Key points are not available for this paper at this time.
Introduction Diabetes is a complex, multi-faceted endocrine disorder affecting various systems in the body, leading to elevated blood glucose levels. This condition arises from partial or complete insulin deficiency, impaired insulin functioning, or a combination of both factors.1 Nowadays, diabetes is recognised as a systemic condition, with serious consequences that extend far beyond glycaemic control, affecting various organs or overall health outcomes. Globally, it is recognised as a significant health crisis, and India stands amongst the most affected nations by this escalating epidemic. According to The International Diabetes Federation (IDF) Diabetes Atlas (11th edition, 2025), 589 million adults aged 20–79 are living with diabetes, representing 11.1% of the global population in this age group. Projections indicate that the number will increase to 853 million (13%) by 2050. Notably, about 252 million adults with diabetes remain undiagnosed, and nearly one in four affected individuals, approximately 158 million amongst them, are over 65 years old. In addition, roughly 635 million adults (12%) have impaired glucose tolerance. In 2024, diabetes contributed to over 1 trillion USD in health care costs, which accounts for 12% of worldwide health spending and is linked to more than 3.4 million deaths, accounting for 9.3% of global mortality. Furthermore, an estimated 9.1 million people have type 1 diabetes, with the majority (69%) aged 20–59. Meanwhile, hyperglycaemia during pregnancy is increasingly common, affecting one in every five live births an estimated 23 million pregnancies annually.2,3 As of 2021, an estimated 541 million people worldwide were suffering either from diabetes or impaired glucose tolerance. Diabetes is also associated with a significant risk of mortality, with estimates indicating more than 6.7 million individuals dying from complications related to diabetes in 2021. The incidence of diabetes amongst children and adolescents is on the rise, with over 1.2 million cases of type 1 diabetes reported in those under the age of 19. The financial impact of diabetes is economically phenomenal, with global health care costs approaching one trillion USD, a figure that is projected to exceed this amount by 2030.4,5 There is an increasing epidemic in comorbidities associated with diabetes, such as cardiovascular diseases, chronic kidney disease, neuropathy, retinopathy, non-alcoholic fatty liver disease and mental health disorders. The presence of comorbidities not only worsens prognosis but also reduces medication adherence. As a result, this reduction further impacts quality of life, complicates therapeutic choices, and increases health care costs.6 The Comorbidome: Concept and Clinical Relevance The ‘comorbidome’ is defined as a graphical and analytical framework that effectively visualise and quantifies the prevalence and risk of death related to various dynamic, interconnected network of comorbidities associated with primary diseases like diabetes. It is often depicted as a ‘solar system’, with the primary disease at its core and the associated comorbidities radiating outward according to their impact and significance. This model provides a clear understanding of the interconnections between diseases and their effects on health.7 In addition, it represents a complex network of related comorbidities along with mortality rates linked to diabetes-related conditions. Understanding these conditions can provide insights into how they interrelate and affect overall health outcomes by creating a visual map of their connections and impacts. In diabetes, the comorbidome offers an organised framework in a structured way to visualise and quantify how various conditions, such as renal dysfunction, cardiovascular diseases, interact with and amplify the effects of hyperglycaemia, insulin resistance, inflammation and metabolic dysfunction.8 Hence, unlike traditional comorbidity indices, which simply score the presence of comorbid conditions, the comorbidome visually maps their interactions and relative impact, which distinguishes it from the broader concept of multimorbidity. This holistic perspective is paramount in type 2 diabetes mellitus, which rarely occurs in isolation. Historically, diabetes management has been critical and filled with complexities due to rising comorbidities associated with the condition. Each and every patient requires a personalised focus on treatment regimens, as there has been a reported decline in medication adherence and quality of life among patients.9,10 However, due to the diversity in comorbidity profiles amongst diabetic patients, a one-size-fits-all model is no longer sustainable. Thus, indicates the need for personalised medication regimen for every patient, which tailors the interventions based on individual variability in genetics, environment, lifestyle and comorbidity burden, and offers a more effective and patient-centric approach.11 Personalising Diabetes Care Through Comorbidome Mapping Mapping the patient-specific comorbidity clusters information in the comorbidome enables clinicians to develop targeted, personalised treatment strategies that improve adherence and quality of life by reducing medication complexities. For example, cardiovascular disease or other associated comorbidities with diabetes may benefit more from newer treatment approaches, which signifies a better benefit-to-risk ratio. As SGLT2 inhibitors or GLP-1 receptor agonists have shown higher cardiac benefits, while in the case of renal involvement, other therapeutic regimens are required. Comorbidome mapping thus supports optimal treatment selection, risk stratification and patient-centric care.12,13 Advances in Comorbidome Research and Clinical Application Recent advancements in data mining, epidemiological modelling, and machine learning techniques reveal distinct comorbidity clusters associated with diabetes. Some researchers are utilising the UK Biobank