Purpose: The purpose of this study was to identify the current health status of adults in the United States with self-reported hearing loss and compare it with US adults with a self-reported excellent or good hearing in three areas: (1) chronic disease states and general health status, (2) medical screening behaviors, and (3) lifestyle behaviors. Methods: A secondary data analysis was conducted using the 2014 data set from the National Health Interview Survey (NHIS), specifically the Sample Adult Public Use File (samadult). For this questionnaire set, one adult per family was randomly selected. This individual self-reported their response to the questionnaire items. Binary regressions were used to analyze the odds ratio to find differences for selected disease states, screenings, and lifestyle behaviors. Respondents were grouped into one of four categories: excellent/good hearing, a little trouble hearing, moderate/a lot of trouble hearing, and deaf. Results: The excellent/good hearing group was used as the comparison group for the other three levels of hearing. There are many differences in likelihood to self-report disease states; the greatest increased likelihoods include tinnitus and heart disease, with tinnitus being 8.6 times more likely for those who identified as having moderate/a lot of hearing loss. Those with any level of hearing loss were 3 to 5 times more likely to self-report heart disease. Regarding lifestyle factors, individuals with any level of hearing loss were less likely to consume alcohol and 2.5 to 9 times more likely to be unable to engage in moderate or vigorous activity on a weekly basis, respectively. Conclusions: There is a difference in the health status of individuals with hearing loss across all three areas examined , and those differences vary based on level of hearing loss, the most notable being the self-reported inability to engage in moderate and vigorous physical activity. Disproportionate rates of tinnitus and heart disease were evident in all levels of hearing loss but most notable in those identifying as having moderate/a lot of trouble hearing. Further interdisciplinary research is necessary to improve the health of individuals with all levels of hearing loss, increase awareness of the hearing/health connection, and decrease hearing loss in general. Approximately 38 million adults in the United States (approximately 15% of adults) suffer from some degree of hearing loss . This nuAlthough the number of Americans with hearing loss is increasing, little is known about the health status of adults with hearing loss. Even less is known about the association between hearing loss and health behaviors. In 2008, Schoenborn and Heyman used data from the National Health Interview Survey (NHIS (2000–2006)) to identify some aspects of health status for adults with hearing loss. They reported that adults who were deaf or had a lot of trouble hearing were almost three times more likely than those with normal hearing to rate their health as fair or poor. Furthermore, adults with deafness or significant hearing loss had higher rates of hypertension and diabetes and were more likely than those with normal hearing to engage in certain health risk behaviors  .Other researchers have also reported associations between hearing loss and specific disease states. For example, diabetes is positively associated with hearing loss ,7. In onSome researchers have also reported a positive association between chronic kidney disease and hearing loss, particularly sudden sensorineural hearing loss ,12. Lin Unfortunately, there is a paucity of research regarding the association between hearing loss and specific health behaviors. The few studies that do exist reveal interesting results. For example, smoking seems to have a negative effect on hearing ,16,17,18One reason for the difference in health status between those with hearing loss and those with normal hearing may be differences in health behaviors between these two groups. If hearing loss is positively associated with negative health status and health risk behaviors, it is vital that public health experts, audiologists, and healthcare providers find ways to intervene and prevent deleterious health outcomes among those with hearing impairments. Therefore, the purpose of this study is to more fully investigate the association between hearing loss, health status, and health behaviors. This study was an observational study using secondary data analysis methods to analyze the responses from the 2014 National Health Interview Survey (NHIS). The NHIS is an annual survey conducted by the US Census Bureau using a face-to-face interview format in participants’ homes. Hence, participants in the current study were non-institutionalized adults in the United States aged 18 to 85 who participated in the 2014 NHIS. The survey required approximately one hour to complete . A total of 36,697 interviews were completed from the 44,552 participating households in the 2014 NHIS. The survey results were labeled “Sample Adult” by the Census Bureau. The response rate for the “Sample Adult” interviews was 80.5%. Sample weighting was based on the 2010 census population estimates. The random sample used by NHIS came from all 50 states and the District of Columbia and was based on the four census regions. Within census regions, participants were selected from both metropolitan and non-metropolitan areas. The sampling design featured a complex, multistage sample method that included stratification, clustering, and oversampling of selected subgroups .n= 4), (2) adult socio-demographics , (3) adult conditions , (4) adult health status and limitations of activity , (5) adult health behaviors , (6) adult healthcare access and utilization , (7) adult selected items , and (8) adult internet and email usage . The interview guide/survey for adults was called the “Sample Adult Questionnaire”. The accompanying data file (samadult) released by the NHIS (identifying information removed) had 769 possible questions. The adult survey included 8 sections. The section titles and the number of questions in each section were as follows: (1) adult identification  excellent/good hearing , (2) a little trouble hearing, (3) moderate trouble/a lot of trouble hearing, and (4) deaf. We analyzed these four hearing groups in relation to their health status indicators and health behaviors. We examined three categories of health indicators: (1) self-reported health status (disease states), (2) self-reported medical behaviors (preventive care), and (3) self-reported health behaviors (lifestyle behaviors). The disease states that we examined included diabetes, hypertension, obesity, stroke, chronic obstructive pulmonary disease (COPD), coronary heart disease, hypercholesterolemia, and cancer. The medical/preventive care behaviors that we examined included various screening tests and vaccinations. The health behaviors that we examined included tobacco use, alcohol use, and physical activity. Descriptive statistics such as mean and standard deviations were used to describe the respondents and their responses on various survey items. Binary regression was used to assess the association between the hearing variables and the health-related outcomes. All analyses were completed using Statistical Product and Service Solutions (SPSS) 23 . The a pParticipants in the 2014 NHIS were sampled from the four census regions: South (35%), West (27%), Midwest (21%), and the Northwest (16%). Participants were White (78%), over age 40 (66%), female (55%), with excellent hearing (48%), and married and living with a spouse (42%). About 1 in 5 adults (19%) reported some level of hearing loss . RegardiAdults with all levels of hearing loss were less likely to rate their health as “about the same as last year” than individuals with excellent/good hearing. Participants with moderate hearing loss/a lot of trouble hearing were almost 3× more likely (OR = 2.93) to rate their health as “worse” than last year compared to those with excellent or good hearing. Individuals who were deaf were slightly more likely (OR = 1.33) to rate their health as “better” than last year compared to those with excellent/good hearing .Degree of hearing loss was associated with the following disease states: tinnitus, heart disease, cancer, COPD, diabetes, ulcers, hypertension, stroke, and high cholesterol. Rates of ulcers, hypertension, diabetes, and cancer increased as degree of hearing loss increased ranging from 2 times (OR = 2.21 for ulcers) to 3.5 times (OR = 3.72 for cancer) more likely to occur when compared to excellent hearing comparison group. Adults reporting “moderate/a lot of trouble hearing” were 5 times (OR = 5.05) more likely to experience heart disease and had the highest