The Impact of Education and Age on Metabolic Disorders

Abstract

Metabolic disorders, such as obesity, elevated blood pressure, dyslipidemias, insulin resistance, hyperglycemia, and hyperuricemia have all been identified as risk factors for an epidemic of important and widespread chronic-degenerative diseases, such as type 2 diabetes and cardiovascular disease, that constitute some of the world's most important public health challenges. Their increasing prevalence can be associated with an aging population and to lifestyles within an obesogenic environment. Taking educational level as a proxy for lifestyle, and using both logistic and linear regressions, we study the relation between a wide set of metabolic biomarkers, and educational level, body mass index (BMI), age, and sex as correlates, in a population of 1,073 students, academic and non-academic staff at Mexico's largest university (UNAM). Controlling for BMI and sex, we consider educational level and age as complementary measures-degree and duration-of exposure to metabolic insults. Analyzing the role of education across a wide spectrum of educational levels (from primary school to doctoral degree), we show that higher education correlates to significantly better metabolic health when compared to lower levels, and is associated with significantly less risk for waist circumference, systolic blood pressure, glucose, glycosylated hemoglobin, triglycerides, high density lipoprotein and metabolic syndrome (all p < 0.05); but not for diastolic blood pressure, basal insulin, uric acid, low density lipoprotein, and total cholesterol. We classify each biomarker, and corresponding metabolic disorder, by its associated set of statistically significant correlates. Differences among the sets of significant correlates indicate various aetiologies and the need for targeted population-specific interventions. Thus, variables strongly linked to educational level are candidates for lifestyle change interventions. Hence, public policy efforts should be focused on those metabolic biomarkers strongly linked to education, while adopting a different approach for those biomarkers not linked as they may be poor targets for educational campaigns.

The Prevalence of Metabolic Disease Multimorbidity and Its Associations With Spending and Health Outcomes in Middle-Aged and Elderly Chinese Adults

“…Our results indicated that metabolic multimorbidity was positively associated with older age, urban area, and health insurance. Consistent with other studies (35,36), the prevalence of metabolic multimorbidity increased with age, which can be attributed to the declining metabolic function during the aging process. Additionally, individuals living in urban areas are more likely to suffer from metabolic multimorbidity than those living in rural areas, which is in line with previous findings (30,37).…”

Beverages Based on Second Quality Citrus Fruits and Maqui Berry, a Source of Bioactive (Poly)phenols: Sorting Out Urine Metabolites upon a Longitudinal Study

“…The rising incidence of metabolic diseases, namely, obesity, metabolic syndrome, or type II diabetes mellitus, is one of the main social challenges ahead now and in the coming years [ 1 ]. These diseases are closely related to dietary habits, in general, and particularly associated with the consumption of sugar-sweetened beverages [ 2 ].…”

Metabolic Physiological Networks: The Impact of Age

“…Over time, the physiological compensatory systems that maintain homeostasis become worn down due to the cumulative impact of metabolic insults, transitioning from healthy to maladaptive states that precede disease onset ( Stephens et al, 2020 ). An already existing medical notion of this system-wide progression of states before the overt onset of disease is metabolic syndrome (MetS), whose prevalence increases strongly with age ( Hildrum et al, 2007 ) and unhealthy lifestyles.…”

“…As metabolic health is an emergent property, arising from the interaction of multiple physiological systems over time, the framework of complexity provides the means for a whole-system analysis ( Lusis et al, 2008 ; Haring et al, 2012 ; Sun et al, 2012 ), rather than a reductionist variable-by-variable approach. In previous work ( Stephens et al, 2020 ), we considered how aging was an important driver of metabolic change across a wide variety of metabolic biomarkers (anthropometric, fasting blood test and vital signs measurements), considering each one individually and noting a substantial degree of heterogeneity as to the impact of aging across them. In contrast, in the present study, we have used Complex Inference Networks ( Stephens et al, 2009 , 2018 ) of these biomarkers as a means to give a more holistic, systems-biology perspective in order to demonstrate how the changes in the coupling between regulated variables and those regulatory systems that try to maintain homeostasis lead to metabolic health changes over a lifetime.…”