Introduction and Objective: Men are underrepresented in the National Diabetes Prevention Program (NDPP), comprising 25% or less of participants. This study evaluated the effects of a group-based remote (i.e. distance learning) diabetes prevention program tailored for Black and Latino men on weight loss and program engagement. Methods: Recruiting through primary care clinics and offered free of charge, we enrolled 301 men who met NDPP eligibility criteria using weight, height, and hemoglobin A1c from the electronic health record (EHR) or ADA diabetes risk score. They were randomized to the tailored men-only Power-Up intervention group or a standard, mixed-gender NDPP comparison. Weight was measured via home-based electronic scales, supplemented by EHR and self-report, at baseline, the 16th core session, and 12-months. The primary outcomes were the percent (%) weight loss at end of core sessions and end of maintenance; and program engagement, defined as attending 8 sessions during months 1-6 and time from first to last session attended at least 9 full months (completion). Results: Participants were on average 52 (±12.5) years old, 61.5% were Black, 50.2% Hispanic/Latino, 48.2% were employed full-time, and 39.7% graduated university/college. The average weight at baseline was 223 (±53) lbs and similar in both Power-Up and standard groups. Men in the Power-Up and standard groups had similar % weight change by the end of core sessions (-1.5% vs -1.1%, p=0.57) and end of maintenance (-1.2% vs -0.2%, p=0.42). The Power-Up group had a higher completion rate than men in the standard groups (18.1% vs. 6.2%, p=0.006). Conclusion: Men assigned to Power-Up were more likely to complete the program than men in the standard NDPP. While overall weight loss was low, these findings highlight that centering men’s health when delivering the NDPP is a promising way to improve program engagement. Additional tailoring may be necessary to more substantially engage men in lifestyle change for diabetes prevention. Disclosure E. Chambers: None. C. Schechter: None. E.A. Walker: None. E. Gil: None. C.J. Gonzalez: None. K.N. Diaz: None. Q. De Jesus: None. K. Pujols: None. J.S. Gonzalez: None. Funding National Institute of Health (R01 DK121896)

Objective: To address previous inconsistencies in reports of differential adherence to diabetes medications, we examined medication adherence and evaluated treatment group differences in a subset of participants in the GRADE study. Research Design and Methods: GRADE participants (T2DM<10 years, HbA1c=6.8-8.5%, on metformin alone) were randomly assigned to add insulin glargine, glimepiride, liraglutide, or sitagliptin. Adherence was measured semiannually for 3 years using a validated 3-item scale (0-100, lowest to highest adherence) in a substudy (N=1739). Analyses evaluated adherence over time and tested treatment group differences in adherence and in the association between adherence and primary (HbA1c 7.0%) and secondary (HbA1c >7.5%) glycemic outcomes. Results: Overall, mean (SD) adherence was high over 3 years of follow-up at 88.7(10.01) and decreased slightly by 3 years relative to baseline (-2.0±14.7,p<0.0001). No inter-group differences were observed until 3 years, when adherence was 5% and 3% higher for the glimepiride and sitagliptin groups, than for liraglutide (both p<0.05). Over follow-up and across groups, a 10-point decrease in adherence was associated with 15% and 19% increased risk of reaching primary (HbA1c 7.0%) and secondary (HbA1c >7.5%) glycemic outcomes (both p<0.0001). Lower adherence was somewhat more predictive of the secondary outcome for those assigned to glargine or liraglutide, compared to glimepiride or sitagliptin (each p<0.05). No other comparisons were significant. Conclusions: Medication adherence was consistently high in GRADE. Observed treatment group differences were small, and of unclear clinical significance. Overall, lower adherence robustly predicted worsening glycemic control, highlighting the importance of ongoing assessment.

