Journal articles: 'Diabetes biomarkers'

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Caveney, Erica J., and Oren J. Cohen. "Diabetes and Biomarkers." Journal of Diabetes Science and Technology 5, no. 1 (January 2011): 192–97. http://dx.doi.org/10.1177/193229681100500127.

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Padilla-Martinez, Felipe, Gladys Wojciechowska, Lukasz Szczerbinski, and Adam Kretowski. "Circulating Nucleic Acid-Based Biomarkers of Type 2 Diabetes." International Journal of Molecular Sciences 23, no. 1 (December 28, 2021): 295. http://dx.doi.org/10.3390/ijms23010295.

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Type 2 diabetes (T2D) is a deficiency in how the body regulates glucose. Uncontrolled T2D will result in chronic high blood sugar levels, eventually resulting in T2D complications. These complications, such as kidney, eye, and nerve damage, are even harder to treat. Identifying individuals at high risk of developing T2D and its complications is essential for early prevention and treatment. Numerous studies have been done to identify biomarkers for T2D diagnosis and prognosis. This review focuses on recent T2D biomarker studies based on circulating nucleic acids using different omics technologies: genomics, transcriptomics, and epigenomics. Omics studies have profiled biomarker candidates from blood, urine, and other non-invasive samples. Despite methodological differences, several candidate biomarkers were reported for the risk and diagnosis of T2D, the prognosis of T2D complications, and pharmacodynamics of T2D treatments. Future studies should be done to validate the findings in larger samples and blood-based biomarkers in non-invasive samples to support the realization of precision medicine for T2D.

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Catalina, Marta Olivera-Santa, Pedro C. Redondo, Maria P. Granados, Carlos Cantonero, Jose Sanchez-Collado, Letizia Albarran, and Jose J. Lopez. "New Insights into Adipokines as Potential Biomarkers for Type-2 Diabetes Mellitus." Current Medicinal Chemistry 26, no. 22 (September 20, 2019): 4119–44. http://dx.doi.org/10.2174/0929867325666171205162248.

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A large number of studies have been focused on investigating serum biomarkers associated with risk or diagnosis of type-2 diabetes mellitus. In the last decade, promising studies have shown that circulating levels of adipokines could be used as a relevant biomarker for diabetes mellitus progression as well as therapeutic future targets. Here, we discuss the possible use of recently described adipokines, including apelin, omentin-1, resistin, FGF-21, neuregulin-4 and visfatin, as early biomarkers for diabetes. In addition, we also include recent findings of other well known adipokines such as leptin and adiponectin. In conclusion, further studies are needed to clarify the pathophysiological significance and clinical value of these biological factors as potential biomarkers in type-2 diabetes and related dysfunctions.

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Kim, Kiyoun, Soohyun Ahn, Johan Lim, Byong Chul Yoo, Jin-Hyeok Hwang, and Woncheol Jang. "Detection of Pancreatic Cancer Biomarkers Using Mass Spectrometry." Cancer Informatics 13s7 (January 2014): CIN.S16341. http://dx.doi.org/10.4137/cin.s16341.

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Background Pancreatic cancer is the fourth leading cause of cancer-related deaths. Therefore, in order to improve survival rates, the development of biomarkers for early diagnosis is crucial. Recently, diabetes has been associated with an increased risk of pancreatic cancer. The aims of this study were to search for novel serum biomarkers that could be used for early diagnosis of pancreatic cancer and to identify whether diabetes was a risk factor for this disease. Methods Blood samples were collected from 25 patients with diabetes (control) and 93 patients with pancreatic cancer (including 53 patients with diabetes), and analyzed using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF/MS). We performed preprocessing, and various classification methods with imputation were used to replace the missing values. To validate the selection of biomarkers identified in pancreatic cancer patients, we measured biomarker intensity in pancreatic cancer patients with diabetes following surgical resection and compared our results with those from control (diabetes-only) patients. Results By using various classification methods, we identified the commonly splitting protein peaks as m/z 1,465, 1,206, and 1,020. In the follow-up study, in which we assessed biomarkers in pancreatic cancer patients with diabetes after surgical resection, we found that the intensities of m/z at 1,465, 1,206, and 1,020 became comparable with those of diabetes-only patients.

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Kulkarni, R. N. "Identifying Biomarkers of Subclinical Diabetes." Diabetes 61, no. 8 (July 23, 2012): 1925–26. http://dx.doi.org/10.2337/db12-0599.

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Tooley, James E., and Kevan C. Herold. "Biomarkers in type 1 diabetes." Current Opinion in Endocrinology & Diabetes and Obesity 21, no. 4 (August 2014): 287–92. http://dx.doi.org/10.1097/med.0000000000000076.

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Laakso, Markku. "Biomarkers for type 2 diabetes." Molecular Metabolism 27 (September 2019): S139—S146. http://dx.doi.org/10.1016/j.molmet.2019.06.016.

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Li, Xuejie, Zhenzhou Zhao, Chuanyu Gao, Lixin Rao, Peiyuan Hao, Dongdong Jian, Wentao Li, Haiyu Tang, and Muwei Li. "The Diagnostic Value of Whole Blood lncRNA ENST00000550337.1 for Pre-Diabetes and Type 2 Diabetes Mellitus." Experimental and Clinical Endocrinology & Diabetes 125, no. 06 (April 13, 2017): 377–83. http://dx.doi.org/10.1055/s-0043-100018.

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Abstract This study aims to investigate long noncoding RNA (lncRNA) as biomarker for pre-diabetes and T2DM. LncRNAs in the peripheral blood of 6 healthy individuals and 6 T2DM patients were collected for microarray analysis. Then 5 candidate biomarkers from the differentially expressed lncRNAs were chosen and verified in a larger independent cohort (control group=20; pre-diabetes group=20; and T2DM group=20). The diagnostic capacity of ENST00000550337.1 was further tested in the third cohort (control group, n=60; pre-diabetes group, n=63; and T2DM group, n=64). A total of 17 lncRNAs were found to be differentially expressed between the 2 groups. 14 lncRNAs of these were upregulated in T2DM patients and 3 were downregulated. 5 upregulated lncRNAs were selected as potential biomarkers and verified in the second cohort, and the expression levels of 3 lncRNAs increased gradually from the control group to the pre-diabetes group to the T2DM group. The diagnostic value of ENST00000550337.1 was then tested in the third cohort, and its high diagnostic value for pre-diabetes and T2DM was confirmed. LncRNA ENST00000550337.1 is a potential diagnostic biomarker for pre-diabetes and T2DM.

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Herzog, Katharina, Tomas Andersson, Valdemar Grill, Niklas Hammar, Håkan Malmström, Mats Talbäck, Göran Walldius, and Sofia Carlsson. "Alterations in Biomarkers Related to Glycemia, Lipid Metabolism, and Inflammation up to 20 Years Before Diagnosis of Type 1 Diabetes in Adults: Findings From the AMORIS Cohort." Diabetes Care 45, no. 2 (December 7, 2021): 330–38. http://dx.doi.org/10.2337/dc21-1238.

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OBJECTIVE Type 1 diabetes is described to have an acute onset, but autoantibodies can appear several years preceding diagnosis. This suggests a long preclinical phase, which may also include metabolic parameters. Here we assessed whether elevations in glycemic, lipid, and other metabolic biomarkers were associated with future type 1 diabetes risk in adults. RESEARCH DESIGN AND METHODS We studied 591,239 individuals from the Swedish AMORIS cohort followed from 1985–1996 to 2012. Through linkage to national patient, diabetes, and prescription registers, we identified incident type 1 diabetes. Using Cox regression models, we estimated hazard ratios for biomarkers at baseline and incident type 1 diabetes. We additionally assessed trajectories of biomarkers during the 25 years before type 1 diabetes diagnosis in a nested case-control design. RESULTS We identified 1,122 type 1 diabetes cases during follow-up (average age of patient at diagnosis: 53.3 years). The biomarkers glucose, fructosamine, triglycerides, the ratio of apolipoprotein (apo)B to apoA-I, uric acid, alkaline phosphatase, and BMI were positively associated with type 1 diabetes risk. Higher apoA-I was associated with lower type 1 diabetes incidence. Already 15 years before diagnosis, type 1 diabetes cases had higher mean glucose, fructosamine, triglycerides, and uric acid levels compared with control subjects. CONCLUSIONS Alterations in biomarker levels related to glycemia, lipid metabolism, and inflammation are associated with clinically diagnosed type 1 diabetes risk, and these may be elevated many years preceding diagnosis.

