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. 2025 Feb 21;41(2):e70036. doi: 10.1002/dmrr.70036

Temporal Trends in the Burden of Diabetes and Its Risk Factors Across the Western Pacific Region Between 1990 and 2044: A Systematic Analysis of the Global Burden of Disease Study 2019

Xinwei Liu 1, Bing Wu 1, Yongqiang Lai 1, Xiyu Zhang 1,2, Hongyu Li 1, Fangqi Qu 1, Chenxi Zhang 1, Yulu Tian 1, Xuelian Fu 3, Jida Li 1, Huiqi Yang 1, Rui Chen 1, Baoguo Shi 4, Yanan Ma 5, Jia Meng 6,✉, Ye Li 1,7,✉
PMCID: PMC11845174  PMID: 39983069

ABSTRACT

Aims

We aimed to assess changes in the burden of diabetes in the Western Pacific region (WPR) between 1990 and 2019, project future trends for 2020–2044, and identify the factors influencing these trends.

Materials and Methods

Data from the Global Burden of Disease Study 2019 were used to calculate the age‐standardised incidence rate (ASIR), age‐standardised death rate (ASDR) and age‐standardised disability‐adjusted life years (DALYs) rate for diabetes in the region. The Nordpred model was used to predict diabetes‐related ASIR and ASDR trends over the next 25 years, and an age‐period‐cohort (APC) model analysed the effects of age, period and cohort. We examined the associations of ASIR, ASDR and age‐standardised DALYs rate per 100,000 population with the socio‐demographic index (SDI).

Results

We observed an increasing trend in the incidence. Mortality increased in the lower‐middle income group and decreased slowly in the high‐ and upper‐middle income groups. High body mass index significantly affected diabetes, with an increasing influence over time, whereas that of tobacco showed a decreasing trend. The incidence of diabetes showed a trend towards occurring at a younger age, in a manner consistent with the economic development trend. Diabetes incidence and mortality showed the opposite trend in the high‐income group, with an increase in SDI.

Conclusions

The burden of diabetes is increasing in the WPR, in association with urbanisation and unhealthy lifestyles. Targeting the risk factors that affect all stages of the disease and managing them through multi‐agency collaboration may improve the quality of life in patients living with the condition.

Keywords: age‐period‐cohort analysis, diabetes, global burden of disease, Western Pacific region

1. Introduction

Diabetes is a global public health issue that places heavy burdens on public health and socio‐economic development [1]. The International Diabetes Federation (IDF) estimates that 537 million adults (aged 20–79 years) worldwide will have diabetes by 2021, resulting in a global health expenditure of $966 billion USD. The number of people living with diabetes is projected to increase by 46% by 2045 [2]. Thus, diabetes represents a steadily growing global health concern. The global community has invested joint efforts to contain diabetes epidemics, through the Global Diabetes Compact in 2021 and the first‐ever global coverage targets for diabetes adopted by the World Health Organization in 2022 [3, 4]. However, diabetes is a disease characterised by a complex aetiology and significant regional differences in its distribution, often associated with multiple complications and long treatment cycles [5, 6], resulting in a substantial disease burden. Therefore, it is urgent to implement targeted interventions based on regional differences to mitigate this burden.

