Comparison of common etiologies of diabetes. Adapted from Moran et al. [4].
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"2036",leadTitle:null,fullTitle:"Insecticides - Advances in Integrated Pest Management",title:"Insecticides",subtitle:"Advances in Integrated Pest Management",reviewType:"peer-reviewed",abstract:"This book contains 30 Chapters divided into 5 Sections. Section A covers integrated pest management, alternative insect control strategies, ecological impact of insecticides as well as pesticides and drugs of forensic interest. Section B is dedicated to chemical control and health risks, applications for insecticides, metabolism of pesticides by human cytochrome p450, etc. Section C provides biochemical analyses of action of chlorfluazuron, pest control effects on seed yield, chemical ecology, quality control, development of ideal insecticide, insecticide resistance, etc. Section D reviews current analytical methods, electroanalysis of insecticides, insecticide activity and secondary metabolites. 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She is Founder/Chairperson of the Department of Zoology (DOZ) and Ex-Controller of Examinations at Shaheed Benazir Bhutto University (SBBU) and Ex-Founder/ Ex-Chairperson of DOZ, Hazara University and Kohat University of Science & Technology. \nShe is the author of 150 high impact research papers, 135 abstracts, 4 authored books and 8 chapters. She is the editor of 5 books and she supervised BS(4), MSc(50), MPhil(40), and Ph.D. (1) students. She has organized and participated in numerous international and national conferences and received multiple awards and fellowships. She is a member of research societies, editorial boards of Journals, and World-Commission on Protected Areas, International Union for Conservation of Nature. 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Nanomedicine has opened a new category of medicines called nanomedicines where the medicine is reduced to the nanoscale size, hoping to enhance its physicochemical properties. The chapter summarizes the nanomedicines that have been approved by the Food and Drug Administration (FDA) or European Medicines Agency (EMA) and the nanomedicines whose clinical trials based on previously published review articles by Anselmo and Mitragotri are ongoing [1, 2].
To gain insight to current trends in nanomedicine research and the most successful types of nanomedicines in the market, the approved nanomedicines are presented in Figure 1. The number of approved nanomedicine products is 29 till 2019 [1]. Liposomes represented 44.8% (13 products). Inorganic nanoparticles ranked second with 41.4% (12 products). Other nanoparticles (polymeric and protein) have only 4 products (13.8%). These findings are very interesting as liposomes are one of the oldest nanomedicines. This opens an argument about the challenges in nanomedicine translation as a new platform requires further investigations to prove its activity and safety. On the other hand, cancer nanotherapeutics is ranked first with 10 products in the market, followed by iron-replacement therapies with 8 products. Also, it is worth to mention that imaging agents (six marketed products) are ranked in third place, especially the inorganic nanoparticles (three products).
Clinically approved nanomedicine for therapy and diagnostic.
Moreover, nanomedicines, currently undergoing clinical trials, are presented in Figure 2. The number of products under clinical trials is 47 till 2019 [1], where liposomes represented 61.7% (29 products) and micelles ranked second with 19.15% (9 products). Other nanoparticles have only nine products (19.15%). Also, these findings are similar to approved nanomedicine, where liposomes are the most used nanomedicine. On the other hand, 39 products are dedicated for cancer treatment. It is worth to mention that 10 products out of the 39 products are loaded with gene therapy and not chemotherapeutic agents.
Nanomedicine currently undergoing clinical trials for therapy and diagnostic.
Generally, the total number of nanomedicines in the market or in clinical trial are 76 products, where liposome formulations were the most used delivery system with 55.26% (42 products), followed by inorganic nanoparticles with 21% (16 products) as presented in Figure 3. According to the World Health Organization in 2015, the first leading cause of death in around 50% of countries is cancer [3]. According to the International Agency for Research on Cancer report that published in 2018 on the global burden of cancer, there are 18.1 million cancer cases and 9.6 million cancer deaths in 2018 [3]. These reports inspired the pharmaceutical industry to invest in this market. As mentioned previously, there are only 10 nanomedicine products out of 29 products available in the market to treat cancer, while there are 39 products out of 47 products for cancer treatment. This number of clinical trials for cancer is mainly derived by the 15 years of support by the US National Cancer Institute through the Centers of Cancer Nanotechnology Excellence (CCNEs) [4].
Total number of nanomedicines approved and under clinical trials (therapeutic and diagnostic).
Nanomedicines are mainly classified into two classes, either inorganic nanoparticles such as gold, silica, and iron oxide or organic nanoparticles such as polymeric, liposomes, and micelles (Figure 4). These nanoparticles are mostly used for therapeutic and diagnostic nanoparticles. Inorganic nanoparticles have been used for a variety of applications including lymph node imaging, hyperthermia, and anemia treatment. Some of them have successfully gone through preclinical studies and clinic trials. Along with inorganic nanoparticles, organic-based nanoparticles have successfully reached the clinical phase and currently reached the market for different applications like vaccination, microbial infection, and cancer.
Clinically approved and investigated nanomedicines including organic nanoparticles and inorganic nanoparticles.
Liposome-based nanomedicine is a type of drug formulation where a drug is encapsulated inside the phospholipid bilayer structure to enhance its bioavailability and therapeutic activity. Liposome formulations are one of the oldest nanomedicines with a well-established technique. Many research efforts were focused on using liposomes to encapsulate several cargos like small molecules such as doxorubicin, nucleic acid such as RNAs, and biological molecules such as vaccines for hepatitis A virus. Furthermore, administration of the liposomes without an encapsulated drug is also a possibility if the liposome subunits have a certain therapeutic effect such as sphingomyelin and cholesterol. PEGylation is an option to consider while using liposomes due to its importance in adding stealth to the delivery system. Most of the approved liposome-based nanomedicines are used for the treatment of cancer diseases. They take a large place in research as 10 out of the 29 approved nanomedicines are liposome-based.
Lipid nanosystems including nanoemulsions and solid lipid-based nanoparticles are another form of nanomedicine, which are usually used to encapsulate hydrophobic cargos to improve permeation and control release profile. Usually, a surfactant is used to ensure a uniform dispersion. Lipid nanomedicine can also encapsulate some gene therapeutics such as siRNA or contrast agents used for imaging such as F-butane. Generally, lipid nanomedicine can improve the pharmacological effect by enhancing drug accumulation in targeted tissues beside its biocompatibility. However, there are several drawbacks like rapid clearance due to reticuloendothelial system (RES) uptake and some limitations for administration routes and challenges regarding system stability [5, 6]. Unlike liposomal-based nanomedicines, lipid-based nanomedicines are not limited for cancer diseases only. Some of the diseases that are treated by lipid-based nanomedicines are amyloidosis, hepatitis B, and hepatic fibrosis. Furthermore, several types of nanoemulsion were loaded with drugs like simvastatin, cinnarizine, coenzyme Q10, and cyclosporine, which used as antihyperlipidemia, antihistaminic, antioxidant, and immunosuppressants, respectively.
