The determinants of complication trajectories in American Indians with type 2 diabetes

Evan L Reynolds, Gulcin Akinci, Mousumi Banerjee, Helen C Looker, Adam Patterson, Robert G Nelson, Eva L Feldman, Brian C Callaghan, Evan L Reynolds, Gulcin Akinci, Mousumi Banerjee, Helen C Looker, Adam Patterson, Robert G Nelson, Eva L Feldman, Brian C Callaghan

Abstract

BACKGROUNDWe aimed to determine whether metabolic syndrome (MetS) affects longitudinal trajectories of diabetic complications, including neuropathy, cardiovascular autonomic neuropathy (CAN), and kidney disease in American Indians with type 2 diabetes.METHODSWe performed a prospective study where participants underwent annual metabolic phenotyping and outcome measurements. The updated National Cholesterol Education Program criteria were used to define MetS and its individual components, using BMI instead of waist circumference. Neuropathy was defined using the Michigan Neuropathy Screening Instrument index, CAN with the expiration/inspiration ratio, and kidney disease with glomerular filtration rate. Mixed-effects models were used to evaluate associations between MetS and these outcomes.RESULTSWe enrolled 141 participants: 73.1% female, a mean (±SD) age of 49.8 (12.3), and a diabetes duration of 19.6 years (9.7 years) who were followed for a mean of 3.1 years (1.7 years). MetS components were stable during follow-up except for declining obesity and cholesterol. Neuropathy (point estimate [PE]: 0.30, 95% CI: 0.24, 0.35) and kidney disease (PE: -14.2, 95% CI: -16.8, -11.4) worsened over time, but CAN did not (PE: -0.002, 95% CI: -0.006, 0.002). We found a significant interaction between the number of MetS components and time for neuropathy (PE: 0.05, 95% CI: 0.01-0.10) but not CAN (PE: -0.003, 95% CI: -0.007, 0.001) or kidney disease (PE: -0.69, 95% CI: -3.16, 1.76). Systolic blood pressure (SBP, unit = 10 mmHg) was associated with each complication: neuropathy (PE: 0.23, 95% CI: 0.07, 0.39), CAN (PE: -0.02, 95% CI: -0.03, -0.02), and kidney disease (PE: -10.2, 95% CI: -15.4, -5.1).CONCLUSIONIn participants with longstanding diabetes, neuropathy and kidney disease worsened during follow-up, despite stable to improving MetS components, suggesting that early metabolic intervention is necessary to prevent complications in such patients. Additionally, the number of MetS components was associated with an increased rate of neuropathy progression, and SBP was associated with each complication.FUNDINGThe following are funding sources: NIH T32NS0007222, NIH R24DK082841, NIH R21NS102924, NIH R01DK115687, the Intramural Program of the NIDDK, the NeuroNetwork for Emerging Therapies, the Robert and Katherine Jacobs Environmental Health Initiative, the Robert E. Nederlander Sr. Program for Alzheimer's Research, and the Sinai Medical Staff Foundation.TRIAL REGISTRATIONClinicalTrials.gov, NCT00340678.

Keywords: Chronic kidney disease; Diabetes; Endocrinology; Neuromuscular disease.

Conflict of interest statement

Conflict of interest: ELF consults for Novartis. BCC consults for a Patient-Centered Outcomes Research Institute grant and DynaMed; receives research support from the American Academy of Neurology; and performs medical legal consultations, including consultations for the Vaccine Injury Compensation Program.

Figures

Figure 1. Participant outcomes during follow-up.
Figure 1. Participant outcomes during follow-up.
(A) Participant-specific neuropathy (MNSI index). (B) CAN (E/I ratio). (C) Kidney disease (GFR). Data shown as mean ± SD. Error bars represent the SD for the outcome measurements within the nearest year of follow-up. The number of participants with outcome measurements in each nearest year of follow-up for MNSI index at baseline: 140, year 1: 117, year 2: 106, year 3: 87, year 4: 59, year 5: 38; for E/I ratio, baseline: 136, year 1: 115, year 2: 96, year 3: 75, year 4: 56, year 5: 13; and for GFR, baseline: 126, year 1: 107, year 2: 86, year 3: 63, year 4: 30, year 5: 5. One outlier was removed in B (E/I ratio = 2.0 at baseline) in order to describe participant trajectories more clearly.
Figure 2. Longitudinal mean changes in neuropathy…
Figure 2. Longitudinal mean changes in neuropathy (MNSI index), CAN (E/I ratio), and kidney disease (GFR) outcomes during follow-up, stratified by the number of MetS components.
(A) Mean neuropathy, (B) mean CAN, (C) mean GFR, and (D) predicted neuropathy from a linear mixed-effects model in each year of follow-up, stratified by the number of MetS components. Mean outcomes based on MetS subgroups with less than 3 participants were excluded from the figures.

