Trends of clinical parameters and incidences of diabetes mellitus complications among patients with type 2 diabetes mellitus in Hong Kong, 2010-2019: a retrospective cohort study

Trajectory of haemoglobin A1c and incidence of cardiovascular disease in patients with type 2 diabetes mellitus

AIM

To evaluate the association between changes in haemoglobin A1c (HbA1c) and the concurrent incidence of cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM) patients.

METHOD

We conducted a retrospective cohort study among T2DM patients with HbA1c measurement after T2DM diagnosis between August 2009 and September 2010. The patients were classified into six subgroups based on baseline HbA1c (<7%; 7%-7.9%; ≥8%) and age (<65; ≥65 years), and then clustered into classes by HbA1c trajectory and CVD incidence over the 12-year follow-up period using joint latent class mixture models. We explored the HbA1c trajectories and CVD incidences in each latent class. Multinomial logistic regression was used to compare the baseline characteristics among different latent classes.

RESULTS

A total of 128 843 T2DM patients were included with a median follow-up period of 11.7 years. Ten latent classes were identified in patients with baseline HbA1c ≥ 8% and age <65 years, while seven classes were identified in the other five groups. Among all the identified latent classes, patients with fluctuating HbA1c trajectories, characterized by alternating periods of increase and decrease, had higher CVD incidences. Male patients, and patients with higher baseline HbA1c and use of antidiabetic drugs were more likely to have a fluctuating HbA1c trajectory. More specifically, patients aged < 65 years with younger age or a smoking habit, and patients aged ≥ 65 years with a longer duration of T2DM were more likely to have a fluctuating HbA1c trajectory.

CONCLUSION

We found that T2DM patients with fluctuating HbA1c trajectories could have a higher CVD risk. Different trajectory-associated characteristics in age subgroups highlight the need for individualized management of T2DM patients.

Expressions of glycemic parameters, lipid profile, and thyroid hormone in patients with type 2 diabetes mellitus and their correlation

OBJECTIVE

This study aimed to investigate the expressions of glycemic parameters, lipid profile, and thyroid hormone in type 2 diabetes mellitus (T2DM) patients and their correlation.

METHODS

Eighty-four patients with T2DM in our hospital were included as the observation group. The T2DM patients were divided into mild group, moderate group, and severe group according to the fasting plasma glucose (FPG) level. Another 84 healthy subjects in the same period of health examination in our hospital were included as the control group. The levels of glycemic parameters, (HbA1c and FPG), lipid profile (TC, TG, LDL-C, and HDL-C) and thyroid hormone (FT3, TSH, and FT4) were measured by automatic biochemical analyzer. The correlation between glycemic parameters, lipid profile, and thyroid hormone was analyzed by Pearson correlation analysis.

RESULTS

The FPG, TC, TG, LDL-C, HbA1c, and TSH levels were significantly elevated, while the HDL-C and FT3 levels were significantly declined in the observation group versus to control group (p < .05). The levels of HbA1c, FPG, TC, LDL-C, and TSH were significantly increased, while the levels of HDL-C and FT3 were decreased in moderate and severe groups, when compared to mild group (p < .05). The levels of HbA1c, FPG, TC, LDL-C and TSH were higher, while the level of FT3 was lower in severe group than those in moderate group (p < .05). Pearson Correlation analysis showed that FT3 level in T2DM patients was positively correlated with FPG, HbAlc, TC, TG, and LDL-C levels (p < .05), but negatively correlated with HDL-C level (p < .05). TSH level was negatively correlated with FPG, HbAlc, TC, TG, and LDL-C levels (p < .05), while positively correlated with HDL-C level.

CONCLUSION

The thyroid hormone levels were of clinical significance in evaluating glycolipid metabolism and severity of T2DM. Clinical detection of glycolipid metabolism and thyroid hormone levels in T2DM patients is of great significance for diagnosis, evaluation, and targeted treatment of the disease.

