Uncovering predictors of low hippocampal volume: Evidence from a large-scale machine-learning-based study in the UK Biobank.

INTRODUCTION: Hippocampal atrophy is an established biomarker for conversion from the normal ageing process to developing cognitive impairment and dementia. This study used a novel hypothesis-free machine-learning approach, to uncover potential risk factors of lower hippocampal volume using information from the world's largest brain imaging study. METHODS: A combination of machine learning and conventional statistical methods were used to identify predictors of low hippocampal volume. We run gradient boosting decision tree modelling including 2891 input features measured before magnetic resonance imaging assessments (median 9.2 years, range 4.2-13.8 years) using data from 42,152 dementia-free UK Biobank participants. Logistic regression analyses were run on 87 factors identified as important for prediction based on Shapley values. False discovery rate adjusted P-value <0.05 was used to declare statistical significance. RESULTS: Older age, male sex, greater height, and whole-body fat free mass were the main predictors of low hippocampal volume with the model also identifying associations with lung function and lifestyle factors including smoking, physical activity, and coffee intake (corrected P<0.05 for all). Red blood cell count and several red blood cell indices such as haemoglobin concentration, mean corpuscular haemoglobin, mean corpuscular volume, mean reticulocyte volume, mean sphered cell volume, and red blood cell distribution width were among many biomarkers associated with low hippocampal volume. CONCLUSION: Lifestyles, physical measures, and biomarkers may affect hippocampal volume, with many of the characteristics potentially reflecting oxygen supply to the brain. Further studies are required to establish causality and clinical relevance of these findings.

Alcohol consumption and the risk of all-cause and cause-specific mortality-a linear and nonlinear Mendelian randomization study.

BACKGROUND: Many observational studies support light-to-moderate alcohol intake as potentially protective against premature death. We used a genetic approach to evaluate the linear and nonlinear relationships between alcohol consumption and mortality from different underlying causes. METHODS: We used data from 278 093 white-British UK Biobank participants, aged 37-73 years at recruitment and with data on alcohol intake, genetic variants, and mortality. Habitual alcohol consumption was instrumented by 94 variants. Linear Mendelian randomization (MR) analyses were conducted using five complementary approaches, and nonlinear MR analyses by the doubly-ranked method. RESULTS: There were 20 834 deaths during the follow-up (median 12.6 years). In conventional analysis, the association between alcohol consumption and mortality outcomes was 'J-shaped'. In contrast, MR analyses supported a positive linear association with premature mortality, with no evidence for curvature (Pnonlinearity ≥ 0.21 for all outcomes). The odds ratio [OR] for each standard unit increase in alcohol intake was 1.27 (95% confidence interval [CI] 1.16-1.39) for all-cause mortality, 1.30 (95% CI 1.10-1.53) for cardiovascular disease, 1.20 (95% CI 1.08-1.33) for cancer, and 2.06 (95% CI 1.36-3.12) for digestive disease mortality. These results were consistent across pleiotropy-robust methods. There was no clear evidence for an association between alcohol consumption and mortality from respiratory diseases or COVID-19 (1.32, 95% CI 0.96-1.83 and 1.46, 95% CI 0.99-2.16, respectively; Pnonlinearity ≥ 0.21). CONCLUSION: Higher levels of genetically predicted alcohol consumption had a strong linear association with an increased risk of premature mortality with no evidence for any protective benefit at modest intake levels.

Phenome-wide association study of ovarian cancer identifies common comorbidities and reveals shared genetics with complex diseases and biomarkers.

