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. 2024 Oct 29;28(11):100402. doi: 10.1016/j.jnha.2024.100402

Spatiotemporal trends of Type 2 diabetes due to low physical activity from 1990 to 2019 and forecasted prevalence in 2050: A Global Burden of Disease Study 2019

Shujin Fan a,b,c,d,1, Jin Xu a,b,1, Jinli Wu a,b,1, Li Yan a,b, Meng Ren a,b,⁎
PMCID: PMC12879310  PMID: 39471776

Abstract

Background

Type 2 diabetes mellitus (T2DM) poses a major global health burden, yet epidemiological research on low physical activity's (LPA) impact is limited. This study examines LPA's global effect on T2DM.

Methods

Analyzing Global Burden of Disease Database (GBD) 2019, we explored LPA-attributable T2DM deaths and Disability-Adjusted Life Years (DALYs) from 1990 to 2019, stratified by year, gender, country, and SDI regions. Estimated Annual Percentage Change (EAPC) assessed trends, and Bayesian models predicted future patterns.

Results

In 2019, LPA accounted for a substantial 8.5% of T2DM deaths and 6.9% of DALYs, representing a noticeable rise since 1990. Age-standardized mortality rates (ASMR) and disability-adjusted life years rates (ASDR) increased globally, particularly in low Socio-Demographic Index (SDI) regions. High and high-middle SDI regions saw a decrease in ASMR, while all regions generally saw an upward trend in ASDR. Projections for 2050 suggest a declining ASMR but an increasing ASDR, indicating a continuing burden of T2DM despite potential mortality reductions.

Conclusion

LPA significantly impacts T2DM, particularly in low SDI regions. Promotion of physical activity is crucial to reduce this burden, particularly in regions where the disease's impact is most severe.

Keywords: GBD, Low physical activity, Type 2 diabetes, Prevalence, Global

1. Introduction

Type 2 diabetes mellitus (T2DM), a prevalent metabolic disease worldwide, poses significant threats to public health. According to research conducted by the Global Burden of Disease Database (GBD) study, approximately 46.2 million individuals suffer from T2DM globally, accounting for 6.28% of the world's population. This figure is projected to escalate to 7079 cases per 100,000 by 2030 [1]. Beyond hyperglycemia, T2DM patients also face elevated risks of cardiovascular, ocular, and renal complications [2]. Data from the World Health Organization (WHO) reveals that 1.5 million deaths are directly attributed to diabetes annually, with nearly half of these fatalities occurring before the age of 70. By 2030, the prevalence of T2DM is anticipated to rise further [3]. Hence, enhancing the prevention and management of T2DM globally is imperative to mitigate its detrimental effects on human health.

The low physical activity's (LPA) lifestyle is intimately linked to the development of diabetes [4]. Research indicates that inadequate physical activity is a primary risk factor for diabetes [5]. Prolonged sedentary behavior and a lack of exercise can contribute to obesity, adipose accumulation, insulin resistance, and consequently, an elevated risk of diabetes [6,7]. Conversely, physical activity aids in weight management, enhances insulin sensitivity, and promotes glucose metabolism. An LPA lifestyle, however, diminishes the body's responsiveness to insulin, leading to elevated blood glucose levels [6]. Previous studies consistently underscore the correlation between LPA and T2DM. A systematic review conducted by Youngshin Song et al. [8] revealed that individuals with low levels of physical activity are at a significantly higher risk of developing T2DM. Similarly, a meta-analysis by Aune et al. [9] demonstrated that higher levels of physical activity correlate with a reduced risk of T2DM.

Despite these compelling findings, comprehensive global epidemiological studies examining the impact of LPA on T2DM prevalence remain scarce. Such investigations are vital as they can inform public health strategies aimed at fostering healthy lifestyles and mitigating the risk of T2DM. For instance, Buckley et al. [10] emphasized the urgent need for global efforts to enhance physical activity levels as a preventative measure against chronic diseases like T2DM. Additionally, a comprehensive understanding of global trends in T2DM attributed to LPA can facilitate the development of targeted intervention strategies. Hemmingsen et al. [11] highlighted the importance of considering physical activity levels in the formulation of T2DM prevention strategies.

In conclusion, there is an urgent need for comprehensive global epidemiological studies that explore the impact of LPA on T2DM. Such research holds the potential to promote healthier lifestyles and alleviate the global burden of T2DM [12]. This study aims to analyze data from the GBD to investigate global epidemiological shifts in T2DM attributable to LPA from 1990 to 2019, and to project these trends up to 2050.

