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Anderson, Jack

https://orcid.org/0009-0006-4967-7372

Mayo Clinic

The University of Sydney

Citations 2

Validation of automated detection of REM sleep without atonia using in-laboratory and in-home recordings
Levendowski, Daniel J. , Lewis, Simon J.G. , Finstuen, Thomas, Mosovsky, Sherri, Parikh, Hersh, Anderson, Jack , Walsh, Christine M. , Lee‐Iannotti, Joyce , Finstuen, Thomas J, St Louis, Erik K, Lewis, Simon J G , Chahine, Lana M. , Galbiati, Andrea , Berka, Chris , Parikh, Hersh, Neylan, Thomas C. , Ferini-Strambi, Luigi , Boeve, Bradley F. , Louis, Erik K. St.

Journal of Clinical Sleep Medicine | November 21, 2024 | Volume: 21 | Issue: 3 | Pages: 583-592

10.5664/jcsm.11488 Get It

0753 Relationship Between OSA and Sleep Biomarker Based Neurodegenerative Disorder Risk in a Clinical Cohort
Mazeika, Gandis, Cho, Yeilim, Anderson, Jack , Levendowski, Daniel J. , Walsh, Christine M. , Lee‐Iannotti, Joyce , Mazeika, Gandis, Tsuang, Debby W. , Neylan, Thomas C. , Lewis, Simon J.G. , Louis, Erik K. St.

SLEEP | April 20, 2024 | Volume: 47 | Issue: Supplement_1 | Pages: A322-A323

10.1093/sleep/zsae067.0753 Get It

Abstract Introduction In this pilot study, sleep biomarker-based neurodegenerative disorder (NDD) risk was assessed in a clinical cohort of patients diagnosed with varying severity of obstructive sleep apnea (OSA). Methods Consecutive in-home multi-night Sleep Profiler records from a community sleep clinic were screened for age >54 years old. Cases with comorbid restless leg syndrome, hypersomnia, missing apnea-hypopnea indexes (AHI), AHI< 5 or single-night recordings were excluded. The 67 remaining records were 40% female, ages 63 + 7.4 years and up to 84 years old. NDD risk was assigned using a machine-learning algorithm combining age and nine sleep biomarkers to assign group probabilities: for Alzheimer’s disease, Lewy Body disease, prodromal synucleinopathy and controls (CG). Sleep biomarkers included: time-REM, non-REM hypertonia, autonomic-activation index, spindle-duration, atypical-N3, time-supine, sleep-efficiency, relative-theta, and theta/alpha. Those with CG probability score >70% were classified as “Probably-normal”. Those with CG probability between 45 - 70% were classified as “Likely-normal”. Insomnia severity >14 and a PHQ 9 > 5 were used as cutoffs for clinical insomnia and depression. Mann-Whitney U-tests were used to evaluate group differences. Results This cohort had 37% mild, 34% moderate, and 29% severe OSA. Self-reported comorbid insomnia and depression were present in 43%. Seventy-three percent of patients were classified as “Probably-normal”, 16% “Likely-normal”, 11% “Likely-NDD” or “Probable-NDD”. The “Probably-normal” group was younger than the rest of the cohort (62 + 6.2 years vs. 68 + 8.6 years, p< 0.01). Of those classified as “Probably-normal” (n=49), OSA was mild in 16, moderate in 17 and severe in 16. Mean AHI trended higher in the “Probably-normal” group than in those with elevated NDD risk (29 + 23.9 vs. 19 + 13.6 events/h, p=0.085). Conclusion Approximately 89% of the clinical cohort was classified as “Probably-normal” or “Likely-normal”. NDD risk was age-dependent. Interactions between OSA severity and NDD risk were not apparent in these findings, possibly related to group-wide effects. NDD risk, as measured by this model, did not appear to be adversely biased by untreated OSA. These results suggest a need for longitudinal studies to compare NDD risk in untreated OSA versus controls without sleep disordered breathing. Support (if any) NIH grants R44AG050326, R44AG054256, RO1AG060477, R33AG064271, ULRR024150, and R34AG056639.