BACKGROUND: Ovarian cancer (OC) is commonly diagnosed among older women who have comorbidities. This hypothesis-free phenome-wide association study (PheWAS) aimed to identify comorbidities associated with OC, as well as traits that share a genetic architecture with OC. METHODS: We used data from 181,203 white British female UK Biobank participants and analysed OC and OC subtype-specific genetic risk scores (OC-GRS) for an association with 889 diseases and 43 other traits. We conducted PheWAS and colocalization analyses for individual variants to identify evidence for shared genetic architecture. RESULTS: The OC-GRS was associated with 10 diseases, and the clear cell OC-GRS was associated with five diseases at the FDR threshold (p = 5.6 × 10-4 ). Mendelian randomizaiton analysis (MR) provided robust evidence for the association of OC with higher risk of "secondary malignant neoplasm of digestive systems" (OR 1.64, 95% CI 1.33, 2.02), "ascites" (1.48, 95% CI 1.17, 1.86), "chronic airway obstruction" (1.17, 95% CI 1.07, 1.29), and "abnormal findings on examination of the lung" (1.51, 95% CI 1.22, 1.87). Analyses of lung spirometry measures provided further support for compromised respiratory function. PheWAS on individual OC variants identified five genetic variants associated with other diseases, and seven variants associated with biomarkers (all, p ≤ 4.5 × 10-8 ). Colocalization analysis identified rs4449583 (from TERT locus) as the shared causal variant for OC and seborrheic keratosis. CONCLUSIONS: OC is associated with digestive and respiratory comorbidities. Several variants affecting OC risk were associated with other diseases and biomarkers, with this study identifying a novel genetic locus shared between OC and skin conditions.

Hypothesis-free discovery of novel cancer predictors using machine learning.

BACKGROUND: Cancer is a leading cause of morbidity and mortality worldwide, and better understanding of the risk factors could enhance prevention. METHODS: We conducted a hypothesis-free analysis combining machine learning and statistical approaches to identify cancer risk factors from 2828 potential predictors captured at baseline. There were 459,169 UK Biobank participants free from cancer at baseline and 48,671 new cancer cases during the 10-year follow-up. Logistic regression models adjusted for age, sex, ethnicity, education, material deprivation, smoking, alcohol intake, body mass index and skin colour (as a proxy for sun sensitivity) were used for obtaining adjusted odds ratios, with continuous predictors presented using quintiles (Q). RESULTS: In addition to smoking, older age and male sex, positively associating features included several anthropometric characteristics, whole body water mass, pulse, hypertension and biomarkers such as urinary microalbumin (Q5 vs. Q1 OR 1.16, 95% CI = 1.13-1.19), C-reactive protein (Q5 vs. Q1 OR 1.20, 95% CI = 1.16-1.24) and red blood cell distribution width (Q5 vs. Q1 OR 1.18, 95% CI = 1.14-1.21), among others. High-density lipoprotein cholesterol (Q5 vs. Q1 OR 0.84, 95% CI = 0.81-0.87) and albumin (Q5 vs. Q1 OR 0.84, 95% CI = 0.81-0.87) were inversely associated with cancer. In sex-stratified analyses, higher testosterone increased the risk in females but not in males (Q5 vs. Q1 ORfemales 1.23, 95% CI = 1.17-1.30). Phosphate was associated with a lower risk in females but a higher risk in males (Q5 vs. Q1 ORfemales 0.94, 95% CI = 0.90-0.99 vs. ORmales 1.09, 95% CI 1.04-1.15). CONCLUSIONS: This hypothesis-free analysis suggests personal characteristics, metabolic biomarkers, physical measures and smoking as important predictors of cancer risk, with further studies needed to confirm causality and clinical relevance.

Genetically instrumented LDL-cholesterol lowering and multiple disease outcomes: A Mendelian randomization phenome-wide association study in the UK Biobank.

