The study evaluates the impact of body mass index (BMI), heart rate and rhythm on coronary artery calcium scoring (CACS) derived from calcium-sensitive virtual non-contrast (VNC) series of photon-counting detector (PCD) computed tomography angiography (CTA) compared to true non-contrast (TNC) series. Patients who underwent cardiac imaging with TNC and CTA on a PCD-CT were included. Agatston scores from TNC and VNC images were used to assign CACS risk category. Analyses considered BMI, heart rhythm and heart rate. Distributions were tested for differences between TNC and VNC derived scores and their correlation was assessed. The final cohort included 88 patients. CACS on VNC showed an underestimation of TNC derived values on median Agatston score TNC = 542 (IQR 200-1294), on median Agatston score VNC = 449 (IQR 130-1183), p < 0.001, percentage difference - 11%. However, linear correlation coefficient was high (r2 = 0.95), and the CAC severity was categorized equivalent in 80%. In approximately 11% of the study cohort, the misclassification of CAC severity could have potentially led to inappropriate treatment following established guidelines. An impact on the significance and extent of the difference in CACS for BMI > 28 kg/m2 and heart rate groups > 69 bpm was found. VNC reconstructions from PCD-CT reliably estimates TNC CACS for BMI ≤ 28 kg/m2 and heart rate ≤ 69 bpm in patients with severe coronary artery disease. Potential underestimation of risk category, especially with increased BMI and heart rate, must be considered for clinical decision making.

Metabolic syndrome (MetS) is a progressive chronic pathophysiological state characterised by abdominal obesity, hypertension, hyperglycaemia, and dyslipidaemia. It is recognised as one of the major clinical syndromes affecting human health, with approximately one-quarter of the global population impacted. MetS increases the risk of developing cardiovascular diseases (CVDs), stroke, type 2 diabetes mellitus (T2DM), and diverse metabolic diseases. Early diagnosis of MetS could potentially reduce the prevalence of these diseases. However, care for the MetS population faces significant challenges due to (i) a lack of comprehensive understanding of the full spectrum of associated diseases, stemming from unclear pathophysiological mechanisms and (ii) frequent underdiagnosis or misdiagnosis of MetS in clinical settings due to inconsistent screening guidelines, limited medical resources, time constraints in clinical practice, and insufficient awareness and training. The increasing availability of healthcare and medical data presents opportunities to apply and innovate with artificial intelligence (AI) in addressing these challenges. This review aims to (i) summarise the spectrum of diseases associated with MetS and (ii) review the diverse AI models applied to MetS and metabolic syndrome-related diseases (MetSRD), where MetSRD collectively refers to diseases and conditions directly associated with MetS.

Our review consists of two phases. Initially, we conducted a literature review on MetS to narrow down the spectrum of MetSRD based on the strength of clinical evidence. We then used the terms 'Metabolic Syndrome' and 'Machine Learning' in combination with the identified MetSRD for further refinement. In total, we identified 52 related diseases in the first phase and 36 articles in the second phase.

We identified a total of 52 MetSRD after the first phase, with T2DM, CVDs, and cancer being the top three. Among the 36 articles obtained in the second phase, we observed the following: (i) The criteria for MetS were inconsistent across the studies. (ii) The primary purpose of AI applications was to identify risk factors for diseases, thereby improving predictions for MetS or MetSRD. Traditional machine learning models, such as Random Forest and Logistic Regression, were found to be the most effective. (iii) In addition to the MetS criteria, AI models explored other factors, including demographic and physiological variables, dietary influences, lipidomic and proteomic indicators, and more.

This review underscores the significant link between MetS and a spectrum of diseases, with a particular focus on underreported conditions such as non-alcoholic fatty liver disease and stroke. Through the analysis of data from diverse sources, AI models, and MetS diagnostic criteria, additional indicators beyond traditional measures have been identified, emphasising the importance of combining both traditional and non-traditional markers to enhance the diagnostic and predictive capabilities for MetS and MetSRD. AI shows great potential in MetS research, particularly through the integration of multi-source data, including clinical metrics, genetic information, and omics data. The amalgamation of traditional machine learning and modern machine learning models is particularly promising, offering a balanced approach to model performance and data complexity. While international definitions provide global applicability, they may not be suitable for all populations and scenarios, necessitating flexible diagnostic criteria and adaptive, explainable algorithms. Ultimately, these will enable personalised diagnostics and targeted interventions.

Despite obesity being associated with negative metabolic and cardiovascular outcomes, there is a subgroup of individuals considered healthy. However, there are questions about the stability of the Metabolically Healthy Obesity phenotype. This is a longitudinal study using the ELSA-Brasil cohort, conducted from 2008/10-2017/19 aiming to describe the trajectory of metabolic status of individuals with obesity, as well as the factors associated with the transition into the unhealthy status. Metabolic status was determined using measures of blood pressure, fasting glucose/glycated hemoglobin, triglycerides, and HDL-cholesterol, no previous diagnosis of alteration in any of these parameters nor taking medication to control them. SPSS v.21.0 was used, considering p < 0.05 as significant. The sample consisted of 190 Metabolically Healthy Individuals with Obesity at baseline, of whom 75.8% transitioned to Metabolically Unhealthy status on the third wave of the study. The baseline data indicates that 8.6% of individuals with obesity were metabolically healthy, and in the follow-up, the prevalence was 5.5%. Alcohol use was a risk factor for metabolic status transition [RR: 1.359 (95%CI: 1.005-1.838)]. Also, each 1 cm increase in waist circumference contributed to a 1% increase in the risk of transitioning from healthy to unhealthy metabolic status [RR: 1.011 (95%CI: 1.004-1.018)]. Being a metabolically healthy individual with obesity is a transient state and alcohol consumption as well as increases in waist circumference are risk factors for the metabolic transition.

The relationship between uncommon phenotypes, such as metabolically healthy obesity and normal weight with metabolic syndrome (MetS), and cardiovascular disease (CVD) risk, remains unclear. We investigated how different combinations of body mass index (BMI) and MetS are associated with overall and specific CVDs and how the number of MetS components influences CVD risk in individuals with obesity.

We performed separate analyses and a meta-analysis of 36,233 individuals from four Swedish cohorts to assess the risk of incident CVDs across BMI/MetS combinations (normal-weight, overweight or obese/MetS yes or no). Participants were followed for CVDs and death through linkage to the Swedish National Registers. Compared to normal weight without MetS, overweight and obesity without MetS had most pronounced association with the risk of heart failure [multivariable hazard ratios, HR (95 % CI) = 1.37 (1.16-1.63) and 1.85 (1.37-2.48), respectively, p < 0.001]. In obese individuals, the risk of incident CVD (composite endpoint) increased with an increasing number of MetS components, but this relationship was not statistically significant in obese participants without additional MetS components, likely due to the small at-risk group. Normal-weight individuals with MetS had an increased risk of myocardial infarction [HR (95 % CI) 2.0 (1.51-2.64)], p < 0.001, and stroke [HR (95 % CI) 1.63 (1.17-2.28), p = 0.004].

Overweight and obesity without MetS showed a greater impact on the risk of heart failure, whereas normal-weight individuals with MetS had a higher risk of myocardial infarction and stroke. In obese individuals, CVD risk increased as the number of MetS components increased.

