Metabolic Syndrome in Offspring of Parents with Metabolic Syndrome: A Meta-Analysis

<b><i>Background:</i></b> The metabolic syndrome (MetS) represents a clustering of risk factors for cardiovascular diseases that includes abdominal obesity, hypertension, dyslipidemia, and insulin resistance. <b><i>Objectives:</i></b> The objective of this study was to reassess the parent-offspring association of MetS since the available findings are still controversial. <b><i>Methods:</i></b> The Cochrane Library, PubMed, Embase, and Web of Science databases were searched to identify relevant articles. All studies comparing MetS status between the offspring of parents with MetS and offspring of parents without MetS were included in the analysis. <b><i>Results:</i></b> A total of 9 studies met the inclusion criteria and they were analyzed. Offspring of at least 1 parent with MetS had a higher risk of MetS (OR 3.88, 95% CI 2.58–5.83, <i>p</i> &#x3c; 0.001). Sons and daughters of fathers with MetS both had a higher risk of MetS (OR 2.31, 95% CI 1.70–3.12, <i>p</i> &#x3c; 0.001, and OR 1.73, 95% CI 1.37–2.18, <i>p</i> &#x3c; 0.001, respectively). Sons and daughters of mothers with MetS both had a higher risk of MetS (OR 1.95, 95% CI 1.37–2.76, <i>p</i> = 0.0002, and OR 1.91, 95% CI 1.54–2.35, <i>p</i> &#x3c; 0.001, respectively). <b><i>Conclusion:</i></b> This meta-analysis showed that there is a higher risk of MetS in the offspring of parents with MetS. However, there was no differential association of MetS according to gender and/or age of the offspring.

Childhood Socioeconomic Status, Comorbidity of Chronic Kidney Disease Risk Factors, and Kidney Function Among Adults in the Midlife in the United States (MIDUS) Study

Background: There is a lack of empirical effort that systematically investigates the clustering of comorbidity among known risk factors (obesity, hypertension, diabetes, hypercholesterolemia, and elevated inflammation) of chronic kidney disease (CKD) and how different types of comorbidity may link differently to kidney function among healthy adult samples. This study modeled the clustering of comorbidity among risk factors, examined the association between the clustering of risk factors and kidney function, and tested whether the clustering of risk factors was associated with childhood SES.Methods: The data were from 2,118 participants (ages 25-84) in the Midlife in the United States (MIDUS) Study. Risk factors included obesity, elevated blood pressure (BP), high total cholesterol levels, poor glucose control, and increased inflammatory activity. Glomerular filtration rate (eGFR) was estimated from serum creatinine, calculated with the CKD-EPI formula. The clustering of comorbidity among risk factors and its association with kidney function and childhood SES were examined using latent class analysis (LCA).Results: A five-class model was optimal: (1) Low Risk (class size = 36.40%; low probability of all risk factors), (2) Obese (16.42%; high probability of large BMI and abdominally obese), (3) Obese and Elevated BP (13.37%; high probability of being obese and having elevated BP), (4) Non-Obese but Elevated BP (14.95%; high probability of having elevated BP, hypercholesterolemia, and elevated inflammation), and (5) High Risk (18.86%; high probability for all risk factors). Obesity was associated with kidney hyperfiltration, while comorbidity between obesity and hypertension was linked to compromised kidney filtration. As expected, the High Risk class showed the highest probability of having eGFR < 60 ml/min/1.73 m2 (P = .12; 95%CI = .09 - .17). Finally, low childhood SES, controlling for education, adult SES, age, gender, and race, was associated with a higher probability of being in the High Risk rather than the Low Risk class (b = -0.20, SE = 0.07, OR [95%CI] = 0.82 [0.71-0.95]).Conclusion: These results highlight the importance of considering the impact of childhood SES on risk factors known to be associated with chronic kidney disease.

Cardiovascular risk factors in a child population: The Health Oriented Pedagogical Project (HOPP)

Aims: The main aim of the present study was to investigate the clustering of risk factors for cardiovascular diseases and metabolic syndrome in a large, healthy representative Norwegian child population. Methods: From a population of 2817, parents of 2297 children agreed to participate. Values of waist circumference (WC), total cholesterol (TC), high-density lipoprotein (HDL), systolic blood pressure (sysBP), haemoglobin-A1c (HbA1c) and Andersen aerobic fitness test were used to test clustering of cardiometabolic risk factors in this sample. Expected distributions of probability for zero to five risk factors are, respectively, 23.7%, 39.6%, 26.4%, 8.8%, 1.5% and 0.1%. A cardiometabolic risk score from zero to five for each individual was derived by adding the number of variables in the least desirable quartile (highest for WC, sysBP, TC and HbA1c; lowest for aerobic fitness and HDL). Results: A risk ratio of 5.8 (95% confidence interval 0.7–46.9) was found for five risk factors, though the small sample size rendered the results non-significant. An explorative analysis combining children with four and five risk factors did not reveal any significant clustering either. Conclusions: No clustering of risk factors was found among Norwegian children aged 6–12 years.

Development of innovative methods for risk assessment in high-rise construction based on clustering of risk factors

The article analyses risks in high-rise construction in terms of investment value with account of the maximum probable loss in case of risk event. The authors scrutinized the risks of high-rise construction in regions with various geographic, climatic and socio-economic conditions that may influence the project environment. Risk classification is presented in general terms, that includes aggregated characteristics of risks being common for many regions. Cluster analysis tools, that allow considering generalized groups of risk depending on their qualitative and quantitative features, were used in order to model the influence of the risk factors on the implementation of investment project. For convenience of further calculations, each type of risk is assigned a separate code with the number of the cluster and the subtype of risk. This approach and the coding of risk factors makes it possible to build a risk matrix, which greatly facilitates the task of determining the degree of impact of risks. The authors clarified and expanded the concept of the price risk, which is defined as the expected value of the event, 105 which extends the capabilities of the model, allows estimating an interval of the probability of occurrence and also using other probabilistic methods of calculation.

Co-occurrence of behavioral risk factors for chronic non-communicable diseases in adolescents: Prevalence and associated factors

ABSTRACT Objective To examine the prevalence of the behavioral risk factors – both isolated and clustered – for chronic diseases, among adolescents. Additionally, its association with various social and demographic variables was estimated. Methods This was a cross-sectional study conducted on 1,039 high school students, from public and private schools, elected for convenience, in Rio de Janeiro, Brazil. A Chi-square test, Mann-Whitney U test, as well as crude and adjusted ordinal logistic regression were used to assess the association between the variables. Results The most frequently observed risk factors were sedentary behavior (68.8%), alcohol consumption (36.8%), and overweight (26.8%). The clustering of risk factors was observed in 67.5% of the students. Being a girl (OR=1.28; 95%CI=1.01–1.63), Caucasian (OR=1.35; 95%CI=1.06–1.72) or private school student (OR=1.46; 95%CI=1.12–1.88) increased the chance of the clustering of risk factors. The co-occurrence of risk factors was predominantly observed in the case of smoking (OR=4.94; 95%CI=1.46–16.75), alcohol consumption (OR=1.43; 95%CI=1.09–1.88), high consumption of ultra-processed foods (OR=1.57; 95%CI=1.19–2.07), and sedentary behavior (OR=1.40; 95%CI=1.07–1.82). Conclusion The co-occurrence of behavioral risk factors was observed to be higher among girls, Caucasian adolescents, and private school students, as well as, among smokers, alcohol users and adolescents with sedentary habits and a high consumption of ultra-processed foods.