Delayed bedtime on non-school days associates with higher weight and waist circumference in children: Cross-sectional and longitudinal analyses with Mendelian randomisation.

Sleep duration has been linked with obesity in population-based studies. Less is known about bedtimes and, especially, if discrepancy between bedtimes on school and non-school days associate with adiposity in children. The associations of self-reported bedtimes with the body mass index z-score (BMIz) and waist-to-height ratio (WtHr) were examined among children with a mean (SD) age of 11.2 (0.85) years in cross-sectional (n = 10,245) and longitudinal (n = 5085) study settings. The causal relationship of whether BMIz contributes to bedtimes, was further examined in a subset of 1064 participants by exploiting Mendelian randomisation (MR). After adjusting for sleep duration and other confounders, every 0.5 h later bedtime on non-school nights and a delay in bedtime in non-school nights compared with school nights associated with 0.048 (95% CI 0.027; 0.069) and 0.08 (95% CI 0.056; 0.105) higher BMIz as well as 0.001 (95% CI 0; 0.002) and 0.004 (95% CI 0.003; 0.005) with higher WtHr, respectively. Moreover, every 0.5-h delay in bedtime in non-school nights compared with school nights associated with 0.001 (95% CI 0; 0.002) greater increase in WtHr in the 2.5 years follow-up. Thus, a 2-h delay in bedtime at the age of 11 years corresponds with a 0.6 cm increase in waist circumference. The MR analysis did not indicate an opposite causal relationship: higher BMIz was not causing delayed bedtimes. Later bedtime on non-school days and discrepancy in bedtimes associated with increased BMIz and WtHr, while longitudinally these predicted higher WtHr, independently of sleep duration. Promoting early bedtimes, especially on weekends, should be considered in obesity prevention among school-aged children.

Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.

AIMS/HYPOTHESIS: Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets. METHODS: We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets. RESULTS: The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10-9; although not withstanding correction for multiple testing, p>9.3×10-9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A and MFF; p<2.7×10-6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10-6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10-11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10-8] and negatively with tubulointerstitial fibrosis [p=2.0×10-9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10-16], and SNX30 expression correlated positively with eGFR [p=5.8×10-14] and negatively with fibrosis [p<2.0×10-16]). CONCLUSIONS/INTERPRETATION: Altogether, the results point to novel genes contributing to the pathogenesis of DKD. DATA AVAILABILITY: The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages ( https://t1d.hugeamp.org/downloads.html ; https://t2d.hugeamp.org/downloads.html ; https://hugeamp.org/downloads.html ).

Apolipoprotein C-III predicts cardiovascular events and mortality in individuals with type 1 diabetes and albuminuria.

OBJECTIVES: We studied apolipoprotein C-III (apoC-III) in relation to diabetic kidney disease (DKD), cardiovascular outcomes, and mortality in type 1 diabetes. METHODS: The cohort comprised 3966 participants from the prospective observational Finnish Diabetic Nephropathy Study. Progression of DKD was determined from medical records. A major adverse cardiac event (MACE) was defined as acute myocardial infarction, coronary revascularization, stroke, or cardiovascular mortality through 2017. Cardiovascular and mortality data were retrieved from national registries. RESULTS: ApoC-III predicted DKD progression independent of sex, diabetes duration, blood pressure, HbA1c , smoking, LDL-cholesterol, lipid-lowering medication, DKD category, and remnant cholesterol (hazard ratio [HR] 1.43 [95% confidence interval 1.05-1.94], p = 0.02). ApoC-III also predicted the MACE in a multivariable regression analysis; however, it was not independent of remnant cholesterol (HR 1.05 [0.81-1.36, p = 0.71] with remnant cholesterol; 1.30 [1.03-1.64, p = 0.03] without). DKD-specific analyses revealed that the association was driven by individuals with albuminuria, as no link between apoC-III and the outcome was observed in the normal albumin excretion or kidney failure categories. The same was observed for mortality: Individuals with albuminuria had an adjusted HR of 1.49 (1.03-2.16, p = 0.03) for premature death, while no association was found in the other groups. The highest apoC-III quartile displayed a markedly higher risk of MACE and death than the lower quartiles; however, this nonlinear relationship flattened after adjustment. CONCLUSIONS: The impact of apoC-III on MACE risk and mortality is restricted to those with albuminuria among individuals with type 1 diabetes. This study also revealed that apoC-III predicts DKD progression, independent of the initial DKD category.