2. Methods

2.1. Data source

GBD is a comprehensive and extensive repository of health-related data created and maintained by the Institute for Health Metrics and Evaluation (IHME), aiming to collect, organize, and analyze global data on disease burden. The latest GBD study, which records data from 1990 to 2019, includes various metrics such as mortality, morbidity, DALYs, 369 diseases and injuries, and 87 risk factors in 204 countries and territories. In GBD 2019, type 2 diabetes was defined as the ICD-10 code E11-E11.1 and E11.3-E11.9, as well as the ICD-9 code 250.00, 250.02, 250.10, 250.12, 250.20, 250.22, 250.30, 250.32, 250.50, 250.52, 250.60, 250.62, 250.70, 250.72, 250.80, 250.82, 250.90 and 250.92. It provides an important and useful tool to inform clinicians, researchers, and policy makers, promotes accountability, and improves lives worldwide [13]. In the GBD 2019, self-reported data on physical activity levels were obtained from two standardized questionnaires: the Global Physical Activity Questionnaire (GPAQ) and the International Physical Activity Questionnaire (IPAQ) [14]. The energy expenditure of different physical activities was measured in terms of metabolic equivalents (METs, minutes/week), which indicated the metabolic rate during activity to the metabolic rate at rest. A previous meta-analysis that examined the dose–response relationship between physical activity and health outcomes established a theoretical minimum risk exposure level of 3000–4500 MET-minutes per week [15]. Based on this level, we classified LPA as < 3000 MET-minutes per week. To estimate the burdens of T2DM attributable to LPA, the Comparative Risk Assessment framework was established, which involved several steps: reviewing the literature and performing meta-regression to obtain the relative risks of LPA for different health outcomes; using various methods to estimate the exposure levels and distributions of LPA; defining the optimal exposure level as the one that had the theoretically lowest risk according to published trials and cohort studies; estimating the population attributable fractions (PAFs); computing the number of deaths and DALYs by multiplying the PAFs and considering the mediating effects [16]. In our study, PAFs indicated how much type 2 diabetes risk could be lowered in a specific population and year if the people in that population had done at least 3000 MET-minutes of physical activity per week in the past. PAF was calculated as the following formula:

PAFasly=∑n=1tRRasy(n)Pasly(n)-1/∑n=1tRRas(n)Pasly(n)

where ‘a’ represents age group, ‘s’ represents sex, ‘l’ represents location, ‘y’ represents year, RRasy represents the relative risks of type 2 diabetes for different levels of LPA exposure (n) compared to the reference level (t), Pasly represents the proportion of the population that is exposed to LPA at a certain level (n), PAFasly was the PAF for the burden of type 2 diabetes attributable to LPA [17]. The following outlines the methods for obtaining each parameter in the PAF formula: Relative risk (RR) has been derived from systematic reviews and meta-regression analyses that are based on previously published studies assessing the risk of T2DM at different levels of physical activity. For the association between LPA (<3000 MET-minutes/week) and T2DM, we utilised pooled data from multiple high-quality studies that provided relative risk estimates for T2DM at various levels of physical activity. The proportion exposed (P) indicates the percentage of a specific population that is exposed to LPA, with data sourced from the GBD 2019 database. By employing standardised survey data (such as GPAQ and IPAQ) provided in the GBD 2019, we were able to estimate the prevalence of LPA across different genders, ages, regions, and years, corresponding to the proportion exposed. The theoretically minimum risk exposure level is defined based on the lowest risk levels observed in published trials that assessed the dose-response relationship between physical activity and T2DM risk. In the PAF calculation, we use the theoretically minimum risk exposure level as a reference point to evaluate the additional risk associated with LPA relative to this minimum risk level. The GBD 2019 Data Input Sources Tool website serves as a comprehensive repository of the original data sources utilized for disease and mortality estimations (http://ghdx.healthdata.org/gbd-2019/data-input-sources). The data used in this study were obtained from the GBD database (https://vizhub.healthdata.org/gbd-results/).

