Cancer risk in patients treated with denosumab compared with alendronate: A population-based cohort study

BACKGROUND

Antiresorptive treatment is currently used in millions of patients with osteoporosis and cancer worldwide. Early studies of denosumab suggested a small signal in ovarian cancer incidence and emerging data suggest that denosumab stimulates germ cell proliferation in the gonads. This study aims to determine the association between the use of denosumab and the risk of reproductive cancers compared with the use of alendronate.

RESEARCH DESIGN AND METHODS

Using a cohort study design, we used the Danish nationwide registries to identify a population of subjects ≥50 years of age during 2010-2017 who started denosumab after being on alendronate treatment for at least six months. The cohort was matched 1:2 with patients who had been treated with alendronate alone for at least six months. The risk of reproductive cancers and the risk difference between groups were estimated using the Longitudinal Targeted Maximum Likelihood Estimation (L-TMLE) method.

RESULTS

We identified 6054 Danish individuals who underwent treatment with denosumab. These individuals were matched with 12,108 receiving alendronate. The absolute risk of reproductive cancer was 1.05 % (95 % CI 0.75-1.34) after three years for denosumab users and was not different 0.03 % (-0.34-0.39) than for alendronate users. In supplemental analyses, there was no increased risk of non-reproductive cancers associated with the use of denosumab (risk difference of 0.54 % (-0.41-1.19). Analysis comparing denosumab users with the general population gave similar results.

CONCLUSION

There was no difference in the risk of cancer following treatment with denosumab compared to treatment with alendronate assessed after a short follow-up of 3 years.

Self-reported and tracked nighttime smartphone use and their association with overweight and cardiometabolic risk markers

Nighttime smartphone use is associated with sleep problems, which in turn have a bidirectional association with overweight. We aim to investigate whether nighttime smartphone use and sleep are related to overweight and metabolic dysfunction in adult populations. We used data from three population samples (aged 16-89) from the SmartSleep Study, which included survey data (N = 29,838), high-resolution tracking data (N = 3446), follow-up data (N = 1768), and cardiometabolic risk markers (N = 242). Frequent self-reported nighttime smartphone use was associated with 51% higher odds (95% CI: 1.32; 1.70) of overweight compared with no use. Tracked nighttime smartphone use was also associated with overweight. Similar results were found for obesity as an outcome. No consistent associations were found between nighttime smartphone use and cardiometabolic risk markers in a small subsample of healthy young women. Poor sleep quality (vs. good sleep quality) was associated with overweight (OR = 1.19, 85% CI: 1.10; 1.28). Overall, frequent nighttime smartphone use was consistently associated with overweight and a higher BMI across diverse population samples. The bidirectional interplay between nighttime smartphone use, sleep, and overweight may create a vicious circle of metabolic dysfunction over time. Therefore, nighttime smartphone use may be a potential target point for public health interventions to reduce overweight at the population level.

Tracked and self-reported nighttime smartphone use, general health, and healthcare utilization: results from the SmartSleep Study

STUDY OBJECTIVES

Nighttime smartphone use is an increasing public health concern. We investigated whether nighttime smartphone use is associated with general health and primary healthcare utilization.

METHODS

4,520 individuals (age 35.6 ± 9.7 years, 35% male) provided self-reported information on smartphone use frequency, symptoms of depression, and general health (one-item perceived health and cross-symptom composite score). A subset of the study sample (n=3,221) tracked their nighttime smartphone use. Primary healthcare utilization, i.e., the number of weeks in which at least one service from the patient's general practitioner was billed in 2020, was extracted from Danish population registries. Statistical analysis comprised logistic and multiple linear regression, controlling for sociodemographics.

RESULTS

319 individuals (7%) reported using their smartphone almost every night or more. More frequent self-reported nighttime smartphone use was associated with poor general health across all measures. Using the smartphone almost every night or more was associated with 2.8 [95CI: 1.9,4.1] fold higher odds of reporting poor health and with an average of 1.4 [95CI: 0.7,2.1] additional GP utilizations per year compared to no use. Associations were also found for the cross-symptom composite score across all symptoms. Further adjustment for symptoms of depression attenuated some associations. Smartphone use towards the end of the sleep period (sleep-offset use) was associated with poorer self-reported general health, but not with healthcare utilization.

CONCLUSION

Nighttime smartphone use frequency is associated with poor general health and healthcare utilization. Further studies should investigate the underlying causal structure and nighttime smartphone use as a transdiagnostic intervention target.

Nighttime smartphone use, sleep quality, and mental health: investigating a complex relationship

STUDY OBJECTIVES

This study investigated the complex relationship between nighttime smartphone use, sleep, and mental health among adult populations in Denmark.

METHODS

Data from three interconnected samples (aged 16-89 years) from the SmartSleep Study included 5798 individuals with survey and register data; 4239 individuals also provided high-resolution smartphone tracking data. Logistic regression models and causal discovery algorithms, which suggest possible causal pathways consistent with the underlying data structure, were used to infer the relationship between self-reported and tracked nighttime smartphone use, self-reported sleep quality, mental health indicators, and register-based psychotropic medication use.

RESULTS

Frequent self-reported nighttime smartphone use was associated with high perceived stress (OR: 2.24, 95% CI = 1.42 to 3.55) and severe depressive symptoms (OR: 2.96, 95% CI = 2.04 to 4.28). We found no clear associations between tracked nighttime smartphone use and mental health outcomes, except for the cluster that used their smartphones repeatedly during the sleep period, which was associated with severe depressive symptoms (OR = 1.69, 95% CI = 1.24 to 2.31). Poor sleep quality (vs. good sleep quality) was associated with high perceived stress (OR = 5.07, 95% CI = 3.72 to 6.90), severe depressive symptoms (OR = 9.67, 95% CI = 7.09 to 13.19), and psychotropic medication use (OR = 2.13, 95% CI = 1.36 to 3.35). The causal discovery models suggest that nighttime smartphone use affects mental health through both problematic smartphone use and poor sleep quality.

