👤 Elina Hyppönen

Coffee is a widely consumed beverage, which has been extensively studied for its potential effects on health. We aimed to map genetic evidence for the effect of habitual coffee consumption on health. We searched PubMed, Embase, Cochrane Database of Systematic Reviews, Cumulative Index to Nursing and Allied Health Literature and two preprint repositories from inception to 30 September 2022, and included fifty-nine studies, spanning 160 disease or biomarker associations. We evaluated the articles for certainty of evidence using a modified GRADE tool and robustness of the associations by comparing Mendelian randomisation (MR) sensitivity analyses. Coffee consumption was associated with smaller grey matter brain volume in one study, and there was probable evidence for an increased risk of Alzheimer’s disease and younger age of onset of Huntington’s disease. MR studies provided probable evidence for an association with increased risk of oesophageal and digestive cancers, but protective effects for hepatocellular carcinomas and ovarian cancer. We found probable evidence for increased risk of type 2 diabetes mellitus, osteoarthritis, rheumatoid arthritis, menopausal disorders, glaucoma, higher total cholesterol, LDL-cholesterol and ApoB, and lowered risk of migraines, kidney disease and gallstone disease. Future studies should aim to understand underlying mechanisms of disease, expand knowledge in non-European cohorts and develop quality assessment tools for systematic reviews of MR studies. Show less

Sub-optimal metabolism is linked to dementia risk, yet metabolic traits rarely occur in isolation. Using data from 308,019 UK Biobank participants, we examined associations of six diverse metabolic subgroups (I-VI) - previously derived via a self-organising map (SOM) that captures patterns of co-occurring metabolic biomarker traits in the population - and 39 individual biomarkers, with incident all-cause dementia, Alzheimer's disease (AD), and vascular dementia (VaD). Biomarker associations were assessed using both linear and nonlinear (restricted cubic spline) models. After adjusting for age, sex, socioeconomic, and lifestyle factors, subgroup analyses showed that participants in the two leanest and two most adipose subgroups had higher risk of dementia outcomes compared to others. Subgroups with high adiposity exhibited elevated VaD risk, which was linked to hypertension, hyperglycaemia, and liver stress (Subgroup II); inflammation, microalbuminuria, and low apolipoprotein A1 (III). For AD, the risk was elevated in the lean subgroups (IV, V), characterised by low body mass index (BMI), triglycerides, and urate, and high sex-hormone binding globulin; as well as for adipose Subgroup II. APOE-ε4 allele count had limited influence on dementia associations with metabolic subgroups and biomarkers. This marked metabolic heterogeneity in dementia risk suggests that metabolic profiling could inform targeted prevention strategies. Interpretation of these findings is supported by previously reported MRI profiles of the metabolic subgroups, providing biological context. Show less

Bipolar disorder (BPD) is a debilitating mood disorder with an unclear etiology. A better understanding of the underlying pathophysiological mechanisms will help to identify novel targets for improved treatment options and prevention strategies. In this metabolome-wide Mendelian randomization study, we screened for metabolites that may have a causal role in BPD. We tested a total of 913 circulating metabolite exposures assessed in 14,296 Europeans using a mass spectrometry-based platform. For the BPD outcome, we used summary data from the largest and most recent genome-wide association study reported to date, including 41,917 BPD cases. We identified 33 metabolites associated with BPD (p Overall, our findings support the notion that arachidonic acid and other polyunsaturated fatty acids may represent potential targets for BPD. Show less

This study aimed to explore the association between depression and body mass index (BMI), and to investigate whether genetic susceptibility to high BMI is different among individuals with or without depression. We used data on 251,125 individuals of white British ancestry from the UK Biobank. We conducted Mendelian randomization (MR) analysis to test for a causal association between depression and BMI using a major depressive disorder (MDD)-related genetic risk score (GRS We found observational and genetic evidence for an association between depression and BMI (MR beta: 0.09, 95% confidence interval [CI] 0.04-0.13). Further, the contribution of genetic risk to high BMI was higher among individuals with depression compared to controls. Carrying 10 additional BMI increasing alleles was associated with 0.24 standard deviation (SD; 95%CI 0.23-0.25) higher BMI among depressed individuals compared to 0.20 SD (95%CI 0.19-0.21) higher in controls, which corresponds to 3.4 kg and 2.8 kg extra weight for an individual of average height. Amongst the individual loci, the evidence for interaction was most notable for a variant near MC4R, a gene known to affect both appetite regulation and the hypothalamic-pituitary adrenal axis (p Genetic predisposition to high BMI was higher among depressed than to nondepressed individuals. This study provides support for a possible role of MC4R in the link between depression and obesity. Show less

Although hundreds of genome-wide association studies-implicated loci have been reported for adult obesity-related traits, less is known about the genetics specific for early-onset obesity and with only a few studies conducted in non-European populations to date. Searching for additional genetic variants associated with childhood obesity, we performed a trans-ancestral meta-analysis of 30 studies consisting of up to 13 005 cases (≥95th percentile of body mass index (BMI) achieved 2-18 years old) and 15 599 controls (consistently <50th percentile of BMI) of European, African, North/South American and East Asian ancestry. Suggestive loci were taken forward for replication in a sample of 1888 cases and 4689 controls from seven cohorts of European and North/South American ancestry. In addition to observing 18 previously implicated BMI or obesity loci, for both early and late onset, we uncovered one completely novel locus in this trans-ancestral analysis (nearest gene, METTL15). The variant was nominally associated with only the European subgroup analysis but had a consistent direction of effect in other ethnicities. We then utilized trans-ancestral Bayesian analysis to narrow down the location of the probable causal variant at each genome-wide significant signal. Of all the fine-mapped loci, we were able to narrow down the causative variant at four known loci to fewer than 10 single nucleotide polymorphisms (SNPs) (FAIM2, GNPDA2, MC4R and SEC16B loci). In conclusion, an ethnically diverse setting has enabled us to both identify an additional pediatric obesity locus and further fine-map existing loci. Show less

