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Fructose intake has increased substantially throughout the developed world and is associated with obesity, type 2 diabetes and non-alcoholic fatty liver disease. Currently, our understanding of the metabolic and mechanistic implications for immune cells, such as monocytes and macrophages, exposed to elevated levels of dietary fructose is limited. Here, we show that fructose reprograms cellular metabolic pathways to favour glutaminolysis and oxidative metabolism, which are required to support increased inflammatory cytokine production in both LPS-treated human monocytes and mouse macrophages. A fructose-dependent increase in mTORC1 activity drives translation of pro-inflammatory cytokines in response to LPS. LPS-stimulated monocytes treated with fructose rely heavily on oxidative metabolism and have reduced flexibility in response to both glycolytic and mitochondrial inhibition, suggesting glycolysis and oxidative metabolism are inextricably coupled in these cells. The physiological implications of fructose exposure are demonstrated in a model of LPS-induced systemic inflammation, with mice exposed to fructose having increased levels of circulating IL-1β after LPS challenge. Taken together, our work underpins a pro-inflammatory role for dietary fructose in LPS-stimulated mononuclear phagocytes which occurs at the expense of metabolic flexibility., Myeloid cells are able to utilize a variety of monosaccharides from our diet, including fructose. Here the authors show that when monocytes are reliant on fructose as a carbon energy source they are reprogrammed towards oxidative metabolism, glutamine anaplerosis and a pro-inflammatory phenotype owing to excess pro-inflammatory cytokine production.

Background:Some individuals living with obesity may be relatively metabolically healthy, whilst others suffer from multiple conditions that may be linked to adverse metabolic effects or other factors. The extent to which the adverse metabolic component of obesity contributes to disease compared to the non-metabolic components is often uncertain. We aimed to use Mendelian randomisation (MR) and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases.Methods:We selected 37 chronic diseases associated with obesity and genetic variants associated with different aspects of excess weight. These genetic variants included those associated with metabolically ‘favourable adiposity’ (FA) and ‘unfavourable adiposity’ (UFA) that are both associated with higher adiposity but with opposite effects on metabolic risk. We used these variants and two sample MR to test the effects on the chronic diseases.Results:MR identified two sets of diseases. First, 11 conditions where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here, MR with the FA and UFA genetics showed opposing effects on risk of disease: coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, polycystic ovary syndrome, heart failure, atrial fibrillation, chronic kidney disease, renal cancer, and gout. Second, 9 conditions where the non-metabolic effects of excess weight (e.g. mechanical effect) are likely a cause. Here, MR with the FA genetics, despite leading to lower metabolic risk, and MR with the UFA genetics, both indicated higher disease risk: osteoarthritis, rheumatoid arthritis, osteoporosis, gastro-oesophageal reflux disease, gallstones, adult-onset asthma, psoriasis, deep vein thrombosis, and venous thromboembolism.Conclusions:Our results assist in understanding the consequences of higher adiposity uncoupled from its adverse metabolic effects, including the risks to individuals with high body mass index who may be relatively metabolically healthy.Funding:Diabetes UK, UK Medical Research Council, World Cancer Research Fund, National Cancer Institute.

Background The UK Biobank is a large prospective cohort, based in the UK, that has deep phenotypic and genomic data on roughly a half a million individuals. Included in this resource are data on approximately 78,000 individuals with “non-white British ancestry.” While most epidemiology studies have focused predominantly on populations of European ancestry, there is an opportunity to contribute to the study of health and disease for a broader segment of the population by making use of the UK Biobank’s “non-white British ancestry” samples. Here, we present an empirical description of the continental ancestry and population structure among the individuals in this UK Biobank subset. Results Reference populations from the 1000 Genomes Project for Africa, Europe, East Asia, and South Asia were used to estimate ancestry for each individual. Those with at least 80% ancestry in one of these four continental ancestry groups were taken forward (N = 62,484). Principal component and K-means clustering analyses were used to identify and characterize population structure within each ancestry group. Of the approximately 78,000 individuals in the UK Biobank that are of “non-white British” ancestry, 50,685, 6653, 2782, and 2364 individuals were associated to the European, African, South Asian, and East Asian continental ancestry groups, respectively. Each continental ancestry group exhibits prominent population structure that is consistent with self-reported country of birth data and geography. Conclusions Methods outlined here provide an avenue to leverage UK Biobank’s deeply phenotyped data allowing researchers to maximize its potential in the study of health and disease in individuals of non-white British ancestry.

