Exploring Novel Biomarkers for Early Diagnosis, Disease Progression, and Treatment Response in Diabetes

Abstract

Diabetes (DM) exists as an unending metabolic disorder which presents impaired insulin secretion or action that triggers hyperglycemia. Early identification and complications prediction are limited by the symptomatic techniques that use fasting plasma glucose (FPG) and verbal glucose resistance test (OGTT) and hemoglobin A1C (HBA1C). Advances in atomic science and proteomics and metabolomics and genomics research resulted in developing sensitive and specific modern biomarkers. However, metabolic, incendiary, oxidative push, circulating microRNAs and lipidomics markers make way demonstrative and restorative bits of knowledge. Moreover, counterfeit insights (AI) and machine learning (ML) change biomarkers, prescient modeling, and accuracy pharmaceutical revelations. AI control investigations when combined with multi-amic approaches demonstrate high promise for better diabetes screening success and developing therapeutic strategies.

Keywords

Diabetes Biomarkers, Insulin Resistance, Oxidative Stress, Inflammatory Markers (TNF-?, IL-6), Precision Medicine, Artificial Intelligence in Diabetes Diagnosis

Introduction

DM is sometimes a perilous metabolic bustle similar to hyperglycemia and shares due to the deficit in discharge, on lapping ^[1]. The need for the recognizable trial of unused biomarkers to monitor early disease determination, infection spreading, and assessment of treatment reaction is needed because of the expanded predominance of diabetes around the globe. The conventional symptomatic markers of diabetes, namely verbal glucose resistance, glycated shapes of hemoglobin (HBA1C) and calm dying, are instances of standard markers that have restricted early arrange diabetes recognizable proof and look at complications ^[2]. Later on, there has been increasingly focus on discovering formerly unused biomarkers that will be capable to forward treatment results and to give more information into malady science. Recent advancement in molecular science, proteomics, metabolomics and genomics have found new biomarkers for diabetes with high specificity and sensitivity. New research finds that diabetes development or its complications can be affected by oxidative stress markers, circulation microRNAs, (which stands for miRNAs), proteomics and lipidomics profiles or by inflammation mediators ^[3,4]. As an example, the dysfunctional beta cells and insulin resistance have been reported to be related to the oxidative stress biomarkers such as glutathione (GSH) and malondialdehyde (MDA)^[5.6]. Similarly, circulating miRNAs including the miR-375 and MIR had-126 have been associated with vascular problems and pancreatic beta cell function ^[7,8,9].

Studies of proteome and fat also show the role of haptoglobin, ceramids and Apoc-III in the development of diabetes and risk of cardiovascular disease ^[10,11]. They aid in personalized treatment approaches, allow for better diagnosis at an earlier stage and offer better knowledge on how the treatment is metabolized.

limitation of tradition diagnosis markers

Traditional diabetes markers such as hemoglobin A1C (HBA1C), oral glucose tolerance (OGTT) testing (FPG) also suffers from diabetes and prediction problems during early stages. Diabetes problems are diagnosed through such FPGs, though they are not sensitive enough to detect postprandial hyperglycemia, a third pioneer ^[12]. Aside from this, FPG levels are also often influenced by a number of circumstances such as stress, illness, and a modern diet, which in turn have many outcomes ^[13]. OGTT is the most commonly employed test to diagnose prediabetes and diabetes because it is a measure of glucose tolerance based on two hours of blood glucose measurement after glucose administration. In other words, this can result in false diagnosis ^[14]. In addition, OGTT results are further affected by test diets and training as well as test reproducibility ^[15].

HBA1C is another common diagnostic indicator that tries to hunt for long term glycemic control and show the average blood glucose levels in the last two to three months. One of many disadvantages is that one cannot identify sudden changes in glucose mirrors in the blood ^[16]. In addition, HBA1c measurement is also affected by the anemia, hemoglobin disease and chronic kidney disease, and these will cause the misdiagnosis of diabetes ^[17]. Also reported were ethnic differences in HBA1C values ??that could affect accuracy of tests in different groups ^[18].

