Lawrence E. Kline

Hypnotics' association with mortality or cancer: a matched cohort study

Objectives An estimated 6%–10% of US adults took a hypnotic drug for poor sleep in 2010. This study extends previous reports associating hypnotics with excess mortality. Setting A large integrated health system in the USA. Design Longitudinal electronic medical records were extracted for a one-to-two matched cohort survival analysis. Subjects Subjects (mean age 54 years) were 10 529 patients who received hypnotic prescriptions and 23 676 matched controls with no hypnotic prescriptions, followed for an average of 2.5 years between January 2002 and January 2007. Main outcome measures Data were adjusted for age, gender, smoking, body mass index, ethnicity, marital status, alcohol use and prior cancer. Hazard ratios (HRs) for death were computed from Cox proportional hazards models controlled for risk factors and using up to 116 strata, which exactly matched cases and controls by 12 classes of comorbidity. Results As predicted, patients prescribed any hypnotic had substantially elevated hazards of dying compared to those prescribed no hypnotics. For groups prescribed 0.4–18, 18–132 and >132 doses/year, HRs (95% CIs) were 3.60 (2.92 to 4.44), 4.43 (3.67 to 5.36) and 5.32 (4.50 to 6.30), respectively, demonstrating a dose–response association. HRs were elevated in separate analyses for several common hypnotics, including zolpidem, temazepam, eszopiclone, zaleplon, other benzodiazepines, barbiturates and sedative antihistamines. Hypnotic use in the upper third was associated with a significant elevation of incident cancer; HR=1.35 (95% CI 1.18 to 1.55). Results were robust within groups suffering each comorbidity, indicating that the death and cancer hazards associated with hypnotic drugs were not attributable to pre-existing disease. Conclusions Receiving hypnotic prescriptions was associated with greater than threefold increased hazards of death even when prescribed <18 pills/year. This association held in separate analyses for several commonly used hypnotics and for newer shorter-acting drugs. Control of selective prescription of hypnotics for patients in poor health did not explain the observed excess mortality.

Evaluation of immobility time for sleep latency in actigraphy

BACKGROUND Actigraphy has become an important tool in sleep research, however, most actigraphic models have had little evaluation of their sleep scoring software. Some reports have described poor agreement of actigraph and polysomnogram (PSG) results. In this study, we examined the accuracy of the Actiwatch-L instrument with Actiware((R)) software version 5.0 (Minimitter-Respironics, Bend, Oregon). METHODS We analyzed the sleep latencies and total sleep times in 33 actigraph recordings and compared performance to simultaneous polysomnography. For scoring sleep latency, the default criterion for scoring sleep onset (10min of immobility) was compared with criteria of 4, 5, 6, and 15min of immobility, and low, middle, and high activity thresholds were compared. RESULTS Of 4, 5, 6, 10 and 15min of actigraphic immobility, the 5min criterion yielded the most accurate sleep latencies. The 5min criterion also yielded near-optimal estimates for total sleep time and wake after sleep onset. CONCLUSIONS We found that a default 10-min immobility parameter for sleep onset was not as accurate as 5min for scoring sleep latency and total sleep time in this clinical sample. There is a need for expanded samples to further evaluate algorithm scoring parameters and the search for superior alternatives.

Wrist actigraphic scoring for sleep laboratory patients: algorithm development.

