Skip to main content
Download PDF

The Presence of Diabetes Mellitus or Pre-diabetes Mellitus Increases Mortality from Heart Disease in a Taiwanese Population: A 10-year Follow-Up Study

BMC Cardiovascular Disorders volume 23, Article number: 375 (2023) Cite this article

Abstract

Background

We evaluated hyperglycemia-associated mortality in the Taiwanese population by conducting a 10-year retrospective cohort study. Methods: From 2007 to 2017, all participants, regardless of their age or underlying diseases, were identified at a Health Screening Center in Taiwan. Overall, 114,534 participants were included in the analysis. They were classified into three subgroups according to glycemia and smoking status by combining survival for data analysis. Results: The mean follow-up time, age, and body mass index (BMI) were 8.14 ± 2.22 years, 40.95 ± 12.14 years, and 23.24 ± 3.65 kg/m2, respectively. The cumulative death rate increased from 0.9% in the normal fasting blood glucose(FBG) subgroup to approximately 6% in the diabetes FBG subgroup. After adjusting for age, gender, BMI, high-density lipoprotein, triglycerides, waist circumference(WC), and smoking status, the hazard ratio (HR) for all-cause, cancer, and heart disease mortality in the diabetes mellitus(DM) subgroup was 1.560, 1.381, and 1.828, respectively.HR was 0.989 in all-cause, 0.940 in cancer, and 1.326 in heart disease in the pre-DM subgroup.

Conclusion

Being tested for pre-DM is related to a higher risk of death from heart disease in the Taiwanese population at baseline. Therefore, cardiovascular risk must be actively measured among diabetes patients every visit.

Peer Review reports

Introduction

Several global challenges are related to hyperglycemia, and the most important challenge is diabetes mellitus (DM). The International diabetes foundation (IDF) released the 10th edition (latest edition) in 2021, confirming that diabetes is one of the fastest growing issues in the 21st century, with an estimate of 537 million adults with type 2 DM. IDF predicted that the diabetes population will increase to 51% in 2045, and it indicated that globally, 783 million people will have diabetes in 2045. Approximately 541 million people were estimated to have impaired glucose tolerance in 2021. Moreover, IDF had estimated that more than 6.7 million people aged 20–79 would die due to diabetes-related causes in 2021. Furthermore, [1, 2]. Lifestyles have changed in the modern economy; people are becoming physically sedentary and eating more carbohydrates. The healthcare system, especially in Taiwan, is threatened by the increasing prevalence of hyperglycemic population. Most healthcare-related costs are covered by national health insurance. Previous studies reported that insulin resistance was the main reason for the increasing number of patients with DM in Asia [3].

DM causes two types of vascular complications: microvascular and macrovascular. Macrovascular complications include coronary artery disease, ischemic cerebrovascular disease, and peripheral arterial disease (PAD), whereas the microvascular complication includes retinopathy. DM worsens and accelerates the course of atherosclerotic cardiovascular disease (CVD). Moreover, it is associated with an increased predisposition to the formation of modified lipoproteins, which are more atherogenic. In addition, oxidatively modified LDL particles are phagocytized by macrophages, leading to the formation of foam cells. S [4,5,6]. Furthermore, the increased risk of atherosclerosis in the population with DM characteristically results in several consequences: (1) A higher risk of stent restenosis following endovascular treatments, such as percutaneous coronary intervention and percutaneous transluminal angioplasty; (2) A higher severity of peripheral arterial disease in patients with peripheral arterial disease coexisting with diabetes; and (3) A higher amputation rate in patients with peripheral arterial disease coexisting with diabetes [7].

Similarly, hyperglycemia is associated with cognitive morbidity and mortality [8,9,10]. Globally, patients with DM have a higher mortality rate than those without diabetes [11]. Moreover, an Asian study indicated that patients with DM are more susceptible to cancer, with a prevalence rate of 8–18% [12, 13].

However, among patients with DM, hyperglycemia is rarely listed as the first cause on the death certificate. .

. We introduce a novel article analyzing the hyperglycemic risk of Pre-DM and DM among all-cause mortality, heart disease mortality and cancer mortality in middle-aged Asian populations.

It was well-known in the literature that DM increases all-cause mortality. However, the impact of DM and pre-DM on difference cause of mortality is uncertain.

Therefore, this study aimed to evaluate whether DM and pre-DM increased different cause of mortality in the Taiwanese population after 10 years follow up.

Methods

Study population

This retrospective cohort study retrieved data from a large private health examination institute (MJ Health Screening Center) in Taiwan from 2007 to 2017. The institute offers paid health examinations in large districts in Taiwan with a general payment of approximately USD 200–730. The demographics of the enrolled patients were assumed to be similar to those of the general Taiwanese population [14].

