Furthering Precision Medicine Genomics With Healthy Living Medicine

The Hidden Dangers of Sedentary Living: Insights into Molecular, Cellular, and Systemic Mechanisms

With the aging of the global population, neurodegenerative diseases are emerging as a major public health issue. The adoption of a less sedentary lifestyle has been shown to have a beneficial effect on cognitive decline, but the molecular mechanisms responsible are less clear. Here we provide a detailed analysis of the complex molecular, cellular, and systemic mechanisms underlying age-related cognitive decline and how lifestyle choices influence these processes. A review of the evidence from animal models, human studies, and postmortem analyses emphasizes the importance of integrating physical exercise with cognitive, multisensory, and motor stimulation as part of a multifaceted approach to mitigating cognitive decline. We highlight the potential of these non-pharmacological interventions to address key aging hallmarks, such as genomic instability, telomere attrition, and neuroinflammation, and underscore the need for comprehensive and personalized strategies to promote cognitive resilience and healthy aging.

Mode of transport, genetic susceptibility, and incidence of coronary heart disease

BACKGROUND

Car use has been associated with higher risk of coronary heart disease (CHD). However, whether the associations of transport modes with CHD vary by genetic susceptibility to CHD are unknown. This study aims to investigate the associations of genetic susceptibility and modes of transport with incidence of CHD.

METHODS

We included 339,588 white British participants from UK Biobank with no history of CHD or stroke at baseline or within two years of follow-up (52.3% in work). Genetic susceptibility to CHD was quantified through weighted polygenic risk scores derived from 300 single-nucleotide polymorphisms related to CHD risk. Categories of transport mode included exclusive car use and alternatives to the car (e.g., walking, cycling and public transport), separately for non-commuting (e.g., getting about [n=339,588] excluding commuting for work), commuting (in the sub-set in work [n=177,370] who responded to the commuting question), and overall transport (transport mode for both commuting and non-commuting [n=177,370]). We used Cox regression with age as the underlying timescale to estimate hazard ratios (HR) of CHD (n=13,730; median 13.8-year follow-up) and tested the interaction between genetic susceptibility and travel modes with adjustment for confounders.

RESULTS

Compared to those using alternatives to the car, hazards of CHD were higher for exclusive use of cars for overall transport (HR: 1.16, 95% confidence interval (CI): 1.08-1.25), non-commuting (HR: 1.08, 95% CI: 1.04-1.12) and commuting (HR: 1.16, 95% CI: 1.09-1.23), after adjusting for confounders plus genetic susceptibility. HRs of CHD were 1.45 (95% CI: 1.38-1.52) and 2.04 (95% CI: 1.95-2.12) for the second and third tertile of genetic susceptibility to CHD, respectively, compared to the first. There was, in general, no strong evidence of interactions between genetic susceptibility and categories of overall, non-commuting and commuting transport. Estimated 10-year absolute risk of CHD was lower for the alternatives to the car across strata of genetic susceptibility, compared with exclusive use of cars for overall, non-commuting and commuting transport.

CONCLUSION

Exclusive use of cars was associated with a relatively higher risk of CHD across all strata of genetic susceptibility. Using alternatives to the car should be encouraged for prevention of CHD for the general population including individuals at high genetic risk.

Effects of a Lifestyle-Modification Program on Blood-Glucose Regulation and Health Promotion in Diabetic Patients: A Randomized Controlled Trial

Background

We aimed to investigate the efficacy of the lifestyle intervention (LSI) program in controlling blood glucose regulation and health promotion in type 2 diabetic (T2D) patients.

Methods

Thirty adults with a diagnosed with diabetes were randomly assigned to LSI and control groups. The LSI group maintained their daily routines after participating twice in the LSI program, while control group maintained 4 weeks of daily life without participating in an intervention.

Results

HbA1c levels in the LSI group decreased significantly after participation (p = 0.025) compared with levels before the study, but there was no significant difference between the groups. The weight and body mass index (BMI) of the LSI group tended to decrease significantly compared with the control group (p = 0.054 and p = 0.055, respectively), and the waist circumference (WC) of the LSI group decreased significantly compared with that of the control group (p = 0.048). In the effects of the LSI program according to the polymorphism of GCKR genes, changes in glycated albumin (GA) (%), HbA1c, WC, BMI, and weight showed a significant decrease in the non-risk (TT genotype) GCKR group compared with the risk group (CC and TC genotype).

Conclusion

Application of the four-week LSI program to diabetics revealed positive effects on blood-glucose control and improvement in obesity indicators. In particular, the risk group with variations in the GCKR gene was associated with more genetic effects on indicators such as blood glucose and obesity than was the non-risk group.

Ambiguity in Communicating Intensity of Physical Activity: Survey Study

Background

Communicating physical activity information with sufficient details, such as activity type, frequency, duration, and intensity, is vital to accurately delineate the attributes of physical activity that bring positive health impact. Unlike frequency and duration, intensity is a subjective concept that can be interpreted differently by people depending on demographics, health status, physical fitness, and exercise habits. However, activity intensity is often communicated using general degree modifiers, degree of physical exertion, and physical activity examples, which are the expressions that people may interpret differently. Lack of clarity in communicating the intensity level of physical activity is a potential barrier to an accurate assessment of exercise effect and effective imparting of exercise recommendations.

Objective

This study aimed to assess the variations in people’s perceptions and interpretations of commonly used intensity descriptions of physical activities and to identify factors that may contribute to these variations.

Methods

A Web-based survey with a 25-item questionnaire was conducted using Amazon Mechanical Turk, targeting adults residing in the United States. The questionnaire included questions on participants’ demographics, exercise habits, overall perceived health status, and perceived intensity of 10 physical activity examples. The survey responses were analyzed using the R statistical package.

Results

The analyses included 498 responses. The majority of respondents were females (276/498, 55.4%) and whites (399/498, 79.9%). Numeric ratings of physical exertion after exercise were relatively well associated with the 3 general degree descriptors of exercise intensity: light, moderate, and vigorous. However, there was no clear association between the intensity expressed with those degree descriptors and the degree of physical exertion the participants reported to have experienced after exercise. Intensity ratings of various examples of physical activity differed significantly according to respondents’ characteristics. Regression analyses showed that those who reported good health or considered regular exercise was important for their health tended to rate the intensity levels of the activity examples significantly higher than their counterparts. The respondents’ age and race (white vs nonwhite) were not significant predictors of the intensity rating.

Conclusions

This survey showed significant variations in how people perceive and interpret the intensity levels of physical activities described with general severity modifiers, degrees of physical exertion, and physical activity examples. Considering that these are among the most widely used methods of communicating physical activity intensity in current practice, a possible miscommunication in assessing and promoting physical activity seems to be a real concern. We need to adopt a method that represents activity intensity in a quantifiable manner to avoid unintended miscommunication.