Abstract
Background
This study investigated whether visit-to-visit fasting plasma glucose (FPG) variability, as measured by the coefficient of variation (CV), increased peripheral artery disease (PAD) risk.
Methods
Individuals with type 2 diabetes from the National Diabetes Care Management Program during the period 2002–2004, ≥ 30 years of age, and free of PAD (n = 30,932) were included and monitored until 2011. Cox proportional hazards regression models were implemented to analyze related determinants of PAD.
Results
A total of 894 incident cases of PAD were identified during an average 8.2 years of follow-up, resulting in a crude incidence rate of 3.53 per 1000 person-years. Both FPG-CV and HbA1c were significantly associated with PAD after multivariate adjustment, with corresponding hazard ratios of 1.24 [95% confidence interval (CI) 1.04–1.47] for FPG-CV in the third tertile and 1.50 (95% CI 1.10–2.04) for HbA1c ≥ 10%. The findings of the sensitivity analysis remained consistent after excluding potential confounders, demonstrating the consistency of the results.
Conclusions
The associations between HbA1c, variability in FPG-CV, and PAD suggest a linked pathophysiological mechanism, suggesting the crucial role of glycemic variability in clinical management and therapeutic goals in preventing PAD in type 2 diabetes.
Keywords: HbA1c, Fasting plasma glucose, Glycemic variability, Peripheral artery disease
Background
Peripheral artery disease (PAD) remains a main cause of non-traumatic lower limb amputations and is linked with cardiovascular complications and long-term disability in type 2 diabetes [1, 2]. Early screening and interventions for modifiable risk factors for PAD can lead to reduction of risk and related complications. A number of traditional risk factors are linked with PAD [3, 4]; however, there is a need to further investigate non-conventional risk factors, especially in vulnerable populations.
PAD is defined as systemic atherosclerosis, mostly in the lower limbs [1, 3, 4]. Recent evidence suggested that glycemic variation might play a crucial role in the atherosclerosis pathogenesis and may confer additional risk for diabetes-related complications, independent of HbA1c level [5, 6]. In addition, there is increasing evidence suggesting that glycemic variability, as derived from the visit-to-visit fasting plasma glucose (FPG) measurements by coefficient of variation (CV), is an independent risk factor for ischemic stroke, diabetic peripheral polyneuropathy, Alzheimer’s disease, and all-cause or cause-specific mortality in persons with type 2 diabetes [7–11]. Moreover, animal and in vitro studies have also shown that oscillating glucose levels may have more harmful effects, including increased atherosclerosis, than chronic sustained hyperglycemia, that are involved in the pathogenesis of PAD [12–14]. However, no large-scale studies to date have specifically evaluated the relationship between PAD and variability in glycemic level, as measured by FPG-CV. Therefore, this large, retrospective cohort study investigated whether variability in glycemic level, as estimated by FPG-CV, has independent and significant associations with PAD in persons with type 2 diabetes.
Methods
Study population
Our data were extracted from 2 nationwide, population-based databases. The National Diabetes Care Management Program (NDCMP) under the Taiwan National Health Insurance (NHI) system was used to obtain diabetes outcome data for 2002–2004. In addition, the NHI administrative database was used for both baseline characteristics and follow-up assessments during the entry (or index) date and December 31, 2011. Additional financial incentives provided by The NDCMP for high-quality healthcare services such as increased frequency of monitoring, annual diabetes-related physical examinations and laboratory tests, and enhanced self-care education [15]. The program requires health care providers in the specialties of nephrology, family medicine, endocrinology, cardiology, internal medicine, and others to participate in clinical training and education programs for certification in Taiwan’s Diabetes Shared Care system program. These providers become eligible for participation of this program and voluntarily enroll patients with diabetes to this program. The purpose of these continuing clinical training and education programs is for enhancing the quality of care through clinical practice standardization, including assessment and diagnosis of diabetes complications. The coordinated physician-led multidisciplinary teams, including physicians and medical care staff, are responsible for the care of patients with diabetes as adhering to established clinical guideline. A physical or history examination suggestive of PAD required confirmed diagnosis by the ankle-brachial index (ABI) [16]. The physician-led multidisciplinary teams can refer patients requiring further diagnostic evaluation to a specialist.
A national retrospective cohort study, the Taiwan Diabetes Study, included 63,084 Chinese individuals with diabetes who participated in the NDCMP during 2002–2004. The index date was the NDCMP entry date. Individuals with a clinical diagnosis of diabetes according to the American Diabetes Association (ADA) criteria [International Classification of Diseases, ninth revision, Clinical Modification (ICD-9-CM) diagnosis code 250] were invited by their physicians to enroll. The NDCMP initially included 63,084 diabetic patients during 2002–2004. Of these, we excluded persons with PAD (ICD-9:443.9 or 440.21), type 1 diabetes (ICD-9: 250.x1/x3), or gestational diabetes (ICD-9: 648.83), and those aged < 30 years at baseline (Additional file 1: Figure S1).
Enrollees underwent comprehensive health assessment at the time of NDCMP entry to assess systolic blood pressure (SBP) and diastolic blood pressure (DBP), body measurements, blood and urine tests, and history of diseases and complications were also obtained. A standardized, computerized questionnaire was designed to collect information from all participants, which was administered by a case manager to record medication use, previous or current disease status, and lifestyle habits. Blood was drawn from an antecubital vein in the morning after a 12-h overnight fast and was sent within 4 h for analysis of FPG, HbA1c, low-density lipoprotein-cholesterol (LDL-C), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), and creatinine. The interval for follow-up was every 3–6 months. All patients repeated the tests on each follow-up anniversary as those performed during program entry.
Participants with missing data for lifestyle behaviors, sociodemographic and diabetes-related factors, diabetic micro- and macrovascular complications, blood biochemical indices, and comorbidities were excluded. Those lost to follow-up and those with less than 3 months of follow-up including those with an entry date until PAD, death, or withdrawal from the NHI of less than 3 months were also excluded from the analysis. The last criterion was used to rule out the potential bias due to reverse causality. Finally, 30,932 patients were included in the study. The study protocol had been approved by the Ethical Review Board at China Medical University Hospital (CMUH102-REC3-016). Informed consent of the study participants was not required because the dataset used in this study consists of de-identified secondary data released for research purposes.
Data sources for assessments at baseline and follow-up visits
The Taiwan government launched the NHI program in 1995, which covered nearly 99% of the entire 23.74 million Taiwan population in 1999 [17]. In 2014, more than 99.6% of the population was covered by the NHI program while 100% of hospitals and 92% of clinics island-wide were under the contracts with the NHI Bureau. The NHI Research Database consisted of information on demographics, prescriptions, and diagnoses from inpatient and ambulatory care claims. The proportion of enrollee withdrawals was very low because of the comprehensive coverage of the NHI program, and bias due to loss of follow-up was thus negligible. To enhance the validity of the claims data, the NHI Bureau in Taiwan sampled the medical charts on a routine basis every 3 months and anonymous experts randomly reviewed 50–100 inpatient and ambulatory care claims from each hospital and clinic, for improvement of the accuracy of coding [18]. The NDCMP was also covered by the NHI program. This study used the datasets for inpatient care based on admission and ambulatory care visits from 2002 to 2011. Every individual had a unique personal identification number (PIN). For privacy and security purposes, patient identity data in the NHI Research Database were cryptographically scrambled. Each patient was linked with the PIN in all NHI datasets without being identified.
