Abstract
Background
Multimorbidity is increasing over time and causes a major global health challenge among individuals with type 2 diabetes (T2D). The growing prevalence of multimorbidity coupled with its complex management leads to a significant concern for the healthcare system. Evidence of existing studies on multimorbidity showed inconsistencies across different regions in the world. This study aimed to summarize and estimate the pooled global prevalence of multimorbidity and its associated factors among patients with T2D.
Methods
A systematic review and meta-analysis were conducted, including studies up to January 2025. PubMed, HINARI, CINAHL/EBSCO, the Cochrane Library, ProQuest, Google Scholar, and Google were used to search studies. The quality of the selected articles was evaluated using the Joanna Briggs Institute (JBI) checklist. The pooled prevalence was estimated using a random effect model with a 95% confidence interval (CI), while a fixed effect model was used to assess the pooled adjusted odds ratios (AORs) for the exposure variables. Heterogeneity was assessed using I² statistics and Cochran’s Q test (p-value). Subgroup analysis and meta-regression were used to identify the sources of variation. Publication bias was assessed using a funnel plot and Egger’s test (p-value < 0.05).
Results
In this review, a total of 32 studies and 2,934,500 participants were included. The pooled global prevalence of multimorbidity among T2D was 83.17%, concordant conditions (related to T2D) was 50.21%, discordant conditions (unrelated to T2D) was 47.24%, and combined conditions (having both chronic conditions) was 58.90%. Age 40–59 years (AOR = 2.36, 95% CI: 1.17–3.55), being retired (AOR = 1.31, 95% CI: 0.96–1.66), non-formal education (AOR = 2.93, 95% CI: 1.44–4.42), being widowed (AOR = 2.00, 95% CI: 1.04–2.96), and being a current smoker (AOR = 2.33, 95% CI: 1.16–3.50) were the predictors of multimorbidity. Being an urban resident (AOR: 1.69, 95% CI: 0.95–2.44) was associated with concordant chronic conditions.
Conclusions
Multimorbidity is highly prevalent among type 2 diabetes, emphasizing a major public health challenge. It is important to develop a well-designed and comprehensive strategy to enhance the prevention and management of multimorbidity. Particular emphasis should be given to older adults, people who have a low level of education, retired individuals, smokers, widowed individuals, and people living in urban residences.
Trial registration
PROSPERO-CRD42025639310. Registered on January 16/2025.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-25570-3.
Keywords: Concordant, Discordant, Global, Multimorbidity, T2D
Background
Multimorbidity (multiple long-term conditions) is a global health concern affecting most patients living with T2D. It is defined as the coexistence of two or more long-term chronic conditions alongside with T2D [1, 2]. Multimorbidity is classified as concordant and discordant chronic conditions based on the presence or absence of shared pathophysiologic characteristics and management strategies [1, 3–5].
Concordant chronic conditions are conditions that share the same pathophysiologic risk profile and management strategies as T2D [4, 6]. These can include retinopathy, nephropathy, neuropathy, hypertension, dyslipidemia, and chronic kidney disease (CKD) [1, 7, 8]. In contrast, discordant chronic conditions refer to the presence of additional chronic conditions alongside with T2D that do not share the same pathophysiologic risk profile and management strategies as T2D [1, 4]. Asthma, chronic obstructive pulmonary disease (COPD), depression, and osteoarthritis are some of the discordant chronic conditions that co-occur with T2D [1, 9].
The probability of developing at least one chronic conditions is increased by twofold or more among patients with T2D [10, 11]. The prevalence of multimorbidity is higher in high and middle-income countries [9, 12–19], and growing significantly in Northern and sub-Saharan Africa [20, 21].
Individuals with multimorbidity often encounter numerous challenges, including exposure to treatment burden, poor medication adherence, reduced quality of life, adverse health outcomes, polypharmacy, higher rate of hospital admission, and greater financial burden on both patients and their families or caregivers [1, 22–24]. These challenges also place a substantial burden on the health care system as well as on the country at large [24].
The development and progression to a multimorbid state depend on multiple factors, which can emerge at any stage or severity of chronic conditions, as well as the associated burden [10]. This problem may arise from various socio-economical, personal, and environmental factors [25], which include advanced age, genetic predisposition, socio-economic status (e.g., occupation, education, income), unhealthy lifestyle behavior, poor glycemic control, duration of diabetes, and psychological factors [26, 27].
Managing patients with multimorbidity can be overwhelming, as each chronic condition requires specific self-care practice [28]. To effectively address the specific need of diabetes patients with multimorbidity, the care should be comprehensive, patients-centered, and integrated [1, 29]. However, the evidence showed that the management of T2D patients with multimorbidity did not achieve the desired goal [29].
Given that strengthening the management of patients living with multimorbidity is essential. Accordingly, identifying the existing prevalence and associated predictor is a key step for managing and preventing multimorbidity. However, the findings remain inconsistent across different regions in the world [9, 12–17, 30]. Thus, it is essential to summarize and synthesize the available evidence. This review study provides better direction and comprehensive understanding to policymakers and healthcare professionals to manage T2D patients with multimorbidity. Therefore, this study aimed to estimate the pooled prevalence of multimorbidity and its associated factors among patients with T2D.
Objectives
To assess the pooled global prevalence of multimorbidity and its subcategories (concordant, discordant, and combined) among patients with T2D.
To identify factors associated with multimorbidity and its subcategories among patients with T2D.
Methods
Study design and settings
Systematic review and meta-analysis were carried out to estimate the pooled global prevalence of multimorbidity and its associated factors among patients with T2D. The study included primary research conducted in primary, secondary, and tertiary care facilities as well as community-based studies.
Protocol registration and reporting
The review protocol has been registered in the international prospective register of systematic reviews (PROSPERO) with a registration number of CRD42025639310. It was reported based on the 2020 Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guideline [31] (Supplementary Table 1).
Search strategy
For this review, we conducted a comprehensive search of various databases, including PubMed, HINARI, CINAHL/EBSCO, Cochrane Library, and ProQuest, whereas we used websites such as Google Scholar and Google. To ensure the retrieval of relevant articles, we applied Boolean operators (AND/OR), parentheses, truncation, quotations, and Medical Subject Headings (MeSH). As part of our search formulation, the search strategies were entered into the Polyglot Search Translator, which adjusted the search string according to the requirements of each database [32].
