Exploring pleiotropy in Mendelian randomisation analyses: What are genetic variants associated with 'cigarette smoking initiation' really capturing?

Genetic variants used as instruments for exposures in Mendelian randomisation (MR) analyses may have horizontal pleiotropic effects (i.e., influence outcomes via pathways other than through the exposure), which can undermine the validity of results. We examined the extent of this using smoking behaviours as an example. We first ran a phenome-wide association study in UK Biobank, using a smoking initiation genetic instrument. From the most strongly associated phenotypes, we selected those we considered could either plausibly or not plausibly be caused by smoking. We examined associations between genetic instruments for smoking initiation, smoking heaviness and lifetime smoking and these phenotypes in UK Biobank and the Avon Longitudinal Study of Parents and Children (ALSPAC). We conducted negative control analyses among never smokers, including children. We found evidence that smoking-related genetic instruments were associated with phenotypes not plausibly caused by smoking in UK Biobank and (to a lesser extent) ALSPAC. We observed associations with phenotypes among never smokers. Our results demonstrate that smoking-related genetic risk scores are associated with unexpected phenotypes that are less plausibly downstream of smoking. This may reflect horizontal pleiotropy in these genetic risk scores, and we would encourage researchers to exercise caution this when using these and genetic risk scores for other complex behavioural exposures. We outline approaches that could be taken to consider this and overcome issues caused by potential horizontal pleiotropy, for example, in genetically informed causal inference analyses (e.g., MR) it is important to consider negative control outcomes and triangulation approaches, to avoid arriving at incorrect conclusions.

Genetic inhibition of angiopoietin-like protein-3, lipids, and cardiometabolic risk.

BACKGROUND AND AIMS: RNA-based, antibody-based, and genome editing-based therapies are currently under investigation to determine if the inhibition of angiopoietin-like protein-3 (ANGPTL3) could reduce lipoprotein-lipid levels and atherosclerotic cardiovascular disease (ASCVD) risk. Mendelian randomisation (MR) was used to determine whether genetic variations influencing ANGPTL3 liver gene expression, blood levels, and protein structure could causally influence triglyceride and apolipoprotein B (apoB) levels as well as coronary artery disease (CAD), ischaemic stroke (IS), and other cardiometabolic diseases. METHODS: RNA sequencing of 246 explanted liver samples and genome-wide genotyping was performed to identify single-nucleotide polymorphisms (SNPs) associated with liver expression of ANGPTL3. Genome-wide summary statistics of plasma protein levels of ANGPTL3 from the deCODE study (n = 35 359) were used. A total of 647 carriers of ANGPTL3 protein-truncating variants (PTVs) associated with lower plasma triglyceride levels were identified in the UK Biobank. Two-sample MR using SNPs that influence ANGPTL3 liver expression or ANGPTL3 plasma protein levels as exposure and cardiometabolic diseases as outcomes was performed (CAD, IS, heart failure, non-alcoholic fatty liver disease, acute pancreatitis, and type 2 diabetes). The impact of rare PTVs influencing plasma triglyceride levels on apoB levels and CAD was also investigated in the UK Biobank. RESULTS: In two-sample MR studies, common genetic variants influencing ANGPTL3 hepatic or blood expression levels of ANGPTL3 had a very strong effect on plasma triglyceride levels, a more modest effect on low-density lipoprotein cholesterol, a weaker effect on apoB levels, and no effect on CAD or other cardiometabolic diseases. In the UK Biobank, the carriers of rare ANGPTL3 PTVs providing lifelong reductions in median plasma triglyceride levels [-0.37 (interquartile range 0.41) mmol/L] had slightly lower apoB levels (-0.06 ± 0.32 g/L) and similar CAD event rates compared with non-carriers (10.2% vs. 10.9% in carriers vs. non-carriers, P = .60). CONCLUSIONS: PTVs influencing ANGPTL3 protein structure as well as common genetic variants influencing ANGPTL3 hepatic expression and/or blood protein levels exhibit a strong effect on circulating plasma triglyceride levels, a weak effect on circulating apoB levels, and no effect on ASCVD. Near-complete inhibition of ANGPTL3 function in patients with very elevated apoB levels may be required to reduce ASCVD risk.

Coeliac disease and type 2 diabetes risk: a nationwide matched cohort and Mendelian randomisation study.

