Ong KL, Stafford LK, McLaughlin SA, Boyko EJ, Vollset SE, Smith AE, Dalton BE, Duprey J, Cruz JA, Hagins H. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet (2023)
Roth, G. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), The Lancet 2018; 392, 1736–1788 (2018).
Turnbull, F. et al. Intensive glucose control and macrovascular outcomes in type 2 diabetes. Diabetologia 52, 2288–2298 (2009).
Google Scholar
Forbes, J. M. & Cooper, M. E. Mechanisms of diabetic complications. Physiol. Rev. 93, 137–188 (2013).
Google Scholar
Mauricio, D., Alonso, N. & Gratacòs, M. Chronic diabetes complications: the need to move beyond classical concepts. Trends Endocrinol. Metabolism. 31, 287–295 (2020).
Google Scholar
Solomon, S. D. et al. Diabetic retinopathy: a position statement by the American diabetes association. Diabetes Care. 40, 412 (2017).
Google Scholar
Thomas, M. C. et al. Diabetic kidney disease. Nat. Reviews Disease Primers. 1, 1–20 (2015).
Vincent, A. M., Callaghan, B. C., Smith, A. L. & Feldman, E. L. Diabetic neuropathy: cellular mechanisms as therapeutic targets. Nat. Reviews Neurol. 7, 573–583 (2011).
Google Scholar
Moulton, K. S. Angiogenesis in atherosclerosis: gathering evidence beyond speculation. Curr. Opin. Lipidol. 17, 548–555 (2006).
Google Scholar
Carter, A. et al. Intimal neovascularisation is a prominent feature of atherosclerotic plaques in diabetic patients with critical limb ischaemia. Eur. J. Vasc. Endovasc. Surg. 33, 319–324 (2007).
Google Scholar
Hayden, M. R. & Tyagi, S. C. Vasa vasorum in plaque angiogenesis, metabolic syndrome, type 2 diabetes mellitus, and atheroscleropathy: a malignant transformation. Cardiovasc. Diabetol. 3, 1–16 (2004).
Google Scholar
Monzer, N. L. et al. The cardiac autonomic response to acute psychological stress in type 2 diabetes. Plos One. 17, e0265234 (2022).
Google Scholar
Buckert, M. et al. Cross-sectional associations of self-perceived stress and hair cortisol with metabolic outcomes and microvascular complications in type 2 diabetes. Front. Public. Health. 12, 1289689 (2024).
Google Scholar
Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361–369 (2018).
Google Scholar
Zaharia, O. P. et al. Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. Lancet Diabetes Endocrinol. 7, 684–694 (2019).
Google Scholar
Tanabe, H. et al. Factors associated with risk of diabetic complications in novel cluster-based diabetes subgroups: a Japanese retrospective cohort study. J. Clin. Med. 9, 2083 (2020).
Google Scholar
Herder, C. & Roden, M. A novel diabetes typology: towards precision diabetology from pathogenesis to treatment. Diabetologia 65, 1770–1781 (2022).
Google Scholar
Dennis, J. M., Shields, B. M., Henley, W. E., Jones, A. G. & Hattersley, A. T. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 7, 442–451 (2019).
Google Scholar
Safai, N., Ali, A., Rossing, P. & Ridderstråle, M. Stratification of type 2 diabetes based on routine clinical markers. Diabetes Res. Clin. Pract. 141, 275–283 (2018).
Google Scholar
Zou, X., Zhou, X., Zhu, Z. & Ji, L. Novel subgroups of patients with adult-onset diabetes in Chinese and US populations. Lancet Diabetes Endocrinol. 7, 9–11 (2019).
Google Scholar
Bayoumi, R. et al. Etiologies underlying subtypes of long-standing type 2 diabetes. Plos One. 19, e0304036 (2024).
Google Scholar
Wesolowska-Andersen, A. et al. Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: an IMI DIRECT study. Cell. Rep. Med. 4;3(1):100477 (2022).
UdlerMS et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS Med. 15 (9), e1002654 (2018).
Schön, M. et al. Analysis of type 2 diabetes heterogeneity with a tree-like representation: insights from the prospective German diabetes study and the LURIC cohort. Lancet Diabetes Endocrinol. 12, 119–131 (2024).
Google Scholar
Wagner, R. et al. Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes. Nat. Med. 27, 49–57 (2021).
Google Scholar
Li, Y. et al. Genetic subtypes of prediabetes, healthy lifestyle, and risk of type 2 diabetes. Diabetes 1;73(7):1178-1187 (2024).