Biomarkers for Early Diagnosis of Diabetes

Metabolic biomarkers

If diabetes is diagnosed early, it can be avoided to maintain treatment conversion. Metabolic biomarkers, such as glycated hemoglobin (HBA1C), insulin resistance markers, and glucose levels, are examples of metabolic biomarkers that are important indications of those who are prone to diabetes. Glucose still remains as a key biomarker for diabetes and it is being determined with diabetic oral glucose resistance (OGTT) testing and sobber plasma glucose (FPG) ^[1]. On the other hand, despite the potential limitation on glucose levels reliability on the diagnosis of early metabolism, such results are still remarkable as food consumption, stress, and circadian rhythms ^[13,20] cause a significant variation. Additionally, OGTT is prone to a lot of the time and is vulnerable to the error of predicting conflicting results ^[15]. HBA1C is another important metabolic biomarker as more reliable and practical alternative to FPG and OGTT, reflecting medium term blood glucose over 2-3 month ^[16]. Unlike glucose base testing it does not require fasting and does not reflect temporary fluctuations in level of blood glucose as HBA1C is not affected by them. However, the disadvantages are that diabetic diabetes cannot be classified in patients with hemoglobinopathy, anemia, or chronic kidney disease ^[18,24]. As well reports have emerged suggesting there are also ethnic differences in the measurement of HBA1C, which raises concerns over the applicability of much of the work to so many groups ^[25]. However, HBA1C is often used for diabetes screening even in the presence of these challenges, since there is a strong association with glucose periodontal long-term control of risk of complications ^[26]. Blood glucose levels and HBA1C became interest early predictors of diabetic’s risk from markers of resistance to insulin. One way that glucose tolerance can be assessed given levels of glucose and insulin in the cold is with a homeostasis model AKA using that as a common metric ^[27]. Increased HOMA-IR measurements correlated with an increased risk of diabetes in the metabolic syndrome and obese patients ^[28]. Additionally, obokins like leptin and obonectin greatly affect the sensitivity of insulin and glucose to body weight. Spot associated IR is often accompanied by increased leptin levels; but in our case, higher obnectin levels, which should be more resistant to insulin and more prone to type 2 diabetes were found ^[29]. These new biomarkers were found to provide valuable information on metabolic changes happening before the onset of the disease which can help in improving the current early detection methods. ^[30]

Inflammatory Biomarkers

The chronic low-grade inflammation makes a significant contribution to insulin resistance, pancreatic beta cell inequity and progression of type 2 diabetes (T2D). Two important, inflammatory biomarkers, were interleukin 6 (IL6) and tumor necrosis factor alpha (TNF$), were thoroughly explored for their role in metabolic disorders and as initial predictors of diabetes risk. In other words, obesity is associated with the inflammation and the inflammation results in insulin resistance and hypoglycemia dysregulation which leads to the release of these inflammatory cytokines, and these cytokines are from immune cells, adipose tissue and macrophages. ^[31]

Tumor Necrosis Factor-Alpha (TNF-α)

Important testing biomarker for diabetes is TNF-î± It does that because it’s a major regulator of inflammation and has been shown to actually interfere with insulin signaling^[32]. Reducing the phosphorylation of Layered-1 (IRS-1) with Glucose Receptor Blocking results in decreasing muscle and adipose tissue sensitivity to insulin. In addition, the fact that a rise in TNF$ values ??is already rising in people with obesity and diabetes mellitus type 2 further supports an involvement in the relationship with long-lasting inflammation and metabolic disorders ^[33]. TNF-He triggers oxidative damage and NF-îM activation along with wearing of pancreatic beta cell death in order to further increase hyperglycemia.^[34] Insulin resistance and circulation are both extremely correlated both clinically and epidemiologically. For instance, obese patients with metabolic syndrome have increased TNF concentration and TNF function in incipient diabetes ^[35,36]. These results are interpretas as a useful biomarker for early identification of diabetes, and insulin sensitivity ^[37] it may be a potential and potential target for diabetes treatment.

