Metabolic alterations constitute a growing challenge for global public health, with significant implications for cardiovascular morbidity and mortality. Early identification of these alterations, even from the presence of a single component, is crucial for effectively preventing and managing chronic diseases.

To determine the prevalence of metabolic states based on one or more alterations in the Peruvian population and to evaluate possible sex disparities.

An analytical cross-sectional study used data from two Peruvian national databases: Surveillance of Nutritional Indicators by Life Stages (VIANEV) and PERU MIGRANT. Data from 885 adults from VIANEV and 986 participants from PERU MIGRANT with complete information for all study variables were analyzed. Graphs were generated to illustrate metabolic states according to different combinations of diagnostic criteria. Bar charts were created to visualize the individual prevalences of each state. Ordinal logistic regression was employed to examine sex disparities and the outcome.

The prevalence of metabolic alterations (at least one alteration) ranged from 87.04% to 87.55%, depending on the criteria used. Significant discrepancies were found in the prevalences of hyperglycemia and abdominal obesity according to the different diagnostic criteria applied. The ordinal logistic regression analysis showed that men had a lower probability of presenting metabolic alterations compared to women, regardless of the diagnostic method used.

This study reveals a high prevalence of metabolic alterations in the Peruvian population, with notable variations depending on the diagnostic criteria employed. The observed discrepancies underscore the need to re-evaluate these criteria for the Peruvian population. The identified disparities between sexes suggest the importance of developing differentiated prevention and management strategies.

Research suggests associations between physical activity, sedentary behavior, sleep, and metabolic syndrome, but most has focused on healthy populations and individual behaviors. We investigated associations of 24-hr movement behavior compositions with cardiometabolic risk factors and metabolic syndrome in adults receiving rehabilitation for other health conditions.

 This cross-sectional study assessed 24-hr movement behaviors using thigh-worn accelerometers and metabolic outcomes via blood analyses in 145 adults attending outpatient rehabilitation. Regression models tested associations of five 24-hr time-use behaviors (time in bed, sedentary time, standing, light-intensity stepping, and moderate- to vigorous-intensity stepping) with cardiometabolic risk factors and metabolic syndrome severity score (a cumulative measure of risk derived from metabolic risk factors).

 Participants (64 [SD 12] years old; 52% women; 66% with metabolic syndrome, with 6 [SD 0.7] days of 24-hr data) spent 41% of a 24-hr day sedentary, 15% standing, 3% in light-intensity stepping, 2% in moderate- to vigorous-intensity stepping, and 38% in bed. Adjusted models indicated that a higher proportion of light-intensity stepping was associated with lower triglycerides, more time in bed was associated with a higher metabolic syndrome severity score, and more time stepping was associated with a lower metabolic syndrome severity score. There was no evidence of associations between the overall compositions and outcomes.

The consistently observed small proportions of physical activity, with lack of variation between participants, may not be sufficient to counteract the impact of high sedentary time on metabolic outcomes in adults attending outpatient rehabilitation.

 Future research may focus on exploring ways to increase light-intensity stepping in sedentary older adults with various health conditions.

Hyperhomocysteinemia (HH) is a metabolic condition linked to cardiovascular and cognitive health risks. This study investigated the prevalence of HH and cardiovascular metabolic syndrome (MS) among patients with symptoms such as fatigue, joint pain, muscle weakness, vertigo, paresthesia, and aphthous stomatitis. The objective was to explore the associations between HH, MS, and quality of life, emphasizing the role of personalized dietary interventions.

A prospective study was conducted between 2019 and 2023, including 86 patients aged 18 years or older who underwent nutrigenetic testing and provided anthropometric data. Participants were divided into three groups: those with HH (45.3%), those without HH or MS (31.4%), and those with MS but without HH (23.3%). Nutrigenetic analyses assessed genetic predispositions related to nutrient metabolism.