Background: Circulating lipids and insulin-like growth factor 1 (IGF-I) have been reliably associated with breast cancer. Observational studies suggest an interplay between lipids and IGF-I, however, whether these relationships are causal and if pathways from these phenotypes to breast cancer overlap is unclear. Methods: Mendelian randomization (MR) was conducted to estimate the relationship between lipids or IGF-I and breast cancer risk using genetic summary statistics for lipids (low-density lipoprotein cholesterol, LDL-C; high-density lipoprotein cholesterol, HDL-C; triglycerides, TGs), IGF-I and breast cancer from GLGC/UKBB (N = 239,119), CHARGE/UKBB (N = 252,547), and Breast Cancer Association Consortium (N = 247,173), respectively. Cross-sectional observational and MR analyses were conducted to assess the bi-directional relationship between lipids and IGF-I in SHIP (N = 3,812) and UKBB (N = 422,389), and using genetic summary statistics from GLGC (N = 188,577) and CHARGE/UKBB (N = 469,872). Results: In multivariable MR (MVMR) analyses, the OR for breast cancer per 1-SD increase in HDL-C and TG was 1.08 [95% confidence interval (CI), 1.04–1.13] and 0.94 (95% CI, 0.89–0.98), respectively. The OR for breast cancer per 1-SD increase in IGF-I was 1.09 (95% CI, 1.04–1.15). MR analyses suggested a bi-directional TG–IGF-I relationship (TG–IGF-I β per 1-SD: −0.13; 95% CI, −0.23 to −0.04; and IGF-I–TG β per 1-SD: −0.11; 95% CI, −0.18 to −0.05). There was little evidence for a causal relationship between HDL-C and LDL-C with IGF-I. In MVMR analyses, associations of TG or IGF-I with breast cancer were robust to adjustment for IGF-I or TG, respectively. Conclusions: Our findings suggest a causal role of HDL-C, TG, and IGF-I in breast cancer. Observational and MR analyses support an interplay between IGF-I and TG; however, MVMR estimates suggest that TG and IGF-I may act independently to influence breast cancer. Impact: Our findings should be considered in the development of prevention strategies for breast cancer, where interventions are known to modify circulating lipids and IGF-I.

To understand the consequences of higher adiposity uncoupled from its adverse metabolic effects, we selected 37 diseases associated with obesity and genetic variants associated with different aspects of excess weight including metabolically “favourable adiposity” (FA) and “unfavourable adiposity” (UFA). Mendelian randomisation (MR) identified two sets of diseases. First, 12 conditions where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here MR with the FA and UFA genetics showed opposing effects on the risk of disease, including colorectal and ovarian cancer, and gout. Second, 7 conditions where the non-metabolic effects of excess weight (e.g. mechanical effect) is likely a cause. Here MR with the FA genetics, despite leading to lower metabolic risk, and MR with the UFA genetics, were both associated with higher disease risk, including osteoarthritis and venous thromboembolism. Individuals with high BMI are at higher risk of some diseases despite being relatively metabolically healthy.

Clear cell renal cell carcinoma (ccRCC) is characterized by large intracellular lipid droplets containing free and esterified cholesterol; however, the functional significance of cholesterol accumulation in ccRCC cells is unknown. We demonstrate that, surprisingly, genes encoding cholesterol biosynthetic enzymes are repressed in ccRCC, suggesting a dependency on exogenous cholesterol. Mendelian randomization analyses based on 31,000 individuals indicate a causal link between elevated circulating high-density lipoprotein (HDL) cholesterol and ccRCC risk. Depriving ccRCC cells of either cholesterol or HDL compromises proliferation and survival in vitro and tumor growth in vivo; in contrast, elevated dietary cholesterol promotes tumor growth. Scavenger Receptor B1 (SCARB1) is uniquely required for cholesterol import, and inhibiting SCARB1 is sufficient to cause ccRCC cell-cycle arrest, apoptosis, elevated intracellular reactive oxygen species levels, and decreased PI3K/AKT signaling. Collectively, we reveal a cholesterol dependency in ccRCC and implicate SCARB1 as a novel therapeutic target for treating kidney cancer. Significance: We demonstrate that ccRCC cells are auxotrophic for exogenous cholesterol to maintain PI3K/AKT signaling pathway and ROS homeostasis. Blocking cholesterol import through the HDL transporter SCARB1 compromises ccRCC cell survival and tumor growth, suggesting a novel pharmacologic target for this disease. This article is highlighted in the In This Issue feature, p. 2945

