Resting metabolic rate (RMR) declines with aging and is related to changes in health status, but how specific health impairments impact basal metabolism over time has been largely unexplored. We analyzed the association of RMR with 15 common age-related chronic diseases for up to 13 years of follow-up in a population of 997 participants to the Baltimore Longitudinal Study of Aging. At each visit, participants underwent measurements of RMR by indirect calorimetry and body composition by DEXA. Linear regression models and linear mixed effect models were used to test cross-sectional and longitudinal associations of RMR and changes in disease status. Cancer and diabetes were associated with higher RMR at baseline. Independent of covariates, prevalent COPD and cancer, as well as incident diabetes, heart failure, and CKD were associated with a steeper decline in RMR over time. Chronic diseases seem to have a two-phase association with RMR. Initially, RMR may increase because of the high cost of resiliency homeostatic mechanisms. However, as the reserve capacity becomes exhausted, a catabolic cascade becomes unavoidable, resulting in loss of total and metabolically active mass and consequent RMR decline. Resting metabolic rate (RMR) changes over the life span and has been related to changes in health status. RMR reflects the energy expended by the human body in a prolonged resting state in the absence of food digestion, physical or cognitive activities . As suchIn an analysis of data from the Baltimore Longitudinal Study of Aging (BLSA), subjects in good health and functional status had lower RMR than those affected by chronic diseases and functional limitations, independent of age, sex and body composition . Also, iOverall, these data suggest that while healthy aging is associated with a progressive decline of RMR, independent of changes in body composition, superimposed adverse changes in health and functional status tend to attenuate such decline. This attenuation has been attributed to the potential extra-energetic cost of maintaining homeostasis in response to disease-related processes . HoweverParticipants were community-dwelling persons recruited from the Baltimore and Washington DC areas. The BLSA has been continually recruiting healthy participants since 1958 and following them indefinitely regardless of any intervening change in health and functional status. The visit frequency depends on age: visits occur every 4 years until the subject reaches age 60, biannually from the age of 60 to 79, and yearly thereafter. A description of the sample and selection criteria has been previously reported ,13.The sample for the current analysis consists of 997 persons aged 24 to 95 years followed from March 2006 to December 2019. All participants underwent a comprehensive medical history interview, clinical examination, laboratory and cardiovascular testing, respiratory testing, cognitive testing, total body dual-energy x-ray absorptiometry (DEXA) imaging, and RMR assessment. Standard questionnaires inquired about different types of activities performed during a typical week, and the measure of physical activity considered encompassed all the high intensity activities, including brisk walking .Fifteen conditions that occur with higher frequency and are associated with high disability and mortality risk in the aging population were selected “a priori” for this study and included: chronic heart failure (CHF), myocardial infarction (MI), cerebrovascular accidents (stroke or transient ischemic attack), hypertension, type 2 diabetes mellitus (T2D), anemia, peripheral artery disease (PAD), cognitive impairment, depression, Parkinson’s disease (PD), chronic kidney disease (CKD), cataract, chronic obstructive pulmonary disease (COPD), cancer, and osteoarthritis (OA). The presence of these conditions was assessed at baseline and every follow-up. −1.154 × (Age) −0.203 ×  × (1.210 if black) [CHF was defined as ejection fraction < 40%, assessed through echocardiography. Diagnosis of hypertension was based on measured systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg, or self-report history of taking anti-hypertensive medications. T2D was defined as having: (1) history of T2D or hypoglycemic treatment; or (2) fasting plasma glucose (FPG) ≥ 126 mg/dl and 2-hr post oral glucose tolerance test (OGTT) plasma glucose (2hrG) ≥ 200 mg/dl at the same visit; or (3) FPG ≥ 126 mg/dl or 2hrG ≥ 200 mg/dl during two consecutive visits, in accordance with American Diabetes Association criteria. PAD was defined as ankle-brachial index (ABI) measured by Doppler stethoscope < 0.9 . Cognitif black) . Anemia RMR was estimated using indirect calorimetry  ,20 evaluTotal body dual-energy x-ray absorptiometry (DEXA) was performed using the Prodigy Scanner  and analyzed with version 10.51.006 software. DEXA uses tissue absorption of x-ray beams to identify different body composition components  and provide quantitative data on body composition ,24.p < 0.05 was considered statistically significant.Statistical analyses were performed using SAS version 9.4 and RStudio version 1.2.1335. Covariates considered in the models were sex , age, total body fat mass, and total body lean mass, since it is well known that these variables contribute to basal energy expenditure. Parameters such as smoking status, alcohol intake, physical activity level, height and weight, and subcutaneous or visceral distribution of the adipose tissue (assessed through computed tomography) were considered as possible covariates but then removed from the models when they did not contribute significantly to model fit. Linear regression models were run to test the cross-sectional associations of RMR with the presence of each of the 15 selected conditions, adjusting for sex, age, lean body mass, and fat body mass as measured by DEXA.Change), i.e., the individual specific slope, was calculated by adding the individual random effect coefficient  to the fixed effect coefficient .Linear mixed effect (LME) models were used to estimate the longitudinal rate of change in RMR as described previously . In summChange with separate models fitted for each disease. Two hypotheses were evaluated: First, we tested whether participants affected by one specific chronic condition at baseline experienced different changes in RMR compared to those who did not have that condition at baseline (no disease). Secondly, we tested the hypothesis whether the development of a chronic condition over the follow-up period  was associated with changes in RMR differently from those participants who remained free from that condition. Each model included baseline age, sex, lean body mass, fat body mass, and longitudinal changes in the latter two variables as covariates. Multiple linear regression analysis was used to examine the determinants of RMRp < 0.001), although on average the value of the β coefficients per kg of estimated lean body mass was 4-fold higher than the coefficient for fat mass, a finding that is consistent with other data reported in the literature [Results of the regression model assessing associations of RMR with 15 selected chronic conditions, after accounting for age, sex, and body composition, are shown in terature ,27,28.p < 0.05), while incident OA was associated with an increase in RMR with marginal significance (p = 0.05)  A.Prevalent COPD and cancer were associated with a steeper decline in RMR, independent of age, sex, and body composition B. No othIn this study, we explored the cross-sectional and longitudinal associations of RMR and prevalent and incidence of major chronic diseases, independent of relevant covariates. Older age was associated with lower resting energetic expenditure at baseline, while baseline T2D and cancer were correlated with higher expenditure levels. Longitudinally, aging was associated with a decline in RMR over time. The presence of incident T2D, CHF, and CKD, as well as that of prevalent COPD and cancer, was also associated with a longitudinal decrease in RMR. Incident OA was associated with an increase in RMR.A number of studies have shown that chronic inflammation is associated with aging and age-associated chronic diseases and may contribute to increased catabolic rates and age-associated changes in body composition, including sarcopenia and muscle wasting . Based oThe cross-sectional association between cancer and higher basal metabolism we observed is not surprising, since among the modifications that occur in a cancerous cell there is an increase in energy needs to support accelerated growth and survival . CanceroThe findings regarding the cross-sectional association between T2D and increased basal metabolism are consistent with previous studies ,33. SeveAnother hypothesis that could explain the increased basal metabolism observed in these conditions is the accumulation of senescent cells. Cell senescence is the defense mechanism triggered by genomic instability due to the exposure to a stressor, such as oxidative and genotoxic insults, chromatin perturbation, oncogene activation, mitochondrial dysfunction, or protein misfolding . The celExpectedly, CHF was associated with a steeper decline in RMR as CHF has long been connected with a deleterious wasting syndrome, termed cardiac cachexia, a strong prognostic factor for CHF . The patConceivably, diseases characterized by intense fatigue, shortness of breath , peripheral neuropathy, and infections (T2D) can lead to exercise intolerance. Reduced physical activity determines a loss of the most metabolically active muscle fibers, muscle denervation and atrophy, muscle infiltration of connective tissue, and a decrease in mitochondrial oxidative capacity ,53. Notap = 0.05). OA is characterized by an inflammatory pathogenesis and has been correlated with an accumulation of senescent cells, both of which are potential mechanisms leading to the increased energy expenditure we observed, as discussed previously [OA was the only condition associated with an increase in RMR over time, with marginal significance (eviously ,55.A possible interpretation of our findings is that the combined effect of chronic diseases and aging exerts a two-phase differential effect on body composition and energy consumption. A progressive impairment of metabolic homeostasis caused by chronic diseases that have systemic effects, such as CHF, COPD and diabetes, challenges energy production; and the availability of energy at the cellular, parenchymal, and organismal level is a fundamental requirement for resiliency strategies. This progressive imbalance between damage accumulation and its functional consequences and shrinking of reserve capacity is non-linear. In the initial response phase, the RMR increases because strategies to