Background: The correlation between white cell count and metabolically healthy obesity (MHO) remains elusive among obese American adults. This study endeavors to elucidate this association.Methods: This study enrolled 6,580 obese patients from the National Health and Nutrition Examination Survey (NHANES). Obesity phenotypes were defined by presence/absence of metabolic syndrome components. Weighted multivariate logistic regression analyses were used to assess the association between white cell count and MHO occurrence. Restricted cubic spline analysis characterized dose–response relationships, and stratified analyses explored these relationships across sociodemographic and lifestyle factors.Results: In this study, MHO prevalence is 11.9% among obese adults. The risk of MHO was inversely correlated with WBC [OR (95%): 0.81 (0.76, 0.86), p < 0.001], lymphocytes [OR (95%): 0.56 (0.47, 0.68), p < 0.001], monocytes [OR (95%): 0.41 (0.22, 0.75), p = 0.004], and neutrophils count [OR (95%): 0.82 (0.76, 0.88), p < 0.001]. WBC and neutrophils showed L-shaped associations, while lymphocytes, monocytes, and RBCs had linear patterns. Furthermore, stratified analyses demonstrated blood cell counts consistently predicted MHO risk across subgroups.Conclusion: In this study, we provide novel insights into the association between blood cell count and the presence of MHO among obese individuals. Blood cell count is an accessible biomarker for dynamically tracking the presence of MHO.

The relationship between serum calcium and occurrence of MHO (metabolically healthy obesity) and MUNO (metabolically unhealthy non-obesity) remains unclear, and distinguishing these two phenotypes is difficult within primary healthcare units. This study explores that relationship. This survey included 28590 adults from the National Health and Nutrition Examination Survey (NHANES) 2001-2018. Obesity phenotypes were categorized based on BMI and presence or absence of metabolic syndrome components. Weighted multivariate logistic regression analyses were used to assess the association between serum calcium levels and the obesity phenotype. Restricted cubic spline analysis characterized dose-response relationships, and stratified analyses explored these relationships across sociodemographic and lifestyle factors. The overall prevalence of MHO and MUNO were 2.6% and 46.6%, respectively. After adjusting for covariates, serum calcium exhibited a negative association with MHO [OR (95%): 0.49 (0.36,0.67), p < 0.001], while exhibiting a positive association with MUNO [OR (95%): 1.48 (1.26,1.84), p < 0.001]. Additionally, we found a non-linear association between serum calcium levels and the incidences of MHO and MUNO. Stratified analyses demonstrated a strong negative correlation between serum calcium levels and MHO occurrence across various subgroups. There was no significant interaction between calcium and stratified variables except sex; the association between calcium and the occurrence of MHO was remarkable in female patients. Meanwhile, the predictive ability of serum calcium level for the occurrence of MUNO among all patients was consistent across various subgroups. There was a significant interaction between calcium level and stratified variables based on age, sex, race, and smoking status; the association was remarkable in older (≥ 40 years old), white, none or less smoking, and female patients. A significant correlation was identified between serum calcium levels and MHO or MUNO. The findings suggest that serum calcium levels may serve as an indicator for more accurate assessment and diagnosis of MUNO and MHO, especially among individuals with abdominal obesity.

Background: The causal relationship between basal metabolic rate (BMR) and infectious diseases remains elusive. This study aims to clarify this association. Methods: This study analyzed genome-wide association studies (GWASs) data from the UK Biobank and FinnGen cohorts to investigate the association between BMR and infectious diseases in European populations. Mendelian randomization (MR) analysis was initially employed, followed by multivariable Mendelian randomization (MVMR) to account for potential confounders. Mediation analysis further confirmed significant relationships. Sensitivity analyses were conducted to validate the study findings. Results: Using two sample MR, genetically predicted BMR was positively linked to skin and soft tissue infections (SSTIs) (OR: 1.31, 95% CI: 1.18–1.47, P < .001), osteomyelitis (OR: 1.95, 95% CI: 1.36–2.80, P < .001) (1.36 ± 2.80), all-cause infections (OR: 1.36, 95% CI: 1.26–1.48, P < .001) and sepsis (OR: 1.36, 95% CI: 1.23–1.51, P < .001). MVMR analysis confirmed BMR's direct causal effect on SSTIs, osteomyelitis, all-cause infections, and sepsis, except for BMI and other factors affecting osteomyelitis. Mediation analysis revealed VAT as a mediator in the linkage between BMR and SSTIs and all-cause infections. HbA1c mediated the path from BMR to osteomyelitis, while CRP and BMI exhibited mediation effects in the BMR-all-cause infections relationship. Conclusion: The study revealed a significant link between increased BMR and elevated risks of SSTIs, osteomyelitis, and bacterial infections, highlighting the intricate BMR-immune connection and its implications for disease control. Key message What is already known on this topic: High BMR is positively correlated with COVID-19 and associated with proinflammatory and immunological activation, but the relationship between BMR and other infectious diseases remains largely unexplored. What this study adds: Higher BMR significantly raises the risk of SSTIs, osteomyelitis, all-cause infections, and sepsis. VAT, HbA1c, CRP, and BMI may mediate the BMR-infection relationship. How this study might affect research, practice, or policy: A higher BMR may be a valuable indicator associated with an increased risk for SSTIs, osteomyelitis, and sepsis. Modulating BMR might hold promise as a clinically relevant intervention to prevent specific infectious diseases.

Metabolic flexibility is a core feature of metabolic health, referring to the capacity to switch efficiently between glucose and lipid substrates in response to nutritional intake, hormonal status, and energetic demand [1]. In healthy states, the body preferentially utilizes glucose and stores energy after feeding, while enhancing fatty acid mobilization and oxidation during fasting or exercise. When this flexibility is impaired, nutrient excess may be redirected toward insulin resistance, ectopic lipid deposition, mitochondrial dysfunction, and chronic inflammation. Thus, the interplay between glucose and lipid homeostasis provides an important framework for understanding obesity, diabetes, steatotic liver disease, and other metabolism-related disorders.Public databases, large cohorts, and multi-omics resources have increasingly enabled metabolic risk to be assessed as an integrated phenotype rather than through isolated biomarkers. These developments have facilitated the use of accessible composite indices, including the triglyceride-glucose index (TyG), visceral adiposity index (VAI), lipid accumulation product (LAP) and metabolic score for insulin resistance (METS-IR), to characterize integrated risks arising from glucose-lipid abnormalities