References

    1. Lillioja S, et al. Insulin resistance and insulin secretory dysfunction as precursors of non-insulin-dependent diabetes mellitus. Prospective studies of Pima Indians. N Engl J Med. 1993;329(27):1988–1992. doi: 10.1056/NEJM199312303292703.
    1. Bennett PH, et al. Diabetes mellitus in American (Pima) Indians. Lancet. 1971;2(7716):125–128.
    1. Knowler WC, et al. Diabetes mellitus in the Pima Indians: incidence, risk factors and pathogenesis. Diabetes Metab Rev. 1990;6(1):1–27. doi: 10.1002/dmr.5610060101.
    1. Knowler WC, et al. Diabetes incidence in Pima Indians: contributions of obesity and parental diabetes. Am J Epidemiol. 1981;113(2):144–156. doi: 10.1093/oxfordjournals.aje.a113079.
    1. Cho P, et al. Diabetes-related mortality among American Indians and Alaska natives, 1990-2009. Am J Public Health. 2014;104(suppl 3):S496–S503.
    1. Knowler WC, et al. Obesity in the Pima Indians: its magnitude and relationship with diabetes. Am J Clin Nutr. 1991;53(suppl 6):1543S–1551S.
    1. Howard BV, et al. Studies of the etiology of obesity in Pima Indians. Am J Clin Nutr. 1991;53(suppl 6):1577S–1585S.
    1. de Courten MP, et al. Hypertension in Pima Indians: prevalence and predictors. Public Health Rep. 1996;111(Suppl 2):40–43.
    1. Howard BV, et al. Plasma and lipoprotein cholesterol and triglyceride concentrations in the Pima Indians: distributions differing from those of Caucasians. Circulation. 1983;68(4):714–724. doi: 10.1161/01.CIR.68.4.714.
    1. Nelson RG, et al. Incidence of end-stage renal disease in type 2 (non-insulin-dependent) diabetes mellitus in Pima Indians. Diabetologia. 1988;31(10):730–736. doi: 10.1007/BF00274774.
    1. Jaiswal M, et al. Burden of diabetic peripheral neuropathy in Pima Indians with type 2 diabetes. Diabetes Care. 2016;39(4):e63–e64. doi: 10.2337/dc16-0082.
    1. Callaghan BC, et al. Enhanced glucose control for preventing and treating diabetic neuropathy. Cochrane Database Syst Rev. 2012;(6):CD007543. doi: 10.1002/14651858.CD007543.pub2.
    1. Isomaa B, et al. The metabolic syndrome influences the risk of chronic complications in patients with type II diabetes. Diabetologia. 2001;44(9):1148–1154. doi: 10.1007/s001250100615.
    1. Costa LA, et al. Aggregation of features of the metabolic syndrome is associated with increased prevalence of chronic complications in Type 2 diabetes. Diabet Med. 2004;21(3):252–255. doi: 10.1111/j.1464-5491.2004.01124.x.
    1. Metascreen Writing Committee, et al. The metabolic syndrome is a risk indicator of microvascular and macrovascular complications in diabetes: results from Metascreen, a multicenter diabetes clinic-based survey. Diabetes Care. 2006;29(12):2701–2707. doi: 10.2337/dc06-0942.
    1. Callaghan BC, et al. Association between metabolic syndrome components and polyneuropathy in an obese population. JAMA Neurol. 2016;73(12):1468–1476. doi: 10.1001/jamaneurol.2016.3745.
    1. Callaghan BC, et al. Metabolic syndrome components are associated with symptomatic polyneuropathy independent of glycemic status. Diabetes Care. 2016;39(5):801–807. doi: 10.2337/dc16-0081.
    1. Callaghan BC, et al. Diabetes and obesity are the main metabolic drivers of peripheral neuropathy. Ann Clin Transl Neurol. 2018;5(4):397–405. doi: 10.1002/acn3.531.
    1. Reynolds EL, et al. The metabolic drivers of neuropathy in India. J Diabetes Complications. 2020;34(10):107653. doi: 10.1016/j.jdiacomp.2020.107653.
    1. Callaghan BC, et al. Central obesity is associated with neuropathy in the severely obese. Mayo Clin Proc. 2020;95(7):1342–1353. doi: 10.1016/j.mayocp.2020.03.025.
