Cluster analysis of phenotypes, job exposure, and inflammatory patterns in elderly and nonelderly asthma patients.

BACKGROUND: Asthma has been identified as different phenotypes due to various risk factors. Age differences may have potential effects on asthma phenotypes. Our study aimed to identify potential asthma phenotypes among adults divided by age as either younger or older than 65 years. We also compared differences in blood granulocyte patterns, occupational asthmagens, and asthma control-related outcomes among patient phenotype clusters. METHODS: We recruited nonelderly (<65 years old) (n = 726) and elderly adults (≥65 years old) (n = 201) with mild-to-severe asthma. We conducted a factor analysis to select 17 variables. A two-step cluster analysis was used to classify subjects with asthma phenotypes, and a discriminant analysis was used to verify the classification of cluster results. RESULTS: There were three clusters with different characteristics identified in both the nonelderly and elderly asthmatic adults. In the nonelderly patient group, cluster 2 (obese, neutrophilic phenotypes) had a 1.85-fold significantly increased risk of asthma exacerbations. Cluster 3 (early-onset, atopy, and smoker with an eosinophil-predominant pattern) had a 2.37-fold risk of asthma exacerbations and higher oral corticosteroid (OCS) use than cluster 1 (late-onset and LMW exposure with paucigranulocytic blood pattern). Among elderly patients, cluster 2 had poor lung function and more ex-smokers. Cluster 3 (early-onset, long asthma duration) had the lowest paucigranulocytic blood pattern percentages in the elderly group. CONCLUSIONS: The novelty of the clusters was found in age-dependent clusters. We identified three distinct phenotypes with heterogeneous characteristics, asthma exacerbations and medicine use in nonelderly and elderly asthmatic patients, respectively. Classification of age-stratified asthma phenotypes may lead to precise identification of patients, which provides personalized disease management.

Development of a Portable All-Wavelength PPG Sensing Device for Robust Adaptive-Depth Measurement: A Spectrometer Approach with a Hydrostatic Measurement Example.

Photoplethysmography (PPG), a noninvasive optical sensing technology, has been widely used to measure various physiological indices. Over-the-counter PPG devices are typically composed of a single-wavelength light source, namely, single-wavelength PPG (SW-PPG). It is known that signals of SW-PPG are easily contaminated or distorted by measurement conditions such as motion artifacts, wearing pressure, and skin type. Since lights of different wavelengths can penetrate skin tissues at different depths, how to effectively construct a multiwavelength PPG (MW-PPG) device or even an all-wavelength PPG (AW-PPG) device has attracted great attention. There is also a very interesting question, that is, what could be the potential benefits of using MW-PPG or AW-PPG devices? This paper demonstrates the construction of an AW-PPG portable device and conducts a preliminary evaluation. The presented device consists of four light-emitting diodes, a chip-scale spectrometer, a microcontroller, a Bluetooth Low Energy transceiver, and a phone app. The maximum ratio combining algorithm (MRC) is used to combine the PPG signals derived from different wavelengths to achieve a better signal-to-noise ratio (S/N). The PPG signals from the developed MRC-AW-PPG device versus those from the conventional SW-PPG device are compared in terms of different hydrostatic pressure conditions. It has been observed that the MRC-AW-PPG device can provide more stable PPG signals than that of a conventional PPG device. The results shine a light on the potential benefits of using multiple wavelengths for the next generation of noninvasive PPG sensing.