Albumin-based nanomedicines are another form of nanosystems, where albumin, especially human serum albumin (protein), is used as a carrier. Albumin nanosystems can be loaded with different cargos via a simple self-assembly procedure of albumin in aqueous solution with simple crosslinking step. The main advantage of albumin is biocompatibility. Despite that, only 2 out of the 29 listed approved nanomedicines and 2 out of the 65 nanomedicines under clinical trials are albumin-based. It is currently used in imaging and delivering drugs that treat cancer diseases.
Micelles are self-assembled nanosystem by amphiphilic molecules that have a hydrophilic part and a hydrophobic one. They have several advantages like high permeability and solubility, which improve drug bioavailability. However, they still have some drawbacks like insufficient control to drug release and cytotoxicity due to amphiphilic molecule use, which interact with cell membrane [5, 7]. Although several reports used block copolymeric micelles to reduce clearance and increase bioavailability of chemotherapeutic agents and other types of drugs, there are no approved micelle-based nanomedicines. However, there are currently nine micelle-based nanomedicines undergoing clinical trials. Majority of them are used for cancer treatment.
Polymeric nanoparticles are one of the most commonly used nanosystems for drug delivery. Several polymers have been used like ethyl cellulose, poly(lactic-co-glycolic acid), polylactic acid, cyclodextrin, alginate, and chitosan. Depending on the nature of the polymer, either hydrophilic or hydrophobic, there are several techniques that have been used to prepare polymeric nanoparticles. Several advantages like relative stability and prolonged duration of action make polymeric nanoparticles a promising platform for the market. However, there are no marketed products based on polymeric nanoparticles. Only three products are currently on clinical trials for cancer.
Inorganic-based nanomedicines have several subtypes. Due to degradability and biocompatibility issues, few types have been used for therapeutic purpose, while other types for diagnostic purpose like imaging agents. One of these subtypes are metal oxide nanoparticles such as hafnium oxide nanoparticles which enhance tumor cell death via electron production through their stimulation with external radiation. Another subtype is in the form of colloids such as iron dextran colloids, iron gluconate colloid, and other similar derivatives that are usually used for the treatment of iron-deficiency anemia. The last subtype mentioned is iron−/silica−/gold-based nanomedicines, either as nanoparticles with drugs arranged on the surface for the treatment of cancer or as nanoshells/nanoparticles used for thermal ablation of tumors. There are 12 products in the market that belong to this type. Eight products used iron-replacement therapies. On the other hand, four products are currently on clinical trials for treating cancer.
The pharmacokinetic parameters of nanomedicines are similar to free drugs with addition phase after drug administration, which is the liberation phase beside the standard absorption, distribution, metabolism, and excretion (ADME). This new phase is controlled by particle nature, size, shape, and surface properties. It is worth to mention that particle size is very important for absorption and elimination. Particles with particle size <5 nm is easily excreted from the kidney, while larger particle size could be eliminated by the liver or engulfed by mononuclear-phagocyte system. Moreover, particle size and shape can affect particle accumulation in targeted tissues like ellipsoidal shape that has better distribution and retention in tumor tissue than spherical one. Surface modification of nanoparticles can affect particle uptake and elimination. Many nanoparticles are coated for active and passive targeting. Passive targeting is a non-specific retention in target tissue like solid cancer tissue by enhanced permeability and retention mechanism. Active targeting is the selective uptake of nanomedicine by specific cells. Target moieties could be protein, antibody, or small molecule selective to specific tissues or cells. This mechanism is mainly controlled by homing to overexpressed cell surface receptors.
The Food and Drug Administration classified nanoscale materials to nanomaterials as “materials used in the manufacture of nanomedicine” or nanomedicine as “final products,” The FDA approved 51 nanomedicines by the year 2016, 40% of which were in clinical trials between 2014 and 2016. According to the FDA evaluation of nanomedicines, it includes the physicochemical properties, followed by pharmacokinetics evaluation of nanomedicines. The pharmacokinetics evaluation includes (1) rate and amount of absorption, (2) retention in circulation, (3) half-life and complete elimination, (4) bioavailability differences, (5) distribution or accumulation to the body or specific tissue for active targeting, (6) decomposition or metabolism, (7) elimination, and (8) toxicity assessment of nanomedicines. On the other hand, the European Medicines Agency defined nanomedicines as “drugs composed of nanomaterials 1–100 nm in size, and these are classified into liposomes, nanoparticles, magnetic nanoparticles, gold NPs, quantum dots, dendrimers, polymeric micelles, viral and non-viral vectors, carbon nanotubes, and fullerenes.” EMA has approved eight commercially available nanomedicines as first-generation nanomedicines. Currently, there are 48 nanomedicines in clinical trials (Phases 1–3) in the EU. EMA evaluates the pharmacokinetics and pharmacodynamics of nanomedicines through investigation of their chemical composition and physicochemical properties [8].
Most pharmaceutical industries are focusing on developing new products for cancer as it is the first cause of death in 50% of the countries. Nanomedicine products have a good share in this market with many approved products to treat several types of cancer at various stages. Abraxane® is a famous albumin-particle bound paclitaxel nanomedicine loaded for advanced non-small cell lung cancer, metastatic breast cancer, and metastatic pancreatic cancer. Doxil®, the first approved nanomedicine by the FDA in 1995, is a PEGylated liposome loaded with doxorubicin for ovarian cancer, HIV-associated Kaposi’s sarcoma, and multiple myeloma. Marqibo® is a liposomal vincristine for Philadelphia chromosome-negative acute lymphoblastic leukemia. Hensify® is the recently approved nanomedicine for cancer in 2019 by the FDA. It is the hafnium oxide nanoparticles stimulated with external radiation to enhance tumor cell death via electron production for locally advanced squamous cell carcinoma. Most of the approved nanomedicines are non-PEGylated except Doxil and Onivyde, which is interesting as most reports have proven the importance of nanomedicine coating with PEG. Furthermore, all nanomedicine products do not have active target moiety. So, all of these products follow passive targeting approach without even stealth characteristics.
Iron-replacement therapy to treat anemia is surprisingly another area for nanomedicine due to the significance of nanoscale iron-oxide colloid system in improving iron absorption to the body. The main advantage of iron-oxide nanomedicine is replacing the injection of free iron with its associated toxicity. Most of these nanosystems are coated with either polysaccharide or polymer to reduce iron toxicity. CosmoFer® is the first approved iron dextran colloid by the FDA in 1996. Injectafer® is the most recent one in 2013 by the FDA, which is iron carboxymaltose colloid.
Another area for nanomedicine, especially the inorganic ones, is diagnostics, mainly imaging agents. Iron-oxide nanomedicines are also approved as contrasting agents for magnetic resonance imaging, which is used to generate contrasted images for different types of cancers. The magnetic property and small particle size allow the distribution of iron-oxide nanomedicine in tumor tissue, which provide a precise imaging of cancer borders. Additionally, perflutren is also used as ultrasound contrast agent in either lipid- or albumin-based nanomedicines. Phospholipid-stabilized microbubble is another form of nanomedicine as ultrasound contrast agent, which is approved in 2001 by the EMA. Its main mechanism is encapsulating air bubbles, which act as reflectors for ultrasound.
