Metabolically Healthy Non-obese Research Articles

Central and Peripheral Sensitivity to Thyroid Hormones Correlate to Metabolically Obesity Phenotypes in Chinese Euthyroid Adults: A Cross-Sectional Study.

Thyroid hormones impact lipid metabolism and glucose homoeostasis through both central and peripheral regulation; however, little research has delved into the association between thyroid hormone sensitivity and metabolically obese phenotypes. We aimed to investigate the correlation between indices of central and peripheral sensitivity to thyroid hormones and metabolically obese phenotypes in euthyroid Chinese adults. This cross-sectional study included 20,084 euthyroid individuals. Central thyroid hormone sensitivity was assessed using the thyroid feedback quantile-based index (TFQI), parametric thyroid feedback quantile-based index (PTFQI), thyroid-stimulating hormone index (TSHI), and thyrotropin thyroxine resistance index (TT4RI), while peripheral thyroid hormone sensitivity was measured by FT3/FT4. Metabolically obesity phenotypes included metabolically healthy non-obesity (MHNO), unhealthy non-obesity (MUNO), metabolically healthy obesity (MHO), and unhealthy obesity (MUO). Multinomial logistic regression and restricted cubic spline analyses were conducted to investigate the association between thyroid hormone sensitivity indices and metabolically obese phenotypes risk. Subgroup analysis was also performed to examine this association stratified by sex and age. Mediation analysis was performed to estimate direct and indirect effects of BMI. Prevalence of MHNO, MUNO, MHO and MUO was 66.1% (n=13,273), 21.3% (n=4271), 5.3% (n=1055), and 7.4% (n=1485) respectively. After adjustment for potential confounders, the odds ratios (ORs) (95% CI) for MUNO and MUO were increased with all elevated thyroid hormones sensitivity indices (per SD increase) [MUNO: TFQI 1.14(1.09-1.19), PTFQI 1.18(1.23-1.23); TSHI 1.26(1.19-1.33), TT4RI 1.41, (1.31-1.53), FT3/FT4 1.20(1.14-1.25) and [MUO: TFQI 1.20(1.11-1.31), PTFQI 1.26(1.16-1.37), TSHI 1.39 (1.26-1.54), TT4RI 1.70(1.48-1.95), FT3/FT4 1.26 (1.16-1.37)] (p value<0.001), and only TSHI and TT4RI (per SD increase) significantly increased the risk of MHO (TSHI: OR=1.12, 95% CI 1.01-1.24; TT4RI: OR=1.25, 95%CI 1.08-1.4) (p value<0.05). Non-linear relationships were observed between central thyroid hormones sensitivity indices and MUNO and MUO(p for nonlinearity<0.05). Conversely, a linear relationship between FT3/FT4 and metabolically obese phenotypes was noted in all subjects (p for nonlinearity>0.05). Besides, subgroup analysis indicated that this association remained consistent among sex and age (p for interaction>0.05). The proportions mediated by BMI on the association of TFQI, PTFQI, TSHI, TT4RI, FT3/FT4 and risk of metabolically unhealthy conditions were 13.73%, 25.38%, 22.75%, 17.94% and 62.28%, respectively. In euthyroid adults, central and peripheral sensitivity to thyroid hormones indices are positively associated with metabolically obese phenotypes risk, especially MUNO and MUO phenotypes.

Systemic Inflammation Across Metabolic Obesity Phenotypes: A Cross-Sectional Study of Korean Adults Using High-Sensitivity C-Reactive Protein as a Biomarker.

Chronic systemic inflammation is a hallmark of obesity. This cross-sectional study aimed to investigate the association between metabolic obesity phenotypes and inflammatory markers in Korean adults (N = 21,112; mean age: 50.9 ± 16.6). Metabolic obesity phenotypes were categorized into metabolically healthy non-obesity (MHNO), metabolically unhealthy non-obesity (MUNO), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO) based on body mass index and the presence of any metabolic abnormalities. High-sensitivity C-reactive protein (hs-CRP) levels were measured. Multiple linear regression was used to determine the association between obesity phenotypes and hs-CRP levels. In the male sample, compared to the MHNO type, the MUNO, MHO, and MUO types were associated with a 22.3% (95% confidence interval; CI: 14.7-30.3%), 15.8% (95% CI: 2.6-30.7%), and 12.5% (95% CI: 3.0-22.9%) increase in the hs-CRP levels, respectively. The association between metabolic obesity types and hs-CRP levels was stronger among the female sample; compared to the MHNO type, the MUNO, MHO, and MUO types were associated with a 30.2% (95% CI: 22.8-38.2%), 16.0% (95% CI: 6.5-26.4%), and 22.8% (95% CI: 13.6-32.8%) increase in the hs-CRP levels, respectively. Our findings indicate a varying profile of systemic inflammation across different metabolic obesity phenotypes.

