Metabolic health's central role in chronic kidney disease progression: a 20-year study of obesity-metabolic phenotype transitions.

This study investigates the risk of chronic kidney disease (CKD) across four metabolic phenotypes: Metabolically Healthy-No Obesity (MH-NO), Metabolically Unhealthy-No obesity (MU-NO), Metabolically Healthy-Obesity (MH-O), and Metabolically Unhealthy-Obesity (MU-O). Data from the Tehran Lipid and Glucose Study, collected from 1999 to 2020, were used to categorize participants based on a BMI ≥ 30 kg/m2 and metabolic health status, defined by the presence of three or four of the following components: high blood pressure, elevated triglycerides, low high-density lipoprotein, and high fasting blood sugar. CKD, characterized by a glomerular filtration rate < 60 ml/min/1.72 m2. The hazard ratio (HR) of CKD risk was evaluated using Cox proportional hazard models. The study included 8731 participants, with an average age of 39.93 years, and identified 734 incidents of CKD. After adjusting for covariates, the MU-O group demonstrated the highest risk of CKD progression (HR 1.42-1.87), followed by the MU-NO group (HR 1.33-1.67), and the MH-O group (HR 1.18-1.54). Persistent MU-NO and MU-O posed the highest CKD risk compared to transitional states, highlighting the significance of exposure during early adulthood. These findings emphasize the independent contributions of excess weight and metabolic health, along with its components, to CKD risk. Therefore, preventive strategies should prioritize interventions during early-adulthood.

Relationship between metabolically healthy obesity and coronary artery calcification.

There is a lack of consensus regarding universally accepted criteria for metabolic health (MH). A simple definition of MH was systematically derived in a recent prospective cohort study. The present cross-sectional study aimed to explore the applicability of these criteria in Korean population, using coronary calcification as an indicator of cardiovascular risk. In total, 1049 healthy participants, who underwent coronary artery calcification testing at university hospital health promotion centers between January and December 2022, were included. Applying the main components of the newly derived definition, MH was defined as follows: (1) systolic blood pressure < 130 mmHg and no use of blood pressure-lowering medication; (2) waist circumference < 90 cm for males and < 85 cm for females; and (3) absence of diabetes. Multivariate logistic regression was conducted to examine the odds ratio (OR) and 95 % confidence interval (CI) for coronary artery calcium score across different phenotypes. The prevalence of coronary artery calcification in this study was 41.1 %. Compared with metabolically healthy, normal weight subjects, those with the metabolically healthy obesity phenotype did not exhibit increased odds for coronary atherosclerosis. (OR 0.93 [95 % CI 0.48-1.79]) Conversely, metabolically unhealthy subjects had increased risk, regardless of their body mass index category (OR 3.10 [95 % CI 1.84-5.24] in metabolically unhealthy normal weight; OR 3.21 [95 % CI 1.92-5.37] in metabolically unhealthy overweight; OR 2.73 [95 % CI 1.72-4.33] in metabolically unhealthy obese phenotype). These findings suggest that the new definition for MH has the potential to effectively distinguish individuals at risk for cardiovascular disease from those who are not.

Transition in metabolic health phenotypes across general adiposity categories and association with the risk of depression: a prospective analysis.