Fritsche, A. et al. Different effects of lifestyle intervention in high-and low-risk prediabetes: results of the randomized controlled prediabetes lifestyle intervention study (PLIS). Diabetes 70, 2785–2795 (2021).
Google Scholar
Group, D. E. R. Intensive diabetes therapy and glomerular filtration rate in type 1 diabetes. N. Engl. J. Med. 365, 2366–2376 (2011).
Control, D., Interventions, C. T. E. D. & Group, C. S. R. Intensive diabetes treatment and cardiovascular outcomes in type 1 diabetes: the DCCT/EDIC study 30-year follow-up. Diabetes Care. 39, 686–693 (2016).
Control, D., Interventions, C. T. E. D. & Group, C. R. Retinopathy and nephropathy in patients with type 1 diabetes four years after a trial of intensive therapy. N. Engl. J. Med. 342, 381–389 (2000).
Zoungas, S. et al. Effects of intensive glucose control on microvascular outcomes in patients with type 2 diabetes: a meta-analysis of individual participant data from randomised controlled trials. Lancet Diabetes Endocrinol. 5, 431–437 (2017).
Google Scholar
Bancks, M. P. et al. Type 2 diabetes subgroups, risk for complications, and differential effects due to an intensive lifestyle intervention. Diabetes Care. 44, 1203–1210 (2021).
Google Scholar
Sulaj, A. et al. Six-month periodic fasting in patients with type 2 diabetes and diabetic nephropathy: a proof-of-concept study. J. Clin. Endocrinol. Metabolism. 107, 2167–2181 (2022).
Veelen, A., Erazo-Tapia, E., Oscarsson, J. & Schrauwen, P. Type 2 diabetes subgroups and potential medication strategies in relation to effects on insulin resistance and beta-cell function: A step toward personalised diabetes treatment? Mol. Metabolism. 46, 101158 (2021).
Google Scholar
Seebauer, L. & Szendrödi, J. Die Neuen subtypen des (Prä–) diabetes auf dem Weg in die praxis. Die Diabetol. 19, 941–951 (2023).
Association, A. D. Diagnosis and classification of diabetes mellitus. Diabetes Care. 37, S81–S90 (2014).
Zaharia, O. P. et al. Diabetes clusters and risk of diabetes-associated diseases-Authors’ reply. Lancet Diabetes Endocrinol. 7, 828–829 (2019).
Google Scholar
Varun, K. et al. Elevated markers of DNA damage and senescence are associated with the progression of albuminuria and restrictive lung disease in patients with type 2 diabetes. EBioMedicine 90, (2023).
Foesleitner, O. et al. Diffusion tensor imaging in anisotropic tissues: application of reduced gradient vector schemes in peripheral nerves. Eur. Radiol. Experimental. 8, 37 (2024).
Mooshage, C. M. et al. Association of small fiber function with microvascular perfusion of peripheral nerves in patients with type 2 diabetes: study using quantitative sensory testing and magnetic resonance neurography. Clin. Neuroradiol. 34, 55–66 (2024).
Google Scholar
Mooshage, C. M. et al. A diminished sciatic nerve structural integrity is associated with distinct peripheral sensory phenotypes in individuals with type 2 diabetes. Diabetologia 67, 275–289 (2024).
Google Scholar
Mooshage, C. M. et al. Magnetization transfer ratio of the sciatic nerve differs between patients in type 1 and type 2 diabetes. Eur. Radiol. Experimental. 8, 6 (2024).
Tsilingiris, D. et al. Sensory phenotypes provide insight into the natural course of diabetic polyneuropathy. Diabetes 73, 135–146 (2024).
Google Scholar
Tsilingiris, D. et al. Dysmetabolism-related early sensory deficits and their relationship with peripheral neuropathy development. J. Clin. Endocrinol. Metabolism. 108, e979–e988 (2023).
Google Scholar
Mooshage, C. M. et al. Diametrical effects of glucose levels on microvascular permeability of peripheral nerves in patients with type 2 diabetes with and without diabetic neuropathy. Diabetes 72, 290–298 (2023).
Google Scholar
Jende, J. M. et al. Sciatic nerve microvascular permeability in type 2 diabetes decreased in patients with neuropathy. Ann. Clin. Transl. Neurol. 9, 830–840 (2022).
Google Scholar
Zilliox, L. et al. Assessing autonomic dysfunction in early diabetic neuropathy: the survey of autonomic symptoms. Neurology 76, 1099–1105 (2011).
