High fasting plasma glucose and the burden of colon and rectum cancer in China trends mortality and disability from 1990 to 2021

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Understanding the temporal trends and contributing factors to this burden is crucial for effective prevention and intervention strategies. Methods Data were extracted from the Global Burden of Disease (GBD) Study for the period between 1990 and 2021. Age-standardized mortality rates (ASMR), disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) were analyzed using joinpoint regression to identify significant changes over time. The analysis also included a decomposition of the factors contributing to the increase in CRC deaths, including aging, epidemiological changes, and population growth. Results In 2021, the burden of CRC attributable to HFPG in China was substantial, with 18,440 deaths, predominantly among males. Males had higher age-standardized rates across all outcomes, with an ASMR of 1.26 per 100,000 compared to 0.62 per 100,000 in females. The joinpoint analysis revealed significant increases in ASMR, DALYs, and YLLs during the late 1990s and early 2000s, particularly among males. A significant rise in YLDs was observed, indicating an increasing burden of chronic disability. Comparatively, global trends mirrored those in China, with slight increases in ASMR, DALYs, and YLLs, but a more pronounced global rise in YLDs. Decomposition analysis showed that aging was the primary driver of the increase in CRC deaths, particularly among males, while epidemiological changes contributed to reduced mortality in females. Conclusions The burden of CRC attributable to HFPG in China has risen significantly over the past three decades, driven primarily by aging and, to a lesser extent, epidemiological changes and population growth. These findings highlight the need for targeted interventions to address the growing impact of HFPG on CRC outcomes, particularly the increasing chronic disability burden. Colon and rectum cancer high fasting plasma glucose China Global Burden of Disease Study epidemiology joinpoint analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Colon and rectum cancer (CRC) is one of the leading causes of cancer-related morbidity and mortality worldwide, with a particularly high burden in China [ 1 – 3 ]. In recent years, there has been growing evidence linking metabolic disorders, including hyperglycemia, to the risk and progression of CRC [ 4 ]. High fasting plasma glucose (HFPG) has emerged as a significant risk factor for CRC, contributing to both cancer incidence and adverse outcomes in affected patients [ 5 , 6 ]. The mechanisms underlying this association are thought to involve insulin resistance, chronic inflammation, and the activation of oncogenic pathways, all of which can promote tumor development and progression [ 7 , 8 ]. Despite the increasing recognition of HFPG as a modifiable risk factor, its impact on CRC burden in the Chinese population over time has not been fully elucidated. Cancer is one of the major contributors to the burden of diseases among the global elderly population [ 9 ]. The growing prevalence of HFPG in China, driven by lifestyle changes and the increasing incidence of obesity and diabetes, has raised concerns about its potential role in the rising burden of CRC [ 10 ]. Previous studies have reported that elevated glucose levels may enhance colorectal tumorigenesis through several mechanisms, including the upregulation of insulin-like growth factors, which can promote cell proliferation and inhibit apoptosis [ 11 ]. Additionally, hyperglycemia has been associated with increased oxidative stress and DNA damage, further contributing to cancer risk [ 12 ]. In China, where the incidence of diabetes and prediabetes is among the highest in the world and still increasing, the impact of HFPG on CRC has become an urgent public health concern [ 13 , 14 ]. Evidence from Iran similarly highlights the significant contribution of modifiable risk factors like HFPG and dietary risks to the burden of cancers, emphasizing the necessity for targeted prevention strategies in aging populations [ 15 ]. However, comprehensive analyses examining the temporal trends and population-level impact of HFPG on CRC in China remain limited, highlighting the need for more in-depth studies. This study aims to fill this gap by analyzing data from the Global Burden of Disease (GBD) Study to assess the impact of HFPG on CRC burden in China over the past three decades [ 16 ]. By examining age-standardized mortality rates (ASMR), disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs), we aim to provide a detailed understanding of how HFPG has contributed to the increasing burden of CRC. Furthermore, we will explore the differences in this burden across gender, time periods, and contributing factors such as aging, epidemiological changes, and population growth. This analysis will provide valuable insights into the public health implications of HFPG in relation to CRC and help inform targeted intervention strategies to mitigate this growing burden. Methods Data source and study population Data for this study were extracted from the GBD database using the GBD Results Tool, a platform developed by the Institute for Health Metrics and Evaluation (IHME) that enables detailed analysis of disease burden metrics. These include deaths, DALYs, YLDs, and YLLs attributable to HFPG. In the GBD methodology, HFPG is defined as the population-level average fasting plasma glucose (FPG) concentration, measured in mmol/L, with FPG treated as a continuous exposure. The IHME classifies high FPG as any value exceeding the theoretical minimum-risk exposure level (TMREL), which is defined as 4.9–5.3 mmol/L. The TMREL represents the level of FPG associated with the lowest risk of developing diseases such as type 2 diabetes and cardiovascular conditions [ 16 , 17 ]. Any FPG values above this threshold are considered to contribute to an increased risk of morbidity and mortality. Malignant neoplasms of the colon and rectum, as classified under ICD-10 codes C18-C21, are included in the analysis of the disease burden attributable to HFPG [ 16 , 17 ]. The GBD database compiles data on HFPG and its association with disease burden across a wide range of populations, adjusting for factors such as age, sex, and geography. The data used in this study were retrieved for the period from 1990 to 2021, and metrics were reported both as absolute numbers and age-standardized rates per 100,000 population. This allowed for a comprehensive examination of the temporal trends in CRC burden attributable to HFPG, with a focus on sex-based differences in these trends over the study period. As this study exclusively utilized publicly available, de-identified data from the GBD database, it did not require ethical approval or informed consent. The research adhered to ethical guidelines and principles to ensure that all data were responsibly handled, without any risk to individual participants. Joinpoint regression analysis Joinpoint regression analysis was used to identify points in time where significant changes in the trends of these metrics occurred. The Joinpoint software (version 5.2.0) was utilized for this analysis, which fits a series of joined straight lines on a log scale to the trend data. The annual percent change was calculated for each segment between joinpoints, and the average annual percent change (AAPC) was computed to summarize the overall trend across the entire study period. Statistical significance was assessed using a Monte Carlo permutation method, with a P -value of less than 0.05 considered statistically significant [ 18 , 19 ]. Age-period-cohort (APC) analysis Additionally, an APC analysis was conducted to disentangle the effects of age, time period, and birth cohort on CRC mortality and other outcomes attributable to HFPG. The APC analysis allowed for the assessment of how these three factors interact and contribute to the observed trends [ 20 ]. Age effects capture the influence of biological and physiological changes as individuals age, period effects reflect external factors such as changes in healthcare or environmental exposures that affect all age groups simultaneously, and cohort effects represent the impact of being born in a particular time period, which includes generational influences like early-life exposures and lifestyle factors that persist throughout life. The APC analysis was performed using the R package "Epi". Decomposition analysis To investigate the factors contributing to the observed changes in CRC mortality attributable to HFPG, a decomposition analysis was performed. This approach decomposes the total change in CRC deaths into three components: aging, population growth, and epidemiological changes. The aging component quantifies the effect of changes in the population’s age structure over time, reflecting the increasing number of individuals at higher risk for CRC due to an aging population. The population growth component accounts for the increase in the overall population size, which leads to more individuals at risk, thereby increasing the total number of CRC deaths. The epidemiological changes component captures the effects of changes in the distribution of risk factors, such as HFPG prevalence, as well as improvements in healthcare, early diagnosis, and treatment outcomes. For this analysis, we used age-specific mortality rates adjusted for changes in these three factors. The decomposition was performed separately for males and females, and the relative contribution of each component was estimated for the period from 1990 to 2021. The method follows a standard decomposition approach, with the total change in deaths calculated as the sum of these three components. This analysis provides insights into how demographic changes and evolving risk factor exposure have driven the increasing CRC burden in China, particularly due to aging and population growth. Software and data handling All statistical analyses were performed using R statistical software (version 4.3.1). Data handling, including extraction, cleaning, and preparation for analysis, was conducted in accordance with standard epidemiological practices. The statistical significance of all trends and decomposition results was determined at the 5% level. Results Burden of CRC attributable to HFPG in China, 2021 In 2021, the burden of CRC attributable to HFPG in China was substantial, with significant differences observed across age groups and between sexes. The total number of deaths due to CRC linked to HFPG was 18,440, with males accounting for a greater proportion (11,657) compared to females (6,783). The ASMR further highlighted this disparity, with males experiencing a rate of 1.26 per 100,000 people, nearly double that of females at 0.62 per 100,000. DALYs, which combine the years lost due to disability and premature death, totaled 429,386, with a higher burden observed in males (280,621 DALYs) than in females (148,765 DALYs). The age-standardized DALY rate for males was 27.79 per 100,000, significantly higher than the 13.41 per 100,000 observed in females. The burden of CRC due to HFPG was also reflected in the YLDs and YLLs. YLDs amounted to 21,182, with males again showing higher numbers (13,594) compared to females (7,588). The age-standardized YLD rates were 1.32 per 100,000 for males and 0.68 per 100,000 for females, indicating a considerable impact of chronic disability due to CRC in males. YLLs were markedly higher, with a total of 408,204 YLLs, including 267,028 in males and 141,176 in females. The age-standardized YLL rate was 26.48 per 100,000 for males, more than double the rate of 12.73 per 100,000 observed in females (Table 1 ). Table 1 All-age cases and age-standardized deaths, DALYs, YLDs, and YLLs rates in 2021 for CRC attributable to HFPG in China. Measure All-ages cases Age-standardized rates per 100 000 people Total Male Female Total Male Female Deaths 18440 (9208, 28705) 11657 (5380, 18689) 6783 (3392, 10957) 0.9 (0.45, 1.41) 1.26 (0.58, 2.00) 0.62 (0.31, 