Decreased plasma kallikrein activity is associated with reduced kidney function in individuals with type 1 diabetes.

AIMS/HYPOTHESIS: Plasma kallikrein is the central mediator of the plasma kallikrein-kinin system, which is involved both in vascular control and thrombin formation cascades. The plasma kallikrein-kinin system has also been considered protective in pathological conditions, but the impact of plasma kallikreins on diabetic nephropathy remains unknown. The objective of this cross-sectional study was to explore the association of plasma kallikrein with diabetic nephropathy. METHODS: We measured plasma kallikrein activity in 295 individuals with type 1 diabetes at various stages of diabetic nephropathy, and we tested the genetic association between the plasma kallikrein-kinin system and kidney function in 4400 individuals with type 1 diabetes. RESULTS: Plasma kallikrein activity was associated with diabetes duration (p < 0.001) and eGFR (p < 0.001), and plasma kallikrein activity was lower with more advanced diabetic nephropathy, being lowest in individuals on dialysis. The minor alleles of the KNG1 rs5030062 and rs710446 variants, which have previously been associated with increased plasma pre-kallikrein and/or factor XI (FXI) protein levels, were associated with higher eGFR (rs5030062 ß = 0.03, p = 0.01; rs710446 ß = 0.03, p = 0.005) in the FinnDiane cohort of 4400 individuals with type 1 diabetes. CONCLUSIONS/INTERPRETATION: Plasma kallikrein activity and genetic variants known to increase the plasma kallikrein level are associated with higher eGFR in individuals with type 1 diabetes, suggesting that plasma kallikrein might have a protective effect in diabetic nephropathy.

Soluble receptor for AGE in diabetic nephropathy and its progression in Finnish individuals with type 1 diabetes.

AIMS/HYPOTHESIS: Activation of the receptor for AGE (RAGE) has been shown to be associated with diabetic nephropathy. The soluble isoform of RAGE (sRAGE) is considered to function as a decoy receptor for RAGE ligands and thereby protects against diabetic complications. A possible association between sRAGE and diabetic nephropathy is still, however, controversial and a more comprehensive analysis of sRAGE with respect to diabetic nephropathy in type 1 diabetes is therefore warranted. METHODS: sRAGE was measured in baseline serum samples from 3647 participants with type 1 diabetes from the nationwide multicentre Finnish Diabetic Nephropathy (FinnDiane) Study. Associations between sRAGE and diabetic nephropathy, as well as sRAGE and diabetic nephropathy progression, were evaluated by regression, competing risks and receiver operating characteristic curve analyses. The non-synonymous SNP rs2070600 (G82S) was used to test causality in the Mendelian randomisation analysis. RESULTS: Baseline sRAGE concentrations were highest in participants with diabetic nephropathy, compared with participants with a normal AER or those with microalbuminuria. Baseline sRAGE was associated with progression from macroalbuminuria to end-stage renal disease (ESRD) in the competing risks analyses, but this association disappeared when eGFR was entered into the model. The SNP rs2070600 was strongly associated with sRAGE concentrations and with progression from macroalbuminuria to ESRD. However, Mendelian randomisation analysis did not support a causal role for sRAGE in progression to ESRD. CONCLUSIONS/INTERPRETATION: sRAGE is associated with progression from macroalbuminuria to ESRD, but does not add predictive value on top of conventional risk factors. Although sRAGE is a biomarker of diabetic nephropathy, in light of the Mendelian randomisation analysis it does not seem to be causally related to progression from macroalbuminuria to ESRD.