Ascertainment of outcome
The primary outcome was PAD, identified through record linkage with inpatient care and ambulatory care data in the NHI Research Database. The PAD incidence was based on coding according to the ICD-9-CM as 443.9 or 440.21. To enhance the accuracy of PAD diagnoses by excluding false positives, all PAD incidences met at least 1 of the following criteria: at least 1 inpatient or 3 ambulatory care claims. The study cohort was then followed up from the entry (index) date to December 31, 2011, or until a PAD event, withdrawal from the NHI, or death. A total of 894 newly diagnosed PAD patients were identified with an average of 8.20 years of follow-up.
For the 1-year period preceding cohort entry, ambulatory care and inpatient claims data were used to determine comorbidities, based on at least 3 ambulatory care claims or 1 claim for inpatient admission, including coronary artery disease.
Comorbidities within 12 months prior to the index date were tabulated using outpatient and inpatient claims data, based on at least 3 ambulatory care claims or 1 inpatient care claim. Any history of hypoglycemia (ICD-9-CM codes 250, 251.0–251.2), hyperlipidemia (ICD-9-CM code 272), hypertension (ICD-9-CM codes 401–405), atrial fibrillation (ICD-9-CM code 427.31), coronary artery disease (CAD) (ICD-9-CM codes 410–413, 414.01–414.05, 414.8, and 414.9), congestive heart failure (CHF) (ICD-9-CM codes 428, 398.91, and 402.x1), chronic hepatitis (ICD-9-CM codes 571, 572.2, 572.3, 572.8, 573.1, 573.2, 573.3, 573.8, and 573.9), cancer (ICD-9-CM codes 140–149, 150–159, 160–165, 170–175, 179–189, 190–199, 200, 202, 203, 210–213, 215–229, 235–239, 654.1, 654.10, 654.11, 654.12, 654.13, and 654.14), albuminuria (ICD-9-CM code 791.0), retinopathy (ICD-9-CM codes 250.5 and 362.0), or chronic obstructive pulmonary disease (COPD) (ICD-9-CM codes 490–496) was recognized as a comorbidity.
Statistical analysis
The FPG-CV measurements for each patient from ambulatory visits within the 1st year of the entry (index) date were calculated for those with more than 2 FPG results. The average number of FPG measurements for this sample was 3.07, with a standard deviation of 0.95. The FPG-CV was divided by the square root of the ratio of total visits, divided by total visits minus 1 to correct for impact of the number of visits might on variation [19]. The multiple imputation approach was applied to handle missing CVs [20, 21]. Patients were grouped into tertiles according to FPG-CV. The extended Cox proportional hazards model with the Lunn-McNeil method was employed to weigh competing risks of PAD, and all-cause mortality was assessed by fitting a proportional sub-distribution hazards regression model focusing on cause-specific hazards for a competing risk of death [22]. Age, sex, and traditional variables were adjusted to compute hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs). FPG-CV and HbA1c were assessed simultaneously with 3 multivariate models, with covariates being entered in hierarchical order: (1) adjustment for age (continuous) and sex; (2) additional adjustment for alcohol consumption (yes/no), smoking and tobacco use (yes/no), diabetes duration, antihypertensive treatment (yes/no), type of hypoglycemic drugs (no medication, 1 oral hypoglycemic drug, combination of 2, 3, or > 3 oral hypoglycemic drugs, insulin monotherapy, and insulin plus oral hypoglycemic drugs), and obesity defined as body mass index ≥ 27 kg/m2, the obesity criteria of the Ministry of Health and Welfare in Taiwan; and (3) additional adjustment for HbA1c (< 6, 6–8, 8–10, ≥ 10%), congestive heart failure, coronary artery disease, hypoglycemia, atrial fibrillation, hypertension, cancer, chronic hepatitis, chronic obstructive pulmonary disease, and hyperlipidemia. In addition, restricted cubic splines in Cox models were used to evaluate whether a linear or non-linear relationship of FPG-CV and HbA1c existed as a continuous variable with the risk of PAD. For sensitivity analyses, patients with diabetic ketoacidosis, hyperglycemic, hyperosmolar nonketotic coma, atrial fibrillation, myocardial infarction, and hypoglycemia were excluded (n = 1728). The statistical significance was defined as a 2-tailed p < 0.05. The SAS statistical package for Windows (Version 9.4, SAS; Cary, NC, USA) was used for analyses.
Results
There were 894 incident cases of PAD after an average 8.2 years of follow-up with a crude incidence of 3.53 per 1000 person-years (3.63 for men, 3.44 for women). The incidence of PAD was 2.93, 3.61, and 4.06 per 1000 person-years in the first, second, and third tertiles of FPG-CV, respectively. The Pearson correlation coefficient between HbA1c and FPG-CV was 0.235, showing a positive weak relationship. The baseline characteristics grouped according to PAD status was presented in Table 1. There were differences according to diabetes duration, type of hypoglycemic medication use, hypertension medication, CAD, and retinopathy.
Table 1.
Baseline characteristics according to peripheral vascular disease status in persons with type 2 diabetes enrolled in the National Diabetes Care Management Program, Taiwan (n = 30,932)
| Variables | Peripheral vascular disease | p value | |
|---|---|---|---|
| No (N = 30,038) | Yes (N = 894) | ||
| Socio-demographic factors | |||
| Male, n (%) | 14,008 (46.63) | 421 (47.09) | 0.81 |
| Age (years), mean (SD) | 60.99 (11.16) | 61.73 (10.87) | 0.05 |
| Lifestyle behaviors, n (%) | |||
| Smoking | 4517 (15.04) | 143 (16.00) | 0.46 |
| Alcohol drinking | 2518 (8.38) | 67 (7.49) | 0.38 |
| Diabetes-related variables | |||
| Duration of diabetes (years), mean (SD) | 6.80 (6.75) | 7.97 (7.41) | < 0.001 |
| Type of hypoglycemic drug use, n (%) | 0.006 | ||
| No medication | 365 (1.22) | 5 (0.56) | |
| One oral hypoglycemic drug | 5134 (17.09) | 113 (12.64) | |
| Two oral hypoglycemic drugs | 12,536 (41.73) | 353 (39.49) | |
| Three oral hypoglycemic drugs | 5537 (18.43) | 175 (19.57) | |
| > 3 oral hypoglycemic drugs | 1575 (5.24) | 59 (6.60) | |
| Insulin | 885 (2.95) | 18 (2.01) | |
| Insulin+ oral hypoglycemic drug | 4006 (13.34) | 171 (19.13) | |
| Drug-related variables, n (%) | |||
| Hypertension drug treatment | 11,671 (38.85) | 316 (35.35) | 0.04 |
| Comorbidity, n (%) | |||
| Obesity (BMI ≥ 27) | 10,927 (36.38) | 304 (34.00) | 0.16 |
| CAD | 2603 (8.67) | 99 (11.07) | 0.01 |
| Stroke | 1519 (5.06) | 52 (5.82) | 0.35 |
| CHF | 785 (2.61) | 19 (2.13) | 0.43 |
| Cancer | 630 (2.10) | 19 (2.13) | 1.00 |
| Hyperlipidemia | 7621 (25.37) | 220 (24.61) | 0.63 |
| Hypertension | 13,736 (45.73) | 397 (44.41) | 0.45 |
| Atrial fibrillation | 150 (0.50) | 3 (0.34) | 0.81 |
| Chronic hepatitis | 2900 (9.65) | 78 (8.72) | 0.38 |
| COPD | 1365 (4.54) | 42 (4.70) | 0.89 |
| Hypoglycemia | 118 (0.39) | 7 (0.78) | 0.10 |
| Albuminuria | 297 (0.99) | 6 (0.67) | 0.44 |
| Retinopathy | 583 (1.94) | 31 (3.47) | 0.002 |
| Cardiovascular risk factors, n (%) | |||
| SBP ≥ 130/DBP ≥85 (mmHg) | 20,730 (69.01) | 607 (67.90) | 0.50 |
| TG ≥ 150 (mg/dL) | 13,075 (43.53) | 397 (44.41) | 0.63 |
| HDL: female < 50; male < 40 (mg/dL) | 15,595 (51.92) | 448 (50.11) | 0.30 |
| LDL ≥ 100 (mg/dL) | 20,929 (69.68) | 618 (69.13) | 0.75 |
| eGFR < 60 (mL/min/1.73 m2) | 7967 (26.52) | 240 (26.85) | 0.86 |
Differences in continuous variables were tested using the student’s t-test. Differences in categorical variables were tested using the Chi-square test or Fisher’s exact test
CHF congestive heart failure, CAD coronary artery disease, SBP systolic blood pressure, DBP diastolic blood pressure, COPD chronic obstructive pulmonary disease, HDL high-density lipoprotein, TG triglyceride, eGFR estimated glomerular filtration rate, LDL low-density lipoprotein
Table 2 shows baseline characteristics in participants grouped according to tertiles of FPG-CV. Persons with lower FPG-CV were found to be associated with higher proportion of male and obesity, lower mean diabetes duration and HbA1c, and lower prevalence of smoking, 3 or more oral hypoglycemic drug use, insulin injections, insulin injections plus oral hypoglycemic drug use, stroke, cancer, hypertension, COPD, and hypoglycemia, as well as higher prevalence of 1 oral hypoglycemic drug use, 2 oral hypoglycemic drug use, and hyperlipidemia. Figure 1 depicts the cumulative risk for PAD estimated by Kaplan–Meier method within subgroups defined by HbA1c and FPG-CV; patients with HbA1c ≥ 10% or FPG-CV > 34.8% had an increased risk of PAD (both log-rank tests p < 0.001).