maintain a homeostatic equilibrium are energetically more expensive than normal physiological mechanisms of a healthy person. With depletion of reserve capacity, the extra-energy is no longer required, energy is scarce, and catabolism becomes the prevalent process, with complex changes in body composition not fully captured by traditional technology. This theory is consistent with many studies showing a rapid decline in body weight and blood pressure in the years prior to death ,57. FiguTo the best of our knowledge, no previous research has examined the longitudinal relationship between the incidence of 15 chronic diseases typical of the aging population and trends in basal energy expenditure over time. Although the mechanisms that link energy metabolism to the pathogenesis and the natural history of specific diseases appear to be complex and far from understood, this study provides an inkling of the directionality and strength of this relationship and allows some early speculation regarding underlying pathways.The limitations of this study concern the exceptionally healthy and well-educated status of the BLSA participants relative to the general population, and the unstable interval between visits. Moreover, complete information regarding all the parameters considered were unavailable for all the subjects: depending on the testing needed for a specific diagnosis, the population considered for each disease varied slightly. Finally, some pathologies were significantly less prevalent than others, leading to a lower statistical power in evaluating their association with the outcome of interest. However, we consider our study to provide solid and novel information in the complex and broad field of energy metabolism in aging.The effect of changes in body composition during disease progression appeared to affect RMR with a magnitude that could not be accounted for by adjustments for DEXA measurements. DEXA is unable to discriminate the contractile, metabolically active components of muscle tissue from other molecules, mainly of connective tissue, that tend to accumulate with aging. Utilization of more precise techniques, such as dilution of deuterated creatine; whole-body potassium counter; and high-resolution, three-dimensional computed topography, should be considered in future studies to reliably assess the contribution of skeletal muscle to RMR . Further research on larger populations is needed to confirm these results and obtain more complete information. Outlining the pathways through which a pathological condition affects the energetic balance of the human body could provide more complete knowledge about the condition itself, an increased awareness of the metabolic trends in healthy aging in contrast to health deterioration, and a better understanding of the mechanisms employed to contrast or respond to an insult. Furthermore, defining a specific pattern of progressive metabolic change resulting from an active pathological process could eventually allow clinical inferences in relation to staging and prognosis. If the metabolic alteration proves to be a sign of the body fighting against the disease, and the features of this connection can be fully characterized, the ability to directly measure this battle through RMR may provide an excellent tool to assess the response of a specific individual to a particular stressor.


Hearing loss is the most common sensory defect, and it affects over 6% of the population worldwide. Approximately 50–60% of hearing loss patients are attributed to genetic causes. Currently, more than 100 genes have been reported to cause non-syndromic hearing loss. It is possible and efficient to screen all potential disease-causing genes for hereditary hearing loss by whole exome sequencing (WES).We collected 5 consanguineous pedigrees from Pakistan with hearing loss and applied WES in selected patients for each pedigree, followed by bioinformatics analysis and Sanger validation to identify the causal genes.GIPC3 (c.937 T > C), LOXHD1 (c.6136G > A) and TMPRSS3 (c.941 T > C). The remaining 2 pedigrees each contained two candidate variants: TECTA (c.4045G > A) and MYO15A (c.3310G > T and c.9913G > C) for one pedigree and DFNB59 (c.494G > A) and TRIOBP (c.1952C > T) for the other pedigree. The candidate variants were validated in all available samples by Sanger sequencing.Variants in 7 genes were identified and validated in these pedigrees. We identified single candidate variant for 3 pedigrees: The candidate variants in hearing-loss genes were validated to be co-segregated in the pedigrees, and they may indicate the aetiologies of hearing loss in such patients. We also suggest that WES may be a suitable strategy for hearing-loss gene screening in clinical detection. Approximately 50% ~ 60% of hearing loss patients are attributed to genetic causes [https://hereditaryhearingloss.org/), and the total number of genes related to hearing loss is expected to be several hundred.Hearing loss is the most common sensory defect, and it affects ~ 1/500 newborns [c causes . Hereditc causes  that canc causes . CurrentGJB2 [SLC26A4 [There are mature gene panels for hearing-loss detection, and the genes involved range from 4 to more than 100. However, except for several genes, such as GJB2 –7 or SLC[SLC26A4 –10, most[SLC26A4 , 12.Recessive inheritance hearing loss is worth studying because such patients usually have unaffected parents, which makes the disorder seem to be “sudden onset”, and this situation is more difficult to prevent. Consanguineous pedigrees represent a suitable natural model to study recessive disorders . In PakiIn this study, we collected 5 consanguineous pedigrees with hearing loss from Pakistan and applied WES to identify the causal genes. We identified several variants in hearing-loss genes that co-segregated in the pedigrees, and they may indicate the aetiologies of hearing loss in such patients.In the present study, we collected 5 consanguineous pedigrees containing 22 patients with hearing loss from rural areas in Pakistan. All the patients showed different degrees of hearing loss. The most likely inheritance mode for these pedigrees was autosomal recessive Fig. . The stuAccording to the manufacturer’s instructions, genomic DNA was isolated from the peripheral blood leukocytes of all participants using a DNA QIAamp mini kit . One patient from each pedigree was selected, and WES was performed. Exons were captured using the BGI-Exome kit V4 and sequenced by BGI-seq 500 with 100 bp paired-end sequencing.http://provean.jcvi.org/), PolyPhen2 [http://genetics.bwh.harvard.edu/pph2/), MutationTaster [http://www.mutationtaster.org/) and CADD [https://cadd.gs.washington.edu/snv), to predict the functional impact of candidate variants [Low-quality reads were removed by SOAPnuke , and theolyPhen2  , all patients showed severe deafness, and one patient (III1) was selected for WES. In the second pedigree (HL2), all patients showed congenital profound deafness and muteness, and one patient (IV1) was selected for WES. In the third pedigree (HL3), all patients showed moderate deafness (their hearing loss started after seizures), and one patient (V1) was selected for WES. In the fourth pedigree (HL4), all patients showed congenital profound deafness, and one patient (IV1) was selected for WES. In the fifth pedigree (HL5), all patients showed moderate deafness, and one patient (III5) was selected for WES.WES was applied in the selected patients. The average depth of the target region was 146X with a coverage of 99.85%, and the coverage of the target region that was sequenced at least 10 times (depth > = 10 X) was 98.20%  were sequenced for the HL1 pedigree, 6 samples (III1 ~ 2 and IV1 ~ 4) were sequenced for the HL2 pedigree, 4 samples (IV1 and V1 ~ 3) were sequenced for the HL3 pedigree, and 21 samples  were sequenced for the HL5 pedigree. For the HL4 pedigree, the initial samples collected were degraded, and we failed to collect additional samples. All the variants selected for Sanger sequencing were co-segregated in the pedigrees except for the HL4 pedigree , non-syndromic genetic deafness and audiogenic seizures. Currently, 11 pathogenic variants in this gene have been identified in ClinVar.In this study, we identified several variants in genes reported to cause hearing loss that co-segregated in the pedigrees. For the HL1 pedigree, the variant in the  regions . The PDZLOXHD1 gene was likely the causative variant. A highly conserved stereociliary protein is encoded by LOXHD1, which contains 15 PLAT domains that is responsible for protein interactions with the plasma membrane [Loxhd1 maintained the cochlear hair cells’ function in mice [LOXHD1 was the causal gene of autosomal recessive deafness (type 77). Currently, 28 pathogenic variants in this gene have been identified in ClinVar.For the HL2 pedigree, the variant in the membrane . It was  in mice . LOXHD1 TECTA and two compound heterozygous variants in MYO15A were the likely candidate variants. A protein with 2155 amino acid residues is encoded by TECTA, which is a non-cellular matrix overlying the cochlear neuroepithelium. The function of this protein is to amplify and transmit sound [TECTA was the causal gene of autosomal recessive deafness (type 21), and 40 pathogenic variants in this gene have been identified in ClinVar. MYO15A encodes a protein that plays a crucial role in hair cells of the inner ear to maintain normal hearing [MYO15A was the causal gene of autosomal recessive deafness (type 3), and 112 pathogenic variants in this gene have been identified in ClinVar. Take the inheritance mode of this pedigree  into consideration, we preferred that the homozygous variant in TECTA was more likely to be responsible than the compound heterozygous variants in MYO15A.For the HL3 pedigree, a homozygous variant in it sound , 29. TEC hearing . MYO15A DFNB59 and TRIOBP. A protein with 352 amino acids is encoded by DFNB59, and is responsible for the signal transmit of auditory nerve [DFNB59 was the causal gene of autosomal recessive deafness (type 59), and 9 pathogenic variants in this gene have been identified in ClinVar. A protein with 652 amino acids is encoded by TRIOBP, which regulates adherens junctions and recognizes actin cytoskeleton [TRIOBP remains unclear, and no pathologies except hearing loss were caused by pathogenic variants in this gene. TRIOBP was the causal gene of autosomal recessive deafness (type 28), and 26 pathogenic variants in this gene have been identified in ClinVar. It was predicted to be likely benign in the deafness variation database for the variant in TRIOBP. Therefore, we thought the variant in DFNB59 was more likely to be the causal variant than the TRIOBP variant.For the HL4 pedigree, homozygous