PDF herunterladen
Exp Clin Endocrinol Diabetes 2022; 130(02): 101-109
DOI: 10.1055/a-1192-3761
Article

Progression of Diabetic Complications in Subgroups of People with Long Term Diabetes Type 1 According to Clinical Course

Christian Gerdes
1   Department of Internal Medicine III, Jena University Hospital, Jena, Germany
,
Christoph Werner
1   Department of Internal Medicine III, Jena University Hospital, Jena, Germany
,
Christof Kloos
1   Department of Internal Medicine III, Jena University Hospital, Jena, Germany
,
Thomas Lehmann
2   Department of Medical Statistics, Jena University Hospital, Information and Documentation, Jena, Germany
,
Gunter Wolf
1   Department of Internal Medicine III, Jena University Hospital, Jena, Germany
,
Ulrich Alfons Müller
1   Department of Internal Medicine III, Jena University Hospital, Jena, Germany
3   Practice for Endocrinology and Diabetes, Centre for Ambulatory Medicine, Jena University Hospital, Jena, Germany
,
Nicolle Müller
1   Department of Internal Medicine III, Jena University Hospital, Jena, Germany
› Institutsangaben Funding: The study was financed by house-funds of the listed institutions.
› Weitere Informationen
Auch verfügbar auf
    Lizenzen und Reprints

Abstract

Aims Prevention and prediction of microvascular complications are important aims of medical care in people with type 1 diabetes. Since the course of the disease is heterogenous, we tried to identify subgroups with specific risk profiles for microvascular complications.

Methods Retrospective analysis of a cohort of 285 people (22637 consultations) with >10 years of type 1 diabetes. Persons were grouped into slow (<15 years), fast (>15 years) and non progressors according to the average onset of microvascular complications. Generalized estimating equations for binary outcomes were applied and pseudo coefficients of determination were calculated.

Results Progression to microvascular disease was associated with age (OR: 1.034 [1.001–1.068]; p=0.04), diabetes duration (OR: 1.057 [1.021–1.094]; p=0.002), HbA1c (OR: 1.035 [1.011–1.060]; p=0.005), BMI (OR: 0.928 [0.866–0.994]; p=0.034) and the social strata index (OR: 0.910 [0.830–0.998]; p=0.046). Generalized estimating equations predicted 31.02% and exclusion of HbA1c marginally reduced the value to 28.88%. The proportion of patients with LADA was higher in fast than slow progressors [13 (26.5%) vs. 14 (11.9%); p=0.019]. A generalized estimating equation comparing slow to fast progressors revealed no significant markers.

Conclusion In our analysis, we were able to confirm known risk factors for microvascular disease in people with type 1 diabetes. Overall, prediction of individual risk was difficult, the effect of individual markers minor and we could not find differences regarding slow or fast progression. We therefore emphasis the need for additional markers to predict individual risk for microvascular disease.

Key words

diabetes mellitus type 1 - microvascular complications - classification - disease progression - social status - biomarkers