1.00) DALYs 429386 (210526, 671812) 280621 (129365, 451739) 148765 (72628, 242540) 20.25 (9.93, 31.64) 27.79 (12.84, 44.76) 13.41 (6.52, 21.82) YLDs 21182 (9703, 35246) 13594 (5982, 23404) 7588 (3472, 12790) 0.98 (0.45, 1.64) 1.32 (0.58, 2.26) 0.68 (0.31, 1.14) YLLs 408204 (201353, 636524) 267028 (121994, 428215) 141176 (69390, 230986) 19.26 (9.50, 30.00) 26.48 (12.13, 42.41) 12.73 (6.23, 20.72) DALYs, disability-adjusted life-years; YLDs, years lived with disability; YLLs, years of life lost; CRC, colon and rectum cancer; HFPG, high fasting plasma glucose. Age and sex distribution of CRC burden attributable to HFPG in China, 2021 The distribution of CRC burden attributable to HFPG across different age groups revealed a clear pattern of increasing impact with age, particularly in males. Figure 1 shows the total numbers of deaths, DALYs, YLDs, and YLLs in 2021, stratified by age and sex. Males consistently exhibited a higher burden across all metrics, with the greatest number of deaths, DALYs, YLDs, and YLLs observed in the older age groups, particularly in those aged 60 and above. The total number of deaths peaked in the 65–79 age groups for both males and females, but males experienced a larger share, reflecting the greater overall mortality due to HFPG-related CRC. Similarly, DALYs, YLDs, and YLLs also showed a marked increase with age, with the highest values observed in older age groups, and again, males bearing a greater burden. These findings are consistent with the age-specific rates shown in Fig. 2 , where deaths and YLLs sharply increased from age 60 onward, with the 85–89 and 90–94 age groups experiencing the highest rates for both males and females. The rates of DALYs and YLDs also escalated with age, underscoring the chronic disability burden of CRC, with a pronounced gender disparity favoring males. Together, Figs. 1 and 2 highlight the significant and increasing burden of CRC attributable to HFPG in China, particularly among older males. Temporal trends in CRC burden attributable to HFPG from 1990 to 2021 From 1990 to 2021, the burden of CRC attributable to HFPG in China showed a consistent upward trend across all measured outcomes, including deaths, DALYs, years YLDs, and YLLs. The number of deaths and age-standardized death rates increased steadily over the three decades, with males consistently exhibiting higher rates and a more pronounced rise compared to females (Fig. 3 A). Similarly, DALYs and age-standardized DALY rates followed this upward trajectory, reflecting the growing impact of HFPG on both mortality and morbidity, with males experiencing a significantly higher burden (Fig. 3 B). The number of YLDs and age-standardized YLD rates also increased gradually, indicating the rising chronic disability associated with CRC, again with a greater impact on males (Fig. 3 C). The most substantial increase was observed in YLLs, where both the number and age-standardized YLL rates showed a marked rise, highlighting the growing premature mortality burden due to CRC, particularly among males (Fig. 3 D). These trends underscore the escalating public health challenge posed by HFPG-related CRC in China, with a clear and growing gender disparity. Age-specific comparison of CRC burden attributable to HFPG between 1990 and 2021 The comparison of CRC burden attributable to HFPG between 1990 and 2021 in China shows that while there were slight increases in deaths, DALYs, and YLLs across all age groups, the most notable rise was observed in YLDs. The number and crude rates of deaths and YLLs have shown only modest elevations over the three decades, with these changes being more pronounced in older age groups, particularly among those aged 60 and above (Fig. 4 A, 4 D). Similarly, the increase in DALYs reflects a moderate rise in the overall burden of CRC, largely driven by the slight increases in both fatal and non-fatal outcomes (Fig. 4 B). In contrast, YLDs, which represent the chronic disability burden, have significantly escalated, especially in older populations, indicating a growing impact of long-term morbidity associated with CRC (Fig. 4 C). This differential pattern of change underscores the increasing importance of addressing chronic disability in the management of CRC related to HFPG. Trends in CRC burden attributable to HFPG: a comparison between China and global levels Between 1990 and 2021, the age-standardized rates for CRC attributable to HFPG showed both similarities and differences when comparing China to the global average. In China, the ASMR remained relatively stable, with only a slight increase from 0.87 to 0.9 per 100,000. Similarly, DALYs and YLLs showed minimal changes, with increases of 0.25 and 0.15 per 100,000, respectively. The most significant rise in China was observed in YLDs, which increased from 0.41 to 0.98 per 100,000, highlighting a growing burden of chronic disability associated with CRC (Table 2 , Fig. 5 A). Globally, the trends were somewhat parallel, with a small but statistically significant increase in ASMR, DALYs, and YLLs, each rising by approximately 0.3 per 100,000. However, like in China, the most notable global change was in YLDs, which rose by 1.41 per 100,000, reflecting a similar global trend towards increased chronic disability burden due to CRC attributable to HFPG (Table 2 , Fig. 5 B). These findings underscore the escalating global and national public health challenge of managing CRC linked to HFPG, particularly the growing impact of long-term disability. Table 2 Change of age-standardized rates in deaths, DALYs, YLDs, and YLLs for CRC attributable to HFPG between 1990 and 2021 in China and global level. Measure China Global 1990 2021 Change 1990 2021 Change Deaths 0.87 (0.43, 1.34) 0.9 (0.45, 1.41) 0.24 (-0.10–0.59) 0.89 (0.45, 1.34) 0.98 (0.51, 1.49) 0.31 (0.19–0.43) * DALYs 19.38 (9.52, 29.9) 20.25 (9.93, 31.64) 0.25 (-0.14–0.65) 18.46 (9.27, 28.03) 20.31 (10.46, 30.81) 0.31 (0.19–0.43) * YLDs 0.41 (0.19, 0.69) 0.98 (0.45, 1.64) 2.92 (2.63–3.21) * 0.63 (0.3, 1.02) 0.97 (0.46, 1.57) 1.41 (1.33–1.49) * YLLs 18.97 (9.29, 29.24) 19.26 (9.5, 30) 0.15 (-0.20–0.49) 17.84 (8.94, 27.02) 19.34 (9.98, 29.36) 0.27 (0.14–0.39) * DALYs, disability-adjusted life-years; YLDs, years lived with disability; YLLs, years of life lost; CRC, colon and rectum cancer; HFPG, high fasting plasma glucose; * , p < 0.05. Joinpoint analysis of CRC burden attributable to HFPG in China The joinpoint analysis and trends presented in Table 3 reveal significant variations in the age-standardized mortality, DALY, YLD, and YLL rates for CRC attributable to HFPG in China between 1990 and 2021. Across the entire period, the AAPC for ASMR showed modest increases, particularly during certain periods such as 1995–2000 where the rates increased significantly for both sexes. However, from 2000 onwards, the trends generally stabilized, with minor fluctuations observed in more recent years (Table 3 , Fig. 6 A). For DALYs, a similar pattern was observed, with significant increases in the late 1990s followed by a stabilization or slight decrease in the 2000s, particularly in females where a notable reduction was observed from 2000 to 2007. Conversely, YLD rates displayed a consistent increase throughout the period, especially during the late 1990s and early 2000s, with males experiencing more pronounced increases. The YLL rates mirrored the mortality trends, with initial increases followed by periods of stabilization, particularly in the last decade, where only minor changes were noted (Table 3 , Fig. 6 B-D). These findings highlight the temporal dynamics and gender differences in the burden of CRC attributable to HFPG in China, with specific periods of increased rates, especially during the late 1990s and early 2000s. Table 3 Trends in age-standardized mortality, DALY, YLD, and YLL rates (per 100,000 persons) among both sexes, males, and females from 1990 to 2021 for CRC attributable to HFPG in China. Age-standardized mortality rate Age-standardized DALY rate Age-standardized YLD rate Age-standardized YLL rate Gender Period APC (95% CI) AAPC (95% CI) Period APC (95% CI) AAPC (95% CI) Period APC (95% CI) AAPC (95% CI) Period APC (95% CI) AAPC (95% CI) Both 1990–1996 -0.24 (-0.82–0.34) 0.25 (-0.14–0.65) 1990–1996 -0.69 (-1.20 - -0.19) * 0.24 (-0.10–0.59) 1990–1995 0.25 (-0.24–0.75) 2.92 (2.63–3.21) * 1990–1996 -0.73 (-1.23 - -0.22) * 0.15 (-0.20–0.49) 1995–2000 3.95 (2.25–5.67) * 1995–2000 3.42 (1.94–4.92) * 1995–2000 5.24 (4.50–5.97) 1995–2000 3.36 (1.89–4.86) * 2000–2007 -1.27 (-1.81 - -0.72) * 2000–2007 -1.03 (-1.51 - -0.55) * 2000–2007 3.04 (2.67–3.41) 2000–2007 -1.15 (-1.62 - -0.67) * 2007–2010 1.66 (-1.60–5.03) 2007–2010 1.46 (-1.39–4.40) 2007–2010 4.83 (2.60–7.10) 2007–2010 1.33 (-1.50–4.25) 2010–2021 -0.20 (-0.43–0.02) 2010–2021 0.11 (-0.10–0.31) 2010–2019 2.94 (2.70–3.18) 2010–2021 -0.01 (-0.21–0.19) 2019–2021 0.58 (-1.66–2.88) Female 1990–1996 -0.50 (-1.00–0.00) -0.51 (-0.85 - -0.16) * 1990–1996 -0.92 (-1.27 - -0.56) * -0.58 (-0.83 - -0.34) * 1990–1996 0.66 (0.19–1.14) * 2.24 (1.83–2.65) * 1990–1996 -0.95 (-1.31 - -0.59) * -0.68 (-0.93 - -0.43) * 1996–2000 2.84 (1.36–4.35) * 1996–2000 2.30 (1.26–3.36) * 1996–1999 5.93 (3.06–8.87) * 1996–2000 2.24 (1.18–3.32) * 2000–2006 -2.09 (-2.72 - -1.46) * 2000–2007 -2.05 (-2.38 - -1.71) * 1999–2016 2.14 (2.03–2.25) * 2000–2007 -2.17 (-2.51 - -1.83) * 2006–2015 -1.13 (-1.44 - -0.82) * 2007–2015 -1.08 (-1.34 - -0.82) * 2016–2019 4.05 (1.29–6.88) * 2007–2015 -1.21 (-1.47 - -0.94) * 2015–2019 0.88 (-0.57–2.36) 2015–2019 1.19 (0.14–2.24) * 2019–2021 -0.23 (-3.07–2.68) 2015–2019 1.07 (0.01–2.13) * 2019–2021 -2.26 (-5.26–0.84) 2019–2021 -1.60 (-3.78–0.64) 2019–2021 -1.68 (-3.90–0.58) Male 1990–1996 -0.00 (-0.49–0.49) 0.63 (0.30–0.96) * 1990–1996 -0.50 (-1.01–0.02) 0.74 (0.45–1.03) * 1990–1995 0.18 (-0.27–0.62) 3.33 (3.10–3.55) * 1990–1995 -0.93 (-1.58 - -0.27) * 0.63 (0.37–0.89) * 1996–2000 4.82 (3.39–6.27) * 1995–2000 4.24 (2.74–5.76) * 1995–2000 5.84 (5.19–6.49) * 1995–20011 3.03 (2.37–3.69) * 2000–2007 -0.59 (-1.03 - -0.14) * 2000–2007 -0.29 (-0.76–0.18) 2000–2007 3.71 (3.38–4.03) * 2001–2007 -0.65 (-1.25 - -0.04) * 2007–2011 2.24 (0.87–3.62) * 2007–2011 2.44 (1.00–3.90) * 2007–2011 5.71 (4.73–6.69) * 2007–2011 2.42 (1.03–3.84) * 2011–2019 0.03 (-0.34–0.41) 2011–2021 0.17 (-0.07–0.41) 2011–2019 3.03 (2.77–3.30) * 2011–2021 0.05 (-0.18–0.28) 2019–2021 -2.18 (-5.03–0.75) 2019–2021 0.29 (-1.76–2.38) DALYs, disability-adjusted life-years; YLDs, years lived with disability; YLLs, years of life lost; AAPC, average annual percent change presented for full period; APC, annual percent change; CRC, colon and rectum cancer; HFPG, high fasting plasma glucose; CI, confidence interval. * , p < 0.05. APC analysis of CRC deaths attributable to HFPG The APC analysis in deaths due to CRC attributable to HFPG in China from 1990 to 2021 reveals distinct patterns across different age groups, birth cohorts, and time periods. The age-specific APC trends showed that younger age groups experienced smaller fluctuations, while older age groups exhibited more pronounced increases in CRC mortality over time (Fig. 7 A). In terms of birth cohorts, the analysis indicated that more recent cohorts (those born closer to 1990) experienced higher APCs in CRC mortality, with each line connecting the age-specific APCs for these cohorts showing an upward trend (Fig. 7 B). The period-specific APC analysis further demonstrated that CRC mortality rates fluctuated more significantly in older age groups across different time periods, with noticeable variations during specific periods (Fig. 7 C). Finally, the birth cohort-specific APC trends highlighted that older cohorts experienced more stable or declining trends, whereas younger cohorts exhibited increasing APCs, particularly in the later years of the study period (Fig. 7 D). These findings emphasize the varying impact of HFPG on CRC mortality across different age groups, birth cohorts, and time periods in China. Decomposition of CRC deaths attributable to HFPG The decomposition analysis of changes in the number of deaths due to CRC attributable to HFPG in China from 1990 