Several clinical applications have been studied using nanomedicine. Diprivan® is the first FDA-approved nanomedicine in 1989 for anesthesia. Another field for nanomachine is vaccination with two products, which are Epaxal® for hepatitis A and Inflexal V® for influenza. Both vaccines are liposome-based nanomedicine due to the similarity of liposome structure to cell structure. Another famous liposome product is AmBisome®, which is a liposome loaded with amphotericin B for treating systemic fungal infections with reduced toxicity. Abelcet® is another approved lipid-based nanomedicine loaded with amphotericin B. Finally, Visudyne® is a liposomal verteporfin for treatment of subfoveal choroidal neovascularization from age-related macular degeneration, pathologic, or ocular histoplasmosis.
Nanomedicines are currently in the middle of the road with great potentials but require many development considerations regarding assessment of physicochemical properties, pharmacokinetic properties, and pharmacodynamic applications. Based on the recent trends with 47 products in clinical trial phases, it is expected that within the next few years, more products will be available for several applications, especially cancer.
The authors declare no conflict of interest.
Historically, cystic fibrosis (CF) caused fatal respiratory failure in early childhood [1, 2], but proactive multidisciplinary care has increased life expectancy to ~44 years [3]. With longer survival, co-morbidities have become more prevalent, the commonest being cystic fibrosis-related diabetes (CFRD) [4, 5]. This is associated with poorer clinical status [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], quality of life [22, 23], and life expectancy [16, 24, 25] relative to non-diabetic CF patients.
CFRD is distinct from other diabetes mellitus etiologies, including type 1 (T1D) and type 2 (T2D) (see Table 1) [4, 5]. It is caused primarily by chronic pancreatitis [26, 27, 28, 29, 30] with progressive insulin deficiency [9, 11, 31], particularly during first-phase insulin secretion [8, 9, 11, 19, 32, 33, 34, 35, 36, 37, 38, 39, 40]. Variations in peripheral insulin sensitivity also contribute to CFRD [20, 41]; hyperglycemia progressively induces insulin resistance via downregulation of glucose transporters [42, 43, 44], and insulin sensitivity decreases with inflammation, use of exogenous glucocorticoids, and puberty [45, 46, 47, 48, 49]. In CF, the depleted and dysfunctional pancreatic β-cells may be unable to compensate for this, producing early intermittent hyperglycemia progressing to fasting hyperglycemia [35, 44, 50].
Type 1 diabetes | Type 2 diabetes | CFRD | |
---|---|---|---|
Prevalence | 0.2% | 11% | 35% (likely underestimated due to lack of testing) |
Onset | Usually acute | Insidious | Insidious |
Peak age of onset | Childhood or adulthood | Adulthood | Ages 18–24 |
Usual body habitus | Normal | Overweight | Underweight, normal, or sometimes overweight (due to CF therapy success) |
Likely pathophysiology | β-cell dysfunction & destruction, primarily autoimmune with genetic & possible environmental causes | Peripheral insulin resistance & subsequent β-cell stress | β-cell destruction due to inspissated pancreatic secretions, inflammation, and replacement with fibrosis & amyloid, plus a component of β-cell dysfunction |
Insulin deficiency | Nearly complete | Partial and variable | Severe but not complete |
Insulin resistance | Variable | Severe | Variable depending on circumstances (e.g. glycemic control, pubertal stage, use of glucocorticoids, inflammation) |
Ketoacidosis risk | High | Low | Low |
Pharmacological & dietary therapy |
|
|
|
Complications | Microvascular & macrovascular disease | Microvascular & macrovascular disease |
|
Likeliest cause of death | Macrovascular disease | Macrovascular disease | CF pulmonary disease |
Comparison of common etiologies of diabetes. Adapted from Moran et al. [4].
CFRD is usually preceded by a spectrum of abnormal glucose tolerance (AGT) on oral glucose tolerance testing (OGTT), including impaired fasting glucose (IFG), indeterminate glucose tolerance (INDET), and impaired glucose tolerance (IGT) [4, 51]. There may be ‘waxing and waning’ of glucose tolerance between these categories [19, 52, 53, 54, 55], probably due to variations in insulin sensitivity [35, 44]. Nevertheless, large prospective cohort studies report overall deterioration in CF patients’ glucose tolerance over life [16, 20, 53, 54, 56]. The date of onset of CFRD is considered to be the first time a patient meets diagnostic criteria, even if glucose abnormalities subsequently resolve due to improvement in insulin sensitivity [4]. This is because studies utilizing this definition report correlations between CFRD duration, microvascular disease prevalence [57], and mortality [16, 56].
Taken together, these factors explain why CFRD becomes more common with age. Prevalence is ~1.5% in CF patients aged <10 years, but ~15% in those aged 11–17 and ~50% in those aged ≥18 [8, 16, 58]. The American Diabetes Association (ADA) recommends annual screening from age 10, using 2-h OGTT [59]. CFRD can also be diagnosed using clinical status, random blood glucose, fasting plasma glucose, and glycated hemoglobin (HbA1c) [4, 60, 61]. In clinically-stable outpatients with CF, diagnostic criteria are identical to those used for other etiologies of diabetes mellitus [4], and are shown in Table 2. Recently, continuous glucose monitoring (CGM) has also been used to investigate glucose abnormalities in CF patients. This method is not yet widely recommended for diagnosis of diabetes, but it is often used to monitor glycemic control or assist insulin dosage [62]. Moreover, CGM often detects even earlier CF-related glucose abnormalities than OGTT, in the form of intermittent postprandial glucose excursions [63].
Glucose measurement method | Diagnostic criteria | ||
---|---|---|---|
Normal ranges | Pre-diabetic ranges | Diabetic ranges | |
Clinical status | Classical symptoms of hyperglycemia, including polyuria, polydipsia, and hyperglycemic crisis, may assist diagnosis of diabetes when combined with other positive diagnostic tests. Some CF-specific definitions also consider unexplained decline in lung function & nutritional status to be classical symptoms. | ||
HbA1c | ≤5.6% (38 mmol/mol) | 5.7–6.4% (39–46 mmol/mol) | ≥6.5% (48 mmol/mol) |
Random blood glucose | — | — | ≥11.1 mmol/L (200 mg/dL) |
Fasting plasma glucose | <5.6 mmol/L (100 mg/dL) | IFG: ≥5.6 mmol/L (100 mg/dL), <7.0 mmol/L (126 mg/dL) | ≥7.0 mmol/L (126 mg/dL) |
2-h OGTT | 0 min: <5.6 mmol/L (100 mg/dL) 2 h: <7.8 mmol/L (140 mg/dL) | All categories constitute AGT IFG: 0 min: ≥5.6 mmol/L (100 mg/dL), <7.0 mmol/L (126 mg/dL) 2 h: N/A INDET: 0 min: <7.0 mmol/L (126 mg/dL) OGTT midpoints: ≥11.1 mmol/L (200 mg/dL) 2 h: <7.8 mmol/L (140 mg/dL) IGT: 0 min: <7.0 mmol/L (126 mg/dL) 2 h: ≥7.8 mmol/L (140 mg/dL), <11.1 mmol/L (200 mg/dL) | 0 min: ≥7.0 mmol/L (126 mg/dL) AND/OR 2 h: ≥11.1 mmol/L (200 mg/dL) |
CGM | Usually <7.8 mmol/L (140 mg/dL) | Elevations ≥7.8 mmol/L (140 mg/dL) are referred to as glucose excursions, but there are no standardized criteria correlating them with AGT or diabetes. |
Diagnostic criteria of glucose measurement methods commonly used in CF. Diagnosis must occur during clinical stability, defined as no pulmonary exacerbations during the past 6 weeks and no current systemic glucocorticoids. It is also recommended that any positive fasting plasma glucose, HbA1c, or OGTT is repeated at a later date. Non-CGM diagnostic criteria are from the American Diabetes Association [59, 64]. CGM diagnostic criteria are from the Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group [65].