Association of metabolically healthy obesity with risk of heart failure and left ventricular dysfunction among older adults.

Obesity is major cause of heart failure (HF), but it is related with a better prognosis among the elderly. Therefore, we aimed to examine whether metabolically healthy obesity (MHO) in late life increases HF risk and is reflected in impaired left ventricular (LV) function. The participants were grouped into four metabolic phenotypes based on obesity and metabolic status: metabolically healthy non-obesity (MHN), MHO, metabolically unhealthy non-obesity (MUN), metabolically unhealthy obesity (MUO). Association of metabolic phenotypes with LV function was evaluated using multiple linear regression models. And association between metabolic phenotypes and risk of HF was assessed using multivariable logistic regression models. In addition, we validated the association of metabolic phenotypes and HF risk in a separate longitudinal cohort. In the primary cohort of 6335 participant, there were 434 participants diagnosed with HF. Compared to MHN participants, the risk of HF was higher among older individuals with MUN (OR = 1.51 [95% CI: 1.14-1.99]) and MUO (OR = 2.01 [95% CI: 1.39-2.91]), but not older individuals with MHO (OR = 0.86 [95% CI: 0.30-2.43). Regarding to LV function, worse LV diastolic function was noted among MUN and MUO individuals rather than MHO individuals. Older adults with MHO were also not associated with risk of HF in the validation cohort. Among older individuals, the metabolic health status might modify the association of obesity with risk of HF and LV diastolic dysfunction. Worse LV diastolic function and higher risk of HF were just noted in individuals with MUO, but not in those with MHO.

Effect of Obesity and Metabolic Health Status on Metabolic-Associated Steatotic Liver Disease among Renal Transplant Recipients Using Hepatic Steatosis Index

Background/Objectives: Obesity and metabolic conditions increase the risk of metabolic-associated steatotic liver disease (MASLD). This study examined the risk of MASLD in 137 renal transplant recipients (RTRs) from a single-center hospital on the basis of their obesity and metabolic health status. Methods: Participants were categorized into four groups: metabolically healthy nonobese (MHNO), metabolically healthy obese (MHO), metabolically abnormal nonobese (MANO), and metabolically abnormal obese (MAO). MASLD was assessed using the hepatic steatosis index (HSI), calculated as 8 × (aspartate aminotransferase/alanine aminotransferase ratio) + body mass index + 2 (if diabetic) + 2 (if woman). The HSI scores were 29.50 ± 4.55, 38.08 ± 5.44, 33.61 ± 5.23, and 39.86 ± 4.13 in the MHNO, MHO, MANO, and MAO groups, respectively (p &lt; 0.05). Results: Overall, 25.55% of the participants (57.14% men) were classified as having MASLD (HSI &gt; 36). A multivariate-adjusted regression analysis revealed significantly higher HSI scores in the MAO group than in the MHNO group. Both MHO and MANO groups also had significantly higher HSI scores. The odds ratios for more severe MASLD were 2.74 (95% CI: 0.88–8.52) for the MANO group and 74.59 (95% CI: 13.29–418.68) for the MAO group compared with the MHNO group. Conclusions: These findings suggest that RTRs with obesity have a higher risk of MASLD, but even those with a normal weight and metabolic abnormalities are at increased risk.

Comparison of Esophageal Dysmotility and Reflux Burden in Patients with Different Metabolic Obesity Phenotypes Based on High-Resolution Impedance Manometry and 24-h Impedance-pH

