Open access peer-reviewed chapter
Abstract
Type 2 diabetes is a complicated condition that develops as a result of the interplay of several genetic variations with a person’s lifestyle and environmental circumstances. Due to its strong ties to aging, it adds to the complexity of these patients and raises the risk that geriatric diseases like sarcopenia, malnutrition, and frailty might appear in them. In fact, chronic inflammation is thought to be the primary mechanism causing metabolic and vascular alterations as people age. There is still little knowledge about the phases of chronic inflammation that influence the development of damage to target organs, and there is also limited knowledge about the relationship between chronic inflammation and metabolic diseases. The interindividual variability in type 2 diabetes patients is further exacerbated by aging-related alterations in inflammatory and metabolic markers. Clustering, or the grouping of individuals, can help identify novel type 2 diabetes phenotypes and further clarify the pathophysiological causes of the condition. The aim of this work is to identify a potential model of treatment personalization that could be especially helpful for family medicine physicians who regularly treat complex heterogeneous patients in light of the rising demand for personalized care for patients with type 2 diabetes.
Keywords
- type 2 diabetes
- chronic diseases
- frail older adults
- inflammation
- precision medicine
1. Introduction
The prevalence of type 2 diabetes (T2D) is at an epidemic level everywhere. According to figures from the International Diabetes Federation, there are currently about 537 million individuals worldwide, aged 20–79, diagnosed with diabetes, and by the year 2045, those numbers are expected to rise to a total of 780 million, respectively. The risk of macrovascular and microvascular complications is increased by T2D, which also lowers life expectancy and quality of life [1]. In T2D, both insulin secretion and action are compromised. Although there has been disagreement about their relative significance, genetic research has led to the realization that β-cell dysfunction is the primary cause of the disorder. According to studies, T2D heritability can range from 30 to 70% [2]. Although genetic predisposition is an important component in determining the onset and severity of T2D, non-genetic variables including nutrition, physical exercise and body weight also have a significant impact on the disease’s development [3].
2. T2D is a heterogeneous, complex disease, associated with aging and the development of multimorbidity, caused by the interaction of multiple changes at genetic loci with lifestyle and environmental factors
About 80% of all cases of diabetes are T2D, making it the most prevalent kind. About 10% of cases of diabetes are type 1, and about 5% of cases are latent autoimmune diabetes of the adult (LADA). The remaining cases of diabetes are maturity-onset diabetes of the young (MODY) and other monogenic types of diabetes, but also secondary diabetes [4].
Common polymorphisms that raise the risk of T2D have been successfully identified by genome-wide association studies (GWAS). The initial investigations, published in 2007, involved thousands of subjects and identified 10 genomic locations responsible for increasing the risk of T2D. The same variations were found in several of these investigations. But those studies demonstrated that the majority of the common polymorphisms discovered by GWAS only elevated the risk of T2D by about 15% - 40% [1, 5]. GWAS studies have had great success and have identified more than 700 new T2D risk loci so far, showing that a larger sample size significantly boosts the statistical ability to find additional association signals. Although novel risk variants for T2D might be statistically significant, their contribution to our understanding of the pathophysiology of T2D is still limited [6].
Some studies also proposed the “palette model of diabetes”, according to which T2D is caused by flaws in a number of aetiological pathways. These pathways include differences in fat distribution, glucagon and incretin secretion and action, insulin action in muscle and liver, beta cell mass, and activity, suggesting a theory in which each person with T2D experiences the onset of diabetes as a result of a variety of flaws in these pathways. For many people, the flaw in each pathway may be slight, but diabetes develops when enough pathways are impacted. However, one or two of these routes may experience a more severe malfunction, also resulting in diabetes [7].