Circulating lipids have been associated with breast cancer (BCa). This association may, in part, be due to an effect of lipids on insulin-like growth factors (IGFs), which have been reliably associated with BCa. In two-sample Mendelian randomization (MR) analyses, we found that low density lipoprotein (LDL-C) was associated with IGFBP-3 (beta:0.08 SD; 95%CI:0.02,0.15; p = 0.01, per SD increase in LDL-C) and IGFBP-3 was associated with postmenopausal BCa (OR:1.09; 95%CI:1.00,1.19; p = 0.05, per SD increase in IGFBP-3). We also found that triglycerides were associated with IGF-I (beta:-0.13SD; 95%CI:-0.22,-0.03, per SD increase in triglycerides) and that IGF-I was associated with overall BCa (OR:1.10;95%CI:1.02,1.18, per SD increase in IGF-I). Taken together, these results suggest that IGFBP-3 may be a potential causal step between LDL-C and postmenopausal BCa and IGF-I a potential causal for triglycerides. Our two-step MR results build on evidence linking circulating lipids and IGFs with BCa, however, multivariable MR analyses are currently unable to support this relationship due to weak instruments.

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Importance: Preclinical and epidemiological studies indicate a potential chemopreventive role of statins in epithelial ovarian cancer risk.Objective: To evaluate the association of genetically proxied inhibition of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase (ie, genetic variants related to lower function of HMG-CoA reductase, target of statins) with epithelial ovarian cancer among the general population and in BRCA1/2 mutation carriers.Design, Setting, and Participants: Single-nucleotide polymorphisms (SNPs) in HMGCR, NPC1L1, and PCSK9 associated with low-density lipoprotein (LDL) cholesterol in a genome-wide association study (GWAS) meta-analysis (N ≤196 475) were used to proxy therapeutic inhibition of HMG-CoA reductase, Niemann-Pick C1-Like 1 (NPC1L1) and proprotein convertase subtilisin/kexin type 9 (PCSK9), respectively. Summary statistics were obtained for these SNPs from a GWAS meta-analysis of case-control analyses of invasive epithelial ovarian cancer in the Ovarian Cancer Association Consortium (OCAC; N = 63 347) and from a GWAS meta-analysis of retrospective cohort analyses of epithelial ovarian cancer among BRCA1/2 mutation carriers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA; N = 31 448). Across the 2 consortia, participants were enrolled between 1973 and 2014 and followed up through 2015. OCAC participants came from 14 countries and CIMBA participants came from 25 countries. SNPs were combined into multi-allelic models and mendelian randomization estimates representing lifelong inhibition of targets were generated using inverse-variance weighted random-effects models.Exposures: Primary exposure was genetically proxied inhibition of HMG-CoA reductase and secondary exposures were genetically proxied inhibition of NPC1L1 and PCSK9 and genetically proxied circulating LDL cholesterol levels.Main Outcomes and Measures: Overall and histotype-specific invasive epithelial ovarian cancer (general population) and epithelial ovarian cancer (BRCA1/2 mutation carriers), measured as ovarian cancer odds (general population) and hazard ratio (BRCA1/2 mutation carriers).Results: The OCAC sample included 22 406 women with invasive epithelial ovarian cancer and 40 941 control individuals and the CIMBA sample included 3887 women with epithelial ovarian cancer and 27 561 control individuals. Median ages for the cohorts ranged from 41.5 to 59.0 years and all participants were of European ancestry. In the primary analysis, genetically proxied HMG-CoA reductase inhibition equivalent to a 1-mmol/L (38.7-mg/dL) reduction in LDL cholesterol was associated with lower odds of epithelial ovarian cancer (odds ratio [OR], 0.60 [95% CI, 0.43-0.83]; P = .002). In BRCA1/2 mutation carriers, genetically proxied HMG-CoA reductase inhibition was associated with lower ovarian cancer risk (hazard ratio, 0.69 [95% CI, 0.51-0.93]; P = .01). In secondary analyses, there were no significant associations of genetically proxied inhibition of NPC1L1 (OR, 0.97 [95% CI, 0.53-1.75]; P = .91), PCSK9 (OR, 0.98 [95% CI, 0.85-1.13]; P = .80), or circulating LDL cholesterol (OR, 0.98 [95% CI, 0.91-1.05]; P = .55) with epithelial ovarian cancer.Conclusions and Relevance: Genetically proxied inhibition of HMG-CoA reductase was significantly associated with lower odds of epithelial ovarian cancer. However, these findings do not indicate risk reduction from medications that inhibit HMG-CoA reductase; further research is needed to understand whether there is a similar association with such medications.