AIMS/HYPOTHESIS: While the association between coeliac disease and type 1 diabetes is well documented, the association of coeliac disease with type 2 diabetes risk remains undetermined. We conducted a nationwide cohort and Mendelian randomisation analysis to investigate this link. METHODS: This nationwide matched cohort used data from the Swedish ESPRESSO cohort including 46,150 individuals with coeliac disease and 219,763 matched individuals in the comparator group selected from the general population, followed up from 1969 to 2021. Data from 9053 individuals with coeliac disease who underwent a second biopsy were used to examine the association between persistent villous atrophy and type 2 diabetes. Multivariable Cox regression was employed to estimate the associations. In Mendelian randomisation analysis, 37 independent genetic variants associated with clinically diagnosed coeliac disease at p<5×10-8 were used to proxy genetic liability to coeliac disease. Summary-level data for type 2 diabetes were obtained from the DIAGRAM consortium (80,154 cases) and the FinnGen study (42,593 cases). RESULTS: Over a median 15.7 years' follow-up, there were 6132 (13.3%) and 30,138 (13.7%) incident cases of type 2 diabetes in people with coeliac disease and comparator individuals, respectively. Those with coeliac disease were not at increased risk of incident type 2 diabetes with an HR of 1.00 (95% CI 0.97, 1.03) compared with comparator individuals. Persistent villous atrophy was not associated with an increased risk of type 2 diabetes compared with mucosal healing among participants with coeliac disease (HR 1.02, 95% CI 0.90, 1.16). Genetic liability to coeliac disease was not associated with type 2 diabetes in DIAGRAM (OR 1.01, 95% CI 0.99, 1.03) or in FinnGen (OR 1.01, 95% CI 0.99-1.04). CONCLUSIONS/INTERPRETATION: Coeliac disease was not associated with type 2 diabetes risk.

Hyperglycaemia, diabetes and risk of fragility fractures: observational and Mendelian randomisation studies.

AIMS/HYPOTHESIS: Fragility fractures may be a complication of diabetes, partly caused by chronic hyperglycaemia. We hypothesised that: (1) individuals with hyperglycaemia and diabetes have increased risk of fragility fracture; (2) hyperglycaemia is causally associated with increased risk of fragility fracture; and (3) diabetes and fragility fracture jointly associate with the highest risk of all-cause mortality. METHODS: In total, 117,054 individuals from the Copenhagen City Heart Study and the Copenhagen General Population Study (the Copenhagen studies) and 390,374 individuals from UK Biobank were included for observational and one-sample Mendelian randomisation (MR) analyses. Fragility fractures were defined as fractures at the hip, spine and arm (humerus/wrist), collected from national health registries. Summary data for fasting glucose and HbA1c concentrations from 196,743 individuals in the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) were combined with data on fragility fractures from the Copenhagen studies in two-sample MR analyses. RESULTS: Higher fasting and non-fasting glucose and HbA1c concentrations were associated with higher risk of any fragility fracture (p<0.001). Individuals with vs without diabetes had HRs for fragility fracture of 1.50 (95% CI 1.19, 1.88) in type 1 diabetes and 1.22 (1.13, 1.32) in type 2 diabetes. One-sample MR supported a causal association between high non-fasting glucose concentrations and increased risk of arm fracture in the Copenhagen studies and UK Biobank combined (RR 1.41 [1.11, 1.79], p=0.004), with similar results for fasting glucose and HbA1c in two-sample MR analyses (ORs 1.50 [1.03, 2.18], p=0.03; and 2.79 [1.12, 6.93], p=0.03, respectively). The corresponding MR estimates for any fragility fracture were 1.18 (1.00, 1.41), p=0.06; 1.36 (0.89, 2.09), p=0.15; and 2.47 (0.95, 6.43), p=0.06, respectively. At age 80 years, cumulative death was 27% in individuals with fragility fracture only, 54% in those with diabetes only, 67% in individuals with both conditions and 17% in those with neither. CONCLUSIONS/INTERPRETATION: Hyperglycaemia and diabetes are risk factors for fragility fracture and one- and two-sample MR analyses supported a causal effect of hyperglycaemia on arm fractures. Diabetes and previous fragility fracture jointly conferred the highest risk of death in the general population.

Genome-wide association and Mendelian randomisation analysis among 30,699 Chinese pregnant women identifies novel genetic and molecular risk factors for gestational diabetes and glycaemic traits.