OBJECTIVE:To address previous inconsistencies in reports of differential adherence to diabetes medications, we examined medication adherence and evaluated treatment group differences in a substudy of participants in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE). RESEARCH DESIGN AND METHODS:GRADE participants (type 2 diabetes duration <10 years, HbA1c 6.8%-8.5%, on metformin alone) were randomly assigned to add insulin glargine, glimepiride, liraglutide, or sitagliptin. Adherence was measured semiannually for 3 years using a validated three-item scale (0-100, lowest to highest adherence) in a substudy (N = 1,739). Analyses included evaluation of adherence over time and testing treatment group differences in adherence and in the association between adherence and primary (HbA1c ≥7.0%) and secondary (HbA1c >7.5%) glycemic outcomes. RESULTS:Overall mean ± SD adherence (average of participant-level mean ± SD) was high over 3 years of follow-up at 88.7 ± 10.01, on a scale of 0-100, and decreased slightly by 3 years relative to baseline (-2.0 ± 14.7; P < 0.0001). No intergroup differences were observed until 3 years, when adherence was 5% and 3% higher for the glimepiride and sitagliptin groups, respectively, than for liraglutide (both P < 0.05). Over follow-up and across groups, a 10-point decrease in adherence was associated with 15% and 19% increased risk of reaching primary (HbA1c ≥7.0%) and secondary (HbA1c >7.5%) glycemic outcomes (both P < 0.0001). Lower adherence was somewhat more predictive of the secondary outcome for those assigned to glargine or liraglutide, compared with glimepiride or sitagliptin (each P < 0.05). No other comparisons were significant. CONCLUSIONS:Medication adherence was consistently high in GRADE. Observed treatment group differences were small and of unclear clinical significance. Overall, lower adherence robustly predicted worsening glycemic control, highlighting the importance of ongoing assessment.

We present machine learning (ML) approaches that enable longitudinal monitoring of patient-reported symptoms for people with multiple sclerosis (pwMS) by harnessing passively collected data from sensors in smartphones and fitness trackers. For each patient, we divided collected data into discrete periods. From each period, we extract patient-level behavioral features from the current period (action features) and the previous period (context features). Next, we apply Support Vector Machine with Radial Bias Function Kernel and AdaBoost to predict the presence of depressive symptoms (every 2-weeks) and high global MS symptom burden, severe fatigue, and poor sleep quality (every 4-weeks). We collected ~12,500 days of passive sensor and behavioral health data from 104 pwMS participants who completed 12-weeks and a subset of 44 pwMS who completed 24-weeks of data collection. Among the best-performing models with the least sensor data requirement, ML algorithm predicts depressive symptoms with an accuracy of 80.6% (35.5% improvement over baseline; F1-score: 0.76), high global MS symptom burden with an accuracy of 77.3% (51.3% improvement over baseline; F1-score: 0.77), severe fatigue with an accuracy of 73.8% (45.0% improvement over baseline; F1-score: 0.74), and poor sleep quality with an accuracy of 72.0% (28.1% improvement over baseline; F1-score: 0.70). Sensor data were largely sufficient for predicting symptom severity, while the prediction of depressive symptoms benefited from minimal active patient input (i.e., response to two brief questions on the day before prediction). Our digital phenotyping approach using passive sensors on smartphones and fitness trackers may help patients with real-world, continuous, self-monitoring of common symptoms in their own environment and assist clinicians with better triage of patient needs for timely interventions in MS.

Introduction and Objective: Self-efficacy and diabetes distress are inextricably linked to coping with diabetes. The role of diabetes distress in the relationship between diabetes self-efficacy and glycemic control in T2DM over time is understudied. The objective of this study was to evaluate the longitudinal associations of self-efficacy with overall diabetes self-management, medication adherence, diabetes distress, and glycemic control. Methods: Within the context of a randomized controlled trial comparing telephonic self-management support to enhanced standard of care among 812 predominantly socioeconomically disadvantaged ethnic minority adults with suboptimal control of T2DM (HbA1c >7.5%), we investigated between-group differences in self-efficacy and examined self-management, medication adherence, and diabetes distress, self-reported on validated scales at 6-months, as potential mediators between baseline diabetes self-efficacy and HbA1c at 12-months using multiple-linear regression. Results: Participants (M[SD] age = 59.2 [10.8], M[SD] HbA1c = 9.3 [1.8], 57% females, and 86% Hispanic/Latino) were mostly less educated (75% less than grade 12 or GED ). An increase of 4.19 points for self-efficacy relative to the baseline in the intervention group was significant (95% CI = -6.08, -2.30; p <.001 ) at 12-month follow-up. There was a significant indirect effect of baseline self-efficacy on lowered 12-month HbA1c through increased medication adherence at 6-month follow-up (ab = -0.005, 95% CI = -0.007, -0.003; p <.001). Results did not support overall self-management or diabetes distress as mediators of self-efficacy. Conclusion: These associations are consistent with Bandura’s self-efficacy theory and suggest that diabetes self-efficacy is responsive to intervention and predictive of glycemic outcomes among disadvantaged adults with suboptimal control of T2DM. R. Fang: None. C. Schechter: None. E.A. Walker: None. J.S. Gonzalez: None. This study was supported by grant R 18 DK098742 from the National Institutes of Health. This study was also partially supported by the Einstein Mount Sinai Diabetes Research Center (P30 DK020541) and the New York Regional Center for Diabetes Translation Research (P30 DK111022). Dr. Gonzalez is supported by grants R01 DK104845, R01 DK121298, R01 DK121896 and R18 DK098742 from the National Institutes of Health.

