A simple and versatile method for frequent 24 h blood sample collection in healthy older adults

Relationships Between 24-hour LH and Testosterone Concentrations and With Other Pituitary Hormones in Healthy Older Men

Objective

To investigate the relationship between LH and testosterone (T), which characteristics associate with the strength of this relationship, and their interrelationships with GH, TSH, cortisol, and ACTH.

Design

Hormones were measured in serum samples collected every 10 minutes during 24 hours from 20 healthy men, comprising 10 offspring of long-lived families and 10 control subjects, with a mean (SD) age of 65.6 (5.3) years. We performed cross-correlation analyses to assess the relative strength between 2 timeseries for all possible time shifts.

Results

Mean (95% CI) maximal correlation was 0.21 (0.10-0.31) at lag time of 60 minutes between LH and total T concentrations. Results were comparable for calculated free, bioavailable, or secretion rates of T. Men with strong LH-T cross-correlations had, compared with men with no cross-correlation, lower fat mass (18.5 [14.9-19.7] vs. 22.3 [18.4-29.4] kg), waist circumference (93.6 [5.7] vs. 103.1 [12.0] cm), high-sensitivity C-reactive protein (0.7 [0.4-1.3] vs. 1.8 [0.8-12.3] mg/L), IL-6 (0.8 [0.6-1.0] vs. 1.2 [0.9-3.0] pg/mL), and 24-hour mean LH (4.3 [2.0] vs. 6.1 [1.5] U/L), and stronger LH-T feedforward synchrony (1.5 [0.3] vs. 1.9 [0.2]). Furthermore, T was positively cross-correlated with TSH (0.32 [0.21-0.43]), cortisol (0.26 [0.19-0.33]), and ACTH (0.26 [0.19-0.32]).

Conclusions

LH is followed by T with a delay of 60 minutes in healthy older men. Men with a strong LH-T relationship had more favorable body composition, inflammatory markers, LH levels, and LH-T feedforward synchrony. We observed positive correlations between T and TSH, cortisol, and ACTH.

Interrelationships Between Pituitary Hormones as Assessed From 24-hour Serum Concentrations in Healthy Older Subjects

CONTEXT

Hormones of the hypothalamic-pituitary-target gland axes are mostly investigated separately, whereas the interplay between hormones might be as important as each separate hormonal axis.

OBJECTIVE

Our aim is to determine the interrelationships between GH, TSH, ACTH, and cortisol in healthy older individuals.

DESIGN

We made use of 24-hour hormone serum concentrations assessed with intervals of 10 minutes from 38 healthy older individuals with a mean age (SD) of 65.1 (5.1) years from the Leiden Longevity Study. Cross-correlation analyses were performed to assess the relative strength between 2 24-hour hormone serum concentration series for all possible time shifts. Cross-approximate entropy was used to assess pattern synchronicity between 2 24-hour hormone serum concentration series.

RESULTS

Within an interlinked hormonal axis, ACTH and cortisol were positively correlated with a mean (95% confidence interval) correlation coefficient of 0.78 (0.74-0.81) with cortisol following ACTH concentrations with a delay of 10 minutes. Between different hormonal axes, we observed a negative correlation coefficient between cortisol and TSH of -0.30 (-0.36 to -0.25) with TSH following cortisol concentrations with a delay of 170 minutes. Furthermore, a positive mean (95% confidence interval) correlation coefficient of 0.29 (0.22-0.37) was found between TSH and GH concentrations without any delay. Moreover, cross-approximate entropy analyses showed that GH and cortisol exhibit synchronous serum concentration patterns.

CONCLUSIONS

This study demonstrates that interrelations between hormones from interlinked as well as different hypothalamic-pituitary-target gland axes are observed in healthy older individuals. More research is needed to determine the biological meaning and clinical consequences of these observations.

