Serum creatinine levels and risk of nonalcohol fatty liver disease in a middle-aged and older Chinese population: A cross-sectional analysis

Association between the cardiometabolic index and NAFLD and fibrosis

Composed of obesity and lipid parameters, the cardiometabolic index (CMI) has emerged as a novel diagnostic tool. Originally developed for diabetes diagnosis, its application has expanded to identifying patients with cardiovascular diseases, such as atherosclerosis and hypertension. However, the relationship between CMI and non-alcoholic fatty liver disease (NAFLD) and liver fibrosis in the US population remains unclear. This cross-sectional study analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning 2017-2020, involving 2996 participants aged 20 years or older. Vibration controlled transient elastography using a FibroScan® system (model 502, V2 Touch) with controlled attenuation parameter measurements identified NAFLD at a threshold of ≥ 274 dB/m, while liver stiffness measurement (LSM) results (median, ≥ 8.2 kPa) indicated fibrosis. A multifactorial logistic regression model explored the relationship between CMI and NAFLD and fibrosis. The effectiveness of CMI in detecting NAFLD and liver fibrosis was assessed through receiver operating characteristic curve analysis. Controlling for potential confounders, CMI showed a significant positive association with NAFLD (adjusted OR = 1.44, 95% CI 1.44-1.45) and liver fibrosis (adjusted OR = 1.84, 95% CI 1.84-1.85). The Areas Under the Curve for predicting NAFLD and fibrosis were 0.762 (95% CI 0.745 ~ 0.779) and 0.664(95% CI 0.633 ~ 0.696), respectively, with optimal cut-off values of 0.462 and 0.527. There is a positive correlation between CMI and NAFLD and fibrosis, which is a suitable and simple predictor of NAFLD and fibrosis.

Prevalence and risk factors for impaired renal function among Asian patients with nonalcoholic fatty liver disease

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is associated with impaired renal function, and both diseases often occur alongside other metabolic disorders. However, the prevalence and risk factors for impaired renal function in patients with NAFLD remain unclear. The objective of this study was to identify the prevalence and risk factors for renal impairment in NAFLD patients.

METHODS

All adults aged 18-70 years with ultrasound-diagnosed NAFLD and transient elastography examination from eight Asian centers were enrolled in this prospective study. Liver fibrosis and cirrhosis were assessed by FibroScan-aspartate aminotransferase (FAST), Agile 3+ and Agile 4 scores. Impaired renal function and chronic kidney disease (CKD) were defined by an estimated glomerular filtration rate (eGFR) with value of < 90 mL/min/1.73 m2 and < 60 mL/min/1.73 m2, respectively, as estimated by the CKD-Epidemiology Collaboration (CKD-EPI) equation.

RESULTS

Among 529 included NAFLD patients, the prevalence rates of impaired renal function and CKD were 37.4% and 4.9%, respectively. In multivariate analysis, a moderate-high risk of advanced liver fibrosis and cirrhosis according to Agile 3+ and Agile 4 scores were independent risk factors for CKD (P< 0.05). Furthermore, increased fasting plasma glucose (FPG) and blood pressure were significantly associated with impaired renal function after controlling for the other components of metabolic syndrome (P< 0.05). Compared with patients with normoglycemia, those with prediabetes [FPG ≥ 5.6 mmol/L or hemoglobin A1c (HbA1c) ≥ 5.7%] were more likely to have impaired renal function (P< 0.05).

CONCLUSIONS

Agile 3+ and Agile 4 are reliable for identifying NAFLD patients with high risk of CKD. Early glycemic control in the prediabetic stage might have a potential renoprotective role in these patients.

TG: A Mediator of the Relationship of Serum Uric Acid to Creatinine Ratio and Nonalcoholic Fatty Liver Disease in Non-Obese Patients with Type 2 Diabetes

Background

The study estimated the association between NAFLD and SUA/Cr in Chinese non-obese patients with type 2 diabetes mellitus (T2DM) and also investigated mediating effect of TG.