Introduction: The relationship between the metabolically healthy obesity (MHO) phenotype and the occurrence of gastroesophageal reflux disease (GERD) and inefficient esophageal motility (IEM) is still unclear. Thus, we assessed the association between different metabolic obesity phenotypes and GERD and IEM using empirical data. Methods: We collected clinical and test data of 712 patients, including 24-h multichannel intraluminal impedance-pH (24-h MII-pH) monitoring, high-resolution manometry (HRM), and endoscopy. We divided 567 individuals into four categories according to their metabolic obesity phenotype: metabolically unhealthy non-obesity (MUNO), metabolically unhealthy obesity (MUO), metabolically healthy non-obesity (MHNO), and MHO. We compared differences in the 24-h MII-pH monitoring, HRM, and endoscopy findings among the four metabolic obesity phenotypes. Results: Patients with the MUNO, MHO, or MUO phenotype showed a greater risk of IEM and GERD (pathologic acid exposure time [AET] >6%) compared with patients with the MHNO phenotype. Regarding the HRM results, patients with the MHNO or MUNO phenotype had a lower integrated relaxation pressure, esophageal sphincter pressure, and esophagogastric junction contractile integral, and more ineffective swallows than patients with the MHO or MUO phenotype (p < 0.05). In terms of 24-h MII-pH, patients with the MHO or MUO phenotype had a higher total, upright, and supine AET; a higher total number of reflux episodes (TRs); and a lower mean nocturnal baseline impedance and post-reflux swallow-induced peristaltic wave index compared with those with the MHNO or MUNO phenotype (all p < 0.05). Considering the odds ratio of 19.086 (95% confidence interval 6.170–59.044) for pathologic AET and 3.659 (95% confidence interval 1.647–8.130) for IEM, patients with the MUO phenotype had the greatest risk after adjusting for all confounding variables. Conclusion: Obesity and metabolic disorders increase the risk of GERD and IEM. Obesity has a greater impact on esophageal dysmotility and pathologic acid exposure than metabolic diseases.

Association of dietary behavior patterns of middle-aged and older adults with their obesity metabolic phenotype: a cross-sectional study

BackgroundMiddle-aged and elderly individuals are the most susceptible groups for metabolic diseases, with their dietary behaviors being significant influencing factors. Exploring the association between overall dietary behaviors and obesity metabolic phenotypes is crucial for early prevention and control of chronic diseases, precision treatment and personalized interventions.MethodsWe conducted a cross-sectional study of 15,160 middle-aged and older adults between June 2019 and August 2021 to collect information on their body mass index (BMI), biochemical indices and disease history. The population was classified into four categories by the criteria of obesity metabolic phenotypes: metabolically healthy non-obesity (MHNO), metabolically unhealthy non-obesity (MUNO), metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). Scores were calculated based on compliance with healthy eating behavior patterns (appropriate or light dietary taste, moderately soft and hard food, slightly hot food temperature, medium or slow eating speed, daily intake of dietary supplements and eating with others), and the population was categorized into subgroups 0–2 (did not meet and met only 1 or 2), 3–4 (met 3 or 4), 5–6 (met 5 or 6). The relationship between dietary behavior patterns and different obesity metabolic phenotypes in middle-aged and elderly people were analyzed by multi-categorical logistic regression model.ResultsCompared with the 5–6 subgroup, the dietary behavior patterns of 0–2 and 3–4 scores were risk factors for MUNO, MHO and MUO (P < 0.05), and the lower the scores of the dietary behavior patterns, the higher the multiplicity of the occurrence of MUNO, MHO and MUO, especially for females and adults between 45–60 years old. Appropriate or light dietary taste, moderately soft and hard food, and slightly hot food temperature were protective factors for MUNO and MUO (P < 0.05); medium or slow eating speed and daily intake of dietary supplements were protective factors for MUNO, MHO and MUO (P < 0.05).ConclusionDietary behavior patterns in middle-aged and older adults are associated with different obesity metabolic phenotypes, and healthy dietary behaviors may be beneficial for the prevention and control of MUNO, MHO and MUO.

Association between metabolic obesity phenotypes and the risk of developing prostate cancer: a propensity score matching study based on Xinjiang.

Obesity-induced metabolic dysfunction increases the risk of developing tumors, however, the relationship between metabolic obesity phenotypes and prostate cancer (PCa) remains unclear. The term metabolic obesity phenotypes was introduced based on metabolic status and BMI categories. Participants were categorized into four groups: metabolically healthy nonobesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy nonobesity (MUNO), and metabolically unhealthy obesity (MUO). Propensity score matching was conducted based on age, ethnicity, marriage, etc. Univariate and multivariate conditional logistic regression analyses were used to assess the relationship between metabolic obesity phenotypes, metabolic risk factors, and PCa. Sensitivity analysis was performed to verify the robustness of the results. After propensity score matching among 564 PCa patients and 1418 healthy individuals, 209 were selected for each of the case and control groups. There were no statistically significant differences in the basic characteristics between the two groups. Univariate and multivariate conditional logistic regression suggested that the risk of developing PCa in both MHO and MUO individuals was higher than in MHNO individuals. Specifically, the risk of developing PCa in MHO individuals was 2.166 times higher than in MHNO individuals (OR=2.166, 95%CI: 1.133-4.139), and the risk in MUO individuals was is 2.398 times higher than in MHNO individuals(OR=2.398, 95%CI:1.271-4.523). Individuals with hyperglycemia and elevated triglycerides also had a higher risk of developing PCa (hyperglycemia:OR=1.488, 95%CI: 1.001-2.210; elevated triglycerides: OR=2.292, 95%CI: 1.419-3.702). Those with more than or equal to three metabolic risk factors had an increased risk of PCa (OR=1.990, 95%CI: 1.166-3.396). Sensitivity analysis indicated an increased risk of PCa in MUO individuals compared to MHNO individuals. In this retrospective study, individuals with MHO and MUO had a higher risk of developing PCa.