reported levels of tinnitus. This group also had the greatest increased risk (3–5×) of COPD (OR = 4.83), stroke (OR= 4.44), and hypertension (OR = 3.43). Individuals identifying as deaf were in the lowest risk group for COPD (OR = 2.59), stroke (OR = 2.05), and hypertension (OR = 3.38), but still at least twice as likely as the comparison group (excellent/good hearing) to have the aforementioned chronic diseases .The likelihood of having tinnitus was much greater for those with any level of hearing loss but did not consistently increase as hearing difficulty increased. Adults who reported hearing loss of any level were between nearly 4 to 9 times (OR= 3.9 to OR = 8.66) more likely to report experiencing tinnitus than their peers with excellent/good hearing . IndividParticipants with any level of hearing loss were less likely to have seen a dentist in the past 12 months (OR = 0.76–0.80); however, they were up to twice as likely to have completed other preventative health screenings the last year when compared with participants with excellent/good hearing  dependinHIV testing was low across all participants, with slightly more than 1/3 reporting they had been tested for HIV . Men whoRegarding lifestyle behaviors of all the participants, many consumed alcohol (63%), had never smoked cigarettes (60%), were sleeping 7–8 h on average each night (58%), and were engaging in moderate physical activity on a weekly basis (58%) .Participants with hearing loss varied in their lifestyle  behaviors. Overall, those with any level of hearing loss had similar hours of sleep, were about 50% less likely to report never being a smoker (OR = 0.48–0.64), and were much less likely to consume alcohol (12+ drinks a year) (OR = 0.05–0.06) than those reporting excellent/good hearing .Respondents with a little trouble hearing had similar alcohol consumption rates as those with excellent/good hearing but were 2.5 times more likely than those with excellent/good hearing to report difficulty with engaging in moderate (OR = 2.71) and vigorous physical activity (OR = 2.54) .Individuals reporting moderate/a lot of trouble hearing were twice as likely (OR = 2.23) to get more than 8 h of sleep per night. They were also more than twice as likely than as adults with excellent/good hearing to be former smokers (OR = 2.44). With exercise, however, they were more than 4 times more likely than those with normal hearing to report being unable to engage in moderate exercise (OR = 4.19) and vigorous exercise (OR = 4.15) .Participants identifying as deaf also reported being three times more likely (OR = 3.01) to get more than 8 h sleep than adults who identified as having excellent/good hearing. They were also almost half as likely to consume alcohol less than 1 day a week (OR = 0.55). Finally, most dramatically different were the exercise habits of people who were deaf. This group was more than 9 times more likely than those with normal hearing to report not being able to engage in moderate physical activity (OR = 9.45) and 7 times less likely to not engage vigorous activity (OR = 7.03) . The purpose of the current study was to investigate the association between hearing loss, health status, and health behaviors. Overall, hearing loss was associated with greater risk of self-reported disease and poor health status. Those who were deaf reported their health as “worse” than last year three times more often those with excellent/good hearing. For most of the disease states assessed, individuals with any level of hearing loss had a greater likelihood of disease when compared to adults with excellent/good hearing. For example, individuals with moderate/a lot of difficulty hearing had a greater likelihood of being diagnosed with COPD, stroke, high cholesterol, and tinnitus compared to adults in other hearing acuity categories. Furthermore, people with any level of hearing loss reported suffering from tinnitus and heart disease at disproportionately high rates compared to those with excellent/good hearing status.The positive association between hearing loss and disease was a noteworthy finding. The heaviest disease burden seemed to fall upon individuals with moderate/a lot of hearing loss—even more so than those who were deaf. Adults with moderate/a lot of hearing loss were most likely to report having hypertension, COPD, stroke, high cholesterol, ulcers, hepatitis, long-term liver conditions, and tinnitus than individuals at any other level of hearing loss. It should be noted that tinnitus itself is associated with noise-induced hearing loss, hormone changes, and heart disease . In contPotential reasons for such differences in self-reported disease by hearing level may be the result of increased communication barriers faced by those with hearing loss when they seek medical care and obtain follow-up care with specialty physicians. Adults with hearing loss may be less motivated to seek medical care and/or seek it less frequently because of their tendency to withdraw socially, avoid engagement with others, and to disengage from conversations/situations they are already engaged in . PatientSuch findings regarding physicians’ negative perceptions of adults with hearing loss point to the need for improved physician training during medical school and residency ,25. SpecDifficulties with comprehension and memory may also be a contributing factor to the higher self-reported disease rates reported by adults with hearing loss. Hearing acuity and memory have been shown to have an inverse association . Tun andOverwhelmingly, the largest health disparity between individuals with some form of hearing loss and those who reported excellent/good hearing was in moderate and vigorous physical activity. Those with hearing loss reported greater difficulty in engaging in moderate and vigorous physical activity compared to those with excellent/good hearing. Regular physical activity has been associated with better weight control, reduction in the risk for cardiovascular disease (CVD), Type 2 diabetes, and some cancers . A conneAll people face some barriers to engaging in regular, moderate, or vigorous activity. For example, Freene et al. (2011)  identifiWhatever the reason(s) that individuals with hearing loss report greater difficulty exercising, we know that negative health outcomes increase as inactivity increases. This may be one reason adults with hearing loss were more likely to be diagnosed with conditions such as high cholesterol, hypertension, diabetes, cancer, and stroke.Some of these barriers to exercise could be overcome by educating health coaches and others in related fields about how to better communicate with individuals with hearing loss. Online training modules could be constructed and required for licensure through such agencies as the National Commission for Health Education Credentialing (NCHEC), National Council on Strength and Fitness (NCSF), American Council on Exercise (ACE), National Academy of Sports Medicine (NASM), International Sports Sciences Association (ISSA), and CrossFit. Targeting the importance of moderate and vigorous exercise to those with hearing loss through education and health risk messaging would be a great first step in improving the activity level for individuals with hearing loss. Furthermore, inter-professional education between physicians and audiologists is needed to better tailor and customize messages and materials regarding exercise and how to reduce barriers to exercise among those with hearing impairments.Regarding lifestyle and prevention, some types of hearing loss can be preventable—for example, noise-induced hearing loss, loss caused by a singular or regularly occurring loud event/episode. Causes may include loud music, operation of loud machinery, or a single episode of uniquely loud stimuli . Early and consistent hearing screenings with proper referral and follow-up may help to reduce the negative health impact of hearing loss of any kind. Improved access to, use of, and social awareness of assistive devices such as hearing aids, cochlear implants, Frequency Modulation FM systems, sound-fields, infrared systems, and induction loops may also have a positive effect on the health of individuals with hearing loss. Such primary and secondary prevention efforts would help public health experts to meet the specific hearing objectives in Healthy People 2020 .The results of this study should be viewed with its potential limitations in mind. First, the NHIS used self-reported data. Self-reported data, especially responses gathered via personal interview, have the potential for socially desirable responses which can create bias in the results. Therefore, healthy behaviors such as exercise and preventive screenings may have been over-reported. Second, the NHIS