Google Scholar
Fong, D. S. et al. Diabetic retinopathy. Diabetes Care. 26, s99–s102 (2003).
Google Scholar
Kopf, S. et al. Breathlessness and restrictive lung disease: an important diabetes-related feature in patients with type 2 diabetes. Respiration 96, 29–40 (2018).
Google Scholar
Kopf, S. et al. Diabetic pneumopathy–a new diabetes-associated complication: mechanisms, consequences and treatment considerations. Front. Endocrinol. 12, 765201 (2021).
Ciardullo, S., Ballabeni, C., Trevisan, R. & Perseghin, G. Liver fibrosis assessed by transient elastography is independently associated with albuminuria in the general united States population. Dig. Liver Disease. 53, 866–872 (2021).
Google Scholar
Tsilingiris, D. et al. Utility of bioelectrical phase angle for cardiovascular risk assessment among individuals with and without diabetes mellitus. Diabetes Vasc. Dis. Res. 21, 14791641231223701 (2024).
Google Scholar
Schimpfle, L. et al. Phase angle of bioelectrical impedance analysis as an indicator for diabetic polyneuropathy in type 2 diabetes mellitus. J. Clin. Endocrinol. Metabolism 15;109(11):e2110-e2119. (2024).
DeFronzo, R. A., Tobin, J. D. & Andres, R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am. J. Physiology-Endocrinology Metabolism. 237, E214 (1979).
Google Scholar
Bischof, M. G. et al. Hepatic glycogen metabolism in type 1 diabetes after long-term near normoglycemia. Diabetes 51, 49–54 (2002).
Google Scholar
Schadewaldt, P., Nowotny, B., Straßburger, K., Kotzka, J. & Roden, M. Indirect calorimetry in humans: a postcalorimetric evaluation procedure for correction of metabolic monitor variability. Am. J. Clin. Nutr. 97, 763–773 (2013).
Google Scholar
Morgenstern, J. et al. Neuron-specific biomarkers predict hypo-and hyperalgesia in individuals with diabetic peripheral neuropathy. Diabetologia 64, 2843–2855 (2021).
Google Scholar
Kender, Z. et al. Sciatic nerve fractional anisotropy and neurofilament light chain protein are related to sensorimotor deficit of the upper and lower limbs in patients with type 2 diabetes. Front. Endocrinol. 14, 1046690 (2023).
Ware, J. E., Kosinski, M. & Keller, S. D. A 12-Item Short-Form health survey: construction of scales and preliminary tests of reliability and validity. Med. Care. 34, 220–233 (1996).
Google Scholar
Gräfe, K., Zipfel, S., Herzog, W. & Löwe, B. Screening psychischer störungen Mit dem gesundheitsfragebogen für patienten (PHQ-D). Diagnostica 50, 171–181 (2004).
Rossing, P. et al. KDIGO 2022 clinical practice guideline for diabetes management in chronic kidney disease. Kidney Int. 102, S1–S127 (2022).
Ziegler, D., Keller, J., Maier, C. & Pannek, J. Diabetic neuropathy. Exp. Clin. Endocrinol. Diabetes. 122, 406–415 (2014).
Google Scholar
Dyck, P. J. et al. Diabetic polyneuropathies: update on research definition, diagnostic criteria and Estimation of severity. Diab./Metab. Res. Rev. 27, 620–628 (2011).
Tesfaye, S. et al. Diabetic neuropathies: update on definitions, diagnostic criteria, Estimation of severity, and treatments. Diabetes Care. 33, 2285 (2010).
Google Scholar
Ziegler, D. et al. Increased prevalence of cardiac autonomic dysfunction at different degrees of glucose intolerance in the general population: the KORA S4 survey. Diabetologia 58, 1118–1128 (2015).
Google Scholar
Vinik, A. I. & Ziegler, D. Diabetic cardiovascular autonomic neuropathy. Circulation 115, 387–397 (2007).
Google Scholar
Wilkinson, C. P. et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110, 1677–1682 (2003).
Google Scholar
Pellegrino, R. et al. Interpretative strategies for lung function tests. Eur. Respir. J. 26, 948–968 (2005).
Google Scholar
Hillier, T. A. & Pedula, K. L. Characteristics of an adult population with newly diagnosed type 2 diabetes: the relation of obesity and age of onset. Diabetes Care. 24, 1522–1527 (2001).
Google Scholar
Rooney, M. R. et al. Global prevalence of prediabetes. Diabetes Care. 46, 1388–1394 (2023).
