Ambulant 24-h glucose rhythms mark calendar and biological age in apparently healthy individuals

Accuracy of Flash Glucose Monitoring During Postprandial Rest and Different Walking Conditions in Overweight or Obese Young Adults

Aims: To investigate the accuracy of FreeStyle Libre^TM flash glucose monitoring (FGM) relevant to plasma glucose (PG) measurements during postprandial rest and different walking conditions in overweight/obese young adults. Methods: Data of 40 overweight/obese participants from two randomized crossover studies were pooled into four trials: (1) sitting (SIT, n = 40); (2) walking continuously for 30 min initiated 20 min before individual postprandial glucose peak (PPGP) (20iP + CONT, n = 40); (3) walking continuously for 30 min initiated at PPGP (iP + CONT, n = 20); and (4) accumulated walking for 30 min initiated 20 min before PPGP (20iP + ACCU, n = 20). Paired FGM and PG were measured 4 h following breakfast. Results: The overall mean absolute relative difference (MARD) between PG and FGM readings was 16.4 ± 8.6% for SIT, 16.2 ± 4.7% for 20iP + CONT, 16.7 ± 12.2% for iP + CONT, and 19.1 ± 6.8% for 20iP + ACCU. The Bland-Altman analysis showed a bias of -1.03 mmol⋅L^-1 in SIT, -0.89 mmol⋅L^-1 in 20iP + CONT, -0.82 mmol⋅L^-1 in iP + CONT, and -1.23 mmol⋅L^-1 in 20iP + ACCU. The Clarke error grid analysis showed that 99.6-100% of the values in all trials fell within zones A and B. Conclusion: Although FGM readings underestimated PG, the FGM accuracy was overall clinically acceptable during postprandial rest and walking in overweight/obese young adults.

A Tool to Explore Discrete-Time Data: The Time Series Response Analyser

The analysis of time series data is common in nutrition and metabolism research for quantifying the physiological responses to various stimuli. The reduction of many data from a time series into a summary statistic(s) can help quantify and communicate the overall response in a more straightforward way and in line with a specific hypothesis. Nevertheless, many summary statistics have been selected by various researchers, and some approaches are still complex. The time-intensive nature of such calculations can be a burden for especially large data sets and may, therefore, introduce computational errors, which are difficult to recognize and correct. In this short commentary, the authors introduce a newly developed tool that automates many of the processes commonly used by researchers for discrete time series analysis, with particular emphasis on how the tool may be implemented within nutrition and exercise science research.

Chinese clinical guidelines for continuous glucose monitoring (2018 edition)

Blood glucose monitoring is an important part of diabetes management. Continuous glucose monitoring (CGM) technology has become an effective complement to conventional blood glucose monitoring methods and has been widely applied in clinical practice. The indications for its use, the accuracy of the generated data, the interpretation of the CGM results, and the application of the results must be standardized. In December 2009, the Chinese Diabetes Society (CDS) drafted and published the first Chinese Clinical Guideline for Continuous Glucose Monitoring (2009 edition), providing a basis for the standardization of CGM in clinical application. Based on the updates of international guidelines and the increasing evidence of domestic studies, it is necessary to revise the latest CGM guidelines in China so that the recent clinical evidence can be effectively translated into clinical benefit for diabetic patients. To this end, the CDS revised the Chinese Clinical Guideline for Continuous Glucose Monitoring (2012 Edition) based on the most recent evidence from international and domestic studies.

Postprandial glycemic response in a non-diabetic adult population: the effect of nutrients is different between men and women

Background

There is a growing interest in the pathopysiological consequences of postprandial hyperglycemia. It is well known that in diabetic patients 2 h plasma glucose is a better risk predictor for coronary heart disease than fasting plasma glucose. Data on the glycemic response in healthy people are scarce.

Objective

To evaluate the effect of macronutrients (carbohydrates, fats, and proteins) and fiber on postprandial glycemic response in an observational study of a non-diabetic adult population.

