This research utilized data from the GBD 2019. The GBD estimation methodology involves identifying multiple relevant data sources for each disease or injury, including censuses, surveys, civil registries, disease registries, health services, environmental monitors, imaging, notifications, and others. These are identified via systematic reviews of published studies, government/organizational websites, reports, and GBD collaborator datasets. Each newly identified and obtained source receives a unique identifier from the librarian team and inclusion into the Global Health Data Exchange (GHDx: https://ghdx.healthdata.org/). The GHDx publicly provides the metadata and available data for all sources used in GBD estimates, allowing to identify the specific sources used to estimate any disease/injury outcome (12, 13).

Our approach adhered to the methodological principles and analytic techniques specified in GBD 2019, which are elaborated in other publications (14). This initiative offered epidemiological insights and quantifications for 369 diseases and injuries in 204 countries and territories spanning from 1990 to 2019 (13). Utilizing comprehensive datasets across different ages, time periods, regions, and health categories, the GBD network employed uniform Bayesian methods to produce disease-specific estimates. This integration of diverse data sources with modeling techniques enabled the estimation of global disease burden across nations and regions despite gaps in population health information (12).

In the GBD 2019, risk factors are stratified into four tiers within a causality framework, ranging from the most general (Level 1, such as NCDs) to the most specific (Level 4, like type 2 diabetes), to gauge their impact on disease burden (15). For diagnosing type 2 diabetes, the primary benchmark is fasting plasma glucose levels above 126 mg/dl or ongoing treatment with medication or insulin for T2DM. Yet, in the GBD 2019, additional indicators such as oral glucose tolerance, and postprandial glucose tests were also recognized, albeit varying from the main diagnostic criteria (4). Consequently, these alternate diagnostic approaches, serving as data inputs, underwent adjustment before the start of the modeling phase. Physical inactivity, categorized as Level 2, was defined as less than 3,000-4,500 metabolic equivalent (MET) minutes per week, averaged across occupational, household, transport, and leisure activities. The MET minutes per week was calculated as follows: minutes of activity/day × days per week × MET level (16). Data on physical inactivity were compiled from 376 sources worldwide (17).

We sourced data on the impact of physical inactivity on type 2 diabetes globally from 1990 to 2019, by country, region, and gender, from the GHDx. These data encompassed 204 countries and territories, divided into five groups based on the sociodemographic index (SDI): high, high-middle, middle, low-middle, and low. SDI, a measure of regional development, is calculated from the total fertility rate among females under 25, education attainment for individuals aged 15 and older, and the lagged per capita gross domestic product, with values ranging from 0 (least developed) to 1 (most developed) (12). Furthermore, these countries were grouped into 21 geographical regions ( Table 1 ). For detailed analysis, age was segmented into 15 categories, including fourteen 5-year intervals from 25-94 years and one category for ages ≥95 years.

To assess the global, regional, and national burden of type 2 diabetes due to physical inactivity, we utilized metrics including deaths, age-standardized mortality rates (ASMR), DALYs, and age-standardized disability rates (ASDR). The estimated annual percentage change (EAPC) was employed to analyze the trends in age-standardized rates (ASR) from 1990 to 2019 (18). Trends in ASR were categorized as increasing, decreasing, or stable based on whether the EAPC and its 95% CI were greater than 0, less than 0, or included 0, respectively (19). These analyses were performed using the R software (version 4.0.3).

Globally, the number of type 2 diabetes deaths associated with physical inactivity markedly rose from 49.8 thousand (95% UI: 24.5-84.6) in 1990 to 125.2 thousand (95% UI: 62.1-208.3) in 2019 ( Table 1 , Figure 1A ). Despite this rise in absolute numbers, the global ASMR showed a slight increase from 1.5 (95% UI: 0.8-2.5) to 1.6 (95% UI: 0.8-2.7) per 100 000, with an EAPC of 0.26 (95% CI: 0.13-0.39), indicating a modest rise in the mortality rate over the three decades ( Table 1 , Figure 1C ).

There was a significant gender difference observed. The number of male fatalities attributed to type 2 diabetes due to physical inactivity increased from 18.9 thousand (95% UI: 8.5-33.8) in 1990 to 54 thousand (95% UI: 25.7-91.5) in 2019, and female deaths rose from 30.9 thousand (95% UI: 16-50.9) to 71.2 thousand (95% UI: 37-115.6). In males, the ASMR modestly increased from 1.4 to 1.6 per 100 000, whereas it remained relatively stable at 1.6 per 100 000 in females. A more pronounced EAPC was observed in males (0.51; 95% CI: 0.37-0.65) compared to females (0.09; 95% CI: -0.04-0.23), as shown in Table 1 .