HbA1c = glycated hemoglobin. OGTT = oral glucose tolerance testing. IFG = impaired fasting glucose. AGT = abnormal glucose tolerance. INDET = indeterminate glucose tolerance. IGT = impaired glucose tolerance. CGM = continuous glucose monitoring.
This chapter compiles research on use of each glucose measurement method in CF patients, with special focus on pre-diabetic patients. The benefits and limitations of each method will be explored to help ascertain when their usage might be appropriate. In the process, we will examine correlations between early glucose abnormalities and clinical decline. Finally, we will review preliminary evidence of improved long-term outcomes with insulin treatment of early glucose abnormalities, supporting their detection and management in routine practice.
The ADA allows diagnosis of CFRD following one random blood glucose measurement ≥11.1 mmol/L, provided that it is combined with polyuria, polydipsia, or hyperglycemic crisis [59]. However, symptomatic hyperglycemia or hyperglycemic crisis is extremely rare in CFRD [4]. In Lanng et al.’s seminal 5-year prospective cohort study of 191 CF patients receiving annual OGTT, only 33% of those diagnosed with CFRD had polyuria or polydipsia [54]. Moreover, in a cross-sectional study of all 60 patients aged ≥10 years at a Brazilian CF center, age at diagnosis was significantly lower for patients diagnosed using OGTT as opposed to clinical criteria (13.5 years vs. 22.3 years), implying much earlier detection of disease [66].
Some centers compensate by accepting unexplained decline in lung function or nutritional status as classical symptoms of hyperglycemia (see Section 3) [67]. In one cross-sectional study of 91 CF patients not known to be diabetic, these modified clinical criteria detected OGTT-diagnosed CFRD with 58% sensitivity [68], which is an improvement over other studies but still suboptimal for a screening test.
HbA1c, i.e. glycated hemoglobin as a percentage of total hemoglobin, is commonly used to monitor glycemic control in diabetes mellitus. It usually reflects average blood glucose over the life of an erythrocyte (~3 months) [64, 69]. However, CF patients with CFRD, INDET or IGT rarely have a significantly-higher HbA1c than those with normal glucose tolerance (NGT) [11, 70, 71, 72, 73], and even statistically-significant differences tend to be of <1% magnitude [8, 34, 40, 74, 75]. Godbout et al.’s study of 13 CFRD patients also found that HbA1c did not correlate with mean plasma glucose, as calculated using fingerprick self-monitoring [76].
Numerous hypotheses have been espoused to explain HbA1c’s relatively poor correlation with glucose tolerance in CF. These include insufficient duration of transient CF-related post-prandial hyperglycemia, which is often limited to the early phase of insulin secretion; alteration of hemoglobin glycation by hypoxia; iron deficiency, which is a common comorbidity of CF; and increased erythrocyte turnover in the context of chronic inflammation [1, 4, 5, 76, 77]. This implies that HbA1c may vary with degree of inflammation [78], and that trends in HbA1c may be more useful for predicting deterioration in glucose tolerance. Supporting this, Lanng et al.’s 5-year prospective cohort study found significant differences in median HbA1c between patients who consistently had NGT (5.2%), patients who varied between NGT and IGT (5.3%), patients who developed CFRD during the study (5.8%), and patients who entered the study with a diagnosis of CFRD (6.5%) [54].
It has also been hypothesized that poor correlation between mean plasma glucose and HbA1c may be confounded by use of fingerprick tests to measure glucose, since these can easily miss CF-related hyperglycaemic peaks due to their relative infrequency [76]. In two studies of CF and CFRD patients, mean plasma glucose was estimated using 2–7 days of CGM rather than fingerprick self-monitoring, and strongly correlated with HbA1c (r = 0.86–0.89) [75, 79].
These findings have regenerated interest in potentially using HbA1c to screen for CF-related glucose abnormalities, especially because it is much more convenient than OGTT. However, computing HbA1c thresholds suitable for CFRD screening has proved challenging. Some studies do report almost 100% sensitivity for OGTT-defined CFRD using HbA1c thresholds of 6.0–7.5% [40, 80, 81, 82], but all have small sample sizes, and most either did not calculate sensitivity to CF-related AGT [81] or report low values, ~20–50% [80, 82]. Therefore, HbA1c may not detect CFRD and its complications until late. Moreover, most evidence suggests that the diagnostic threshold for CFRD, HbA1c ≥6.5%, has poor sensitivity compared to OGTT [54, 83, 84, 85].
Lowering the diagnostic threshold for HbA1c abnormalities does increase sensitivity to both CFRD and AGT, but the thresholds required to achieve sufficient sensitivity for screening generally have unacceptably low specificity [60]. There is also wide variation in the sensitivities and specificities reported by different studies using the same HbA1c threshold; this may be due to differences in type of HbA1c assay [74, 86] and timing of the studies relative to the institution’s routine OGTT screening [87]. Yung et al., conducting a cross-sectional study of 91 CF patients not known to be diabetic, but also not previously routinely screened, found that HbA1c ≥6.1% had 83% sensitivity for OGTT-diagnosed CFRD [68]. However, more recent studies with similar designs report only 30–50% sensitivity [39, 82, 88, 89].
Given this uncertainty, the current advice from the ADA is that HbA1c should not be used to screen for CF-related glucose abnormalities [59]. HbA1c is still recommended for monitoring glycemic control in CFRD, although normal results must be interpreted with caution [4, 78]. It has also been suggested that HbA1c might be a useful adjunct to OGTT in screening, as its results may fluctuate less and hence, may more accurately predict long-term risk of glucose abnormalities. In a recent 6-year retrospective cohort study of 50 NGT adults with CF followed up with annual OGTT, HbA1c ≥5.6% had OR 3.49 for development of IGT or CFRD [90].