Interleukin-6 (IL-6)

Another inflammatory cytokine involved in metabolism regulation is IL-6: it has two parts. Insulin resistance and risk of diabetes are related to chronic research, but it has particular advantages, as with the stimulation of glucose metabolism during training ^[38]. The course of action also benefits ^[39,40] as another marker of systemic inflammation, and improved insulin signaling in peripheral organs, and liver glucose production. Further, there is positive correlation between increased IL-6 mirrors reported by diabetic patients, and type 2 diabetes incidence in prospective cohort studies ^[41]. Also, IL-6 most likely initiated the activity of the activator of Janus kinase/signal converter and transcriptional signaling pathway activator (JAK/STAT), which has been shown to well promote systemic inflammation and insulin resistance ^[42]. Nevertheless, the part of IL-6 in diabetes is diverse, as there have been proved by the research that training in IL-6 generations can benefit glucose metabolism, particularly in training. ^[43]

Biomarkers for Monitoring Disease Progression

Oxidative Stress Markers

Oxidative stress is important in diabetic disease in a progression following the course of diabetic disease with insulin resistance and beta cell dysfunction and diabetic complications. Determining biomarkers of oxidative stress allows for development of disease progression and treatment. Malondiahide (MDA) is used as a measure of the main oxidative stress biomarker due to oxidative damage when diabetes occurs. Superoxide squumutase is also known as a reduced activity of the enzyme in the diabetic state. Thus, this is the spontaneously generated enzyme of superoxides radicals with more oxidative stress ^[44,45]. Moreover, growth of insulin resistance is correlated with lowering of glutathione levels (GSH), a major antioxidant against reactive oxygen species. ^[46]

Circulating microRNAs

In particular, small, non-encoded RNAs referred to as circulating microRNAs (miRNAs) play a role in gene expression and are correlated with the development and complications of diabetes is known to control the pancreatic insulin secretion and beta cell function ^[47]. miR-126 is a supposed disease marker, associated with the endothelial cell function and the complications involved in diabetes ^[48]. Additionally, miR-21 was correlated with inflammation and the development of diabetic nephropathy. ^[49] The development of proteomy and lipidomer diabetes can be investigated in greater detail via proteomic and fatty agent approaches. APOC-III, one of the important biomarkers, is known to be able to grow up with diabetic dyslipidemia and is correlated to the cardiovascular risk of disease ^[50]. Another category of sphingolipids is exacerbating problem related to diabetes through inducing better insulin resistance and metabolic disorders ^[51]. In addition, it was connected to haptoglobin, a glycoprotein that participates in inflammation and oxidative stress and that is associated with disease surveillance. ^[52]

Biomarkers for Treatment Response in Diabetes

There is a need to develop biomarkers to evaluate effects of the diabetes therapy to better manage glucose and also determine indications for this treatment to decrease complications. The three major biomarkers for treatment are the glucose change markers, pharmacological gamer markers for personalized treatment, and antidiabetic drug response biomarkers.

Glycemic variability markers assess

The blood glucose variation index also takes into account the mesoglycemia (HBA1C) in addition to the variability of blood glucose levels that cause complications of diabetes. Implicit CGM based metrics such as time within the region (TIR), moderate intensity distal blood glucose (MAGE), and coefficient of change in glucose (CV%) can actually provide data of actual glucose variation ^[53]. A major predictor for adapting therapies that stabilize glucose levels, including GLP-1 receptor and SGLT2 inhibitor, is large glucose variability and inducing oxidative stress and cardiovascular risk. ^[54]

Pharmacogenomic markers for personalized therapy

Pharmacogenomic markers have the potential to be based on genetic differences to be used in pharmacologically based individualized treatment in expectations in particular, since everyone responds differently to antidiabetic drugs. For example, variations in SLC22A1 (fabric carrier family 22 member 1) affected metformin absorption ^[55,56] during TCF7L2-polymorphism. KCNJ11 and CYP2C9 Gente tests may help to offer personalized treatments, enhance treatment outcomes, and decrease risk of undesirable adverse drug reactions ^[57,58]. Pharmacogenomics used to treat diabetes allows precision medicine (same as optimization of drug response in all patients. ^[59]

