Propensity score analysis for health care disparities: a deweighting approach.

BACKGROUND: Propensity score weighting is a useful tool to make causal or unconfounded comparisons between groups. According to the definition by the Institute of Medicine (IOM), estimates of health care disparities should be adjusted for health-status factors but not for socioeconomic status (SES) variables. There have been attempts to use propensity score weighting to generate estimates that are concordant with IOM's definition. However, the existing propensity score methods do not preserve SES distributions in minority and majority groups unless SES variables are independent of health status variables. METHODS: The present study introduces a deweighting method that uses two types of propensity scores. One is a function of all covariates of health status and SES variables and is used to weight study subjects to adjust for them. The other is a function of only the SES variables and is used to deweight the subjects to preserve the original SES distributions. RESULTS: The procedure of deweighting is illustrated using a dataset from a right heart catheterization (RHC) study, where it was used to examine whether there was a disparity between black and white patients in receiving RHC. The empirical example provided promising evidence that the deweighting method successfully preserved the marginal SES distributions for both racial groups but balanced the conditional distributions of health status given SES. CONCLUSIONS: Deweighting is a promising tool for implementing the IOM-definition of health care disparities. The method is expected to be broadly applied to quantitative research on health care disparities.

Project ECHO Brain Health: Assessing the Impact of a Pilot Program to Promote Self-Efficacy Among Community Health Workers.

This mixed-methods study sought to examine the impact of the Project ECHO Brain Health program on participating community health workers' (CHWs') self-efficacy to address dementia, promote brain health, and advocate for research among Latinx South Texas communities. Using an explanatory sequential design, quantitative data collected from pre- and post-program surveys were analyzed to inform the collection of qualitative data, followed by an interpretation of all findings to better understand the impact of the program on self-efficacy. Pre- and post-surveys were collected from 25 CHWs, 13 of whom later participated in individual interviews. There was a statistically significant increase in mean self-efficacy scores between the pre- and post-surveys among participants. Three categories reflecting the experiences of participants were identified from the qualitative data: addressing training needs; impact on CHWs and their practice; and community of learning. The findings suggest that Project ECHO Brain Health program is a timely intervention that may facilitate increased self-efficacy among CHWs as they navigate the impacts of dementia in their communities.

Subclassification estimation of the weighted average treatment effect.

Weighting and subclassification are popular approaches using propensity scores (PSs) for estimation of causal effects. Weighting is appealing in that it gives consistent estimators for various causal estimands if appropriate weights are well defined and the PS model is correctly specified. Subclassification is known to be more robust to model misspecification than weighting, but its application to diverse causal estimands is limited. In this article, we propose generalized stratum weights to implement subclassification estimators for various causal estimands. These weights include stratum weights for the average treatment effect (ATE) of the overall population and those for the ATE of the treated as special cases. For this, we incorporate strata into the expression of the weighted average treatment effect (WATE). Particularly, we identify stratum weights for the ATE for the overlap population (ATO), for which the weighting estimator is known to be most efficient among the class of WATE estimators. We show that the identified stratum weights for ATO are equivalent to the optimal stratum weights, which are the inverse variances of the stratum-specific estimators. Simulation studies demonstrate that the proposed subclassification estimator for ATO is more robust to model misspecification than the weighting estimator for ATO. We also propose augmented subclassification estimators, which are shown to be less biased than the subclassification estimators when only the outcome model is correctly specified. The practical utility of the proposed methods is illustrated in a study of right heart catheterization.

Assessing the effect of patient navigator assistance for psychosocial support services on health-related quality of life in a randomized clinical trial in Latino breast, prostate, and colorectal cancer survivors.

