The glycemic fluctuation in peri-percutaneous coronary intervention (PCI) period is closely related to the short-term and long-term prognosis in patients with coronary heart disease (CHD). At present, there are few studies on glycemic fluctuation and its influencing factors in peri- PCI period in these patients.
To explore the characteristics and influencing factors of glycemic fluctuation during peri-PCI period in patients with CHD and type 2 diabetes (T2DM) .
One hundred and fifty-six patients undergoing PCI in the cardiology ward of Jiangsu Provincial People's Hospital from April 2021 to November 2021 were selected. General demographics were collected by general demographics questionnaire. Perioperative glycemic fluctuation was collected by glycemic data questionnaire. The patients were stratified according to the normal reference value range of blood glucose fluctuation evaluation indicators: normal standard deviation of blood glucose level (SDBG) group (<2.0 mmol/L, n=58) and high SDBG group (≥2.0 mmol/L, n=98) by the SDBG level; normal amplitude of postprandial glycemic excursions (PPGE) group (<2.2 mmol/L, n=28) and high PPGE group (≥2.2 mmol/L, n=128) by the PPGE; normal largest amplitude of glycemic excursion (LAGE) group (<4.4 mmol/L, n=39) and high LAGE group (≥4.4 mmol/L, n=117) by the LAGE. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. The Hospital Anxiety and Depression Scale (HAD) was used to identify depression and anxiety. The Numeric Rating Scale (NRs) used to assess pain intensity in peri-PCI period. Pearson and Spearman correlation analyses were used to study the correlation of factors related to blood glucose fluctuation during the peri-PCI period. Multiple linear regression analysis was used to explore the factors associated with blood glucose fluctuation during the peri-PCI period.
Normal and high SDBG groups had statistically significant differences in course of diabetes, use of glucose control regimen, mean values of HbA1c, changes in dieatary intake during perioperative period, operation start time, operation duration, and NRs score; as well as stent/balloon implantation prevalence (P<0.05). HbA1c in normal SDBG group was lower than that in high SDBG group. There were statistically significant differences in use of glucose control regimen, mean values of BMI and TC, as well as perioperative dietary intake between normal and high PPGE groups (P<0.05). The mean values of BMI and TC of normal PPGE group were higher than those of high PPGE group. There were statistically significant differences in the duration of diabetes, use of glucose control regimen, mean values of TC, HbA1c and NRS score between normal and high LAGE groups (P<0.05). The normal LAGE group had higher TC and lower HbA1c than high LAGE group. The results of repeated measures ANOVA showed that the mean values of SDBG, PPGE and LAGE on the day before PCI, the day of PCI and the day after PCI were significantly different (P<0.05). Correlation analysis showed that during the peri-PCI period, SDBG was positively correlated with age, NRs score and PSQI score (r=0.216, 0.188, 0.295, P<0.05). PPGE was positively correlated with age, duration of diabetes (rs=0.179, P<0.05) and NRs score (rs=0.165, P<0.05), and negatively correlated with BMI and TG (rs=-0.254, -0.196, P<0.05). LAGE was positively correlated with the duration of diabetes, HbA1c and HAD score (rs=0.355, 0.171, 0.158, P<0.05). Multiple linear regression analysis showed that age; diet, PSQI score, and the time from the final meal before PCI to the start of PCI were independent factors influencing SDBG, with an explanatory degree of 19.3% (P<0.05). Education level, BMI, glucose control regimen, the time from the end of PCI to the first meal after PCI, and the time from the final meal before PCI to the start of PCI were independent factors influencing PPGE, with an explanation degree of 21.3% (P<0.05). The duration of diabetes, systolic blood pressure, PSQI score, location of domicile, perioperative exercise time, NRs score, the time from the end of PCI to the first meal after PCI; and time of surgery initiation were independent factors influencing LAGE, with an explanation degree of 47.8% (P<0.05) .
The influencing factors of blood glucose fluctuation during the peri-PCI period in patients with CHD and T2DM may include age, diabetes course, systolic blood pressure, registered permanent residence, education level, BMI, diet, perioperative exercise time, sleep quality, pain level, the time from the end of PCI to the first meal after PCI; the time from the final meal before PCI to the start of PCI, time of surgery initiation, and glycemic control plan. Based on these factors, personalized plans can be designed to control blood sugar fluctuations to improve the prognosis.
