A Mendelian Trait for Olfactory Sensitivity Affects Odor Experience and Food Selection

SummaryHumans vary in acuity to many odors [1–4], with variation within olfactory receptor (OR) genes contributing to these differences [5–9]. How such variation also affects odor experience and food selection remains uncertain [10], given that such effects occur for taste [11–15]. Here we investigate β-ionone, which shows extreme sensitivity differences [4, 16, 17]. β-ionone is a key aroma in foods and beverages [18–21] and is added to products in order to give a pleasant floral note [22, 23]. Genome-wide and in vitro assays demonstrate rs6591536 as the causal variant for β-ionone odor sensitivity. rs6591536 encodes a N183D substitution in the second extracellular loop of OR5A1 and explains >96% of the observed phenotypic variation, resembling a monogenic Mendelian trait. Individuals carrying genotypes for β-ionone sensitivity can more easily differentiate between food and beverage stimuli with and without added β-ionone. Sensitive individuals typically describe β-ionone in foods and beverages as “fragrant” and “floral,” whereas less-sensitive individuals describe these stimuli differently. rs6591536 genotype also influences emotional associations and explains differences in food and product choices. These studies demonstrate that an OR variant that influences olfactory sensitivity can affect how people experience and respond to foods, beverages, and other products

CATA questions for sensory product characterization: Raising awareness of biases

Research into sensory product characterizations by consumers using CATA (check-all-that-apply) methodology is entering the stage where better understanding is required of ways that CATA question implementation influences sensory profiles and product discrimination. With this aim, five studies were conducted. Focusing on strategies that have been suggested as ways to reduce primacy and order bias in CATA data, this research explored the effect of these initiatives on the elicited sensory product profiles and conclusions drawn with regards to sample differences. The use of within-subjects randomization of CATA terms did not significantly affect frequency of use of CATA terms but significantly affected conclusions regarding differences among samples, compared to the evaluation of multiple samples with CATA ballots where terms are presented in the same order. The use of multiple shorter CATA questions defined by sensory modality also yielded results that significantly differed from when single longer CATA questions with terms from multiple sensory modalities were used. CATA question length and the use of single/mixed sensory modalities both appeared to be contributing factors to these differences. ‘Dynamics of sensory perception’, which refers to effects on CATA results arising when CATA terms are assessed in the sensory evaluation process (during/after sample consumption) was confirmed as a source of bias. A key learning from this research was that sensory product characterization and differences among samples are subject to multiple minor biases related to how the question is formulated and that the exact experimental conditions under which CATA data are generated should be reported

Implementation Support for a Social Risk Screening and Referral Process in Community Health Centers.

Evidence is needed about how to effectively support health care providers in implementing screening for social risks (adverse social determinants of health) and providing related referrals meant to address identified social risks. This need is greatest in underresourced care settings. The authors tested whether an implementation support intervention (6 months of technical assistance and coaching study clinics through a five-step implementation process) improved adoption of social risk activities in community health centers (CHCs). Thirty-one CHC clinics were block-randomized to six wedges that occurred sequentially. Over the 45-month study period from March 2018 to December 2021, data were collected for 6 or more months preintervention, the 6-month intervention period, and 6 or more months postintervention. The authors calculated clinic-level monthly rates of social risk screening results that were entered at in-person encounters and rates of social risk-related referrals. Secondary analyses measured impacts on diabetes-related outcomes. Intervention impact was assessed by comparing clinic performance based on whether they had versus had not yet received the intervention in the preintervention period compared with the intervention and postintervention periods. In assessing the results, the authors note that five clinics withdrew from the study for various bandwidth-related reasons. Of the remaining 26, a total of 19 fully or partially completed all 5 implementation steps, and 7 fully or partially completed at least the first 3 steps. Social risk screening was 2.45 times (95% confidence interval [CI], 1.32-4.39) higher during the intervention period compared with the preintervention period; this impact was not sustained postintervention (rate ratio, 2.16; 95% CI, 0.64-7.27). No significant difference was seen in social risk referral rates during the intervention or postintervention periods. The intervention was associated with greater blood pressure control among patients with diabetes and lower rates of diabetes biomarker screening postintervention. All results must be interpreted considering that the Covid-19 pandemic began midway through the trial, which affected care delivery generally and patients at CHCs particularly. Finally, the study results show that adaptive implementation support was effective at temporarily increasing social risk screening. It is possible that the intervention did not adequately address barriers to sustained implementation or that 6 months was not long enough to cement this change. Underresourced clinics may struggle to participate in support activities over longer periods without adequate resources, even if lengthier support is needed. As policies start requiring documentation of social risk activities, safety-net clinics may be unable to meet these requirements without adequate financial and coaching/technical support