Patients with HH exhibited reduced quality of life, with lower Short Form-12 Health Survey (SF-12) scores compared to other groups. Sex-specific nutrient needs and age-related changes in dietary requirements were identified. Metabolic conditions, including obesity, hypertension, and hypercholesterolemia, inversely impacted nutrient utilization. Physical activity positively correlated with higher demands for folic acid, vitamin B12, zinc, and magnesium.

Nutritional interventions targeting these needs effectively improved metabolic health and alleviated symptoms. HH significantly impacts quality of life and metabolic health. Personalized dietary and lifestyle modifications tailored to genetic predispositions, sex, and age are critical for mitigating cardiometabolic risks. These findings lay the groundwork for targeted interventions aimed at improving health outcomes in individuals with HH and MS.

Background: Metabolic syndrome (MetS) is a clinical construct that conglomerates risk factors interconnected with cardiovascular diseases and type 2 diabetes. More than a thousand million individuals in the world were diagnosed with MetS in 2018. Objective: Our objective was to examine the prevalence of MetS and its components among Mexican adults. Methods: Data from 1733 adults aged ≥20 years who participated in the Mexican National Health and Nutrition Survey 2021. Sociodemographic, and clinical factors were gathered and analyzed. To define MetS, we used the harmonized diagnosis criteria. Results: The prevalence of MetS in Mexican adults was 45.3% (43.7% in men and 46.8% in women). This was mainly driven by increased abdominal obesity (AO) 79.8% and dyslipidemia (low high-density lipoprotein [HDL]-cholesterol and hypertriglyceridemia) 77.1%. The proportion of subjects with a least one MetS component was 90.5% and with any combination of two components was 25.2% and for three was 28.9%. The most frequent combination of MetS components was the cluster of AO, low HDL-cholesterol, and hypertriglyceridemia (15.6%). Conclusions: A high prevalence of MetS was registered in Mexico in 2021. Women and adults aged 40 years or older were the groups with the highest prevalence of MetS and its components. The health system in Mexico must promote strategies for the prevention and control of MetS and its components in adults.

The proportion of noodles in the Korean diet is increasing, but the effect of noodle intake on metabolic syndrome has not been sufficiently investigated. Therefore, we investigated noodle consumption and its relation to metabolic syndrome in Korean adults.

This study was conducted on 10,505 adults using the combined data of the 2012-2016 Korea National Health and Nutrition Examination Survey (KNHANES). Noodle intake was evaluated with a food frequency question-naire (FFQ) based on 112 food items. To compute odds ratios (ORs) and their 95% confidence intervals (CIs) controlled for confounders, multivariable logistic regression models were used.

Compared to people in the lowest levels of noodle intake, the OR of the metabolic syndrome of those in the highest levels was 1.48 (95% CI, 1.16-1.90; p-trend = 0.002). This positive association was also found for hypertriglyceridemia and abdominal obesity, which were metabolic syndrome components. Specifically, the odds of having hypertriglyceridemia were 38% (OR, 1.38; 95% CI, 1.14-1.66; p-trend < 0.001) higher for people with high noodle consumption compared to those with low noodle consumption in the overall population. The tendency for people who consume a lot of noodles to have raised odds of metabolic syndrome was observed when analyzed by the type of noodles.

This study suggested noodle intake was positively related to met-abolic syndrome and its components in Korean adults. Further clinical trials and prospective cohort studies are required to identify a causal relationship between noodle intake and metabolic syndrome in Koreans.

