Resume of results
We studied 93 laboratory-based polysomnography samples, 36 from people aged 75 years and older and 57 from people aged between 65 and 74 years. Moreover, OSA and other sleep pathologies were common in both groups. Older people also receive more medications than younger people.
We observed that the localization of sleep spindles changed in the oldest individuals, with slow spindles becoming more central in the N2 sleep stage (or less Frontal) while there was no difference for fast spindles or in the N3 sleep stage. By comparing sleep stages, we observed that density, frequency and duration reached greater values in N2 than in N3 for both slow and fast spindles. The SS-S amplitude was lower and the SS-F amplitude was greater in N2. Older age group influenced the SS-S density variation, with the N2 mean value reaching a higher value. Gender influenced SS-F amplitude variation between sleep stages, with females reaching higher values.
We also observed that the SS-S density was greater than the SS-F density, especially in N2, with no clear effect of age, but the difference was greater in the 65+ group. Therefore, we could hypothesize that SS densities in the oldest age group tend to be closer between S and F because of either a decrease in the number of slow spindles or an increase in the number of fast spindles. Our study cannot answer this question.
We observed that the frequency of sleep spindles did not change through the N2 or N3 sleep stage, regardless of age, OSA status, or sex.
Amplitude was greater for slow spindles in both sleep stages, without changing through age but with an influence of OSA diagnosis in N2 in the youngest patients.
Finally, the duration was also longer in the slow spindles in the N2 stage, and the effect of age was unclear. Group age was not statistically significant in the multivariate model, but exact age was significant in the 65+ group. The small sample size of the 75+ group may not have enabled us to confirm this difference.
The significant differences we found here concern the N2 sleep stage, which led us to question the potentially different roles of sleep spindles throughout sleep stages, in parallel with the different roles attributed to slow and fast spindles themselves.
Bibliography comparisons
Population data
The medical data of our participants could be considered concerning because of the high percentage of sleep pathology diagnoses in older people and the high rate of sleep-modifying drug consumption.
The Haute Autorité de Santé (the first independent French public scientific authority) report on OSA and its treatments showed similar results. OSA is found in approximately 20 to 50% of people after 60 years of age, while only moderate and severe OSA are considered, where we decided to consider mild OSA as well, leading to higher values.18
In addition, we studied participants using laboratory-based polysomnography as part of their medical care and did not recruit them for our research. This means that they all had a sleep complaint, some sleep symptoms, or comorbidities and risk factors, leading them to undergo polysomnography. We tried to lower this bias by including OSA diagnosis as a cofactor in the analysis.
We excluded participants treated with benzodiazepines and related because of their clinical effects on sleep and their effects on sleep spindles.19,20 However, these treatments were used by 96 of the 340 participants screened (28.24%). This was not biased because of the care course of the participants. In a 2017 report on benzodiazepine consumption in France, the Agence Nationale de Sécurité du Médicament et des produits de santé (the French public agency that allows access to health products and ensures their security) presented a growth in consumption with age, with maximal use in women older than 80 years (38.3%). Nevertheless, there are encouraging data about the global consumption of benzodiazepines, with the annual consumption rate decreasing between 2012 and 2015.21 We may suppose that the rate we observed in our study reflects a continuous decrease since 2015. However, there is still a very high rate of benzodiazepine consumption in older people at high risk of comorbidities and polymedication.
Sleep spindle characteristics
The first SS topography studies described results from young people in the N2 sleep stage, with a slow frequency peak (< 12.5 Hz) of frontal and central distribution or centro-parietal distribution (depending on the EEG montage), while fast spindles (> 12.5 Hz) were found on every derivation (frontal, central, parietal, occipital).22-23
More recently, but still in healthy young population samples (mean age 29.7 ± 6), an SS topography study revealed a central distribution for the SS-F and a centro-frontal distribution for the SS-S in N2 and a central distribution for the SS-F and a frontal distribution for the SS-S in N3.24
Our results are consistent with prior studies, with a predominant central distribution for all types of SS in both groups in sleep stage N3, N2 for SS-F in both groups, and a central distribution for SS-S in N2 in the 75+ group. Simultaneously, it was centrofrontal in the 65+ group.
Some specific SS-S originating from the frontal area seemed to be lost between the 65+ and 75+ participants.
