Delirium as a Presenting Symptom of COVID-19.

BACKGROUND: Delirium is a common neurologic manifestation of coronavirus disease 2019 (COVID-19) in older adults who present to the emergency department (ED). OBJECTIVE: To investigate clinical characteristics associated with delirium as a presenting symptom of COVID-19 in older adults and develop a logistic regression to predict the likelihood of delirium. METHOD: We compared clinical characteristics in an age- and gender-matched sample of 68 delirious individuals with 68 nondelirious individuals (Mage = 78) who presented to the ED with COVID-19. RESULTS: The delirious group was more likely to have neurologic, psychiatric, and cardiovascular comorbidities; a prior history of delirium; and deliriogenic medications in their medication list. They were less likely to present with respiratory symptoms and more likely to present with sepsis, hypoxia, higher heart rate, and higher sodium. The delirious group had higher mortality (51%) than the nondelirious group (32%). Delirium developed within an average of 2 days of initial COVID-19 symptom onset, with symptom onset to ED within an average of 4 days and symptom onset to death within an average of 11 days. Logistic regression based on five delirium predictors correctly predicted 80% of those with delirium (75% sensitivity at 86% specificity). CONCLUSION: Our results are largely consistent with prior studies and suggest that delirium is a common, early occurring, and lethal manifestation of COVID-19 in older adults presenting to the ED, in most cases causing acute on chronic neurocognitive dysfunction strongly influenced by inflammatory and hypoxic-ischemic mechanisms.

Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative.

IMPORTANCE: Depression is an illness affecting a large percentage of the world's population throughout the lifetime. To date, there is no available biomarker for depression detection and tracking of symptoms relies on patient self-report. OBJECTIVE: To explore and validate features extracted from recorded voice samples of depressed subjects as digital biomarkers for suicidality, psychomotor disturbance, and depression severity. DESIGN: We conducted a cross-sectional study over the course of 12 months using a frequently visited web form version of the PHQ9 hosted by Mental Health America (MHA) to ask subjects for anonymous voice samples via a separate web form hosted by NeuroLex Laboratories. Subjects were asked to provide demographics, answers to the PHQ9, and two voice samples. SETTING: Online only. PARTICIPANTS: Users of the MHA website. MAIN OUTCOMES AND MEASURES: Performance of statistical models using extracted voice features to predict psychomotor disturbance, suicidality, and depression severity as indicated by the PHQ9. RESULTS: Voice features extracted from recorded audio of depressed subjects were able to predict PHQ9 question 9 and total scores with an area under the curve of 0.821 and a mean absolute error of 4.7, respectively. Psychomotor Disturbance prediction was less powerful with an area under the curve of 0.61. CONCLUSION AND RELEVANCE: Automated voice analysis using short recordings of patient speech may be used to augment depression screen and symptom management.

Investigating voice as a biomarker: Deep phenotyping methods for early detection of Parkinson's disease.

Voice technology has grown tremendously in recent years and using voice as a biomarker has also been gaining evidence. We demonstrate the potential of voice in serving as a deep phenotype for Parkinson's Disease (PD), the second most common neurodegenerative disorder worldwide, by presenting methodology for voice signal processing for clinical analysis. Detection of PD symptoms typically requires an exam by a movement disorder specialist and can be hard to access and inconsistent in findings. A vocal digital biomarker could supplement the cumbersome existing manual exam by detecting and quantifying symptoms to guide treatment. Specifically, vocal biomarkers of PD are a potentially effective method of assessing symptoms and severity in daily life, which is the focus of the current research. We analyzed a database of PD patient and non-PD subjects containing voice recordings that were used to extract paralinguistic features, which served as inputs to machine learning models to predict PD severity. The results are presented here and the limitations are discussed given the nature of the recordings. We note that our methodology only advances biomarker research and is not cleared for clinical use. Specifically, we demonstrate that conventional machine learning models applied to voice signals can be used to differentiate participants with PD who exhibit little to no symptoms from healthy controls. This work highlights the potential of voice to be used for early detection of PD and indicates that voice may serve as a deep phenotype for PD, enabling precision medicine by improving the speed, accuracy, accessibility, and cost of PD management.

5-Day Injectable Extended-release Buprenorphine Initiation in Non-opioid-tolerant Individuals in a Carceral Setting: A Case Series.