In total, 114,534 participants, including 56,885 males and 57,649 females, had undergone the annual health examination at the MJ Health Screening Center, and they were followed up for at least 8.14 ± 2.22 years. We retrived twice blood glucose data from two healthchecks at different times. (Fig. 1)The mortality rate was verified in 2021 using the Death Registration from the ministry of Health and Welfare in Taiwan. The cause of death was defined in the tenth revision of the International Classification of Disease (ICD)-10, including heart diseases (ICD-10-CM code I01-I02.0, I05-I09, I20-I25, I27, I30-I52) and cancers (C00-C97). Survival years were estimated from 1996 to 2021.We retreived death records from 2007 to 2017.

Data used in this research were authorized by the MJ Health Research Foundation (Authorization Code: MJHRF2019016A) and were received from the same foundation. This study was approved by the Tri-service General Hospital Institutional Review Board (number: A202005160), and it was conducted in accordance with the principles stated in the Declaration of Helsinki. The Tri-service General Hospital is under the supervision of the School of medicine, National Defense Medical Center. Any interpretations or conclusions presented in this paper do not represent the views of the MJ Health Research Foundation. All participants in the research provided written informed consent before the health examination to authorize the data analysis. Personal identification data were removed by the MJ Health Research Foundation; hence, the participants remained anonymous during the entire research process. Details of the study population and data collection are described and reported elsewhere.

2.2. Definition of Baseline Parameters:

Clinical data and baseline parameters, including BMI, blood pressure, WC, high-density lipoprotein (HDL), triglycerides (TG), and fasting blood glucose (FBG), were collected. The Homogeneous Direct method (TOSHIBA C8000) was used for HDL cholesterol, the GPO-POD-ESPT method (TOSHIBA C8000) was used for TG level, and the HK.G-6-PD.NADP method (TOSHIBA C8000) was used for FBG level. Waist measurements of the upper hip bone and the bottom of the ribs, with no clothing interfering with the measurement were taken at the center. Smoking status were defined as follow: non-smoker, second hand smoker, smoker and quit smoking (individuals ever had cigarettes exposure) .

Definition of DM and hyperglycemia

In 2022, the American Diabetes Association (ADA) defined a FBG level of > 100 mg/dl as hyperglycemia. Moreover, FBG levels of 100–125 mg/dl (5.6–6.9 mmol/dl) could be used for the diagnosis of prediabetes, whereas patients with twice the defined FBG levels, i.e., those with FBG of > 126 mg/dl (7.0 mmol/L) were defined diabetes mellitus. Fasting was defined as no caloric intake for ≥ 8 h [15].

Subgroup analysis

We classified the patients into the following three subgroups according to their FBG status: diabetes mellitus (DM) subgroup: In twice separately tests, FBG values > 126 mg/dl; pre-diabetes mellitus (pre-DM) subgroup: FBG 100–125 mg/dl, and normal: FBG values < 100 mg/dl.

Statistical analysis

The patients were examined from 2007 to 2017. The primary endpoint was the data of the cause of death, classified into all-cause mortality, mortality due to cancer, and mortality due to heart disease, associated with the death registry of the Ministry of Health and Welfare of Taiwan. An independent t-test was performed to compare follow-up time, age, and BMI between the participants with and without survival. We constructed normal and abnormal categories of WC, TG, and HDL levels by combining survival for data analysis. Furthermore, we constructed three subgroups of FBG level and smoking status by combining survival for data analysis. The Pearson chi-square test was used for dichotomous variables. Kaplan–Meier (KM) curve was used to analyze the cumulative survival of each category (all-cause mortality, mortality due to cancer, and mortality due to heart diseases). Crude hazard ratio (HR) with 95% confidence intervals (95% CI) was estimated using Cox regression models to assess the association between FBG levels and the risk of mortality. Finally, cox proportional hazards regression models were used to estimate the HR and 95% CI for mortality risk between the normal FBG, pre-DM, and DM subgroups after adjusting for gender, age, BMI, FBG, smoking status, HDL, TG, and WC. A forest plot was used to compare the risk between all-cause mortality, mortality due to heart disease,, and mortality due to cancer. The SPSS 22.0 (IBM, Armonk, NY, USA) was used for statistical analysis. The p-value of < 0.05 indicated statistical significance.

Results

Characteristics of the participants

Overall, 114,534 participants were included in the analysis. Basic patient characteristics and the distribution of demographics by FBG status are summarized in Tables 1 and 2, respectively. The means of follow-up time, age, and BMI were 8.14 ± 2.22 years, 40.95 ± 12.14 years, and 23.24 ± 3.65 kg/m2, respectively. During the follow-up, 1579 (1.4%) cases died. Among the participants, significant differences (p < 0.001) were observed in the cumulative incidences of mortality between BMI, WC, FBS, TG, HDL, gender, and smoking status. The cumulative incidence of mortality increased from 0.9% in the normal FBG group participants to 6.0% in the DM group participants (p < 0.001).