Background Black and Latino men are at increased risk for poor diabetes health outcomes but are underrepresented in lifestyle interventions for weight loss and diabetes prevention. Although relatively few men participate in the National Diabetes Prevention Program (NDPP), it remains the most widely available evidence-based approach to type 2 diabetes prevention in the United States. Thus, an NDPP tailored to Black and Latino men has the potential to address prior limitations of NDPP implementation and reduce gender, racial, and ethnic diabetes disparities. It also provides an opportunity to define a population for targeted outreach and evaluate the reach of our recruitment methods and interventions. Objective We tailored the US Centers for Disease Control and Prevention Prevent T2 curriculum for the NDPP for Black and Latino men, called Power-Up, and will evaluate its effects in comparison to standard mixed-gender NDPP groups via virtual delivery. The primary aim of the project is to assess the effect of Power-Up versus NDPP on weight loss among men with prediabetes. The secondary aim is to compare the engagement and retention of men with prediabetes in Power-Up versus NDPP. We will also examine the reach of our recruitment methods and engagement in our screening, consenting, and assessment procedures prior to the point of randomization. We hypothesized that men randomized to Power-Up would achieve greater percent weight loss from baseline at 16 weeks (end of Core sessions) and 1 year (end of Maintenance sessions) than men randomized to standard, mixed-gender NDPP. Power-Up is also expected to have better engagement and retention. Methods Using the electronic health record (EHR) systems of a large academic medical center and a network of small to medium independent primary care practices throughout New York City, we identified Black and Latino men who met eligibility criteria for NDPP and enrolled them in a randomized controlled trial in which they were assigned 1:1 to receive Power-Up or the standard, mixed-gender NDPP over 1 year via online videoconferencing. Coaches delivering these interventions were trained according to the standards for the NDPP. Power-Up will be delivered by men coaches. Weight will be collected with home-based electronic scales for primary outcome analyses. Engagement will be assessed by session attendance logs. Results We identified 11,052 men for outreach based on EHR data, successfully screened 26% of them, consented and enrolled 22% of these, and randomly assigned 48% of consented participants. Primary and secondary outcome analyses will be assessed among randomized men. Conclusions This study highlights the effort required to reach and engage Black and Latino men for virtually delivered diabetes prevention programs. Forthcoming trial results for weight loss and engagement will further inform efforts to address disparities in diabetes prevention through tailored programming for Black and Latino men. Trial Registration ClinicalTrials.gov NCT04104243; https://clinicaltrials.gov/study/NCT04104243 International Registered Report Identifier (IRRID) DERR1-10.2196/64405