2.2. Study design

We collected data on death and Disability-Adjusted Life Years (DALYs) caused by T2DM due to LPA from 1990 to 2019. This included the Age-standardized mortality rates (ASMR) and disability-adjusted life years rates (ASDR) by year, sex, country and five Socio-Demographic Index (SDI) regions. DALYs are calculated by adding years of life lost (YLLs) and years lived with disability (YLDs). The percent of death or DALYs refers to the proportion of deaths or DALYs of T2DM caused by low physical activity within the total number of deaths or DALYs of T2DM in a specific time period and geographic area. The age-standardized rate (ASR) is calculated by applying age-specific rates to the standard population which enables more accurate comparisons of disease burden between populations and minimizes the influence of differences in population age structures. The SDI is a composite measure the overall development and socio-economic status of a country or region, ranging from 0 to 1. It combines multiple indicators related to education, income, and fertility to provide a summary measure of social and economic development. All countries and territories were divided into five SDI regions based on the SDI index. We displayed the data in charts and figures, including the disease trend over the years and the distribution of the disease by age and countries. To have a more comprehensive understanding of the disease distribution and impact, we divided the decomposition of the disease into three factors: aging, demographic structure, and epidemiological changes. We also made predictions about the future trend of the disease using Bayesian Age-Period-Cohort (BAPC) projections.

2.3. Statistical analysis

The Estimated Annual Percentage Change (EAPC) is a method used in epidemiology to estimate the annual percentage change in disease incidence or mortality rates over a specific period. To calculate the EAPC of the ASMR and ASDR, a log-linear regression model is fitted to the natural logarithm of the rates over time. The equation for this model can be expressed as: ln (ASR) = β0 + β1*year + ε, where ln (ASR) represents the natural logarithm of the ASR, β0 is the intercept or constant term, β1 represents the slope of the regression line, indicating the rate of change in the logarithm of the ASR over the year, year represents the exact year ranging from 1990 to 2019, ε represents the random error term. The coefficients β0 and β1 are estimated using methods such as ordinary least squares (OLS) regression. The slope coefficient β1 is of particular interest as it reflects the estimated annual percentage change in the ASR. Once the coefficients are estimated, the EAPC can be calculated using the formula: EAPC = (exp (β1) − 1) * 100, where exp (β1) represents the exponential of the slope coefficient, which can be interpreted as the average annual percentage change in the ASR over the year. The magnitude of the EAPC represents the rate of change in the ASMR and ASDR. A positive EAPC with the lower limit of 95% CI higher than 0 indicates an increasing trend of the ASR, while a negative EAPC with the upper limit of 95% CI lower than 0 suggests a decreasing trend. Additionally, we explored the impact of SDI on EAPC of the ASMR and ASDR through Spearman rank correlation analysis and linear regression analysis was employed for fitting the data. We employed a decomposition analysis approach to investigate the factors contributing to changes in death cases and DALYs using a validated algorithm. The study focused on aging, population, and epidemiological change, with each factor assigned a value representing its impact on the overall effect. Positive magnitudes indicate promoting factors, while negative magnitudes represent negative ones. Additionally, we used the BAPC model to predict death and DALYs, ASMR and ASDR until 2050. The BAPC model decomposes variations into age, period, and cohort effects, assuming that they can be described as a function of these three factors. We fitted the BAPC model to estimate the age, period and cohort effects and used it to project the future death and DALYs, ASMR, and ASDR by extrapolating the estimated effects beyond the overall data. All analyses were performed by using R software (Version 4.3.1), with P values less than 0.05 considered statistically significant.

3. Results

3.1. The burden and trend of T2DM attributable to low physical activity

In 2019, LPA was responsible for 125,195.38 deaths of T2DM, accounting for 8.5% of the total deaths, and 4,549,207.17 DALYs, representing 6.9% of the total DALYs. Comparatively, in 1990, there were 49,781.11 deaths (8.2% of total deaths) and 1,719,775.43 DALYs (6.8% of total DALYs), as outlined in Table 1. Both death counts and DALYs have steadily climbed from 1990 to 2019, as illustrated in Fig. 1A and 1B. Regarding T2DM-attributable ASMR for LPA, it was 1.62 per 100,000 in 2019, up from 1.48 per 100,000 in 1990. The ASDR saw a more significant increase, rising from 45.00 per 100,000 in 1990 to 55.92 per 100,000 in 2019. While the ASMR remained relatively stable with a marginal increase over this period, exhibiting an EAPC of 0.26%, the ASDR showed a more pronounced rise with an EAPC of 0.84%.

Table 1.

Deaths, DALYs, ASMR, ASDR and EAPC of Type 2 diabetes attributable to Low physical activity globally and 5 SDI regions, and change from 1990 to 2019.