In 2003, the ADA briefly sanctioned fasting plasma glucose as an alternative to OGTT in CFRD screening, because there were insufficient data supporting insulin therapy for CFRD without fasting hyperglycemia [91]. However, subsequent studies have demonstrated similar insulin-induced clinical improvements in patients with and without fasting hyperglycemia [16, 92], and treatment of CFRD without fasting hyperglycemia is now standard practice [4]. Only 16–25% of patients diagnosed with CFRD on OGTT have fasting hyperglycemia [8, 54, 68, 81].
Use of fasting glucose to detect pre-diabetic stages on the glucose tolerance spectrum remains somewhat contentious in CF. Most studies report that fasting plasma glucose does not significantly differ between CF patients with NGT, INDET or IGT [39, 72, 93]. The ADA does use fasting glucose to define one pre-diabetic glucose tolerance category, IFG (5.6–6.9 mmol/L), and suggested in 2003 that screening OGTTs could be limited to IFG patients [94]. A prospective cohort study of 1128 CF patients aged 10–64 found that this approach would reduce number of OGTTs by 67%, but miss 17.8% of CFRD and IGT [94]. In a cross-sectional analysis of 73 children with CF, IFG had 100% sensitivity for CFRD, but only 25% sensitivity for IGT [11].
Finally, like HbA1c, there is debate regarding the utility of IFG as an adjunctive test for predicting long-term risk of CFRD. Frohnert et al. found no significant relationship [95], but Schmid et al. found that IFG generated OR 2.72 for CFRD [96].
As discussed above, other conventional diagnostic tests have <100% sensitivity for CFRD compared to OGTT. Therefore, OGTT remains the recommended screening test in CF. It is also the only test with standardized definitions of multiple pre-diabetic glucose abnormalities, all demonstrated to predict development of CFRD [96].
Nevertheless, there are several issues with the 2-h OGTT. It may be more inconvenient and resource-intensive than other glucose measurement methods, which is of particular concern in CF because patients and clinics already face a high treatment burden from other aspects of CF care [97]. It also requires patient co-operation, which can be difficult when assessing children [93]. Patients are expected to consume at least 150 g (600 kcal) of carbohydrates for 3 days before an OGTT, then fast for 8 h overnight and be tested early the next morning [59]. They must drink a solution containing a 1.75 g/kg glucose load, preferably within 5 min, then lie or sit quietly for 2 h [64]. In a standard OGTT, venous blood is sampled twice: immediately before ingestion of the load, and at 120 min (BG120). Many CF centers also take hourly or 30-minutely samples to detect post-prandial hyperglycemia that resolves before 2 h [59]. As described earlier, these transient post-prandial glucose excursions are very common in CF, due to selective impairment of early insulin secretion. Our group previously performed OGTT with 30-minutely sampling in 33 children with CF aged 10–19, and found that peak venous insulin concentration was delayed until 90–120 min, producing an early venous glucose peak at 60–90 min [9] (Figure 1).
Venous blood glucose (□) and insulin ( ▓ ) in 30-minutely samples over a 2-h oral glucose tolerance test, as measured in 33 children with CF aged 10–19. Boxes indicate interquartile range, horizontal lines indicate median, whiskers indicate 5th and 95th percentiles. Figure taken from Hameed et al. [9].
The inconvenience of OGTT may contribute to poor patient uptake of CFRD screening [98, 99, 100]. In 2018, the Cystic Fibrosis Foundation Patient Registry reported that the average CF center was screening just 61.3% of adolescents and 32.8% of adults [100]. Rates of utilization of other glucose measurement methods, such as HbA1c and fasting glucose, were much higher (92.3% for adolescents and 89.6% for adults), suggesting that the main barrier to screening is the OGTT itself [100]. Suggested solutions include shortening the OGTT to 60 or 90 min [83] or replacing it with the 50-g non-fasting 1-h glucose challenge test [89, 101], which is currently used to screen for gestational diabetes mellitus in healthy women [101]. These modified OGTT protocols are not standard recommended practice [4].
There are also other issues with the OGTT that likely cannot be resolved by simply shortening it. Its diagnostic thresholds are not specific to CF and may be insensitive to CF-related clinical decline (see Section 3). OGTT results also frequently fluctuate in CF, with a large multicenter prospective cohort study finding a variability coefficient 1.5–1.8 times higher than in the general population [55]. Similarly, in two 4–5 year prospective cohort studies, 18–58% of AGT patients demonstrated overall improvement in glucose tolerance category, while only 14–22% demonstrated deterioration [19, 54].
Finally, even with venous sampling at additional timepoints, the peak blood glucose measurements recorded during OGTT may poorly reflect peak blood glucose achieved by CF patients in daily life [4, 60, 61]. After all, the OGTT’s 1.75 g/kg load contains less glucose than most CF patients’ everyday meals [61, 98]. This has prompted research into CF-related glucose abnormalities using CGM, a technology that can screen for glucose excursions over a longer interval of everyday life and high calorie CF diet.
Most CGM systems consist of two parts: a sterile sensor, worn subcutaneously for up to 14 days, and a transmitter attached to the sensor that measures interstitial fluid glucose every 30 s, recording an average every 5 min [97] (Figure 2). Some systems do not require a separate sensor, instead measuring interstitial fluid glucose via an electrical current applied across intact skin, but issues have been reported with skin reactions and inaccuracy [102]. Interstitial fluid glucose reflects capillary glucose with a 4–20 min delay [103].
Continuous glucose monitor sensor, before and after attachment of the transmitter. ‘CGM set’ and ‘Continuous Glucose Monitor’ by Sara Bassett are licensed under CC BY-NC-SA 2.0.
CGM has been validated against OGTT in children with CF of all glucose tolerance categories [104] and non-diabetic adolescents and adults with CF [105]. A subsequent study of this latter group found that they differed significantly from healthy controls in mean CGM glucose (+14.1%) and presence of CGM peaks ≥11.1 mmol/L (+33%), but not in the conventional diagnostic measures of fasting glucose, BG120, and HbA1c [106]. Moreover, 70% of CF patients undertaking simultaneous CGM and OGTT had their CGM peak outside OGTT [106]. This was the beginning of a substantial body of evidence demonstrating the superior sensitivity of CGM to CF-related glucose excursions above OGTT diagnostic thresholds, with numerous studies finding CGM glucose peaks ≥7.8 or 11.1 mmol/L in 71–93% of patients classified as NGT on recent OGTT [14, 31, 85, 98, 107, 108]. In a 5-year prospective cohort study of 21 adults with CF, 83% had their CGM peak and BG120 fall in different diagnostic categories, and for 93% the CGM-identified category was worse. Again, this suggests the superior sensitivity of CGM over OGTT [98].