Regarding the SDI, high SDI regions witnessed a decrease in ASMR from 1.2 (95% UI: 0.6-2) per 100 000 in 1990 to 0.9 (95% UI: 0.4-1.5) per 100 000 in 2019, reflecting a significant reduction in mortality with an EAPC of -1.48 (95% CI: -1.81–1.15). The high-middle SDI regions observed a relatively constant ASMR, shifting marginally from 1.4 (95% UI: 0.7-2.2) per 100 000 to 1.3 (95% UI: 0.7-2) per 100 000 over the period 1990-2019, with an EAPC of -0.2 (95% CI: -0.33–0.07). Conversely, the low-middle SDI regions experienced the most notable ASMR increase, from 1.9 (95% UI: 1-3.2) per 100 000 in 1990 to 2.5 (95% UI: 1.3-4.1) per 100 000 in 2019, and an EAPC of 1.07 (95% CI: 0.91-1.24) ( Table 1 , Figure 1C ). In the GBD regions, South Asia recorded the highest mortality in 2019 with 26.2 thousand deaths (95% UI: 13.1-43.7). The top ASMR in 2019 were in Oceania (11 per 100 000; 95% UI: 5.3-19.3) and Southern Sub-Saharan Africa (6.7 per 100 000; 95% UI: 3.2-11.2) ( Table 1 , Figure 2A ). ASMR showed declining trends in regions like High-Income Asia Pacific and High-Income North America, while remaining stable in East Asia. However, ASMR were on the rise in Southern Sub-Saharan Africa and Central Asia, with Central Asia experiencing the sharpest increase (EAPC: 3.78; 95% CI: 3.47-4.08) ( Table 1 , Figure 2B ).

Nationally, India recorded the highest count of type 2 diabetes fatalities attributable to physical inactivity, with numbers reaching 5238.9 (95% UI: 2483.5-9416) in 1990 and escalating to 19070.6 (95% UI: 9589.6-32291.6) by 2019. Qatar had the most elevated ASMR in 1990 at 19.3 (95% UI: 11.1-29) per 100 000, while Fiji reported the highest in 2019 at 29 (95% UI: 14.8-49.6) per 100 000. Conversely, Guatemala exhibited the lowest ASMR in 1990 (0.1 per 100 000; 95% UI: 0.1-0.4), and Ukraine had the lowest in 2019 (0.1 per 100 000; 95% UI: 0.1-0.2). Among 121 countries that experienced an increase in ASMR, Uzbekistan showed the most rapid growth (EAPC: 5.85; 95% CI: 5.26-6.44) ( Figures 2A, B ; Supplementary Table 1 ).

In 2019, the pattern of type 2 diabetes deaths due to physical inactivity in different age groups formed an inverted V-shape, with its peak in the 80-84 age group. The majority of these deaths were among individuals aged 60-89, predominantly in the middle and low-middle SDI regions ( Figure 1E ). Mortality rates in different age groups for patients with type 2 diabetes due to physical inactivity escalated with advancing age, particularly post the age of 80 ( Figure 1G ; Supplementary Figure 1A ). On a global scale, the EAPC in mortality rates exhibited an inverted U-shaped relationship with age. Negative EAPC values were observed in the 25-29 and 70-74 age groups, while positive values were seen in the 30-64 and 75+ age groups. The sharpest decline in mortality was in the 25-29 age group, and the most significant increase was in the 40-44 age group. However, in low SDI regions, mortality rates rose consistently across all age groups ( Supplementary Figure 1B ).

Globally, the number of DALYs for type 2 diabetes associated with physical inactivity more than doubled from approximately 1.7 million (95% UI: 0.8-3.1) in 1990 to 4.5 million (95% UI: 2.2-8.0) in 2019 ( Table 1 , Figure 1B ). Over the same period, the ASDR rose from 45 (95% UI: 21.3-79.5) per 100 000 to 55.9 (95% UI: 27.2-97.6) per 100 000, with an EAPC of 0.84 (95% CI: 0.78-0.89) ( Table 1 , Figure 1D ).

A notable gender disparity was also observed for DALYs and ASDR. In males, DALYs increased from 691.5 thousand (95% UI: 288.5-1292.4) in 1990 to 2.0 million (95% UI: 0.9-3.7) in 2019. Over the same timeframe, female DALYs rose from 1.0 million (95% UI: 0.5-1.8) to 2.5 million (95% UI: 1.3-4.3). The ASDR slightly increased from 40.5 per 100 000 (95% UI: 17.9-73.9) to 54.3 per 100 000 (95% UI: 25-97.6) in males, while rising at a slower pace in females from 49.1 (95% UI: 24.1-84.6) per 100 000 to 57.5 (95% UI: 28.6-98.4) per 100 000. Males demonstrated a higher EAPC (0.99; 95% CI: 0.93-1.05) than females (0.73; 95% CI: 0.66-0.79) ( Table 1 ).

