BACKGROUND: There have been no studies that use longitudinal data with more than 2 measurements and methods of longitudinal data analysis to identify risk factors for incident albuminuria over time more effectively. STUDY DESIGN: Longitudinal study. SETTINGS & PARTICIPANTS: A subgroup of participants in the Strong Heart Study, a population-based sample of American Indians, in central Arizona, Oklahoma, and North and South Dakota. Participants with diabetes without albuminuria were followed up for a mean of 4 years. PREDICTORS: Age, sex, study center, high-density lipoprotein and low-density lipoprotein cholesterol levels, triglyceride level, body mass index, systolic blood pressure, use of antihypertensive medication, smoking, hemoglobin A(1c) level, fasting glucose level, type of diabetes therapy, diabetes duration, plasma creatinine level, and urinary albumin-creatinine ratio (UACR). OUTCOMES & MEASUREMENTS: Albuminuria was defined as UACR of 30 mg/g or greater. Urine creatinine and albumin were measured by using the picric acid method and a sensitive nephelometric technique, respectively. RESULTS: Of 750 and 568 participants with diabetes without albuminuria and with normal plasma creatinine levels at the first and second examinations, 246 and 132 developed albuminuria by the second and third examinations, respectively. Incident albuminuria was predicted by baseline UACR, fasting glucose level, systolic blood pressure, plasma creatinine level, study center, current smoking, and use of angiotensin-converting enzyme inhibitors and antidiabetic medications. UACR of 10 to 30 mg/g increased the odds of developing albuminuria 2.7-fold compared with UACR less than 5 mg/g. LIMITATIONS: Single random morning urine specimen. CONCLUSIONS: Many risk factors identified for incident albuminuria can be modified. Control of blood pressure and glucose level, smoking cessation, and use of angiotensin-converting enzyme inhibitors may reduce the incidence of albuminuria.
BACKGROUND: There have been no studies that use longitudinal data with more than 2 measurements and methods of longitudinal data analysis to identify risk factors for incident albuminuria over time more effectively. STUDY DESIGN: Longitudinal study. SETTINGS & PARTICIPANTS: A subgroup of participants in the Strong Heart Study, a population-based sample of American Indians, in central Arizona, Oklahoma, and North and South Dakota. Participants with diabetes without albuminuria were followed up for a mean of 4 years. PREDICTORS: Age, sex, study center, high-density lipoprotein and low-density lipoprotein cholesterol levels, triglyceride level, body mass index, systolic blood pressure, use of antihypertensive medication, smoking, hemoglobin A(1c) level, fasting glucose level, type of diabetes therapy, diabetes duration, plasma creatinine level, and urinary albumin-creatinine ratio (UACR). OUTCOMES & MEASUREMENTS: Albuminuria was defined as UACR of 30 mg/g or greater. Urine creatinine and albumin were measured by using the picric acid method and a sensitive nephelometric technique, respectively. RESULTS: Of 750 and 568 participants with diabetes without albuminuria and with normal plasma creatinine levels at the first and second examinations, 246 and 132 developed albuminuria by the second and third examinations, respectively. Incident albuminuria was predicted by baseline UACR, fasting glucose level, systolic blood pressure, plasma creatinine level, study center, current smoking, and use of angiotensin-converting enzyme inhibitors and antidiabetic medications. UACR of 10 to 30 mg/g increased the odds of developing albuminuria 2.7-fold compared with UACR less than 5 mg/g. LIMITATIONS: Single random morning urine specimen. CONCLUSIONS: Many risk factors identified for incident albuminuria can be modified. Control of blood pressure and glucose level, smoking cessation, and use of angiotensin-converting enzyme inhibitors may reduce the incidence of albuminuria.
Authors: E T Lee; T K Welty; R Fabsitz; L D Cowan; N A Le; A J Oopik; A J Cucchiara; P J Savage; B V Howard Journal: Am J Epidemiol Date: 1990-12 Impact factor: 4.897
Authors: B Vasquez; E V Flock; P J Savage; M Nagulesparan; L J Bennion; H R Baird; P H Bennett Journal: Diabetologia Date: 1984-02 Impact factor: 10.122
Authors: Conall M O'Seaghdha; Shih-Jen Hwang; Ashish Upadhyay; James B Meigs; Caroline S Fox Journal: Am J Kidney Dis Date: 2010-11 Impact factor: 8.860
Authors: Stacey E Jolly; Carolyn J Noonan; Yvette D Roubideaux; Jack H Goldberg; Sven O E Ebbesson; Jason G Umans; Barbara V Howard Journal: Nephron Clin Pract Date: 2010-04-21
Authors: Stacey E Jolly; Suying Li; Shu-Cheng Chen; Andrew S Narva; Claudine T Jurkovitz; Keith C Norris; Michael G Shlipak Journal: Am J Nephrol Date: 2008-11-14 Impact factor: 3.754
Authors: V Saroja Voruganti; Nora Franceschini; Karin Haack; Sandra Laston; Jean W MacCluer; Jason G Umans; Anthony G Comuzzie; Kari E North; Shelley A Cole Journal: Eur J Hum Genet Date: 2013-12-04 Impact factor: 4.246
Authors: Sunny S Singh; Jeanine E Roeters-van Lennep; Roosmarijn F H Lemmers; Thijs T W van Herpt; Aloysius G Lieverse; Eric J G Sijbrands; Mandy van Hoek Journal: Acta Diabetol Date: 2020-02-05 Impact factor: 4.280
Authors: Ying Zhang; Elisa T Lee; Barbara V Howard; Lyle G Best; Jason G Umans; Jeunliang Yeh; Wenyu Wang; Fawn Yeh; Tauqeer Ali; Richard B Devereux; Giovanni de Simone Journal: Diabetes Care Date: 2013-06-04 Impact factor: 19.112