Globally, the disease burden of diabetes shows significant spatial heterogeneity. For instance, the Middle East and North Africa, North America, the Caribbean and Western Pacific region (WPR) have higher prevalence rates of diabetes, whereas Africa and Southeast Asia exhibit lower rates [7]. The 10th edition of the IDF Diabetes Atlas reports that one in eight adults in the WPR has diabetes, totalling 206 million individuals. The WPR currently has the highest number of diabetes‐related deaths (2.3 million) among all IDF regions [8]. The WPR has become the hardest hit area in terms of diabetes [9], as it includes many countries with high incidences (e.g., Malaysia and Vietnam) [10]. Most existing studies on the matter have been conducted on regions (e.g., Europe and Africa) [11, 12], and on a few individual countries (e.g., Brazil, China and Korea) [13, 14]. There is a lack, however, of spatial‐regional studies in the WPR. The high levels of economic, cultural and health‐related diversity in the WPR have led to significant differences in terms of diabetes burden between different countries in the region. Most existing studies on the matter have focused on past and present diabetes trends [15, 16], but few have focused on future predictions. Studies on the persistence of diabetes over time have generally analysed fragmented and isolated periods. Studies have used an age‐period‐cohort (APC) model to analyse the trends in the incidence and mortality of diabetes in China and India [17], but few have focused on incidence, mortality and disability‐adjusted life years (DALYs) diabetes trends from a full‐cycle perspective.

In this context, this study used data from the Global Burden of Disease Study 2019 (GBD 2019) to analyse the temporal trends and influencing factors of diabetes in six WPR countries. We used an expanded time period and explored the associated risk factors and mechanisms of action in terms of morbidity, mortality and disability. This enabled the dynamic capturing of diabetes across time and space, and a thorough characterisation of which people in the region are most at risk of developing diabetes across different points in the disease cycle. Improving the morbidity of diabetes in the WPR will significantly reduce its global burden, and provide a reference for other regions.

2. Methods

2.1. Data Source

The GBD 2019 project collected data through a systematic assessment of censuses, household surveys, disease registries, environmental surveillance and other sources [18]. This project estimated the incidence, mortality and DALYs for 369 diseases and injuries, and 87 associated risk factors, in 204 countries and territories, between 1990 and 2019. Previous studies have shown that the occurrence of diabetes is associated with socio‐economic development [19]. According to the World Bank 2022 criteria [20], countries in the WPR were grouped into high‐income, upper‐middle‐income and lower‐middle‐income groups. Countries with a high incidence of diabetes and relatively good institutions were selected from each economic subgroup. The final countries included in the study were Japan and New Zealand in high‐income countries, China and Malaysia in upper‐middle‐income countries and the Philippines and Vietnam in lower‐middle‐income countries (Table 1). We analysed indicators from the GBD 2019 such as incidence, mortality, DALYs and risk factors for diabetes across the six selected countries.

TABLE 1.

Countries included in the study.

Group Country
High income Japan, New Zealand
Upper‐middle income China, Malaysia
Lower‐middle income Philippines, Vietnam
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2.2. Model Selection

The APC model is a statistical analysis method widely used in demography, sociology, economics and epidemiology [21]. Our APC analysis revealed three types of time‐varying phenomena: age, period and cohort effects. The age effect is a variation linked to the biological and social processes of ageing that is specific to individuals. This usually indicates the different risks associated with different age groups. The period effect represents the variation in risk across all age groups within different periods. The cohort effect denotes the impact on an individual or group owing to differences in birth year or time spent experiencing an event [22]. Given the specificity of the model, it is required that the data for the age and period groups be structured identically, both at 5‐year intervals. However, when using 5‐year averaged data, individuals in a 5‐year age group within a 5‐year period were ultimately born in a 10‐year range, resulting in a halving of the temporal resolution and masking some significant variations in cohort effects [23]. In contrast, using data with 1 year selected every 5 years ensures that all individuals in the 5‐year age group in 1 year were born within the past 5 years, thereby improving the accuracy of birth year matching and measurement precision [24]. Therefore, this study divided participants into 18 groups and selected 1 year every 5 years for research. Age‐specific diabetes incidence, mortality and DALYs data of the population were analysed for the years 1994, 1999, 2004, 2009, 2014 and 2019. APC analysis was performed by using the apc_ie package for STATA, version 15.1 (StataCorp, College Station, TX, USA).