AIMS/HYPOTHESIS: Gestational diabetes mellitus (GDM) is the most common disorder in pregnancy; however, its underlying causes remain obscure. This study aimed to investigate the genetic and molecular risk factors contributing to GDM and glycaemic traits. METHODS: We collected non-invasive prenatal test (NIPT) sequencing data along with four glycaemic and 55 biochemical measurements from 30,699 pregnant women during a 2 year period at Shenzhen Baoan Women's and Children's Hospital in China. Genome-wide association studies (GWAS) were conducted between genotypes derived from NIPTs and GDM diagnosis, baseline glycaemic levels and glycaemic levels after glucose challenges. In total, 3317 women were diagnosed with GDM, while 19,565 served as control participants. The results were replicated using two independent cohorts. Additionally, we performed one-sample Mendelian randomisation to explore potential causal associations between the 55 biochemical measurements and risk of GDM and glycaemic levels. RESULTS: We identified four genetic loci significantly associated with GDM susceptibility. Among these, MTNR1B exhibited the highest significance (rs10830963-G, OR [95% CI] 1.57 [1.45, 1.70], p=4.42×10-29), although its effect on type 2 diabetes was modest. Furthermore, we found 31 genetic loci, including 14 novel loci, that were significantly associated with the four glycaemic traits. The replication rates of these associations with GDM, fasting plasma glucose levels and 0 h, 1 h and 2 h OGTT glucose levels were 4 out of 4, 6 out of 9, 10 out of 11, 5 out of 7 and 4 out of 4, respectively. Mendelian randomisation analysis suggested that a genetically regulated higher lymphocytes percentage and lower white blood cell count, neutrophil percentage and absolute neutrophil count were associated with elevated glucose levels and an increased risk of GDM. CONCLUSIONS/INTERPRETATION: Our findings provide new insights into the genetic basis of GDM and glycaemic traits during pregnancy in an East Asian population and highlight the potential role of inflammatory pathways in the aetiology of GDM and variations in glycaemic levels. DATA AVAILABILITY: Summary statistics for GDM; fasting plasma glucose; 0 h, 1 h and 2h OGTT; and the 55 biomarkers are available in the GWAS Atlas (study accession no.: GVP000001, https://ngdc.cncb.ac.cn/gwas/browse/GVP000001) .

Genetic relations between type 1 diabetes, coronary artery disease and leukocyte counts.

AIMS/HYPOTHESIS: Type 1 diabetes is associated with excess coronary artery disease (CAD) risk even when known cardiovascular risk factors are accounted for. Genetic perturbation of haematopoiesis that alters leukocyte production is a novel independent modifier of CAD risk. We examined whether there are shared genetic determinants and causal relationships between type 1 diabetes, CAD and leukocyte counts. METHODS: Genome-wide association study summary statistics were used to perform pairwise linkage disequilibrium score regression and heritability estimation from summary statistics (ρ-HESS) to respectively estimate the genome-wide and local genetic correlations, and two-sample Mendelian randomisation to estimate the causal relationships between leukocyte counts (335,855 healthy individuals), type 1 diabetes (18,942 cases, 501,638 control individuals) and CAD (122,733 cases, 424,528 control individuals). A latent causal variable (LCV) model was performed to estimate the genetic causality proportion of the genetic correlation between type 1 diabetes and CAD. RESULTS: There was significant genome-wide genetic correlation (rg) between type 1 diabetes and CAD (rg=0.088, p=8.60 × 10-3) and both diseases shared significant genome-wide genetic determinants with eosinophil count (rg for type 1 diabetes [rg(T1D)]=0.093, p=7.20 × 10-3, rg for CAD [rg(CAD)]=0.092, p=3.68 × 10-6) and lymphocyte count (rg(T1D)=-0.052, p=2.76 × 10-2, rg(CAD)=0.176, p=1.82 × 10-15). Sixteen independent loci showed stringent Bonferroni significant local genetic correlations between leukocyte counts, type 1 diabetes and/or CAD. Cis-genetic regulation of the expression levels of genes within shared loci between type 1 diabetes and CAD was associated with both diseases as well as leukocyte counts, including SH2B3, CTSH, MORF4L1, CTRB1, CTRB2, CFDP1 and IFIH1. Genetically predicted lymphocyte, neutrophil and eosinophil counts were associated with type 1 diabetes and CAD (lymphocyte OR for type 1 diabetes [ORT1D]=0.67, p=2.02-19, ORCAD=1.09, p=2.67 × 10-6; neutrophil ORT1D=0.82, p=5.63 × 10-5, ORCAD=1.17, p=5.02 × 10-14; and eosinophil ORT1D=1.67, p=5.45 × 10-25, ORCAD=1.07, p=2.03 × 10-4. The genetic causality proportion between type 1 diabetes and CAD was 0.36 ± 0.16 (pLCV=1.30 × 10-2), suggesting a possible intermediary causal variable. CONCLUSIONS/INTERPRETATION: This study sheds light on shared genetic mechanisms underlying type 1 diabetes and CAD, which may contribute to their co-occurrence through regulation of gene expression and leukocyte counts and identifies cellular and molecular targets for further investigation for disease prediction and potential drug discovery.

Circulating white blood cell traits and colorectal cancer risk: A Mendelian randomisation study.