BACKGROUND: After a diagnosis of prostate, breast, or colorectal cancer, Latinos experience higher mortality rates and lower health-related quality of life (HRQOL) in comparison with other ethnic/racial groups. Patient navigation (PN) and lay community health workers or promotores are effective in increasing cancer screening and early-stage diagnosis among Latinos. However, little is known about the effect of PN on HRQOL among Latino cancer survivors. METHODS: Latinos previously diagnosed with breast, prostate, or colorectal cancer (n = 288) were randomized to 1 of 2 conditions: 1) the Patient Navigator LIVESTRONG Cancer Navigation Services (PN-LCNS) survivor care program or 2) PN only. HRQOL was measured with the Functional Assessment of Cancer Therapy-General, and cancer-specific HRQOL was measured with the Functional Assessment of Cancer Therapy-Breast, the Functional Assessment of Cancer Therapy-Prostate, and the Functional Assessment of Cancer Therapy-Colorectal for breast, prostate, and colorectal cancer survivors, respectively, at the baseline and at 3 follow-up time points. Generalized estimating equation analyses were conducted to estimate the effect of condition on HRQOL with adjustments for covariates and baseline HRQOL. RESULTS: PN-LCNS demonstrated a significant improvement in HRQOL in comparison with PN only for colorectal cancer survivors but not for breast and prostate cancer survivors. CONCLUSIONS: Enhanced PN improves HRQOL among Latino colorectal cancer survivors. Future research should identify the best strategies for engaging Latino survivors in PN programs. PN programs should also be adapted to address HRQOL concerns among Latina breast cancer survivors.

Machine learning outcome regression improves doubly robust estimation of average causal effects.

BACKGROUND: Doubly robust estimation produces an unbiased estimator for the average treatment effect unless both propensity score (PS) and outcome models are incorrectly specified. Studies have shown that the doubly robust estimator is subject to more bias than the standard weighting estimator when both PS and outcome models are incorrectly specified. METHOD: We evaluated whether various machine learning methods can be used for estimating conditional means of the potential outcomes to enhance the robustness of the doubly robust estimator to various degrees of model misspecification in terms of reducing bias and standard error. We considered four types of methods to predict the outcomes: least squares, tree-based methods, generalized additive models and shrinkage methods. We also considered an ensemble method called the Super Learner (SL), which is a linear combination of multiple learners. We conducted simulations considering different scenarios by the complexity of PS and outcome-generating models and some ranges of treatment prevalence. RESULTS: The shrinkage methods performed well with robust doubly robust estimates in term of bias and mean squared error across the scenarios when the models became rich by including all 2-way interactions of the covariates. The SL performed similarly to the best method in each scenario. CONCLUSIONS: Our findings indicate that machine learning methods such as the SL or the shrinkage methods using interaction models should be used for more accurate doubly robust estimators.

Serum microRNA profiles among dioxin exposed veterans with monoclonal gammopathy of undetermined significance.

Previously an increased risk for monoclonal gammopathy of undetermined significance (MGUS), a precursor of multiple myeloma (MM), was reported among Vietnam veterans exposed to Agent Orange and its contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Dysregulated expression of certain microRNAs (miRNAs) was demonstrated in MGUS and MM. Given the important role of miRNAs in cellular homeostasis, the aim of this study was to determine if there was an association between serum levels of selected miRNAs and TCDD in 47 MGUS cases identified in our previous investigation using serum specimens and exposure data archived by the Air Force Health Study (AFHS). A total of 13 miRNA levels (let-7a, let-7i, miR-16, miR-20a, miR-21, miR-34a, miR-106b, miR-146a, miR-181a, miR-192, miR-205, miR-335, and miR-361) was measured in serum stored during the 2002 AFHS follow-up and the relationship to lipid-adjusted serum TCDD levels in 1987 was determined. miR-34a showed the strongest relationship with TCDD; after age-adjustment, this positive association was more pronounced. In contrast, the other 12 miRNAs displayed absolute values of age adjusted coefficient estimates below 1.16 and non-significant p-values. The observed strong positive association between high body burdens of TCDD and miR-34a, a tumor suppressor regulated by p53, in this MGUS population warrants clarification of the TCDD-miR-34a relationship and its role in the pathogenesis of MGUS and risk for MM.

A randomised controlled trial of the Learning Skills Together (LST) intervention to improve dementia family caregivers' self-efficacy with complex care.