CONCLUSION

The complex relationship between nighttime smartphone use, sleep, and poor mental health may create a vicious circle over time, and nighttime smartphone use may constitute a potential leverage point for public health interventions aimed at improving sleep and mental health.

Cohort profile: The SmartSleep Study, Denmark, combining evidence from survey, clinical and tracking data

PURPOSE

The SmartSleep Study is established to comprehensively assess the impact of night-time smartphone use on sleep patterns and health. An innovative combination of large-scale repeated survey information, high-resolution sensor-driven smartphone data, in-depth clinical examination and registry linkage allows for detailed investigations into multisystem physiological dysregulation and long-term health consequences associated with night-time smartphone use and sleep impairment.

PARTICIPANTS

The SmartSleep Study consists of three interconnected data samples, which combined include 30 673 individuals with information on smartphone use, sleep and health. Subsamples of the study population also include high-resolution tracking data (n=5927) collected via a customised app and deep clinical phenotypical data (n=245). A total of 7208 participants are followed in nationwide health registries with full data coverage and long-term follow-up.

FINDINGS TO DATE

We highlight previous findings on the relation between smartphone use and sleep in the SmartSleep Study, and we evaluate the interventional potential of the citizen science approach used in one of the data samples. We also present new results from an analysis in which we use 803 000 data points from the high-resolution tracking data to identify clusters of temporal trajectories of night-time smartphone use that characterise distinct use patterns. Based on these objective tracking data, we characterise four clusters of night-time smartphone use.

FUTURE PLANS

The unprecedented size and coverage of the SmartSleep Study allow for a comprehensive documentation of smartphone activity during the entire sleep span. The study has been expanded by linkage to nationwide registers, which allow for further investigations into the long-term health and social consequences of night-time smartphone use. We also plan new rounds of data collection in the coming years.

Amantadine for COVID-19 treatment (ACT) study: a randomized, double-blinded, placebo-controlled clinical trial

OBJECTIVES

The COVID-19 pandemic has revealed a severe need for effective antiviral treatment. The objectives of this study were to assess if pre-emptive treatment with amantadine for COVID-19 in non-hospitalized persons ≥40 years or adults with comorbidities was able to prevent disease progression and hospitalization. Primary outcomes were clinical status on day 14.

METHODS

Between 9 June 2021 and 27 January 2022, this randomized, double-blinded, placebo-controlled, single-centre clinical trial included 242 subjects with a follow-up period of 90 days. Subjects were randomly assigned 1:1 to either amantadine 100 mg or placebo twice daily for 5 days. The inclusion criteria were confirmed SARS-CoV-2 infection and at least one of (a) age ≥40 years, age ≥18 years and (b) at least one comorbidity, or (c) body mass index ≥30. The study protocol was published at www.

CLINICALTRIALS

gov (unique protocol #02032021) and at www.clinicaltrialregister.eu (EudraCT-number 2021-001177-22).

RESULTS

With 121 participants in each arm, we found no difference in the primary endpoint with 82 participants in the amantadine arm, and 92 participants in the placebo arm with no limitations to activities, respectively, and 25 and 37 with limitations to activities in the amantadine arm and the placebo arm, respectively. No participants in either group were admitted to hospital or died. The OR of having state severity increased by 1 in the amantadine group versus placebo was 1.8 (CI 1.0-3.3, [p 0.051]). On day 7, one participant was hospitalized in each group; throughout the study, this increased to five and three participants for amantadine versus placebo treatment (p 0.72). Similarly, on day 7, there was no difference in the status of oropharyngeal swabs. Most participants (108 in each group) were SARS-CoV-2 RNA positive (p 0.84).

CONCLUSION

We found no effect of amantadine on disease progression of SARS-CoV-2 infection.

Night-time smartphone use, sleep duration, sleep quality, and menstrual disturbances in young adult women: A population-based study with high-resolution tracking data

Study Objectives

To explore the relationship among night-time smartphone use, sleep duration, sleep quality, and menstrual disturbances in young adult women.

Methods

Women aged 18-40 years were included in the SmartSleep Study in which they objectively tracked their smartphone use via the SmartSleep app between self-reported sleep onset and offset times (n = 764) and responded to a survey (n = 1068), which included background characteristics, sleep duration, sleep quality (Karolinska Sleep Questionnaire), and menstrual characteristics (International Federation of Gynecology and Obstetrics' definitions).

Results

The median tracking time was four nights (interquartile range: 2-8). Higher frequency (p = .05) and longer duration (p = .02) of night-time smartphone use were associated with long sleep duration (≥9 h), but not with poor sleep quality or short sleep duration (<7 h). Short sleep duration was associated with menstrual disturbances (OR = 1.84, 95% confidence interval [CI] = 1.09 to 3.04) and irregular menstruation (OR = 2.17, 95% CI = 1.08 to 4.10), and poor sleep quality was associated with menstrual disturbances (OR = 1.43, 95% CI = 1.19 to 1.71), irregular menstruation (OR = 1.34, 95% CI = 1.04 to 1.72), prolonged bleedings (OR = 2.50, 95% CI = 1.44 to 4.43) and short-cycle duration (OR = 1.40, 95% CI = 1.06 to 1.84). Neither duration nor frequency of night-time smartphone use was associated with menstrual disturbances.

Conclusions

Night-time smartphone use was associated with longer sleep duration, but not with menstrual disturbances in adult women. Short sleep duration and sleep quality were associated with menstrual disturbances. Further investigation of the effects of night-time smartphone use on sleep and female reproductive function in large prospective studies is needed.