A number of methods exist for calculating and predicting disease burden. Nordpred was developed to meet the requirements of long‐term predictions based on the APC model [25]. Nordpred represents a well‐established prediction and estimation method. Therefore, we chose to use Nordpred to predict future trends in diabetes burden. Population estimates and standard population structures were obtained from the United Nations World Population Prospects 2019 Revision (https://population.un.org/wpp/) and the World (WHO 2000–2025) Standard (https://seer.cancer.gov/stdpopulations/world.who.html) [26]. The calculation was based on the Nordpred package for R, version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria).

2.3. Selection of Influencing Factors

The GBD 2019 estimates attributable mortality, years of life lost, years lived with disability and DALYs for 87 risk factors and combinations of risk factors globally, regionally, and for 204 countries and territories [27]. The 87 risk factors in the GBD study were divided into four major categories: environmental and occupational, behavioural, metabolic and dietary risks [28]. The risk assessment framework of the GBD 2019 selected level 2 risk factors associated with mortality and DALYs of diabetes. The risk factors for diabetes in the GBD 2019 were air pollution, alcohol use, dietary risk, high body‐mass index (BMI), high fasting plasma glucose, low physical activity, non‐optimal temperature and tobacco. In previous studies, the risk factors for diabetes were age, sex, dietary risk, high BMI, high fasting plasma glucose, low physical activity and tobacco smoking [18, 29, 30]. Based on the eight risk factors provided by the GBD 2019 database combined with risk factors from previous studies, we excluded alcohol use and high fasting plasma glucose from this list. We therefore included six indicators in our risk factor analysis for this study: air pollution, dietary risk, high BMI, low physical activity, non‐optimal temperature and tobacco.

3. Results

3.1. Past, Present and Future Trends in Diabetes Burden Between Different Economic Groups

Figure 1A and Table S1 show the trends in age‐standardised incidence rate (ASIR) of diabetes by region between 1990 and 2044. Between 1990 and 2019, the ASIR of diabetes in the WPR showed an overall upward trend from 200.89/100,000 to 228.12/100,000, and is projected to reach 248.03/100,000 by 2044. The high‐income group in WPR exhibited a similar trend in terms of diabetes ASIR, which was lower than the global and WPR averages. In the high‐income group, Japan showed a significantly higher ASIR in males compared with females. Malaysia, an upper‐middle‐income country, maintained the highest ASIR for diabetes across the study districts. In Vietnam, which is a lower‐middle‐income country, the ASIR for diabetes increased continually. By 2044, the ASIR of diabetes in Malaysia and Vietnam are projected to reach 391.24/100,000 and 387.95/100,000, respectively—which are higher than those of the other regions in this study.

FIGURE 1.

FIGURE 1

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ASIR (A) and ASDR (B) of diabetes per 100,000 population in different regions between 1990 and 2044. ASIR, age‐standardised incidence rate; ASDR, age‐standardised death rate.

Trends in age‐standardised death rate (ASDR) for diabetes by region are shown in Figure 1B and Table S2. From the past to the future, the ASDR of diabetes showed a downward trend in the WPR, and an upward trend globally. Within the WPR, the high‐ and upper‐middle income groups were consistent with the overall WPR trends, while the lower‐middle income group showed an increasing trend. Japan, a high‐income country, had the lowest ASDR for diabetes across the study area. Although Malaysia had the highest ASIR, its ASDR showed a substantial decline (from 25.89/100,000 to 12.86/100,000) over the three periods of 2000–2004, 2005–2009 and 2010–2014. Based on this, the ASDR of Malaysia was significantly higher than that of China, which was in the upper‐middle income group. The lower‐middle income group had a consistently higher ASDR of diabetes than the global and WPR averages—particularly in the Philippines, which had the highest ASDR (at 49.64 per 100,000).