Current studies have presented conflicting findings regarding the associations between light at night (LAN) exposure and the risk of overweight/obesity, hypertension, and diabetes. Our study systematically summarized the evidence of the association between LAN exposure and the risk of overweight/obesity, hypertension, and diabetes. We searched five databases (PubMed, Web of Science, Embase, Scopus, and Cochrane) for observational studies published up to 1 August 2023. The pooled odds ratio (ORs) and 95% confidence intervals (CIs) were estimated by random-effects models for the association. Eighteen studies were included in the meta-analysis. Compared with the group with the lowest level of LAN, the group with the highest level of LAN is associated with an increased risk of overweight/obesity (pooled OR = 1.19, 95%CI: 1.13-1.26), hypertension (pooled OR: 1.86, 95% CI:1.28-2.72), and diabetes (pooled OR = 1.21, 95%CI: 1.07-1.31). Our meta-analysis demonstrated LAN exposure is associated with increased risk of overweight/obesity, hypertension, and diabetes.

This study aimed to evaluate the association of lactation duration with incident ischemic heart disease (IHD) and to determine the potential health gains from scaling up breastfeeding practice.

130,147 parous postmenopausal females without IHD were included at baseline (2004-2008) from the China Kadoorie Biobank study. Lactation duration was self-reported and measured as lifetime, per child, and first child, respectively. Incident IHD was identified during follow-up (2004-2015). The dose-response associations between lactation duration and IHD were examined using Cox models with restricted cubic splines. Stratification analyses were conducted by socioeconomic status (SES) and residence. The number of preventable IHD cases was estimated using the population attributable fraction and potential impact fraction in various scenarios.

The study shows that parous postmenopausal females who ever lactated have significantly lower risks of IHD, with adjusted hazard ratios (aHRs) varying from 0.71 (95%CI: 0.63-0.80) to 0.85 (95%CI: 0.75-0.96) for a lifetime, from 0.70 (0.63-0.78) to 0.82 (0.72-0.93) for per-child, and from 0.80 (0.74-0.87) to 0.92 (0.85-0.99) for the first-child, appearing as U-shaped associations. Similar associations are found in females with low SES and urban residence. The scaling up of breastfeeding to near-universal levels could have prevented up to 115,000 new IHD cases among Chinese females aged over 40 years in 2019.

Lactation demonstrates potential benefits in reducing IHD risk, appearing as U-shaped associations among Chinese parous postmenopausal females, especially for those with low SES in urban areas. Scaling up breastfeeding practices serves as a promising strategy for reducing the IHD burden in China.

This prospective study aimed to examine the individual and combined population attributable fractions (PAFs) of stroke and its subtypes associated with reproductive factors among Chinese postmenopausal women, highlighting variations across socioeconomic status (SES) stratas.

Data were from 138,873 Chinese postmenopausal women enrolled in the China Kadoorie Biobank. Reproductive factors evaluated in this study included early age at menarche, early age at menopause, advanced age at first live birth, high parity, history of stillbirth, history of miscarriage or termination, and non-lactation. PAFs were calculated using hazard ratios, estimated using Cox proportional hazard regression, and prevalence of the seven reproductive factors. PAF for each reproductive risk factor and combined PAFs for all factors were estimated in total population and across SES classes.

Of the 138,873 included participants, 17,042 developed strokes during a median follow-up period of 8.9 years. Across SES classes, the greatest attributable fractions of total stroke cases were observed for high parity among low-SES women (PAF 17.2%, 95% confidence interval [CI] 13.7%, 20.6%), history of miscarriage or termination among medium-SES women (PAF 11.4%, 95% CI 8.2%, 14.5%), and no history of lactation among high-SES women (PAF 3.1%, 95% CI 1.7%, 4.9%). A multiplicatively estimated 20.5% (95% CI 20.4%, 20.5%) and 3.1% (95% CI 1.7%, 4.9%) of stroke cases were attributable to the seven reproductive risk factors in low-SES and high-SES women, respectively.

A large fraction of stroke cases among Chinese postmenopausal women were associated with reproductive factors. Targeted cardiovascular prevention strategies are warranted among women with different SES to mitigate risks associated with different reproductive profiles.

Obesity directly contributes to the progression of cardiovascular disease, but little is known about the association and risk attribution of normal-weight obesity subtypes with the incidence of major vascular events (MVEs) and their subtypes.

This is a prospective cohort study based on the China Kadoorie Biobank (CKB). A total of 308,071 individuals with no prior vascular diseases or cancer were included at baseline. The incidence of MVEs and their subtypes were recorded during follow-up. Adjusted hazard ratios (HRs) for each disease were yielded by Cox regression.

During a median follow-up of 10.3 years, 62,040 MVEs occurred, with the adjusted HRs (95 % confidence intervals) were 1.11 (1.09-1.13) for normal-weight general obesity (NWGO), 1.27 (1.23-1.31) for normal-weight central obesity (NWCO), and 1.30 (1.27-1.33) for normal-weight central and general obesity (NWCGO). For subtypes of MVEs, increased waist circumference (WC) was associated with excess risk of ischaemic heart disease (IHD) independent of body fat percent (BF%) levels (HR range: 1.30-1.69 in men; 1.36-1.55 in women), while the risk plateaued with rising BF% within each WC quartile. However, even in men with lower WC (≤78 cm [median]), the risks of cerebrovascular disease (CeVD), particularly ischaemic stroke (IS), were increased with higher BF% (all P < 0.01). Conversely, in women, independent dose-response associations were primarily observed between increasing WC and CeVD, with the highest risk observed for IS (HR 1.38, 1.31-1.47).

This study provided novel, sex-specific evidence that normal-weight obesity subtypes were associated with distinct risks of subtypes of MVEs, with elevated risks predominantly attributable to WC in women and both WC and BF% in men.

Previous studies indicated that metabolic heterogeneity of obesity would affect the risk of cardiovascular disease (CVD). However, the alterations in CVD risk associated with transitions between various metabolic health statuses influenced by obesity status remain unclear.

We utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a longitudinal cohort study involving Chinese residents aged 45 years and older. Baseline data were collected in 2011-2012, with follow-up surveys conducted up to 2020. Participants in the study were categorized into four body mass index-metabolic phenotypes: metabolically healthy normal weight (MHNW), metabolically healthy overweight/obesity (MHOO), metabolically unhealthy normal weight (MUNW) and metabolically unhealthy overweight/obesity (MUOO). Transitions in these phenotypes over 4 years were analysed. Cox regression models were used to assess the associations of these phenotypes and their transitions with CVD incidence.

Among 7721 participants, 1353 (17.5%) developed CVD during the follow-up period. Both overweight/obese and metabolically unhealthy statuses were associated with increased CVD risk. The highest risk was observed in the MUOO group (hazard ratio [HR]: 1.74, 95% confidence interval [CI]: 1.50-2.09, p < 0.0001), followed by the MUNW (HR 1.35, 95% CI: 1.13-1.66, p < 0.001) and MHOO (HR: 1.29, 95% CI: 1.08-1.56, p = 0.002) groups compared to the MHNW group. The deteriorations of obesity and metabolic health status elevated the incidence of CVD, whereas improvements in these statuses reduced the risk of CVD. Additionally, alterations in metabolic health status conferred greater benefits in overweight/obese individuals compared to those with normal weight.