Characteristics of the epicardial adipose tissue measured by low-dose chest computed tomography according to the metabolic health/obesity status

BackgroundEpicardial adipose tissue (EAT) is associated with the development of cardiovascular disease and long-term survival. This study aimed to assess the characteristics of EAT according to the metabolic health and obesity status using low-dose chest computed tomography (CT). MethodsA total of 1074 asymptomatic adults who underwent a medical health check-up were enrolled. Subjects were categorized into the following four groups according to the metabolic health and obesity status: metabolically healthy non-obese (MHNO); metabolically unhealthy non-obese (MUNO); metabolically healthy obese (MHO); and metabolically unhealthy obese (MUO). EAT on low-dose chest CT was measured by using automatic, quantitative measurement software. ResultsMUO showed the highest EAT volume and lowest EAT radiodensity in comparison with MHNO (p < 0.001). The MUNO (n = 70), MHO (n = 259), and MUO (n = 231) groups had increased EAT volume (β [95 % CI], 37.65 [23.11,52.18], 56.79 [47.56,66.02], 84.85 [74.59,95.11] respectively, all p < 0.001), decreased EAT radiodensity (β [95 % CI], − 3.22 [− 4.59,− 1.85], − 4.48 [− 5.30,− 3.66], − 6.03 [− 6.90,− 5.16] respectively, all p < 0.001) in comparison with the MHNO (n = 514) group by using multivariable linear regression models. ConclusionsBoth metabolic abnormalities and obesity were closely associated with EAT characteristics. Characteristics of EAT are similar in MHO and MUNO. This finding suggests that MHO is not a favorable condition in terms of cardiac health, as assessed by the characteristics of EAT. The combination of obesity and metabolically unhealthy status has a synergistic adverse effect on EAT. Measurement of EAT could be a useful imaging biomarker for evaluation of an individual's metabolic health/obesity status.

Metabolic obesity phenotypes and all-cause mortality among the Chinese oldest-old population: a prospective cohort study.

The obesity paradox has been reported among older adults. However, whether the favorable effect of obesity is dependent on metabolic status remains largely unknown. We aimed to explore the association of metabolic obesity phenotypes and their changes with all-cause mortality among the Chinese oldest-old population. This prospective cohort study included 1207 Chinese oldest old (mean age: 91.8 years). Metabolic obesity phenotypes were determined by central obesity and metabolic status, and participants were classified into metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), metabolically healthy non-obesity (MHN), and metabolically unhealthy non-obesity (MUN). The hazard ratios (HRs) and 95% confidence intervals (95% CIs) were estimated by Cox regression models. During 5.3 years of follow-up, 640 deaths were documented. Compared with non-obesity, obesity was associated with a decreased mortality risk among participants with metabolically healthy (HR, 0.75; 95% CI, 0.63-0.91) while this association was insignificant among metabolically unhealthy. Compared to MHO, MHN (HR, 1.27; 95% CI, 1.06-1.53) and MUN (HR, 1.49; 95% CI, 1.10-2.02) were significantly associated with an increased mortality risk. Compared to those with stable MHO, those transited from MHO to MUO demonstrated a higher mortality risk (HR, 1.81; 95% CI, 1.06-3.11). MHO predicts better survival among the Chinese oldest-old population. These findings suggest that ensuring optimal management of metabolic health is beneficial and taking caution in weight loss based on the individual body weight for the metabolically healthy oldest-old adults.

Rising Trends in Metabolically Healthy Obesity in Cancer Patients and Its Impact on Cardiovascular Events: Insights from a Contemporary Nationwide Analysis in the USA (2016-2020).