Introduction and Objective: We examined medication adherence as a predictor of glycemic outcomes and evaluated treatment group differences in the GRADE study. Methods: GRADE participants (T2DM <10 years, HbA1c = 6.8-8.5%, on metformin monotherapy) were randomly assigned to add insulin glargine, glimepiride, liraglutide, or sitagliptin. The Emotional Distress Substudy followed 1,739 participants who self-reported adherence to the assigned medication regimen biannually up to 3 years on a 3-item validated scale (scored 0-100). We examined: a) levels of adherence and differences by treatment group over time; b) adherence as a predictor of primary (HbA1c ≥7.0) and secondary (HbA1c >7.5) glycemic outcomes; and c) treatment group differences in the association between adherence and glycemic outcomes. Results: Across treatment groups, adherence decreased slightly over 3 years (M±SD = -2.0 ± 14.7, p < 0.0001) but was high overall (M±SD = 88.7 ± 10.01). No group differences were observed at 6, 12, or 24 months. However, at 3 years adherence was 5.1% higher for the glimepiride and 3% higher for sitagliptin groups than for liraglutide (both p < 0.05); no other group differences were significant. Across groups, a 10-point decrease in adherence score was associated with 15% and 19% increased risk of reaching primary and secondary glycemic outcomes, respectively (both p < 0.0001). Adherence predicted the primary glycemic outcome conisistently across groups but was somewhat more predictive of reaching the secondary glycemic outcome for those assigned to glargine or liraglutide, as compared to glimepiride or sitagliptin (each p < 0.05). No other group comparisons were significant. Conclusion: Medication adherence was consistently high in GRADE and observed treatment group differences were small, supporting previously reported trial outcomes. Nevertheless, lower adherence robustly predicted worsening glycemic control, highlighting the importance of ongoing assessment. J.S. Gonzalez: None. H. Wen: None. M.R. Gramzinski: None. N.M. Butera: None. D. Uschner: None. D.J. Wexler: Other Relationship; Novo Nordisk. H. Petrovitch: None. B. Fattaleh: None. E.A. Walker: None. C.J. Hoogendoorn: None. C.A. Presley: None. G. Crespo-Ramos: None. V. Lagari: None. H. Krause-Steinrauf: None. A.L. Cherrington: None. National Institute of Diabetes and Digestive and Kidney Diseases (U01DK098246; U34DK088043; R01DK104845); The National Heart, Lung, and Blood Institute; The Centers for Disease Control and Prevention

BackgroundPrevious studies have shown that thalamic and hippocampal neurodegeneration is associated with clinical decline in Multiple Sclerosis (MS). However, contributions of the specific thalamic nuclei and hippocampal subfields require further examination.ObjectiveUsing 7 Tesla (7T) magnetic resonance imaging (MRI), we investigated the cross-sectional associations between functionally grouped thalamic nuclei and hippocampal subfields volumes and T1 relaxation times (T1-RT) and subsequent clinical outcomes in MS.MethodsHigh-resolution T1-weighted and T2-weighted images were acquired at 7T (n=31), preprocessed, and segmented using the Thalamus Optimized Multi Atlas Segmentation (THOMAS, for thalamic nuclei) and the Automatic Segmentation of Hippocampal Subfields (ASHS, for hippocampal subfields) packages. We calculated Pearson correlations between hippocampal subfields and thalamic nuclei volumes and T1-RT and subsequent multi-modal rater-determined and patient-reported clinical outcomes (∼2.5 years after imaging acquisition), correcting for confounders and multiple tests.ResultsSmaller volume bilaterally in the anterior thalamus region correlated with worse performance in gait function, as measured by the Patient Determined Disease Steps (PDDS). Additionally, larger volume in most functional groups of thalamic nuclei correlated with better visual information processing and cognitive function, as measured by the Symbol Digit Modalities Test (SDMT). In bilateral medial and left posterior thalamic regions, there was an inverse association between volumes and T1-RT, potentially indicating higher tissue degeneration in these regions. We also observed marginal associations between the right hippocampal subfields (both volumes and T1-RT) and subsequent clinical outcomes, though they did not survive correction for multiple testing.ConclusionUltrahigh field MRI identified markers of structural damage in the thalamic nuclei associated with subsequently worse clinical outcomes in individuals with MS. Longitudinal studies will enable better understanding of the role of microstructural integrity in these brain regions in influencing MS outcomes.