Most of this evidence, particularly in children, is limited by small sample sizes [14, 85, 98, 107, 108] and lack of non-CF controls [14, 85, 98, 108]. However, it is logical that the increased duration and frequency of glucose monitoring facilitated by CGM, and the opportunity to incorporate the patient’s usual diet and physical activity, facilitates more sensitive detection of glucose excursions [109]. CGM is also generally easier and better tolerated than OGTT [78]. While sensors and transmitters are expensive, and staff do require training on their usage, they have become more user-friendly, smaller and cheaper over time [73, 110]. The newest devices can be inserted rapidly during a clinic appointment, do not require calibration against fingerpricks, and can be removed by patients or carers without medical supervision [97].
CGM does have one major disadvantage compared to OGTT. The clinical significance of the mild glucose excursions that it detects are still being determined; consequently, there is no standardized system for recognizing and describing clinically relevant CGM findings, and no universally accepted threshold for initiation of treatment [97]. Common variables computed by CGM software include average sensor glucose, maximum glucose, area under the curve of glucose per day (AUCglucose/day), percentage time spent above thresholds (e.g. 7.8 or 11.1 mmol/L), number of excursions ≥11.1 mmol/L, and measures of glycemic variability, such as standard deviation of average sensor glucose [103]. All these parameters have been correlated with HbA1c in CF patients [75], and many have been correlated with clinical outcomes. However, these studies report heterogeneous findings and rarely include substantial prospective follow-up (see Section 3) [84].
Given all these factors, CGM is not yet widely recommended for CFRD diagnosis or screening [4]. However, it is used in some centers for diagnosis and screening, follow-up of borderline diagnostic tests, and investigation of patients who cannot or refuse to undergo OGTT [31, 111, 112]. Like HbA1c, it may also be useful as an adjunctive test for predicting long-term risk of CF-related glucose abnormalities. In a prospective cohort study of 17 children with CF, all those who had glucose excursions ≥11.1 mmol/L on CGM developed either CFRD or IGT with INDET over a period of 2.5 years, irrespective of their glucose tolerance at baseline [107].
CFRD is well-understood to have a differing profile of sequelae as compared to T1D or T2D. Macrovascular disease is uncommon outside of case reports [1, 4, 5, 113], and although screening for microvascular disease should be routinely undertaken [59], microvascular complications are uncommon until at least 5–10 years of CFRD with fasting hyperglycemia [57, 114, 115]. Therefore they are substantially predated by declines in lung function [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 116, 117, 118] and nutritional status [7, 9, 10, 11, 12, 14, 117], both of which are significant predictors of early mortality in CF [10, 11, 16, 18, 25, 56, 119]. Four large cohort studies also report higher annual frequency in diabetic vs. non-diabetic CF patients of pulmonary exacerbations requiring intravenous antibiotics or hospitalization [10, 21, 39, 120], and it was recently demonstrated that diabetic CF patients have reduced recovery of baseline forced expiratory volume in 1 sec as a percentage of predicted (FEV1%) following pulmonary exacerbations [116].
A causative relationship between CFRD, impaired lung function, and poor nutritional status is implied by the clinical improvements seen following insulin therapy [13, 92, 120, 121, 122], and is also biologically plausible on several accounts. Insulin is a powerfully anabolic hormone, therefore insulin deficiency combined with CF’s increased metabolic requirements promotes catabolism with nutritional decline [9, 93, 123, 124]. Regarding lung function and pulmonary exacerbations, hyperglycemia is known to promote respiratory tract infections (RTIs) both systemically, via pro-inflammatory and immunosuppressive effects [125, 126], and locally, via glucose leakage into airway secretions, which could promote pathogen growth [125, 127, 128, 129, 130]. Several cohort studies report higher prevalence in diabetic vs. non-diabetic CF patients of certain RTIs, including Pseudomonas aeruginosa [10, 19, 117, 131], Staphylococcus aureus [132, 133], and Burkholderia cepacia [10, 117, 132].
Finally, hyperglycemia can also impair lung function through non-infective pathways. It has been associated with restrictive lung disease in T1D and T2D (via non-enzymatic glycation of collagen and elastin) [134], and with inflammatory and proteolytic lung destruction in CFRD [135, 136, 137]. Lung proteolysis may be exacerbated by protein catabolism [19, 122], which can furthermore weaken respiratory muscles [138, 139] and impair immunoprotein synthesis during RTIs [61]. This may explain why lung function in CF also correlates with nutritional status [6, 7, 140, 141, 142].
Numerous cohort and case-control studies examining the 1–5 years before CFRD diagnosis report decline in lung function [19, 35, 38, 92, 143, 144, 145, 146] and nutritional status [19, 35, 38, 92, 143, 144] in pre-diabetic patients, or significantly reduced values compared to non-diabetic CF controls [12, 17]. This suggests that pre-diabetic glucose abnormalities are clinically significant. Two case-control studies focusing specifically on pediatric populations also report that pre-diabetic children with CF have significantly lower height and weight velocities than non-diabetic CF controls [145, 146], with one study demonstrating differences up to 11 years before CFRD diagnosis [146]. These differing velocities produce steadily-widening gaps in height-for-age and weight-for-age, reaching statistically-significant sizes after CFRD diagnosis, usually around ages 15–19 [18, 146]. Importantly, this growing disparity seems to occur even if aggressive insulin therapy is commenced at diagnosis [144], and although it may narrow with prolonged therapy, it may not fully correct [18, 144, 147]. Therefore, optimizing clinical outcomes in CFRD may require treatment of pre-diabetic abnormalities, highlighting the importance of glucose measurement systems that can sensitively predict clinical decline.
Traditional OGTT diagnostic thresholds are not specific to CF – in fact, they were originally designed to predict T2D-associated microvascular disease in Pima Native Americans [148]. This may explain their apparent insensitivity to CF clinical outcomes. A few studies do report poorer lung function or nutritional status in IGT vs. NGT CF patients [37, 72], and several more identify IGT as a significant risk factor for substantial decline in FEV1% over 4–5 years [19, 149]. However, most studies attempting to correlate IGT with contemporary lung function and nutritional status find no significant relationship [19, 33, 34, 39, 53, 70, 71, 72, 73, 150, 151, 152].
A more successful non-conventional OGTT parameter is the additional glucose tolerance category of INDET, defined as blood glucose ≥11.1 mmol/L at an OGTT midpoint – most commonly 60 min (BG60) – as opposed to 0 or 120 min [4]. BG60 has been shown to inversely correlate with BMI in children with CF, and correlates with FEV1% and forced vital capacity as a percentage of predicted (FVC%) in both children [7] and adults [150]. In a subsequent study, INDET patients had mean FEV1% comparable to CFRD patients, representing a significant reduction compared to NGT and IGT patients [71]. INDET has also been confirmed to predict development of CFRD (OR 2.81 over ~3.5 years) [93, 96].
Other OGTT parameters shown to predict FEV1% in non-diabetic CF patients include higher peak glucose (BGmax) [9, 33, 72, 153], higher AUCglucose [124, 153], and reduced insulin secretion [34, 35, 72, 124]. Finally, a few studies have correlated FEV1% with trajectories of deterioration in glucose tolerance [41, 154]. One prospective cohort study recruited 152 non-diabetic CF patients, and stratified them according to whether their glucose tolerance on OGTT improved, deteriorated or remained stable over 2 years [41]. While all patients experienced a decline in FEV1%, the extent of decline only reached statistical significance in patients of stable or deteriorating glucose tolerance, and those of deteriorating glucose tolerance also had a much larger drop than those of stable glucose tolerance (−6.1% vs. −1.6%) [41].