variants were detected in both ry nerve . DFNB59 skeleton . CurrentTMPRSS3 may cause the disorder. The protein encoded by this gene plays a crucial role in activating the ENaC sodium channel [+ concentration in the inner ear [TMPRSS3 was the causal gene of autosomal recessive deafness (type 8), and 23 pathogenic variants in this gene have been identified in ClinVar.For the HL5 pedigree, the variant in  channel , and it nner ear . TMPRSS3GIPC3, LOXHD1, TECTA, MYO15A, DFNB59, TRIOBP and TMPRSS3, respectively. The density may indicate the degree of understanding or focus for different genes. Genes with low density, such as LOXHD1 and TRIOBP, may have potential research value.We calculated the density of reported pathogenic variants in these genes, which were 11.7/kb, 4.2/kb, 6.2/kb, 10.6/kb, 8.5/kb, 3.7/kb and 16.8/kb for The majority of causal genes we identified for these pedigrees were not common hearing-loss genes. If we applied a common hearing-loss gene panel to screen these patients, we would obtain negative results, and the causal gene/variant for the patients would be missed. Therefore, WES may be a better strategy than panel sequencing for hearing-loss screening even in clinical detection.In conclusion, we applied WES in five consanguineous pedigrees (one patient per pedigree) from Pakistan with hearing loss, followed by Sanger sequencing for all available samples among the pedigrees to identify the causal genes for them. Several variants in hearing-loss genes were validated to be co-segregated in the pedigrees, and they may indicate the aetiologies of hearing loss in such patients. Moreover, we suggest that WES may be a suitable strategy for hearing-loss screening in clinical detection.Additional file 1.Additional file 2.


Coronavirus disease 2019 (COVID-19) poses a challenge to routine healthcare practice.The suspension of the national screening program combined with fewer referrals through primary care  has led to fewer colorectal cancer diagnoses in the Netherlands.This retrospective cohort study included all patients with colorectal cancer who were operated on between March 9 and June 30, 2020, in four hospitals across the Netherlands. One of these was an academic hospital, and the other three were district general hospitals. The primary outcome was the number of colorectal cancer resections performed. Secondary endpoints were the number and risk factors of postoperative complications in patients undergoing colorectal cancer surgery.Multivariate logistic regression analysis was performed to ascertain the risk of developing a complication  in patients who underwent colorectal cancer surgery after correcting for possible confounders. Odds ratio (OR) with 95% confidence intervals (CI) were used to quantify the risk of postoperative complications. Two-sided P values below 0.05 were considered statistically significant.One hundred sixty-two patients underwent colorectal resectional surgery for malignant pathology. There were 88 lower staged (stages 0–II) and 74 higher staged (stages III–IV) colorectal cancers. Of the 162 patients, 21 had been tested for COVID-19 (13.0%), two of whom tested positive (9.5%). Postoperative complications occurred in 46 (28.4%) patients. An overall reduction in the overall numbers of colorectal resections occurred over the time of the study Fig. . After wThe current study showed a decline of colorectal surgical procedures during the study period. This appears to be a result of the suspension of the national colorectal screening program as well as a reduction in the number of referrals from primary care. The result of this has been a decrease in the apparent incidence of lower staged tumors. This is unsurprising, since colorectal cancers diagnosed at the national screening program are detected at an early stage compared to patients who present with colorectal-related symptoms.3This study also showed that the number of patients presenting with symptomatic colorectal cancer (non-screened patients) decreased during the COVID-19 period which could be a representation of the decreased referrals by primary care.Since the ongoing COVID-19 pandemic puts an enormous strain on healthcare systems worldwide, this study showed that, if necessary, suspension of the national screening program is acceptable as long as explicit attention is paid to the continuation primary care referrals.


Disability in activities of daily living (ADL) is common in elderly people. Dietary diversity is associated with several age-related diseases. The evidence on dietary diversity score (DDS) and ADL disability is limited. This study was based on the China Health and Nutrition Survey. Prospective data of 5004 participants were analyzed. ADL disability was defined as the inability to perform at least one of the five self-care tasks. Cox proportional regression models were conducted to estimate the association of cumulative average DDS with the risk of ADL disability. Logistic regression models were performed to estimate the odds ratios for the average DDS, the baseline DDS, and the recent DDS prior to the end of the survey in relation to ADL disability, respectively. The results indicate that higher average DDS was associated with a decreased risk of ADL disability . The association was stronger among participants who did not had comorbidity at baseline than those who did (P-interaction 0.035). The average DDS is the most pronounced in estimating the association of DDS with ADL disability of the three approaches. In summary, higher DDS has beneficial effects on ADL disability, and long-term dietary exposure is more preferable in the investigation of DDS and ADL. The world’s population is aging at a faster speed than it used to be according to a report from the World Health Organization . It is pActivities of daily living (ADL) is an index to represent the capability of skills essential to manage basic physical needs, comprised of the following areas: grooming/personal hygiene, dressing, toileting/continence, transferring/ambulating, and eating . The preDietary diversity is an important aspect of our diet. Keeping a diverse diet is one of the key recommendations of Chinese dietary guidelines . EpidemiThis study was based on the China Health and Nutrition Survey (CHNS). Details about the CHNS has been described elsewhere . In brieParticipants’ diet intakes in the past 3 days were recorded by dietary recall and household food weight inventory. Details about the dietary survey process have been published . All fooIn phase 1997, 2000, 2004, 2006, and 2015, participants aged 55 years and above were asked whether she or he could finish some self-care tasks . For each question, participants were asked “Do you have any difficulty doing this?”: no difficulty; having some difficulty, but can still do it; need help to do it; cannot do it at all; and unknown. Participants who chose “need help to do it” or “cannot do it at all” in at least one of the five activities were defined as ADL disability . Information on sociodemographic characteristics, lifestyle behaviors, and personal health and medical history were collected in the CHNS. Baseline age, gender, living region, residency, education level, per capita household income, smoking status, alcohol consumption, physical activity, body mass index (BMI), and medical history  were obtained from the phase at entry. 2. Since blood pressure was measured, participants who had been previously diagnosed with hypertension or with systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg were all regarded as hypertensive patients. Other medical histories were based on the self-report from participants. Comorbidity was defined as having at least one of the four diseases  at the baseline survey. Per capita household income was estimated at each phase, and missing values for per capita household income was replaced by the medians of each survey site. Income was grouped into tertiles and was labeled as low, middle, and high at each phase. Physical activity was measured in terms of metabolic equivalent of task (MET)-hours per week . MissingContinuous variables were presented as means and standard deviations or medians and quartiles according to the distribution of data; categorical variables were presented as percentages. Normally distributed continuous variables were tested between groups by one-way analysis of variance, for categorical variables, chi-square tests were performed for difference across groups. To estimate the trends of nutrient intakes across DDS tertiles , linear regression models were conducted. We assigned the midpoint values of DDS tertiles and treated the variables as continuous in the linear regression model. Values of nutrient intakes were transformed to log to reach normality.Person years were measured from baseline survey date until the last survey date . To assess the effect of DDS on the risk of ADL disability, hazard ratios (HRs) for DDS and ADL disability were estimated by Cox proportional hazard regression models. Multivariate models were conducted. In the first model, covariates including age at entry (continuous), gender (men or women), living region (southern or northern China), residency , income , and education level (primary school and below or middle school and higher) were adjusted. In the second model, smoking status (smoker or not), physical activity (≤100 or >100 MET-hours/week), BMI (continuous), and comorbidity (yes or no) were additionally adjusted. Sensitivity analyses were performed by (1) excluding individuals whose follow-up time was less than 5 years and (2) the adjustment of alcohol consumption (regular consumer or not) and phase at entry . Subgroup analyses were conducted in participants with different baseline characteristics , and comorbidity).To assess the short-term and the long-term effects of DDS on ADL disability, we analyze the DDS value reported in different surveys with the odds of ADL disability. Individuals involved in three or more dietary surveys were included. Logistic regression models were performed to estimate the odds ratios for the average DDS across phases, the baseline DDS, and the recent DDS prior to the end of the survey in relation to ADL disability, respectively. p values were two-sided, and statistical significance was defined as p < 0.05.All the statistics were conducted in R 4.0.2. All The average DDS among participants was 3.78 ± 1.04 (ranged from 1.33 to 8.00). DDS was positively associated with intakes of protein, fat, dietary fiber, vitamin A, riboflavin, niacin, vitamin C, vitamin E, calcium, phosphorus, potassium, magnesium, iron, zinc, and selenium. Meanwhile, higher DDS was inversely associated with intakes of carbohydrate, sodium, and manganese .During a median follow-up of 9 years , 601 ADL was reported. Absolute ADL rates according to DDS tertiles  were 14.4, 10.5, and 8.7 per 1000 person years, respectively. After the adjustment of covariates, higher DDS was associated with decreased risk of ADL disability .In the sensitivity analysis, the association of DDS with ADL disability did not change by excluding individuals whose follow-up time was less than 5 years, or additional adjustment of alcohol consumption and phase at entry .The significant association between DDS and ADL disability was observed in all subgroups based on baseline characteristics, including gender, living region, age at entry, and comorbidity. However, the association was more pronounced in participants living in southern China, participants aged over 65 years at entry, and participants without comorbidities than the others. A significant interaction term between DDS and comorbidity was observed .We found both the average DDS across phases and the baseline DDS were associated with lower odds of ADL disability. The association was more pronounced for the average DDS. The association between the recent DDS prior to the end of the survey and ADL disability was insignificant . This study found that higher DDS was inversely associated with the risk of ADL disability in Chinese adults. The association was stable after the adjustment of physical activity, BMI, and comorbidity. To our best knowledge, the present study is the first one revealing the beneficial effect of dietary diversity on ADL disability.In the western population, the Mediterranean diet is most frequently investigated when addressing the effect of dietary patterns on functional capacity. A meta-analysis showed that higher adherence to the Mediterranean diet is associated with decreased risk of frailty and functional disability in the elderly . BesidesOur study observed that each additional point on DDS was associated with a nearly thirty percent reduction in the risk of ADL disability. The benefits of DDS on ADL might be related to the following mechanisms. First, aging is associated with a loss of muscle mass, leading to frailty, sarcopenia, and functional disability . More diAn interaction between DDS and comorbidity on the risk of ADL disability was observed in this study. The association of DDS with ADL disability was more pronounced among participants without comorbidity at baseline than those with comorbidity. We assumed that this might be because, among participants with comorbidity, the progression of disease played a more important role in the loss of ADL independence than the impact of diet. However, we should note that, even in individuals with comorbidity, the beneficial effect of DDS still existed.In this study, we calculated the cumulative average DDS to address participants’ long-term dietary exposure. To compare the difference between different dietary exposure measures, we estimated the OR for the average DDS across phases, the baseline DDS, and the recent DDS prior to the end of the survey in relation to ADL disability. The results indicate that when estimating the effect of DDS on ADL disability, the average DDS is the most pronounced of the three approaches, which is consistent with previous studies ,45. The The strengths of this study include prospective design and population-based samples, which provided advantages for causal inference. Repeated dietary surveys allowed us to capture participants’ long-term dietary exposure. There are several limitations. First, although comorbidities were considered in our analysis, other factors that may influence ADL  were not included because of a lack of data. Second, the present study was based on a dynamic cohort study, participants joined the survey at a wide age range; however, in the subgroup and sensitivity analyses, we took participants’ age at entry and phase at entry into consideration, which could partly mark up for this defect. Third, the ADL disability was self-reported. Participants might overestimate or underestimate their abilities to finish some tasks because of social desirability or misunderstanding.In summary, this study found that higher DDS has a beneficial effect on the risk of ADL disability and long-term dietary exposure is more preferable in the investigation of DDS and ADL.


Progression-free survival (PFS) in glioma patients varies widely, even when stratifying for known predictors . Neuronal activity has been shown to accelerate tumor growth in an animal model, suggesting that brain activity may be valuable as a PFS predictor. We investigated whether postoperative oscillatory brain activity, assessed by resting-state magnetoencephalography is of additional value when predicting PFS in glioma patients.We included 27 patients with grade II–IV gliomas. Each patient’s oscillatory brain activity was estimated by calculating broadband power (0.5–48 Hz) in 56 epochs of 3.27 s and averaged over 78 cortical regions of the Automated Anatomical Labeling atlas. Cox proportional hazard analysis was performed to test the predictive value of broadband power towards PFS, adjusting for known predictors by backward elimination.p = 0.022). Post-hoc univariate analysis showed that higher broadband power also predicted shorter overall survival ; p = 0.038).Higher broadband power predicted shorter PFS after adjusting for known prognostic factors  1.15–5.70); Our findings suggest that postoperative broadband power is of additional value in predicting PFS beyond already known predictors.The online version of this article (10.1007/s11060-019-03386-7) contains supplementary material, which is available to authorized users. Glioma prognosis is currently determined by factors such as molecular tumor subtype, tumor grade, performance status and age . Still, One proposed factor associated with glioma growth is neuronal activity. By introducing light-sensitive proteins into the brain, neurons can be activated through light (optogenetics) . This teNeuronal activity in patients may be assessed non-invasively using magnetoencephalography (MEG) or electroencephalography (EEG). MEG records the magnetic fields induced by synchronous postsynaptic neuronal activity and, unlike EEG, is not perturbed by the skull and scalp , 10. TheTo gain a deeper understanding of predictors of PFS in the postoperative clinical setting, we investigated whether higher postoperative broadband power predicts shorter PFS in a cohort of glioma patients. For clinical relevance, we adjusted for already known predictors thereby investigating the added value of broadband power as a predictor. As a post-hoc analysis, we investigated the relationship between broadband power and OS.This retrospective study cohort, which combined several datasets of patients previously reported on, consisted of de novo glioma patients who underwent tumor resection at the Brain Tumor Center Amsterdam  between 2010 and 2017 , 14. IncA cohort of healthy controls, previously described in the setting of studies on multiple sclerosis, was used to normalize indices of broadband power , 16. TheIn the glioma patients, PFS was defined as the time (in weeks) between the MEG recording and the date of radiological or clinical progression, as determined by a multidisciplinary tumor board including a neuroradiologist, neurosurgeon, radiation oncologist, medical oncologist and a neurologist. Patients without progression in the follow-up period (until December 2018) were censored after the last contact.MEG data were obtained using a 306-channel whole-head system  with a sampling frequency of 1250 Hz. Brain activity was recorded for 5 min in a magnetically shielded room  during an eyes-closed resting-state in supine position. Patients were instructed to lay still, keep their eyes closed and to stay awake. From the raw data, malfunctioning channels were excluded after visual inspection and artefacts were filtered offline using the temporal extension of Signal Space Separation in MaxFilter software  , 20. A 32-test for categorical data.Data were analyzed using IBM SPSS Statistics for Windows . P-values smaller than 0.05 were considered statistically significant. Group differences between healthy controls and patients were assessed with Student’s t tests for continuous variables and χp < 0.1). To avoid overfitting, the maximum number of covariates was determined by dividing the number of patients with progression by six . We. We13]. Even though overall survival is less intuitively related to tumor growth speed than PFS (as second line treatment effects may compound confounding factors later in the disease), we explored the predictive value of broadband power towards OS in post-hoc analyses. In this cohort of 38 patients, 13 patients died within follow-up, with a median OS of 167 (range 25–327) weeks. In the PFS analyses, the maximal number of covariates was set to one per six cases of progression, or in this case, death . As onlyp = 0.038; see Table p = 0.020) and epilepsy  to be significant predictors, see Table The univariate cohort consisted of 38 patients see Fig. . This mop = 0.051; 0.061; 0.064; 0.076; 0.093; 0.203; 15.8%). The multivariate model revealed statistically non-significant results three times .We assessed the robustness of both models by performing leave-one-out analyses, repeating all analyses, each time excluding one patient. This resulted in a univariate non-significant model six times . According to our results, assessment of broadband power could thus improve the current prognosis prediction. Although we did not directly measure tumor growth, these findings also support the hypothesis that higher levels of neuronal activity are associated with faster tumor growth. Activity recorded at the sensor-level did not predict PFS, emphasizing the importance of reconstructing activity at the source-level , 32.Older age was associated with a slightly longer PFS (HR 0.93), whereas we expected age to predict a slightly shorter PFS (previously reported HR 1.02–1.06) . This coResults from the post-hoc analyses also suggest a relationship between broadband power and overall survival. The univariate model showed broadband power to be predictive of OS, the multivariate model verified this connection. Even though this multivariate model was overfitted and thus had limited statistical power, it suggests that the predictive value of broadband power cannot be explained by other predictors and is an independent predictor of OS.In our patients, broadband power was assessed after tumor resection, which is a relevant phase of the disease in terms of treatment strategy. Part of the current cohort has been reported on with respect to preoperative broadband power, showing that higher oscillatory activity before tumor resection significantly predicts PFS . Hazard Broadband power was based on all AAL regions, because Derks et al. showed similar results in the preoperative setting for tumor regions specifically and when including all AAL regions . From a Glioma prognosis and treatment are determined preoperatively and confirmed or adapted postoperatively, based predominantly on histopathology, molecular tumor subtyping, radiological imaging and patient condition. Our results suggest that broadband power could contribute to assessment of prognosis in the postoperative disease phase.Additionally, broadband power may speculatively be useful in dynamically monitoring disease course. If broadband power is indeed a valid proxy of the neuronal activity that leads to accelerated tumor growth, (forthcoming) tumor progression may conceivably be detected with broadband power before radiological or clinical progression is observable . If so, A foreseen barrier towards clinical implementation of our current results is the fact that MEG is costly and not widely available in hospitals around the world, although new technical developments may change this situation rapidly . For nowEven more valuable than improving survival prediction and monitoring, is the possibility of developing new treatment targets based on the mechanism hypothesized to underlie the predictive value of broadband power for PFS. NLGN3 has been suggested to play a key role in glioma growth and might therefore be a viable treatment target . Indeed,First, although very well-characterized, the study cohort should be considered small, so caution must be applied when interpreting our results. The robustness of the results was evaluated with leave-one-out analyses, showing a 11–16% model instability, probably due to the low sample size. Nevertheless, our study results are in line with previous findings .Furthermore, the study population does not accurately reflect the general diffuse glioma patient population: our patients generally had prognostically favorable tumors, were relatively young and had high performance status. This selection bias possibly resulted in the surprising finding that older age was associated with longer PFS. Although the effect was small (HR 0.95), it was significant, underlining that we should interpret our findings with caution when extrapolating to the general diffuse glioma population.Most patients used an AED. Even though AED dosing was titrated in order to achieve seizure freedom and may thus not reach levels necessary for the dose-dependent lowering of neuronal activity, we cannot exclude the possibility that AED use influenced our results .Last, we used broadband activity as a proxy of neuronal firing. Although we did not directly measure neuronal activity, assessment through MEG remains its most accurate non-invasive proxy , 12.Postoperative broadband power as measured with MEG emerges as a valuable addition to currently known predictors of PFS and OS in glioma patients. Larger prospective cohort studies to investigate the relationship between broadband power and tumor growth are needed to translate these findings to clinical practice. Our findings may be a next step towards improving accuracy of glioma prognosis and disease monitoring, and may offer new leads towards new glioma therapy.Supplementary file1 (DOCX 35 kb)Below is the link to the electronic supplementary material.


Quantifying hearing thresholds via mobile self-assessment audiometric applications has been demonstrated repeatedly with heterogenous results regarding the accuracy. One important limitation of several of these applications has been the lack of appropriate calibration of their core technical components (sound generator and headphones). The current study aimed at evaluating accuracy and reliability of a calibrated application (app) for pure-tone screening audiometry by self-assessment on a tablet computer: Audimatch app installed on Apple iPad 4 in combination with Sennheiser HDA-280 headphones. In a repeated-measures design audiometric thresholds collected by the app were compared to those obtained by standardized automated audiometry and additionally test-retest reliability was evaluated. Sixty-eight participants aged 19–65 years with normal hearing were tested in a sound-attenuating booth. An equivalence test revealed highly similar hearing thresholds for the app compared with standardized automated audiometry. A test-retest reliability analysis within each method showed a high correlation coefficient for the app (Spearman rank correlation: rho = 0.829) and for the automated audiometer (rho = 0.792). The results imply that the self-assessment of audiometric thresholds via a calibrated mobile device represents a valid and reliable alternative for stationary assessment of hearing loss thresholds, supporting the potential usability within the area of occupational health care. Hearing loss together with its negative personal and socio-economic consequences represents a serious health issue across cultures and around the globe. According to the World Health Organization  about 46Hearing tests are necessary for the detection of hearing loss and are usually obtained by audiologists in clinical settings where the patient is seated in a sound-attenuating room (booth) during the testing. This procedure is time consuming, personnel-intensive and hence cost-intensive for the health care system. Therefore, audiometry is not implemented as a general assessment across the adult population, but rather performed selectively in individuals who are either suspected to have hearing loss or might be at risk of hearing loss . A cost-Automated audiometry in general has successfully shown to deliver hearing thresholds and several studies have provided evidence on the validity of hearing thresholds by automated audiometry comparable to manual audiometry . To dateThe validity of a freely available iOS-based audiometric application (uHear) for hearing loss has been investigated in several studies . This deA self-assessment, tablet-based hearing test (ShoeBox audiometry) was validated in adults using a calibrated audiometric headphone revealing hearing thresholds within an acceptable range compared to conventional clinical audiometry . The autFor hearing tests, it is crucial that measurements of hearing levels gathered by different devices are comparable. Audiometric devices differ in their hardware components, i.e., in the tone-generating module and in the type of headphones. Headphones differ in frequency characteristics depending on their specific details of construction. Thus, the use of different types of headphones  can lead to large variations in amplitude of the test signal depending on their frequency response . ConsequAccurate interpretation of hearing thresholds requires calibration of the audiometric device, as comparability with established hearing tests cannot be ensured otherwise. To comply with this prerequisite a newly developed self-assessment tablet-based hearing test, calibrated with audiometric headphones, was validated and its reliability was tested in the current study. This app was developed for adults as a screening test. To evaluate the accuracy and reliability of the audiometric application we measured hearing-thresholds with the new app and compared these to standardized automated audiometry. For the comparison of thresholds, automated audiometry based on the Hughson-Westlake algorithm was applied, as this ensures a standardized procedure and high test-retest reliability. To answer the question whether differences in hearing thresholds between devices were sufficiently similar, an equivalence test (TOST-P) was computed for all frequencies . FurtherThe newly developed audiometric application was compared to standardized automated audiometry using a repeated-measures within-subject design. A conventional audiometer with automatic recording of hearing thresholds standardized according to M = 42.1 years; SD = 13.74) and was roughly balanced according to gender . Two participants had to be measured a second time on a separate day as their data was not stored correctly during the initial measurement.Sixty-eight participants were recruited either via local advertisements or from the database of the Department of Neurophysiology and Pathophysiology of the University Medical Center Hamburg-Eppendorf. Participants received remuneration for their participation (10 €/hour). Inclusion criteria for the participants were: normal hearing (according to self-report), no hearing aids, and an intact outer auditory canal. Participants were recruited so that the sample evenly covered the age range between 19 and 65 years  were conducted in a sound-attenuating room , which fulfilled the requirements of The test session started with an explanation of the entire test procedure and written consent by the participants. An inspection of the outer ear and two screening tests for hearing according to Weber and Rinne were performed, before the main testing started.The audiometric procedure of AM and app was technically similar .The investigator provided a standardized instruction according to A standardized instruction was implemented in the app as a startup-wizard, which each participant had to execute. To guarantee voluntary participation, the user could terminate the test at any time. The startup-wizard included the following: a reminder to take a comfortable seating position and to avoid noise, e.g., through unnecessary movements; a reminder to draw full attention to the test. The headphones were placed by the users themselves. The correct left/right positioning of the headphones was tested by presenting test tones to each ear. Instructions on the screen requested the user to take off glasses, head covers, and jewelry that could interfere with the correct placement of the headphones.no if you are not sure.” The inter-stimulus interval was fixed to 0.7 s. When a threshold was determined for one frequency, the sound pressure level was increased by 20 dB for the subsequent frequency. Following the familiarization, the order of the eleven test frequencies was: 1 to 8 kHz, again 1 kHz and finally down to 125 Hz. For the opposite ear the threshold of the first ear at 1 kHz was taken as the starting level.A familiarization procedure at 1 kHz was conducted before the main test. At all times pure tones with a fixed duration of 1 s were used as test tones and it was not possible to respond before the end of the tone. Following the tone, participants had to respond to the question “Did you hear a tone?” with “yes” or “no.” The choice screen initiated the response interval. If the response time lasted longer than 3 s, the following message was presented: “choose At the end of the procedure, app test results were presented to the participants in five grades according to the criteria of the The total duration of the four audiometric tests was approximately 60–80 min, including the instruction and breaks, which were scheduled after each audiometric test. During the breaks with self-determined duration, participants had the possibility for refreshment. The duration of the breaks did not exceed 5 min. All test sessions were conducted within the timespan from 9 a.m. to 6 p.m. Resulting hearing thresholds were explained to participants after the complete test-procedure with reference to hearing thresholds appropriate for age and gender according to ISO 7029 .The main goals of the analysis were (1) to evaluate the accuracy of the app, i.e., the agreement of hearing thresholds recorded by the app compared to the AM, and (2) to assess the reliability of the app, i.e., evaluating the degree of precision of measurements of the app in a test-retest analysis. The first goal was addressed by testing whether differences in hearing thresholds between devices fall within an acceptable range of deviation to be expected for audiometric measurements using an equivalence test and furthermore by calculating the correlation between the audiograms of app and AM thresholds. The second goal was evaluated by test-retest reliability as estimated by correlating hearing thresholds of the same participants and the same device (correlations within app and within AM thresholds).p < 0.05 was defined for all statistical tests.Data analysis was performed using R and R Studio . For the comparison of thresholds, ears of participants were treated as independent units of observation, as hearing loss can occur in one ear independently from the other one. A significance level of a priori defined range  are rejected. In this case the H1, i.e., the mean value lies within the accepted range of deviation, will be accepted. The critical t-value for the H01 is calculated with the mean values of hearing thresholds in the AM (M1) and the app (M2) according to With μivalence . The test-value for H02 according to:and Diff being the standard deviation of the differences and n the sample size. Hence H01 would be rejected if t1 ≤ −tα, (n–1) and H02 would be rejected if t2 ≥ t1–α, (n–1), respectively, with alpha being the significance level. As 11 tests (one for each frequency) were performed, testing the hypothesis that the devices did not differ in accuracy, a Bonferroni correction for multiple comparisons was applied by dividing the significance level by the number of tests: p = 0.05/11 = 0.0045. The Bonferroni correction reduces the probability of false positives. Of note, with the TOST-P this is the probability that the test erroneously assumes that the two devices do not differ in their thresholds, although they do differ.with sThe interval of equivalence was defined as the maximally tolerable deviation (δ and −δ) due to the measurement uncertainty according to As a second approach to quantify the agreement of the measured values, correlation coefficients between hearing thresholds obtained by the two devices (app and AM) were computed. A Spearman rank correlation was calculated, because hearing thresholds were not normally distributed.rtt), can be considered as the method’s degree of precision. It is assumed that the true values of participants do not differ between measurements  of 0.5, 1, 2, 4 kHz: Normal hearing (≤25 dB HL), mild (26–40 dB), moderate (41–60 dB), severe (61–80 dB), or profound hearing loss like deafness (>81 dB) on the better hearing ear. Two participants had mild hearing loss with ∼35 dB HL on the better hearing ear. This result was obtained identically by the app and the AM. This decision of using the WHO criteria for hearing loss bears the potential disadvantage that unilateral hearing loss, or hearing loss in single frequencies is missing. Further results are very detailed and contain all single frequencies and both ears. The following results are reported for 136 ears of 68 participants.Descriptive analyses of the mean hearing levels (in dB HL) revealed that thresholds measured with the app were slightly higher than thresholds measured with the AM . This reThe distribution of differences between hearing thresholds of the app and the AM across subjects show that at t1 approximately 97% of all differences fall within the range of ± 10 dB HL and at t2: 96% . These vMDiff = −0.48; SD = 4.63, t(135) = 23.984, p < 0.001) and the largest at 1500 Hz  = 14.566, p < 0.001). Results of the TOST-P for all frequencies can be found in For the statistical evaluation of similarity of the hearing thresholds obtained with the app and the AM, the data was analyzed between devices using the first run (t1) only. The equivalence test revealed that differences in hearing thresholds fall into the interval of equivalence for all frequencies . The smaAPP, AM = 0.777 (p < 0.001). A scatter plot of the hearing thresholds indicates a strong association between the two devices  for measurements with the app and rhott, AM = 0.792 (p < 0.001) for measurements with the AM 125 = 0.728 (p < 0.001) at 125 Hz up to a very high correlation at 8000 Hz rho(tt)8000 = 0.919 (p < 0.001). Test-retest reliability values within frequencies in the AM vary from rho(tt)125 = 0.614 (p < 0.001) at 125 Hz up to a very high correlation at 8000 Hz rho(tt)8000 = 0.902 (p < 0.001).Hearing thresholds within devices show a strong correlation between the first and the second session, also when considering single frequencies . CorrelaMD = 12.38 min (IQR = 2.179) and with the AM it was MD = 16.51 min (IQR = 3.45). Minimum and maximum times to complete the test with the app were 10.05 min and 19.73 min and with the AM were 13 and 28.5 min. The time to complete the hearing test with the app was significantly shorter than with the AM at t1: The average difference was MDiff = −4.27 min , t(63) = −14.335, p < 0.001.To assess whether durations to complete the test with the app and the AM are comparable, the duration of each audiometric test was measured. For both devices the time interval included the familiarization phase, but not the instruction. Due to transmission errors, durations of four subjects were lost in the data set of the app. The median of durations at t1 to complete the test with the app was MD = 11.98 min (IQR = 2.566) and with the AM it was MD = 16.23 min (IQR = 3.49). Within audiometric tests no significant differences between t1 and t2 durations were observed , V = 0.713, p = 0.096. For this comparison the duration values of seven participants were lost, due to the transmission error of the app data.To assess whether a learning effect had occurred in the use of the app, reflected in a shorter duration of the second testing, a two-sided Wilcoxon signed rank test was computed. The duration with the app at t2 was The results of the current validation study revealed similar audiometric thresholds for the mobile screening audiometer application compared to standardized automated audiometry. Differences between the audiograms of both devices ranged within a clinically acceptable range as indicated by an equivalence test. The convergence of both audiograms revealed by each device was demonstrated by the correlation of hearing thresholds. The degree of reliability of the mobile screening audiometer was quantified by a test-retest reliability analysis. The resulting correlation coefficient revealed a high degree of precision for the app and showed similar levels compared to standardized automated audiometry.The first aim of the present study was to investigate the validity of the mobile screening audiometer. To that end, we compared the hearing thresholds of the app with hearing thresholds of a standard automated audiometer as used in clinical context. An equivalence test (TOST-P) indicated a good agreement between the auditory thresholds of both devices. The test revealed that mean differences between app and AM are significantly smaller than the limits of uncertainty that are common to audiometric procedures. Therefore, we concluded that the hearing thresholds gathered with the app seem to be within a clinically acceptable range of deviation compared to hearing thresholds obtained with a standard automated audiometer. Agreement of thresholds is also supported by the significant correlation between hearing thresholds of the two devices. In the current study 97% of detected thresholds were within the clinically acceptable range of difference (10 dB HL) compared to standardized automated audiometry. Compared to other studies investigating the validity of the mobile audiometric applications, the corresponding percentage of values within the acceptable range in the current study is comparable  or even Mobile audiometric devices without calibrated headphones show larger differences in hearing thresholds compared with conventional audiometry . SeveralApplications which were tested with calibrated headphones resulted in much higher accuracies than the uncalibrated devices . In factMost mobile audiometric devices suffer from a limited range of sound intensities due to the technical limitations of headphones or the acoustic converter. At the lower boundary of sound intensities other studies report a technical limitation at 10 dB HL  or 15 dBThe second aim of the study was to evaluate the degree of reliability of measurements of the app by performing a test-retest analysis. This analysis revealed a high degree of precision for the app with slightly higher correlation coefficients compared to the audiometer. Only the evaluation of one other computerized and self-assessment hearing test  was repoTaken together, the findings of the present study support the assumption that the app under investigation showed acceptable values of accuracy and high reliability which are in the range of automated clinical audiometers . The devThe duration to complete an audiometric test with the app was on average 4.27 min shorter than with the AM, pointing to a potential time-saving advantage for the investigator and reducing task-related fatigue for the participant. Three main features, which could affect the duration of the test, were different between the app and the audiometer: the duration of the test tones, the interval between response and the next tone, and the type of question the participants had to answer (press a button only when a tone is present vs. press different buttons for tone present/not present). These three differences could have led to faster detection of thresholds without strongly affecting the validity of the new audiometric device, as differences between both devices were within the limits (97% of thresholds were within 10 dB difference).A learning effect for the execution of the app was not detected, as test durations in the first compared to the second run were similar. Similar durations for both runs also indicate that the instructions for the app were sufficiently well understood before the first run. Also, an effect of fatigue seems unlikely, given similar test durations at the beginning and the end of the whole testing session.Test sessions were conducted in a sound-insulated booth and it is therefore not evident whether results can be compared to the measurement of hearing thresholds in a noisy surrounding, like in a standard office. Thus, field studies with environmental noise could provide more insight on the accuracy and validity of the audiometric thresholds gathered by the app, for example in a waiting room of an otolaryngologist. Importantly, a validation with audiologically impaired patients would be necessary for the estimation of sensitivity and specificity of the app for clinically relevant hearing loss.The audiometric application was designed for self-assessment. However, even though the whole audiometric procedure can be performed by the participant, the system is not intended for the use in private homes, outside the range of a trained person, as special headphones are needed and regular calibration of the iPad/headphone combination is a prerequisite. Apart from the calibration of the system, which has to be performed by a specialized company, this audiometric screening test can be operated by the user. Some supervision by trained personnel is helpful when starting the app, but it does not require the guidance of a health care professional. This stands also in contrast to the operation of the automated audiometer (which was used for comparison) for which the placement of the headphones and the instruction of the participants had to be done by a trained person.A further limitation of the study is concerning the sampling method. Gender and age range are well-known factors which influence hearing thresholds . To avoiTwo authors of the current article were involved in the development of the app-based mobile hearing test which was evaluated in this study. This fact was disclosed before the start of the study so that a potential influence on the design of the study, data collection or the rational of data analysis could be contained beforehand.Given the experimental settings of the present study, i.e., measuring audiometric thresholds in normal hearing participants in a sound-attenuated room, the results provide first evidence that the mobile self-assessment screening audiometer with calibrated headphones is measuring hearing thresholds, which are comparable to those obtained by a standard automated audiometer. Thus, the app could serve as a valid screening audiometer. Future studies could explore the validity of the app for patients with hearing loss or for children.The datasets generated for this study are available on request to the corresponding author.This research was conducted in conformity to the regulations and ethical standards of the Hamburg Medical Association (Ärztekammer Hamburg). The ethical review board of the Hamburg Medical Association waived the requirement for ethical approval for this study after screening the study protocol given that the experimental execution and data handling did not meet the criteria for an obligatory ethical review according to national guidelines and local legislation. Data storage and analysis were fully anonymized. Written informed consent was collected by all participants before participation according to the Declaration of Helsinki.TS and GS conceived and designed the study. AC performed the testing. AC, TS, and GS analyzed the data. JN contributed materials or analysis tools. AC and TS wrote the manuscript. All authors discussed the results, edited, and approved the manuscript.GS and JN received reimbursements or salary from Sonormed GmbH  to participate in the development of the app-based hearing test evaluated in the current study. JN received salary from Mindo Software SLU . The company was involved in the software development of the app. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Supplemental Digital Content is available in the text. Diet may affect susceptibility of the inner ear to noise and age-related effects that lead to tinnitus and hearing loss. This study used complementary single nutrient and dietary pattern analysis based on statistical grouping of usual dietary intake in a cross-sectional analysis of tinnitus and hearing difficulties in a large population study sample.The research was conducted using the UK Biobank resource. Tinnitus was based on report of ringing or buzzing in one or both ears that lasts more than five minutes at a time and is currently experienced at least some of the time. Identification of a hearing problem was based on self-reported difficulties with hearing. Usual dietary intake and dietary patterns  were estimated based on between two and five administrations of the Oxford Web-Q 24-hour dietary recall questionnaire over the course of a year for 34,576 UK adult participants aged 40 to 69.In a multivariate model, higher intake of vitamin B12 was associated with reduced odds of tinnitus, while higher intakes of calcium, iron, and fat were associated with increased odds . A dietary pattern characterised by high protein intake was associated with reduced odds of tinnitus . Higher vitamin D intake was associated with reduced odds of hearing difficulties , as were dietary patterns high in fruit and vegetables and meat and low in fat .There were associations between both single nutrients and dietary patterns with tinnitus and hearing difficulties. Although the size of the associations was small, universal exposure for dietary factors indicates that there may be a substantial impact of diet on levels of tinnitus and hearing difficulties in the population. This study showed that dietary factors might be important for hearing health. Tinnitus refers to the perception of sound without an external source. Prevalence of tinnitus among UK adults aged 40 to 69 years was estimated at 16.9% . HearingThe role of diet in tinnitus has been identified as a research priority by both patients and clinicians .There isTaken together, these studies suggest a small but significant impact of diet on tinnitus in the general population. The literature on diet and hearing in humans has been reviewed in detail elsewhere . Most prDespite the limitations of single nutrient analyses, some general conclusions about single nutrient intake and hearing may be possible. Higher intake of lipids, carbohydrates, and sugars is typically associated with poorer hearing. In recent studies, polyunsaturated fats were associated with better hearing, while high-density lipoprotein and triglycerides were associated with poorer audiometric hearing  and inciWith respect to micronutrients, inconsistent associations between vitamins A, B, C, and E, and magnesium, and hearing have been reported . BesidesAn alternative and complementary approach to single nutrient analysis is dietary pattern analysis , which iWe are aware of two studies that have examined dietary patterns in relation to hearing. Spankovich and LePrell (2013) carried out cross-sectional analysis of data from the National Health and Nutritional Examination Survey. Higher dietary quality .The analysis was conducted using the UK Biobank resource . UK Biobz score relative to national levels that are then summed to provide an overall deprivation score. Lower scores represent less deprived (more affluent) socioeconomic status. In the analyses described later, Townsend scores were categorized into quartiles from least to most deprived sections of the study sample. Race was coded according to “White” or “Nonwhite” background.Data on sex, race (based on 2001 UK Census categories), and area of residence were recorded for each participant. Area of residence was translated to Townsend deprivation score, a proxy measure of socioeconomic status . TownsenA subset of the UK Biobank was included in the present analysis. Participants were excluded from analysis on the basis of (i) outside age range of 40 to 69 years, (ii) no self-report hearing or tinnitus data, (iii) estimated energy intake <597 or >4300 kcal/day or >5 SD above or below the mean, (iv) having completed less than two instances of the 24-hr dietary recall questionnaire, (v) estimated dietary intake more than 5 SDs above the mean for any nutrient, and (vi) self-reported use of a dietary supplement at each instance of completion of the dietary recall questionnaire. At least two instances of completion of the dietary recall questionnaire were required for estimation of usual dietary intake . RegularIdentification of a hearing problem was based on a response of “yes” to the question “Do you have any difficulty with your hearing?”. Estimates of the accuracy of self-report vary according to the level of hearing impairment, though they are typically around 90% sensitivity with 70% specificity for moderate levels of hearing impairment . IdentifDietary assessment was based on the Oxford Web-Q, a detailed computerized questionnaire on the intake of 200 commonly consumed food and beverages consumed in the previous 24 hr . The WebCardiovascular disease was identified if a participant reported any cardiovascular problem including heart attack, heart failure, stroke, angina, intermittent claudication, transient ischemic attack, and arterial embolism or deep venous thrombosis. High cholesterol was identified on the basis of participant report of high cholesterol or if the participant reported they were currently taking medication for high cholesterol. Hypertension was identified on the basis of participant report of high blood pressure, currently took medication for high blood pressure, or a measured systolic blood pressure greater than 140 mm Hg or diastolic pressure greater than 90 mm Hg. Diabetes was identified on the basis of participant report of diabetes or the use of medication for diabetes.Body mass index (BMI) was calculated as the participants’ weight (in kilograms) divided by height squared (in meters). All medications that were being taken regularly  were recorded. Short-term medications  were not recorded. All medications known to have ototoxic properties  were coded as such. Occupation-related noise exposure was identified on the basis of any reported noise exposure in response to the question “Have you ever worked in a noisy place where you had to shout to be heard?” Music-related noise exposure was identified on the basis of any reported exposure in response to the question “Have you ever listened to music for more than 3 hr per week at a volume which you would need to shout to be heard or, if wearing headphones, someone else would need to shout for you to hear them?” The criterion for work- and music-related noise roughly corresponds to exposure exceeding 85 dB(A) . ParticiBoth raw nutrient intake and nutrient intake adjusted for individual variation in energy intake were analyzed. Two methods were used to adjust for variation in individual energy intake : (i) nutLogistic regression analyses were carried out to model cross-sectional associations between micro- and macronutrients and dietary patterns with hearing difficulties and tinnitus. Nutrients and dietary pattern components were entered as quintile data. Classification of dietary intake into quintiles is commonly used in dietary epidemiology because analysis by quintiles of intake does not require the assumption of a linear dose–response relationship and reduces the impact of outlying data points. Quintile 1 (the lowest level of intake) was the reference category in the following analyses. All nutrient/dietary components were entered simultaneously into the regression models. Potential confounders were identified based on having been implicated with hearing loss or tinnitus in previous research  and checThere were 47,072 participants who had completed at least two instances of the Web-Q and hearing and tinnitus questions. Participants who were regular supplement users  were excluded. Table With respect to completion of the Web-Q, 10% (3451) of participants completed the Web-Q five times, 24% (8327) four, 31%  three, and 25%  on two occasions. There was representation of each season of the year  and day of the week .Principal components analysis yielded six components with Eigenvalues greater than 1. The six components accounted for 78% of variation in diet, following varimax rotation Table . To assihttp://links.lww.com/EANDH/A550).The final regression models for tinnitus included age, sex, race, ototoxic medication, occupation-related noise exposure, smoking status, alcohol consumption, and cardiovascular disease. Higher intake of vitamin B12 and dietary pattern factor 3 (High protein) was associated with reduced odds of tinnitus (Table http://links.lww.com/EANDH/A551).The final regression models for hearing included age, sex, race, ototoxic medication, BMI, occupation-related noise exposure, smoking status, alcohol consumption, physical activity, and hypertension. Higher intake of fat and saturated fat was associated with higher odds of reporting hearing difficulties Table . Higher There were no interactions between age, sex, BMI or race and B12, calcium, iron, fat or factor 3 dietary intakes and tinnitus. There were no interactions between age, sex, BMI or race and vitamin D, factor 1, 2, or 3.Around one-third of UK Biobank participants completed a test of speech recognition in noise . PatternAll analyses were rerun including the 11,008 participants who were excluded from the analysis earlier having been identified as regular users of dietary supplements. A similar pattern of results was obtained for both tinnitus and hearing difficulties.This study reports evidence of associations between both single nutrients and dietary patterns with tinnitus and hearing difficulties. The size of the associations was generally small, but the universal exposure that the dietary data represent implies there may be a substantial impact of diet on levels of tinnitus and hearing difficulties within the population.Higher intakes of calcium, iron and fat were associated with increased odds of tinnitus while higher intakes of vitamin B12 and dietary pattern factor 3 (High protein) were associated with reduced odds of tinnitus. Adjustment for hearing difficulties made no difference to patterns of associations between tinnitus and dietary factors, suggesting that possible impacts of diet on tinnitus are independent of hearing.The present study is the only study we are aware of to suggest a specific association between levels of dietary B12 and tinnitus. The association between factor 3 (High protein) intake and reduced odds of tinnitus may be related to vitamin B12; meat, fish, and poultry are sources of vitamin B12. A recent Korean population study similarly reported that lower intakes of protein, vitamin B2, and vitamin B3 were associated with increased tinnitus . One stuWe previously reported that “persistent tinnitus” was associated with higher fruit and vegetable, bread, fish, and egg intake. Dairy and caffeinated coffee intake was associated with reduced odds of persistent tinnitus . “BotherHigher intake of fat and starch was associated with increased risk of tinnitus. Abnormalities in the calcium signaling pathway in outer hair cells have previously been linked to tinnitus , and calHigher intake of vitamin D and dietary patterns characterized by high fruit and vegetable intake (factor 1) and high protein (factor 3) was associated with reduced odds of hearing difficulties. A dietary pattern associated with high saturated fat intake (factor 2) was associated with increased risk of hearing difficulties.The association between a high-fat diet and poorer hearing is consistent with a growing body of research in relation to a diet high in saturated fats and poor hearing . A diet Consistent with other dietary pattern analyses showing that healthy diets were associated with reduced risk of audiometric poor hearing  and selfpoorer mid- and high-frequency audiometic hearing. Higher levels of vitamin D were associated with better hearing in the present study. The association between factor 3 (High protein) intake and better hearing may be related to vitamin D intake, as oily fish, red meat, and liver are sources of vitamin D. In contrast, a Korean population study  reportedpoorer hearing while high-protein diet was no longer associated with hearing. Possible explanations are that tinnitus may mediate the impact of diet on hearing, the self-reported measures of hearing are confounded, or that one or both associations are spurious. Disentangling relationships between tinnitus, hearing, and dietary factors may require mediation analysis based on longitudinal observational data, followed by experimental studies with random allocation to dietary condition.In models of dietary factors with hearing, addition of tinnitus as a covariate resulted in intake of B12 associated with It was not possible to establish causal associations based on the cross-sectional correlational design on the present study or to examine the time course of exposure to dietary factors and development of hearing difficulties/tinnitus. The associations described in the present study may be due to confounding with other dietary or nondietary factors or to insufficient control of measured confounders. Following the precedent of previous single nutrient studies, the analyses were not adjusted for type-1 statistical errors; some of the statistically significant associations reported in the present study may be false positives. Encouragingly, the present study reported similar associations  that have been reliably reported in previous independent studies. Additionally observational prospective cohort studies and/or intervention studies of dietary supplementation would provide more convincing evidence of causal associations.Hearing was indexed with a self-reported measure rather than measured thresholds, although self-reported hearing difficulty is a reliable index of audiometrically identified hearing impairment , and almNutrients were estimated from dietary intake rather than biomarkers; circulating levels are likely to differ from those estimated from intake due to homeostatic mechanisms, individual differences in absorption, bioavailability, errors in nutrient estimation based on food/beverage intake, and other factors . DietaryOne disadvantage of single nutrient approaches is that single nutrients are not consumed in isolation. Nutrient intakes correlate with each other, and single nutrient analysis does not take into account the possibility of biochemical interactions between nutrients. One approach to address the limitations of single nutrient analysis is to examine dietary patterns rather than single nutrients. This was the first study to use dietary pattern analysis based on statistical identification of dietary patterns within a population in relation to hearing and tinnitus. A focus on dietary patterns is complementary to the single nutrient approach that the majority of previous dietary hearing research used. The limitations of dietary pattern analyses are that dietary patterns identified from the data must be interpreted post hoc, similar diets may be named differently across studies, and dietary patterns identified in one population may not be generalizable to other populations in which dietary groupings may be different. The effects of single nutrients that do significantly affect health may be diluted and missed, and associations with overall dietary patterns may not inform the biological mechanism underlying disease.The UK Biobank recorded use of dietary supplements, but the exact formulation of supplements was not recorded, and it was not possible to include intake from supplements or adjust for intake from supplements in the analysis. Users of dietary supplements differ from nonusers in various ways including health, demographics, lifestyle, and diet . EncouraInformation on occupation- and music-related noise exposure was based on self-reported levels of noise exposure rather than direct measurement of noise levels experienced by participants. Some residual confounding with noise exposure may therefore be likely.This study suggested a substantial impact of diet on levels of tinnitus and hearing difficulties.Dietary factors are thought to influence susceptibility to tinnitus and hearing loss. This study examines cross-sectional associations between tinnitus, hearing difficulties, and nutrient intakes and dietary patterns using a validated 24-hr recall dietary questionnaire in a large population study sample of 34,576 UK adults. Dietary nutrients and patterns were estimated based on repeated administrations of a questionnaire over one year. After adjusting for confounders, higher intakes of calcium, iron, and fat were associated with increased odds of tinnitus, while higher intakes of vitamin B12 and a dietary pattern high in meat intake were associated with reduced odds of tinnitus. Higher intake of vitamin D and diets high in fruit, vegetable, and meat were associated with reduced odds of hearing difficulties. A high-fat diet was associated with increased risk of hearing difficulties. The study suggests that a diet low in fat and high in vitamins and antioxidants may be important for hearing health.The authors thank Avni Vayas for assisting with interpretation of the factors in the dietary pattern analysis.