Cardiometabolic Clustering Research Articles

Association between calcium intake from different food sources during childhood and cardiometabolic risk on adolescence: The Generation XXI birth cohort.

Calcium intake has been associated with lower adiposity, but few studies explored the longitudinal relation of calcium from different sources and cardiometabolic markers in young population. Prospectively estimate the association between dairy and non-dairy calcium intake at 4, 7, and 10 years (y) of age and cardiometabolic risk at 13 y. The sample included 4017 participants from the Generation XXI birth cohort. Dietary data were collected from a 3-day food diary. Cardiometabolic clusters at 13 y were estimated by a probabilistic Gaussian mixture model (z-score of waist circumference [WC], HOMA-IR; HDL cholesterol, triglycerides, and systolic blood pressure [BP]). Multivariable linear and logistic regression models were used to estimate associations. Calcium intake (/100 mg), after adjustment for confounders, was negatively and significantly associated with body mass index (BMI) (β = -0.02, 95% CI: -0.04; -0.01), WC (cm) (β = -0.23, 95% CI: -0.36; -0.11), and diastolic BP (mmHg) (β = -0.14, 95% CI: -0.26; -0.03). After additional adjustment for total energy intake, associations lose statistical significance. Calcium intake from milk at 7 y was inversely associated with WC (β = -0.25, 95% CI: -0.48; -0.03) and from yogurt at 10 y was associated with higher BMI (β = 0.08, 95% CI: 0.03; 0.13) and WC (β = 0.54, 95% CI: 0.12; 0.96). Calcium from vegetables at 4, 7, 10 y reduces later cardiometabolic risk (OR = 0.71; OR = 0.84; OR = 0.98, respectively). This study supports a protective effect of calcium on adolescents' cardiometabolic health, especially from vegetables.

Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts

It remains unclear how to meaningfully classify people living with multimorbidity (multiple long-term conditions (MLTCs)), beyond counting the number of conditions. This paper aims to identify clusters of MLTCs in different age groups and associated risks of adverse health outcomes and service use. Latent class analysis was used to identify MLTCs clusters in different age groups in three cohorts: Secure Anonymised Information Linkage Databank (SAIL) (n=1,825,289), UK Biobank (n=502,363), and the UK Household Longitudinal Study (UKHLS) (n=49,186). Incidence rate ratios (IRR) for MLTC clusters were computed for: all-cause mortality, hospitalisations, and general practice (GP) use over 10 years, using <2 MLTCs as reference. Information on health outcomes and service use were extracted for a ten year follow up period (between 01st Jan 2010 and 31st Dec 2019 for UK Biobank and UKHLS, and between 01st Jan 2011 and 31st Dec 2020 for SAIL). Clustering MLTCs produced largely similar results across different age groups and cohorts. MLTC clusters had distinct associations with health outcomes and service use after accounting for LTC counts, in fully adjusted models. The largest associations with mortality, hospitalisations and GP use in SAIL were observed for the "Pain+" cluster in the age-group 18-36 years (mortality IRR=4.47, hospitalisation IRR=1.84; GP use IRR=2.87) and the "Hypertension, Diabetes & Heart disease" cluster in the age-group 37-54 years (mortality IRR=4.52, hospitalisation IRR=1.53, GP use IRR=2.36). In UK Biobank, the "Cancer, Thyroid disease & Rheumatoid arthritis" cluster in the age group 37-54 years had the largest association with mortality (IRR=2.47). Cardiometabolic clusters across all age groups, pain/mental health clusters in younger groups, and cancer and pulmonary related clusters in older age groups had higher risk for all outcomes. In UKHLS, MLTC clusters were not significantly associated with higher risk of adverse outcomes, except for the hospitalisation in the age-group 18-36 years. Personalising care around MLTC clusters that have higher risk of adverse outcomes may have important implications for practice (in relation to secondary prevention), policy (with allocation of health care resources), and research (intervention development and targeting), for people living with MLTCs. This study was funded by the National Institute for Health and Care Research (NIHR; Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (multimorbidity)-NIHR202020).

Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction

BackgroundMachine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis. MethodsAll adult discharges for STEMI in the National Inpatient Sample (October 2015 to December 2019) were included, excluding patients with prior myocardial infarction. Machine-learning clustering analysis was used to define clusters based on 21 clinical attributes of interest. Main outcomes of the study were cluster-based comparison of risk profile, in-hospital clinical outcomes and utilization of invasive management. Binomial hierarchical multivariable logistic regression with adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) was used to detect the between-cluster differences. ResultsOut of overall 470,960 STEMI cases, the machine-learning analysis revealed 4 different clusters with 205,640 (cluster 0: ‘behavioural risk cluster’), 146,400 (cluster 1: ‘least comorbidity cluster’), 45,100 (cluster 2: ‘diabetes with end-organ damage cluster’) and 73,820 (cluster 3: ‘cardiometabolic cluster’) cases. Attributes with the highest importance for clustering were hypertension and diabetes. After multivariable adjustment, patients from ‘diabetes with end-organ damage cluster’ exhibited the worst mortality, MACCE and ischemic stroke (p < 0.001 for all), as well as the lowest utilization of invasive management (p < 0.001 for all), in comparison to other clusters. Patients from ‘behavioural risk cluster’ exhibited the best in-hospital prognosis and the highest utilization of invasive management, compared to other clusters (p < 0.001 for all). ConclusionsMachine learning driven clustering of inpatients with STEMI reveals important population subgroups with distinct prevalence, risk profile, prognosis and management. Data driven approaches may identify high risk phenogroups and warrants further study.

Patterns and trajectories of multimorbidity prior to dementia: a UK Biobank study

AbstractBackgroundThe co‐occurrence of multiple long term conditions (LTCs), termed multimorbidity, is a risk factor for dementia. However, we lack an understanding of how and when specific LTCs accumulate over time to impact dementia risk.Method338,270 individuals from the UK Biobank with linked electronic health records up to January 2022 were studied, excluding those with either a dementia diagnosis or age of latest follow up &lt; 65 years. LTC diagnoses were derived using hospital episode statistics and GP records (age of diagnosis computed using month/year of birth and month/year of diagnosis). We performed an age‐stratified analysis in which we a) cluster individuals based on 43 non‐dementia LTCs diagnosed within age bins (0‐55, 55‐65, 65‐70 years), and b) compute risk of incident dementia using logistic regression with multimorbidity cluster as predictor and incident dementia as outcome. Diagnosis data was submitted to PCA, and the resulting factor scores were kMeans‐clustered. 6 clusters were derived per age bin.ResultWe named clusters based on the most prevalent LTCs within that cluster (Figure 1). Cardiometabolic, arthritis, gastrointestinal, and mixed clusters existed at all ages. Mental health and respiratory clusters were present exclusively in the younger age bin (0‐55) whereas hypertension and eye disorder clusters were exclusive to older ages (55‐65 and 65‐70). Across the lifespan, compared to being disease free, multimorbidity was significantly associated with incident dementia (OR&gt;1). We also observed age‐specific multimorbidity preceding dementia onset. From 0‐55 years, being in the mixed cluster presented the highest dementia risk (OR = 5.13, 95% CI = [4.61, 5.73]). Being in the cardiometabolic cluster was associated with highest risk in 55‐65 (OR = 6.24 [5.48, 7.1]) and 65‐70 (OR = 6.99 [5.91, 8.27]) ranges. The most predictive trajectory of incident dementia involved the mixed cluster at 55, followed by the cardiometabolic clusters from 55‐65 and 65‐70. Table 1 displays the top 10 trajectories associated with dementia. All p‐values are bonferroni‐holmes corrected and &lt; 0.01.ConclusionWe present multimorbidity pathways linked to increased risk of dementia. We describe how and when diseases cluster together prior to dementia diagnosis.