1990
Location Deathcase Death_percent ASMR ASDR DALYs DALYs_percent
Global 49781.11 (24516.53, 84623.93) 0.08 (0.04, 0.14) 1.48 (0.75, 2.46) 45.00 (21.34, 79.47) 1719775.43 (782033.40, 3071194.26) 0.07 (0.03, 0.12)
High SDI 12698.13 (6138.14, 21321.56) 0.10 (0.05, 0.17) 1.21 (0.58, 2.03) 40.49 (17.80, 74.82) 420335.72 (187172.00, 765887.77) 0.09 (0.04, 0.15)
High-middle SDI 12572.85 (6481.82, 20294.23) 0.10 (0.05, 0.16) 1.36 (0.71, 2.16) 43.02 (21.15, 72.53) 445748.56 (213452.18, 758999.36) 0.08 (0.04, 0.13)
Middle SDI 13055.71 (6108.40, 22874.01) 0.07 (0.03, 0.13) 1.65 (0.81, 2.81) 48.08 (22.40, 84.40) 466859.48 (208934.32, 839380.22) 0.06 (0.03, 0.11)
Low-middle SDI 8249.43 (4088.09, 14158.05) 0.07 (0.04, 0.12) 1.93 (0.98, 3.21) 51.33 (25.51, 90.20) 280194.36 (131638.40, 505040.93) 0.06 (0.03, 0.11)
Low SDI 3135.96 (1421.29, 5878.96) 0.05 (0.02, 0.09) 1.82 (0.86, 3.25) 47.90 (21.76, 88.05) 104472.59 (44734.01, 200820.24) 0.05 (0.02, 0.09)
2019
 EAPC(1990-2019)
Location Death_case Death_percent ASMR ASDR DALYs DALYs_percent ASMR ASDR
Global 125195.38 (62095.75, 208346.77) 0.09 (0.04, 0.14) 1.62 (0.81, 2.68) 55.92 (27.16, 97.60) 4549207.17 (2188516.16, 7969495.46) 0.07 (0.03, 0.12) 0.26 (0.13 to 0.39) 0.84 (0.78 to 0.89)
High SDI 19636.29 (9612.33, 32216.36) 0.11 (0.05, 0.17) 0.91 (0.45, 1.50) 49.72 (23.04, 91.82) 878974.95 (410795.94, 1574373.87) 0.09 (0.04, 0.15) −1.48 (−1.81 to −1.15) 0.63 (0.54 to 0.73)
High-middle SDI 25584.10 (13381.40, 40527.24) 0.10 (0.05, 0.16) 1.30 (0.68, 2.05) 47.56 (23.77, 81.85) 963081.55 (481367.22, 1662414.54) 0.08 (0.04, 0.13) −0.2 (−0.33 to −0.07) 0.39 (0.29 to 0.49)
Middle SDI 44194.28 (21587.94, 74536.20) 0.08 (0.04, 0.14) 2.10 (1.03, 3.48) 63.83 (30.05, 110.33) 1545749.93 (707733.96, 2717547.06) 0.07 (0.03, 0.12) 0.89 (0.82 to 0.96) 1.04 (0.99 to 1.09)
Low-middle SDI 27377.15 (14024.03, 45501.49) 0.08 (0.04, 0.13) 2.53 (1.29, 4.12) 68.11 (34.22, 115.41) 872087.73 (429422.40, 1524246.58) 0.06 (0.03, 0.10) 1.07 (0.91 to 1.24) 1.19 (1.09 to 1.29)
Low SDI 8243.18 (3927.69, 14331.84) 0.06 (0.03, 0.10) 2.12 (1.02, 3.62) 58.51 (27.26, 105.95) 283472.67 (125338.64, 520123.45) 0.05 (0.02, 0.09) 0.73 (0.56 to 0.89) 0.96 (0.84 to 1.09)
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Data in parentheses are 95% uncertainty intervals. DALYs = disability-adjusted life-years. SDI = Socio-Demographic Index. ASMR = age-standardized mortality rate. ASDR = age-standardized DALYs rate. EAPC = Estimated Annual Percentage Change.

Fig. 1.

Fig. 1

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Change in death and DALYs of T2DM due to LPA from 1990 to 2019. (A) Death percent of T2DM attributable to low physical activity. (B) DALYs percent of T2DM attributable to low physical activity. (C) Number of deaths and ASMR of T2DM attributable to low physical activity. (D) Number of DALYs and ASDR attributable to low physical activity. T2DM: type 2 diabetes. LPA: low physical activity. DALYs = disability-adjusted life-years. ASMR = age-standardized mortality rate. ASDR = age-standardized DALYs rate.

