Baseline and change in serum lipid and uric acid level over time and incident of nonalcoholic fatty liver disease (NAFLD) in Chinese adults.

Observational studies have shown that non-alcoholic fatty liver disease (NAFLD) is strongly associated with metabolic dysfunction. However, there is a paucity of research on whether changes in indicators of serum metabolism contribute to the development of NAFLD. This study was conducted with 4084 participants who underwent healthy physical examinations at Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China, in 2022 and 2023. Baseline and follow-up measurements, including anthropometric data, abdominal ultrasound and blood samples were collected. The diagnosis of NAFLD was based on the 2010 Chinese Guidelines on Diagnosis and Treatment of NAFLD. Multiple logistic regression was utilized to analyze the odds ratios (ORs) for the 1-year risk of NAFLD in connection with both baseline metabolic indicators and changes in metabolic indicators observed over the course of 1 year. A total of 3425 study participants who were free of NAFLD at baseline, including 1146 men and 2279 women, were included in the final analysis. The mean age was 34.43 ± 7.20 years. Participants who developed NAFLD were older, male and had higher levels of body mass index (BMI), blood pressure, fasting blood glucose (FBG), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), free triiodothyronine (fT3), uric acid (UA), alanine aminotransferase (ALT) and aspartate aminotransferase (AST); and lower levels of high-density lipoprotein cholesterol (HDL-C) and free thyroxine (fT4) (all P values < 0.05). The multivariable model showed that baseline BMI, diastolic blood pressure (DBP), TG, TC, HDL-C, LDL-C, UA, fT4, fT3, ALT and changes in TG, HDL-C, and UA were associated with the 1-year risk of developing NAFLD. The risk of NAFLD increased by 56% [OR 1.56, 95% Confidence Interval (CI) 1.32-1.87] and 40% (OR 1.40, 95% CI 1.19-1.64) for each standard deviation (SD) increase in altered TG values (1.01 mmol/L) and altered UA values (55 µmol/L) respectively. Conversely, for each SD (0.27 mmol/L) increase in HDL-C change, the 1-year risk of incident NAFLD was reduced by 50% (OR 0.50, 95% CI 0.40-0.62). The present study suggested that increases in TG and UA, and decreases in HDL-C, significantly increase the risk of developing NAFLD. Therefore, more attention should be paid to these factors in the management and prevention of NAFLD.

Hand grip strength is inversely associated with total daily insulin dose requirement in patients with type 2 diabetes mellitus: a cross-sectional study.

Background: Short-term (2 weeks to 3 months) insulin intensive therapy using continuous subcutaneous insulin infusion (CSII) can improve islet beta cell function and prolong glycemic remission in patients with newly diagnosed type 2 diabetes mellitus (T2DM). However, the total daily insulin dose (TDD, IU/kg/d) required to achieve near-normoglycemic control with CSII still needs to be frequently adjusted based on blood glucose monitoring. Although real-time continuous glucose monitoring (rtCGM), which measures the interstitial fluid glucose concentration continuously without much difficulty, facilitates the adjustment of insulin dosage, its adoption in the T2DM population is strictly limited by insurance coverage and lack of awareness of rtCGM among clinicians. Thus, it is of clinical significance to identify easy-to-use parameters that may allow a more rapid and accurate prediction of TDD requirement. This study aimed to explore the association between hand grip strength (HGS) and TDD requirement in patients with T2DM receiving CSII therapy. Methods: A total of 180 eligible patients with T2DM were enrolled in the study and divided into three groups based on their HGS: low (L), medium (M), and high (H). The TDD requirement was calculated on day 7 or 8 of CSII treatment. Anthropometric parameters, including HGS, skeletal muscle mass, skeletal muscle index (SMI) and 6-m gait speed, and laboratory data, were collected on the morning of the second day after admission, within the first 24 h of CSII therapy. These parameters were used to identify significant predictors of TDD requirement using Pearson or Spearman correlation test, and stepwise multiple regression analysis. Results: There were no significant differences in age, duration of T2DM, waist-to-hip ratio (WHR), body mass index (BMI), blood pressure, liver function, estimated glomerular filtration rate, triglyceride, total cholesterol, glycosylated hemoglobin A1c (HbA1c), homeostatic model assessment of insulin resistance (HOMA-IR), and homeostasis model assessment of beta cell function (HOMA-ß) among the groups. The H group had higher body muscle mass-to-fat ratio (BMFR), skeletal muscle mass-to-fat ratio (SMFR), SMI, 6-m gait speed, and lower TDD requirement than the M and L groups. The HGS negatively correlated with TDD requirement (r = -0.33, p < 0.001) after adjusting for sex, age, BMI, WHR, HbA1c, Ln (HOMA-ß), Ln (HOMA-IR), Ln (BMFR), Ln (SMFR), SMI, and 6-m gait speed. Multivariate stepwise regression analysis indicated that HGS was an independent predictor of TDD requirement in patients with T2DM (ß = -0.45, p < 0 001). Conclusion: Lower HGS is associated with an increased TDD requirement in T2DM patients. HGS may facilitate the prediction of TDD requirement in T2DM patients receiving CSII therapy.