A large number of genetic loci are associated with adult body mass index. However, the genetics of childhood body mass index are largely unknown. We performed a meta-analysis of genome-wide association studies of childhood body mass index, using sex- and age-adjusted standard deviation scores. We included 35 668 children from 20 studies in the discovery phase and 11 873 children from 13 studies in the replication phase. In total, 15 loci reached genome-wide significance (P-value < 5 × 10(-8)) in the joint discovery and replication analysis, of which 12 are previously identified loci in or close to ADCY3, GNPDA2, TMEM18, SEC16B, FAIM2, FTO, TFAP2B, TNNI3K, MC4R, GPR61, LMX1B and OLFM4 associated with adult body mass index or childhood obesity. We identified three novel loci: rs13253111 near ELP3, rs8092503 near RAB27B and rs13387838 near ADAM23. Per additional risk allele, body mass index increased 0.04 Standard Deviation Score (SDS) [Standard Error (SE) 0.007], 0.05 SDS (SE 0.008) and 0.14 SDS (SE 0.025), for rs13253111, rs8092503 and rs13387838, respectively. A genetic risk score combining all 15 SNPs showed that each additional average risk allele was associated with a 0.073 SDS (SE 0.011, P-value = 3.12 × 10(-10)) increase in childhood body mass index in a population of 1955 children. This risk score explained 2% of the variance in childhood body mass index. This study highlights the shared genetic background between childhood and adult body mass index and adds three novel loci. These loci likely represent age-related differences in strength of the associations with body mass index. Show less

Coffee, a major dietary source of caffeine, is among the most widely consumed beverages in the world and has received considerable attention regarding health risks and benefits. We conducted a genome-wide (GW) meta-analysis of predominately regular-type coffee consumption (cups per day) among up to 91,462 coffee consumers of European ancestry with top single-nucleotide polymorphisms (SNPs) followed-up in ~30 062 and 7964 coffee consumers of European and African-American ancestry, respectively. Studies from both stages were combined in a trans-ethnic meta-analysis. Confirmed loci were examined for putative functional and biological relevance. Eight loci, including six novel loci, met GW significance (log10Bayes factor (BF)>5.64) with per-allele effect sizes of 0.03-0.14 cups per day. Six are located in or near genes potentially involved in pharmacokinetics (ABCG2, AHR, POR and CYP1A2) and pharmacodynamics (BDNF and SLC6A4) of caffeine. Two map to GCKR and MLXIPL genes related to metabolic traits but lacking known roles in coffee consumption. Enhancer and promoter histone marks populate the regions of many confirmed loci and several potential regulatory SNPs are highly correlated with the lead SNP of each. SNP alleles near GCKR, MLXIPL, BDNF and CYP1A2 that were associated with higher coffee consumption have previously been associated with smoking initiation, higher adiposity and fasting insulin and glucose but lower blood pressure and favorable lipid, inflammatory and liver enzyme profiles (P<5 × 10(-8)).Our genetic findings among European and African-American adults reinforce the role of caffeine in mediating habitual coffee consumption and may point to molecular mechanisms underlying inter-individual variability in pharmacological and health effects of coffee. Show less

The pubertal height growth spurt is a distinctive feature of childhood growth reflecting both the central onset of puberty and local growth factors. Although little is known about the underlying genetics, growth variability during puberty correlates with adult risks for hormone-dependent cancer and adverse cardiometabolic health. The only gene so far associated with pubertal height growth, LIN28B, pleiotropically influences childhood growth, puberty and cancer progression, pointing to shared underlying mechanisms. To discover genetic loci influencing pubertal height and growth and to place them in context of overall growth and maturation, we performed genome-wide association meta-analyses in 18 737 European samples utilizing longitudinally collected height measurements. We found significant associations (P < 1.67 × 10(-8)) at 10 loci, including LIN28B. Five loci associated with pubertal timing, all impacting multiple aspects of growth. In particular, a novel variant correlated with expression of MAPK3, and associated both with increased prepubertal growth and earlier menarche. Another variant near ADCY3-POMC associated with increased body mass index, reduced pubertal growth and earlier puberty. Whereas epidemiological correlations suggest that early puberty marks a pathway from rapid prepubertal growth to reduced final height and adult obesity, our study shows that individual loci associating with pubertal growth have variable longitudinal growth patterns that may differ from epidemiological observations. Overall, this study uncovers part of the complex genetic architecture linking pubertal height growth, the timing of puberty and childhood obesity and provides new information to pinpoint processes linking these traits. Show less

Circulating levels of 25-hydroxyvitamin D (25OHD) are positively associated with high density lipoprotein (HDL) cholesterol. We sought to replicate a previously reported interaction between APOA5 genotype and vitamin D, and to examine whether HDL-associated genetic loci modify the association between serum 25OHD and HDL cholesterol. We examined whether 42 single nucleotide polymorphisms (SNPs) modify the association between serum 25OHD and HDL cholesterol in the 1958 British Birth cohort (aged 45 years, n = 4978). We identified a borderline interaction between the SNP rs12272004 (near the APOA5) and serum 25OHD on HDL cholesterol (P(interaction) = 0.05). The interaction was particularly prominent among the samples collected during winter (P(interaction) = 0.001). None of the other loci showed an interaction with serum 25OHD concentrations on HDL cholesterol. Our study in 4978 British Whites provides further support that APOA5 genotype modifies the association between vitamin D metabolites and HDL cholesterol. Show less

Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10(-8) to P = 10(-190)). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function. Show less