Response biomarkers for anti-diabetic drugs

Measureable indications of the effectiveness of the drug in the individual patient are given by biomarkers, ie antidiabetic drugs. An example is that increased levels of SOBER-C peptide with better glycemic control is due and can be used to predict responses to GLP-1 receptor agonist ^[60]. With regards to improving weight loss and insulin sensitivity in patients treated with GLP-1 receptor agonists, fibroblast growth factor 21 was considered as a biomarker ^[61]. In addition, metformin can be monitored with the effects on their concentrations of lactic acid and AMPK activation, reflected in mitochondrial function and drug resistance ^[62].

Future Perspectives and Emerging Trends

As these multi-amic technology, precision medicine, and artificial intelligence (AI) and machine learning (ML) are accelerating their development in the diabetes biomarker research, and these will be the approach on the signage of the era^[63]. These techniques can change diabetes management by enabling a greater depth of insight into how to tailor care, which treatment will work optimally, the disease pathophysiology. ^[64]

AI and machine learning in biomarker discovery

 These techniques can change diabetes management by enabling a greater depth of insight into how to tailor care, which treatment will work optimally, the disease pathophysiology. ^[65]

Multi-omics approach in diabetes research

In understanding of pathogenesis diabetes, a multiomics strategy for diabetes research approaches from genetics, transcriptomics, proteomics, lipidomics and metabolomics is necessary. The multiomics analysis helps to discover the molecular networks that are interconnected which influence diabetes development and response to diabetes treatment versus single biomarker analysis. For instance, transcriptome studies revealed that gene expression changes in pancreatic beta cells exposed to glucose stress; whereas, metabolic analysis showed particular lipid species linked with insulin resistance ^[66]. Mass spectrometry in combination with progression of high slew rate sequencing are employed to identify of multi-Aamics signals that enable identification of diabetes types and prediction of drug efficacy.

Potential for precision medicine in diabetes management

Accurate medical diabetes therapy is to change treatment according to the genetic makeup, metabolic process profile and environmental condition of each individual. Previous studies have already determined in pharmacological genomics how genetic variation affects individuals’ response to antidiabetic drugs such as cloth and metformin sulfonyl. Future research will attempt to integrate a number of OMICS data records into AI based predictive models to design individual treatments. For instance, predictive algorithms, disease history based on patient’s biomarker profile, and lifestyle factors can adopt personalized glycemic targets and medication regimes ^[67]. It enables the classification of the patients in specific subgroups in a precision medicine way. This is because, there is clinical research information and BIAB available, the treatment increases and side effects are lower.

CONCLUSION

Diabetes This is a worldwide health issue and requires progressed observation, recognition, and treatment steps. Despite the fact that frequently utilized, conventional symptomatic markers, for example, HBA1C, OGTT, and Calm plasma glucose are less exact, reproducible, and touchy to non-type glycemic variable. Through progress in biomarkers for atomic, metabolic, fiery, and oxidative stretch, treatment campaigns have been appraised and illness and early discovery are checked. Glucose, hemoglobin An affront resistance markers (TNF-INS±, IL-6), oxidative harm markers as well as circulating microRNAs are biomarkers that describe the complex pathophysiology of diabetes and its complications. Additionally, identifiable proof of markers and the prediction of infection through artificial intelligence (AI) and machine learning (ML) has completely disrupted the problem of discerning the high-risk patients and the improvement of methods to be able to enhance the treatment programs. Thanks to the combination of multiamic advances such as transcriptomics, proteomics, lipidomics, metabolomics and genomes, advances have been made to understand diabetes heterogeneity. It allows for distinguishing proof of unused treatment targets and subtypes of infections. These propels aid the ability to arrange treatment for diabetes based on an individual’s hereditary, physiological, and way of life qualities, or accuracy medication, for short. AI underpins information, multiomics, and custom-made treatment investigation. End of the utilize is to anticipate malady courses, make strides the adequacy of medications, and give better diagnostic rebellious of diabetes. Through coordination of these advancements into clinical care, people in the healthcare occupations can be more effectively produced and more patient centered diabetes care that ultimately reduces the burden of diabetes complications and increases the life expectancy of millions of people worldwide.