Time in range (TIR) is a new indicator of glycemic management in diabetes mellitus which has been thriving in recent years. Studies have confirmed that TIR is closely associated with chronic complications of diabetes. Previous studies have confirmed a close association between TIR and chronic complications of diabetes. Current studies on TIR and diabetic kidney disease (DKD) mainly focus on proteinuria, however the role of glomerular filtration rate (eGFR) in it is often neglected, and there are few studies on the cut points of TIR in evaluating glycemic control.
To investigate the relationship between TIR and the development of DKD in type 2 diabetes mellitus (T2DM), so as to provide theoretical foundations for the timely clinical detection, diagnosis and treatment of DKD in patients with T2DM.
A total of 214 T2DM patients admitted to the Department of Endocrinology in Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from July 2021 to December 2021 were included. The general data, laboratory indices and medication use were collected. The included patients were divided into group of DKD〔UACR ≥ 30 mg/g and/or eGFR < 60 ml·min-1 (1.73 m2) -1, n=58〕 and group of T2DM alone〔UACR<30 mg/g and eGFR≥60 ml·min-1 (1.73 m2) -1, n=156〕 based on the urinary albumin/creatinine ratio (UACR) and eGFR results, the included patients were further divided into TIR1 group (TIR>85%, n=90), TIR2 group (70%<TIR≤85%, n=51), TIR3 group (40%<TIR≤70%, n=57), and TIR4 group (TIR≤40%, n=16) using TIR values of 40%, 70%, and 85% as the cut points. Multivariate Logistic regression analysis was used to analyze the relationship between TIR and the development of DKD in T2DM patients.
The detection rate of DKD in T2DM patients tended to increase with decreasing TIR levels (Ptrend <0.05). The results of multivariate Logistic regression analysis showed that TIR was an influencing factor for the development of DKD in T2DM patients after adjusting for variables〔OR=0.976, 95%CI (0.953, 0.999), P=0.047〕; TIR3 and TIR4 groups were influencing factors for the development of DKD in T2DM patients compared to TIR1 group〔OR=5.287, 95%CI (1.897, 14.737), P=0.001; OR=4.712, 95%CI (1.143, 19.424), P=0.032〕 after adjusting for various confounding variables, and the incidence risk of DKD in T2DM patients tended to increase with decreasing TIR levels (Ptrend=0.010) .
TIR is an influencing factor for the development of DKD in T2DM patients; the incidence rate of DKD in T2DM patients increases significantly with the decreasing levels of TIR.
Postprandial glucose excursions is the main cause of elevated glycated hemoglobin levels in patients with diabetes mellitus. Controlling postprandial glucose is also an important strategy for preventing and treating chronic complications of diabetes.
To evaluate the effect of protein preload meal pattern on postprandial glucose excursions in patients with type 1 diabetes mellitus (T1DM) .
This study is a randomized, open-label, within-subject crossover clinical registration study. We selected thirty-one T1DM patients aged 18-45 years with a course of disease >1 year who were admitted to the First People's Hospital of Yunnan Province from February 2019 to December 2021. After ten hours fasting, all patients ate two isocaloric test meals with the same ingredients, one is protein preload meals and another is mixed meals, on the 4th and 7th days of wearing continuous glucose monitoring systems (CGM), respectively. CGM was used to analyze the CGMS data 5 hours after a meal, including peak postprandial glucose, time to peak postprandial glucose, average blood glucose level; area under curve (AUC) for blood glucose, incremental area under curve (iAUC) for blood glucose, mean amplitude of glycemic excursions (MAGE), the incremental glucose peak (?Peak) and low (?Low), and the proportion of time of hypoglycemia and hyperglycemia events. We also used a generalized linear mixed model to compare the difference in blood glucose excursions during five hours post-prandial.