to 2021 highlights the contributions of aging, epidemiological changes, and population growth (Fig. 8 ). Aging was the primary factor driving the increase in CRC deaths across both sexes, with a particularly strong impact observed in males. Epidemiological changes contributed to an increase in mortality among males, while in females, these changes led to a reduction in CRC mortality. Population growth had a more pronounced effect on males, contributing significantly to the rise in CRC deaths, whereas its impact on females was comparatively smaller. These results reflect the differing roles of demographic and epidemiological factors in influencing CRC mortality trends attributable to HFPG in China over the study period. Discussion The present study provides a comprehensive analysis of the burden of CRC attributable to HFPG in China from 1990 to 2021, revealing significant trends and demographic patterns. The findings highlight a substantial increase in CRC-related deaths, DALYs, YLDs, and YLLs over the study period, with a particularly pronounced burden observed in males. Joinpoint regression analysis identified key periods where significant changes in these trends occurred, with notable increases during the late 1990s and early 2000s. The decomposition analysis demonstrated that the rising burden of CRC attributable to HFPG was primarily driven by population aging, with additional contributions from epidemiological changes and population growth. Interestingly, while epidemiological changes led to an increase in mortality among males, they contributed to a reduction in mortality among females. Furthermore, the APC analysis revealed distinct age, period, and cohort effects, underscoring the complex interplay of demographic and temporal factors in shaping the observed trends. These findings underscore the growing public health challenge posed by HFPG-related CRC in China, particularly the increasing chronic disability burden and the significant gender disparities in outcomes. HFPG is recognized globally as a major cause of mortality and disability and is considered an independent risk factor for CRC development [ 11 , 21 , 22 ]. A meta-analysis of six prospective cohort studies involving 62,814 CRC cases reported a pooled relative risk (RR) of 1.015 (1.012 to 1.019) per 18 mg/dL increase in FPG, with similar RRs across sexes and anatomical sites [ 23 ]. A population-based cohort study found that glycemic control in patients with diabetes was independently linked to the risk of developing colonic adenoma and CRC, showing a biological gradient [ 6 ]. Prospective data from the Malmö Diet and Cancer cardiovascular cohort, which included 5,144 subjects, showed that elevated blood glucose levels increase the likelihood of developing colon cancer [ 24 ]. Additionally, a previous meta-analysis of observational studies involving 28,999 participants indicated that diabetes mellitus was linked to a higher likelihood of developing colorectal adenoma and advanced adenoma [ 25 ]. Moreover, inadequate control of fasting blood glucose is linked to an increased mortality risk and reduced survival in colon cancer patients [ 26 ]. The potential mechanisms of this association include hyperinsulinemia, adipose tissue dysfunction-induced inflammation and immunological surveillance impairment [ 27 ]. These studies collectively emphasized the heightened risk of colorectal cancer across the diabetes mellitus population. The number of individuals diagnosed with diabetes mellitus has significantly increased over the past three decades, rising from 148.5 million in 1990 to 437.9 million in 2019 [ 28 ]. The global prevalence of patients with both CRC and diabetes mellitus is expected to continue growing [ 29 , 30 ]. In China, from 1990 to 2019, the number of diabetes patients increased by 160%, from 35.14 million to 91.70 million, and the age-standardized prevalence rate rose from 4788 per 100,000 to 8170 per 100,000 [ 31 ]. The prevalence of diabetes in Chinese adults aged 20–79 years is projected to increase from 8.2% to 9.7% between 2020 and 2030 [ 32 ]. The results of this study highlight a concerning upward trend in the burden of CRC attributable to HFPG, both globally and in China. This trend is closely linked to the rising prevalence of diabetes mellitus, which has become a significant public health issue. In China, the increase in HFPG-related CRC reflects broader global patterns, yet it is exacerbated by the rapid economic and lifestyle transitions that the country has undergone in recent decades. The shift towards a more sedentary lifestyle, coupled with increased consumption of calorie-dense foods, has contributed to a surge in diabetes cases, which in turn has elevated the risk of CRC. This is particularly alarming given the size of China’s population, where even a small increase in the prevalence of HFPG can translate into a substantial rise in the absolute number of CRC cases [ 33 ]. To address this growing burden, cancer prevention strategies and public health interventions should be promoted, and a comprehensive framework for therapeutic agendas should be implemented. Comparatively, while the global burden of CRC associated with HFPG is increasing, the situation in China is particularly severe. Studies have shown that the prevalence of diabetes in China has outpaced that in many other countries, making HFPG a more prominent risk factor for CRC within the Chinese population [ 33 ]. This is further supported by the findings of the GBD study, which indicates a sharper rise in CRC cases and mortality rates in China compared to global averages, largely driven by the increasing prevalence of HFPG [ 33 , 34 ]. The differences in trends between China and other regions can be attributed to the unique demographic and epidemiological characteristics of the Chinese population, including rapid urbanization and aging [ 35 , 36 ]. The aging population, as evidenced by the global rise in the elderly demographic, exacerbates the impact of HFPG on CRC incidence. According to a report by Saeid Safiri et al., the global elderly population has steadily increased, with projections suggesting that by 2050, 17% of the world’s population will be aged 65 and above [ 9 ]. This shift towards an older population means that more individuals are living with chronic conditions, including diabetes, for longer periods, which increases the cumulative burden of diseases like CRC. Aging further amplifies the effects of diabetes on CRC risk, as older adults tend to have a more sedentary lifestyle, lower metabolic resilience, and reduced capacity to manage chronic diseases, making them particularly vulnerable to the compounding effects of HFPG [ 37 ]. In China, the elderly population is growing rapidly, and with this demographic shift, the burden of CRC attributable to HFPG is expected to escalate. The interplay of aging, increased diabetes prevalence, and lifestyle changes necessitates more focused interventions, such as tailored public health policies aimed at preventing both diabetes and CRC. Aging-related factors, such as declining immune function and the presence of comorbidities, may also enhance the progression of CRC, necessitating comprehensive screening and early detection programs targeting the elderly [ 15 , 38 ]. The upward trend in CRC burden attributable to HFPG in China underscores the need for targeted public health interventions. While global efforts to control diabetes and related metabolic disorders have shown some success, the rapid rise of these conditions in China suggests that more aggressive and culturally tailored strategies are needed. This includes not only enhancing diabetes prevention and management programs but also integrating cancer prevention strategies that specifically address the challenges of aging populations. Despite the significant contributions of this study, several limitations must be acknowledged. First, the reliance on data from the GBD database introduces potential inaccuracies due to the use of model-based estimates and variability in data quality across regions, particularly in areas where healthcare data are less reliable. This could lead to under- or over-estimation of the true burden of CRC attributable to HFPG. Second, this study did not account for potential confounding factors such as diet, physical activity, and other lifestyle behaviors that are independently linked to CRC risk. These factors could influence the observed associations between HFPG and CRC outcomes. Third, the study’s observational nature limits the ability to establish a causal relationship between HFPG and CRC, as it is based on population-level data and correlations rather than controlled experiments. Additionally, while the study focused on the Chinese population, the findings may not be fully generalizable to other countries or populations with different healthcare systems, genetic backgrounds, and environmental exposures. Future studies should prioritize integrating individual-level data and consider potential confounders more comprehensively. Longitudinal studies tracking patients over time would help clarify causal relationships and identify specific mechanisms linking HFPG to CRC. Comparative studies across diverse populations are also necessary to assess whether similar trends are evident in different regions, which would provide further insight into the global applicability of these findings. Conclusions This study underscores the significant impact of HFPG on the burden of CRC in China. The increasing prevalence of HFPG as a major modifiable risk factor calls for urgent and coordinated public health interventions. Addressing HFPG through enhanced diabetes management and preventive strategies may be a key approach to mitigating the growing CRC burden. Future research should focus on exploring the underlying mechanisms linking HFPG to CRC development, while also examining the effectiveness of integrated metabolic and oncologic interventions. Expanding comparative studies across diverse populations will further clarify whether similar trends are evident globally. Moreover, longitudinal and patient-level studies are needed to establish stronger causal relationships and to identify modifiable factors that can be targeted to prevent CRC progression. Comprehensive strategies that focus on metabolic health could play a crucial role in reducing CRC incidence and improving patient outcomes in the future. Declarations Consent to publish Not applicable. Data availability statement The data utilized in this study were sourced from the GBD 2021 database, which is publicly available. The datasets analyzed in this study can be accessed at the IHME website: http://ghdx.healthdata.org/gbd-results-tool. Ethical statement Not applicable. This study used de-identified GBD 2021 data, complying with the Declaration of Helsinki, relevant guidelines, and GATHER standards[39], without requiring ethical approval or informed consent. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding The author(s) declare that financial support was received for the research and/or publication of this article. All expenses associated with this study were self-funded by the authors. Authors' contributions Yaqin Qian: Conceptualization, Methodology, Formal analysis, Writing - Original Draft. Zhouwei Zhan: Data Curation, Investigation, Writing - Review & Editing. Bijuan Chen: Software, Validation, Visualization, Writing - Review & Editing. 