Background Higher adiposity increases the risk of colorectal cancer (CRC), but whether this relationship varies by anatomical sub-site or by sex is unclear. Further, the metabolic alterations mediating the effects of adiposity on CRC are not fully understood. Methods We examined sex- and site-specific associations of adiposity with CRC risk and whether adiposity-associated metabolites explain the associations of adiposity with CRC. Genetic variants from genome-wide association studies of body mass index (BMI) and waist-to-hip ratio (WHR, unadjusted for BMI; N = 806,810), and 123 metabolites from targeted nuclear magnetic resonance metabolomics (N = 24,925), were used as instruments. Sex-combined and sex-specific Mendelian randomization (MR) was conducted for BMI and WHR with CRC risk (58,221 cases and 67,694 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium, Colorectal Cancer Transdisciplinary Study, and Colon Cancer Family Registry). Sex-combined MR was conducted for BMI and WHR with metabolites, for metabolites with CRC, and for BMI and WHR with CRC adjusted for metabolite classes in multivariable models. Results In sex-specific MR analyses, higher BMI (per 4.2 kg/m2) was associated with 1.23 (95% confidence interval (CI) = 1.08, 1.38) times higher CRC odds among men (inverse-variance-weighted (IVW) model); among women, higher BMI (per 5.2 kg/m2) was associated with 1.09 (95% CI = 0.97, 1.22) times higher CRC odds. WHR (per 0.07 higher) was more strongly associated with CRC risk among women (IVW OR = 1.25, 95% CI = 1.08, 1.43) than men (IVW OR = 1.05, 95% CI = 0.81, 1.36). BMI or WHR was associated with 104/123 metabolites at false discovery rate-corrected P ≤ 0.05; several metabolites were associated with CRC, but not in directions that were consistent with the mediation of positive adiposity-CRC relations. In multivariable MR analyses, associations of BMI and WHR with CRC were not attenuated following adjustment for representative metabolite classes, e.g., the univariable IVW OR for BMI with CRC was 1.12 (95% CI = 1.00, 1.26), and this became 1.11 (95% CI = 0.99, 1.26) when adjusting for cholesterol in low-density lipoprotein particles. Conclusions Our results suggest that higher BMI more greatly raises CRC risk among men, whereas higher WHR more greatly raises CRC risk among women. Adiposity was associated with numerous metabolic alterations, but none of these explained associations between adiposity and CRC. More detailed metabolomic measures are likely needed to clarify the mechanistic pathways.

Observational epidemiological studies are prone to confounding, measurement error, and reverse causation, undermining their ability to generate reliable causal estimates of the effect of risk factors to inform cancer prevention and treatment strategies. Mendelian randomization (MR) is an analytical approach that uses genetic variants to proxy potentially modifiable exposures (e.g. environmental factors, biological traits, and druggable pathways) to permit robust causal inference of the effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within population health research in cardio-metabolic disease, but also holds much promise for identifying possible interventions (e.g., dietary, behavioural, or pharmacological) for cancer prevention and treatment. However, some methodological and conceptual challenges in the implementation of MR are particularly pertinent when applying this method to cancer aetiology and prognosis, including reverse causation arising from disease latency and selection bias in studies of cancer progression. These issues must be carefully considered to ensure appropriate design, analysis, and interpretation of such studies.In this review, we provide an overview of the key principles and assumptions of MR focusing on applications of this method to the study of cancer aetiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared to conventional epidemiological studies. Lastly, limitations of MR and recent methodological developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer.