Besides genetic factors, non-genetic factors are considered to have a significant impact on the development of diabetes. There is strong evidence that T2D may be prevented by altering one’s lifestyle: losing weight by dietary changes that follow the most recent guidelines for intake of whole grains, fiber, fruit, and vegetables, as well as an increase in physical activity [1]. The degree of long-term weight loss and commitment to lifestyle modifications are substantially correlated with the risk reduction of T2D, and this preventive impact has been shown to persist for many years after active intervention. However, to determine the ideal diet to prevent T2D, more carefully controlled intervention trials are required. Currently, a diet low in saturated fat and high in fiber, whole grains, fruit, and vegetables, as well as a Mediterranean-type diet, may be advised for the prevention of T2D in prediabetes. There is currently insufficient data to suggest that altering one’s lifestyle can prevent microvascular or macrovascular complications in patients with T2D [8]. According to many studies, the fluctuation of additional risk variables, such as blood pressure, heart rate, plasma lipids, body weight, and serum uric acid concentrations, may contribute to the development of diabetic complications together with the variability of blood sugar levels. Additionally, when present simultaneously, the heterogeneity of each risk factor may also have cumulative consequences [9].
The intricacy of T2D is particularly noticeable in senior people because the disease’s frequency dramatically increases beyond the age of 50. As a result, in these individuals, the effects of aging, such as a rise in comorbidities, difficulty controlling blood sugar, a propensity to develop sarcopenia, malnutrition, and frailty, are much more prominent. The levels of laboratory markers ultimately represent the combined impact of all illnesses in a particular person, which complicates therapy [10]. Frailty and multimorbidity are prevalent among elderly diabetics and are linked to a variety of negative outcomes, such as disability and death. The likelihood of negative outcomes increases proportionately with the number of morbidities and the degree of frailty, but it still remains unclear how multimorbidity and frailty relate to glycemic control. The pattern and clustering of morbidities may have a substantial impact on the prediction of unfavorable outcomes. Therefore, comprehensive diabetes treatment recommendations that use a holistic approach are necessary, including screening for and management of conflicting illnesses, also including mental health problems like depression [11].
More current recommendations emphasize the customization of T2D care, which should take a number of medical and individualized aspects into account. The diabetic phenotype, accessible biomarkers (autoantibodies and genetic testing), and the existence of medical comorbidities are important medical considerations that should typically be taken into account. In addition to patient criteria including treatment preferences, age, and life expectancy, treatment options should take into account the existence of additional difficulties, multimorbidity, and, particularly in elderly patients, the presence of frailty [12]. In the future, therapy decisions may be guided by profiling scores in conjunction with clinical and genetic indicators, particularly in T2D patients [13].
3. Anthropometric characteristics and lifestyle factors associated with T2D
There is ample evidence from preclinical and clinical studies that suggests that adipose people are more prone to developing cardiovascular disease (CVD) and premature death from it [14, 15]. Obesity is also acknowledged as the most significant modifiable risk factor for prediabetes and T2D. Depending on the degree of obesity, the distribution of fat tissue, the age of beginning, and the length of obesity, metabolic syndrome (MS) can develop very quickly [16]. Long used in medical settings to evaluate the health concerns related to obesity are specific anthropometric measurements that are thought of as surrogate assessments for identifying obesity. The most fundamental anthropometric measures are those of height, weight, waist, hip, and limb circumferences [16, 17]. Body mass index (BMI) has been used for many years to evaluate physical nutrition, or the presence of overweight or obesity, both in research and in daily life. This measure’s primary drawback is that it does not accurately represent the body’s composition, making it difficult to judge the distribution of accumulated fat, which is crucial for determining risk for various morbidities [17].