Design

Cross-sectional study. 150 non-diabetic adults performed continuous glucose monitoring for 6 days. During this period they recorded food and beverage intake. The participants were instructed not to make changes in their usual diet and physical exercise.

Variables analyzed included clinical parameters (age, sex, body weight, height, body mass index, blood pressure, and waist measurement), meal composition (calories, carbohydrates, fats, proteins, and fiber) and glycemic postprandial responses separated by sexes.

The study period was defined from the start of dinner to 6 h later.

Results

A total of 148 (51% women) subjects completed all study procedures. Dinner intake was higher in males than in females (824 vs 531 kcal). Macronutrient distribution was similar in both sexes. No significant differences were found in fiber intake between men and women (5.5 g vs 4.5 g).

In both sexes, the higher intake of carbohydrates corresponded to a significantly higher glycemic response (p = 0.0001 in women, p = 0.022 in men). Moreover, in women, as fat intake was higher, a flattening of the postprandial glycemic curve was observed (p = 0.003). With respect to fiber, a significantly lower glycemic response was observed in the group of women whose fiber intake at dinner was higher (p = 0.034).

Conclusions

Continuous glucose monitoring provides important information about glucose levels after meals. In this study, the postprandial glycemic response in women was different from that of men, and carbohydrates were the main determinant of elevated postprandial glucose levels.

Meal Timing, Aging, and Metabolic Health

A growing body of evidence suggests that meal timing is an important factor for metabolic regulation and that the circadian clock tightly interacts with metabolic functions. The proper functioning of the circadian clock is critical for maintaining metabolic health. Therefore, chrononutrition, a novel discipline which investigates the relation between circadian rhythms, nutrition, and metabolism, has attracted increasing attention in recent years. Circadian rhythms are strongly affected by obesity, type 2 diabetes, and other dietary-induced metabolic diseases. With increasing age, the circadian system also undergoes significant changes which contribute to the dysregulation of metabolic rhythms. Metabolic diseases are a major health concern, particularly in light of a growing aging population, and effective approaches for their prevention and treatment are urgently needed. Recently, animal studies have impressively shown beneficial effects of several dietary patterns (e.g., caloric restriction or time-restricted feeding) on circadian rhythms and metabolic outcomes upon nutritional challenges. Whether these dietary patterns show the same beneficial effects in humans is, however, less well studied. As indicated by recent studies, dietary approaches might represent a promising, attractive, and easy-to-adapt strategy for the prevention and therapy of circadian and metabolic disturbances in humans of different age.

Metformin inhibits mitochondrial adaptations to aerobic exercise training in older adults

Metformin and exercise independently improve insulin sensitivity and decrease the risk of diabetes. Metformin was also recently proposed as a potential therapy to slow aging. However, recent evidence indicates that adding metformin to exercise antagonizes the exercise‐induced improvement in insulin sensitivity and cardiorespiratory fitness. The purpose of this study was to test the hypothesis that metformin diminishes the improvement in insulin sensitivity and cardiorespiratory fitness after aerobic exercise training (AET) by inhibiting skeletal muscle mitochondrial respiration and protein synthesis in older adults (62 ± 1 years). In a double‐blinded fashion, participants were randomized to placebo (n = 26) or metformin (n = 27) treatment during 12 weeks of AET. Independent of treatment, AET decreased fat mass, HbA1c, fasting plasma insulin, 24‐hr ambulant mean glucose, and glycemic variability. However, metformin attenuated the increase in whole‐body insulin sensitivity and VO₂max after AET. In the metformin group, there was no overall change in whole‐body insulin sensitivity after AET due to positive and negative responders. Metformin also abrogated the exercise‐mediated increase in skeletal muscle mitochondrial respiration. The change in whole‐body insulin sensitivity was correlated to the change in mitochondrial respiration. Mitochondrial protein synthesis rates assessed during AET were not different between treatments. The influence of metformin on AET‐induced improvements in physiological function was highly variable and associated with the effect of metformin on the mitochondria. These data suggest that prior to prescribing metformin to slow aging, additional studies are needed to understand the mechanisms that elicit positive and negative responses to metformin with and without exercise.

