The Association Between METS-IR and Serum Ferritin Level in United States Female: A Cross-Sectional Study Based on NHANES

L-shaped association between lean body mass to visceral fat mass ratio with hyperuricemia: a cross-sectional study

BACKGROUND

Insufficient attention has been given to examining the correlation between body composition and hyperuricemia, leading to inconsistent findings. The primary objective of this research is to explore the association between lean body mass index (LMI), visceral fat mass index (VFMI), and hyperuricemia. A specific emphasis will be placed on assessing the link between the ratio of lean body mass to visceral fat mass (LMI/VFMI) and hyperuricemia.

METHODS

The present study employed a cross-sectional design and involved a total of 9,646 individuals who participated in the National Health and Nutrition Examination Survey (NHANES). To explore the associations among the variables, logistic and linear regressions were employed. Additionally, subgroup analyses and sensitivity analyses were conducted based on various characteristics.

RESULTS

The results showed that LMI was positively associated with hyperuricemia (for Per-SD: OR = 1.88, 95%CI: 1.75, 2.01; for quartiles [Q4:Q1]: OR = 5.37, 95%CI: 4.31, 6.69). Meanwhile, VFMI showed a positive association with hyperuricemia (for Per-SD: OR = 2.02, 95%CI: 1.88, 2.16; for quartiles [Q4:Q1]: OR =8.37, 95%CI: 6.70, 10.47). When considering the effects of In LMI/VFMI, an L-shaped negative association with hyperuricemia was observed (for Per-SD: OR = 0.45, 95%CI: 0.42, 0.49; for quartiles [Q4:Q1]: OR = 0.16, 95%CI: 0.13, 0.20). Subgroup and sensitivity analyses demonstrated the robustness of this association across different subgroups. Additionally, the segmented regression analysis indicated a saturation effect of 5.64 for the In LMI/VFMI with hyperuricemia (OR = 0.20, 95%CI: 0.17, 0.24). For every 2.72-fold increase of In LMI/VFMI, the risk of hyperuricemia was reduced by 80%.

CONCLUSION

The LMI/VFMI ratio is non-linearly associated with serum uric acid. Whether this association is causal needs to be confirmed in further longitudinal studies or Mendelian randomization.

Association between lean body mass to visceral fat mass ratio and bone mineral density in United States population: a cross-sectional study

BACKGROUND

Studies have explored the correlation between body composition and bone mineral density (BMD), but there has yet to be a consensus. Thus, the present study aims to comprehensively investigate the association between lean body mass, adipose tissue, and BMD.

METHODS

We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) (2011-2018) with 11,227 subjects. Multiple linear regression, smoothed curve fitting, threshold, and saturation effect analysis were used to explore the association between lean body mass, visceral fat mass, and BMD. Also, we used the lean body mass to visceral fat mass ratio (Log LM/VFM) as a proxy variable to analyze its association with BMD alone.

RESULTS

After adjusting for potential confounding factors, the results showed a positive correlation between lean mass and total BMD (for continuous: β = 0.078, P < 0.001; for quartile: β = 0.138, P < 0.001), while visceral fat mass was negatively correlated (for continuous: β = -0.027, P < 0.001; for quartile: β = -0.065, P < 0.001). A positive correlation was observed when the alternative variable Log LM/VFM was analyzed separately for its association with BMD (for continuous: β = 0.034, P < 0.001; for quartile: β = 0.084, P < 0.001). In addition, subgroup analyses for gender, age, body mass index, hypertension, and diabetes showed that all subgroups except the diabetes subgroup showed a substantial degree of robustness (P < 0.05). The smoothed curve fitting showed a nonlinear relationship between Log LM/VFM and BMD, and there was a threshold effect with a critical value of 2.60.

CONCLUSION

Maintaining a proper ratio of lean body mass and visceral fat mass is beneficial for increasing BMD.

The association of body mass index and weight waist adjustment index with serum ferritin in a national study of US adults

BACKGROUND

Abnormal serum ferritin levels are associated with a variety of diseases. Meanwhile, abnormal serum ferritin is influenced by a variety of risk factors, but its correlation with obesity remains poorly described.

OBJECTIVE

This study aimed to investigate the association of body mass index (BMI) and weight waist adjustment index (WWI) with serum ferritin in US adults.

METHODS

Participants in this study took part in the National Health and Nutrition Examination Survey (NHANES) prior to the pandemic from 2017 to March 2020. Serum ferritin was used as the sole response variable and BMI and WWI were used as independent variables. Multiple linear regression was used to assess the relationship between serum ferritin and the independent variables, and smoothed curve fitting and threshold effects analysis were performed to assess the presence of non-linear relationships. To validate the sensitive individuals for the correlation between the independent and the dependent variables, a subgroup analysis was performed.