Observational studies have suggested a protective role for eosinophils in colorectal cancer (CRC) development and implicated neutrophils, but the causal relationships remain unclear. Here, we aimed to estimate the causal effect of circulating white blood cell (WBC) counts (N = ~550 000) for basophils, eosinophils, monocytes, lymphocytes and neutrophils on CRC risk (N = 52 775 cases and 45 940 controls) using Mendelian randomisation (MR). For comparison, we also examined this relationship using individual-level data from UK Biobank (4043 incident CRC cases and 332 773 controls) in a longitudinal cohort analysis. The inverse-variance weighted (IVW) MR analysis suggested a protective effect of increased basophil count and eosinophil count on CRC risk [OR per 1-SD increase: 0.88, 95% CI: 0.78-0.99, P = .04; OR: 0.93, 95% CI: 0.88-0.98, P = .01]. The protective effect of eosinophils remained [OR per 1-SD increase: 0.88, 95% CI: 0.80-0.97, P = .01] following adjustments for all other WBC subtypes, to account for genetic correlation between the traits, using multivariable MR. A protective effect of increased lymphocyte count on CRC risk was also found [OR: 0.84, 95% CI: 0.76-0.93, P = 6.70e-4] following adjustment. Consistent with MR results, a protective effect for eosinophils in the cohort analysis in the fully adjusted model [RR per 1-SD increase: 0.96, 95% CI: 0.93-0.99, P = .02] and following adjustment for the other WBC subtypes [RR: 0.96, 95% CI: 0.93-0.99, P = .001] was observed. Our study implicates peripheral blood immune cells, in particular eosinophils and lymphocytes, in CRC development, highlighting a need for mechanistic studies to interrogate these relationships.

Differing genetic variants associated with liver fat and their contrasting relationships with cardiovascular diseases and cancer.

BACKGROUND & AIMS: The mechanisms underlying the association of steatotic liver disease with cardiovascular and cancer outcomes are poorly understood. We aimed to use MRI-derived measures of liver fat and genetics to investigate causal mechanisms that link higher liver fat to various health outcomes. METHODS: We conducted a genome-wide association study on 37,358 UK Biobank participants to identify genetic variants associated with liver fat measured from MRI scans. We used a Mendelian randomisation approach to investigate the causal effect of liver fat on health outcomes independent of BMI, alcohol consumption and lipids using data from published genome-wide association studies and FinnGen. RESULTS: We identified 13 genetic variants associated with liver fat that had differing effects on the risks of health outcomes. Genetic variants associated with impaired hepatic triglyceride export showed liver fat-increasing alleles to be correlated with a reduced risk of coronary artery disease and myocardial infarction but an elevated risk of type 2 diabetes, while variants associated with enhanced de novo lipogenesis showed liver fat-increasing alleles to be linked to a higher risk of myocardial infarction and coronary artery disease. Genetically higher liver fat content increased the risk of non-alcohol-related cirrhosis, hepatocellular carcinoma, and intrahepatic bile duct and gallbladder cancers, exhibiting a dose-dependent relationship, irrespective of the mechanism. CONCLUSION: This study provides fresh insight into the heterogeneous effect of liver fat on health outcomes. It challenges the notion that liver fat per se is an independent risk factor for cardiovascular disease, underscoring the dependency of this association on the specific mechanisms that drive fat accumulation in the liver. However, excess liver fat, regardless of the underlying mechanism, appears to be causally linked to cirrhosis and cancers in a dose-dependent manner. IMPACT AND IMPLICATION: This research advances our understanding of the heterogeneity in mechanisms influencing liver fat accumulation, providing new insights into how liver fat accumulation may impact various health outcomes. The findings challenge the notion that liver fat is an independent risk factor for cardiovascular disease and highlight the mechanistic effect of some genetic variants on fat accumulation and the development of cardiovascular diseases. This study is of particular importance for healthcare professionals including physicians and researchers, as well as patients, as it allows for more targeted and personalised treatment by understanding the relationship between liver fat and various health outcomes. The findings emphasise the need for a personalised management approach and a reshaping of risk assessment criteria. It also provides room for prioritising a clinical intervention aimed at reducing liver fat content (likely via intentional weight loss) that could help protect against liver-related fibrosis and cancer.

Pancreatitis affects gut microbiota via metabolites and inflammatory cytokines: an exploratory two-step Mendelian randomisation study.

Previous studies have observed relationships between pancreatitis and gut microbiota; however, specific changes in gut microbiota abundance and underlying mechanisms in pancreatitis remain unknown. Metabolites are important for gut microbiota to fulfil their biological functions, and changes in the metabolic and immune environments are closely linked to changes in microbiota abundance. We aimed to clarify the mechanisms of gut-pancreas interactions and explore the possible role of metabolites and the immune system. To this end, we conducted two-sample Mendelian randomisation (MR) analysis to evaluate the casual links between four different types of pancreatitis and gut microbiota, metabolites, and inflammatory cytokines. A two-step MR analysis was conducted to further evaluate the probable mediating pathways involving metabolites and inflammatory cytokines in the causal relationship between pancreatitis and gut microbiota. In total, six potential mediators were identified in the causal relationship between pancreatitis and gut microbiota. Nineteen species of gut microbiota and seven inflammatory cytokines were genetically associated with the four types of pancreatitis. Metabolites involved in glucose and amino acid metabolisms were genetically associated with chronic pancreatitis, and those involved in lipid metabolism were genetically associated with acute pancreatitis. Our study identified alterations in the gut microbiota, metabolites, and inflammatory cytokines in pancreatitis at the genetic level and found six potential mediators of the pancreas-gut axis, which may provide insights into the precise diagnosis of pancreatitis and treatment interventions for gut microbiota to prevent the exacerbation of pancreatitis. Future studies could elucidate the mechanism underlying the association between pancreatitis and the gut microbiota.

