Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study over 12 months.

Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.

Improved measurement of disease progression in people living with early Parkinson's disease using digital health technologies.

BACKGROUND: Digital health technologies show promise for improving the measurement of Parkinson's disease in clinical research and trials. However, it is not clear whether digital measures demonstrate enhanced sensitivity to disease progression compared to traditional measurement approaches. METHODS: To this end, we develop a wearable sensor-based digital algorithm for deriving features of upper and lower-body bradykinesia and evaluate the sensitivity of digital measures to 1-year longitudinal progression using data from the WATCH-PD study, a multicenter, observational digital assessment study in participants with early, untreated Parkinson's disease. In total, 82 early, untreated Parkinson's disease participants and 50 age-matched controls were recruited and took part in a variety of motor tasks over the course of a 12-month period while wearing body-worn inertial sensors. We establish clinical validity of sensor-based digital measures by investigating convergent validity with appropriate clinical constructs, known groups validity by distinguishing patients from healthy volunteers, and test-retest reliability by comparing measurements between visits. RESULTS: We demonstrate clinical validity of the digital measures, and importantly, superior sensitivity of digital measures for distinguishing 1-year longitudinal change in early-stage PD relative to corresponding clinical constructs. CONCLUSIONS: Our results demonstrate the potential of digital health technologies to enhance sensitivity to disease progression relative to existing measurement standards and may constitute the basis for use as drug development tools in clinical research.

Parkinson's disease can impact a person's ability to move, which can result in slow or rigid movements. Wearable sensors can be used to measure these symptoms and could be particularly useful to detect changes early in the course of the disease when symptoms may be subtle. We developed a wearable sensor-based method to measure movement in people with early Parkinson's disease that uses wrist and foot-worn sensors. Our results demonstrate that our sensor-based measurements can accurately quantify progressive changes in movement function. Such measurements may allow researchers to more accurately evaluate how well treatments designed to slow the course of Parkinson's disease are working in the future.

Towards interpretable speech biomarkers: exploring MFCCs.

While speech biomarkers of disease have attracted increased interest in recent years, a challenge is that features derived from signal processing or machine learning approaches may lack clinical interpretability. As an example, Mel frequency cepstral coefficients (MFCCs) have been identified in several studies as a useful marker of disease, but are regarded as uninterpretable. Here we explore correlations between MFCC coefficients and more interpretable speech biomarkers. In particular we quantify the MFCC2 endpoint, which can be interpreted as a weighted ratio of low- to high-frequency energy, a concept which has been previously linked to disease-induced voice changes. By exploring MFCC2 in several datasets, we show how its sensitivity to disease can be increased by adjusting computation parameters.

The Influence of Greenspace Exposure on Affect in People With and Those Without Schizophrenia: Exploratory Study.

BACKGROUND: Exposure to natural vegetation (ie, "greenspace") is related to beneficial outcomes, including higher positive and lower negative affect, in individuals with and those without mental health concerns. Researchers have yet to examine dynamic associations between greenspace exposure and affect within individuals over time. Smartphone-based ecological momentary assessment (EMA) and passive sensors (eg, GPS, microphone) allow for frequent sampling of data that may reveal potential moment-to-moment mechanisms through which greenspace exposure impacts mental health. OBJECTIVE: In this study, we examined associations between greenspace exposure and affect (both self-reported and inferred through speech) in people with and those without schizophrenia spectrum disorder (SSD) at the daily level using smartphones. METHODS: Twenty people with SSD and 14 healthy controls reported on their current affect 3 times per day over 7 days using smartphone-based EMA. Affect expressed through speech was labeled from ambient audio data collected via the phone's microphone using Linguistic Inquiry and Word Count (LIWC). Greenspace exposure, defined as the normalized difference vegetation index (NDVI), was quantified based on continuous geo-location data collected from the phone's GPS. RESULTS: Overall, people with SSD used significantly more positive affect words (P=.04) and fewer anger words (P=.04) than controls. Groups did not significantly differ in mean EMA-reported positive or negative affect, LIWC total word count, or NDVI exposure. Greater greenspace exposure showed small to moderate associations with lower EMA-reported negative affect across groups. In controls, greenspace exposure on a given day was associated with significantly lower EMA-reported anxiety on that day (b=-0.40, P=.03, 95% CI -0.76 to -0.04) but significantly higher use of negative affect words (b=0.66, P<.001, 95% CI 0.29-1.04). There were no significant associations between greenspace exposure and affect at the daily level among participants with SSD. CONCLUSIONS: Our findings speak to the utility of passive and active smartphone assessments for identifying potential mechanisms through which greenspace exposure influences mental health. We identified preliminary evidence that greenspace exposure could be associated with improved mental health by reducing experiences of negative affect. Future directions will focus on furthering our understanding of the relationship between greenspace exposure and affect on individuals with and those without SSD.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study.

Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.

Smartphone-based mobility metrics capture daily social motivation and behavior in schizophrenia.

Impaired social functioning contributes to reduced quality of life and is associated with poor physical and psychological well-being in schizophrenia, and thus is a key psychosocial treatment target. Low social motivation contributes to impaired social functioning, but is typically examined using self-report or clinical ratings, which are prone to recall biases and do not adequately capture the dynamic nature of social motivation in daily life. In the current study, we examined the utility of global positioning system (GPS)-based mobility data for capturing social motivation and behavior in people with schizophrenia. Thirty-one participants with schizophrenia engaged in a 60-day mobile intervention designed to increase social motivation and functioning. We examined associations between twice daily self-reports of social motivation and behavior (e.g., number of social interactions) collected via Ecological Momentary Assessment (EMA) and passively collected daily GPS mobility metrics (e.g., number of hours spent at home) in 26 of these participants. Findings suggested that greater mobility on a given day was associated with more EMA-reported social interactions on that day for four out of five examined mobility metrics: number of hours spent at home, number of locations visited, probability of being stationary, and likelihood of following one's typical routine. In addition, greater baseline social functioning was associated with less daily time spent at home and lower probability of following a daily routine during the intervention. GPS-based mobility thus corresponds with social behavior in daily life, suggesting that more social interactions may occur at times of greater mobility in people with schizophrenia, while subjective reports of social interest and motivation are less associated with mobility for this population.

Metadata Framework to Support Deployment of Digital Health Technologies in Clinical Trials in Parkinson's Disease.

Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors. We demonstrate how this framework can be applied to data collected with different types of DHTs deployed in the WATCH-PD clinical study of Parkinson's disease. This framework provides a means to pre-specify and therefore standardize aspects of the use of DHTs, promoting comparability of DHTs across future studies.

Acute Sleep Disruption Does Not Diminish Pulsatile Growth Hormone Secretion in Pubertal Children.

Context: In children, growth hormone (GH) pulses occur after sleep onset in association with slow-wave sleep (SWS). There have been no studies in children to quantify the effect of disrupted sleep on GH secretion. Objective: This study aimed to investigate the effect of acute sleep disruption on GH secretion in pubertal children. Methods: Fourteen healthy individuals (aged 11.3-14.1 years) were randomly assigned to 2 overnight polysomnographic studies, 1 with and 1 without SWS disruption via auditory stimuli, with frequent blood sampling to measure GH. Results: Auditory stimuli delivered during the disrupted sleep night caused a 40.0 ± 7.8% decrease in SWS. On SWS-disrupted sleep nights, the rate of GH pulses during N2 sleep was significantly lower than during SWS (IRR = 0.56; 95% CI, 0.32-0.97). There were no differences in GH pulse rates during the various sleep stages or wakefulness in disrupted compared with undisrupted sleep nights. SWS disruption had no effect on GH pulse amplitude and frequency or basal GH secretion. Conclusion: In pubertal children, GH pulses were temporally associated with episodes of SWS. Acute disruption of sleep via auditory tones during SWS did not alter GH secretion. These results indicate that SWS may not be a direct stimulus of GH secretion.

AMH is Higher Across the Menstrual Cycle in Early Postmenarchal Girls than in Ovulatory Women.

CONTEXT: Adolescents have more small, growing follicles and larger ovaries than normal women and are prone to anovulatory cycles (ANOV). It is unknown if a higher antral follicle count (AFC) per se contributes to ANOV in early postmenarchal girls. OBJECTIVE: To determine the relationship between AMH (an AFC biomarker), other reproductive hormones, and ANOV in postmenarchal girls and to compare AMH in girls and regularly cycling adults. METHODS: A total of 23 girls (1.7 ± 0.2 years postmenarche) and 32 historic adult controls (≤34 years) underwent serial hormone measurements during 1 to 2 menstrual cycles. Girls also had pelvic ultrasounds. AMH was measured 5 times/subject using the Ansh ultrasensitive ELISA. RESULTS: Girls had higher AMH than women (5.2 ± 0.3 vs. 3.3 ± 0.4 ng/mL; P < 0.01) and girls with more ovulatory (OV) cycles tended to have lower AMH than those with ANOV (2 OV 4.5 ± 0.2, 1 OV 5.7 ± 1.1, 0 OV 6.8 ± 1.1 ng/mL; P = 0.1). In girls, AMH correlated with natural-log (ln) transformed LH (r = 0.5, P = 0.01), ln_androstenedione (r = 0.6, P = 0.003), ln_testosterone (r = 0.5, P = 0.02), and ovarian volume (r = 0.7, P < 0.01) but not with FSH, estradiol, P4, or body mass index. In women, AMH correlated with estradiol and P4 (both r = -0.4, P ≤ 0.03) but not with ln_LH or body mass index. CONCLUSIONS: In postmenarchal girls, AMH is higher than in ovulatory women and is associated with LH, androgens, and a propensity for anovulatory cycles. The cause of the transient increase in AMH and AFC during late puberty and the steps underlying the transition to a mature ovary deserve further study.

mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson's disease.

Accurately monitoring motor and non-motor symptoms as well as complications in people with Parkinson's disease (PD) is a major challenge, both during clinical management and when conducting clinical trials investigating new treatments. A variety of strategies have been relied upon including questionnaires, motor diaries, and the serial administration of structured clinical exams like part III of the MDS-UPDRS. To evaluate the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms, we carried out a project (project BlueSky) encompassing four clinical studies, in which 60 healthy volunteers (aged 23-69; 33 females) and 95 people with PD (aged 42-80; 37 females; years since diagnosis 1-24 years; Hoehn and Yahr 1-3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this paper, we investigated (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle. Future papers will explore methods for estimating symptom severity based on sensor data. We found that 38% of participants who were asked to complete an electronic motor diary at home missed ~25% of total possible entries and otherwise made entries with an average delay of >4 h. During clinical evaluations by PD specialists, self-reports of dyskinesia were marked by ~35% false negatives and 15% false positives. Compared with live evaluation, the video evaluation of part III of the MDS-UPDRS significantly underestimated the subtle features of tremor and extremity bradykinesia, suggesting that these aspects of the disease may be underappreciated during remote assessments. On the other hand, live and video raters agreed on aspects of postural instability and gait. Our results highlight the significant opportunity for objective, high-resolution, continuous monitoring afforded by wearable technology to improve upon the monitoring of PD symptoms.

Early breast development in overweight girls: does estrogen made by adipose tissue play a role?

BACKGROUND: Girls who are overweight/obese (OB) develop breast tissue but do not undergo menarche (the first menstrual period) significantly earlier than girls of normal weight (NW). It has been proposed that estrogen synthesized by adipose tissue may be contributory, yet OB do not have higher serum estrogen levels than NW matched on breast stage. We hypothesized that estrogen synthesized locally, in mammary fat, may contribute to breast development. This hypothesis would predict that breast development would be more advanced than other estrogen-sensitive tissues as a function of obesity and body fat. METHODS: Eighty premenarchal girls (26 OB, 54 NW), aged 8.2-14.7 years, underwent dual-energy x-ray absorptiometry to calculate percent body fat (%BF), Tanner staging of the breast, breast ultrasound for morphological staging, trans-abdominal pelvic ultrasound, hand x-ray (bone age, BA), a blood test for reproductive hormones, and urine collection to determine the vaginal maturation index (VMI), an index of estrogen exposure in urogenital epithelial cells. RESULTS: When controlling for breast morphological stage determined by ultrasound, %BF was not associated with serum estrogen or gonadotropin (LH and FSH) levels or with indices of systemic estrogen action (uterine volume, endometrial thickness, BA advancement, and VMI). Tanner breast stage did not correlate with breast morphological stage and led to misclassification of chest fatty tissue as breast tissue in some OB. CONCLUSIONS: These studies do not support the hypothesis that estrogen derived from total body fat or local (mammary) fat contributes to breast development in OB girls.

The Relationship Between Progesterone, Sleep, and LH and FSH Secretory Dynamics in Early Postmenarchal Girls.

CONTEXT: During puberty, LH pulse frequency increases during sleep; in women, LH pulse frequency slows during sleep in the early/middle follicular phase (FP) of the menstrual cycle. The origin and significance of this developmental transition are unknown. OBJECTIVE: To determine the relationship between progesterone (P4) exposure, sleep-related slowing of LH pulses in the FP, and the intercycle FSH rise, which promotes folliculogenesis, in early postmenarchal girls. METHODS: 23 girls (gynecologic age 0.4 to 3.5 years) underwent hormone measurements and pelvic ultrasounds during two consecutive cycles and one frequent blood sampling study with concurrent polysomnography during the FP. RESULTS: Subjects demonstrated one of four patterns during cycle 1 that represent a continuum of P4 exposure: ovulatory cycles with normal or short luteal phase lengths or anovulatory cycles ± follicle luteinization. Peak serum P4 and urine pregnanediol (Pd) in cycle 1 were inversely correlated with LH pulse frequency during sleep in the FP of cycle 2 (r = -0.5; P = 0.02 for both). The intercycle FSH rise and folliculogenesis in cycle 2 were maintained after anovulatory cycles without P4 or Pd exposure or nocturnal slowing of LH pulse frequency in the FP. CONCLUSIONS: During late puberty, rising P4 levels from follicle luteinization and ovulation may promote a slower LH pulse frequency during sleep in the FP. However, a normal FSH rise and follicle growth can occur in the absence of P4-associated slowing. These studies therefore suggest that an immature LH secretory pattern during sleep is unlikely to contribute to menstrual irregularity in the early postmenarchal years.