At the regional SDI level, deaths, DALYs, ASMR, and ASDR due to LPA are detailed in Table 1. Across all SDI regions, there was a notable increase in deaths and DALYs from 1990 to 2019. During this period, the percentage of deaths in various SDI regions exhibited distinct patterns. High, middle, low-middle, and low SDI regions saw a gradual increase followed by a decline, but high-middle SDI regions remained relatively stable with a slight upward trend. In terms of DALYs percentage, middle, low-middle, and low SDI regions initially increased slowly before decreasing. High SDI regions showed a trend from ascent to descent. High-middle SDI regions had minor changes with overall modest variations. Globally, the ASMR trend slightly increased from 1990 to 2019, with middle, low-middle, and low SDI regions consistently rising. However, high and high-middle SDI regions saw a decrease in ASMR (Fig. 1C). As for ASDR, all SDI regions generally trended upward. Notably, low-middle, middle, and low SDI regions increased until 2017 and then declined. Unlike ASMR, high and high-middle SDI regions showed an upward ASDR trend (Fig. 1D). EAPC data reflects consistent growth in ASMR and ASDR in most regions, except for a reduction in ASMR in high and high-middle SDI regions. Detailed country-level data can be found in Supplement Table S1.

3.2. The regional distribution of T2DM attributable to low physical activity

The regional spread of ASMR in 1990 and 2019 bears striking similarities (Fig. 2A and B). The higher ASMR rates persistently cluster in developing nations across Africa, South America, and Southeast Asia. Notably, a significant surge in ASMR is evident in specific Asian and African countries in 2019, compared to 1990. Nations like Bahrain, Oman, Qatar, Sri Lanka in Asia, and African countries such as Eswatini, Lesotho, Botswana, and South Africa have witnessed a substantial uptick in ASMR.

Fig. 2.

Fig. 2

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World map of ASMR and ASDR of T2DM attributable to low physical activity in 1990 and 2019, and the change of ASMR and ASDR globally from 1990 to 2019. (A) ASMR of T2DM attributable to low physical activity in 1990. (B) ASMR of T2DM attributable to low physical activity in 2019. (C) ASDR of T2DM attributable to low physical activity in 1990. (D) ASDR of T2DM attributable to low physical activity in 2019. (E) The EAPC of ASMR of T2DM attributable to low physical activity from 1990 to 2019. (F) The EAPC of ASDR of T2DM attributable to low physical activity from 1990 to 2019. ASMR = age-standardized mortality rate. ASDR = age-standardized DALYs rate. EAPC = Estimated Annual Percentage Change. T2DM: type 2 diabetes. LPA: low physical activity.

The territorial allocation of ASDR in 1990 and 2019 mirrors the distribution trend seen in ASMR (Fig. 2C and D). Both ASMR and ASDR exhibit comparable dispersions at the country level. Similarly to ASMR, there's a conspicuous rise in ASDR in specific developing nations in 2019, when juxtaposed with 1990 data. This increase is particularly notable in African countries such as Eswatini, Morocco, Egypt, Lesotho, Botswana, Libya, and Asian countries including Bahrain, Qatar, Afghanistan, and Oman.

The EAPC distribution maps provide a thorough perspective on the yearly regional variations in ASMR and ASDR across the designated period (Fig. 2E and F). These visual aids are invaluable for attaining a profound comprehension of how ASMR and ASDR evolved annually in each country over the two decades. For a more detailed breakdown of the regional distribution, please refer to Supplement Table S2. We also conducted a correlation analysis between EAPC and SDI to determine if changes in EAPC are related to SDI, as illustrated in Supplement Fig. S1.

3.3. The age and sex distribution in T2DM attributable to low physical activity

In 2019, deaths were primarily observed in middle-aged and older individuals populations, exhibiting a comparable distribution among both males and females (Fig. 3A). The age distribution of DALYs also focused on middle-aged and older individuals cohorts, revealing no noteworthy gender disparities (Fig. 3B). Specifically, in 2019, the death rate experienced a notable surge in the 75–79 age bracket, again with a similar distribution among males and females. The rate of DALYs steadily climbed with age, and higher rates predominantly affected middle-aged and older individuals demographics. Gender distribution of DALYs rates demonstrated minimal variations (Fig. 3C and D). The age and gender profiles of deaths, DALYs, and corresponding rates in 1990 and 2019 shared striking similarities. Detailed data showcasing the age and gender distributions across various countries in 1990 and 2019 are available in Supplement Figure S2 and Supplement Table S3.