Assessing causal links between age at menarche and adolescent mental health: a Mendelian randomisation study.

BACKGROUND: The timing of puberty may have an important impact on adolescent mental health. In particular, earlier age at menarche has been associated with elevated rates of depression in adolescents. Previous research suggests that this relationship may be causal, but replication and an investigation of whether this effect extends to other mental health domains is warranted. METHODS: In this Registered Report, we triangulated evidence from different causal inference methods using a new wave of data (N = 13,398) from the Norwegian Mother, Father, and Child Cohort Study. We combined multiple regression, one- and two-sample Mendelian randomisation (MR), and negative control analyses (using pre-pubertal symptoms as outcomes) to assess the causal links between age at menarche and different domains of adolescent mental health. RESULTS: Our results supported the hypothesis that earlier age at menarche is associated with elevated depressive symptoms in early adolescence based on multiple regression (ß = - 0.11, 95% CI [- 0.12, - 0.09], pone-tailed < 0.01). One-sample MR analyses suggested that this relationship may be causal (ß = - 0.07, 95% CI [- 0.13, 0.00], pone-tailed = 0.03), but the effect was small, corresponding to just a 0.06 standard deviation increase in depressive symptoms with each earlier year of menarche. There was also some evidence of a causal relationship with depression diagnoses during adolescence based on one-sample MR (OR = 0.74, 95% CI [0.54, 1.01], pone-tailed = 0.03), corresponding to a 29% increase in the odds of receiving a depression diagnosis with each earlier year of menarche. Negative control and two-sample MR sensitivity analyses were broadly consistent with this pattern of results. Multivariable MR analyses accounting for the genetic overlap between age at menarche and childhood body size provided some evidence of confounding. Meanwhile, we found little consistent evidence of effects on other domains of mental health after accounting for co-occurring depression and other confounding. CONCLUSIONS: We found evidence that age at menarche affected diagnoses of adolescent depression, but not other domains of mental health. Our findings suggest that earlier age at menarche is linked to problems in specific domains rather than adolescent mental health in general.

Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes.

BACKGROUND: Higher maternal pre-pregnancy body mass index (BMI) is associated with adverse pregnancy and perinatal outcomes. However, whether these associations are causal remains unclear. METHODS: We explored the relation of maternal pre-/early-pregnancy BMI with 20 pregnancy and perinatal outcomes by integrating evidence from three different approaches (i.e. multivariable regression, Mendelian randomisation, and paternal negative control analyses), including data from over 400,000 women. RESULTS: All three analytical approaches supported associations of higher maternal BMI with lower odds of maternal anaemia, delivering a small-for-gestational-age baby and initiating breastfeeding, but higher odds of hypertensive disorders of pregnancy, gestational hypertension, preeclampsia, gestational diabetes, pre-labour membrane rupture, induction of labour, caesarean section, large-for-gestational age, high birthweight, low Apgar score at 1 min, and neonatal intensive care unit admission. For example, higher maternal BMI was associated with higher risk of gestational hypertension in multivariable regression (OR = 1.67; 95% CI = 1.63, 1.70 per standard unit in BMI) and Mendelian randomisation (OR = 1.59; 95% CI = 1.38, 1.83), which was not seen for paternal BMI (OR = 1.01; 95% CI = 0.98, 1.04). Findings did not support a relation between maternal BMI and perinatal depression. For other outcomes, evidence was inconclusive due to inconsistencies across the applied approaches or substantial imprecision in effect estimates from Mendelian randomisation. CONCLUSIONS: Our findings support a causal role for maternal pre-/early-pregnancy BMI on 14 out of 20 adverse pregnancy and perinatal outcomes. Pre-conception interventions to support women maintaining a healthy BMI may reduce the burden of obstetric and neonatal complications. FUNDING: Medical Research Council, British Heart Foundation, European Research Council, National Institutes of Health, National Institute for Health Research, Research Council of Norway, Wellcome Trust.

Causal relationships between anthropometric traits, bone mineral density, osteoarthritis and spinal stenosis: A Mendelian randomisation investigation.

