Risk of 16 cancers across the full glycemic spectrum: a population-based cohort study using the UK Biobank.

INTRODUCTION: Diabetes is observed to increase cancer risk, leading to hypothesized direct effects of either hyperglycemia or medication. We investigated associations between glycosylated hemoglobin (HbA1c) across the whole glycemic spectrum and incidence of 16 cancers in a population sample with comprehensive adjustment for risk factors and medication. RESEARCH DESIGN AND METHODS: Linked data from the UK Biobank and UK cancer registry for all individuals with baseline HbA1c and no history of cancer at enrollment were used. Incident cancer was based on International Classification of Diseases - 10th Edition diagnostic codes. Age-standardized incidence rates were estimated by HbA1c category. Associations between HbA1c, modeled as a restricted cubic spline, and cancer risk were estimated using Cox proportional hazards models. RESULTS: Among 378 253 individuals with average follow-up of 7.1 years, 21 172 incident cancers occurred. While incidence for many of the 16 cancers was associated with hyperglycemia in crude analyses, these associations disappeared after multivariable adjustment, except for pancreatic cancer (HR 1.55, 95% CI 1.22 to 1.98 for 55 vs 35 mmol/mol), and a novel finding of an inverse association between HbA1c and premenopausal breast cancer (HR 1.27, 95% CI 1.00 to 1.60 for 25 vs 35 mmol/mol; HR 0.71, 95% CI 0.54 to 0.94 for 45 vs 35 mmol/mol), not observed for postmenopausal breast cancer. Adjustment for diabetes medications had no appreciable impact on HRs for cancer. CONCLUSIONS: Apart from pancreatic cancer, we did not demonstrate any independent positive association between HbA1c and cancer risk. These findings suggest that the potential for a cancer-inducing, direct effect of hyperglycemia may be misplaced

Advancing drug development for atrial fibrillation by prioritising findings from human genetic association studies

Background: Drug development for atrial fibrillation (AF) has failed to yield new approved compounds. We sought to identify and prioritise potential druggable targets with support from human genetics, by integrating the available evidence with bioinformatics sources relevant for AF drug development. Methods: Genetic hits for AF and related traits were identified through structured search of MEDLINE. Genes derived from each paper were cross-referenced with the OpenTargets platform for drug interactions. Confirmation/validation was demonstrated through structured searches and review of evidence on MEDLINE and ClinialTrials.gov for each drug and its association with AF. Findings: 613 unique drugs were identified, with 21 already included in AF Guidelines. Cardiovascular drugs from classes not currently used for AF (e.g. ranolazine and carperitide) and anti-inflammatory drugs (e.g. dexamethasone and mehylprednisolone) had evidence of potential benefit. Further targets were considered druggable but remain open for drug development. Interpretation: Our systematic approach, combining evidence from different bioinformatics platforms, identified drug repurposing opportunities and druggable targets for AF. Funding: KK is supported by UCL BHF Accelerator AA/18/6/34223

HbA1c and brain health across the entire glycaemic spectrum.

AIM: To understand the relationship between HbA1c and brain health across the entire glycaemic spectrum. MATERIALS AND METHODS: We used data from the UK Biobank cohort consisting of 500,000 individuals aged 40-69?years. HbA1c and diabetes diagnosis were used to define baseline glycaemic categories. Our outcomes included incident all-cause dementia, vascular dementia (VD), Alzheimer's dementia (AD), hippocampal volume (HV), white matter hyperintensity (WMH) volume, cognitive function and decline. The reference group was normoglycaemic individuals (HbA1c ?35 & <42 mmol/mol). Our maximum analytical sample contained 449,973 individuals with complete data. RESULTS: Prediabetes and known diabetes increased incident VD (HR 1.54; 95% CI = 1.04, 2.28 and HR 2.97; 95% CI = 2.26, 3.90, respectively). Known diabetes increased all-cause and AD risk (HR 1.91; 95% CI = 1.66, 2.21 and HR 1.84; 95% CI = 1.44, 2.36, respectively). Prediabetes and known diabetes elevated the risks of cognitive decline (OR 1.42; 1.48, 2.96 and OR 1.39; 1.04, 1.75, respectively). Prediabetes, undiagnosed and known diabetes conferred higher WMH volumes (3%, 22% and 7%, respectively) and lower HV (36, 80 and 82?mm3 , respectively), whereas low-normal HbA1c had 1% lower WMH volume and 12?mm3 greater HV. CONCLUSION: Both prediabetes and known diabetes are harmful in terms of VD, cognitive decline and AD risks, as well as lower HV. Associations appeared to be somewhat driven by antihypertensive medication, which implies that certain cardiovascular drugs may ameliorate some of the excess risk. Low-normal HbA1c levels, however, are associated with more favourable brain health outcomes and warrant more in-depth investigation

