Metabolic effects of a 13-weeks lifestyle intervention in older adults: The Growing Old Together Study

Leisure time physical activity is associated with improved diastolic heart function and is partly mediated by unsupervised quantified metabolic health

Objectives

To investigate the association between leisure time physical activity (LTPA) and MRI-based diastolic function and the mediating role of metabolic health.

Methods

This cross-sectional analysis comprised 901 participants (46% women, mean age (SD): 56 (6) years (The Netherlands, 2008-2012)). LTPA was assessed via questionnaire, quantified in metabolic equivalent of tasks (METs)-minutes per week and participants underwent abdominal and cardiovascular MRI. Confirmatory factor analysis was used to construct the metabolic load factor. Piecewise structural equation model with adjustments for confounders was used to determine associations between LTPA and diastolic function and the mediating effect of metabolic load.

Results

Significant differences in mitral early/late peak filling rate (E/A) ratio per SD of LTPA (men=1999, women=1870 MET-min/week) of 0.18, (95% CI= 0.03 to 0.33, p=0.021) were observed in men, but not in women: -0.01 (-0.01 to 0.34, p=0.058). Difference in deceleration time of mitral early filling (E-DT) was 0.13 (0.01 to 0.24, p=0.030) in men and 0.17 (0.05 to 0.28, p=0.005) in women. Metabolic load, including MRI-based visceral and subcutaneous adipose tissue, fasting glucose, high-density lipoprotein cholesterol and triglycerides, mediated these associations as follows: E/A-ratio of 0.030 (0.000 to 0.067, 19% mediated, p=0.047) in men but not in women: 0.058 (0.027 to 0.089, p<0.001) and E-DT not in men 0.004 (-0.012 to 0.021, p=0.602) but did in women 0.044 (0.013 to 0.057, 27% mediated, p=0.006).

Conclusions

A larger amount of LTPA was associated with improved diastolic function where confirmatory factor analysis-based metabolic load partly mediated this effect. Future studies should assess whether improving indicators of metabolic load alongside LTPA will benefit healthy diastolic function even more.

Effect of behavioral weight-loss program on biomarkers of cardiometabolic disease risk: Heart Health Study randomized trial

OBJECTIVE

This study aimed to determine whether novel biomarkers of cardiometabolic health improve in response to a 12-month behavioral weight-loss intervention and to compare benefits of diet alone with diet plus physical activity for these biomarkers.

METHODS

Participants (N = 374) were randomized to either diet alone (DIET), diet plus 150 min/wk of prescribed moderate-intensity physical activity (DIET + PA150), or diet plus 250 min/wk of prescribed moderate-intensity physical activity (DIET + PA250). Biomarker concentrations were determined using nuclear magnetic resonance spectroscopy. Mixed models assessed for a time effect, group effect, or group by time interaction.

RESULTS

All groups significantly improved body weight (time: p < 0.0001), Lipoprotein Insulin Resistance Index score (time: p < 0.0001), Diabetes Risk Index score (time: p < 0.0001), branched-chain amino acid concentration (time: p < 0.0001), and GlycA concentration (time: p < 0.0001), with no group effect or group by time interactions.

CONCLUSIONS

All intervention groups prompted a notable beneficial change among biomarkers of insulin resistance and cardiometabolic health. However, the addition of at least moderate-intensity physical activity to a diet-only intervention did not provide any additional benefit. These findings highlight that an average weight loss of approximately 10% profoundly impacts biomarkers of insulin resistance and cardiometabolic disease in adults with overweight or obesity.

Theory-derived intervention to improve oral health of older adults: study protocol for a randomised controlled trial

INTRODUCTION

Changing health behaviours is an important and difficult task. Despite growing interest in behavioural theories and models, there is a paucity of research examining their validity in explaining oral health behaviours, and there is a need for interventional studies to assess their effectiveness in improving oral health. This study aims to test the explanatory power of the dominant psychological theories, develop theory-derived intervention and evaluate its effectiveness in improving oral health of older adults.