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Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, Malanda B: IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045 . Diabetes research and clinical practice 2018, 138 :271-281. Liu J, Shen M, Zhuang G, Zhang L: Investigating the temporal trends of diabetes disease burden in China during 1990-2019 from a global perspective . Frontiers in endocrinology 2024, 15 :1324318. Liu J, Liu M, Chai Z, Li C, Wang Y, Shen M, Zhuang G, Zhang L: Projected rapid growth in diabetes disease burden and economic burden in China: a spatio-temporal study from 2020 to 2030 . The Lancet regional health Western Pacific 2023, 33 :100700. Zheng Y, Ley SH, Hu FB: Global aetiology and epidemiology of type 2 diabetes mellitus and its complications . Nature reviews Endocrinology 2018, 14 (2):88-98. Li N, Lu B, Luo C, Cai J, Lu M, Zhang Y, Chen H, Dai M: Incidence, mortality, survival, risk factor and screening of colorectal cancer: A comparison among China, Europe, and northern America . Cancer letters 2021, 522 :255-268. Tsoi KK, Hirai HW, Chan FC, Griffiths S, Sung JJ: Cancer burden with ageing population in urban regions in China: projection on cancer registry data from World Health Organization . British medical bulletin 2017, 121 (1):83-94. Wen D, Zou W, Wen X, Yang Y, Chen Y, He Y, Wang G, Shan B: Urban-rural disparity in colorectal cancer incidence and increasing trend in relation to socioeconomic development and urbanization in China . The Journal of international medical research 2018, 46 (10):4181-4196. Seum T, Hoffmeister M, Brenner H: Risk-Adapted Starting Ages of Colorectal Cancer Screening for People With Diabetes or Metabolic Syndrome . Alimentary pharmacology & therapeutics 2024. Gallo A, Pellegrino S, Pero E, Agnitelli MC, Parlangeli C, Landi F, Montalto M: Main Disorders of Gastrointestinal Tract in Older People: An Overview . 2024, 6 (1):313-336. Stevens GA, Alkema L, Black RE, Boerma JT, Collins GS, Ezzati M, Grove JT, Hogan DR, Hogan MC, Horton R et al : Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement . Lancet (London, England) 2016, 388 (10062):e19-e23. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8603727","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590091371,"identity":"8b304e73-93a8-4d4a-8fe6-94270cd3417d","order_by":0,"name":"yaqin qian","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"yaqin","middleName":"","lastName":"qian","suffix":""},{"id":590091372,"identity":"b5241ccc-64e4-45cc-a02f-785980718cc5","order_by":1,"name":"zhouwei zhan","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University,Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"zhouwei","middleName":"","lastName":"zhan","suffix":""},{"id":590091373,"identity":"741f6071-2ac3-4b3c-b1e1-94c889c49665","order_by":2,"name":"bijuan chen","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"bijuan","middleName":"","lastName":"chen","suffix":""},{"id":590091374,"identity":"ec8da954-dfd0-4404-b65e-34d657d3ad5f","order_by":3,"name":"fan wang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"fan","middleName":"","lastName":"wang","suffix":""},{"id":590091375,"identity":"15304f91-84d3-44b0-9784-605803f0b0a8","order_by":4,"name":"ying yin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYDAC5gMMBz9USDDzszcf+ADkJ4BFefBpYUtgPCxxxoJdsudY4gxitTAf4G2r4DeY4WNInBaDYzwGByTOSEgbSPB8bOZhsMvTnZHA+OBtG4O8OT4tBRUSxubSvRuBWpKLzW4kMBvObWMw3NmAQ8v9HrAtyZZzzm5/zMPAnLjtRgKbNG8bQ4LBATy28LZJ1G+4kfMQaEs9SAv7b2K0MBvcyGEEajkMtoUZnxbJY2wFwECWYAYGsmHjHIPjidvOPGyWnHNOwnADDi18x5g3f/xQUQeKyocNbyqqE7cdTz744U2ZjTwuWxQOcBjAOUw8YDZjA5CQwK4eCOQb2B/AOYw/cKobBaNgFIyCkQwAN9Jk+Jp4PsUAAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"ying","middleName":"","lastName":"yin","suffix":""}],"badges":[],"createdAt":"2026-01-14 16:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8603727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8603727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102749089,"identity":"c598a52f-2a0b-4468-b57c-acb261916290","added_by":"auto","created_at":"2026-02-16 09:11:57","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2623400,"visible":true,"origin":"","legend":"\u003cp\u003eThe numbers of deaths, DALYs, YLDs, and YLLs in 2021 due to CRC attributable to HFPG in China, stratified by age and sex. (A) The total numbers of deaths by age and sex. (B) The total numbers of DALYs by age and sex. (C) The total numbers of YLDs by age and sex. (D) The total numbers of YLLs by age and sex. These crude numbers provide an overview of the absolute burden of CRC attributable to HFPG in 2021, highlighting the overall scale of the disease. CRC, colon and rectum cancer; HFPG, high fasting plasma glucose; DALYs, disability-adjusted life years; YLDs, years lived with disability; YLLs, years of life lost.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8603727/v1/5f95a416676ec759c7af68b8.jpg"},{"id":102749208,"identity":"26764577-ba89-4c10-a2b1-149ea9349bfc","added_by":"auto","created_at":"2026-02-16 09:12:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2362748,"visible":true,"origin":"","legend":"\u003cp\u003eThe age-specific rates of deaths, DALYs, YLDs, and YLLs in 2021 due to CRC attributable to HFPG in China, stratified by sex. (A) The rate of deaths by age and sex. (B) The rate of DALYs by age and sex. (C) The rate of YLDs by age and sex. (D) The rate of YLLs by age and sex. DALYs, disability-adjusted life years; YLDs, years lived with disability; YLLs, years of life lost; CRC, colon and rectum cancer; HFPG, high fasting plasma glucose.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8603727/v1/9829be82207ff2a42daedbcc.jpg"},{"id":102709956,"identity":"11f5d0cf-0558-432d-bd76-886e11def7b4","added_by":"auto","created_at":"2026-02-15 15:05:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3713486,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends in the number and age-standardized rates of deaths, DALYs, YLDs, and YLLs due to CRC attributable to HFPG in China from 1990 to 2021, stratified by sex. (A) Trends in the number of deaths and age-standardized death rates. (B) Trends in the number of DALYs and age-standardized DALY rates. (C) Trends in the number of YLDs and age-standardized YLD rates. (D) Trends in the number of YLLs and age-standardized YLL rates. DALYs, disability-adjusted life years; YLDs, years lived with disability; YLLs, years of life lost; CRC, colon and rectum cancer; HFPG, high fasting plasma glucose.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8603727/v1/773e191afa9a6589fac43797.jpg"},{"id":102709951,"identity":"72633463-3540-49f4-8769-ad9676fcff72","added_by":"auto","created_at":"2026-02-15 15:05:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2805176,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the number and crude rates of deaths, DALYs, YLDs, and YLLs due to CRC attributable to HFPG in China by age group between 1990 and 2021. (A) The number and crude rates of deaths across different age groups in 1990 and 2021. (B) The number and crude rates of DALYs by age group in 1990 and 2021. (C) The number and crude rates of YLDs by age group in 1990 and 2021. (D) The number and crude rates of YLLs by age group in 1990 and 2021. DALYs, disability-adjusted life years; YLDs, years lived with disability; YLLs, years of life lost; CRC, colon and rectum cancer; HFPG, high fasting plasma glucose.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8603727/v1/c0e9d9758f27f3a40ef314e2.jpg"},{"id":102709954,"identity":"d4d8437c-c72b-430a-89b5-61875f2fcf6d","added_by":"auto","created_at":"2026-02-15 15:05:07","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1370213,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized trends in CRC burden attributable to HFPG from 1990 to 2021, comparing China and global data. (A) ASMR, DALYs, YLDs, and YLLs rates in China. (B) Age-standardized global rates for the same metrics, highlighting similar patterns with a global perspective. CRC, colon and rectum cancer; HFPG, high fasting plasma glucose; ASMR, age-standardized mortality rates; DALYs, disability-adjusted life years; YLDs, years lived with disability; YLLs, years of life lost.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8603727/v1/17309a4c2552e5f295520a1e.jpg"},{"id":102749345,"identity":"7f71ecc7-8c5a-448b-9b64-e447a99aef3e","added_by":"auto","created_at":"2026-02-16 09:12:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2659127,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint analysis of trends in CRC burden attributable to HFPG in China from 1990 to 2021. (A) Trends in ASMR. (B) Trends in DALYs rates. (C) Trends in YLDs rates. (D) Trends in YLLs rates. CRC, colon and rectum cancer; HFPG, high fasting plasma glucose; ASMR, age-standardized mortality rates; DALYs, disability-adjusted life years; YLDs, years lived with disability; YLLs, years of life lost.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8603727/v1/c827c3b79ead3dd1d589d75f.jpg"},{"id":102709958,"identity":"33312577-0ec8-4919-8850-3e662e0e684b","added_by":"auto","created_at":"2026-02-15 15:05:07","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1805662,"visible":true,"origin":"","legend":"\u003cp\u003eAge-specific and period-specific annual percent changes (APC) in deaths due to CRC attributable to HFPG in China from 1990 to 2021. (A) The age-specific APC in CRC deaths according to time periods; each line connects the age-specific APC for a 5-year period. (B) The age-specific APC in CRC deaths according to birth cohorts; each line connects the age-specific APC for a 5-year birth cohort. (C) The period-specific APC in CRC deaths according to age groups; each line connects the period-specific APC for a 5-year age group. (D) The birth cohort-specific APC in CRC deaths according to age groups; each line connects the birth cohort-specific APC for a 5-year age group. CRC, colon and rectum cancer; HFPG, high fasting plasma glucose.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8603727/v1/0c25c23363c2d540991736bc.jpg"},{"id":102709953,"identity":"fb7d4bea-70c3-40b3-a614-c1930c3929eb","added_by":"auto","created_at":"2026-02-15 15:05:07","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":220690,"visible":true,"origin":"","legend":"\u003cp\u003eDecomposition of the changes in the number of deaths due to CRC attributable to HFPG in China from 1990 to 2021, stratified by sex. The figure breaks down the increase in deaths into three contributing factors: aging, epidemiological change, and population growth. CRC, colon and rectum cancer; HFPG, high fasting plasma glucose.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8603727/v1/eeeb66589295808287347dbf.jpg"},{"id":104401830,"identity":"69b1a5dd-1c98-483c-9e84-de30084694ba","added_by":"auto","created_at":"2026-03-11 12:13:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":20409474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8603727/v1/3c5c99a6-deea-49a1-a6dd-48bb57de7826.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"High fasting plasma glucose and the burden of colon and rectum cancer in China trends mortality and disability from 1990 to 2021","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColon and rectum cancer (CRC) is one of the leading causes of cancer-related morbidity and mortality worldwide, with a particularly high burden in China [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In recent years, there has been growing evidence linking metabolic disorders, including hyperglycemia, to the risk and progression of CRC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. High fasting plasma glucose (HFPG) has emerged as a significant risk factor for CRC, contributing to both cancer incidence and adverse outcomes in affected patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The mechanisms underlying this association are thought to involve insulin resistance, chronic inflammation, and the activation of oncogenic pathways, all of which can promote tumor development and progression [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite the increasing recognition of HFPG as a modifiable risk factor, its impact on CRC burden in the Chinese population over time has not been fully elucidated.