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Mi, Qing-Sheng, Metthew Weiland, Ruiqun Qi, and Li Zhou. "Global miRNA expression profiles uncover serum miRNAs as novel biomarkers for diabetes staging in NOD mice (P3267)." Journal of Immunology 190, no. 1_Supplement (May 1, 2013): 192.19. http://dx.doi.org/10.4049/jimmunol.190.supp.192.19.

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Abstract Type 1 diabetes (T1D) is an autoimmune diseases resulting from T cell-mediated pancreatic beta cell destruction. New biomarkers are urgently needed for earlier T1D risk prediction and progression. MicroRNAs (miRNAs) regulate beta cell and immune cell development and function, and are involved in T1D development. Recently, it has been discovered that serum contains large amounts of stable miRNAs derived from immune cells and other tissues/cells, and that serum miRNAs might serve as biomarkers for disease prediction and progression. Yet little is known about serum miRNAs in T1D. We hypothesize that serum miRNAs could be a novel class of blood-based biomarker for diabetes staging. Serum RNAs were isolated from nonobese diabetic (NOD) mice at different stages (3-4, 7-8, 16-19 weeks, and newly diagnosed diabetes) during diabetes development. Serum miRNA expression profiles were performed by real-time RT-PCR MicroRNA Arrays. Twenty-three miRNAs appeared to have specific expression patterns during diabetes development. Single qRT-PCR further confirmed that serum miR-150, miR-146b, and miR-215 are significantly upregulated starting at the stage of insulitis, destructive insulitis and diabetes, respectively. Our data highly suggest that serum miRNAs are potential novel biomarkers for diabetes prediction and staging, and that miR-150, miR-146b and miR-215 could be used as serum biomarkers for diabetes staging or therapy response in the NOD mouse.

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Zhao, Xuemei, Vijay Modur, Leonidas N. Carayannopoulos, and Omar F. Laterza. "Biomarkers in Pharmaceutical Research." Clinical Chemistry 61, no. 11 (November 1, 2015): 1343–53. http://dx.doi.org/10.1373/clinchem.2014.231712.

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Abstract BACKGROUND Biomarkers are important tools in drug development and are used throughout pharmaceutical research. CONTENT This review focuses on molecular biomarkers in drug development. It contains sections on how biomarkers are used to assess target engagement, pharmacodynamics, safety, and proof-of-concept. It also covers the use of biomarkers as surrogate end points and patient selection/companion diagnostics and provides insights into clinical biomarker discovery and biomarker development/validation with regulatory implications. To survey biomarkers used in drug development—acknowledging that many pharmaceutical development biomarkers are not published—we performed a focused PubMed search employing “biomarker” and the names of the largest pharmaceutical companies as keywords and filtering on clinical trials and publications in the last 10 years. This yielded almost 500 entries, the majority of which included disease-related (approximately 60%) or prognostic/predictive (approximately 20%) biomarkers. A notable portion (approximately 8%) included HER2 (human epidermal growth factor receptor 2) testing, highlighting the utility of biomarkers for patient selection. The remaining publications included target engagement, safety, and drug metabolism biomarkers. Oncology, cardiovascular disease, and osteoporosis were the areas with the most citations, followed by diabetes and Alzheimer disease. SUMMARY Judicious biomarker use can improve pharmaceutical development efficiency by helping to select patients most appropriate for treatment using a given mechanism, optimize dose selection, and provide earlier confidence in accelerating or discontinuing compounds in clinical development. Optimal application of biomarker technology requires understanding of candidate drug pharmacology, detailed modeling of biomarker readouts relative to pharmacokinetics, rigorous validation and qualification of biomarker assays, and creative application of these elements to drug development problems.

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Norrman, Annika Emilia, Taina Tervahartiala, Ella Sahlberg, Timo Sorsa, Hellevi Ruokonen, Lisa Grönroos, Jukka H. Meurman, et al. "Salivary Biomarkers and Oral Health in Liver Transplant Recipients, with an Emphasis on Diabetes." Diagnostics 11, no. 4 (April 7, 2021): 662. http://dx.doi.org/10.3390/diagnostics11040662.

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Salivary biomarkers have been linked to various systemic diseases. We examined the association between salivary biomarkers, periodontal health, and microbial burden in liver transplant (LT) recipients with and without diabetes, after transplantation. We hypothesized that diabetic recipients would exhibit impaired parameters. This study included 84 adults who received an LT between 2000 and 2006 in Finland. Dental treatment preceded transplantation. The recipients were re-examined, on average, six years later. We evaluated a battery of salivary biomarkers, microbiota, and subjective oral symptoms. Periodontal health was assessed, and immunosuppressive treatments were recorded. Recipients with impaired periodontal health showed higher matrix metalloproteinase-8 (MMP-8) levels (p < 0.05) and MMP-8/tissue inhibitor of matrix metalloproteinase 1 (TIMP1) ratios (p < 0.001) than recipients with good periodontal health. Diabetes post-LT was associated with impaired periodontal health (p < 0.05). No difference between groups was found in the microbial counts. Salivary biomarker levels did not seem to be affected by diabetes. However, the advanced pro-inflammatory state induced by and associated with periodontal inflammation was reflected in the salivary biomarker levels, especially MMP-8 and the MMP-8/TIMP-1 molar ratio. Thus, these salivary biomarkers may be useful for monitoring the oral inflammatory state and the course of LT recipients.

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Jasmadi, Rima Novisca, and Intanri Kurniati. "BIOMARKER YANG BERPOTENSI MENDETEKSI RISIKO DIABETES MELLITUS GESTASIONAL PADA MASA PRAKONSEPSI." JIMKI: Jurnal Ilmiah Mahasiswa Kedokteran Indonesia 8, no. 1 (February 26, 2020): 58–63. http://dx.doi.org/10.53366/jimki.v8i1.38.