Longitudinal Associations Between Physical Health Conditions in Childhood and Attention-Deficit/Hyperactivity Disorder Symptoms at Age 17 Years

Although evidence suggests significant cross-sectional relationships between attention-deficit/hyperactivity disorder (ADHD) and several physical health conditions, less is known about their longitudinal associations. We investigated the cumulative effect of childhood physical health conditions on ADHD symptoms at age 17 years, controlling for environmental factors, ADHD medication status, and ADHD symptoms at age 3 years. Using Millennium Cohort Study data (weighted n= 8,059), we assessed whether 4 physical health clusters (sensory, neurological, atopic, and cardio-metabolic) were associated with scores on the ADHD subscale from the Strengths and Difficulties Questionnaire at age 17 years. Environmental factors were grouped into 5 cumulative risk indices: prenatal, perinatal, postnatal environment, postnatal maternal well-being, and sociodemographic factors. Regression analyses determined whether each physical health cluster was associated with ADHD score while controlling for environmental factors, ADHD medication, and earlier symptoms. Sensory, neurological, and cardio-metabolic clusters were all significantly associated with ADHD symptoms (β range= 0.04-0.09, p< .001). The overall model explained 2% of the variance. This rose to 21% (ΔR2= 0.06) after adjusting for confounders. The sensory (β= 0.06) and neurological (β= 0.06) clusters remained significant (R2= 0.21, ΔR2= 0.06), but the cardio-metabolic cluster was no longer a significant predictor. Sensory or neurological conditions in childhood were associated with higher ADHD symptoms aged 17 after adjustment of confounders. This was not the case for atopic or cardio-metabolic conditions. These findings have implications for the care of children with sensory/neurological conditions and future research examining ADHD etiopathophysiology.

Health status and mental distress in people with cancer and comorbid conditions: The Australian National Health Survey analysis.

Data on the impact of specific comorbidities on health outcomes is limited. We compared health status and mental distress between individuals with and without cancer according to comorbidity type. A cross-sectional analysis using data from the Australian National Health Survey 2017-18 including all respondents aged ≥25 years with and without a history of cancer. The odds of poor health and mental distress were reported according to cancer status, and specific individual and cluster of comorbidities. There were 1982 individuals (52% female) with cancer and 12,635 (51% female) without cancer. Individuals with cancer were older, and more likely to have a comorbidity compared with those without cancer. They were more likely to report poor health than those without cancer for each specific comorbidity; except for skin conditions and infectious diseases; with the adjusted odds ratio (aOR) ranging from 1.34 (95% CI = 1.01-1.79) for digestive disorders to 2.93 (95% CI = 1.62-5.29) for blood conditions. The strongest association with poor health (aOR 2.79, 95% CI = 2.27-3.43) and mental distress (aOR 9.01, 95% CI = 7.25-11.20) was observed for those with a comorbid mental illness. Exploratory cluster analysis identified four distinct comorbidity clusters: low comorbidity, musculoskeletal, respiratory and cardiometabolic; cancer survivors in the cardiometabolic cluster had a higher odds of reporting poor health (aOR 3.50, 95% CI = 2.48-4.92) and mental distress (aOR 2.33, 95% CI = 1.53-3.55) than those with a low comorbidity. Comorbidities in cancer survivors were common and associated with inferior health status, although the magnitude of the effect varied by comorbidity type. Risk assessment and management of comorbidities should be an important priority for cancer care and research.

Clustering of Cardiometabolic Risk Factors and Dementia Incidence in Older Adults: ACross-Country Comparison in England, the United States, and China.

There is mixed evidence for an association between cardiometabolic risk factors and dementia incidence. This study aimed to determine whether different latent classes of cardiometabolic conditions were associated with dementia risk in older adults across England, the United States, and China. A total of 4511 participants aged 50 and older were drawn from the English Longitudinal Study of Ageing (ELSA), 5112 from Health and Retirement Study (HRS), and 9022 from China Health and Retirement Longitudinal Study (CHARLS). Latent class analyses were performed across each data set utilizing 7 baseline cardiometabolic conditions: obesity, low high-density lipoprotein cholesterol, systolic and diastolic blood pressure, hyperglycemia, diabetes, and inflammation. Confounder-adjusted Cox proportional hazards regressions were conducted to estimate dementia incidence by cardiometabolic latent classes. Three similar cardiometabolic classes were identified across all countries: (i) "relatively healthy/healthy obesity," (ii) "obesity-hypertension," and (iii) "complex cardiometabolic." Across the 3 samples, a total of 1230 individuals developed dementia over a median of 6.8-12.2years. Among ELSA and HRS participants, the "complex cardiometabolic" group had a higher dementia risk when compared to the "healthy obesity" groups (England: adjusted hazard ratio [AdjHR]=1.62 [95% confidence interval {CI}=1.11-2.37]; United States: AdjHR=1.31 [95% CI=1.02-1.68]). However, in CHARLS participants, the "obesity-hypertension" group had a greater risk of dementia when compared to the "relatively healthy" group (AdjHR=1.28 [95% CI=1.04-1.57]). This study provides evidence that in western populations, complex cardiometabolic clusters are associated with higher rates of dementia incidence, whereas in a Chinese sample, a different cardiometabolic profile seems to be linked to an increased risk of dementia.