BACKGROUND: Injectable extended-release buprenorphine (XR-Bup) addresses several barriers to the implementation of treatment with medications for opioid use disorder (MOUD) in carceral settings due to lower risk of diversion and reduced operational procedures. However, there is no standardized approach or guideline for initiating sublingual buprenorphine (SL-Bup) and transitioning to XR-Bup in persons with opioid use disorder (OUD) who are not actively using opioids, a clinical scenario commonly encountered in carceral settings. METHODS: We conducted a retrospective case series of non-opioid-tolerant men with OUD at a Montana Department of Corrections facility who initiated XR-Bup using a 5-day induction protocol between May 1, 2023, and November 1, 2023. Primary outcome was receipt of the initial XR-Bup injection. Secondary outcomes were toleration of SL-Bup induction protocol and active continuation of XR-Bup at time of discharge. RESULTS: Sixteen individuals initiated the SL-Bup induction protocol, and all were successfully transitioned to XR-Bup with no severe adverse effects. There were no required dose changes or severe adverse effects from SL-Bup induction. Two (12%) elected to discontinue XR-Bup due to commonly reported adverse effects. Fourteen (88%) remained on XR-Bup at discharge. CONCLUSIONS: Five-day induction of SL-Bup and transition to XR-Bup may be considered for non-opioid-tolerant individuals with OUD in carceral settings.

Comparing psychometric characteristics of a computerized cognitive test (BrainCheck Assess) against the Montreal cognitive assessment.

Introduction: Previous validation studies demonstrated that BrainCheck Assess (BC-Assess), a computerized cognitive test battery, can reliably and sensitively distinguish individuals with different levels of cognitive impairment (i.e., normal cognition (NC), mild cognitive impairment (MCI), and dementia). Compared with other traditional paper-based cognitive screening instruments commonly used in clinical practice, the Montreal Cognitive Assessment (MoCA) is generally accepted to be among the most comprehensive and robust screening tools, with high sensitivity/specificity in distinguishing MCI from NC and dementia. In this study, we examined: (1) the linear relationship between BC-Assess and MoCA and their equivalent cut-off scores, and (2) the extent to which they agree on their impressions of an individual's cognitive status. Methods: A subset of participants (N = 55; age range 54-94, mean/SD = 80/9.5) from two previous studies who took both the MoCA and BC-Assess were included in this analysis. Linear regression was used to calculate equivalent cut-off scores for BC-Assess based on those originally recommended for the MoCA to differentiate MCI from NC (cut-off = 26), and dementia from MCI (cut-off = 19). Impression agreement between the two instruments were measured through overall agreement (OA), positive percent agreement (PPA), and negative percent agreement (NPA). Results: A high Pearson correlation coefficient of 0.77 (CI = 0.63-0.86) was observed between the two scores. According to this relationship, MoCA cutoffs of 26 and 19 correspond to BC-Assess scores of 89.6 and 68.5, respectively. These scores are highly consistent with the currently recommended BC-Assess cutoffs (i.e., 85 and 70). The two instruments also show a high degree of agreement in their impressions based on their recommended cut-offs: (i) OA = 70.9%, PPA = 70.4%, NPA = 71.4% for differentiating dementia from MCI/NC; (ii) OA = 83.6%, PPA = 84.1%, NPA = 81.8% for differentiating dementia/MCI from NC. Discussion: This study provides further validation of BC-Assess in a sample of older adults by showing its high correlation and agreement in impression with the widely used MoCA.

Lower leg muscle involvement in Duchenne muscular dystrophy: an MR imaging and spectroscopy study.

OBJECTIVE: To describe the involvement of lower leg muscles in boys with Duchenne muscular dystrophy (DMD) by using MR imaging (MRI) and spectroscopy (MRS) correlated to indices of functional status. SUBJECTS AND METHODS: Nine boys with DMD (mean age, 11 years) and eight healthy age- and BMI-matched boys (mean age, 13 years) prospectively underwent lower leg MRI, 1H-MRS of tibialis anterior (TA) and soleus (SOL) for lipid fraction measures, and 31P-MRS for pH and high-energy phosphate measures. DMD subjects were evaluated using the Vignos lower extremity functional rating, and tests including 6 min walk test (6MWT) and 10 m walk. RESULTS: DMD subjects had highest fatty infiltration scores in peroneal muscles, followed by medial gastrocnemius and soleus. Compared to controls, DMD boys showed higher intramuscular fat (P = 0.04), lipid fractions of TA and SOL (P = 0.02 and 0.003, respectively), pH of anterior compartment (P = 0.0003), and lower phosphocreatine/inorganic phosphorus ratio of posterior compartment (P = 0.02). The Vignos rating correlated with TA (r = 0.79, P = 0.01) and SOL (r = 0.71, P = 0.03) lipid fractions. The 6MWT correlated with fatty infiltration scores of SOL (r = -0.76, P = 0.046), medial (r = -0.80, P = 0.03) and lateral (r = -0.84, P = 0.02) gastrocnemius, intramuscular fat (r = -0.80, P = 0.03), and SOL lipid fraction (r = -0.89, P = 0.007). Time to walk 10 m correlated with anterior compartment pH (r = 0.78, P = 0.04). CONCLUSION: Lower leg muscles of boys with DMD show a distinct involvement pattern and increased adiposity that correlates with functional status. Lower leg MRI and 1H-MRS studies may help to noninvasively demonstrate the severity of muscle involvement.