Background: Obesity or overweight raises the risk of developing 13 types of cancer, representing 40% of all cancers diagnosed in the United States annually. Given the ongoing debate surrounding the impact of metabolically healthy obesity (MHO) on cardiovascular outcomes, it is crucial to comprehend the incidence of Major Adverse Cardiovascular and Cerebrovascular Events (MACCEs) and the influence of MHO on these outcomes in cancer patients. Methods: Data of hospitalized cancer patients with and without obesity were analyzed from the National Inpatient Sample 2016-2020. Metabolically healthy patients were identified by excluding diabetes, hypertension, and hyperlipidemia using Elixhauser comorbidity software, v.2022.1. After that, we performed a multivariable regression analysis for in-hospital MACCEs and other individual outcomes. Results: We identified 3,111,824 cancer-related hospitalizations between 2016 and 2020. The MHO cohort had 199,580 patients (6.4%), whereas the MHnO (metabolically healthy non-obese) cohort had 2,912,244 patients (93.6%). The MHO cohort had a higher proportion of females, Blacks, and Hispanics. Outcomes including in-hospital MACCEs (7.9% vs. 9.5%; p < 0.001), all-cause mortality (6.1% vs. 7.5%; p < 0.001), and acute myocardial infarction (AMI) (1.5% vs. 1.6%; p < 0.001) were lower in the MHO cohort compared to the MHnO cohort. Upon adjusting for the baseline characteristics, the MHO group had lower odds of in-hospital MACCEs [adjusted odds ratio (AOR) = 0.93, 95% CI (0.90-0.97), p < 0.001], all-cause mortality [AOR = 0.91, 95% CI (0.87-0.94); p < 0.001], and acute ischemic stroke (AIS) [AOR = 0.76, 95% CI (0.69-0.84); p < 0.001], whereas there were higher odds of acute myocardial infarction (AMI) [AOR = 1.08, 95% CI (1.01-1.16); p < 0.001] and cardiac arrest (CA) [AOR = 1.26, 95% CI (1.01-1.57); p = 0.045] in the MHO cohort compared to the MHnO cohort. Conclusions: Hospitalized cancer patients with MHO exhibited a lower prevalence of in-hospital MACCEs than those with MHnO. Additional prospective studies and randomized clinical trials are imperative to validate these findings, particularly in stratifying MHO across various cancer types and their corresponding risks of in-hospital MACCEs.

Metabolic Obesity Phenotypes and Incident Cardiovascular Outcomes in Middle-Aged and Older Korean Adults: A Longitudinal 10-Year Analysis of the Korean Genome and Epidemiology Study.

Background: This study investigated the association of four metabolic obesity phenotypes with incident coronary artery disease and stroke in a large-scale, community population-based, prospective Korean cohort observed for over 10 years. Methods: The study participants included 7374 adults aged 40-69 years, drawn from the Korean Genome and Epidemiology Study. Participants with different metabolic obesity phenotypes were categorized according to body weight and metabolic health status into four groups: metabolically healthy nonobese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy nonobese (MUHNO), and metabolically unhealthy obese (MUHO). Combined cardiovascular events were defined as coronary artery disease and stroke. We used multivariate Cox proportional hazards regression models to prospectively assess hazard ratios (HRs) with 95% confidence intervals (CIs) for incident coronary artery disease or stroke over 10 years after the baseline survey. Results: During the follow-up period, newly developed coronary artery disease, stroke, and combined cardiovascular events were diagnosed in 151 (2.0%), 137 (1.9%), and 283 (3.8%) participants, respectively. After adjusting for confounding variables, the HRs (95% CIs) for incident combined cardiovascular events were 1.81 (1.34-2.46) in the MUHO group, 1.29 (0.92-1.81) in the MUHNO group, and 1.21 (0.81-1.79) in the MHO group compared with those in the MHNO group. Conclusions: This study revealed distinct risks associated with four metabolic obesity phenotypes concerning incident coronary artery disease and stroke. After adjusting for potential confounding variables, the results indicated that MUHO, but not MUHNO or MHO, showed a higher risk of developing coronary artery disease and stroke than MHNO.

The transition of metabolic phenotypes and cardiovascular events: Panasonic cohort study 16.

The study objective was to investigate whether changes in metabolic phenotype affect the risk of cardiovascular events. All 117,589 participants were included in this retrospective cohort study. The metabolic phenotypes of the participants were assessed at two points (the second evaluation was set 2 years after the first evaluation), and the incidence rate of cardiovascular events was observed for 11 years. The main outcome was 3-point major adverse cardiac events (MACE), which comprises cardiovascular death, nonfatal coronary artery disease, and nonfatal stroke incidence. Of the participants, 2748 (2.3%) cases of 3-point MACE were identified during follow-up. The stable metabolically healthy obesity group had a higher risk of 3-point MACE than those with stable metabolically healthy nonobesity (MHNO). Additionally, the change from metabolically healthy obesity to MHNO for 2 years decreased the risk of 3-point MACE (hazard ratio [HR], 1.12: 95% CI: 0.84-1.47) to the same level as stable MHNO. However, the change from metabolically abnormal nonobesity and metabolically abnormal obesity to MHNO for 2 years maintained a higher risk of 3-point MACE (HR, 1.66 [95% CI: 1.36-2.01]; HR, 1.91 [95% CI: 1.22-2.81]) than those with stable MHNO. Change in metabolic phenotype is associated with incident 3-point MACE.