Fig. 3.

Fig. 3

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Sex and age composition of T2DM across 5 SDI regions. (A) Number of deaths of T2DM attributable to low physical activity by sex and age in 2019. (B) Number of DALYs of T2DM attributable to low physical activity by sex and age in 2019. (C) Rate of deaths of T2DM attributable to low physical activity by sex and age in 2019. (D) Rate of DALYs of T2DM attributable to low physical activity by sex and age in 2019. DALYs = disability-adjusted life-years. SDI = Socio-Demographic Index. ASMR = age-standardized mortality rate. T2DM: type 2 diabetes.

3.4. The decomposition of T2DM attributable to low physical activity

From 1990 to 2019, various factors have influenced the rise in deaths and DALYs worldwide (Fig. 4A and B). The combined effects of aging, epidemiological shifts, and population growth have resulted in a net surge of 75,415 deaths. Specifically, aging contributed to 22,931, epidemiological changes accounted for 45,896, and population growth added 6,588 deaths. Notably, population growth and aging were the primary factors, responsible for 60.86% and 30.41% of the increase, respectively (Fig. 4A). In terms of DALYs, these three factors collectively led to an increase of 2,829,433 DALYs. Among them, aging caused an additional 550,478, epidemiological changes contributed 1,632,716, and population growth led to 646,239 more DALYs. Population growth played the biggest role, accounting for 57.7% of the increase, while epidemiological changes also significantly impacted, contributing 22.84% to the rise in DALYs (Fig. 4B).

Fig. 4.

Fig. 4

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Different composition in T2DM attributable to LPA across 5 SDI regions. (A) The decomposition of the change of number of deaths from 1990 to 2019. (B) The decomposition of the change of number of DALYs from 1990 to 2019. T2DM: type 2 diabetes. LPA: low physical activity. SDI = Socio-Demographic Index. DALYs = disability-adjusted life-years.

The influence of these factors varies slightly among different SDI regions between 1990 and 2019. In low, low-middle, and middle SDI regions, each factor contributed to an escalation in deaths. However, in high-middle and high SDI regions, a decline in epidemiological changes led to a reduction in the associated burden (Fig. 4A). Regarding DALYs, the findings align with the global trends, indicating that all factors played a role in their increase, with population growth having the most significant impact (Fig. 4B). Detailed outcomes for each country over the 20-year span can be accessed in the supplementary materials, along with results for global, SDI regions, and individual countries every five years (Supplement Table S4).

3.5. Estimated trends of T2DM attributable to low physical activity until 2050

The BAPC findings for global deaths, DALYs, ASMR, and ASDR are displayed in Fig. 5. It is anticipated that global deaths will steadily decline up to 2050. Male deaths are expected to rise gradually, whereas female deaths are forecasted to drop at a swifter rate, ultimately leading to an overall reduction in total deaths (Fig. 5A). Predictions suggest that global DALYs will keep increasing until 2050. Males are likely to encounter a more substantial surge, while females are expected to see a more gradual rise (Fig. 5B). By 2050, the global ASMR is projected to decline, mirroring the trend in death cases. Male ASMR is expected to undergo relatively minor fluctuations, whereas female ASMR is forecasted to decrease significantly (Fig. 5C). Regarding global ASDR, it is expected to continue escalating until 2050. Female ASDR is anticipated to steadily decrease at a consistent rate, while male ASDR is predicted to climb more noticeably, resulting in an overall uptick in total ASDR (Fig. 5D).

Fig. 5.

Fig. 5

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The prediction of T2DM trend attributable to LPA across gender. (A) The projection of number of deaths of T2DM attributable to low physical activity. (B) The projection of number of DALYs of T2DM attributable to low physical activity. (C) The projection of ASMR of T2DM attributable to low physical activity. (D) The projection of ASDR of T2DM attributable to low physical activity. T2DM: type 2 diabetes. LPA: low physical activity. ASMR = age-standardized mortality rate. ASDR = age-standardized DALYs rate. DALYs = disability-adjusted life-years.

