Age-related changes in sleep spindle characteristics in individuals over 75 years of age: a retrospective and comparative study

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We know main modifications of the macro-architecture with a shorter sleep, occurring earlier and being more fragmented. We have been studying sleep micro-architecture through its pathological modification in sleep, psychiatric or neurocognitive disorders whereas we are still unable to say if the sleep micro-architecture of an old and very old person is rather normal, under physiological changes, or a concern for a future disorder to appear. We wanted to evaluate age-related changes in sleep spindle characteristics in individuals over 75 years of age compared with younger individuals. Methods This was an exploratory study based on retrospective and comparative laboratory-based polysomnography data registered in the normal care routine for people over 75 years of age compared to people aged 65–74 years. We were studying their sleep spindle characteristics (localization, density, frequency, amplitude, and duration) in the N2 and N3 sleep stages. ANOVA and ANCOVA using age, sex and OSA were applied. Results We included 36 participants aged > 75 years and 57 participants aged between 65 and 74 years. An OSA diagnosis was most common in both groups. Older adults receive more medication to modify their sleep. Spindle localization becomes more central after 75 years of age. Changes in the other sleep spindle characteristics between the N2 and N3 sleep stages and between the slow and fast spindles were conformed to literature data, but age was a relevant modifier only for density and duration. Conclusion We observed the same sleep spindle characteristics in both age groups except for localization. We built our study on a short sample, and participants were not free of all sleep disorders. We could establish normative values through further studies with larger samples of people without any sleep disorders to understand the modifications in normal aging and pathological conditions and to reveal the predictive biomarker function of sleep spindles. polysomnography sleep architecture spindle aging oldest old Figures Figure 1 Figure 2 Introduction The global population is increasingly aging. The 2022 world population prospects of the United Nations project population aged 65 and above to increase from 10–16% by 2050; 2020 Insee projections announced a doubling of the number of people older than 75 years between 2013 and 2070. 1,2 Older people are the most affected by neurocognitive disorders, sleep disorders and polypharmacy. 3–6 Sleep undergoes many age-related changes during normal aging and even more with associated medical conditions. 7–12 We define sleep stages with the help of the microarchitecture and its specific patterns and graphic elements, such as the K-complex and sleep spindle (SS), in the second sleep stage (N2). Various pathologies, such as cognitive disorders, cause clinical and architecture sleep disorders that can be observed through the study of microsleep. Some changes have been described as predictors of subsequent neurocognitive disorders and neurodegenerative pathologies, such as Alzheimer's disease or Dementia with Lewy bodies. 13–15 However, macro- and microsleep architectures have no precise definition for normality or abnormality in the oldest populations, even though many authors have conducted small trials or extensive cohort studies on age-related changes in sleep. To our knowledge, no study has focused on the modifications of the microsleep architecture in very old people, although this population is mainly concerned with the accumulation of sleep pathologies, neurodegenerative pathologies, polymedication, and the entanglement of these points. This exploratory study aimed to verify whether we could identify statistically significant age-related changes in sleep spindle characteristics using laboratory-based polysomnography data from a population sample of elderly (65–74 years) and oldest people (75 and older) registered through normal care routines. We focused on changes in sleep spindle (SS) characteristics, including localization, density, frequency, amplitude, and duration. We hypothesized that even a short monocentric retrospective study could reveal such changes in SS characteristics. Light modifications should be observed as they are when studying younger people: density, amplitude, and duration decrease with age, whereas frequency and localization are not affected. Methods Overview The analysis was based on data collected from laboratory-based polysomnography of patients registered between August 2012 and May 2021 at the Sleep Laboratory of the University Hospital of Besançon. Participants We selected people aged over 75 years at the time of polysomnography to constitute our research group (further named “75+”) and created a control group of people aged between 65 and 74 years (further named “65+”). Polysomnography was recorded as part of the normal care routine of the participants, and we assumed that they all had a sleep complaint, a sleep disorder or both. To obtain comparable sleep data, we excluded participants with neurological pathologies that could have substantially changed their sleep records (Parkinson's disease, stroke, epilepsy, multiple system atrophy, and meningioma) or who had a mental illness (bipolar disorder). We also excluded patients treated with benzodiazepines or related or antipsychotic drugs. We compiled the date of birth, age, and date of polysomnography to create both groups and then anonymized participants, giving each record a consecutive number when added to a group. We listed their treatments to identify those that could modify sleep without having a systematic effect (including opioids, pregabalin, methylphenidate, pramipexole, and rotigotine) and manually reviewed their medical files to check their cognitive status at the time of polysomnography: no disorder, minor (mND) or major (MND) neurocognitive disorder. This study was approved by the Clinical Research and Innovation Office of the University Hospital of Besançon, 25 000 Besançon, France. All participants received a study notice and a nonobjection form to sign and send them back if they did not want to be included. Polysomnography All polysomnography data were recorded as part of the usual care procedure. The data used in this study were retrospectively collected, and the care procedures were not changed. Each participant underwent single-night laboratory-based polysomnography with the following montage: nine electroencephalography electrodes (C3, C4, Cz, F3, F4, T3, T4, O1, O2) with a 35 Hz high-pass filter, 0.35 Hz low-pass filter, and 500 Hz sampling frequency. Left and right electrooculogram, bilateral chin and shins electromyogram, single bipolar electrocardiogram, finger pulse oximetry, chest and abdominal excursion, airflow, and body position. The signals were analyzed using BrainRT™ analysis software (Onafhankelijke Software Groep, Kontich, Belgium) and digitally stored, and sleep staging was performed according to standardized AASM criteria. 16 We manually reviewed the polysomnography data to exclude those with too many artifacts, unstable sleep, or insufficient sleep time. We then selected the most stable 10-minute extracts of the N2 sleep stage and sleep stage 3 (further called N3) of the first three sleep cycles; older people rarely reach a fourth cycle, especially when sleeping in the laboratory. We added to each individual dataset the following items extracted from the polysomnography: obstructive sleep apnea diagnosis (OSA) and its severity based on the apnea-hypopnea index (AHI) following the AASM Scoring Manual Version 2.2, central sleep apnea, prior use of mandibular advancement device or continuous positive airway pressure therapy, and other sleep disorders such as periodic limb movements or nocturnal hypoxemia. Sleep spindles Polysomnography data were restricted to electroencephalogram data only and registered in the European Data Format (EDF) using EDF browser version 1.93 64-bit. Python was used through Anaconda Navigator 2.1.2 to upload the dataset, and spindle detection was assessed through YASA (Yet Another Spindle Algorithm) version 0.6.0 developed by Raphael Vallat. 17 We used the following main criteria to detect and record spindles: a frequency between 9 and 12.5 Hz (the “Slow” Sleep Spindle group, called SS-S), 12.5 and 16 Hz (the “Fast” Sleep Spindle group, called SS-F), and a duration between 0.5 and 2 seconds. SS had to be detected on two or more electrodes to be counted. Individual data were analyzed and pooled into large datasets for statistical analysis at the group level and between groups. Analysis After visual verification and validation, the collected data were registered in Microsoft Excel using a unique code for each item. Finally, the results were analyzed with descriptive statistics using SPSS software for Windows (version 18.0; SPSS, Inc., Chicago, IL, USA). Qualitative variables were described in terms of effective and percentage. We performed a Pearson chi2 test to evaluate the links between SS localization regarding sleep stage and SS type in both population groups (75 + and 65+), applying Cramer’s V correlation test. Quantitative variables are described as the mean, median, standard deviation, and range. Sociodemographic data were also compared using Pearson chi2 test and either Phi or Cramer’s V correlation tests. For the main sleep spindle characteristics, we applied ANOVA to compare both age groups; two models of ANCOVA were used to adjust the results: Model 1 used exact age and global OSA as covariates; Model 2 used group age, sex and global OSA as covariates. The significance level was set at 5% for all the statistical analyses. Results Between August 2012 and May 2021, 96 participants were identified in the 75+ group. Ninety-one were screened after applying the exclusion criteria for polysomnography records, and 38 patients were excluded because of pathology or treatment. After the polysomnography review, we excluded 17 additional records, leading to 36 participants in the 75+ group being included in the analysis. The same procedure was performed for the 65+ group, and 274 participants were identified. Two participants in this group refused their data to be used. Excluding duplicates, pathologies and treatments led to a sample of 143 participants. Eighty-six more participants were excluded after the polysomnography review, resulting in 57 participants in the control group. These data are presented in the flow chart (Figure 1). Figure 1 . Flow chart. We identified 370 participants recorded between August 2012 and May 2021 who were 65 years old or more. We excluded 30 records because of duplicated and wrong protocol; 144 because they weren't fitting with medical file inclusion criteria. Finally 103 participants because of the polysomnography themselve. At the end, 36 patients were included in 75+ group and 57 in 65+ control group. Population The 36 participants in the 75+ group had a mean age of 79.5 years (± 3.5 years) and were divided into 18 participants aged between 75 and 79 years, 14 between 80 and 84 years and only 4 between 85 and 89 years. The 57 participants of the 65+ group had a mean age of 68.2 years (± 2.23) and were divided into 42 participants aged between 65 and 69 years and 15 participants aged between 70 and 74 years. There was no significant difference between the 75+ and 65+ groups regarding sex ratio (1.25 and 0.5; p = 0.052), global (97.2% and 89.5%, p = 0.105) or stratified OSA diagnosis (mild 16.7% and 12.3%, p = 0.556; moderate 22.2% and 40.4%, p = 0.077; severe 58.3% and 36.8%, p = 0.055). The groups were also comparable in terms of associated sleep disorder diagnosis (49.1% and 55.6%, p = 0.671) and neurocognitive disorder (ND) frequency: no ND for 31 and 55 participants (p = 0.104); mND for five and two participants (p = 0.104). No participant had MND. Only 7.5% of the participants were diagnosed with central sleep apnea syndrome, all of whom were in the 75+ group (p = 0.001). Five patients had continuous positive airway pressure therapy because of known OSA but did not use it during polysomnography. We found a significant difference in the “Other” treatment category: 16 participants from the 75+ group compared with only 4 from the 65+ group (p < 0.001) were treated with drugs that could modify their sleep while treating some pain (opioids), restless leg syndrome (pramipexole, ropinirole), or excessive daytime sleepiness (methylphenidate). All the data are presented in Table 1. Table 1. Main socio-medical characteristics of the participants of both groups, presented as effective (percentage). Group 65+ 75+ p Effective N 57 (61.3%) 36 (38.7%) Gender Women 19 (33.3%) 20 (55.6%) Men 38 (66.7%) 16 (44.4%) Sex ratio W/M 0.5 1.25 0.052 Age Mean (SD) 68.2 (± 2.23) 79.5 (± 3.5) OSA Mild 7 (12.3%) 6 (16.7%) 0.556 Moderate 23 (40.4%) 8 (22.2%) 0.077 Severe 21 (36.8%) 21 (58.3%) 0.055 Global 51 (89.5%) 35 (97.2%) 0.105 CSA 0 7 (19.4%) 0.001 Sleep apnea treatment MAD 0 0 CPAP 4 (7%) 1 (2.8%) 0.645 Other diagnosis N 28 (49.1%) 20 (55.6%) 0.671 Neurocognitive Disorders None 55 (96.5%) 31 (86.1%) 0.104 Minor 2 (3.5%) 5 (13.9%) 0.104 Major 0 0 Treatments Anti-depressants 7 (12.3%) 7 (19.4%) 0.383 Melatonin 3 (5.3%) 1 (2.8%) 1 Other 4 (7%) 16 (44.4%) < 0.001 Groups were comparable in terms of gender, OSA, sleep apnea treatment, other diagnosis, neurocognitive disorder, anti-depressants and melatonin. There was a significant difference in CSA frequency (none in 65+ group) and in other treatments frequency (a lot more in the 75+ group). OSA: Obstructive Sleep Apnea. Mild for AHI 5-15/hour. Moderate for AHI 15-30/hour. Severe for AHI > 30/hour. CSA: Central Sleep Apnea. MAD: Mandibular Advancement Device. CPAP: Continuous Positive Airway Pressure therapy Qualitative characteristic: Localization We analyzed the localization source by grouping the electrodes to construct a variable with four values: central (C: C3, C4, and Cz), frontal (F: F3 and F4), temporal (T: T3 and T4), and occipital (O: O1 and O2). Between the groups, SS-S localization was significantly different in stage N2, revealing a predominant C in the 75+ group and a greater difference between C and F in the 65+ group, with a strong correlation (chi2 10.443 (3), p = 0.015, Cramer’s V 0.389). For SS-F (chi2 3.279 (3), p = 0.351, Cramer’s V = 0.225), predominant C localization was found in both groups; there was no significant difference, and the correlation was moderate between group appurtenance and localization (Table 2 and Figure 2A). There was no significant difference in N3 between the groups, with C localization being predominant for both types of SS in both groups (Figure 2B). Figure 2A. SS localization in N2 according to SS type and age group. Values are presented as percentage of each localization class for one type of SS per group. Central localization is highly predominant in all SS type in the 75+ group while it is only predominant for SS-F in the 65+ group. Figure 2B. SS localization in N3 according to SS type and age group. Values are presented as percentage of each localization class for one type of SS per group. Central localization is predominant for every SS type and there is no significant difference between groups. S: Slow; F: Fast. Error bars show standard error. ***: Statistically significant difference with p < 0.005 Table 2. Sleep Spindles Localization in both sleep stages, comparing age groups Sleep Stage N2 Sleep Stage N3 SS-S SS-F SS-S SS-F 65+ (%) 75+ (%) 65+ (%) 75+ (%) 65+ (%) 75+ (%) 65+ (%) 75+ (%) Frontal 45.7 11.8 20.6 19.4 23.9 22.6 13.8 20 Central 31.4 47.1 67.6 74.2 39.1 35.5 75.9 80 Temporal 20 29.4 8.8 0 23.9 19.4 10.3 0 Occipital 2.9 11.8 2.9 6.5 13 22.6 0 0 Pearson 10.443 3.279 1.251 1.813 df 3 3 3 2 p 0.015 0.351 0.741 0.404 Cramer 0.389 0.225 0.127 0.203 Grouping electrodes into 4 localizations: Central (C3, C4, and Cz), Frontal (F3 and F4), Temporal (T3 and T4), and Occipital (O1 and O2). The main localization is always Central except for SS-S in N2 where the distribution is more balanced between Central and Frontal in the 65+ group. Quantitative characteristics: Density, frequency, amplitude, and duration For the subsequent analysis of sleep spindle characteristics between N2 and N3, our statistical model included three main covariates: age (75+ or 65+), sex (female or male) and global OSA diagnosis (Table 3). Table 3. Sleep stages comparison of the sleep spindles characteristics N2 N3 Global Covariates influence (p) 65+ 75+ 65+ 75+ p age group sex OSA Density SS-S 2.952 3.317 1.748 1.842 < 0.001 0.05 0.505 1 SS-F 1.991 1.228 0.293 0.402 < 0.001 0.671 0.896 0.504 Frequency SS-S 10.82 10.419 10.763 10.579 < 0.001 0.177 0.2 0.598 SS-F 13.407 13.702 13.189 13.51 0.004 0.571 0.503 0.524 Amplitude SS-S 34.959 37.134 36.072 37.816 < 0.001 0.506 0.197 0.762 SS-F 31.409 33.219 28.975 31.432 < 0.001 0.148 0.022 0.191 Duration SS-S 0.836 0.874 0.793 0.795 0.001 0.065 0.158 0.339 SS-F 0.787 0.791 0.667 0.722 0.222 0.28 0.901 0.941 Values are presented as Mean. We present the mean values of each age group for both Slow and Fast Sleep Spindles. ANCOVA include the age group (75+ or 65+), OSA diagnosis (yes or no) and gender (female or male) for calculation of statistical difference. SS density was greater in N2 than in N3 for SS-S (p < 0.001) and SS-F (p < 0.001), with a significant influence from the age group (p 0.05) on slow spindles, with density being greater in the 75+ group. We observed a significantly greater mean frequency of SS-S (p < 0.001) and SS-F (p = 0.004) in N2 than in N3, with no statistically significant influence from the covariates. Amplitudes were lower in N2 than in N3 for SS-S (p < 0.001) and greater in N2 for SS-F (p < 0.001) in the covariate model, with gender influencing the SS-F: females had spindles with greater amplitudes. The duration of SS was longer in N2 for SS-S (p = 0.001) and SS-F without reaching a significant level (p = 0.222), with no clear influence from the covariates, but the age group almost reached significance (longer spindles in the 75+). We compared the SS-S and SS-F characteristics in both age groups in a multivariate model using global OSA and exact age as covariates. The Model 1 results are presented in Table 4. In the 75+ group, considering exact age and global OSA as covariates, SS density was not significantly different between S and F in stage 2 (p 0.409) or in stage 3 (p 0.319). SS frequency was not different (p 0.206 in N2 and p 0.109 in N3) between SS-S and SS-F. Amplitude changed between SS-S and SS-F in both stages, with SS-S being greater than SS-F in both sleep stages (p < 0.001) with no significant influence of covariates. Duration was significantly different in N2 (p 0.001) but not in N3 (p 0.74), being longer in SS-S than in SS-F. In the 65+ group, the SS-S density was significantly greater in N2 (p < 0.001) but not in N3 (p = 0.797). Frequencies were not different in N2 (p = 0.395) or in N3 (p = 0.983) between SS-S and SS-F. Amplitudes were greater in SS-S in N2 (p < 0.001), with a significant influence of global OSA (p = 0.010), leading to greater values; they were also greater in SS-S in N3 (p = 0.039), with no clear influence of the two covariates. SS-S duration increased in N2 (p < 0.001) with increasing age (p = 0.012) but did not change in N3 (p = 0.606). All these results are presented as Model 1 in Table 4. Finally, ANCOVA with a multivariate model was applied to compare S/F spindle characteristics with age group, global OSA and sex as covariates in Model 2 (Table 5). For density, the N2 SS-S was significantly greater than the SS-F (p = 0.007), with no clear influence from the covariates, while the density did not differ in N3 (p = 0.965). For frequencies, N2 values did not differ between SS-S and SS-F (p = 0.55), and the N3 values did not differ (p = 0.422). Table 4. Spindle types comparison of the sleep spindles characteristics in both groups, with ANCOVA - model 1. 75+ Global Covariates influence (p) 65+ Global Covariates influence (p) S F p exact age OSA S F p exact age OSA Density N2 3.317 1.228 0.409 0.525 0.975 2.952 1.991 < 0.001 0.144 0.763 N3 1.842 0.402 0.319 0.24 0.089 1.748 0.293 0.797 0.704 0.508 Frequency N2 10.419 13.702 0.206 0.54 0.692 10.82 13.407 0.395 0.594 0.798 N3 10.579 13.51 0.109 0.944 0.122 10.763 13.189 0.983 0.883 0.592 Amplitude N2 37.134 33.219 < 0.001 0.72 0.985 34.959 31.409 < 0.001 0.231 0.01 N3 37.816 31.432 < 0.001 0.201 0.195 36.072 28.975 0.039 0.763 0.827 Duration N2 0.874 0.791 0.001 0.571 0.933 0.836 0.787 < 0.001 0.012 0.069 N3 0.795 0.7212 0.74 0.567 0.009 0.793 0.667 0.606 0.517 0.814 Model 1 includes exact age and OSA diagnosis as covariates. We see amplitude and duration are different between slow and fast spindles in both age groups. OSA: Obstructive Sleep Apnea. Table 5. Spindle types comparison of the sleep spindles characteristics with ANCOVA - model 2. 75+ 65+ Global Covariates influence (p) S F S F p age group sex OSA Density N2 3.317 1.228 2.952 1.991 0.007 0.104 0.879 0.824 N3 1.842 0.402 1.748 0.293 0.965 0.694 0.331 0.341 Frequency N2 10.419 13.702 10.82 13.407 0.55 0.199 0.364 0.2 N3 10.579 13.51 10.763 13.189 0.422 0.677 0.266 0.546 Amplitude N2 37.134 33.219 34.959 31.409 < 0.001 0.176 0.736 0.184 N3 37.816 31.432 36.072 28.975 < 0.001 0.138 0.042 0.933 Duration N2 0.874 0.791 0.836 0.787 < 0.001 0.37 0.423 0.065 N3 0.795 0.7212 0.793 0.667 0.93 0.411 0.476 0.251 Model 2 includes age group (75+ or 65+), OSA (yes or no) and gender (female or male). In multivariate model, age does not affect any of the modifications we saw in the model 1; while gender affects amplitude variation in N3. OSA: Obstructive Sleep Apnea. For amplitudes, the SS-S amplitude was greater than the SS-F amplitude (p < 0.001) in both stages, with the influence of sex in stage 3 (p = 0.042), with females reaching greater amplitudes. For duration, the SS-S was longer than the SS-F for N2, and the global OSA tended to be the most explanatory factor (p = 0.065); there was no difference for N3. These results are shown in Table 5 as Model 2. Discussion 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) 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. 42 Moreover, 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. Conclusion This retrospective monocentric study was able to verify changes in sleep spindle characteristics between sleep stages and between sleep spindle types (slow or fast). There were few slight age-related changes in the number of sleep spindles between the over 65 age group and the over 75 age group. It seems that density and duration were the most affected characteristics through age when looking at the exact age, but the differences were lost when strictly comparing the age groups. Most of our results were consistent with those in the literature, as the localization of the sleep spindles was mostly central. However, the values and means of the main characteristics of sleep spindles changed significantly among all studies, including the most recent and extensive studies. It is still necessary to conduct wider longitudinal studies with old and oldest participants to analyze their microsleep architecture and its evolution over several decades. Therefore, we could finally define typical values and abnormal patterns linked to one or another diagnosis, revealing the role of micro sleep architecture as a predictive biomarker of neurodegenerative pathologies and their evolution and gravity. Declarations Ethics approval and consent to participate : This study was approved by the Clinical Research and Innovation Office of the University Hospital of Besançon, 25 000 Besançon, France. All participants received a study notice and a nonobjection form to sign and send them back if they did not want to be included. Consent for publication : Not applicable. Availability of data and materials : The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests : The authors declare that they have no competing interests Funding : Not applicable. Authors' contributions : BP have made the design of the work, the acquisition and analysis, the interpretation of data and drafted the work. PV have made substantial contributions to the conception and substantively revised it. HB have made substantial contributions to the conception, helped with the acquisition and interpretation. SG have made substantial contributions to the conception, took part to the acquisition, analysis and interpretation, substantially revised the work. All the authors have approved the submitted version and have agreed both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. Acknowledgements : Not applicable. References Raftery AE, Chunn JL, Gerland P, Ševčíková H. 