The role of blood pressure in risk of ischemic and hemorrhagic stroke in type 1 diabetes.

BACKGROUND: Hypertension is one of the strongest risk factors for stroke in the general population, while systolic blood pressure has been shown to independently increase the risk of stroke in type 1 diabetes. The aim of this study was to elucidate the association between different blood pressure variables and risk of stroke in type 1 diabetes, and to explore potential nonlinearity of this relationship. METHODS: We included 4105 individuals with type 1 diabetes without stroke at baseline, participating in the nationwide Finnish Diabetic Nephropathy Study. Mean age at baseline was 37.4 ± 11.9 years, median duration of diabetes 20.9 (interquartile range 11.5-30.4) years, and 52% were men. Office systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured. Based on these pulse pressure (PP) and mean arterial pressure (MAP) were calculated. Strokes were classified based on medical and autopsy records, as well as neuroimaging. Cox proportional hazard models were performed to study how the different blood pressure variables affected the risk of stroke and its subtypes. RESULTS: During median follow-up time of 11.9 (9.21-13.9) years, 202 (5%) individuals suffered an incident stroke; 145 (72%) were ischemic and 57 (28%) hemorrhagic. SBP, DBP, PP, and MAP all independently increased the risk of any stroke. SBP, PP, and MAP increased the risk of ischemic stroke, while SBP, DBP, and MAP increased the risk of hemorrhagic stroke. SBP was strongly associated with stroke with a hazard ratio of 1.20 (1.11-1.29)/10 mmHg. When variables were modeled using restricted cubic splines, the risk of stroke increased linearly for SBP, MAP, and PP, and non-linearly for DBP. CONCLUSIONS: The different blood pressure variables are all independently associated with increased risk of stroke in individuals with type 1 diabetes. The risk of stroke, ischemic stroke, and hemorrhagic stroke increases linearly at blood pressure levels less than the current recommended treatment guidelines.

The Genetic Landscape of Renal Complications in Type 1 Diabetes.

Diabetes is the leading cause of ESRD. Despite evidence for a substantial heritability of diabetic kidney disease, efforts to identify genetic susceptibility variants have had limited success. We extended previous efforts in three dimensions, examining a more comprehensive set of genetic variants in larger numbers of subjects with type 1 diabetes characterized for a wider range of cross-sectional diabetic kidney disease phenotypes. In 2843 subjects, we estimated that the heritability of diabetic kidney disease was 35% (P=6.4×10-3). Genome-wide association analysis and replication in 12,540 individuals identified no single variants reaching stringent levels of significance and, despite excellent power, provided little independent confirmation of previously published associated variants. Whole-exome sequencing in 997 subjects failed to identify any large-effect coding alleles of lower frequency influencing the risk of diabetic kidney disease. However, sets of alleles increasing body mass index (P=2.2×10-5) and the risk of type 2 diabetes (P=6.1×10-4) associated with the risk of diabetic kidney disease. We also found genome-wide genetic correlation between diabetic kidney disease and failure at smoking cessation (P=1.1×10-4). Pathway analysis implicated ascorbate and aldarate metabolism (P=9.0×10-6), and pentose and glucuronate interconversions (P=3.0×10-6) in pathogenesis of diabetic kidney disease. These data provide further evidence for the role of genetic factors influencing diabetic kidney disease in those with type 1 diabetes and highlight some key pathways that may be responsible. Altogether these results reveal important biology behind the major cause of kidney disease.

The serum uric acid concentration is not causally linked to diabetic nephropathy in type 1 diabetes.