    1. Lu B, et al. Determination of peripheral neuropathy prevalence and associated factors in Chinese subjects with diabetes and pre-diabetes - ShangHai Diabetic neuRopathy Epidemiology and Molecular Genetics Study (SH-DREAMS) PLoS One. 2013;8(4):e61053. doi: 10.1371/journal.pone.0061053.
    1. Andersen ST, et al. Risk factors for incident diabetic polyneuropathy in a cohort with screen-detected type 2 diabetes followed for 13 years: ADDITION-Denmark. Diabetes Care. 2018;41(5):1068–1075. doi: 10.2337/dc17-2062.
    1. Tesfaye S, et al. Vascular risk factors and diabetic neuropathy. N Engl J Med. 2005;352(4):341–350. doi: 10.1056/NEJMoa032782.
    1. Hanewinckel R, et al. Metabolic syndrome is related to polyneuropathy and impaired peripheral nerve function: a prospective population-based cohort study. J Neurol Neurosurg Psychiatry. 2016;87(12):1336–1342. doi: 10.1136/jnnp-2016-314171.
    1. Ziegler D, et al. Prevalence of polyneuropathy in pre-diabetes and diabetes is associated with abdominal obesity and macroangiopathy: the MONICA/KORA Augsburg Surveys S2 and S3. Diabetes Care. 2008;31(3):464–469. doi: 10.2337/dc07-1796.
    1. Straub RH, et al. Impact of obesity on neuropathic late complications in NIDDM. Diabetes Care. 1994;17(11):1290–1294. doi: 10.2337/diacare.17.11.1290.
    1. Han L, et al. Peripheral neuropathy is associated with insulin resistance independent of metabolic syndrome. Diabetol Metab Syndr. 2015;7:14.
    1. Laitinen T, et al. Cardiovascular autonomic dysfunction is associated with central obesity in persons with impaired glucose tolerance. Diabet Med. 2011;28(6):699–704. doi: 10.1111/j.1464-5491.2011.03278.x.
    1. Lu Y, et al. The association and predictive value analysis of metabolic syndrome combined with resting heart rate on cardiovascular autonomic neuropathy in the general Chinese population. Diabetol Metab Syndr. 2013;5(1):73. doi: 10.1186/1758-5996-5-73.
    1. Stein PK, et al. The relationship of heart rate and heart rate variability to non-diabetic fasting glucose levels and the metabolic syndrome: the Cardiovascular Health Study. Diabet Med. 2007;24(8):855–863. doi: 10.1111/j.1464-5491.2007.02163.x.
    1. Callaghan BC, et al. The prevalence and determinants of cognitive deficits and traditional diabetic complications in the severely obese. Diabetes Care. 2020;43(3):683–690. doi: 10.2337/dc19-1642.
    1. et al. Cardiovascular autonomic neuropathy in context of other complications of type 2 diabetes mellitus. Biomed Res Int. 2013;2013:507216.
    1. Valensi P, et al. Cardiac autonomic neuropathy in diabetic patients: influence of diabetes duration, obesity, and microangiopathic complications--the French multicenter study. Metabolism. 2003;52(7):815–820. doi: 10.1016/S0026-0495(03)00095-7.
    1. Dimova R, et al. Risk factors for autonomic and somatic nerve dysfunction in different stages of glucose tolerance. J Diabetes Complications. 2017;31(3):537–543. doi: 10.1016/j.jdiacomp.2016.11.002.
    1. Chung JO, et al. Anemia, bilirubin, and cardiovascular autonomic neuropathy in patients with type 2 diabetes. Medicine (Baltimore) 2017;96(15):e6586. doi: 10.1097/MD.0000000000006586.
    1. Witte DR, et al. Risk factors for cardiac autonomic neuropathy in type 1 diabetes mellitus. Diabetologia. 2005;48(1):164–171. doi: 10.1007/s00125-004-1617-y.
    1. Thomas G, et al. Metabolic syndrome and kidney disease: a systematic review and meta-analysis. Clin J Am Soc Nephrol. 2011;6(10):2364–2373. doi: 10.2215/CJN.02180311.
    1. Chen J, et al. The metabolic syndrome and chronic kidney disease in U.S. adults. Ann Intern Med. 2004;140(3):167–174. doi: 10.7326/0003-4819-140-3-200402030-00007.