Our search approach was guided by the CoCoPop framework (Co = condition = context, Pop = population). Using this framework, we developed a search strategy focusing on the following terms: condition (prevalence of multimorbidity, concordant and discordant), context (health care settings), and population (type 2 diabetes), furthermore, we incorporated alternative keywords, and the detailed search strategies were as follows: (Prevalence OR proportion OR magnitude OR epidemiology OR pattern OR trend OR burden) AND (multimorbidity OR multiple chronic conditions OR multi-morbidity OR chronic comorbidity OR polymorbidity OR chronic conditions OR long-term condition OR chronic diseases OR multiple long-term conditions OR concordant chronic conditions OR discordant chronic conditions) AND (type 2 diabetes OR type 2 diabetes mellitus OR type II diabetes OR type 2 DM OR T2D OR T2DM) AND (primary care OR secondary care OR tertiary care OR hospital). All articles were retrieved during the period from January 20 to March 27, 2025 (Supplementary Table 2).
Eligibility criteria
The review included all adults aged 18 years and older living with T2D. The review included articles reporting on the prevalence of multimorbidity and its subcategories, such as concordant, discordant, and combined chronic conditions. It covered research from all healthcare settings and community-based studies. Observational studies such as cross-sectional, cohort, and case-control studies were considered. Both published and unpublished full-text articles in English were included.
Review studies focusing on type 1 diabetes (T1D), gestational diabetes, or other forms of diabetes were excluded. Additionally, studies reporting on the prevalence of a single chronic condition among individuals with T2D, magazines, newspapers, and books were excluded from the study. There were no restrictions on the year of publication.
Outcomes of the review study
The primary outcome of this review study was multimorbidity, while concordant, discordant, and combined chronic conditions were considered secondary outcomes (Supplementary Table 3).
Outcome ascertainment
Multimorbidity
It is defined as the presence of two or more clinically confirmed chronic conditions in addition to T2D, and no one chronic conditions is considered as an index [1, 2].
Concordant chronic conditions (related to T2D)
These refer to the presence of at least one chronic conditions in addition to T2D and are more likely to share the same pathophysiologic pathway and management strategies as T2D [1, 7, 8].
Discordant chronic conditions (unrelated to T2D)
These refer to the presence of at least one chronic conditions in addition to T2D but do not share the same pathophysiologic pathway and management strategies as T2D [1, 4].
Combined chronic conditions (both concordant and discordant)
It refers to the co-existence of at least one concordant and one discordant chronic conditions in addition to T2D [6, 8]
Study selection and screening
Following an extensive search, the retrieved data were exported to Endnote-21 and then to Ryann software. This software assists in compiling articles from multiple databases and search engines onto a single platform. Prior to screening, duplicate and ineligible articles were removed. Two independent reviewers (OY and AW) participated in the screening process. During the screening process, they addressed their differences through discussion and successfully resolved the issue.
Quality assessment
The quality of the selected articles was evaluated using the Joanna Briggs Institute (JBI) checklist for observational studies [33]. It is used to assess various aspects of the study designs, such as selection bias, measurement bias, confounding, and reporting bias. The checklist contained eight questions for cross-sectional and ten for cohort studies. Each item was assessed with four response options: “Yes,” “No,” “Unclear,” or “Not Applicable.” The quality of each included paper was evaluated by two independent reviewers (AW and OY). The reviewers independently judged the eligibility of each paper, and any differences in opinion were resolved through discussion [34]. The JBI checklist does not provide a formal classification of risk of bias; instead, it relies on the reviewer’s judgment to categorize it as having high, medium, or low risk of bias [35]. Accordingly, the reviewers agreed to classify studies with a score of ≤ 49% as high risk, those scoring 50–69% as moderate risk, and those scoring ≥ 70% as low risk of bias (Supplementary Table 4).
Data extraction
The data were extracted using Microsoft Excel. The extracted data included study characteristics such as author name, year of publication, country, study design, sample size, study setting, income level [36], data collection procedure, quality appraisal, and funding source. Furthermore, the data extraction format incorporated the key outcome of interest, including the prevalence of multimorbidity, concordant, discordant and combined chronic conditions, the exposure variables, and statistical results (Supplementary Table 5).
Data processing and analysis
The retrieved data were exported from Microsoft Excel to Stata version 17 for analysis. The results were described using graphs, tables and using narrative sentence. The pooled prevalence was evaluated using a random effect model with a 95% confidence interval (CI). A fixed effect model was used to assess the pooled Adjusted Odds Ratios (AORs) for the exposure variables.
Heterogeneity was assessed using I² square statistics and Cochran’s Q test. In a Cochran’s Q test, a p-value of < 0.05 suggests heterogeneity. The I² square statistic of 0 to 30% suggests minimal heterogeneity, 30 to 60% suggests moderate heterogeneity, and 60 to 100% suggests substantial heterogeneity [37, 38]. Further variability in effect size across studies was also inspected using the forest plot test. In conjunction with the forest plot test, heterogeneity and outliers of the studies were evaluated using the Galbraith plot test. In addition, sensitivity analysis was conducted to evaluate the influence of individual studies on the pooled estimate by systematically removing studies one at a time (leave-one-out analysis) and observing variations in the pooled estimate.
Sources of study variation were evaluated using subgroup analysis and meta-regression. The subgroup analysis was evaluated using publication year, study design, study settings, data collection technique, and level of income. Further, meta-regression was conducted to identify potential sources of heterogeneity, using sample size and publication year as covariates. Random-effect univariate and multivariable meta-regression models were also implemented to identify sources of heterogeneity. In the multivariable regression model, a p-value of ≤ 0.05 indicates that the moderator variable was significantly associated with the effect size, and it explains sources of heterogeneity.
Publication bias was evaluated using both a funnel plot and Egger’s test. The funnel plot was visually inspected to determine the symmetry of study distribution, with a symmetrical appearance indicating the absence of publication bias. Additionally, Egger’s test was performed to assess statistical significance, where a p-value < 0.05 suggests the presence of publication bias. Bias was addressed using the trim and fill analysis.
Results
Study selection
In this systematic review and meta-analysis, a total of 2,147 studies were initially retrieved from various databases. After removing duplicates, 1,507 articles were screened based on title, abstract, outcome of interest, and study design. Finally, 32 studies with 2,934,500 participants were included for full review [8, 9, 12–18, 30, 39–59] (Fig. 1).
Fig. 1.