BackgroundType 2 diabetes develops for many years before diagnosis. We aimed to reveal early metabolic features characterising liability to adult disease by examining genetic liability to adult type 2 diabetes in relation to detailed metabolic traits across early life.Methods and FindingsData were from up to 4,761 offspring from the Avon Longitudinal Study of Parents and Children cohort. Linear models were used to examine effects of a genetic risk score (GRS, including 162 variants) for adult type 2 diabetes on 4 repeated measures of 229 traits from targeted nuclear magnetic resonance (NMR) metabolomics. These traits included lipoprotein subclass-specific cholesterol and triglyceride content, amino and fatty acids, inflammatory glycoprotein acetyls, and others, and were measured in childhood (age 8y), adolescence (age 16y), young-adulthood (age 18y), and adulthood (age 25y). For replication, two-sample Mendelian randomization (MR) was conducted using summary data from genome-wide association studies of metabolic traits from NMR in an independent sample of adults (N range 13,476 to 24,925; mean (SD) age range 23.9y (2.1y) to 61.3y (2.9y)). Among ALSPAC participants (49.7% male), the prevalence of type 2 diabetes was very low across time points (< 5 cases when first assessed at age 16y; 7 cases (0.4%) when assessed at age 25y). At age 8y, type 2 diabetes liability (per SD-higher GRS) was associated with lower lipids in high-density lipoprotein (HDL) particle subtypes – e.g. −0.03 SD (95% CI = −0.06, −0.003; P = 0.03) for total lipids in very-large HDL. At age 16y, associations remained strongest with lower lipids in HDL and became stronger with pre-glycemic traits including citrate (−0.06 SD, 95% CI = −0.09, −0.02; P = 1.41×10−03) and with glycoprotein acetyls (0.05 SD, 95% CI = 0.01, 0.08; P = 0.01). At age 18y, associations were stronger with branched chain amino acids including valine (0.06 SD; 95% CI = 0.02, 0.09; P = 1.24×10−03), while at age 25y, associations had strengthened with VLDL lipids and remained consistent with previously altered traits including HDL lipids. Results of two-sample MR in an independent sample of adults indicated persistent patterns of effect of type 2 diabetes liability, with higher type 2 diabetes liability positively associated with VLDL lipids and branched chain amino acid levels, and inversely associated with HDL lipids – again for large and very large HDL particularly (−0.004 SD (95% CI = −0.007, −0.002; P = 8.45×10−04) per 1 log odds of type 2 diabetes for total lipids in large HDL). Study limitations include modest sample sizes for ALSPAC analyses and limited coverage of protein and hormonal traits; insulin was absent as it is not quantified by NMR and not consistently available at each time point. Analyses were restricted to white-Europeans which reduced confounding by population structure but limited inference to other ethnic groups.ConclusionsOur results support perturbed HDL lipid metabolism as one of the earliest features of type 2 diabetes liability which precedes higher branched chain amino acid and inflammatory glycoprotein acetyl levels. This feature is apparent in childhood as early as age 8y, decades before the clinical onset of disease.Author summaryWhy was this study done?Type 2 diabetes develops for many years before diagnosis. Clinical disease is characterised by numerous metabolic perturbations that are detectable in circulation, but which of these reflect the developmental stages of type 2 diabetes – as opposed to independent causes of type 2 diabetes or markers of other disease processes – is unknown. Revealing traits specific to type 2 diabetes development could inform the targeting of key pathways to prevent the clinical onset of disease and its complications.Genetic liability to type 2 diabetes is less prone to confounding than measured type 2 diabetes or blood glucose and may help reveal early perturbations in the blood that arise in response to type 2 diabetes liability itself.What did the researchers do and find?We examined effects of genetic liability to adult type 2 diabetes, based on a genetic risk score including 162 variants, on detailed metabolic traits measured on the same individuals across four stages of early life – childhood (age 8y), adolescence (age 16y), young-adulthood (age 18y), and adulthood (age 25y).We found that higher type 2 diabetes liability was associated most consistently across ages with lower lipid content in certain subtypes of HDL particles. Effects were more gradual on higher lipid content in VLDL particles and on higher branched chain amino acid and inflammatory glycoprotein acetyl levels.What do these findings mean?Signs of type 2 diabetes liability are detectable in the blood in childhood, decades before the disease becomes noticeable. These signs, taken to reflect the early features of, or coincident with, disease, likely involve lower lipid content in HDL particles, followed by higher levels of branched chain amino acids and inflammation.Genetic risk scores for adult diseases can be integrated with metabolic measurements taken earlier in life to help to reveal the timing at which signs of disease liability become visible and the traits most central to its development.