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Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia. Sleep Med. nov 2017;39:54‑61. Espiritu JRD. Aging-Related Sleep Changes. Clin Geriatr Med. févr 2008;24(1):1‑14. Nicolas A, Petit D, Rompré S, Montplaisir J. Sleep spindle characteristics in healthy subjects of different age groups. Clin Neurophysiol. mars 2001;112(3):521‑7. Crowley K. The effects of normal aging on sleep spindle and K-complex production. Clin Neurophysiol. oct 2002;113(10):1615‑22. Guadagni V, Byles H, Tyndall AV, Parboosingh J, Longman RS, Hogan DB, et al. Association of sleep spindle characteristics with executive functioning in healthy sedentary middle‐aged and older adults. J Sleep Res [Internet]. avr 2021 [cité 29 mai 2021];30(2). Disponible sur: https://onlinelibrary.wiley.com/doi/10.1111/jsr.13037 Fillmore P, Gao C, Diaz J, Scullin MK. The Prospective Sleeping Brain: Age-Related Differences in Episodic Future Thinking and Frontal Sleep Spindles. J Cogn Neurosci. 1 juin 2021;33(7):1287‑94. Martin N, Lafortune M, Godbout J, Barakat M, Robillard R, Poirier G, et al. Topography of age-related changes in sleep spindles. Neurobiol Aging. févr 2013;34(2):468‑76. Djonlagic I, Mariani S, Fitzpatrick AL, Van Der Klei VMGTH, Johnson DA, Wood AC, et al. Macro and micro sleep architecture and cognitive performance in older adults. Nat Hum Behav. janv 2021;5(1):123‑45. Campos-Beltrán D, Marshall L. Changes in sleep EEG with aging in humans and rodents. Pflüg Arch - Eur J Physiol. mai 2021;473(5):841‑51. Taillard J, Gronfier C, Bioulac S, Philip P, Sagaspe P. Sleep in Normal Aging, Homeostatic and Circadian Regulation and Vulnerability to Sleep Deprivation. Brain Sci. 29 juill 2021;11(8):1003. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Sep, 2024 Read the published version in BMC Geriatrics → Version 1 posted Editorial decision: Revision requested 05 Aug, 2024 Reviews received at journal 03 Aug, 2024 Reviews received at journal 30 Jul, 2024 Reviewers agreed at journal 24 Jul, 2024 Reviewers agreed at journal 23 Jul, 2024 Reviewers invited by journal 23 Jul, 2024 Editor invited by journal 17 Jul, 2024 Editor assigned by journal 16 Jul, 2024 Submission checks completed at journal 16 Jul, 2024 First submitted to journal 15 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4743069","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336098396,"identity":"e544e146-0781-4a07-8532-59342ed51012","order_by":0,"name":"Bastien Poirson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACAyA+AGLwM/AwMDA2wCXkCGuRbEDVYoxXC4RxgFgt5tLNBw9X/LHJMz5+9pgE4w4be/7ZzQ8fFzAY5OPSYjnnWMLBs21pxWZn8tIkGM+kJc64c8zYeAaDgWUDDi0GN3IMDjY2HE7cdoPHTPpv2+EEhhs5bNI8DH8McOiAaGn4czhx8wweMwnGtv/28hAtBgS0sB1O3CAB1nKAcQMhLWC/NLYBvXAmx9iC8Uxy4sYbacbGPAa4tQBD7PDHhj82if3tZwxvMO6ws5e7kfzwMU8Fbi0MEjgcjFMDTi2jYBSMglEwChAAAPNyVivfhVt2AAAAAElFTkSuQmCC","orcid":"","institution":"CHU de Besançon, Service de Gériatrie","correspondingAuthor":true,"prefix":"","firstName":"Bastien","middleName":"","lastName":"Poirson","suffix":""},{"id":336098397,"identity":"dcc51862-fdcb-4b7a-828a-2289e42c3649","order_by":1,"name":"Pierre Vandel","email":"","orcid":"","institution":"Lausanne University Hospital and University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Pierre","middleName":"","lastName":"Vandel","suffix":""},{"id":336098398,"identity":"ab714ac1-4a99-4ba5-a579-da6f62c5574e","order_by":2,"name":"Hubert Bourdin","email":"","orcid":"","institution":"CHU de Besançon, Unité d’Explorations du Sommeil et de la Vigilance","correspondingAuthor":false,"prefix":"","firstName":"Hubert","middleName":"","lastName":"Bourdin","suffix":""},{"id":336098399,"identity":"9d3f97cb-ee9c-49d0-aaa3-fa50893691e8","order_by":3,"name":"Silvio Galli","email":"","orcid":"","institution":"CHU de Besançon, Unité d’Explorations du Sommeil et de la Vigilance","correspondingAuthor":false,"prefix":"","firstName":"Silvio","middleName":"","lastName":"Galli","suffix":""}],"badges":[],"createdAt":"2024-07-15 13:06:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4743069/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4743069/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12877-024-05364-9","type":"published","date":"2024-09-20T15:58:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62653949,"identity":"79bdcc1e-b5dd-4689-94ba-93ccf4d17fba","added_by":"auto","created_at":"2024-08-17 01:19:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44537,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFlow chart.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWe identified 370 participants recorded between August 2012 and May 2021 who were 65 years old or more. We excluded 30 records because of duplicated and wrong protocol; 144 because they weren't fitting with medical file inclusion criteria. Finally 103 participants because of the polysomnography themselve. At the end, 36 patients were included in 75+ group and 57 in 65+ control group.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"OnlineFIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-4743069/v1/404df73f7f7ddc97844560f5.png"},{"id":62653950,"identity":"b3d2c357-4998-456e-af20-7807d1124218","added_by":"auto","created_at":"2024-08-17 01:19:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003e\u003cem\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003c/em\u003e\u003c/u\u003e\u003cem\u003e SS localization in N2 according to SS type and age group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eValues are presented as percentage of each localization class for one type of SS per group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCentral localization is highly predominant in all SS type in the 75+ group while it is only predominant for SS-F in the 65+ group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cem\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/em\u003e\u003c/u\u003e\u003cem\u003e SS localization in N3 according to SS type and age group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eValues are presented as percentage of each localization class for one type of SS per group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCentral localization is predominant for every SS type and there is no significant difference between groups.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eS: Slow; F: Fast. Error bars show standard error.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e***: Statistically significant difference with p \u0026lt; 0.005\u003c/em\u003e\u003c/p\u003e","description":"","filename":"OnlineFIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-4743069/v1/0d9585906f58663d6714032e.png"},{"id":65104142,"identity":"d12722c7-b8b9-415d-9025-2ae7c977df5a","added_by":"auto","created_at":"2024-09-23 16:12:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":924640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4743069/v1/249a8169-6b21-414d-a6ad-21830b10ef95.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age-related changes in sleep spindle characteristics in individuals over 75 years of age: a retrospective and comparative study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global population is increasingly aging. The 2022 world population prospects of the United Nations project population aged 65 and above to increase from 10\u0026ndash;16% by 2050; 2020 Insee projections announced a doubling of the number of people older than 75 years between 2013 and 2070.\u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOlder people are the most affected by neurocognitive disorders, sleep disorders and polypharmacy.\u003csup\u003e3\u0026ndash;6\u003c/sup\u003e Sleep undergoes many age-related changes during normal aging and even more with associated medical conditions.\u003csup\u003e7\u0026ndash;12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe define sleep stages with the help of the microarchitecture and its specific patterns and graphic elements, such as the K-complex and sleep spindle (SS), in the second sleep stage (N2).\u003c/p\u003e \u003cp\u003eVarious pathologies, such as cognitive disorders, cause clinical and architecture sleep disorders that can be observed through the study of microsleep. Some changes have been described as predictors of subsequent neurocognitive disorders and neurodegenerative pathologies, such as Alzheimer's disease or Dementia with Lewy bodies.\u003csup\u003e13\u0026ndash;15\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHowever, macro- and microsleep architectures have no precise definition for normality or abnormality in the oldest populations, even though many authors have conducted small trials or extensive cohort studies on age-related changes in sleep.\u003c/p\u003e \u003cp\u003eTo our knowledge, no study has focused on the modifications of the microsleep architecture in very old people, although this population is mainly concerned with the accumulation of sleep pathologies, neurodegenerative pathologies, polymedication, and the entanglement of these points.\u003c/p\u003e \u003cp\u003eThis exploratory study aimed to verify whether we could identify statistically significant age-related changes in sleep spindle characteristics using laboratory-based polysomnography data from a population sample of elderly (65\u0026ndash;74 years) and oldest people (75 and older) registered through normal care routines.\u003c/p\u003e \u003cp\u003eWe focused on changes in sleep spindle (SS) characteristics, including localization, density, frequency, amplitude, and duration.\u003c/p\u003e \u003cp\u003eWe hypothesized that even a short monocentric retrospective study could reveal such changes in SS characteristics. Light modifications should be observed as they are when studying younger people: density, amplitude, and duration decrease with age, whereas frequency and localization are not affected.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview\u003c/h2\u003e \u003cp\u003eThe analysis was based on data collected from laboratory-based polysomnography of patients registered between August 2012 and May 2021 at the Sleep Laboratory of the University Hospital of Besan\u0026ccedil;on.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eWe selected people aged over 75 years at the time of polysomnography to constitute our research group (further named \u0026ldquo;75+\u0026rdquo;) and created a control group of people aged between 65 and 74 years (further named \u0026ldquo;65+\u0026rdquo;).\u003c/p\u003e \u003cp\u003ePolysomnography was recorded as part of the normal care routine of the participants, and we assumed that they all had a sleep complaint, a sleep disorder or both.\u003c/p\u003e \u003cp\u003eTo obtain comparable sleep data, we excluded participants with neurological pathologies that could have substantially changed their sleep records (Parkinson's disease, stroke, epilepsy, multiple system atrophy, and meningioma) or who had a mental illness (bipolar disorder). We also excluded patients treated with benzodiazepines or related or antipsychotic drugs.