Background and Objectives: Numerous studies have explored the biomarker atherogenic index of plasma (AIP) in relation to metabolic syndrome (MetS), showing its potential utility in assessing this condition. However, the existing evidence remains inconsistent and inconclusive. Therefore, this study aimed to evaluate the association between AIP and MetS and assess its predictive accuracy. Materials and Methods: A comprehensive search of PubMed, EMBASE, and Scopus was conducted using a predefined search strategy to identify relevant studies. Eligible studies diagnosed MetS based on the International Diabetes Federation criteria. The primary outcomes were the mean difference (MD) in AIP between MetS patients and healthy controls, as well as the area under the curve (AUC) for AIP in predicting MetS. Results: Thirteen studies involving 17,689 participants met the inclusion criteria and were included in the systematic review and meta-analysis. AIP levels were significantly higher in MetS patients compared to healthy controls, with an MD of 0.309 (95% CI 0.214, 0.405). In contrast, the difference in AIP levels between type 2 diabetes mellitus (T2DM) patients with MetS and normoglycemic MetS patients was not statistically significant (MD 0.142, 95% CI -0.091, 0.376). The predictive accuracy of AIP for MetS yielded an AUC of 0.864 (95% CI 0.856, 0.871). Conclusions: AIP levels are significantly elevated in MetS patients compared to healthy individuals, supporting AIP's potential role as a biomarker for MetS. However, AIP levels did not differ significantly between T2DM patients with MetS and normoglycemic MetS patients. The predictive accuracy of AIP for MetS is acceptable, indicating that AIP may serve as a useful tool in MetS diagnosis. Further research is warranted to clarify its diagnostic and prognostic significance in clinical settings.

Type 2 diabetes mellitus (T2DM) is a multifactorial metabolic disorder characterized by insulin resistance and beta cell dysfunction, resulting in hyperglycemia. Olive oil, a cornerstone of the Mediterranean diet, has attracted considerable attention due to its potential health benefits, including reducing the risk of developing T2DM. This literature review aims to critically examine and synthesize existing research regarding the impact of olive oil on the expression of genes relevant to T2DM. This paper also seeks to provide an immunological and genetic perspective on the signaling pathways of the main components of extra virgin olive oil. Key bioactive components of olive oil, such as oleic acid and phenolic compounds, were identified as modulators of insulin signaling. These compounds enhanced the insulin signaling pathway, improved lipid metabolism, and reduced oxidative stress by decreasing reactive oxygen species (ROS) production. Additionally, they were shown to alleviate inflammation by inhibiting the NF-κB pathway and downregulating pro-inflammatory cytokines and enzymes. Furthermore, these bioactive compounds were observed to mitigate endoplasmic reticulum (ER) stress by downregulating stress markers, thereby protecting beta cells from apoptosis and preserving their function. In summary, olive oil, particularly its bioactive constituents, has been demonstrated to enhance insulin sensitivity, protect beta cell function, and reduce inflammation and oxidative stress by modulating key genes involved in these processes. These findings underscore olive oil's therapeutic potential in managing T2DM. However, further research, including well-designed human clinical trials, is required to fully elucidate the role of olive oil in personalized nutrition strategies for the prevention and treatment of T2DM.

Neighborhood characteristics serve as risk factors for metabolic syndrome (MetS). However, the intermediary factors linking this relationship remain understudied. Thus, we investigated the sex-specific mediating role of C-reactive protein, physical activity (PA), and perceived stress in the associations of perceived neighborhood social environment (PNSE) with MetS severity among Black adults.

This cross-sectional study included 3185 adults (64% women) from exam 1 (2000-2004) of the Jackson Heart Study. MetS severity Z scores were calculated based on the Adult Treatment Panel III criteria formula. PNSE included neighborhood violence, problems, and social cohesion. Men and women were analyzed separately. A bootstrap resampling technique with 95% bias-corrected CI (95% BC CI) was used to evaluate whether C-reactive protein, PA, and perceived stress mediated the association between each PNSE and MetS severity, adjusting for covariates. All PNSE factors were directly related to MetS severity in women but not in men. In women, neighborhood problems were indirectly associated with MetS severity mediated through PA (β=0.02 [95% BC CI, 0.00-0.05]). In men, neighborhood violence, problems, and social cohesion were indirectly associated with MetS severity mediated through PA (β=0.05 [95% BC CI, 0.01-0.10]; β=0.03 [95% BC CI, 0.00-0.06]; and β=-0.04 [95% BC CI, -0.09 to -0.01], respectively). Neither C-reactive protein nor perceived stress mediated such associations in either women or men.