Previous studies have shown a relationship between uric acid concentration and progression of renal disease. Here we studied causality between the serum uric acid concentration and progression of diabetic nephropathy in 3895 individuals with type 1 diabetes in the FinnDiane Study. The renal status was assessed with the urinary albumin excretion rate and estimated glomerular filtration rate (eGFR) at baseline and at the end of the follow-up. Based on previous genomewide association studies on serum uric acid concentration, 23 single nucleotide polymorphisms (SNPs) with good imputation quality were selected for the SNP score. This score was used to assess the causality between serum uric acid and renal complications using a Mendelian randomization approach. At baseline, the serum uric acid concentration was higher with worsening renal status. In multivariable Cox regression analyses, baseline serum uric acid concentration was not independently associated with progression of diabetic nephropathy over a mean follow-up of 7 years. However, over the same period, baseline serum uric acid was independently associated with the decline in eGFR. In the cross-sectional logistic regression analyses, the SNP score was associated with the serum uric acid concentration. Nevertheless, the Mendelian randomization showed no causality between uric acid and diabetic nephropathy, eGFR categories, or eGFR as a continuous variable. Thus, our results suggest that the serum uric acid concentration is not causally related to diabetic nephropathy but is a downstream marker of kidney damage.

Progress in Defining the Genetic Basis of Diabetic Complications.

PURPOSE OF REVIEW: Diabetic complications affecting the kidneys, retina, nerves, and the cardiovasculature are the major causes of morbidity and mortality in diabetes. This paper aims to review the current understanding of the genetic basis of these complications, based on recent findings especially from genome-wide association studies. RECENT FINDINGS: Variants in or near AFF3, RGMA-MCTP2, SP3-CDCA7, GLRA3, CNKSR3, and UMOD have reached genome-wide significance (p value <5 × 10-8) for association with diabetic kidney disease, and recently, GRB2 was reported to be associated at genome-wide significance with diabetic retinopathy. While some loci affecting cardiovascular disease in the general population have been replicated in diabetes, GLUL affects the risk of cardiovascular disease specifically in diabetic subjects. Genetic findings are emerging for diabetic complications, although the studies remain relatively small compared to those for type 1 and type 2 diabetes. In addition to pinpointing specific loci, the studies also reveal biological information on correlated traits and pathways.

Integrated analysis of blood DNA methylation, genetic variants, circulating proteins, microRNAs, and kidney failure in type 1 diabetes.

Variation in DNA methylation (DNAmet) in white blood cells and other cells/tissues has been implicated in the etiology of progressive diabetic kidney disease (DKD). However, the specific mechanisms linking DNAmet variation in blood cells with risk of kidney failure (KF) and utility of measuring blood cell DNAmet in personalized medicine are not clear. We measured blood cell DNAmet in 277 individuals with type 1 diabetes and DKD using Illumina EPIC arrays; 51% of the cohort developed KF during 7 to 20 years of follow-up. Our epigenome-wide analysis identified DNAmet at 17 CpGs (5'-cytosine-phosphate-guanine-3' loci) associated with risk of KF independent of major clinical risk factors. DNAmet at these KF-associated CpGs remained stable over a median period of 4.7 years. Furthermore, DNAmet variations at seven KF-associated CpGs were strongly associated with multiple genetic variants at seven genomic regions, suggesting a strong genetic influence on DNAmet. The effects of DNAmet variations at the KF-associated CpGs on risk of KF were partially mediated by multiple KF-associated circulating proteins and KF-associated circulating miRNAs. A prediction model for risk of KF was developed by adding blood cell DNAmet at eight selected KF-associated CpGs to the clinical model. This updated model significantly improved prediction performance (c-statistic = 0.93) versus the clinical model (c-statistic = 0.85) at P = 6.62 × 10-14. In conclusion, our multiomics study provides insights into mechanisms through which variation of DNAmet may affect KF development and shows that blood cell DNAmet at certain CpGs can improve risk prediction for KF in T1D.

Genetic and epigenetic background of diabetic kidney disease.