3.2. Full‐Cycle Description of the Disease Burden at Incidence, Exacerbation or Delay, and Diabetes‐Related Death

Figure 2A and Tables S3–S5 show our APC analysis results for the incidence of diabetes over the entire region, and in the six individual WPR countries. In the age effect analysis, China's age effect coefficient was the first to exceed 0, and age became a risk factor for diabetes in the country beginning at 15–19 years of age. Malaysia's age effect coefficient was > 0 at age 20–24, which was in line with that of the overall WPR and the global value. The age effect coefficient of the Philippines was > 0 at 25–29 years of age. Age in Japan, New Zealand and Vietnam was the latest to become a risk factor, at 30–34 years.

FIGURE 2.

FIGURE 2

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Effects of age, period and cohort on the incidence of diabetes in different regions (A) and the ASIR trends of diabetes in three income groups by SDI between 1990 and 2019 (B). (A) Error bars represent the 95% confidence intervals. (B) High income (left panel 1), upper‐middle income (left panel 2) and lower‐middle income (left panel 3) groups. The expected values based on SDI and disease incidence in all locations are shown as the black line. The shaded regions represent the 95% confidence intervals. ASIR, age‐standardised incidence rate; SDI, socio‐demographic index.

The age effect coefficients of China and the overall WPR peaked at 45–49 and 50–54 years of age, respectively, which were earlier than the global age effect coefficient peak. The Japanese and global age effect coefficients both reached their maximum values at 55–59 years. Compared to the global age effect coefficient, those of Malaysia, the Philippines and Vietnam peaked at 65–69 years—a delay of 10 years. The latest age effect coefficient peak occurred in New Zealand, at 75–79 years—a full 20 years after the global age effect coefficient peak. For both the period and cohort effects, the three economic subgroups exhibited the same trend, with the risk of diabetes onset increasing over time and decreasing in the birth cohort.

The relationships between the ASIR and socio‐demographic index (SDI) in the six countries between 1990 and 2019 are shown in Figure 2B. Over this period, the global SDI and ASIR values showed trends of steady growth. The upper‐middle and lower‐middle income groups consistently followed a global trend, whereas the high income group exhibited heterogeneity and non‐stationarity. The relationship between SDI and ASIR in New Zealand was defined by an SDI equal to 0.8 as the cut‐off point. When it was below this threshold, it exhibited an inverted ‘U’ shape. When it was > 0.8, ASIR increased rapidly. There were two inflection points in the relationship between SDI and ASIR in Japan. When the SDI was 0.825, the ASIR decreased while when it was 0.85, the ASIR increased.

Data from the GBD 2019 were extracted to analyse the contributions of the different risk factors to age‐standardised death and DALYs for diabetes (Figures 3A and 4A, Tables S10 and S11). In the different economic groups, the risk factors for death and DALYs were mainly manifested in air pollution, dietary risk, high BMI, low physical activity, non‐optimal temperature and tobacco. Among these factors, the contribution of high BMI to diabetes showed an upward trend in all of the countries. The contribution of high BMI to deaths increased by 17.3% in the Philippines and to DALYs by 20.1% in Malaysia. The contribution of tobacco as an influencing factor for death and DALYs in patients with diabetes exhibited a decreasing trend.

FIGURE 3.

FIGURE 3

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Percentage of diabetes age‐standardised deaths attributable to six risk factors in study regions in 1990 and 2019 (A), effects of age, period and cohort on diabetes mortality in different regions (B) and trends in ASDR of diabetes in the three income groups by SDI between 1990 and 2019 (C). (B) Error bars represent the 95% confidence intervals. (C) High income (left panel 1), upper‐middle income (left panel 2) and lower‐middle income (left panel 3) groups. The expected values based on SDI and disease mortality in all locations are shown as the black line. The shaded regions represent the 95% confidence intervals. ASDR, age‐standardised death rate; SDI, socio‐demographic index.

FIGURE 4.