Serum albumin and albuminuria predict the progression of chronic kidney disease in patients with newly diagnosed type 2 diabetes: a retrospective study.

BACKGROUND: Diabetes-related kidney disease is associated with end-stage renal disease and a high mortality rate. However, data on risk factors associated with kidney disease in patients with newly diagnosed type 2 diabetes mellitus (DM) remains insufficient. The aim of the present study was to identify the risk factors significantly associated with chronic kidney disease progression in patients with newly diagnosed type 2 DM. METHODS: We reviewed a total of 254 consecutive patients who were newly diagnosed with type 2 diabetes at Nanjing First Hospital from January to December 2014. They were observed for two years, and baseline and biochemical variables were used to identify significant predictors of kidney failure progression. Kidney failure progression was defined as a ≥ 30% increase in serum creatine level. RESULTS: The mean age of patients was 58.96 years, 37.4% were women, and 57.1% had hypertension. Kidney function progressed in 40 patients (15.75%). Multivariable logistic regression analyses showed that serum albumin (p = 0.015) and microalbuminuria (p < 0.001) were associated with kidney failure progression in patients with newly diagnosed type 2 DM. Those with lower estimated glomerular filtration rate (eGFR; 30-60 ml/min/1.73 m2) at baseline had lower serum albumin levels compared to those of patients with higher eGFR. The albuminuria levels were higher in patients with lower eGFR than in those with eGFR ≥ 90 ml/min/1.73 m2. Receiver operating characteristic curve analysis showed that the area under the curve was 0.754 (95% CI [0.670-0. 0.837]). CONCLUSIONS: The overall rate of chronic kidney disease progression is relatively high, and low serum albumin and high albuminuria levels are associated with kidney failure progression in newly diagnosed diabetic patients.

Association of Higher Circulating Insulin Antibody with Increased Mean Amplitude Glycemic Excursion in Patients with Type 2 Diabetes Mellitus: A Cross-Sectional, Retrospective Case-Control Study.

Insulin antibody (IA) may potentially affect a patient's glycemic control due to its variability in both binding and/or releasing insulin. However, the association between IA titer and daily glycemic variability (GV) is still unknown. We thus performed this cross-sectional, retrospective case-control study to assess the relationship between IA titer and mean amplitude glycemic excursion (MAGE) in type 2 diabetes mellitus (T2DM) patients using a continuous glucose monitoring (CGM) system. We recruited 100 eligible patients (IA > 5%, IA positive) and divided them into two groups-a low (L) group and a high (H) group-based on their IA titer. The control (C) group consisted of 47 patients (IA ≤ 5%, IA negative) matched for age, BMI, gender, and glycosylated hemoglobin A1c (HbA1c). The CGM determined the GV of enrolled patients. The primary outcome was the relationship between the IA titer and the MAGE, and the secondary outcome was the differences of GV among the three groups. We found that patients in the H group had higher levels of blood glucose fluctuation parameters than those in the L and C groups. The Ln(IA) was positively correlated with Ln(MAGE) even after adjusting for age, gender, BMI, HbA1c, and fasting and postprandial C-peptide(r = 0.423, p < 0.001). Multiple linear stepwise regression analysis revealed that Ln(IA) was an independent factor of Ln(MAGE) (beta = 0.405, p < 0.001). In conclusion, the higher circulating IA titer was associated with increased MAGE in T2DM patients, indicating that those patients with elevated IA titer should receive GV assessment and individualized treatment.

Efficacy Comparison of Preprandial and Postprandial Prandilin 25 Administration in Patients with Newly Diagnosed Type 2 Diabetes Using a Continuous Glucose Monitoring System.