\u003c/p\u003e \u003cp\u003eCancer is one of the major contributors to the burden of diseases among the global elderly population [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The growing prevalence of HFPG in China, driven by lifestyle changes and the increasing incidence of obesity and diabetes, has raised concerns about its potential role in the rising burden of CRC [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies have reported that elevated glucose levels may enhance colorectal tumorigenesis through several mechanisms, including the upregulation of insulin-like growth factors, which can promote cell proliferation and inhibit apoptosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, hyperglycemia has been associated with increased oxidative stress and DNA damage, further contributing to cancer risk [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In China, where the incidence of diabetes and prediabetes is among the highest in the world and still increasing, the impact of HFPG on CRC has become an urgent public health concern [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Evidence from Iran similarly highlights the significant contribution of modifiable risk factors like HFPG and dietary risks to the burden of cancers, emphasizing the necessity for targeted prevention strategies in aging populations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, comprehensive analyses examining the temporal trends and population-level impact of HFPG on CRC in China remain limited, highlighting the need for more in-depth studies.\u003c/p\u003e \u003cp\u003eThis study aims to fill this gap by analyzing data from the Global Burden of Disease (GBD) Study to assess the impact of HFPG on CRC burden in China over the past three decades [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. By examining age-standardized mortality rates (ASMR), disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs), we aim to provide a detailed understanding of how HFPG has contributed to the increasing burden of CRC. Furthermore, we will explore the differences in this burden across gender, time periods, and contributing factors such as aging, epidemiological changes, and population growth. This analysis will provide valuable insights into the public health implications of HFPG in relation to CRC and help inform targeted intervention strategies to mitigate this growing burden.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and study population\u003c/h2\u003e \u003cp\u003eData for this study were extracted from the GBD database using the GBD Results Tool, a platform developed by the Institute for Health Metrics and Evaluation (IHME) that enables detailed analysis of disease burden metrics. These include deaths, DALYs, YLDs, and YLLs attributable to HFPG. In the GBD methodology, HFPG is defined as the population-level average fasting plasma glucose (FPG) concentration, measured in mmol/L, with FPG treated as a continuous exposure. The IHME classifies high FPG as any value exceeding the theoretical minimum-risk exposure level (TMREL), which is defined as 4.9\u0026ndash;5.3 mmol/L. The TMREL represents the level of FPG associated with the lowest risk of developing diseases such as type 2 diabetes and cardiovascular conditions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Any FPG values above this threshold are considered to contribute to an increased risk of morbidity and mortality. Malignant neoplasms of the colon and rectum, as classified under ICD-10 codes C18-C21, are included in the analysis of the disease burden attributable to HFPG [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe GBD database compiles data on HFPG and its association with disease burden across a wide range of populations, adjusting for factors such as age, sex, and geography. The data used in this study were retrieved for the period from 1990 to 2021, and metrics were reported both as absolute numbers and age-standardized rates per 100,000 population. This allowed for a comprehensive examination of the temporal trends in CRC burden attributable to HFPG, with a focus on sex-based differences in these trends over the study period. As this study exclusively utilized publicly available, de-identified data from the GBD database, it did not require ethical approval or informed consent. The research adhered to ethical guidelines and principles to ensure that all data were responsibly handled, without any risk to individual participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eJoinpoint regression analysis\u003c/h3\u003e\n\u003cp\u003eJoinpoint regression analysis was used to identify points in time where significant changes in the trends of these metrics occurred. The Joinpoint software (version 5.2.0) was utilized for this analysis, which fits a series of joined straight lines on a log scale to the trend data. The annual percent change was calculated for each segment between joinpoints, and the average annual percent change (AAPC) was computed to summarize the overall trend across the entire study period. Statistical significance was assessed using a Monte Carlo permutation method, with a \u003cem\u003eP\u003c/em\u003e-value of less than 0.05 considered statistically significant [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAge-period-cohort (APC) analysis\u003c/h3\u003e\n\u003cp\u003eAdditionally, an APC analysis was conducted to disentangle the effects of age, time period, and birth cohort on CRC mortality and other outcomes attributable to HFPG. The APC analysis allowed for the assessment of how these three factors interact and contribute to the observed trends [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Age effects capture the influence of biological and physiological changes as individuals age, period effects reflect external factors such as changes in healthcare or environmental exposures that affect all age groups simultaneously, and cohort effects represent the impact of being born in a particular time period, which includes generational influences like early-life exposures and lifestyle factors that persist throughout life. The APC analysis was performed using the R package \"Epi\".\u003c/p\u003e\n\u003ch3\u003eDecomposition analysis\u003c/h3\u003e\n\u003cp\u003eTo investigate the factors contributing to the observed changes in CRC mortality attributable to HFPG, a decomposition analysis was performed. This approach decomposes the total change in CRC deaths into three components: aging, population growth, and epidemiological changes. The aging component quantifies the effect of changes in the population\u0026rsquo;s age structure over time, reflecting the increasing number of individuals at higher risk for CRC due to an aging population. The population growth component accounts for the increase in the overall population size, which leads to more individuals at risk, thereby increasing the total number of CRC deaths. The epidemiological changes component captures the effects of changes in the distribution of risk factors, such as HFPG prevalence, as well as improvements in healthcare, early diagnosis, and treatment outcomes. For this analysis, we used age-specific mortality rates adjusted for changes in these three factors. The decomposition was performed separately for males and females, and the relative contribution of each component was estimated for the period from 1990 to 2021. The method follows a standard decomposition approach, with the total change in deaths calculated as the sum of these three components. This analysis provides insights into how demographic changes and evolving risk factor exposure have driven the increasing CRC burden in China, particularly due to aging and population growth.\u003c/p\u003e\n\u003ch3\u003eSoftware and data handling\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using R statistical software (version 4.3.1). Data handling, including extraction, cleaning, and preparation for analysis, was conducted in accordance with standard epidemiological practices. The statistical significance of all trends and decomposition results was determined at the 5% level.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBurden of CRC attributable to HFPG in China, 2021\u003c/h2\u003e \u003cp\u003eIn 2021, the burden of CRC attributable to HFPG in China was substantial, with significant differences observed across age groups and between sexes. The total number of deaths due to CRC linked to HFPG was 18,440, with males accounting for a greater proportion (11,657) compared to females (6,783). The ASMR further highlighted this disparity, with males experiencing a rate of 1.26 per 100,000 people, nearly double that of females at 0.62 per 100,000. DALYs, which combine the years lost due to disability and premature death, totaled 429,386, with a higher burden observed in males (280,621 DALYs) than in females (148,765 DALYs). The age-standardized DALY rate for males was 27.79 per 100,000, significantly higher than the 13.41 per 100,000 observed in females. The burden of CRC due to HFPG was also reflected in the YLDs and YLLs. YLDs amounted to 21,182, with males again showing higher numbers (13,594) compared to females (7,588). The age-standardized YLD rates were 1.32 per 100,000 for males and 0.68 per 100,000 for females, indicating a considerable impact of chronic disability due to CRC in males. YLLs were markedly higher, with a total of 408,204 YLLs, including 267,028 in males and 141,176 in females. The age-standardized YLL rate was 26.48 per 100,000 for males, more than double the rate of 12.73 per 100,000 observed in females (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAll-age cases and age-standardized deaths, DALYs, YLDs, and YLLs rates in 2021 for CRC attributable to HFPG in China.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAll-ages cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAge-standardized rates per 100 000 people\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18440 (9208, 28705)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11657 (5380, 18689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6783 (3392, 10957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9 (0.45, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.26 (0.58, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62 (0.31, 1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDALYs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e429386 (210526, 671812)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280621 (129365, 451739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148765 (72628, 242540)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.25 (9.93, 31.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.79 (12.84, 44.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.41 (6.52, 21.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYLDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21182 (9703, 35246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13594 (5982, 23404)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7588 (3472, 12790)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98 (0.45, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.32 (0.58, 2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68 (0.31, 1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYLLs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e408204 (201353, 636524)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267028 (121994, 428215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141176 (69390, 230986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.26 (9.50, 30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.48 (12.13, 42.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.73 (6.23, 20.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eDALYs, disability-adjusted life-years; YLDs, years lived with disability; YLLs, years of life lost; CRC, colon and rectum cancer; HFPG, high fasting plasma glucose.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAge and sex distribution of CRC burden attributable to HFPG in China, 2021\u003c/h3\u003e\n\u003cp\u003eThe distribution of CRC burden attributable to HFPG across different age groups revealed a clear pattern of increasing impact with age, particularly in males. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the total numbers of deaths, DALYs, YLDs, and YLLs in 2021, stratified by age and sex. Males consistently exhibited a higher burden across all metrics, with the greatest number of deaths, DALYs, YLDs, and YLLs observed in the older age groups, particularly in those aged 60 and above. The total number of deaths peaked in the 65\u0026ndash;79 age groups for both males and females, but males experienced a larger share, reflecting the greater overall mortality due to HFPG-related CRC. Similarly, DALYs, YLDs, and YLLs also showed a marked increase with age, with the highest values observed in older age groups, and again, males bearing a greater burden. These findings are consistent with the age-specific rates shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, where deaths and YLLs sharply increased from age 60 onward, with the 85\u0026ndash;89 and 90\u0026ndash;94 age groups experiencing the highest rates for both males and females. The rates of DALYs and YLDs also escalated with age, underscoring the chronic disability burden of CRC, with a pronounced gender disparity favoring males. Together, Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlight the significant and increasing burden of CRC attributable to HFPG in China, particularly among older males.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTemporal trends in CRC burden attributable to HFPG from 1990 to 2021\u003c/h2\u003e \u003cp\u003eFrom 1990 to 2021, the burden of CRC attributable to HFPG in China showed a consistent upward trend across all measured outcomes, including deaths, DALYs, years YLDs, and YLLs. The number of deaths and age-standardized death rates increased steadily over the three decades, with males consistently exhibiting higher rates and a more pronounced rise compared to females (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Similarly, DALYs and age-standardized DALY rates followed this upward trajectory, reflecting the growing impact of HFPG on both mortality and morbidity, with males experiencing a significantly higher burden (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The number of YLDs and age-standardized YLD rates also increased gradually, indicating the rising chronic disability associated with CRC, again with a greater impact on males (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The most substantial increase was observed in YLLs, where both the number and age-standardized YLL rates showed a marked rise, highlighting the growing premature mortality burden due to CRC, particularly among males (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). These trends underscore the escalating public health challenge posed by HFPG-related CRC in China, with a clear and growing gender disparity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAge-specific comparison of CRC burden attributable to HFPG between 1990 and 2021\u003c/h2\u003e \u003cp\u003eThe comparison of CRC burden attributable to HFPG between 1990 and 2021 in China shows that while there were slight increases in deaths, DALYs, and YLLs across all age groups, the most notable rise was observed in YLDs. The number and crude rates of deaths and YLLs have shown only modest elevations over the three decades, with these changes being more pronounced in older age groups, particularly among those aged 60 and above (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Similarly, the increase in DALYs reflects a moderate rise in the overall burden of CRC, largely driven by the slight increases in both fatal and non-fatal outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In contrast, YLDs, which represent the chronic disability burden, have significantly escalated, especially in older populations, indicating a growing impact of long-term morbidity associated with CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). This differential pattern of change underscores the increasing importance of addressing chronic disability in the management of CRC related to HFPG.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTrends in CRC burden attributable to HFPG: a comparison between China and global levels\u003c/h2\u003e \u003cp\u003eBetween 1990 and 2021, the age-standardized rates for CRC attributable to HFPG showed both similarities and differences when comparing China to the global average. In China, the ASMR remained relatively stable, with only a slight increase from 0.87 to 0.9 per 100,000. Similarly, DALYs and YLLs showed minimal changes, with increases of 0.25 and 0.15 per 100,000, respectively. The most significant rise in China was observed in YLDs, which increased from 0.41 to 0.98 per 100,000, highlighting a growing burden of chronic disability associated with CRC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Globally, the trends were somewhat parallel, with a small but statistically significant increase in ASMR, DALYs, and YLLs, each rising by approximately 0.3 per 100,000. However, like in China, the most notable global change was in YLDs, which rose by 1.41 per 100,000, reflecting a similar global trend towards increased chronic disability burden due to CRC attributable to HFPG (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). These findings underscore the escalating global and national public health challenge of managing CRC linked to HFPG, particularly the growing impact of long-term disability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChange of age-standardized rates in deaths, DALYs, YLDs, and YLLs for CRC attributable to HFPG between 1990 and 2021 in China and global level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.43, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.45, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24 (-0.10\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89 (0.45, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.51, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31 (0.19\u0026ndash;0.43) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDALYs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.38 (9.52, 29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.25 (9.93, 31.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25 (-0.14\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.46 (9.27, 28.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.31 (10.46, 30.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31 (0.19\u0026ndash;0.43) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYLDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41 (0.19, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.45, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.92 (2.63\u0026ndash;3.21) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63 (0.3, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.46, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.41 (1.33\u0026ndash;1.49) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYLLs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.97 (9.29, 29.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.26 (9.5, 30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15 (-0.20\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.84 (8.94, 27.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.34 (9.98, 29.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27 (0.14\u0026ndash;0.39) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eDALYs, disability-adjusted life-years; YLDs, years lived with disability; YLLs, years of life lost; CRC, colon and rectum cancer; HFPG, high fasting plasma glucose; \u003csup\u003e*\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eJoinpoint analysis of CRC burden attributable to HFPG in China\u003c/h2\u003e \u003cp\u003eThe joinpoint analysis and trends presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveal significant variations in the age-standardized mortality, DALY, YLD, and YLL rates for CRC attributable to HFPG in China between 1990 and 2021. Across the entire period, the AAPC for ASMR showed modest increases, particularly during certain periods such as 1995\u0026ndash;2000 where the rates increased significantly for both sexes. However, from 2000 onwards, the trends generally stabilized, with minor fluctuations observed in more recent years (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). For DALYs, a similar pattern was observed, with significant increases in the late 1990s followed by a stabilization or slight decrease in the 2000s, particularly in females where a notable reduction was observed from 2000 to 2007. Conversely, YLD rates displayed a consistent increase throughout the period, especially during the late 1990s and early 2000s, with males experiencing more pronounced increases. The YLL rates mirrored the mortality trends, with initial increases followed by periods of stabilization, particularly in the last decade, where only minor changes were noted (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-D). These findings highlight the temporal dynamics and gender differences in the burden of CRC attributable to HFPG in China, with specific periods of increased rates, especially during the late 1990s and early 2000s.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrends in age-standardized mortality, DALY, YLD, and YLL rates (per 100,000 persons) among both sexes, males, and females from 1990 to 2021 for CRC attributable to HFPG in China.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAge-standardized mortality rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAge-standardized DALY rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eAge-standardized YLD rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eAge-standardized YLL rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAAPC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAPC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAAPC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAPC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAAPC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAPC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAAPC (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.24 (-0.82\u0026ndash;0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25 (-0.14\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.69 (-1.20 - -0.19) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24 (-0.10\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.25 (-0.24\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.92 (2.63\u0026ndash;3.21) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.73 (-1.23 - -0.22) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.15 (-0.20\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1995\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.95 (2.25\u0026ndash;5.67) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1995\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.42 (1.94\u0026ndash;4.92) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1995\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.24 (4.50\u0026ndash;5.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1995\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.36 (1.89\u0026ndash;4.86) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.27 (-1.81 - -0.72) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2000\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.03 (-1.51 - -0.55) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2000\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.04 (2.67\u0026ndash;3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2000\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.15 (-1.62 - -0.67) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2007\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66 (-1.60\u0026ndash;5.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2007\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.46 (-1.39\u0026ndash;4.