Ethnic differences in physical and mental multimorbidity in working age adults with a history of depression and/or anxiety.

The current study used data from an ethnically diverse population from South London to examine ethnic differences in physical and mental multimorbidity among working age (18-64 years) adults in the context of depression and anxiety. The study included 44 506 patients who had previously attended Improving Access to Psychological Therapies services in the London Borough of Lambeth. Multinomial logistic regression examined cross-sectional associations between ethnicity with physical and mental multimorbidity. Patterns of multimorbidity were identified using hierarchical cluster analysis. Within 44 056 working age adults with a history of depression or anxiety from South London there were notable ethnic differences in physical multimorbidity. Adults of Black Caribbean ethnicity were more likely to have physical multimorbidity [adjusted relative risk ratio (aRRR) = 1.25, 95% confidence interval (CI) 1.15-1.36] compared to adults of White ethnicity. Relative to adults of White ethnicity, adults of Asian ethnicity were more likely to have physical multimorbidity at higher thresholds only (e.g. 4 + conditions; aRRR = 1.53, 95% CI 1.17-2.00). Three physical (atopic, cardiometabolic, mixed) and three mental (alcohol/substance use, common/severe mental illnesses, personality disorder) multimorbidity clusters emerged. Ethnic minority groups with multimorbidity had a higher probability of belonging to the cardiometabolic cluster. In an ethnically diverse population with a history of common mental health disorders, we found substantial between- and within-ethnicity variation in rates of physical, but not mental, multimorbidity. The findings emphasised the value of more granular definitions of ethnicity when examining the burden of physical and mental multimorbidity.

IS THE AMOUNT OF WEIGHT LOSS AFTER BARIATRIC SURGERY A FACTOR DETERMINING CARDIOMETABOLIC RISK REDUCTION?

Objective: This study aimed to investigate whether differential weight loss amount appropriately reflects improvements in cardiometabolic health in patients undergoing bariatric surgery. Materials and Methods: Patients who underwent bariatric surgery (BS) and were followed up for six months were divided into three groups according to their weight loss (Group-1: Low weight loss; Group-2: Moderate weight loss; Group-3: High weight loss). Before and after surgery, patients were evaluated for metabolic syndrome (MetS) using waist circumference, blood pressure, glucose, HDL-C, and triglyceride data, and a clustered cardiometabolic risk (CMR) score was obtained for each patient using a calculator available in the literature online. Changes in each MetS criterion and CMR score in the groups before and after the operation, and their relations with each other were compared. Results: Sixty-six patients were included in the study. It was observed that the prevalence of MetS, which was 74.2% before the operation, decreased to 25.8%. A significant difference was observed between the groups in the decrease in weight, waist circumference, fat mass, fasting blood sugar, and HOMA-IR values. However, no significant difference was found between the groups in the change in MetS parameters and CMR scores. While a significant negative correlation was found between the amount of weight loss and the difference in the CMR score, no relation was observed between each MetS parameter and the amount of weight loss. It was also found that each 1% increase in weight loss was associated with a 57% decrease in the CMR score. Conclusion: Although there is a significant decrease in cardiometabolic risk parameters after bariatric surgery, the amount of weight loss of the patients does not affect the individual parameters. However, using a scoring system to evaluate and monitor patients’ cardiometabolic cluster risks will make it possible to follow the gradual changes in patients after surgery, making interventions by physicians and nutritionists more targeted and efficient.