BACKGROUND: The association between obesity and depression may partly depend on the contextual metabolic health. The effect of change in metabolic health status over time on subsequent depression risk remains unclear. We aimed to assess the prospective association between metabolic health and its change over time and the risk of depression across body mass index (BMI) categories. METHODS: Based on a nationally representative cohort, we included participants enrolled at the wave 2 (2004-2005) of the English Longitudinal Study of Ageing and with follow-up for depression at wave 8 (2016-2017). Participants were cross-classified by BMI categories and metabolic health (defined by the absence of hypertension, diabetes, and hypercholesterolemia) at baseline or its change over time (during waves 3-6). Logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the risk of depression at follow-up stratified by BMI category and metabolic health status with adjustment for potential confounders. RESULTS: The risk of depression was increased for participants with metabolically healthy obesity compared with healthy nonobese participants, and the risk was highest for those with metabolically unhealthy obesity (OR 1.62, 95% CI 1.18-2.20). Particularly hypertension and diabetes contribute most to the increased risk. The majority of metabolically healthy participants converted to unhealthy metabolic phenotype (50.1% of those with obesity over 8 years), which was associated with an increased risk of depression. Participants who maintained metabolically healthy obesity were still at higher risk (1.99, 1.33-2.72), with the highest risk observed for those with stable unhealthy metabolic phenotypes. CONCLUSIONS: Obesity remains a risk factor for depression, independent of whether other metabolic risk factors are present or whether participants convert to unhealthy metabolic phenotypes over time. Long-term maintenance of metabolic health and healthy body weight may be beneficial for the population mental well-being.

Metabolically healthy obesity, transition from metabolic healthy to unhealthy status, and carotid atherosclerosis.

BACKGROUND: Evidence of the effects of metabolically healthy obesity (MHO) on atherosclerosis is limited; the transition effects of metabolic health and obesity phenotypes have been ignored. We examined the association between metabolic health and the transition to atherosclerosis risk across body mass index (BMI) categories in a community population. METHODS: This cross-sectional study was based on a national representative survey that included 50,885 community participants aged ≥40 years. It was conducted from 01 December 2017 to 31 December 2020, in 13 urban and 13 rural regions across Hunan China. Metabolic health was defined as meeting less than three abnormalities in blood pressure, glucose, high-density lipoprotein cholesterol, triglycerides, or waist circumference. The participants were cross-classified at baseline based on their metabolic health and obesity. In addition, the relationship between atherosclerosis and transitions in metabolic health status based on 4733 participants from baseline to the second survey after 2 years was considered. The relationship between metabolic health status and the risk of transition to Carotid atherosclerosis (CA) was assessed using logistic regression and Cox proportional hazards regression analyses. RESULTS: In this study, the mean age of the participants was 60.7 years (standard deviation [SD], 10.91), 53.0% were female, and 51.2% had CA. As compared with metabolically healthy normal weight (MHN), those with MHO phenotype (odd ratio [OR] 1.10, 95% confidence interval [CI] 1.02-1.21), metabolically unhealthy normal weight (OR 1.27, 95% CI 1.19-1.35), metabolically unhealthy overweight (OR 1.41, 95% CI 1.33-1.48), and metabolically unhealthy obese (OR 1.54, 95% CI 1.44-1.64) had higher risk for CA. However, during the follow-up of 2 years, almost 33% of the participants transitioned to a metabolically unhealthy status. As compared with stable healthy normal weight, transition from metabolically healthy to unhealthy status (hazard ratios [HR] 1.21, 95% [CI] 1.02-1.43) and stable metabolically unhealthy overweight or obesity (MUOO) (HR 1.32, 95% CI 1.17-1.48) were associated with higher risk of CA. CONCLUSIONS: In the community population, obesity remains a risk factor for CA despite metabolic health. However, the risks were highest for metabolically unhealthy status across all BMI categories. A large proportion of metabolically healthy overweight or participants with obesity converts to an unhealthy phenotype over time, which is associated with an increased risk of CA.

Healthy overweight and obesity in the young: Prevalence and risk of major adverse cardiovascular events.