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ABSTRAK Pendahuluan: Diabetes mellitus gestasional adalah gangguan intoleransi glukosa pada masa kehamilan. Diabetes mellitus gestasional merupakan komplikasi yang paling sering terjadi pada kehamilan, ditemukan pada 5-9% dari kehamilan. Ibu hamil yang menderita diabetes mellitus gestasional dapat meningkatkan risiko hipertensi selama kehamilan, persalinan secara cesar, dan macrosomia (berat badan bayi yang lahir lebih dari 4000 gram). Risiko jangka panjang yang dapat dialami oleh ibu hamil dengan diabetes mellitus gestasional yaitu peningkatan risiko menderita penyakit diabetes serta penyakit kardiovaskular dan pada bayi yang dilahirkan akan meningkatkan risiko terjadinya obesitas, intoleransi glukosa, dan diabetes. Pembahasan: Proses patogenik terjadinya diabetes mellitus gestasional sudah di mulai dari sebelum kehamilan. Identifikasi wanita yang berisiko tinggi mengalami diabetes mellitus gestasional akan sangat bermanfaat apabila dilakukan sebelum kehamilan agar dapat dilakukan intervensi pada saat prakonsepsi untuk mengurangi risiko terjadinya diabetes mellitus gestasional pada saat hamil nantinya. Ada beberapa jenis biomarker yang bisa digunakan untuk mendeteksi risiko diabetes mellitus gestasional, di antaranya total adiponectin, sex hormone-binding globulin (SHBG), total high-density lipoprotein (HDL), low-density lipoprotein (LDL) peak diameter dan gamma-glutamyltransferase (GGT). Kesimpulan: Penggunaan lebih dari satu biomarker memiliki skor yang lebih tinggi dalam mengidentifikasi diabetes mellitus gestasional dibandingkan hanya dengan satu biomarker saja. Wanita yang diperiksa dengan 3 atau 4 biomarker memiliki peluang teridentifikasi diabetes mellitus gestasional 10 kali lipat lebih besar. Kata kunci: Biomarker, Diabetes mellitus gestasional, Prakonsepsi ABSTRACT Introduction: Gestational diabetes mellitus is a glucose intolerance disorders during pregnancy. Gestational diabetes mellitus is the most common complication in pregnancy, found in 5-9% of pregnancies. Pregnant women that suffer gestational diabetes mellitus can increase risk of hypertension during pregnancy, caesarean delivery, and macrosomia (babies’ weight more than 4000 grams). Long-term risks that can be experienced by pregnant women with gestational diabetes mellitus are an increased risk of suffering from diabetes and cardiovascular disease and in babies born will increase the risk of obesity, glucose intolerance, and diabetes. Discussion: The pathogenic process of gestational diabetes mellitus begins before pregnancy. Identification of women at high risk of having gestational diabetes mellitus will be very useful if done before pregnancy, that intervention can be done at the time of preconception to reduce the risk of developing gestational diabetes mellitus in future pregnancy. There are several types of biomarkers that can be used to detect the risk of gestational diabetes mellitus, including total adiponectin, sex hormone-binding globulin Tinjauan Pustaka JIMKI Volume 8 No.1 | November 2019 – Februari 2020 59 (SHBG), total high-density lipoprotein (HDL), low-density lipoprotein (LDL) peak diameter and gamma-glutamyltransferase (GGT). Conclusion: Use of more than one biomarker has a higher score in identifying gestational diabetes mellitus compared to just one biomarker. Women examined with 3 or 4 biomarkers had a 10-fold greater chance of being identified as gestational diabetes mellitus. Keyword: Biomarkers, Gestational diabetes mellitus, Preconception

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Yang, Mingjun, Boni Song, Juxiang Liu, Zhitong Bing, Yonggang Wang, and Linmiao Yu. "Gene signature for prognosis in comparison of pancreatic cancer patients with diabetes and non-diabetes." PeerJ 8 (November 11, 2020): e10297. http://dx.doi.org/10.7717/peerj.10297.

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Background Pancreatic cancer (PC) has much weaker prognosis, which can be divided into diabetes and non-diabetes. PC patients with diabetes mellitus will have more opportunities for physical examination due to diabetes, while pancreatic cancer patients without diabetes tend to have higher risk. Identification of prognostic markers for diabetic and non-diabetic pancreatic cancer can improve the prognosis of patients with both types of pancreatic cancer. Methods Both types of PC patients perform differently at the clinical and molecular levels. The Cancer Genome Atlas (TCGA) is employed in this study. The gene expression of the PC with diabetes and non-diabetes is used for predicting their prognosis by LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression. Furthermore, the results are validated by exchanging gene biomarker with each other and verified by the independent Gene Expression Omnibus (GEO) and the International Cancer Genome Consortium (ICGC). The prognostic index (PI) is generated by a combination of genetic biomarkers that are used to rank the patient’s risk ratio. Survival analysis is applied to test significant difference between high-risk group and low-risk group. Results An integrated gene prognostic biomarker consisted by 14 low-risk genes and six high-risk genes in PC with non-diabetes. Meanwhile, and another integrated gene prognostic biomarker consisted by five low-risk genes and three high-risk genes in PC with diabetes. Therefore, the prognostic value of gene biomarker in PC with non-diabetes and diabetes are all greater than clinical traits (HR = 1.102, P-value < 0.0001; HR = 1.212, P-value < 0.0001). Gene signature in PC with non-diabetes was validated in two independent datasets. Conclusions The conclusion of this study indicated that the prognostic value of genetic biomarkers in PCs with non-diabetes and diabetes. The gene signature was validated in two independent databases. Therefore, this study is expected to provide a novel gene biomarker for predicting prognosis of PC with non-diabetes and diabetes and improving clinical decision.

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Gasecka, Aleksandra, Dominika Siwik, Magdalena Gajewska, Miłosz J. Jaguszewski, Tomasz Mazurek, Krzysztof J. Filipiak, Marek Postuła, and Ceren Eyileten. "Early Biomarkers of Neurodegenerative and Neurovascular Disorders in Diabetes." Journal of Clinical Medicine 9, no. 9 (August 30, 2020): 2807. http://dx.doi.org/10.3390/jcm9092807.

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Diabetes mellitus (DM) is a common disease worldwide. There is a strong association between DM and neurovascular and neurodegenerative disorders. The first group mainly consists of diabetic retinopathy, diabetic neuropathy and stroke, whereas, the second group includes Alzheimer’s disease, Parkinson’s disease, mild cognitive impairment and dementia. The aforementioned diseases have a common pathophysiological background including insulin resistance, oxidative stress, atherosclerosis and vascular injury. The increasing prevalence of neurovascular and neurodegenerative disorders among diabetic patients has resulted in an urgent need to develop biomarkers for their prediction and/or early detection. The aim of this review is to present the potential application of the most promising biomarkers of diabetes-related neurodegenerative and neurovascular disorders, including amylin, β-amyloid, C-reactive protein (CRP), dopamine, gamma-glutamyl transferase (GGT), glycogen synthase kinase 3β, homocysteine, microRNAs (mi-RNAs), paraoxonase 1, phosphoinositide 3-kinases, tau protein and various growth factors. The most clinically promising biomarkers of neurovascular and neurodegenerative complications in DM are hsCRP, GGT, homocysteine and miRNAs. However, all biomarkers discussed in this review could become a part of the potential multi-biomarker screening panel for diabetic patients at risk of neurovascular and neurodegenerative complications.

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Miao, Yan, Paul A. Smink, Dick de Zeeuw, and Hiddo J. Lambers Heerspink. "Drug-Induced Changes in Risk/Biomarkers and Their Relationship with Renal and Cardiovascular Long-Term Outcome in Patients with Diabetes." Clinical Chemistry 57, no. 2 (February 1, 2011): 186–95. http://dx.doi.org/10.1373/clinchem.2010.148395.

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BACKGROUND Optimal renal and cardiovascular risk management in diabetic patients includes optimal maintenance of blood pressure and control of glucose and lipids. Although the optimal control of these risk factors or “risk/biomarkers” has proven to be effective, it often is difficult to achieve. Consequently, the risk for renal and cardiovascular complications remains devastatingly high. Many risk/biomarkers have been discovered that accurately predict long-term renal and cardiovascular outcome. However, the aim of measuring risk/biomarkers may not be only to determine an individual's risk, but also to use the risk/biomarker level to guide therapy and thereby improve long-term clinical outcome. CONTENT This review describes the effects of various drugs on novel risk/biomarkers and the relationship between (drug induced) short-term changes in risk/biomarkers and long-term renal and cardiovascular outcome in patients with diabetes. SUMMARY In post hoc analyses of large trials, the short-term reductions in albuminuria, transforming growth factor-β, and N-terminal pro-B–type natriuretic peptide (NT-proBNP) induced by inhibitors of the renin-angiotensin-aldosterone system were associated with a decreased likelihood of long-term adverse renal and cardiovascular outcomes. However, the few studies that systematically investigated the utility of prospectively targeting novel risk/biomarkers such as hemoglobin or NT-proBNP failed to demonstrate long-term cardiovascular protection. The latter examples suggest that although a risk/biomarker may have superior prognostic ability, therapeutically changing such a risk/biomarker does not necessarily improve long-term outcome. Thus, to establish the clinical utility of other novel risk/biomarkers, clinical trials must be performed to prospectively examine the effects of therapeutically-induced changes in single or multiple risk/biomarkers on long-term risk management of patients with diabetes.

