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Challenges for the effective management of interstitial lung diseases (ILDs) include difficulties with the early detection of disease, accurate prognostication with baseline data, and accurate and precise response to therapy. The purpose of this Review is to describe the clinical and research gaps in the diagnosis and prognosis of ILD, and how machine learning can be applied to image biomarker research to close these gaps. Machine-learning algorithms can identify ILD in at-risk populations, predict the extent of lung fibrosis, correlate radiological abnormalities with lung function decline, and be used as endpoints in treatment trials, exemplifying how this technology can be used in care for people with ILD. Advances in image processing and analysis provide further opportunities to use machine learning that incorporates deep-learning-based image analysis and radiomics. Collaboration and consistency are required to develop optimal algorithms, and candidate radiological biomarkers should be validated against appropriate predictors of disease outcomes.

Rationale: Idiopathic pulmonary fibrosis is a rare, irreversible, and progressive disease of the lungs. Common genetic variants, in addition to non-genetic factors, have been consistently associated with IPF. Rare variants identified by candidate gene, family-based, and exome studies have also been reported to associate with IPF. However, the extent to which rare variants genome-wide may contribute to the risk of IPF remains unknown. Objectives: We used whole-genome sequencing to investigate the role of rare variants, genome-wide, on IPF risk. Methods: As part of the Trans-Omics for Precision Medicine Program, we sequenced 2,180 cases of IPF. Association testing focused on the aggregated effect of rare variants (minor allele frequency ≤0.01) within genes or regions. We also identified individual variants that are influential within genes and estimated the heritability of IPF based on rare and common variants. Measurements and Main Results: Rare variants in both TERT and RTEL1 were significantly associated with IPF. A single rare variant in each of the TERT and RTEL1 genes was found to consistently influence the aggregated test statistics. There was no significant evidence of association with other previously reported rare variants. The SNP-heritability of IPF was estimated to be 32% (s.e. 3%). Conclusions: Rare variants within the TERT and RTEL1 genes and well-established common variants have the largest contribution to IPF risk overall. Efforts in risk profiling or development of therapies for IPF that focus on TERT, RTEL1, common variants, and environmental risk factors are likely to have the largest impact on this complex disease.

Background Limited data are available on the association between clinically measured disease severity markers and quality of life (QOL) in idiopathic pulmonary fibrosis (IPF). The study examined the associations between objective disease severity metrics and QOL in a contemporary IPF population. Methods This study evaluated baseline data from patients enrolled in the multicenter, US-based Idiopathic Pulmonary Fibrosis Prospective Outcomes Registry between June 2014 and July 2018. Disease severity metrics included FVC % predicted, diffusing capacity for carbon monoxide (Dlco) % predicted, supplemental oxygen use with activity, supplemental oxygen use at rest, and two summary scores (the Gender-Age-Lung Physiology index, based on gender, age, and % predicted values for Dlco and FVC; and the Composite Physiologic Index, based on % predicted values for Dlco, FVC, and FEV1). Multivariable adjusted regression models were used to examine cross-sectional associations between each severity measure and St. George's Respiratory Questionnaire (SGRQ) total score. Results Among 829 patients with complete SGRQ data, the median (interquartile range) SGRQ score at enrollment was 40 (26-53), with higher scores indicating worse QOL. Modest SGRQ impairments were observed with increasing Gender-Age-Lung Physiology score (2.9 [1.8-4.0] per 1-point increase] and with increasing Composite Physiologic Index scores (3.0 [2.4-3.6] per 5-point increase). Substantial SGRQ impairments were observed for oxygen use with activity (15.6 [12.9-18.2]), oxygen use at rest (16.2 [13.0-19.4]), and decreasing Dlco (5.0 [4.0-6.1] per 10% decrease in % predicted). Conclusions Objective measures of disease severity, including severity scores, physiologic parameters, and supplemental oxygen use, are associated with worse QOL in patients with IPF. Trial Registry ClinicalTrials.gov; No.: NCT01915511; URL: www.clinicaltrials.gov.

Postmenopausal women are more susceptible to poor wound healing. This phenomenon can be reversed by estrogen replacement therapy in non-diabetic individuals. Postmenopausal women with type 2 diabetes are more susceptible to wound healing complications, potentially secondary to an estrogen deficiency. Few studies have examined the mechanism of action and effects of estrogens on diabetic wound healing in females. It appears that multiple factors influence delayed wound healing among individuals with diabetes including: an imbalance in cytokines, growth factors, extracellular matrix (ECM) turnover, and oxidant stress (OS). Estrogens have been shown to regulate the expression of genes important for extracellular matrix turnover, including collagen and matrix metalloproteinases (MMP).For this reason, the effects of 17β-estradiol (E2) on MMP-2, MMP-13, and MMP-14 and estrogen receptor alpha and beta (ER-α and -β) expression in the wound tissue of estrogen-deficient female mice with established type 2 diabetes mellitus (C57BL/6J-m Leprdb/2+) were studied.Topical E2 upregulates ERα in wound tissue thereby improving and accelerating diabetic wound healing in estrogen deficient mice.The mechanism appears to decrease MMP-2, MMP-13, and MMP-14 mediated tissue matrix destruction and increasing collagen content. .