AIMS: Lipid-lowering medications are widely used to control blood cholesterol levels and manage a range of cardiovascular and lipid disorders. We aimed to explore the possible associations between LDL lowering and multiple disease outcomes or biomarkers. METHODS: We performed a Mendelian randomization phenome-wide association study (MR-PheWAS) in 337 475 UK Biobank participants to test for associations between four proposed LDL-C-lowering genetic risk scores (PCSK9, HMGCR, NPC1L1 and LDLR) and 1135 disease outcomes, with follow-up MR analyses in 52 serum, urine, imaging and clinical biomarkers. We used inverse-variance weighted MR in the main analyses and complementary MR methods (weighted median, weighted mode, MR-Egger and MR-PRESSO) as sensitivity analyses. We accounted for multiple testing with false discovery rate correction (P < 2.0 × 10-4 for phecodes, P < 1.3 × 10-2 for biomarkers). RESULTS: We found evidence for an association between genetically instrumented LDL lowering and 10 distinct disease outcomes, suggesting potential causality. All genetic instruments were associated with hyperlipidaemias and cardiovascular diseases in the expected directions. Biomarker analyses supported an effect of LDL-C lowering through PCSK9 on lung function (FEV [beta per 1 mg/dL lower LDL-C -1.49, 95% CI -2.21, -0.78]; FVC [-1.42, 95% CI -2.29, -0.54]) and through HMGCR on hippocampal volume (beta per 1 mg/dL lower LDL-C 6.09, 95% CI 1.74, 10.44). CONCLUSIONS: We found genetic evidence to support both positive and negative effects of LDL-C lowering through all four LDL-C-lowering pathways. Future studies should further explore the effects of LDL-C lowering on lung function and changes in brain volume.

Schizophrenia and co-morbidity risk: Evidence from a data driven phenomewide association study.

Schizophrenia is a chronic debilitating psychiatric disorder with significant morbidity and mortality. In this study, we used information from 337,484 UK Biobank participants and performed PheWAS using schizophrenia genetic risk score on 1135 disease outcomes. Signals that passed the false discovery rate threshold were further analyzed for evidence on the causality of the association. We extended the analysis to 30 serum, four urine, and six neuroimaging biomarkers to identify biomarkers that could be affected by schizophrenia. Schizophrenia GRS was associated with 54 (39 distinct) disease outcomes including schizophrenia in the PheWAS analysis. Of these, a causal association were found with 10 distinct diseases in the MR analysis. Schizophrenia causally linked with higher odds of anxiety (OR = 1.41, 95%CI 1.12 to 1.21), bipolar disorder (OR = 1.52, 95%CI 1.36 to 1.70), major depressive disorder (OR = 1.12, 95%CI 1.08 to 1.16) and suicidal ideation (OR = 1.30, 95%CI 1.19 to 1.42). Lower odds were found for several diseases including type 1 diabetes, coronary atherosclerosis and some musculoskeletal disorders. In analyses using biomarkers, schizophrenia was associated with lower serum 25(OH)D, gamma glutamyltransferase, cystatin C, serum creatinine. However, we did not find association with any of the brain imaging markers. Our analyses confirmed the co-existence of schizophrenia with other mental health disorders but did not otherwise suggest strong effects on disease risk. Biomarker analyses reflected associations which could be explained by unhealthy lifestyles, suggesting patients with schizophrenia may benefit from screening for and managing broader health aspects.

Association Between Metabolically Different Adiposity Subtypes and Osteoarthritis: A Mendelian Randomization Study.