The study highlights the importance of maintaining and promoting metabolic health, particularly in overweight/obese individuals, to reduce CVD risk. Metabolic health status plays a more crucial role than obesity status in predicting CVD incidence.

To investigate the association of metabolic status newly defined or obesity with asymptomatic intracranial arterial stenosis (aICAS) among populations in rural China.

The cross-sectional study is based on the Rose asymptomatic IntraCranial Artery Stenosis (RICAS) cohort, which enrolled 2005 participants aged 40 years or older without a history of clinical stroke or transient ischemic attack. Metabolically healthy status (MH) was defined by a newly proposed criterion: (1) systolic blood pressure (SBP) < 130 mmHg and without antihypertensive medication; (2) a waist-to-hip ratio (WHR) below 1.03 for men and below 0.95 for women; (3) no diabetes. All participants were categorized based on their metabolic status and obesity. Multivariate logistic regression models were used to investigate the association between metabolic status or obesity and aICAS.

Among 2005 participants, 1597 (79.65%) were defined as metabolically unhealthy status (MU) according to the new criterion. MU was significantly associated with aICAS (OR 2.02, 95% CI 1.11-3.68, P = 0.021), especially moderate-to-severe aICAS (OR 2.43, 95% CI 1.04-5.72, P = 0.042). The prevalence of aICAS increased with the numbers of metabolic disorders (P for linear trend <0.001). Both metabolically unhealthy normal-weight (MUN) (OR 2.11, 95% CI 1.10-4.03, P = 0.025) and metabolically unhealthy obesity (MUO) (OR 3.30, 95% CI 1.64-6.64, P = 0.001) were significantly correlated with aICAS, but not metabolically healthy obesity (MHO). Subgroup analysis further confirmed the association between MU and aICAS risk only in men (P for interaction = 0.042).

MU defined by the new criterion was significantly associated with aICAS, especially with moderate-to-severe aICAS. This novel criterion effectively identifies individuals with a high prevalence of aICAS among populations with obesity, which could be crucial for stroke prevention.

Childhood obesity poses a significant public health challenge, yet the molecular intricacies underlying its pathobiology remain elusive. Leveraging extensive multi-omics profiling (methylome, miRNome, transcriptome, proteins and metabolites) and a rich phenotypic characterization across two parts of Europe within the population-based Human Early Life Exposome project, we unravel the molecular landscape of childhood obesity and associated metabolic dysfunction. Our integrative analysis uncovers three clusters of children defined by specific multi-omics profiles, one of which characterized not only by higher adiposity but also by a high degree of metabolic complications. This high-risk cluster exhibits a complex interplay across many biological pathways, predominantly underscored by inflammation-related cascades. Further, by incorporating comprehensive information from the environmental risk-scape of the critical pregnancy period, we identify pre-pregnancy body mass index and environmental pollutants like perfluorooctanoate and mercury as important determinants of the high-risk cluster. Overall, our work helps to identify potential risk factors for prevention and intervention strategies early in the life course aimed at mitigating obesity and its long-term health consequences.

Excessive sugar intake increases the energy metabolic burden and the risk of cardiovascular disease (CVD). Patients on peritoneal dialysis absorb much more glucose than the World Health Organization recommends, but the link to CVD is unclear.

To identify the association between peritoneal glucose absorption, lipid metabolism, and CVD.

We applied generalized additive mixed effects and mixed effects Cox proportional hazard models to evaluate the impact of peritoneal glucose absorption on lipid profiles and CVD risk. We performed subgroup analyses by using protein intake (normalized protein nitrogen appearance [nPNA] and normalized protein catabolic rate [nPCR] were used to assess protein intake) and high-sensitivity C-reactive protein (hs-CRP).

After multivariable adjustment, peritoneal glucose absorption per 10 g/d increase was associated with an increase in cholesterol of 0.145 (95% confidence interval [CI]: 0.086-0.204) mmol/L. No link with the total risk of CVD was observed; however, protein intake and hs-CRP levels affected the relationship between glucose absorption and CVD risk. Patients with values for nPNA and nPCR <1.0 g/(kg·d) were associated with a lower risk of CVD (hazard ratio [HR] 95% CI: 0.68 (0.46-0.98)) with glucose absorption per 10 g/d increase. While patients with hs-CRP levels ≥3 mg/d or values for nPNA or nPCR ≥1.0 g/(kg·d) were associated with a higher risk of CVD (HR 95% CI: 1.32 (1.07-1.63); 1.31 (1.02-1.68)) for glucose absorption per 10 g/d increase.

Our study found a positive correlation between peritoneal glucose absorption and lipid profiles. Increased glucose absorption was associated with a lower risk of CVD in lower protein intake patients and a higher risk of CVD in higher hs-CRP or protein intake levels in patients on peritoneal dialysis.

The relationship between metabolically healthy obesity (MHO) and cardiovascular disease (CVD) risk remains debated. The critical point may be the lack of consensus on MHO's definition and diagnostic criteria.

This study aimed to investigate the association of MHO status with arteriosclerosis-CVD (ASCVD) risk in Chinese under new diagnostic criteria.

Participants aged 35-79 in the 2009 China Health and Nutrition Survey cohort were included. The 10-year ASCVD risk was predicted by the prediction for ASCVD risk in China, and participants with a predicted risk of ⩾ 10% were classified into the high-risk group. The Bayesian network (BN) models were constructed to characterize the multivariable probabilistic connections between metabolically obesity phenotypes and ASCVD risk.

The 10-year ASCVD risk score and the proportion of individuals at ASCVD high risk were significantly different between metabolically obesity phenotypes (P< 0.001). BN reasoning results showed that MHO individuals were not significantly associated with a 10-year ASCVD risk. Among metabolically unhealthy individuals, the conditional probability of high ASCVD risk increased with the Body Mass Index (BMI), with the conditional probability of high ASCVD risk was 24.63% (95% CI: 22.81-26.55%), 32.97% (95% CI: 30.75-35.27%) and 40.2% (95% CI: 36.64-43.86%) for metabolically unhealthy normal weight (MUNW), metabolically healthy overweight weight (MHOW), and metabolically unhealthy obesity (MUO) group, respectively. Subgroup analysis showed that MHO individuals were at increased risk of CVD compared with metabolically healthy normal weight (MHNW) individuals only in females.

These results showed that there was no significant increase in ASCVD risk of MHO phenotype based on the new diagnostic criteria, suggesting that MHO is in a relatively healthy state.

Previous evidence showed that ambient air pollution and cardiovascular mortality are related. However, there is a lack of evidence towards the modification effect of long-term lifestyle on the association between short-term ambient air pollution and death from cardiovascular events.

A total of 14,609 death from major adverse cardiovascular events (MACE) were identified among the China Kadoorie Biobank participants from 2013 to 2018. Ambient air pollution exposure including particulate matter 2.5 (PM2.5), SO2, NO2, CO, and O3 from the same period were obtained from space-time model reconstructions based on remote sensing data. Case-crossover design and conditional logistic regression was applied to estimate the effect of short-term exposure to air pollutants on MACE mortality.