PURPOSE:The purpose of the 12-month randomized controlled trial was to evaluate the effectiveness of a Telephonic Self-Management Support (T-SMS) program among adults with type 2 diabetes (T2D). METHODS:Eight hundred twelve adults with T2D participated in NYC Care Calls (mean age = 59.2, SD = 10.8; female = 57%; mean A1C = 9.3, SD = 1.8; Latino = 86%) and were randomly assigned to T-SMS or enhanced usual care (EUC). A1C (primary outcome), blood pressure, and body mass index (secondary outcomes) were extracted from electronic medical records. Secondary patient-reported outcomes, including depressive symptoms, diabetes distress, medication adherence, and self-management activities, were assessed by telephone in English or Spanish. For T-SMS, the number of assigned phone calls was based on baseline A1C, depressive symptoms, and/or diabetes distress. Analyses were conducted under the intention-to-treat principle. RESULTS:A1C decreased over 12 months in both T-SMS (0.72% percentage points; 95% CI, 0.53-0.91) and EUC (0.66% percentage points; 95% CI, 0.46-0.85; Ps < .001). Diabetes distress and self-management also improved over time in both arms (Ps < .05). Compared to EUC, participants in the T-SMS arm did not differ in outcomes. CONCLUSIONS:The T-SMS and EUC groups were found not to have an appreciable outcome difference. It is unclear whether improvements in A1C across both conditions represent a secular trend or indicate that print-based educational intervention may have a positive impact on self-management and well-being.

We compared the characteristics, risk factors, and outcomes of post-COVID conditions (PCC) in people with multiple sclerosis (pwMS) with healthy controls and identified relevant risk factors and associated neurological outcomes in pwMS.

Importance: Management of long-term consequences after acute COVID-19 in people with multiple sclerosis and related disorders (pwMSRD) is challenging due to overlapping clinical presentations. There have been limited investigations of post-COVID sequelae in pwMSRD. Objective: We assessed whether pwMSRD were more susceptible to post-COVID sequelae when compared to controls. Design: This cross-sectional study leveraged a multi-center cohort of pwMSRD and controls. Setting: A one-time web-based survey was conducted between August and December 2022. Participants: Out of the 2,156 participants who consented, the analysis included 1,972 after excluding 184 due to missing data. Main Exposure: Diagnosis of MSRD. Main Outcomes and Measures: We surveyed 71 symptoms that emerged at least 1 month after the initial acute COVID-19 and were either new onset or worsening from the pre-COVID baseline. We assessed whether each participant experienced (1) ≥1 new symptom, (2) ≥1 worsening symptom from baseline. Results: The study included 969 pwMSRD (799 [82.5%] women, mean age 51.8 [SD 12.1] years) and 1,003 controls (796 [79.4%] women, mean age 45.2 [SD 10.3] years). 613 pwMSRD (63.5%) and 614 controls (61.2%) experienced acute COVID-19. Compared to controls, pwMSRD had higher odds of developing a new symptom (OR=1.55; 95%CI=1.22-1.98; p<.01) and experiencing a worsening symptom from baseline (OR=3.39; 95%CI=2.64-4.36; p<.01). PwMSRD were more likely to develop new symptoms involving the pulmonary as well as head, eyes, ears, nose, and throat systems, and have worsening systemic, musculoskeletal, and neuropsychiatric symptoms from baseline. Acute COVID-19 severity mediated >20% of the association between MSRD diagnosis and post-COVID sequelae. In the subgroup of pwMS, having post-COVID sequelae was associated with worse functional disability. Conclusions and Relevance: Compared to controls, pwMSRD experienced an increased risk of post-COVID sequelae involving multiple organ systems. Post-COVID sequelae was associated with greater disability in pwMS. The findings highlighted the importance of recognizing and managing long-term symptoms following acute COVID-19 in this vulnerable population. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The study is supported in part by NINDS R01NS098023 and NINDS R01NS124882 ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of University of Pittsburgh gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes De-identified data are available upon request to the corresponding author and with permission from the participating institutions.