Google Scholar
Szendroedi, J. et al. Cohort profile: the German diabetes study (GDS). Cardiovasc. Diabetol. 15, 1–14 (2016).
Nordström*, A., Hadrévi, J., Olsson, T., Franks, P. W. & Nordström, P. Higher prevalence of type 2 diabetes in men than in women is associated with differences in visceral fat mass. J. Clin. Endocrinol. Metabolism. 101, 3740–3746 (2016).
Ali, J. et al. Overall clinical features of type 2 diabetes mellitus with respect to gender. Cureus 15, (2023).
Steinberg, J. et al. Analysis of female enrollment and participant sex by burden of disease in US clinical trials between 2000 and 2020. JAMA Netw. Open., Jun 1;4(6):e2113749 (2021).
Hartwig, S. et al. Anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? Pooled analysis of four German population-based cohort studies and comparison with a nationwide cohort study. BMJ Open. 6 (1), e009266 (2016).
Google Scholar
Kinmonth, A. L., Woodcock, A., Griffin, S., Spiegal, N. & Campbell, M. J. Randomised controlled trial of patient centred care of diabetes in general practice: impact on current wellbeing and future disease risk. Bmj 317, 1202–1208 (1998).
Google Scholar
Gatling, W., Guzder, R., Turnbull, J., Budd, S. & Mullee, M. The Poole diabetes study: how many cases of type 2 diabetes are diagnosed each year during normal health care in a defined community? Diabetes Res. Clin. Pract. 53, 107–112 (2001).
Google Scholar
Kristófi, R. et al. Cardiovascular and renal disease burden in type 1 compared with type 2 diabetes: a two-country nationwide observational study. Diabetes Care. 44, 1211–1218 (2021).
Google Scholar
Parving, H-H. et al. Prevalence of microalbuminuria, arterial hypertension, retinopathy, and neuropathy in patients with insulin dependent diabetes. Br. Med. J. (Clin Res. Ed). 296, 156–160 (1988).
Google Scholar
Ismail-Beigi, F. et al. Effect of intensive treatment of hyperglycaemia on microvascular outcomes in type 2 diabetes: an analysis of the ACCORD randomised trial. Lancet 376, 419–430 (2010).
Google Scholar
Duckworth, W. et al. Glucose control and vascular complications in veterans with type 2 diabetes. N. Engl. J. Med. 360, 129–139 (2009).
Google Scholar
Pop-Busui, R., Lu, J., Lopes, N. & Jones, T. L. Prevalence of diabetic peripheral neuropathy and relation to glycemic control therapies at baseline in the BARI 2D cohort. J. Peripheral Nerv. Syst. 14, 1–13 (2009).
Google Scholar
Kirthi, V. et al. Prevalence of peripheral neuropathy in pre-diabetes: a systematic review. BMJ Open. Diabetes Res. Care. 9, e002040 (2021).
Google Scholar
Ziegler, D., Herder, C. & Papanas, N. Neuropathy in prediabetes. Diab./Metab. Res. Rev. 39, e3693 (2023).
Google Scholar
Soumiya, B. et al. Prevalence and risk factors of retinopathy in type 1 diabetes: A Cross-Sectional study. Cureus 15, (2023).
Romero-Aroca, P. et al. Differences in incidence of diabetic retinopathy between type 1 and 2 diabetes mellitus: a nine-year follow-up study. Br. J. Ophthalmol. 101, 1346–1351 (2017).
Google Scholar
Bhattacharyya M, Nickols-Richardson SM, Miller AL, Bhattacharyya R, Frankhauser F, Miller LE. Prevalence and determinants of undiagnosed liver steatosis and fibrosis in a nationally representative sample of US adults. Cureus 15, (2023).
Mertens J, Weyler J, Dirinck E, Vonghia L, Kwanten W, Mortelmans L, Peleman C, Chotkoe S, Spinhoven M, Vanhevel F. Prevalence, risk factors and diagnostic accuracy of non-invasive tests for NAFLD in people with type 1 diabetes. 5. https://doi org/101016/j jhepr 2023:100753 (2023).
De Vries, M., Westerink, J., Kaasjager, K. H. & De Valk, H. W. Prevalence of nonalcoholic fatty liver disease (NAFLD) in patients with type 1 diabetes mellitus: a systematic review and meta-analysis. J. Clin. Endocrinol. Metabolism. 105, 3842–3853 (2020).
Hidmark, A. et al. Electrical muscle stimulation induces an increase of VEGFR2 on Circulating hematopoietic stem cells in patients with diabetes. Clin. Ther. 39, 1132–1144 (2017). e1132.