In Table 2, people with diabetes are older and have an increased BMI with gradual increase in value from normal-pre-diabetes-diabetes category. Inversion of the difference between normal and abnormal proportions observed in WC, TG and HDL. In addition, same trend is noted between quitted smoker and second-hand smoker.

Fig. 1
figure 1

Flowchart of participants enrolment

Full size image
Table 1 Distribution of the demographics
Full size table
Table 2 Distribution of the demographic by FBG status
Full size table

Cox regression analysis of mortality risk in patients with and without DM

Table 3 shows the association between DM and the risk of mortality. Compared with the normal FBG subgroup, significantly increased risk of all-cause mortality was noted in the pre-DM (HR: 1.982, 95% CI: 1.795–2.187) and DM subgroups (HR: 6.209, 95% CI: 5.427–7.104). Similar results were observed when we used a separate analysis for mortality risk assessment due to cancer and heart disease.

Table 3 Cox regression analysis of mortality risk in patients with and without DM
Full size table

Moreover, we used cox proportional-hazards model with Kaplan–Meier (KM) curve to analyze the cumulative survival of each category (all-cause mortality, mortality due to cancer, and mortality due to heart diseases); these are presented in Fig. 2. Regardless of whether we compared the cumulative survival of the all-cause mortality category, cancer, or heart disease category, after approximately 10 years of follow-up, we were able to see a significant decrease in cumulative survival in the DM group versus the pre-DM and normal subgroups.

Fig. 2
figure 2

Cox regression model for mortality (unadjusted) among (a) all-cause, (b) cancer, (c) and heart disease

Full size image

Pooled estimated of adjusted HRs for mortality cause according to categories of FBG subgroups

Figure 3 displays another pooled estimate of adjusted HRs for all-cause mortality, heart disease mortality, and cancer mortality according to the categories of FBG. Compared to the normal FBG subgroup, the HR for all-cause mortality in DM subgroup was 1.560 (95% CI: 1.338–1.819, p < 0.001). A significantly higher risk of heart disease-related mortality was observed in participants of the DM subgroup (HR: 1.828, 95% CI: 1.117–2.992, p < 0.001). When we focused on the HRs for cancer-related mortality, the DM subgroup had significantly higher rates than the normal FBG subgroup (HR: 1.381, 95% CI: 1.093–1.746, p < 0.05).

We can see the data on Fig. 3,Compared to the normal FBG subgroup, the HR for all-cause mortality in pre-DM subgroup was 0.989 (95% CI: 0.888–1.102, p > 0.05), there was no significantly increase. Compared to the normal FBG subgroup, the HR for heart disease-related mortality in pre-DM subgroup was 1.326 (95% CI: 0.920–1.911, p > 0.05), there was no significantly increase. When we focused on the HRs for cancer-related mortality, the pre-DM subgroup also didn’t have significantly higher rates than the normal FBG subgroup 0.940 (95% CI: 0.806–1.097, p > 0.05).

The HRs for mortality causes between the pre-DM and normal FBG subgroups did not significantly increase after adjusting for age, gender, BMI, HDL, TG, WC, and smoking status.

Fig. 3
figure 3

Forrest plot of hazard ratio of mortality by Cox regression. HRs were adjusted for age, gender, BMI, HDL, TG, WC, and smoking status

*:p < 0.05; **:p < 0.01; ***:p < 0.001

Full size image

Discussion

Current study of a general Taiwanese population showed that fasting blood glucose levels within the pre-diabetes range (100–125 mg/dl) do not significantly increase the risk of all-cause mortality, cancer, or heart disease. However, once pre-diabetes progresses to diabetes (FBG > 125 mg/dl), there is a significant increase in the risk of all-cause mortality, cancer, and heart disease. Therefore, it is important to prevent the progression of pre-diabetes to diabetes through lifestyle changes such as healthy diet and regular exercise.

There is one meta-analysis conducted by China researchers including 31,662 article, 129 studies. 75% of studies are in Europe and Asia. The results demonstrated that having prediabetes will increase the risk of heart disease (relative risk 1.09; 1.04–1.14 95% CI). In comparison, prediabetes increases the risk of all-cause mortality with similar results to heart disease (RR 1.07, 1.03–1.12 95% CI). Xiaoyan Cai et al. revealed all-cause mortality had relative risk of 1.03 in pre-DM group. In the subgroup analysis in above Chinese study, participant’s aged < 60 had relative risk of 1.16 [16]. In addition, Chinese researchers found similar HR with 0.89 in all-cause mortality after adjusted for age, sex, BMI, cigarette smoking, blood pressure and HDL cholesterol, LDL cholesterol and triglycerides in same population as this study. Though, the mean age was 57.7 ± 8.9 [17]. In our study, the overall average of participant’s was 40.86 ± 12.08. Scarcely the relative risk between all-cause mortality and heart disease. In addition, another meta-analysis conducted by European researchers depict being pre-diabetes had a HR 1.27 in CV mortality (1.02–1.58 95% CI) [18].

