Correction: A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study.

[This corrects the article DOI: 10.2196/35696.].

A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study.

BACKGROUND: Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion. OBJECTIVE: This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging. METHODS: Carefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age. RESULTS: The first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes. CONCLUSIONS: Estimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory. TRIAL REGISTRATION: ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19209.

A Biological Age Model Designed for Health Promotion Interventions: Protocol for an Interdisciplinary Study for Model Development.

BACKGROUND: Actions to improve healthy aging and delay morbidity are crucial, given the global aging population. We believe that biological age estimation can help promote the health of the general population. Biological age reflects the heterogeneity in functional status and vulnerability to disease that chronological age cannot. Thus, biological age assessment is a tool that provides an intuitively meaningful outcome for the general population, and as such, facilitates our understanding of the extent to which lifestyle can increase health span. OBJECTIVE: This interdisciplinary study intends to develop a biological age model and explore its usefulness. METHODS: The model development comprised three consecutive phases: (1) conducting a cross-sectional study to gather candidate biomarkers from 100 individuals representing normal healthy aging people (the derivation cohort); (2) estimating the biological age using principal component analysis; and (3) testing the clinical use of the model in a validation cohort of overweight adults attending a lifestyle intervention course. RESULTS: We completed the data collection and analysis of the cross-sectional study, and the initial results of the principal component analysis are ready. Interpretation and refinement of the model is ongoing. Recruitment to the validation cohort is forthcoming. We expect the results to be published by December 2021. CONCLUSIONS: We expect the biological age model to be a useful indicator of disease risk and metabolic risk, and further research should focus on validating the model on a larger scale. TRIAL REGISTRATION: ClinicalTrials.gov NCT03680768, https://clinicaltrials.gov/ct2/show/NCT03680768 (Phase 1 study); NCT04279366 https://clinicaltrials.gov/ct2/show/NCT04279366 (Phase 3 study). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19209.

In vivo demonstration of Pseudomonas aeruginosa biofilms as independent pharmacological microcompartments.

BACKGROUND: Pseudomonas aeruginosa is difficult to eradicate from the lungs of cystic fibrosis (CF) patients due to biofilm formation. Organs and blood are independent pharmacokinetic (PK) compartments. Previously, we showed in vitro biofilms behave as independent compartments impacting the pharmacodynamics. The present study investigated this phenomenon in vivo. METHODS: Seaweed alginate beads with P. aeruginosa resembling biofilms, either freshly produced (D0) or incubated for 5 days (D5) were installed s.c in BALB/c mice. Mice (n = 64) received tobramycin 40 mg/kg s.c. and were sacrificed at 0.5, 3, 6, 8, 16 or 24 h after treatment. Untreated controls (n = 14) were sacrificed, correspondingly. Tobramycin concentrations were determined in serum, muscle tissue, lung tissue and beads. Quantitative bacteriology was determined. RESULTS: The tobramycin peak concentrations in serum was 58.3 (±9.2) mg/L, in lungs 7.1 mg/L (±2.3), muscle tissue 2.8 mg/L (±0.5) all after 0.5 h and in D0 beads 19.8 mg/L (±3.5) and in D5 beads 24.8 mg/L (±4.1) (both 3 h). A 1-log killing of P. aeruginosa in beads was obtained at 8h, after which the bacterial level remained stable at 16 h and even increased in D0 beads at 24 h. Using the established diffusion retardation model the free tobramycin concentration inside the beads showed a delayed buildup of 3 h but remained lower than the MIC throughout the 24 h. CONCLUSIONS: The present in vivo study based on tobramycin exposure supports that biofilms behave as independent pharmacological microcompartments. The study indicates, reducing the biofilm matrix would increase free tobramycin concentrations and improve therapeutic effects.

Formation of Pseudomonas aeruginosa inhibition zone during tobramycin disk diffusion is due to transition from planktonic to biofilm mode of growth.