    1. Ryu S, et al. Time-dependent association between metabolic syndrome and risk of CKD in Korean men without hypertension or diabetes. Am J Kidney Dis. 2009;53(1):59–69. doi: 10.1053/j.ajkd.2008.07.027.
    1. Rashidi A, et al. Are patients who have metabolic syndrome without diabetes at risk for developing chronic kidney disease? Evidence based on data from a large cohort screening population. Clin J Am Soc Nephrol. 2007;2(5):976–983. doi: 10.2215/CJN.01020207.
    1. Ninomiya T, et al. Metabolic syndrome and CKD in a general Japanese population: the Hisayama Study. Am J Kidney Dis. 2006;48(3):383–391. doi: 10.1053/j.ajkd.2006.06.003.
    1. Luk AOY, et al. Metabolic syndrome predicts new onset of chronic kidney disease in 5,829 patients with type 2 diabetes: a 5-year prospective analysis of the Hong Kong Diabetes Registry. Diabetes Care. 2008;31(12):2357–2361. doi: 10.2337/dc08-0971.
    1. Lucove J, et al. Metabolic syndrome and the development of CKD in American Indians: the Strong Heart Study. Am J Kidney Dis. 2008;51(1):21–28. doi: 10.1053/j.ajkd.2007.09.014.
    1. Kurella M, et al. Metabolic syndrome and the risk for chronic kidney disease among nondiabetic adults. J Am Soc Nephrol. 2005;16(7):2134–2140. doi: 10.1681/ASN.2005010106.
    1. Kitiyakara C, et al. The metabolic syndrome and chronic kidney disease in a Southeast Asian cohort. Kidney Int. 2007;71(7):693–700. doi: 10.1038/sj.ki.5002128.
    1. Bonnet F, et al. Waist circumference and the metabolic syndrome predict the development of elevated albuminuria in non-diabetic subjects: the DESIR Study. J Hypertens. 2006;24(6):1157–1163. doi: 10.1097/.
    1. Watanabe H, et al. Metabolic syndrome and risk of development of chronic kidney disease: the Niigata preventive medicine study. Diabetes Metab Res Rev. 2010;26(1):26–32. doi: 10.1002/dmrr.1058.
    1. Tozawa M, et al. Metabolic syndrome and risk of developing chronic kidney disease in Japanese adults. Hypertens Res. 2007;30(10):937–943. doi: 10.1291/hypres.30.937.
    1. Sun F, et al. Metabolic syndrome and the development of chronic kidney disease among 118 924 non-diabetic Taiwanese in a retrospective cohort. Nephrology (Carlton) 2010;15(1):84–92. doi: 10.1111/j.1440-1797.2009.01150.x.
    1. Lee SJ, et al. Metabolic syndrome status over 2 years predicts incident chronic kidney disease in mid-life adults: a 10-year prospective cohort study. Sci Rep. 2018;8(1):12237. doi: 10.1038/s41598-018-29958-7.
    1. Partanen J, et al. Natural history of peripheral neuropathy in patients with non-insulin-dependent diabetes mellitus. N Engl J Med. 1995;333(2):89–94. doi: 10.1056/NEJM199507133330203.
    1. Alicic RZ, et al. Diabetic Kidney disease: challenges, progress, and possibilities. Clin J Am Soc Nephrol. 2017;12(12):2032–2045. doi: 10.2215/CJN.11491116.
    1. Hirode G, Wong RJ. Trends in the prevalence of metabolic syndrome in the United States, 2011-2016. JAMA. 2020;323(24):2526–2528. doi: 10.1001/jama.2020.4501.
    1. Looker HC, et al. Changes in BMI and weight before and after the development of type 2 diabetes. Diabetes Care. 2001;24(11):1917–1922. doi: 10.2337/diacare.24.11.1917.
    1. No authors listed. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) Lancet. 1998;352(9131):837–853. doi: 10.1016/S0140-6736(98)07019-6.
    1. Chaudhry ZW, et al. Stability of body weight in type 2 diabetes. Diabetes Care. 2006;29(3):493–497. doi: 10.2337/diacare.29.03.06.dc05-1703.
    1. No authors listed. United Kingdom prospective diabetes study (UKPDS) 13: relative efficacy of randomly allocated diet, sulphonylurea, insulin, or metformin in patients with newly diagnosed non-insulin dependent diabetes followed for three years. BMJ. 1995;310(6972):83–88. doi: 10.1136/bmj.310.6972.83.