A Computerized Cognitive Test Battery for Detection of Dementia and Mild Cognitive Impairment: Instrument Validation Study.

BACKGROUND: Early detection of dementia is critical for intervention and care planning but remains difficult. Computerized cognitive testing provides an accessible and promising solution to address these current challenges. OBJECTIVE: The aim of this study was to evaluate a computerized cognitive testing battery (BrainCheck) for its diagnostic accuracy and ability to distinguish the severity of cognitive impairment. METHODS: A total of 99 participants diagnosed with dementia, mild cognitive impairment (MCI), or normal cognition (NC) completed the BrainCheck battery. Statistical analyses compared participant performances on BrainCheck based on their diagnostic group. RESULTS: BrainCheck battery performance showed significant differences between the NC, MCI, and dementia groups, achieving 88% or higher sensitivity and specificity (ie, true positive and true negative rates) for separating dementia from NC, and 77% or higher sensitivity and specificity in separating the MCI group from the NC and dementia groups. Three-group classification found true positive rates of 80% or higher for the NC and dementia groups and true positive rates of 64% or higher for the MCI group. CONCLUSIONS: BrainCheck was able to distinguish between diagnoses of dementia, MCI, and NC, providing a potentially reliable tool for early detection of cognitive impairment.

Modular MR-compatible lower leg exercise device for whole-body scanners.

PURPOSE: To develop a modular MR-compatible lower leg exercise device for muscle testing using a clinical 3 T MR scanner. MATERIALS AND METHODS: An exercise device to provide isotonic resistance to plantar- or dorsiflexion was constructed from nonferrous materials and designed for easy setup and use in a clinical environment. Validation tests were performed during dynamic MR acquisitions. For this purpose, the device was tested on the posterior lower leg musculature of five subjects during 3 min of exercise at 30% of maximum voluntary plantarflexion during 31-phosphorus MR spectroscopy ((31)P-MRS). Measures of muscle phosphocreatine (PCr), inorganic phosphate (Pi), and pH were obtained before, during, and after the exercise protocol. RESULTS: At the end of exercise regimen, muscle PCr showed a 28% decrease from resting levels (to 21.8 ± 3.9 from 30.4 ± 3.0 mM) and the average PCr recovery rate was 35.3 ± 8.3 s. Muscle Pi concentrations increased 123% (to 14.6 ± 4.7 from 6.5 ± 3.3 mM) and pH decreased 1.5% (to 7.06 ± 0.14 from 7.17 ± 0.07) from resting levels. CONCLUSION: The described MR-compatible lower leg exercise was an effective tool for data acquisition during dynamic MR acquisitions of the calf muscles. The modular design allows for adaptation to other whole-body MR scanners and incorporation of custom-built mechanical or electronic interfaces and can be used for any MR protocol requiring dynamic evaluation of calf muscles.

Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort.

AIM: Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905-22) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired participants and 2) comparing the performance of the original linguistic features with the performance of expanded features. METHODS: Data were derived from a subset of the FHS Cognitive Aging Cohort. We analyzed a sub-selection of 98 participants, which provided 127 unique audio files and clinical observations (n = 127, female = 47%, cognitively impaired = 43%). We built on previous work which extracted original linguistic features from transcribed audio files by extracting expanded features. We used both feature sets to train logistic regression classifiers to distinguish cognitively normal from cognitively impaired participants and compared the predictive power of the original and expanded linguistic feature sets, and participants' Mini-Mental State Examination (MMSE) scores. RESULTS: Based on the area under the receiver-operator characteristic curve (AUC) of the models, both the original (AUC = 0.882) and expanded (AUC = 0.883) feature sets outperformed MMSE (AUC = 0.870) in classifying cognitively impaired and cognitively normal participants. Although the original and expanded feature sets had similar AUC, the expanded feature set showed better positive and negative predictive value [expanded: positive predictive value (PPV) = 0.738, negative predictive value (NPV) = 0.889; original: PPV = 0.701, NPV = 0.869]. CONCLUSIONS: Linguistic analysis has been shown to be a potentially powerful tool for clinical use in classifying cognitive impairment. This study expands the work of several others, but further studies into the plausibility of speech analysis in clinical use are vital to ensure the validity of speech analysis for clinical classification of cognitive impairment.

