Background and aims: Regional muscle distribution is associated with abdominal obesity and metabolic syndrome. However, the relationship between muscle distribution and nonalcoholic fatty liver disease (NAFLD) remains unclear. This study was to determine the relationship between regional muscle distribution and the risk and severity of NAFLD. Methods: This cross-sectional study ultimately included 3161 participants. NAFLD diagnosed by ultrasonography was classified into three groups (non, mild, and moderate/severe). We estimated the regional body muscle mass (lower limbs, upper limbs, extremities, and trunk) through multifrequency bioelectrical impedance analysis (BIA). The relative muscle mass was defined as the muscle mass adjusted for the body mass index (BMI). Results: NAFLD participants accounted for 29.9% (945) of the study’s population. Individuals with a higher lower limb, extremity, and trunk muscle mass had a lower risk of NAFLD (p < 0.001). Patients with moderate/severe NAFLD had a lower muscle mass of the lower limbs and trunk than patients with mild NAFLD (p < 0.001), while the muscle mass of the upper limbs and extremities did not differ significantly between the two groups. Moreover, similar results were found for both sexes and among different age groups. Conclusions: A higher muscle mass of the lower limbs, extremities, and trunk was negatively associated with the risk of NAFLD. A lower muscle mass of the limbs and trunk was inversely associated with the severity of NAFLD. This study provides a new theoretical basis for the development of individualized exercise prescriptions for the prevention of NAFLD in non-NAFLD patients.
Abstract Background The relationship between obesity and non-Hodgkin’s lymphoma (NHL) was controversial, which may be due to the crudeness definition of obesity based on body mass index (BMI). As obesity and metabolic abnormalities often coexist, we aimed to explore whether the classification of obesity based on metabolic status can help to evaluate the real impact of obesity on the readmission of NHL. Methods In this retrospective cohort study, utilizing the 2018 Nationwide Readmissions Database, we identified NHL-related index hospitalizations and followed them for non-elective readmission. The patients with NHL were classified as metabolically healthy non-obese (MHNO) and obese (MHO) and metabolically unhealthy non-obese (MUNO) and obese (MUO). Readmission rates for each phenotype were calculated at 30-day intervals. Multiple COX regression was used to analyze the association of metabolic-defined obesity with 30-day, 90-day, and 180-day readmission rates in patients with NHL. Results There were 22,086 index hospitalizations with NHL included. In the multivariate COX regression, MUNO was associated with increased 30-day (HR = 1.113, 95% CI 1.036–1.195), 90-day (HR = 1.148, 95% CI 1.087–1.213), and 180-day readmission rates (HR = 1.132, 95% CI 1.077–1.189), and MUO was associated with increased 30-day (HR=1.219, 95% CI: 1.081-1.374), 90-day (HR = 1.228, 95% CI 1.118–1.348), and 180-day readmission rates (HR = 1.223, 95% CI 1.124–1.33), while MHO had no associations with readmission rates. Conclusions The presence of metabolic abnormalities with or without obesity increased the risk of non-selective readmission in patients with NHL. However, obesity alone had no associations with the risk of non-selective readmission, suggesting that interventions for metabolic abnormalities may be more important in reducing readmissions of NHL patients.
Introduction This study aimed to examine the risk of common diseases among people with pre-diabetes and explored the relationship between pre-diabetes and multimorbidity (in this case, two or more comorbid diseases). Methods An observational multicohort study using data from the UK Biobank database and the National Inpatient Sample (NIS) database (2016–2018) was conducted. We analysed 461 535 participants and 17 548 442 patients aged 18 years or older from both databases, of whom 14.0% and 0.7% were diagnosed with pre-diabetes, respectively. A total of 76 common diseases of various body systems were selected as adverse health outcomes for analysis. Results Among 64 523 individuals with pre-diabetes in the UK Biobank, the mean age was 60 years, 35 304 (54.7%) were female. There were 24 non-overlapping diseases associated with pre-diabetes with significant multiple test results in both databases, and most of them are circulatory system diseases. Compared with normoglycaemia, the confounder-adjusted HR in the UK Biobank for pre-diabetes was 1.46 (95% CI 1.43 to 1.49) for accompanying complex multimorbidity (ie, four or more pre-diabetes-related diseases), the corresponding confounder-adjusted OR in the NIS study was 10.03 (95% CI 9.66 to 10.40). Conclusion Pre-diabetes was associated with a significantly higher risk of multimorbidity. Pre-diabetes, thus, might represent an important target for multimorbidity prevention, and stronger emphasis on its management seems necessary to reduce the risk of the development of multiple comorbidities, especially before the onset of overt diabetes.