The PRISMA flow diagram showed data retrieval, screening, and included studies among patients with T2D
Among the included studies, twenty-three studies reported on multimorbidity [12–14, 16, 18, 30, 39–51, 53–56]. Six studies were reported on both multimorbidity and its subcategories [9, 15, 17, 57, 58]. One study reported on the prevalence of both concordant and discordant chronic conditions [8], whereas two studies reported on the prevalence of concordant chronic conditions only [52, 59].
Characteristics of the included studies
Studies reported from the period of 2012 to 2025 were retrieved and reviewed. Out of a total of 2,147 articles, 32 were included in the systematic review and meta-analysis. Among these, twenty-three studies reported on multimorbidity, such as three from India [16, 49, 53], one from Italy [45], one from Bangladesh [44], one from Turkey [14], one from Japan [50], one from Nepal [48], one form Ireland [18], one from Croatia [47], one from Netherlands [51], one from Peru [54], two from Spain [12, 55], three from UK [30, 41, 43], three from United States (US) [13, 40, 56], and three from China [39, 42, 46].
Six studies reported both multimorbidity and its subcategories (concordant and discordant chronic conditions), one from Finland [15], one from Australia [9], one from Ireland [57], one from UK [58], one from Ireland [57], and one from India [17]. Additionally, one from Israel [8] reported on both concordant and discordant chronic conditions. Furthermore, two studies from Ethiopia reported only on concordant chronic conditions [52, 59].
Based on the location of studies, twelve were conducted in Europe [12, 15, 18, 30, 41, 43, 45, 47, 51, 55, 57, 58], thirteen in Asia [8, 14, 16, 17, 39, 42, 44, 46, 48–50, 53, 58], three in North America [13, 40, 56], one in Australia [9], one in South America [54], and two in Africa [52, 59].
Concerning study design, seventeen studies were cross-sectional [13–17, 41, 42, 44, 46–49, 52, 53, 56, 57, 59], and twelve studies were retrospective cohort [8, 9, 12, 30, 39, 40, 43, 45, 50, 51, 54, 55], three were prospective cohort [18, 58].
Based on the level of income, eighteen studies were conducted in high-income countries [8, 9, 12, 13, 15, 18, 30, 40, 41, 43, 45, 50, 51, 55–58], six studies in upper-middle-income countries [14, 39, 42, 46, 47, 54], six studies in low-middle-income countries [16, 17, 44, 48, 49, 53] and two studies in low-income countries [52, 59].
In terms of study settings, nine studies were conducted in primary care settings [8, 9, 15, 16, 18, 47, 50, 51, 57], seven studies were conducted in tertiary care settings [14, 30, 46, 48, 49, 52, 59], twelve studies were conducted in primary, secondary, and tertiary care settings [8, 12, 13, 39–45, 54–56] and four studies were community-based studies [17, 53, 58].
Regarding data collection techniques, twenty studies were conducted using review of health records records [8, 9, 12–15, 30, 39–41, 43, 45, 46, 48, 50–52, 54, 55, 59], nine studies were conducted using both interview and review of health records [17, 18, 42, 44, 47, 53, 56, 58], and three studies were conducted using face-to-face interviews [16, 49, 57].
The quality of the reviewed studies was evaluated using the JBI standardized checklist for cohort and cross-sectional studies. The minimum score of the studies was 5/8 for cross-sectional studies and 7/10 for cohort studies. This indicated that all articles had an acceptable risk of bias, as all scored above 50%, and therefore, all articles were included in the study (Table 1).
Table 1.
Characteristics of the included studies among patients with type 2 diabetes
| Author/ year | Country | Income level | Study setting | Study design | Sample size | Data source | Prevalence | Quality assessment |
|---|---|---|---|---|---|---|---|---|
| Heikkala, E., et al, 2021[15] | Finland | High income | Primary care | Cross-sectional | 4,545 | Review of health record | 93 | 8/8 |
| Pati, S. and F. Schellevis, 2017 [16] | India | Lower-middel income | Primary care | Cross-sectional | 912 | Interview | 84 | 8/8 |
| Chiang, J.I., et al, 2020 [9] | Australia | High income | Primary care | Retrospective cohort | 69,718 | Review of health record | 90 | 8/8 |
| Khadka, T., et al, 2023 [48] | Nepal | Lower-middel income | Tertiary care | Cross-sectional | 107 | Review health records | 70.1 | 5/8 |
| Akın, S. and C. Bölük, 2019 [14] | Turkey | Upper-middel income | Tertiary care | Cross-sectional | 1,024 | Review of health record | 98.5 | 8/8 |
| Mata-Cases, M., et al, 2019 [12] | Spain | High income | Primary care and above | Retrospective cohort | 373,185 | Review of health record | 82 | 9/10 |
| Koto, R., et al, 2023 [50] | Japan | High income | Primary care | Retrospective cohort | 93,801 | Review of health record | 85.6 | 8/10 |
| Li, Y., et al, 2024 [39] | China | Upper-middel income | Primary care and above | Retrospective cohort | 10,421 | Review of health record | 90 | 9/10 |
| Ji, Q., et al., 2024 [42] | China | Upper-middel income | Primary care and above | Cross-sectional | 25,454 | Interview and health record | 79.8 | 8/8 |
| Li, X., et al, 2021 [46] | China | Upper-middel income | Tertiary care | Cross-sectional | 4,777 | Review of health record | 94 | 8/8 |
| Lin, P.-J., et al, 2018 [40] | US | High income | Primary care and above | Retrospective cohort | 138,466 | Review of health record | 83 | 9/10 |
| Brali Lang., et al 2016 [47] | Croatia | Upper-middel income | Primary care | Cross-sectional | 7,979 | Interview and health record | 78 | 8/8 |
| Alonso-Morán, E., et al , 2014 [55] | Spain | High income | Primary care and above | Retrospective cohort | 149,015 | Review of health record | 66.8 | 9/10 |
| Luijks, H., et al, 2012 [51] | Netherlands | High income | Primary care | Retrospe ctive cohort | 714 | Review of health record | 84.6 | 9/10 |
| O’Shea, M.P., M, 2015 [57] | Ireland | High income | Primary care | Cross-sectional | 498 | Interview | 77.9 | 8/8 |