Google Scholar
Schumacher, D. et al. Compensatory mechanisms for Methylglyoxal detoxification in experimental & clinical diabetes. Mol. Metabolism. 18, 143–152 (2018).
Google Scholar
Jende J, Gröner J, Oikonomou D, Heiland S, Kopf S, Pham M, Nawroth PP, Bendszus M, Kurz FT. Diabetic neuropathy differs between type 1 and type 2 diabetes. 20118
Kopf, S. et al. Deep phenotyping neuropathy: an underestimated complication in patients with pre-diabetes and type 2 diabetes associated with albuminuria. Diabetes Res. Clin. Pract. 146, 191–201 (2018).
Google Scholar
Kender, Z., Groener, J. B., Reismann, P. & Kopf, S. A Metformin Hatása a vérzsírértékekre, illetve a Szív És érrendszeri Kockázatra Sztatinkezelésben Nem részesülő 2-es Típusú Cukorbetegekben. Orv. Hetil. 160, 1346–1352 (2019).
Google Scholar
Groener, J. B. et al. Asprosin response in hypoglycemia is not related to hypoglycemia unawareness but rather to insulin resistance in type 1 diabetes. PloS One. 14, e0222771 (2019).
Google Scholar
Jende, J. M. et al. Association of serum cholesterol levels with peripheral nerve damage in patients with type 2 diabetes. JAMA Netw. Open. 2, e194798–e194798 (2019).
Google Scholar
Groener, J. B. et al. Understanding diabetic neuropathy—from subclinical nerve lesions to severe nerve fiber deficits: a cross-sectional study in patients with type 2 diabetes and healthy control subjects. Diabetes 69, 436–447 (2020).
Google Scholar
Jende, J. M. et al. Diabetic polyneuropathy is associated with pathomorphological changes in human dorsal root ganglia: a study using 3T MR neurography. Front. NeuroSci. 14, 570744 (2020).
Google Scholar
Jende, J. M. et al. Structural nerve remodeling at 3-T MR neurography differs between painful and painless diabetic polyneuropathy in type 1 or 2 diabetes. Radiology 294, 405–414 (2020).
Google Scholar
Jende, J. M. et al. Troponin T parallels structural nerve damage in type 2 diabetes: a cross-sectional study using magnetic resonance neurography. Diabetes 69, 713–723 (2020).
Google Scholar
Lou, B. et al. Elevated 4-hydroxynonenal induces hyperglycaemia via Aldh3a1 loss in zebrafish and associates with diabetes progression in humans. Redox Biol. 37, 101723 (2020).
Google Scholar
Jende, J. M. et al. Diffusion tensor imaging of the sciatic nerve as a surrogate marker for nerve functionality of the upper and lower limb in patients with diabetes and prediabetes. Front. NeuroSci. 15, 642589 (2021).
Google Scholar
Jende, J. M. et al. Fractional anisotropy and troponin T parallel structural nerve damage at the upper extremities in a group of patients with prediabetes and type 2 Diabetes–A study using 3t magnetic resonance neurography. Front. NeuroSci. 15, 741494 (2022).
Google Scholar
Jende, J. M. et al. Magnetic resonance neurography reveals smoking-associated decrease in sciatic nerve structural integrity in type 2 diabetes. Front. NeuroSci. 15, 811085 (2022).
Google Scholar
Monzer, N. et al. Associations of childhood neglect with the ACTH and plasma cortisol stress response in patients with type 2 diabetes. Front. Psychiatry. 12, 679693 (2021).
Google Scholar
Jende, J. M. et al. Troponin T is negatively associated with 3 Tesla magnetic resonance peripheral nerve perfusion in type 2 diabetes. Front. Endocrinol. 13, 839774 (2022).
Kender, Z. et al. Diabetic neuropathy is a generalized phenomenon with impact on hand functional performance and quality of life. Eur. J. Neurol. 29, 3081–3091 (2022).
Google Scholar
Loft, A. et al. A macrophage-hepatocyte glucocorticoid receptor axis coordinates fasting ketogenesis. Cell Metabol. 34, 473–486 (2022). e479.
Google Scholar
Al-Dabet, M. M. et al. Reversal of the renal hyperglycemic memory in diabetic kidney disease by targeting sustained tubular p21 expression. Nat. Commun. 13, 5062 (2022).