OBJECTIVE: In this Mendelian randomization (MR) study, the objective was to investigate the causal effect of metabolically different adiposity subtypes on osteoarthritis. METHODS: We performed 2-sample MR using summary-level data for osteoarthritis (10,083 cases and 40,425 controls) from a genome-wide association using the UK Biobank, and for site-specific osteoarthritis from the Arthritis Research UK Osteoarthritis Genetics consortium. We used 3 classes of genetic instruments, which all increase body mass index but are associated with different metabolic profiles (unfavorable, neutral, and favorable). Primary analysis was performed using inverse variance weight (IVW), with additional sensitivity analysis from different MR methods. We further applied a nonlinear MR using UK Biobank data to understand the nature of the adiposity-osteoarthritis relationship. RESULTS: Greater metabolically unfavorable and metabolically neutral adiposity were associated with higher odds of osteoarthritis (IVW odds ratio [OR] 1.56 [95% confidence interval (95% CI) 1.31, 1.85] and OR 1.60 [95% CI 1.15, 2.23], respectively). The estimate for the association between metabolically favorable adiposity and osteoarthritis was similar, although with notable imprecision (OR 1.55 [95% CI 0.70, 3.41]). Using site-specific osteoarthritis, metabolically unfavorable, neutral, and favorable adiposity were all associated with higher odds of knee osteoarthritis (OR 1.44 [95% CI 1.04, 1.98], OR 2.28 [95% CI 1.04, 4.99], and OR 6.80 [95% CI 2.08, 22.19], respectively). We found generally consistent estimates with a wider confidence interval crossing the null from other MR methods. The nonlinear MR analyses suggested a nonlinear relationship between metabolically unfavorable adiposity and osteoarthritis (Pnonlinear  = 0.003). CONCLUSION: Metabolic abnormalities did not explain the association between greater adiposity and the risk of osteoarthritis, which might suggest that the association is largely due to a mechanical effect on the joints.

Milk consumption and risk of twelve cancers: A large-scale observational and Mendelian randomisation study.

BACKGROUND & AIMS: Milk consumption is a modifiable lifestyle factor that has been associated with several cancer types in observational studies. Limited evidence exists regarding the causality of these relationships. Using a genetic variant (rs4988235) near the lactase gene (LCT) locus that proxies milk consumption, we conducted a comprehensive survey to assess potential causal relationships between milk consumption and 12 types of cancer. METHODS: Our analyses were conducted using white British participants of the UK Biobank (n = up to 255,196), the FinnGen cohort (up to 260,405), and available cancer consortia. We included cancers with previous evidence of an association with milk consumption in observational studies, as well as cancers common in both UK Biobank and FinnGen populations (>1000 cases). We evaluated phenotypic associations of milk intake and cancer incidence in the UK Biobank, and then used a Mendelian randomisation (MR) approach to assess causality in the UK Biobank, FinnGen consortium, and combined analyses incorporating additional consortia data for five cancers. In MR meta-analyses, case numbers for cancers of breast, ovary, uterus, cervix, prostate, bladder and urinary tract, colorectum, and lung ranged between 6000 and 148,000 cases, and between 780 and 1342 cases for cancers of the liver, mouth, stomach and diffuse large B-cell lymphoma. RESULTS: In observational analyses, milk consumption was associated with higher risk of bladder and urinary tract cancer (OR 1.23, 95% CI 1.03-1.47), but not with any other cancer. This association was not confirmed in the MR analysis, and genetically predicted milk consumption showed a significant association only with lower risk of colorectal cancer (0.89, 0.81-0.98 per additional 50 g/day). In the MR analyses conducted among individual cohorts, genetically predicted milk consumption provided evidence for an association with lower colorectal cancer in the FinnGen cohort (0.85, 0.74-0.97), and in the UK Biobank greater risk of female breast cancer (1.12, 1.03-1.23), and uterine cancer in pre-menopausal females (3.98, 1.48-10.7). CONCLUSION: In a comprehensive survey of milk-cancer associations, we confirm of a protective role of milk consumption for colorectal cancer. Our analyses also provide some suggestion for higher risks of breast cancer and premenopausal uterine cancer, warranting further investigation.

Metabolic profile predicts incident cancer: A large-scale population study in the UK Biobank.