When humans is in postparidal status, our islet β cells in pancrease produce insulin into blood system for blood glucose control. The bondary of insulin and insulin receptors in cell membranes facilitate glucose uptake by the cell. As a result, leading to the decreased of blood glucose. Deteroirtion of the insulin production in islet β cells or dysfunction of the regulation between glucose transporter translocation in cell membrane, or both, lead to hyperglycemia. Anminal studys in late twentieth century revealed an increased in inflammation mediators ,for example, the tumor necrosis factor( TNF)- α, interleukin(IL)-6, C-reative protein among obese mice [19, 20]. Insulin receptor substrate(IRS)-1 and − 2 tyrosine phosphorylated after the binding of insulin and insulin receptor regulated the normal glucoe metabolism. Insulin resistance was casused by Serine phosphorylation of IRS substrates by jun N-terminal kinase (JNK1), and inhibitor of NF-κB-kinase β (IKKβ, an NF-κB-activating kinase) [21] Decreased insulin secretion and insulin resistance leads to hyperglycemia.

Hyperglycemia may be detected using fasting blood glucose levels, oral glucose tolerance test, and HbA1c levels. These three methods measure different physiological processes. A1c has stablity and measurability in randaom versus fasting blood glucose. However, A1c could be affected by genetically variation of hemoglobin structure [22].A Dutch study by vant Riet et al. found that the correlation coefficient between glucose tolerance test and HbA1c levels differs between known diabetes, newly diagnosed diabetes, and impaired glucose tolerance [23]. The information about the measurement of glycemia remains unclear [24]. Additionally, a study from India reported considerable disagreement regarding the use of the FBG criterion (FBG > 126 mg/dl) or HbA1c ≥ 6.5% to determine DM [25] The HbA1c and glucose correlation coefficients were either insignificant or uncorrelated [23] The result of Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial, account > 12,500 people, documented clear graded relationship between FBG ≥ 100 (5.6mmol/L) and A1c levels. And is unaffected by geography or ethnic group [26].Among our study population, most cases were of impaired glucose tolerance (FBG: 100.24 ± 18.96; Table 1).Thailand researchers stuided correlation between HbA1c and FBG in 3 hemoglobulin group(normal, hbE and high hbA2), came to conclusion with a positive association between estimated average glucose and FBG. Estimated averge glucose was calculated through equation from HbA1c [27].

German study investigating the impact of hyperglycemia on remodeling of the heart by cardiac MR imaging, which include 390 individuals (39%) was prediabetes and 49 (4.9%) were found to have unknown type 2 diabetes. A positive association was established between hyperglycemia and hypertrophy of the left ventricle. Net effect of hyperglycemia resulted in a concentric of left ventricle, which was unconstrained of other confounders, such as hypertension. To summarized, concentric remodeling of left ventrile result in arterial stiffness, smaller left ventricle size and higher thickness of left ventricle [28]An Asian study revealed that the fasting hyperglycemia was a risk factor of atrial fibrillation in patients with acute myocardial infarction [29]. During Coronavirus Disease 2019 (COVID-19) pandemic, a study found an increased deathrate in patients with new-onset hyperglycemia without diabetes, than those having pre-existing diabetes (41.7% vs. 14.8%, respectively; <0.001).

Saudi Arabia researchers conducted 679 recruited anticipants, with approximately the same age with this study. 32 ± 11.8,40 ± 12.14, respectively [30]. BMI and TG showed correlation with pre-diabetes, but not in the HDL variance and smoking status. Another middle east study investigates a city with approximately 3.5 million populations, with mean age 35.7 ± 15.44 years, reported BMI, WC, and dyslipidemia are correlated with DM. However, dyslipidemia merely reach statistic significant (P = 0.004 in men and 0.064 in women) in pre-diabetes. Neither the smoking status nor WC [31]the differences in correlation between hyperglycemia and dyslipidemia cross studies might be attributed to the different methods used for defining prediabetes, as well as the difference in age between the populations studied.

The current study findings provide epidemiological evidence indicating that in the Taiwanese population, experiencing Pre-DM (FBG 100-125 mg/dl) is associated with increased mortality due to heart disease. Using FBG data in large number of populations, with the Death Registration from ministry of Health and Welfare in Taiwan, and a retrospective follow-up of 10 years, we were able to examine and compare hyperglycemia with pre-DM and DM related mortality issues in Taiwan. The results of the current study form a cornerstone of highlightening the heart-relate mortality issue in pre-diabete group.