Table 2.
Baseline characteristics based on HbA1c level and FPG-CV in patients with type 2 diabetes enrolled in the National Diabetes Care Management Program, Taiwan (n = 30,932)
| Variables | HbA1c (%) | FPG-CV (%) | p-value | ||||||
|---|---|---|---|---|---|---|---|---|---|
| < 6.0 (N = 2663) | 6.0–8.0 (N = 13,535) | 8.0–10.0 (N = 9229) | ≥ 10.0 (N = 5505) | p-value | ≤ 17.5 (N = 10,214) | 17.5–34.8 (N = 10,199) | > 34.8 (N = 10,519) | ||
| Sociodemographic factors | |||||||||
| Male, n (%) | 1496 (56.18) | 6406 (47.33) | 3959 (42.9) | 2568 (46.65) | < 0.001 | 4880 (47.78) | 4645 (45.54) | 4904 (46.62) | 0.006 |
| Age (years), mean (SD) | 62.84 (11.93) | 62.11 (11.02) | 60.52 (10.85) | 58.27 (11.03) | < 0.001 | 61.07 (11.13) | 61.04 (11.03) | 60.93 (11.29) | 0.64 |
| Lifestyle behaviors, n (%) | |||||||||
| Smoking | 377 (14.16) | 1897 (14.02) | 1394 (15.1) | 992 (18.02) | < 0.001 | 1407 (13.78) | 1479 (14.50) | 1774 (16.86) | < 0.001 |
| Alcohol drinking | 255 (9.58) | 1133 (8.37) | 730 (7.91) | 467 (8.48) | 0.05 | 858 (8.4) | 818 (8.02) | 909 (8.64) | 0.27 |
| Diabetes-related variables | |||||||||
| Duration of diabetes (years), mean (SD) | 5.00 (5.98) | 6.60 (6.70) | 7.69 (7.01) | 6.84 (6.69) | < 0.001 | 6.13 (6.32) | 7.01 (6.78) | 7.35 (7.13) | < 0.001 |
| Type of hypoglycemic drug use, n (%) | < 0.001 | < 0.001 | |||||||
| No medication | 104 (3.91) | 180 (1.33) | 57 (0.62) | 29 (0.53) | 177 (1.73) | 101 (0.99) | 92 (0.87) | ||
| One oral hypoglycemic drug | 991 (37.21) | 2993 (22.11) | 866 (9.38) | 397 (7.21) | 2473 (24.21) | 1566 (15.35) | 1208 (11.48) | ||
| Two oral hypoglycemic drugs | 1135 (42.62) | 6412 (47.37) | 3640 (39.44) | 1702 (30.92) | 4520 (44.25) | 4441 (43.54) | 3928 (37.34) | ||
| Three oral hypoglycemic drugs | 234 (8.79) | 2086 (15.41) | 2114 (22.91) | 1278 (23.22) | 1663 (16.28) | 2044 (20.04) | 2005 (19.06) | ||
| > 3 oral hypoglycemic drugs | 40 (1.50) | 485 (3.58) | 635 (6.88) | 474 (8.61) | 423 (4.14) | 587 (5.76) | 624 (5.93) | ||
| Insulin | 55 (2.07) | 327 (2.42) | 332 (3.6) | 189 (3.43) | 175 (1.71) | 237 (2.32) | 491 (4.67) | ||
| Insulin+ oral hypoglycemic drug | 104 (3.91) | 1052 (7.77) | 1585 (17.17) | 1436 (26.09) | 783 (7.67) | 1223 (11.99) | 2171 (20.64) | ||
| HbA1c (%), mean (SD) | 5.47 (0.45) | 6.99 (0.55) | 8.83 (0.57) | 11.43 (1.28) | < 0.001 | 7.69 (1.73) | 8.12 (1.80) | 8.76 (2.13) | < 0.001 |
| FPG-CV (%),mean (SD) | 26.29 (23.24) | 27.83 (23.21) | 33.92 (25.26) | 43.14 (31.76) | < 0.001 | 10.15 (4.72) | 25.41 (4.90) | 60.30 (25.79) | < 0.001 |
| Drug-related variables, n (%) | |||||||||
| Hypertension drug treatment | 1063 (39.92) | 5572 (41.17) | 3610 (39.12) | 1742 (31.64) | < 0.001 | 4005 (39.21) | 3960 (38.83) | 4022 (38.24) | 0.35 |
| Comorbidity, n (%) | |||||||||
| Obesity (BMI ≥ 27) | 961 (36.09) | 5066 (37.43) | 3439 (37.26) | 1765 (32.06) | < 0.001 | 3796 (37.16) | 3756 (36.83) | 3679 (34.97) | 0.002 |
| CAD | 226 (8.49) | 1241 (9.17) | 839 (9.09) | 396 (7.19) | < 0.001 | 884 (8.65) | 906 (8.88) | 912 (8.67) | 0.81 |
| Stroke | 169 (6.35) | 654 (4.83) | 497 (5.39) | 251 (4.56) | 0.002 | 432 (4.23) | 516 (5.06) | 623 (5.92) | < 0.001 |
| CHF | 83 (3.12) | 375 (2.77) | 227 (2.46) | 119 (2.16) | 0.03 | 240 (2.35) | 280 (2.75) | 284 (2.70) | 0.15 |
| Cancer | 56 (2.10) | 280 (2.07) | 216 (2.34) | 97 (1.76) | 0.13 | 191 (1.87) | 206 (2.02) | 252 (2.40) | 0.02 |
| Hyperlipidemia | 627 (23.54) | 3592 (26.54) | 2414 (26.16) | 1208 (21.94) | < 0.001 | 2738 (26.81) | 2619 (25.68) | 2484 (23.61) | < 0.001 |
| Hypertension | 1282 (48.14) | 6565 (48.50) | 4296 (46.55) | 1990 (36.15) | < 0.001 | 4545 (44.5) | 4769 (46.76) | 4819 (45.81) | 0.005 |
| Atrial fibrillation | 18 (0.68) | 66 (0.49) | 46 (0.50) | 23 (0.42) | 0.48 | 47 (0.46) | 46 (0.45) | 60 (0.57) | 0.39 |
| Chronic hepatitis | 268 (10.06) | 1332 (9.84) | 899 (9.74) | 479 (8.70) | 0.08 | 941 (9.21) | 992 (9.73) | 1045 (9.93) | 0.19 |
| COPD | 138 (5.18) | 621 (4.59) | 427 (4.63) | 221 (4.01) | 0.10 | 397 (3.89) | 468 (4.59) | 542 (5.15) | < 0.001 |
| Hypoglycemia | 17 (0.64) | 65 (0.48) | 36 (0.39) | 7 (0.13) | 0.001 | 24 (0.23) | 39 (0.38) | 62 (0.59) | < 0.001 |
| Cardiovascular risk factors, n (%) | |||||||||
| SBP ≥ 130/DBP ≥ 85 (mmHg) | 1811 (68.01) | 9480 (70.04) | 6459 (69.99) | 3587 (65.16) | < 0.001 | 7056 (69.08) | 7044 (69.07) | 7237 (68.80) | 0.88 |
| TG ≥ 150 (mg/dL) | 934 (35.07) | 5460 (40.34) | 4319 (46.8) | 2759 (50.12) | < 0.001 | 4166 (40.79) | 4390 (43.04) | 4916 (46.73) | < 0.001 |