This could be due to alterations in the frontal area observed in older people, according to recent studies showing a negative association between age and cortical thickness, or a correlation between cortical thickness and EEG alterations, especially for sigma power in NREM sleep.25,26
Fjell et al. also found a brain size reduction with large interindividual variability, predominantly in the frontal area, which could be due to changes in the synaptic network, leading to a worse detection of SS through frontal external electrodes.27
According to the studies by Münch et al. and Mander et al., it could also be linked to memory loss. One study showed frontal aging with worse adaptation of the frontal area to sleep deprivation compared to younger people when specifically studying EEG power density in the delta and theta ranges.28
The other study revealed a regionally selective deficit in fast sleep spindle density with the greatest impairment over the prefrontal area, without a significant link with gray matter volume.29
Our results support the same hypothesis that age differences in spindle topographic distribution might be the consequence of differences in spindle generation rather than differences in the detection limit.
In 2021, McConnel et al. developed a new concept. SS-F split between the early ones in the N2 sleep stage, with a frequency range between 14.5 and 17.5 Hz, and the late SS-F in the N3 sleep stage, with a frequency between 10-14 Hz.30 Again, our results are consistent, and we found a significant difference, with a greater mean frequency for SS-F in N2 than in N3 in both the 65+ group and the 75+ group. Many differences between these studies and ours must be considered, mainly in terms of the participants' age. To the best of our knowledge, this is the first study to specifically examine the sleep spindle characteristics of participants aged 75+ years.
The SS density is defined by significant interindividual variability and high sensitivity to perturbations. Through decades of studies, SS mean density has been described by many researchers and trials to define a norm: from 2.7 ± 2.1 SS per minute on a single participant using electromagnetic tomography in 2001 by Anderer et al. to 3.3 per minute for good sleepers and 3.51 per minute for people living with psychophysiological insomnia by Normand et al.; always over young participants; through SS mean densities of 2.54 per minute before and 2.4 per minute after treatment by cognitive behavioral therapy in a 2017 study by Dang-Vu et al. led on insomniac people.31–33
A review by Espiritu et al. in 2008 showed a large range of SS mean densities obtained between studies, and they could only conclude a decrease in sleep spindle number and density with aging.34–36
In 2021, Guadagni et al. described SS densities more precisely in the oldest sample population (68.2 ± 5.6 years old).37 They found a mean density of 2.4-2.46 SS per minute in central and frontal electrodes in N2 and lower values in N3: 1.46-1.62 in central and 1.66-1.8 in frontal electrodes. SS were recorded in a 10-16 Hz frequency range or 12-16 Hz for eight participants.
Here, by studying older people and more participants, we wanted to determine whether the mean densities would be around the same range for younger people or in another field. Our results are similar to those of Guadagni et al. but with slightly greater mean densities of both N2 and N3 in both age groups. The most important point is that we reached a known significant difference between the N2 and N3 values, and we added the influence of age, with lower values in the oldest group for both sleep stages and both sleep spindle types, at a statistically significant level for the N2/N3 slow spindle comparison.
This difference was not observed in the study by Fillmore et al., who studied SS characteristics through a different protocol, namely, a frequency range of 10-16 Hz, a frontal area only, a young group (18-29 years old) and an older but larger group (50-84 years old).38
Here, we decided to study two successive age groups for more accurate comparisons instead of young versus old or only a senior sample population. Moreover, our inclusion criteria were not as strict as those reported in the literature. Indeed, patients receiving benzodiazepines were excluded, but those receiving restless leg syndrome treatments or opioids were not excluded, which may have biased the results because our age groups were not comparable.
It seems that SS characteristics are even more sensitive to study protocols and inclusion criteria than they are susceptible to aging. For another example, Martin et al. studied SS characteristics in a 60–73-year-old population without neurological pathology and no treatment that could have modified sleep, with an AHI < 10, and this time split the SS between slow (11–13 Hz) and fast (13–15 Hz). These results differed from our findings and those of other studies: the mean density was between 2.4 (SS-S) and 2.6 (SS-F)/minute, the mean frequency was between 12.8 and 13 Hz, the mean amplitude was < 25 µv, and the mean duration was < 0.68 seconds.39
To limit these variations, Djonlagic et al. based their study on macro- and microsleep architectures of polysomnography registered through two large cohorts (MESA and MrOS).40
They found that both the SS-S (center frequency, 11 Hz) and SS-F (center frequency, 15 Hz) mean densities decreased for every successive age group (decades) in both cohorts between 50 and 80 years of age, which is concordant with our results. They observed the same type of age-related decrease for SS amplitude and duration, whereas the SS frequency increased for each age group. Our results are not consistent about these points, with increased SS amplitude for both types and very slightly increased durations as developed earlier, while we registered an increase in frequencies.