and other larger databases to identify five clusters: cardio-renal diseases, neurovascular, metabolic, inflammatory, musculoskeletal and neuropsychiatric. These identified clusters not only differ in associated complications but also in their progression and treatment approaching responses.14 Moreover, mapping these comorbidities within the comorbidome enables risk stratification and personalised interventions, directly improving patient outcomes.15 Hence, this reveals that the comorbidities exert an independent effect on disease outcomes, and the comorbidome should be integral to clinical decision-making Box 1.Box 1: Case illustration: Application of the diabetes comorbidome in clinical practiceChallenges and Future Directions Despite the promising benefits of the comorbidome, there are several challenges to consider when implementing comorbidome-guided personalised care regimens. One major issue is the lack of standardised tools to visualise and measure the interconnected networks of comorbidities. Existing indexes, such as the Charlson and Elixhauser Scores, have limited scope and fail to capture the dynamic interactions among different comorbidities.16,17 The integration of electronic health records (EHRs) to create a comorbidome has not yet been optimized in comorbidity mapping algorithms. In addition, challenges such as interoperability, data quality and privacy concerns limit the functionality of interconnected comorbidome frameworks.18 The integration of emerging techniques like artificial intelligence in forming a comorbidome offers hope and collates the collection and integration of omics data with comorbidity profile can yield highly personalised risk maps for personalised treatment regimen and reduction of medication regimen complexity and yield a groundwork for precision medicine for the future.19 Hence, it underscores the need for future research, such as longitudinal or network-based studies, which should focus on capturing the evolving interactions amongst various comorbidities. Pathways to Clinical Implementation The process of translating the concept of comorbidome into clinical practice assumes an extensive, multifactorial, and prolonged approach. Foremost, there exists the necessity of the establishment of validated standardised tools that quantified and visualises the comorbidity networks and their mortalities, such as Charlson and Elixhauser scores, which largely expresses a narrow focus.7,20 Combining diabetes comorbidome with EHR will make real-time risk stratification and allow personalized care planning, although there are many challenges associated with privacy, interoperability, and quality to be tackled. Comorbidities associated with diabetes should require effective treatment care plans which should be personalized using multidisciplinary collaborations between endocrinologists, cardiologists, nephrologists, etc., and primary care providers to provide extensive patient-based care.21,22 In addition, the synchronisation of AI and omics data in diabetes comorbidome has the potential to bring a substantial benefit in terms of creating personalised risk road maps and the discovery of comorbidity clusters that can assist in the promotion of precision medicine in diabetes as well. On a systemic level, the current primary health care programs by the government of India, like National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke, are the key towards inculcating the community-based strategies in the management of chronic diseases, which is in line with comorbidome-guided care. The integration of the concept of comorbidome into primary healthcare can significantly enhance comprehensive comorbidity management. By facilitating early detection of coexisting conditions and enabling risk stratification in patients, healthcare providers can tailor interventions more effectively. Simultaneously, government investments in health information technology and standardized data reporting across healthcare centers will support the population-level implementation of comorbidome framework, ultimately improving healthcare outcomes and resource allocations. Therefore, the integrated management of diabetes with a focus on patient-centered care underscores the necessity of adopting comorbidity-based strategies worldwide. This approach is endorsed by organisations such as the IDF and the World Health Organization, which offer guidance for the development of diabetes treatment.23,24 Conclusion Diabetes is a complex and escalating global health challenge that is intricately linked with a wide array of comorbidities that significantly impact patient outcomes in the form of increased care costs and overall quality of life. The concept of comorbidome, which is a web of comorbid conditions associated with diabetes, offers a more holistic and patient-centered approach. Moreover, the adoption of the comorbidome concept in clinical practice, supported by advancements in data science such as artificial intelligence and omics technologies, can help clinicians to select and stratify the risks and in the selection of patient-centric personalized treatment regimens, which not only enhance the medication adherence of the patient towards associated comorbidities but also improve the overall patient outcomes. However, the introduction of the comorbidome concept requires overcoming significant challenges such as the development of visualising aids, robust data integration and multidisciplinary collaborations. Nowadays, several national health programs and international guidelines recognise and underscore the value of such approaches and suggest the need for continued investment. Thus, the adoption of comorbidome-guided strategies into diabetes management holds promising results not only for enhancing personalised care but also for enhancing in population-level health outcomes. This paradigm shift not only paves the way for more individualised, comprehensive and effective patient care.
Abhishek Pachauri (Wed,) studied this question.