All PNSEs (violence, problems, and social cohesion) were directly related to MetS severity in women only. PA mediated the relationship between each PNSE and MetS in a sex-specific manner. Efforts focusing on local conditions are needed to better understand why such disparities exist for at-risk minoritized groups.

The co-occurrence of diabetes and mental illnesses such as bipolar disorder (BD), obsessive-compulsive disorder (OCD), and schizophrenia creates significant barriers for both people with diabetes (PWD) and their healthcare teams. This literature review provides an analysis of the relationship between diabetes and mental illnesses through exploring epidemiology, shared risk factors, and clinical implications. The aim is to enhance the understanding of these complex comorbidities to guide and improve future research and clinical practice.

Recent research suggests a strong link between mental illness, metabolic syndrome, and diabetes. Studies show that BD has a robust relationship with metabolic disease and the antipsychotic medications used in treatment for many mental illnesses are strongly associated with weight gain and metabolic disease. However, there is limited research exploring the bidirectional relationship that diabetes has with BD, schizophrenia, and OCD. While research exists on the link between diabetes and mental conditions such as depression and anxiety, little research has examined schizophrenia, OCD and BD. The findings noted in this review suggest gaps in treatment options, healthcare services, and social support. While this paper provides a foundation for future progress, advancement in this field will require a collaborative effort from researchers, healthcare professionals, and community outreach programs to effectively close the gaps in care noted in these patient populations.

Metabolic syndrome (MetS) has been suggested to play a role in congenital defects. This study investigated the association of MetS and its components with orofacial clefts (OFCs).

We conducted a case-control study in Northeast Thailand. Ninety-four cases with cleft lip, with or without cleft palate, were frequency matched with 94 controls on the infant's age and mother's education. We administered a mother's health questionnaire and collected anthropometric measurements and blood samples. Multiple logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup analyses were performed among infants without a family history of OFCs, mothers who were not currently breastfeeding, and mothers who were >6 months postpartum.

When compared to mothers of normal weight, the OR associated with OFCs were 2.44 (95% CI, 1.04-5.76, P = .04) in overweight mothers, and 3.30 (95% CI, 1.14-9.57, P = .03) in obese mothers. Low HDL-C raised the risk of OFCs 2.95 times (95% CI, 1.41-6.14, P = .004) compared to normal HDL-C levels. Mothers with 4 or 5 features of MetS were 2.77 times as likely to have the affected child than those who did not (95% CI, 0.43-17.76), but this difference was not statistically significant (P = .28). Subgroup analyses showed similar results, uncovering an additional significant association between underweight mothers and OFCs.

The results indicate a robust association between underweight and overweight/obese maternal body mass index and increased OFC risk. Additionally, low HDL-C in mothers is linked to an elevated risk of OFCs. Further research is needed to evaluate if promoting strategies to maintain optimal body weight and enhance HDL-C levels in reproductive-age and pregnant women icould contribute to a reduction of the risk of OFCs in their progeny.

This study aimed to investigate the association between age-specific and sex-specific continuous metabolic syndrome severity score (cMetS-S) and the risk of developing type 2 diabetes mellitus (T2DM). Additionally, the study aimed to assess the added value of cMetS-S in predicting T2DM compared with traditional MetS criteria.

The study used a longitudinal cohort design, following participants for 18 years.

The research was conducted within the Tehran Lipid and Glucose Study, a community-based study in Tehran, Iran.

A total of 6957 participants aged 20-60 years were included in the study.

The cMetS-S of each participant was determined using age-specific and sex-specific equations and Cox proportional hazard regression models were used to analyse the association between cMetS-S and T2DM using continuous and quantile approaches.

The outcome measure was the association between cMetS-S and the development of T2DM during the 18-year follow-up.