Pseudomonas aeruginosa PAO1 (tobramycin MIC = 0.064 µg/mL) was used to perform agar diffusion tests employing tobramycin-containing tablets. Bacterial growth and formation of inhibition zones were studied by stereomicroscopy and by blotting with microscope slides and staining with methylene blue, Alcian blue and a fluorescent lectin for the P. aeruginosa PSL, which was studied by confocal laser scanning microscopy. Diffusion of tobramycin from the deposit was modelled using a 3D geometric version of Fick's second law of diffusion. The time-dependent gradual increase in the minimum biofilm eradication concentration (MBEC) was studied using a Calgary Biofilm Device. The early inhibition zone was visible after 5 h of incubation. The corresponding calculated tobramycin concentration at the border was 1.9 µg/mL, which increased to 3.2 µg/mL and 6.3 µg/mL after 7 h and 24 h, respectively. The inhibition zone increased to the stable final zone after 7 h of incubation. Bacterial growth and small aggregate formation (young biofilms) took place inside the inhibition zone until the small aggregates contained less than ca. 64 cells and production of polysaccharide matrix including PSL had begun; thereafter, the small bacterial aggregates were killed by tobramycin. Bacteria at the border of the stable inhibition zone and beyond continued to grow to a mature biofilm and produced large amount of polysaccharide-containing matrix. Formation of the inhibition zone during agar diffusion antimicrobial susceptibility testing is due to a switch from a planktonic to biofilm mode of growth and gives clinically important information about the increased antimicrobial tolerance of biofilms.

Modelling of ciprofloxacin killing enhanced by hyperbaric oxygen treatment in Pseudomonas aeruginosa PAO1 biofilms.

OUTLINE: In chronic lung infections by Pseudomonas aeruginosa (PA) the bacteria thrive in biofilm structures protected from the immune system of the host and from antibiotic treatment. Increasing evidence suggests that the susceptibility of the bacteria to antibiotic treatment can be significantly enhanced by hyperbaric oxygen treatment. The aim of this study is to simulate the effect of ciprofloxacin treatment in a PAO1 biofilm model with aggregates in agarose when combined with hyperbaric oxygen treatment. This is achieved in a reaction-diffusion model that describes the combined effect of ciprofloxacin diffusion, oxygen diffusion and depletion, bacterial growth and killing, and adaptation of the bacteria to ciprofloxacin. In the model, the oxygen diffusion and depletion use a set of parameters derived from experimental results presented in this work. The part of the model describing ciprofloxacin killing uses parameter values from the literature in combination with our estimates (Jacobs, et al., 2016; Grillon, et al., 2016). Micro-respirometry experiments were conducted to determine the oxygen consumption in the P. aeruginosa strain PAO1. The parameters were validated against existing data from an HBOT experiment by Kolpen et al. (2017). The complete oxygen model comprises a reaction-diffusion equation describing the oxygen consumption by using a Michaelis-Menten reaction term. The oxygen model performed well in predicting oxygen concentrations in both time and depth into the biofilm. At 2.8 bar pure oxygen pressure, HBOT increases the penetration depth of oxygen into the biofilm almost by a of factor 4 in agreement with the scaling that follows from the stationary balance between the consumption term and diffusion term. CONCLUSION: In the full reaction-diffusion model we see that hyperbaric oxygen treatment significantly increases the killing by ciprofloxacin in a PAO1 biofilm in alignment with the experimental results from Kolpen et al. (Kolpen, et al., 2017; Kolpen, et al. 2016). The enhanced killing, in turn, lowers the oxygen consumption in the outer layers of the biofilm, and leads to even deeper penetration of oxygen into the biofilm.

A method to investigate the biomechanical alterations in Perthes' disease by hip joint contact modeling.

Perthes' disease is a destructive hip joint disorder characterized by malformation of the femoral head in young children. While the morphological changes have been widely studied, the biomechanical effects of these changes still need to be further elucidated. The objective of this study was to develop a method to investigate the biomechanical alterations in Perthes' disease by finite element (FE) contact modeling using MRI. The MRI data of a unilateral Perthes' case was obtained to develop the three-dimensional FE model of the hip joint. The stress and contact pressure patterns in the unaffected hip were well distributed. Elevated concentrations of stress and contact pressure were found in the Perthes' hip. The highest femoral cartilage von Mises stress 3.9 MPa and contact pressure 5.3 MPa were found in the Perthes' hip, whereas 2.4 MPa and 4.9 MPa in the healthy hip, respectively. The healthy bone in the femoral head of the Perthes' hip carries additional loads as indicated by the increase of stress levels around the necrotic-healthy bone interface. Identifying the biomechanical changes, such as the location of stress and contact pressure concentrations, is a prerequisite for the preoperative planning to obtain stress relief for the highly stressed areas in the malformed hip. This single-patient study demonstrated that the biomechanical alterations in Perthes' disease can be evaluated individually by patient-specific finite element contact modeling using MRI. A multi-patient study is required to test the strength of the proposed method as a pre-surgery planning tool.