-17.6] to metabolic risks and 5808 [95% UI: 3449-9359] or 0.9% [95% UI: 0.6-1.1] to low physical activity in Ethiopia. While the age-adjusted proportion of all-cause mortality attributable to CMU decreased significantly between 1990 and 2015, it increased from 10.8% [95% UI: 8.8-13.3] to 14.5% [95% UI: 11.7-18.0] for dietary risks and from 17.0% [95% UI: 15.4-18.7] to 24.2% [95% UI: 22.2-26.1] for metabolic risks. In 2015, Ethiopia ranked among the top four countries (of 15 Eastern SSA countries) in terms of mortality and DALYs based on the age-standardized proportion of disease attributable to dietary and metabolic risks. CONCLUSIONS: In Ethiopia, while there was a decline in mortality and DALYs attributable to CMU over the last two and half decades, the burden attributable to dietary and metabolic risks have increased during the same period. Lifestyle and metabolic risks of NCDs require more attention by the primary health care system of the country.

The prevalence and burden of mental and substance use disorders in Australia: Findings from the Global Burden of Disease Study 2015.

OBJECTIVES: Timely and accurate assessments of disease burden are essential for developing effective national health policies. We used the Global Burden of Disease Study 2015 to examine burden due to mental and substance use disorders in Australia. METHODS: For each of the 20 mental and substance use disorders included in Global Burden of Disease Study 2015, systematic reviews of epidemiological data were conducted, and data modelled using a Bayesian meta-regression tool to produce prevalence estimates by age, sex, geography and year. Prevalence for each disorder was then combined with a disorder-specific disability weight to give years lived with disability, as a measure of non-fatal burden. Fatal burden was measured as years of life lost due to premature mortality which were calculated by combining the number of deaths due to a disorder with the life expectancy remaining at the time of death. Disability-adjusted life years were calculated by summing years lived with disability and years of life lost to give a measure of total burden. Uncertainty was calculated around all burden estimates. RESULTS: Mental and substance use disorders were the leading cause of non-fatal burden in Australia in 2015, explaining 24.3% of total years lived with disability, and were the second leading cause of total burden, accounting for 14.6% of total disability-adjusted life years. There was no significant change in the age-standardised disability-adjusted life year rates for mental and substance use disorders from 1990 to 2015. CONCLUSION: Global Burden of Disease Study 2015 found that mental and substance use disorders were leading contributors to disease burden in Australia. Despite several decades of national reform, the burden of mental and substance use disorders remained largely unchanged between 1990 and 2015. To reduce this burden, effective population-level preventions strategies are required in addition to effective interventions of sufficient duration and coverage.

Incidence, prevalence and mortality rates of malaria in Ethiopia from 1990 to 2015: analysis of the global burden of diseases 2015.

BACKGROUND: In Ethiopia there is no complete registration system to measure disease burden and risk factors accurately. In this study, the 2015 global burden of diseases, injuries and risk factors (GBD) data were used to analyse the incidence, prevalence and mortality rates of malaria in Ethiopia over the last 25 years. METHODS: GBD 2015 used verbal autopsy surveys, reports, and published scientific articles to estimate the burden of malaria in Ethiopia. Age and gender-specific causes of death for malaria were estimated using cause of death ensemble modelling. RESULTS: The number of new cases of malaria declined from 2.8 million [95% uncertainty interval (UI) 1.4-4.5 million] in 1990 to 621,345 (95% UI 462,230-797,442) in 2015. Malaria caused an estimated 30,323 deaths (95% UI 11,533.3-61,215.3) in 1990 and 1561 deaths (95% UI 752.8-2660.5) in 2015, a 94.8% reduction over the 25 years. Age-standardized mortality rate of malaria has declined by 96.5% between 1990 and 2015 with an annual rate of change of 13.4%. Age-standardized malaria incidence rate among all ages and gender declined by 88.7% between 1990 and 2015. The number of disability-adjusted life years lost (DALY) due to malaria decreased from 2.2 million (95% UI 0.76-4.7 million) in 1990 to 0.18 million (95% UI 0.12-0.26 million) in 2015, with a total reduction 91.7%. Similarly, age-standardized DALY rate declined by 94.8% during the same period. CONCLUSIONS: Ethiopia has achieved a 50% reduction target of malaria of the millennium development goals. The country should strengthen its malaria control and treatment strategies to achieve the sustainable development goals.

National mortality burden due to communicable, non-communicable, and other diseases in Ethiopia, 1990-2015: findings from the Global Burden of Disease Study 2015.

BACKGROUND: Ethiopia lacks a complete vital registration system that would assist in measuring disease burden and risk factors. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) estimates to describe the mortality burden from communicable, non-communicable, and other diseases in Ethiopia over the last 25 years. METHODS: GBD 2015 mainly used cause of death ensemble modeling to measure causes of death by age, sex, and year for 195 countries. We report numbers of deaths and rates of years of life lost (YLL) for communicable, maternal, neonatal, and nutritional (CMNN) disorders, non-communicable diseases (NCDs), and injuries with 95% uncertainty intervals (UI) for Ethiopia from 1990 to 2015. RESULTS: CMNN causes of death have declined by 65% in the last two-and-a-half decades. Injury-related causes of death have also decreased by 70%. Deaths due to NCDs declined by 37% during the same period. Ethiopia showed a faster decline in the burden of four out of the five leading causes of age-standardized premature mortality rates when compared to the overall sub-Saharan African region and the Eastern sub-Saharan African region: lower respiratory infections, tuberculosis, HIV/AIDS, and diarrheal diseases; however, the same could not be said for ischemic heart disease and other NCDs. Non-communicable diseases, together, were the leading causes of age-standardized mortality rates, whereas CMNN diseases were leading causes of premature mortality in 2015. Although lower respiratory infections, tuberculosis, and diarrheal disease were the leading causes of age-standardized death rates, they showed major declines from 1990 to 2015. Neonatal encephalopathy, iron-deficiency anemia, protein-energy malnutrition, and preterm birth complications also showed more than a 50% reduction in burden. HIV/AIDS-related deaths have also decreased by 70% since 2005. Ischemic heart disease, hemorrhagic stroke, and ischemic stroke were among the top causes of premature mortality and age-standardized death rates in Ethiopia in 2015. CONCLUSIONS: Ethiopia has been successful in reducing deaths related to communicable, maternal, neonatal, and nutritional deficiency diseases and injuries by 65%, despite unacceptably high maternal and neonatal mortality rates. However, the country's performance regarding non-communicable diseases, including cardiovascular disease, diabetes, cancer, and chronic respiratory disease, was minimal, causing these diseases to join the leading causes of premature mortality and death rates in 2015. While the country is progressing toward universal health coverage, prevention and control strategies in Ethiopia should consider the double burden of common infectious diseases and non-communicable diseases: lower respiratory infections, diarrhea, tuberculosis, HIV/AIDS, cardiovascular disease, cancer, and diabetes. Prevention and control strategies should also pay special attention to the leading causes of premature mortality and death rates caused by non-communicable diseases: cardiovascular disease, cancer, and diabetes. Measuring further progress requires a data revolution in generating, managing, analyzing, and using data for decision-making and the creation of a full vital registration system in the country.