AIMS: To investigate the prevalence of metabolically healthy overweight/obesity and to study its longitudinal association with major adverse cardiovascular and renal events (MARCE). METHODS AND RESULTS: The study was conducted in 1210 young-to-middle-age subjects grouped according to their BMI and metabolic status. The risk of MARCE was evaluated during 17.4 years of follow-up. Forty-eight-percent of the participants had normal weight, 41.9% had overweight, and 9.3% had obesity. Metabolically healthy status was found in 31.1% of subjects with normal weight and in 20.0% of those with overweight/obesity. During the follow-up, there were 108 MARCE. In multivariate Cox analysis adjusted for confounders and risk factors, no association was found between MARCE and overweight/obesity (p = 0.49). In contrast, metabolic status considered as a two-class variable (0 versus at least one metabolic abnormality) was a significant predictor of MARCE (HR, 2.11; 95%CI, 1.21-3.70, p = 0.009). Exclusion of atrial fibrillation from MARCE (N = 87) provided similar results (HR, 2.11; 95%CI, 1.07-4.16, p = 0.030). Inclusion of average 24 h BP in the regression model attenuated the strength of the associations. Compared to the group with healthy metabolic status, the metabolically unhealthy overweight/obesity participants had an increased risk of MARCE with an adjusted HR of 2.33 (95%CI, 1.05-5.19, p = 0.038). Among the metabolically healthy individuals, the CV risk did not differ according to BMI group (p = 0.53). CONCLUSION: The present data show that the risk of MARCE is not increased in young metabolically healthy overweight/obesity suggesting that the clinical approach to people with high BMI should focus on parameters of metabolic health rather than on BMI.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Association between alternative healthy eating index (AHEI) with metabolic health status in adolescents with overweight and obesity.

BACKGROUND: There has been lack of evidence on the association between healthy dietary patterns and metabolic health status of adolescents. The present study aimed to evaluate the association between alternative healthy eating index (AHEI) and metabolic health status among a relatively representative sample of Iranian adolescents with overweight/obesity. METHODS: Adolescents with extra body weight (n = 203, aged 12-18 y), were selected for this cross-sectional study by a multistage cluster random-sampling method. Habitual dietary intakes and diet quality of individuals were assessed using validated food frequency questionnaire and AHEI-2010, respectively. Data on other covariates were also gathered by pre-tested questionnaires. To determine fasting glucose, insulin and lipid profiles, fasting blood samples were collected. Participants were categorized as having metabolically healthy overweight/obesity (MHO) or metabolically unhealthy overweight/obesity (MUO) phenotypes, based on two approaches (International Diabetes Federation (IDF) and combination of IDF with Homeostasis Model Assessment Insulin Resistance (HOMA-IR)). RESULTS: The overall prevalence of MUO was 38.9% (based on IDF criteria) and 33.0% (based on IDF/HOMA-IR criteria). After considering all potential confounders, participants in highest tertiles of AHEI-2010 had lower odds of MUO profile according to both IDF (OR = 0.05; 95% CI: 0.01-0.15) and IDF/HOMA-IR (OR = 0.05; 95% CI: 0.02-0.19) definitions. This association was stronger in adolescents with overweight compared with obese ones and also among girls than boys. Moreover, each unit increase in AHEI-2010 score was associated with lower risk of MUO based on both criteria. CONCLUSIONS: Higher adherence to AHEI-2010 was inversely associated with odds of MUO in Iranian adolescents with overweight/obesity.

Risk of incident chronic kidney disease in metabolically healthy obesity and metabolically unhealthy normal weight: A systematic review and meta-analysis.

Studies have reported inconsistent results about the risk of incident chronic kidney disease (CKD) in people with metabolically healthy obesity (MHO). We designed this systematic review and meta-analysis to evaluate the risk of developing CKD in people with MHO and metabolically unhealthy normal weight (MUNW). We used a predefined search strategy to retrieve eligible studies from multiple databases up to June 20, 2022. Random-effects model meta-analyses were implied to estimate the overall hazard ratio (HR) of incident CKD in obesity phenotypes. Eight prospective cohort studies, including approximately 5 million participants with a median follow-up ranging between 3 and 14 years, were included in this meta-analysis. Compared to the metabolically healthy normal weight (MHNW), the mean differences in cardiometabolic and renal risk factors in MHO, MUNW, and metabolically unhealthy obesity (MUO) were evaluated with overall HR of 1.42, 1.49, and 1.84, respectively. Compared to MHNW, the mean estimated glomerular filtration rate (eGFR) and high-density lipoprotein (HDL) were significantly lower, and low-density lipoprotein (LDL), blood pressure, blood glucose, and triglycerides were higher in MHO and MUNW. In conclusion, MHO and MUNW are not benign conditions and pose a higher risk for incident CKD. Obesity, whether in the presence or absence of metabolic health, is a risk factor for CKD.