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.

A Pilot Characterization of the Human Chronobiome

Physiological function, disease expression and drug effects vary by time-of-day. Clock disruption in mice results in cardio-metabolic, immunological and neurological dysfunction; circadian misalignment using forced desynchrony increases cardiovascular risk factors in humans. Here we integrated data from remote sensors, physiological and multi-omics analyses to assess the feasibility of detecting time dependent signals - the chronobiome – despite the “noise” attributable to the behavioral differences of free-living human volunteers. The majority (62%) of sensor readouts showed time-specific variability including the expected variation in blood pressure, heart rate, and cortisol. While variance in the multi-omics is dominated by inter-individual differences, temporal patterns are evident in the metabolome (5.4% in plasma, 5.6% in saliva) and in several genera of the oral microbiome. This demonstrates, despite a small sample size and limited sampling, the feasibility of characterizing at scale the human chronobiome “in the wild”. Such reference data at scale are a prerequisite to detect and mechanistically interpret discordant data derived from patients with temporal patterns of disease expression, to develop time-specific therapeutic strategies and to refine existing treatments.

A viewpoint on considering physiological principles to study stress resistance and resilience with aging

Adaptation to stress is identified as one of the seven pillars of aging research. Our viewpoint discusses the importance of the distinction between stress resistance and resilience, highlights how integration of physiological principles is critical for further understanding in vivo stress resistance and resilience, and advocates for the use of early warning signs to prevent a tipping point in stress resistance and resilience.

The aging clock: circadian rhythms and later life

Circadian rhythms play an influential role in nearly all aspects of physiology and behavior in the vast majority of species on Earth. The biological clockwork that regulates these rhythms is dynamic over the lifespan: rhythmic activities such as sleep/wake patterns change markedly as we age, and in many cases they become increasingly fragmented. Given that prolonged disruptions of normal rhythms are highly detrimental to health, deeper knowledge of how our biological clocks change with age may create valuable opportunities to improve health and longevity for an aging global population. In this Review, we synthesize key findings from the study of circadian rhythms in later life, identify patterns of change documented to date, and review potential physiological mechanisms that may underlie these changes.

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.

Association between the rs7903146 Polymorphism in the TCF7L2 Gene and Parameters Derived with Continuous Glucose Monitoring in Individuals without Diabetes

BACKGROUND

The rs7903146-T allele in the transcription factor 7-like 2 (TCF7L2) gene has been associated with impaired pancreatic insulin secretion, enhanced liver glucose production, and an increased risk of type 2 diabetes. Nevertheless, the impact of rs7903146 on daily glucose trajectories remains unclear. Continuous glucose monitoring (CGM) can estimate glycemia and glycemic variability based on consecutive glucose measurements collected over several days. The purpose of the present study was to investigate the associations of rs7903146 with glycemia and glycemic variability in middle-aged participants without diabetes.

METHODS

Complete data from 235 participants without diabetes from the Leiden Longevity Study were available. Participants were divided into two groups based on rs7903146 genotype; rs7903146-CC genotype carriers (N = 123) and rs7903146-CT/TT genotype carriers (N = 112). Validated parameters of glycemia (e.g., mean 24h glucose level) and glycemic variability (e.g., 24h standard deviation) were derived from data collected with a CGM system for a 72-hour period.

RESULTS

The study population was on average 64.7 years old (standard deviation = 5.9) and composed of 49.8% of women. Compared with rs7903146-CC carriers, rs7903146-CT/TT carriers exhibited a trend towards a higher mean 24-hour glucose level (5.21 versus 5.32 mmol/L; p-value = 0.15) and a significantly higher mean nocturnal glucose (3:00am- 6:00am; 4.48 versus 4.67 mmol/L; p-value = 0.03) that was explained for 34.6% by body weight and percentage body fat. No differences in measures of glycemic variability between the genotype groups were observed.

CONCLUSION

Despite limited sample size, our study indicates that the rs7903146-T allele in TCF7L2 was associated with a higher mean nocturnal glucose dependent on body composition, which might suggest that rs7902146 affects liver-specific aspects of glucose metabolism.