Joint effect of BMI and metabolic status on mortality among adults: a population-based longitudinal study in United States

We explored the joint effects of different metabolic obesity phenotypes on all-cause and disease-specific mortality risk among the American population. Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 1999–2018. Mortality outcome data were from mortality files linked to National Death Index record and follow-up information was up to December 31, 2019. 50,013 participants were finally included. Four metabolic obesity phenotypes were defined based on obesity and metabolic status: metabolically healthy obese (MHO), metabolically unhealthy obese (MUO), metabolically healthy non-obese (MHNO), and metabolically unhealthy non-obese (MUNO). Population-weighted Cox proportional hazards models were used to explore the all-cause and disease-specific mortality risk of metabolic obesity phenotypes. The all-cause mortality risk of MUO and MUNO was significantly higher than MHNO. MUNO was associated with a significantly increased risk of death from heart disease (HR: 1.40, 95% CI 1.16–1.70), hypertension (HR: 1.68, 95% CI 1.34–2.12), diabetes (HR: 2.29, 95% CI 1.67–3.15), and malignant neoplasms (HR:1.29, 95% CI 1.09–1.53). Metabolic unhealth significantly increased the risk of all-cause mortality, regardless of obesity status. Among individuals with metabolic unhealthy status, obesity significantly reduced the risk of all-cause mortality (HR: 0.91, 95% CI 0.85–0.98). Our study highlights the importance of identifying and characterizing metabolic obesity phenotypes in obese and metabolically abnormal patients, as well as healthy adults. Comprehensive evaluation of obesity and metabolic status is necessary to adopt appropriate interventions and treatment measures and maximize patient benefit.

Association of metabolic health and obesity with coronary heart disease in adult cancer survivors.

The metabolically healthy obese (MHO) phenotype is associated with an increased risk of coronary heart disease (CHD) in the general population. However, association of metabolic health and obesity phenotypes with CHD risk in adult cancer survivors remains unclear. We aimed to investigate the associations between different metabolic health and obesity phenotypes with incident CHD in adult cancer survivors. We used National Health Insurance Service (NHIS) to identify a cohort of 173,951 adult cancer survivors aged more than 20 years free of cardiovascular complications. Metabolically healthy nonobese (MHN), MHO, metabolically unhealthy nonobese (MUN), metabolically unhealthy obese (MUO) phenotypes were created using as at least three out of five metabolic health criteria along with obesity (body mass index ≥ 25.0 kg/m2). We used Cox proportional hazards model to assess CHD risk in each metabolic health and obesity phenotypes. During 1,376,050 person-years of follow-up, adult cancer survivors with MHO phenotype had a significantly higher risk of CHD (hazard ratio [HR] = 1.52; 95% confidence intervals [CI]: 1.41 to 1.65) as compared to those without obesity and metabolic abnormalities. MUN (HR = 1.81; 95% CI: 1.59 to 2.06) and MUO (HR = 1.92; 95% CI: 1.72 to 2.15) phenotypes were also associated with an increased risk of CHD among adult cancer survivors. Adult cancer survivors with MHO phenotype had a higher risk of CHD than those who are MHN. Metabolic health status and obesity were jointly associated with CHD risk in adult cancer survivors.

The classification of obesity based on metabolic status redefines the readmission of non-Hodgkin’s lymphoma—an observational study

BackgroundThe 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.MethodsIn 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.ResultsThere 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.ConclusionsThe 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.

The effects of weight loss and improved metabolic health status on the risk of non-alcoholic fatty liver disease—results from a prospective cohort in China