BACKGROUND: Nearly two-thirds of family caregivers of persons living with Alzheimer's disease or related dementias (AD/ADRD) provide complex care, including medical care. Family caregivers typically receive little to no training on how to provide this care. Furthermore, family caregivers simultaneously grapple with the presence of behavioral and psychological symptoms of dementia (BPSD), diminished communication abilities, and comorbidities such as diabetes. We developed Learning Skills Together (LST), a 6-week digitally delivered psychoeducational program, to facilitate family caregiver abilities to administer complex care tasks. The goal of the present study is to test the efficacy of LST and to reduce adverse outcomes associated with caregiving, such as depressive symptomology and negative appraisal of BPSD. METHODS: To test the efficacy of LST, we will conduct a two-arm single-site randomized controlled trial (RCT) with N = 200 family caregivers of persons living with AD/ADRD. Eligible family caregivers will be randomly assigned to participate in either the LST intervention or a structurally equivalent control condition focused on healthy living. All family caregivers will complete four surveys, including a baseline survey administered prior to randomization, a post-intervention survey, and a 3- and 6-month follow-up survey to assess change in study outcomes. Between-group comparisons of each outcome will be evaluated using generalized estimating equation models. Mediation analyses will assess family caregiver self-efficacy as the intervention's mechanism of change in depressive symptomology and BPSD. We will also examine caregiver race, ethnicity, and gender as effect modifiers of the intervention. DISCUSSION: LST findings will inform the field of AD/ADRD and caregiving regarding optimally supporting family caregivers in managing complex care tasks. If efficacious, the LST intervention will support family caregivers in preserving their own mental health while providing complex care. TRIAL REGISTRATION: Clinical Trials.gov NCT05846984 . This study was registered on May 6, 2023.

A randomised controlled trial of the Learning Skills Together (LST) intervention to improve dementia family caregivers' self-efficacy with complex care.

Background: Nearly two-thirds of family caregivers of persons living with Alzheimer's disease or related dementias (AD/ADRD) provide complex care, including medical care. Family caregivers typically receive little to no training on how to provide this care. Furthermore, family caregivers simultaneously grapple with the presence of behavioral and psychological symptoms of dementia (BPSD), diminished communication abilities, and comorbidities such as diabetes. We developed Learning Skills Together (LST), a six-week digitally delivered psychoeducational program, to facilitate family caregiver abilities to administer complex care tasks. The goal of the present study is to test the efficacy of LST and to reduce adverse outcomes associated with caregiving, such as depressive symptomology and negative appraisal of BPSD. Methods: To test the efficacy of LST, we will conduct a two-arm single-site randomised controlled trial (RCT) with N = 200 family caregivers of persons living with AD/ADRD. Eligible family caregivers will be randomly assigned to participate in either the LST intervention or a structurally equivalent control condition focused on healthy living. All family caregivers will complete four surveys, including a baseline survey administered prior to randomisation, a post-intervention survey, and a three- and six-month follow-up survey to assess change in study outcomes. Between-group comparisons of each outcome will be evaluated using generalized estimating equation models. Mediation analyses will assess family caregiver self-efficacy as the intervention's mechanism of change in depressive symptomology and BPSD. We will also examine caregiver race, ethnicity, and gender as effect modifiers of the intervention. Discussion: LST findings will inform the field of AD/ADRD and caregiving regarding optimally supporting family caregivers in managing complex care tasks. If efficacious, the LST intervention will support family caregivers in preserving their own mental health while providing complex care.

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration.

Introduction: The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods: Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results: One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion: Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

Erratum: Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration - CORRIGENDUM.

[This corrects the article DOI: 10.1017/cts.2024.2.].

Practicable confidence intervals for current status data.