ACKNOWLEDGMENT

We acknowledge the various knowledge database and scientific sources utilized in compiling this chapters.

CONFLICT OF INTEREST

The authors claim no conflict of interest

FUNDING

Not Applicable.

REFERENCES

1. Davies MJ, Aroda VR, Collins BS, Gabbay RA, Green J, Maruthur NM, Rosas SE, Del Prato S, Mathieu C, Mingrone G, Rossing P. Management of hyperglycaemia in type 2 diabetes, 2022. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2022 Dec;65(12):1925-66.
2. Atkinson FS, Brand-Miller JC, Foster-Powell K, Buyken AE, Goletzke J. International tables of glycemic index and glycemic load values 2021: a systematic review. The American journal of clinical nutrition. 2021 Nov 1;114(5):1625-32.
3. Chen HX, Chen W, Liu X, Liu YR, Zhu SL. A review of the open charm and open bottom systems. Reports on Progress in Physics. 2017 May 2;80(7):076201.
4. Zampetaki A, Kiechl S, Drozdov I, Willeit P, Mayr U, Prokopi M, Mayr A, Weger S, Oberhollenzer F, Bonora E, Shah A. Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circulation research. 2010 Sep 17;107(6):810-7.
5. Giacco F, Brownlee M. Oxidative stress and diabetic complications. Circulation research. 2010 Oct 29;107(9):1058-70.
6. Robertson B, Hernquist L, Cox TJ, Di Matteo T, Hopkins PF, Martini P, Springel V. The evolution of the MBH-σ relation. The Astrophysical Journal. 2006 Apr 10;641(1):90.
7. Varón A, Vinh LS, Wheeler WC. POY version 4: phylogenetic analysis using dynamic homologies. Cladistics. 2010 Feb;26(1):72-85.
8. Zampetaki A, Kiechl S, Drozdov I, Willeit P, Mayr U, Prokopi M, Mayr A, Weger S, Oberhollenzer F, Bonora E, Shah A. Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circulation research. 2010 Sep 17;107(6):810-7.
9. Zhao M, Golaz JC, Held IM, Guo H, Balaji V, Benson R, Chen JH, Chen X, Donner LJ, Dunne JP, Dunne K. The GFDL global atmosphere and land model AM4. 0/LM4. 0: 2. Model description, sensitivity studies, and tuning strategies. Journal of Advances in Modeling Earth Systems. 2018 Mar;10(3):735-69.
10. Chaurasia B, Summers SA. Ceramides–lipotoxic inducers of metabolic disorders. Trends in Endocrinology & Metabolism. 2015 Oct 1;26(10):538-50.
11. Nielsen BB, Nielsen S. Learning and innovation in international
12. strategic alliances: An empirical test of the role of trust and tacitness. Journal of management Studies. 2009 Sep;46(6):1031-  56
13. Mohan K, Sarkar M, Prakash BS. Efficiency of heatsynch protocol in estrous synchronization, ovulation and conception of dairy buffaloes (Bubalus bubalis). Asian-Australasian Journal of Animal Sciences. 2009 Apr 30;22(6):774-80.
14. Bonora E, Tuomilehto J. The pros and cons of diagnosing diabetes with A1C. Diabetes care. 2011 Apr 22;34(Suppl 2):S184.
15. Bartoli G, Hönisch B, Zeebe RE. Atmospheric CO2 decline during the Pliocene intensification of Northern Hemisphere glaciations. Paleoceanography. 2011 Dec;26(4).
16. Ko TC, Yu W, Sakai T, Sheng H, Shao J, Beauchamp RD, Thompson EA. TGF-β1 effects on proliferation of rat intestinal epithelial cells are due to inhibition of cyclin D1 expression. Oncogene. 1998 Jul;16(26):3445-54.
17. Nathan DM, Davidson MB, DeFronzo RA, Heine RJ, Henry RR, Pratley R, Zinman B. Impaired fasting glucose and impaired glucose tolerance: implications for care. Diabetes care. 2007 Mar 1;30(3):753-9.