It is rarer for studies to report correlations between OGTT parameters and nutritional status [33, 34, 35, 41, 71, 72, 154], possibly because intensive dietician management of CF mitigates nutritional decline [133, 154]. Nevertheless, one seminal prospective cohort study inversely correlated age-adjusted height and BMI with AUCglucose [8], and a recent cross-sectional study found that lower-than-median insulin secretion at 60 min is independently associated with worse BMI [150]. In children, BMI (calculated as weight in kg divided by the square of height in meters) may be a less sensitive measure of nutritional status than weight-for-age, as poor linear growth may mask decline [146]. Nevertheless, Wooldridge et al. report a direct correlation between AUCinsulin and BMI z-score in 146 NGT children with CF aged 5–20 [123], and our group has found that AUCglucose inversely correlates with age-adjusted weight, height and BMI in children aged ≤10 years [153]. Furthermore, in an earlier cohort study of 33 children aged 10–19, we found that higher BGmax was associated with decline in weight z-score, FEV1% and FVC% over the past 12 months, and BGmax ≥8.2 mmol/L had 87% sensitivity and 70% specificity for a clinically significant decline in weight z-score [9]. By contrast, BG120 was no better than chance at detecting decline in weight z-score, and the conventional diagnostic threshold of 11.1 mmol/L had only 10% sensitivity [9]. These findings led us to propose an alternative system for classifying CF-related glucose abnormalities on OGTT, the Cystic Fibrosis Insulin Deficiency (CFID) stages (Table 3) [9].
Diagnostic category | o-min OGTT glucose | Max OGTT glucose | 2-h OGTT glucose |
---|---|---|---|
CFID1 | <7.0 mmol/L | ≥8.2 mmol/L | <11.1 mmol/L |
CFID2 | <7.0 mmol/L | ≥11.1 mmol/L | <11.1 mmol/L |
CFID3 | <7.0 mmol/L | N/A | ≥11.1 mmol/L |
CFID4 | ≥7.0 mmol/L | N/A | N/A |
Cystic fibrosis insulin deficiency (CFID) classification system of CF-related glucose abnormalities, as proposed by Hameed et al. [9].
Six main studies have explored the clinical significance of CGM-based measures of CF-related early glucose abnormalities [9, 98, 111, 152, 155, 156]. Their results are compelling but heterogeneous. Taylor-Cousar et al. conducted a 5-year prospective cohort study of 17 originally non-diabetic CF patients, 7 of whom developed CFRD during observation [98]. In this subgroup, there was significant inverse correlation between peak glucose and BMI, and a trend towards correlation with FEV1% [98]. Leclercq et al. also examined peak glucose, stratifying 38 NGT CF patients according to whether they had any peaks ≥11.1 mmol/L during 72-h CGM [155]. In the ‘yes’ group, there was significantly lower FEV1% and FVC%, and increased risk of colonization with P. aeruginosa [155].
In the aforementioned study undertaken by our research group in 33 children with CF aged 10–19, we also showed that percentage time ≥7.8 mmol/L on CGM predicted 12-month rate of decline in weight z-score, FVC%, and FEV1%. Similarly, on receiver operator characteristic (ROC) analysis, ≥4.5% time at ≥7.8 mmol/L on CGM was a sensitive and specific predictor of clinically significant decline in weight z-score and FVC% [9]. Frost et al. subsequently used these parameters to interpret the CGM results of 59 adults being investigated for CF-related glucose abnormalities [112]. They found that percentage time ≥7.8 mmol/L on CGM correlated with baseline FEV1% and 12-month rate of decline [112].
In Chan et al.’s study of 88 children with CF aged 10–18, 12-month decline in FEV1% and FVC% was predicted by multiple other CGM parameters: peak glucose, number of daily glucose excursions >11.1 mmol/L, mean amplitude of glycemic excursions, and standard deviation [152]. Brugha et al. investigated another glycemic variability measure, glucose interquartile ranges, in a 7-year retrospective cohort study [111]. On ROC analysis, ranges >1.95 mmol/L predicted CFRD with 60% sensitivity and 98% specificity, but did not correlate with BMI or FEV1% [111].
Finally, our group recently conducted a cross-sectional study of 18 children with CF aged ≤5 years [156]. Even in this very young group, history of P. aeruginosa was predicted by mean glucose and percentage time at ≥7.8 mmol/L, and levels of inflammatory markers in bronchoalveolar lavage fluid were predicted by peak glucose, mean glucose, percentage time at ≥7.8 mmol/L, and standard deviation [156].
Three studies report a weak inverse correlation between HbA1c and lung function in non-diabetic CF patients (r = −0.25–0.3) [72, 73, 88], and one of these also found a direct correlation with number of infective pulmonary exacerbations per year [73]. In two more studies, HbA1c ≥ 5.5–5.8% predicted poorer FVC% [74] or FEV1% [82]. Therefore HbA1c, despite its insensitivity to CF-related glucose abnormalities, may be a useful harbinger of clinical decline when elevated.
Several studies have also investigated fructosamine, glycated albumin, and 1,5-anhydroglucitol as alternatives to HbA1c in CF. These biomarkers are not dependent on the lifespan of erythrocytes, and have been shown to correlate with mean plasma glucose in CF as estimated using CGM [75]. However, evidence of their ability to predict glucose abnormalities and clinical decline in CF is currently mixed [11, 74, 157]. In one study, fractional serum fructosamine (FSF) ≥3.70 μmol/g predicted IGT and CFRD with 100% sensitivity and 67% specificity, and patients with elevated FSF also had significantly lower median FEV1% (47% vs. 90%) [157].
Early evidence suggests that fasting glucose, including IFG, does not correlate with clinical status in CF [53, 95]. In one case-control study, IFG actually predicted better lung function than normal fasting glucose in some patient subgroups, particularly children with simultaneous IGT [95]. It was hypothesized that IFG may represent a physiological adaptation to CF, with hepatic glucose production upregulated to meet increased baseline metabolic requirements [95].
Our institute, the Sydney Children’s Hospital, provides one example of integrating multiple glucose measurement methods into routine practice. Children with CF are screened annually for glucose abnormalities from age 10, using OGTT with 30-minutely sampling. CGM is used to follow up borderline OGTTs, or to investigate children with clinically-suspected glucose abnormalities who have normal OGTTs or are unable to undergo OGTT. CGM excursions ≥11.1 mmol/L over 72 h of monitoring are considered severe abnormalities that warrant further investigation for possible insulin therapy. Moreover, some pre-diabetic children on OGTT are randomized to insulin therapy via the CF-IDEA trial (
Ultimately, the most clinically relevant measures of CF-related early glucose abnormalities are those that alter patient management. Therefore the long-term effects of actively treating early abnormalities is an important research question. Most studies have focused on insulin therapy, as insulin is currently the only recommended pharmacotherapy for CFRD (in part because of its anabolic effects) [59]. Emerging research has also explored oral anti-hypoglycemics [158], incretin modifiers [159], and CFTR modulators [160, 161].