Although confidence intervals (CIs) for binary isotonic regression and current status survival data have been well studied theoretically, their practical application has been limited, in part because of poor performance in small samples and in part because of computational difficulties. Ghosh, Banerjee, and Biswas (2008, Biometrics 64, 1009-1017) described three approaches to constructing CIs: (i) the Wald-based method; (ii) the subsampling-based method; and (iii) the likelihood-ratio test (LRT)-based method. In simulation studies, they found that the subsampling-based method and LRT-based method tend to have better coverage probabilities than a simple Wald-based method that may perform poorly in realistic sample sizes. However, software implementing these approaches is currently unavailable. In this article, we show that by using transformations, simple Wald-based CIs can be improved with small and moderate sample sizes to have competitive performance with LRT-based method. Our simulations further show that a simple nonparametric bootstrap gives approximately correct CIs for the data generating mechanisms that we consider. We provide an R package that can be used to compute the Wald-type and the bootstrap CIs and demonstrate its practical utility with two real data analyses.

Profiling compliers and noncompliers for instrumental variable analysis with covariates: A weighting approach.

Instrumental variable (IV) estimation is an essential tool to estimate the causal effect of a treatment in randomized experiments when noncompliance exists. In such studies, standard statistical approaches can be biased because compliers and noncompliers can differ in unmeasured ways that affect both the compliance behavior and outcome. Based on a few assumptions such as monotonicity, the IV estimand represents the causal effect of compliers. Profiling compliers and noncompliers has important implications because the IV estimand applies only to compliers. A method for estimating the covariate means for compliers and noncompliers has recently been proposed in political sciences literature. However, this approach requires an assumption that the instrument is randomly assigned, which confines the application of this approach to randomized experiments. In this study, we present two weighting methods for profiling compliers and noncompliers when the instrument and compliance behavior are confounded by several covariates. The proposed approach can be used for both experimental and nonexperimental studies, and hence is more broadly applicable. For the development, an instrumental propensity score is adopted to account for confounded instruments. We demonstrate the utility of the proposed methods based on simulation and real data experiments.

Effects of Adjusting for Instrumental Variables on the Bias and Precision of Propensity Score Weighted Estimators: Analysis Under Complete, Near, and No Positivity Violations.

Purpose: To demonstrate that using an instrumental variable (IV) with monotonicity reduces the accuracy of propensity score (PS) weighted estimators for the average treatment effect (ATE). Methods: Monotonicity in the relationship between a binary IV and a binary treatment variable is an important assumption to identify the ATE for compliers who would only take treatment when encouraged by the IV. We perform theoretical and numerical investigations to study the impact of using the IV that satisfies monotonicity on the PS of treatment in terms of the positivity assumption, which requires that the PS be strictly between 0 and 1, and the accuracy of PS weighted estimators. Two versions of monotonicity that result in one-sided or two-sided noncompliance are considered. Results: The PS adjusting for the IV always violates the positivity assumption when noncompliance occurs in one direction (one-sided noncompliance) and is more extreme than without the IV under two-sided noncompliance. These results are valid if the probability of being encouraged to get treatment and the compliance score, the probability of being a complier, are strictly between 0 and 1. Conclusion: Using a binary IV with monotonicity as a covariate for the PS model makes the estimated PSs unnecessarily extreme, reducing the accuracy of the PS weighted estimators.

Heterogeneity of COVID-19 symptoms and associated factors: Longitudinal analysis of laboratory-confirmed COVID-19 cases in San Antonio.

Few studies have examined heterogeneous associations of risk factors with Coronavirus Disease-2019 (COVID-19) symptoms by type. The objectives of this study were to estimate the prevalence of and risk factors associated with COVID-19 symptoms and to investigate whether the associations differ by the type of symptoms. This study obtained longitudinal data over 6 months from laboratory-confirmed COVID-19 cases in a citywide sample in San Antonio. Sixteen symptoms of COVID-19 infection, measured at baseline and three follow-up times (1, 3, and 6 months), were analyzed using generalized estimating equations (GEE) to investigate potential risk factors while accounting for the repeated measurements. The risk factors included time in months, sociodemographic characteristics, and past or current medical and psychiatric conditions. To obtain interpretable results, we categorized these sixteen symptoms into five categories (cardiopulmonary, neuro-psychological, naso-oropharyngeal, musculoskeletal, and miscellaneous). We fitted GEE models with a logit link using each category as the outcome variable. Our study demonstrated that the associations were heterogeneous by the categories of symptoms. The time effects were the strongest for naso-oropharyngeal symptoms but the weakest for neuro-psychological symptoms. Female gender was associated with increased odds of most of the symptoms. Hispanic ethnicity was also associated with higher odds of neuro-psychological, musculoskeletal, and miscellaneous symptoms. Depression was the most robust psychiatric condition contributing to most of the symptoms. Different medical conditions seemed to contribute to different symptom expressions of COVID-19 infection.