[32]

The HRs for all-cause mortality in the DM subgroup, regardless of age and sex (Table 2; Fig. 3), demonstrated a similar results to that of the Sweden cohort [33]. The increased mortality rate among DM population have been reported since 1985 [34]. The higher risk of mortality among DM people is brought on by the cardiovascular consequences of DM. The Multiple Risk Factor Intervention Trail Group trials showed how diabetes affects cardiovascular system of males between the ages of 35 and 57. Using the fasting blood glucose as a subgroup, Eberly et al. demonstrated that a fasting glucose level greater than 126 mg/dl predicts an increased risk of mortality [35, 36]. Another recent study by Mansor et al., using the US National Health and Nutrition Examination Survey data, revealed that males aged 30–84 years with diabetes had a higher mortality rate than females from 1999 to 2010 [37].

The cumulative survival of the pre-DM and DM subgroups significantly decreased after approximately 10 years in the cox proportional-hazards model using the Kaplan–Meier (KM) curve of all-cause mortality, heart disease, and cancer mortality (Fig. 2). Fortunately, the advancement in technology led to a decrease in death rates due to vascular disease among individuals with [38,39,40,41] and without diabetes [39, 42].A similar trend was also reported in Asian countries [43].

In summary, the current study has three main strengths. First, to the best of our knowledge, this is the first study with youngest age of participants to be conducted in Taiwan that shows how hyperglycemia(DM or pre-DM) affects mortalities. Second, the profound effect of deteriorated glucose metabolism started from heart disease. Mortality from heart disease had higher HR than all-cause mortality and cancer in pre-diabetes populations. The influence of having a DM diagnosis increase the all-cause mortality was well-established in the NHID database [40,41,42,43]. The presence of DM appears to be a risk factor for all-cause mortality and mortality due to cancer and heart disease worldwide [14, 42]. Third, the population size enrolled in our study is sufficient to represent the general Taiwanese population. Forth, The ethnic groups we studied represent the most important productive forces in a country, our study demonstrate how pre-DM or DM impact those young adults from the begining.

This study also had some limitations.

First, the lack of sufficient HbA1c data in our database stems from the fact that HbA1c is not a routine test among the MJ Health Screening Center .Hence, we chose FBG instead of HbA1c for subgroup analysis. Additionally, FBG is more immediate and accessible in day-to-day practice.

Second, we were unable to discover the detailed competing cause of death, socio-economic factors the drug record, amount of cigarette exposure and detailed occupational data of each individual in this study. The MJ database was compulsive with adult health examination, which is paid by Taiwan government and self-paid physical health examination. Third, the cause of mortality, especially in heart disease and cancer, we can’t precisely classify heart disease and type of cancer.

Conclusions

The current study offers epidemiological evidence indicating that being tested as diabetes or pre-DM, is linked to a higher mortality due to heart disease than all-cause and cancer in the Taiwanese population. How to early implement heart prevention in those pre-diabetes population is a public issue for government in Taiwan.Moreover, more precautions should be adopted for pre-DM populations (FBG 100–125 mg/dl) because the all-cause mortality risk will be increased approximately 50% when they progress to be diagnosed with DM.

Data availability

Data are available from the MJ Health Research Foundation. Due to the legal restrictions imposed by the government of Taiwan in relation to the “Personal Information Protection Act,” data cannot be made publicly available.

Abbreviations

BMI:

Body mass undex

FBG:

Fasting blood glucose

WC:

Waist cirvumference

HR:

Hazard ratio

DM:

Diabetes mellitus

IDF:

International diabetes foundation

PAD:

Peripheral arterial disease

CVD:

Cardiovascular disease

ICD:

International Classification of Disease

HDL:

High-density lipoprotein

TG:

Triglycerides

ADA:

American Diabetes Association

KM:

Kaplan–Meier

HR:

Crude hazard ratio

References

  1. WHITING D. IDF Diabetes Atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract. 2011;94:311–21.

    Article  PubMed  Google Scholar 

  2. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, Stein C, Basit A, Chan JC, Mbanya JC. IDF diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Research and Clinical Practice; 2021. p. 109119.

  3. Ma RC, Chan JC. Type 2 diabetes in East Asians: similarities and differences with populations in Europe and the United States. Volume 1281. Annals of the New York Academy of Sciences; 2013. pp. 64–91. 1.

  4. Warraich HJ, Rana JS. Dyslipidemia in diabetes mellitus and cardiovascular disease. Cardiovasc Endocrinol. 2017;6(1):27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Eckel RH, Bornfeldt KE, Goldberg IJ. Cardiovascular disease in diabetes, beyond glucose. Cell Metabol. 2021;33(8):1519–45.