BACKGROUND AND AIMS: Analyses to predict the risk of cancer typically focus on single biomarkers, which do not capture their complex interrelations. We hypothesized that the use of metabolic profiles may provide new insights into cancer prediction. METHODS: We used information from 290,888 UK Biobank participants aged 37 to 73 years at baseline. Metabolic subgroups were defined based on clustering of biochemical data using an artificial neural network approach and examined for their association with incident cancers identified through linkage to cancer registry. In addition, we evaluated associations between 38 individual biomarkers and cancer risk. RESULTS: In total, 21,973 individuals developed cancer during the follow-up (median 3.87 years, interquartile range [IQR] = 2.03-5.58). Compared to the metabolically favorable subgroup (IV), subgroup III (defined as "high BMI, C-reactive protein & cystatin C") was associated with a higher risk of obesity-related cancers (hazard ratio [HR] = 1.26, 95 % CI = 1.21 to 1.32) and hematologic-malignancies (e.g., lymphoid leukemia: HR = 1.83, 95%CI = 1.44 to 2.33). Subgroup II ("high triglycerides & liver enzymes") was strongly associated with liver cancer risk (HR = 5.70, 95%CI = 3.57 to 9.11). Analysis of individual biomarkers showed a positive association between testosterone and greater risks of hormone-sensitive cancers (HR per SD higher = 1.32, 95%CI = 1.23 to 1.44), and liver cancer (HR = 2.49, 95%CI =1.47 to 4.24). Many liver tests were individually associated with a greater risk of liver cancer with the strongest association observed for gamma-glutamyl transferase (HR = 2.40, 95%CI = 2.19 to 2.65). CONCLUSIONS: Metabolic profile in middle-to-older age can predict cancer incidence, in particular risk of obesity-related cancer, hematologic malignancies, and liver cancer. Elevated values from liver tests are strong predictors for later risk of liver cancer.

Metabolic profile-based subgroups can identify differences in brain volumes and brain iron deposition.

AIMS: To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia. MATERIALS AND METHODS: Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self-organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition. RESULTS: In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high-density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (ßstandardized -0.20, 95% confidence interval [CI] -0.24 to -0.16), HV (ßstandardized -0.09, 95% CI -0.13 to -0.04), WMH volume (ßstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (ßstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C-reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (ßstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (ßstandardized -0.15, 95% CI -0.16 to -0.14) and HV (ßstandardized -0.11, 95% CI -0.12 to -0.10), and between BP and WMH volume (ßstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP). CONCLUSIONS: Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations.

Joint Analysis of Phenotypic and Genomic Diversity Sheds Light on the Evolution of Xenobiotic Metabolism in Humans.

Variation in genes involved in the absorption, distribution, metabolism, and excretion of drugs (ADME) can influence individual response to a therapeutic treatment. The study of ADME genetic diversity in human populations has led to evolutionary hypotheses of adaptation to distinct chemical environments. Population differentiation in measured drug metabolism phenotypes is, however, scarcely documented, often indirectly estimated via genotype-predicted phenotypes. We administered seven probe compounds devised to target six cytochrome P450 enzymes and the P-glycoprotein (P-gp) activity to assess phenotypic variation in four populations along a latitudinal transect spanning over Africa, the Middle East, and Europe (349 healthy Ethiopian, Omani, Greek, and Czech volunteers). We demonstrate significant population differentiation for all phenotypes except the one measuring CYP2D6 activity. Genome-wide association studies (GWAS) evidenced that the variability of phenotypes measuring CYP2B6, CYP2C9, CYP2C19, and CYP2D6 activity was associated with genetic variants linked to the corresponding encoding genes, and additional genes for the latter three. Instead, GWAS did not indicate any association between genetic diversity and the phenotypes measuring CYP1A2, CYP3A4, and P-gp activity. Genome scans of selection highlighted multiple candidate regions, a few of which included ADME genes, but none overlapped with the GWAS candidates. Our results suggest that different mechanisms have been shaping the evolution of these phenotypes, including phenotypic plasticity, and possibly some form of balancing selection. We discuss how these contrasting results highlight the diverse evolutionary trajectories of ADME genes and proteins, consistent with the wide spectrum of both endogenous and exogenous molecules that are their substrates.

Healthy Lifestyle, Genetic Risk and Brain Health: A Gene-Environment Interaction Study in the UK Biobank.