Aims People with type 2 diabetes (T2DM) have an increased risk of cardiovascular disease (CVD). We examined depressive symptoms (DS) and diabetes distress (DD) in relation to the estimated 10-year risk of CVD in adults with T2DM enrolled in the GRADE Emotional Distress Substudy. Methods Linear regression models examined the associations of baseline DS and DD with estimated 10-year risk of CVD using the Atherosclerotic Cardiovascular Disease (ASCVD) risk score, adjusting for age, sex, race/ethnicity, education, income, diabetes duration, diabetes-related complications, and HbA1c. Results A total of 1,605 GRADE participants were included: 54% Non-Latino (NL) White, 19% Latino, 18% NL-Black, 66% male, mean age 57.5 (SD=10.25) years, diabetes duration 4.2 (SD=2.8) years, and HbA1c 7.5% (SD=0.5%). After incorporating covariates, only DS, especially cognitive-affective symptoms, were associated with ASCVD risk (estimate=0.15 [95% CI: 0.04, 0.26], p=0.006). Higher DS remained significantly associated with higher ASCVD risk when adding DD to covariates (estimate=0.19 [95% CI: 0.07, 0.30], p=0.002). DD was not associated with ASCVD risk when accounting for covariates. Conclusions Depressive symptoms, particularly cognitive-affective symptoms, are associated with increased 10-year predicted ASCVD risk among adults with early T2DM. Diabetes distress is not significantly associated with the predicted ASCVD risk when accounting for covariates.

Few studies examined blood biomarkers informative of patient-reported outcome (PRO) of disability in people with multiple sclerosis (MS). We examined the associations between serum multi-protein biomarker profiles and patient-reported MS disability. In this cross-sectional study (2017-2020), adults with diagnosis of MS (or precursors) from two independent clinic-based cohorts were divided into a training and test set. For predictors, we examined seven clinical factors (age at sample collection, sex, race/ethnicity, disease subtype, disease duration, disease-modifying therapy [DMT], and time interval between sample collection and closest PRO assessment) and 19 serum protein biomarkers potentially associated with MS disease activity endpoints identified from prior studies. We trained machine learning (ML) models (Least Absolute Shrinkage and Selection Operator regression [LASSO], Random Forest, Extreme Gradient Boosting, Support Vector Machines, stacking ensemble learning, and stacking classification) for predicting Patient Determined Disease Steps (PDDS) score as the primary endpoint and reported model performance using the held-out test set. The study included 431 participants (mean age 49 years, 81% women, 94% non-Hispanic White). For binary PDDS score, combined feature input of routine clinical factors and the 19 proteins consistently outperformed base models (comprising clinical features alone or clinical features plus one single protein at a time) in predicting severe (PDDS ≥ 4) versus mild/moderate (PDDS < 4) disability across multiple machine learning approaches, with LASSO achieving the best area under the curve (AUCPDDS = 0.91) and other metrics. For ordinal PDDS score, LASSO model comprising combined clinical factors and 19 proteins as feature input (R2PDDS = 0.31) again outperformed base models. The two best-performing LASSO models (i.e., binary and ordinal PDDS score) shared six clinical features (age, sex, race/ethnicity, disease subtype, disease duration, DMT efficacy) and nine proteins (cluster of differentiation 6, CUB-domain-containing protein 1, contactin-2, interleukin-12 subunit-beta, neurofilament light chain [NfL], protogenin, serpin family A member 9, tumor necrosis factor superfamily member 13B, versican). By comparison, LASSO models with clinical features plus one single protein at a time as feature input did not select either NfL or glial fibrillary acidic protein (GFAP) as a final feature. Forcing either NfL or GFAP as a single protein feature into models did not improve performance beyond clinical features alone. Stacking classification model using five functional pathways to represent multiple proteins as meta-features implicated those involved in neuroaxonal integrity as significant contributors to predictive performance. Thus, serum multi-protein biomarker profiles improve the prediction of real-world MS disability status beyond clinical profile alone or clinical profile plus single protein biomarker, reaching clinically actionable performance.