METHODS AND ANALYSIS

440 community dwelling older adults will be recruited. To be eligible for this trial, one needs to be 55-79 years old, having at least 8 natural teeth, and with no life-threatening disease, impaired cognitive function, or radiotherapy in the head and neck region. At the initial visit, each participant will be required to complete a detailed questionnaire which collects information on sociodemographic background, oral health behaviours and domains of three psychological theories and models: (1) health belief model, (2) theory of planned behaviour and (3) social cognitive theory. The theory or model that best explains the health behaviours will be selected for designing the oral health intervention. The effectiveness of the theory-derived intervention will be evaluated in a randomised controlled trial. Participants will be randomly assigned to two groups, receiving theory-derived intervention and conventional health education, respectively. At baseline and at 12 and 24 months post intervention, each participant will complete a short questionnaire and undergo an oral examination (dental check-up). The effectiveness of the interventions will be evaluated using behavioural outcomes (diet, toothbrushing, interdental cleaning) and clinical outcomes (oral hygiene, dental caries and periodontal conditions).

ETHICS AND DISSEMINATION

This study has been approved by the Institutional Review Board of National University of Singapore (Ref: NUS-IRB-2020-417). Findings will be presented in international conferences and peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04946292.

PLIS: A metabolomic response monitor to a lifestyle intervention study in older adults

The response to lifestyle intervention studies is often heterogeneous, especially in older adults. Subtle responses that may represent a health gain for individuals are not always detected by classical health variables, stressing the need for novel biomarkers that detect intermediate changes in metabolic, inflammatory, and immunity-related health. Here, our aim was to develop and validate a molecular multivariate biomarker maximally sensitive to the individual effect of a lifestyle intervention; the Personalized Lifestyle Intervention Status (PLIS). We used ¹ H-NMR fasting blood metabolite measurements from before and after the 13-week combined physical and nutritional Growing Old TOgether (GOTO) lifestyle intervention study in combination with a fivefold cross-validation and a bootstrapping method to train a separate PLIS score for men and women. The PLIS scores consisted of 14 and four metabolites for females and males, respectively. Performance of the PLIS score in tracking health gain was illustrated by association of the sex-specific PLIS scores with several classical metabolic health markers, such as BMI, trunk fat%, fasting HDL cholesterol, and fasting insulin, the primary outcome of the GOTO study. We also showed that the baseline PLIS score indicated which participants respond positively to the intervention. Finally, we explored PLIS in an independent physical activity lifestyle intervention study, showing similar, albeit remarkably weaker, associations of PLIS with classical metabolic health markers. To conclude, we found that the sex-specific PLIS score was able to track the individual short-term metabolic health gain of the GOTO lifestyle intervention study. The methodology used to train the PLIS score potentially provides a useful instrument to track personal responses and predict the participant's health benefit in lifestyle interventions similar to the GOTO study.

A recurrent neural network architecture to model physical activity energy expenditure in older people

Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being.

Unlike dietary restriction, rapamycin fails to extend lifespan and reduce transcription stress in progeroid DNA repair-deficient mice

Dietary restriction (DR) and rapamycin extend healthspan and life span across multiple species. We have recently shown that DR in progeroid DNA repair‐deficient mice dramatically extended healthspan and trippled life span. Here, we show that rapamycin, while significantly lowering mTOR signaling, failed to improve life span nor healthspan of DNA repair‐deficient Ercc1^∆/− mice, contrary to DR tested in parallel. Rapamycin interventions focusing on dosage, gender, and timing all were unable to alter life span. Even genetically modifying mTOR signaling failed to increase life span of DNA repair‐deficient mice. The absence of effects by rapamycin on P53 in brain and transcription stress in liver is in sharp contrast with results obtained by DR, and appoints reducing DNA damage and transcription stress as an important mode of action of DR, lacking by rapamycin. Together, this indicates that mTOR inhibition does not mediate the beneficial effects of DR in progeroid mice, revealing that DR and rapamycin strongly differ in their modes of action.