Comparison of 3.0 T proton magnetic resonance spectroscopy short and long echo-time measures of intramyocellular lipids in obese and normal-weight women.

PURPOSE: To compare correlations of intramyocellular lipids (IMCL) measured by short and long echo-time proton magnetic resonance spectroscopy (1H-MRS) with indices of body composition and insulin resistance in obese and normal-weight women. MATERIALS AND METHODS: We quantified IMCL of tibialis anterior (TA) and soleus (SOL) muscles in 52 premenopausal women (37 obese and 15 normal weight) using single-voxel 1H-MRS PRESS at 3.0 T with short (30 msec) and long (144 msec) echo times. Statistical analyses were performed to determine correlations of IMCL with body composition as determined by computed tomography (CT) and insulin resistance indices and to compare correlation coefficients from short and long echo-time data. Signal-to-noise ratio (SNR), linewidth, and coefficients of variation (CV) of short and long echo-time spectra were calculated. RESULTS: Short and long echo-time IMCL from TA and SOL significantly correlated with body mass index (BMI) and abdominal fat depots (r = 0.32 to 0.70, P = <0.05), liver density (r = -0.39 to -0.50, P < 0.05), and glucose area under the curve as a measure of insulin resistance (r = 0.47 to 0.49, P < 0.05). There was no significant difference between correlation coefficients of short and long echo-time spectra (P > 0.5). Short echo-time IMCL in both muscles showed significantly higher SNR (P < 0.0001) and lower CVs when compared to long echo-time acquisitions. Linewidth measures were not significantly different between groups. CONCLUSION: IMCL quantification using short and long echo-time 1H-MRS at 3.0 T is useful to detect differences in muscle lipid content in obese and normal-weight subjects. In addition, IMCL correlates with body composition and markers of insulin resistance in this population with no significant difference in correlations between short and long echo-times. Short echo-time IMCL quantification of TA and SOL muscles at 3.0 T was superior to long echo-time due to better SNR and better reproducibility.

Breath-hold 1H-magnetic resonance spectroscopy for intrahepatic lipid quantification at 3 Tesla.

OBJECTIVE: To compare breath-hold 1H-magnetic resonance spectroscopy (1H-MRS) with respiratory-gated 1H-MRS and computed tomography (CT) for quantification of hepatic lipid content. METHODS: Twenty-three premenopausal women underwent breath-hold point-resolved single-voxel 1H-MRS of the liver followed by respiratory-gated 1H-MRS at 3 Tesla and CT slice through the liver. Interscan variability for 1H-MRS was assessed in 6 volunteers. Pearson correlation coefficients, Bland-Altman 95% limit of agreement, and concordance correlation coefficients were calculated. RESULTS: There was a strong correlation between breath-hold and respiratory-gated 1H-MRS (r = 0.94, P < 0.0001; concordance correlation coefficient, 0.75). Using Bland-Altman analysis, all but 2 data points were within the limits of agreement. Both 1H-MRS techniques had low interscan variability. There was an inverse correlation of both 1H-MRS techniques with CT attenuation values of the liver. CONCLUSIONS: Breath-hold 1H-MRS is a reliable method to measure hepatic lipid content at 3 Tesla. Breath-hold 1H-MRS of the liver provides data that closely correlates with that obtained from longer-duration respiratory-gated technique.

Assessing the Utility of Language and Voice Biomarkers to Predict Cognitive Impairment in the Framingham Heart Study Cognitive Aging Cohort Data.