Diabetic kidney disease (DKD) is a severe diabetic complication that affects up to half of the individuals with diabetes. Elevated blood glucose levels are a key underlying cause of DKD, but DKD is a complex multifactorial disease, which takes years to develop. Family studies have shown that inherited factors also contribute to the risk of the disease. During the last decade, genome-wide association studies (GWASs) have emerged as a powerful tool to identify genetic risk factors for DKD. In recent years, the GWASs have acquired larger number of participants, leading to increased statistical power to detect more genetic risk factors. In addition, whole-exome and whole-genome sequencing studies are emerging, aiming to identify rare genetic risk factors for DKD, as well as epigenome-wide association studies, investigating DNA methylation in relation to DKD. This article aims to review the identified genetic and epigenetic risk factors for DKD.

Interaction between genetic susceptibility to obesity and food intake on BMI in Finnish school-aged children.

Diet modulates the genetic risk of obesity, but the modulation has been rarely studied using genetic risk scores (GRSs) in children. Our objectives were to identify single nucleotide polymorphisms (SNPs) that drive the interaction of specific foods with obesity and combine these into GRSs. Genetic and food frequency data from Finnish Health in Teens study was utilized. In total, 1142 11-year-old subjects were genotyped on the Metabochip array. BMI-GRS with 30 well-known SNPs was computed and the interaction of individual SNPs with food items and their summary dietary scores were examined in relation to age- and sex-specific BMI z-score (BMIz). The whole BMI-GRS interacted with several foods on BMIz. We identified 7-11 SNPs responsible for each interaction and these were combined into food-specific GRS. The most predominant interaction was witnessed for pizza (p < 0.001): the effect on BMIz was b - 0.130 (95% CI - 0.23; - 0.031) in those with low-risk, and 0.153 (95% CI 0.072; 0.234) in high-risk. Corresponding, but weaker interactions were verified for sweets and chocolate, sugary juice drink, and hamburger and hotdog. In total 5 SNPs close to genes NEGR1, SEC16B, TMEM18, GNPDA2, and FTO were shared between these interactions. Our results suggested that children genetically prone to obesity showed a stronger association of unhealthy foods with BMIz than those with lower genetic susceptibility. Shared SNPs of the interactions suggest common differences in metabolic gene-diet interactions, which warrants further investigation.

Haptoglobin Genotype Does Not Confer a Risk of Stroke in Type 1 Diabetes.

The exon copy number variant in the haptoglobin gene is associated with cardiovascular and kidney disease. For stroke, previous research is inconclusive. We aimed to study the relationship between the haptoglobin Hp1/2 genotype and stroke in individuals with type 1 diabetes from the Finnish Diabetic Nephropathy Study. We included two partially overlapping cohorts: one with haptoglobin genotypes determined using genotyping for 179 individuals with stroke and 517 matched control subjects, and the other using haptoglobin genotype imputation for a larger cohort of 500 individuals with stroke and 3,806 individuals without stroke. We observed no difference in the Hp1-1, Hp2-1, and Hp2-2 genotype frequencies between individuals with or without stroke, neither in the genotyping nor the imputation cohorts. Haptoglobin genotypes were also not associated with the ischemic or hemorrhagic stroke subtypes. In our imputed haptoglobin cohort, 61% of individuals with stroke died during follow-up. However, the risk of death was not related to the haptoglobin genotype. Diabetic kidney disease and cardiovascular events were common in the cohort, but the haptoglobin genotypes were not associated with stroke when stratified by these complications. To conclude, the Hp1/2 genotypes did not affect the risk of stroke or survival after stroke in our cohort with type 1 diabetes.

Whole-exome sequencing identifies novel protein-altering variants associated with serum apolipoprotein and lipid concentrations.