It is already known that earlier diagnosis and treatment of CFRD, via OGTT screening programs, improves life expectancy and resolves historical sex differences in clinical outcomes (females with CFRD previously did worse than males) [16, 24]. Seven studies were identified trialing insulin therapy for CF patients who were pre-diabetic on OGTT [92, 122, 143, 162, 163, 164]. Five report statistically-significant improvements in lung function [122, 163, 165], nutritional status [122, 143, 164, 165], or rate of decline in either variable [163, 164], either intra-individually or relative to untreated controls. Moreover, five out of six studies assessing tolerability found no significantly-increased incidence of symptomatic hypoglycemia [92, 122, 143, 162, 164, 165]. Finally, one additional study has assessed the efficacy of insulin therapy initiated based on CGM, via retrospective analysis of all non-diabetic adults at a British CF center who had a CGM ordered between 2013 and 2016 [112]. Insulin was initiated if patients spent >4.5% time at >7.8 mmol/L on CGM, and if they recorded no clear triggers for these glucose excursions in a contemporary food diary. Patients treated with insulin demonstrated statistically-significant improvements in FEV1% and weight within 3 months of treatment, and maintained an improvement in weight and annual rate of lung function decline at 12 months [112].
All this suggests that treatment of CF-related AGT may be beneficial. However, results are difficult to generalize, due to heterogeneity in studies’ inclusions criteria, types of controls, and insulin regimens [166]. Studies are also limited by small sample sizes [92, 112, 122, 143, 162, 163, 164, 165], short durations [92, 112, 122, 143, 162, 165], and mixed analysis of pre-diabetic and diabetic patients [92, 122], highlighting the need for large long-term randomized control trials. One such trial, CF-IDEA (
As patients with CF live longer, CFRD becomes an increasingly prevalent serious co-morbidity, associated with significant decline in lung function and nutritional status. Evidence suggests that this decline may begin years earlier, in the pre-diabetic phase. Currently, OGTT is the most sensitive licensed diagnostic tool for identifying pre-diabetic CF-related glucose abnormalities, but its utility is limited by inconvenience, high variability of results, and insensitivity of traditional diagnostic categories to CF-related glucose excursions and clinical decline. Development of standardized interpretation systems for CGM may revolutionize detection of clinically relevant early glucose abnormalities. Results of randomized controlled trials of insulin treatment prior to onset of CFRD may alter the point at which insulin is offered.
SH, AJ and CFV have received funding from the National Health and Medical Research Council of Australia, Australasian Cystic Fibrosis Research Trust, Regional Diabetes Support Scheme, Sydney Children’s Hospital Foundation, and Australasian Pediatric Endocrine Care Grant from Pfizer, and industry support from Novo Nordisk, Medtronic, and Abbott Diagnostics. BP has been awarded a fellowship from the Thoracic Society of Australia and New Zealand and Vertex, and a postgraduate scholarship from the National Health and Medical Research Council of Australia.
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\\n\\nPolicy last updated: 2016-06-08
\\n"}]'},components:[{type:"htmlEditorComponent",content:'Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
\n\nIntechOpen only publishes manuscripts for which it has publishing rights. This is governed by a publication agreement between the Author and IntechOpen. This agreement is accepted by the Author when the manuscript is submitted and deals with both the rights of the publisher and Author, as well as any obligations concerning a particular manuscript. However, in accepting this agreement, Authors continue to retain significant rights to use and share their publications.
\n\nHOW COPYRIGHT WORKS WITH OPEN ACCESS LICENSES?
\n\nAgreement samples are listed here for the convenience of prospective Authors:
\n\n\n\nDEFINITIONS
\n\nThe following definitions apply in this Copyright Policy:
\n\nAuthor - in order to be identified as an Author, three criteria must be met: (i) Substantial contribution to the conception or design of the Work, or the acquisition, analysis, or interpretation of data for the Work; (ii) Participation in drafting or revising the Work; (iii) Approval of the final version of the Work to be published.
\n\nWork - a Chapter, including Conference Papers, and any and all text, graphics, images and/or other materials forming part of or accompanying the Chapter/Conference Paper.
\n\nMonograph/Compacts - a full manuscript usually written by a single Author, including any and all text, graphics, images and/or other materials.
\n\nCompilation - a collection of Works distributed in a Book that IntechOpen has selected, and for which the coordination of the preparation, arrangement and publication has been the responsibility of IntechOpen. Any Work included is accepted in its entirety in unmodified form and is published with one or more other contributions, each constituting a separate and independent Work, but which together are assembled into a collective whole.
\n\nIntechOpen - Registered publisher with office at 5 Princes Gate Court, London, SW7 2QJ - UNITED KINGDOM
\n\nIntechOpen platform - IntechOpen website www.intechopen.com whose main purpose is to host Monographs in the format of Book Chapters, Long Form Monographs, Compacts, Conference Proceedings and Videos.
\n\nVideo Lecture – an audiovisual recording of a lecture or a speech given by a Lecturer, recorded, edited, owned and published by IntechOpen.
\n\nTERMS
\n\nAll Works published on the IntechOpen platform and in print are licensed under a Creative Commons Attribution 3.0 Unported License, a license which allows for the broadest possible reuse of published material.
\n\nCopyright on the individual Works belongs to the specific Author, subject to an agreement with IntechOpen. The Creative Common license is granted to all others to:
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Creative Commons Attribution 3.0 Unported (CC BY 3.0) | \n\t\t\t\n\t\t\t 5 October 2011 (2011-10-05) \n\t\t\t | \n\t\t\tCurrently | \n\t\t
The CC BY 3.0 license permits Works to be freely shared in any medium or format, as well as the reuse and adaptation of the original contents of Works (e.g. figures and tables created by the Authors), as long as the source Work is cited and its Authors are acknowledged in the following manner:
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\n\nThe same principles apply to Works published under the CC BY-NC-SA 3.0 license, with the caveats that (1) the content may not be used for commercial purposes, and (2) derivative works building on this content must be distributed under the same license. The restrictions contained in these license terms may, however, be waived by the copyright holder(s). Users wishing to circumvent any of the license terms are required to obtain explicit permission to do so from the copyright holder(s).
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\n\nAll rights to Books and all other compilations published on the IntechOpen platform and in print are reserved by IntechOpen.
\n\nThe copyright to Books and other compilations is subject to separate copyright from those that exist in the included Works.
\n\nAll Long Form Monographs/Compacts are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others.
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\n\nContent reuse:
\n\n© {year} {authors' full names}. Originally published in {short citation} under {license version} license. Available from: {DOI}
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\n\nPolicy last updated: 2016-06-08
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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). 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