\u003c/p\u003e \u003cp\u003e We compiled the date of birth, age, and date of polysomnography to create both groups and then anonymized participants, giving each record a consecutive number when added to a group.\u003c/p\u003e \u003cp\u003eWe listed their treatments to identify those that could modify sleep without having a systematic effect (including opioids, pregabalin, methylphenidate, pramipexole, and rotigotine) and manually reviewed their medical files to check their cognitive status at the time of polysomnography: no disorder, minor (mND) or major (MND) neurocognitive disorder.\u003c/p\u003e \u003cp\u003e This study was approved by the Clinical Research and Innovation Office of the University Hospital of Besan\u0026ccedil;on, 25 000 Besan\u0026ccedil;on, France. All participants received a study notice and a nonobjection form to sign and send them back if they did not want to be included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePolysomnography\u003c/h2\u003e \u003cp\u003eAll polysomnography data were recorded as part of the usual care procedure. The data used in this study were retrospectively collected, and the care procedures were not changed.\u003c/p\u003e \u003cp\u003eEach participant underwent single-night laboratory-based polysomnography with the following montage: nine electroencephalography electrodes (C3, C4, Cz, F3, F4, T3, T4, O1, O2) with a 35 Hz high-pass filter, 0.35 Hz low-pass filter, and 500 Hz sampling frequency. Left and right electrooculogram, bilateral chin and shins electromyogram, single bipolar electrocardiogram, finger pulse oximetry, chest and abdominal excursion, airflow, and body position.\u003c/p\u003e \u003cp\u003eThe signals were analyzed using BrainRT\u0026trade; analysis software (Onafhankelijke Software Groep, Kontich, Belgium) and digitally stored, and sleep staging was performed according to standardized AASM criteria.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe manually reviewed the polysomnography data to exclude those with too many artifacts, unstable sleep, or insufficient sleep time. We then selected the most stable 10-minute extracts of the N2 sleep stage and sleep stage 3 (further called N3) of the first three sleep cycles; older people rarely reach a fourth cycle, especially when sleeping in the laboratory.\u003c/p\u003e \u003cp\u003eWe added to each individual dataset the following items extracted from the polysomnography: obstructive sleep apnea diagnosis (OSA) and its severity based on the apnea-hypopnea index (AHI) following the AASM Scoring Manual Version 2.2, central sleep apnea, prior use of mandibular advancement device or continuous positive airway pressure therapy, and other sleep disorders such as periodic limb movements or nocturnal hypoxemia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSleep spindles\u003c/h2\u003e \u003cp\u003ePolysomnography data were restricted to electroencephalogram data only and registered in the European Data Format (EDF) using EDF browser version 1.93 64-bit.\u003c/p\u003e \u003cp\u003ePython was used through Anaconda Navigator 2.1.2 to upload the dataset, and spindle detection was assessed through YASA (Yet Another Spindle Algorithm) version 0.6.0 developed by Raphael Vallat.\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe used the following main criteria to detect and record spindles: a frequency between 9 and 12.5 Hz (the \u0026ldquo;Slow\u0026rdquo; Sleep Spindle group, called SS-S), 12.5 and 16 Hz (the \u0026ldquo;Fast\u0026rdquo; Sleep Spindle group, called SS-F), and a duration between 0.5 and 2 seconds. SS had to be detected on two or more electrodes to be counted.\u003c/p\u003e \u003cp\u003eIndividual data were analyzed and pooled into large datasets for statistical analysis at the group level and between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis\u003c/h2\u003e \u003cp\u003eAfter visual verification and validation, the collected data were registered in Microsoft Excel using a unique code for each item. Finally, the results were analyzed with descriptive statistics using SPSS software for Windows (version 18.0; SPSS, Inc., Chicago, IL, USA).\u003c/p\u003e \u003cp\u003eQualitative variables were described in terms of effective and percentage.\u003c/p\u003e \u003cp\u003eWe performed a Pearson chi2 test to evaluate the links between SS localization regarding sleep stage and SS type in both population groups (75\u0026thinsp;+\u0026thinsp;and 65+), applying Cramer\u0026rsquo;s V correlation test.\u003c/p\u003e \u003cp\u003eQuantitative variables are described as the mean, median, standard deviation, and range. Sociodemographic data were also compared using Pearson chi2 test and either Phi or Cramer\u0026rsquo;s V correlation tests.\u003c/p\u003e \u003cp\u003eFor the main sleep spindle characteristics, we applied ANOVA to compare both age groups; two models of ANCOVA were used to adjust the results: Model 1 used exact age and global OSA as covariates; Model 2 used group age, sex and global OSA as covariates.\u003c/p\u003e \u003cp\u003eThe significance level was set at 5% for all the statistical analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBetween August 2012 and May 2021, 96 participants were identified in the 75+ group. Ninety-one were screened after applying the exclusion criteria for polysomnography records, and 38 patients were excluded because of pathology or treatment. After the polysomnography review, we excluded 17 additional records, leading to 36 participants in the 75+ group being included in the analysis.\u003c/p\u003e\n\u003cp\u003eThe same\u0026nbsp;procedure was performed for the 65+ group, and\u0026nbsp;274 participants\u0026nbsp;were identified. Two participants\u0026nbsp;in this group refused their data to be used.\u0026nbsp;Excluding duplicates, pathologies and treatments led to a sample of 143 participants. Eighty-six\u0026nbsp;more participants were excluded after the polysomnography review, resulting in 57 participants in the control group.\u003c/p\u003e\n\u003cp\u003eThese data are presented in the flow chart (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eFigure 1\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eFlow chart.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWe identified 370 participants recorded between August 2012 and May 2021 who were 65 years old or more. We excluded 30 records because of duplicated and wrong protocol; 144 because they weren\u0026apos;t fitting with medical file inclusion criteria. Finally 103 participants because of the polysomnography themselve. At the end, 36 patients were included in 75+ group and 57 in 65+ control group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePopulation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;36 participants\u0026nbsp;in\u0026nbsp;the 75+ group had a mean age of 79.5 years (\u0026plusmn; 3.5\u0026nbsp;years)\u0026nbsp;and were\u0026nbsp;divided into 18 participants\u0026nbsp;aged\u0026nbsp;between 75\u0026nbsp;and\u0026nbsp;79\u0026nbsp;years, 14 between 80\u0026nbsp;and\u0026nbsp;84\u0026nbsp;years\u0026nbsp;and only 4 between 85 and 89 years.\u003c/p\u003e\n\u003cp\u003eThe 57 participants of the 65+ group had a mean age of 68.2 years (\u0026plusmn; 2.23) and were divided into 42 participants aged between 65 and 69 years and 15 participants aged between 70 and 74 years.\u003c/p\u003e\n\u003cp\u003eThere was no significant difference between\u0026nbsp;the\u0026nbsp;75+ and 65+ groups regarding sex ratio (1.25 and 0.5; p\u0026nbsp;=\u0026nbsp;0.052), global (97.2% and 89.5%, p\u0026nbsp;=\u0026nbsp;0.105) or stratified OSA diagnosis (mild 16.7% and 12.3%, p = 0.556; moderate 22.2% and 40.4%, p = 0.077; severe 58.3% and 36.8%, p = 0.055).\u003c/p\u003e\n\u003cp\u003eThe groups were also comparable in terms of associated sleep disorder diagnosis (49.1% and 55.6%, p = 0.671) and neurocognitive disorder (ND) frequency: no ND for 31 and 55 participants (p = 0.104); mND for five and two participants (p = 0.104). No participant had MND.\u003c/p\u003e\n\u003cp\u003eOnly 7.5% of the participants were diagnosed with central sleep apnea syndrome, all of whom were in the 75+ group (p = 0.001). Five patients had continuous positive airway pressure therapy because of known OSA but did not use it during polysomnography.\u003c/p\u003e\n\u003cp\u003eWe found a significant difference in the \u0026ldquo;Other\u0026rdquo; treatment category: 16 participants from the 75+ group compared with only 4 from the 65+ group (p \u0026lt; 0.001) were treated with drugs that could modify their sleep while treating some pain (opioids), restless leg syndrome (pramipexole, ropinirole), or excessive daytime sleepiness (methylphenidate). All the data are presented in Table 1.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 1.\u003c/u\u003e\u003c/strong\u003e Main socio-medical characteristics of the participants of both groups, presented as effective (percentage).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEffective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57 (61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eW/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.2 (\u0026plusmn;\u0026nbsp;2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.5 (\u0026plusmn;\u0026nbsp;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eOSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 (58.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51 (89.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35 (97.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSleep apnea treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCPAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOther diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28 (49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eNeurocognitive Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55 (96.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31 (86.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMinor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMajor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eTreatments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAnti-depressants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMelatonin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eGroups were comparable in terms of gender, OSA, sleep apnea treatment, other diagnosis, neurocognitive disorder, anti-depressants and melatonin. There was a significant difference in CSA frequency (none in 65+ group) and in other treatments frequency (a lot more in the 75+ group). \u003cem\u003eOSA: Obstructive Sleep Apnea. Mild for AHI 5-15/hour. Moderate for AHI 15-30/hour. Severe for AHI \u0026gt; 30/hour. CSA: Central Sleep Apnea. MAD: Mandibular Advancement Device. CPAP: Continuous Positive Airway Pressure therapy\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eQualitative characteristic: Localization\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the localization source by grouping the electrodes to construct a variable with four values: central (C: C3, C4, and Cz), frontal (F: F3 and F4), temporal (T: T3 and T4), and occipital (O: O1 and O2).\u003c/p\u003e\n\u003cp\u003eBetween\u0026nbsp;the groups, SS-S localization was significantly different in stage N2, revealing a\u0026nbsp;predominant C in the 75+ group and\u0026nbsp;a greater difference\u0026nbsp;between C and F in the 65+ group, with a strong correlation\u0026nbsp;(chi2 10.443 (3), p = 0.015, Cramer\u0026rsquo;s V 0.389). For SS-F (chi2 3.279 (3), p = 0.351, Cramer\u0026rsquo;s V = 0.225),\u0026nbsp;predominant C localization was found in\u0026nbsp;both groups; there was no significant difference, and the correlation was moderate between group appurtenance and localization\u0026nbsp;(Table 2 and Figure 2A).