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Bjornstad, Petter, and David M. Maahs. "Diabetes Complications in Childhood Diabetes: New Biomarkers and Technologies." Current Pediatrics Reports 3, no. 2 (April 4, 2015): 177–86. http://dx.doi.org/10.1007/s40124-015-0081-0.

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Gromova, Mariya, Annegret Vaggelas, Gabriele Dallmann, and Diane Seimetz. "Biomarkers: Opportunities and Challenges for Drug Development in the Current Regulatory Landscape." Biomarker Insights 15 (January 2020): 117727192097465. http://dx.doi.org/10.1177/1177271920974652.

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Biomarkers are widely used at every stage of drug discovery and development. Utilisation of biomarkers has a potential to make drug discovery, development and approval processes more efficient. An overview of the current global regulatory landscape is presented in this article with particular emphasis on the validation and qualification of biomarkers, as well as legal framework for companion diagnostics. Furthermore, this article shows how the number of approved drugs with at least 1 biomarker used during development (biomarker acceptance) is affected by the recent advances in the biomarker regulations. More than half of analysed approvals were supported by biomarker data and there has been a slight increase in acceptance of biomarkers in recent years, even though the growth is not continuous. For certain pharmacotherapeutic groups, approvals with biomarkers are more common than without. Examples include immunosuppressants, immunostimulants, drugs used in diabetes, antithrombotic drugs, antineoplastic agents and antivirals. As a conclusion, potential benefits, challenges and opportunities of using biomarkers in drug discovery and development in the current regulatory landscape are summarised and discussed.

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Goldfine, Allison B., Robert W. Gerwien, Janice A. Kolberg, Sheila O'Shea, Sarah Hamren, Glenn P. Hein, Xiaomei M. Xu, and Mary Elizabeth Patti. "Biomarkers in Fasting Serum to Estimate Glucose Tolerance, Insulin Sensitivity, and Insulin Secretion." Clinical Chemistry 57, no. 2 (February 1, 2011): 326–37. http://dx.doi.org/10.1373/clinchem.2010.156133.

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BACKGROUND Biomarkers for estimating reduced glucose tolerance, insulin sensitivity, or impaired insulin secretion would be clinically useful, since these physiologic measures are important in the pathogenesis of type 2 diabetes mellitus. METHODS We conducted a cross-sectional study in which 94 individuals, of whom 84 had 1 or more risk factors and 10 had no known risk factors for diabetes, underwent oral glucose tolerance testing. We measured 34 protein biomarkers associated with diabetes risk in 250-μL fasting serum samples. We applied multiple regression selection techniques to identify the most informative biomarkers and develop multivariate models to estimate glucose tolerance, insulin sensitivity, and insulin secretion. The ability of the glucose tolerance model to discriminate between diabetic individuals and those with impaired or normal glucose tolerance was evaluated by area under the ROC curve (AUC) analysis. RESULTS Of the at-risk participants, 25 (30%) were found to have impaired glucose tolerance, and 11 (13%) diabetes. Using molecular counting technology, we assessed multiple biomarkers with high accuracy in small volume samples. Multivariate biomarker models derived from fasting samples correlated strongly with 2-h postload glucose tolerance (R2 = 0.45, P < 0.0001), composite insulin sensitivity index (R2 = 0.91, P < 0.0001), and insulin secretion (R2 = 0.45, P < 0.0001). Additionally, the glucose tolerance model provided strong discrimination between diabetes vs impaired or normal glucose tolerance (AUC 0.89) and between diabetes and impaired glucose tolerance vs normal tolerance (AUC 0.78). CONCLUSIONS Biomarkers in fasting blood samples may be useful in estimating glucose tolerance, insulin sensitivity, and insulin secretion.

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Speake, Cate, and Jared M. Odegard. "Evaluation of Candidate Biomarkers of Type 1 Diabetes via the Core for Assay Validation." Biomarker Insights 10s4 (January 2015): BMI.S29697. http://dx.doi.org/10.4137/bmi.s29697.

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Recognizing an increasing need for biomarkers that predict clinical outcomes in type 1 diabetes (T1D), JDRF, a major funding organization for T1D research, recently instituted the Core for Assay Validation (CAV) to accelerate the translation of promising assays from discovery to clinical implementation via a process of coordinated evaluation of biomarkers. In this model, the CAV facilitates the validation of candidate assay methods as well as qualification of proposed biomarkers for a specific clinical use in well-characterized patients. We describe here a CAV-driven pilot project aimed at identifying biomarkers that predict the rate of decline in beta cell function after diagnosis. In a formalized pipeline, candidate assays are first assessed for general rationale, technical precision, and biological associations in a cross-sectional cohort. Those with the most favorable characteristics are then applied to placebo arm subjects of T1D intervention trials to assess their predictive correlation with beta cell function. We outline a go/no-go process for advancing candidate assays in a defined qualification pipeline that also allows for the discovery of novel predictive biomarker combinations. This strategy could be a model for other collaborative biomarker development efforts in and beyond T1D.

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Gluhovschi, Cristina, Gheorghe Gluhovschi, Ligia Petrica, Romulus Timar, Silvia Velciov, Ioana Ionita, Adriana Kaycsa, and Bogdan Timar. "Urinary Biomarkers in the Assessment of Early Diabetic Nephropathy." Journal of Diabetes Research 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4626125.

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Diabetic nephropathy (DN) is a frequent and severe complication of diabetes mellitus (DM). Its diagnosis in incipient stages may allow prompt interventions and an improved prognosis. Towards this aim, biomarkers for detecting early DN can be used. Microalbuminuria has been proven a remarkably useful biomarker, being used for diagnosis of DN, for assessing its associated condition—mainly cardiovascular ones—and for monitoring its progression. New researches are pointing that some of these biomarkers (i.e., glomerular, tubular, inflammation markers, and biomarkers of oxidative stress) precede albuminuria in some patients. However, their usefulness is widely debated in the literature and has not yet led to the validation of a new “gold standard” biomarker for the early diagnosis of DN. Currently, microalbuminuria is an important biomarker for both glomerular and tubular injury. Other glomerular biomarkers (transferrin and ceruloplasmin) are under evaluation. Tubular biomarkers in DN seem to be of a paramount importance in the early diagnosis of DN since tubular lesions occur early. Additionally, biomarkers of inflammation, oxidative stress, podocyte biomarkers, and vascular biomarkers have been employed for assessing early DN. The purpose of this review is to provide an overview of the current biomarkers used for the diagnosis of early DN.

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Jin, Yulan, and Jin-Xiong She. "Novel Biomarkers in Type 1 Diabetes." Review of Diabetic Studies 9, no. 4 (2012): 224–35. http://dx.doi.org/10.1900/rds.2012.9.224.

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Shin, Andrew C. "Are BCAAs Mere Biomarkers of Diabetes?" Diabetes Research - Open Journal 3, no. 1 (April 28, 2017): e4-e8. http://dx.doi.org/10.17140/droj-3-e009.

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Bonifacio, Ezio. "Predicting Type 1 Diabetes Using Biomarkers." Diabetes Care 38, no. 6 (May 21, 2015): 989–96. http://dx.doi.org/10.2337/dc15-0101.

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Dias, Stephanie, Carmen Pheiffer, Yoonus Abrahams, Paul Rheeder, and Sumaiya Adam. "Molecular Biomarkers for Gestational Diabetes Mellitus." International Journal of Molecular Sciences 19, no. 10 (September 26, 2018): 2926. http://dx.doi.org/10.3390/ijms19102926.