survey used broad, general response items for participants to rate their hearing. Such loosely defined responses may have led to a lack of specificity and accuracy. This may have negatively impacted the internal validity of the results regarding classification of hearing loss. Third, the nature of the NHIS data is cross-sectional, and the statistical analyses used by the investigators of the current study preclude causal references.Further research is needed to better understand the reasons adults with hearing loss report higher levels of specific diseases and why they have greater difficulty engaging in regular physical activity/exercise. Utilizing mixed research methods and qualitative research in particular may help to elucidate why these difficulties exist. Examining these health outcomes/behaviors in relation to variables such as age of onset of hearing loss, percent of life with hearing loss, hours of device use, support systems, possible habilitation or rehabilitation, and type of device would provide invaluable information.Finally, more efforts are needed to improve health professionals’ awareness of and sensitivity to those with hearing loss and how hearing loss impacts health. Such efforts should include education and training for future healthcare providers, medical social workers, patient educators, health educators, and community health workers. Inter-professional workshops/seminars/trainings to improve communication with patients/clients regarding the health implications of hearing loss need to be established, and research needs to be done on how to best address this growing population.


This research aims to validate a modified visually based Montreal Cognitive Assessment for hearing-aid users (MoCA-HA). This population should be the target of cognitive screening due to high risk of developing dementia.Case-control study.The participants were recruited from referral hearing-aid center and memory clinic in central London, United Kingdom.75 hearing-aid users were recruited. Of these, thirty were cognitively intact controls with hearing impairment (NC-HI); thirty had mild cognitive impairment with hearing impairment (MCI-HI); fifteen had dementia with hearing impairment (D-HI).The baseline characteristics and analysis of the MoCA-HA for the NC-HI were recorded. The MoCA-HA performance of the MCI-HI cohort and D-HI cohort were also studied.The cutpoint of <26 yields 93.3% sensitivity with 80% specificity in distinguishing MCI-HI from NC-HI. The specificity increased to 95.6% in screening for all cognitive impairment (MCI-HI and D-HI) from NC-HI.The MoCA-HA has been validated with a cutpoint which is comparable to the traditional MoCA. This tool may help clinicians to early identify older adult hearing-aid users for appropriate cognitive evaluation. One in every three adults over the age of 65 suffer from disabling hearing loss . A recenSince current available screening tools for cognitive impairment require patients to follow orally presented instructions, normal hearing thresholds are implicitly assumed when conducting the test. However, mishearing or misinterpreting the test instructions and test items due to hearing loss can lead to underestimations of cognitive ability . Timely The Montreal cognitive assessment (MoCA) has excellent validity in identifying mild cognitive impairment (MCI) compared to other commonly used screening tools . HoweverAnother possible way of MoCA modification is to adapt commonly used dementia screening tools for a visual as opposed to an auditory presentation. Lin et al. have demonstrated the utility of a visually adapted version of the MoCA for the severely hearing-impaired (HI-MoCA) by examining the performance of cognitively intact individuals with cochlear-implants . HoweverThis study aims to expand upon Lin et al.’s work by examining the performance of the adapted MoCA in older adults with no restriction of hearing loss severity. Moreover, it aims to test the ability of the visually adapted MoCA to distinguish individuals with a diagnosis of MCI and dementia from those without and establish an optimum cutpoint. The cutpoint may differ from the traditional MoCA due to the different test delivery modality and cognitive ability of the hearing-impaired cohort .clinicaltrial.gov with Identifier: NCT03648502.The project was approved by the UK National Health Services (NHS) Ethical Committee IRAS247176. The study was under the University College London Joint Research Office (JRO) sponsorship ID 18/0306. The study protocol was registered in A sample of 30 adults aged ≥65 were recruited via recruitment flyers and posters distributed in the hearing aid center at the Royal National Throat Nose Ear Hospital (RNTNEH), London, United Kingdom. The inclusion criteria were age ≥65 years with documented hearing loss  who are not on the cochlear implant waiting list. To ensure participants in the study had normal cognition, only those with a General Practitioner’s Assessment of Cognition (GPCOG) score of equal 9 or GPCOG score = 5–8 with informant score = 4–6 were recruited .A sample of 30 adults diagnosed with mild cognitive impairment (MCI-HI) and 15 with dementia (D-HI) aged ≥65 were recruited via clinician referral and research registry in the memory clinics at Camden and Islington NHS Foundation Trust, London, United Kingdom. The RNTNEH, where the control NC-HI cohort was recruited, is also based within the Camden and Islington borough. The diagnosis of MCI and dementia cases were based on the ICD-10 criteria .The diagnostic assessment was done within NHS Memory Services, which are specialist diagnostic services for the assessment of patients with suspected dementia referred by primary care doctors. Diagnostic assessments are conducted by medical practitioners, under the supervision of consultant old-age psychiatrists, and following ICD-10 criteria. The assessment consists of a clinical interview of patient and collateral obtained from relative or friend, review of medical and psychiatric history, assessment of functional needs inclusive of sensory impairment, review of psychiatric and physical health needs medication, any use of alcohol and drugs and their potential impact of cognition, assessment of mental state and cognitive testing. The main tool for cognitive testing is Addenbrooke’s Cognitive Examination v3, a validated clinical tool for the diagnosis of mild cognitive impairment and dementia. Additional neuropsychological testing is used if required and the diagnostic assessment includes a brain scan if clinically appropriate after initial assessment. The recruitments were done within 2 weeks of their last follow-up with the service to ensure the current status of the diagnosis of MCI and dementia.The inclusion criteria were age ≥65 years with documented hearing loss  who are not in the cochlear implant waiting list.The exclusion criteria for all groups were uncorrected visual impairment; physical disability(s) which might inhibit performance on the written/drawing elements of the tests as evaluated by the researchers, and congenital/childhood-onset hearing loss (<18 years old age) as reported by participants.Audiograms were conducted for every participant according to the British Society of Audiology protocol  during tA version of the MoCA adapted for people with hearing impairment/hearing aids users was used. The original MoCA has a total score of 30 with 7 subcategories which are Visuospatial/executive, Naming, Memory , Attention, Language, Abstract and Orientation.www.mocatest.org.The hearing-impaired MoCA (HI-MoCA) developed by via the Microsoft PowerPoint program. The tool was also adapted according to guidance for the visually impaired population to ensure good visibility for the older adults with possible visual and hearing impairments. The duration of each slide timed was set according to the previously published paper by In the final version, the instructions were presented visually on the screen The slides were presented to the participants by the administrator. The participants told the administrator when they were ready to move on to the next slide. The administrator guided the participants to read the instruction on the screen without further explanation by the administrator. The decision to have the administrator progressing the test to the next slide was suggested by the PPI volunteers, since they judged that some older people may not be comfortable when operating computer screens.www.mocatest.org. There were some changes from the The participants responded to each slide verbally except when they were prompted to draw in the visuospatial/executive sub-tests. Their responses for this task were recorded in the original record form (MoCA 8.3) which can be downloaded from The