| Lin, P.-J., et al, 2015 [56] | US | High income | Primary care and above | Cross-sectional | 161,174 | Interview and health record | 88 | 8/8 |
| Umeh, K, 2022 [41] | UK | High income | Primary care and above | Cross-sectional | 280 | Review of health record | 74.67 | 8/8 |
| Eilat-Tsanani, S., et al, 2021[8] | Israel | High income | Primary care and above | Retrospective cohort | 9,725 | Review of health record | 16.6 | 9/10 |
| Negussie, Y.M., et al, 2023 [59] | Ethiopia | Low-income | Tertiary care | Cross-sectional | 398 | Interview and health record | 45.5 | 8/8 |
| Ejeta, A., et al, 2021 [52] | Ethiopia | Low-income | Primary care | Cross-sectional | 333 | Interview and health record | 72.73 | 8/8 |
| Chiang, J.I., et al, [58], 2020 | UK | High income | Community | Prospective cohort | 59,657 | Interview and health record | 90 | 9/10 |
| Chiang, J.I., et al, 2020, [58] | Taiwan | High income | Community | Prospective cohort | 59,657 | Interview and health record | 80 | 9/10 |
| Zghebi, S.S., et al, 2020, [43] | UK | High income | Primary care and above | Retrospective cohort | 108,588 | Review of health record | 77 | 9/10 |
| Teljeur, C., et al, 2013, [18] | Ireland | High incom | Primary care facility | Prospective cohort | 424 | Interview and health record | 90 | 9/10 |
| Iglay, K., et al, 2016, [13] | China | Upper-middle income | Tertiary care facility | Cross-sectional | 4,777 | Review of health record | 94 | 8/8 |
| Soji, D.J., et al , 2021, [17] | India | Lower-middle income | Community | Cross-sectional | 400 | Interview and health record | 74 | 8/8 |
| Yogesh, M., et al.,2024 [53] | India | Lower-middle income | Community | Cross-sectional | 800 | Interview and health record | 62.5 | 8/8 |
| Bernabe-Ortiz, A., et al, 2022, [54] | Peru | Upper-middle income | Primary care and above | Retrospective cohort | 9,582 | Review of health record | 74 | 9/10 |
| Nowakowska, M., et al.,2019, [30] | UK | High income | Tertiary care facility | Retrospective cohort | 102,394 | Review of health record | 75 | 9/10 |
| Mannan, A., et al.,2022, [44] | Bangladesh | Lower-middle income | Primary care and above | Cross-sectional | 2,136 | Interview and health record | 80.7 | 8/8 |
| Jha, P.N. and V. Kusum, [49] | India | Lower-middle income | Tertiary care facility | Cross-sectional | 872 | Interview | 94 | 5/8 |
| Guerrero-Fernández de Alba, I., et al, [45] | Italy | High income | Primary care and above | Retrospective cohort | 197,992 | Review of health record | 97.6 | 9/10 |
Meta-analysis
The pooled global prevalence of multimorbidity among patients with T2D
A total of twenty-nine studies were included to estimate the pooled global prevalence of multimorbidity. The study indicated that the pooled global prevalence of multimorbidity was 83.17% (95% CI: 79.72–86.63). Turkey had the highest prevalence (98.5%, 95% CI = 95.51–101.49.51.49) [14], followed by India (62.5%, 95% CI = 59.72–65.28) [55]. The I² test and the Cochran’s Q statistics demonstrated that there was a substantial heterogeneity across the studies (I² = 97.6%, p < 0.001) (Fig. 2). In addition, the Galbraith plot revealed the presence of both outliers and heterogeneity (Fig. 3).
Fig. 2.
The forest plot showed the random pooled prevalence of multimorbidity among patients with T2D
Fig. 3.
The Galbraith plot showed outliers and variation across the studies on multimorbidity among patients with T2D
Sensitivity test
The sensitivity test demonstrated that all individual studies fell within the confidence interval, and none had a significant impact on the pooled prevalence. This indicates consistency in meta-analysis estimates and reinforces the robustness of the findings (Table 2).
Table 2.
Sensitivity test showed the random effect of each study on the pooled estimate of multimorbidity among T2D
| Author (year) | Estimate | 95% confidence interval |
|---|---|---|
| Heikkala, E, et al (2021) [15] | 82.82 | 79.32-86.32 |
| Pati, S. and F. Schellevis, et al (2017) [16] | 83.14 | 79.57-86.72 |
| Chiang, J.I, et al (2020) [9] | 82.93 | 79.39- 86.47 |
| Chiang, J.I, et al (2020) [58] | 83.29 | 79.72- 86.86 |
| Chiang, J.I, et al (2020) [58] | 82.93 | 79.39- 86.47 |
| Akin,S, et al (2019) | 82.63 | 79.23-86.03 |
| Khadka, T, et al (2023) [48] | 83.64 | 80.18-87.09 |
| Mata-Cases, M, et al (2019) [12] | 83.22 | 79.64-86.79 |
| Koto, R, et al (2023) [50] | 83.09 | 79.52-86.66 |
| Zghebi, S.S, et al (2020) [43] | 83.39 | 79.85-86.95 |
| Li, Y, et al (2024) [39] | 82.93 | 79.39 -86.47 |
| Ji, Q, et al (2024) [42] | 83.29 | 79.72-86.87 |
| Li, X, et al (2021) [46] | 82.79 | 79.30-86.27 |
| Lin, P.-J, et al (2018) [40] | 83.18 | 79.60-86.75 |
| Brali Lang, et al (2016) [46] | 83.36 | 79.79-86.92 |
| Bernabe-Ortiz, A, et al (2022) [54] | 83.50 | 79.98-87.02 |
| Alonso-Morán, E, et al (2014) [55] | 83.76 | 80.39- 87.13 |
| Luijks, H, et al (2012) [51] | 83.12 | 79.55-86.69 |
| Teljeur, C, et al (2013) [18] | 82.93 | 79.39-86.47 |
| OShea, M.P., M,et al (2015) [57] | 83.36 | 79.80-86.92 |
| Lin, P.-J, et al (2015) [56] | 83.00 | 79.44-86.56 |
| Umeh, K, et al (2022) [41] | 83.48 | 79.94-87.01 |
| Iglay, K, et al (2022) | 82.66 | 79.24-86.08 |
| Soji, D.J., et al (2021) [17] | 83.50 | 79.98-87.02 |
| Nowakowska, M, et al (2019) [30] | 83.47 | 79.93-86.99 |
| Yogesh, M, et al (2024) [53] | 83.91 | 80.68-87.14 |
| Mannan, A, et al. (2022) [44] | 83.26 | 79.69-86.83 |
| Jha, P.N. and V. Kusum (2020) [49] | 82.79 | 79.30-86.27 |
| Guerrero-Fernández de Alba, I, et al. (2020) [45] | 82.69 | 79.72-86.08 |
| Overall | 83.17 | 79.72-86.63 |
Heterogeneity assessment
The forest plot test and the Galbraith plot revealed high heterogeneity. As indicated by the forest plot, the I² value was 97.0%, and a Cochran’s Q statistic indicated a p-value of < 0.001. These suggest a need for further analyses, including subgroup analysis and meta-regression.