Comparing Methods for Measurement Error Detection in Serial 24-h Hormonal Data

Measurement errors commonly occur in 24-h hormonal data and may affect the outcomes of such studies. Measurement errors often appear as outliers in such data sets; however, no well-established method is available for their automatic detection. In this study, we aimed to compare performances of different methods for outlier detection in hormonal serial data. Hormones (glucose, insulin, thyroid-stimulating hormone, cortisol, and growth hormone) were measured in blood sampled every 10 min for 24 h in 38 participants of the Leiden Longevity Study. Four methods for detecting outliers were compared: (1) eyeballing, (2) Tukey’s fences, (3) stepwise approach, and (4) the expectation-maximization (EM) algorithm. Eyeballing detects outliers based on experts’ knowledge, and the stepwise approach incorporates physiological knowledge with a statistical algorithm. Tukey’s fences and the EM algorithm are data-driven methods, using interquartile range and a mathematical algorithm to identify the underlying distribution, respectively. The performance of the methods was evaluated based on the number of outliers detected and the change in statistical outcomes after removing detected outliers. Eyeballing resulted in the lowest number of outliers detected (1.0% of all data points), followed by Tukey’s fences (2.3%), the stepwise approach (2.7%), and the EM algorithm (11.0%). In all methods, the mean hormone levels did not change materially after removing outliers. However, their minima were affected by outlier removal. Although removing outliers affected the correlation between glucose and insulin on the individual level, when averaged over all participants, none of the 4 methods influenced the correlation. Based on our results, the EM algorithm is not recommended given the high number of outliers detected, even where data points are physiologically plausible. Since Tukey’s fences is not suitable for all types of data and eyeballing is time-consuming, we recommend the stepwise approach for outlier detection, which combines physiological knowledge and an automated process.

The 24-hour serum profiles of bone markers in healthy older men and women

The process of bone turnover displays variations over 24 h, with C-terminal cross-linked telopeptide of type 1 collagen (CTX) and osteocalcin exhibiting a nadir in the afternoon and a peak in the night. In contrast, N-terminal propeptide of type 1 procollagen (P1NP) did not display an apparent 24-hour rhythm. Other emerging novel biomarkers of bone, sclerostin and Dickkopf-related protein 1 (DKK1), are markers of osteocyte activity with limited data available regarding their 24-hour profiles. In this study, we aimed to extend available data on 24-hour profiles of CTX, osteocalcin, and P1NP and to assess the 24-hour profiles of sclerostin and DKK1 in healthy older men and women and to compare these between men and women. We measured these five bone markers in EDTA plasma collected every 4 h during 24 h in 37 healthy older men and women (range 52-76 years). Differences between time points were determined using repeated measures ANOVA and cosinor analyses were performed to determine circadian rhythmicity. The circadian rhythm of CTX was confirmed by the cosinor model, with women showing larger amplitude compared to men. Osteocalcin showed higher levels during nighttime compared to daytime in both men and women. For P1NP levels we observed a small but significant increase in the night in men. Sclerostin and DKK1 did not show a circadian rhythm, but sclerostin levels differed between time points. Because of the large intraindividual variation, DKK1 as measured in this study cannot be considered a reliable marker for diagnostic or research purposes. In conclusion, when measuring CTX, osteocalcin, P1NP, or sclerostin either in clinical practice or in a research setting, one should consider the 24-hour profiles of these bone markers.

Familial longevity is characterized by high circadian rhythmicity of serum cholesterol in healthy elderly individuals

