Per-protocol analysis of the ZINC trial for HIV disease among alcohol users

Zinc in Human Health and Infectious Diseases

During the last few decades, the micronutrient zinc has proven to be an important metal ion for a well-functioning immune system, and thus also for a suitable immune defense. Nowadays, it is known that the main cause of zinc deficiency is malnutrition. In particular, vulnerable populations, such as the elderly in Western countries and children in developing countries, are often affected. However, sufficient zinc intake and homeostasis is essential for a healthy life, as it is known that zinc deficiency is associated with a multitude of immune disorders such as metabolic and chronic diseases, as well as infectious diseases such as respiratory infections, malaria, HIV, or tuberculosis. Moreover, the modulation of the proinflammatory immune response and oxidative stress is well described. The anti-inflammatory and antioxidant properties of zinc have been known for a long time, but are not comprehensively researched and understood yet. Therefore, this review highlights the current molecular mechanisms underlying the development of a pro-/ and anti-inflammatory immune response as a result of zinc deficiency and zinc supplementation. Additionally, we emphasize the potential of zinc as a preventive and therapeutic agent, alone or in combination with other strategies, that could ameliorate infectious diseases.

Use of Machine Learning to Estimate the Per-Protocol Effect of Low-Dose Aspirin on Pregnancy Outcomes: A Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE

In randomized clinical trials (RCTs), per-protocol effects may be of interest in the presence of nonadherence with the randomized treatment protocol. Using machine learning in per-protocol effect estimation can help avoid model misspecification owing to strong parametric assumptions, as is common with standard methods (eg, logistic regression).

OBJECTIVES

To demonstrate the use of ensemble machine learning with augmented inverse probability weighting (AIPW) for per-protocol effect estimation in RCTs and to evaluate the per-protocol effect size of aspirin on pregnancy.

DESIGN, SETTING, AND PARTICIPANTS

This secondary analysis used data from 1227 women in the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial, a multicenter, block-randomized, double-blind, placebo-controlled clinical trial of the effect of daily low-dose aspirin on pregnancy outcomes in women at high risk of pregnancy loss. Participants were recruited at 4 university medical centers in the US from June 15, 2007, to July 15, 2012. Women were followed up for 6 menstrual cycles for attempted pregnancy and 36 weeks of gestation if pregnancy occurred. Follow-up was completed on August 17, 2012. Data analyses were performed on July 9, 2021.

EXPOSURES

Daily low-dose (81 mg) aspirin taken at least 5 of 7 days per week for at least 80% of follow-up time relative to placebo.

MAIN OUTCOMES AND MEASURES

Pregnancy detected using human chorionic gonadotropin (hCG) levels.

RESULTS

Among the 1227 women included in the analysis (mean SD age, 28.74 [4.80] years), 1161 (94.6%) were non-Hispanic White and 858 (69.9%) adhered to the protocol. Five machine learning models were combined into 1 meta-algorithm, which was used to construct an AIPW estimator for the per-protocol effect. Compared with adhering to placebo, adherence to the daily low-dose aspirin protocol for at least 5 of 7 days per week was associated with an increase in the probability of hCG-detected pregnancy of 8.0 (95% CI, 2.5-13.6) more hCG-detected pregnancies per 100 women in the sample, which is substantially larger than the estimated intention-to-treat estimate of 4.3 (95% CI, -1.1 to 9.6) more hCG-detected pregnancies per 100 women in the sample.

CONCLUSIONS AND RELEVANCE

These findings suggest that a low-dose aspirin protocol is associated with increased hCG-detected pregnancy in women who adhere to treatment for at least 5 days per week. With the presence of nonadherence, per-protocol treatment effect estimates differ from intention-to-treat estimates in the EAGeR trial. The results of this secondary analysis of clinical trial data suggest that machine learning could be used to estimate per-protocol effects by adjusting for confounders related to nonadherence in a more flexible way than traditional regressions.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00467363.