| HDL: female < 50; male < 40 (mg/dL) | 1315 (49.38) | 7036 (51.98) | 4955 (53.69) | 2737 (49.72) | < 0.001 | 5178 (50.7) | 5358 (52.53) | 5507 (52.35) | 0.01 |
| LDL ≥ 100 (mg/dL) | 1688 (63.39) | 9228 (68.18) | 6521 (70.66) | 4110 (74.66) | < 0.001 | 7202 (70.51) | 7056 (69.18) | 7289 (69.29) | 0.07 |
| eGFR < 60 (mL/min/1.73 m2) | 827 (31.06) | 3858 (28.5) | 2333 (25.28) | 1189 (21.6) | < 0.001 | 2340 (22.91) | 2661 (26.09) | 3206 (30.48) | < 0.001 |
Differences in continuous variables tested by ANOVA. Differences in categorical variables tested by chi-square or Fisher’s exact test
CHF congestive heart failure, CAD coronary artery disease, SBP systolic blood pressure, DBP diastolic blood pressure, COPD chronic obstructive pulmonary disease, HDL high-density lipoprotein, TG triglyceride, eGFR estimated glomerular filtration rate, LDL low-density lipoprotein
Fig. 1.
Risk of peripheral vascular disease by a HbA1c level and b FPG-CV level
Table 3 shows the HRs of PAD based on subgroups of FPG-CV, with and without adjustment for lifestyles, FPG, and comorbidity. FPG-CV was associated with PAD risks. We observed an independent association between FPG-CV in the third tertile and PAD incidence in the third multivariate model, with an HR of 1.24 (95% CI 1.04–1.47), and found that HbA1c ≥ 10% was also independently associated with increased PAD risk (HR: 1.50, 95% CI 1.00–2.40, p < 0.05). With using restricted cubic splines, we observed a non-linear increasing association between HbA1c and risk of PAD, with leveling off at 6-9%; a linear increasing trend for FPG-CV was also observed, with leveling off at ≥ 35% (Fig. 2). Based on the statistics of likelihood ratio tests for HbA1c and FPG-CV, we found HbA1c had a higher predictive power on PAD than FPG-CV (12.51 vs. 6.0) whereas FPG-CV had a higher predictive power on death (54.62 vs 112.69).
Table 3.
Hazard ratios (HRs) of peripheral vascular disease based on HbA1c and FPG-CV levels in persons with diabetes enrolled in the National Diabetes Care Management Program, Taiwan (n = 30,932)
| Variables | n | Cases | Person-years | IR | Peripheral vascular disease (N = 894) | ||
|---|---|---|---|---|---|---|---|
| Age and gender-adjusted | Multivariate-adjusteda | Multivariate-adjustedb | |||||
| HbA1c (%) | |||||||
| < 6 | 2663 | 59 | 21,708.26 | 2.72 | 1.00 | 1.00 | 1.00 |
| 6–8 | 13,535 | 377 | 111,972.54 | 3.37 | 2.31 (1.72, 3.11)*** | 1.20 (0.90, 1.58) | 1.19 (0.90, 1.57) |
| 8–10 | 9229 | 247 | 75,713.95 | 3.26 | 2.29 (1.69, 3.10)*** | 1.06 (0.79, 1.42) | 1.05 (0.78, 1.41) |
| ≥ 10 | 5505 | 211 | 44,117.07 | 4.78 | 3.52 (2.59, 4.76)*** | 1.52 (1.12, 2.06)** | 1.50 (1.10, 2.04)** |
| p for trend | < 0.001 | 0.01 | 0.01 | ||||
| FPG-CV (%) | |||||||
| ≤ 17.5 | 10,214 | 251 | 85,777.39 | 2.93 | 1.00 | 1.00 | 1.00 |
| 17.5–34.8 | 10,199 | 306 | 84,671.99 | 3.61 | 1.34 (1.13, 1.59)*** | 1.18 (1.00, 1.40) | 1.17 (0.99, 1.39) |
| > 34.8 | 10,519 | 337 | 83,062.43 | 4.06 | 1.44 (1.21, 1.71)*** | 1.25 (1.05, 1.48)* | 1.24 (1.04, 1.47)* |
| p for trend | < 0.001 | 0.01 | 0.01 | ||||
Multivariate-adjusteda for age, gender, alcohol consumption, smoking, diabetes duration, type of hypoglycemic medication, hypertension medication and obesity
Multivariate-adjustedb for cardiovascular risk factors, stroke, coronary artery disease, cancer, congestive heart failure, hyperlipidemia, atrial fibrillation, hypertension, chronic hepatitis, hypoglycemia and chronic obstructive pulmonary disease in addition to the variables in first multivariate model
IR incidence density rate = number of incident cases/person-years * 1000
* p < 0.05; ** p < 0.01; *** p < 0.001
Fig. 2.
Restricted multivariable cubic spline plots for a HbA1c level and b FPG-CV level (n = 30,932). HRs were adjusted for age, sex, alcohol consumption, smoking, diabetes duration, type of hypoglycemic medication, antihypertensive medication, obesity, cardiovascular risk factors, coronary artery disease, stroke, cancer, congestive heart failure, hyperlipidemia, atrial fibrillation, hypertension, chronic obstructive pulmonary disease, chronic hepatitis, and hypoglycemia
Sensitivity analysis demonstrated that the PAD HRs remained similar for patients in the second to third tertiles of FPG-CV [1.01 (95% CI 1.02–1.44) and 1.27 (95% CI 1.07–1.52)]. The linear trend for the risk of PAD remained significant for FPG-CV, even after patients with various types of comorbidity were excluded (p for trend = 0.006) (Table 4).