A total of 1124 T2DM cases were recorded over 18 years of follow-up. In the fully adjusted model, a 1-SD increase in the cMetS-S was associated with future T2DM (HR 1.72; 95% CI 1.54 to 1.91). Men and women had HRs of 1.65 (95% CI 1.40 to 1.95) and 1.83 (95% CI 1.59 to 2.10) for T2DM per 1-SD increase in cMetS-S, respectively. Higher cMetS-S was associated with increased risk of diabetes in both prediabetic (HR 1.42;95% CI 1.23 to 1.64) and normoglycaemic individuals (HR 2.11;95% CI 1.76 to 2.54); this association was more significant in normoglycaemic individuals. Unlike the traditional-based MetS definitions, the cMetS-S improved diabetes prediction (p<0.001).

The cMetS-S is strongly associated with future diabetes in prediabetic and normoglycaemic individuals independent of MetS components during a long term. As the relationship between cMetS-S and T2DM is more pronounced in normoglycaemic individuals than in those with pre-diabetes, implementing the evaluation of cMetS-S can serve as an early identification tool for individuals at risk of T2DM prior to the onset of pre-diabetes.

High-density lipoprotein cholesterol (HDL-C) is one of 5 components [high blood pressure, glucose, triglycerides, waist circumference, low HDL-C], 3 of which, needed to diagnose metabolic syndrome (MetS). Evolving research shows that higher HDL-C is not necessarily cardioprotective in midlife women, supporting a need to re-evaluate HDL-C's contribution to risks related to MetS. We tested whether risk of future diabetes and higher carotid intima-media thickness (cIMT) differ by HDL-C status in midlife women diagnosed with MetS based on the other 4 components.

1) no MetS, 2) MetS with HDL-C ≥ 50 mg/dL (MetS hiHDL), and 3) MetS with HDL-C < 50 mg/dL (MetS loHDL). cIMT was measured 13.8 ± 0.6 years post baseline. Incident diabetes was assessed yearly.

Among 2773 women (1350 (48 %) of them had cIMT), 2383 (86 %) had no MetS, 117 (4 %) had MetS hiHDL, 273 (10 %) had MetS loHDL. Compared with no MetS, both MetS- hiHDL and loHDL groups had higher cIMT and diabetes risk. Risk of having high cIMT did not differ between MetS loHDL vs. hiHDL groups. Adjusting for levels of MetS criteria other than HDL-C at baseline explained the associations of each of the two MetS groups with cIMT. Conversely, after adjustment, associations of MetS hiHDL and MetS loHDL with incident diabetes persisted.

In midlife women, HDL-C status matters for predicting risk of incident diabetes but not higher cIMT beyond other MetS components.

This prospective cohort study aimed to investigate the association between ultra-processed food (UPF) and the risk of metabolic syndrome (MetS), as well as to assess whether fruit and vegetable intake and weight change modify this association.

We included 1915 healthy participants who participated in the Tehran Lipid and Glucose Study (TLGS), all of whom had complete demographic, anthropometric, and dietary measurements. A validated food frequency questionnaire was used to assess UPF consumption based on the NOVA classification system. MetS was defined according to the Joint Interim Statement. Multivariable adjusted Cox regression was used to estimate hazard ratios (HRs) for MetS events across tertiles of UPF. The effect of fruit and vegetable consumption and weight change on this association was assessed using joint classification by Cox regression.

UFP consumption showed no association with MetS risk after adjusting for confounders. However, after adjustment for dietary fiber, fruits, and vegetables, the highest tertile of UPF consumption was positively linked to MetS risk, compared to the lowest tertile. There was a significant interaction between fruit, vegetable, and dietary fiber intake and UPF consumption concerning the risk of MetS (All P values < 0.05). Among individuals consuming less than 248 g/day of fruit, the risk of MetS increased by 54% (confidence interval: 1.13-2.10) in the highest UPF tertile. Consuming vegetables and dietary fiber below the median (258 g/day and 42.2 g/day, respectively) increased the risk of MetS in the third tertile of UPF. However, consuming vegetables and fiber ≥ median intake, reduced the risk of MetS among those with the lowest UPF consumption. Furthermore, the risk of MetS was observed in the third tertile of UPF consumption among individuals with fruit and vegetable consumption < 537 g/day. UPF consumption was not associated with the risk of MetS in different weight change statuses.