Subgroup analysis
To assess potential heterogeneities and variability in the pooled estimate, subgroup analyses were performed based on publication year, study design, study setting, level of income, and data collection technique. All subgroup analyses showed substantial heterogeneity, with I² values varying between 93.4% and 98.6% and p-value < 0.001 (Table 3).
Table 3.
Sub-group analysis among T2D patients with multimorbidity
| Subgroups | Subgroup categories | Number of studies | Test of heterogeneity (I2) |
Effect size (%) | Random effect (95% CI) | p-value |
|---|---|---|---|---|---|---|
| Publication year | < 2015 | 3 | 98.6 | 80.46 | (66.57-94.35) | <0.001 |
| 2015-2020 | 10 | 96.8 | 84.87 | (80.58-95.62) | <0.001 | |
| ≥ 2020 | 16 | 97.8 | 82.76 | (77.99-87.52) | <0.001 | |
| Study design | Cross-sectional | 15 | 97.9 | 82.60 | (77.41-87.80) | <0.001 |
| Prospective cohort | 3 | 93.4 | 86.66 | (80.09-93.23) | <0.001 | |
| Retrospective cohort | 11 | 97.6 | 83.00 | (77.32-88.69) | <0.001 | |
| Study setting | Primary care | 8 | 96.9 | 81.22 | (76.23-86.20) | <0.001 |
| Tertiary care | 5 | 96.7 | 86.31 | (74.96-97.64) | <0.001 | |
| Primary, secondary and tertiary care | 12 | 97.5 | 82.58 | (77.31-87.86) | <0.001 | |
| Community | 4 | 98.4 | 85.38 | (81.54-89.22) | <0.001 | |
| Data collection technique | Review of health records | 17 | 97.9 | 85.29 | (76.13-94.46) | <0.001 |
| Interview | 3 | 96.6 | 85.29 | (76.13-94.46) | <0.001 | |
| Interview and health records | 9 | 97.2 | 80.33 | (74.54-88.11) | <0.001 | |
| Level of income | High income | 17 | 96.9 | 84.27 | (80.23—88.31) | <0.001 |
| Low-middle income | 6 | 98.3 | 77.54 | (68.67-86.42) | <0.001 | |
| High-middle income | 6 | 97.7 | 85.71 | (77.86-93.56) | <0.001 | |
| Continents | Europe | 12 | 97.3 | 82.21 | (77.12-87.29) | <0.001 |
| Asia | 12 | 96.1 | 82.76 | (76.73-88.79) | <0.001 | |
| North and South America, Australia | 5 | 97.1 | 86.49 | (78.82-94.17) | <0.001 |
Meta regression
Meta-regression was conducted to assess the source of heterogeneity, using sample size and year of publication as covariates. Multivariable random-effects meta-regression analyses were conducted to assess potential heterogeneity. The analysis revealed that sample size (coefficient = 1.26, standard error = 1.10, p-value = 0.91) and year of publication (regression coefficient = −0.003, standard error = 0.08, p-value = 0.97) were not statistically significant and did not contribute to heterogeneity.
Publication bias
We evaluated studies for potential publication bias using a funnel plot and Egger test. The funnel plot test demonstrated asymmetrical distribution (Fig. 4). The Egger’s test showed significant publication bias with a coefficient (4.38), standard error (0.013), and a p-value < 0.001 (95% CI: 4.36–4.41). So, both tests indicated a need to adjust the studies using trim and fill analysis. In the trim and fill meta-analysis, the total number of studies increased to 33, incorporating four additional imputed studies that were expected to be missed from the observed studies. After accounting for the missed or unpublished articles, the random pooled estimate of multimorbidity was 80.92% (95% CI: 77.26–84.57) with a p-value of < 0.001 (Fig. 5).
Fig. 4.
The funnel plot showed publication bias among T2D patients living with multimorbidity
Fig. 5.
Trim and fill analysis showed multimorbid studies among patients with T2D
Factors associated with multimorbidity
This study indicated that age, cigarette smoking, occupation, education and marital status were associated with multimorbidity. Individuals aged 40–59 were 2.36 times more likely to have multimorbidity compared to those aged 18–39 years (AOR = 2.36, 95% CI: 1.17–3.55) (Fig. 6). Current smokers were 2.33 times more likely to have multimorbidity compared to non-smokers (AOR = 2.33, 95% CI: 1.16–3.50) (Fig. 7). Retired individuals were 1.31 times more likely to have multimorbidity compared to employed individuals (AOR = 1.31, 95% CI: 0.96–1.66) (Fig. 8). Individuals with non-formal education were 2.93 times more likely to have multimorbidity compared to those with college and above (AOR = 2.93, 95% CI: 1.44–4.42) (Fig. 9). People who were widowed were 2.00 times more likely to have multimorbidity compared to those married individuals (AOR = 2.00, 95% CI: 1.04–2.96) (Fig. 10). Single findings were summarized in a table (Supplementary Table 6).
Fig. 6.
The fixed effect model showed the pooled estimate of age 40–59 compared with age 18–39 years among patients with T2D
Fig. 7.
The fixed effect model showed the pooled estimate of smokers compared with non-smokers among patients with T2D
Fig. 8.
The fixed effect model showed the pooled estimate of retired individuals compared with employed individuals among patients with T2D
Fig. 9.
The fixed effect model showed the pooled estimate of non-formal education compared to college and above among patients with T2D
Fig. 10.
The fixed effect model showed the pooled estimate of widowed individuals compared to married individuals among patients with T2D
The pooled global prevalence of concordant chronic conditions among patients with T2D
A total of nine studies were included to estimate the pooled global prevalence of concordant chronic condition. The pooled global prevalence of concordant chronic conditions was 50.21% (95% CI: 35.90–64.51.90.51), with substantial heterogeneity observed (I² = 100, p < 0.001) (Fig. 11). The highest prevalence was reported in Taiwan (76.10%) [18], and the lowest prevalence was reported in Israel (16.60%) [8]. The Galbraith plot revealed that the majority of the studies deviated significantly from the regression line, indicating substantial heterogeneity (Fig. 12).
Fig. 11.
Forest plot showed the pooled prevalence of concordant chronic conditions among patients with 2DM from a random effect model
Fig. 12.
The Galbraith plot showed outliers and variation across the studies on concordant chronic conditions among patients with T2D
Sensitivity test
Sensitivity analysis was evaluated by excluding each study one at a time to assess its effect on the pooled prevalence of concordant chronic conditions. The result showed that all studies fell within the confidence interval, suggesting that no single study had a significant effect on the pooled prevalence (Fig. 13).
Fig. 13.
A sensitivity test showed the effect of individual studies on the pooled estimate of concordant chronic conditions among patients with T2D
Heterogeneity assessment
In this systematic review and meta-analysis, fhe forest plot test and the Galbraith plot revealed high heterogeneity, as indicated by the forest plot the I² value was 100% and a Cochran’s Q statistic with a p-value indicated < 0.001. Hence, to further explore the sources of heterogeneity, we performed a subgroup analysis and meta-regression.
Subgroup analysis
Subgroup analysis was evaluated using study design and level of income. In this analysis, the sources of variability across the group were not identified. The I2 test variation ranged from 99.1% to 100% (Table 4).
Table 4.
Sub-group analysis among T2D patients with concordant chronic conditions
| Subgroups | Subgroup categories | Number of studies | Test of heterogeneity (I2) |
Effect size (%) | Random effect (95% CI) | p-value |
|---|---|---|---|---|---|---|
| Healthcare settings | Primary care | 3 | 100 | 35.84 | (-4.68-76.35) | <0.001 |
| Tertiary care | 3 | 99.1 | 48.38 | (51.39-81.49) | <0.001 | |
| Community | 3 | 99.9 | 66.44 | (21.66-75.11) | <0.001 | |
| Continents | Europe | 3 | 99.9 | 36.09 | (6.85-63.33) | <0.001 |
| Asia | 4 | 100 | 57.13 | (35.47-78.78) | <0.001 | |
| Africa | 2 | 98.4 | 59.12 | (32.44-85.80) | <0.001 | |
| Study design | Cross-sectional | 6 | 99.9 | 50.34 | (24.89-75.79) | <0.001 |
| Prospective cohort | 3 | 100 | 49.97 | (16.11-83.82) | <0.001 | |
| Level of income | High income | 6 | 100.0 | 44.66 | (27.09-62.23) | <0.001 |
| Low and middle income | 3 | 97.0 | 61.42 | (45.61-77.23) | <0.001 | |
| Data collection technique | Review of health records | 5 | 100 | 45.12 | (14.18-76.07) | <0.001 |
| Interview and health records | 4 | 99.9 | 56.65 | (42.50-70.81) | <0.001 |
Meta regression
Meta-regression was performed to examine the sources of heterogeneity using sample size and year of publication. The multivariable random-effects meta-regression analyses showed that sample size (coefficient = 0.00, standard error = 5.78, P-value = 0.06) and year of publication (regression coefficient = −0.31, standard error = 0.28, P-value = 0.31) were not found statistically significant and sources of heterogeneity not identified.
Publication bias
We evaluated studies for potential publication bias using a funnel plot and Egger test. The visual inspection of the funnel plot shows an asymmetrical distribution (Fig. 14). However, Egger’s test revealed no significant publication bias, with a coefficient of (−0.86), standard error (0.71), and p-value (0.26).
Fig. 14.
The funnel plot showed publication bias among T2D patients living with concordant chronic conditions
Factor associated with concordant chronic conditions
This study revealed that factors such as urban residence were associated with concordant chronic conditions. Urban residents were 1.69 times more likely to have concordant chronic conditions compared to rural residents (AOR: 1.69, 95% CI: 0.95–2.44) (Fig. 15). Individual studies were summarized in the table (Supplementary Table 7).
Fig. 15.
The fixed-effect model showed the pooled prevalence of urban residence compared with rural residence among individuals with T2D who have a concordant chronic condition
The pooled global prevalence of discordant chronic conditions among patients with T2D
A total of seven studies were included to estimate the pooled global prevalence of discordant chronic conditions. The pooled global prevalence of discordant chronic conditions was 47.24% (27.84–66.63), with substantial heterogeneity observed (I²=100%, p < 0.001) (Fig. 16). The highest prevalence was reported in Australia (83.4%) [9], and the lowest prevalence was reported in Finland (8.0%) [15]. The Galbraith plot also indicated no outliers but showed substantial heterogeneity (Fig. 17).
Fig. 16.
The forest plot showed the pooled prevalence of discordant chronic conditions among patients with T2D from a random effect model
Fig. 17.
The Galbraith plot showed outliers and variation across studies on discordant chronic conditions among patients with T2D
Sensitivity test
Sensitivity analysis was conducted by excluding one study at a time to observe variations in the pooled estimate. The sensitivity graph revealed that all individual studies remained within the confidence interval, with none significantly influencing the pooled prevalence. This suggests that the findings were stable (Fig. 18).
Fig. 18.
Sensitivity test showed the effect of individual study on the pooled estimate of discordant chronic conditions among patients with T2D
Heterogeneity assessment
The forest plot test and the Galbraith plot revealed high heterogeneity, as indicated by the forest plot; the I² value was 100%, and a Cochran’s Q statistic with a p-value indicated < 0.001. As a result, to identify the source of variation, we conducted a subgroup analysis and meta-regression.
Subgroup analysis
The subgroup analysis was evaluated based on the study design, continents, health care settings, year of publication, and data collection technique. The results indicated that the sources of variation could not be identified (Table 5).
Table 5.
