No abstract

Background: Many CpGs become hyper or hypo-methylated with age. Multiple methods have been developed by Horvath et al. to estimate DNA methylation (DNAm) age including Pan-tissue, Skin & Blood, PhenoAge, and GrimAge. Pan-tissue and Skin & Blood try to estimate chronological age in the normal population whereas PhenoAge and GrimAge use surrogate markers associated with mortality to estimate biological age and its departure from chronological age. Here, we applied Horvath's four methods to calculate and compare DNAm age in 499 subjects with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study using DNAm data measured by Illumina EPIC array in the whole blood. Association of the four DNAm ages with development of diabetic complications including cardiovascular diseases (CVD), nephropathy, retinopathy, and neuropathy, and their risk factors were investigated. Results: Pan-tissue and GrimAge were higher whereas Skin & Blood and PhenoAge were lower than chronological age (p < 0.0001). DNAm age was not associated with the risk of CVD or retinopathy over 18-20 years after DNAm measurement. However, higher PhenoAge (β = 0.023, p = 0.007) and GrimAge (β = 0.029, p = 0.002) were associated with higher albumin excretion rate (AER), an indicator of diabetic renal disease, measured over time. GrimAge was also associated with development of both diabetic peripheral neuropathy (OR = 1.07, p = 9.24E−3) and cardiovascular autonomic neuropathy (OR = 1.06, p = 0.011). Both HbA1c (β = 0.38, p = 0.026) and T1D duration (β = 0.01, p = 0.043) were associated with higher PhenoAge. Employment (β = − 1.99, p = 0.045) and leisure time (β = − 0.81, p = 0.022) physical activity were associated with lower Pan-tissue and Skin & Blood, respectively. BMI (β = 0.09, p = 0.048) and current smoking (β = 7.13, p = 9.03E−50) were positively associated with Skin & Blood and GrimAge, respectively. Blood pressure, lipid levels, pulse rate, and alcohol consumption were not associated with DNAm age regardless of the method used. Conclusions: Various methods of measuring DNAm age are sub-optimal in detecting people at higher risk of developing diabetic complications although some work better than the others.

Decreased red cell deformability is characteristic of several disorders. In some cases, the extent of defective deformability can predict severity of disease or occurrence of serious complications. Ektacytometry uses laser diffraction viscometry to measure the deformability of red blood cells subject to either increasing shear stress or an osmotic gradient at a constant value of applied shear stress. However, direct deformability measurements are difficult to interpret when measuring heterogenous blood that is characterized by the presence of both rigid and deformable red cells. This is due to the inability of rigid cells to properly align in response to shear stress and results in a distorted diffraction pattern marked by an exaggerated decrease in apparent deformability. Measurement of the degree of distortion provides an indicator of the heterogeneity of the erythrocytes in blood. In sickle cell anemia, this is correlated with the percentage of rigid cells, which reflects the hemoglobin concentration and hemoglobin composition of the erythrocytes. In addition to measuring deformability, osmotic gradient ektacytometry provides information about the osmotic fragility and hydration status of erythrocytes. These parameters also reflect the hemoglobin composition of red blood cells from sickle cell patients. Ektacytometry measures deformability in populations of red cells and does not, therefore, provide information on the deformability or mechanical properties of individual erythrocytes. Regardless, the goal of the techniques described herein is to provide a convenient and reliable method for measuring the deformability and cellular heterogeneity of blood. These techniques may be useful for monitoring temporal changes, as well as disease progression and response to therapeutic intervention in several disorders. Sickle cell anemia is one well-characterized example. Other potential disorders where measurements of red cell deformability and/or heterogeneity are of interest include blood storage, diabetes, Plasmodium infection, iron deficiency, and the hemolytic anemias due to membrane defects.