Background: Deep vein thrombosis (DVT) is the formation of a thrombus/clot in the deep veins, when part of this clot breaks off it can travel to the lungs, resulting in pulmonary embolism. These two conditions together are known as venous thromboembolism (VTE), a leading cause of death and disability worldwide. Despite the prevalence of VTE, we do not fully understand what causes it and it is often overlooked as a major public health problem. Confirming and identifying risk factors associated with DVT is likely to lead to a reduction in the incidence, morbidity and mortality of VTE especially where these risk factors are modifiable. We can do this, by exploiting the availability of summary genetic data from genome-wide association studies (GWAS) of numerous phenotypes, including DVT, which permits hypothesis-free causal inference. Objectives: To identify novel risk factors for DVT and to assess the causality of factors previously shown to be associated with DVT. Methods: Two-sample Mendelian randomization (MR) was performed using summarised genetic data. Inverse variance weighted (IVW) estimates were calculated and validated by additional methods more robust to horizontal pleiotropy (MR Egger, simple mode, weighted mode, and weighted median). Bidirectional and heterogeneity sensitivity analyses were performed to further evaluate our findings. Results: Forty-seven exposures passed an exposure-exposure correlation-adjusted Bonferroni P-value threshold (5.43E-05). These included previously hypothesised risk factors for DVT (e.g. body mass index, varicose veins, height, hyperthyroidism) and novel associations (e.g. prospective memory, basal metabolic rate). Conclusion: Our analyses confirmed causal associations of risk factors previously associated with DVT and highlighted several novel risk factors for the disease. Our study demonstrates the utility of using a hypothesis free Mendelian randomization approach for the identification of novel disease risk factors.

BackgroundRapid pre - and postnatal growth have been associated with later life adverse health outcomes, which could implicate (as a mediator) circulating insulin-like-growth-factor I (IGF-I), an important regulator of growth. We investigated associations of prenatal (birth weight and length) and postnatal growth in infancy and childhood with circulating IGF-I measured at 11.5 years of age.MethodsWe analysed 11.5-year follow-up data from 17,046 Belarusian children who participated in the Promotion of Breastfeeding Intervention Trial (PROBIT) since birth.ResultsComplete data were available for 5422 boys and 4743 girls (60%). We stratified the analyses by sex, as there was evidence of interaction between growth and sex in their associations with IGF-I. Weight and length/height velocity during childhood were positively associated with IGF-I at 11.5 years; associations increased with age at growth assessment and were stronger for length/height gain than for weight gain. The change in internal run-normalized IGF-I z-score at 11.5 years was 0.038 (95% CI -0.004,0.080) per standard deviation (SD) increase in length gain at 0-3 months amongst girls and 0.025 (95% CI - 0.011,0.060) amongst boys, increasing to 0.336 (95% CI 0.281,0.391;) and 0.211 (95% CI 0.165,0.256) for girls and boys, respectively, for growth during 6.5-11.5 years.ConclusionPostnatal growth velocities in childhood are positively associated with levels of circulating IGF-I in pre-adolescents. Future studies should focus on assessing whether IGF-I is on the causal pathway between early growth and later health outcomes, such as cancer and diabetes.