We aimed to investigate the interrelationships among polygenic risk scores (PRS), healthy lifestyle factors (HLFs), and colorectal cancer (CRC) risk in individuals with prediabetes. To investigate whether adherence to HLFs influence CRC risk in those with elevated PRS within this specific population. Data from 22,408 prediabetes participants without CRC at baseline were analyzed from the UK Biobank. HLFs were graded using healthy lifestyle scores (HLSs) and classified as favorable, intermediate, or unfavorable, while the PRS for CRC was categorized as high, medium, or low. Cox proportional hazards models were used to calculate hazard ratios (HR) and 95% confidence intervals (CI) for CRC risk. High PRS (HR: 2.36; 95% CI: 1.86–3.00) and medium PRS (HR: 1.42; 95% CI: 1.09–1.83) prediabetes were associated with increased CRC risk compared to those with low PRS. HLFs were linked to lower CRC risk in a dose–response manner, with never smoking (HR: 0.69; 95% CI: 0.57–0.84) and maintaining a healthy BMI (HR: 0.64; 95% CI: 0.49–0.82) associated with reduced CRC risk. Adherence to favorable HLFs may reduce the CRC risk in those with medium (HR: 0.51; 95% CI: 0.27–0.95) and high PRS (HR: 0.62; 95% CI: 0.39–0.99) over 15 years of follow-up. In participants with high PRS and unfavorable HLFs, the excess risk due to the additive interaction between PRS and HLFs was 1.41% (p < 0.01), especially for women (1.07%). There is an additive interaction of PRS and HLFs on CRC risk in individuals with prediabetes. Adopting favorable HLFs should be integrated into the management of prediabetes individuals to reduce the risk of CRC.
Abstract Background: A high plasma level of small dense low-density lipoprotein cholesterol (sdLDL-C) is one of the key features of diabetic dyslipidemia. However, the relationship between sdLDL-C and diabetic peripheral neuropathy (DPN) has not been investigated. Therefore, we measured sdLDL-C in patients with type 2 diabetes mellitus (T2DM) ± DPN, determined the factors affecting sdLDL-C levels, and characterized the relationship between sdLDL-C and DPN in T2DM. Methods: sdLDL-C was measured in the T2DM patients with (n=702) and without (n=780) DPN and the characteristics of these two groups were compared. Correlation analysis was used to identify factors associated with sdLDL-C levels. Finally, multiple regression analysis was used to further characterize the relationship between sdLDL-C and DPN in patients with T2DM. Results: The serum sdLDL-C levels were higher in T2DM patients with DPN than in those without. sdLDL-C positively correlated with systolic blood pressure, diastolic blood pressure, total cholesterol, low-density lipoprotein cholesterol, urinary microalbumin–creatinine ratio, and urinary microalbumin in T2DM patients with DPN ( P =0.017, P <0.001, P <0.001, P <0.001, P =0.005, and P =0.002, respectively) and negatively correlated with high-density lipoprotein cholesterol ( P =0.01). Furthermore, sdLDL-C was independently associated with DPN in patients with T2DM (odds ratio=5.857, 95% confidence interval [CI]: 2.60–13.20, P <0.001). Receiver operating characteristic analysis was performed to identify the optimal cut-off value of sdLDL-C for the prediction of DPN, which was found to be 0.985 mmol/L (area under the curve=0.571; 95% CI: 0.541–0.600; sensitivity, 64.8%; specificity, 47.7%, P <0.001). Conclusions: sdLDL-C was increased in T2DM patients with DPN and was found to be an independent predictor of the presence of DPN.