BackgroundThe impact of weight loss and/or improved metabolic status on the risk of non-alcoholic fatty liver disease (NAFLD) has yet to be determined.MethodsA total of 35,322 participants without NAFLD were followed. NAFLD risk was compared between consistently metabolically healthy non-obese (MHNO) and non-MHNO who lost weight to become non-obese and/or improved their metabolic health, using Cox proportional hazards and logistic regression models.ResultsFollowing 148,186 person-years, 8,409 participants had onset NAFLD, with an incidence rate of 56.75 (95% CI: 55.57, 57.94) per 1,000 person-years. Metabolically healthy obese (MHO), metabolically unhealthy obese (MUO), and metabolically unhealthy non-obese (MUNO) at baseline were associated with increased NAFLD risk, with hazard ratios of 4.48 (95%CI:4.24, 4.73), 8.85 (95%CI:7.95, 9.84), and 10.70 (95%CI:9.73, 11.78). Weight loss and/or metabolic status improvements could significantly reduce NAFLD risk by 79.46 to 41.46%. Specifically, after weight loss from MHO to MHNO, the reduction in NAFLD risk [OR decreased from 12.01 (95%CI:9.40, 15.35) to 4.14 (95%CI:3.08, 5.57)] was greater than that of the MUNO subgroup whose metabolic status improved to MHNO [OR decreased from 5.53 (95%CI:5.15, 5.94) to 2.71 (95%CI:2.50, 3.93)]. In the MUO subgroup, the group with the greatest risk reduction of NAFLD was the weight and metabolic state both improvement group [MUO to MHNO, OR decreased from 22.74 (95%CI:17.61, 29.37) to 4.67 (95%CI:3.05, 7.16)], followed by the weight loss only group [MUO to MUNO, OR decreased to 6.83 (95%CI:4.87, 9.57)], and finally the group with the least and insignificant risk reduction was the metabolic state improvement group [MUO to MHO, OR decreased to 13.38 (95%CI:9.17,19.53)]. NAFLD risk was negatively correlated with the duration of improvement (p &amp;lt; 0.001).ConclusionIndividuals with non-MHNO were more likely to develop NAFLD than those with consistent MHNO, but metabolic improvements and weight loss can alleviate the risk. Their NAFLD risk was negatively correlated with improvement duration. However, it remained higher than in individuals with consistent MHNO at an average follow-up of 4.2 years.

The short- and long-term readmission of four major categories of digestive system cancers: does obesity or metabolic disorder matter?

Patients with digestive system cancers (DSCs) are at a high risk for hospitalizations; however, the risk factors for readmission remain unknown. Here, we established a retrospective cohort study to assess the association between metabolic obesity phenotypes and readmission risks of DSC. A total of 142,753 and 74,566 patients at index hospitalization were ultimately selected from the Nationwide Readmissions Database (NRD) 2018 to establish the 30-day and 180-day readmission cohorts, respectively. The study population was classified into four groups: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO). Multivariate Cox regression analysis was used to estimate the effect of metabolic obesity phenotypes on DSC readmission. The MUNO phenotype had 1.147-fold (95% CI: 1.066, 1.235; p < 0.001) increased 180-day readmission risks in patients with neoplasm of the upper digestive tract. The MUNO phenotype had 1.073-fold (95% CI: 1.027, 1.121; p = 0.002) increased 30-day readmission risks and 1.067-fold (95% CI: 1.021, 1.115; p = 0.004) increased 180-day readmission risks in patients with neoplasm of the lower digestive tract. The MUNO and MUO phenotypes were independent risk factors of readmission in patients with liver or pancreatic neoplasm. Metabolic obesity status was independently associated with a high risk of severe and unplanned hospitalization within 30 days or 180 days. Both obesity and metabolic abnormalities are associated with a high risk for the poor prognosis of DSC patients. The effect of metabolic categories on the short- or long-term readmission of liver or pancreas cancers may be stronger than that of obesity.

Association of metabolically unhealthy non-obese and metabolically healthy obese individuals with arterial stiffness and 10-year cardiovascular disease risk: a cross-sectional study in Chinese adults

BackgroundThe relationship between metabolically healthy obese individuals (MHO) and cardiovascular disease (CVD) risk is disputed. This study investigated the association of metabolically unhealthy non-obese(MUNO) individuals and MHO with arterial stiffness and 10-year CVD risk.MethodsA total of 13,435 participants were enrolled and further divided into the metabolically healthy non-obese (MHNO) phenotype (n = 4927), MUNO phenotype (n = 1971), MHO phenotype (n = 2537) and metabolically unhealthy obese (MUO) phenotype (n = 4000) according to body mass index (BMI) and metabolic status. We used brachial ankle pulse wave velocity (baPWV) to measure arterial stiffness and the Framingham risk score (FRS) to evaluate the 10-year CVD risk.ResultsThe MUO and MUNO phenotypes had higher mean baPWV values than the MHO and MHNO phenotypes, regardless of age (1446.19 ± 233.65 vs. 1423.29 ± 240.72 vs. 1283.57 ± 213.77 vs. 1234.08 ± 215.99 cm/s, P < 0.001). Logistic regression analysis indicated that the MUNO and MUO phenotypes were independently correlated with elevated baPWV and 10-year CVD risk, while the MHO phenotype was independently associated with only the 10-year CVD risk. In metabolically healthy subjects, BMI showed a dose-dependent increase in the risk of elevated baPWV, with an adjusted OR of 1.007 (95% CI 1.004–1.010, P < 0.001). However, in metabolically unhealthy participants, the estimate for the relationship between elevated baPWV and BMI was nonsignificant.ConclusionsThe MUNO phenotype exhibits increased arterial stiffness and 10-year CVD risk. However, BMI is positively and dose-dependently correlated with arterial stiffness only in metabolically healthy subjects. We speculate that metabolic status may be a strong confounder in the obesity–elevated baPWV association.