\u003c/p\u003e\n\u003cp\u003eThere was no significant difference in N3 between the groups, with C localization being predominant for both types of SS in both groups (Figure 2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eFigure 2A.\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;SS localization in N2 according to SS type and age group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eValues are presented as percentage of each localization class for one type of SS per group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCentral localization is highly predominant in all SS type in the 75+ group while it is only predominant for SS-F in the 65+ group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eFigure 2B.\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;SS localization in N3 according to SS type and age group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eValues are presented as percentage of each localization class for one type of SS per group.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCentral localization is predominant for every SS type and there is no significant difference between groups.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eS: Slow; F: Fast. Error bars show standard error.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e***: Statistically significant difference with p \u0026lt; 0.005\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 2.\u003c/u\u003e\u003c/strong\u003e Sleep Spindles Localization in both sleep stages, comparing age groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eSleep Stage N2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eSleep Stage N3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eSS-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eSS-F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eSS-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eSS-F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e65+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e75+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e65+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e75+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e65+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e75+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e65+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e75+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOccipital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ePearson\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e10.443\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e3.279\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1.251\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1.813\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.015\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0.351\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0.741\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0.404\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eCramer\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0.389\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0.225\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0.127\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0.203\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\n \u003cp\u003eGrouping electrodes into 4 localizations: Central (C3, C4, and Cz), Frontal (F3 and F4), Temporal (T3 and T4), and Occipital (O1 and O2). The main localization is always Central except for SS-S in N2 where the distribution is more balanced between Central and Frontal in the 65+ group.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eQuantitative characteristics: Density,\u0026nbsp;\u003c/em\u003e\u003cem\u003efrequency, amplitude, and duration\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the subsequent analysis of sleep spindle characteristics between N2 and N3, our statistical model included three main covariates: age (75+ or 65+), sex (female or male) and global OSA diagnosis (Table 3).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"83.42151675485009%\" colspan=\"10\" valign=\"bottom\" style=\"width: 99.8469%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 3.\u003c/u\u003e\u003c/strong\u003e Sleep stages comparison of the sleep spindles characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.400352733686066%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.052910052910052%\" valign=\"bottom\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.98236331569665%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eCovariates influence (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.908450704225352%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.922535211267606%\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.380281690140846%\" valign=\"bottom\"\u003e\n \u003cp\u003eage group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" valign=\"bottom\"\u003e\n \u003cp\u003esex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" valign=\"bottom\"\u003e\n \u003cp\u003eOSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.908450704225352%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDensity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS-S\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e2.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e3.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\"\u003e\n \u003cp\u003e1.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.922535211267606%\"\u003e\n \u003cp\u003e1.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.380281690140846%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.451942740286299%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS-F\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.247443762781186%\"\u003e\n \u003cp\u003e1.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.247443762781186%\"\u003e\n \u003cp\u003e1.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.429447852760736%\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.202453987730062%\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.656441717791411%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.541922290388548%\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.611451942740286%\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.611451942740286%\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.908450704225352%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS-S\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e10.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e10.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\"\u003e\n \u003cp\u003e10.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.922535211267606%\"\u003e\n \u003cp\u003e10.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.380281690140846%\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.451942740286299%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS-F\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.247443762781186%\"\u003e\n \u003cp\u003e13.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.247443762781186%\"\u003e\n \u003cp\u003e13.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.429447852760736%\"\u003e\n \u003cp\u003e13.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.202453987730062%\"\u003e\n \u003cp\u003e13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.656441717791411%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.541922290388548%\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.611451942740286%\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.611451942740286%\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.908450704225352%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmplitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS-S\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e34.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e37.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\"\u003e\n \u003cp\u003e36.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.922535211267606%\"\u003e\n \u003cp\u003e37.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.380281690140846%\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.451942740286299%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS-F\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.247443762781186%\"\u003e\n \u003cp\u003e31.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.247443762781186%\"\u003e\n \u003cp\u003e33.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.429447852760736%\"\u003e\n \u003cp\u003e28.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.202453987730062%\"\u003e\n \u003cp\u003e31.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.656441717791411%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.541922290388548%\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.611451942740286%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.611451942740286%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.908450704225352%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS-S\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.922535211267606%\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.380281690140846%\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.451942740286299%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS-F\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.247443762781186%\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.247443762781186%\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.429447852760736%\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.202453987730062%\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.656441717791411%\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.541922290388548%\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.611451942740286%\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.611451942740286%\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"10\" valign=\"bottom\"\u003e\n \u003cp\u003eValues are presented as Mean. We present the mean values of each age group for both Slow and Fast Sleep Spindles. ANCOVA include the age group (75+ or 65+), OSA diagnosis (yes or no) and gender (female or male) for calculation of statistical difference.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSS density was greater in N2 than in N3 for SS-S (p \u0026lt; 0.001) and SS-F (p \u0026lt; 0.001), with a significant influence from the age group (p 0.05) on slow spindles, with density being greater in the 75+ group.\u003c/p\u003e\n\u003cp\u003eWe observed a significantly greater mean frequency of SS-S (p \u0026lt; 0.001) and SS-F (p = 0.004) in N2 than in N3, with no statistically significant influence from the covariates.\u003c/p\u003e\n\u003cp\u003eAmplitudes were lower in N2 than in N3 for SS-S (p \u0026lt; 0.001) and greater in N2 for SS-F (p \u0026lt; 0.001) in the covariate model, with gender influencing the SS-F: females had spindles with greater amplitudes.\u003c/p\u003e\n\u003cp\u003eThe duration of SS was longer in N2 for SS-S (p = 0.001) and SS-F without reaching a significant level (p = 0.222), with no clear influence from the covariates, but the age group almost reached significance (longer spindles in the 75+).\u003c/p\u003e\n\u003cp\u003eWe\u0026nbsp;compared the\u0026nbsp;SS-S\u0026nbsp;and\u0026nbsp;SS-F characteristics in both age groups in a multivariate model using global OSA and exact age as covariates.\u0026nbsp;The\u0026nbsp;Model 1 results are presented in Table 4.\u003c/p\u003e\n\u003cp\u003eIn the 75+ group, considering exact age and global OSA as covariates, SS density was not significantly different between S and F in stage 2 (p 0.409) or in stage 3 (p 0.319). SS frequency was not different (p 0.206 in N2 and p 0.109 in N3) between SS-S and SS-F. Amplitude changed between SS-S and SS-F in both stages, with SS-S being greater than SS-F in both sleep stages (p \u0026lt; 0.001) with no significant influence of covariates. Duration was significantly different in N2 (p 0.001) but not in N3 (p 0.74), being longer in SS-S than in SS-F.