The risk for T2D in adults definitely rises with BMI over 30 kg/m2, although it appears to begin to increase even within the normal BMI range (from 22 and 24 kg/m2) for both genders [18]. Numerous studies have confirmed a significant connection between the increase in BMI and the development of T2D [19, 20]. Furthermore, many studies have confirmed the association between the existence of central/visceral obesity and the development of T2D, independent of BMI. White adipose tissue (WAT) is known to contribute to the regulation of total glucose levels and energy homeostasis [21, 22]. The strong association between the accumulation of visceral fat and the onset of insulin resistance and, consequently, hyperinsulinemia is primarily attributed to a more harmful secretory, lipolytic and proinflammatory profile of adipocytes in visceral fat depots. [23, 24]. Since increased visceral adiposity is recognized as an independent risk factor for T2D, anthropometric indicators of central obesity such as waist circumference, waist-to-hip ratio, and waist-to-height ratio are used in daily clinical practice and research. The prevalence of T2D increases with increasing waist circumference, and it is considered an independent predictor of T2D development even after adjustment for BMI [25, 26]. Therefore, central obesity is considered to be a better overall predictor of the onset of T2D than BMI alone [19, 27]. Even though many anthropometrics have been investigated, including BMI, waist circumference, waist-to-hip ratio, and waist-to-height ratio, studies remain inconsistent, and conclusive results have not been reached [28, 29].
The fact that T2D is mostly an age-related disease contributes to the complexity of these patients by increasing their potential for the development of multiple comorbidities and geriatric conditions, such as sarcopenia, malnutrition, and frailty. The metabolic, inflammatory, and hormonal parameters of the affected individuals vary as a result of these situations, which increases interindividual variation. In elderly people with T2D, we recognize at least two metabolic types of frailty: one characterized by obesity and high insulin resistance (sarcopenic obesity phenotype) and another characterized by muscle and body mass loss and low insulin resistance (anorexic malnourished phenotype). So far, it has not been clarified whether a lower BMI (<25 kg/m2) reflects the presence of frailty or only reduced body mass without the presence of frailty (Figures 1 and 2) [30, 31]. Also, it is still not clear how many phenotypic forms women with MS can have, considering possible differences in BMI, renal function, and the existence of CVD and frailty [30].
Figure 1.
Gender (men)-dependent distribution of older diabetic patients according to the frailty status (orange – Nonfrail, green – Pre-frail, red – Frail) and BMI categories (<25 kg/m2, 25–30 kg/m2, >30 kg/m2).
Figure 2.
Gender (women)-dependent distribution of older diabetic patients according to the frailty status (orange – Nonfrail, green – Pre-frail, red – Frail) and BMI categories (<25 kg/m2, 25–30 kg/m2, >30 kg/m2).
We can say that the anthropometric parameters used so far and the known genetic and environmental risk factors, including lifestyle, do not provide a complete understanding of the pathophysiology of T2D. Considering the important role of chronic inflammation in this and many other chronic diseases, grouping based on the values of inflammatory markers in combination with existing parameters could contribute to the discovery of hidden phenotypes of patients with T2D [30, 31].
4. T2D as a systemic inflammatory disease – Immune-metabolic disturbances in age-related metabolic and vascular conditions
There are number of key studies published over the past two decades that have provided foundational insights into the immunometabolism field. Low grade inflammation has been implicated in the development of T2D, CVD and other common aging diseases. Inflammation is associated with increased recruitment of inflammatory and immune cells from the circulation to the tissue via dysfunctional vascular endothelial cell [32]. Distinctly from acute inflammation, for which the time course is well-known, the phases of chronic inflammation associated with aging and the development of chronic age-related diseases, are yet poorly identified. That being the case, just a few discrete mechanisms have been identified thus far. [33, 34]. It is believed that the mechanisms of chronic inflammation show a dynamic of change that runs parallel to the progression of damage to the end organs and a decrease in the entropy of the whole body [35, 36].
There are numerous sources of inflammation in older individuals, and the main considerable are senescent cells and chronically activated innate immune system [35]. Various stimuli, like molecules that are released from damaged tissues and disturbed gut microbiota, can trigger receptors of the innate immune system, leading to increased production of proinflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), IL-18, IL-6 which can trigger a persistent inflammatory state in the body and alter the etiology of disorders linked with inflammation when nucleotide-binding domain, leucine-rich–containing family, pyrin domain–containing-3 (NLRP3) is abnormally activated (Figure 3) [37, 38].
Figure 3.
Immune-metabolic disturbances in age-related disease (T2D).