    Article  CAS  Google Scholar 

  6. Jakubiak GK, Cieślar G, Stanek A. Nitrotyrosine, Nitrated Lipoproteins, and Cardiovascular Dysfunction in patients with type 2 diabetes: what do we know and what remains to be explained? Antioxidants. 2022;11(5):856.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jakubiak GK, Pawlas N, Cieślar G, Stanek A. Pathogenesis and clinical significance of in-stent restenosis in patients with diabetes. Int J Environ Res Public Health. 2021;18(22):11970.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Stratton IM, Adler AI, Neil HAW, Matthews DR, Manley SE, Cull CA, Hadden D, Turner RC, Holman RR. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000;321(7258):405–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bordier L, Doucet J, Boudet J, Bauduceau B. Update on cognitive decline and dementia in elderly patients with diabetes. Diabetes Metab. 2014;40(5):331–7.

    Article  CAS  PubMed  Google Scholar 

  10. Ott A, Stolk R, Van Harskamp F, Pols H, Hofman A, Breteler M. Diabetes mellitus and the risk of dementia: the Rotterdam Study. Neurology. 1999;53(9):1937–7.

    Article  CAS  PubMed  Google Scholar 

  11. Raghavan S, Vassy JL, Ho YL, Song RJ, Gagnon DR, Cho K, Wilson PWF, Phillips LS. Diabetes Mellitus-Related all-cause and Cardiovascular Mortality in a national cohort of adults. J Am Heart Assoc. 2019;8(4):e011295.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Chen Y, Wu F, Saito E, Lin Y, Song M, Luu HN, Gupta PC, Sawada N, Tamakoshi A, Shu X-O. Association between type 2 diabetes and risk of cancer mortality: a pooled analysis of over 771,000 individuals in the Asia Cohort Consortium. Diabetologia. 2017;60(6):1022–32.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Barone BB, Yeh HC, Snyder CF, Peairs KS, Stein KB, Derr RL, Wolff AC, Brancati FL. Long-term all-cause mortality in cancer patients with preexisting diabetes mellitus: a systematic review and meta-analysis. JAMA. 2008;300(23):2754–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Wu X, Tsai SP, Tsao CK, Chiu ML, Tsai MK, Lu PJ, Lee JH, Chen CH, Wen C, Chang SS, Hsu CY, Wen CP. Cohort Profile: the Taiwan MJ Cohort: half a million chinese with repeated health surveillance data. Int J Epidemiol. 2017;46(6):1744–1744 g.

    Article  PubMed  Google Scholar 

  15. Li G, Han L, Wang Y, Zhao Y, Li Y, Fu J, Li M, Gao S, Willi SM. Evaluation of ADA HbA1c criteria in the diagnosis of pre-diabetes and diabetes in a population of chinese adolescents and young adults at high risk for diabetes: a cross-sectional study. BMJ open. 2018;8(8):e020665.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Cai X, Zhang Y, Li M, Wu JH, Mai L, Li J, Yang Y, Hu Y, Huang Y. Association between prediabetes and risk of all cause mortality and cardiovascular disease: updated meta-analysis. BMJ. 2020;370:m2297.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lu J, He J, Li M, Tang X, Hu R, Shi L, Su Q, Peng K, Xu M, Xu Y, Chen Y, Yu X, Yan L, Wang T, Zhao Z, Qin G, Wan Q, Chen G, Dai M, Zhang D, Gao Z, Wang G, Shen F, Luo Z, Qin Y, Chen L, Huo Y, Li Q, Ye Z, Zhang Y, Du R, Cheng D, Liu C, Wang Y, Wu S, Yang T, Deng H, Li D, Lai S, Bloomgarden ZT, Chen L, Zhao J, Mu Y, Ning G, Wang W, Bi Y, Group ftCS. Predictive value of fasting glucose, postload glucose, and Hemoglobin A1c on risk of diabetes and complications in chinese adults. Diabetes Care. 2019;42(8):1539–48.

    Article  CAS  PubMed  Google Scholar 

  18. Schlesinger S, Neuenschwander M, Barbaresko J, Lang A, Maalmi H, Rathmann W, Roden M, Herder C. Prediabetes and risk of mortality, diabetes-related complications and comorbidities: umbrella review of meta-analyses of prospective studies. Diabetologia. 2022;65(2):275–85.

    Article  CAS  PubMed  Google Scholar 

  19. Hotamisligil GS, Shargill NS, Spiegelman BM. Adipose expression of tumor necrosis factor-α: direct role in obesity-linked insulin resistance. Science. 1993;259(5091):87–91.

    Article  CAS  PubMed  Google Scholar 

  20. Kershaw EE, Flier JS. Adipose tissue as an endocrine organ. J Clin Endocrinol Metabolism. 2004;89(6):2548–56.

    Article  CAS  Google Scholar 

  21. Odegaard JI, Chawla A. Alternative macrophage activation and metabolism. Annu Rev Pathol. 2011;6:275.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Nathan DM, Singer DE, Hurxthal K, Goodson JD. The clinical information value of the glycosylated hemoglobin assay. N Engl J Med. 1984;310(6):341–6.

