Prediabetes, an intermediate stage between normal blood sugar levels and a diabetes mellitus diagnosis, is increasing in prevalence. Severe prediabetes is associated with a similar risk of ...complications as diabetes, but its relationship with peripheral arterial disease remains underexplored.
We conducted a retrospective cohort study involving 36,950 adult patients, utilizing electronic medical records from the National Taiwan University Hospital between 2014 and 2019. We employed multivariable Cox regression and Kaplan-Meier analysis with the log-rank test to analyze major adverse limb events (MALE) and major adverse cardiovascular events (MACE) in relation to normal glucose regulation (NGR) and prediabetes.
During the 131,783 person-years follow-up, 17,754 cases of prediabetes and 19,196 individuals with normal glucose regulation (NGR) were identified. Kaplan-Meier analysis revealed an increased incidence of both MALE and MACE in individuals with prediabetes. (log-rank p = 0.024 and < 0.001). Prediabetes exhibited a significant association with an elevated risk of MALE (adjusted hazard ratio (aHR) 1.26 95% CI 1.10-1.46, p = 0.001) and MACE (aHR 1.46 1.27-1.67, p < 0.001). Furthermore, in individuals with prediabetes, the elevation in the risk of MALE commenced before HbA1c levels surpassed 5.0% (for HbA1c 5.0-5.5%: aHR 1.78 (1.04-3.04), p = 0.036; HbA1c 5.5-6.0%: aHR 1.29 1.06-1.58, p = 0.012; aHbA1c 6.0-6.5%: aHR 1.39 1.14-1.70, p < 0.001). Similarly, the onset of increased MACE risk was observed when HbA1c levels exceeded 5.5% (for HbA1c 5.5-6.0%: aHR 1.67 1.39-2.01, p < 0.001; HbA1c 6.0-6.5%: HR 2.10 1.76-2.51, p < 0.001). Factors associated with both MALE and MACE in prediabetes include advanced age, male gender, higher body mass index, and a history of heart failure or atrial fibrillation.
We demonstrated higher susceptibility to MALE and MACE in prediabetes compared to normoglycemic counterparts, notwithstanding lower HbA1c levels. Complications may manifest at an earlier prediabetes trajectory. Intensive lifestyle modification may improve the prognosis of severe prediabetes.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The glycemic continuum often indicates a gradual decline in insulin sensitivity leading to an increase in glucose levels. Although prediabetes is an established risk factor for both macrovascular and ...microvascular diseases, whether prediabetes is independently associated with the risk of developing atrial fibrillation (AF), particularly the occurrence time, has not been well studied using a high-quality research design in combination with statistical machine-learning algorithms.
Using data available from electronic medical records collected from the National Taiwan University Hospital, a tertiary medical center in Taiwan, we conducted a retrospective cohort study consisting 174,835 adult patients between 2014 and 2019 to investigate the relationship between prediabetes and AF. To render patients with prediabetes as comparable to those with normal glucose test, a propensity-score matching design was used to select the matched pairs of two groups with a 1:1 ratio. The Kaplan-Meier method was used to compare the cumulative risk of AF between prediabetes and normal glucose test using log-rank test. The multivariable Cox regression model was employed to estimate adjusted hazard ratio (HR) for prediabetes versus normal glucose test by stratifying three levels of glycosylated hemoglobin (HbA1c). The machine-learning algorithm using the random survival forest (RSF) method was further used to identify the importance of clinical factors associated with AF in patients with prediabetes.
A sample of 14,309 pairs of patients with prediabetes and normal glucose test result were selected. The incidence of AF was 11.6 cases per 1000 person-years during a median follow-up period of 47.1 months. The Kaplan-Meier analysis revealed that the risk of AF was significantly higher in patients with prediabetes (log-rank p < 0.001). The multivariable Cox regression model indicated that prediabetes was independently associated with a significant increased risk of AF (HR 1.24, 95% confidence interval 1.11-1.39, p < 0.001), particularly for patients with HbA1c above 5.5%. The RSF method identified elevated N-terminal natriuretic peptide and altered left heart structure as the two most important risk factors for AF among patients with prediabetes.