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Gestational diabetes mellitus (GDM) is a growing public health problem worldwide. The condition is associated with perinatal complications and an increased risk for future metabolic disease in both mothers and their offspring. In recent years, molecular biomarkers received considerable interest as screening tools for GDM. The purpose of this review is to provide an overview of the current status of single-nucleotide polymorphisms (SNPs), DNA methylation, and microRNAs as biomarkers for GDM. PubMed, Scopus, and Web of Science were searched for articles published between January 1990 and August 2018. The search terms included “gestational diabetes mellitus”, “blood”, “single-nucleotide polymorphism (SNP)”, “DNA methylation”, and “microRNAs”, including corresponding synonyms and associated terms for each word. This review updates current knowledge of the candidacy of these molecular biomarkers for GDM with recommendations for future research avenues.

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Cassiday, Laura. "Candidate biomarkers for type 1 diabetes." Journal of Proteome Research 7, no. 2 (February 2008): 482. http://dx.doi.org/10.1021/pr083717a.

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Stefan-van Staden, Raluca-Ioana, Grigorina Mitrofan, and Constantin Ionescu-Tirgoviste. "Pattern Recognition of Diabetes Related Biomarkers." Electroanalysis 30, no. 11 (September 14, 2018): 2628–34. http://dx.doi.org/10.1002/elan.201800523.

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Sauriasari, Rani, Dhonna Dwi Safitri, and Nuriza Ulul Azmi. "Current updates on protein as biomarkers for diabetic kidney disease: a systematic review." Therapeutic Advances in Endocrinology and Metabolism 12 (January 2021): 204201882110496. http://dx.doi.org/10.1177/20420188211049612.

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Background: In the past decade, researchers have been focused on discovering protein biomarkers for diabetic kidney disease. This paper aims to search for, analyze, and synthesize current updates regarding the development of these efforts. Methods: We systematically searched the ScienceDirect, SpringerLink, and PubMed databases for observational studies of protein biomarkers in patients with diabetes mellitus. We included studies published between January 2018 and April 2020, that were based on a population of patients with type-1 or type-2 diabetes mellitus aged ⩾18 years, with an observational design such as cross-sectional, case–control, or cohort studies. The dependent variable of the research results was in the form of protein biomarkers from urine, plasma, or serum. Results: Following the screening process, 20 research articles with available full text met the inclusion criteria. These could be categorized as glomerular biomarkers (ANGPTL4, beta-2 microglobulin, Smad1, and glypican-5); inflammatory biomarkers (MCP-1 and adiponectin); and tubular biomarkers (NGAL, VDBP, megalin, sKlotho, and KIM-1). The development of a panel of biomarkers showed more promising results than those for a single biomarker in diagnosing diabetic kidney disease. Conclusion: All the biomarkers discussed in this review showed promising results for predicting diabetic kidney disease because they correlate with albuminuria, eGFR, or both. However, of the 11 protein biomarkers, none have prognostic value beyond albuminuria and eGFR.

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Hariadi, Hariadi, Maia Thalia Giani, and Silvia Handika Anggraeni. "Potential Biomarkers as Early Detection of Diabetic Cardiomyopathy." Cardiovascular and Cardiometabolic Journal (CCJ) 2, no. 2 (September 30, 2021): 95. http://dx.doi.org/10.20473/ccj.v2i2.2021.95-109.

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Abstract: Diabetes mellitus (DM) is one of the most prevalent and burdensome among chronic disease worldwide. Its complications accelerate mortality rate within population. Diabetic cardiomyopathy (DCM) is one of diabetes macrovascular complications, which symptoms are frequently unforeseen. Advances in pathogenesis understanding DCM underlying mechanisms remain not fully perceived. Current diagnostic approach of DCM can hardly determine diabetic patients with asymptomatic cardiomyopathy. Previous studies suggested biomarkers might detect early stage DCM. There are numerous selective biomarkers representing several pathophysiological pathways, such as myocardial fibrosis, inflammatory response, cardiomyocyte apoptosis, and metabolic dysregulation in the development of diabetic heart anomaly It was also reported those biomarkers are useful for the prognostic assessment of the disease. However, not all biomarkers are cardiac specific and can be an auspicious diagnostic tool candidate. Recent studies show that there are certain biomarkers, such as microRNA, H-FABP, IGFBP7, and some other novel cardiac biomarkers were more specifically associated with the pathological mechanism of DCM. In this review, we aimed to discuss the role of several potential cardiac biomarkers as early detection in DCM that may predict future incident of DCM, and contribute to improving mortality prediction in patients with subclinical DCM.Keywords: Biomarker; Diabetic Cardiomyopathy

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Kaczmarek, Karolina. "Gestational diabetes mellitus – biomarkers as a diagnosis of the future." Farmacja Polska 75, no. 6 (June 30, 2019): 306–9. http://dx.doi.org/10.32383/farmpol/116215.

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Zhang, Wen, Lili Gong, Song Yang, Yali Lv, Feifei Han, He Liu, and Lihong Liu. "Lipidomics Profile Changes of Type 2 Diabetes Mellitus with Acute Myocardial Infarction." Disease Markers 2019 (November 16, 2019): 1–7. http://dx.doi.org/10.1155/2019/7614715.

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The morbidity and mortality of cardiovascular disease (CVD)/acute myocardial infarction (AMI) of type 2 diabetes mellitus (T2DM) patients are extremely higher than those without T2DM. Biomarkers can be used to predict the occurrence of acute myocardial infarction, thus effectively reducing the incidence of CVD events, particularly in T2DM patients. Lipids have been shown to be biomarkers and potential therapeutic targets for human diseases. The aim of our study was to investigate the prognostic value of lipid biomarkers for predicting AMI in T2DM patients. A total of 420 subjects were recruited in this research. Liquid Chromatography-Electrospray Ionization-Quadrupole Time of Flight-Mass Spectrometer- (LC-ESI-QTOF-MS-) and Liquid Chromatography/Mass Spectrometer- (LC/MS-) based metabolomic methods were applied to characterize metabolic profiles in each plasma sample. In the first untargeted set, 40 T2DM patients with AMI, 40 T2DM patients without AMI, and 40 control subjects were gender- and age-matched. Eight lipid metabolites showed a significant difference among three groups. Then, in the second set, targeted metabolic profiling assays for these 8 lipid biomarker concentrations in plasma were performed; another 100 T2DM patients with AMI, 100 T2DM patients without AMI, and 100 control subjects were selected independently. Receiver operating characteristic (ROC) curves were constructed, and the area under the ROC curves (AUC) was calculated to determine the potential biomarkers. ROC curve analysis showed that the AUC value of lysophosphatidylcholine (LysoPC) 18:0 is more than 0.7, indicating that LysoPC 18:0 may be a potential sensitive and specific biomarker for T2DM with AMI. The changed plasma concentrations of lipids were significantly associated with T2DM with AMI, which showed great value to be biomarkers, though it requires a prospective cohort study for further validation.

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Odubanjo, Anthony A., Rohini Kalisetti, Robert Adrah, Adeniyi Ajenifuja, Blessey Joseph, and Mohammed Zaman. "Severe Myopericarditis in Diabetic Ketoacidosis—All Troponin are Not Myocardial Infarction." Clinical Medicine Insights: Case Reports 11 (January 1, 2018): 117954761876335. http://dx.doi.org/10.1177/1179547618763356.

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Uncontrolled diabetes and acute coronary syndrome share a complex dynamic that results in significant ambiguity when interpreting biomarker elevations in this setting. This is concerning because myocardial infarction has been shown to be the most common cause of death in the first 24 hours of admission for uncontrolled diabetes. Literature shows that elevation in cardiac biomarkers in patients with uncontrolled diabetes could be from viral myopericarditis, although a clear clinical significance is still lacking.1 It is, however, clear that elevation in cardiac biomarkers portends a poor long-term prognosis in patients with uncontrolled diabetes mellitus. We present a rare case of myopericarditis in a middle-aged patient with uncontrolled diabetes. The patient had elevated troponin I level reaching a peak of 7.3 ng/mL with associated ST elevations on electrocardiography. Coronary angiogram was subsequently done revealing clean coronaries. To our knowledge, this is the first description of myopericarditis in uncontrolled diabetes without a known cause.