Is glycaemia associated with poorer brain health and risk of dementia? Cross sectional and follow-up analysis of the UK Biobank

ABSTRACTINTRODUCTIONTo understand the relationship across the glycaemic spectrum, with brain health.METHODSUK Biobank participants. HbA1c and diabetes diagnosis define baseline glycaemic categories. Outcomes: incident vascular dementia (VD), Alzheimer’s dementia (AD), hippocampal volume (HV), white matter hyperintensity (WMH) volume, cognitive function and decline. Reference group: normoglycaemic individuals (HbA1c 35-&lt;42 mmol/mol).RESULTSPre- and known diabetes increased incident VD, (HR 1.54, 95%CI=1.04;2.28 and 2.97, 95%CI=2.26;3.90). Known diabetes increased AD risk (HR 1.84, 95%CI=1.44;2.36). Pre- and known diabetes elevated risks of cognitive decline (OR 1.42, 1.48;2.96 and 1.39, 1.04;1.75). Pre-diabetes, undiagnosed and known diabetes conferred higher WMH volumes (4%, 26%, 5%,) and lower HV (22.4mm3, 15.2mm3, 62.2mm3). Low-normal HbA1c had 2% lower WMH volume and 13.6mm3 greater HV.DISCUSSIONPre and known diabetes increase VD risks; known diabetes increases AD risk. Low-normal HbA1c associates with favourable neuroimaging outcomes. Our findings may have implications for cardiovascular medication in hyperglycaemia for brain health.</jats:sec

The vitamin D binding protein axis modifies disease severity in Lymphangioleiomyomatosis

Background: Lymphangioleiomyomatosis (LAM) is a rare disease of women. Decline in lung function is variable making appropriate targeting of therapy difficult. We used unbiased serum proteomics to identify markers associated with outcome in LAM. Methods: 101 women with LAM and 22 healthy controls were recruited from the National Centre for LAM (Nottingham, UK). 152 DNA and serum samples with linked lung function and outcome data were obtained from patients in the NHLBI LAM Registry (USA). Proteomic analysis was performed on a discovery cohort of 50 LAM and 20 control sera using a SCIEX SWATH mass spectrometric workflow. Protein levels were quantitated by ELISA and SNPs in GC encoding Vitamin D Binding Protein (VTDB) genotyped. Results: Proteomic analysis showed VTDB was 2.6 fold lower in LAM than controls. Serum VTDB was lower in progressive compared with stable LAM (p=0.001) and correlated with diffusing capacity (p=0.01). Median time to death or lung transplant was reduced by 46 months in those with CC genotypes at rs4588 and 38 months in those with non-A containing haplotypes at rs7041/4588 (p=0.014 and 0.008 respectively). Conclusions: The VTDB axis is associated with disease severity and outcome, and GC genotype could help predict transplant free survival in LAM

The vitamin D binding protein axis modifies disease severity in Lymphangioleiomyomatosis

Background: Lymphangioleiomyomatosis (LAM) is a rare disease of women. Decline in lung function is variable making appropriate targeting of therapy difficult. We used unbiased serum proteomics to identify markers associated with outcome in LAM. Methods: 101 women with LAM and 22 healthy controls were recruited from the National Centre for LAM (Nottingham, UK). 152 DNA and serum samples with linked lung function and outcome data were obtained from patients in the NHLBI LAM Registry (USA). Proteomic analysis was performed on a discovery cohort of 50 LAM and 20 control sera using a SCIEX SWATH mass spectrometric workflow. Protein levels were quantitated by ELISA and SNPs in GC encoding Vitamin D Binding Protein (VTDB) genotyped. Results: Proteomic analysis showed VTDB was 2.6 fold lower in LAM than controls. Serum VTDB was lower in progressive compared with stable LAM (p=0.001) and correlated with diffusing capacity (p=0.01). Median time to death or lung transplant was reduced by 46 months in those with CC genotypes at rs4588 and 38 months in those with non-A containing haplotypes at rs7041/4588 (p=0.014 and 0.008 respectively). Conclusions: The VTDB axis is associated with disease severity and outcome, and GC genotype could help predict transplant free survival in LAM