Leukocyte subtypes, gelatinases, and their tissue inhibitors in a group of subjects with asymptomatic carotid atherosclerosis.

In a cohort of subjects with asymptomatic carotid atherosclerosis (ACA), we have evaluated the neutrophil and lymphocyte count and their ratio (NLR), the gelatinases (MMP-2 and MMP-9) and their tissue inhibitors (TIMP-1 and TIMP-2). At baseline, no difference was observed between ACA subjects and subject control group regarding neutrophil and lymphocyte count while was evident in ACA subjects a significant increase in MMP-2, MMP-9 and TIMP-2 associated to a significant decrease in TIMP-1. Dividing the ACA according to the number of cardiovascular risk factors (CRFs) we have observed an increase in lymphocyte count in the subgroup with 3-5 CRFs. Evaluating the leukocyte subtypes according to all the surrogate markers of insulin resistance has been noted, in the subgroups that exceed the medians of these markers, a significant increase in neutrophil and lymphocyte count without any variation of the NLR. Effecting the same evaluation for the MMP/TIMP pattern we observed, instead, that the same subgroups tend to show a decrease in MMP-2 and an increase in MMP-9. No difference instead for TIMP-1 and TIMP-2. The abnormality of the MMP/TIMP pattern, bearing in mind the cardiometabolic clustering present in this cohort of ACA subjects, would induce to use drugs able not only to cure the cardiometabolic risk factors but also to influence the MMP/TIMP profile.

A longitudinal analysis of PM2.5 exposure and multimorbidity clusters and accumulation among adults aged 45-85 in China.

While previous studies have emphasised the role of individual factors in understanding multimorbidity disparities, few have investigated contextual factors such as air pollution (AP). We first use cross-sectional latent class analysis (LCA) to assess the associations between PM2.5 exposure and multimorbidity disease clusters, and then estimate the associations between PM2.5 exposure and the development of multimorbidity longitudinally using growth curve modelling (GCM) among adults aged 45-85 in China. The results of LCA modelling suggest four latent classes representing three multimorbidity patterns (respiratory, musculoskeletal, cardio-metabolic) and one healthy pattern. The analysis shows that a 1 μg/m3 increase in cumulative exposure to PM2.5 is associated with a higher likelihood of belonging to respiratory, musculoskeletal or cardio-metabolic clusters: 2.4% (95% CI: 1.02, 1.03), 1.5% (95% CI: 1.01, 1.02) and 3.3% (95% CI: 1.03, 1.04), respectively. The GCM models show that there is a u-shaped association between PM2.5 exposure and multimorbidity, indicating that both lower and higher PM2.5 exposure is associated with increased multimorbidity levels. Higher multimorbidity in areas of low AP is explained by clustering of musculoskeletal diseases, whereas higher AP is associated with cardio-metabolic disease clusters. The study shows how multimorbidity clusters vary contextually and that PM2.5 exposure is more detrimental to health among older adults.

The Ratios of Triglycerides and C-reactive protein to High density-lipoprotein -cholesterol as valid biochemical markers of the Nascent Metabolic Syndrome

ABSTRACT Aims: Metabolic syndrome (MetS), a cardiometabolic cluster, is a major global problem. The ratio of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C) is a good biomarker of MetS in certain populations C-reactive protein (CRP) has also been also shown to be a biomarker of MetS. Since CRP captures inflammation, we compared the ratios of TG to HDL-C and CRP to HDL-C in patients with nascent MetS. Methods: Patients with MetS (n = 58) and matched controls (n = 44) were recruited. Fasting blood samples were obtained for routine laboratories, insulin, and adipokines. TG and CRP ratios to HDL-C were calculated. Data were analyzed statistically. Results: Both the TG to HDL-C and CRP to HDL-C ratios were significantly increased in MetS and both increased with increasing severity of MetS. Whilst both correlated with cardiometabolic features and insulin resistance, only the CRP to HDL-C ratio correlated significantly with adiponectin and leptin. Receiver operating characteristic curve analysis showed that both ratios showed excellent discrimination for MetS with no significant differences between ratios. Conclusions: Thus both the TG to HDL-C and CRP to HDL-C ratios are significantly increased in patients with nascent MetS and appear to be valid biomarkers of MetS. However, these preliminary findings with CRP: HDL-C need confirmation in large prospective studies and could have important implications for assessing cardiometabolic risk in African Americans, an under-served population.