Predictive markers of metabolically healthy obesity in children and adolescents: can AST/ALT ratio serve as a simple and reliable diagnostic indicator?

This study aimed to estimate the prevalence of metabolically healthy obesity (MHO) according to two different consensus-based criteria and to investigate simple, measurable predictive markers for the diagnosis of MHO. Five hundred and ninety-three obese children and adolescents aged 6-18 years were included in the study. The frequency of MHO was calculated. ROC analysis was used to estimate the predictive value of AST/ALT ratio, waist/hip ratio, MPV, TSH, and Ft4 cut-off value for the diagnosis of MHO. The prevalence of MHO was 21.9% and 10.2% according to 2018 and 2023 consensus-based MHO criteria, respectively. AST/ALT ratio cut-off value for the diagnosis of MHO was calculated as ≥ 1 with 77% sensitivity and 52% specificity using Damanhoury et al.'s criteria (AUC = 0.61, p = 0.02), and 90% sensitivity and 51% specificity using Abiri et al.'s criteria (AUC = 0.70, p = 0.01). Additionally, using binomial regression analysis, only the AST/ALT ratio is independently and significantly associated with the diagnosis of MHO (p = 0.03 for 2018 criteria and p = 0.04 for 2023 criteria). CONCLUSION: The ALT/AST ratio may be a useful indicator of MHO in children and adolescents. WHAT IS KNOWN: • Metabolically healthy obesity refers to people who are obese but do not have any of the standard cardio-metabolic risk factors. • Metabolically healthy obesity is not entirely harmless; the metabolic characteristics of individuals with this phenotype are less favorable than those of healthy lean groups. Moreover, it is not a constant state, and there may be a transition to metabolically unhealthy phenotypes over time. WHAT IS NEW: • The prevalence of MHO is 21.9% and 10.2% according to 2018 and 2023 consensus-based metabolically healthy obesity criteria, respectively. • The ALT/AST ratio may be a useful indicator of metabolically healthy obesity in children and adolescents.

Association between Metabolically Healthy Status and Risk of Gastrointestinal Cancer.

PURPOSE: Although obesity is associated with numerous diseases, the risks of disease may depend on metabolically healthy status. Nevertheless, it is unclear to whether metabolically healthy status affects risk of gastrointestinal (GI) cancer in general Chinese population. MATERIALS AND METHODS: A total of 114,995 participants who met the criteria were included from the Kailuan Study. The study participants were divided into four groups according to body mass index (BMI)/waist circumference (WC) and metabolic status. Incident of GI cancer (esophageal cancer, gastric cancer, liver cancer, biliary cancer, pancreatic cancer, and colorectal cancer) during 2006-2020 were confirmed by review of medical records. The Cox proportional hazard regression models were used to assess the association metabolically healthy status with the risk of GI cancer by calculating the hazard ratios (HR) and 95% confidence interval (CI). RESULTS: During a mean 13.76 years of follow-up, we documented 2,311 GI cancers. Multivariate Cox regression analysis showed that compared with the metabolically healthy normal-weight group, metabolically healthy obese (MHO) participants demonstrated an increased risk of developing GI cancer (HR, 1.54; 95% CI, 1.11 to 2.13) by BMI categories. However, such associations were not found for WC category. These associations were moderated by age, sex, and anatomical site of the tumor. Individuals with metabolic unhealthy normal-weight or metabolic unhealthy obesity phenotype also have an increased risk of GI cancer. CONCLUSION: MHO phenotype was associated with increased risk of GI cancer. Moreover, individuals who complicated by metabolic unhealthy status have an increased risk of developing GI cancer. Hence, clinicians should consider the risk of incident GI cancer in people with abnormal metabolically healthy status and counsel them about metabolic fitness and weight control.