Changes in the body’s shape and structure that occur with aging, including muscle loss and an increase in visceral fat, significantly contribute to inflammation process and the development of insulin resistance, which combined together raise the risk for metabolic and vascular complications in older individuals [36]. Obesity augments age-related inflammation, which is mainly the effect of proinflammatory cytokines and other proinflammatory mediators produced by dysfunctional adipocytes and monocyte/macrophages that abundantly infiltrate adipose tissue of obese individuals [39, 40]. Additionally, metabolic intermediates such as free fatty acids, advanced glycation end products, and oxidized lipoproteins that are overproduced in obesity and obesity-related diseases, such as MS and T2D, have been identified as potent proinflammatory signals (Figure 3) [37]. The proinflammatory cytokines induce insulin resistance, which further exacerbates metabolic disorders and inflammation, leading to the accelerated atherosclerosis and target organ damage (Figures 1 and 2) [36, 41].
Since many patients newly diagnosed with T2D already have associated complications, pre-existing tissue damage due to end-organ disease in cardiometabolic states, which occurs between the ages of 55 and 60, can further enhance all of these mechanisms [42].
Knowledge about T2D and patients suffering from it is still not complete, but it is known that T2D develops more often in obese people, since fat tissue is the source of inflammation, and inflammatory mechanisms are involved in the development of comorbidities that eventually exhaust the reserves of homeostatic mechanisms and cause the onset of weakness and illness. The aforementioned findings call for better integration in a practical sense with the goal of a patient-oriented approach, especially at the level of primary health care.
5. Metabolic reprogramming in chronic inflammation and the balance between the tolerogenic and inflammatory immune axis in cardiometabolic diseases, especially T2D
We have known for a long time that target organ disease is closely related to the development of macrovascular and microvascular complications, including entities such as ischemic (atherosclerotic) heart disease, congestive heart failure (CHF), stroke, chronic kidney disease (CKD), peripheral arterial disease, neuropathy and retinopathy in patients with T2D [43]. Distinctly from acute inflammation, for which the time course is well-known, the phases of chronic inflammation associated with aging and the development of chronic age-related diseases, are poorly identified yet [33, 34, 44]. When the tissue stress level reaches a certain threshold, inflammation is likely to develop. How this occurs in a real-life setting is not completely understood. The evidence suggests that the immune and inflammatory cell population that migrates to the tissues of the target organs may differ from the population of cells known to contribute to atherosclerosis in large arteries, which primarily consists of macrophages, dendritic cells (DCs), NK cells, CD8+ (cytotoxic) T lymphocytes, and Th1 lymphocytes. [44, 45]. In target organ damage, a pivotal role is attributed to the subset of CD4+ T lymphocytes, termed T helper 17 (Th17) lymphocytes, which produces cytokines of the IL-17 family (IL-21, IL-22, IL-26, and IL-17A and F), which act to create an inflammatory environment. The role of Th17 lymphocytes in sustaining chronic inflammation has already been recognized in autoimmune and other inflammation-mediated diseases, such as inflammatory bowel disease, osteoarthritis, and periodontitis [46, 47].
Namely, in inflamed tissue microenvironment, the bias is turning from the predomination of anti-inflammatory T regulatory (Treg) lymphocytes toward a predomination of the proinflammatory Th1/Th17 pathway, which is associated with increased production of cytokines of the IL-17 family – known as a trigger for maintaining inflammation and tissue damage [48, 49]. Besides changes in the cytokine profile, changes in metabolic conditions can also shift the balance between Treg and Th17 cell lines. In particular, these two cell lines are distinguished by a high degree of flexibility to circumstances in the microenvironment, allowing the immune system to be functionally adjusted to changes in physiological parameters [50, 51]. Inflammation and tissue remodeling/fibrosis must coexist with incomplete Th17/Treg polarization, with the balance fluctuating between either of these processes’ predominance. For example, besides increased production of anti-inflammatory cytokines, an expansion of Treg is also associated with increased production of Transforming Growth Factor Beta (TGF-β) - a major fibrotic factor [52]. The cytokine IL-17A is the best-investigated member of the IL-17 cytokine family, and its role in the development of CVD and target organ damage has been demonstrated in both experimental and clinical conditions [53, 54]. Some of the proposed mechanisms include increased mobilization of inflammatory and immune cells (monocytes, neutrophils, and T lymphocytes) from circulation to tissues, increased production of proinflammatory molecules, such as cytokines, chemokines, and adhesion molecules, and induction of extra-cellular matrix degradation and tissue fibrosis [55, 56].