Google Scholar
Mooshage, C. M. et al. Insulin resistance is associated with reduced capillary permeability of thigh muscles in patients with type 2 diabetes. J. Clin. Endocrinol. Metabolism. 109, e137–e144 (2024).
Google Scholar
Joshi, P. et al. Impact of the-1T > C single-nucleotide polymorphism of the CD40 gene on the development of endothelial dysfunction in a pro-diabetic microenvironment. Atherosclerosis 394, 117386 (2024).
Google Scholar
Kender, Z. et al. Six-month periodic fasting does not affect somatosensory nerve function in type 2 diabetes patients. Front. Endocrinol. 14, 1143799 (2023).
Reinisch, I. et al. Adipocyte p53 coordinates the response to intermittent fasting by regulating adipose tissue immune cell landscape. Nat. Commun. 15, 1391 (2024).
Google Scholar
von Rauchhaupt E, Rodemer C, Kliemank E, Bulkescher R, Campos M, Kopf S, Fleming T, Herzig S, Nawroth PP, Szendroedi J. Glucose load following prolonged fasting increases oxidative stress-linked response in individuals with diabetic complications. Diabetes Care . Sep 1;47(9):1584-1592. (2024).
Elwakiel, A. et al. Factor XII signaling via uPAR-integrin β1 axis promotes tubular senescence in diabetic kidney disease. Nat. Commun. 15, 7963 (2024).
Google Scholar
Pham, M. et al. Magnetic resonance neurography detects diabetic neuropathy early and with proximal predominance. Ann. Neurol. 78, 939–948 (2015).
Google Scholar
Pham, M. et al. Proximal neuropathic lesions in distal symmetric diabetic polyneuropathy: findings of high-resolution magnetic resonance neurography. Diabetes Care. 34, 721–723 (2011).
Google Scholar
Jende, J. M. et al. Diabetic neuropathy differs between type 1 and type 2 diabetes: insights from magnetic resonance neurography. Ann. Neurol. 83, 588–598 (2018).
Google Scholar
Davis, W. A., Knuiman, M., Kendall, P., Grange, V. & Davis, T. M. Glycemic exposure is associated with reduced pulmonary function in type 2 diabetes: the Fremantle diabetes study. Diabetes Care. 27, 752–757 (2004).
Google Scholar
Lange, P. et al. Diabetes mellitus and ventilatory capacity: a five year follow-up study. Eur. Respir. J. 3, 288–292 (1990).
Google Scholar
Davies, M. J. et al. Management of hyperglycemia in type 2 diabetes, 2022. A consensus report by the American diabetes association (ADA) and the European association for the study of diabetes (EASD). Diabetes Care. 45, 2753–2786 (2022).
Google Scholar
Galindo, R. J., Trujillo, J. M., Wang, C. C. L. & McCoy, R. G. Advances in the management of type 2 diabetes in adults. BMJ Med. Sep 4;2(1):e000372 (2023).
Landmann, G. et al. Local hyperexcitability of C-nociceptors May predict responsiveness to topical Lidocaine in neuropathic pain. Plos One. 17, e0271327 (2022).
Google Scholar
Jonas, R. et al. Tuning in C-nociceptors to reveal mechanisms in chronic neuropathic pain. Ann. Neurol. 83, 945–957 (2018).
Google Scholar
Røikjer, J. et al. Perception threshold tracking: validating a novel method for assessing function of large and small sensory nerve fibers in diabetic peripheral neuropathy with and without pain. Pain 164, 886–894 (2023).
Google Scholar
Flechtner-Mors M, Schwab K, Fröhlich-Reiterer E, Kapellen T, Meissner T, Rosenbauer J, Stachow R, Holl R. Overweight and obesity based on four reference systems in 18,382 paediatric patients with type 1 diabetes from Germany and Austria. Journal of diabetes research 2015, 370753 (2015).
Muggeo, M. et al. Long-term instability of fasting plasma glucose, a novel predictor of cardiovascular mortality in elderly patients with non–insulin-dependent diabetes mellitus: the Verona diabetes study. Circulation 96, 1750–1754 (1997).
Google Scholar
Khunti, K. et al. Effectiveness of a diabetes education and self management programme (DESMOND) for people with newly diagnosed type 2 diabetes mellitus: three year follow-up of a cluster randomised controlled trial in primary care. Bmj 344, e2333 (2012).
Google Scholar
Hsieh, F. Y., Bloch, D. A. & Larsen, M. D. A simple method of sample size calculation for linear and logistic regression. Stat. Med. 17, 1623–1634 (1998).
Google Scholar