Table 4.
Sensitivity analyses for evaluating the potential bias due to comorbidities, by excluding persons with diabetic ketoacidosis, hyperglycemic hyperosmolar nonketotic coma, atrial fibrillation, myocardial infarction, and hypoglycemia
| Variables | n | Cases | Person-years | IR | Multivariate-adjusted HR 95% CI |
|---|---|---|---|---|---|
| Model I | |||||
| HbA1c (%) | |||||
| < 6 | 2607 | 56 | 21,284.98 | 2.63 | 1.00 |
| 6–8 | 13,335 | 368 | 110,444.51 | 3.33 | 1.21 (0.91, 1.61) |
| 8–10 | 9077 | 242 | 74,554.23 | 3.25 | 1.07 (0.79, 1.45) |
| ≥ 10 | 5386 | 205 | 43,260.94 | 4.74 | 1.53 (1.11, 2.09)** |
| p for trend | 0.01 | ||||
| FPG-CV (%) | |||||
| ≤ 17.5 | 10,074 | 244 | 84,668.74 | 2.88 | 1.00 |
| 17.5–34.8 | 10,048 | 303 | 83,481.24 | 3.63 | 1.19 (1.01, 1.42)* |
| > 34.8 | 10,283 | 324 | 81,394.68 | 3.98 | 1.23 (1.04, 1.47)* |
| p for trend | 0.02 | ||||
| Model II | |||||
| HbA1c (%) | |||||
| < 6 | 2629 | 58 | 21,421.83 | 2.71 | 1.00 |
| 6–8 | 13,412 | 372 | 110,998.85 | 3.35 | 1.18 (0.89, 1.56) |
| 8–10 | 9144 | 245 | 75,038.39 | 3.26 | 1.04 (0.77, 1.40) |
| ≥ 10 | 5436 | 211 | 43,554.03 | 4.84 | 1.51 (1.11, 2.05)** |
| p for trend | 0.01 | ||||
| FPG-CV (%) | |||||
| ≤ 17.5 | 10,126 | 246 | 85,067.16 | 2.89 | 1.00 |
| 17.5–34.8 | 10,105 | 304 | 83,885.01 | 3.62 | 1.19 (1.00, 1.41)* |
| > 34.8 | 10,390 | 336 | 82,060.93 | 4.09 | 1.26 (1.06, 1.50)** |
| p for trend | 0.007 | ||||
| Model III | |||||
| HbA1c (%) | |||||
| < 6 | 2604 | 59 | 21,261.14 | 2.78 | 1.00 |
| 6–8 | 13,181 | 372 | 109,131.66 | 3.41 | 1.18 (0.89, 1.57) |
| 8–10 | 8981 | 245 | 73,772.11 | 3.32 | 1.05 (0.78, 1.41) |
| ≥ 10 | 5365 | 204 | 43,072.94 | 4.74 | 1.47 (1.08, 2.00)* |
| p for trend | 0.02 | ||||
| FPG-CV (%) | |||||
| ≤ 17.5 | 9968 | 248 | 83,751.3 | 2.96 | 1.00 |
| 17.5–34.8 | 9938 | 301 | 82,597.88 | 3.64 | 1.17 (0.99, 1.39) |
| > 34.8 | 10,225 | 331 | 80,888.67 | 4.09 | 1.24 (1.04, 1.47)* |
| p for trend | 0.01 | ||||
| Model IV | |||||
| HbA1c (%) | |||||
| < 6 | 2645 | 59 | 21,577.48 | 2.73 | 1.00 |
| 6–8 | 13,469 | 374 | 111,541.73 | 3.35 | 1.18 (0.89, 1.56) |
| 8–10 | 9183 | 247 | 75,373.75 | 3.28 | 1.05 (0.78, 1.41) |
| ≥ 10 | 5482 | 211 | 43,970.42 | 4.80 | 1.50 (1.11, 2.04)** |
| p for trend | 0.01 | ||||
| FPG-CV (%) | |||||
| ≤ 17.5 | 10,167 | 249 | 85,419.13 | 2.92 | 1.00 |
| 17.5–34.8 | 10,153 | 305 | 84,359.17 | 3.62 | 1.18 (0.99, 1.39) |
| > 34.8 | 10,459 | 337 | 82,685.08 | 4.08 | 1.25 (1.05, 1.48)* |
| p for trend | 0.009 | ||||
| Model V | |||||
| HbA1c (%) | |||||
| < 6 | 2646 | 59 | 21,595.5 | 2.73 | 1.00 |
| 6–8 | 13,470 | 372 | 111,547.16 | 3.33 | 1.17 (0.88, 1.54) |
| 8–10 | 9193 | 246 | 75,477.85 | 3.26 | 1.03 (0.77, 1.39) |
| ≥ 10 | 5498 | 210 | 44,089.87 | 4.76 | 1.46 (1.08, 1.99)* |
| p for trend | 0.02 | ||||
| FPG-CV (%) | |||||
| ≤ 17.5 | 10,190 | 248 | 85,621.37 | 2.90 | 1.00 |
| 17.5–34.8 | 10,160 | 304 | 84,395.44 | 3.60 | 1.18 (1.00, 1.40) |
| > 34.8 | 10,457 | 335 | 82,693.58 | 4.05 | 1.25 (1.05, 1.48)* |
| p for trend | 0.008 | ||||
| Model VI | |||||
| HbA1c (%) | |||||
| < 6 | 2500 | 56 | 20,470.2 | 2.74 | 1.00 |
| 6–8 | 12,803 | 352 | 106,329.25 | 3.31 | 1.15 (0.86, 1.54) |
| 8–10 | 8716 | 239 | 71,749.68 | 3.33 | 1.06 (0.78, 1.43) |
| ≥ 10 | 5185 | 197 | 41,763.28 | 4.72 | 1.46 (1.06, 2.00)* |
| p for trend | 0.02 | ||||
| FPG-CV (%) | |||||
| ≤ 17.5 | 9734 | 235 | 81,922.2 | 2.87 | 1.00 |
| 17.5–34.8 | 9657 | 293 | 80,391.77 | 3.64 | 1.21 (1.02, 1.44)* |
| > 34.8 | 9813 | 316 | 77,998.43 | 4.05 | 1.27 (1.07, 1.52)** |
| p for trend | 0.006 | ||||
Multivariate-adjusted for age, gender, alcohol consumption, smoking, diabetes duration, type of hypoglycemic medication, hypertension medication, obesity cardiovascular risk factors, stroke, coronary artery disease, cancer, congestive heart failure, hyperlipidemia, atrial fibrillation, hypertension, chronic hepatitis, hypoglycemia and chronic obstructive pulmonary disease
Model I: persons with HHNK being excluded (N = 527)
Model II: persons with DKA being excluded (N = 311)
Model III: persons with myocardial infarction being excluded (N = 801)
Model IV: persons with atrial fibrillation being excluded (N = 153)
Model V: persons with hypoglycemia being excluded (N = 125)
Model VI: persons with HHNK, DKA, myocardial infarction, atrial fibrillation and hypoglycemia being excluded (N = 1728)
* p < 0.05; ** p < 0.01; *** p < 0.001
Discussion
This study investigated HbA1c levels and FPG variability in relation to PAD in persons with type 2 diabetes. We identified a non-conventional risk factor, FPG-CV, was associated with greater risk for PAD in persons with type 2 diabetes during a follow-up period of 8.2 years. Moreover, our findings demonstrated an association between FPG-CV and PAD incidence, independent of HbA1c. These findings remained consistent in sensitivity analysis by excluding potential confounders, demonstrating the robustness of our study results.