    Article  CAS  PubMed  Google Scholar 

  23. van ‘t Riet E, Alssema M, Rijkelijkhuizen JM, Kostense PJ, Nijpels G, Dekker JM. Relationship between A1C and glucose levels in the general dutch population: the new Hoorn study. Diabetes Care. 2010;33(1):61–6.

    Article  Google Scholar 

  24. Lippi G, Targher G. Glycated hemoglobin (HbA1c): old dogmas, a new perspective? Clin Chem Lab Med. 2010;48(5):609–14.

    Article  CAS  PubMed  Google Scholar 

  25. Prakaschandra R, Naidoo DP. Fasting plasma glucose and the HbA1c are not optimal screening modalities for the diagnosis of New Diabetes in previously undiagnosed asian Indian Community participants. Ethn Dis. 2018;28(1):19–24.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ramachandran A, Riddle MC, Kabali C, Gerstein HC. o.b.o.t.O. Investigators, Relationship between A1C and fasting plasma glucose in Dysglycemia or Type 2 diabetes: an analysis of baseline data from the ORIGIN trial. Diabetes Care. 2012;35(4):749–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sriwimol W, Choosongsang P, Choosongsang P, Petkliang W, Treerut P. Associations between HbA1c-derived estimated average glucose and fasting plasma glucose in patients with normal and abnormal hemoglobin patterns. Scand J Clin Lab Invest. 2022;82(3):192–8.

    Article  CAS  PubMed  Google Scholar 

  28. Markus MRP, Rospleszcz S, Ittermann T, Baumeister SE, Schipf S, Siewert-Markus U, Lorbeer R, Storz C, Ptushkina V, Peters A, Meisinger C, Bamberg F, Nauck M, Bahls M, Völzke H, Felix SB, Bülow R, Rathmann W, Dörr M. Glucose and insulin levels are associated with arterial stiffness and concentric remodeling of the heart. Cardiovasc Diabetol. 2019;18(1):145.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Li M, Gao Y, Guo K, Wu Z, Lao Y, Li J, Huang X, Feng L, Dong J, Yuan Y. Association between Fasting Hyperglycemia and New-Onset Atrial Fibrillation in patients with Acute myocardial infarction and the impact on short- and long-term prognosis. Front Cardiovasc Med. 2021;8:667527.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Al Amri T, Bahijri S, Al-Raddadi R, Ajabnoor G, Al Ahmadi J, Jambi H, Borai A, Tuomilehto J. The Association between Prediabetes and Dyslipidemia among Attendants of Primary Care Health Centers in Jeddah, Saudi Arabia. Diabetes Metab Syndr Obes. 2019;12:2735–43.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Bahijri SM, Jambi HA, Al Raddadi RM, Ferns G, Tuomilehto J. The prevalence of diabetes and Prediabetes in the Adult Population of Jeddah, Saudi Arabia–A Community-Based survey. PLoS ONE. 2016;11(4):e0152559.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Singh AK, Singh R. Hyperglycemia without diabetes and new-onset diabetes are both associated with poorer outcomes in COVID-19. Diabetes Res Clin Pract. 2020;167:108382.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Jansson SPO, Andersson DKG, Svärdsudd K. Mortality Trends in subjects with and without diabetes during 33 years of follow-up. Diabetes Care. 2009;33(3):551–6.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Muggeo M, Verlato G, Bonora E, Bressan F, Girotto S, Corbellini M, Gemma M, Moghetti P, Zenere M, Cacciatori V. The Verona diabetes study: a population-based survey on known diabetes mellitus prevalence and 5-year all-cause mortality. Diabetologia. 1995;38(3):318–25.

    Article  CAS  PubMed  Google Scholar 

  35. Eberly LE, Cohen JD, Prineas R, Yang L, M.R.F.I.T R, Group. Impact of incident diabetes and incident nonfatal cardiovascular disease on 18-year mortality: the multiple risk factor intervention trial experience. Diabetes Care. 2003;26(3):848–54.

    Article  PubMed  Google Scholar 

  36. Statistical design considerations in the NHLI multiple risk factor intervention trial (MRFIT). J Chronic Dis, 1977. 30(5): p. 261–75.

  37. Mansour O, Golden SH, Yeh HC. Disparities in mortality among adults with and without diabetes by sex and race. J Diabetes Complications. 2020;34(3):107496.

    Article  PubMed  Google Scholar 

  38. Pearson-Stuttard J, Bennett J, Cheng YJ, Vamos EP, Cross AJ, Ezzati M, Gregg EW. Trends in predominant causes of death in individuals with and without diabetes in England from 2001 to 2018: an epidemiological analysis of linked primary care records. Lancet Diabetes Endocrinol. 2021;9(3):165–73.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Gregg EW, Cheng YJ, Srinivasan M, Lin J, Geiss LS, Albright AL, Imperatore G. Trends in cause-specific mortality among adults with and without diagnosed diabetes in the USA: an epidemiological analysis of linked national survey and vital statistics data. Lancet. 2018;391(10138):2430–40.