Until recently, immune memory was considered an exclusive feature of the T cell-mediated adaptive immune system [57]. However, in recent years this paradigm has changed, and there is a growing body of evidence that long-term adaptive changes may also affect monocytes/macrophages, resulting in their enhanced responses to repeated stimulation with infectious and noninfectious challenges [58].
Genes engaged in immunological activities and in maintaining glycolytic metabolic pathways are those whose activity is impacted by trained immunity, as shown by the findings of transcriptional and epigenetic research [59]. Induction of post-translational histone modifications (also known as epigenetic alterations) and rewiring of cellular metabolism are two of the main processes behind trained immunity, which have been supported by studies. These pathways cause chromatin to be more accessible to inflammatory stimuli and for proinflammatory cytokine production to rise with time. The change from oxidative phosphorylation to glycolysis in cell energy metabolism is a crucial stage in the process of epigenetic reprogramming [48, 60]. This process is regulated by activating the Akt/mTOR/Hif pathway, resulting in increased production of lactate and disruption of the tricarboxylic acid cycle (TCA), also known as the Krebs cycle (Figure 4). These metabolic adjustments are necessary to fulfill the demands of activated immune cells, which must quickly produce adenosine triphosphate (ATP), the energy-storing molecule required to carry out immune cell operations and produce new components [62, 63]. The intracellular quantities of several metabolites, including as citrate, succinate, and fumarate, rise as Krebs cycle activity drops and some other metabolic pathways are engaged. The development of epigenetic alterations and histone modifications were shown to be accelerated by these compounds’ increased cellular availability (Figure 4) [50, 60, 64].
Figure 4.
Metabolic reprogramming in chronic inflammation. Taken and adapted from Majnarić LT [
6. Application of the data clustering method and advanced molecular biological and computer technologies (systems-biological approach) in examining the heterogeneity of people with T2D
The fact that T2D is primarily an age-related illness highlights the variability of T2D-related phenotypes by raising the risk of various comorbidities, malnutrition, sarcopenia, and frailty in these patients [61]. In addition to the well-known relationship between glycemia regulation and the risk of vascular complications and death, new research has also highlighted the significance of patient age and the age at which T2D first manifested [65]. T2D implies heterogeneity in the clinical presentation of the disease, the course of the disease, and also in the responses to certain forms of known pharmacotherapeutic treatments [7]. The common comorbidities of T2D are enhancing interindividual variability; therefore, personalized approach and therapy are needed. Considering the multifactorial nature of T2D, poor knowledge of the molecular connections of the pathophysiological pathways responsible for organ damage and the development of the disease, as well as the great influence of environmental, highly variable factors, today the heterogeneities of T2D are often investigated using a systemic-biological approach [66, 67].
It is a holistic approach to research, which, in contrast to the traditional reductionist approach, seeks, through computer modeling, to integrate a large number of data used to describe clinical individuals suffering from T2D with existing biochemical and molecular-biological data, primarily obtained through genome and transcriptome analysis. Some of the newer technologies, such as single-cell DNA or RNA sequencing, combined with advanced IT (computer) data processing techniques, show hitherto unknown and unimaginable possibilities of providing insight into the connection of pathology and pathophysiology at the tissue and organ level with the clinical expression of the disease itself in patients [68]. One of the key techniques used in data-mining investigations is cluster analysis. Clustering is a classification of a large number of items into classes, grouping of data that is scattered from data in other clusters and has similarities or is located near to one another. Pattern recognition, data analysis, image processing, and biological research are just a few areas where cluster analysis is often implemented. With further development, they could be easily applied at the level of primary health care to easily predict the development of chronic diseases, as well as their complications [69].