Our study’s strengths include use of a large national population database of type 2 diabetes cases, a retrospective cohort study design with a relatively long follow-up period, standard data collection procedures, and adjusted for a large numbers of candidate confounders, which further validate our results. Our findings are crucial to the clinical management of PAD in type 2 diabetes. First, glycemic variability as determined by FPG-CV levels may be considered an additional non-conventional risk factor for PAD in type 2 diabetes. Second, the current practice of relying mainly on HbA1c alone but not glycemic variability may be inadequate. It may be necessary to achieve both a glycemic target and glycemic control stability. Further well-designed studies are warranted to determine whether minimizing glycemic variability has a crucial impact on PAD development in type 2 diabetes.
PAD increases diabetes-related complications; therefore, recognizing the risk factors for PAD is crucial [4, 23]. Prior studies’ evidence indicates elevated HbA1c is an independent determinant for PAD in type 2 DM. The present study confirms previous findings that elevated HbA1c is an independent factor for PAD in persons with type 2 diabetes [1, 3, 23, 24]. Nonetheless, studies on the benefit of lowering blood glucose alone, using HbA1c as a therapeutic target, did not show improvement in claudication symptoms or slowing of PAD progression in type 2 diabetes [25]. Interpretation of this finding is complicated, partly because HbA1c level merely presents the average blood glucose in the preceding 8–12 weeks and fails to reflect variability in glycemia, which may indicate an additional factor for development of diabetic vascular complications independent of HbA1c [6, 26]. Our results add to the emerging concept that glycemic variability as determined by FPG-CV confers additional risk of PAD, indicating the need for further research.
Numerous processes are linked to the development of PAD, which is considered a marker of systemic atherosclerosis [1, 3, 23, 24]. Conventional atherosclerotic risk factors, consisting of age, diabetes, smoking, hypertension, dyslipidemia, microalbuminuria, and obesity are associated with PAD [3, 4, 23, 27]. Several potential mechanisms may link glycemic variability to the occurrence of PAD from a pathophysiological point of view. First, evidence has suggested that oscillating glucose may trigger increases in oxidative stress, endothelial dysfunction or damage, advanced glycation end-products and inflammatory cytokines, resulting in a higher rate of occurrence and acceleration of atherosclerosis independently of sustained hyperglycemia [12–14, 28–33]. Second, A relationship of cardiovascular autonomic dysfunction with atherosclerosis has been observed [34, 35]. Exaggerated glycemic fluctuations were demonstrated to adversely affect endothelial vessel hearing with result of hypoxia and blood flow disorders in neuronal cells, which may lead to autonomic dysfunction [36]. Previous study has revealed that patients with type 2 diabetes with PAD had lower heart rate variability (HRV) indices than patients without PAD, which reflects a cardiovascular autonomic dysfunction [34]. Moreover, visit to visit variability in FPG could be an important risk factor for long term changes in left cardiac structure and function in patients with type 2 DM [37]. In this regard, the associations between PAD and cardiovascular autonomic dysfunction could be partly explained by glycemic fluctuations. Third, glycemic fluctuations might cause insulin resistance, beta cell dysfunction and apoptosis, inducing cellular metabolic memory, and epigenetic changes [12–14, 38]. According to these findings, we hypothesize that variability in glycemia may be a crucial pathological mechanism in PAD. Future research is warranted to explore the plausible biological mechanism by which glycemic variability contributes to the pathogenesis of PAD.
The current study has several limitations. First, given its observational nature, the possibility of selection bias and unrecognized confounding variables may have affected the findings, despite use of multivariate regression analysis. Second, glycemic variability can be assessed using a variety of methods, and FPG-CV is only one such method. To date, there is little consensus about a preferred method for assessment of the role of glycemic variability in diabetic complications. Methods for precise measurement of glycemic variability are also needed to compare the predictive ability of each method for diabetic complications including PAD [6, 39]. Third, we only considered the 1st-year FPG-CV and could not consider time-varying FPG-CV because the data was not availability. FPG-CV may have been changeable, while the doctors are treating. Thus, we examined the concordance of DM treatment between the 1st year and the 2nd year after entry to the study and found all types of anti-diabetes medications use had agreement greater than 90%, ranging from 94 to 99%. Fourth, the diagnosis of PAD was not validated through reviewing medical records and was only determined by ICD codes, and thus relied on the accuracy of diagnostic in our database. The NHI Bureau in Taiwan selects a sample of medical records routinely and endeavors to verify the accuracy of diagnoses in the database, thereby improving the accuracy of coding [17, 18]. The insurance claims are routinely scrutinized by medical specialists under anonymous peer review based on standard diagnostic criteria, and coding errors are penalized. Further, to improve accurate diagnosis of PAD, we included only those incident cases in which medical service was administered for PAD during either ≥ 3 outpatient visits or ≥ 1 hospitalization. Similar methods for PAD ascertainment have been adopted by previous studies [40, 41]. Fifth, PAD is often asymptomatic [42], and asymptomatic PAD or mild PAD symptoms might have been overlooked. Therefore, the risk of PAD could be underestimated. In this study, we only considered patients with symptoms and those seeking medical care, i.e., those diagnosed in clinical practice. Fifth, the effects of glycemic variability on PAD severity were not examined. Further studies are needed to determine whether the effects of glycemic variability are correlated with ABI severity. Finally, all of our study subjects in our study were ethnic Chinese from Taiwan, and generalizability of our results to other ethnic groups needs to be further confirmed.
Conclusions
Both visit-to-visit variability in glycemia, determined by FPG-CV level, and HbA1c represented risk factors for PAD besides other conventional risk factors in persons with type 2 diabetes. Our study’s findings show that HbA1c level and glucose fluctuation may be targeted for PAD prevention in type 2 diabetes. Further study is required to clarify the underlying mechanisms and verify whether FPG-CV or HbA1c level is a valuable therapeutic target.
Supplementary information
Additional file 1: Figure S1. Flow chart of recruitment procedures for the study. Table S1. Hazard ratios (HRs) of peripheral vascular disease based on HbA1c and FPG-CV levels in persons with diabetes enrolled in the National Diabetes Care Management Program, Taiwan (n=30,932).
Acknowledgements
This study was funded primarily by the Bureau of National Health Insurance (DOH94-NH-1007), the Ministry of Science and Technology of Taiwan (MOST 104-2314-B-039-016, MOST 105-2314-B-039-021-MY3, MOST 105-2314-B-039-025-MY3, MOST 107-2314-B-039-049, MOST 108-2314-B-039-039, MOST 108-2314-B-039-035-MY3 and MOST 108-2314-B-039-031-MY2), and China Medical University (CMU108-S-07).
Authors’ contributions
CPY, TCL, and CCL contributed equally to the design of the study and the direction of its implementation, including supervision of the field activities, quality assurance and control. CSL, CHL, and KLH supervised the field activities. CPY and CCL helped conduct the literature review and prepare the Methods and the Discussion sections of the text. TCL, CIL, and SYY designed the study’s analytic strategy and conducted the data analysis. All authors read and approved the final manuscript.
Funding
Not applicable.
Availability of data and materials
The datasets generated and/or analyzed during the current study are not publicly available due to the policy declared by National Health Insurance in Taiwan but are available from the corresponding author on reasonable request.