    1. Kallioinen N, et al. Sources of inaccuracy in the measurement of adult patients’ resting blood pressure in clinical settings: a systematic review. J Hypertens. 2017;35(3):421–441. doi: 10.1097/HJH.0000000000001197.
    1. Warne DK, et al. Comparison of body size measurements as predictors of NIDDM in Pima Indians. Diabetes Care. 1995;18(4):435–439. doi: 10.2337/diacare.18.4.435.
    1. Mak RH, et al. Wasting in chronic kidney disease. J Cachexia Sarcopenia Muscle. 2011;2(1):9–25. doi: 10.1007/s13539-011-0019-5.
    1. Callaghan BC, et al. Better diagnostic accuracy of neuropathy in obesity: a new challenge for neurologists. Clin Neurophysiol. 2018;129(3):654–662. doi: 10.1016/j.clinph.2018.01.003.
    1. Tanamas SK, et al. Long-term effect of losartan on kidney disease in American Indians with type 2 diabetes: a follow-up analysis of a randomized clinical trial. Diabetes Care. 2016;39(11):2004–2010. doi: 10.2337/dc16-0795.
    1. Nelson RG, et al. Changing course of diabetic nephropathy in the Pima Indians. Diabetes Res Clin Pract. 2008;82(suppl 1):S10–S14.
    1. Grundy SM, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement: executive summary. Crit Pathw Cardiol. 2005;4(4):198–203. doi: 10.1097/00132577-200512000-00018.
    1. Feldman EL, et al. A practical two-step quantitative clinical and electrophysiological assessment for the diagnosis and staging of diabetic neuropathy. Diabetes Care. 1994;17(11):1281–1289. doi: 10.2337/diacare.17.11.1281.
    1. Herman WH, et al. Use of the Michigan Neuropathy Screening Instrument as a measure of distal symmetrical peripheral neuropathy in type 1 diabetes: results from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications. Diabet Med. 2012;29(7):937–944. doi: 10.1111/j.1464-5491.2012.03644.x.
    1. Feng Y, et al. The Semmes Weinstein monofilament examination as a screening tool for diabetic peripheral neuropathy. J Vasc Surg. 2009;50(3):675–682. doi: 10.1016/j.jvs.2009.05.017.
    1. Ewing DJ, et al. The value of cardiovascular autonomic function tests: 10 years experience in diabetes. Diabetes Care. 1985;8(5):491–498. doi: 10.2337/diacare.8.5.491.
    1. Spallone V, et al. Cardiovascular autonomic neuropathy in diabetes: clinical impact, assessment, diagnosis, and management Diabetes Metab Res Rev. 2011;27(7):639–653. doi: 10.1002/dmrr.1239.
    1. Tesfaye S, et al. Diabetic neuropathies: update on definitions, diagnostic criteria, estimation of severity, and treatments. Diabetes Care. 2010;33(10):2285–2293. doi: 10.2337/dc10-1303.
    1. Spallone V, et al. Recommendations for the use of cardiovascular tests in diagnosing diabetic autonomic neuropathy. Nutr Metab Cardiovasc Dis. 2011;21(1):69–78. doi: 10.1016/j.numecd.2010.07.005.
    1. Sletten DM, et al. COMPASS 31: a refined and abbreviated Composite Autonomic Symptom Score. Mayo Clin Proc. 2012;87(12):1196–1201. doi: 10.1016/j.mayocp.2012.10.013.
    1. Myers BD, et al. Progression of overt nephropathy in non-insulin-dependent diabetes. Kidney Int. 1995;47(6):1781–1789. doi: 10.1038/ki.1995.246.
    1. Vileikyte L, et al. The development and validation of a neuropathy- and foot ulcer-specific quality of life instrument. Diabetes Care. 2003;26(9):2549–2555. doi: 10.2337/diacare.26.9.2549.
    1. Stetson DS, et al. Effects of age, sex, and anthropometric factors on nerve conduction measures. Muscle Nerve. 1992;15(10):1095–1104. doi: 10.1002/mus.880151007.

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