final MoCA used was the MoCA version for hearing aids users (MoCA-HA) which incorporated all the changes suggested by the volunteer end-users and the health care professionals. The test was completed within 15 min. The MoCA-HA was used for all the participants recruited in this study.The sample size was calculated for using Receiver Operating Curve (ROC) analysis using the EasyROC tool  in distipost hoc analysis. When the baseline characteristics shown significant difference, subgroup analysis with matched controls was done as a sensitivity analysis method to account for the differences. Matched controls analysis was done by repeating the sensitivity analysis after eliminating each unmatched control case until the baseline characteristics of interest were matched.The statistical analysis was done with IBM statistic SPSS program version 25. The baseline characteristics of the NC-HI, MCI-HI, and D-HI were compared with one-way ANOVA with Tukey-Kramer The Receiver Operating Characteristic [ROC] curve was computed to evaluate the overall effectiveness of a newly developed binary outcome diagnostic tool, MoCA-HA. The calculation was made against the ICD-10 gold standard of the diagnosis of MCI and dementia. The AUC of the plot, which determines the diagnostic property of this tool was also conducted . The higJ) value via formula J = (sensitivity + specificity)−1 (The appropriate cutpoint for distinguishing between the NC-HI and MCI-HI was identified with the highest Youden index (icity)−1 . The Youicity)−1 .F = 12.43, p < 0.005 and F = 10.47, p < 0.005, respectively, as shown in F = 0.24, p = 0.79] as demonstrated in There were significant differences in the mean age and years of education of the NC-HI, MCI-HI, and D-HI cohort, p < 0.005) and the D-HI cohort by 5.53 years  (p = 0.03). There was no significant difference between the MCI-HI and the D-HI mean ages (p = 0.34).The mean age of the NC-HI cohort was significantly lower than the MCI-HI cohort by 8.53 years  (p = 0.02) and the D-HI cohort by 5.53 years  (p < 0.005). There was no significant difference between the MCI-HI and D-HI mean years of education (p = 0.08).The mean years of education of the NC-HI cohort was significantly higher than the MCI-HI cohort by 2.80 years   = 81.45 (p < 0.005) as demonstrated in Overall; the total MoCA-HA mean score was 27.27(SD = 2.16) for the normal cognition (NC-HI) participants, and mean score = 22.03 (SD = 3.06) for the MCI-HI, and mean score = 15.20 (SD = 4.21) for the D-HI. The mean scores were significantly differenced among the three cohorts N = 9) was also statistically significant at 0.84 with SE = 0.07 .For determining the diagnostic property of MoCA-HA in screening for MCI-HI, the AUC was calculated for the NC-HI and MCI-HI cohort . With thWith the whole NC-HI control cohort, the highest Youden index was 0.733, which resulted in the MoCA-HA cutpoint of 25.50 . With the matched NC-HI control cohort, the highest Youden index was 0.489, which resulted in the same MoCA-HA cutpoint score of 25.50  in practice. The MoCA-HA only provides integer scores, therefore the overall score below 26 may be utilized as cutpoint.N = 14) was also statistically significant at 0.998, SE = 0.005 .For determining the diagnostic property of MoCA-HA in screening for D-HI, the AUC was calculated for the NC-HI and D-HI cohort . With thWith the whole NC-HI control cohort, the highest Youden index was 0.967 which resulted in the MoCA-HA cutpoint of 22.5 .With the 14 aged-matched NC-HI control cohort, the highest Youden index was 0.933 which resulted in the MoCA-HA cutpoint of 21.5 . The second highest Youden index was 0.929 which resulted in the MoCA-HA cut-point of 22.5 . In practice, the MoCA-HA only provides integer scores, therefore the overall score below 23 may be utilized as cutpoint to maximize the sensitivity of screening for dementia.We have demonstrated the use of MoCA-HA  for the hearing-impaired older adults. When implemented among adults aged ≥65 who used hearing aids, the MoCA-HA had an outstanding diagnostic property with AUC of >0.9. When considering only the matched control cohort of mean age >80 years, the MoCA-HA still has an excellent AUC of 0.84 (AUC 0.8–0.9).The cutpoint of the MoCA-HA was found to be <26 which is similar to the traditional MoCA cutpoint in screening for the MCI-HI. This cutpoint also yield similarly high sensitivity of 93.3% as the traditional MoCA with a specificity of 80.0%. When considering all cognitive impairment stages (MCI-HI and D-HI), using a cutpoint of <26 could screen for potential cognitive impairment with even higher sensitivity up to 95.6% while maintaining the specificity of 80%.It is important to have a version of the MoCA suitable for older adults with hearing impairment since previous research has shown that they may be at a higher risk of developing MCI or dementia  and inteSince cochlear implant candidates with prolonged and severe to profound hearing loss may have poorer speech production pre-operatively , a writtAnother difference among cochlear implant candidates and other older adults with hearing impairment (hearing aids users) is that most of the cochlear implant candidates need to be physically fit enough to undergo surgery. Moreover, the candidates may tend to be younger since cochlear implant at a younger age was associated with a better outcome in older adults . In our Since years of formal education has previously been found to be a risk factor for dementia , lower eThis study only evaluated the hearing ability of the cohorts by means of an audiogram. However, it is well established in the scientific literature that auditory processing disorder is also a possible diagnostic marker of cognitive dysfunction in older patients as well as peripheral type hearing loss. Further studies that apply an auditory processing test battery on these populations should be conducted in order to evaluate their hearing ability in more detail .All recruitment and testing were at one site, which may limit generalizability. The findings of the study need to be validated at other sites and with larger samples. More sample with age/education years-matched controls could be beneficial in the implementation of this tool in a broader context.Previous research has shown that performance on the original, verbally presented MoCA test performance is affected by hearing loss , and whiThe MoCA-HA was well accepted by clinicians and patients. Our recently published work using the MoCA-HA has shown that the MoCA-HA results were not affected by the participants’ hearing levels . Using oThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the UK National Health Services (NHS) Ethical Committee IRAS247176. The study was under the University College London Joint Research Office (JRO) sponsorship ID 18/0306. The patients/participants provided their written informed consent to participate in this study.NU was the main author who conducted the research planning, recruitment/testing, and analysis under supervision of JS, SC, and DB. CN and MH conducted the recruitment and testing of D-HI participants. All authors contributed to the final manuscript.The 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Obsessive-Compulsive Disorder (OCD), characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions), is associated with dysfunction in fronto-striatal circuits. There are currently no fast-acting pharmacological treatments for OCD. However, recent clinical studies demonstrated that an intravenous infusion of ketamine rapidly reduces OCD symptoms. To probe mechanisms underlying ketamine’s therapeutic effect on OCD-like behaviors, we used the SAPAP3 knockout (KO) mouse model of compulsive grooming. Here we recapitulate the fast-acting therapeutic effect of ketamine on compulsive behavior, and show that ketamine increases activity of dorsomedial prefrontal neurons projecting to the dorsomedial striatum in KO mice. Optogenetically mimicking this increase in fronto-striatal activity reduced compulsive grooming behavior in KO mice. Conversely, inhibiting this circuit in wild-type mice increased grooming. Finally, we demonstrate that ketamine blocks the exacerbation of grooming in KO mice caused by optogenetically inhibiting fronto-striatal activity. These studies demonstrate that ketamine increases activity in a fronto-striatal circuit that causally controls compulsive grooming behavior, suggesting this circuit may be important for ketamine’s therapeutic effects in OCD. Intravenous infusion of ketamine rapidly reduces obsessive-compulsive disorder symptoms. Here, the authors show in mice that ketamine acts by increasing activity in a fronto-striatal circuit that causally controls compulsive grooming behaviour. Available pharmacological