BACKGROUND: Dyslipidemia is a major risk factor for cardiovascular disease, and diabetes impacts the lipid metabolism through multiple pathways. In addition to the standard lipid measurements, apolipoprotein concentrations provide added awareness of the burden of circulating lipoproteins. While common genetic variants modestly affect the serum lipid concentrations, rare genetic mutations can cause monogenic forms of hypercholesterolemia and other genetic disorders of lipid metabolism. We aimed to identify low-frequency protein-altering variants (PAVs) affecting lipoprotein and lipid traits. METHODS: We analyzed whole-exome (WES) and whole-genome sequencing (WGS) data of 481 and 474 individuals with type 1 diabetes, respectively. The phenotypic data consisted of 79 serum lipid and apolipoprotein phenotypes obtained with clinical laboratory measurements and nuclear magnetic resonance spectroscopy. RESULTS: The single-variant analysis identified an association between the LIPC p.Thr405Met (rs113298164) and serum apolipoprotein A1 concentrations (p=7.8×10-8). The burden of PAVs was significantly associated with lipid phenotypes in LIPC, RBM47, TRMT5, GTF3C5, MARCHF10, and RYR3 (p<2.9×10-6). The RBM47 gene is required for apolipoprotein B post-translational modifications, and in our data, the association between RBM47 and apolipoprotein C-III concentrations was due to a rare 21 base pair p.Ala496-Ala502 deletion; in replication, the burden of rare deleterious variants in RBM47 was associated with lower triglyceride concentrations in WES of >170,000 individuals from multiple ancestries (p=0.0013). Two PAVs in GTF3C5 were highly enriched in the Finnish population and associated with cardiovascular phenotypes in the general population. In the previously known APOB gene, we identified novel associations at two protein-truncating variants resulting in lower serum non-HDL cholesterol (p=4.8×10-4), apolipoprotein B (p=5.6×10-4), and LDL cholesterol (p=9.5×10-4) concentrations. CONCLUSIONS: We identified lipid and apolipoprotein-associated variants in the previously known LIPC and APOB genes, as well as PAVs in GTF3C5 associated with LDLC, and in RBM47 associated with apolipoprotein C-III concentrations, implicated as an independent CVD risk factor. Identification of rare loss-of-function variants has previously revealed genes that can be targeted to prevent CVD, such as the LDL cholesterol-lowering loss-of-function variants in the PCSK9 gene. Thus, this study suggests novel putative therapeutic targets for the prevention of CVD.

Epigenome-wide meta-analysis identifies DNA methylation biomarkers associated with diabetic kidney disease.

Type 1 diabetes affects over nine million individuals globally, with approximately 40% developing diabetic kidney disease. Emerging evidence suggests that epigenetic alterations, such as DNA methylation, are involved in diabetic kidney disease. Here we assess differences in blood-derived genome-wide DNA methylation associated with diabetic kidney disease in 1304 carefully characterised individuals with type 1 diabetes and known renal status from two cohorts in the United Kingdom-Republic of Ireland and Finland. In the meta-analysis, we identify 32 differentially methylated CpGs in diabetic kidney disease in type 1 diabetes, 18 of which are located within genes differentially expressed in kidneys or correlated with pathological traits in diabetic kidney disease. We show that methylation at 21 of the 32 CpGs predict the development of kidney failure, extending the knowledge and potentially identifying individuals at greater risk for diabetic kidney disease in type 1 diabetes.

The Relationship Between Body Fat Distribution and Nonalcoholic Fatty Liver in Adults With Type 1 Diabetes.