BACKGROUND: There is a need for fast, accessible, low-cost, and accurate diagnostic methods for early detection of cognitive decline. Dementia diagnoses are usually made years after symptom onset, missing a window of opportunity for early intervention. OBJECTIVE: To evaluate the use of recorded voice features as proxies for cognitive function by using neuropsychological test measures and existing dementia diagnoses. METHODS: This study analyzed 170 audio recordings, transcripts, and paired neuropsychological test results from 135 participants selected from the Framingham Heart Study (FHS), which includes 97 recordings of cognitively normal participants and 73 recordings of cognitively impaired participants. Acoustic and linguistic features of the voice samples were correlated with cognitive performance measures to verify their association. RESULTS: Language and voice features, when combined with demographic variables, performed with an AUC of 0.942 (95% CI 0.929-0.983) in predicting cognitive status. Features with good predictive power included the acoustic features mean spectral slope in the 500-1500 Hz band, variation in the F2 bandwidth, and variation in the Mel-Frequency Cepstral Coefficient (MFCC) 1; the demographic features employment, education, and age; and the text features of number of words, number of compound words, number of unique nouns, and number of proper names. CONCLUSION: Several linguistic and acoustic biomarkers show correlations and predictive power with regard to neuropsychological testing results and cognitive impairment diagnoses, including dementia. This initial study paves the way for a follow-up comprehensive study incorporating the entire FHS cohort.

By the numbers: ratings and utilization of behavioral health mobile applications.

Although >10,000 behavioral health applications ("apps") are currently available on the Apple and Google Play marketplaces, they have been minimally evaluated or regulated and little is known about "real world" usage patterns. This investigation combined data from online behavioral health app rating frameworks and a mobile health market research firm to identify the most downloaded apps as well as determine rating and ranking concordance between frameworks. Findings demonstrated that the most commonly downloaded apps focus on relaxation, mindfulness, and meditation skills and that they often have notably discordant reviews across rating frameworks. Our results suggest that there is a growing need for: (1) standardized behavioral health app quality and effectiveness measures, (2) up-to-date behavioral health app guidance for clinicians and consumers, and (3) evidence-based apps that incorporate revealed consumer preferences.

Depression Screening from Voice Samples of Patients Affected by Parkinson's Disease.

Depression is a common mental health problem leading to significant disability world wide. Depression is not only common but also commonly co-occurs with other mental and neurological illnesses. Parkinson's Disease gives rise to symptoms directly impairing a person's ability to function. Early diagnosis and detection of depression can aid treatment, but diagnosis typically requires an interview with a health provider or structured diagnostic questionnaire. Thus, unobtrusive measures to monitor depression symptoms in daily life could have great utility in screening depression for clinical treatment. Vocal biomarkers of depression are a potentially effective method of assessing depression symptoms in daily life, which is the focus of the current research. We have a database of 921 unique patients with Parkinson's disease and their self assessment of whether they felt depressed or not. Voice recordings from these patients were used to extract paralinguistic features, which served as inputs to machine-learning and deep learning techniques to predict depression. The results are presented here and the limitations are discussed given the nature of the recordings which lack language content. Our models achieved accuracies as high as 0.77 in classifying depressed and non-depressed subjects accurately using their voice features and PD severity. We found depression and severity of Parkinson's Disease had a correlation coefficient of 0.3936, providing a valuable feature when predicting depression from voice. Our results indicate a clear correlation between feeling depressed and the severity of the Parkinson's disease. Voice may be an effective digital biomarker to screen for depression among patients suffering from Parkinson's Disease.

Parkinson's Disease Classification of mPower Walking Activity Participants.

Motion sensor data collected using Sage Bionetwork's mPower application on the Apple iPhone to record participant activities is analyzed to classify samples as positive or negative for Parkinson's Diagnosis. Pre-processing of the data showed differences in the time and frequency dimensions for features derived from Apple Core motion data. Several classic machine learning classification algorithms were trained on seventy-seven derived data points for best precision, recall, and F-1 score. Accuracy as high as ninety-two percent were achieved, with the best results attained from decision tree and multi-layered artificial neural network algorithms. This research shows that motion data produced on the Apple iPhone using the mPower application shows promise as an accessible platform to classify participants for presence of Parkinson's Disease signs.

Acceptability of mHealth augmentation of Collaborative Care: A mixed methods pilot study.