Ethics approval and consent to participate
This study was approved by the Ethical Review Board of China Medical University Hospital (CMUH102-REC3-016). Informed consent of the study participants was not required because the dataset used in this study consists of de-identified secondary data released for research purposes.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Cheng-Chieh Lin and Chun-Pai Yang contributed equally to this work.
Contributor Information
Chun-Pai Yang, Email: neuralyung@gmail.com.
Cheng-Chieh Lin, Email: cclin@mail.cmuh.org.tw.
Chia-Ing Li, Email: t6446@mail.cmuh.org.tw.
Chiu-Shong Liu, Email: liucs@ms14.hinet.net.
Chih-Hsueh Lin, Email: d5496@mail.cmuh.org.tw.
Kai-Lin Hwang, Email: kailin@tdtv.tinp.net.tw.
Shing-Yu Yang, Email: yz123kimo@yahoo.com.tw.
Tsai-Chung Li, Email: tcli@mail.cmu.edu.tw.
Supplementary information
Supplementary information accompanies this paper at 10.1186/s12933-019-0978-y.
References
- 1.Beach KW, Bedford GR, Bergelin RO, Martin DC, Vandenberghe N, Zaccardi M, Strandness DE., Jr Progression of lower-extremity arterial occlusive disease in type II diabetes mellitus. Diabetes Care. 1988;11(6):464–472. doi: 10.2337/diacare.11.6.464. [DOI] [PubMed] [Google Scholar]
- 2.Shu J, Santulli G. Update on peripheral artery disease: epidemiology and evidence-based facts. Atherosclerosis. 2018;275:379–381. doi: 10.1016/j.atherosclerosis.2018.05.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Fowkes FG, Rudan D, Rudan I, Aboyans V, Denenberg JO, McDermott MM, Norman PE, Sampson UK, Williams LJ, Mensah GA, et al. Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010: a systematic review and analysis. Lancet (London, England) 2013;382(9901):1329–1340. doi: 10.1016/S0140-6736(13)61249-0. [DOI] [PubMed] [Google Scholar]
- 4.Kullo IJ, Rooke TW. CLINICAL PRACTICE. Peripheral artery disease. N Engl J Med. 2016;374(9):861–871. doi: 10.1056/NEJMcp1507631. [DOI] [PubMed] [Google Scholar]
- 5.Nalysnyk L, Hernandez-Medina M, Krishnarajah G. Glycaemic variability and complications in patients with diabetes mellitus: evidence from a systematic review of the literature. Diabetes Obes Metab. 2010;12(4):288–298. doi: 10.1111/j.1463-1326.2009.01160.x. [DOI] [PubMed] [Google Scholar]
- 6.Gorst C, Kwok CS, Aslam S, Buchan I, Kontopantelis E, Myint PK, Heatlie G, Loke Y, Rutter MK, Mamas MA. Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis. Diabetes Care. 2015;38(12):2354–2369. doi: 10.2337/dc15-1188. [DOI] [PubMed] [Google Scholar]
- 7.Muggeo M, Zoppini G, Bonora E, Brun E, Bonadonna RC, Moghetti P, Verlato G. Fasting plasma glucose variability predicts 10-year survival of type 2 diabetic patients: the Verona Diabetes Study. Diabetes Care. 2000;23(1):45–50. doi: 10.2337/diacare.23.1.45. [DOI] [PubMed] [Google Scholar]
- 8.Lin CC, Yang CP, Li CI, Liu CS, Chen CC, Lin WY, Hwang KL, Yang SY, Li TC. Visit-to-visit variability of fasting plasma glucose as predictor of ischemic stroke: competing risk analysis in a national cohort of Taiwan Diabetes Study. BMC Med. 2014;12:165. doi: 10.1186/s12916-014-0165-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Lin CC, Li CI, Liu CS, Lin WY, Chen CC, Yang SY, Lee CC, Li TC. Annual fasting plasma glucose variation increases risk of cancer incidence and mortality in patients with type 2 diabetes: the Taichung Diabetes Study. Endocr Relat Cancer. 2012;19(4):473–483. doi: 10.1530/ERC-12-0038. [DOI] [PubMed] [Google Scholar]
- 10.Li TC, Yang CP, Tseng ST, Li CI, Liu CS, Lin WY, Hwang KL, Yang SY, Chiang JH, Lin CC. Visit-to-Visit variations in fasting plasma glucose and HbA1c associated with an increased risk of Alzheimer disease: Taiwan Diabetes Study. Diabetes Care. 2017;40(9):1210–1217. doi: 10.2337/dc16-2238. [DOI] [PubMed] [Google Scholar]
- 11.Yang CP, Li CI, Liu CS, Lin WY, Hwang KL, Yang SY, Li TC, Lin CC. Variability of fasting plasma glucose increased risks of diabetic polyneuropathy in T2DM. Neurology. 2017;88(10):944–951. doi: 10.1212/WNL.0000000000003682. [DOI] [PubMed] [Google Scholar]
- 12.Piconi L, Quagliaro L, Assaloni R, Da Ros R, Maier A, Zuodar G, Ceriello A. Constant and intermittent high glucose enhances endothelial cell apoptosis through mitochondrial superoxide overproduction. Diabetes Metab Res Rev. 2006;22(3):198–203. doi: 10.1002/dmrr.613. [DOI] [PubMed] [Google Scholar]
- 13.Azuma K, Kawamori R, Toyofuku Y, Kitahara Y, Sato F, Shimizu T, Miura K, Mine T, Tanaka Y, Mitsumata M, et al. Repetitive fluctuations in blood glucose enhance monocyte adhesion to the endothelium of rat thoracic aorta. Arterioscler Thromb Vasc Biol. 2006;26(10):2275–2280. doi: 10.1161/01.ATV.0000239488.05069.03. [DOI] [PubMed] [Google Scholar]
- 14.Quagliaro L, Piconi L, Assaloni R, Martinelli L, Motz E, Ceriello A. Intermittent high glucose enhances apoptosis related to oxidative stress in human umbilical vein endothelial cells: the role of protein kinase C and NAD(P)H-oxidase activation. Diabetes. 2003;52(11):2795–2804. doi: 10.2337/diabetes.52.11.2795. [DOI] [PubMed] [Google Scholar]
- 15.Chen TT, Chung KP, Lin IC, Lai MS. The unintended consequence of diabetes mellitus pay-for-performance (P4P) program in Taiwan: are patients with more comorbidities or more severe conditions likely to be excluded from the P4P program? Health Serv Res. 2011;46(1 Pt 1):47–60. doi: 10.1111/j.1475-6773.2010.01182.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Gerhard-Herman MD, Gornik HL, Barrett C, Barshes NR, Corriere MA, Drachman DE, Fleisher LA, Fowkes FG, Hamburg NM, Kinlay S, et al. 2016 AHA/ACC guideline on the management of patients with lower extremity peripheral artery disease: a Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2017;135(12):e726–e779. doi: 10.1161/CIR.0000000000000471. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Wen CP, Tsai SP, Chung WS. A 10-year experience with universal health insurance in Taiwan: measuring changes in health and health disparity. Ann Intern Med. 2008;148(4):258–267. doi: 10.7326/0003-4819-148-4-200802190-00004. [DOI] [PubMed] [Google Scholar]
- 18.The National Health Insurance Statistics; 2014. https://www.nhi.gov.tw/english/Content_List.aspx?n=8162ED7256773F45&topn=616B97F8DF2C3614. Accessed 20 Nov 2019.