treatments (serotonin reuptake inhibitors or SRIs) rarely produce complete symptom remission and take 2–3 months to produce meaningful symptom relief3. Often high SRI doses are required with increased side effect burden, and ~30–40% of OCD patients remain refractory to these treatments5. Identifying effective, fast-acting treatments will help reduce OCD morbidity.Obsessive-compulsive disorder (OCD) is characterized by recurring, intrusive thoughts (obsessions), and repetitive behaviors (compulsions)7. Pioneering human studies have shown that low-dose ketamine, which acts on brain glutamate receptor pathways (among others), has rapid, robust therapeutic effects in depression and other disorders11. A randomized controlled clinical trial comparing a single low-dose intravenous ketamine to placebo in OCD patients showed ketamine’s therapeutic effect was rapid (within hours), and half those who received the drug reported remarkable OCD symptom relief (lasting up to 7 days)12. A recent rodent study showed that ketamine rapidly reversed a pharmacologically induced perseverative hyperlocomotion behavior13. However, the mechanisms underlying ketamine’s therapeutic effect in OCD (and other disorders) are unknown. This knowledge gap is largely due to the fact that no preclinical studies have addressed the circuit effects of ketamine on compulsive behavior in rodent models, a necessary step towards identifying mechanism. Understanding how ketamine modulates neural circuits involved in compulsive behavior will help identify pathways for the rational development of fast-acting therapeutic interventions.Increasing evidence indicates that disruptions in glutamate signaling may play a role in OCD symptoms21. OCD is associated with dysfunction in two frontal cortical regions: the orbitofrontal cortex (OFC) and dorsal anterior cingulate cortex (dACC). Previous mechanistic work has largely focused on the OFC, a region implicated in assigning value to action outcomes22. In contrast, little mechanistic work has addressed the causal role of the dACC in compulsive behavior. The dACC is important for controlling action selection and has also been implicated in fear expression, making it uniquely situated to control both the motor and affective components of OCD24. The dACC has been associated with conflict monitoring in OCD27, and several structural imaging studies have shown decreased ACC volumes in OCD29. The fronto-striatal circuit consisting of dACC projections to the dorsal medial striatum (DMS) is of particular interest given its role in controlling cognitive flexibility and goal-oriented behavior, functions that are disrupted in OCD30. Alterations in this circuit may lead to perseverative behaviors by disrupting the balance between goal-oriented and habitual behavior33. Indeed, several studies support a correlation between increased reliance on habitual learning and severity of compulsions34. Determining the causal role of the dACC-DMS circuit in compulsive behavior—and how this circuit is modulated by ketamine—could provide critical insights into OCD pathology that are necessary for developing better treatment options.Neuroimaging studies in OCD patients consistently show altered activity of fronto-striatal circuits implicated in action selection, emotion regulation, and cognitive flexibility, although there is conflicting evidence about the directionality of these alterations37. One rodent model of compulsive behavior is the SAP90/PSD95-associated protein 3 (SAPAP3) knock-out (KO) mouse, which has a prominent compulsive grooming phenotype that is reduced by SRI treatment38. Mice lacking SAPAP3 also demonstrate impaired cognitive flexibility, aberrant habit formation, and altered glutamatergic signaling in the dorsal striatum, a region implicated in OCD pathology40. A recent analysis of post-mortem brains demonstrated reduced expression of the SAPAP3 protein in the striatum of OCD patients, and variants of the Sapap3 gene have been associated with early-onset OCD and trichotillomania, another compulsive disorder41. Previous studies using this genetic model to investigate fronto-striatal circuits have focused on the role of the OFC, with little work addressing the rodent dACC homolog—the dorsomedial prefrontal cortex (dmPFC)43. Given the importance of the dmPFC-DMS circuit for controlling behavioral flexibility, we hypothesized that alterations in the dmPFC-DMS circuit would be associated with compulsive behaviors in this mouse31. The SAPAP3 KO mouse thus provides a platform for testing the causal role of the dmPFC-DMS circuit in compulsive behavior and how novel therapeutics such as ketamine modulate compulsive grooming and its underlying neural circuitry.Rodent studies are beginning to provide insights into cortico-striatal mechanisms of OCD-related behaviorHere we demonstrate that ketamine rapidly attenuates compulsive grooming behavior in SAPAP3 KO mice (referred to as “KO mice” from here on). Using in vivo fiber photometry recording from dmPFC-DMS projection neurons, we show that KO mice lack normal modulation of dmPFC-DMS circuit activity associated with grooming behavior in WT mice. Furthermore, ketamine selectively increases activity of this circuit in KO but not WT mice during grooming. Optogenetically mimicking this increased activity by stimulating dmPFC-DMS projections was sufficient to rescue the compulsive grooming phenotype in KO mice. Conversely, optogenetic inhibition of dmPFC-DMS projections increased grooming in WT mice, demonstrating bidirectional control of grooming behavior via this fronto-striatal circuit. These studies demonstrate that ketamine increases activity in a fronto-striatal circuit that causally controls compulsive grooming behavior, suggesting a central role for this circuit in mediating ketamine’s therapeutic effects in OCD.t-test, P = 0.0083, df = 28; WT = 15, KO = 15). To assess circuit activity during behavior, we used fiber photometry to record calcium signals from fronto-striatal projection neurons expressing the calcium indicator GCaMP6m. To selectively express GCaMP6m in dmPFC-DMS projection neurons, we injected a retrograde canine adenovirus carrying Cre recombinase (CAV2-Cre) into the DMS and a Cre-dependent GCaMP6m virus into dmPFC. We then implanted an optical fiber in the dmPFC to record from this DMS-projecting subpopulation  and frequency  of calcium transient peaks during grooming epochs and saw a significant increase in peak frequency in the KO mice. We then generated a z-scored peri-event time histogram (PETH) of dmPFC-DMS projection neuron activity aligned to the onset of grooming behavior . To assess whether this circuit is also modulated with cessation of grooming, we then focused on the transition out of grooming behavior. We generated PETHs aligned to the end of grooming epochs and saw that both WT . In summary, WT animals have a decrease in fronto-striatal activity during grooming. KO animals, however, selectively lack this robust dip in neural activity at grooming onset, suggesting altered encoding of grooming initiation and/or maintenance in this circuit. Furthermore, both WT and KO animals had similar increases in neural activity after grooming ends, potentially indicating that different circuits control grooming initiation and termination in the dmPFC. These results together support the hypothesis that dmPFC-DMS circuit modulation is involved in normal grooming behavior, and that engagement of this circuit is markedly altered in compulsive groomers.First, we wanted to observe natural neural dynamics in the dmPFC-DMS projection underlying healthy versus compulsive grooming in WT and KO littermates, respectively. We first confirmed that KO mice had increased grooming compared to WT littermates , whereas there was no difference in grooming frequency between WT-saline and WT-ketamine groups at any timepoint . A similar pattern of results emerged when we quantified total grooming duration . Similar to the grooming frequency data, post hoc analysis of grooming duration revealed that KO-saline and KO-ketamine groups significantly differed in grooming duration at 1 h and 1 day time points , whereas there was no difference in WT grooming duration between treatment groups . These data indicate that ketamine selectively attenuates compulsive over-grooming in KO animals without disrupting normal WT grooming behavior.After establishing baseline differences in dmPFC-DMS circuit activity between normal and compulsive grooming, we tested whether ketamine could rescue the behavioral and circuit deficits observed in SAPAP3 KO mice. We first performed a behavioral pharmacology experiment to determine whether ketamine could attenuate compulsive grooming in KO mice similar to its rapid therapeutic effect observed