Lifestyle-Intervention-Induced Reduction of Abdominal Fat Is Reflected by a Decreased Circulating Glycerol Level and an Increased HDL Diameter

Scope

Abdominal obesity is one of the main modifiable risk factors of age‐related cardiometabolic disease. Cardiometabolic disease risk and its associated high abdominal fat mass, cholesterol, and glucose concentrations can be reduced by a healthier lifestyle. Hence, the aim is to understand the relation between lifestyle‐induced changes in body composition, and specifically abdominal fat, and accompanying changes in circulating metabolic biomarkers.

Methods and results

Data from the Growing Old Together (GOTO) study was used, which is a single arm lifestyle intervention in which 164 older adults (mean age 63 years, BMI 23–35 kg/m²) changed their lifestyle during 13 weeks by 12.5% caloric restriction plus 12.5% increase in energy expenditure. It is shown here that levels of circulating metabolic biomarkers, even after adjustment for body mass index, specifically associate with abdominal fat mass. The applied lifestyle intervention mainly reduces abdominal fat mass (−2.6%, SD = 3.0) and this reduction, when adjusted for general weight loss, is highly associated with decreased circulating glycerol concentrations and increased HDL diameter.

Conclusion

The lifestyle‐induced reduction of abdominal fat mass is particularly associated, independent of body mass index or general weight loss, with decreased circulating glycerol concentrations and increased HDL diameter.

Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks

Human Activity Recognition (HAR) is a growing field of research in biomedical engineering and it has many potential applications in the treatment and prevention of several diseases. Due to the recent advancement in technology, devices that collect position and orientation measurements (e.g. accelerometers and gyroscopes) are becoming ubiquitous. These measurements can then be used to train machine learning models for HAR. In this research, we propose one recurrent neural network architecture and a data augmentation approach for building robust and accurate models for HAR. We compared models with Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers. The proposed data augmentation approach was used to make the models robust to the cases where one or more sensors are missing. In this empirical study, we could also understand some relations between the ideal locations of the sensors in the participants and the types of activities performed. The proposed approaches were tested in the GOTOv dataset from a study which involved 35 participants performing 16 sedentary, ambulatory and lifestyle activities in a semi-structured environment. The results presented, clearly show that the models are able to detect these activities in a robust way.

Electromembrane Extraction of Highly Polar Compounds: Analysis of Cardiovascular Biomarkers in Plasma

Cardiovascular diseases (CVDs) represent a major concern in today’s society, with more than 17.5 million deaths reported annually worldwide. Recently, five metabolites related to the gut metabolism of phospholipids were identified as promising predictive biomarker candidates for CVD. Validation of those biomarker candidates is crucial for applications to the clinic, showing the need for high-throughput analysis of large numbers of samples. These five compounds, trimethylamine N-oxide (TMAO), choline, betaine, l-carnitine, and deoxy-l-carnitine (4-trimethylammoniobutanoic acid), are highly polar compounds and show poor retention on conventional reversed phase chromatography, which can lead to strong matrix effects when using mass spectrometry detection, especially when high-throughput analysis approaches are used with limited separation of analytes from interferences. In order to reduce the potential matrix effects, we propose a novel fast parallel electromembrane extraction (Pa-EME) method for the analysis of these metabolites in plasma samples. The evaluation of Pa-EME parameters was performed using multi segment injection–capillary electrophoresis–mass spectrometry (MSI-CE-MS). Recoveries up to 100% were achieved, with variability as low as 2%. Overall, this study highlights the necessity of protein precipitation prior to EME for the extraction of highly polar compounds. The developed Pa-EME method was evaluated in terms of concentration range and response function, as well as matrix effects using fast-LC-MS/MS. Finally, the developed workflow was compared to conventional sample pre-treatment, i.e., protein precipitation using methanol, and fast-LC-MS/MS. Data show very strong correlations between both workflows, highlighting the great potential of Pa-EME for high-throughput biological applications.