Wrist actigraphy is employed increasingly in sleep research and clinical sleep medicine. Critical evaluation of the performance of new actigraphs and software is needed. Actigraphic sleep–wake estimation was compared with polysomnographic (PSG) scoring as the standard in a clinical sleep laboratory. A convenience sample of 116 patients undergoing clinical sleep recordings volunteered to participate. Actiwatch‐L recordings were obtained from 98 participants, along with 18 recordings using the newer Spectrum model (Philips Electronics), but some of the actigraphic recordings could not be adequately aligned with the simultaneous PSGs. Of satisfactory alignments, 40 Actiwatch recordings were used as a training set to empirically develop a new Scripps Clinic algorithm for sleep–wake scoring. The Scripps Clinic algorithm was then prospectively evaluated in 39 Actiwatch recordings and 16 Spectrum recordings, producing epoch‐by‐epoch sleep–wake agreements of 85–87% and kappa statistics averaging 0.52 (indicating moderate agreement). Wake was underestimated by the scoring algorithm. The correlations of PSG versus actigraphic wake percentage estimates were r = 0.6690 for the Actiwatch and r = 0.2197 for the Spectrum. In general, using a different weighting of activity counts from previous and subsequent epochs, the Scripps Clinic algorithm discriminated sleep–wake more successfully than the manufacturer’s Actiware algorithms. Neither algorithm had fully satisfactory agreement with PSG. Further evaluations of algorithms for these actigraphs are needed, along with controlled comparisons of different actigraphic designs and software.

Melatonin effects on luteinizing hormone in postmenopausal women: a pilot clinical trial NCT00288262

BackgroundIn many mammals, the duration of the nocturnal melatonin elevation regulates seasonal changes in reproductive hormones such as luteinizing hormone (LH). Melatonins effects on human reproductive endocrinology are uncertain. It is thought that the same hypothalamic pulse generator may both trigger the pulsatile release of GnRH and LH and also cause hot flashes. Thus, if melatonin suppressed this pulse generator in postmenopausal women, it might moderate hot flashes. This clinical trial tested the hypothesis that melatonin could suppress LH and relieve hot flashes.MethodsTwenty postmenopausal women troubled by hot flashes underwent one week of baseline observation followed by 4 weeks of a randomized controlled trial of melatonin or matched placebo. The three randomized treatments were melatonin 0.5 mg 2.5–3 hours before bedtime, melatonin 0.5 mg upon morning awakening, or placebo capsules. Twelve of the women were admitted to the GCRC at baseline and at the end of randomized treatment for 24-hour sampling of blood for LH. Morning urine samples were collected twice weekly to measure LH excretion. Subjective responses measured throughout baseline and treatment included sleep and hot flash logs, the CESD and QIDS depression self-ratings, and the SAFTEE physical symptom inventory.ResultsUrinary LH tended to increase from baseline to the end of treatment. Contrasts among the 3 randomized groups were statistically marginal, but there was relative suppression combining the groups given melatonin as contrasted to the placebo group (p < 0.01 one-tailed, Mann-Whitney U = 14.) Similar but not significant results were seen in blood LH. There were no significant contrasts among groups in hot flashes, sleep, depression, or side-effect measures and no significant adverse effects of any sort.ConclusionThe data are consistent with the hypothesis that melatonin suppresses LH in postmenopausal women. An effect related to the duration of nocturnal melatonin elevation is suggested. Effects of melatonin on reproductive endocrinology should be studied further in younger women and in men. Larger studies of melatonin effects on postmenopausal symptoms would be worthwhile.

Genetic variants associated with sleep disorders

Objective The diagnostic boundaries of sleep disorders are under considerable debate. The main sleep disorders are partly heritable; therefore, defining heritable pathophysiologic mechanisms could delineate diagnoses and suggest treatment. We collected clinical data and DNA from consenting patients scheduled to undergo clinical polysomnograms, to expand our understanding of the polymorphisms associated with the phenotypes of particular sleep disorders. Methods Patients at least 21 years of age were recruited to contribute research questionnaires, and to provide access to their medical records, saliva for deoxyribonucleic acid (DNA), and polysomnographic data. From these complex data, 38 partly overlapping phenotypes were derived indicating complaints, subjective and objective sleep timing, and polysomnographic disturbances. A custom chip was used to genotype 768 single-nucleotide polymorphisms (SNPs). Additional assays derived ancestry-informative markers (eg, 751 participants of European ancestry). Linear regressions controlling for age, gender, and ancestry were used to assess the associations of each phenotype with each of the SNPs, highlighting those with Bonferroni-corrected significance. Results In peroxisome proliferator-activated receptor gamma, coactivator 1 beta (PPARGC1B), rs6888451 was associated with several markers of obstructive sleep apnea. In aryl hydrocarbon receptor nuclear translocator-like (ARNTL), rs10766071 was associated with decreased polysomnographic sleep duration. The association of rs3923809 in BTBD9 with periodic limb movements in sleep was confirmed. SNPs in casein kinase 1 delta (CSNK1D rs11552085), cryptochrome 1 (CRY1 rs4964515), and retinoic acid receptor-related orphan receptor A (RORA rs11071547) were less persuasively associated with sleep latency and time of falling asleep. Conclusions SNPs associated with several sleep phenotypes were suggested, but due to risks of false discovery, independent replications are needed before the importance of these associations can be assessed, followed by investigation of molecular mechanisms.