Methods

All patients were divided into NAFLD group (n = 420) and non-NAFLD group (n = 347). The differences of biochemical indicators between the two groups were compared. The link between SUA/Cr and other parameters was checked through Spearman correlation analysis. Differences in the incidence rate of NAFLD between SUA/Cr and TG 3 tertile subgroups were tested by chi-squared. To explore the independent influence of SUA/Cr and TG on NAFLD, logistic regression was performed. The predictive value of SUA/Cr and SUA/Cr combined with BMI for NAFLD was analyzed using ROC curves. In addition, to confirm whether TG has a mediating effect on the link of SUA/Cr and NAFLD, we conducted a mediating analysis.

Results

NAFLD group had higher SUA/Cr values than individuals without NAFLD (P < 0.01). SUA/Cr was linked with TC and TG (r = 0.081, 0.215 respectively). NAFLD prevalence increased progressively from quartile 1 to quartile 3 of SUA/Cr (44% vs 57% vs 62%). Prevalence of NAFLD increased from quartile 1 to quartile 3 of TG (35.8% vs 58.7% vs 69.9%). Analysis of the logistic regression revealed that SUA/Cr and TG were statistically linked with NAFLD. The ROC curve pointed out that the area under the curve (AUC), sensitivity and specificity of SUA/Cr were 0.59, 0.629 and 0.522, respectively. The AUC, sensitivity and specificity for SUA/Cr combined with BMI were 0.719, 0.644 and 0.677, separately. The mediation analysis showed a statistically direct effect of SUA/Cr on NAFLD (β=0.148, 95% CI: 0.0393, 0.2585). The function of SUA/Cr on NAFLD partially mediated by TG (β=0.1571, 95% CI: 0.0704, 0.2869).

Conclusion

SUA/Cr was significantly associated with NAFLD in non-obese T2DM patients, and TG partially mediated this association. SUA/Cr can be applied to predict for NAFLD.

Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China

Objective

Insulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the "common soil" of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings.

Methods

We analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models.

Results

The LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc.

Conclusion

The ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.

Gut ecological networks reveal associations between bacteria, exercise, and clinical profile in non-alcoholic fatty liver disease patients

IMPORTANCE

Our study is applying a community-based approach to examine the influence of exercise on gut microbiota (GM) and discover GM structures linked with NAFLD improvements during exercise. The majority of microbiome research has focused on finding specific species that may contribute to the development of human diseases. However, we believe that complex diseases, such as NAFLD, would be more efficiently treated using consortia of species, given that bacterial functionality is based not only on its own genetic information but also on the interaction with other microorganisms. Our results revealed that exercise significantly changes the GM interaction and that structural alterations can be linked with improvements in intrahepatic lipid content and metabolic functions. We believe that the identification of these characteristics in the GM enhances the development of exercise treatment for NAFLD and will attract general interest in this field.

Creatinine accelerates APAP-induced liver damage by increasing oxidative stress through ROS/JNK signaling pathway

Serum creatinine is an endogenous biomarker to estimate glomerular filtration rate (GFR) and is commonly used to assess renal function in clinical practice. Acetaminophen (APAP), the most available analgesic and antipyretic medication, is recommended as the drug of choice for pain control in patients with renal diseases. However, an overdose of APAP can lead to severe acute liver injury, which is also the most common cause of acute liver failure in western countries. The role of creatinine in APAP-induced liver injury is unclear and should be further explored. Herein, clinical data on patients with drug-induced liver injury revealed that the creatinine concentration between 82-442 μmol/L for female and 98–442 μmol/L for male is positively correlated with alanine aminotransferase (ALT), aspartate aminotransferase (AST). While there was no correlation between creatinine and ALT and AST when creatinine concentration is over 442 μmol/L. In addition, mice were administrated with creatinine intraperitoneally for 1 week before APAP injection to investigated the pathophysiological role of creatinine in APAP-induced acute liver injury. The results showed that creatinine administration aggravated hepatic necrosis and elevated serum lactate dehydrogenase (LDH) and ALT levels in mice upon APAP injection. The mechanism study demonstrated that creatinine could increase the production of reactive oxygen activation (ROS) and the activation of c-Jun N-terminal kinase (JNK). Furthermore, the liver injury was alleviated and the difference between APAP-treated mice and APAP combined with creatinine-treated mice was blunted after using specific ROS and JNK inhibitors. Significantly, creatinine stimulation aggravates APAP-induced cell death in HepaRG cells with the same mechanism. In summary, this study proposed that creatinine is closely related with liver function of drug-induced liver injury and exacerbates APAP-induced hepatocyte death by promoting ROS production and JNK activation, thus providing new insight into the usage of APAP in patients with kidney problems.