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Maalmi, Haifa, Christian Herder, Klaus Strassburger, Sofia Urner, Karin Jandeleit-Dahm, Oana-Patricia Zaharia, Yanislava Karusheva, et al. "Biomarkers of Inflammation and Glomerular Filtration Rate in Individuals with Recent-Onset Type 1 and Type 2 Diabetes." Journal of Clinical Endocrinology & Metabolism 105, no. 12 (September 3, 2020): e4370-e4381. http://dx.doi.org/10.1210/clinem/dgaa622.

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Abstract Context While inflammation has been associated with kidney function in long-standing diabetes, its possible association in newly diagnosed diabetes is unknown. Objective To investigate cross-sectional and prospective associations between biomarkers of inflammation and kidney function in recent-onset diabetes. Methods The study included individuals with type 1 and type 2 diabetes with known diabetes duration of <1 year from the German Diabetes Study. Baseline serum concentrations of 74 biomarkers were measured using proximity extension assay technology and their associations with estimated glomerular filtration rate (eGFR) and kidney function decline over 5 years were tested using multiple linear and logistic regression analysis. Results The cross-sectional analysis included 165 individuals with type 1 diabetes and 291 with type 2 diabetes. Baseline eGFR was higher in type 1 compared with type 2 diabetes (102 ± 15 vs 90 ± 16 mL/min/1.73 m2; P < 0.0001). After full adjustment for covariates and multiple testing, 7 biomarkers were associated with lower baseline eGFR in type 1 diabetes and 24 were associated with lower baseline eGFR in type 2 diabetes. Among these biomarkers, 6 biomarkers (CD5, CCL23, CST5, IL-10RB, PD-L1, TNFRSF9) were inversely associated with eGFR in both diabetes types. The prospective analysis did not detect associations between inflammatory biomarkers and kidney function decline. No evidence of an interaction between diabetes type and inflammatory biomarkers was found. Conclusion Several biomarkers of inflammation associate with lower baseline eGFR in recent-onset type 1 and type 2 diabetes, but do not associate with kidney function loss during the first 5 years after the diagnosis of diabetes.

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Lichtenauer, Michael, Peter Jirak, Vera Paar, Brigitte Sipos, Kristen Kopp, and Alexander E. Berezin. "Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication." Applied Sciences 11, no. 10 (May 12, 2021): 4397. http://dx.doi.org/10.3390/app11104397.

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Heart failure (HF) and type 2 diabetes mellitus (T2DM) have a synergistic effect on cardiovascular (CV) morbidity and mortality in patients with established CV disease (CVD). The aim of this review is to summarize the knowledge regarding the discriminative abilities of conventional and novel biomarkers in T2DM patients with established HF or at higher risk of developing HF. While conventional biomarkers, such as natriuretic peptides and high-sensitivity troponins demonstrate high predictive ability in HF with reduced ejection fraction (HFrEF), this is not the case for HF with preserved ejection fraction (HFpEF). HFpEF is a heterogeneous disease with a high variability of CVD and conventional risk factors including T2DM, hypertension, renal disease, older age, and female sex; therefore, the extrapolation of predictive abilities of traditional biomarkers on this population is constrained. New biomarker-based approaches are disputed to be sufficient for improving risk stratification and the prediction of poor clinical outcomes in patients with HFpEF. Novel biomarkers of biomechanical stress, fibrosis, inflammation, oxidative stress, and collagen turn-over have shown potential benefits in determining prognosis in T2DM patients with HF regardless of natriuretic peptides, but their role in point-to-care and in routine practice requires elucidation in large clinical trials.

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Khan, Rabia Sannam, and Haroon Malik. "Diagnostic Biomarkers for Gestational Diabetes Mellitus Using Spectroscopy Techniques: A Systematic Review." Diseases 11, no. 1 (January 25, 2023): 16. http://dx.doi.org/10.3390/diseases11010016.

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Gestational diabetes mellitus (GDM) is associated with adverse maternal and foetal consequences, along with the subsequent risk of type 2 diabetes mellitus (T2DM) and several other diseases. Due to early risk stratification in the prevention of progression of GDM, improvements in biomarker determination for GDM diagnosis will enhance the optimization of both maternal and foetal health. Spectroscopy techniques are being used in an increasing number of applications in medicine for investigating biochemical pathways and the identification of key biomarkers associated with the pathogenesis of GDM. The significance of spectroscopy promises the molecular information without the need for special stains and dyes; therefore, it speeds up and simplifies the necessary ex vivo and in vivo analysis for interventions in healthcare. All the selected studies showed that spectroscopy techniques were effective in the identification of biomarkers through specific biofluids. Existing GDM prediction and diagnosis through spectroscopy techniques presented invariable findings. Further studies are required in larger, ethnically diverse populations. This systematic review provides the up-to-date state of research on biomarkers in GDM, which were identified via various spectroscopy techniques, and a discussion of the clinical significance of these biomarkers in the prediction, diagnosis, and management of GDM.

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Papachristoforou, Eleftheria, Aikaterini Kountouri, Eirini Maratou, Dimitris Kouretas, Zoi Skaperda, Maria Tsoumani, Panagiotis Efentakis, Ignatios Ikonomidis, Vaia Lambadiari, and Konstantinos Makrilakis. "Association of Hypoglycemia with Biomarkers of Oxidative Stress and Antioxidants: An Observational Study." Healthcare 10, no. 8 (August 10, 2022): 1509. http://dx.doi.org/10.3390/healthcare10081509.

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Hypoglycemia has been associated with complications from the vasculature. The contributing effects of oxidative stress (OS) on these actions have not been sufficiently studied, especially in daily, routine clinical practice. We examined the association of hypoglycemia encountered in daily clinical practice with biomarkers of OS and endogenous antioxidant activity in persons with diabetes [type 1 (T1D) or type 2 (T2D)], as well as individuals without diabetes, with a history of hypoglycemia. Several biomarkers of OS (MDA, ADMA, ox-LDL, 3-NT, protein carbonyls, 4-HNE, TBARS) and antioxidant capacity (TAC, superoxide scavenging capacity, hydroxyl radical scavenging capacity, reducing power, ABTS) were measured. Blood was drawn at the time of hypoglycemia detection and under euglycemic conditions on a different day. A total of 31 participants (mean age [±SD] 52.2 ± 21.1 years, 45.2% males) were included in the study. There were 14 (45.2%) persons with T2D, 12 (38.7%) with T1D, and 5 (16.1%) without diabetes. We found no differences in the examined biomarkers. Only TBARS, a biomarker of lipid peroxidation, showed lower values during hypoglycemia (p = 0.005). This finding needs confirmation in more extensive studies, given that MDA, another biomarker of lipid peroxidation, was not affected. Our study suggests that hypoglycemia encountered in daily clinical practice does not affect OS.

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Cho, William C. S., Tai-Tung Yip, Wai-Shing Chung, Albert W. N. Leung, Christopher H. K. Cheng, and Kevin K. M. Yue. "Potential Biomarkers Found by Protein Profiling May Provide Insight for the Macrovascular Pathogenesis of Diabetes Mellitus." Disease Markers 22, no. 3 (2006): 153–66. http://dx.doi.org/10.1155/2006/450762.