Consuming more fruits and vegetables mitigated the adverse effect of UPF on the risk of developing MetS.

Evening chronotype is known to be associated with various chronic diseases and cardiovascular risk factors. Metabolic syndrome is a group of conditions that together raise the risk of coronary heart disease, diabetes, stroke, and other serious health problems. Only a few studies have been published on the association between chronotype and metabolic syndrome in unselected population data, with conflicting results. The aim of this study was to evaluate the association between chronotype and metabolic syndrome at population level by using unselected Northern Finland Birth cohort 1966 (NFBC1966) database. The study population consists of participants with NFBC66 (n = 5,113, 57% female) at the age of 46 yr old. Chronotype was determined with shortened Morningness-Eveningness Questionnaires and expressed as morning (44%), intermediate (44%), and evening types (12%). Metabolic syndrome was determined according to the definition of International Diabetes Federation. One-way ANOVA, Kruskal-Walli's test, and χ2 tests were used to compare the chronotype groups, followed by logistic regression analysis (adjusted with alcohol consumption, smoking, marital status, level of education, and leisure-time physical activity). In women, the prevalence of metabolic syndrome was statistically significantly higher in the evening type group: 23, 24, and 34% for morning, intermediate, and evening groups, respectively (P < 0.001). In logistic regression analysis, evening chronotype was associated with higher risk of having metabolic syndrome (OR 1.5; CI 95% 1.2 to 2.0). In this population-based birth cohort study, the evening chronotype was independently associated with higher prevalence of metabolic syndrome in women.NEW & NOTEWORTHY Only a few studies have been conducted on the association between chronotype and metabolic syndrome in unselected population data, with conflicting results. In this population-based cohort study of 5,113 participants, the evening chronotype associated with metabolic syndrome in women when there was no such association in men. The result supports a previous South Korean population study of 1,620 participants, in which the association was also found in women, but not in men.

In order to better understand which metabolic differences are related to insulin resistance in metabolic syndrome (MetSyn), we used hyperinsulinemic-euglycemic (HE) clamps in individuals with MetSyn and related peripheral insulin resistance to circulating biomarkers.

In this cross-sectional study, HE-clamps were performed in treatment-naive men (n = 97) with MetSyn. Subjects were defined as insulin-resistant based on the rate of disappearance (Rd). Machine learning models and conventional statistics were used to identify biomarkers of insulin resistance. Findings were replicated in a cohort with n = 282 obese men and women with (n = 156) and without (n = 126) MetSyn. In addition to this, the relation between biomarkers and adipose tissue was assessed by nuclear magnetic resonance imaging.

Peripheral insulin resistance is marked by changes in proteins related to inflammatory processes such as IL-1 and TNF-receptor and superfamily members. These proteins can distinguish between insulin-resistant and insulin-sensitive individuals (AUC = 0.72 ± 0.10) with MetSyn. These proteins were also associated with IFG, liver fat (rho 0.36, p = 1.79 × 10-9) and visceral adipose tissue (rho = 0.35, p = 6.80 × 10-9). Interestingly, these proteins had the strongest association in the MetSyn subgroup compared to individuals without MetSyn.

MetSyn associated with insulin resistance is characterized by protein changes related to body fat content, insulin signaling and pro-inflammatory processes. These findings provide novel targets for intervention studies and should be the focus of future in vitro and in vivo studies.

Cardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these disorders. Early detection and intervention for individuals with metabolic abnormalities can help mitigate the risk of developing more serious cardiometabolic conditions. This study aimed to develop an image-derived phenotype (IDP) for metabolic abnormality from unenhanced abdominal computed tomography (CT) scans using deep learning. We used this IDP to classify individuals with metabolic syndrome and predict future occurrence of cardiometabolic disorders.