Our study found that prediabetes is independently associated with a higher risk of AF. Furthermore, alterations in left heart structure make a significant contribution to this elevated risk, and these structural changes may begin during the prediabetes stage.
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Atrial fibrillation (AF) is prevalent in patients with type 2 diabetes mellitus (T2DM). Glycemic variability (GV) is associated with risk of micro- and macrovascular diseases. However, whether the GV ...can increase the risk of AF remains unknown.
The cohort study used a database from National Taiwan University Hospital, a tertiary medical center in Taiwan. Between 2014 and 2019, a total of 27,246 adult patients with T2DM were enrolled for analysis. Each individual was assessed to determine the coefficients of variability of fasting glucose (FGCV) and HbA1c variability score (HVS). The GV parameters were categorized into quartiles. Multivariate Cox regression models were employed to estimate the relationship between the GV parameters and the risk of AF, transient ischemic accident (TIA)/ischemic stroke and mortality in patients with T2DM.
The incidence rates of AF and TIA/ischemic stroke were 21.31 and 13.71 per 1000 person-year respectively. The medium follow-up period was 70.7 months. In Cox regression model with full adjustment, the highest quartile of FGCV was not associated with increased risk of AF Hazard ratio (HR): 1.12, 95% confidence interval (CI) 0.96-1.29, p = 0.148 or TIA/ischemic stroke (HR: 1.04, 95% CI 0.83-1.31, p = 0.736), but was associated with increased risk of total mortality (HR: 1.33, 95% CI 1.12-1.58, p < 0.001) and non-cardiac mortality (HR: 1.41, 95% CI 1.15-1.71, p < 0.001). The highest HVS was significantly associated with increased risk of AF (HR: 1.29, 95% CI 1.12-1.50, p < 0.001), total mortality (HR: 2.43, 95% CI 2.03-2.90, p < 0.001), cardiac mortality (HR: 1.50, 95% CI 1.06-2.14, p = 0.024) and non-cardiac mortality (HR: 2.80, 95% CI 2.28-3.44, p < 0.001) but was not associated with TIA/ischemic stroke (HR: 0.98, 95% CI 0.78-1.23, p = 0.846). The Kaplan-Meier analysis showed significantly higher risk of AF, cardiac and non-cardiac mortality according to the magnitude of GV (log-rank test, p < 0.001).
Our data demonstrate that high GV is independently associated with the development of new-onset AF in patients with T2DM. The benefit of maintaining stable glycemic levels to improve clinical outcomes warrants further studies.
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Background Peripheral arterial disease (PAD) is a severe complication in patients with type 2 diabetes. Glycemic variability (GV) is associated with increased risks of developing microvascular and ...macrovascular diseases. However, few studies have focused on the association between GV and PAD. Methods and Results This cohort study used a database maintained by the National Taiwan University Hospital, a tertiary medical center in Taiwan. For each individual, GV parameters were calculated, including fasting glucose coefficient of variability (FGCV) and hemoglobin A1c variability score (HVS). Multivariate Cox regression models were constructed to estimate the relationships between GV parameters and composite scores for major adverse limb events (MALEs) and major adverse cardiovascular events (MACEs). Between 2014 and 2019, a total of 45 436 adult patients with prevalent type 2 diabetes were enrolled for analysis, and GV was assessed during a median follow-up of 64.4 months. The average number of visits and time periods were 13.38 and 157.87 days for the HVS group and 14.27 and 146.59 days for the FGCV group, respectively. The incidence rates for cardiac mortality, PAD, and critical limb ischemia (CLI) were 5.38, 20.11, and 2.41 per 1000 person-years in the FGCV group and 5.35, 20.32, and 2.50 per 1000 person-years in HVS group, respectively. In the Cox regression model with full adjustment, the highest FGCV quartile was associated with significantly increased risks of MALEs (hazard ratio HR, 1.57 95% CI, 1.40-1.76;
<0.001) and MACEs (HR, 1.40 95% CI, 1.25-1.56;
<0.001). Similarly, the highest HVS quartile was associated with significantly increased risks of MALEs (HR, 1.44 95% CI, 1.28-1.62;
<0.001) and MACEs (HR, 1.28 95% CI, 1.14-1.43;
<0.001). The highest FGCV and HVS quartiles were both associated with the development of PAD and CLI (FGCV: PAD HR, 1.57;
<0.001, CLI HR, 2.19;
<0.001; HVS: PAD HR, 1.44;
<0.001, CLI HR, 1.67;
=0.003). The Kaplan-Meier analysis showed significantly higher risks of MALEs and MACEs with increasing GV magnitude (log-rank
<0.001). Conclusions Among individuals with diabetes, increased GV is independently associated with the development of MALEs, including PAD and CLI, and MACEs. The benefit of maintaining stable glycemic levels for improving clinical outcomes warrants further studies.