Genotyping Sleep Disorders Patients

Objective The genetic susceptibility factors underlying sleep disorders might help us predict prognoses and responses to treatment. Several candidate polymorphisms for sleep disorders have been proposed, but there has as yet inadequate replication or validation that the candidates may be useful in the clinical setting. Methods To assess the validity of several candidate associations, we obtained saliva deoxyribonucleic acid (DNA) samples and clinical information from 360 consenting research participants who were undergoing clinical polysomnograms. Ten single nucleotide polymorphisms (SNPs) were genotyped. These were thought to be related to depression, circadian sleep disorders, sleep apnea, restless legs syndrome (RLS), excessive sleepiness, or to slow waves in sleep. Results With multivariate generalized linear models, the association of TEF rs738499 with depressive symptoms was confirmed. Equivocal statistical evidence of association of rs1801260 (the C3111T SNP in the CLOCK gene) with morningness/eveningness and an association of Apolipoprotein E (APOE) rs429358 with the Epworth Sleepiness Scale (ESS) were obtained, but these associations were not strong enough to be of clinical value by themselves. Predicted association of SNPs with sleep apnea, RLS, and slow wave sleep were not confirmed. Conclusion The SNPs tested would not, by themselves, be of use for clinical genotyping in a sleep clinic.

Zolpidem for postanoxic spasticity.

A 28-year-old male sustained anoxic brain damage following aborted cardiac arrest, and subsequently developed severe muscular rigidity and spasticity involving all extremities. The spasticity was refractory to the standard regimens used for spastic hypertonia. Zolpidem dramatically inhibited muscular rigidity, spasticity, and dystonic posturing in a dose-dependent manner, resulting in a sustained improvement of his global performance over four years. The authors postulate a central mechanism of action by selective inhibition of GABAergic inhibitory neurons, and suggest a controlled clinical study to investigate the potential efficacy of zolpidem in relieving spasticity related to postanoxic brain injury.

Type III home sleep testing versus pulse oximetry: is the respiratory disturbance index better than the oxygen desaturation index to predict the apnoea-hypopnoea index measured during laboratory polysomnography?

Objectives In its guidelines on the use of portable monitors to diagnose obstructive sleep apnoea, the American Academy of Sleep Medicine endorses home polygraphy with type III devices recording at a minimum airflow the respiratory effort and pulse oximetry, but advises against simple pulse oximetry. However, oximetry is widely available and simple to use in the home. This study was designed to compare the ability of the oxygen desaturation index (ODI) based on oximetry alone with a stand-alone pulse oximeter (SPO) and from the oximetry channel of the ApneaLink Plus (ALP), with the respiratory disturbance index (RDI) based on four channels from the ALP to predict the apnoea–hypopnoea index (AHI) from laboratory polysomnography. Design Cross-sectional diagnostic accuracy study. Setting Sleep medicine practice of a multispecialty clinic. Participants Patients referred for laboratory polysomnography with suspected sleep apnoea. We enrolled 135 participants with 123 attempting the home sleep testing and 73 having at least 4 hours of satisfactory data from SPO and ALP. Interventions Participants had home testing performed simultaneously with both a SPO and an ALP. The 2 oximeter probes were worn on different fingers of the same hand. The ODI for the SPO was calculated using Profox software (ODISOX). For the ALP, RDI and ODI were calculated using both technician scoring (RDIMAN and ODIMAN) and the ALP computer scoring (RDIRAW and ODIRAW). Results The receiver–operator characteristic areas under the curve for AHI ≥5 were RDIMAN 0.88 (95% confidence limits 0.81–0.96), RDIRAW 0.86 (0.76–0.94), ODIMAN 0.86 (0.77–0.95), ODIRAW 0.84 (0.75–0.93) and ODISOX 0.83 (0.73–0.93). Conclusions We conclude that the RDI and the ODI, measured at home on the same night, give similar predictions of the laboratory AHI, measured on a different night. The differences between the two methods are small compared with the reported night-to-night variation of the AHI.