INTRODUCTION: The aim of this study was to determine the clinical efficacy of preprandial and postprandial Prandilin 25 (premixed insulin lispro 25) administration in patients with newly diagnosed type 2 diabetes mellitus (T2DM) using a continuous glucose monitoring (CGM) system. METHODS: This was a single-center, self-controlled comparative clinical trial. Newly diagnosed T2DM patients with hemoglobin A1c > 8.0% were hospitalized and received Prandilin 25 plus metformin treatment. Glycemic control was reached after a 7-to-8-day run-in period. Patients underwent 2 days of treatment consisting of preprandial Prandilin 25 on day 1 and postprandial Prandilin 25 on day 2 at the same dosage. The primary outcome was the 24-h mean amplitude of glycemic excursion (24 hMAGE); secondary outcomes were other daily glycemic variability parameters, including 24-h mean blood glucose (24hMBG), 24-h standard deviation of blood glucose (24hSDBG), large amplitude of glycemic excursion (LAGE), incremental area under the curve (AUC) values for different glucose levels, postprandial glucose excursion, and incidence of hypoglycemia, which were assessed using a CGM system. RESULTS: Eighty-five patients completed this study. There was no statistically significant difference in 24hMAGE, 24hMBG, 24hSDBG, or LAGE between the preprandial injection group and the postprandial injection group. Similarly, there was no between-treatment difference in the AUC for a blood glucose level below 3.9 mmol/L, in the AUC for a blood glucose level above 10.0 mmol/L, or in the percentages of time that the blood glucose level was below 3.9 mmol/L or above 10.0 mmol/L. Further analysis showed that the pre-meal glucose, peak height, and time to peak after each meal, the relative areas under the CGM curve at 1-4 h after each meal, as well as the incidence of hypoglycemia, were similar for the preprandial and postprandial Prandilin 25 groups. CONCLUSION: In patients with T2DM managed with premixed insulin lispro 25, postprandial injection (within 30 min of meal onset) may be an acceptable alternative to preprandial injection when the regular preprandial insulin dose is omitted. TRIAL REGISTRATION: Chinese Clinical Trial Register identifier: ChiCTR1800015828.

Comparison of Efficacy and Economic Value of Prandilin 25 and Humalog Mix 25 in Patients with Newly Diagnosed Type 2 Diabetes by a Continuous Glucose Monitoring System.

INTRODUCTION: To determine the clinical efficacy and economic value of insulin lispro 25-Prandilin 25 vs. insulin lispro 25-Humalog mix 25 in treatment of newly diagnosed type 2 diabetes mellitus (T2DM) by a continuous glucose monitoring system (CGMS). METHODS: This was a single-center, randomized, case-crossover clinical trial. Participants were randomly allocated to two groups and underwent two kinds of insulin lispro 25 treatment separated by a 1-day washout period. In total, 81 patients with newly diagnosed T2DM with hemoglobin A1c (HbA1c) above 9% were hospitalized and randomly divided to receive Prandilin 25/Humalog mix 25 or Humalog mix 25/Prandilin 25 treatment. All participants were subjected to metformin therapy simultaneously. Glycemic control was reached after 7-8 days Prandilin 25 or Humalog mix 25 treatment; each patient received continuous glucose monitoring (CGM) for 5 consecutive days (from day 1 to day 5). On day 3 of CGM performance, Prandilin 25 treatment was switched to Humalog mix 25 treatment at the same dosage or vice versa. Parameters representing glycemic variability (GV) and postprandial glucose excursions, including 24-h mean blood glucose (24hMBG), 24-h standard deviation of blood glucose (24hSDBG), 24-h mean amplitude of glycemic excursion (24hMAGE), large amplitude of glycemic excursion (LAGE), incremental area under the curve (AUC) for different glucose levels, and postmeal relative areas under the CGM curve (AUCpp) for 1-4 h of each meal, were calculated for each patient. RESULTS: No significant differences were found in the 24hMAGE, 24hMBG, 24hSDBG, LAGE, mean 1-h preprandial blood glucose and the incidence of hypoglycemia between the Prandilin 25 treatment group and Humalog mix 25 treatment group. Similarly, there were no between-treatment differences for AUC and time when blood glucose was below 3.9 mmol/l, between 3.9 mmol/l and 10.0 mmol/l, or above 10.0 mmol/l. Further analysis showed the AUCpp for 1-4 h of each meal for two kinds of treatments were similar. However, the mean estimated cost of Prandilin 25 was only 85% of Humalog mix 25 in one treatment course. CONCLUSION: Prandilin 25 is non-inferior in clinical efficacy compared with Humalog mix 25. In view of the significant difference in the cost of the two kinds of insulin lispro 25, Prandilin 25 is a much more cost-effective anti-diabetes drug for management of T2DM. TRIAL REGISTRATION: Chinese Clinical Trial Register identifier, ChiCTR1800015829.