A multi-stage deep learning approach was used to extract the IDP from the liver region of unenhanced abdominal CT scans. In a cohort of over 2,000 individuals the IDP was used to classify individuals with metabolic syndrome. In a subset of over 1,300 individuals, the IDP was used to predict future occurrence of hypertension, type II diabetes, and fatty liver disease.

For metabolic syndrome (MetS) classification, we compared the performance of the proposed IDP to liver attenuation and visceral adipose tissue area (VAT). The proposed IDP showed the strongest performance (AUC 0.82) compared to attenuation (AUC 0.70) and VAT (AUC 0.80). For disease prediction, we compared the performance of the IDP to baseline MetS diagnosis. The models including the IDP outperformed MetS for type II diabetes (AUCs 0.91 and 0.90) and fatty liver disease (AUCs 0.67 and 0.62) prediction and performed comparably for hypertension prediction (AUCs of 0.77).

This study demonstrated the superior performance of a deep learning IDP compared to traditional radiomic features to classify individuals with metabolic syndrome. Additionally, the IDP outperformed the clinical definition of metabolic syndrome in predicting future morbidities. Our findings underscore the utility of data-driven imaging phenotypes as valuable tools in the assessment and management of metabolic syndrome and cardiometabolic disorders.

Metabolic syndrome is one of the most common health problems for people around the world. The aim of our study was to assess the prevalence of metabolic syndrome among adults without prior diagnosis of cardiovascular disease, diabetes, and chronic kidney disease. We also plan to assess the influence of certain lifestyle components on prevalence of metabolic syndrome. The study involved cardiovascularly healthy patients undergoing lab tests, measurements, and the HLPCQ questionnaire (The Healthy Lifestyle and Personal Control Questionnaire). The data were used to diagnose metabolic syndrome. Out of 1044 patients from 10 primary care facilities, 23.3% met the metabolic syndrome criteria, showing a strong link with increased blood pressure, cholesterol, and fasting glucose. Lower scores in the Organized physical exercise subscale of the HLPCQ questionnaire were noted in those with metabolic syndrome. Comparing the subscale of HLPCQ questionnaire, the lower results in Organized physical exercise subscale were found among the participants with metabolic syndrome, both male and females. Metabolic syndrome, a significant risk factor for cardiovascular disease, should be screened for actively, even in apparently healthy populations. Results obtained in our study from analysis of HLPCQ show that screening for metabolic syndrome should be preceded by prevention based on regular physical activity and proper eating habits.

Obesity, dyslipidaemia and insulin resistance are associated with hypopituitarism. The association between these conditions and Sheehan's syndrome (SS) caused by post-partum pituitary gland necrosis is poorly understood. This study aimed to assess cardiovascular risk surrogate markers in SS patients, and we compared clinical, biochemical and radiological testing with healthy controls.

In this cross-sectional study, we studied 45 patients with SS on standard replacement therapy and compared them with healthy controls. All subjects underwent anthropometric, inflammatory marker and hormonal measurement (adrenocorticotropic hormone (ACTH), stimulated cortisol, insulin-like growth factor-1 (IGF-1), thyroxine (T4), follicle-stimulating hormone (FSH), luteinising hormone (LH), oestradiol (E2), prolactin (Prl), insulin, interleukin-6 (IL-6) and high-sensitivity C-reactive protein (hs-CRP)). Carotid intima-media thickness (CIMT), flow-mediated dilation (FMD) and echocardiography were also performed.