In previous research that used clustering methods on patients with T2D, several subtypes of patients were discovered, the most common of which are severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes. In addition to a personalized approach in therapy and patient monitoring, based on regularly gathered data, it may be possible to identify homogenous groupings of individuals with probable disease development at the beginning, which may be used to target such patients for therapeutic and preventive interventions. There are several efficient treatment options for treating T2D, including insulin and oral pharmaceuticals, the latter of which have various ways of action. To move toward more specialized treatments, it will be required to determine which subgroups of T2D patients benefit most from the currently available medicines. Key methodological details as well as the potential metabolic pathways that may be impacted in each patient subgroup, still need to be better addressed [70].
The growing number of elderly people with T2D in primary health care requires the implementation of newer ways of approaching the patient in everyday work. Grouping older patients with T2D into discrete phenotypic subgroups (clusters) could be a way to reduce the complexity of these patients and, most importantly, risk stratification for negative outcomes, but it could also be used as a model for a personalized treatment approach. In prospective monitoring, defined clusters can indicate the speed and probability of the occurrence of certain health outcomes, which is ultimately essential in primary health care for earlier and better prevention of possible complications. The distribution of persons with a unique diagnosis of T2D into spontaneously gathered subgroups (clusters) using clinical, sociodemographic, and inflammatory characteristics may also enable the association of genetic polymorphisms of cytokine genes with the existence of certain phenotypes, which would contribute to the discovery and understanding of specific pathogenic pathways [31, 69]. Replace the entirety of this text with the main body of your chapter. The body is where the author explains experiments presents, and interprets data of one’s research. Authors are free to decide how the main body will be structured. However, you are required to have at least one heading. Please ensure that either British or American English is used consistently in your chapter.
7. Early intervention and intensive management of patients with diabetes, cardiorenal, and metabolic diseases
The global burden of obesity and T2D has driven an accelerated increase in the incidence of cardiorenal and other metabolic diseases. Many studies have shown that the etiological mechanisms of T2D, CKD, atherosclerotic cardiovascular disease (ASCVD), fatty liver disease associated with metabolic dysfunction (MAFLD) and heart failure (HF) overlap, and that patients with CKD and T2D are especially exposed to an additional high risk from progression of CKD and CVD [70]. Some comorbidities are conventionally considered to be factors of increased cardiovascular risk, and some are concomitant complications of T2D [71].
The need for developing an integrated approach to the classification of the aforementioned diseases/disorders is highlighted in light of recent knowledge regarding the common pathophysiological basis of the mentioned comorbidities, with a special emphasis on the importance of chronic inflammation, which is at the center of the common pathophysiological mechanism (Figure 3) [72, 73, 74]. Based on this fact, a multi-consortium program was created with experts from cardiology, nephrology, endocrinology, and primary health care, which aims to emphasize the extremely important role of early intervention and intensive care for patients with complex chronic diseases such as T2D [75]. One of the most crucial health care techniques for facilitating early diagnosis and treatment, which can enhance quality of life and avoid premature mortality, is preventive health screening [76]. In addition to the perceived benefits of preventive screening, there is growing awareness of the importance of improving individual health behavior, which can lead to multiple health benefits [77]. Finally, the goals of early and intensive intervention are primary prevention of disease and comorbidity or secondary prevention, which aims to reduce further deterioration of the disease and reduce dysfunction and mortality. These efforts seek to reduce clinical inertia with the goal of improving patient adherence and for the long-term benefit of their health condition (Figure 5).
Figure 5.
Proposal of intensive management of patients suffering from complex diseases.