OBJECTIVE: Obesity, which is associated with nonalcoholic fatty liver (NAFL), has increased among people with type 1 diabetes. Therefore, we explored the associations between body fat distribution and NAFL in this population. RESEARCH DESIGN AND METHODS: This study included 121 adults with type 1 diabetes from the Finnish Diabetic Nephropathy (FinnDiane) Study for whom NAFL was determined by magnetic resonance imaging. Body composition was assessed by dual-energy X-ray absorptiometry. Genetic data concerning PNPLA3 rs738409 and TM6SF2 rs58542926 were available as a directly genotyped polymorphism. Associations between body fat distribution, waist-to-height ratio (WHtR), BMI, and NAFL were explored using logistic regression. A receiver operating characteristic (ROC) curve was used to determine the WHtR and BMI thresholds with the highest sensitivity and specificity to detect NAFL. RESULTS: Median age was 38.5 (33-43.7) years, duration of diabetes was 21.2 (17.9-28.4) years, 52.1% were women, and the prevalence of NAFL was 11.6%. After adjusting for sex, age, duration of diabetes, and PNPLA3 rs738409, the volume (P = 0.03) and percentage (P = 0.02) of visceral adipose tissue were associated with NAFL, whereas gynoid, appendicular, and total adipose tissues were not. The area under the curve between WHtR and NAFL was larger than BMI and NAFL (P = 0.04). The WHtR cutoff of 0.5 showed the highest sensitivity (86%) and specificity (55%), whereas the BMI of 26.6 kg/m2 showed 79% sensitivity and 57% specificity. CONCLUSIONS: Visceral adipose tissue is associated with NAFL in adults with type 1 diabetes, and WHtR may be considered when screening for NAFL in this population.

The Low-Expression Variant of FABP4 Is Associated With Cardiovascular Disease in Type 1 Diabetes.

Fatty acid binding protein 4 (FABP4) is implicated in the pathogenesis of cardiometabolic disorders. Pharmacological inhibition or genetic deletion of FABP4 improves cardiometabolic health and protects against atherosclerosis in preclinical models. As cardiovascular disease (CVD) is common in type 1 diabetes, we examined the role of FABP4 in the development of complications in type 1 diabetes, focusing on a functional, low-expression variant (rs77878271) in the promoter of the FABP4 gene. For this, we assessed the risk of CVD, stroke, coronary artery disease (CAD), end-stage kidney disease, and mortality using Cox proportional hazards models for the FABP4 rs77878271 in 5,077 Finnish individuals with type 1 diabetes. The low-expression G allele of rs77878271 increased the risk of CVD, independent of confounders. Findings were tested for replication in 852 Danish and 3,678 Finnish individuals with type 1 diabetes. In the meta-analysis, each G allele increased the risk of stroke by 26% (P = 0.04), CAD by 26% (P = 0.006), and CVD by 17% (P = 0.003). In Mendelian randomization, a 1-SD unit decrease in FABP4 increased risk of CAD 2.4-fold. Hence, in contrast with the general population, among patients with type 1 diabetes the low-expression G allele of rs77878271 increased CVD risk, suggesting that genetically low FABP4 levels may be detrimental in the context of type 1 diabetes.

The pattern-recognition molecule H-ficolin in relation to diabetic kidney disease, mortality, and cardiovascular events in type 1 diabetes.

H-ficolin recognizes patterns on microorganisms and stressed cells and can activate the lectin pathway of the complement system. We aimed to assess H-ficolin in relation to the progression of diabetic kidney disease (DKD), all-cause mortality, diabetes-related mortality, and cardiovascular events. Event rates per 10-unit H-ficolin-increase were compared in an observational follow-up of 2,410 individuals with type 1 diabetes from the FinnDiane Study. DKD progression occurred in 400 individuals. The unadjusted hazard ratio (HR) for progression was 1.29 (1.18-1.40) and 1.16 (1.05-1.29) after adjustment for diabetes duration, sex, HbA1c, systolic blood pressure, and smoking status. After adding triglycerides to the model, the HR decreased to 1.07 (0.97-1.18). In all, 486 individuals died, including 268 deaths of cardiovascular causes and 192 deaths of complications to diabetes. HRs for all-cause mortality and cardiovascular mortality were 1.13 (1.04-1.22) and 1.05 (0.93-1.17), respectively, in unadjusted analyses. These estimates lost statistical significance in adjusted models. However, the unadjusted HR for diabetes-related mortality was 1.19 (1.05-1.35) and 1.18 (1.02-1.37) with the most stringent adjustment level. Our results, therefore, indicate that H-ficolin predicts diabetes-related mortality, but neither all-cause mortality nor fatal/non-fatal cardiovascular events. Furthermore, H-ficolin is associated with DKD progression, however, not independently of the fully adjusted model.