Twenty-six T1DM patients were included in the statistical analysis. There was no significant difference in fasting blood glucose, peak blood glucose, mean blood glucose from 0 to 300 min, mean blood glucose from 180 to 300 min, the proportion of time when blood glucose>10 mmol/L (%), the proportion of time when blood glucose>13.9 mmol/L (%), MAGE, and ?Peak between protein preload meals and mixed meals (P>0.05). The peak time and ?Low of the protein preload meals were higher than those of the mixed meals, and the mean blood glucose (0-180 min) of the protein preload meals was lower than that of the mixed meals (P<0.05). No hypoglycemia event occurred in the mixed meals group. iAUC0-30, iAUC0-60, iAUC0-90, iAUC0-120, iAUC0-150, iAUC0-180, and iAUC0-210 of protein preload meals were lower than those of mixed meals (P<0.05). The blood glucose excursion at 0-30 min, 31-60 min, and 181-210 min of protein preload meals were significantly lower than those of mixed meals (P<0.05) .
This study showed that protein preload meal pattern can improve postprandial glucose levels and reduce postprandial glucose variability in adults with T1DM.
Recent small subcortical infarct (RSSI) is one of the manifestations of lacunar infarction. It is a common brain disease and can lead to the clinical outcome of disability or dementia in many patients. However, the relationship of infarction burden and cognitive impairment with blood glucose fluctuation in type 2 diabetes mellitus (T2DM) patients with RSSI is not very clear.
To explore the correlation of blood glucose variability (GV) with infarction burden and cognitive impairment in T2DM patients with RSSI, and based on this, to build a risk prediction model.
A total of 140 patients with T2DM and RSSI who were treated in the Second Affiliated Hospital of Zhengzhou University from January 2021 to June 2022 were retrospectively selected. The basic clinical data of the patients were collected. The 72-hour continuous blood glucose monitoring was performed. The infarct burden was evaluated by the magnetic resonance imaging performance (the study subjects were divided into the high infarction burden group including 45 cases and the low infarction burden group including 95 cases according to the imaging performance). The cognitive function was evaluated by the Montreal Cognitive Assessment (MoCA). Spearman correlation analysis was used to explore the correlation between GV and cognitive function (MoCA score). Multivariate Logistic regression analysis was used to explore the influencing factors of infarction burden and cognitive dysfunction in T2DM patients with RSSI. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of GV on cognitive impairment in T2DM patients with RSSI, and the nomogram predictive model was constructed and the predictive value was analyzed.
In terms of GV-related indicators, the high infarction burden group had higher standard deviation (SD) and percentage of coefficient of variation (%CV) and lower time in range (TIR) than the low infarction burden group, with statistically significant differences (P<0.05). The results of Spearman correlation analysis showed that SD (rs=0.272, P=0.001) and %CV (rs=0.391, P<0.001) were directly proportional to MoCA score, and TIR (rs=-0.325, P<0.001) was inversely proportional to the MoCA score in T2DM patients with RSSI. The results of multivariate Logistic regression analysis showed that elevated SD〔OR=4.201, 95%CI (1.380, 12.788), P=0.011〕 and %CV〔OR=1.218, 95%CI (1.096, 1.354), P<0.001〕were risk factors for high infarction burden in patients with T2DM and RSSI, while increased TIR〔OR=0.866, 95%CI (0.814, 0.921), P<0.001〕 was a protective factor. Elevated SD〔OR=2.947, 95%CI (1.150, 7.548), P=0.024〕 and %CV〔OR=1.174, 95%CI (1.072, 1.287), P=0.001〕were risk factors for cognitive impairment, while elevated TIR〔OR=0.954, 95%CI (0.917, 0.992), P=0.018〕 was a protective factor in T2DM patients with RSSI. The area under the curve (AUC) of %CV for predicting cognitive impairment in patients with T2DM and RSSI was 0.758〔95%CI (0.660, 0.856), P<0.001〕, with an optimal cut-off value of 29.5%, 66.7% sensitivity and 76.0% specificity. The AUC of TIR in predicting cognitive impairment in T2DM patients with RSSI was 0.714〔95%CI (0.624, 0.804), P<0.001〕, with an optimal cut-off value of 60.5%, 97.2% sensitivity and 44.2% specificity. The nomogram prediction model based on SD, %CV, and TIR for the risk of cognitive impairment in T2DM patients with RSSI demonstrated great clinical benefits, and the internal correction suggested that the actual prediction results were similar to the ideal prediction results.
Elevated GV indicators such as SD and %CV may be independent risk factors, and increased TIR may be a protective factor for high infarct burden and cognitive dysfunction in T2DM patients with RSSI. %CV and TIR had good predictive value for cognitive dysfunction in T2DM patients with RSSI.