We found MACE mortality was significantly associated with PM2.5 (relative percent increase 2.91% per 10 µg/m3 increase, 95% CI 1.32-4.53), NO2 (5.37% per 10 µg/m3 increase, 95% CI 1.56-9.33), SO2 (6.82% per 10 µg/m3 increase, 95% CI 2.99-10.80), and CO (2.24% per 0.1 mg/m3 increase, 95% CI 1.02-3.48). Stratified analyses indicated that drinking was associated with elevated risk of MACE mortality with NO2 and SO2 exposure; physical inactivity was associated with higher risk of death from MACE when exposed to PM2.5; and people who had balanced diet had lower risk of MACE mortality when exposed to CO and NO2.

The study results showed that short-term exposure to ambient PM2.5, NO2, SO2, and CO would aggravate the risk of cardiovascular mortality, yet healthy lifestyle conduct might mitigate such negative impact to some extent.

Preserved ratio impaired spirometry (PRISm) and airflow obstruction are recognized as critical early signs of chronic obstructive pulmonary disease (COPD). While these conditions arise from concurrent exposure to toxicants and essential nutrients, how vitamin D modifies the pulmonary toxicity induced by polycyclic aromatic hydrocarbons (PAHs) and the metabolic mechanisms involved is still unclear.

Based on the National Health and Nutrition Examination Survey (NHANES) 2007-2012, data on urinary PAH metabolites (ΣOH-PAHs), serum vitamin D metabolite levels [Σ25(OH)D], and pulmonary function tests [forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and FEV1/FVC] from 2189 participants, including 369 subjects with early lung dysfunction, defined as PRISm or airflow obstruction. Multiple metabolic disorder indicators were calculated using biochemical markers. The interaction effects between vitamin D and PAHs were evaluated using multiple linear and logistic regression models. Causal mediation analyses and structural equation modeling were employed to investigate the mediating roles of metabolic indicators.

PAHs and vitamin D had opposite effects on lung function parameters [FEV1: β (95 CIs) = -0.01 (-0.02, -0.01) vs. 0.01 (0.01, 0.04); FVC: β (95 CIs) = -0.01 (-0.02, 0.01) vs. 0.04 (0.01, 0.06)] and risk of early lung dysfunction [OR (95 CIs) = 1.22 (1.06, 1.40) vs. 0.52 (0.37, 0.73)]. Decreased associations of ΣOH-PAHs with FEV1, FVC, and early lung dysfunction, as well as with metabolic score-visceral adiposity index (MSV) were visualized with increased Σ25(OH)D among subjects with body mass index (BMI) < 28 kg/m2. Furthermore, 2.18 %, 18.20 %, 5.70 %, and 4.70 % of the associations of co-exposure to ΣOH-PAHs and Σ25(OH)D with FEV1, FVC, FEV1/FVC, and early lung dysfunction disease were mediated by MSV.

Our findings indicated that vitamin D antagonizes the hazardous effects of PAHs on early lung dysfunction by metabolic alteration, providing new insight into the identification of the underlying high-risk populations and accessible prevention and intervention measures for attenuating PAH-induced lung dysfunction.

To evaluate the associations between biological aging, metabolic heterogeneity of obesity, and rheumatoid arthritis (RA).

This prospective cohort study analyzed 268,184 individuals from the UK Biobank. Biological age was estimated using phenotypic age (PhenoAge), Klemera-Doubal methods (KDM-BA), and telomere length. We calculated KDM-BA acceleration and PhenoAge acceleration after subtracting the effect of chronological age by regression residual. The metabolic heterogeneity of obesity can be evaluated by four BMI metabolic phenotypes, namely metabolically unhealthy normal weight (MUNW), metabolically healthy normal weight (MHNW), metabolically unhealthy overweight/obesity (MUOO), and metabolically healthy overweight/obesity (MHOO). Cox models were employed to estimate the associations between biological aging, metabolic heterogeneity of obesity, and RA risk.

A total of 2842 patients experienced RA during a mean follow-up time of 12.21 years. A standard deviation (SD) increase in KDM-BA acceleration and PhenoAge acceleration was associated with an increased risk of RA by 13% (hazard ratio = 1.13; 95% CI, 1.09-1.17) and 39% (HR = 1.39; 95% CI, 1.34-1.44), respectively. A SD increase in telomere length was associated with a reduced risk of RA by 5% (HR = 0.95; 95% CI, 0.91-0.98). Compared to the MHNW group, the MUOO group was associated with a 51% increase in the risk of incident RA. In the joint effect analysis, compared to the MHNW + KDM-BA younger subgroup, the HR (95% CI) for RA was 1.68 (1.48, 1.90) in the MUOO + KDM-BA older subgroup.

Accelerated biological aging may heighten the susceptibility to RA, particularly in individuals with obesity or metabolic dysfunction. Key Points •Accelerated biological aging increases the risk of developing RA. •Overweight/obese people with a healthy metabolism have a higher risk of RA than those with normal weight and healthy metabolism. •The BMI metabolic phenotype has a strong modifying effect on the association between KDM-BA/PhenoAge and RA risk.

Chronological age (CA) does not reflect individual variation in the aging process. However, existing biological age predictors are mostly based on European populations and overlook the widespread nonlinear effects of clinical biomarkers.

Using data from the prospective Dongfeng-Tongji (DFTJ) cohort of elderly Chinese, we propose a physiological aging index (PAI) based on 36 routine clinical biomarkers to measure aging progress. We first determined the optimal level of each biomarker by restricted cubic spline Cox models. For biomarkers with a U-shaped relationship with mortality, we derived new variables to model their distinct effects below and above the optimal levels. We defined PAI as a weighted sum of variables predictive of mortality selected by a LASSO Cox model. To measure aging acceleration, we defined ΔPAI as the residual of PAI after regressing on CA. We evaluated the predictive value of ΔPAI on cardiovascular diseases (CVD) in the DFTJ cohort, as well as nine major chronic diseases in the UK Biobank (UKB).

In the DFTJ training set (n = 12,769, median follow-up: 10.38 years), we identified 25 biomarkers with significant nonlinear associations with mortality, of which 11 showed insignificant linear associations. By incorporating nonlinear effects, we selected CA and 17 clinical biomarkers to calculate PAI. In the DFTJ testing set (n = 15,904, 5.87 years), PAI predict mortality with a concordance index (C-index) of 0.816 (95% confidence interval, [0.796, 0.837]), better than CA (C-index = 0.771 [0.755, 0.788]) and PhenoAge (0.799 [0.784, 0.814]). ΔPAI was predictive of incident CVD and its subtypes, independent of traditional risk factors. In the external validation set of UKB (n = 296,931, 12.80 years), PAI achieved a C-index of 0.749 (0.746, 0.752) to predict mortality, remaining better than CA (0.706 [0.702, 0.709]) and PhenoAge (0.743 [0.739, 0.746]). In both DFTJ and UKB, PAI calibrated better than PhenoAge when comparing the predicted and observed survival probabilities. Furthermore, ΔPAI outperformed any single biomarker to predict incident risks of eight age-related chronic diseases.