Multimodal Wearable Sensors to Measure Gait and Voice.

BACKGROUND: Traditional measurement systems utilized in clinical trials are limited because they are episodic and thus cannot capture the day-to-day fluctuations and longitudinal changes that frequently affect patients across different therapeutic areas. OBJECTIVES: The aim of this study was to collect and evaluate data from multiple devices, including wearable sensors, and compare them to standard lab-based instruments across multiple domains of daily tasks. METHODS: Healthy volunteers aged 18-65 years were recruited for a 1-h study to collect and assess data from wearable sensors. They performed walking tasks on a gait mat while instrumented with a watch, phone, and sensor insoles as well as several speech tasks on multiple recording devices. RESULTS: Step count and temporal gait metrics derived from a single lumbar accelerometer are highly precise; spatial gait metrics are consistently 20% shorter than gait mat measurements. The insole's algorithm only captures about 72% of steps but does have precision in measuring temporal gait metrics. Mobile device voice recordings provide similar results to traditional recorders for average signal pitch and sufficient signal-to-noise ratio for analysis when hand-held. Lossless compression techniques are advised for signal processing. CONCLUSIONS: Gait metrics from a single lumbar accelerometer sensor are in reasonable concordance with standard measurements, with some variation between devices and across individual metrics. Finally, participants in this study were familiar with mobile devices and had high acceptance of potential future continuous wear for clinical trials.

Healthy Post-Menarchal Adolescent Girls Demonstrate Multi-Level Reproductive Axis Immaturity.

Context: Menstrual irregularity after menarche has been attributed to immature estrogen positive feedback activity (E+FB) but data are conflicting. Objective: To determine the hypothalamic-pituitary-ovarian contributions to menstrual irregularity in adolescents. Methods: Twenty-three healthy girls [aged 12.8 to 17.6 years; 0.4 to 3.5 years postmenarche; body mass index (BMI) percentile, 41.0 to 99.3] underwent serial hormone measurements and pelvic ultrasounds during two consecutive menstrual cycles. Hormones and follicle growth were compared with 65 adult historic controls with ovulatory cycles (OVs). Results: Girls had anovulatory cycles (ANOVs; 30%), OVs with a short luteal phase (short OVs; 22%), or OVs with normal luteal phase (normal OVs; 48%) without differences in cycle length, chronologic or gynecologic age, or BMI. Adolescents showed a spectrum of E+FB [midcycle LH adjusted for preovulatory estradiol (E2)]; only normal OV girls were comparable to adults. All OV girls had lower E2, progesterone, and gonadotropins during the luteal phase and luteal-follicular transition compared with adults. Normal OV girls also had lower follicular phase LH and FSH levels, a longer follicular phase, a slower dominant follicle growth rate, and smaller estimated preovulatory follicle size than adults. Follicular phase E2 and inhibin B levels were lower in normal OV girls than in adults even after adjusting for differences in FSH and follicle size. Conclusions: Early postmenarchal girls with normal OVs demonstrate mature E+FB but continue to have lower gonadotropin levels, diminished ovarian responsiveness, and decreased corpus luteum sex steroid synthesis compared with adults, indicating that reproductive axis maturity requires coordinated development of all components of the hypothalamic-pituitary-ovarian axis.

Central precocious puberty in Boston boys: A 10-year single center experience.

