Loki Natarajan’s research while affiliated with University of California, San Diego and other places

Background: Incidence of cardiometabolic disease among U.S. Hispanics/Latinos is higher than in non-Hispanic Whites. Prolonged sitting duration is prevalent in older adults, and compounded with menopause, greatly increases cardiometabolic risk in postmenopausal women. Metabolomic analyses of interventions to reduce sitting are lacking and mechanistic understanding of health-promoting behavior change in postmenopausal Latinas is needed. Methods: To address this knowledge gap, an exploratory analysis investigated the plasma metabolome impact of a 12-week increased standing intervention among sedentary postmenopausal Latinas with overweight or obesity. From a parent-randomized controlled trial, a subset of Best Responders (n = 43) was selected using parameters of highest mean change in sitting bout duration and total sitting time; baseline variable-Matched Controls (n = 43) were selected using random forest modeling. Targeted LC-MS/MS analysis of archived baseline and 12-week plasma samples was conducted. Metabolite change was determined using a covariate-controlled general linear model and multivariate testing was performed. A false discovery rate correction was applied to all analyses. Results: Best Responders significantly changed time sitting (−110.0 ± 11.0 min; −21%), standing (104.6 ± 10.1 min; 40%), and sitting in bouts >30 min (−102.3 ± 13.9 min; −35%) compared to Matched Controls (7.1 ± 9.8 min, −7.8 ± 9.0 min, and −4.6 ± 12.7 min, respectively; all p < 0.001). Twelve-week metabolite change was significantly different between the two groups for 24 metabolites (FDR < 0.05). These were primarily related to amino acid metabolism, improved blood flow, and ATP production. Enzyme enrichment analysis predicted significant changes regulating glutamate, histidine, phenylalanine, and mitochondrial short-chain fatty acid catabolism. Pathway analysis showed significant intervention effects on glutamate metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis, potentially indicating reduced cardiometabolic disease risk. Conclusions: Replacing nearly two hours of daily sitting time with standing and reduced prolonged sitting bouts significantly improved metabolomic profiles associated with cardiometabolic risk among postmenopausal Latinas.

Purpose The association between cardiovascular health (CVH) with perceived quality of life (PQoL) and variations by sex and Hispanic ethnicity is not well understood. Methods This study included 583 participants (42% Hispanic, 56% female, mean age 59 years). Linear regression modeled the covariate-adjusted associations between CVH, using the combined 7 components of Life’s Simple 7 (LS7; ideal and intermediate, compared to poor), and PQoL (total and physical, social, and cognitive health domains). For individual LS7 components, we assessed effect modification by sex and Hispanic ethnicity. Results Compared to individuals with poor CVH, those with intermediate (β [95% CI] = 0.22 [0.09, 0.35]) and ideal (β [95% CI] = 0.22 [0.08, 0.36]) CVH had higher overall PQoL. This effect was dominated by the physical PQoL domain. Of LS7 components, ideal body mass index (BMI) (β [95% CI] = 0.17 [0.03, 0.31]) and physical activity (β [95% CI] = 0.26 [0.12, 0.40]) were associated with overall PQoL. Ideal diet (β [95% CI] = 0.32 [0.08, 0.56]) and fasting plasma glucose (β [95% CI] = 0.32 [0.06, 0.58]) were associated with the physical PQoL domain. A higher PQoL score was associated with intermediate BMI in women, and physical PQoL was associated with smoking for women. A BMI*Hispanic interaction resulted in larger associations between intermediate/ideal BMI and physical PQoL in non-Hispanics. Conclusion Ideal or intermediate CVH health factors and health behaviors were associated with higher PQoL. Sex and ethnicity differences suggest that perceived quality of life is associated with BMI for women and non-Hispanics.

Sensor devices, such as accelerometers, are widely used for measuring physical activity (PA). These devices provide outputs at fine granularity (e.g., 10–100 Hz or minute‐level), which while providing rich data on activity patterns, also pose computational challenges with multilevel densely sampled data, resulting in PA records that are measured continuously across multiple days and visits. On the other hand, a scalar health outcome (e.g., BMI) is usually observed only at the individual or visit level. This leads to a discrepancy in numbers of nested levels between the predictors (PA) and outcomes, raising analytic challenges. To address this issue, we proposed a multilevel longitudinal functional principal component analysis (mLFPCA) model to directly model multilevel functional PA inputs in a longitudinal study, and then implemented a longitudinal functional principal component regression (FPCR) to explore the association between PA and obesity‐related health outcomes. Additionally, we conducted a comprehensive simulation study to examine the impact of imbalanced multilevel data on both mLFPCA and FPCR performance and offer guidelines for selecting optimal methods.