FMR1, circadian genes and depression: suggestive associations or false discovery?

Background There are several indications that malfunctions of the circadian clock contribute to depression. To search for particular circadian gene polymorphisms associated with depression, diverse polymorphisms were genotyped in two samples covering a range of depressed volunteers and participants with normal mood. Methods Depression mood self-ratings and DNA were collected independently from a sample of patients presenting to a sleep disorders center (1086 of European origin) and from a separate sample consisting of 399 participants claiming delayed sleep phase symptoms and 406 partly-matched controls. A custom Illumina Golden Gate array of 768 selected single nucleotide polymorphisms (SNPs) was assayed in both samples, supplemented by additional SNPlex and Taqman assays, including assay of 41 ancestry-associated markers (AIMs) to control stratification. Results In the Sleep Clinic sample, these assays yielded Bonferroni-significant association with depressed mood in three linked SNPs of the gene FMR1: rs25702 (nominal P=1.77E-05), rs25714 (P=1.83E-05), and rs28900 (P=5.24E-05). This FMR1 association was supported by 8 SNPs with nominal significance and a nominally-significant gene-wise set test. There was no association of depressed mood with FMR1 in the delayed sleep phase case–control sample or in downloaded GWAS data from the GenRED 2 sample contrasting an early-onset recurrent depression sample with controls. No replication was located in other GWAS studies of depression. Our data did weakly replicate a previously-reported association of depression with PPARGC1B rs7732671 (P=0.0235). Suggestive associations not meeting strict criteria for multiple testing and replication were found with GSK3B, NPAS2, RORA, PER3, CRY1, MTNR1A and NR1D1. Notably, 16 SNPs nominally associated with depressed mood (14 in GSK3B) were also nominally associated with delayed sleep phase syndrome (P=3E10-6). Conclusions Considering the inconsistencies between samples and the likelihood that the significant three FMR1 SNPs might be linked to complex polymorphisms more functionally related to depression, large gene resequencing studies may be needed to clarify the import for depression of these circadian genes.