in human OCD patients. First, we established baseline grooming levels for naive KO mice and their WT littermates. We then divided the mice into two experimental groups, to receive either saline or ketamine (30 mg/kg i.p.) injection. To validate that ketamine injections were successful, we measured locomotor activity for 10 min post-injection . We then quantified the peak frequency and peak amplitude of calcium transients during grooming behavior after saline and ketamine injection. We saw no differences between genotypes or drug conditions in the peak frequency during grooming behavior . However, in KO animals ketamine significantly increased peak amplitude of calcium transients compared to KO-saline, WT-saline, and WT-ketamine groups during grooming epochs, while no change was observed for WT animals between treatment conditions . From our fiber photometry data, we removed 2 KO animals that did not behaviorally respond to ketamine (see “Methods” for exclusion criteria). KO mice that did not respond to ketamine treatment did not show the same ketamine-induced increase in peak amplitude compared to the SAPAP3 KO mice that did respond to ketamine, though this difference could be due to low sample number of non-responders . Next, we generated PETHs of dmPFC-DMS projection activity aligned to grooming onset. WT-saline and WT-ketamine groups both showed a dip in neural activity at grooming onset . We then generated PETHs aligned to the end of grooming epochs and saw that WT-saline and WT-ketamine groups had a similar increase in neural activity immediately after the termination of grooming behavior . These results indicate that loss of SAPAP3 sensitizes dmPFC-DMS circuitry to ketamine-induced increases in activity, while this circuitry appears resistant to such effects in WT animals. In addition, because ketamine did not restore KO dmPFC-DMS projection activity to WT levels, but instead further increased activity in this projection, it is possible that the higher fronto-striatal activity we observed during pre-injection baseline grooming , but still had a delay of ~30 s to grooming onset. The fact that this increase in grooming behavior was not time-locked to the onset of the laser suggests that inhibiting this projection is permissive of grooming rather than directly driving the behavior. We averaged the amount of time spent grooming across 5-min epochs of laser on versus laser off and saw that in the eNpHR3.0 group grooming significantly increased for the first 5 min while the laser was on . Analysis revealed a significant main effect of virus with a significant interaction between virus and laser. At baseline, there were no grooming differences between eYFP and eNpHR3.0 groups. Mice expressing eNpHR3.0 but not eYFP had a significant laser-evoked increase in grooming duration from baseline .Our observation that WT animals have a dip in dmPFC-DMS projection activity at the onset of grooming suggested that reduced activity in this circuit may be permissive of grooming. To test this hypothesis, we used optogenetic inhibition to decrease activity in the dmPFC-DMS projection and examine whether this manipulation could causally increase grooming behavior in WT animals. We injected WT mice in the dmPFC with either an eYFP control virus or halorhodopsin (eNpHR3.0) under control of the CaMKIIα promoter and implanted optical fibers in the DMS to bilaterally inhibit fronto-striatal terminals . We then calculated the laser-induced change in grooming duration and saw that eNpHR3.0 mice had a significant increase in the duration of grooming epochs compared to eYFP mice during behavior-dependent inhibition . These results indicate that inhibiting the dmPFC-DMS projection produces a hyper-grooming phenotype and that acute inhibition during an individual grooming event can prolong grooming in WT mice.Next, we took advantage of the temporal precision of optogenetics to perform a “behavior-dependent” inhibition experiment in which we investigated whether we could extend individual grooming events in WT mice by briefly inhibiting the dmPFC-DMS projection when animals start grooming. Grooming behavior was scored during a 20-min session consisting of a 5 min baseline with no laser, followed by 5 min during which the start of each grooming event would trigger the laser to stay on until the mouse stopped grooming Fig. . This waP = 0.0252, df = 1, F = 6.695; laser P = 0.0241, df = 1, F = 6.830; virus P = 0.4258, df = 1, F = 0.6840; subject P = 0.0043, df = 11, F = 5512, residual df = 11; eYFP = 6 KO mice, ChR2 = 7 KO mice). We then calculated the laser-induced change in grooming duration and saw that ChR2-KO mice had a significant decrease in the duration of grooming events compared to eYFP-KO mice . These results together demonstrate that using brief closed loop stimulation and inhibition of the dmPFC-DMS projection bidirectionally decreases and increases, respectively, the duration of individual grooming events.Given that the therapeutic effect of ketamine was associated with increased neural activity in fronto-striatal projection neurons during grooming in KO animals, we reasoned that optogenetically elevating activity in this circuit might likewise have a therapeutic effect on compulsive grooming. We injected channelrhodopsin-2 (ChR2) under control of the CaMKIIα promoter or the eYFP control virus (AAV5-CaMKIIa-eYFP) into the dmPFC and implanted an optical fiber in the DMS Fig. . We thenP = 0.0430, df = 2, F = 3.311; laser P < 0.0001, df = 2, F = 22.22; genotype P = 0.0018, df = 1, F = 11.64; subject P < 0.0001, df = 31, F = 4.936, residual df = 62; WT-ChR2 = 18, SAPAP3 KO-ChR2 = 15). Interestingly, stimulation of dmPFC terminals did not alter the number of grooming bouts that KO mice engaged in, demonstrating that stimulation of dmPFC only altered duration of grooming . Since stimulating this projection had an acute effect of attenuating compulsive grooming, we tested whether repeated stimulation of this projection could produce a lasting therapeutic effect. Mice received three additional stimulation sessions over 2 weeks, totaling 50 min of laser stimulation (see “Methods” for details). We then scored grooming behavior in the absence of laser 4 days after the last stimulation to look for lasting therapeutic effects. KO-ChR2 cumulative grooming behavior was indeed significantly reduced after this repeated stimulation compared to pre-stimulation baseline grooming behavior, an effect that was not present in KO-eYFP, WT-eYFP, and WT-ChR2 groups . Specifically, when we compared the change in grooming duration after repeated stimulation between KO-ChR2 and WT-ChR2 groups, we saw that KO-ChR2 had a significantly greater reduction in grooming behavior relative to WT-ChR2 . These optogenetic experiments together demonstrate causal control of compulsive grooming behavior via manipulation of the dmPFC-DMS projection. Furthermore, these results indicate that minimal repeated stimulation of this projection may have lasting therapeutic effects on compulsive behavior.Next, we performed a standard time-dependent stimulation protocol to determine the effects of continuous stimulation on overall grooming behavior. We recorded grooming behavior for a 5-min baseline period, a 5-min laser on period (10 Hz stimulation), and a final 5-min laser off period Fig. . At baseP = 0.0181, df = 3, F = 4.230; treatment P = 0.1692, df = 3, F = 1.858; time P = 0.0003, df = 1, F = 18.68; subject P = 0.0027, df = 20, F = 3.686, residual df = 20; saline eYFP-KO = 5, ketamine eYFP-KO = 6, saline KO-NpHR = 6, ketamine KO-NpHR = 7). KO-NpHR mice also showed a significant reduction in grooming frequency post-ketamine . Twenty-four hours post-injection we recorded grooming behavior for 15 min, including a 5-min baseline period, a 5-min laser on period (continuous light), and a final 5-min laser off period. During the laser on period NpHR-KO mice that received saline showed significantly higher grooming behavior compared to NpHR-KO mice that received ketamine and eYFP-KO mice . We observed no significant alterations in grooming frequency . This experiment demonstrated that the KO grooming phenotype can be exacerbated by inhibiting PL-DMS activity, lending support to our hypothesis that the increased activity observed in the KOs baseline fiber photometry recordings is a compensatory mechanism. Furthermore, ketamine’s ability to block the increase in grooming caused by inhibiting PL-DMS activity provides additional support to the hypothesis that the ketamine-induced increase in PL-DMS activity reduces grooming behavior in KO mice.Our observation that optogenetically stimulating dmPFC-DMS projections rescues the compulsive grooming behavior in KO mice supported our hypothesis that ketamine may exert