FIGURE 4

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Percentage of diabetes age‐standardised DALYs attributable to six risk factors in study regions in 1990 and 2019 (A), effects of age, period and cohort on DALYs of diabetes in different regions (B) and trends in age‐standardised DALYs rate of diabetes in the three income groups by SDI between 1990 and 2019 (C). (B) Error bars represent the 95% confidence intervals. (C) High income (left panel 1), upper‐middle income (left panel 2) and lower‐middle income (left panel 3) groups. The expected values based on SDI and disease DALYs rate in all locations are shown as the black line. The shaded regions represent the 95% confidence intervals. DALYs, disability‐adjusted life years; SDI, socio‐demographic index.

The results of our APC analyses of mortality and DALYs in the study area are represented in Figures 3B and 4B, and Tables S4, S6–S9. In terms of mortality, the age‐effect coefficients of the studied regions showed continuous upward trends after the 15–19 age group. The age effect coefficients for Japan, New Zealand, Malaysia, Vietnam and globally were between 45 and 49 years, and age was a risk factor for diabetes‐related mortality. At the age of 50–54 years, age became a risk factor for diabetes‐related mortality in China, the Philippines and the WPR as a whole.

In terms of DALYs, the age effect coefficients of the high income group showed initial upward trends, and the age effect coefficients in Japan and New Zealand were > 0 at the ages of 35–39 and 40–44 years, respectively. Age was identified as a risk factor for diabetes DALYs. China, Malaysia and Vietnam showed an upward trend after an inflection point at the age of 5–9. At the age of 35–39, Malaysia's age effect coefficient was > 0, while those of China and Vietnam did not reach above 0 until age 40–44. The Philippines, WPR and the global regions all had a point of inflection at age 10–14, and the Philippines' age effect coefficient was > 0 at age 50–54—delayed by 10 years compared to the WPR and the global values.

Analyses of the relationships among diabetes ASDR, age‐standardised DALYs rate and SDIs in the six countries between 1990 and 2019 are shown in Figures 3C and 4C. The relationship between ASDR and SDI showed heterogeneity among the three economic subgroups. The ASDR of the high income group showed the opposite trend, with an increasing SDI. Malaysia's ASDR showed a significant downward trend when the SDI was 0.65, and its ASDR slowly increased when the SDI was 0.7. China's ASDR showed a relatively stable trend with SDI changes. The relationship between age‐standardised DALYs rate and SDI was similar to that between ASDR and SDI.

4. Discussion

The WPR has the most patients with diabetes worldwide, and the overall burden of diabetes continues to increase in this region. The incidence of diabetes is increasing year over year in the WPR. In Malaysia and Vietnam, the incidence of diabetes will likely continue to increase over the period spanning 1990–2044. Vietnam is projected to experience the most significant increase in the incidence of diabetes, from 165.51/100,000 in 1990 to 387.95/100,000 in 2044. Diabetes‐related mortality showed an upward trend in the lower‐middle income group, among which the Philippines had the most significant projected future increase. Diabetes‐related mortality showed a decreasing trend in the high‐ and upper‐middle income groups. In our attribution analysis, high BMI was the dominant factor leading to death and DALYs of diabetes, and was the only factor that aggravated the impact on death and DALYs. In contrast, tobacco reduced the impact on death and DALYs. Our APC results showed that the exposure risk of the incidence of diabetes presented a significant trend in the younger age groups. The incidence of diabetes in the WPR changed in the same direction as the overall SDI. The diabetes‐related mortality of the high income group decreased with increasing SDI. The incidence of diabetes increased with increasing SDI in the high income group.