As previously mentioned, obesity has a very important role in the pathogenesis of many metabolic diseases, including metabolic syndrome, T2D, CKD, and CVD (Figure 5). Therefore, while treating these individuals, it is crucial to take anthropometric measurements, notably the BMI. If it is greater than 30 kg/m2, we may diagnose obesity and take the appropriate action. European Society of Endocrinology guidelines also recommend thyroid function testing in all obese patients, given the high prevalence of hypothyroidism in obesity. For hypercortisolism, male hypogonadism and female gonad dysfunction, hormone testing is recommended only in case of clinical suspicion of an underlying endocrine disorder. Reducing body weight and changing one’s lifestyle are the major objectives of treating obesity, and it has been demonstrated that doing so will enhance metabolic processes generally, improve the management of chronic disorders like T2D, and lower mortality and complication rates [78].
Weight loss should be accomplished by dietary changes, increased and modified physical activity, and cognitive-behavioral treatment. The approach to the obese patient is based on five key determinants: identification, evaluation, advice, setting objectives, and monitoring. Pharmacotherapy, especially GLP-1 receptor agonists (semaglutide and liraglutide), also finds its place in the treatment of obesity [79]. A BMI of 40 kg/m2 or higher is required for bariatric surgery, or a BMI of 35 kg/m2 if a patient has obesity-related diseases like diabetes, arterial hypertension, dyslipidemia, etc. and has failed to achieve and maintain a desirable body mass using other methods [80, 81]. Alternative weight management services and therapies will need to be investigated for patients for whom surgery is inappropriate or for those who choose not to have surgery, which may lead to an increase in referrals to other multidisciplinary services.
Recommendations for the approach to patients with metabolic syndrome emphasize the importance of treating each individual component of the metabolic syndrome, and the focus is on the treatment of MAFLD, which is defined as liver steatosis with the presence of any of the three criteria: excess body weight/obesity, metabolic dysregulation, T2D [75, 81]. Since CVD is the leading cause of death in patients with MAFLD, non-pharmacological treatments such as weight management, diet and exercise modifications, and bariatric surgery are also recommended for the treatment of MAFLD [82].
As ASCVD is the leading cause of morbidity and mortality in patients with T2D, it is important to recognize patients with existing risk factors for the development of ASCVD and those with pre-existing disease in time. These patients require an aggressive approach to the treatment of risk factors to prevent ASCVD and possible complications including the occurrence of cardiovascular events [83]. It is not uncommon for patients with T2D to develop CKD with or without ASCVD, which is associated with a high risk of worsening ASCVD and the development of CHF [75]. The treatment of CKD in these patients implies a lifestyle change with special emphasis on a diet with reduced salt and protein intake, and the pharmacotherapeutic approach includes RAAS inhibitors (ACEi or ARB), SGLT2 inhibitors and non-steroidal MRAs [84, 85].
8. Precision medicine: the potential to improve the health of individuals with T2D
Patients with T2D, as we mentioned previously, usually have more comorbidities, which increases variability among patients and requires patient-directed (personalized) treatment [86]. A more thorough comprehension of the unique phenotypes and genotypes of T2D patients might lead to better care for them using a tailored strategy, which is notably evident in the method used to treat older T2D patients [43].
Current guidelines for the treatment of T2D recommend determining the target values of glucose, HbA1c and pharmacotherapy based on the clinical characteristics of the patient and parameters such as blood glucose, general health status, life expectancy, arterial pressure values, fasting glucose and evening glycemia [87]. The guidelines recommend the use of GLP-1 receptor agonists or SGLT2i in patients with ASCVD or at high risk for ASCVD and in patients with CKD. SGLT2i are also recommended in patients with heart failure. GLP-1 is recommended for use in patients with stroke or TIA [88]. If the planned treatment goals are not achieved with metformin, the specified pharmacotherapy, and lifestyle changes, it is recommended to include a second line of pharmacotherapy with further monitoring. BMI, the risk of hypoglycemia, the possibility of adequate application of the therapy, and its availability, including the price, are also taken into account when choosing therapy [89].
These recommendations are based on clinical trials that provide evidence of efficacy, tolerability, and side effects, but research does not provide information about how an individual will respond to a particular treatment [90]. Therefore, it is expected that a personalized approach to the patient could be a useful tool for the treatment of a complicated disease such as T2D. This approach would enable individualization of therapy, but also prognosis and prevention, which will affect the reduction of treatment costs and avoid the failure associated with the algorithmic approach “one size fits all” [12, 91].