OBJECTIVE: Spinal stenosis is a common condition among older individuals, with significant morbidity attached. Little is known about its risk factors but degenerative conditions, such as osteoarthritis (OA) have been identified for their mechanistic role. This study aims to explore causal relationships between anthropometric risk factors, OA, and spinal stenosis using Mendelian randomisation (MR) techniques. DESIGN: We applied two-sample MR to investigate the causal relationships between genetic liability for select risk factors and spinal stenosis. Next, we examined the genetic relationship between OA and spinal stenosis with linkage disequilibrium score regression and Causal Analysis Using Summary Effect estimates MR method. Finally, we used multivariable MR (MVMR) to explore whether OA and body mass index (BMI) mediate the causal pathways identified. RESULTS: Our analysis revealed strong evidence for the effect of higher BMI (odds ratio [OR] = 1.54, 95%CI: 1.41-1.69, p-value = 2.7 × 10-21), waist (OR = 1.43, 95%CI: 1.15-1.79, p-value = 1.5 × 10-3) and hip (OR = 1.50, 95%CI: 1.27-1.78, p-value = 3.3 × 10-6) circumference on spinal stenosis. Strong evidence of causality was also observed for higher bone mineral density (BMD): total body (OR = 1.21, 95%CI: 1.12-1.29, p-value = 1.6 × 10-7), femoral neck (OR = 1.35, 95%CI: 1.09-1.37, p-value = 7.5×10-7), and lumbar spine (OR = 1.38, 95%CI: 1.25-1.52, p-value = 4.4 × 10-11). We detected high genetic correlations between spinal stenosis and OA (rg range: 0.47-0.66), with Causal Analysis Using Summary Effect estimates results supporting a causal effect of OA on spinal stenosis (ORallOA = 1.6, 95%CI: 1.41-1.79). Direct effects of BMI, BMD on spinal stenosis remained after adjusting for OA in the MVMR. CONCLUSIONS: Genetic susceptibility to anthropometric risk factors, particularly higher BMI and BMD can increase the risk of spinal stenosis, independent of OA status. These results may inform preventative strategies and treatments.

The relationship between sex hormone-binding protein and non-alcoholic fatty liver disease using Mendelian randomisation.

BACKGROUND: The incidence of non-alcohol fatty liver disease (NAFLD) has been increasing annually with the improvement of living standards. Numerous epidemiological observations have linked sex hormone-binding protein (SHBG) levels to NAFLD. However, evidence of the causal role of SHBG in the development and progression of NAFLD is still absent. Therefore, a systematic assessment of the causal relationship is needed. METHOD: A two-sample Mendelian randomisation (MR) analysis was conducted. Genome-wide association study (GWAS) data for SHBG were obtained online from the IEU database (ebi-a-GCST90012111) as exposure. GWAS data from the NAFLD of the Finngen consortium were used for preliminary analysis, while NAFLD data from another GWAS involving 8434 participants were used for replication and meta-analyses. Causal effects were investigated with inverse variance weighted (IVW), weighted median and MR-Egger regression. Sensitivity analyses including Cochran's Q test, leave-one-out analysis and MR-Egger intercept analysis were simultaneously conducted to assess heterogeneity and pleiotropy. RESULTS: After rigorous selection, 179 single-nucleotide polymorphisms (SNPs) were identified as strongly correlated instrumental variables. Preliminary analysis suggested a significant causal relationship between genetically determined serum SHBG levels and NAFLD [odds ratio (OR) IVW = .54, 95% confidence interval (CI) = .30-.98, p = .043], supported by the results of the replication analysis (ORIVW = .61, 95% CI = .46-.81, p = .0006) and further meta-analysis (OR = .59, 95% CI = .46-.77, p < .0001). CONCLUSION: The genetic tendency to high levels of SHBG was causally correlated with a reduced risk of NAFLD, indicating that circulating high levels of SHBG was a protective factor for NAFLD.

Proteomic associations with forced expiratory volume: a Mendelian randomisation study.

BACKGROUND: A decline in forced expiratory volume (FEV1) is a hallmark of respiratory diseases that are an important cause of morbidity among the elderly. While some data exist on biomarkers that are related to FEV1, we sought to do a systematic analysis of causal relations of biomarkers with FEV1. METHODS: Data from the population-based AGES-Reykjavik study were used. Serum proteomic measurements were done using 4782 DNA aptamers (SOMAmers). Data from 1479 participants with spirometric data were used to assess the association of SOMAmer measurements with FEV1 using linear regression. Bi-directional two-sample Mendelian randomisation (MR) analyses were done to assess causal relations of observationally associated SOMAmers with FEV1, using genotype and SOMAmer data from 5368 AGES-Reykjavik participants and genetic associations with FEV1 from a publicly available GWAS (n = 400,102). RESULTS: In observational analyses, 530 SOMAmers were associated with FEV1 after multiple testing adjustment (FDR < 0.05). The most significant were Retinoic Acid Receptor Responder 2 (RARRES2), R-Spondin 4 (RSPO4) and Alkaline Phosphatase, Placental Like 2 (ALPPL2). Of the 257 SOMAmers with genetic instruments available, eight were associated with FEV1 in MR analyses. Three were directionally consistent with the observational estimate, Thrombospondin 2 (THBS2), Endoplasmic Reticulum Oxidoreductase 1 Beta (ERO1B) and Apolipoprotein M (APOM). THBS2 was further supported by a colocalization analysis. Analyses in the reverse direction, testing whether changes in SOMAmer levels were caused by changes in FEV1, were performed but no significant associations were found after multiple testing adjustments. CONCLUSIONS: In summary, this large scale proteogenomic analyses of FEV1 reveals circulating protein markers of FEV1, as well as several proteins with potential causality to lung function.