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2007\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.83 (2.60\u0026ndash;7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2007\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.33 (-1.50\u0026ndash;4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.20 (-0.43\u0026ndash;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2010\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11 (-0.10\u0026ndash;0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.94 (2.70\u0026ndash;3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2010\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.01 (-0.21\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2019\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.58 (-1.66\u0026ndash;2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.50 (-1.00\u0026ndash;0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.51 (-0.85 - -0.16) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.92 (-1.27 - -0.56) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.58 (-0.83 - -0.34) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.66 (0.19\u0026ndash;1.14) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.24 (1.83\u0026ndash;2.65) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.95 (-1.31 - -0.59) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.68 (-0.93 - -0.43) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1996\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.84 (1.36\u0026ndash;4.35) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1996\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.30 (1.26\u0026ndash;3.36) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1996\u0026ndash;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.93 (3.06\u0026ndash;8.87) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1996\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.24 (1.18\u0026ndash;3.32) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.09 (-2.72 - -1.46) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2000\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.05 (-2.38 - -1.71) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1999\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.14 (2.03\u0026ndash;2.25) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2000\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-2.17 (-2.51 - -1.83) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.13 (-1.44 - -0.82) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2007\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.08 (-1.34 - -0.82) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2016\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.05 (1.29\u0026ndash;6.88) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2007\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.21 (-1.47 - -0.94) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 (-0.57\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2015\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19 (0.14\u0026ndash;2.24) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2019\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.23 (-3.07\u0026ndash;2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2015\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.07 (0.01\u0026ndash;2.13) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.26 (-5.26\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2019\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.60 (-3.78\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2019\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.68 (-3.90\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00 (-0.49\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63 (0.30\u0026ndash;0.96) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.50 (-1.01\u0026ndash;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.45\u0026ndash;1.03) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18 (-0.27\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.33 (3.10\u0026ndash;3.55) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.93 (-1.58 - -0.27) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.63 (0.37\u0026ndash;0.89) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1996\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.82 (3.39\u0026ndash;6.27) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1995\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.24 (2.74\u0026ndash;5.76) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1995\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.84 (5.19\u0026ndash;6.49) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1995\u0026ndash;20011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.03 (2.37\u0026ndash;3.69) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.59 (-1.03 - -0.14) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2000\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.29 (-0.76\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2000\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.71 (3.38\u0026ndash;4.03) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2001\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.65 (-1.25 - -0.04) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2007\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.24 (0.87\u0026ndash;3.62) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2007\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.44 (1.00\u0026ndash;3.90) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2007\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.71 (4.73\u0026ndash;6.69) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2007\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.42 (1.03\u0026ndash;3.84) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2011\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 (-0.34\u0026ndash;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2011\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17 (-0.07\u0026ndash;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2011\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.03 (2.77\u0026ndash;3.30) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2011\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.05 (-0.18\u0026ndash;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.18 (-5.03\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2019\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.29 (-1.76\u0026ndash;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eDALYs, disability-adjusted life-years; YLDs, years lived with disability; YLLs, years of life lost; AAPC, average annual percent change presented for full period; APC, annual percent change; CRC, colon and rectum cancer; HFPG, high fasting plasma glucose; CI, confidence interval. \u003csup\u003e*\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAPC analysis of CRC deaths attributable to HFPG\u003c/h2\u003e \u003cp\u003eThe APC analysis in deaths due to CRC attributable to HFPG in China from 1990 to 2021 reveals distinct patterns across different age groups, birth cohorts, and time periods. The age-specific APC trends showed that younger age groups experienced smaller fluctuations, while older age groups exhibited more pronounced increases in CRC mortality over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In terms of birth cohorts, the analysis indicated that more recent cohorts (those born closer to 1990) experienced higher APCs in CRC mortality, with each line connecting the age-specific APCs for these cohorts showing an upward trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The period-specific APC analysis further demonstrated that CRC mortality rates fluctuated more significantly in older age groups across different time periods, with noticeable variations during specific periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Finally, the birth cohort-specific APC trends highlighted that older cohorts experienced more stable or declining trends, whereas younger cohorts exhibited increasing APCs, particularly in the later years of the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). These findings emphasize the varying impact of HFPG on CRC mortality across different age groups, birth cohorts, and time periods in China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDecomposition of CRC deaths attributable to HFPG\u003c/h2\u003e \u003cp\u003eThe decomposition analysis of changes in the number of deaths due to CRC attributable to HFPG in China from 1990 to 2021 highlights the contributions of aging, epidemiological changes, and population growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Aging was the primary factor driving the increase in CRC deaths across both sexes, with a particularly strong impact observed in males. Epidemiological changes contributed to an increase in mortality among males, while in females, these changes led to a reduction in CRC mortality. Population growth had a more pronounced effect on males, contributing significantly to the rise in CRC deaths, whereas its impact on females was comparatively smaller. These results reflect the differing roles of demographic and epidemiological factors in influencing CRC mortality trends attributable to HFPG in China over the study period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provides a comprehensive analysis of the burden of CRC attributable to HFPG in China from 1990 to 2021, revealing significant trends and demographic patterns. The findings highlight a substantial increase in CRC-related deaths, DALYs, YLDs, and YLLs over the study period, with a particularly pronounced burden observed in males. Joinpoint regression analysis identified key periods where significant changes in these trends occurred, with notable increases during the late 1990s and early 2000s. The decomposition analysis demonstrated that the rising burden of CRC attributable to HFPG was primarily driven by population aging, with additional contributions from epidemiological changes and population growth. Interestingly, while epidemiological changes led to an increase in mortality among males, they contributed to a reduction in mortality among females. Furthermore, the APC analysis revealed distinct age, period, and cohort effects, underscoring the complex interplay of demographic and temporal factors in shaping the observed trends. These findings underscore the growing public health challenge posed by HFPG-related CRC in China, particularly the increasing chronic disability burden and the significant gender disparities in outcomes.\u003c/p\u003e \u003cp\u003eHFPG is recognized globally as a major cause of mortality and disability and is considered an independent risk factor for CRC development [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A meta-analysis of six prospective cohort studies involving 62,814 CRC cases reported a pooled relative risk (RR) of 1.015 (1.012 to 1.019) per 18 mg/dL increase in FPG, with similar RRs across sexes and anatomical sites [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A population-based cohort study found that glycemic control in patients with diabetes was independently linked to the risk of developing colonic adenoma and CRC, showing a biological gradient [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Prospective data from the Malm\u0026ouml; Diet and Cancer cardiovascular cohort, which included 5,144 subjects, showed that elevated blood glucose levels increase the likelihood of developing colon cancer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, a previous meta-analysis of observational studies involving 28,999 participants indicated that diabetes mellitus was linked to a higher likelihood of developing colorectal adenoma and advanced adenoma [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, inadequate control of fasting blood glucose is linked to an increased mortality risk and reduced survival in colon cancer patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The potential mechanisms of this association include hyperinsulinemia, adipose tissue dysfunction-induced inflammation and immunological surveillance impairment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These studies collectively emphasized the heightened risk of colorectal cancer across the diabetes mellitus population.