Metabolically healthy obese individuals are still at high risk for diabetes: Application of the marginal structural model.

AIM: To assess the effect of obesity phenotype on the incidence of diabetes, considering phenotype as a time-varying exposure. METHODS: We used community-based cohort data from the Korean Genome and Epidemiology Study, with a 16-year follow-up period. Obesity phenotype was determined using body mass index and metabolic syndrome criteria. The influence of obesity phenotype on the occurrence of diabetes was evaluated using a Cox proportional hazard model and a marginal structural model (MSM). RESULTS: Obesity phenotypes were defined in 6265 individuals, with diabetes identified in 903 (14.4%) during the follow-up period. Individuals with metabolically healthy obesity (MHO) exhibited a higher risk of diabetes compared to those with metabolically healthy normal weight (MHNW), with a hazard ratio (HR) of 1.48 (95% confidence interval [CI] 1.15-1.90). This association remained significant after applying the MSM (HR 1.49, 95% CI 1.01-2.20). Moreover, various sensitivity analyses consistently demonstrated a higher risk of diabetes in individuals with MHO compared to those with MHNW. CONCLUSIONS: Even when obesity phenotype was treated as a time-varying exposure, individuals with MHO were still at higher risk for developing diabetes than those with MHNW. Consequently, such individuals should aim to avoid transitioning to a metabolically unfavourable state and strive to reduce their body weight to a normal range.

Conversions between metabolically unhealthy and healthy obesity from midlife to late-life.

INTRODUCTION: Metabolically healthy obesity may be a transient phenotype, but studies with long follow-up, especially covering late-life, are lacking. We describe conversions between cross-categories of body mass index (BMI) and metabolic health in 786 Swedish twins with up to 27 years of follow-up, from midlife to late-life. METHODS: Metabolic health was defined as the absence of metabolic syndrome (MetS). We first visualized conversions between BMI-metabolic health phenotypes in 100 individuals with measurements available at ages 50-64, 65-79, and ≥80. Next, we modeled conversion in metabolic health status by BMI category in the full sample using Cox proportional hazards regression. RESULTS: The proportion of individuals with MetS and with overweight or obesity increased with age. However, one-fifth maintained a metabolically healthy overweight or obesity across all three age categories. Among those metabolically healthy at baseline, 59% converted to MetS during follow-up. Conversions occurred 56% more often among individuals with metabolically healthy obesity, but not overweight, compared to normal weight. Among those with MetS at baseline, 60% regained metabolic health during follow-up, with no difference between BMI categories. CONCLUSIONS: Conversions between metabolically healthy and unhealthy status occurred in both directions in all BMI categories. While conversions to MetS were more common among individuals with obesity, many individuals maintained or regained metabolic health during follow-up.

Is metabolic-healthy obesity associated with risk of dementia? An age-stratified analysis of the Whitehall II cohort study.

BACKGROUND: Metabolically healthy obesity is hypothesized to be a benign condition but whether this is the case for dementia remains debated. We examined the role of age at assessment of metabolic-obesity phenotypes in associations with incident dementia. METHODS: Obesity (body mass index ≥ 30 kg/m2) and poor metabolic health (≥ 2 of elevated serum triglycerides, low HDL-C, elevated blood pressure, and elevated serum fasting glucose) were used to define four metabolic-obesity phenotypes (metabolically healthy (MHNO) and unhealthy non-obesity (MUNO), metabolically healthy (MHO) and unhealthy obesity (MUO)) at < 60, 60 to < 70, and ≥ 70 years using 6 waves of data from the Whitehall II study and their associations with incident dementia was examined using Cox regression. RESULTS: Analyses with exposures measured < 60, 60 to < 70, and ≥ 70 years involved 410 (5.8%), 379 (5.6%), and 262 (7.4%) incident dementia cases over a median follow-up of 20.8, 10.3, and 4.2 years respectively. In analyses of individual components, obesity before 60 years (HR 1.41, 95% CI: [1.08, 1.85]) but not at older ages was associated with dementia; unhealthy metabolic status when present < 60 years (HR 1.33, 95% CI: [1.08, 1.62]) and 60 to < 70 years (HR 1.32, 95% CI: [1.07, 1.62]) was associated with dementia. Compared to the metabolically healthy non-obesity group, the risk of dementia was higher in those with metabolically healthy obesity before 60 years (1.69; 95% CI: [1.16, 2.45]); this was not the case when metabolic-obesity phenotype was present at 60 to < 70 years or ≥ 70 years. Analyses at older ages were on smaller numbers due to death and drop-out but inverse probability weighting to account for missing data yielded similar results. CONCLUSIONS: Individuals with metabolically healthy obesity before age 60 had a higher risk of incident dementia over a 27-year follow-up; the excess risk dissipates when metabolic health and obesity are measured after 70 years.