Systemic immune-inflammation index (SII), a novel inflammatory indicator based on platelets, neutrophils and lymphocytes, has been shown to be associated with prognostic value in several solid tumors. However, its prognostic value in nonalcoholic fatty liver disease (NAFLD) has not been reported yet. Therefore, the present study aimed to investigate the prognostic value of SII in individuals with NAFLD. Data was collected from the 2005 to 2014 National Health and Nutrition Examination Survey (NHANES, https://www.cdc.gov/nchs/nhanes/index.htm), and vital status was derived from the National Death Index (NDI) up to 31 December 2015. NAFLD was diagnosed based on Hepatic Steatosis Index (HSI). Multivariate Cox regression and Kaplan–Meier survival curves were performed to measure the hazard ratios (HRs) and 95% confidence interval (CI). Our study investigated the relationship between SII and all-cause mortality by using two-part linear regression models with penalized splines, as well as Cox models with penalized splines. A total of 10,787 NAFLD participants (44.14% men) aged ≥20 years old were enrolled. There were 776 deaths from all causes after a mean follow-up period of 5.6 years. According to the full adjusted Cox regression analysis, the low log2-SII group (quartile 1) and the highest log2-SII group (quartile 4) were significantly associated with increased mortality from all causes (aHR =1.86; 95% CI: 1.47–2.37; p < 0.0001). After controlling for confounders, an increase in log2-SII was associated with an increased all-cause mortality risk of 41% for every unit raised (aHR = 1.41; 95% CI: 1.26–1.57; p < 0.0001). After adjusting for multiple potential confounders, the association between log2-SII and all-cause mortality was nonlinear, and the threshold value was 8.8. There was no association between an increase of one unit in log2-SII and all-cause mortality below the threshold (aHR = 0.90, 95% CI: 0.71–1.15, p = 0.419). However, a higher log2-SII was associated with a higher risk of death from any cause when it exceeded the threshold (aHR = 1. 73, 95% CI: 1.49–2.02, p < 0.001). Based on a study of US NAFLD patients, it was found that the baseline log2-SII is associated with all-cause mortality. Elevated SII is associated with poor survival among NAFLD patients.KEY MESSAGESUsing a large nationally representative survey of individuals among US adults, the study demonstrated that log2-SII was J-shaped and associated with all-cause death among individuals with NAFLD.Spline analyses demonstrated that the association between log2-SII and all-cause mortality was non-linear after adjusting for multiple potential confounders, and the threshold value was 8.8.Higher log2-SII associated with poor survival in NAFLD. Using a large nationally representative survey of individuals among US adults, the study demonstrated that log2-SII was J-shaped and associated with all-cause death among individuals with NAFLD. Spline analyses demonstrated that the association between log2-SII and all-cause mortality was non-linear after adjusting for multiple potential confounders, and the threshold value was 8.8. Higher log2-SII associated with poor survival in NAFLD.
OBJECTIVE: 12,13-dihydroxy-9Z-octadecenoic acid (12,13-diHOME) was one of the newly found lipokines. The goal of this study was to investigate whether the 12,13-diHOME was associated with related metabolic markers of nonalcoholic fatty liver disease (NAFLD) in a Chinese population with type 2 diabetes (T2DM) and obesity.METHODS: This cross-sectional study enrolled 202 subjects with T2DM. Anthropometric parameters, 12,13-diHOME, serum lipids levels, fasting blood-glucose (FBG), serum glycosylated hemoglobin (HbA1c), fasting insulin (FINS), homeostasis model assessment of insulin resistance (HOMA-IR), liver and kidney function parameters were collected. NAFLD was diagnosed based on abdominal ultrasonography examination results. A computer-aided ultrasound quantitative method was applied to evaluate the liver fat content (LFC).RESULTS: The number of the patients with fatty liver was 139 (68.81%) and those with non-fatty liver was 63 (31.19%). Subjects with NAFLD had a higher body mass index (BMI), diastolic blood pressure, serum alanine aminotransferase (ALT), triglyceride (TG), HOMA-IR, LFC, p<0.05 for all. But no significant difference was found in plasma 12,13-diHOME level (p=0.967), though its level trend was higher in non-NAFLD group. Plasma 12,13-diHOME was positively correlated with aspartate aminotransferase (AST), total cholesterol (TC), high density lipoprotein cholesterol (HDLC), blood urea nitrogen (BUN), free fatty acid (FFA), C-peptide, FINS and HOMAIR. It was negatively correlated with height, body weight, glomerular filtration rate (eGFR) and HbA1c.CONCLUSIONS: Although 12,13-diHOME was correlated with AST, TC, HDL-C, BUN, FFA, C-peptide, FINS, HOMA-IR, eGFR and HbA1c, there was no significant difference in 12,13-diHOME level between the two groups. However, more research should be carried on about this newly found lipokine.