Genetic susceptibility and lifestyle affect the risk of dementia but there is little direct evidence for their associations with preclinical changes in brain structure. We investigated the association of genetic dementia risk and healthy lifestyle with brain morphometry, and whether effects from elevated genetic risk are modified by lifestyle changes. We used prospective data from up to 25,894 UK Biobank participants (median follow-up of 8.8 years), and defined healthy lifestyle according to American Heart Association criteria as BMI < 30, no smoking, healthy diet and regular physical activity). Higher genetic risk was associated with lower hippocampal volume (beta −0.16 cm3, 95% CI −0.22, −0.11) and total brain volume (−4.34 cm3, 95% CI −7.68, −1.01) in participants aged ≥60 years but not <60 years. Healthy lifestyle was associated with higher total brain, grey matter and hippocampal volumes, and lower volume of white matter hyperintensities, with no effect modification by age or genetic risk. In conclusion, adverse effects of high genetic risk on brain health were only found in older participants, while adhering to healthy lifestyle recommendations is beneficial regardless of age or genetic risk.

Considering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology.

Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. Here, we show that considering hormone-sensitive cancers as a single disease in the UK Biobank reveals shared genetic aetiology. We observe that a significant proportion of variance in disease liability is explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale is estimated as 10.06% (SE 0.70%). Moreover, we find 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Pair-wise analysis also estimates positive genetic correlations between some pairs of hormone-sensitive cancers although they are not statistically significant. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers.

Cross-sectional metabolic subgroups and 10-year follow-up of cardiometabolic multimorbidity in the UK Biobank.

We assigned 329,908 UK Biobank participants into six subgroups based on a self-organizing map of 51 biochemical measures (blinded for clinical outcomes). The subgroup with the most favorable metabolic traits was chosen as the reference. Hazard ratios (HR) for incident disease were modeled by Cox regression. Enrichment ratios (ER) of incident multi-morbidity versus randomly expected co-occurrence were evaluated by permutation tests; ER is like HR but captures co-occurrence rather than event frequency. The subgroup with high urinary excretion without kidney stress (HR = 1.24) and the subgroup with the highest apolipoprotein B and blood pressure (HR = 1.52) were associated with ischemic heart disease (IHD). The subgroup with kidney stress, high adiposity and inflammation was associated with IHD (HR = 2.11), cancer (HR = 1.29), dementia (HR = 1.70) and mortality (HR = 2.12). The subgroup with high liver enzymes and triglycerides was at risk of diabetes (HR = 15.6). Multimorbidity was enriched in metabolically favorable subgroups (3.4 ≤ ER ≤ 4.0) despite lower disease burden overall; the relative risk of co-occurring disease was higher in the absence of obvious metabolic dysfunction. These results provide synergistic insight into metabolic health and its associations with cardiovascular disease in a large population sample.

Vitamin D and brain health: an observational and Mendelian randomization study.

BACKGROUND: Higher vitamin D status has been suggested to have beneficial effects on the brain. OBJECTIVES: To investigate the association between 25-hydroxyvitamin D [25(OH)D], neuroimaging features, and the risk of dementia and stroke. METHODS: We used prospective data from the UK Biobank (37-73 y at baseline) to examine the association between 25(OH)D concentrations with neuroimaging outcomes (N = 33,523) and the risk of dementia and stroke (N = 427,690; 3414 and 5339 incident cases, respectively). Observational analyses were adjusted for age, sex, ethnicity, month, center, and socioeconomic, lifestyle, sun behavior, and illness-related factors. Nonlinear Mendelian randomization (MR) analyses were used to test for underlying causality for neuroimaging outcomes (N = 23,901) and dementia and stroke (N = 294,514; 2399 and 3760 cases, respectively). RESULTS: Associations between 25(OH)D and total, gray matter, white matter, and hippocampal volumes were nonlinear, with lower volumes both for low and high concentrations (adjusted P-nonlinear ≤ 0.04). 25(OH)D had an inverse association with white matter hyperintensity volume [per 10 nmol/L 25(OH)D; adjusted ß: -6.1; 95% CI: -11.5, -7.0]. Vitamin D deficiency was associated with an increased risk of dementia and stroke, with the strongest associations for those with 25(OH)D <25 nmol/L (compared with 50-75.9 nmol/L; adjusted HR: 1.79; 95% CI: 1.57, 2.04 and HR: 1.40; 95% CI: 1.26, 1.56, respectively). Nonlinear MR analyses confirmed the threshold effect of 25(OH)D on dementia, with the risk predicted to be 54% (95% CI: 1.21, 1.96) higher for participants at 25 nmol/L compared with 50 nmol/L. 25(OH)D was not associated with neuroimaging outcomes or the risk of stroke in MR analyses. Potential impact fraction suggests 17% (95% CI: 7.22, 30.58) of dementia could be prevented by increasing 25(OH)D to 50 nmol/L. CONCLUSIONS: Low vitamin D status was associated with neuroimaging outcomes and the risks of dementia and stroke even after extensive covariate adjustment. MR analyses support a causal effect of vitamin D deficiency on dementia but not on stroke risk.