Managing Multimorbidity (Multiple Chronic Diseases) Amid COVID-19 Pandemic: A Community Based Study From Odisha, India.

While most of the studies to date demonstrate the deleterious effect of multiple chronic diseases on COVID-19 risk and outcome, there is sparse information available on the effect of the pandemic on multimorbidity management, with no reports yet from India. We sought to explore the effect of COVID-19 pandemic on routine and emergency care for multimorbidity among community-dwelling adults in Odisha, India. A community-based cross-sectional study was undertaken pandemic lockdown, in Khurda district of Odisha, India. Around 600 individuals having at least one chronic disease residing in rural, urban residential and slums were interviewed using a specifically developed questionnaire MAQ COVID-19. The association of socio-demographic characteristics and multimorbidity with pandemic-related care challenges was examined by multiple logistic regression. Principal Component Analysis was employed to minimize the dimensionality of factors related to multimorbidity care. Multimorbidity was highly prevalent in younger age group (46–60 years) with cardio-metabolic clusters being dominant. Individuals with multimorbidity experienced significantly higher care challenges than those with single condition (AOR = 1.48, 95% CI = 1.01–2.05) with notable disruption in treatment and routine check-up. Most frequently cited concerns were—physician consultation (43%), diagnostic-services (26%), transport (33%), and mobility restrictions (21%). Multivariate analysis revealed older adults living alone in urban residence to have higher challenges than their rural counterparts. Patient activation for self-care, multimorbidity literacy, and technology-enabled tele-consultation could be explored as potential interventions. Future studies should qualitatively explore the challenges of physicians as well as garner an in-depth understanding of multimorbidity management in the vulnerable subgroups.

Metabolites that activate the inflammasome in nascent metabolic syndrome.

Metabolic Syndrome (MetS) is a cardio-metabolic cluster that increases the risk of type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD). Whilst it affects 35% of the American adult population, its pathogenesis remains to be elucidated. Both insulin resistance and increased inflammation appear to be pivotal mechanisms. The NOD-like receptor family pyrin domain containing protein 3 (NLRP3) inflammasome, an intracellular multi-protein complex, is crucial in the activation of Caspase 1, resulting in an increase in both IL-1and IL-18. In this preliminary report we examined the relationship between metabolites from our exploratory metabolomics studies with the NLRP3 inflammasome activity in the adipose tissue of patients with nascent MetS. This study comprised patients with nascent MetS matched with controls. All patients in this study had normal renal and hepatic function. Metabolites were analyzed from frozen early morning urine samples and correlated with adipose tissue Caspase 1, interleukin-1, and interleukin-18 density. Caspase 1, a marker of NLRP3 inflammasome activity, was significantly elevated in patients with nascent MetS compared to controls. Isoleucine, GABA, Carnitine and PC34: 2 were also significantly increased in patients with MetS. Caspase1 correlated positively with Isoleucine, GABA, Carnitine, and PC34:2. We make the novel observation that the NLRP3 inflammasome activity is correlated with certain metabolites (Isoleucine, GABA, Carnitine and PC34:2) and hypothesize that they could trigger increased NLRP3 Inflammasome activity in MetS. However, these preliminary ,hypothesis generating novel findings need confirmation in larger studies of the metabolome and inflammasome.

The platelet to high density lipoprotein -cholesterol ratio is a valid biomarker of nascent metabolic syndrome.