Our results highlight the potential of PAI and ΔPAI as integrative biomarkers to evaluate aging acceleration and facilitate the development of targeted intervention strategies for healthy aging.

Numerous studies have indicated an obesity paradox in observational research on aging health, where being normal weight or underweight adversely affects cognitive function, while moderate obesity may offer protective benefits. This study aims to investigate the association between body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), abdominal volume index (AVI), and the joint effect of BMI and HC on cognitive impairment in older Chinese people.

A total of 10,579 participants aged 65 years and older from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) were included in this cross-sectional study. BMI, WC, HC, WHtR, WHR, and AVI were calculated from height, weight, WC, and HC measurements, where weight, WC, and HC were obtained by direct measurement. Mini-Mental State Examination was used to assess cognitive impairment. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated using binary logistic regression. Non-linear correlations were investigated using restricted cubic spline curves.

In multivariate logistic regression models fully adjusted for confounding variables, our analyses showed significant negative associations of WC [OR 0.93 (95%CI 0.88-0.98), P = .012], HC [OR 0.92 (95%CI 0.87-0.97), P = .004], lower WHR (Q2) [OR 0.85 (95%CI 0.72-1.00), P = .044], and AVI [OR 0.93 (95%CI 0.88-0.98), P = .011] with cognitive impairment. Nonlinear curve analysis showed that the risk of cognitive impairment was lowest when the BMI was about 25.5 kg/m², suggesting that the optimal BMI for older Chinese people to maintain good cognitive ability may be in the overweight range. In addition, there was a non-linear "N" shaped relationship between HC and cognitive impairment, with HC having the highest risk of cognitive impairment at about 82 cm and the lowest risk at about 101 cm. The joint effects analysis indicated that the lowest risk was observed among those with normal or higher BMI but higher HC compared with participants with normal BMI levels and lower HC levels.

In older Chinese people, a low-waisted and high-hip circumference body figure is favorable for cognitive function in older people. It also found a significant association between AVI and cognitive impairment. The joint analysis of BMI and HC suggests that maintaining a normal or higher BMI with a higher HC may be more conducive to maintaining good cognitive function.

To describe weight management and perceptions in China.

Data were from the Adelphi Real World Obesity Disease Specific Programme™, a cross-sectional survey between April and July 2022 of physicians managing people with obesity or overweight (PwO) and PwO in real-world clinical practice in China. At data collection, eligible PwO were aged ≥18 years, under weight management and/or had a body mass index (BMI) ≥28 kg/m2.

In total, 100 physicians and 801 PwO were enrolled. More than two thirds of PwO (70.7%; 531/751) were not diagnosed with obesity until a BMI ≥30 kg/m2. Most PwO (78%; 625/801) were on treatment for at least one obesity-related complication (ORC). Physicians commonly initiated weight loss discussions with PwO who already had an ORC (48.0%; 48/100). According to physicians and PwO, the mean target BMI was set at 25.8 kg/m2 and 24.3 kg/m2, and the mean target percentage weight loss was 19.6% and 23.7%, respectively. Over a median 6.4 months of weight management, the mean percentage weight loss was 4.1%. Few PwO achieved the weight loss target set by their physician (9.9%; 69/695) or themselves (2.0%; 14/696). Most physicians and PwO were unsatisfied with the current weight loss (92.3% [739/801] and 82.0% [650/793], respectively).

These findings suggest that earlier intervention may be needed to address obesity as a disease. Most physicians and PwO recognized the importance of normal weight, but few PwO achieved weight loss targets, which may suggest an unmet need for improved weight management.

This study aimed to identify the main dietary patterns in Hechuan and clarify how they are associated with obesity phenotypes.

A cross-sectional study was conducted based on a baseline survey of a general population cohort study in southwest China. A semi-quantitative food frequency questionnaire (FFQ) was used to investigate the dietary habits of the participants in the past year. Principal component analysis was conducted to identify the main dietary patterns, and multinomial logistic regression analysis was conducted to describe the association between the major dietary patterns and obesity phenotypes.

Three major dietary patterns were identified. The participants who followed the wheaten food dietary pattern had a higher likelihood of having metabolically normal obesity (MHO) (odds ratio (OR) 1.05, 95% confidence interval (CI) 1.02-1.08), metabolically abnormal normal weight (MUNW) (OR 1.08, 95%CI 1.00-1.16), and metabolically abnormal obesity (MUO) (OR 1.07, 95%CI 1.04-1.11). Specifically, those with the highest wheaten food dietary pattern were 1.60 times more likely to have MHO (OR 1.60, 95%CI 1.25-2.05), 2.62 times more likely to have MUNW (OR 2.62, 95%CI 1.28-5.37), and 2.01 times more likely to have MUO (OR 2.01, 95%CI 1.51-2.69) than those with the lowest wheaten food dietary pattern.

The wheaten food dietary pattern may increase the risk of obesity and metabolic abnormalities. Therefore, timely interventions should be carried out for this group of people.

Obesity and metabolic abnormalities were associated with an increased risk of cardiovascular disease. However, it is unclear how metabolic weight phenotypes relate to cardiovascular diseases in postmenopausal women. This study aimed to explore the relationships in postmenopausal women.

We included 15,575 postmenopausal women aged 35-75 years (median age, 60.6) without cardiovascular disease at baseline from a subcohort of the China Patient-centered Evaluative Assessment of Cardiac Events Million Persons Project. Metabolically unhealthy phenotype was defined as having ≥2 risk factors of metabolic syndrome: blood pressure ≥130/85 mm Hg or current use of antihypertensive drugs, fasting glucose ≥5.6 mmol/L or current use of antidiabetic agents, triglycerides ≥1.7 mmol/L, and high-density lipoprotein cholesterol <1.3 mmol/L. Cox regression analysis was used to evaluate the risks of cardiovascular diseases. Over a median follow-up period of 3.55 (interquartile range, 2.59-4.44) years, a total of 1354 cardiovascular events occurred. Compared to metabolically healthy normal weight, the multivariate-adjusted hazard ratios and their 95% confidence intervals were 1.41 (1.16-1.72) for metabolically unhealthy normal weight, 1.42 (1.16-1.73) for metabolically healthy overweight/obesity, and 1.75 (1.48-2.08) for metabolically unhealthy overweight/obesity. Subdividing overweight/obesity into separate groups revealed higher total cardiovascular disease risk only in metabolically unhealthy individuals across body mass index categories.

In postmenopausal women, both metabolically healthy overweight/obesity and metabolically unhealthy normal weight were associated with a higher risk of cardiovascular disease compared to metabolically healthy normal weight, and the greatest risk was observed in the metabolically unhealthy overweight/obesity category.

The concept of metabolic health, particularly in obesity, has attracted a lot of attention in the scientific community, and is being increasingly used to determine the risk of cardiovascular diseases and diabetes mellitus-related complications. This Review assesses the current understanding of metabolically healthy obesity (MHO). First, we present the historical evolution of the concept. Second, we discuss the evidence for and against its existence, the usage of different definitions of MHO over the years and the efforts made to provide novel definitions of MHO. Third, we highlight epidemiological data with regard to cardiovascular risk in MHO, which is estimated to be moderately elevated using widely used definitions of MHO when compared with individuals with metabolically healthy normal weight, but potentially not elevated using a novel definition of MHO. Fourth, we discuss novel findings about the physiological mechanisms involved in MHO and how such knowledge helps to identify and characterize both people with MHO and those with metabolically unhealthy normal weight. Finally, we address how the concept of MHO can be used for risk stratification and treatment in clinical practice.