Supplementary Material



Publikationsverlauf

Eingereicht: 19. Februar 2020
Eingereicht: 28. Mai 2020

Angenommen: 02. Juni 2020

Artikel online veröffentlicht:
10. August 2020

© 2020. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Classification of diabetes mellitus. Geneva: World Health Organization; 2019. Licence: CC BY-NC-SA 3.0 IGO Available from: https://apps.who.int/iris/handle/10665/325182, Accessed: 05.10.2019.
    Suche in Google Scholar
  • 2 Therapy of type 1 diabetes mellitus. German Diabetes Association. 2018 [Available from: https://www.deutsche-diabetes-gesellschaft.de/leitlinien/evidenzbasierte-leitlinien.html, Accessed: 05.10.2019.
    PubMedSuche in Google Scholar
  • 3 Imagawa A, Hanafusa T, Miyagawa J. et al. A proposal of three distinct subtypes of type 1 diabetes mellitus based on clinical and pathological evidence. Ann Med 2000; 32: 539-543
    CrossrefPubMedSuche in Google Scholar
  • 4 Woittiez NJ, Roep BO. Impact of disease heterogeneity on treatment efficacy of immunotherapy in type 1 diabetes: Different shades of gray. Immunotherapy 2015; 7: 163-174
    CrossrefPubMedSuche in Google Scholar
  • 5 Hirsch IB, Brownlee M. Beyond hemoglobin A1c – need for additional markers of risk for diabetic microvascular complications. JAMA 2010; 303: 2291-2292
    CrossrefPubMedSuche in Google Scholar
  • 6 Diabetes C, Complications Trial Research G Nathan DM, Genuth S, Lachin J. et al. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 1993; 329: 977-986
    CrossrefPubMedSuche in Google Scholar
  • 7 Dulon M, Bardehle D, Blettner M. Assessing social inequality in microcensus data and German national health examination survey. Gesundheitswesen 2003; 65: 629-635
    Thieme ConnectPubMedSuche in Google Scholar
  • 8 Naik RG, Brooks-Worrell BM, Palmer JP. Latent autoimmune diabetes in adults. J Clin Endocrinol Metab 2009; 94: 4635-4644
    CrossrefPubMedSuche in Google Scholar
  • 9 National Guideline on Neuropathy in Diabetes in Adults - Long Version 2011. German Diabetes Association. 2011 Available from: www.dm-neuropathie.versorgungsleitlinien.de, Accessed: 31.10.2019
    PubMedSuche in Google Scholar
  • 10 Grading diabetic retinopathy from stereoscopic color fundus photographs – an extension of the modified Airlie House classification. ETDRS report number 10 Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology 1991; 98 (Suppl. 05) 786-806
    CrossrefPubMedSuche in Google Scholar
  • 11 Ou HT, Lee TY, Li CY. et al. Incidence of diabetes-related complications in Chinese patients with type 1 diabetes: A population-based longitudinal cohort study in Taiwan. BMJ Open 2017; 7: e015117
    CrossrefPubMedSuche in Google Scholar
  • 12 Raile K, Galler A, Hofer S. et al. Diabetic nephropathy in 27,805 children, adolescents, and adults with type 1 diabetes: Effect of diabetes duration, A1C, hypertension, dyslipidemia, diabetes onset, and sex. Diabetes Care 2007; 30: 2523-2528
    CrossrefPubMedSuche in Google Scholar
  • 13 Veglio M, Sivieri R. Prevalence of neuropathy in IDDM patients in Piemonte, Italy. The Neuropathy Study Group of the Italian Society for the Study of Diabetes, Piemonte Affiliate. Diabetes Care 1993; 16: 456-461
    CrossrefPubMedSuche in Google Scholar
  • 14 Forga L, Goni MJ, Ibanez B. et al. Influence of Age at diagnosis and time-dependent risk factors on the development of diabetic retinopathy in patients with type 1 diabetes. J Diabetes Res 2016; 9898309
    PubMedSuche in Google Scholar
  • 15 Arikan E, Sabuncu T, Ozer EM. et al. The clinical characteristics of latent autoimmune diabetes in adults and its relation with chronic complications in metabolically poor controlled Turkish patients with Type 2 diabetes mellitus. J Diabetes Complications 2005; 19: 254-258
    CrossrefPubMedSuche in Google Scholar
  • 16 Hawa MI, Buchan AP, Ola T. et al. LADA and CARDS: A prospective study of clinical outcome in established adult-onset autoimmune diabetes. Diabetes Care 2014; 37: 1643-1649
    CrossrefPubMedSuche in Google Scholar
  • 17 Klein R, Knudtson MD, Lee KE. et al. The Wisconsin Epidemiologic Study of Diabetic Retinopathy: XXII the twenty-five-year progression of retinopathy in persons with type 1 diabetes. Ophthalmology 2008; 115: 1859-1868
    CrossrefPubMedSuche in Google Scholar
  • 18 Tesfaye S, Chaturvedi N, Eaton SE. et al. Vascular risk factors and diabetic neuropathy. N Engl J Med 2005; 352: 341-350
    CrossrefPubMedSuche in Google Scholar
  • 19 Volzke H, Alte D, Schmidt CO. et al. Cohort profile: The study of health in Pomerania. Int J Epidemiol 2011; 40: 294-307
    CrossrefPubMedSuche in Google Scholar
  • 20 Ku E, McCulloch CE, Mauer M. et al. Association between blood pressure and adverse renal events in type 1 diabetes. Diabetes Care 2016; 39: 2218-2224
    CrossrefPubMedSuche in Google Scholar
  • 21 Lachin JM, Genuth S, Nathan DM. et al. Effect of glycemic exposure on the risk of microvascular complications in the diabetes control and complications trial – revisited. Diabetes 2008; 57: 995-1001
    CrossrefPubMedSuche in Google Scholar
  • 22 Kilpatrick ES, Rigby AS, Atkin SL. The effect of glucose variability on the risk of microvascular complications in type 1 diabetes. Diabetes Care 2006; 29: 1486-1490
    CrossrefPubMedSuche in Google Scholar
  • 23 Nathan DM, Turgeon H, Regan S. Relationship between glycated haemoglobin levels and mean glucose levels over time. Diabetologia 2007; 50: 2239-2244
    CrossrefPubMedSuche in Google Scholar
  • 24 Kilpatrick ES, Rigby AS, Atkin SL. Variability in the relationship between mean plasma glucose and HbA1c: Implications for the assessment of glycemic control. Clin Chem 2007; 53: 897-901
    CrossrefPubMedSuche in Google Scholar
  • 25 Keenan HA, Costacou T, Sun JK. et al. Clinical factors associated with resistance to microvascular complications in diabetic patients of extreme disease duration: the 50-year medalist study. Diabetes Care 2007; 30: 1995-1997
    CrossrefPubMedSuche in Google Scholar
  • 26 Muhlhauser I, Overmann H, Bender R. et al. Social status and the quality of care for adult people with type I (insulin-dependent) diabetes mellitus – a population-based study. Diabetologia 1998; 41: 1139-1150
    CrossrefPubMedSuche in Google Scholar
  • 27 Beck RW, Bergenstal RM, Riddlesworth TD. et al. Validation of time in range as an outcome measure for diabetes clinical trials. Diabetes Care 2019; 42: 400-405
    CrossrefPubMedSuche in Google Scholar
  • 28 Lu J, Ma X, Zhou J. et al. Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care 2018; 41: 2370-2376
    CrossrefPubMedSuche in Google Scholar
  • 29 Espinola-Klein C, Rupprecht HJ, Blankenberg S. et al. Impact of infectious burden on progression of carotid atherosclerosis. Stroke 2002; 33: 2581-2586
    CrossrefPubMedSuche in Google Scholar
  • 30 Barutta F, Bruno G, Matullo G. et al. MicroRNA-126 and micro-/macrovascular complications of type 1 diabetes in the EURODIAB Prospective Complications Study. Acta Diabetol 2017; 54: 133-139
    CrossrefPubMedSuche in Google Scholar
  • 31 Doganay S, Evereklioglu C, Er H. et al. Comparison of serum NO, TNF-alpha, IL-1beta, sIL-2R, IL-6 and IL-8 levels with grades of retinopathy in patients with diabetes mellitus. Eye (Lond) 2002; 16: 163-170
    CrossrefPubMedSuche in Google Scholar