👤 Suna Onengut-Gumuscu

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Rachel G Miller, Trevor J Orchard, Suna Onengut-Gumuscu +3 more · 2021 · Diabetic medicine : a journal of the British Diabetic Association · Blackwell Publishing · added 2026-04-24
We aimed to identify long-term HbA1c trajectories and examine associated characteristics in an observational, childhood-onset (<17 years) type 1 diabetes cohort. Data are from the Epidemiology of Diab Show more
We aimed to identify long-term HbA1c trajectories and examine associated characteristics in an observational, childhood-onset (<17 years) type 1 diabetes cohort. Data are from the Epidemiology of Diabetes Complications study, comprising 405 participants with ≥2 of seven possible HbA1c measurements over follow-up (1988-2013) and available DNA (baseline mean diabetes duration 21 years, 53% men). HbA1c trajectories were estimated using latent class growth models. Baseline and change in participant characteristics were compared across trajectories. Five HbA1c trajectories were identified: low (51%), intermediate stable (22%), improved (19%), high stable (6%), and worsened (2%; not included in analyses). Age, diabetes duration, diabetes onset age, and sex did not differ across trajectories. Characteristics did not differ significantly between intermediate stable and low trajectories at baseline, though albumin excretion rate (AER, p = 0.0002) and estimated glomerular filtration rate (eGFR, p = 0.001) worsened slightly more in intermediate stable over time. Improved and high stable trajectories had higher baseline LDL-c (p = 0.002 and 0.003, respectively). Improved trajectory increased median self-monitoring of blood glucose from <1 to 3.5 times/day (p < 0.0001) and had larger LDL-c improvement (p = 0.004) but greater worsening of AER (p < 0.0001) and eGFR (p < 0.0001) than low. The A allele of rs12970134 (near MC4R) was associated with improved (p = 0.0003) or high stable (p = 0.001) HbA1c trajectory, both patterns with high baseline HbA1c. Long-term HbA1c trajectories were primarily associated with modifiable factors in this type 1 diabetes cohort. The intermediate stable pattern had a risk factor profile that suggests some protection against adverse metabolic effects of chronic hyperglycaemia, warranting further study. Show less
📄 PDF DOI: 10.1111/dme.14545
MC4R
Rachel G Miller, Stuart J McGurnaghan, Suna Onengut-Gumuscu +5 more · 2021 · Journal of diabetes and its complications · Elsevier · added 2026-04-24
To examine candidate insulin resistance single nucleotide polymorphisms (SNPs) for associations with glycemic control, insulin resistance, BMI, and complications in an observational type 1 diabetes (T Show more
To examine candidate insulin resistance single nucleotide polymorphisms (SNPs) for associations with glycemic control, insulin resistance, BMI, and complications in an observational type 1 diabetes (T1D) cohort: the Pittsburgh Epidemiology of Diabetes Complications (EDC) study. In 422 European-ancestry participants, we assessed associations using additive models between 15 candidate SNPs and 25-year mortality, cardiovascular disease, microalbuminuria, overt nephropathy and proliferative retinopathy, and 25-year mean HbA1c, estimated glucose disposal rate (eGDR, inverse measure of insulin resistance), and BMI. The A allele of rs12970134 was associated with higher mean HbA1c (β = +0.34 ± 0.09, p = 0.00009) and nominally associated with worse eGDR (p = 0.02). Further analyses suggest the HbA1c association may be modified by diabetes therapy regimen: rs12970134 AA genotype was associated with higher HbA1c under non-intensive therapy conditions (<3 insulin injections/day or monitoring blood glucose<3 times/day [p = 0.004]), but not under intensive therapy (≥3 injections/day or insulin pump and monitoring glucose≥3 times/day [p = 0.71]). There were no significant associations between any SNPs and BMI or complications. rs12970134, near MC4R, is strongly associated with HbA1c in this cohort. Further exploration of this genomic region is warranted, as it may hold promise for discovering new therapeutic targets to improve glycemic control in T1D. Show less
📄 PDF DOI: 10.1016/j.jdiacomp.2020.107842
MC4R
Qian Wang, Andrew T Grainger, Ani Manichaikul +3 more · 2015 · BMC genetics · BioMed Central · added 2026-04-24
Individuals with dyslipidemia often develop type 2 diabetes, and diabetic patients often have dyslipidemia. It remains to be determined whether there are genetic connections between the 2 disorders. A Show more
Individuals with dyslipidemia often develop type 2 diabetes, and diabetic patients often have dyslipidemia. It remains to be determined whether there are genetic connections between the 2 disorders. A female F2 cohort, generated from BALB/cJ (BALB) and SM/J (SM) Apoe-deficient (Apoe(-/-)) strains, was started on a Western diet at 6 weeks of age and maintained on the diet for 12 weeks. Fasting plasma glucose and lipid levels were measured before and after 12 weeks of Western diet. 144 genetic markers across the entire genome were used for quantitative trait locus (QTL) analysis. One significant QTL on chromosome 9, named Bglu17 [26.4 cM, logarithm of odds ratio (LOD): 5.4], and 3 suggestive QTLs were identified for fasting glucose levels. The suggestive QTL near the proximal end of chromosome 9 (2.4 cM, LOD: 3.12) was replicated at both time points and named Bglu16. Bglu17 coincided with a significant QTL for HDL (high-density lipoprotein) and a suggestive QTL for non-HDL cholesterol levels. Plasma glucose levels were inversely correlated with HDL but positively correlated with non-HDL cholesterol levels in F2 mice on either chow or Western diet. A significant correlation between fasting glucose and triglyceride levels was also observed on the Western diet. Haplotype analysis revealed that "lipid genes" Sik3, Apoa1, and Apoc3 were probable candidates for Bglu17. We have identified multiple QTLs for fasting glucose and lipid levels. The colocalization of QTLs for both phenotypes and the sharing of potential candidate genes demonstrate genetic connections between dyslipidemia and type 2 diabetes. Show less
📄 PDF DOI: 10.1186/s12863-015-0292-y
APOC3