Phenotypic subgroups can be formed based on six factors including age at diagnosis, BMI, presence of glutamate decarboxylase antibodies (GADA), and insulin resistance as measured by homeostatic model assessment 2 of beta cell function (HOMA2-B) and insulin resistance (HOMA2-IR) measured by C-peptide concentration [68, 91]. It was discovered that some of them are exposed to a higher risk of developing diabetic nephropathy or retinopathy by dividing them into subgroups based on the aforementioned features. The ability to treat individuals with a higher risk of acquiring specific problems early and specifically thanks to this grouping [68].
It is also crucial to note that T2D is frequently a polygenic condition that is impacted by environmental variables. Due to the enormous effect of environmental variables, it is challenging to assess the significance of each of the hundreds of genetic variations linked to the development of T2D. To help with the diagnosis and treatment of T2D, clustering of genetic variations to assess the total genetic risk for acquiring the illness from the condition is a promising strategy [90, 92].
In the case of elderly patients with a complex disease like T2D, the separation into discrete phenotypic subgroups, as was done in Bosnić’s doctoral dissertation, can be a way of deconstructing the complexity of these patients and can be used as a model to guide personalized treatment, which can ultimately contribute recognition of hidden pathophysiological mechanisms in these patients [13]. This research approach could be especially beneficial for family physicians, who are in the position of dealing with the complexity of these patients on a daily basis [13, 14]. Additionally, pharmacogenomics, which enables the creation of a genetically customized therapy strategy to obtain the optimum individual response, offers new treatment choices for people with T2D. In order to obtain the intended therapeutic effectiveness and drug response, pharmacokinetics and pharmacodynamics are optimized taking into consideration a person’s genetic profile [6].
9. Conclusion
In this chapter, we considered the need for a more integrative approach in the treatment of patients with T2D. The need for this approach arose from the knowledge of changes in anthropometric characteristics and lifestyle that are associated with T2D, the recommendations of current guidelines for the treatment of T2D that support the treatment of target organs, and the discovery of pathophysiological mechanisms responsible for damage to the target organ, based on the existence of chronic inflammation. The aforementioned findings encourage future investigation into the use of various approaches for locating elderly individuals who are afflicted with chronic illnesses like T2D. The personalized approach described in this chapter, which draws on previously known information as well as new techniques for grouping patients with T2D, may present new opportunities for personalized risk stratification, treatment, and monitoring of patients with T2D and other chronic diseases, particularly in older people. This would significantly benefit primary care physicians’ day-to-day work as well as that of other healthcare professionals.
Ethics approval and consent to participate
The Expert and Ethics Council of the Health Centre Slavonski Brod approved the study (ID: 1433-1/020]).
Abbreviations
| T2D | type 2 diabetes |
| LADA | latent autoimmune diabetes of the adult |
| MODY | maturity onset diabetes of the young |
| GWAS | genome-wide association studies |
| MS | metabolic syndrome |
| BMI | body mass index |
| WAT | white adipose tissue |
| TNF-α | tumor necrosis factor-α |
| IL-1β | interleukin-1beta |
| NLRP3 | nucleotide-binding domain, leucine-rich–containing family, pyrin domain–containing-3 |
| CHF | congestive heart failure |
| CKD | chronic kidney disease |
| DCs | dendritic cells |
| Treg | T regulatory |
| TGF-β | transforming growth factor beta |
| TCA | tricarboxylic acid cycle |
| ASCVD | atherosclerotic cardiovascular disease |
| MAFLD | fatty liver disease associated with metabolic dysfunction |
| HF | heart failure |
| GADA | glutamate decarboxylase antibodies |
| HOMA2-B | homeostatic model assessment 2 of beta cell function |
| HOMA2-IR | homeostatic model assessment 2 of insulin resistance. |
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