Fast LC-ESI-MS/MS analysis and influence of sampling conditions for gut metabolites in plasma and serum

In the past few years, the gut microbiome has been shown to play an important role in various disorders including in particular cardiovascular diseases. Especially the metabolite trimethylamine-N-oxide (TMAO), which is produced by gut microbial metabolism, has repeatedly been associated with an increased risk for cardiovascular events. Here we report a fast liquid chromatography tandem mass spectrometry (LC-MS/MS) method that can analyze the five most important gut metabolites with regards to TMAO in three minutes. Fast liquid chromatography is unconventionally used in this method as an on-line cleanup step to remove the most important ion suppressors leaving the gut metabolites in a cleaned flow through fraction, also known as negative chromatography. We compared different blood matrix types to recommend best sampling practices and found citrated plasma samples demonstrated lower concentrations for all analytes and choline concentrations were significantly higher in serum samples. We demonstrated the applicability of our method by investigating the effect of a standardized liquid meal (SLM) after overnight fasting of 25 healthy individuals on the gut metabolite levels. The SLM did not significantly change the levels of gut metabolites in serum.

Dietary Protein and Muscle in Aging People: The Potential Role of the Gut Microbiome

Muscle mass, strength, and physical function are known to decline with age. This is associated with the development of geriatric syndromes including sarcopenia and frailty. Dietary protein is essential for skeletal muscle function. Resistance exercise appears to be the most beneficial form of physical activity for preserving skeletal muscle and a synergistic effect has been noted when this is combined with dietary protein. However, older adults have shown evidence of anabolic resistance, where greater amounts of protein are required to stimulate muscle protein synthesis, and response is variable. Thus, the recommended daily amount of protein is greater for older people. The aetiologies and mechanisms responsible for anabolic resistance are not fully understood. The gut microbiota is implicated in many of the postulated mechanisms for anabolic resistance, either directly or indirectly. The gut microbiota change with age, and are influenced by dietary protein. Research also implies a role for the gut microbiome in skeletal muscle function. This leads to the hypothesis that the gut microbiome might modulate individual response to protein in the diet. We summarise the existing evidence for the role of the gut microbiota in anabolic resistance and skeletal muscle in aging people, and introduce the metabolome as a tool to probe this relationship in the future.

High Adiposity Is Associated With Higher Nocturnal and Diurnal Glycaemia, but Not With Glycemic Variability in Older Individuals Without Diabetes

BACKGROUND

It is well known that adiposity is a risk factor for insulin resistance and type 2 diabetes mellitus. In the present study, we aimed to investigate the associations of measures of adiposity with indices of glycemia and of glycemic variability over a 72-h period in non-diabetic older adults.

METHODS

This cross-sectional study was conducted in non-diabetic individuals from the Active and Healthy Aging Study (N = 228), Switchbox (N = 116), and the Growing Old Together Study (N = 94). Body mass index (BMI) and waist circumference were measured, and indices of glycemia and glycemic variability were derived from continuous glucose monitoring (CGM) using the Mini-Med^® CGM system. Associations between adiposity and CGM were studied separately for the three cohorts, and derived estimates were subsequently meta-analyzed.

RESULTS

After meta-analyzing the results from the separate cohorts, individuals with a higher BMI had higher levels of glycemia. Individuals with BMI between 30 and 35 kg/m² had 0.28 mmol/L [95% confidence interval (CI): 0.12-0.44] higher 72 h-mean glucose concentration, 0.26 mmol/L (0.10-0.42) higher diurnal glucose (6:00 a.m. to 0:00 a.m.), and 0.39 mmol/L (0.19; 0.59) higher nocturnal glucose (3:00 a.m. to 6:00 a.m.) than participants with a normal weight (BMI 18.5-25 kg/m²). However, no associations were observed between higher BMI and glycemic variability. Results for glycemia and glycemic variability were similarly observed for a high waist circumference.