4.1. The Future Burden of Diabetes Remains Significant

4.1.1. The Projected Future Incidence of Diabetes Continues to Rise

The number of patients with diabetes in WPR ranks among the highest worldwide. Against this backdrop, the incidence of diabetes in the WPR will continue to grow now and in the future, bringing a more severe disease burden. The WPR has witnessed an economic boom, and the region is experiencing rapid economic growth, with gross domestic product (GDP) per capita increasing from $1435 USD in 1980 to $21,242 USD in 2021, an increase of ∼14.8‐fold [31]. Rapid development has contributed to improved healthcare policies, medical technology and increased health literacy, all of which have had a positive impact on diabetes detection. However, rapid economic development comes with costs such as the destruction of the natural environment and the adoption of unhealthy lifestyles [32]. The continued increase in such exposure factors has led to an increase in the number of susceptible populations, with a significant upward trend in the incidence of diabetes. Among them, Malaysia and Vietnam have shown the most significant increases in diabetes incidence. The high incidence of diabetes in Malaysia may be related to its lifestyle and food culture. According to the National Health and Morbidity Survey Report (NHMS) for 2019 in Malaysia, 1/2 of the adults had overweightness or obesity and 1/4 were physically inactive [33]. Moreover, there has been a surge in the incidence of diabetes in Vietnam, which has been validated by key studies [16, 34]. Khan et al. analysed the global incidence of type 2 diabetes and reported an increasing worldwide trend [35]. In contrast, our study systematically analysed the reasons for the increasing incidence trend in Vietnam, from macro‐ to micro‐health service utilisation. This trend may be related to the relatively slow response to chronic disease management, the late establishment of a national prevention and control system, and the need to strengthen the concept of disease prevention in Vietnam. For example, Vietnam's regulations on the management of noncommunicable diseases in primary healthcare only began in 2018 [36]. In addition, accessibility to healthcare resources remains limited in the country.

In this study, the incidence of diabetes in the WPR was observed to be increasing, but a few areas—such as the United States and France—have been successful in kerbing it [37, 38]. The United States covers a wider population in terms of health education, while France focuses on the development of preventive exercises and other public health interventions such as the 2013 Sugar Act. Based on the measures taken by these countries, the WPR should aim to promote prevention‐oriented approaches and all‐round health interventions for the people of the region. The WPR should make full use of the National Disease Surveillance System, and implement effective and economical interventions to prevent the occurrence of diabetes—such as improving the management of dietary structures and promoting physical activity.

4.1.2. High and Upper‐Middle Income Countries Are the First to Reach an Inflection Point in the Decline of Diabetes‐Related Mortality, While Lower‐Middle Income Ones Continue to Experience Rising Diabetes‐Related Mortality Rates

The mortality rate of diabetes in the lower‐middle income group continued to increase, which is consistent with previous research findings [1, 39, 40]. This study analysed the reasons for this outcome from both horizontal and vertical perspectives. Examining the longitudinal progression within the lower‐middle income group, it appeared to be related to untempered urbanisation and westernisation during economic development, leading to increased environmental risks and lifestyle changes. Moreover, when comparing different income groups horizontally, an increasing trend was observed to be associated with underdeveloped health systems and the use of older medicines and technologies for treating diseases. Among these, the mortality burden of diabetes in the Philippines is expected to increase at the most significant rate in the future, and maintain its high mortality rate. The main reasons for this are the lack of health resources and the low levels of healthcare accessibility and quality. The Philippines Healthcare Access and Quality index was 31 in 1980 and 46 in 2019 [41], remaining at a low level. This results in poor access to healthcare services among patients with diabetes, leading to increased mortality. Mortality from diabetes in the high‐ and upper‐middle income groups showed a downward trend, but the trend was relatively flat. Among high income groups, Japan had the lowest diabetes‐related mortality rate. Compared with other countries, Japan has carried out extensive and sustained health management interventions. It implemented the ‘Healthy Japan 21’ plan, which started with health management and set 79 goals in 9 areas—including diet, physical activity, and diabetes—and was implemented by multiple actors—including the government, schools and health personnel. The ‘Healthy Japan 21 (second term)’ plan was then launched to further expand this health management system to cover the whole life cycle from infancy to old age [42]. These health plans were broad interventions that were followed over time. Thus, Japan is in a leading position regarding drug development. As early as 2015, Japan launched the world's first once‐weekly oral long‐acting hypoglycaemic drug, Trelagliptin [43]. This glucose‐lowering agent reduces the number of insulin doses needed, and can improve treatment adherence compared with other once‐daily glucose‐lowering agents [44]. Diabetes mortality in Malaysia, an upper‐middle income country, declined significantly between 2000 and 2014, but remained significantly higher than that in China (which is in the same income group). In contrast, the sharp decline in mortality in a given period may be the result of the positive impact on diabetes prevention and control caused by the cumulative effects of measures such as governance of public spaces, shifts in the perspective of health management, and shifting of the prevention gateway over time. For example, school nutrition programs were introduced in the 1970s, smoking was banned in public places and places of worship, and in the mid‐1990s, the Chronic Care Model was developed and the healthcare system shifted to a patient‐centred model [45, 46]. However, the mortality rate remains high. This is associated with a higher rate of obesity or overweightness, lack of physical activity, and poorer adherence to health management in patients with diabetes.