its therapeutic effect on grooming behavior through increased dmPFC-DMS activity. In addition, our baseline fiber photometry data showed that KO mice, prior to ketamine, have increased dmPFC-DMS activity which we hypothesized may represent an incomplete compensatory mechanism to try to limit grooming behavior. We therefore further reasoned that (1) inhibiting the dmPFC-DMS projection would exacerbate grooming behavior in KO mice, and (2) that ketamine should block this increase in grooming. To provide more direct evidence in support of this hypothesis, we next sought to combine optogenetic inhibition with ketamine administration to test whether ketamine would occlude the optogenetic effect. We therefore optogenetically inhibited dmPFC-DMS activity in KO mice 24 h after they received either saline or ketamine 50. Optical stimulation of dmPFC terminals during our ex vivo DMS slice recordings indicated that ketamine administration did not alter presynaptic release probability, as measured by paired-pulse ratios (PPR), in SAPAP3 KO mice. Ketamine did, however, increase PPR in WT mice which was unexpected given the lack of both behavioral and fiber photometry effects of ketamine in WT mice. Ketamine also mitigated the differences between KO and WT mice in the frequency of asynchronous release from dmPFC synaptic terminals, which could indicate increased dmPFC synaptic activity in SAPAP3 KO-ketamine mice. This would be in line with our fiber photometry results, where ketamine increased in vivo dmPFC-DMS activity. Interestingly, recent work has demonstrated that protein kinase A activation can increase the number of vesicles released by regulating the readily releasable pool without altering release probability, showing increased frequency in aEPSCs without altering PPR51. Of note, we do not have post-injection behavioral data for the animals used in our slice experiments and so the variability in this data set could potentially be accounted for by KO-ketamine non-responders. Given our fiber photometry data showing that SAPAP3 KO-ketamine non-responders do not have an increase in dmPFC-DMS activity, the potential inclusion of such non-responders in our slice physiology experiments could occlude more robust ketamine-induced changes in our slice data. Overall, these results suggest that ketamine likely exerts its behavioral effect on grooming not via altered intrinsic excitability of dmPFC cells, but rather via altered in vivo recruitment of dmPFC neurons during compulsive behavior. Furthermore, our slice results support the hypothesis that the in vivo activity differences we observed between WT and SAPAP3 KO mice could result in increased multivesicular release or increased release frequency of dmPFC-DMS projections in the SAPAP3 KO mice. However, we cannot exclude the possibility that ketamine administration may also alter presynaptic inputs onto dmPFC-DMS neurons, contributing to the ketamine-induced increase in dmPFC-DMS activity. For example, in another mouse model of compulsivity, repetitive checking behavior is driven by reduced mPFC glutamatergic activity due to feed-forward inhibition from BLA inputs52.To determine circuit changes associated with the anti-compulsive effect of ketamine, we used in vivo fiber photometry recording of fronto-striatal projection neurons to assess genotype- and drug-related differences in dmPFC-DMS circuit activity during grooming. Our baseline fiber photometry recordings showed several key differences in fronto-striatal circuit activity between WT and SAPAP3 KO mice. WT fronto-striatal projection neurons had a drop-in activity at the onset of grooming, but no such modulation was observed in SAPAP3 KO mice. The sustained neural activity during grooming observed in SAPAP3 KO mice appeared to be driven by an increased frequency of calcium transients compared to WT mice. Interestingly, upon transition out of grooming, both WT and SAPAP3 KO mice showed an increase in neural activity relative to pre-grooming baseline levels. This could indicate that, with respect to grooming behavior, dmPFC-DMS projection neurons have two states (low activity permits grooming whereas high activity suppresses grooming). Loss of the low activity state with maintenance of the high activity state in SAPAP3 KO mice could represent compensatory mechanisms in the dmPFC to try to modulate compulsive grooming behavior. Furthermore, these two activity states could be driven by different synaptic inputs onto the dmPFC, which may be differentially dysregulated in SAPAP3 KO mice. In contrast to these in vivo circuit differences, ex vivo prefrontal slice recordings did not reveal any genotype differences in basal cellular properties of dmPFC neurons. Other studies have demonstrated altered endocannabinoid signaling in the striatum of SAPAP3 KO mice, which could affect local release properties of glutamatergic projections without changing overall prefrontal cell body activity53, while the ketamine-induced increase in glutamatergic transmission may allow for dmPFC-DMS projection neurons to more successfully restore normal behavior. Furthermore, it seems unlikely that the WT dip in fronto-striatal activity is directly driving grooming behavior but may instead represent a permissive signal for grooming. The timing of our optogenetic inhibition effect corroborates this idea: inhibiting the dmPFC-DMS projection in WT mice did not immediately drive grooming behavior when the laser was turned on, but rather gradually increased the probability of grooming episodes, consistent with a permissive role of this circuit. In addition, saline injection, which appeared to exacerbate grooming duration in SAPAP3 KO animals 24 h post-injection, reduced SAPAP3 KO fronto-striatal activity during grooming, further supporting a model in which decreased fronto-striatal projection activity promotes the initiation of grooming behavior. Therefore, the dmPFC-DMS projection neurons may represent a permissive signal that normally promotes grooming behavior through decreased activity, but is disrupted in SAPAP3 KO mice. A recent study demonstrated that cortico-striatal limbic loops, such as the dmPFC-DMS projection, are capable of modulating cortico-striatal motor loops, providing a potential anatomical substrate for permissive dmPFC-DMS gating of DLS that can be causally tested in future studies54. The additional boost in prefrontal glutamatergic signaling provided by ketamine may therefore further dampen the dmPFC’s permissive signal to a threshold that is sufficient to overcome dysregulated downstream grooming motor circuitry and restore healthy grooming levels55. Indeed, both the pharmacological (ketamine) and optogenetic manipulations we employed that increase activity in the dmPFC-DMS projection had a convergent effect of decreasing compulsive grooming in SAPAP3 KO mice  that directly drives compulsive groomingice Fig. , while oice Fig.  and SAPAice Fig. , further52. We now add to this growing body of scientific work by demonstrating the causal role of the dmPFC-DMS circuit in compulsive behavior. In our study, we demonstrate that bidirectional manipulation dmPFC-DMS activity can acutely alter grooming behavior in both WT and SAPAP3 KO mice. Our rescue of compulsive grooming in the SAPAP3 KO mice by increasing dmPFC-DMS activity mirrors the acute therapeutic effect on compulsive behavior seen in SAPAP3 KO mice when lateral orbitofrontal (OFC) terminals are stimulated in the dorsal striatum36. It is possible that increasing dmPFC and lateral OFC projection activity in the striatum both act to offset the increased striatal drive that SAPAP3 KO mice receive from the secondary motor cortex53. Of particular interest, however, is that our repeated stimulation of dmPFC-DMS projections induced a long-lasting reduction in grooming 4 days after the final stimulation session. Others have demonstrated similar lasting changes in compulsive behavior through chronic manipulations of fronto-striatal circuitry. Of note, repeated stimulation of projections to the ventromedial striatum from ventral and medial OFC in WT mice produce a delayed but sustained increase in grooming behavior, mimicking a compulsive grooming phenotype35. Our finding together with this previous work suggests that stimulating fronto-striatal projections may induce robust and lasting plasticity at these synapses. Further studies into the mechanisms of such fronto-striatal plasticity may be of particular importance for understanding the etiology of OCD and for designing novel therapeutic interventions. By dissecting how each of these circuits contributes to compulsive behavior, we can begin to build a more complete picture and better identify points of convergence for developing targeted therapeutic