RESULTS

A final total of 7552 participants were included in this study. Both independent variables had a positive relationship with serum ferritin, with effect values of (β = 0.68, 95% CI: 0.17-1.19) when BMI was the independent variable and (β = 8.62, 95% CI: 3.53-13.72) when WWI was the independent variable in the fully adjusted model. This positive association between the two obesity-related indexes and serum ferritin became more significant as BMI and WWI increased (P for trend < 0.001). In subgroup analyses, the positive association between the independent variables and serum ferritin was more pronounced in participants who were male, 40-59 years old, white, and had diabetes and hypertension. In addition, smoothed curve fitting and threshold effects analysis demonstrated a linear positive association of BMI and WWI with serum ferritin.

CONCLUSIONS

In the US adult population, while there was a linear positive association of WWI and BMI with serum ferritin, the effect values between WWI and serum ferritin were more significant. Male, 40-59 years old, white, participants with diabetes and hypertension should be cautious that higher WWI might entail a risk of higher serum ferritin levels.

Association between serum ferritin and liver stiffness in adults aged ≥20 years: A cross-sectional study based on NHANES

The importance of serum ferritin has been demonstrated in many liver diseases, but its relationship with liver stiffness remains unclear. The objective of this study was to investigate the association between serum ferritin levels and participants' liver stiffness measurement (LSM) in the United States population. We conducted a screening of participants from National Health and Nutrition Examination Survey (NHANES) 2017.1 to 2020.3 to ensure that participants included in this study had complete serum ferritin and LSM information. Association between the independent variable (serum ferritin) and the dependent variable (LSM) was investigated by multiple linear regression and subgroup analysis was performed to identify sensitive individuals, and we subsequently assessed whether there was a non-linear relationship between the 2 using smoothed curve fitting and threshold effect models. The final 7143 participants were included in this study. There was a positive association between participants' serum ferritin concentration and LSM, with an effect value of (β = 0.0007, 95% confidence interval (CI): 0.0002-0.0011) in the all-adjusted model. The smoothing curve and threshold effect models indicated a non-linear positive correlation between serum ferritin and LSM, which was more pronounced when serum ferritin concentration exceeded 440 ng/mL. Subsequent subgroup analysis showed that this positive correlation was more pronounced in males (β = 0.0007, 95% CI: 0.0001-0.0012), age >60 years (β = 0.00015, 95% CI: 0.0007-0.0023), black participants (β = 0.00018, 95% CI: 0.0009-0.0026), and participants with body mass index (BMI) <25 kg/m2 (β = 0.00012, 95% CI: 0.0005-0.0020). In U.S. adults, there was a positive correlation between serum ferritin levels and liver stiffness, which was more pronounced when serum ferritin exceeded 440 ng/mL. Our study suggested that regular serum ferritin testing would be beneficial in monitoring changes in liver stiffness. Male, age >60 years, black participants, and those with a BMI < 25 kg/m2 should be of greater consideration.

Correlation between hearing impairment and the Triglyceride Glucose Index: based on a national cross-sectional study

Objective

Data from the National Health and Nutrition Examination Survey (NHANES) were used to assess the association between the triglyceride-glucose (TyG) index and hearing impairment (HI).

Methods

We used eight survey cycles from NHANES 2001-2012 and 2015-2018 to conduct this cross-sectional study. HI was designed as an dependent variable, and the TyG index was selected as an exposure factor (independent variable). The correlation between the two variables was assessed using multiple logistic regression. In order to assess whether there was a non-linear relationship between the TyG index and HI, the TyG index was distributed and a test for trend was conducted (P for trend), followed by smooth curve fitting (penalized spline) and generalized additive model (GAM) regression. We also performed a subgroup analysis to identify sensitive groups whose responses were clearly associated with independent variables.

Results

10,906 participants were finally included in the study, and those with a higher TyG index had a higher frequency of hearing impairment. There was a linear positive correlation between the TyG index and HI. For the low-frequency HI, however, this positive correlation was not statistically significant (OR = 1.05, 95% CI: 0.98, 1.14); however, it was more stable for the high-frequency HI (OR = 1.12, 95% CI: 1.03, 1.22). Additionally, as the TyG index increased, this positive association increased as well (P for trend = 0.05). The HPTA test showed a positive association with more severe HI (simultaneous) as the independent variable increased (OR = 1.14, 95% CI: 1.05-1.24), and this association was even more significant with increasing severity (P for trend 0.05). According to the subgroup analysis, the positive association between TyG index and high-frequency HI was more significant in females, 40-69 years old, without hypertension or diabetes, and when strict high-frequency HI was significant in males, females, 40-69 years old, with hypertension and diabetes.