The vitamin D binding protein axis modifies disease severity in lymphangioleiomyomatosis

Background: Lymphangioleiomyomatosis (LAM) is a rare disease of women. Decline in lung function is variable making appropriate targeting of therapy difficult. We used unbiased serum proteomics to identify markers associated with outcome in LAM.Methods: 101 women with LAM and 22 healthy controls were recruited from the National Centre for LAM (Nottingham, UK). 152 DNA and serum samples with linked lung function and outcome data were obtained from patients in the NHLBI LAM Registry (USA). Proteomic analysis was performed on a discovery cohort of 50 LAM and 20 control sera using a SCIEX SWATH mass spectrometric workflow. Protein levels were quantitated by ELISA and SNPs in GC encoding Vitamin D Binding Protein (VTDB) genotyped.Results: Proteomic analysis showed VTDB was 2.6 fold lower in LAM than controls. Serum VTDB was lower in progressive compared with stable LAM (p=0.001) and correlated with diffusing capacity (p=0.01). Median time to death or lung transplant was reduced by 46?months in those with CC genotypes at rs4588 and 38?months in those with non-A containing haplotypes at rs7041/4588 (p=0.014 and 0.008 respectively).Conclusions: The VTDB axis is associated with disease severity and outcome, and GC genotype could help predict transplant free survival in LAM

Type 2 diabetes risks and determinants in second-generation migrants and mixed ethnicity people of South Asian and African Caribbean descent in the UK.

AIMS/HYPOTHESIS: Excess risks of type 2 diabetes in UK South Asians (SA) and African Caribbeans (AC) compared with Europeans remain unexplained. We studied risks and determinants of type 2 diabetes in first- and second-generation (born in the UK) migrants, and in those of mixed ethnicity. METHODS: Data from the UK Biobank, a population-based cohort of ~500,000 participants aged 40-69 at recruitment, were used. Type 2 diabetes was assigned using self-report and HbA1c. Ethnicity was both self-reported and genetically assigned using admixture level scores. European, mixed European/South Asian (MixESA), mixed European/African Caribbean (MixEAC), SA and AC groups were analysed, matched for age and sex to enable comparison. In the frames of this cross-sectional study, we compared type 2 diabetes in second- vs first-generation migrants, and mixed ethnicity vs non-mixed groups. Risks and explanations were analysed using logistic regression and mediation analysis, respectively. RESULTS: Type 2 diabetes prevalence was markedly elevated in SA (599/3317 = 18%) and AC (534/4180 = 13%) compared with Europeans (140/3324 = 4%). Prevalence was lower in second- vs first-generation SA (124/1115 = 11% vs 155/1115 = 14%) and AC (163/2200 = 7% vs 227/2200 = 10%). Favourable adiposity (i.e. lower waist/hip ratio or BMI) contributed to lower risk in second-generation migrants. Type 2 diabetes in mixed populations (MixESA: 52/831 = 6%, MixEAC: 70/1045 = 7%) was lower than in comparator ethnic groups (SA: 18%, AC: 13%) and higher than in Europeans (4%). Greater socioeconomic deprivation accounted for 17% and 42% of the excess type 2 diabetes risk in MixESA and MixEAC compared with Europeans, respectively. Replacing self-reported with genetically assigned ethnicity corroborated the mixed ethnicity analysis. CONCLUSIONS/INTERPRETATION: Type 2 diabetes risks in second-generation SA and AC migrants are a fifth lower than in first-generation migrants. Mixed ethnicity risks were markedly lower than SA and AC groups, though remaining higher than in Europeans. Distribution of environmental risk factors, largely obesity and socioeconomic status, appears to play a key role in accounting for ethnic differences in type 2 diabetes risk

Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits.

The role of rare variants in complex traits remains uncharted. Here, we conduct deep whole genome sequencing of 1457 individuals from an isolated population, and test for rare variant burdens across six cardiometabolic traits. We identify a role for rare regulatory variation, which has hitherto been missed. We find evidence of rare variant burdens that are independent of established common variant signals (ADIPOQ and adiponectin, P = 4.2 × 10-8; APOC3 and triglyceride levels, P = 1.5 × 10-26), and identify replicating evidence for a burden associated with triglyceride levels in FAM189B (P = 2.2 × 10-8), indicating a role for this gene in lipid metabolism

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution.

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF &lt;5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants