Physical and cognitive impact following SARS-CoV-2 infection in a large population-based case-control study.

BACKGROUND: Persistent symptoms are common after SARS-CoV-2 infection but correlation with objective measures is unclear. METHODS: We invited all 3098 adults who tested SARS-CoV-2 positive in Iceland before October 2020 to the deCODE Health Study. We compared multiple symptoms and physical measures between 1706 Icelanders with confirmed prior infection (cases) who participated, and 619 contemporary and 13,779 historical controls. Cases participated in the study 5-18 months after infection. RESULTS: Here we report that 41 of 88 symptoms are associated with prior infection, most significantly disturbed smell and taste, memory disturbance, and dyspnea. Measured objectively, cases had poorer smell and taste results, less grip strength, and poorer memory recall. Differences in grip strength and memory recall were small. No other objective measure associated with prior infection including heart rate, blood pressure, postural orthostatic tachycardia, oxygen saturation, exercise tolerance, hearing, and traditional inflammatory, cardiac, liver, and kidney blood biomarkers. There was no evidence of more anxiety or depression among cases. We estimate the prevalence of long Covid to be 7% at a median of 8 months after infection. CONCLUSIONS: We confirm that diverse symptoms are common months after SARS-CoV-2 infection but find few differences between cases and controls in objective parameters measured. These discrepancies between symptoms and physical measures suggest a more complicated contribution to symptoms related to prior infection than is captured with conventional tests. Traditional clinical assessment is not expected to be particularly informative in relating symptoms to a past SARS-CoV-2 infection.

Persistent symptoms are commonly reported after SARS-CoV-2 infection, and this is often described as long Covid. We compared different symptoms reported following SARS-CoV- 2 infection with the results obtained during various medical evaluations that are often used to assess health, such as blood tests, smell tests, taste tests, hearing tests, etc. We compared symptoms and test results between 1,706 Icelanders who had been infected previously with SARS-CoV-2 infection (cases) and 14,398 individuals who had not been infected (controls). Out of 88 assessed symptoms, 41 were more common in cases than controls. However, relatively few differences were seen in the results obtained from the various medical evaluations (cases had poorer smell and taste test results, slightly less grip strength, and slightly poorer memory recall than controls). The differences seen between symptoms and results of medical evaluations suggests that conventional clinical tests may not be informative in relating symptoms to a past SARS-CoV-2 infection.

Multiomics study of nonalcoholic fatty liver disease.

Nonalcoholic fatty liver (NAFL) and its sequelae are growing health problems. We performed a genome-wide association study of NAFL, cirrhosis and hepatocellular carcinoma, and integrated the findings with expression and proteomic data. For NAFL, we utilized 9,491 clinical cases and proton density fat fraction extracted from 36,116 liver magnetic resonance images. We identified 18 sequence variants associated with NAFL and 4 with cirrhosis, and found rare, protective, predicted loss-of-function variants in MTARC1 and GPAM, underscoring them as potential drug targets. We leveraged messenger RNA expression, splicing and predicted coding effects to identify 16 putative causal genes, of which many are implicated in lipid metabolism. We analyzed levels of 4,907 plasma proteins in 35,559 Icelanders and 1,459 proteins in 47,151 UK Biobank participants, identifying multiple proteins involved in disease pathogenesis. We show that proteomics can discriminate between NAFL and cirrhosis. The present study provides insights into the development of noninvasive evaluation of NAFL and new therapeutic options.

Predicting the probability of death using proteomics.

Predicting all-cause mortality risk is challenging and requires extensive medical data. Recently, large-scale proteomics datasets have proven useful for predicting health-related outcomes. Here, we use measurements of levels of 4,684 plasma proteins in 22,913 Icelanders to develop all-cause mortality predictors both for short- and long-term risk. The participants were 18-101 years old with a mean follow up of 13.7 (sd. 4.7) years. During the study period, 7,061 participants died. Our proposed predictor outperformed, in survival prediction, a predictor based on conventional mortality risk factors. We could identify the 5% at highest risk in a group of 60-80 years old, where 88% died within ten years and 5% at the lowest risk where only 1% died. Furthermore, the predicted risk of death correlates with measures of frailty in an independent dataset. Our results show that the plasma proteome can be used to assess general health and estimate the risk of death.

Automatic fundus image quality assessment on a continuous scale.

Fundus photography is commonly used for screening, diagnosis, and monitoring of various diseases affecting the eye. In addition, it has shown promise in the diagnosis of brain diseases and evaluation of cardiovascular risk factors. Good image quality is important if diagnosis is to be accurate and timely. Here, we propose a method that automatically grades image quality on a continuous scale which is more flexible than binary quality classification. The method utilizes random forest regression models trained on image features discovered automatically by combining basic image filters using simulated annealing as well as features extracted with the discrete Fourier transform. The method was developed and tested on images from two different fundus camera models. The quality of those images was rated on a continuous scale from 0.0 to 1.0 by five experts. In addition, the method was tested on DRIMDB, a publicly available dataset with binary quality ratings. On the DRIMDB dataset the method achieves an accuracy of 0.981, sensitivity of 0.993 and specificity of 0.958 which is consistent with the state of the art. When evaluating image quality on a continuous scale the method outperforms human raters.

Sequence Variants in TAAR5 and Other Loci Affect Human Odor Perception and Naming.

Olfactory receptor (OR) genes in humans form a special class characterized by unusually high DNA sequence diversity, which should give rise to differences in perception and behavior. In the largest genome-wide association study to date based on olfactory testing, we investigated odor perception and naming with smell tasks performed by 9,122 Icelanders, with replication in a separate sample of 2,204 individuals. We discovered an association between a low-frequency missense variant in TAAR5 and reduced intensity rating of fish odor containing trimethylamine (p.Ser95Pro, pcombined = 5.6 × 10-15). We demonstrate that TAAR5 genotype affects aversion to fish odor, reflected by linguistic descriptions of the odor and pleasantness ratings. We also discovered common sequence variants in two canonical olfactory receptor loci that associate with increased intensity and naming of licorice odor (trans-anethole: lead variant p.Lys233Asn in OR6C70, pcombined = 8.8 × 10-16 and pcombined = 1.4 × 10-9) and enhanced naming of cinnamon (trans-cinnamaldehyde; intergenic variant rs317787-T, pcombined = 5.0 × 10-17). Together, our results show that TAAR5 genotype variation influences human odor responses and highlight that sequence diversity in canonical OR genes can lead to enhanced olfactory ability, in contrast to the view that greater tolerance for mutations in the human OR repertoire leads to diminished function.