Numerous studies have indicated a growing prevalence of hyperuricemia. Elevated levels of serum uric acid (SUA) have been established as influential factors in conditions such as obesity, metabolic syndrome, diabetes mellitus, gout, and cardiovascular disease. Overweight and obesity are closely related to an increase in SUA. Our objective is to demonstrate the mediating role of liver enzyme in the correlation between body mass index (BMI) and SUA.

A total of 5925 adults aged 18 to 65 were included in this cross-sectional study. Logistic regression and mediation analysis were used to investigate the relationship between BMI and hyperuricemia as well as liver enzyme levels. Standard methods were used to determine the biochemical indexes, including SUA, liver enzymes, and blood lipids in the collected samples.

The study revealed that the prevalence of hyperuricemia was 28.0%. Furthermore, the prevalence of overweight and obesity was as high as 48.5%, with 70.7% of this subgroup presenting with hyperuricemia. There was a positive correlation between BMI and hyperuricemia, and elevated levels of liver enzymes (ALT, AST, GGT) were associated with a higher risk of hyperuricemia. The study also observed a positive correlation between BMI and liver enzymes (ALT, AST, GGT). The study findings suggested that ALT, AST, and GGT played significant mediating roles in the relationship between BMI and SUA. Specifically, the unadjusted model revealed that ALT and GGT accounted for 22.12% and 18.13% of the mediation effects, respectively.

The study found that BMI is associated with hyperuricemia, where liver enzyme abnormalities may have a mediating role. It is suggested that being overweight or obese may affect liver enzyme levels, leading to increased SUA levels. Controlling weight and liver enzyme levels may help prevent and treat hyperuricemia.

From January to April 2020, the COVID-19 pandemic affected eastern China, leading medical students to adopt home protection measures that significantly altered their physical activity and lifestyle habits. This study aimed to assess the prevalence of musculoskeletal pain (MSP) among medical students during home protection and to analyze the influence of demographic factors, physical exercise habits during normal school life, home protection data on MSP. This research provides a foundation for developing interventions to reduce MSP among medical students. A cross-sectional survey was conducted at Nanjing Medical University in Jiangsu Province from January to April 2020, utilizing the Nordic Musculoskeletal Questionnaire (NMQ) to measure MSP. Multivariate logistic regression identified factors influencing MSP. A total of 859 students participated, with a 40.2% prevalence of MSP during home protection. Multivariate analysis showed that female sex (OR: 1.648; 95% CI: 1.157-2.347), irregular physical exercise during school (OR: 1.648; 95% CI: 1.157-2.347), and less than or equal to six hours of sleep per day during home protection (OR: 1.474; 95% CI: 1.020-2.131) were significant risk factors. The high prevalence of MSP underscores the need for interventions that focus on gender-specific risks, promote regular physical exercise, and ensure adequate sleep.

The association between obesity indicators and sleep quality remains unclear among elderly Chinese people. Therefore, we aimed to assess this association by utilizing data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS).

A total of 10,505 participants aged 65 and above from the 2018 CLHLS were included. Calculate body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and weight-adjusted-waist index (WWI) based on measured weight, height, and waist circumference. Based on BMI values, individuals were classified as underweight (<18.5 kg/m2), normal weight (18.5-23.9 kg/m2) and overweight or obesity (BMI ≥24 kg/m2). In the survey, sleep quality was rated in a 5-point format ("1 = very good," "2 = good," "3 = fair," "4 = poor," or "5 = very poor"), and we categorized "1" and "2" as good sleep quality and "3," "4," and "5" as poor sleep quality. Logistic regression models were used to evaluate odds ratios (ORs) and 95% confidence intervals (CIs), with subgroup analysis and restricted-cubic-spline (RCS) conducted.

The prevalence of poor sleep quality was 47.06%. There are significant differences in obesity indicators and other factors between the two groups of people with good sleep and poor sleep. After adjusting for potential confounding factors (including demographics, socioeconomic status, lifestyle behaviors, health-related issues and activities of daily living), our analyses revealed significant negative associations of BMI [OR 0.96 (95% CI 0.95-0.98)], WC [OR 0.99 (95% CI 0.98-0.99)] and WHtR [OR 0.18 (95% CI 0.09-0.35)] with poor sleep quality. RCS regression also showed that BMI, WC, WHtR and WWI were all strongly negatively correlated with poor sleep quality.

In elderly Chinese people, overweight/obese elderly people may have a better sleep quality compared to elderly people with normal weight, while underweight elderly people are unfavorable for sleep quality.

The associations of different obesity and metabolic phenotypes during midlife with the risk of incident dementia remain unclear. This study aimed to investigate the associations between metabolic heterogeneity of obesity and long-term risk of dementia.

We conducted prospective analyses from three cohorts, including the UK Biobank (UKB), Atherosclerosis Risk in Communities (ARIC) study, and Framingham Offspring Study (FOS). Eligible participants were those aged 45-65 years with valid assessments of body mass index (BMI) and metabolic status at the study baseline. Obesity was defined as a BMI of ≥ 30.0 kg/m2, while metabolic abnormality was defined as meeting ≥ 2 of the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) criteria. Metabolic heterogeneity of obesity was evaluated based on obesity and metabolic phenotypes and grouped as metabolically normal non-obesity (MNNO), metabolically abnormal non-obesity (MANO), metabolically normal obesity (MNO), and metabolically abnormal obesity (MAO).

Included in this study were 295,823 participants aged 56.3 ± 5.9 years from the UKB, 12,547 participants aged 54.0 ± 5.7 years from the ARIC, and 2,004 participants aged 53.9 ± 5.9 years from the FOS. Over 4,348,208 person-years, a total of 6,190 participants (3,601 in the UKB, 2,405 in the ARIC, and 184 in the FOS) developed incident dementia. In the pooled analysis of three cohorts, metabolic abnormality was associated with a hazard ratio (HR) of 1.41 (95% confidence interval [CI]: 1.10-1.80) for dementia, while obesity was associated with an HR of 1.20 (1.03-1.41). Compared with MNNO, individuals with MANO and MAO had increased risks of dementia (pooled HR: 1.33, 95% CI: 1.04-1.71 for MANO and 1.48, 1.16-1.89 for MAO). However, there was no significant difference in the risk of dementia among MNO (pooled HR: 1.10, 95% CI: 0.98-1.24). In addition, participants who recovered from MANO to MNNO had a lower risk of dementia (pooled HR: 0.79, 95% CI: 0.64-0.97), as compared with stable MANO.

Metabolic abnormality has a stronger association with dementia than obesity. Metabolically abnormal non-obesity and obesity, but not metabolically normal obesity, are associated with higher risks of incident dementia as compared with metabolically normal non-obesity. Recovering from an abnormal metabolic status to normal reduces the risk of dementia in populations without obesity. Our findings highlight the important role of metabolic status in the development of dementia and recommend the stratified management of obesity based on metabolic status.