18. Rao Kondapally Seshasai S, Kaptoge S, Thompson A, Di Angelantonio E, Gao P, Sarwar N, Whincup PH, Mukamal KJ, Gillum RF, Holme I, Njølstad I. Diabetes mellitus, fasting glucose, and risk of cause-specific death. The New England journal of medicine. 2011;364(9):829-41.
19. Herman WH, Cohen RM. Racial and ethnic differences in the relationship between HbA1c and blood glucose: implications for the diagnosis of diabetes. The Journal of Clinical Endocrinology & Metabolism. 2012 Apr 1;97(4):1067-72.
20. Mohan V, Radhika G, Sathya RM, Tamil SR, Ganesan A, Sudha V. Dietary carbohydrates, glycaemic load, food groups and newly detected type 2 diabetes among urban Asian Indian population in Chennai, India (Chennai Urban Rural Epidemiology Study 59). British journal of nutrition. 2009 Nov;102(10):1498-506.
21. Selvin E, Ning Y, Steffes MW, Bash LD, Klein R, Wong TY, Astor BC, Sharrett AR, Brancati FL, Coresh J. Glycated hemoglobin and the risk of kidney disease and retinopathy in adults with and without diabetes. diabetes. 2011 Jan 1;60(1):298-305.
22. Sumner JA, Griffith JW, Mineka S. Overgeneral autobiographical memory as a predictor of the course of depression: A meta-analysis. Behaviour research and therapy. 2010 Jul 1;48(7):614-25
23. International Expert Committee. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes care. 2009 Jul;32(7):1327.
24. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner R. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. diabetologia. 1985 Jul; 28:412-9.
25. Muniyappa R, Hall G, Kolodziej TL, Karne RJ, Crandon SK, Quon MJ. Cocoa consumption for 2 wk enhances insulin-mediated vasodilatation without improving blood pressure or insulin resistance in essential hypertension. The American journal of clinical nutrition. 2008 Dec 1;88(6):1685-96.
26. Kadowaki T, Yamauchi T, Kubota N, Hara K, Ueki K, Tobe K. Adiponectin and adiponectin receptors in insulin resistance, diabetes, and the metabolic syndrome. The Journal of clinical investigation. 2006 Jul 3;116(7):1784-92.
27. Yamauchi T, Iwabu M, Okada-Iwabu M, Kadowaki T. Adiponectin receptors: a review of their structure, function and how they work. Best practice & research Clinical endocrinology & metabolism. 2014 Jan 1;28(1):15-23.
28. Hotamisligil GS, Shargill NS, Spiegelman BM. Adipose expression of tumor necrosis factor-α: direct role in obesity-linked insulin resistance. Science. 1993 Jan 1;259(5091):87-91.
29. Hotamisligil GS, Budavari A, Murray D, Spiegelman BM. Reduced tyrosine kinase activity of the insulin receptor in obesity-diabetes. Central role of tumor necrosis factor-alpha. The Journal of clinical investigation. 1994 Oct 1;94(4):1543-9.
30. Uysal KT, Wiesbrock SM, Marino MW, Hotamisligil GS. Protection from obesity-induced insulin resistance in mice lacking TNF-α function. Nature. 1997 Oct 9;389(6651):610-4.
31. Ehses JA, Ellingsgaard H, Böni-Schnetzler M, Donath MY. Pancreatic islet inflammation in type 2 diabetes: from α and β cell compensation to dysfunction. Archives of physiology and biochemistry. 2009 Oct 1;115(4):240-7.
32. Donath MY, Shoelson SE. Type 2 diabetes as an inflammatory disease. Nature reviews immunology. 2011 Feb;11(2):98-107.
33. Tilg H, Hotamisligil GS. Nonalcoholic fatty liver disease: cytokine-adipokine interplay and regulation of insulin resistance. Gastroenterology. 2006 Sep 1;131(3):934-45.