\u003c/p\u003e\n\u003cp\u003eIn the 65+ group,\u0026nbsp;the\u0026nbsp;SS-S density was significantly\u0026nbsp;greater\u0026nbsp;in N2 (p \u0026lt; 0.001) but not in N3 (p\u0026nbsp;=\u0026nbsp;0.797). Frequencies were not different in N2 (p = 0.395)\u0026nbsp;or\u0026nbsp;in N3 (p = 0.983) between SS-S and SS-F.\u003c/p\u003e\n\u003cp\u003eAmplitudes were\u0026nbsp;greater\u0026nbsp;in SS-S in N2 (p \u0026lt; 0.001),\u0026nbsp;with a significant influence of global OSA (p = 0.010),\u0026nbsp;leading to\u0026nbsp;greater\u0026nbsp;values; they were also\u0026nbsp;greater\u0026nbsp;in SS-S in N3 (p = 0.039),\u0026nbsp;with no clear influence of the two covariates.\u003c/p\u003e\n\u003cp\u003eSS-S duration increased in N2 (p \u0026lt; 0.001) with increasing age (p = 0.012) but did not change in N3 (p = 0.606). All these results are presented as Model 1 in Table 4.\u003c/p\u003e\n\u003cp\u003eFinally,\u0026nbsp;ANCOVA with a multivariate model\u0026nbsp;was applied\u0026nbsp;to compare S/F spindle characteristics with age group, global OSA and\u0026nbsp;sex\u0026nbsp;as covariates\u0026nbsp;in Model\u0026nbsp;2 (Table 5).\u003c/p\u003e\n\u003cp\u003eFor\u0026nbsp;density, the\u0026nbsp;N2 SS-S was significantly\u0026nbsp;greater\u0026nbsp;than\u0026nbsp;the\u0026nbsp;SS-F (p = 0.007),\u0026nbsp;with no clear influence from the covariates, while\u0026nbsp;the density did not differ in N3\u0026nbsp;(p = 0.965).\u003c/p\u003e\n\u003cp\u003eFor frequencies, N2 values did not differ between SS-S and SS-F (p = 0.55), and the N3 values did not differ (p = 0.422).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 4.\u003c/u\u003e\u003c/strong\u003e Spindle types comparison of the sleep spindles characteristics in both groups, with ANCOVA - model 1.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e75+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCovariates influence (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e65+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCovariates influence (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eexact age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eexact age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDensity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.144\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmplitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" valign=\"bottom\"\u003e\n \u003cp\u003eModel 1 includes exact age and OSA diagnosis as covariates. We see amplitude and duration are different between slow and fast spindles in both age groups. \u003cem\u003eOSA: Obstructive Sleep Apnea.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1337\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"10\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 5.\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSpindle types comparison of the sleep spindles characteristics with ANCOVA - model 2.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.529543754674645%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.786836200448766%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.269259536275243%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e75+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.269259536275243%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e65+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.293941660433807%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.851159311892296%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eCovariates influence (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.517189835575486%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.783258594917788%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.285500747384155%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.370702541106128%\" valign=\"bottom\"\u003e\n \u003cp\u003eage group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.014947683109119%\" valign=\"bottom\"\u003e\n \u003cp\u003esex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.370702541106128%\" valign=\"bottom\"\u003e\n \u003cp\u003eOSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.517189835575486%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDensity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.783258594917788%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e3.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e1.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e2.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e1.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.285500747384155%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.370702541106128%\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.014947683109119%\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.370702541106128%\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.729632945389436%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e1.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e1.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.5183527305282%\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026857654431513%\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.996418979409132%\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026857654431513%\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.517189835575486%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.783258594917788%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e10.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e13.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e10.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e13.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.285500747384155%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.370702541106128%\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.014947683109119%\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.370702541106128%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.729632945389436%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e10.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e10.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e13.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.5183527305282%\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026857654431513%\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.996418979409132%\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026857654431513%\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.517189835575486%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmplitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.783258594917788%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e37.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e33.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e34.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e31.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.285500747384155%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.370702541106128%\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.014947683109119%\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.370702541106128%\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.729632945389436%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e37.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e31.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e36.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e28.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.5183527305282%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026857654431513%\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.996418979409132%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026857654431513%\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.517189835575486%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.783258594917788%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.164424514200299%\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.285500747384155%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.370702541106128%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.014947683109119%\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.370702541106128%\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.729632945389436%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e0.7212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.175470008952551%\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.5183527305282%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026857654431513%\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.996418979409132%\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026857654431513%\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"10\" valign=\"bottom\"\u003e\n \u003cp\u003eModel 2 includes age group (75+ or 65+), OSA (yes or no) and gender (female or male). In multivariate model, age does not affect any of the modifications we saw in the model 1; while gender affects amplitude variation in N3. \u003cem\u003eOSA: Obstructive Sleep Apnea.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor amplitudes,\u0026nbsp;the\u0026nbsp;SS-S\u0026nbsp;amplitude\u0026nbsp;was\u0026nbsp;greater\u0026nbsp;than\u0026nbsp;the\u0026nbsp;SS-F\u0026nbsp;amplitude\u0026nbsp;(p \u0026lt; 0.001) in both stages, with\u0026nbsp;the\u0026nbsp;influence of\u0026nbsp;sex\u0026nbsp;in stage 3 (p\u0026nbsp;=\u0026nbsp;0.042),\u0026nbsp;with\u0026nbsp;females reaching\u0026nbsp;greater\u0026nbsp;amplitudes.\u003c/p\u003e\n\u003cp\u003eFor duration, the SS-S was longer than the SS-F for N2, and the global OSA tended to be the most explanatory factor (p = 0.065); there was no difference for N3. These results are shown in Table 5 as Model 2.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eResume of results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe studied 93 laboratory-based polysomnography\u0026nbsp;samples, 36 from people aged 75 years and older\u0026nbsp;and 57 from people aged between 65 and 74 years. Moreover,\u0026nbsp;OSA and other sleep pathologies were common in both\u0026nbsp;groups. Older people\u0026nbsp;also receive more medications\u0026nbsp;than younger people.\u003c/p\u003e\n\u003cp\u003eWe\u0026nbsp;observed\u0026nbsp;that\u0026nbsp;the\u0026nbsp;localization of sleep\u0026nbsp;spindles changed\u0026nbsp;in the oldest\u0026nbsp;individuals, with\u0026nbsp;slow\u0026nbsp;spindles\u0026nbsp;becoming more\u0026nbsp;central\u0026nbsp;in\u0026nbsp;the\u0026nbsp;N2 sleep stage (or less Frontal) while there was no difference for fast\u0026nbsp;spindles or\u0026nbsp;in\u0026nbsp;the\u0026nbsp;N3 sleep stage.\u0026nbsp;By comparing\u0026nbsp;sleep stages, we\u0026nbsp;observed\u0026nbsp;that density, frequency and duration\u0026nbsp;reached greater\u0026nbsp;values in N2\u0026nbsp;than in\u0026nbsp;N3 for both slow and fast spindles.\u0026nbsp;The\u0026nbsp;SS-S amplitude\u0026nbsp;was\u0026nbsp;lower and\u0026nbsp;the\u0026nbsp;SS-F\u0026nbsp;amplitude was greater\u0026nbsp;in N2. Older age group influenced\u0026nbsp;the\u0026nbsp;SS-S density variation, with\u0026nbsp;the\u0026nbsp;N2 mean value reaching\u0026nbsp;a\u0026nbsp;higher\u0026nbsp;value. Gender influenced SS-F amplitude variation between sleep stages,\u0026nbsp;with females\u0026nbsp;reaching higher values.\u003c/p\u003e\n\u003cp\u003eWe also\u0026nbsp;observed that the\u0026nbsp;SS-S density was\u0026nbsp;greater\u0026nbsp;than\u0026nbsp;the\u0026nbsp;SS-F\u0026nbsp;density, especially in N2, with no clear\u0026nbsp;effect\u0026nbsp;of age,\u0026nbsp;but the difference was\u0026nbsp;greater\u0026nbsp;in the 65+ group.