CONCLUSION

High adiposity associates with constant higher mean glucose levels over the day in non-diabetic older adults.

Phenome and genome based studies into human ageing and longevity: An overview

Human ageing is an extremely personal process leading across the life course of individuals to large population heterogeneity in the decline of functional capacity, health and lifespan. The extremes of this process are witnessed by the healthy vital 100-year-olds on one end and the 60-year-olds suffering from multiple morbid conditions on the other end of the spectrum. Molecular studies into the basis of this heterogeneity have focused on a range of endpoints and methodological approaches. The phenotype definitions most prominently investigated in these studies are either lifespan-related or biomarker based indices of the biological ageing rate of individuals and their tissues. Unlike for many complex, age-related diseases, consensus on the ultimate set of multi-biomarker ageing or lifespan-related phenotypes for genetic and genomic studies has not been reached yet. Comparable to animal models, hallmarks of age-related disease risk, healthy ageing and longevity include immune and metabolic pathways. Potentially novel genomic regions and pathways have been identified among many (epi)genomic studies into chronological age and studies into human lifespan regulation, with APOE and FOXO3A representing yet the most robust loci. Functional analysis of a handful of genes in cell-based and animal models is ongoing. The way forward in human ageing and longevity studies seems through improvements in the interpretation of the biology of the genome, in application of computational and systems biology, integration with animal models and by harmonization of repeated phenotypic and omics measures in longitudinal and intervention studies. This article is part of a Special Issue entitled: Model Systems of Aging - edited by "Houtkooper Riekelt".

Small nucleoli are a cellular hallmark of longevity

Animal lifespan is regulated by conserved metabolic signalling pathways and specific transcription factors, but whether these pathways affect common downstream mechanisms remains largely elusive. Here we show that NCL-1/TRIM2/Brat tumour suppressor extends lifespan and limits nucleolar size in the major C. elegans longevity pathways, as part of a convergent mechanism focused on the nucleolus. Long-lived animals representing distinct longevity pathways exhibit small nucleoli, and decreased expression of rRNA, ribosomal proteins, and the nucleolar protein fibrillarin, dependent on NCL-1. Knockdown of fibrillarin also reduces nucleolar size and extends lifespan. Among wildtype C. elegans, individual nucleolar size varies, but is highly predictive for longevity. Long-lived dietary restricted fruit flies and insulin-like-peptide mutants exhibit small nucleoli and fibrillarin expression, as do long-lived dietary restricted and IRS1 knockout mice. Furthermore, human muscle biopsies from individuals who underwent modest dietary restriction coupled with exercise also display small nucleoli. We suggest that small nucleoli are a cellular hallmark of longevity and metabolic health conserved across taxa.

Animal lifespan is plastic and is regulated by conserved signalling pathways. Here, Tiku et al. show that longevity-enhancing mutations or interventions are associated with reduced nucleolar size in worms, flies, mice and humans, and that nucleolar size can predict life-expectancy in individual worms.

Good Ol' Fat: Links between Lipid Signaling and Longevity

Aging is the single greatest risk factor for the development of disease. Understanding the biological molecules and mechanisms that modulate aging is therefore critical for the development of health-maximizing interventions for older people. The effect of fats on longevity has traditionally been disregarded as purely detrimental. However, new studies are starting to uncover the possible beneficial effects of lipids working as signaling molecules on health and longevity. These studies highlight the complex links between aging and lipid signaling. In this review we summarize accumulating evidence that points to changes in lipid metabolism, and in particular lipid signaling, as an underlying mechanism for healthy aging.

High Liver Enzyme Concentrations are Associated with Higher Glycemia, but not with Glycemic Variability, in Individuals without Diabetes Mellitus

BACKGROUND

Elevated concentrations of liver enzymes have been associated with an increased risk of developing type 2 diabetes mellitus. However, it remains unclear to which specific aspects of diurnal glucose metabolism these associate most. We aimed to investigate the associations between liver enzyme concentrations and 24 h-glucose trajectories in individuals without diabetes mellitus from three independent cohorts.