Genome-Wide Association Study of Peripheral Artery Disease.

BACKGROUND: Peripheral artery disease (PAD) affects >200 million people worldwide and is associated with high mortality and morbidity. We sought to identify genomic variants associated with PAD overall and in the contexts of diabetes and smoking status. METHODS: We identified genetic variants associated with PAD and then meta-analyzed with published summary statistics from the Million Veterans Program and UK Biobank to replicate their findings. Next, we ran stratified genome-wide association analysis in ever smokers, never smokers, individuals with diabetes, and individuals with no history of diabetes and corresponding interaction analyses, to identify variants that modify the risk of PAD by diabetic or smoking status. RESULTS: We identified 5 genome-wide significant (Passociation ≤5×10-8) associations with PAD in 449 548 (Ncases=12 086) individuals of European ancestry near LPA (lipoprotein [a]), CDKN2BAS1 (CDKN2B antisense RNA 1), SH2B3 (SH2B adaptor protein 3) - PTPN11 (protein tyrosine phosphatase non-receptor type 11), HDAC9 (histone deacetylase 9), and CHRNA3 (cholinergic receptor nicotinic alpha 3 subunit) loci (which overlapped previously reported associations). Meta-analysis with variants previously associated with PAD showed that 18 of 19 published variants remained genome-wide significant. In individuals with diabetes, rs116405693 at the CCSER1 (coiled-coil serine rich protein 1) locus was associated with PAD (odds ratio [95% CI], 1.51 [1.32-1.74], Pdiabetes=2.5×10-9, Pinteractionwithdiabetes=5.3×10-7). Furthermore, in smokers, rs12910984 at the CHRNA3 locus was associated with PAD (odds ratio [95% CI], 1.15 [1.11-1.19], Psmokers=9.3×10-10, Pinteractionwithsmoking=3.9×10-5). CONCLUSIONS: Our analyses confirm the published genetic associations with PAD and identify novel variants that may influence susceptibility to PAD in the context of diabetes or smoking status.

Genetic risk score predicts risk for overweight and obesity in Finnish preadolescents.

Common genetic variants predispose to obesity with varying contribution by age. We incorporated known genetic variants into genetic risk scores (GRSs) and investigated their associations with overweight/obesity and central obesity in preadolescents. Furthermore, we compared GRSs with lifestyle factors, and tested if they predict the change in body size and shape in a 4-year follow-up. We utilized 1142 subjects from the Finnish Health in Teens (Fin-HIT) cohort. Overweight and obesity were defined with age- and gender-specific body mass index (BMI) z-score (BMIz), while central obesity by the waist-to-height ratio (WHtR). Background data on parental language, eating habits, leisure-time physical activity (LTPA) and sleep duration were included. Genotyping was performed with the Metabochip platform. Weighted, standardized GRSs were derived. Of the11-year-old children, 25.5% were at least overweight and 90.8% had Finnish speaking background. BMI-GRS was associated with higher risk for overweight with odds ratio (95% confidence interval) of 1.39 (1.20; 1.60) and obesity 1.41 (1.08; 1.83), but not with central obesity. BMI-GRS was weakly and inversely associated with the changes in BMIz and WHtR in the 4-year follow-up. Waist-to-hip ratio-GRS was not related to any obesity measures at baseline nor in the follow-up. The effect of BMI-GRS is similar to that of low LTPA on overweight. An interaction between parental language and BMI-GRS was noted (P = .019): BMI-GRS associated more strongly with overweight in Swedish than in Finnish speakers. We further identified two suggestive genetic variants near LOC101926977 and LOC105369677 associated with BMIz in preadolescents which were replicated in the adult population. In preadolescents, known genetic predisposing factors induce a risk for overweight comparable to low LTPA. However, the GRS was poor in predicting short-term changes in BMI or WHtR.