From the above, it can be seen that there are gaps in the lower‐middle income group, such as imperfect health management systems and a lack of health resources. Lower‐middle income countries should learn from well‐developed national health policies, health education, and health systems in high income ones. However, the lower‐middle income group is constrained by lagging economic development, making it more difficult to quickly form a comprehensive health management system. Therefore, the rational allocation of scarce health resources is the quickest and most effective way to kerb the rise in diabetes‐related mortality. On this basis, the healthcare system should be gradually improved, hospital management should be strengthened, the efficiency of the use of medical resources should be improved, and the problem of limited health resources should be alleviated.

4.2. Incidence of Diabetes Poses a Younger Trend

The age effect coefficient for the onset of diabetes showed that multiple age groups in the WPR were affected by diabetes. Age 20–24 years was observed to mark the start of age as a risk factor for diabetes. The incidence of diabetes in the WPR showed a youthful trend, which is consistent with the increasing trend of diabetes incidence in younger age groups observed by Sun et al. [47, 48, 49] We focused on the WPR, which has a heavier burden of diabetes than the rest of the world and systematically analysed the causes behind this youthful trend at both societal and individual levels. The WPR is rapidly urbanising, with a rise of 2.3× between 1975 and 2020 in the proportion of the population living in cities in the region. Rapid urbanisation has been accompanied by the emergence of unhealthy lifestyles, and obesity has become a prominent manifestation in people affected by such lifestyles. By 2035, the rate of obesity in adults is expected to double, increasing from 8% to 19% in males and from 9% to 16% in females [50]. Obesity poses a dual threat to both societal development and public health. The trend towards disease risk in younger individuals is even more pronounced in China, which is an upper‐middle income country. This may be attributable to rapid economic development. China's GDP grew 2.6‐fold between 2007 and 2015. Meanwhile, the total volume of industrial emissions increased by a factor of 1.8× [51], significantly impacting the environment. Coupled with the fact that young people are responsible for managing their families, society and work, they experience increased stressors and are prone to sleep deprivation, sedentary behaviour and irregular diets—all of which can contribute to the development of diabetes.

To improve the current state of diabetes becoming younger in onset, it must be managed in a phased manner. A supportive health environment is created through health promotion, and basic prevention is conducted on multiple levels—including the environment, education and employment. Through primary prevention targets, modifiable or preventable behavioural factors such as unhealthy diets, tobacco use and physical activity can be used to control the risk factors for and reduce the incidence of diabetes. Individuals and patients at high risk of developing diabetes can be screened, controlled, and treated through secondary and tertiary prevention, thus reducing diabetes DALYs.

4.3. SDI has a Bidirectional Effect on Incidence and Mortality in High Income Countries

When SDI was associated with the incidence of diabetes, it varied in the same direction as SDI in the WPR. Diabetes is caused by a variety of factors. With the rapid societal development, resources are overconsumed, ecology can be threatened and living environments may change dramatically. As a result, lifestyles have become individualised, diverse and complex [52]. The difficulty in preventing diabetes and the increase in its exposure factors make it difficult to comprehensively prevent and manage diabetes in a timely manner.