The metabolic syndrome (MetS) is a major global problem, and inflammation and insulin resistance appear to be key underpinnings in this cardio-metabolic cluster. MetS predisposes to an increased risk of diabetes and atherosclerotic cardiovascular disease (ASCVD). It has a procoagulant diathesis which included increased platelet activity and impaired fibrinolysis. High density lipoprotein (HDL) appears to be anti-thrombotic. Accordingly, we examined the ratios between platelets to HDL-cholesterol(C) and adiponectin (Adipo) in patients with nascent MetS without the confounding of diabetes, ASCVD and smoking to determine their validity as biomarkers of MetS. Patients with nascent MetS (n = 58) and matched controls (n = 44) were recruited. Fasting blood samples were obtained for complete blood counts, basic metabolic panel, lipids, insulin, and Adipo. Ratios of platelets to HDL-C and Adipo were calculated. Following adjustment for adiposity, only the platelet: HDL ratio was significantly increased in MetS and increased with severity of MetS. Receiver operating characteristic curve analysis showed that the platelet: HDL-C area under the curve (AUC) significantly added to both platelets and platelet lymphocyte ratio AUCs. Also the platelet: HDL-C ratio correlated with all cardio-metabolic features of MetS, high sensitivity C-reactive protein, insulin resistance chemerin, and leptin. The ratio of platelets: HDL-C is significantly increased in patients with nascent MetS and appear to be a valid biomarker of MetS. It could also emerge as a biomarker for athero-thrombotic risk. However, these preliminary findings need confirmation in large prospective studies.

Chemerin Ratios to HDL-cholesterol and Adiponectin as Biomarkers of Metabolic Syndrome

ABSTRACT Aims Metabolic Syndrome (MetS) a global problem, which comprises a cardio-metabolic cluster of risk factors, increases the risk for type-2 diabetes (T2DM) and atherosclerotic cardiovascular diseases (ASCVD). To date, the best laboratory-based biomarker for MetS appears to be high-sensitivity C-reactive protein (hsCRP). Chemerin, a novel adipokine is increased in MetS and appears to contribute to both insulin resistance and inflammation. In this pilot study, we tested if the chemerin:HDL-C or chemerin:adiponectin ratios are better biomarkers for predicting MetS than hsCRP. Patients and Methods We enrolled patients and controls with nascent MetS, uncomplicated by diabetes, ASCVD, macro-inflammation, and smoking using rigorous criteria. Fasting blood samples were obtained in order to calculate insulin resistance in the liver (HOMA-IR) and adipose tissue (ADIPO-IR) and for measurement of chemerin and adiponectin levels. Statistical analyses including receiver operating characteristic (ROC) curves were used to evaluate data. Results We observed the chemerin:HDL-C ratio is significantly increased in MetS and increases with severity of MetS (p < .001). The chemerin: adiponectin ratio was not significantly increased following adjustment for age and waist circumference. The chemerin:HDL-C ratio correlated with BMI, WC, triglycerides, plasma glucose, HDL-C, and both HOMA-IR and ADIPO-IR. ROC curve analysis showed that the chemerin:HDL-C ratio area under the curve (AUC) was greater than the AUC for hsCRP. Conclusion In this preliminary report, we demonstrate that the ratio of chemerin to HDL-C is a valid biomarker of MetS and appears to be a better predictor than hsCRP. These findings need to be confirmed in larger studies.

Multimorbidity among Two Million Adults in China.

To explore the multimorbidity prevalence and patterns among middle-aged and older adults from China. Data on thirteen chronic diseases were collected from 2,097,150 participants aged over 45 years between January 1st 2011 and December 31st 2015 from Beijing Medical Claim Data for Employees. Association rule mining and hierarchical cluster analysis were applied to assess multimorbidity patterns. Multimorbidity prevalence was 51.6% and 81.3% in the middle-aged and older groups, respectively. The most prevalent disease pair was that of osteoarthritis and rheumatoid arthritis (OARA) with hypertension (HT) (middle-aged: 22.5%; older: 41.8%). Ischaemic heart disease (IHD), HT, and OARA constituted the most common triad combination (middle-aged: 11.0%; older: 31.2%). Among the middle-aged group, the strongest associations were found in a combination of cerebrovascular disease (CBD), OARA, and HT with IHD in males (lift = 3.49), and CBD, OARA, and COPD with IHD in females (lift = 3.24). Among older patients, glaucoma and cataracts in females (lift = 2.95), and IHD, OARA, and glaucoma combined with cataracts in males (lift = 2.45) were observed. Visual impairment clusters, a mixed cluster of OARA, IHD, COPD, and cardiometabolic clusters were detected. Multimorbidity is prevalent among middle-aged and older Chinese individuals. The observations of multimorbidity patterns have implications for improving preventive care and developing appropriate guidelines for morbidity treatment.