Background Sedentary behavior has been identified as a significant risk factor for Metabolic Syndrome (MetS). However, it is unclear if the sedentary pattern measurement approach (posture vs. movement) impacts observed associations or if associations differ for Hispanic/Latino communities, who have higher risk of MetS. Methods Participants from the Community of Mine (CoM) study (N = 602) wore hip-based accelerometers for 14 days and completed MetS-associated biomarker assessment (triglycerides, blood pressure, fasting glucose, HDL cholesterol, waist circumference). Sedentary patterns were classified using both cutpoints (movement-based) and the Convolutional Neural Network Hip Accelerometer Posture (CHAP) algorithm (posture-based). We used logistic regression to estimate associations between MetS with sedentary patterns overall and stratified by Hispanic/Latino ethnicity. Results CHAP and cutpoint sedentary patterns were consistently associated with MetS. When controlling for total sedentary time and moderate to vigorous physical activity, only CHAP-measured median sedentary bout duration (OR = 1.15, CI: 1.04, 1.28) was significant. In stratified analysis, CHAP-measured median bout duration and time spent in sedentary bouts ≥ 30 min were each associated with increased odds of MetS, but the respective associations were stronger for Hispanic/Latino ethnicity (OR = 1.71 and 1.48; CI = 1.28–2.31 and 1.12–1.98) than for non-Hispanic/Latino ethnicity (OR = 1.43 and 1.40; CI = 1.10–1.87 and 1.06–1.87). Conclusions The way sedentary patterns are measured can impact the strength and precision of associations with MetS. These differences may be larger in Hispanic/Latino ethnic groups and warrants further research to inform sedentary behavioral interventions in these populations.

INTRODUCTION Bumetanide, a loop diuretic, was identified as a candidate drug for repurposing for Alzheimer's disease (AD) based on its effects on transcriptomic apolipoprotein E signatures. Cross‐sectional analyses of electronic health records suggest that bumetanide is associated with decreased prevalence of AD; however, temporality between bumetanide exposure and AD development has not been established. METHODS We evaluated Medicare claims data using Cox proportional hazards regression to evaluate the association between time‐dependent use of bumetanide and time to first AD diagnosis while controlling for patient characteristics. Multiple sensitivity analyses were conducted to test the robustness of the findings. RESULTS We sampled 833,561 Medicare beneficiaries, 60.8% female, with mean (standard deviation) age of 70.4 (12). Bumetanide use was not significantly associated with AD risk (hazard ratio 1.05; 95% confidence interval, 0.99–1.10). DISCUSSION Using a nationwide dataset and a retrospective cohort study design, we were not able to identify a time‐dependent effect of bumetanide lowering AD risk. Highlights Bumetanide was identified as a candidate for repurposing for Alzheimer's disease (AD). We evaluated the association between bumetanide use and risk of AD. We used Medicare data and accounted for duration of bumetanide use. Bumetanide use was not significantly associated with risk of AD.

Lactate elevation is a well-characterized biomarker of mitochondrial dysfunction, but its role in diabetic kidney disease (DKD) is not well defined. Urine lactate was measured in patients with type 2 diabetes (T2D) in 3 cohorts (HUNT3, SMART2D, CRIC). Urine and plasma lactate were measured during euglycemic and hyperglycemic clamps in participants with type 1 diabetes (T1D). Patients in the HUNT3 cohort with DKD had elevated urine lactate levels compared with age- and sex-matched controls. In patients in the SMART2D and CRIC cohorts, the third tertile of urine lactate/creatinine was associated with more rapid estimated glomerular filtration rate decline, relative to first tertile. Patients with T1D demonstrated a strong association between glucose and lactate in both plasma and urine. Glucose-stimulated lactate likely derives in part from proximal tubular cells, since lactate production was attenuated with sodium-glucose cotransporter-2 (SGLT2) inhibition in kidney sections and in SGLT2-deficient mice. Several glycolytic genes were elevated in human diabetic proximal tubules. Lactate levels above 2.5 mM potently inhibited mitochondrial oxidative phosphorylation in human proximal tubule (HK2) cells. We conclude that increased lactate production under diabetic conditions can contribute to mitochondrial dysfunction and become a feed-forward component to DKD pathogenesis.

The Mann-Whitney-Wilcoxon rank sum test (MWWRST) is a widely used method for comparing two treatment groups in randomized control trials, particularly when dealing with highly skewed data. However, when applied to observational study data, the MWWRST often yields invalid results for causal inference. To address this limitation, Wu et al. (Causal inference for Mann-Whitney-Wilcoxon rank sum and other nonparametric statistics, Stat. Med. 33 (2014), pp. 1261-1271) introduced an approach that incorporates inverse probability weighting (IPW) into this rank-based statistic to mitigate confounding effects. Subsequently, Mao (On causal estimation using U-statistics, Biometrika 105 (2018), pp. 215-220), Zhang et al. (Estimating Mann Whitney-type causal effects, J. Causal Inference 7 (2019), ARTICLE ID 20180010), and Ai et al. (A Mann-Whitney test of distributional effects in a multivalued treatment, J. Stat. Plan. Inference 209 (2020), pp. 85-100) extended this IPW estimator to develop doubly robust estimators. Nevertheless, each of these approaches has notable limitations. Mao's method imposes stringent assumptions that may not align with real-world study data. Zhang et al.'s (Estimating Mann Whitney-type causal effects, J. Causal Inference 7 (2019), ARTICLE ID 20180010) estimators rely on bootstrap inference, which suffers from computational inefficiency and lacks known asymptotic properties. Meanwhile, Ai et al. (A Mann-Whitney test of distributional effects in a multivalued treatment, J. Stat. Plan. Inference 209 (2020), pp. 85-100) primarily focus on testing the null hypothesis of equal distributions between two groups, which is a more stringent assumption that may not be well-suited to the primary practical application of MWWRST. In this paper, we aim to address these limitations by leveraging functional response models (FRM) to develop doubly robust estimators. We demonstrate the performance of our proposed approach using both simulated and real study data.