The biological clock, whose function deteriorates with increasing age, determines bodily circadian (i.e. 24h) rhythms, including that of cholesterol metabolism. Dampening of circadian rhythms has been associated with aging and disease. Therefore, we hypothesized that individuals with a familial predisposition for longevity have a higher amplitude circadian serum cholesterol concentration rhythm. The aim of this study was to investigate circadian rhythmicity of serum cholesterol concentrations in offspring of nonagenarian siblings and their partners. Offspring from nonagenarian siblings (n = 19), and their partners as controls (n = 18), were recruited from the Leiden Longevity Study. Participants (mean age 65 years) were studied in a controlled in‐hospital setting over a 24‐h period, receiving three isocaloric meals at 9:00 h, 12:00 h and 18:00 h. Lights were off between 23:00 h and 8:00 h. Serum total cholesterol (TC), HDL cholesterol (HDL‐C), non‐HDL‐C and triglycerides (TG) were determined every 30 min over a 24‐h period. Serum TC concentrations were higher during day than during night in offspring (5.2 vs. 4.7 mm, P < 0.001) and in controls (5.3 vs. 5.0 mm, P < 0.001). The difference in TC concentrations between day and night tended to be greater in offspring than in controls (0.5 vs. 0.3 mm, P = 0.109), reaching statistical significance in females (P = 0.045). Notably, the day–night serum differences in non‐HDL‐C were twofold greater in offspring than in controls (0.43 vs. 0.21 mm, P = 0.044) and most explicit in females (0.53 vs. 0.22, P = 0.078). We conclude that familial longevity is characterized by a high circadian rhythmicity of non‐HDL‐C in healthy elderly offspring from nonagenarian siblings.

Growth hormone secretion is diminished and tightly controlled in humans enriched for familial longevity

Reduced growth hormone (GH) signaling has been consistently associated with increased health and lifespan in various mouse models. Here, we assessed GH secretion and its control in relation with human familial longevity. We frequently sampled blood over 24 h in 19 middle‐aged offspring of long‐living families from the Leiden Longevity Study together with 18 of their partners as controls. Circulating GH concentrations were measured every 10 min and insulin‐like growth factor 1 (IGF‐1) and insulin‐like growth factor binding protein 3 (IGFBP3) every 4 h. Using deconvolution analysis, we found that 24‐h total GH secretion was 28% lower (P = 0.04) in offspring [172 (128–216) mU L⁻¹] compared with controls [238 (193–284) mU L⁻¹]. We used approximate entropy (ApEn) to quantify the strength of feedback/feedforward control of GH secretion. ApEn was lower (P = 0.001) in offspring [0.45 (0.39–0.53)] compared with controls [0.66 (0.56–0.77)], indicating tighter control of GH secretion. No significant differences were observed in circulating levels of IGF‐1 and IGFBP3 between offspring and controls. In conclusion, GH secretion in human familial longevity is characterized by diminished secretion rate and more tight control. These data imply that the highly conserved GH signaling pathway, which has been linked to longevity in animal models, is also associated with human longevity.

Familial Longevity Is Not Associated with Major Differences in the Hypothalamic-Pituitary-Gonadal Axis in Healthy Middle-Aged Men

CONTEXT

A trade-off between fertility and longevity possibly exists. The association of the male hypothalamic-pituitary-gonadal (HPG) axis with familial longevity has not yet been investigated.

OBJECTIVE

To study 24-h hormone concentration profiles of the HPG axis in men enriched for familial longevity and controls.

DESIGN

We frequently sampled blood over 24 h in 10 healthy middle-aged male offspring of nonagenarian participants from the Leiden Longevity Study together with 10 male age-matched controls. Individual 24-h luteinizing hormone (LH) and testosterone concentration profiles were analyzed by deconvolution analyses to estimate secretion parameters. Furthermore, the temporal relationship between LH and testosterone was assessed by cross-correlation analysis. We used (cross-)approximate entropy to quantify the strength of feedback and/or feedforward control of LH and testosterone secretion.

RESULTS

Mean [95% confidence interval (CI)] total LH secretion of the offspring was 212 (156-268) U/L/24 h, which did not differ significantly (p = 0.51) from the total LH secretion of controls [186 (130-242) U/L/24 h]. Likewise, mean (95% CI) total testosterone secretion of the offspring [806 (671-941) nmol/L/24 h] and controls [811 (676-947) nmol/L/24 h] were similar (p = 0.95). Other parameters of LH and testosterone secretion were also not significantly different between offspring and controls. The temporal relationship between LH and testosterone and the strength of feedforward/feedback regulation within the HPG axis were similar between offspring of long-lived families and controls.

CONCLUSION

This relatively small study suggests that in healthy male middle-aged participants, familial longevity is not associated with major differences in the HPG axis. Selection on both fertility and health may in part explain the results.

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.