Sex- and site-specific differences in colorectal cancer risk among people with type 2 diabetes

Purpose: The prevalence of colorectal cancer is higher among patients with type 2 diabetes mellitus (T2D) than among patients without diabetes. Furthermore, men are at higher risk for developing colorectal cancer than women in the general population and also subsite-specific risks differ per sex. The aim was to evaluate the impact of T2D on these associations. Methods: A population-based matched cohort study was performed using data from the PHARMO Database Network. Patients with T2D were selected and matched (1:4) to diabetes free controls. Cox proportional hazards models were used to estimate hazard ratios (HRs) for CRC and its subsites. HRs were determined per sex and adjusted for age and socioeconomic status. The ratio of distal versus proximal colon cancer was calculated for people with T2D and controls per sex and stratified by age. Results: Over 55,000 people with T2D were matched to > 215,000 diabetes free controls. Men and women with T2D were 1.3 times more likely to develop colorectal cancer compared to controls. Men with T2D were at higher risk to develop distal colon cancer (hazard ratio (95% confidence interval), 1.42 (1.08–1.88)), and women with T2D were at higher risk for developing proximal colon cancer (hazard ratio (95% confidence interval), 1.58 (1.13–2.19)). For rectal cancer, no statistically significant risk was observed for both men and women. Conclusions: Sex-specific screening strategies and prevention protocols should be considered for people with T2D. More tailored screening strategies may optimize the effectiveness of colorectal cancer screening in terms of reducing incidence and mortality

Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes

CONTEXT: There is a need for novel biomarkers and better understanding of the pathophysiology of diabetic kidney disease. OBJECTIVE: To investigate associations between plasma metabolites and kidney function in people with type 2 diabetes (T2D). DESIGN: 3089 samples from individuals with T2D, collected between 1999 and 2015, from 5 independent Dutch cohort studies were included. Up to 7 years follow-up was available in 1100 individuals from 2 of the cohorts. MAIN OUTCOME MEASURES: Plasma metabolites (n = 149) were measured by nuclear magnetic resonance spectroscopy. Associations between metabolites and estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and eGFR slopes were investigated in each study followed by random effect meta-analysis. Adjustments included traditional cardiovascular risk factors and correction for multiple testing. RESULTS: In total, 125 metabolites were significantly associated (PFDR = 1.5×10-32 - 0.046; β = -11.98-2.17) with eGFR. Inverse associations with eGFR were demonstrated for branched-chain and aromatic amino acids (AAAs), glycoprotein acetyls, triglycerides (TGs), lipids in very low-density lipoproteins (VLDL) subclasses, and fatty acids (PFDR < 0.03). We observed positive associations with cholesterol and phospholipids in high-density lipoproteins (HDL) and apolipoprotein A1 (PFDR < 0.05). Albeit some metabolites were associated with UACR levels (P < 0.05), significance was lost after correction for multiple testing. Tyrosine and HDL-related metabolites were positively associated with eGFR slopes before adjustment for multiple testing (PTyr = 0.003; PHDLrelated < 0.05), but not after. CONCLUSIONS: This study identified metabolites associated with impaired kidney function in T2D, implying involvement of lipid and amino acid metabolism in the pathogenesis. Whether these processes precede or are consequences of renal impairment needs further investigation

Heritability estimates for 361 blood metabolites across 40 genome-wide association studies

Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2 total), and the proportion of heritability captured by known metabolite loci (h2 Metabolite-hits) for 309 lipids and

Author Correction: Heritability estimates for 361 blood metabolites across 40 genome-wide association studies (Nature Communications, (2020), 11, 1, (39), 10.1038/s41467-019-13770-6)

The original version of the Supplementary Information associated with this Article included an incorrect Supplementary Data 1 file, in which additional delimiters were included in the first column for a number of rows, resulting in column shifts for some of these rows. The HTML has been updated to include a corrected version of Supplementary Data 1; the original incorrect version of Supplementary Data 1 can be found as Supplementary Information associated with this Correction. In addition, the original version of this Article contained an error in the author affiliations. An affiliation of Abdel Abdellaoui with Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands was inadvertently omitted. This has now been corrected in both the PDF and HTML versions of the Article