Estimating the effect of lipid-lowering agents on novel subtypes of adult-onset diabetes.

AIMS: The aims of the present study were to assess the effects of lipid-lowering drugs [HMG-CoA reductase inhibitors, proprotein convertase subtilisin/kexin type 9 inhibitors, and Niemann-Pick C1-Like 1 (NPC1L1) inhibitors] on novel subtypes of adult-onset diabetes through a Mendelian randomisation study. MATERIALS AND METHODS: We first inferred causal associations between lipid-related traits [including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), apolipoproteins A-I, and apolipoproteins B] and novel subtypes of adult-onset diabetes. The expression quantitative trait loci of drug target genes for three classes of lipid-lowering drugs, as well as genetic variants within or nearby drug target genes associated with LDL-C, were then utilised as proxies for the exposure of lipid-lowering drugs. Mendelian randomisation analysis was performed using summary data from genome-wide association studies of LDL-C, severe autoimmune diabetes, severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes. RESULTS: There was an association between HMGCR-mediated LDL-C and the risk of SIRD [odds ratio (OR) = 0.305, 95% confidence interval (CI) = 0.129-0.723; p = 0.007], and there was an association of PCSK9-mediated LDL-C with the risk of SIDD (OR = 0.253, 95% CI = 0.120-0.532; p < 0.001) and MOD (OR = 0.345, 95% CI = 0.171-0.696; p = 0.003). Moreover, NPC1L1-mediated LDL-C (OR = 0.109, 95% CI = 0.019-0.613; p = 0.012) and the increased expression of NPC1L1 gene in blood (OR = 0.727, 95% CI = 0.541-0.977; p = 0.034) both showed a significant association with SIRD. These results were further confirmed by sensitivity analyses. CONCLUSIONS: In summary, the different lipid-lowering medications have a specific effect on the increased risk of different novel subtypes of adult-onset diabetes.

Causal effects of dietary habits on COVID-19 susceptibility, hospitalisation and severity: a comprehensive Mendelian randomisation study.

This study aimed to investigate the causal effect of dietary habits on COVID-19 susceptibility, hospitalisation and severity. We used data from a large-scale diet dataset and the COVID-19 Host Genetics Initiative to estimate causal relationships using Mendelian randomisation. The inverse variance weighted (IVW) method was used as the main analysis. For COVID-19 susceptibility, IVW estimates indicated that milk (OR: 0·82; 95 % CI (0·68, 0·98); P = 0·032), unsalted peanut (OR: 0·53; 95 % CI (0·35, 0·82); P = 0·004), beef (OR: 0·59; 95 % CI (0·41, 0·84); P = 0·004), pork (OR: 0·63; 95 % CI (0·42, 0·93); P = 0·022) and processed meat (OR: 0·76; 95 % CI (0·63, 0·92); P = 0·005) were causally associated with reduced COVID-19 susceptibility, while coffee (OR: 1·23; 95 % CI (1·04, 1·45); P = 0·017) and tea (OR: 1·17; 95 % CI (1·05, 1·31); P = 0·006) were causally associated with increased risk. For COVID-19 hospitalisation, beef (OR: 0·51; 95 % CI (0·26, 0·98); P = 0·042) showed negative correlations, while tea (OR: 1·54; 95 % CI (1·16, 2·04); P = 0·003), dried fruit (OR: 2·08; 95 % CI (1·37, 3·15); P = 0·001) and red wine (OR: 2·35; 95 % CI (1·29, 4·27); P = 0·005) showed positive correlations. For COVID-19 severity, coffee (OR: 2·16; 95 % CI (1·25, 3·76); P = 0·006), dried fruit (OR: 1·98; 95 % CI (1·16, 3·37); P = 0·012) and red wine (OR: 2·84; 95 % CI (1·21, 6·68); P = 0·017) showed an increased risk. These findings were confirmed to be robust through sensitivity analyses. Our findings established a causal relationship between dietary habits and COVID-19 susceptibility, hospitalisation and severity.

Dietary factors and the risk of atopic dermatitis: a Mendelian randomisation study.