Release of Oxytocin and Cortisol Is Associated With Neurobehavioral Patterns in Premature Infants.

OBJECTIVE: To examine relationships among salivary oxytocin and cortisol levels in parents and preterm infants and neurobehavioral functioning in preterm infants after skin-to-skin contact. DESIGN: A secondary analysis of a randomized crossover study. SETTING: NICU. PARTICIPANTS: Twenty-eight stable premature infants and their mothers and fathers. METHODS: Participating infants contributed 108 saliva samples that we collected 45 minutes after skin-to-skin contact and tested for oxytocin and cortisol. We randomized data collection by whether the infant was held first by the mother or by the father. We conducted linear regression to test if summary scores on the NICU Network Neurobehavioral Scale were associated with salivary oxytocin and cortisol levels. RESULTS: We found a significant negative relationship between infant oxytocin levels and the Stress scores (b = -0.07, p < .01) and the Excitability scores (b = -1.12, p = .04) among infants held skin-to-skin with their mothers. We found a significant positive relationship between infant oxytocin levels and the Self-Regulatory scores (b = 0.38, p = .05) among infants held skin-to-skin with their mothers. We found a significant positive relationship between infant cortisol level and the Stress scores (b = 0.05, p = .04), Excitability scores (b = 1.06, p = 0.05), and Asymmetrical Reflexes scores (b = 1.21, p = .03) among infants held skin-to-skin with their mothers. We only found a negative significant relationship between infant cortisol levels and the Stress scores (b = -0.03, p = .04) among infants held skin-to-skin with their fathers. CONCLUSION: We found that oxytocin is an important biomarker that may improve infant neurobehavioral functioning. The data showed a difference in oxytocin responses after skin-to-skin contact with mothers compared to fathers.

Collaborative Program to Evaluate Real World Data for Use in Clinical Studies and Regulatory Decision Making.

The 21st Century Cures Act allows the US Food and Drug Administration to consider real world data (RWD) for new indications or post approval study requirements. However, there is limited guidance as to the relative quality of different RWD types. The ACE-RWD program will compare the quality of EHR clinical data, EHR billing data, and linked healthcare claims data to traditional clinical trial data collection methods. ACE-RWD is being conducted alongside 5-10 ancillary studies, with five sponsors, across multiple therapeutic areas. Each ancillary study will be conducted after or in parallel with its parent clinical study at a minimum of two clinical sites. Although not required, it is anticipated that EHR clinical and EHR billing data will be obtained via EHR-to-eCRF mechanisms that are based on the Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR®) standard.

Metabolomic profiles associated with physical activity in White and African American adult men.