34. Uysal KT, Wiesbrock SM, Marino MW, Hotamisligil GS. Protection from obesity-induced insulin resistance in mice lacking TNF-α function. Nature. 1997 Oct 9;389(6651):610-4.
35. Pedersen BK, Febbraio MA. Muscle as an endocrine organ: focus on muscle-derived interleukin-6. Physiological reviews. 2008 Oct;88(4):1379-406.
36. Kern PA, Ranganathan S, Li C, Wood L, Ranganathan G. Adipose tissue tumor necrosis factor and interleukin-6 expression in human obesity and insulin resistance. American journal of physiology-endocrinology and metabolism. 2001 May 1;280(5):E745-51.
37. Pradhan AD, Manson JE, Rifai N, Buring JE, Ridker PM. C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. jama. 2001 Jul 18;286(3):327-34.
38. Spranger J, Kroke A, Mohlig M, Hoffmann K, Bergmann MM, Ristow M, Boeing H, Pfeiffer AF. Inflammatory cytokines and the risk to develop type 2 diabetes: results of the prospective population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Diabetes. 2003 Mar 1;52(3):812-7.
39. Senn JJ, Klover PJ, Nowak IA, Mooney RA. Interleukin-6 induces cellular insulin resistance in hepatocytes. Diabetes. 2002 Dec 1;51(12):3391-9.
40. Carey AL, Steinberg GR, Macaulay SL, Thomas WG, Holmes AG, Ramm G, Prelovsek O, Hohnen-Behrens C, Watt MJ, James DE, Kemp BE. Interleukin-6 increases insulin-stimulated glucose disposal in humans and glucose uptake and fatty acid oxidation in vitro via AMP-activated protein kinase. Diabetes. 2006 Oct 1;55(10):2688-97.
41. Giacco F, Brownlee M. Oxidative stress and diabetic complications. Circulation research. 2010 Oct 29;107(9):1058-70.
42. Maritim AC, Sanders A, Watkins Iii JB. Diabetes, oxidative stress, and antioxidants: a review. Journal of biochemical and molecular toxicology. 2003;17(1):24-38.
43. Robertsen R, Andreassen O, Iversen A. Havbruksnæringens ringvirkninger i Troms. Nofima rapportserie. 2012.
44. Poy MN, Eliasson L, Krutzfeldt J, Kuwajima S, Ma X, Macdonald PE, Pfeffer S, Tuschl T, Rajewsky N, Rorsman P, Stoffel M. A pancreatic islet-specific microRNA regulates insulin secretion. Nature. 2004 Nov 11;432(7014):226-30.
45. Zampetaki A, Kiechl S, Drozdov I, Willeit P, Mayr U, Prokopi M, Mayr A, Weger S, Oberhollenzer F, Bonora E, Shah A. Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circulation research. 2010 Sep 17;107(6):810-7.
46. Chen A, Lu Y, Wang B. Customers’ purchase decision-making process in social commerce: A social learning perspective. International Journal of Information Management. 2017 Dec 1;37(6):627-38.
47. Zhao J, Zhou Y, Li Z, Wang W, Chang KW. Learning gender-neutral word embeddings. arXiv preprint arXiv:1809.01496. 2018 Aug 29.
48. Chaurasia B, Summers SA. Ceramides–lipotoxic inducers of metabolic disorders. Trends in Endocrinology & Metabolism. 2015 Oct 1;26(10):538-50.
49. Galiè N, Hoeper MM, Humbert M, Torbicki A, Vachiery JL, Barbera JA, Beghetti M, Corris P, Gaine S, Gibbs JS, Gomez-Sanchez MA. Guidelines for the diagnosis and treatment of pulmonary hypertension: the Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS), endorsed by the International Society of Heart and Lung Transplantation (ISHLT). European heart journal. 2009 Oct 1;30(20):2493-537.
50. Ceriello A. Diabete e Cancro. Cancer Epidemiol Biomark Prev. 2005; 14:138-47.