4. Discussions

The results of our research indicate that globally, between 1990 and 2019, the number of deaths and DALYs of T2DM attributable to LPA has nearly tripled. This significant increase in deaths and DALYs can be attributable to population aging and growth [18,19]. After age-standardizing, we observed a gradual decrease in the number of deaths of T2DM attributable to LPA worldwide, especially in areas with high and High-middle SDI regions since 2000. However, the prevalence of T2DM attributable to LPA continues to rise globally, in both high and low SDI regions. The decreasing trend in ASMR of T2DM attributable to LPA after 2000 may reflect the combined effect of multiple factors. The efforts of High SDI countries in preventing and treating diabetes may be an important reason for this trend. Studies have shown that these countries have made significant progress in diabetes management [20,21]. However, unhealthy lifestyles are still the main cause of the continued increase in the incidence of T2DM globally and cannot be compulsorily intervened, and the LPA studied in this paper is one of the unhealthy lifestyles. We also explored the changes in the number of deaths and cases of T2DM attributable to LPA in each countries globally from 1990 to 2019 and found that the regions with the highest increase in deaths in the past 20 years are mainly concentrated in developing countries. These countries may have lower levels of public health infrastructure and health education, resulting in insufficient prevention and management of chronic diseases such as diabetes [22]. Additionally, the changes in lifestyle and unhealthy eating habits during the economic development and urbanization of developing countries may also lead to an increase in the mortality rate of T2DM patients. For example, the prevalence of high-sugar and high-fat diet [23,24]. Among these developing countries, there are also countries that have suffered from wars, such as Syria, Lebanon, and Iran, and war may be the primary factor causing the death of diabetes and other patients [25,26].

Our research into the the global deaths and DALYs of T2DM patients attributable to LPA, categorized by age and gender, revealed that there was no significant difference between genders. However, as age increases, the number of deaths and DALYs also increases, especially in Low-middle SDI regions and Low SDI regions. This is likely due to the lack of medical resources, malnutrition, poor environmental hygiene, and other factors that impact the development and management of diseases [[27], [28], [29]]. We observed that after the age of 80, the number of T2DM patients and deaths caused by LPA increased sharply, even doubling compared to before 80 years old. Previous studies have shown that the older individuals are at a higher risk of developing T2DM due to decreased insulin sensitivity, decreased insulin secretion capacity, and reduced physical activity due to mobility limitations [30,31]. Therefore, it is crucial to pay attention to the physical condition of the older individuals and consider adding diabetes screening as a routine examination for the older individuals.

Our analysis of decomposition results indicates that the rise in age and population will contribute to an increase in the number of T2DM patients and deaths attributable to LPA, regardless of the region’s level of economic development. In regions with High SDI and High-middle SDI, the higher economic status and increased investment in preventative medical care have resulted in a decline in infectious diseases [32,33], thereby reducing the T2DM patient mortality rate. However, this decline is still overshadowed by population growth and aging, which contradicts the gradual decrease in ASMR of T2DM after 2000, suggesting that there are other factors influencing the T2DM patient mortality rate. Surprisingly, we observed that in Low SDI areas, aging, population, and the occurrence of infectious diseases do not appear to be influential factors in the rise of the number of T2DM patients and deaths linked to LPA, despite our discovery of a high number of T2DM patients and deaths among the older individuals in Low SDI areas. We believe that the primary influencing factor in Low SDI regions is the burden of chronic diseases, including obesity, hypertension, which are closely related to T2DM [[34], [35], [36]]. Limited medical resources and health education may lead to a lower level of prevention and management of T2DM, further exacerbating the severity of the disease. These factors may collectively contribute to an increased risk of T2DM due to LPA in the older individuals in Low SDI areas, resulting in an increase in incidence and mortality [37,38].

Finally, we forecasted the future number of T2DM patients and the deaths due to LPA, with a forecast time set for 2050. It is pleasing to note that the number of deaths among T2DM patients is expected to decrease gradually worldwide. Neverrheless, the incidence of T2DM continues to exhibit an upward trend.

Despite the clinical and scientific advances that have enhanced our understanding of the pathogenesis and influencing factors of T2DM, as well as improved management of T2DM patients, the prevalence of T2DM has not decreased but rather increased over the past few decades [39], particularly in the older individuals population, where physical inactivity is unavoidable and linked to numerous chronic complications [40,41]. Regular T2DM screening in this population is still essential for developing and implementing based public health and intervention measures, and for assessing and ultimately revise their effectiveness. Previous studies have examined risk factors for T2DM such as BMI, dietary habits, and air pollution [42], but there has yet to be a comprehensive exploration of individual risk factors and their contribution to the global epidemiology of T2DM as discussed in this article. Despite efforts in many countries to promote physical activity for the prevention of T2DM, this study shows a continuous increase in the number of T2DM cases due to physical inactivity, especially in developing countries where technological advancements have led to more sedentary occupations [43].