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Diabetes mellitus (DM) is an alarming threat to health of mankind, yet its pathogenesis is unclear. The purpose of this study was to find potential biomarkers to serve as indicators for the pathogenesis of DM in a time course manner. Based on our previous findings that oxidative stress occurred at week 8, aorta lysate and sera of 102 streptozotocin (STZ)-induced diabetic and 85 control male Sprague-Dawley rats were obtained at the 4th, 8th and 12th week after STZ injection. The protein profiles were studied employing surface-enhanced laser desorption/ionization time-of-flight mass spectrometry technology in attomole sensitivity range. In the aorta, a multiple biomarker panel was discovered at the 4th week. At the 8th week, 4 biomarkers were found, while at the 12th week, 3 biomarkers were identified. In the sera, a triplet of 3 peaks and 2 biomarkers were all discovered to have 100% classification accuracy rate to differentiate the DM and control groups at all time intervals. Besides, 2 biomarkers were also found to have high classification value at week 12. Comparing the aorta and sera from DM and non-DM rats, a bundle of potential biomarkers with significant changes in peak intensities and high classification values were found. Two of the serum biomarkers matched with islet amyloid polypeptide and resistin in the SWISS-PROT knowledgebase. Validation has been conducted using immunoassay kits. These potential biomarkers may provide valuable insight on the pathogenesis of DM and macrovascular complications.

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Kaštelan, Snježana, Ivana Orešković, Filip Bišćan, Helena Kaštelan, and Antonela Gverović Antunica. "Inflammatory and angiogenic biomarkers in diabetic retinopathy." Biochemia medica 30, no. 3 (October 12, 2020): 385–99. http://dx.doi.org/10.11613/bm.2020.030502.

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Diabetic retinopathy (DR) is one of the most common microvascular complications of diabetes mellitus (DM) and a leading cause of blindness in working-age adults in developed countries. Numerous investigations have recognised inflammation and angiogenesis as important factors in the development of this complication of diabetes. Current methods of DR treatment are predominantly used at advanced stages of the disease and could be associated with serious side effects. Therefore, new diagnostic methods are needed in order to identify the initial stages of DR as well as monitoring the effects of applied therapy. Biochemical biomarkers are molecules found in blood or other biological fluid and tissue that indicate the existence of an abnormal condition or disease. They could be a valuable tool in detecting early stages of DR, identifying patients most susceptible to retinopathy progression and monitoring treatment outcomes. Biomarkers related to DR can be measured in the blood, retina, vitreous, aqueous humour and recently in tears. As the retina represents a small part of total body mass, a circulating biomarker for DR needs to be highly specific. Local biomarkers are more reliable as indicators of the retinal pathology; however, obtaining a sample of aqueous humour, vitreous or retina is an invasive procedure with potential serious complications. As a non-invasive novel method, tear analysis offers a promising direction in further research for DR biomarker detection. The aim of this paper is to review systemic and local inflammatory and angiogenic biomarkers relevant to this sight threatening diabetic complication.

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Thipsawat, Sopida. "Early detection of diabetic nephropathy in patient with type 2 diabetes mellitus: A review of the literature." Diabetes and Vascular Disease Research 18, no. 6 (November 2021): 147916412110588. http://dx.doi.org/10.1177/14791641211058856.

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Type 2 diabetes mellitus is a pathology of heterogeneous etiology characterized by hyperglycemia resulting from lack of insulin action, insulin secretion, or both, and the population with diabetes mellitus is predicted to be about 439 million worldwide by 2030. Prolong diabetes has been related with microvascular complications especially diabetic nephropathy. DN is the most common complication of type 2 diabetes mellitus, and it is the leading cause of end-stage renal disease worldwide. It is crucial to diagnose patients who are more sensible to develop DN for better control of the process of disease. Several factors and mechanisms contribute to the development and outcome of diabetic nephropathy. Microalbuminuria is an early marker of DN and use it as a routine for screening, but the renal damages may be happening even without microalbuminuria. There are several significant kidney damage and disease biomarkers which helps in early detection of DN. An early biomarker may allow earlier diagnosis, treatment reduces DN prevalence and slows DN progression. Therefore, this review focuses on laboratory biomarkers that are earlier, more validation of an early and specific biomarker could potentially make it possible for early diagnosis, treatment, and retardation of progression of diabetic nephropathy.

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Wichro, Erika, Tanja Macheiner, Jasmin Schmid, Barbara Kavsek, and Karine Sargsyan. "The Wide and Complex Field of NAFLD Biomarker Research: Trends." ISRN Hepatology 2014 (April 28, 2014): 1–12. http://dx.doi.org/10.1155/2014/846923.

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Background. Nonalcoholic fatty liver disease is now acknowledged as a complex public health issue linked to sedentary lifestyle, obesity, and related disorders like type 2 diabetes and metabolic syndrome. Aims. We aimed to retrieve its trends out of the huge amount of published data. Therefore, we conducted an extensive literature search to identify possible biomarker and/or biomarker combinations by retrospectively assessing and evaluating common and novel biomarkers to predict progression and prognosis of obesity related liver diseases. Methodology. We analyzed finally 62 articles accounting for 157 cohorts and 45,288 subjects. Results. Despite the various approaches, most cohorts were considerably small and rarely comparable. Also, we found that the same standard parameters were measured rather than novel biomarkers. Diagnostics approaches appeared incomparable. Conclusions. Further collaborative investigations on harmonizing ways of data acquisition and identifying such biomarkers for clinical use are necessary to yield sufficient significant results of potential biomarkers.

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Prentice, Ross L., Mary Pettinger, Marian L. Neuhouser, Lesley F. Tinker, Ying Huang, Cheng Zheng, JoAnn E. Manson, Yasmin Mossavar-Rahmani, Garnet L. Anderson, and Johanna W. Lampe. "Application of blood concentration biomarkers in nutritional epidemiology: example of carotenoid and tocopherol intake in relation to chronic disease risk." American Journal of Clinical Nutrition 109, no. 4 (March 27, 2019): 1189–96. http://dx.doi.org/10.1093/ajcn/nqy360.

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ABSTRACT Background Biomarkers provide potential to objectively measure the intake of nutrients and foods, and thereby to strengthen nutritional epidemiology association studies. However, there are only a few established intake biomarkers, mostly based on recovery of nutrients or their metabolites in urine. Blood concentration measures provide a potential biomarker source for many additional nutritional variables, but their use in disease-association studies requires further development. Objective The aim of this study was to apply recently proposed serum-based carotenoid and tocopherol intake biomarkers and to examine their association with the incidence of major cardiovascular diseases, cancers, and diabetes in a subset of Women's Health Initiative (WHI) cohorts. Methods Serum concentrations of α- and β-carotene, lutein plus zeaxanthin (L + Z), and α-tocopherol were routinely measured at baseline in a subset of 5488 enrollees in WHI cohorts. Intake biomarkers for these 4 micronutrients, obtained by combining serum concentrations with participant characteristics, were recently proposed using a 153-woman feeding study within WHI. These biomarker equations are augmented here to include pertinent disease risk factors and are associated with subsequent chronic disease incidence in this WHI subset. Results HRs for a doubling of micronutrient intake differed only moderately from the null for the outcomes considered. However, somewhat lower risks of specific cardiovascular outcomes, breast cancer, and diabetes were associated with a higher intake of α- and β-carotene, lower risk of diabetes was associated with higher L + Z intake, and elevated risks of certain cardiovascular outcomes were associated with a higher intake of α-tocopherol. These patterns remained following the exclusion of baseline users of dietary supplements. Conclusions Concentration biomarkers can be calculated from blood specimens obtained in large epidemiologic cohorts and applied directly in disease-association analyses, without relying on self-reported dietary data. Observed associations between carotenoid and tocopherol biomarkers and chronic disease risk could be usefully evaluated further using stored serum specimens on the entire WHI cohort. This study was registered at www.clinicaltrials.gov as NCT00000611.

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Goetze, Jens P., and David P. Sonne. "Diabetes and its lack of causal biomarkers." Biomarkers in Medicine 10, no. 11 (November 2016): 1121–23. http://dx.doi.org/10.2217/bmm-2016-0251.