METHODS

This cross-sectional study included 436 participants without diabetes mellitus from the Active and Healthy Aging Study, the Switchbox Study, and the Growing Old Together Study. Fasting blood samples were drawn to measure gamma-glutamyltransferase (GGT), alanine transaminase, and aspartate transaminase. Measures of glycemia (e.g., nocturnal and diurnal mean glucose levels) and glycemic variability (e.g., mean amplitude of glucose excursions) were derived from continuous glucose monitoring. Analyses were performed separately for the three cohorts; derived estimates were additionally meta-analyzed.

RESULTS

After meta-analyses of the three cohorts, elevated liver enzyme concentrations, and specifically elevated GGT concentrations, were associated with higher glycemia. More specific, participants in the highest GGT tertile (GGT ≥37.9 U/L) had a 0.39 mmol/L (95% confidence interval: 0.23, 0.56) higher mean nocturnal glucose (3:00 to 6:00 a.m.) and a 0.23 mmol/L (0.10, 0.36) higher diurnal glucose (6:00 to 0:00 a.m.) than participants in the lowest GGT tertile (GGT <21.23 U/L). However, elevated liver enzyme concentrations were not associated with a higher glycemic variability.

CONCLUSION

Though elevated liver enzyme concentrations did not associate with higher glycemic variability in participants without diabetes mellitus, specifically, elevated GGT concentrations associated with higher glycemia.

The effect of standardized food intake on the association between BMI and 1H-NMR metabolites

Multiple studies have shown that levels of ¹H-NMR metabolites are associated with disease and risk factors of disease such as BMI. While most previous investigations have been performed in fasting samples, meta-analysis often includes both cohorts with fasting and non-fasting blood samples. In the present study comprising 153 participants (mean age 63 years; mean BMI 27 kg/m²) we analyzed the effect of a standardized liquid meal (SLM) on metabolite levels and how the SLM influenced the association between metabolites and BMI. We observed that many metabolites, including glycolysis related metabolites, multiple amino acids, LDL diameter, VLDL and HDL lipid concentration changed within 35 minutes after a standardized liquid meal (SLM), similarly for all individuals. Remarkable, however, is that the correlations of metabolite levels with BMI remained highly similar before and after the SLM. Hence, as exemplified with the disease risk factor BMI, our results suggest that the applicability of ¹H-NMR metabolites as disease biomarkers depends on the standardization of the fasting status rather than on the fasting status itself. Future studies are required to investigate the dependency of metabolite biomarkers for other disease risk factors on the fasting status.

Classification for Longevity Potential: The Use of Novel Biomarkers

Background

In older people, chronological age may not be the best predictor of residual lifespan and mortality, because with age the heterogeneity in health is increasing. Biomarkers for biological age and residual lifespan are being developed to predict disease and mortality better at an individual level than chronological age. In the current paper, we aim to classify a group of older people into those with longevity potential or controls.

Methods

In the Leiden Longevity Study participated 1671 offspring of nonagenarian siblings, as the group with longevity potential, and 744 similarly aged controls. Using known risk factors for cardiovascular disease, previously reported markers for human longevity and other physiological measures as predictors, classification models for longevity potential were constructed with multiple logistic regression of the offspring-control status.

Results

The Framingham Risk Score (FRS) is predictive for longevity potential [area under the receiver operating characteristic curve (AUC) = 64.7]. Physiological parameters involved in immune responses and glucose, lipid and energy metabolism further improve the prediction performance for longevity potential (AUCmale = 71.4, AUCfemale = 68.7).

Conclusion

Using the FRS, the classification of older people in groups with longevity potential and controls is moderate, but can be improved to a reasonably good classification in combination with markers of immune response, glucose, lipid, and energy metabolism. We show that individual classification of older people for longevity potential may be feasible using biomarkers from a wide variety of different biological processes.