BACKGROUND: Physical activity (PA) is associated with various health benefits, especially in improving chronic health conditions. However, the metabolic changes in host metabolism in response to PA remain unclear, especially in racially/ethnically diverse populations. OBJECTIVE: This study is to assess the metabolic profiles associated with the frequency of PA in White and African American (AA) men. METHODS: Using the untargeted metabolomics data collected from 698 White and AA participants (mean age: 38.0±8.0, age range: 20-50) from the Louisiana Osteoporosis Study (LOS), we conducted linear regression models to examine metabolites that are associated with PA levels (assessed by self-reported regular exercise frequency levels: 0, 1-2, and ≥3 times per week) in White and AA men, respectively, as well as in the pooled sample. Covariates considered for statistical adjustments included race (only for the pooled sample), age, BMI, waist circumstance, smoking status, and alcohol drinking. RESULTS: Of the 1133 untargeted compounds, we identified 7 metabolites associated with PA levels in the pooled sample after covariate adjustment with a false discovery rate of 0.15. Specifically, compared to participants who did not exercise, those who exercised at a frequency ≥3 times/week showed higher abundances in uracil, orotate, 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) (GPE), threonate, and glycerate, but lower abundances in salicyluric glucuronide and adenine in the pooled sample. However, in Whites, salicyluric glucuronide and orotate were not significant. Adenine, GPE, and threonate were not significant in AAs. In addition, the seven metabolites were not significantly different between participants who exercised ≥3 times/week and 1-2 times/week, nor significantly different between participants with 1-2 times/week and 0/week in the pooled sample and respective White and AA groups. CONCLUSIONS: Metabolite responses to PA are dose sensitive and may differ between White and AA populations. The identified metabolites may help advance our knowledge of guiding precision PA interventions. Studies with rigorous study designs are warranted to elucidate the relationship between PA and metabolites.

Role of endogenous adenine in kidney failure and mortality with diabetes.

Diabetic kidney disease (DKD) can lead to end-stage kidney disease (ESKD) and mortality, however, few mechanistic biomarkers are available for high risk patients, especially those without macroalbuminuria. Urine from participants with diabetes from Chronic Renal Insufficiency Cohort (CRIC), Singapore Study of Macro-Angiopathy and Reactivity in Type 2 Diabetes (SMART2D), and the Pima Indian Study determined if urine adenine/creatinine ratio (UAdCR) could be a mechanistic biomarker for ESKD. ESKD and mortality were associated with the highest UAdCR tertile in CRIC (HR 1.57, 1.18, 2.10) and SMART2D (HR 1.77, 1.00, 3.12). ESKD was associated with the highest UAdCR tertile in patients without macroalbuminuria in CRIC (HR 2.36, 1.26, 4.39), SMART2D (HR 2.39, 1.08, 5.29), and Pima Indian study (HR 4.57, CI 1.37-13.34). Empagliflozin lowered UAdCR in non-macroalbuminuric participants. Spatial metabolomics localized adenine to kidney pathology and transcriptomics identified ribonucleoprotein biogenesis as a top pathway in proximal tubules of patients without macroalbuminuria, implicating mammalian target of rapamycin (mTOR). Adenine stimulated matrix in tubular cells via mTOR and stimulated mTOR in mouse kidneys. A specific inhibitor of adenine production was found to reduce kidney hypertrophy and kidney injury in diabetic mice. We propose that endogenous adenine may be a causative factor in DKD.

Endogenous adenine mediates kidney injury in diabetic models and predicts diabetic kidney disease in patients.

Diabetic kidney disease (DKD) can lead to end-stage kidney disease (ESKD) and mortality; however, few mechanistic biomarkers are available for high-risk patients, especially those without macroalbuminuria. Urine from participants with diabetes from the Chronic Renal Insufficiency Cohort (CRIC) study, the Singapore Study of Macro-angiopathy and Micro-vascular Reactivity in Type 2 Diabetes (SMART2D), and the American Indian Study determined whether urine adenine/creatinine ratio (UAdCR) could be a mechanistic biomarker for ESKD. ESKD and mortality were associated with the highest UAdCR tertile in the CRIC study and SMART2D. ESKD was associated with the highest UAdCR tertile in patients without macroalbuminuria in the CRIC study, SMART2D, and the American Indian study. Empagliflozin lowered UAdCR in nonmacroalbuminuric participants. Spatial metabolomics localized adenine to kidney pathology, and single-cell transcriptomics identified ribonucleoprotein biogenesis as a top pathway in proximal tubules of patients without macroalbuminuria, implicating mTOR. Adenine stimulated matrix in tubular cells via mTOR and stimulated mTOR in mouse kidneys. A specific inhibitor of adenine production was found to reduce kidney hypertrophy and kidney injury in diabetic mice. We propose that endogenous adenine may be a causative factor in DKD.