Metabolic health status and renal disorders: a cross-sectional study.

Previous surveys suggests that body mass index (BMI) may be positively related to development of chronic kidney disease (CKD). However, this association might be altered by metabolic syndrome. Therefore, we aimed to evaluate the association of metabolic health status with CKD. The present cross-sectional study was carried out on 3322 representative sample of Iranian adults. Metabolic syndrome was identified based on the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) and BMI was assessed by anthropometric measurements. Estimated glomerular filtration rate (eGFR) was calculated by modification of diet in renal disease-Chronic Kidney Disease Epidemiology Collaboration (MDRD-EPI) formula. Subjects were categorized into four phenotypes: metabolically healthy normal weight (MHNW), metabolically healthy overweight and obesity (MHO), metabolically unhealthy normal weight (MUHNW), and metabolically unhealthy overweight and obesity (MUHO). Based on multivariate-adjusted models, the risk of CKD was significantly higher in MUHO compared with MHNW (OR: 1.48; p < 0.05). Although MUHNW and MUHO were associated with lower eGFR and albuminuria, the significant association was not observed in case of hematuria. Furthermore, subjects with kidney stones tended to be in MHO (OR: 1.42; p < 0.05) and MUHO phenotypes (OR: 1.64; p < 0.05), in comparison to the MHNW phenotype. The odds of kidney disorders were higher in adults with metabolic syndrome, regardless of BMI. However, this relationship might be strengthened by the concomitance of metabolic syndrome and obesity. To verify our findings, clarify the causality, and elucidate the underlying mechanisms, further research are warranted.

Longitudinal association of serum 25-hydroxyvitamin D levels with metabolically healthy body size transition in children and adolescents: A prospective cohort study with 2 years of follow-up.

BACKGROUND AND AIMS: Although the associations of vitamin D with obesity and metabolic abnormalities have been reported, the role of vitamin D in the transition of obesity phenotype remains unclear but is highly desired since it is crucial to identify potential methods for obesity management. Therefore, we aimed to investigate the relationship between vitamin D and the risk for metabolically unhealthy obesity (MUO) or metabolically healthy obesity (MHO) in metabolically healthy children with 2 years of follow-up. METHODS: Data were collected from a population-based cohort consisting of 6424 metabolically healthy children aged 6-16 years at baseline. Metabolic abnormalities including hypertension, high triglycerides (TG), low high-density lipoprotein cholesterol (HDL-C), hyperglycemia, and hyperuricemia were assessed both at baseline and follow-up. Baseline serum 25-hydroxyvitamin D (25[OH]D) concentrations were measured as exposure. The obesity phenotype transition was evaluated by weight status with the combination of metabolic health status from baseline to follow-up. RESULTS: During a 2-year follow-up, 889 (13.8 %) incident MUO cases occurred. For participants with obesity, each 10 nmol/L increment in 25(OH)D concentrations was associated with a 21 % (95%CI: 13 %∼43 %) and a 7 % (95%CI: 1 %∼14 %) decreased risk in high TG and hyperuricemia, respectively. A 51 % (95%CI: 22 %∼69 %) lower risk of MUO was observed in participants with sufficient vitamin D levels (≥50 nmol/L) compared to those with vitamin D deficiency (<30 nmol/L). Besides, among children who were MHO at baseline, those with sufficient vitamin D levels (≥50 nmol/L) were more likely to transition to metabolically healthy normal weight (MHNW) than vitamin D deficient individuals (<30 nmol/L). CONCLUSIONS: Vitamin D may prevent the development of MUO and help increase the transition from MHO to MHNW. The findings highlight that vitamin D might be an effective nutrient for obesity management.