Accuracy of Continuous Glucose Monitoring Measurements in Normo-Glycemic Individuals

Background

The validity of continuous glucose monitoring (CGM) is well established in diabetic patients. CGM is also increasingly used for research purposes in normo-glycemic individuals, but the CGM validity in such individuals is unknown. We studied the accuracy of CGM measurements in normo-glycemic individuals by comparing CGM-derived versus venous blood-derived glucose levels and measures of glycemia and glycemic variability.

Methods

In 34 healthy participants (mean age 65.7 years), glucose was simultaneously measured every 10 minutes, via both an Enlite^® CGM sensor, and in venous blood sampled over a 24-hour period. Validity of CGM-derived individual glucose measurements, calculated measures of glycemia over daytime (09:00h-23:00h) and nighttime (23:00h-09:00h), and calculated measures of glycemic variability (e.g. 24h standard deviation [SD]) were assessed by Pearson correlation coefficients, mean absolute relative difference (MARD) and paired t-tests.

Results

The median correlation coefficient between CGM and venous glucose measurements per participant was 0.68 (interquartile range: 0.40–0.78), and the MARD was 17.6% (SD = 17%). Compared with venous sampling, the calculated measure of glycemia during daytime was 0.22 mmol/L higher when derived from CGM, but no difference was observed during nighttime. Most measures of glycemic variability were lower with CGM than with venous blood sampling (e.g., 24h SD: 1.07 with CGM and 1.26 with venous blood; p-value = 0.004).

Conclusion

In normo-glycemic individuals, CGM-derived glucose measurements had good agreement with venous glucose levels. However, the measure of glycemia was higher during the day and most measures of glycemic variability were lower when derived from CGM.

Molecular clocks and the human condition: approaching their characterization in human physiology and disease

Molecular clockworks knit together diverse biological networks and compelling evidence from model systems infers their importance in metabolism, immunological and cardiovascular function. Despite this and the diurnal variation in many aspects of human physiology and the phenotypic expression of disease, our understanding of the role and importance of clock function and dysfunction in humans is modest. There are tantalizing hints of connection across the translational divide and some correlative evidence of gene variation and human disease but most of what we know derives from forced desynchrony protocols in controlled environments. We now have the ability to monitor quantitatively ex vivo or in vivo the genome, metabolome, proteome and microbiome of humans in the wild. Combining this capability, with the power of mobile telephony and the evolution of remote sensing, affords a new opportunity for deep phenotyping, including the characterization of diurnal behaviour and the assessment of the impact of the clock on approved drug function.

Glycemic Allostasis during Mental Activities on Fasting in Non-alcohol Users and Alcohol Users with Different Durations of Abstinence

Glycemic allostasis is the process by which blood glucose stabilization is achieved through the balancing of glucose consumption rate and release into the blood stream under a variety of stressors. This paper reviews findings on the dynamics of glycemic levels during mental activities on fasting in non-alcohol users and alcohol users with different periods of abstinence. Referred articles for this review were searched in the databases of PubMed, Scopus, DOAJ and AJOL. The search was conducted in 2013 between January 20 and July 31. The following keywords were used in the search: alcohol action on glycemia OR brain glucose OR cognitive functions; dynamics of glycemia, dynamics of glycemia during mental activities; dynamics of glycemia on fasting; dynamics of glycemia in non-alcohol users OR alcohol users; glycemic regulation during sobriety. Analysis of the selected articles showed that glycemic allostasis during mental activities on fasting is poorly regulated in alcohol users even after a long duration of sobriety (1-4 weeks after alcohol consumption), compared to non-alcohol users. The major contributor to the maintenance of euglycemia during mental activities after the night's rest (during continuing fast) is gluconeogenesis.

Identifying the genomic determinants of aging and longevity in human population studies: progress and challenges

Human lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25–30% and expected to be polygenic. Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research. Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation. The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing. Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data – generated using novel technologies – in a wealth of studies in human populations. Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation.