Association between body fat and sarcopenia in older adults with type 2 diabetes mellitus: A cross-sectional study

ObjectivesTo investigate the association between body fat (BF%) and sarcopenia in older adults with type 2 diabetes mellitus (T2DM) and potential link with increased levels of inflammatory indicators and insulin resistance.MethodsA total of 543 older adults with T2DM were included in this cross-sectional study. Appendicular skeletal muscle (ASM), handgrip strength and gait speed were measured to diagnose sarcopenia according to the updated Asian Working Group for Sarcopenia (AWGS) 2019 criteria. Body composition data were tested using dual-energy X-ray absorptiometry (DEXA). Levels of serum high-sensitive C-reactive protein (hs-CRP), interleukin-6, fasting blood insulin (FINS), hemoglobin A1c (HbA1c), 25-hydroxyvitamin D3 [25(OH) D3] were also determined.ResultsThe prevalence of sarcopenia in all participants was 8.84%, of which 11.90% were male and 5.84% females. The Pearson’s correlation analysis revealed that BF% was negatively correlated with gait speed in men and women (R =-0.195, P=0.001; R = -0.136, P =0.025, respectively). After adjusting for all potential confounders, sarcopenia was positive associated with BF% (male, OR: 1.38, 95% CI: 1.15–1.65, P< 0.001; female, OR: 1.30, 95% CI: 1.07–1.56, P=0.007), and negatively associated with body mass index (BMI) (male, OR: 0.57, 95% CI: 0.44–0.73, P<0.001; female, OR: 0.48, 95% CI: 0.33–0.70, P<0.001). No significant differences were found in hs-CRP, interleukin-6, and insulin resistance between older T2DM adults with and without sarcopenia.ConclusionHigher BF% was linked to an increased risk of sarcopenia in older adults with T2DM, suggesting the importance of assessing BF% rather than BMI alone to manage sarcopenia

Correlation between heart rate variability and perioperative neurocognitive disorders in patients undergoing non-cardiac surgery: A retrospective cohort study.

ObjectiveWith the improvement of medical level, the number of elderly patients is increasing, and the postoperative outcome of the patients cannot be ignored. However, there have been no studies on the relationship between preoperative heart rate variability (HRV) and Perioperative Neurocognitive Disorders (PND). The purpose of this study was to explore the correlation between (HRV) and (PND), postoperative intensive care unit (ICU), and hospital stay in patients undergoing non-cardiac surgery.MethodThis retrospective analysis included 687 inpatients who underwent 24-hour dynamic electrocardiogram examination in our six departments from January 2021 to January 2022. Patients were divided into two groups based on heart rate variability (HRV): high and low. Possible risk factors of perioperative outcomes were screened using univariate analysis, and risk factors were included in multivariate logistic regression to screen for independent risk factors. The subgroup analysis was carried out to evaluate the robustness of the results. The nomogram of PND multi-factor logistic prediction model was constructed. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve was drawn by bootstrap resampling 1000 times for internal verification to evaluate the prediction ability of nomogram.ResultA total of 687 eligible patients were included. The incidence of low HRV was 36.7% and the incidence of PND was 7.6%. The incidence of PND in the low HRV group was higher than that in the high HRV group (11.8% vs 5.2%), the postoperative ICU transfer rate was higher (15.9% than 9.3%P = 0.009), and the hospital stay was longer [15 (11, 19) vs (13), 0.015]. The multivariable logistic regression analysis showed that after adjusting for other factors, decreased low HRV was identified as an independent risk factor for the occurrence of PND (Adjusted Odds Ratio = 2.095; 95% Confidence Interval: 1.160-3.784; P = 0.014) and postoperative ICU admission (Adjusted Odds Ratio = 1.925; 95% Confidence Interval: 1.128-3.286; P = 0.016). This study drew a nomogram column chart for a multivariate logistic regression model, incorporating age and HRV. The calibration curve shows that the predicted value of the model for the occurrence of cardio-cerebrovascular events is in good agreement with the actual observed value, with C-index of 0.696 (95% CI: 0.626 ~ 0.766). Subgroup analysis showed that low HRV was an independent risk factor for PND in patients with gastrointestinal surgery and ASA Ⅲ, aged ≥ 65 years.ConclusionIn patients undergoing non-cardiac surgery, the low HRV was an independent risk factor for PND and postoperative transfer to the ICU, and the hospitalization time of patients with low HRV was prolonged. Through establishing a risk prediction model for the occurrence of PND, high-risk patients can be identified during the perioperative period for early intervention

The GH-IGF-1 Axis in Circadian Rhythm

Organisms have developed common behavioral and physiological adaptations to the influence of the day/night cycle. The CLOCK system forms an internal circadian rhythm in the suprachiasmatic nucleus (SCN) during light/dark input. The SCN may synchronize the growth hormone (GH) secretion rhythm with the dimming cycle through somatostatin neurons, and the change of the clock system may be related to the pulsatile release of GH. The GH—insulin-like growth factor 1 (IGF-1) axis and clock system may interact further on the metabolism through regulatory pathways in peripheral organs. We have summarized the current clinical and animal evidence on the interaction of clock systems with the GH—IGF-1 axis and discussed their effects on metabolism.</p

PND diagnostic code corresponding to ICD-9/10.

ObjectiveWith the improvement of medical level, the number of elderly patients is increasing, and the postoperative outcome of the patients cannot be ignored. However, there have been no studies on the relationship between preoperative heart rate variability (HRV) and Perioperative Neurocognitive Disorders (PND). The purpose of this study was to explore the correlation between (HRV) and (PND), postoperative intensive care unit (ICU), and hospital stay in patients undergoing non-cardiac surgery.MethodThis retrospective analysis included 687 inpatients who underwent 24-hour dynamic electrocardiogram examination in our six departments from January 2021 to January 2022. Patients were divided into two groups based on heart rate variability (HRV): high and low. Possible risk factors of perioperative outcomes were screened using univariate analysis, and risk factors were included in multivariate logistic regression to screen for independent risk factors. The subgroup analysis was carried out to evaluate the robustness of the results. The nomogram of PND multi-factor logistic prediction model was constructed. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve was drawn by bootstrap resampling 1000 times for internal verification to evaluate the prediction ability of nomogram.ResultA total of 687 eligible patients were included. The incidence of low HRV was 36.7% and the incidence of PND was 7.6%. The incidence of PND in the low HRV group was higher than that in the high HRV group (11.8% vs 5.2%), the postoperative ICU transfer rate was higher (15.9% than 9.3%P = 0.009), and the hospital stay was longer [15 (11, 19) vs (13), 0.015]. The multivariable logistic regression analysis showed that after adjusting for other factors, decreased low HRV was identified as an independent risk factor for the occurrence of PND (Adjusted Odds Ratio = 2.095; 95% Confidence Interval: 1.160–3.784; P = 0.014) and postoperative ICU admission (Adjusted Odds Ratio = 1.925; 95% Confidence Interval: 1.128–3.286; P = 0.016). This study drew a nomogram column chart for a multivariate logistic regression model, incorporating age and HRV. The calibration curve shows that the predicted value of the model for the occurrence of cardio-cerebrovascular events is in good agreement with the actual observed value, with C-index of 0.696 (95% CI: 0.626 ~ 0.766). Subgroup analysis showed that low HRV was an independent risk factor for PND in patients with gastrointestinal surgery and ASA Ⅲ, aged ≥ 65 years.ConclusionIn patients undergoing non-cardiac surgery, the low HRV was an independent risk factor for PND and postoperative transfer to the ICU, and the hospitalization time of patients with low HRV was prolonged. Through establishing a risk prediction model for the occurrence of PND, high-risk patients can be identified during the perioperative period for early intervention.</div

Characteristics and variable distribution of the two groups.

Characteristics and variable distribution of the two groups.</p

The ROC curve presents the predictive performance of age, HRV, and the prediction model for the risk of PND established through the results of multi-factor logistic regression analysis for the occurrence of PND.

The ROC curve presents the predictive performance of age, HRV, and the prediction model for the risk of PND established through the results of multi-factor logistic regression analysis for the occurrence of PND.</p

PND univariate analysis.

ObjectiveWith the improvement of medical level, the number of elderly patients is increasing, and the postoperative outcome of the patients cannot be ignored. However, there have been no studies on the relationship between preoperative heart rate variability (HRV) and Perioperative Neurocognitive Disorders (PND). The purpose of this study was to explore the correlation between (HRV) and (PND), postoperative intensive care unit (ICU), and hospital stay in patients undergoing non-cardiac surgery.MethodThis retrospective analysis included 687 inpatients who underwent 24-hour dynamic electrocardiogram examination in our six departments from January 2021 to January 2022. Patients were divided into two groups based on heart rate variability (HRV): high and low. Possible risk factors of perioperative outcomes were screened using univariate analysis, and risk factors were included in multivariate logistic regression to screen for independent risk factors. The subgroup analysis was carried out to evaluate the robustness of the results. The nomogram of PND multi-factor logistic prediction model was constructed. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve was drawn by bootstrap resampling 1000 times for internal verification to evaluate the prediction ability of nomogram.ResultA total of 687 eligible patients were included. The incidence of low HRV was 36.7% and the incidence of PND was 7.6%. The incidence of PND in the low HRV group was higher than that in the high HRV group (11.8% vs 5.2%), the postoperative ICU transfer rate was higher (15.9% than 9.3%P = 0.009), and the hospital stay was longer [15 (11, 19) vs (13), 0.015]. The multivariable logistic regression analysis showed that after adjusting for other factors, decreased low HRV was identified as an independent risk factor for the occurrence of PND (Adjusted Odds Ratio = 2.095; 95% Confidence Interval: 1.160–3.784; P = 0.014) and postoperative ICU admission (Adjusted Odds Ratio = 1.925; 95% Confidence Interval: 1.128–3.286; P = 0.016). This study drew a nomogram column chart for a multivariate logistic regression model, incorporating age and HRV. The calibration curve shows that the predicted value of the model for the occurrence of cardio-cerebrovascular events is in good agreement with the actual observed value, with C-index of 0.696 (95% CI: 0.626 ~ 0.766). Subgroup analysis showed that low HRV was an independent risk factor for PND in patients with gastrointestinal surgery and ASA Ⅲ, aged ≥ 65 years.ConclusionIn patients undergoing non-cardiac surgery, the low HRV was an independent risk factor for PND and postoperative transfer to the ICU, and the hospitalization time of patients with low HRV was prolonged. Through establishing a risk prediction model for the occurrence of PND, high-risk patients can be identified during the perioperative period for early intervention.</div

Abnormal ECG of the two groups.

ObjectiveWith the improvement of medical level, the number of elderly patients is increasing, and the postoperative outcome of the patients cannot be ignored. However, there have been no studies on the relationship between preoperative heart rate variability (HRV) and Perioperative Neurocognitive Disorders (PND). The purpose of this study was to explore the correlation between (HRV) and (PND), postoperative intensive care unit (ICU), and hospital stay in patients undergoing non-cardiac surgery.MethodThis retrospective analysis included 687 inpatients who underwent 24-hour dynamic electrocardiogram examination in our six departments from January 2021 to January 2022. Patients were divided into two groups based on heart rate variability (HRV): high and low. Possible risk factors of perioperative outcomes were screened using univariate analysis, and risk factors were included in multivariate logistic regression to screen for independent risk factors. The subgroup analysis was carried out to evaluate the robustness of the results. The nomogram of PND multi-factor logistic prediction model was constructed. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve was drawn by bootstrap resampling 1000 times for internal verification to evaluate the prediction ability of nomogram.ResultA total of 687 eligible patients were included. The incidence of low HRV was 36.7% and the incidence of PND was 7.6%. The incidence of PND in the low HRV group was higher than that in the high HRV group (11.8% vs 5.2%), the postoperative ICU transfer rate was higher (15.9% than 9.3%P = 0.009), and the hospital stay was longer [15 (11, 19) vs (13), 0.015]. The multivariable logistic regression analysis showed that after adjusting for other factors, decreased low HRV was identified as an independent risk factor for the occurrence of PND (Adjusted Odds Ratio = 2.095; 95% Confidence Interval: 1.160–3.784; P = 0.014) and postoperative ICU admission (Adjusted Odds Ratio = 1.925; 95% Confidence Interval: 1.128–3.286; P = 0.016). This study drew a nomogram column chart for a multivariate logistic regression model, incorporating age and HRV. The calibration curve shows that the predicted value of the model for the occurrence of cardio-cerebrovascular events is in good agreement with the actual observed value, with C-index of 0.696 (95% CI: 0.626 ~ 0.766). Subgroup analysis showed that low HRV was an independent risk factor for PND in patients with gastrointestinal surgery and ASA Ⅲ, aged ≥ 65 years.ConclusionIn patients undergoing non-cardiac surgery, the low HRV was an independent risk factor for PND and postoperative transfer to the ICU, and the hospitalization time of patients with low HRV was prolonged. Through establishing a risk prediction model for the occurrence of PND, high-risk patients can be identified during the perioperative period for early intervention.</div

The PND risk nomogram line plots.

ObjectiveWith the improvement of medical level, the number of elderly patients is increasing, and the postoperative outcome of the patients cannot be ignored. However, there have been no studies on the relationship between preoperative heart rate variability (HRV) and Perioperative Neurocognitive Disorders (PND). The purpose of this study was to explore the correlation between (HRV) and (PND), postoperative intensive care unit (ICU), and hospital stay in patients undergoing non-cardiac surgery.MethodThis retrospective analysis included 687 inpatients who underwent 24-hour dynamic electrocardiogram examination in our six departments from January 2021 to January 2022. Patients were divided into two groups based on heart rate variability (HRV): high and low. Possible risk factors of perioperative outcomes were screened using univariate analysis, and risk factors were included in multivariate logistic regression to screen for independent risk factors. The subgroup analysis was carried out to evaluate the robustness of the results. The nomogram of PND multi-factor logistic prediction model was constructed. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve was drawn by bootstrap resampling 1000 times for internal verification to evaluate the prediction ability of nomogram.ResultA total of 687 eligible patients were included. The incidence of low HRV was 36.7% and the incidence of PND was 7.6%. The incidence of PND in the low HRV group was higher than that in the high HRV group (11.8% vs 5.2%), the postoperative ICU transfer rate was higher (15.9% than 9.3%P = 0.009), and the hospital stay was longer [15 (11, 19) vs (13), 0.015]. The multivariable logistic regression analysis showed that after adjusting for other factors, decreased low HRV was identified as an independent risk factor for the occurrence of PND (Adjusted Odds Ratio = 2.095; 95% Confidence Interval: 1.160–3.784; P = 0.014) and postoperative ICU admission (Adjusted Odds Ratio = 1.925; 95% Confidence Interval: 1.128–3.286; P = 0.016). This study drew a nomogram column chart for a multivariate logistic regression model, incorporating age and HRV. The calibration curve shows that the predicted value of the model for the occurrence of cardio-cerebrovascular events is in good agreement with the actual observed value, with C-index of 0.696 (95% CI: 0.626 ~ 0.766). Subgroup analysis showed that low HRV was an independent risk factor for PND in patients with gastrointestinal surgery and ASA Ⅲ, aged ≥ 65 years.ConclusionIn patients undergoing non-cardiac surgery, the low HRV was an independent risk factor for PND and postoperative transfer to the ICU, and the hospitalization time of patients with low HRV was prolonged. Through establishing a risk prediction model for the occurrence of PND, high-risk patients can be identified during the perioperative period for early intervention.</div