Conclusion

Participants with a higher TyG index may have a higher risk of HI. TyG index and HI risk showed a linear relationship, which became even more significant when HPTA was included.

The metabolic score of insulin resistance is positively correlated with bone mineral density in postmenopausal patients with type 2 diabetes mellitus

The prevalence of type 2 diabetes mellitus (T2DM) complicated with osteoporosis (OP) is increasing yearly. Early prevention, detection and treatment of OP are important in postmenopausal patients with T2DM. This study aimed to explore the correlation between insulin resistance and bone mineral density (BMD), and OP in postmenopausal patients with T2DM. In this study, postmenopausal patients with T2DM who visited our hospital from January 2021 to March 2022 were divided into the OP group (n = 91) and non-OP group (n = 119) according to whether they were complicated with OP or not. The general data of patients, BMD, blood routine, glucose metabolism, lipid metabolism, liver and kidney function indexes were collected, and the homeostatic model assessment for IR (HOMA-IR), the triglyceride-glucose (TyG) index and the metabolic score for IR (METS-IR) were calculated. A weighted multivariate linear regression model assessed the correlation between insulin resistance (IR) related indexes and lumbar spine, femoral neck, and hip BMD. A weighted logistic regression model assessed the odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association between the IR-related indexes and OP risk. The nonlinear relationship was also evaluated by smooth curve fitting (SCF) and a weighted generalized additive model (GAM). Moreover, the Receiver-operating characteristics (ROC) curve was used to analyze the predictive efficiency of METS-IR in postmenopausal patients with T2DM with OP. HOMA-IR, TyG, and METS-IR in the OP group were lower than those in the non-OP group (all P < 0.05). Weighted multiple linear regression after adjusting covariates showed that METS-IR was positively correlated with the lumbar spine, femoral neck, and hip BMD (β_METS-IR = 0.006,0.005,0.005, all P < 0.001). The results of weighted Logistic regression and GAM showed that when METS-IR < 44.5, each unit of increased METS-IR value was associated with a decreased OP risk of 12% (P = 0.002). When METS-IR ≥ 44.5, there was no significant correlation between METS-IR and the risk of OP (OR = 1.00, P = 0.934). Similar trends were not observed in HOMA-IR and TyG. The ROC suggested helpful discriminative power of the METS-IR index for T2DM. We confirmed that METS-IR, as a novel alternative marker of IR, had a positive association with BMD in postmenopausal patients with T2DM, and METS-IR was a protective factor for OP in a specific range.

Association between iron metabolism and non-alcoholic fatty liver disease: results from the National Health and Nutrition Examination Survey (NHANES 2017-2018) and a controlled animal study

BACKGROUND

Iron metabolism may be involved in the pathogenesis of the non-alcoholic fatty liver disease (NAFLD). The relationship between iron metabolism and NAFLD has not been clearly established. This study aimed to clarify the relationship between biomarkers of iron metabolism and NAFLD.

METHODS

Based on the National Health and Nutrition Examination Survey (NHANES), restricted cubic spline models and multivariable logistic regression were used to examine the association between iron metabolism [serum iron (SI), serum ferritin (SF), transferrin saturation (TSAT), and soluble transferrin receptor (sTfR)] and the risk for NAFLD. In addition, stratified subgroup analysis was performed for the association between TSAT and NAFLD. Moreover, serum TSAT levels were determined in male mice with NAFLD. The expression of hepcidin and ferroportin, vital regulators of iron metabolism, were analyzed in the livers of mice by quantitative real-time PCR (qRT-PCR) and patients with NAFLD by microarray collected from the GEO data repository.

RESULTS

Patients with NAFLD showed decreased SI, SF, and TSAT levels and increased STfR levels based on the NHANES. After adjusting for confounding factors, TSAT was significantly negatively correlated with NAFLD. Of note, the relationship between TSAT and NAFLD differed in the four subgroups of age, sex, race, and BMI (P for interaction < 0.05). Consistently, mice with NAFLD exhibited decreased serum TSAT levels. Decreased hepcidin and increased ferroportin gene expression were observed in the livers of patients and mice with NAFLD.

CONCLUSION

Serum TSAT levels and hepatic hepcidin expression were decreased in both patients and mice with NAFLD. Among multiple biomarkers of iron metabolism, lower TSAT levels were significantly associated with a higher risk of NAFLD in the U.S. general population. These findings might provide new ideas for the prediction, diagnosis, and mechanistic exploration of NAFLD.

Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES

Background

Prevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential.

Objective

An XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM.

Methods

All data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort.

Results

A total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa].

Conclusions

XGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.