A systematic review of scope and quality of health economic evaluations conducted in Ethiopia.

There has been an increased interest in health technology assessment and economic evaluations for health policy in Ethiopia over the last few years. In this systematic review, we examined the scope and quality of healthcare economic evaluation studies in Ethiopia. We searched seven electronic databases (PubMed/MEDLINE, EMBASE, PsycINFO, CINHAL, Econlit, York CRD databases and CEA Tufts) from inception to May 2021 to identify published full health economic evaluations of a health-related intervention or programme in Ethiopia. This was supplemented with forward and backward citation searches of included articles, manual search of key government websites, the Disease Control Priorities-Ethiopia project and WHO-CHOICE programme. The quality of reporting of economic evaluations was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. The extracted data were grouped into subcategories based on the subject of the economic evaluation, organized into tables and reported narratively. This review identified 34 full economic evaluations conducted between 2009 and 2021. Around 14 (41%) of studies focussed on health service delivery, 8 (24%) on pharmaceuticals, vaccines and devices, and 4 (12%) on public-health programmes. The interventions were mostly preventive in nature and focussed on communicable diseases (n = 19; 56%) and maternal and child health (n = 6; 18%). Cost-effectiveness ratios varied widely from cost-saving to more than US $37 313 per life saved depending on the setting, perspectives, types of interventions and disease conditions. While the overall quality of included studies was judged as moderate (meeting 69% of CHEERS checklist), only four out of 27 cost-effectiveness studies characterized heterogeneity. There is a need for building local technical capacity to enhance the design, conduct and reporting of health economic evaluations in Ethiopia.

High coffee consumption, brain volume and risk of dementia and stroke.

BACKGROUND: Coffee is a highly popular beverage worldwide, containing caffeine which is a central nervous system stimulant. OBJECTIVES: We examined whether habitual coffee consumption is associated with differences in brain volumes or the odds of dementia or stroke. METHODS: We conducted prospective analyses of habitual coffee consumption on 398,646 UK Biobank participants (age 37-73 years), including 17,702 participants with MRI information. We examined the associations with brain volume using covariate adjusted linear regression, and with odds of dementia (4,333 incident cases) and stroke (6,181 incident cases) using logistic regression. RESULTS: There were inverse linear associations between habitual coffee consumption and total brain (fully adjusted ß per cup -1.42, 95% CI -1.89, -0.94), grey matter (ß -0.91, 95% CI -1.20, -0.62), white matter (ß -0.51, 95% CI -0.83, -0.19) and hippocampal volumes (ß -0.01, 95% CI -0.02, -0.003), but no evidence to support an association with white matter hyperintensity (WMH) volume (ß -0.01, 95% CI -0.07, 0.05). The association between coffee consumption and dementia was non-linear (Pnon-linearity = 0.0001), with evidence for higher odds for non-coffee and decaffeinated coffee drinkers and those drinking >6 cups/day, compared to light coffee drinkers. After full covariate adjustment, consumption of >6 cups/day was associated with 53% higher odds of dementia compared to consumption of 1-2 cups/day (fully adjusted OR 1.53, 95% CI 1.28, 1.83), with less evidence for an association with stroke (OR 1.17, 95% CI 1.00, 1.37, p = 0.055). CONCLUSION: High coffee consumption was associated with smaller total brain volumes and increased odds of dementia.