Liver Fat and Cardiometabolic Risk Factors Among School-Age Children.

Background and AimsNonalcoholic fatty liver disease is a major risk factor for cardiometabolic disease in adults. The burden of liver fat and associated cardiometabolic risk factors in healthy children is unknown. In a population‐based prospective cohort study among 3,170 10‐year‐old children, we assessed whether both liver fat accumulation across the full range and nonalcoholic fatty liver disease are associated with cardiometabolic risk factors already in childhood.Approach and ResultsLiver fat fraction was measured by magnetic resonance imaging, and nonalcoholic fatty liver disease was defined as liver fat fraction ≥5.0%. We measured body mass index, blood pressure, and insulin, glucose, lipids, and C‐reactive protein concentrations. Cardiometabolic clustering was defined as having three or more risk factors out of high visceral fat mass, high blood pressure, low high‐density‐lipoprotein cholesterol or high triglycerides, and high insulin concentrations. Nonalcoholic fatty liver disease prevalences were 1.0%, 9.1%, and 25.0% among children who were normal weight, overweight, and obese, respectively. Both higher liver fat within the normal range (<5.0% liver fat) and nonalcoholic fatty liver disease were associated with higher blood pressure, insulin resistance, total cholesterol, triglycerides, and C‐reactive protein concentrations (P values < 0.05). As compared with children with <2.0% liver fat, children with ≥5.0% liver fat had the highest odds of cardiometabolic clustering (odds ratio 24.43 [95% confidence interval 12.25, 48.60]). The associations remained similar after adjustment for body mass index and tended to be stronger in children who were overweight and obese.ConclusionsHigher liver fat is, across the full range and independently of body mass index, associated with an adverse cardiometabolic risk profile already in childhood. Future preventive strategies focused on improving cardiometabolic outcomes in later life may need to target liver fat development in childhood.

Physical Activity and Cardiometabolic Risk Factor Clustering in Young Adults with Obesity.

There is a paucity of information on the clustering of cardiometabolic risk factors in young adults and how this clustering may vary based on whether or not they perform sufficient levels of physical activity. We analyzed baseline data from 346 young adults (23.3 ± 4.4 yr) participating in the Healthy Body Healthy U clinical trial from 2015 to 2018. Cardiometabolic risk factors were measured according to standard procedures and moderate- to vigorous-intensity physical activity (MVPA) was determined by accelerometry. A cardiometabolic clustering score (ranging from 0 to 5) was created from five biomarkers according to whether or not a standard clinical risk cut point was exceeded (0, no; 1, yes): abdominal circumference (>102 cm (men) or >88 cm (women)), hemoglobin A1c (≥5.7%), HDL cholesterol (<40 mg·dL (men) or <50 mg·dL (women)), systolic blood pressure (≥130 mm Hg), and diastolic blood pressure (≥85 mm Hg). Cardiometabolic dysregulation (CD) was defined as a cardiometabolic clustering score ≥3. Multiple logistic regression determined the independent association between level of MVPA and CD, while adjusting for sex, race/ethnicity, sedentary time, and smoking. The prevalence of CD was 18% (22% in men, 17% in women). We observed a nonlinear graded association between MVPA and CD. Participants performing 150-300 min·wk of MVPA significantly lowered their odds of CD by 66% (odds ratio, 0.34; 95% confidence interval, 0.16-0.75), whereas those exceeding 300 min·wk lowered their odds by 61% (odds ratio, 0.39; 95% confidence interval, 0.18-0.86) compared with those performing <150 min·wk, independent of obesity and the other covariables. Recommended levels of moderate-intensity physical activity is significantly associated with lower odds of CD and thus may prevent or diminish the need for expensive pharmaceutical treatment over the remainder of the life-span.

Selected Cardiometabolic Risk-factor Clusters of Urban Hypertensive Adults in Response to 72-hour ABU Radio Frequency Modulated (F.M.) Call for Free-medical Screening

Selected Cardiometabolic Risk-factor Clusters of Urban Hypertensive Adults in Response to 72-hour ABU Radio Frequency Modulated (F.M.) Call for Free-medical Screening