Again, the means suffered a high interindividual variation, and they found a sex difference in the MESA sample concerning SS-F density, while the only gender effects we observed were amplitudes.
Lam et al. studied a sample population with mild cognitive impairment (MCI) and a mean age of 69.1 years compared to a control group without neurocognitive disorders or treatment and a mean age of 64.8 years 15. Considering SS-S (11-13 Hz) and SS-F (13-16 Hz), the densities were very low for every type of SS: 0.36 SS-S/minute for the control group and 0.43 for the MCI group; 0.41 and 0.22 SS-F/minute for the control and MCI groups. The durations were closer to our results, with 0.74 seconds in the control group and 0.75 seconds in the MCI group, considering NREM sleep overall (N2+N3).
Recently, in a review to synthesize age-related sleep modification tendencies, Campos et al. reported that SS density and amplitude decrease in elderly people, duration decreases throughout life, and topography is increasingly reduced to the central area.41
This review was completed by Taillard et al., who reported that density and amplitude reductions were more prominent in the anterior sites. In contrast, duration reduction is more posterior, and frequency is less affected.42Moreover, fast spindles are more affected than slow spindles, and slow spindles are slower, while fast spindles are faster. They found that these modifications were more significant during the final sleep cycle.
These points could explain some of the differences we observed in the results; as in older studies, we have not focused on specific SS localizations to perform the calculations. In addition, our data were obtained from fragments of the polysomnography night, most of which were from the first or second sleep cycles. In regular practice, older people undergoing polysomnography as part of normal care rarely reach a third cycle at these ages, particularly after having one or more sleep pathologies and sleep-modifying treatments.
Forces and limitations
Due to the retrospective design of the study, we could not collect specific medical data by questioning the participants. We had to check their computerized medical records to find pieces of information we needed, which were not always well informed. We used the same methodology and collected the same data in every medical file from both groups to avoid any information bias.
Second, the two groups were not comparable in terms of every sociomedical characteristic, and there was a significant difference in CSA diagnosis and other treatments. Diagnosis was still infrequent. We selected only short fragments of the most stable sleep in every record, limiting the influence of apneas on the microsleep architecture to limit the risk of selection bias, but this may have modified the whole night microsleep architecture even then.
The use of limited sleep fragments may have affected the results. However, the use of the same methodology for both groups did not lead to measurement bias. This may have artificially increased the mean density values of sleep spindles because they were the key signals used to score the N2 sleep stage and to manually select the sleep fragments used for analysis. This could also increase the mean duration when carefully manually checking for artifacts because a longer spindle is more susceptible to visualization and counting by the human eye.
Third, confounding bias is inherent to observational studies, and there may be some confounding factors that were not considered in this study. However, we limited this risk by collecting the same data on the primary medical status of all the participants, which could have changed the results. For example, the exclusion of patients with neurological pathology or verification of an OSA diagnosis was as prominent in both groups.
This study had several strengths, starting with the unique polysomnography protocol used for every recording. In addition, using the same software and hardware, both are highly recognized for their quality in sleep medicine; in a single sleep study laboratory, working only with trained nurses and technicians to perform polysomnography. Two experienced sleep physicians checked the medical files and sleep records for exclusion criteria and record quality.
SS detection was realized by powerful software that was continuously updated based on older algorithms that all proved to be accurate and efficient. In addition, the initial selection of the most stable sleep fragments and manual rejection of artifacts ensured that we registered and used only quality data for the statistical analysis.
Finally, our study sample was quite large owing to the extended registration period, while it was a monocentric study. Therefore, we added a control group to compare our data with the literature and to make direct comparisons between the age groups. This is visible through the statistical power that we reached with multiple significant results, even for calculations performed over a single group.