A Targeted Multiomics Approach to Identify Biomarkers Associated with Rapid eGFR Decline in Type 1 Diabetes
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A Targeted Multiomics Approach to Identify Biomarkers Associated with Rapid eGFR Decline in Type 1 Diabetes. / Limonte, Christine P.; Valo, Erkka; Montemayor, Daniel; Afshinnia, Farsad; Ahluwalia, Tarunveer S.; Costacou, Tina; Darshi, Manjula; Forsblom, Carol; Hoofnagle, Andrew N.; Groop, Per-Henrik; Miller, Rachel G.; Orchard, Trevor J.; Pennathur, Subramaniam; Rossing, Peter; Sandholm, Niina; Snell-Bergeon, Janet K.; Ye, Hongping; Zhang, Jing; Natarajan, Loki; De Boer, Ian H.; Sharma, Kumar.
I: American Journal of Nephrology, Bind 51, Nr. 10, 2020, s. 839-848.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - A Targeted Multiomics Approach to Identify Biomarkers Associated with Rapid eGFR Decline in Type 1 Diabetes
AU - Limonte, Christine P.
AU - Valo, Erkka
AU - Montemayor, Daniel
AU - Afshinnia, Farsad
AU - Ahluwalia, Tarunveer S.
AU - Costacou, Tina
AU - Darshi, Manjula
AU - Forsblom, Carol
AU - Hoofnagle, Andrew N.
AU - Groop, Per-Henrik
AU - Miller, Rachel G.
AU - Orchard, Trevor J.
AU - Pennathur, Subramaniam
AU - Rossing, Peter
AU - Sandholm, Niina
AU - Snell-Bergeon, Janet K.
AU - Ye, Hongping
AU - Zhang, Jing
AU - Natarajan, Loki
AU - De Boer, Ian H.
AU - Sharma, Kumar
PY - 2020
Y1 - 2020
N2 - Background: Individuals with type 1 diabetes (T1D) demonstrate varied trajectories of estimated glomerular filtration rate (eGFR) decline. The molecular pathways underlying rapid eGFR decline in T1D are poorly understood, and individual-level risk of rapid eGFR decline is difficult to predict. Methods: We designed a case-control study with multiple exposure measurements nested within 4 well-characterized T1D cohorts (FinnDiane, Steno, EDC, and CACTI) to identify biomarkers associated with rapid eGFR decline. Here, we report the rationale for and design of these studies as well as results of models testing associations of clinical characteristics with rapid eGFR decline in the study population, upon which "omics"studies will be built. Cases (n = 535) and controls (n = 895) were defined as having an annual eGFR decline of ≥3 and <1 mL/min/1.73 m2, respectively. Associations of demographic and clinical variables with rapid eGFR decline were tested using logistic regression, and prediction was evaluated using area under the curve (AUC) statistics. Targeted metabolomics, lipidomics, and proteomics are being performed using high-resolution mass-spectrometry techniques. Results: At baseline, the mean age was 43 years, diabetes duration was 27 years, eGFR was 94 mL/min/1.73 m2, and 62% of participants were normoalbuminuric. Over 7.6-year median follow-up, the mean annual change in eGFR in cases and controls was -5.7 and 0.6 mL/min/1.73 m2, respectively. Younger age, longer diabetes duration, and higher baseline HbA1c, urine albumin-creatinine ratio, and eGFR were significantly associated with rapid eGFR decline. The cross-validated AUC for the predictive model incorporating these variables plus sex and mean arterial blood pressure was 0.74 (95% CI: 0.68-0.79; p < 0.001). Conclusion: Known risk factors provide moderate discrimination of rapid eGFR decline. Identification of blood and urine biomarkers associated with rapid eGFR decline in T1D using targeted omics strategies may provide insight into disease mechanisms and improve upon clinical predictive models using traditional risk factors.
AB - Background: Individuals with type 1 diabetes (T1D) demonstrate varied trajectories of estimated glomerular filtration rate (eGFR) decline. The molecular pathways underlying rapid eGFR decline in T1D are poorly understood, and individual-level risk of rapid eGFR decline is difficult to predict. Methods: We designed a case-control study with multiple exposure measurements nested within 4 well-characterized T1D cohorts (FinnDiane, Steno, EDC, and CACTI) to identify biomarkers associated with rapid eGFR decline. Here, we report the rationale for and design of these studies as well as results of models testing associations of clinical characteristics with rapid eGFR decline in the study population, upon which "omics"studies will be built. Cases (n = 535) and controls (n = 895) were defined as having an annual eGFR decline of ≥3 and <1 mL/min/1.73 m2, respectively. Associations of demographic and clinical variables with rapid eGFR decline were tested using logistic regression, and prediction was evaluated using area under the curve (AUC) statistics. Targeted metabolomics, lipidomics, and proteomics are being performed using high-resolution mass-spectrometry techniques. Results: At baseline, the mean age was 43 years, diabetes duration was 27 years, eGFR was 94 mL/min/1.73 m2, and 62% of participants were normoalbuminuric. Over 7.6-year median follow-up, the mean annual change in eGFR in cases and controls was -5.7 and 0.6 mL/min/1.73 m2, respectively. Younger age, longer diabetes duration, and higher baseline HbA1c, urine albumin-creatinine ratio, and eGFR were significantly associated with rapid eGFR decline. The cross-validated AUC for the predictive model incorporating these variables plus sex and mean arterial blood pressure was 0.74 (95% CI: 0.68-0.79; p < 0.001). Conclusion: Known risk factors provide moderate discrimination of rapid eGFR decline. Identification of blood and urine biomarkers associated with rapid eGFR decline in T1D using targeted omics strategies may provide insight into disease mechanisms and improve upon clinical predictive models using traditional risk factors.
KW - Biomarkers
KW - eGFR slope
KW - Omics
KW - Type 1 diabetes
U2 - 10.1159/000510830
DO - 10.1159/000510830
M3 - Journal article
C2 - 33053547
AN - SCOPUS:85094196988
VL - 51
SP - 839
EP - 848
JO - American Journal of Nephrology
JF - American Journal of Nephrology
SN - 0250-8095
IS - 10
ER -
ID: 252717785