- 19.Kilpatrick ES, Rigby AS, Atkin SL. A1C variability and the risk of microvascular complications in type 1 diabetes: data from the Diabetes Control and Complications Trial. Diabetes Care. 2008;31(11):2198–2202. doi: 10.2337/dc08-0864. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Rubin DB. Multiple imputation for nonresponse in surveys. New York: Wiley; 1987. [Google Scholar]
- 21.Schafer J. Analysis of incomplete multivariate data. London: Chapman & Hall; 1997. [Google Scholar]
- 22.Lunn M, McNeil D. Applying Cox regression to competing risks. Biometrics. 1995;51(2):524–532. doi: 10.2307/2532940. [DOI] [PubMed] [Google Scholar]
- 23.Olin JW, Sealove BA. Peripheral artery disease: current insight into the disease and its diagnosis and management. Mayo Clin Proc. 2010;85(7):678–692. doi: 10.4065/mcp.2010.0133. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Criqui MH, Aboyans V. Epidemiology of peripheral artery disease. Circ Res. 2015;116(9):1509–1526. doi: 10.1161/CIRCRESAHA.116.303849. [DOI] [PubMed] [Google Scholar]
- 25.UK Prospective Diabetes Study (UKPDS) Group Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) Lancet (London, England) 1998;352(9131):837–53. doi: 10.1016/S0140-6736(98)07019-6. [DOI] [PubMed] [Google Scholar]
- 26.Smith-Palmer J, Brandle M, Trevisan R, Orsini Federici M, Liabat S, Valentine W. Assessment of the association between glycemic variability and diabetes-related complications in type 1 and type 2 diabetes. Diabetes Res Clin Pract. 2014;105(3):273–284. doi: 10.1016/j.diabres.2014.06.007. [DOI] [PubMed] [Google Scholar]
- 27.Kosiborod M, Gomes MB, Nicolucci A, Pocock S, Rathmann W, Shestakova MV, Watada H, Shimomura I, Chen H, Cid-Ruzafa J, et al. Vascular complications in patients with type 2 diabetes: prevalence and associated factors in 38 countries (the DISCOVER study program) Cardiovasc Diabetol. 2018;17(1):150. doi: 10.1186/s12933-018-0787-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Takahashi H, Iwahashi N, Kirigaya J, Kataoka S, Minamimoto Y, Gohbara M, Abe T, Okada K, Matsuzawa Y, Konishi M, et al. Glycemic variability determined with a continuous glucose monitoring system can predict prognosis after acute coronary syndrome. Cardiovasc Diabetol. 2018;17(1):116. doi: 10.1186/s12933-018-0761-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Teliti M, Cogni G, Sacchi L, Dagliati A, Marini S, Tibollo V, De Cata P, Bellazzi R, Chiovato L. Risk factors for the development of micro-vascular complications of type 2 diabetes in a single-centre cohort of patients. Diabetes Vasc Dis Res. 2018;15(5):424–432. doi: 10.1177/1479164118780808. [DOI] [PubMed] [Google Scholar]
- 30.Magri CJ, Mintoff D, Camilleri L, Xuereb RG, Galea J, Fava S. Relationship of hyperglycaemia, hypoglycaemia, and glucose variability to atherosclerotic disease in type 2 diabetes. J Diabetes Res. 2018;2018:7464320–7464320. doi: 10.1155/2018/7464320. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Caprnda M, Mesarosova D, Ortega PF, Krahulec B, Egom E, Rodrigo L, Kruzliak P, Mozos I, Gaspar L. Glycemic variability and vascular complications in patients with type 2 diabetes mellitus. Folia Med. 2017;59(3):270–278. doi: 10.1515/folmed-2017-0048. [DOI] [PubMed] [Google Scholar]
- 32.Lee M-Y, Hsiao P-J, Huang Y-T, Huang J-C, Hsu W-H, Chen S-C, Shin S-J. Greater HbA1c variability is associated with increased cardiovascular events in type 2 diabetes patients with preserved renal function, but not in moderate to advanced chronic kidney disease. PLoS ONE. 2017;12(6):e0178319–e0178319. doi: 10.1371/journal.pone.0178319. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Dekker RG, 2nd, Qin C, Ho BS, Kadakia AR. The effect of cumulative glycemic burden on the incidence of diabetic foot disease. J Orthop Surg Res. 2016;11(1):143. doi: 10.1186/s13018-016-0474-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Canani LH, Copstein E, Pecis M, Friedman R, Leitao CB, Azevedo MJ, Triches C, Rados DR, Moreas RS, Gross JL. Cardiovascular autonomic neuropathy in type 2 diabetes mellitus patients with peripheral artery disease. Diabetol Metab Syndr. 2013;5(1):54. doi: 10.1186/1758-5996-5-54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Matsutani D, Sakamoto M, Minato S, Kayama Y, Takeda N, Horiuchi R, Utsunomiya K. Visit-to-visit HbA1c variability is inversely related to baroreflex sensitivity independently of HbA1c value in type 2 diabetes. Cardiovasc Diabetol. 2018;17(1):100–100. doi: 10.1186/s12933-018-0743-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Nativel M, Potier L, Alexandre L, Baillet-Blanco L, Ducasse E, Velho G, Marre M, Roussel R, Rigalleau V, Mohammedi K. Lower extremity arterial disease in patients with diabetes: a contemporary narrative review. Cardiovasc Diabetol. 2018;17(1):138–138. doi: 10.1186/s12933-018-0781-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Tang X, Zhong J, Zhang H, Luo Y, Liu X, Peng L, Zhang Y, Qian X, Jiang B, Liu J, et al. Visit-to-visit fasting plasma glucose variability is an important risk factor for long-term changes in left cardiac structure and function in patients with type 2 diabetes. Cardiovasc Diabetol. 2019;18(1):50. doi: 10.1186/s12933-019-0854-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Cardoso CRL, Leite NC, Moram CBM, Salles GF. Long-term visit-to-visit glycemic variability as predictor of micro- and macrovascular complications in patients with type 2 diabetes: The Rio de Janeiro Type 2 Diabetes Cohort Study. Cardiovasc Diabetol. 2018;17(1):33–33. doi: 10.1186/s12933-018-0677-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.DeVries JH. Glucose variability: where it is important and how to measure it. Diabetes. 2013;62(5):1405–1408. doi: 10.2337/db12-1610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Hsu WY, Lin CL, Kao CH. A population-based cohort study on peripheral arterial disease in patients with schizophrenia. PLoS ONE. 2016;11(2):e0148759. doi: 10.1371/journal.pone.0148759. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Lin YS, Tung TH, Wang J, Chen YF, Chen TH, Lin MS, Chi CC, Chen MC. Peripheral arterial disease and atrial fibrillation and risk of stroke, heart failure hospitalization and cardiovascular death: a nationwide cohort study. Int J Cardiol. 2016;203:204–211. doi: 10.1016/j.ijcard.2015.10.091. [DOI] [PubMed] [Google Scholar]
- 42.Hooi JD, Kester AD, Stoffers HE, Overdijk MM, van Ree JW, Knottnerus JA. Incidence of and risk factors for asymptomatic peripheral arterial occlusive disease: a longitudinal study. Am J Epidemiol. 2001;153(7):666–672. doi: 10.1093/aje/153.7.666. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1: Figure S1. Flow chart of recruitment procedures for the study. Table S1. Hazard ratios (HRs) of peripheral vascular disease based on HbA1c and FPG-CV levels in persons with diabetes enrolled in the National Diabetes Care Management Program, Taiwan (n=30,932).
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to the policy declared by National Health Insurance in Taiwan but are available from the corresponding author on reasonable request.