Differences in the levels of inflammatory markers between metabolically healthy obese and other obesity phenotypes in adults: A systematic review and meta-analysis

AimsThe aim of this study was to systematically review and analyze differences in the levels of C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) comparing metabolically healthy but obese (MHO) with metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO) subjects. Data synthesisWe searched PubMed, Embase, Web of Science, and Scopus for studies that matched the relevant search terms. Differences in inflammatory marker levels between MHO and the other three phenotypes were pooled as standardized mean differences (SMD) or differences of medians (DM) using a random-effects model. We included 91 studies reporting data on 435,007 individuals. The CRP levels were higher in MHO than in MHNO subjects (SMD = 0.63, 95% CI: 0.49, 0.76; DM = 0.83 mg/L, 95% CI: 0.56, 1.11). The CRP levels were higher in MHO than in MUNO subjects (SMD = 0.16, 95% CI: 0.05, 0.28; DM = 0.39 mg/L, 95% CI: 0.09, 0.69). The CRP levels were lower in MHO than in MUO individuals (SMD = −0.43, 95% CI: −0.54, −0.31; DM = −0.82 mg/L, 95% CI: −1.16, −0.48). The IL-6 levels in MHO were higher than in MHNO while lower than in MUO subjects. The TNF-α levels in MHO were higher than in MHNO individuals. ConclusionsThis review provides evidence that CRP levels in MHO are higher than in MHNO and MUNO subjects but lower than in MUO individuals. Additionally, IL-6 levels in MHO are higher than in MHNO but lower than in MUO subjects, and TNF-α levels in MHO are higher than in MHNO individuals. Systematic review registrationPROSPERO number: CRD42021234948.

Metabolic phenotypes and risk of end-stage kidney disease in patients with type 2 diabetes.

Obesity often initiates or coexists with metabolic abnormalities. This study aimed to investigate the pathological characteristics and the independent or mutual relations of obesity and metabolic abnormalities with end-stage kidney disease (ESKD) in patients with type 2 diabetes (T2D) and associated diabetic kidney disease (DKD). A total of 495 Chinese patients with T2D and biopsy-confirmed DKD between 2003 and 2020 were enrolled in this retrospective study. The metabolic phenotypes were based on the body weight index (BMI)-based categories (obesity, BMI ≥ 25.0 kg/m2) and metabolic status (metabolically unhealthy status, ≥ 1 criterion National Cholesterol Education Program Adult Treatment Panel III (NCEP/ATP III) excluding waist circumference and hyperglycemia) and were categorized into four types: metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO), and metabolically unhealthy obesity (MUO). The pathological findings were defined by the Renal Pathology Society classification. Cox proportional hazards models were used to estimate hazard ratios (HRs) for ESKD. There are 56 (11.3%) MHNO patients, 28 (5.7%) MHO patients, 176 (35.6%) MUNO patients, and 235 (47.5%) MUO patients. The high prevalence of the Kimmelstiel-Wilson nodule and severe mesangial expansion were associated with obesity, whereas severe IFTA was related to metabolically unhealthy status. In the multivariate analysis, the adjusted HR (aHR) was 2.09 [95% confidence interval (CI) 0.99-4.88] in the MHO group, 2.16 (95% CI 1.20-3.88) in the MUNO group, and 2.31 (95% CI 1.27-4.20) in the MUO group compared with the MHNO group. Furthermore, the presence of obesity was insignificantly associated with ESKD compared with non-obese patients (aHR 1.22, 95% CI 0.88-1.68), while the metabolically unhealthy status was significantly associated with ESKD compared to the metabolically healthy status in the multivariate analysis (aHR 1.69, 95% CI 1.10-2.60). Obesity itself was insignificantly associated with ESKD; however, adding a metabolically unhealthy status to obesity increased the risk for progression to ESKD in T2D and biopsy-proven DKD.