Objective Biomarkers could inform disease worsening and severity in people with MS (pwMS). Few studies have examined blood biomarkers informative of patient-reported outcome (PRO) of disability in pwMS. In this study we examine the associations between serum protein biomarker profiles and patient-reported disability in pwMS. Methods This cross-sectional study included adults with a neurologist-confirmed diagnosis of MS from the University of Pittsburgh Medical Center (Pittsburgh, PA) and the Rocky Mountain MS Clinic (Salt Lake City, Utah) between 2017 and 2020. For exposure, we included 19 serum protein biomarkers potentially associated with MS inflammatory disease activity and 7 key clinical factors (age at sample collection, sex, race/ethnicity, disease subtype, disease duration, disease-modifying treatment, and time interval between sample collection and closest PRO assessment). Using 6 machine learning approaches (Least Absolute Shrinkage and Selection Operator [LASSO] regression, Random Forest [RF], XGBoost, Support-Vector Machines [SVM], stacking ensemble learning, and stacking classification algorithm), we examined model performance in predicting Patient Determined Disease Steps (PDDS) as the primary outcome. We assessed model prediction of Patient-Reported Outcomes Measurement Information System (PROMIS) physical function in a subgroup. We reported model performance using the held-out testing set. Results We included 431 unique participants (mean age 49 years, 81% women, 94% non-Hispanic White). Using binary outcomes, models comprising both routine clinical factors and the 19 proteins as features consistently outperformed base models (containing clinical features alone or clinical features plus single protein) in predicting severe (PDDS≥4, PROMIS<35) versus mild/moderate (PDDS<4, PROMIS≥35) disability for all machine learning approaches, with LASSO achieving the best area under the curve (AUCPDDS=0.91, AUCPROMIS=0.90). Using ordinal/continuous outcomes, LASSO models with combined clinical factors and 19 proteins as features (R2PDDS=0.31, R2PROMIS =0.35) again outperformed base models. The four LASSO models (PDDS, PROMIS; both binary and ordinal/continuous) with combined clinical and protein features shared 2 clinical features (disease subtype, disease duration) and 4 protein biomarkers (CDCP1, IL-12B, NEFL, PRTG). Conclusions Serum protein biomarker profiles improve the prediction of real-world MS disability status beyond clinical profile alone or clinical profile plus individual protein biomarker, reaching clinically actionable performance. ### Competing Interest Statement Zongqi Xia serves on the scientific advisory board of Roche/Genentech and has a research agreement with Octave Biosciences. Wen Zhu serves on the scientific advisory board of Roche/Genentech. John F. Foley has received research support from Biogen, Novartis, Adamas, Octave and Genentech. He received speakers honoraria from Biogen. He has participated in advisory boards with TG Therapeutics, Sandoz, Biogen, and Octave. He has equity interest in Octave. He is the founder of InterPro Bioscience. Ferhan Qureshi is an employee of Octave Bioscience Inc. Fujun Zhang was an employee of Octave Bioscience Inc, when the study analysis was performed. ### Funding Statement Sample collection and biomarker assay are funded in part by Octave Bioscience, INC. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The institutional review boards of the University of Pittsburgh (STUDY19080007) and Rocky Mountain Multiple Sclerosis Clinic (WCG20201562) gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Code for analysis and figures is available at < https://github.com/xialab2016/MSbiomarker.git>. De-identified data are available upon request to the corresponding author and with permission from the participating institution.

PURPOSE:The purpose of this study was to explore how treatment adherence and lifestyle changes required for glycemic control in type 2 diabetes (T2D) are related to quality of life (QoL) among predominantly ethnic minority and socioeconomically disadvantaged adults engaged in making changes to improve T2D self-management.METHODS:Adults with T2D in New York City were recruited for the parent study based on recent A1C (≥7.5%) and randomly assigned to 1 of 2 arms, receiving educational materials and additional self-management support calls, respectively. Substudy participants were recruited from both arms after study completion. Participants (N = 50; 62% Spanish speaking) were interviewed by phone using a semistructured guide and were asked to define QoL and share ways that T2D, treatment, self-management, and study participation influenced their QoL. Interviews were analyzed using thematic analysis.RESULTS:QoL was described as a multidimensional health-related construct with detracting and enhancing factors related to T2D. Detracting factors included financial strain, symptom progression and burden, perceived necessity to change cultural and lifestyle traditions, and dietary and medical limitations. Enhancing factors included social support, diabetes education, health behavior change, sociocultural connection.CONCLUSION:QoL for diverse and socioeconomically disadvantaged adults with T2D is multifaceted and includes aspects of health, independence, social support, culture, and lifestyle, which may not be captured by existing QoL measures. Findings may inform the development of a novel QoL measure for T2D.