OBJECTIVE: Recent studies in the US and abroad suggest that boys are undergoing puberty at a younger age. It is unknown if this secular trend extends to boys with central precocious puberty (CPP), who sit at the extreme end of the pubertal spectrum, and if neuroimaging should remain a standard diagnostic tool. STUDY DESIGN: Retrospective chart review of all boys with CPP seen by Endocrinology at a US pediatric hospital from 2001-2010. RESULTS: Fifty boys had pubertal onset at an average age of 7.31 years (95CI 6.83-7.89), though many did not present until nearly one year thereafter, by which time 30% were mid-to-late pubertal. Boys were predominantly non-Hispanic White and 64% were overweight/obese. The majority (64%) of boys had neurogenic CPP (CNS-CPP) with neurofibromatosis type I being the most common diagnosis. Diagnosis of CPP led to discovery of a neurogenic lesion in only 3 of 32 (9%) CNS-CPP cases. The remaining boys, with idiopathic CPP (36%), were indistinguishable from those with CNS-CPP aside from four boys who endorsed a family history of PP (22% vs. 0% among CNS-CPP cases). Importantly, there was no change in the incidence of male CPP after accounting for the increase in clinic volume during this time period. CONCLUSION: In this contemporary Boston-based cohort of 50 boys with CPP, most cases were neurogenic, consistent with older literature. Several idiopathic cases had a family history of PP but were otherwise indistinguishable from CNS-CPP cases. Thus, neuroimaging remains a critical diagnostic tool. We find no evidence for an increase in the prevalence of male CPP.

The Relationship Between Estrogen and the Decline in Delta Power During Adolescence.

Study Objectives: During adolescence, there is a precipitous decrease in slow-wave sleep (SWS) and its spectral correlate, delta power, which may reflect cortical reorganization. The temporal association between the decrease in delta power and puberty suggests that sex steroids may initiate these changes. This association has not been previously investigated. Methods: To determine whether estrogen triggers the adolescent decline in delta power, we compared delta power in 14 girls with central precocious puberty (CPP) and 6 age-matched, prepubertal controls. Five CPP participants were re-studied 7-14 months after pubertal suppression to determine if the changes in delta power are reversible after restoring a prepubertal hormonal milieu. The change in delta power was also compared between CPP participants and five historic controls from a longitudinal polysomnographic study. Results: CPP participants (6.7-10.5 years) spent 30% of the night in SWS. Delta power (3.7 × 106 ± 2.7 × 105 µV2) predominated in the first 2 non-rapid eye movement episodes and decayed exponentially (tau 0.006 minutes). Age-matched controls demonstrated similar sleep staging (24% SWS) and delta dynamics (3.3 × 106 ± 5.1 × 105 µV2, tau 0.004 minutes). Four out of 5 CPP participants had a significant decrease (26%) in delta power after hormone suppression (p < .05), similar to historic controls. Conclusion: Using an innovative model of girls with CPP studied before and after estrogen suppression, the effects of puberty on the decline in delta power were dissociated from those of chronologic age. The current studies suggest that increased estrogen does not cause the adolescent decline in delta power and indicate that neurodevelopmental changes per se or other factors associated with puberty drive these sleep changes.

Effect of Slow Wave Sleep Disruption on Metabolic Parameters in Adolescents.

STUDY OBJECTIVES: Cross-sectional studies report a correlation between slow wave sleep (SWS) duration and insulin sensitivity (SI) in children and adults. Suppression of SWS causes insulin resistance in adults but effects in children are unknown. This study was designed to determine the effect of SWS fragmentation on SI in children. METHODS: Fourteen pubertal children (11.3-14.1 y, body mass index 29(th) to 97(th) percentile) were randomized to sleep studies and mixed meal (MM) tolerance tests with and without SWS disruption. Beta-cell responsiveness (Φ) and SI were determined using oral minimal modeling. RESULTS: During the disruption night, auditory stimuli (68.1 ± 10.7/night; mean ± standard error) decreased SWS by 40.0 ± 8.0%. SWS fragmentation did not affect fasting glucose (non-disrupted 76.9 ± 2.3 versus disrupted 80.6 ± 2.1 mg/dL), insulin (9.2 ± 1.6 versus 10.4 ± 2.0 µIU/mL), or C-peptide (1.9 ± 0.2 versus 1.9 ± 0.1 ng/mL) levels and did not impair SI (12.9 ± 2.3 versus 10.1 ± 1.6 10(-4) dL/kg/min per µIU/mL) or Φ (73.4 ± 7.8 versus 74.4 ± 8.4 10(-9) min(-1)) to a MM challenge. Only the subjects in the most insulin-sensitive tertile demonstrated a consistent decrease in SI after SWS disruption. CONCLUSION: Pubertal children across a range of body mass indices may be resistant to the adverse metabolic effects of acute SWS disruption. Only those subjects with high SI (i.e., having the greatest "metabolic reserve") demonstrated a consistent decrease in SI. These results suggest that adolescents may have a unique ability to adapt to metabolic stressors, such as acute SWS disruption, to maintain euglycemia. Additional studies are necessary to confirm that this resiliency is maintained in settings of chronic SWS disruption.