\u0026nbsp;Therefore,\u0026nbsp;we could hypothesize that SS densities in\u0026nbsp;the\u0026nbsp;oldest age\u0026nbsp;group tend\u0026nbsp;to be closer between S and F because of either a\u0026nbsp;decrease in the\u0026nbsp;number\u0026nbsp;of slow spindles\u0026nbsp;or an\u0026nbsp;increase in\u0026nbsp;the\u0026nbsp;number of\u0026nbsp;fast\u0026nbsp;spindles. Our study cannot answer this question.\u003c/p\u003e\n\u003cp\u003eWe\u0026nbsp;observed\u0026nbsp;that\u0026nbsp;the\u0026nbsp;frequency of sleep\u0026nbsp;spindles\u0026nbsp;did not change through\u0026nbsp;the\u0026nbsp;N2 or N3 sleep stage,\u0026nbsp;regardless of\u0026nbsp;age, OSA\u0026nbsp;status,\u0026nbsp;or sex.\u003c/p\u003e\n\u003cp\u003eAmplitude was\u0026nbsp;greater\u0026nbsp;for slow spindles in both sleep stages, without changing through age but with an influence of OSA diagnosis in N2 in the youngest patients.\u003c/p\u003e\n\u003cp\u003eFinally,\u0026nbsp;the\u0026nbsp;duration was\u0026nbsp;also\u0026nbsp;longer in\u0026nbsp;the\u0026nbsp;slow spindles in\u0026nbsp;the\u0026nbsp;N2 stage, and\u0026nbsp;the\u0026nbsp;effect of age was unclear. Group\u0026nbsp;age was not statistically significant in the multivariate model,\u0026nbsp;but exact age was significant\u0026nbsp;in\u0026nbsp;the 65+ group.\u0026nbsp;The small\u0026nbsp;sample\u0026nbsp;size\u0026nbsp;of the 75+\u0026nbsp;group may\u0026nbsp;not\u0026nbsp;have\u0026nbsp;enabled us to confirm\u0026nbsp;this\u0026nbsp;difference.\u003c/p\u003e\n\u003cp\u003eThe significant differences we\u0026nbsp;found here concern the N2 sleep stage, which led\u0026nbsp;us to question\u0026nbsp;the\u0026nbsp;potentially different\u0026nbsp;roles\u0026nbsp;of sleep\u0026nbsp;spindles throughout\u0026nbsp;sleep stages, in parallel with\u0026nbsp;the\u0026nbsp;different roles attributed to slow and fast spindles themselves.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBibliography comparisons\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePopulation data\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;medical data of our participants could be considered concerning because of the high percentage of sleep pathology diagnoses in older people and\u0026nbsp;the\u0026nbsp;high rate of sleep-modifying drug consumption.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eHaute Autorité de Santé\u003c/em\u003e (the first independent\u0026nbsp;French public scientific authority) report\u0026nbsp;on OSA and its treatments showed similar results. OSA is found in approximately 20 to 50% of people after 60 years\u0026nbsp;of age,\u0026nbsp;while only moderate and severe OSA\u0026nbsp;are considered, where we decided to consider mild OSA as well, leading to higher values.\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn addition, we studied participants using laboratory-based polysomnography as part of their medical care and\u0026nbsp;did not recruit\u0026nbsp;them for our research. This means\u0026nbsp;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.\u003c/p\u003e\n\u003cp\u003eWe excluded participants treated with benzodiazepines and related because of their clinical effects on sleep and their effects on sleep spindles.\u003csup\u003e19,20\u003c/sup\u003e However, these treatments were used by 96\u0026nbsp;of\u0026nbsp;the 340 participants screened (28.24%). This was not biased\u0026nbsp;because of the care course of\u0026nbsp;the participants. In\u0026nbsp;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\u0026nbsp;growth in consumption\u0026nbsp;with age, with maximal use in women older than 80\u0026nbsp;years\u0026nbsp;(38.3%).\u0026nbsp;Nevertheless, there\u0026nbsp;are\u0026nbsp;encouraging data about\u0026nbsp;the\u0026nbsp;global consumption of benzodiazepines, with\u0026nbsp;the\u0026nbsp;annual consumption rate decreasing between 2012 and 2015.\u003csup\u003e21\u003c/sup\u003e We may suppose that the rate we\u0026nbsp;observed\u0026nbsp;in our study reflects a continuous decrease since 2015. However, there is still a very high rate of benzodiazepine consumption in older people at\u0026nbsp;high risk of comorbidities and\u0026nbsp;polymedication.\u003c/p\u003e\n\u003cp\u003eSleep\u0026nbsp;spindle\u0026nbsp;characteristics\u003c/p\u003e\n\u003cp\u003eThe first\u0026nbsp;SS topography studies described results from young people in the N2 sleep stage, with a slow frequency peak (\u0026lt; 12.5 Hz) of frontal and central distribution or centro-parietal\u0026nbsp;distribution\u0026nbsp;(depending\u0026nbsp;on\u0026nbsp;the EEG montage),\u0026nbsp;while fast spindles (\u0026gt; 12.5 Hz) were found on every derivation (frontal, central, parietal, occipital).\u003csup\u003e22\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eMore recently,\u0026nbsp;but still\u0026nbsp;in\u0026nbsp;healthy young population\u0026nbsp;samples\u0026nbsp;(mean age 29.7 ± 6),\u0026nbsp;an\u0026nbsp;SS topography study\u0026nbsp;revealed\u0026nbsp;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.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOur 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.\u003c/p\u003e\n\u003cp\u003eSome specific SS-S originating from the frontal area\u0026nbsp;seemed\u0026nbsp;to be lost between\u0026nbsp;the 65+ and 75+ participants.\u003c/p\u003e\n\u003cp\u003eThis could be due to alterations in the frontal area\u0026nbsp;observed\u0026nbsp;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.\u003csup\u003e25,26\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eFjell et al. also found a brain size reduction with large interindividual variability, predominantly in the frontal area,\u0026nbsp;which could be due to changes in the synaptic network,\u0026nbsp;leading to a worse detection of SS through frontal external electrodes.\u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the studies by\u0026nbsp;Münch et al. and\u0026nbsp;Mander et al., it could also be linked to memory loss. One\u0026nbsp;study showed\u0026nbsp;frontal aging with worse adaptation of the frontal area to sleep deprivation compared to younger people when specifically studying EEG power density in\u0026nbsp;the delta and theta ranges.\u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe other study revealed\u0026nbsp;a regionally selective deficit in fast sleep spindle density with\u0026nbsp;the greatest impairment over\u0026nbsp;the prefrontal area,\u0026nbsp;without a significant link with gray matter volume.\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOur results support the same hypothesis that age differences in spindle topographic distribution might be the consequence of\u0026nbsp;differences in\u0026nbsp;spindle generation rather than\u0026nbsp;differences in the\u0026nbsp;detection limit.\u003c/p\u003e\n\u003cp\u003eIn 2021, McConnel et al.\u0026nbsp;developed a new concept. SS-F\u0026nbsp;split\u0026nbsp;between the early ones in the N2 sleep stage, with\u0026nbsp;a frequency range between 14.5 and 17.5 Hz, and\u0026nbsp;the\u0026nbsp;late SS-F in\u0026nbsp;the\u0026nbsp;N3\u0026nbsp;sleep stage,\u0026nbsp;with a frequency between 10-14 Hz.\u003csup\u003e30\u003c/sup\u003e Again, our results are consistent, and we found a significant difference,\u0026nbsp;with\u0026nbsp;a greater\u0026nbsp;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\u0026nbsp;in terms of\u0026nbsp;the participants' age. To\u0026nbsp;the best of our knowledge, this is the first study\u0026nbsp;to specifically examine the sleep spindle characteristics\u0026nbsp;of participants aged 75+ years.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;SS density is defined by significant\u0026nbsp;interindividual\u0026nbsp;variability and high sensitivity to perturbations. Through decades of studies, SS mean density\u0026nbsp;has been described by many researchers\u0026nbsp;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.\u0026nbsp;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.\u0026nbsp;led on insomniac people.\u003csup\u003e31–33\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eA review by Espiritu et al. in 2008 showed a large range of SS mean densities obtained between studies, and they could only conclude\u0026nbsp;a\u0026nbsp;decrease in sleep spindle number and density with aging.\u003csup\u003e34–36\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn 2021,\u0026nbsp;Guadagni et al.\u0026nbsp;described SS densities\u0026nbsp;more precisely in\u0026nbsp;the\u0026nbsp;oldest sample population (68.2 ± 5.6 years old).\u003csup\u003e37\u003c/sup\u003e 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.\u003c/p\u003e\n\u003cp\u003eHere,\u0026nbsp;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\u0026nbsp;similar to\u0026nbsp;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.\u003c/p\u003e\n\u003cp\u003eThis difference was not\u0026nbsp;observed\u0026nbsp;in the study by Fillmore et al.,\u0026nbsp;who studied\u0026nbsp;SS characteristics through a different protocol, namely, a\u0026nbsp;frequency range\u0026nbsp;of\u0026nbsp;10-16 Hz,\u0026nbsp;a\u0026nbsp;frontal area only, a young group (18-29 years old) and an older but larger group (50-84 years old).\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eHere,\u0026nbsp;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\u0026nbsp;those reported in the\u0026nbsp;literature. Indeed, patients receiving benzodiazepines were excluded, but\u0026nbsp;those\u0026nbsp;receiving\u0026nbsp;restless leg syndrome treatments or opioids\u0026nbsp;were not excluded, which may have biased the results because our age groups were\u0026nbsp;not comparable.\u003c/p\u003e\n\u003cp\u003eIt seems that SS characteristics are even more sensitive to study protocols and inclusion criteria than\u0026nbsp;they are susceptible to aging. For another example, Martin et al. studied SS characteristics\u0026nbsp;in a\u0026nbsp;60–73-year-old population without neurological pathology\u0026nbsp;and\u0026nbsp;no treatment that could have modified sleep, with an AHI \u0026lt; 10, and this time split\u0026nbsp;the\u0026nbsp;SS between slow (11–13\u0026nbsp;Hz) and fast (13–15\u0026nbsp;Hz). These results differed from our findings and those of\u0026nbsp;other studies:\u0026nbsp;the\u0026nbsp;mean density\u0026nbsp;was\u0026nbsp;between 2.4 (SS-S) and 2.6 (SS-F)/minute,\u0026nbsp;the\u0026nbsp;mean frequency\u0026nbsp;was\u0026nbsp;between 12.8 and 13 Hz,\u0026nbsp;the\u0026nbsp;mean amplitude\u0026nbsp;was\u0026nbsp;\u0026lt; 25 µv, and\u0026nbsp;the\u0026nbsp;mean duration\u0026nbsp;was\u0026nbsp;\u0026lt; 0.68\u0026nbsp;seconds.\u003csup\u003e39\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eTo limit these variations, Djonlagic et al. based their study on macro-\u0026nbsp;and microsleep architectures\u0026nbsp;of polysomnography registered through two large cohorts (MESA and MrOS).\u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThey found that both\u0026nbsp;the\u0026nbsp;SS-S (center\u0026nbsp;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,\u0026nbsp;which is concordant with our results. They observed the same type of age-related decrease for SS amplitude and duration, whereas\u0026nbsp;the SS frequency\u0026nbsp;increased for each age group. Our\u0026nbsp;results are not consistent about these points, with increased SS amplitude for both types\u0026nbsp;and\u0026nbsp;very slightly increased durations as developed earlier,\u0026nbsp;while we registered an increase\u0026nbsp;in\u0026nbsp;frequencies.\u003c/p\u003e\n\u003cp\u003eAgain, the\u0026nbsp;means suffered a high\u0026nbsp;interindividual\u0026nbsp;variation, and they found a sex difference in the MESA sample concerning SS-F density,\u0026nbsp;while the only gender effects we\u0026nbsp;observed were\u0026nbsp;amplitudes.