Association between metabolically healthy obesity and metastasis in lung cancer patients - a systematic review and meta-analysis.

Background: Many clinical trials have looked at the relationship between obesity and lung cancer (LC), however, there is scarcity of literature specifically addressing the association between metabolically healthy obesity and metastasis in LC patients. To address this gap in the body of evidence, the study was conducted to observe the association between metabolically healthy obesity and metastasis in LC patients. Methods: We conducted a pre-registered systematic review by searching six major online databases to identify studies relevant related to our investigation, in adherence with the PRISMA guidelines. A proper data extraction protocol was further established to synthesize the findings from the selected papers through a meta-analysis. Results: Eleven (11) studies met the requisite selection criterion and were included in the study. A random-effect model was used. Obesity was found to have a significant impact on readmission in LC patients. The combined analysis showed a significant effect size of 0.08 (95% CI 0.07 to 0.08), indicating a noticeable impact of obesity. It was also assessed that obese individuals had a 34% reduced risk of LC compared to normal weight individuals. Obesity was associated with a lower risk of surgical complications with a pooled risk ratio of 0.13 (95% CI 0.12 to 0.14). A statistically significant decreased risk of LC (pooled RR = 0.72, 95% CI: 0.68 to 0.77) was also observed in the obese individuals. Conclusion: The analysis reveals that obesity is associated with a noticeable increase in readmissions, although the impact on LC risk itself is negligible. Moreover, obesity appears to have a beneficial effect by reducing the risk of surgical complications. These results highlight the complex relationship between the two aforementioned factors, emphasizing the importance of considering obesity as a significant factor in patient management and healthcare decision-making. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023427612.

Adherence to the Mediterranean diet as a possible additional tool to be used for screening the metabolically unhealthy obesity (MUO) phenotype.

BACKGROUND: The terms metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) categorize subjects with obesity based on the presence or absence of cardio-metabolic risk factors. Detecting MUO phenotype is crucial due to the high risk of cardio-metabolic complications, requiring tailored and intensive follow-up. However, diagnosing MUO is time-consuming and costly. Thus, we aimed to investigate the role of Mediterranean diet (MD) in determining MHO/MUO phenotypes and whether adherence to MD could serve as an additional screening tool for MUO phenotype. METHODS: The study population of this cross-sectional observational study consisted of 275 subjects with obesity. We assessed their lifestyle habits (physical activity and smoking habits), anthropometric measurements (weight, height, waist circumference, body mass index), blood pressure, metabolic parameters, inflammatory marker (high sensitivity C reactive protein levels), adherence to MD (by PREvención con DIetaMEDiterránea (PREDIMED) questionnaire), and MHO/MUO phenotypes. RESULTS: The study included 275 individuals with obesity (256F/19M; 34.0 ± 10.5 years; BMI 38.3 ± 5.95 kg/m2). Among them, 114 (41.5%) exhibited MHO phenotype, while 161 (58.5%) had MUO phenotype. MHO phenotype exhibited favorable anthropometric and cardio-metabolic profiles, characterized by lower waist circumference (p < 0.001), BMI (p < 0.001), insulin resistance (p < 0.001), blood pressure (p < 0.001), inflammation (p < 0.001), and lipid levels (p < 0.001) compared to MUO phenotype. Notably, we found that MHO phenotype had higher adherence to MD (p < 0.001) and consumed more extra virgin olive oil (EVOO) (p < 0.001), vegetables (p < 0.001), fruits (p < 0.001), legumes (p = 0.001), fish (p < 0.001), wine (p = 0.008), and nuts (p = 0.001), while reporting lower intake of red/processed meats (p < 0.001), butter, cream, margarine (p = 0.008), soda drinks (p = 0.006), and commercial sweets (p = 0.002) compared to MUO phenotype. Adherence to MD (p < 0.001) and EVOO (p = 0.015) intake were identified as influential factors in determining the presence of MUO/MHO phenotypes. Furthermore, a PREDIMED score < 5 proved to be the most sensitive and specific cut-point value for predicting the presence of MUO phenotype (p < 0.001). CONCLUSION: High adherence to MD was associated with MHO phenotype. Moreover, we suggest that a specific cut-off of the PREDIMED score could be an indicator to discriminate patients with MUO/MHO phenotypes and therefore help in identifying patients at higher cardiovascular risk who will require specific dietary intervention.