It is worth noting that Type 2 Diabetes Mellitus (T2DM) not only places patients at a higher risk of mortality but also significantly increases the incidence and severity of disabilities. Patients with T2DM often experience complications such as cardiovascular issues, eye problems, and kidney disease, which frequently lead to long-term disabilities, severely impacting their quality of life and social functioning. Low levels of physical activity are a significant risk factor for the development of T2DM and its complications, as they exacerbate issues such as obesity, insulin resistance, and metabolic disorders. Therefore, reducing low physical activity is crucial for alleviating the burden of T2DM and its associated disabilities. Furthermore, the economic impact resulting from the increase in T2DM and related disabilities cannot be overlooked. Disabilities not only impose a heavy financial burden on individuals and families, including medical expenses, rehabilitation costs, and income loss due to decreased work capacity, but they also lead to additional healthcare expenditures and productivity losses for society as a whole. As the number of T2DM patients continues to rise, particularly in developing countries, this economic burden may further intensify, posing a serious challenge to public health systems and national economies. Consequently, when formulating prevention and management strategies for T2DM, it is essential to fully consider the important aspect of disability. By promoting physical activity, improving lifestyle choices, and providing timely medical interventions, we can effectively reduce the incidence of T2DM and its associated disabilities, thereby alleviating the economic burden on individuals, families, and society. Additionally, policymakers should focus on the social integration and employment issues of disabled individuals, providing the necessary support and assistance to ensure they can fully participate in social life and realise their self-worth.

Our study have some limitations: (1) We predict that the confidence interval for T2DM caused by LPA between 2019 and 2050 is large, which may bring about some statistical bias. (2) T1DM and other types of diabetes also have a high incidence globally, but due to lack of data, this study did not include the analysis of diabetes types other than T2DM. (3) In the compositional analysis, we mainly consider the impact of three major factors—population, age, and infectious diseases—on disease occurrence. However, the results cannot be determined solely by these three influencing factors; more social factors need to be considered. (4) Our results mainly discuss the regional distribution based on SDI. We are unable to elaborate on the results of each country, so we can only place the analysis results of different countries in the supplementary files.

In conclusion, this underscores the necessity for public health policymakers, decision-makers, and other stakeholders to focus on this issue and design and implement specific, effective policies to educate individuals about the adverse of physical inactivity and promote physical exercise to prevent T2DM [44].

CRediT authorship contribution statement

Shujin Fan and Jin Xu contributed to data acquisition,analysis, and interpretation and drafted and critically reviewed the manuscript for intellectual content. Jinli Wu contributed to revise the manuscript. Li Yan contributed to the conception and design of the study and critical review of the manuscript. Meng Ren is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility.

Consent for publication

All Authors approved to public the article.

Ethics approval and consent to participate

Not applicable.

Declaration of Generative AI and AI-assisted technologies in the writing process

Not applicable.

Funding

This work was supported by grants from National Natural Science Foundation of China (U20A20352, 82370822, 82470850), Guang Dong Clinical Research Center for Metabolic Diseases (2020B1111170009), Guangdong Basic and Applied Basic Research Foundation (2024A1515010503) and Guangzhou Science and technology projects (2024B03J1342).

Availability of data and materials

This study was performed in line with the principles of the Declaration of King Abdullah International Medical Research Center (KAIMRC). The Biomedical ethics committee in KAIMRC exempts this study from IRB due to public use data.

Declaration of competing interest

There is no competing interest among authors.

Acknowledgments

We acknowledge the IHME for providing the data for this article.

Footnotes

Appendix A

Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jnha.2024.100402.

Appendix A. Supplementary data

The following are Supplementary data to this article:

mmc1.docx (15.1KB, docx)
mmc2.xlsx (474.9KB, xlsx)
mmc3.pdf (490.8KB, pdf)
mmc4.pdf (575.1KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.docx (15.1KB, docx)
mmc2.xlsx (474.9KB, xlsx)
mmc3.pdf (490.8KB, pdf)
mmc4.pdf (575.1KB, pdf)

Data Availability Statement

This study was performed in line with the principles of the Declaration of King Abdullah International Medical Research Center (KAIMRC). The Biomedical ethics committee in KAIMRC exempts this study from IRB due to public use data.

Articles from The Journal of Nutrition, Health & Aging are provided here courtesy of Elsevier

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