Obesity/overweight and metabolic anomalies are known to be associated with elevated cardiovascular disease (CVD) risk. However, there is a paucity of research exploring the association between different body weights, varying metabolic statuses, and the occurrence of CVD in the Chinese population. Thus, we performed this study to explore the relation between different metabolic overweight/obesity phenotypes and the prevalence of CVD.

We analyzed data from 9075 participants in the Risk Evaluation of cAncers in Chinese diabeTic Individuals: A lONgitudinal (REACTION) study. Participants were classified into four metabolic phenotypes based on their metabolic status and obesity/overweight status. Regression analysis was used to evaluate the relationship between CVD and different groups. Additionally, we conducted a subgroup analysis to further explore the relationship between CVD and different metabolic abnormalities.

Compared to metabolically healthy non-overweight/obesity (MHNO) individuals, both overweight/obesity and metabolic anomalies were positively associated with CVD prevalence. Among other metabolically unhealthy and overweight/obesity phenotypes, metabolically healthy overweight/obesity (MHO) generally exhibited a comparatively lower association with CVD. In the elderly, high waist circumference was significantly associated with CVD, rather than body weight. Further analysis revealed that hypertension had the strongest association with CVD.

Elderly individuals should place more emphasis on managing their waist circumference rather than only on BMI. CVD prevention should focus on both body weight management and treatment of metabolic diseases, with particular emphasis on antihypertensive therapy.

We aimed to investigate the associations of changes in metabolic health across categories of body size phenotype with the risk of colorectal cancer in a community-based prospective cohort.

In the current study, a total of 70,987 participants were included. Changes in metabolic health across categories of body size phenotype were assessed between the health examination for the first time in the years 2006 through 2009 and a 2010/2011 health examination. A multivariate Cox proportional hazards model was used to assess the associations of changes in metabolic health across body size phenotype categories with risk of colorectal cancer.

During the median follow-up time of 11.04 years, 428 (0.60%) participants developed colorectal cancer. Compared with metabolically healthy normal-weight (MHNW) participants who remained MH, the risk of colorectal cancer was increased by 144% (95% CI: 1.21-4.95) for participants with metabolically healthy obesity (MHO) who converted to a metabolically unhealthy (MU) phenotype. Participants who were MU at baseline were still at increased risk of colorectal cancer, regardless of obesity status.

The MHO phenotype was a dynamic status over time, and converting to MU during follow-up and being initially MU were associated with having an increased risk of colorectal cancer, regardless of degree of obesity and body size phenotype.

To investigate the association between metabolically healthy obesity (MHO) and left ventricular geometric remodelling in Chinese children.

This cross-sectional study used data from two population-based samples in China, including 2871 children aged 6-11 years. Weight status was defined based on body mass index according to the World Health Organization growth chart. Metabolic status was defined based on the 2018 consensus-based criteria proposed by Damanhoury et al. Obes Rev 2018;19:1476-1491 (blood pressure, lipids and glucose). Left ventricular geometric remodelling was determined as concentric remodelling, eccentric hypertrophy, and concentric hypertrophy. Multinomial logistic regression analysis was used to determine odds ratios (ORs) and 95% confidence intervals (CIs) for the association between categories of weight and metabolic status and left ventricular geometric remodelling.

Compared with children with metabolically healthy normal weight, those with MHO had higher odds of left ventricular geometric remodelling, with adjusted ORs (95% CIs) of 2.01 (1.23-3.28) for concentric remodelling, 6.36 (4.03-10.04) for eccentric hypertrophy, and 17.07 (7.97-36.58) for concentric hypertrophy. Corresponding ORs (95% CIs) were 2.35 (1.47-3.75), 10.85 (7.11-16.55), and 18.56 (8.63-39.94), respectively, for children with metabolically unhealthy obesity. In contrast, metabolically unhealthy normal weight was not associated with higher odds of left ventricular geometric remodelling. Findings were consistent in sensitivity analyses that used different definitions of weight and metabolic status and left ventricular geometric remodelling.

Children with MHO had higher odds of left ventricular geometric remodelling than their metabolically healthy normal weight counterparts. Our findings suggest MHO may not be a benign condition for cardiac health in children.

This retrospective cohort study was designed to evaluate the association between eight systemic inflammation indicators at baseline and the metabolically unhealthy (MU) phenotype after two years of follow-up.

Participants were defined as metabolically healthy (MH) if they met 0-2 of the criteria and metabolically unhealthy (MU) if they met ≥ 3 of the criteria. A many of 4175 subjects aged 20-80 years with a metabolically healthy (MH) phenotype at baseline were enrolled in the study. We compared the clinical characteristics between women and men enrolled at baseline according to the metabolic phenotype at follow-up. The associations between baseline inflammation indicators and MU status at follow-up were evaluated using logistic regression analysis.

922 (22.08%) developed new-onset MU symptoms during follow-up. Logistic regression analysis found that most inflammation indicators were significantly associated with MU phenotype at follow-up, aside from the LMR and SII. After adjusting for potential confounders, only the correlations between CRP level, neutrophil count, and MU phenotype reached significance. In comparison to the control group with a CRP of <0.50 mg/L, the odds ratios (ORs) and 95% confidence intervals (CIs) were 1.61 (1.25-2.09), 1.49 (1.15-1.94), and 1.68 (1.30-2.18) for individuals with CRP levels of 0.50-0.90 mg/L, 0.91-1.72 mg/L, and above 1.72 mg/L, respectively. In the population with a neutrophil count <5.00 ×109 cells/L, the neutrophil count correlated positively and significantly with the MU phenotype. In comparison to the control group with a neutrophil count of <2.75 × 109 cells/L, the ORs and 95% CIs were 1.65 (1.30-2.09) in the population with neutrophil count >4.17 × 109 cells/L.

CRP and neutrophil counts positively correlated with the risk of MU phenotype in Chinese subjects. These composite inflammatory markers (NLR, PLR, LMR, and SII) provide limited advantages for predicting MU risks compared to CRP.

Increasing evidence supports the association between body mass index (BMI), Alzheimer's disease, and vascular markers. Recently, metabolically unhealthy conditions have been reported to affect the expression of these markers. We aimed to investigate the effects of BMI status on Alzheimer's and vascular markers in relation to metabolic health status.

We recruited 1,736 Asians without dementia (71.6 ± 8.0 years). Participants were categorized into underweight, normal weight, or obese groups based on their BMI. Each group was further divided into metabolically healthy (MH) and unhealthy (MU) groups based on the International Diabetes Foundation definition of metabolic syndrome. The main outcome was Aβ positivity, defined as a Centiloid value of 20.0 or above and the presence of vascular markers, defined as severe white matter hyperintensities (WMH). Logistic regression analyses were performed for Aβ positivity and severe WMH with BMI status or interaction terms between BMI and metabolic health status as predictors. Mediation analyses were performed with hippocampal volume (HV) and baseline Mini-Mental State Examination (MMSE) scores as the outcomes, and linear mixed models were performed for longitudinal change in MMSE scores.