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Turowski, Gitta, Line Sletner, Branka Yli, Anne K. Jenum, and Borghild Roald. "Placental morphology and biomarkers in gestational diabetes." Placenta 45 (September 2016): 94. http://dx.doi.org/10.1016/j.placenta.2016.06.114.

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Hill, James O., and John C. Peters. "Biomarkers and functional foods for obesity and diabetes." British Journal of Nutrition 88, S2 (November 2002): S213—S218. http://dx.doi.org/10.1079/bjn2002685.

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Obesity has reached epidemic proportions in many countries around the world. Because of the close relationship between obesity and type 2 diabetes, an epidemic of diabetes is close behind the obesity epidemic. Preventing and treating obesity is becoming an increasing priority. In the United States, over 60% of the adult population is overweight or obese and thus at increased risk of developing diabetes and cardiovascular disease. While the aetiology of obesity and diabetes is complex, diet clearly plays an important role both in the development and management of these diseases. There is interest in functional foods that could help in prevention and/or management of obesity and type 2 diabetes. This could involve food products that help management of ‘hunger’ or that increase ‘satiety’. It could also involve foods that contribute to more inefficient use of ingested energy (i.e. foods that stimulate energy expenditure more than would be expected from their energy content). As the concept of insulin sensitivity becomes generally more accepted by health care professionals and the public, foods may be targeted towards maximizing insulin sensitivity and towards ‘prevention’ of diabetes. In addition to foods that impact upon body weight, these may include foods that affect the glucose and/or insulin levels that are seen either following the ingestion of food or later in the day. The present paper reviews the complex aetiology of obesity and diabetes and considers a potential role for functional foods in prevention and treatment of obesity and diabetes.

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Adiga, Usha, Kathyayani P, and Nandith P.B. "Association of Insulin Based Insulin Resistance with Liver Biomarkers in Type 2 Diabetes mellitus." Journal of Pure and Applied Microbiology 13, no. 2 (June 30, 2019): 1199–205. http://dx.doi.org/10.22207/jpam.13.2.60.

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Adki, Kaveri M., and Yogesh A. Kulkarni. "Potential Biomarkers in Diabetic Retinopathy." Current Diabetes Reviews 16, no. 9 (November 6, 2020): 971–83. http://dx.doi.org/10.2174/1573399816666200217092022.

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Background: Diabetic retinopathy is one of the important complications of diabetes. In major cases, diabetic retinopathy is unnoticed until the irreversible damage to eye occurs and leads to blurred vision and, eventually, blindness. Objective: The pathogenesis and diagnosis of diabetic retinopathy are very complex and not fully understood. Currently, well-established laser techniques and medications are available, but these treatment options have their own shortcomings on biological systems. Biomarkers can help to overcome this problem due to easy, fast and economical options for diagnosis of diabetic retinopathy. Methods: The search terms used were “Diabetic retinopathy”, “Biomarkers in diabetic retinopathy”, “Novel biomarkers in diabetic retinopathy” and “Potential biomarkers of diabetic retinopathy” by using different scientific resources and databases like EBSCO, ProQuest, PubMed and Scopus. Eligibility criteria included biomarkers involved in diabetic retinopathy in the detectable range. Exclusion criteria included the repetition and duplication of the biomarker in diabetic retinopathy. Results: Current review and literature study revealed that biomarkers of diabetic retinopathy can be categorized as inflammatory: tumor necrosis factor-α, monocyte chemoattractant protein-1, transforming growth factor- β; antioxidant: nicotinamide adenine dinucleotide phosphate oxidase; nucleic acid: poly ADP ribose polymerase- α, Apelin, Oncofetal; enzyme: ceruloplasmin, protein kinase C; and miscellaneous: erythropoietin. These biomarkers have a great potential in the progression of diabetic retinopathy hence can be used in the diagnosis and management of this debilitating disease. Conclusion: Above mentioned biomarkers play a key role in the pathogenesis of diabetic retinopathy; hence they can also be considered as potential targets for new drug development.

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Wander, Pandora L., Costas A. Christophi, Maria Rosario G. Araneta, Edward J. Boyko, Daniel A. Enquobahrie, Dana Dabelea, Ronald B. Goldberg, et al. "Adiposity, related biomarkers, and type 2 diabetes after gestational diabetes: The Diabetes Prevention Program." Obesity 30, no. 1 (November 18, 2021): 221–28. http://dx.doi.org/10.1002/oby.23291.

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Abbasi, Ali. "Mendelian randomization studies of biomarkers and type 2 diabetes." Endocrine Connections 4, no. 4 (December 2015): 249–60. http://dx.doi.org/10.1530/ec-15-0087.

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Many biomarkers are associated with type 2 diabetes (T2D) risk in epidemiological observations. The aim of this study was to identify and summarize current evidence for causal effects of biomarkers on T2D. A systematic literature search in PubMed and EMBASE (until April 2015) was done to identify Mendelian randomization studies that examined potential causal effects of biomarkers on T2D. To replicate the findings of identified studies, data from two large-scale, genome-wide association studies (GWAS) were used: DIAbetes Genetics Replication And Meta-analysis (DIAGRAMv3) for T2D and the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for glycaemic traits. GWAS summary statistics were extracted for the same genetic variants (or proxy variants), which were used in the original Mendelian randomization studies. Of the 21 biomarkers (from 28 studies), ten have been reported to be causally associated with T2D in Mendelian randomization. Most biomarkers were investigated in a single cohort study or population. Of the ten biomarkers that were identified, nominally significant associations with T2D or glycaemic traits were reached for those genetic variants related to bilirubin, pro-B-type natriuretic peptide, delta-6 desaturase and dimethylglycine based on the summary data from DIAGRAMv3 or MAGIC. Several Mendelian randomization studies investigated the nature of associations of biomarkers with T2D. However, there were only a few biomarkers that may have causal effects on T2D. Further research is needed to broadly evaluate the causal effects of multiple biomarkers on T2D and glycaemic traits using data from large-scale cohorts or GWAS including many different genetic variants.

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Náplava, Robert, Martin Gřiva, Čestmír Číhalík, Zdeněk Coufal, Milada Špendlíková, and Ota Hlinomaz. "Inflammatory biomarkers and coronary restenosis in patients with type-2 diabetes." Cor et Vasa 52, no. 11-12 (November 1, 2010): 690–94. http://dx.doi.org/10.33678/cor.2010.173.

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Jung, Chan-Young, and Tae-Hyun Yoo. "Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease." Diabetes & Metabolism Journal 46, no. 2 (March 31, 2022): 181–97. http://dx.doi.org/10.4093/dmj.2021.0329.

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Although diabetic kidney disease (DKD) remains the leading cause of end-stage kidney disease eventually requiring chronic kidney replacement therapy, the prevalence of DKD has failed to decline over the past 30 years. In order to reduce disease prevalence, extensive research has been ongoing to improve prediction of DKD onset and progression. Although the most commonly used markers of DKD are albuminuria and estimated glomerular filtration rate, their limitations have encouraged researchers to search for novel biomarkers that could improve risk stratification. Considering that DKD is a complex disease process that involves several pathophysiologic mechanisms such as hyperglycemia induced inflammation, oxidative stress, tubular damage, eventually leading to kidney damage and fibrosis, many novel biomarkers that capture one specific mechanism of the disease have been developed. Moreover, the increasing use of high-throughput omic approaches to analyze biological samples that include proteomics, metabolomics, and transcriptomics has emerged as a strong tool in biomarker discovery. This review will first describe recent advances in the understanding of the pathophysiology of DKD, and second, describe the current clinical biomarkers for DKD, as well as the current status of multiple potential novel biomarkers with respect to protein biomarkers, proteomics, metabolomics, and transcriptomics.

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Journal articles on the topic 'Diabetes biomarkers'

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