Association of serum creatinine with hepatic steatosis and fibrosis: a cross-sectional study

Background

Recent studies have shown that chronic kidney disease (CKD) prevalence is significantly higher in patients with hepatic steatosis (HS); however, it remains unclear whether HS is associated with serum creatinine (SCr). We aimed to explore the association between SCr levels and HS in a Chinese population.

Methods

We performed a cross-sectional study among 56,569 Chinese individuals. SCr level, other clinical and laboratory parameters, abdominal ultrasound and noninvasive fibrosis scores were extracted, and the fibrosis 4 score (FIB-4) was calculated.

Results

A total of 27.1% of the subjects had HS. After 1:1 propensity score matching (PSM) according to sex and age, we included 13,301 subjects with HS and 13,301 subjects without HS. SCr levels were significantly higher in the HS group than in the non-HS group [73.19 ± 15.14(μmoI/L) vs. 71.75 ± 17.49(μmoI/L), p < 0.001]. Univariate and multivariate regression analyses showed a positive association between SCr and the prevalence of HS. Stepwise regression analysis showed that the association between SCr and HS was independent of other metabolic syndrome components. The prevalence of HS increased significantly with increasing SCr levels. Metabolism-related indicators and liver enzymes were significantly higher in the HS group than in the non-HS group; furthermore, these parameters increased with increasing SCr levels. FIB-4 was significantly higher in the HS group than in the non-HS group but did not show an increasing trend with increasing SCr levels.

Conclusions

Our results showed an independent association between SCr level and HS risk in a Chinese population.

Effect of moderate-to-severe hepatic steatosis on neutralising antibody response among BNT162b2 and CoronaVac recipients

Aim

Studies of hepatic steatosis (HS) effect on COVID-19 vaccine immunogenicity are lacking. We aimed to compare immunogenicity of BNT162b2 and CoronaVac among moderate/severe HS and control subjects.

Patients and Methods

295 subjects who received BNT162b2 or CoronaVac vaccines from five vaccination centers were categorized into moderate/severe HS (controlled attenuation parameter ≥268 dB/m on transient elastography) (n=74) or control (n=221) groups. Primary outcomes were seroconversion rates of neutralising antibody by live virus Microneutralization (vMN) assay (titer ≥10) at day 21 (BNT162b2) or day28 (CoronaVac) and day56 (both). Secondary outcome was highest-tier titer response (top 25% of vMN titer; cutoff: 160 [BNT162b2] and 20 [CoronaVac]) at day 56.

Results

For BNT162b2 (n=228 [77.3%]), there was no statistical differences in seroconversion rates (71.7% vs 76.6% [day21]; 100% vs 100% [day56]) or vMN GMT (13.2 vs 13.3, [day21]; 91.9 vs 101.4, [day56]) among moderate/severe HS and control groups respectively. However, lower proportion of moderate/severe HS patients had highest-tier response (5.0% vs 15.5%; p=0.037 [day56]). For CoronaVac (n=67 [22.7%]), there was no statistical differences in seroconversion rates (7.1% vs 15.1%, [day21]; 64.3% vs 83.0%, [day56]) or vMN GMT (5.3 vs 5.8,) at day 28. However, moderate/severe HS patients had lower vMN GMT (9.1 vs 14.8, p=0.021) at day 56 with lower proportion having highest-tier response (21.4% vs 52.8%, p=0.036).

Conclusion

While there was no difference in seroconversion rate between moderate/severe HS and control groups after two doses of vaccine, a lower proportion of moderate/severe HS patients achieved highest-tier response for either BNT162b2 or CoronaVac.