\u003c/p\u003e\n\u003cp\u003eLam et al. studied a sample population with\u0026nbsp;mild cognitive impairment\u0026nbsp;(MCI) and a mean age of 69.1 years compared to a control group without neurocognitive\u0026nbsp;disorders\u0026nbsp;or treatment and a mean age of 64.8 years\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e. Considering SS-S (11-13 Hz) and SS-F (13-16 Hz), the densities were very low for every type of SS: 0.36\u0026nbsp;SS-S/minute for\u0026nbsp;the\u0026nbsp;control\u0026nbsp;group\u0026nbsp;and 0.43 for\u0026nbsp;the\u0026nbsp;MCI\u0026nbsp;group;\u0026nbsp;0.41 and 0.22\u0026nbsp;SS-F/minute\u0026nbsp;for the control and MCI groups. The\u0026nbsp;durations were closer to our results,\u0026nbsp;with 0.74\u0026nbsp;seconds\u0026nbsp;in\u0026nbsp;the\u0026nbsp;control\u0026nbsp;group\u0026nbsp;and 0.75\u0026nbsp;seconds\u0026nbsp;in the MCI group, considering NREM sleep\u0026nbsp;overall\u0026nbsp;(N2+N3).\u003c/p\u003e\n\u003cp\u003eRecently, in a review to synthesize age-related sleep modification tendencies, Campos et al. reported that\u0026nbsp;SS density and amplitude\u0026nbsp;decrease in elderly people, duration decreases throughout life, and topography is\u0026nbsp;increasingly reduced to the central area.\u003csup\u003e41\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThis review was completed by Taillard et al.,\u0026nbsp;who reported\u0026nbsp;that density and amplitude reductions were more prominent in\u0026nbsp;the anterior sites. In contrast, duration reduction is more posterior, and frequency is less affected.\u003csup\u003e42\u003c/sup\u003eMoreover,\u0026nbsp;fast spindles are more affected than slow\u0026nbsp;spindles,\u0026nbsp;and slow\u0026nbsp;spindles\u0026nbsp;are slower,\u0026nbsp;while fast\u0026nbsp;spindles are\u0026nbsp;faster. They found that these modifications were more significant during the final sleep cycle.\u003c/p\u003e\n\u003cp\u003eThese points could explain some\u0026nbsp;of the\u0026nbsp;differences we\u0026nbsp;observed\u0026nbsp;in the results; as\u0026nbsp;in older studies, we have\u0026nbsp;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,\u0026nbsp;particularly after having one or more sleep pathologies and sleep-modifying treatments.\u003c/p\u003e\n\u003cp\u003eForces and limitations\u003c/p\u003e\n\u003cp\u003eDue to\u0026nbsp;the retrospective design of the study, we could\u0026nbsp;not collect specific medical data by questioning\u0026nbsp;the participants. We had to check their computerized medical records to find pieces of information we needed, which were not always well informed. We\u0026nbsp;used the same methodology and collected the same data in every medical file from both\u0026nbsp;groups to avoid any information bias.\u003c/p\u003e\n\u003cp\u003eSecond, the two groups were not comparable in\u0026nbsp;terms of\u0026nbsp;every sociomedical characteristic, and there was a significant difference in CSA diagnosis and\u0026nbsp;other\u0026nbsp;treatments. Diagnosis was still infrequent. We selected only short fragments of the most stable sleep in every record, limiting\u0026nbsp;the influence of apneas on the microsleep architecture to limit the risk of selection bias,\u0026nbsp;but\u0026nbsp;this\u0026nbsp;may have modified the whole night microsleep architecture even then.\u003c/p\u003e\n\u003cp\u003eThe use of limited sleep fragments may have affected the results. However, the\u0026nbsp;use of the\u0026nbsp;same methodology for both groups did not lead to measurement bias.\u0026nbsp;This\u0026nbsp;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\u0026nbsp;to manually select the sleep fragments used for analysis.\u0026nbsp;This\u0026nbsp;could\u0026nbsp;also increase\u0026nbsp;the mean duration when carefully manually checking for artifacts because a longer spindle is more susceptible to\u0026nbsp;visualization and counting by the\u0026nbsp;human eye.\u003c/p\u003e\n\u003cp\u003eThird, confounding bias is inherent to observational studies,\u0026nbsp;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\u0026nbsp;the participants,\u0026nbsp;which could have changed the results. For example,\u0026nbsp;the\u0026nbsp;exclusion\u0026nbsp;of patients with neurological pathology or\u0026nbsp;verification of an OSA diagnosis was as prominent in both\u0026nbsp;groups.\u003c/p\u003e\n\u003cp\u003eThis study had several strengths, starting with the unique polysomnography protocol used for every\u0026nbsp;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\u0026nbsp;polysomnography. Two experienced sleep physicians checked\u0026nbsp;the medical files and sleep records for exclusion criteria and record\u0026nbsp;quality.\u003c/p\u003e\n\u003cp\u003eSS detection was realized by powerful software\u0026nbsp;that was continuously updated\u0026nbsp;based on older algorithms that all proved\u0026nbsp;to be accurate and efficient. In addition, the initial selection of the most stable sleep fragments and manual rejection of artifacts ensured that we\u0026nbsp;registered and used only quality data for the statistical analysis.\u003c/p\u003e\n\u003cp\u003eFinally, 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.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis retrospective monocentric study was able to verify changes in sleep spindle characteristics between sleep stages and between sleep spindle types (slow or fast).\u003c/p\u003e \u003cp\u003eThere were few slight age-related changes in the number of sleep spindles between the over 65 age group and the over 75 age group. It seems that density and duration were the most affected characteristics through age when looking at the exact age, but the differences were lost when strictly comparing the age groups.\u003c/p\u003e \u003cp\u003eMost of our results were consistent with those in the literature, as the localization of the sleep spindles was mostly central. However, the values and means of the main characteristics of sleep spindles changed significantly among all studies, including the most recent and extensive studies.\u003c/p\u003e \u003cp\u003eIt is still necessary to conduct wider longitudinal studies with old and oldest participants to analyze their microsleep architecture and its evolution over several decades. Therefore, we could finally define typical values and abnormal patterns linked to one or another diagnosis, revealing the role of micro sleep architecture as a predictive biomarker of neurodegenerative pathologies and their evolution and gravity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e:\u0026nbsp;This study was approved by the Clinical Research and Innovation Office\u0026nbsp;of\u0026nbsp;the University Hospital of Besan\u0026ccedil;on, 25 000 Besan\u0026ccedil;on, France. All participants received\u0026nbsp;a study notice and a nonobjection form to sign and send\u0026nbsp;them back if they did\u0026nbsp;not want to be included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: BP have made the design of the work, the acquisition and analysis, the interpretation of data and drafted the work. PV have made substantial contributions to the conception and substantively revised it. HB have made substantial contributions to the conception, helped with the acquisition and interpretation. SG have made substantial contributions to the conception, took part to the acquisition, analysis and interpretation, substantially revised the work.\u003c/p\u003e\n\u003cp\u003eAll the authors have approved the submitted version and have agreed both to be personally accountable for the author\u0026apos;s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRaftery AE, Chunn JL, Gerland P, \u0026Scaron;evč\u0026iacute;kov\u0026aacute; H. Bayesian Probabilistic Projections of Life Expectancy for All Countries. Demography. juin 2013;50(3):777‑801.\u003c/li\u003e\n\u003cli\u003eBlanpain N. 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Brain Sci. 29 juill 2021;11(8):1003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"polysomnography, sleep architecture, spindle, aging, oldest old","lastPublishedDoi":"10.21203/rs.3.rs-4743069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4743069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSleep and its architecture are affected and changing through the whole lifespan. We know main modifications of the macro-architecture with a shorter sleep, occurring earlier and being more fragmented. We have been studying sleep micro-architecture through its pathological modification in sleep, psychiatric or neurocognitive disorders whereas we are still unable to say if the sleep micro-architecture of an old and very old person is rather normal, under physiological changes, or a concern for a future disorder to appear. We wanted to evaluate age-related changes in sleep spindle characteristics in individuals over 75 years of age compared with younger individuals.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was an exploratory study based on retrospective and comparative laboratory-based polysomnography data registered in the normal care routine for people over 75 years of age compared to people aged 65\u0026ndash;74 years. We were studying their sleep spindle characteristics (localization, density, frequency, amplitude, and duration) in the N2 and N3 sleep stages. ANOVA and ANCOVA using age, sex and OSA were applied.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe included 36 participants aged\u0026thinsp;\u0026gt;\u0026thinsp;75 years and 57 participants aged between 65 and 74 years. An OSA diagnosis was most common in both groups. Older adults receive more medication to modify their sleep. Spindle localization becomes more central after 75 years of age. Changes in the other sleep spindle characteristics between the N2 and N3 sleep stages and between the slow and fast spindles were conformed to literature data, but age was a relevant modifier only for density and duration.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe observed the same sleep spindle characteristics in both age groups except for localization. We built our study on a short sample, and participants were not free of all sleep disorders. We could establish normative values through further studies with larger samples of people without any sleep disorders to understand the modifications in normal aging and pathological conditions and to reveal the predictive biomarker function of sleep spindles.\u003c/p\u003e","manuscriptTitle":"Age-related changes in sleep spindle characteristics in individuals over 75 years of age: a retrospective and comparative study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 01:19:35","doi":"10.21203/rs.3.rs-4743069/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-05T10:39:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-03T22:22:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-30T07:39:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250370871657674462165896938633739700264","date":"2024-07-24T11:36:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246672656452170286016064827457127177508","date":"2024-07-24T03:46:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-23T23:52:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-17T09:55:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-16T14:44:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-16T14:41:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2024-07-15T13:04:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88724e51-9840-4605-ade6-023e9c295d1c","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T16:04:56+00:00","versionOfRecord":{"articleIdentity":"rs-4743069","link":"https://doi.org/10.1186/s12877-024-05364-9","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2024-09-20 15:58:00","publishedOnDateReadable":"September 20th, 2024"},"versionCreatedAt":"2024-08-17 01:19:35","video":"","vorDoi":"10.1186/s12877-024-05364-9","vorDoiUrl":"https://doi.org/10.1186/s12877-024-05364-9","workflowStages":[]},"version":"v1","identity":"rs-4743069","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4743069","identity":"rs-4743069","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}