Previous studies have revealed an association between dietary factors and atopic dermatitis (AD). To explore whether there was a causal relationship between diet and AD, we performed Mendelian randomisation (MR) analysis. The dataset of twenty-one dietary factors was obtained from UK Biobank. The dataset for AD was obtained from the publicly available FinnGen consortium. The main research method was the inverse-variance weighting method, which was supplemented by MR‒Egger, weighted median and weighted mode. In addition, sensitivity analysis was performed to ensure the accuracy of the results. The study revealed that beef intake (OR = 0·351; 95 % CI 0·145, 0·847; P = 0·020) and white bread intake (OR = 0·141; 95 % CI 0·030, 0·656; P = 0·012) may be protective factors against AD. There were no causal relationships between AD and any other dietary intake factors. Sensitivity analysis showed that our results were reliable, and no heterogeneity or pleiotropy was found. Therefore, we believe that beef intake may be associated with a reduced risk of AD. Although white bread was significant in the IVW analysis, there was large uncertainty in the results given the wide 95 % CI. Other factors were not associated with AD in this study.

Association between n-3 PUFA and lung function: results from the NHANES 2007-2012 and Mendelian randomisation study.

This study aimed to investigate the association between n-3 PUFA and lung function. First, a cross-sectional study was conducted based on the National Health and Nutrition Examination Survey (NHANES) 2007-2012 data. n-3 PUFA intake was obtained from 24-h dietary recalls. A multivariable linear regression model was used to assess the observational associations of n-3 PUFA intake with lung function. Subsequently, a two-sample Mendelian randomisation (MR) was performed to estimate the potential causal effect of n-3 PUFA on lung function. Genetic instrumental variables were extracted from published genome-wide association studies. Summary statistics about n-3 PUFA was from UK Biobank. Inverse variance weighted was the primary analysis approach. The observational study did not demonstrate a significant association between n-3 PUFA intake and most lung function measures; however, a notable exception was observed with significant findings in the highest quartile for forced vital capacity (FVC) and % predicted FVC. The MR results also showed no causal effect of circulating n-3 PUFA concentration on lung function (forced expiratory volume in one second (FEV1), ß = 0·01301, se = 0·01932, P = 0·5006; FVC, ß = -0·001894, se = 0·01704, P = 0·9115; FEV1:FVC, ß = 0·03118, se = 0·01743, P = 0·07359). These findings indicate the need for further investigation into the impact of higher n-3 PUFA consumption on lung health.

Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression.

Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time-for example, a linear trajectory having both an intercept (severity at time zero) and a slope (rate of change). A technique for identifying causal relationships between one exposure and one outcome in observational data whilst avoiding bias due to confounding is two sample Mendelian Randomisation (2SMR). We consider a multivariate approach to 2SMR using a multilevel model for disease progression to estimate the causal effect an exposure has on the intercept and slope. We carry out a simulation study comparing a naïve univariate 2SMR approach to a multivariate 2SMR approach with one exposure that effects both the intercept and slope of an outcome that changes linearly with time since diagnosis. The simulation study results, across six different scenarios, for both approaches were similar with no evidence against a non-zero bias and appropriate coverage of the 95% confidence intervals (for intercept 93.4-96.2% and the slope 94.5-96.0%). The multivariate approach gives a better joint coverage of both the intercept and slope effects. We also apply our method to two Parkinson's cohorts to examine the effect body mass index has on disease progression. There was no strong evidence that BMI affects disease progression, however the confidence intervals for both intercept and slope were wide.

Non-linear Mendelian randomization: detection of biases using negative controls with a focus on BMI, Vitamin D and LDL cholesterol.

Mendelian randomisation (MR) is an established technique in epidemiological investigation, using the principle of random allocation of genetic variants at conception to estimate the causal linear effect of an exposure on an outcome. Extensions to this technique include non-linear approaches that allow for differential effects of the exposure on the outcome depending on the level of the exposure. A widely used non-linear method is the residual approach, which estimates the causal effect within different strata of the non-genetically predicted exposure (i.e. the "residual" exposure). These "local" causal estimates are then used to make inferences about non-linear effects. Recent work has identified that this method can lead to estimates that are seriously biased, and a new method-the doubly-ranked method-has been introduced as a possibly more robust approach. In this paper, we perform negative control outcome analyses in the MR context. These are analyses with outcomes onto which the exposure should have no predicted causal effect. Using both methods we find clearly biased estimates in certain situations. We additionally examined a situation for which there are robust randomised controlled trial estimates of effects-that of low-density lipoprotein cholesterol (LDL-C) reduction onto myocardial infarction, where randomised trials have provided strong evidence of the shape of the relationship. The doubly-ranked method did not identify the same shape as the trial data, and for LDL-C and other lipids they generated some highly implausible findings. Therefore, we suggest there should be extensive simulation and empirical methodological examination of performance of both methods for NLMR under different conditions before further use of these methods. In the interim, use of NLMR methods needs justification, and a number of sanity checks (such as analysis of negative and positive control outcomes, sensitivity analyses excluding removal of strata at the extremes of the distribution, examination of biological plausibility and triangulation of results) should be performed.