Sub-group analysis among T2D patients with discordant chronic conditions
| Subgroups | Subgroup categories | Number of studies | Test of heterogeneity (I2) |
Effect size (%) | Random effect (95% CI) | p-value |
|---|---|---|---|---|---|---|
| Healthcare settings | Primary care | 4 | 100 | 43.9 | (-6.62-94.42) | <0.001 |
| Primary and above | 3 | 99.7 | 52.31 | (44.07-60.55) | <0.001 | |
| Continents | Europe | 4 | 100 | 39.77 | (0.82-78.73) | <0.001 |
| Asia | 3 | 100 | 57.27 | (36.62-77.92) | <0.001 | |
| Study design | Cross-sectional | 100 | 33.76 | (-17.63-85.14) | <0.001 | |
| Prospective cohort | 99.8 | 65.04 | (57.76-72.32) | <0.001 | ||
| Year of publication | <2015 | 2 | 99.10 | 42.10 | (-13.76-97.95) | <0.001 |
| ≥2020 | 5 | 100 | 49.29 | (26.53-72.05) | <0.001 | |
| Data collection technique | Review of health records | 3 | 100 | 53.99 | (-4.73-112.72) | <0.001 |
| Interview and health records | 4 | 99.8 | 42.41 | (31.94-52.87) | <0.001 |
Meta regression
Meta-regression was conducted to assess the source of heterogeneity, using sample size and year of publication. The multivariable random-effects meta-regression analyses indicated that sample size (coefficient = 0.00, standard error = 0.00, p-value = 0.12) and year of publication (coefficient = −0.15, standard error = 0.19, p-value = 0.47) were not identified as sources of heterogeneity.
Publication bias
We evaluated studies for potential publication bias using a funnel plot and Egger test. The funnel plot showed asymmetry (Fig. 19), while Egger’s test indicated no significant bias, with a coefficient of (−1.51), a standard error of (1.30), and a p-value of (0.29).
Fig. 19.
The funnel plot showed publication bias among T2D patients with discordant chronic conditions
The pooled global prevalence of combined chronic conditions among patients with T2D
A total of three studies were included to estimate the pooled global prevalence of combined chronic conditions. The pooled global prevalence of combined chronic conditions was 58.90% (95% CI: 45.76–72.04). It showed substantial heterogeneity (I² = 99.50%, p < 0.001) (Fig. 20). The highest prevalence was reported in Israel [8], and the lowest prevalence was reported in Ireland [57]. Individual factors associated with combined chronic conditions were summarized in the table (Supplementary Table 8). The Galbraith plot showed that there were no extreme values or outliers, but it indicates the presence of potential heterogeneity (Fig. 21).
Fig. 20.
The forest plot showed the pooled prevalence of combined chronic conditions among patients with T2D from a random effect model
Fig. 21.
The Galbraith plot showed outliers and variation across combined chronic conditions among patients with T2D
Sensitivity test
The effect of individual studies on the pooled prevalence was evaluated by excluding each study one at a time to assess any variation in the pooled estimate. Overall, the sensitivity analysis showed that individual studies did not affect the pooled prevalence (Fig. 22).
Fig. 22.
Sensitivity test showed the effect of each study on the pooled estimate of combined chronic conditions among patients with T2D
Heterogeneity assessment
The forest plot test and the Galbraith plot revealed high heterogeneity, as indicated by the forest plot; the I² value was 99.50%, and a Cochran’s Q statistic with a p-value indicated < 0.001. So, meta-regression was evaluated to assess potential sources of heterogeneity.
Meta regression
Meta-regression was assessed to examine the sources of heterogeneity, using sample size. The univariate analyses showed that the sample size was not found to be a source of heterogeneity (coefficient = 0.00, standard error = 0.00, and p-value = 0.81).
Discussion
Multimorbidity is increasing over time and causes a major global health challenge among individuals with type 2 diabetes. The growing prevalence of multimorbidity along with its complex management leads to a significant concern for the healthcare system. As a result, it is essential to strengthen the management of patients with multimorbidity. However, evidence from existing studies showed inconsistencies across different regions in the world. As a result, it is crucial to summarize and synthesize the available evidence. It gives better direction to policymakers and healthcare professionals to manage T2D patients with multimorbidity.
This review study revealed that the pooled global prevalence of multimorbidity was 83.17% (95% CI: 79.72–86.63). This is higher than the study reported in the UK [10], where the age-standardized prevalence of multimorbidity was 33.3% in the least deprived area and 32.7% in the most deprived areas. The possible explanation is that the UK study mainly focused on the clustering pattern of multimorbidity, such as cardiovascular, musculoskeletal, mental health, and respiratory disorders, while the prevalence was also reported in the context of age. Moreover, the current study reported a higher prevalence compared to studies reported in Bangladesh (37.2%) [60], the UK (33.1%) [61, 62], China (25.4%) [63], and Nepal (25.05%) [64]. This variation might be explained by differences in the study population, as the current study particularly focused on individuals with T2D rather than the general population. Furthermore, over time, individuals with T2D experience a progressive increase in the number of chronic conditions [65], potentially contributing to high prevalence.
The current review study also showed that the pooled global prevalence of concordant chronic conditions was 50.21% (95% CI: 35.90–64.51.90.51), which is comparable with the study reported in sub-Saharan Africa (6 to 64%) [20]. This finding suggests that most patients with T2D are more likely to have concordant chronic conditions due to shared pathophysiologic mechanisms and common risk factors [6]. Furthermore, the shared pathophysiologic pathways, along with inadequate coordinated and patient-centered care, may contribute to increasing the prevalence of concordant chronic conditions [1, 66, 67].
The pooled global prevalence of discordant chronic conditions was 47.24% (95% CI: 27.84–66.63), which is in line with the study reported in Europe (43.5%) [68]. The finding highlights that the increased prevalence may be associated with aging, lifestyle change, and the rising burden of non-communicable diseases [69, 70]. Similarly, patients with discordant chronic conditions may face higher health risks due to a lack of an integrated care approach [71, 72], which may contribute to the growing prevalence of discordant chronic conditions among patients with T2D.
Moreover, the pooled global prevalence of combined chronic conditions was 58.90% (95% CI: 45.76–72.04); this demonstrates that the presence of both concordant and discordant chronic conditions together may contribute to greater clinical complexity and ineffective treatment outcomes [73]. Existing evidence also indicated that the interactions between concordant and discordant chronic conditions can lead to diagnosis uncertainty, medication selection challenges, altered risk and benefits of treatment, and care coordination difficulties [73, 74]. Such complexity collectively may contribute to the growing burden of combined chronic conditions.
The odds of multimorbidity were 2.36 times higher among individuals aged 40–59 years compared to those aged 18–39 years. This finding is consistent with a study conducted in the UK [75], which demonstrated that as age increases, patients become more prone to multimorbidity. This may be explained by the fact that aging is a progressive and irreversible pathophysiological process, driven by changes in body metabolism, leading to the decline in the function of tissues and organs, alongside a significant rise in the risk of multimorbidity [76].