When linking SDI to diabetes‐associated mortality, it varied in the opposite direction to the SDI within the high income group, with the Universal Health Coverage Service Coverage Index at 83 in Japan and 85 in New Zealand in 2021 [53]. High income countries generally have a higher capacity to provide basic healthcare. Japan has access to advanced diabetes medications and well‐established health management practices, while New Zealand provides nutritional counselling and psychological support for patients. Thus, these two countries have reduced the loss of years of life caused by diabetes within their borders. SDI changes in the same direction as diabetes incidence in the high income group, but in the opposite direction to mortality, reflecting the two‐way effect of SDI on diabetes in such countries. Therefore, to kerb the negative effect of SDI on diabetes incidence, governments should perfect their health management systems and institutes, and improve healthcare facilities. Society must use public opinion to improve health literacy, and individuals should adopt healthy lifestyles that include healthy diets and moderate levels of physical activity. These strategies have the potential to decrease the incidence of diabetes and diabetes‐related complications.

This study was subject to several key limitations worth noting. First, the data used were all from the GBD 2019 database; therefore, the data source was singular and the secondary data did not fully reflect certain trends. Second, the risk factors in the GBD 2019 study were influenced by the availability and quality of the primary data. This may result in certain risk factors not being included in the analysis, or the data on the risk relationships of some risk factors being sparse, leading to their omission from the analysis. Third, the absence of a typing study on diabetes may confound the relationship between diabetes and other factors, and there may be a few biases introduced when distinguishing causal effects from confounding effects.

5. Conclusion

The incidence of diabetes has continued to increase in the WPR. Although its mortality rate has decreased, this trend has remained relatively flat. However, the disease burden has not yet been alleviated. We analysed the factors that influence it, which indicated that the main ones were the rapid ageing of the population, economic growth, rapid urbanisation, environmental pollution and unhealthy lifestyles. These factors can be effectively controlled. This study will hopefully provide a theoretical basis for the better prevention and management of diabetes, and effectively reduce the significant disability and complications associated with it.

Author Contributions

Conceptualisation: Y. Li and B.S. Statistical analysis: X.Z., J.L. and H.L. Analysis and interpretation of data: F.Q., C.Z. and Y.T. Writing–original draft: X.L., B.W. and Y. Lai. Critical revision of the manuscript for important intellectual content: Y. Li, Y.M. and J.M. Funding acquisition: Y. Li. Visualisation and acquisition of data: J.M., X.F., H.Y. and R.C.

Ethics Statement

The authors have nothing to report.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Peer Review

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1002/dmrr.70036.

Supporting information

Supporting Information S1

DMRR-41-e70036-s001.docx (197.1KB, docx)

Acknowledgements

We thank the Global Burden of Disease Study 2019 for providing publicly available data. Each author contributed to the concept, design, research, data analysis and drafting of the article. I have obtained written permission from all authors.

Funding: This work was supported by the National Natural Science Foundation of China (72174047, 71874045) and Heilongjiang Provincial Natural Science Foundation of China (LH2021G015).

Xinwei Liu, Bing Wu, and Yongqiang Lai contributed equally to this work and shared first authorship.

Ye Li and Yanan Ma contributed equally as senior authors.

Contributor Information

Jia Meng, Email: mengjia1663@126.com.

Ye Li, Email: liye8459@163.com.

Data Availability Statement

The data that support the findings of this study are openly available in the Global Health Data Exchange GBD Results Tool at http://ghdx.healthdata.org/gbd‐results‐tool.

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Associated Data

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

Supplementary Materials

Supporting Information S1

DMRR-41-e70036-s001.docx (197.1KB, docx)

Data Availability Statement

The data that support the findings of this study are openly available in the Global Health Data Exchange GBD Results Tool at http://ghdx.healthdata.org/gbd‐results‐tool.

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