OBJECTIVE: To assess the feasibility and acceptability of a mobile health platform supporting Collaborative Care. METHOD: Collaborative Care patients (n=17) used a smartphone app to transmit PHQ-9 and GAD-7 scores and sensor data to a dashboard used by one care manager. Patients completed usability and satisfaction surveys and qualitative interviews at 4weeks and the care manager completed a qualitative interview. Mobile metadata on app usage was obtained. RESULTS: All patients used the app for 4weeks, but only 35% (n=6) sustained use at 8weeks. Prior to discontinuing use, 88% (n=15) completed all PHQ-9 and GAD-7 measures, with lower response rates for daily measures. Four themes emerged from interviews: understanding the purpose; care manager's role in supporting use; benefits of daily monitoring; and privacy / security concerns. Two themes were user-specific: patients' desire for personalization; and care manager burden. CONCLUSIONS: The feasibility and acceptability of the mobile platform is supported by the high early response rate, however attrition was steep. Our qualitative findings revealed nuanced participant experiences and uncovered some concerns about mobile health. To encourage retention, attention may need to be directed toward promoting patient understanding and provider engagement, and offering personalized patient experiences.

Patient-oriented health technologies: Patients' perspectives and use.

BACKGROUND: For patient-oriented mobile health tools to contribute meaningfully to improving healthcare delivery, widespread acceptance and use of such tools by patients are critical. However, little is known about patients' attitudes toward using health technology and their willingness to share health data with providers. AIMS: To investigate primary care patients' comfort sharing health information through mobile devices, and patients' awareness and use of patient portals. METHODS: Patients (n=918) who visited one of 6 primary care clinics in the Northwest US completed a survey about health technology use, medical conditions, and demographics. RESULTS: More patients were comfortable sharing mobile health information with providers than having third parties store their information (62% vs 30%, Somers D=.33, p<0.001). Patients older than 55 years were less likely to be comfortable sharing with providers (AORs 0.37-0.42, p<0.01). Only 39% of patients knew if their clinic offered a patient portal; however, of these, 67% used it. Health literacy limitations were associated with lower portal awareness (AOR=0.55, p=0.005) but not use. Portal use was higher among patients with a chronic condition (AOR= 3.18, p=0.004). CONCLUSION: Comfort, awareness, and use of health technologies were variable. Practices introducing patient-facing health technologies should promote awareness, address concerns about data security, and provide education and training, especially to older adults and those with health literacy limitations. Patient-facing health technologies provide an opportunity for delivering scalable health education and self-management support, particularly for patients with chronic conditions who are already using patient portals.

Schizophrenia and the gut-brain axis.

Several risk factors for the development of schizophrenia can be linked through a common pathway in the intestinal tract. It is now increasingly recognized that bidirectional communication exists between the brain and the gut that uses neural, hormonal, and immunological routes. An increased incidence of gastrointestinal (GI) barrier dysfunction, food antigen sensitivity, inflammation, and the metabolic syndrome is seen in schizophrenia. These findings may be influenced by the composition of the gut microbiota. A significant subgroup of patients may benefit from the initiation of a gluten and casein-free diet. Antimicrobials and probiotics have therapeutic potential for reducing the metabolic dysfunction and immune dysregulation seen in patients with schizophrenia.

Vertebral bone marrow fat is positively associated with visceral fat and inversely associated with IGF-1 in obese women.

Recent studies have demonstrated an important physiologic link between bone and fat. Bone and fat cells arise from the same mesenchymal precursor cell within bone marrow, capable of differentiation into adipocytes or osteoblasts. Increased BMI appears to protect against osteoporosis. However, recent studies have suggested detrimental effects of visceral fat on bone health. Increased visceral fat may also be associated with decreased growth hormone (GH) and insulin-like growth factor 1 (IGF-1) levels which are important for maintenance of bone homeostasis. The purpose of our study was to assess the relationship between vertebral bone marrow fat and trabecular bone mineral density (BMD), abdominal fat depots, GH and IGF-1 in premenopausal women with obesity. We studied 47 premenopausal women of various BMI (range: 18-41 kg/m², mean 30 ± 7 kg/m²) who underwent vertebral bone marrow fat measurement with proton magnetic resonance spectroscopy (1H-MRS), body composition, and trabecular BMD measurement with computed tomography (CT), and GH and IGF-1 levels. Women with high visceral fat had higher bone marrow fat than women with low visceral fat. There was a positive correlation between bone marrow fat and visceral fat, independent of BMD. There was an inverse association between vertebral bone marrow fat and trabecular BMD. Vertebral bone marrow fat was also inversely associated with IGF-1, independent of visceral fat. Our study showed that vertebral bone marrow fat is positively associated with visceral fat and inversely associated with IGF-1 and BMD. This suggests that the detrimental effect of visceral fat on bone health may be mediated in part by IGF-1 as an important regulator of the fat and bone lineage.