\u003c/p\u003e \u003cp\u003eThe number of individuals diagnosed with diabetes mellitus has significantly increased over the past three decades, rising from 148.5\u0026nbsp;million in 1990 to 437.9\u0026nbsp;million in 2019 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The global prevalence of patients with both CRC and diabetes mellitus is expected to continue growing [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In China, from 1990 to 2019, the number of diabetes patients increased by 160%, from 35.14\u0026nbsp;million to 91.70\u0026nbsp;million, and the age-standardized prevalence rate rose from 4788 per 100,000 to 8170 per 100,000 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The prevalence of diabetes in Chinese adults aged 20\u0026ndash;79 years is projected to increase from 8.2% to 9.7% between 2020 and 2030 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The results of this study highlight a concerning upward trend in the burden of CRC attributable to HFPG, both globally and in China. This trend is closely linked to the rising prevalence of diabetes mellitus, which has become a significant public health issue. In China, the increase in HFPG-related CRC reflects broader global patterns, yet it is exacerbated by the rapid economic and lifestyle transitions that the country has undergone in recent decades. The shift towards a more sedentary lifestyle, coupled with increased consumption of calorie-dense foods, has contributed to a surge in diabetes cases, which in turn has elevated the risk of CRC. This is particularly alarming given the size of China\u0026rsquo;s population, where even a small increase in the prevalence of HFPG can translate into a substantial rise in the absolute number of CRC cases [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. To address this growing burden, cancer prevention strategies and public health interventions should be promoted, and a comprehensive framework for therapeutic agendas should be implemented.\u003c/p\u003e \u003cp\u003eComparatively, while the global burden of CRC associated with HFPG is increasing, the situation in China is particularly severe. Studies have shown that the prevalence of diabetes in China has outpaced that in many other countries, making HFPG a more prominent risk factor for CRC within the Chinese population [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This is further supported by the findings of the GBD study, which indicates a sharper rise in CRC cases and mortality rates in China compared to global averages, largely driven by the increasing prevalence of HFPG [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The differences in trends between China and other regions can be attributed to the unique demographic and epidemiological characteristics of the Chinese population, including rapid urbanization and aging [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The aging population, as evidenced by the global rise in the elderly demographic, exacerbates the impact of HFPG on CRC incidence. According to a report by Saeid Safiri et al., the global elderly population has steadily increased, with projections suggesting that by 2050, 17% of the world\u0026rsquo;s population will be aged 65 and above [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This shift towards an older population means that more individuals are living with chronic conditions, including diabetes, for longer periods, which increases the cumulative burden of diseases like CRC. Aging further amplifies the effects of diabetes on CRC risk, as older adults tend to have a more sedentary lifestyle, lower metabolic resilience, and reduced capacity to manage chronic diseases, making them particularly vulnerable to the compounding effects of HFPG [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn China, the elderly population is growing rapidly, and with this demographic shift, the burden of CRC attributable to HFPG is expected to escalate. The interplay of aging, increased diabetes prevalence, and lifestyle changes necessitates more focused interventions, such as tailored public health policies aimed at preventing both diabetes and CRC. Aging-related factors, such as declining immune function and the presence of comorbidities, may also enhance the progression of CRC, necessitating comprehensive screening and early detection programs targeting the elderly [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The upward trend in CRC burden attributable to HFPG in China underscores the need for targeted public health interventions. While global efforts to control diabetes and related metabolic disorders have shown some success, the rapid rise of these conditions in China suggests that more aggressive and culturally tailored strategies are needed. This includes not only enhancing diabetes prevention and management programs but also integrating cancer prevention strategies that specifically address the challenges of aging populations.\u003c/p\u003e \u003cp\u003eDespite the significant contributions of this study, several limitations must be acknowledged. First, the reliance on data from the GBD database introduces potential inaccuracies due to the use of model-based estimates and variability in data quality across regions, particularly in areas where healthcare data are less reliable. This could lead to under- or over-estimation of the true burden of CRC attributable to HFPG. Second, this study did not account for potential confounding factors such as diet, physical activity, and other lifestyle behaviors that are independently linked to CRC risk. These factors could influence the observed associations between HFPG and CRC outcomes. Third, the study\u0026rsquo;s observational nature limits the ability to establish a causal relationship between HFPG and CRC, as it is based on population-level data and correlations rather than controlled experiments. Additionally, while the study focused on the Chinese population, the findings may not be fully generalizable to other countries or populations with different healthcare systems, genetic backgrounds, and environmental exposures. Future studies should prioritize integrating individual-level data and consider potential confounders more comprehensively. Longitudinal studies tracking patients over time would help clarify causal relationships and identify specific mechanisms linking HFPG to CRC. Comparative studies across diverse populations are also necessary to assess whether similar trends are evident in different regions, which would provide further insight into the global applicability of these findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study underscores the significant impact of HFPG on the burden of CRC in China. The increasing prevalence of HFPG as a major modifiable risk factor calls for urgent and coordinated public health interventions. Addressing HFPG through enhanced diabetes management and preventive strategies may be a key approach to mitigating the growing CRC burden. Future research should focus on exploring the underlying mechanisms linking HFPG to CRC development, while also examining the effectiveness of integrated metabolic and oncologic interventions. Expanding comparative studies across diverse populations will further clarify whether similar trends are evident globally. Moreover, longitudinal and patient-level studies are needed to establish stronger causal relationships and to identify modifiable factors that can be targeted to prevent CRC progression. Comprehensive strategies that focus on metabolic health could play a crucial role in reducing CRC incidence and improving patient outcomes in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data utilized in this study were sourced from the GBD 2021 database, which is publicly available. The datasets analyzed in this study can be accessed at the IHME website: http://ghdx.healthdata.org/gbd-results-tool.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study used de-identified GBD 2021 data, complying with the Declaration of Helsinki, relevant guidelines, and GATHER standards[39], without requiring ethical approval or informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that financial support was received for the research and/or publication of this article. All expenses associated with this study were self-funded by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYaqin Qian:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Writing - Original Draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZhouwei Zhan:\u003c/strong\u003e Data Curation, Investigation, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBijuan Chen:\u003c/strong\u003e Software, Validation, Visualization, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFan Wang:\u003c/strong\u003e Supervision, Project Administration, Writing - Review \u0026amp; Editing, Corresponding Author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYing Yin:\u003c/strong\u003e Supervision, Project Administration, Writing - Review \u0026amp; Editing, Corresponding 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\u003cstrong\u003e388\u003c/strong\u003e(10062):e19-e23.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colon and rectum cancer, high fasting plasma glucose, China, Global Burden of Disease Study, epidemiology, joinpoint analysis","lastPublishedDoi":"10.21203/rs.3.rs-8603727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8603727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe burden of colon and rectum cancer (CRC) attributable to high fasting plasma glucose (HFPG) in China has been increasing, reflecting a significant public health challenge. Understanding the temporal trends and contributing factors to this burden is crucial for effective prevention and intervention strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were extracted from the Global Burden of Disease (GBD) Study for the period between 1990 and 2021. Age-standardized mortality rates (ASMR), disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) were analyzed using joinpoint regression to identify significant changes over time. The analysis also included a decomposition of the factors contributing to the increase in CRC deaths, including aging, epidemiological changes, and population growth.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn 2021, the burden of CRC attributable to HFPG in China was substantial, with 18,440 deaths, predominantly among males. Males had higher age-standardized rates across all outcomes, with an ASMR of 1.26 per 100,000 compared to 0.62 per 100,000 in females. The joinpoint analysis revealed significant increases in ASMR, DALYs, and YLLs during the late 1990s and early 2000s, particularly among males. A significant rise in YLDs was observed, indicating an increasing burden of chronic disability. Comparatively, global trends mirrored those in China, with slight increases in ASMR, DALYs, and YLLs, but a more pronounced global rise in YLDs. Decomposition analysis showed that aging was the primary driver of the increase in CRC deaths, particularly among males, while epidemiological changes contributed to reduced mortality in females.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe burden of CRC attributable to HFPG in China has risen significantly over the past three decades, driven primarily by aging and, to a lesser extent, epidemiological changes and population growth. These findings highlight the need for targeted interventions to address the growing impact of HFPG on CRC outcomes, particularly the increasing chronic disability burden.\u003c/p\u003e","manuscriptTitle":"High fasting plasma glucose and the burden of colon and rectum cancer in China trends mortality and disability from 1990 to 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-15 15:05:02","doi":"10.21203/rs.3.rs-8603727/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1cba418b-5271-4297-a489-989f67ffad9e","owner":[],"postedDate":"February 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T11:42:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-15 15:05:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8603727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8603727","identity":"rs-8603727","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}