Being underweight increased the risk of Aβ positivity (odds ratio [OR] = 2.37, 95% confidence interval [CI] 1.13-4.98), whereas obesity decreased Aβ positivity risk (OR = 0.63, 95% CI 0.50-0.80). Especially, obesity decreased the risk of Aβ positivity (OR = 0.38, 95% CI 0.26-0.56) in the MH group, but not in the MU group. Obesity increased the risk of severe WMH (OR = 1.69, 1.16-2.47). Decreased Aβ positivity mediate the relationship between obesity and higher HV and MMSE scores, particularly in the MH group. Obesity demonstrated a slower decline in MMSE (β = 1.423, p = 0.037) compared to being normal weight, especially in the MH group.

Our findings provide new evidence that metabolic health has a significant effect on the relationship between obesity and Alzheimer's markers, which, in turn, lead to better clinical outcomes.

Dyslipidemia and abnormal cholesterol metabolism are closely related to coronary artery calcification (CAC) and are also critical factors in cardiovascular disease death. In recent years, the atherogenic index of plasma (AIP) has been widely used to evaluate vascular sclerosis. This study aimed to investigate the potential association of AIP between CAC and major adverse cardiovascular events (MACEs).

This study included 1,121 participants whose CACs were measured by multislice spiral CT. Participants' CAC Agatston score, CAC mass, CAC volume, and number of vessels with CACs were assessed. AIP is defined as the base 10 logarithm of the ratio of triglyceride (TG) concentration to high-density lipoprotein-cholesterol (HDL-C) concentration. We investigated the multivariate-adjusted associations between AIP, CAC, and MACEs. The mediating role of the AIP in CAC and MACEs was subsequently discussed.

During a median follow-up of 31 months, 74 MACEs were identified. For each additional unit of log-converted CAC, the MACE risk increased by 48% (HR 1.48 [95% CI 1.32-1.65]). For each additional unit of the AIP (multiplied by 10), the MACEs risk increased by 19%. Causal mediation analysis revealed that the AIP played a partial mediating role between CAC (CAC Agatston score, CAC mass) and MACEs, and the mediating proportions were 8.16% and 16.5%, respectively. However, the mediating effect of CAC volume tended to be nonsignificant (P = 0.137).

An increased AIP can be a risk factor for CAC and MACEs. Biomarkers based on lipid ratios are a readily available and low-cost strategy for identifying MACEs and mediating the association between CAC and MACEs. These findings provide a new perspective on CAC treatment, early diagnosis, and prevention of MACEs.

This study aims to explore the association between a healthy lifestyle and abnormal ambulatory blood pressure (ABP) in Chinese youths.

A school-based sample of 1,296 college students was investigated. A lifestyle score was calculated by synthesizing 5 lifestyle factors, including smoking, alcohol consumption, diet, physical activity, and sleeping. The total score ranged from 0 to 5, with a higher score indicating a healthier lifestyle. This score was then divided into 3 categories representing low adherence to a healthy lifestyle (0-2), medium adherence (3), and high adherence (4-5). Abnormal 24-hour blood pressure (BP) was defined as systolic BP (SBP) ≥ 130 mm Hg and/or diastolic BP (DBP) ≥ 80 mm Hg. Abnormal daytime BP was determined as daytime SBP ≥ 135 mm Hg and/or DBP ≥ 85 mm Hg, while abnormal nighttime BP was characterized as nighttime SBP ≥ 120 mm Hg and/or DBP ≥ 70 mm Hg. We assessed the associations using the binomial regression model.

Mean age was 18.81 years, and 74.5% were women. The prevalence of abnormal 24-hour BP, daytime BP, and nighttime BP are 4.2%, 3.7%, and 9.0%, respectively. We found that participants with a high level of adherence to a healthy lifestyle had a significantly lower prevalence of abnormal 24-hour BP [prevalence ratios (PR) = 0.15, 95% CI: 0.05, 0.48] and abnormal daytime BP (PR = 0.16, 95%CI: 0.05, 0.52), when compared to those with a low level of adherence and after adjusting for the potential covariates.

A healthier lifestyle is associated with a better ambulatory BP profile among youths.

Metabolically healthy obesity is not always a benign condition. It is associated with an increased incidence of cardiovascular disease and all-cause mortality. We investigated the prognostic significance of metabolically healthy obesity by comparing clinical profile-matched metabolically healthy obesity and non-obesity groups.

We analyzed a health insurance dataset with annual health checkup data from Japan. The analyzed data included 168,699 individuals aged <65 years. Obesity was defined as ≥25 kg/m2 body mass index. Metabolically healthy was defined as ≤1 metabolic risk factor (high blood pressure, low high-density lipoprotein cholesterol, high low-density lipoprotein cholesterol, or high hemoglobin A1c). Incidence rates of stroke, myocardial infarction, and all-cause mortality identified from the insurance data were compared between metabolically healthy obesity and non-obesity groups (n = 8644 each) using a log-rank test.

The stroke (obesity: 9.2 per 10,000 person-years; non-obesity: 10.5; log-rank test p = 0.595), myocardial infarction (obesity: 3.7; non-obesity: 3.1; p = 0.613), and all-cause mortality (obesity: 26.6; non-obesity: 23.2; p = 0.304) incidence rates did not differ significantly between the metabolically healthy obesity and non-obesity groups, even when the abdominal obesity was considered in the analysis. The lack of association was also observed in the comparison between the metabolically unhealthy obesity and non-obesity groups (n = 10,965 each). The population with metabolically healthy obesity reported negligibly worse metabolic profiles than the population with non-obesity at the 5.6-year follow-up.

Obesity, when accompanied by a healthy metabolic profile, did not increase the risk of cardiovascular outcomes and all-cause mortality.

Metabolic unhealth (MUH) is closely associated with cardiovascular disease (CVD). Life's Essential 8 (LE8), a recently updated cardiovascular health (CVH) assessment, has some overlapping indicators with MUH but is more comprehensive and complicated than MUH. Given the close relationship between them, it is important to compare these two measurements.

This population-based cross-sectional survey included 20- to 80-year-old individuals from 7 National Health and Nutrition Examination Survey (NHANES) cycles between 2005 and 2018. Based on the parameters provided by the American Heart Association, the LE8 score (which ranges from 0 to 100) was used to classify CVH into three categories: low (0-49), moderate (50-79), and high (80-100). The MUH status was evaluated by blood glucose, blood pressure, and blood lipids. The associations were assessed by multivariable regression analysis, subgroup analysis, restricted cubic spline models, and sensitivity analysis.

A total of 22,582 participants were enrolled (median of age was 45 years old), among them, 11,127 were female (weighted percentage, 49%) and 16,595 were classified as MUH (weighted percentage, 73.5%). The weighted median LE8 scores of metabolic health (MH) and MUH individuals are 73.75 and 59.38, respectively. Higher LE8 scores were linked to lower risks of MUH (odds ratio [OR] for every 10 scores increase, 0.53; 95% CI 0.51-0.55), and a nonlinear dose-response relationship was seen after the adjustment of potential confounders. This negative correlation between LE8 scores, and MUH was strengthened among elderly population.

Higher LE8 and its subscales scores were inversely and nonlinearly linked with the lower presence of MUH. MUH is consistent with LE8 scores, which can be considered as an alternative indicator when it is difficult to collect the information of health behaviors.