51. Pitocco D, Spanu T, Di Leo M, Vitiello R, Rizzi A, Tartaglione L, Fiori B, Caputo S, Tinelli G, Zaccardi F, Flex A. Diabetic foot infections: a comprehensive overview. European Review for Medical & Pharmacological Sciences. 2019 Apr 2;23.
52. Florez JC, Jablonski KA, Bayley N, Pollin TI, de Bakker PI, Shuldiner AR, Knowler WC, Nathan DM, Altshuler D. TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. New England Journal of Medicine. 2006 Jul 20;355(3):241-50.
53. Shu Y, Yu Y, Yang J, McCormick DA. Selective control of cortical axonal spikes by a slowly inactivating K+ current. Proceedings of the National Academy of Sciences. 2007 Jul 3;104(27):11453-8.
54. Pearson RG, Thuiller W, Araújo MB, Martinez?Meyer E, Brotons L, McClean C, Miles L, Segurado P, Dawson TP, Lees DC. Model?based uncertainty in species range prediction. Journal of biogeography. 2006 Oct;33(10):1704-11.
55. Zhou J, Deng Y, Luo F, He Z, Tu Q, Zhi X. Functional molecular ecological networks. MBio. 2010 Oct 29;1(4):10-128.
56. Ali MH, Suleiman N, Khalid N, Tan KH, Tseng ML, Kumar M. Supply chain resilience reactive strategies for food SMEs in coping to COVID-19 crisis. Trends in food science & technology. 2021 Mar 1; 109:94-102.
57. Meneilly GS, Knip A, Tessier D, Canadian Diabetes Association Clinical Practice Guidelines Expert Committee. Diabetes in the elderly. Canadian Journal of Diabetes. 2013 Apr 1;37: S184-90.
58. Faerch K, Blond MB, Bruhn L, Amadid H, Vistisen D, Clemmensen KK, Vainø CT, Pedersen C, Tvermosegaard M, Dejgaard TF, Karstoft K. The effects of dapagliflozin, metformin or exercise on glycaemic variability in overweight or obese individuals with prediabetes (the PRE-D Trial): a multi-arm, randomised, controlled trial. Diabetologia. 2021 Jan; 64:42-55.
59. Foretz M, Guigas B, Bertrand L, Pollak M, Viollet B. Metformin: from mechanisms of action to therapies. Cell metabolism. 2014 Dec 2;20(6):953-66.
60. Zhou G, Myers R, Li Y, Chen Y, Shen X, Fenyk-Melody J, Wu M, Ventre J, Doebber T, Fujii N, Musi N. Role of AMP-activated protein kinase in mechanism of metformin action. The Journal of clinical investigation. 2001 Oct 15;108(8):1167-74.
61. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. New England Journal of Medicine. 2019 Apr 4;380(14):1347-58.
62. Shashikumar SP, Wardi G, Malhotra A, Nemati S. Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”. NPJ digital medicine. 2021 Sep 9;4(1):134.
63. Ting DS, Cheung CY, Lim G, Tan GS, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY, Wong EY. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama. 2017 Dec 12;318(22):2211-23.
64. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome biology. 2017 Dec; 18:1-5.
65. Franks PW, McCarthy MI. Exposing the exposures responsible for type 2 diabetes and obesity. Science. 2016 Oct 7;354(6308):69-73.
66. Alghieth M, Alawaji R, Saleh SH, Alh S. Air Pollution Forecasting Using Deep Learning. International Journal of Online & Biomedical Engineering. 2021 Dec 3;17(14).
67. Villena-Tejada M, Vera-Ferchau I, Cardona-Rivero A, Zamalloa-Cornejo R, Quispe-Florez M, Frisancho-Triveño Z, Abarca-Meléndez RC, Alvarez-Sucari SG, Mejia CR, Yañez JA. Use of medicinal plants for COVID-19 prevention and respiratory symptom treatment during the pandemic in Cusco, Peru: A cross-sectional survey. PloS one. 2021 Sep 22;16(9): e0257165.