    Article  PubMed  Google Scholar 

  40. Harding JL, Shaw JE, Peeters A, Davidson S, Magliano DJ. Age-Specific Trends from 2000–2011 in all-cause and cause-specific mortality in type 1 and type 2 diabetes: a cohort study of more than one million people. Diabetes Care. 2016;39(6):1018–26.

    Article  PubMed  Google Scholar 

  41. Ali MK, Bullard KM, Saaddine JB, Cowie CC, Imperatore G, Gregg EW. Achievement of goals in U.S. diabetes care, 1999–2010. N Engl J Med. 2013;368(17):1613–24.

    Article  CAS  PubMed  Google Scholar 

  42. Preis SR, Pencina MJ, Hwang S-J, D’Agostino Sr RB, Savage PJ, Levy D, Fox CS. Trends in cardiovascular disease risk factors in individuals with and without diabetes mellitus in the Framingham Heart Study. Circulation. 2009;120(3):212–20.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Tsunekawa S, Kamiya H, Nakamura J. Different trends in causes of death in patients with diabetes between Japan and the USA. J Diabetes Investig. 2019;10(3):571–3.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

No funding body participated in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

Authors and Affiliations

  1. Department of Family Medicine, Taichung Armed Forces General Hospital, No. 348, Sec. 2, Zhongshan Rd., Taiping Dist, Taichung, 41148, Taiwan

    Hsuan-Chih Tsai, Po-Sheng Hsu & Chun-Cheng Liao

  2. Department of Occupational Medicine, Taichung Veterans General Hospital, Taichung, 40705, Taiwan

    Hsuan-Chih Tsai

  3. School of Medicine, National Defense Medical Center, Taipei, 11490, Taiwan

    Hsuan-Chih Tsai, Deng-Ho Yang & Chun-Cheng Liao

  4. Department of Medical Education and Research, Taichung Armed Forces General Hospital, Taichung, 41148, Taiwan

    Hsuan-Chih Tsai, Chia-Lien Hung, Deng-Ho Yang & Chun-Cheng Liao

  5. Department of Medical Imaging and Radiological Science, Central Taiwan University of Science and Technology, Takun, Taichung, 40601, Taiwan

    Lung-Fa Pan

  6. Department of Cardiology, Taichung Armed Forces General Hospital, Taichung, 41148, Taiwan

    Lung-Fa Pan

  7. Department of Medical Laboratory Science and Biotechnology, Central Taiwan University of Science and Technology, Taichung, 40601, Taiwan

    Deng-Ho Yang

  8. Department of Surgery, Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung, 813204, Taiwan

    Kuang-Chen Hung

  9. Department of Surgery, National Defense Medical Center, Taipei, 11490, Taiwan

    Kuang-Chen Hung

  10. Department of Surgery, Taichung Armed Forces General Hospital, Taichung, 41148, Taiwan

    Kuang-Chen Hung

  11. Central Taiwan University of Science and Technology, Taichung, 40601, Taiwan

    Kuang-Chen Hung

  12. National Chin-Yi University, Taichung, 411030, Taiwan

    Kuang-Chen Hung

Authors
  1. Hsuan-Chih Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Po-Sheng Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lung-Fa Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chia-Lien Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Deng-Ho Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kuang-Chen Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chun-Cheng Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Investigation, Deng-Ho Yang; Software, Chia-Lien Hung; Supervision, Chun-Cheng Liao and Kuang-Chen Hung; Validation, Lung-Fa Pan; Writing – original draft, Hsuan-Chih Tsai and Po-Sheng Hsu.

Corresponding authors

Correspondence to Kuang-Chen Hung or Chun-Cheng Liao.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was approved by the Tri-service General Hospital Institutional Review Board (number: A202005160), and it was conducted in accordance with the principles stated in the Declaration of Helsinki. The Tri-service General Hospital is under the supervision of the school of medicine, National Defense Medical Center. All participants in the research provided written informed consent before the health examination to authorize the data analysis.

Consent for publication

Not applicable.

Disclosure

All or part of the data used in this research were authorized by the MJ Health Research Foundation and received from the MJ Health Research Foundation. (Authorization Code: MJHRF2019016A). Any interpretation or conclusion described in this paper does not represent the views of MJ Health Research Foundation.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tsai, HC., Hsu, PS., Pan, LF. et al. The Presence of Diabetes Mellitus or Pre-diabetes Mellitus Increases Mortality from Heart Disease in a Taiwanese Population: A 10-year Follow-Up Study. BMC Cardiovasc Disord 23, 375 (2023). https://doi.org/10.1186/s12872-023-03406-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12872-023-03406-5

Keywords

BMC Cardiovascular Disorders

ISSN: 1471-2261

Contact us