Across SDI regions, high SDI areas exhibited a rise in DALYs from 420.3 thousand (95% UI: 187.2-765.9) in 1990 to 879 thousand (95% UI: 410.8-1574.4) by 2019. The ASDR also rose from 40.5 (95% UI: 17.8-74.8) per 100 000 to 49.7 (95% UI: 23-91.8) per 100 000 over the same timeframe, with an EAPC of 0.63 (95% CI: 0.54-0.73). High-middle SDI regions exhibited rises in DALYs from 445.7 thousand (95% UI: 213.5-759) in 1990 to 963.1 thousand (95% UI: 481.4-1662.4) in 2019, and in ASDRs from 43 (95% UI: 21.1-72.5) per 100 000 to 47.6 (95% UI: 23.8-81.9) per 100 000, with an EAPC of 0.39 (95% CI: 0.29-0.49). Similarly, the middle, low-middle, and low SDI regions all exhibited upward trends, with the fastest ASDR increase observed in low-middle SDI regions (EAPC: 1.19; 95% CI: 1.09-1.29) ( Table 1 , Figure 1D ). Among GBD regions, the highest number of DALYs in 2019 occurred in South Asia with 783.8 thousand (95% UI: 374.1-1426.2), followed by North Africa and Middle East with 685 thousand (95% UI: 356.4-1111.5). The highest ASDR in 2019 shifted to Oceania (281.4 per 100 000; 95% UI: 125.3-525.6) and the Caribbean (184.1 per 100 000; 95% UI: 93.5-310.6).Considering the changes of ASDR in different regions over time, ASDR showed declining trends in regions including East Asia and High-Income Asia Pacific, while remaining relatively stable in Eastern Sub-Saharan Africa. However, ASDRs rose in other regions like Southern Latin America and Central Asia, with Central Asia having the fastest increase (EAPC: 3.18; 95% CI: 3.01-3.36) ( Table 1 , Figure 2D ).

Nationally, India recorded the highest DALYs in 1990, totaling 194 thousand (95% UI: 86.5-355.8), while in 2019, India and China emerged as the leading countries in DALYs with 576.3 and 452.1 thousand, respectively. Trinidad and Tobago had the highest ASDR in 1990, at 447.2 (95% CI: 221.3-746) per 100 000, with Fiji leading in 2019, at 672.7 (95% CI: 317.8-1188.4) per 100 000. Guatemala and Mongolia consistently showed the lowest ASDR, recording 4.9 in 1990 and 11.4 in 2019 per 100 000. There were increases in ASDR trends in 157 countries and territories from 1990 to 2019, while 21 countries and territories exhibited downward trends in ASDR ( Figures 2C, D ; Supplementary Table 1 ).

In 2019, the number of DALYs for type 2 diabetes due to physical inactivity in different age groups reflected the observed pattern in mortality rates, peaking in the 65-69 age group ( Figure 1F ). In high and high-middle SD) regions, DALY rates consistently rose with increasing age. In contrast, in middle, low-middle, and low SDI regions, these rates initially surged until the age group of 80-84, followed by a slight variation in individuals aged over 85 ( Figure 1H ; Supplementary Figure 2A ). The trend in the EAPC of DALY rates across different ages paralleled the EAPC trends seen in mortality rates ( Supplementary Figure 2B ).

Between 1990 and 2019, the global numbers of deaths and DALYs from type 2 diabetes associated with physical inactivity was higher among females than males ( Figures 3A, B ). While the ASMR was consistently greater in females, this disparity between male and female ASMR narrowed over the 30-year period. The 2019 data on deaths and DALYs in different age groups due to type 2 diabetes related to physical inactivity are depicted in Figures 3C, D . Similarly, the death and DALY counts were higher in females. Generally, the death rate for type 2 diabetes due to physical inactivity showed an upward trend for both sexes, with the DALY rate significantly increasing in the 75-84 age bracket, but stabilizing after 85 years of age.

The relationship between the ASMR for type 2 diabetes due to physical inactivity and the SDI values in various GBD regions formed an M-shape. The ASMR initially rose until reaching an SDI of around 0.43, after which it declined as SDI values increased. In 2019, the EAPC in ASMR for type 2 diabetes due to physical inactivity showed a slight negative correlation with SDI, especially when SDI exceeded 0.65. Over the span from 1990 to 2019, more than half of the 204 countries analyzed displayed an upward ASMR trend (EAPC and 95% CI above 0) ( Figure 4 ). A similar pattern was noted in the relationship between the age-standardized DALY rate, SDI values, and EAPC in the age-standardized DALY rate ( Figure 5 ).