Atrial fibrillation (AF) is prevalent in patients with type 2 diabetes mellitus (T2DM). Obesity commonly accompanies T2DM, and increases the risk of AF. However, the dose-relationship between body ...mass index (BMI) and AF risk has seldom been studied in patients with diabetes.
This cohort study utilized a database from National Taiwan University Hospital, a tertiary medical center in Taiwan. Between 2014 and 2019, 64,339 adult patients with T2DM were enrolled for analysis. BMI was measured and categorized as underweight (BMI < 18.5), normal (18.5 ≤ BMI < 24), overweight (24 ≤ BMI < 27), obesity class 1 (27 ≤ BMI < 30), obesity class 2 (30 ≤ BMI < 35), or obesity class 3 (BMI ≥ 35). Multivariate Cox regression and spline regression models were employed to estimate the relationship between BMI and the risk of AF in patients with T2DM.
The incidence of AF was 1.97 per 1000 person-years (median follow-up, 70.7 months). In multivariate Cox regression, using normal BMI as the reference group, underweight (HR 1.52, 95% CI 1.25-1.87, p < 0.001) was associated with a significantly higher risk of AF, while overweight was associated with significantly reduced risk of AF (HR 0.82, 95% CI 0.73-0.89, p < 0.001). Kaplan-Meier analysis showed AF risk was highest in the underweight group, followed by obesity class 3, while the overweight group had the lowest incidence of AF (log-rank test, p < 0.001). The cubic restrictive spline model revealed a "J-shaped" or "L-shaped" relationship between BMI and AF risk.
Underweight status confers the highest AF risk in Asian patients with T2DM.
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Background and Aim
The aim of this study is to identify gastric cancer burden in Indigenous Taiwanese peoples and conduct a project to evaluate how to reduce the disparities most effectively in ...Indigenous communities.
Methods
First, we quantified the health disparities in gastric cancer in Indigenous peoples using data from the cancer registries during the period of 2006–2014. Second, we identified parameters that might be associated with Helicobacter pylori infection or help identify a good eradication strategy.
Results
Gastric cancer incidence (24.4 vs 12.3 per 100 000 person‐years) and mortality rates (15.8 vs 6.8 per 100 000 person‐years) were higher in Indigenous than in non‐Indigenous, with 2.19‐fold (95% confidence interval CI: 2.06–2.33) and 2.47‐fold (2.28–2.67) increased risk, respectively. In Indigenous communities, H. pylori infection was more prevalent in Indigenous than in non‐Indigenous (59.4% vs 31.5%, P < 0.01). Regression analyses consistently showed that either the mountain or plain Indigenous had 1.89‐fold (95% CI: 1.34–2.66) and 1.73‐fold (95% CI: 1.24–2.41) increased risk for H. pylori infection, respectively, as compared with non‐Indigenous, adjusting for other baseline characteristics. The high infection rates were similarly seen in young, middle‐aged, and older adults. Program eradication rates using clarithromycin‐based triple therapy were suboptimal (73.7%, 95% CI: 70.0–77.4%); the habits of smoking (1.70‐fold, 95% CI: 1.01–2.39) and betel nut chewing (1.54‐fold, 95% CI: 0.93–2.16) were associated with the higher risk of treatment failure.
Conclusion
Gastric cancer burden is higher in Indigenous Taiwanese peoples than in their non‐Indigenous counterparts. Eliminating the prevalent risk factor of H. pylori infection is a top priority to reduce this health disparity.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The need is growing to create medical big data based on the electronic health records collected from different hospitals. Errors for sure occur and how to correct them should be explored.