Objective Some studies have demonstrated a bidirectional association between obesity and depression, whereas others have not. This discordance might be due to the metabolic health status. We aimed to determine whether the relationship between obesity and depression is dependent on metabolic health status. Methods In total, 9,022,089 participants were enrolled and classified as one of four obesity phenotypes: metabolically healthy nonobesity (MHNO), metabolically unhealthy nonobesity (MUNO), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). We then divided the population into eight phenotypes based on obesity and the number of metabolic risk factors. Furthermore, the associations of eight phenotypes, based on obesity and specific metabolic risk factors, with depression were assessed. Result Among all participants, a higher risk of depression was observed for MUNO, MHO and MUO than for MHNO. The risk was highest for MUO (OR = 1.442; 95% CI = 1.432, 1.451). However, the association between MHO and depression was different for men and women (OR = 0.941, men; OR = 1.132, women). The risk of depression increased as the number of metabolic risk factors increased. Dyslipidemia was the strongest metabolic risk factor. These relationships were consistent among patients ≥ 45 years of age. Conclusions The increased risk of obesity-related depression appears to partly depend on metabolic health status. The results highlight the importance of a favorable metabolic status, and even nonobese populations should be screened for metabolic disorders.
Increased body mass index (BMI) and metabolic abnormalities are controversial prognostic factors of lung cancer. However, the relationship between metabolic overweight/obesity phenotypes and hospital readmission in patients with lung cancer is rarely reported.We established a retrospective cohort using the United States (US) Nationwide Readmissions Database (NRD). We included adult patients diagnosed with lung cancer from January 1, 2018 to November 30, 2018 and excluded patients combined with other cancers, pregnancy, died during hospitalization, low body weight, and those with missing data. The cohort was observed for hospital readmission until December 31, 2018. We defined and distinguished four metabolic overweight/obesity phenotypes: metabolically healthy with normal weight (MHNW), metabolically unhealthy with normal weight (MUNW), metabolically healthy with overweight or obesity (MHO), and metabolically unhealthy with overweight or obesity (MUO). The relationship between metabolic overweight/obesity phenotypes and 30-day readmission risk was assessed by multivariable Cox regression analysis.Of the 115,393 patients included from the NRD 2018 (MHNW [58214, 50.4%], MUNW [44980, 39.0%], MHO [5044, 4.4%], and MUO [7155, 6.2%]), patients with the phenotype MUNW (6531, 14.5%), MHO (771, 15.3%), and MUO (1155, 16.1%) had a higher readmission rate compared to those with MHNW (7901, 13.6%). Compared with patients with the MHNW phenotype, those with the MUNW (hazard ratio [HR], 1.10; 95% CI, 1.06-1.14), MHO (HR, 1.15; 95% CI, 1.07-1.24), and MUO (HR, 1.28; 95% CI, 1.20-1.36) phenotypes had a higher risk of readmission, especially in men, those without surgical intervention, or those aged >60 years. In women, similar results with respect to readmission were observed in people aged >60 years (MUNW [HR, 1.07; 95% CI, 1.01-1.13], MHO [HR, 1.19; 95% CI, 1.06-1.35], and MUO [HR, 1.28; 95% CI, 1.16-1.41]). We also found increased costs for 30-day readmission in patients with MHO (OR, 1.18; 95% CI, 1.07-1.29) and MUO (OR, 1.11; 95% CI, 1.02-1.20).Increased BMI and metabolic abnormalities are independently associated with higher readmission risks in patients with lung cancer, whereas increased BMI also increases the readmission costs. Follow-up and intervention method targeting increased BMI and metabolic abnormalities should be considered for patients with lung cancer.The National Key Research and Development Program of China (2017YFC1309800).
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