The mean age and body mass index (BMI) of SS patients were 48.1 ± 10.0 years and 24.3 ± 4.3 kg/m2, respectively, while those of controls were 44.6 ± 12.0 years and 24.6 ± 3.2 kg/m2, respectively. Systolic blood pressure was significantly higher in SS (124.6 ± 20.8 vs. 117.0 ± 18.6 mm of Hg, P < 0.05). All SS patients were hypothyroid, and all except one were hypocortisolaemic. Triglyceride (TG) levels were significantly higher in SS patients (165.6 ± 83.3 vs. 117.2 ± 56.1, P < 0.01), but no difference in the prevalence of metabolic syndrome (MetS) was found. hs-CRP (9.1 (5.2-18.5) vs. 1.5 (0.6-2.8), P < 0.001) and IL-6 (4.9 (3.7-7.3) vs. 3.1 (2.0-4.2), P < 0.001) were significantly higher in SS patients. CIMT was significantly increased in SS patients, but no difference in FMD was found. Echocardiography revealed no significant difference in left ventricular (LV) dimensions, interventricular thickness, posterior wall thickness, ejection fraction, LV mass and diastolic function.

SS patients show increased cardiovascular risk with hypertension, dyslipidaemia and increased atherosclerotic and inflammatory markers.

In 2016, the Lipid Association of India (LAI) developed a cardiovascular risk assessment algorithm and defined low-density lipoprotein cholesterol (LDL-C) goals for prevention of atherosclerotic cardiovascular disease (ASCVD) in Indians. The recent refinements in the role of various risk factors and subclinical atherosclerosis in prediction of ASCVD risk necessitated updating the risk algorithm and treatment goals.

The LAI core committee held twenty-one meetings and webinars from June 2022 to July 2023 with experts across India and critically reviewed the latest evidence regarding the strategies for ASCVD risk prediction and the benefits and modalities for intensive lipid lowering. Based on the expert consensus and extensive review of published data, consensus statement IV was commissioned.

The young age of onset and a more aggressive nature of ASCVD in Indians necessitates emphasis on lifetime ASCVD risk instead of the conventional 10-year risk. It also demands early institution of aggressive preventive measures to protect the young population prior to development of ASCVD events. Wide availability and low cost of statins in India enable implementation of effective LDL-C-lowering therapy in individuals at high risk of ASCVD. Subjects with any evidence of subclinical atherosclerosis are likely to benefit the most from early aggressive interventions.

This document presents the updated risk stratification and treatment algorithm and describes the rationale for each modification. The intent of these updated recommendations is to modernize management of dyslipidemia in Indian patients with the goal of reducing the epidemic of ASCVD among Indians in Asia and worldwide.

Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models-Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost-each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them.

It is unclear how metabolic syndrome (MetS) and diabetes affect Gal-3 (galectin 3) levels and the resulting implications for heart failure (HF) risk. We assessed relationships of MetS and diabetes with Gal-3, and their joint associations with incident HF.

We included 8445 participants without HF (mean age, 63 years; 59% men; 16% Black race) at ARIC (Atherosclerosis Risk in Communities) study visit 4 (1996-1999). We categorized participants as having MetS only, MetS with diabetes, or neither, and by quartiles of MetS severity Z score. We assessed cross-sectional associations of metabolic risk categories with high Gal-3 level (≥75th percentile) using logistic regression. We used Cox regression to evaluate combined associations of metabolic risk categories and Gal-3 quartiles with HF. In cross-sectional analyses, compared with no MetS and no diabetes, MetS only (odds ratio [OR], 1.24 [95% CI, 1.10-1.41]) and MetS with diabetes (OR, 1.59 [95% CI, 1.32-1.92]) were associated with elevated Gal-3. Over a median follow-up of 20.5 years, there were 1749 HF events. Compared with individuals with neither diabetes nor MetS and with Gal-3 in the lowest quartile, the combination of MetS with diabetes and Gal-3 ≥75th percentile was associated with a 4-fold higher HF risk (hazard ratio, 4.35 [95% CI, 3.30-5.73]). Gal-3 provided HF prognostic information above and beyond MetS, NT-proBNP (N-terminal pro-B-type natriuretic peptide), high-sensitivity cardiac troponin T, and CRP (C-reactive protein) (ΔC statistic for models with versus without Gal-3: 0.003; P=0.004).

MetS and diabetes are associated with elevated Gal-3. The HF risk significantly increased with the combination of greater metabolic risk and higher Gal-3.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.