Prevalence of metabolic syndrome among patients with schizophrenia in Ethiopia.

BACKGROUND: Globally, the prevalence of metabolic syndrome (MetS) is higher among patients with schizophrenia than the general population, and this leads to higher morbidity and mortality in this population. The aim of this study was to investigate the MetS prevalence among patients with schizophrenia in Ethiopia. METHODS: We conducted a cross-sectional analysis of baseline data of 200 patients with schizophrenia recruited from Amanuel Mental Specialized Hospital, Addis Ababa, Ethiopia. Lipid profile and blood glucose levels were measured using Roche Cobas 6000 clinical chemistry analyzer. The prevalence of MetS was assessed based on National Cholesterol Education Program Adult Treatment Panel III criteria. Patients' demographic information, clinical and laboratory data, lifestyle habits, particularly smoking and Khat chewing, were evaluated vis-à-vis MetS. RESULTS: The overall prevalence of MetS in patients with schizophrenia was 21.5% (17.1% male, 29.6% female) where Low HDL-cholesterol value was the most common metabolic disorders components in both males and females subgroups. In the multivariate analysis, the positive and negative symptoms score (PANSS, AOR = 1.03, 95% CI 1.001-1.054) was associated factors with MetS. CONCLUSION: In Ethiopia, patients with schizophrenia were found to have higher prevalence of MetS than the general population. Physicians/health care providers should routinely screen patients with schizophrenia for MetS and initiate timely management of those who develop the syndrome to reduce the health cost from caring for NCDs, improve the patients' quality of life, and prevent premature mortality.

Adiposity and cancer: a Mendelian randomization analysis in the UK biobank.

BACKGROUND: Observational and Mendelian randomization (MR) studies link obesity and cancer, but it remains unclear whether these depend upon related metabolic abnormalities. METHODS: We used information from 321,472 participants in the UK biobank, including 30,561 cases of obesity-related cancer. We constructed three genetic instruments reflecting higher adiposity together with either "unfavourable" (82 SNPs), "favourable" (24 SNPs) or "neutral" metabolic profile (25 SNPs). We looked at associations with 14 types of cancer, previously suggested to be associated with obesity. RESULTS: All genetic instruments had a strong association with BMI (p < 1 × 10-300 for all). The instrument reflecting unfavourable adiposity was also associated with higher CRP, HbA1c and adverse lipid profile, while instrument reflecting metabolically favourable adiposity was associated with lower HbA1c and a favourable lipid profile. In MR-inverse-variance weighted analysis unfavourable adiposity was associated with an increased risk of non-hormonal cancers (OR = 1.22, 95% confidence interval [CI]:1.08, 1.38), but a lower risk of hormonal cancers (OR = 0.80, 95%CI: 0.72, 0.89). From individual cancers, MR analyses suggested causal increases in the risk of multiple myeloma (OR = 1.36, 95%CI: 1.09, 1.70) and endometrial cancer (OR = 1.77, 95%CI: 1.16, 2.68) by greater genetically instrumented unfavourable adiposity but lower risks of breast and prostate cancer (OR = 0.72, 95%CI: 0.61, 0.83 and OR = 0.81, 95%CI: 0.68, 0.97, respectively). Favourable or neutral adiposity were not associated with the odds of any individual cancer. CONCLUSIONS: Higher adiposity associated with a higher risk of non-hormonal cancer but a lower risk of some hormone related cancers. Presence of metabolic abnormalities might aggravate the adverse effects of higher adiposity on cancer. Further studies are warranted to investigate whether interventions on adverse metabolic health may help to alleviate obesity-related cancer risk.