Macronutrient quality and the incidence of metabolically unhealthy phenotypes in adults with normal weight and overweight/obesity.

OBJECTIVE: We aimed to investigate the associations of macronutrient quality indices with the incident metabolically unhealthy normal weight (MUNW) and metabolically unhealthy overweight/obesity (MUO) phenotypes. METHODS: This prospective study included 512 metabolically healthy normal weight and 787 metabolically healthy overweight/obese adults from the third study examination of the Tehran Lipid and Glucose Study. The participants were followed through the sixth study examination. Diet was measured with a food frequency questionnaire. The macronutrient quality index (MQI), carbohydrate quality index (CQI), fat quality index (FQI), and healthy plate quality index (HPPQI) were calculated. Hazard ratio (HR) and 95 % confidence interval (95 % CI) were estimated for incident unhealthy phenotypes using Cox regression. RESULTS: After controlling all possible confounding factors, a one-point higher HPPQI was linked to a 28 % lower risk of MUNW (HR = 0.72; 95 % CI = 0.59, 0.87). Compared to the lowest quartile, the incident MUNW was also lower in the two last quartiles of the HPPQI. A one-unit increase in MQI was associated with a 5 % lower incident MUO (HR = 0.95; 95 % CI = 0.92, 0.99). The incident MUO was also higher for the highest compared to the lowest MQI quartile. In quartiles 2-4 of the HPPQI, incident MUO was lower with respective HRs (95 % CI) of 0.71 (0.54, 0.93), 0.60 (0.45, 0.80), and 0.66 (0.50, 0.86) in the fully-adjusted model. CONCLUSIONS: A higher overall macronutrient quality was independently associated with a lower incident MUO. A higher dietary protein quality was related to a lower risk for MUNW and MUO.

Risk of Cardiovascular Diseases Among Different Metabolic Obesity Phenotypes: A Prospective Observational Study.

Objectives: Various diseases are associated with obesity and metabolism. We sought to investigate the risk of cardiovascular disease (CVD) in diverse metabolic obesity phenotypes. Methods and Results: A prospective observational study of 1517 participants ≥25 years of age without CVD at baseline was conducted. Participants were categorized into four groups based on the condition of central obesity and metabolic health status: metabolically healthy normal weight, metabolically healthy obesity (MHO), metabolically unhealthy normal weight, and metabolically unhealthy obese (MUO). A multivariate Cox regression analysis was used to analyze the relationship between different obesity phenotypes and CVD. During 14830.49 person-years of follow-up, there were 244 incident cases of CVD. Of the 1517 participants, 72 (4.75%) and 812 (53.53%) were classified as having MHO and MUO, respectively. MHO and MUO had a tendency toward a higher risk of CVD [adjusted hazard ratios (HRs) = 1.49, 95% confidence interval (CI): 1.11-2.02 and HR = 1.25, 95% CI: 1.00-1.55, respectively] based on the waist circumference criterion. Conclusion: MHO and MUO can increase the risk of CVD.