Behind the Hospital Ward: In-Hospital Mortality of Type 2 Diabetes Mellitus Patients in Indonesia (Analysis of National Health Insurance Claim Sample Data)

The rising global prevalence of diabetes mellitus, a chronic metabolic disorder, poses significant challenges to healthcare systems worldwide. This study examined in-hospital mortality among patients diagnosed with non-insulin-dependent diabetes mellitus (NIDDM) of ICD-10, or Type 2 Diabetes Mellitus (T2DM), in Indonesia, utilizing hospital claims data spanning from 2017 to 2022 obtained from the Indonesia Health Social Security Agency or Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan. The analysis, which included 610,809 hospitalized T2DM patients, revealed an in-hospital mortality rate of 6.6%. Factors contributing to an elevated risk of mortality included advanced age, the presence of comorbidities, and severe complications. Additionally, patients receiving health subsidies and those treated in government hospitals were found to have higher mortality risks. Geographic disparities were observed, highlighting variations in healthcare outcomes across different regions. Notably, the complication of ketoacidosis emerged as the most significant risk factor for in-hospital mortality, with an odds ratio (OR) of 10.86, underscoring the critical need for prompt intervention and thorough management of complications to improve patient outcomes.

Predicting coronary heart disease in Chinese diabetics using machine learning

Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients complicated with CHD for understanding the occurrence of diabetes/CHD comorbidity. In this study, by analyzing the risk of CHD in more than 300,000 diabetes patients in southwest China, an artificial intelligence (AI) model was proposed to predict the risk of diabetes/CHD comorbidity. Firstly, we statistically analyzed the distribution of four types of features (basic demographic information, laboratory indicators, medical examination, and questionnaire) in comorbidities, and evaluated the predictive performance of three traditional machine learning methods (eXtreme Gradient Boosting, Random Forest, and Logistic regression). In addition, we have identified nine important features, including age, WHtR, BMI, stroke, smoking, chronic lung disease, drinking and MSP. Finally, the model produced an area under the receiver operating characteristic curve (AUC) of 0.701 on the test samples. These findings can provide personalized guidance for early CHD warning for diabetic populations.

Temporal trends in chronic complications of diabetes by sex in community-based people with type 2 diabetes: the Fremantle Diabetes Study

BACKGROUND

Whether recent reductions in cardiovascular disease (CVD) events and mortality in type 2 diabetes apply equally to both sexes is largely unknown. The aim of this study was to characterize temporal changes in CVD events and related outcomes in community-based male and female Australian adults with type 2 diabetes or without known diabetes.

METHODS

Participants from the longitudinal observational Fremantle Diabetes Study Phases I (FDS1; n = 1291 recruited 1993-1996) and II (FDS2; n = 1509 recruited 2008-2011) and four age-, sex- and postcode-matched individuals without diabetes (FDS1 n = 5159; FDS2 n = 6036) were followed for first myocardial infarction, stroke, heart failure hospitalization, lower extremity amputation, CVD death and all-cause mortality. Five-year incidence rates (IRs) for males versus females in FDS1 and FDS2 were calculated, and IR ratios (IRRs) derived.

RESULTS

The FD1 and FDS2 participants were of mean age 64.0 and 65.4 years, respectively, and 48.7% and 51.8% were males. For type 2 diabetes, IRRs for all endpoints were 11-62% lower in FDS2 than FDS1 for both sexes. For participants without diabetes, IRRs were 8-56% lower in FDS2 versus FDS1 apart from stroke in females (non-significantly 41% higher). IRRs for males versus females across FDS phases were not significantly different for participants with type 2 diabetes or those without diabetes (P-values for male * FDS2 interaction ≥ 0.0.083 adjusted for age). For risk factors in participants with type 2 diabetes, greater improvements between FDS1 and FDS2 in smoking rates in males were offset by a greater reduction in systolic blood pressure in females.

CONCLUSIONS

The incidence of chronic complications in Australians with type 2 diabetes and without diabetes has fallen similarly in both sexes over recent decades, consistent with comparably improved overall CVD risk factor management.