Electronic ...health records of 9,197,817 patients and 53,081,148 visits, totaling about 500 million records for 2006–2016, were transmitted from eight hospitals into an integrated database. We randomly selected 10% of patients, accumulated the primary keys for their tabulated data, and compared the key numbers in the transmitted data with those of the raw data. Errors were identified based on statistical testing and clinical reasoning.
Data were recorded in 1573 tables. Among these, 58 (3.7%) had different key numbers, with the maximum of 16.34/1000. Statistical differences (P < 0.05) were found in 34 (58.6%), of which 15 were caused by changes in diagnostic codes, wrong accounts, or modified orders. For the rest, the differences were related to accumulation of hospital visits over time. In the remaining 24 tables (41.4%) without significant differences, three were revised because of incorrect computer programming or wrong accounts. For the rest, the programming was correct and absolute differences were negligible. The applicability was confirmed using the data of 2,730,883 patients and 15,647,468 patient-visits transmitted during 2017–2018, in which 10 (3.5%) tables were corrected.
Significant magnitude of inconsistent data does exist during the transmission of big data from diverse sources. Systematic validation is essential. Comparing the number of data tabulated using the primary keys allow us to rapidly identify and correct these scattered errors.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Outreach (i.e., to invite those who do not use, or who under use screening services) and inreach (i.e., to invite an existing population who have already accessed the medical system) approaches may ...influence people to increase their use of screening test; however, whether their outcomes would be equivalent remains unclear.
A total of 3,363,896 subjects, 50-69 years of age, participated in a colorectal cancer (CRC) screening program using biennial fecal immunochemical tests; 34.5% participated during 2004-2009 when the outreach approach alone was used, and 65.5% participated from 2010-2013 when outreach was integrated with an inreach approach. We compared the outcomes of the two approaches in delivery of screening services.
Coverage rates increased from 21.4% to 36.9% and the positivity rate increased from 4.0% to 7.9%, while referral for confirmatory diagnostic examinations declined from 80.0% to 53.3%. The first period detected CRC in 0.20% of subjects screened, with a positive predictive value (PPV) of 6.1%, and the second detected CRC in 0.34% of subjects, with a PPV of 8.0%. After adjusting for confounders, differences were observed in the PPV for CRC (adjusted relative risk, 1.50; 95% confidence interval CI, 1.41-1.60), cancer detection rate (1.20; 95% CI, 1.13-1.27), and interval cancer rate (0.72; 95% CI, 0.65-0.80). When we focused on the comparison between two approaches during the same study period of 2010-2013, the positivity rate of fecal testing (8.2% vs. 7.6%) and the PPV for CRC detection remained higher (1.07; 95% CI, 1.01-1.12) in subjects who were recruited from the inreach approach.
Outcomes of screening were equivalent or better after integration of outreach and inreach approaches.
The results will encourage makers of health-care policy to adopt the integration approach to deliver screening services.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background
To elucidate the impact of varying anatomic sites on advanced stage of and death from oral cancer.
Methods
A total of 27 717 oral cancers mainly from a population‐based visual inspection ...program in Taiwan from 2004 to 2009 was followed until the end of 2012.
Results
Using lip cancer as reference, the odds ratios (95% confidence interval CI) of advanced stage of cancer were 2.20 (1.92‐2.51) for tongue, 2.60 (2.28‐2.97) for buccal, 2.68 (2.20‐3.28) for floor of mouth, 2.96 (2.52‐3.47) for hard palate, 6.04 (5.17‐7.05) for gingiva, and 10.83 (9.20‐12.74) for oropharynx. The estimated hazard ratios (95% CI) for oral cancer death increased from 1.48 (1.31‐1.67) in buccal, 1.61 (1.43‐1.82) in tongue, 1.68 (1.41‐1.99) in floor of mouth, 1.79 (1.57‐2.05) in gingiva, 1.97 (1.71‐2.26) in hard palate, and 2.15 (1.89‐2.45) in oropharynx.
Conclusion
Different anatomic sites had variations in advanced stage of and death from oral cancer and need vigilant surveillance.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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