Do No Harm: Not Even to Some Degree

EditoRial http://dx.doi.org/10.5664/jcsm.2018 do no Harm: not Even to Some degree Daniel F. Kripke, M.D. 1 ; Robert D. Langer, M.D., M.P.H. 2 ; Lawrence E. Kline, D.O. 1 Scripps Clinic Viterbi Family Sleep Center, La Jolla, CA; 2 Jackson Hole Center for Preventive Medicine, Jackson, WY W e are grateful to Dr. Bianchi and colleagues for summa- rizing questions about our recent paper associating hyp- notics with excess mortality and cancer. 1 We are grateful to the Journal for this opportunity to provide organized answers to these questions. In the absence of adequate randomized trials extending for years, it is clinically important to assess the long-term risks and benefi ts of commonly prescribed hypnotics, using the best data now available and employing the most conservative ana- lytic strategies possible. We have done that. Dr. Bianchi and colleagues asserted that we confused correlation with causal- ity. We did not. We took pains to be clear that ours was an observational study and that some residual confounding was likely. Nonetheless, the robust associations of hypnotics with mortality and cancer that we found, increasing stepwise with increasing exposure, and virtually unchanged with multiple strategies for control, command attention and reassessment of common practice. It has been hard to report calmly that hypnotic use was as- sociated with 3.60- to 5.32-fold mortality risks. It would be wonderful if somebody could prove it is not so—and the risk could be overestimated—but scientifi c ethics require us to re- port what our data showed and to explain the possible implica- tions. Though the main responsibility for warning falls on the manufacturers who were informed of these risks years ago and on their FDA supervision, we physicians have a duty to warn also. We cannot hide risks, even if they might frighten patients out of taking hypnotics. Patients have a right to know. Some suppose that the increased mortality of hypnotic users was due to their insomnia, and that insomnia might explain the 3.60- to 5.32-fold mortality excess. It is true that because of the IRB’s interpretation of Pennsylvania law, we were unable to control explicitly for insomnia or depression. However, in our paper, 1 we referenced several studies which have found that in- somnia is NOT associated with signifi cant mortality. We know of no evidence that insomnia signifi cantly predicts mortality when hypnotic use, comorbidities, and other confounders are adequately controlled. A new example is found in the recently published representative national sample from Taiwan, where those with sleep disorders had signifi cantly less cancer inci- dence than those without sleep disorders among participants who had not received zolpidem, HR = 0.69 (0.62-0.78 95% CI). 2 Use of ≥ 300 mg/y zolpidem without benzodiazepines was associated with a cancer hazard ratio of 6.24 (4.13-9.43 95% CI), but even among the zolpidem users, those with sleep disorders had lower risk. Thus, sleep disorders could not con- ceivably explain the excess mortality or cancer associated with zolpidem prescriptions. Our paper also referenced studies showing that depression does not confound the association of hypnotic use with mor- tality. In Belleville’s Canadian sample, control for depression only reduced the hypnotics and tranquilizers mortality odds ra- tio from 1.40 (1.13-1.75, 95% CI) to 1.36 (1.09-1.70, 95% CI), which was not signifi cant. 3 Depression was not even a signifi - cant mortality risk factor when benzodiazepine use and other covariates were controlled. 4 In the Taiwan sample, the cancer hazard ratio for depression was 0.68 (0.53-0.88, 95% CI). 2 Thus, confounding with depression could not explain mortality and cancer associated with hypnotic prescriptions. What about sleep apnea as an explanation? Young and col- leagues reported that apnea-hypopnea indices < 30 were NOT signifi cantly associated with excess mortality in an adjusted model. 5 With AHI ≥ 30, the risk ratio was only 3.0 (1.4-6.3, 95% CI). The Sleep Heart Health Study had even more sur- prising results, wherein women (who use more hypnotics than men) had no signifi cant increase in mortality associated with any level of sleep apnea. Among men, signifi cant excess mor- tality was associated with AHI ≥ 30 only among those age ≤ 70 years. 6 Among both sexes of all ages combined, AHI ≥ 30 was associated with a hazard ratio of only 1.46 (1.14-1.86 95% CI). These apnea studies were not as extensively controlled for comorbidities as our study. Our hypnotic-associated risk ratios of 3.60 to 5.32 could not be caused by apnea risk ratios of only 1.46 or 3, even in the implausible event that all the patients pre- scribed hypnotics had AHI ≥ 30 but none of the controls. Could excess comorbidities among hypnotic users account for our 3.60- to 5.32-fold hazard ratios? Let us recall the data. In our stratifi ed analyses, we compared hypnotic users with con- trols having exactly the same classes of comorbidities, so co- morbidity diagnoses were matched. Even had we not employed matching and alternative forms of adjustment to control for co- morbidities, how could a 45% excess of comorbidities among the hypnotic users account for a 3.60- to 5.32-fold mortality hazard? It is true that we were unable to adjust for severity of comorbidities when they occurred, but since the diagnoses of comorbidities accounted for only a small amount of the mortal- ity hazard, where is the evidence that severity of comorbidities or uncommon comorbidities could account additionally for a much larger portion of the mortality hazard? No one has offered any explanation of how confounding could produce the signifi cantly different hazard ratios we ob- served between hypnotics and different cancers. Sanofi Aventis Journal of Clinical Sleep Medicine, Vol. 8, No. 4, 2012