Individuals who were current smokers were 2.33 times more likely to have multimorbidity compared to non-smokers. This finding is consistent with the study reported in Italy [77]. The possible justification could be that smoking-induced chronic inflammation, increased cellular and organ damage, poor dietary habits, and physical inactivity lead to the development of multimorbidity [78, 79].
The odds of multimorbidity were 1.31 times higher among retired individuals compared to employed individuals. This is consistent with the study reported in China [80]. A possible explanation is that retired individuals may experience a lose of daily routines, including reducing mental and physical activities, diminished social interaction, and weakened a sense of identity or life purpose [81]. This, in turn, may result in unhealthy behaviors that contribute to the development of multimorbidity.
Individuals with no formal education were 2.93 times more likely to have multimorbidity compared to those with college and above. This is supported by the study reported in Australia [82], Southeast Asia [83], and Denmark [84]. The possible justification could be that people without formal education may have limited health literacy, reduced access to healthcare services, and lower awareness of preventive measures, which can contribute to a higher risk of developing multimorbidity [53].
People who were widowed was two times more likely to have multimorbidity than people who were married, which is supported by the study reported in China [85], Denmark [53], and the UK [62]. The possible explanation is that widowed individuals may be more vulnerable to physiological, social, and financial problems, which can adversely affect their health and lead to the development of multimorbidity [86, 87].
Individuals with T2D living in urban areas were 1.69 times more likely to have concordant chronic conditions compared to rural residents. This is supported by the study reported in the UK [88] and Nepal [64]. This might be due to the fact that urban residents are more likely to be exposed to poor dietary habits (high in carbohydrates, fat, and protein), sedentary behavior, environmental pollution, and stress. These may contribute to the development of concordant chronic conditions [89, 90].
Implication of the study
This review study provides an important insight for clinical practice, which encourages the need for integrated, comprehensive, and patient-centered care to manage type 2 diabetes patients with multimorbidity. Furthermore, it supports policymakers in developing different strategies to effectively manage type 2 diabetes with multimorbidity. Moreover, this study serves as a baseline for further research.
Strength and limitation of the study
This study has some important strengths, including the use of major databases to identify research articles and the employment of a rigorous and comprehensive strategy to conduct the search. In addition, the study investigates multimorbidity in broad perspective in terms of prevalence, subtype, and predictors. However, the study has certain limitations; it did not reflect the true variation due to the heterogeneity of the results. Also, studies were focused on only English-language and observational studies. Moreover, the limited number of studies for the outcome variables (combined chronic condition) as well as the small number of studies reported from low-income countries may limit generalizability. The other limitation of this study is that the absence of clear, standardized criteria for classifying the risk of bias may affect the selection of individual studies.
Conclusions
Multimorbidity is highly prevalent among patients with type 2 diabetes, showing a significant global public health challenge. Factors such as age 40–59 years, being retired, non-formal education, being widowed and being a current smoker were found to be factors associated with multimorbidity. Whereas, being an urban resident was found to be a factor associated with concordant chronic conditions.
To prevent and reduce the burden of multimorbidity, the healthcare professionals should provide health education and psycho-behavioral support, particularly for older adults, individuals with low education, retired individuals, smokers, for widowed individuals, and people living in urban residences. Policymakers should develop a well-designed and comprehensive strategy to enhance the prevention and management of multimorbidity. In addition, further studies are recommended in low- and middle-income countries. Systematic reviews and meta-analyses that include interventional studies and studies reported in multiple languages should be considered. The author recommends further longitudinal study to see the real effect of predictors for multimorbidity; Moreover, qualitative studies that explore barriers to multimorbidity prevention and assessment of the quality of care of patients living with T2D are recommended.
Supplementary Information
Supplementary Material 1. PRISMA check list, 2020.
Supplementary Material 2. Search strategies used to identify existing evidence on multimorbidity among patients with T2D.
Supplementary Material 3. Concordant and discordant chronic conditions included in the systematic reviwe and meta-analysis among patients living with T2D.
Supplementary Material 4. JBI Critical appraisal (quality assessment) for observational studies among patients with T2D.
Supplementary Material 5. Details of data extraction.
Supplementary Material 6. Summary of individual studies showed factors associated with multimorbidity among patients with type 2 diabetes.
Supplementary Material 7. Summary of individual studies reporting factors associated with concordant chronic condition among patients with T2D.
Supplementary Material 8. Summary of individual studies reporting factors associated with combined chronic conditions among patients with T2D.
Acknowledgements
I would like to acknowledge my advisors, Professor Kerstin Erlandsson, Dr. Margarata Westerbotn, Dr. Mignote Hailu, Dr. Debrework Tesgera, and others who participated in the data screening and evaluation process.
Abbreviations
- AOR
Adjusted odds ratio
- CI
Confidence interval
- CoCoPop
Co (condition), Co (context), Pop (population)
- ES
Effect size
- MeSH
Medical Subject Headings
- PRISMA
Preferred Reporting Items for Systematic Review and Meta-analysis
- T2D
Type two diabetes
Authors’ contributions
YMF1developed the protocol of the current review MW5, KE2, DTB3 and MHG3, contributed to the study design, data analysis and comprehensive review of the entire work. AWA1, and OYM4 were involved in the selection, screening, quality assessment and data extraction processes.
Funding
The author declare that no funding was received for this review study
Data availability
All data used in the study are provided within the main document and supplementary files.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 1. PRISMA check list, 2020.
Supplementary Material 2. Search strategies used to identify existing evidence on multimorbidity among patients with T2D.
Supplementary Material 3. Concordant and discordant chronic conditions included in the systematic reviwe and meta-analysis among patients living with T2D.
Supplementary Material 4. JBI Critical appraisal (quality assessment) for observational studies among patients with T2D.
Supplementary Material 5. Details of data extraction.
Supplementary Material 6. Summary of individual studies showed factors associated with multimorbidity among patients with type 2 diabetes.
Supplementary Material 7. Summary of individual studies reporting factors associated with concordant chronic condition among patients with T2D.
Supplementary Material 8. Summary of individual studies reporting factors associated with combined chronic conditions among patients with T2D.
Data Availability Statement
All data used in the study are provided within the main document and supplementary files.