intervention. Furthermore, we provide the first steps in understanding how ketamine can interact with circuits altered in OCD to restore normal goal-directed behavior, providing a preclinical platform for testing neural circuit mechanisms underlying novel fast-acting treatments for OCD.Several rodent studies have highlighted the importance of the orbitofrontal cortex, ventral striatum, and amygdalo-striatal projection in compulsive behaviorAll experiments were performed under a protocol approved by the Institutional Animal Care and Use Committees at the University of California, San Francisco. Homozygous SAPAP3 knockout (KO) mice and their wild-type (WT) littermates were generated from breeding two heterozygotes that were maintained on a C57BL/6J background. For a subset of optogenetic experiments, wild-type C57BL/6J mice purchased from The Jackson Laboratory were also used. The over-grooming phenotype in SAPAP3 KOs emerges around 4 months of age, so all mice were at least 120 days old (average age = 290 days). Both male and female mice were used  on top of a heating pad. Anesthesia was maintained with 1.5–2.0% isoflurane for the duration of surgery. Respiration and toe pinch response were monitored closely. Slow-release buprenorphine (0.5 mg/kg) and ketoprofen (1.6 mg/kg) were administered subcutaneously at the start of surgery. The incision area was shaved and cleaned with ethanol and betadine. Lidocaine (0.5%) was administered topically. An incision was made along the midline, and bregma and lambda were measured to level the skull. Virus was injected and fiber optic ferrule(s) implanted as described below. After securing the ferrule with dental cement, the skin was sutured around the implant and mice recovered in a clean cage atop a heating pad. The following day, mice were monitored for healthy recovery and administered a subsequent dose of ketoprofen.To optogenetically inhibit projections from dmPFC to DMS, 500 nL of AAV5-CaMKIIα-eNpHR3.0-eYFP or AAV5-CaMKIIα-eYFP control virus (UNC Vector Core) was bilaterally injected into the mPFC  using a 10 μl nanofil syringe (World Precision Instruments) with a 33 gauge beveled needle at a rate of 0.1 μl/min. After waiting 15 min, the needle was slowly withdrawn. Two optical fibers in 1.25 mm ceramic ferrules  were then implanted bilaterally in the DMS . Fibers were lowered slowly into the DMS and ferrules were secured to the skull using dental cement (C&B Metabond). These surgeries were done on C57BL/6J WT mice (The Jackson Laboratory), and on SAPAP3 KO animals and WT littermates. For both inhibition and stimulation experiments of dmPFC-DMS projections AAV5-CaMKIIα-eYFP virus, which lacks the light-responsive opsins, was used as the control for surgical procedures, non-specific effects of AAV5 viral expression, and any non-opsin specific behavioral changes induced by light changes when lasers are switched on and off.For stimulation of projections from dmPFC to DMS, 500 nL of AAV5-CaMKIIα-ChR2-eYFP (Addgene) or AAV5-CaMKIIα-eYFP control virus (UNC Vector Core)  was injected unilaterally into the left dmPFC and an optical fiber was implanted unilaterally in the DMS , using the same coordinates and ferrules as described above. These surgeries were done on SAPAP3 KO animals and WT littermates.For recording from dmPFC neurons that project to the DMS, 700 nL of a 50/50 mixture of AAV1.hSyn.mCherry  and CAV2-Cre (Plateforme de Vectorologie de Montpellier) was injected unilaterally into the DMS . To allow CAV2-Cre-induced GCaMP expression in the dmPFC, 1500 nL of AAV1.Syn.Flex.GCaMP6m.WPRE.SV40 (Addgene) was unilaterally injected into the dmPFC . An optical fiber stub in a 2.5 mm ceramic ferrule  was then implanted into the dmPFC  and secured as described above. These surgeries were done on SAPAP3 KOs and WT littermates. GCaMP6m expression was assessed post-perfusion by counting and then averaging the number of GCaMP6m cells present in two brain slices per animal. Images of the implant location were taken with a 6D Widefield Nikon Ti Inverted Microscope. An area of 400 microns by 400 microns was defined directly under the implant site, and cells expressing GCaMP6m were manually counted. The average number of neurons expressing GCaMP6m in this area was 45.5 neurons +/− 10 s.e.m.13 vg/mL) was a gift from The Genetically Encoded Neuronal Indicator and Effector Project (GENIE) & Douglas Kim . pAAV-CaMKIIa-hChR2(H134R)-EYFP  was a gift from Karl Deisseroth . AAV5-CaMKIIa-eNpHR3.0-eYFP  was a gift from Karl Deisseroth and packaged by the UNC Vector Core. AAV5-CaMKIIa-eYFP  was a gift from Karl Deisseroth and packaged by the UNC Vector Core. AAV1-hSyn.mCherry  was a gift from Karl Deisseroth and packaged by the UNC Vector Core. CAV-2-cre virus  was from Plateforme de Vectorologie de Montpellier.pAAV.Syn.Flex.GCaMP6m.WPRE.SV40  and recorded with two video cameras to have both top and side views. All behavioral assays were done in an open top sound dampened chamber. A blind observer manually scored and timestamped grooming behavior. Locomotor activity  was recorded and quantified by Ethovision XT software (Noldus). A TTL pulse triggered by the Ethovision XT software was used to synchronize fiber photometry recording data with behavioral data.48.Grooming behavior for SAPAP3 KOs and WTs was recorded for 10-min sessions as described above. Baseline grooming behavior was measured, and then mice were split into two groups to receive either saline (0.9% sterile sodium chloride) or ketamine . The next day mice were injected intraperitoneally with either saline or ketamine and immediately placed in the behavioral rig. Locomotor recordings were taken for 10 min post-injection to verify successful ketamine injection through induction of a short-term increase in locomotor behavior. No grooming measurements were taken at this time. Grooming was scored at additional behavioral time points of 1 h, 1 day, 3 days, and 7 days post-injection. It should be noted that prior to using the 30 mg/kg dose a small cohort of animals were tested with a 20 mg/kg dose of ketamine  pulse generator controlled by TTL pulses from Ethovision (Noldus) software was used to drive the laser. Laser output was delivered to the animal via an optical fiber  connected to a 1x1 fiber optic rotary joint (Doric Lenses) followed by another optical fiber  which was coupled to the cannula through a ceramic sleeve (Thorlabs). For the time-dependent stimulation protocol, baseline measurements were recorded while mice were not connected to a patch cord and were observed moving freely. To stimulate projections from dmPFC to DMS using ChR2, blue light was generated by a 473 nm laser (Shanghai Laser & Optics Century Co. LTD) . Each session was divided into three epochs: a 5-min baseline period followed by 5 min of laser on and 5 min of laser off post-stimulation. A subset of animals received 3 additional days of stimulation totaling 50 min spread across 2 weeks. After the time-dependent stimulation experiment mice were rested for 2 days before additional stimulation, then rested for 3 days, and then 2 days. In addition, these extra stimulation sessions occurred in three different environments . We then assessed the long-term effects of the laser on grooming behavior 4 days after the final stimulation when mice were untethered. This experiment was performed 6 weeks post-surgery. For the behavior-dependent stimulation protocol  each session was divided into four epochs: 5 min no laser period, 5 min period where the laser was turned on and off with the initiation and termination of grooming, respectively, 5 min no laser period, and 5 min period where the laser was turned on and off with the initiation and termination of grooming, respectively. The laser was triggered on and off by TTL pulses sent by the Ethovision XT software (Noldus). The TTL pulses were controlled by manual keyboard strokes at the start and end of grooming. This experiment was performed 6–9 weeks post-surgery.To inhibit projections from dmPFC to DMS using eNpHR3.0, green light was generated by a 532 nm laser (Shanghai Laser & Optics Century Co. LTD) (5 mW continuous light on each side). For time-dependent bilateral inhibition of dmPFC-DMS terminals in WT animals, each trial began with a 5-min baseline period followed by 10 min of laser on and 5 min of laser off post-stimulation. This experiment was performed ~7 weeks post-surgery. The behavior-dependent bilateral inhibition of dmPFC-DMS terminals in WT animals followed the same protocol as the described above for the behavior-dependent stimulation protocol and occurred 11 weeks post-surgery. For bilateral inhibition of dmPFC-DMS terminals in KO animals, untethered baseline grooming measures were recorded for 10 min. KO mice then received either an injection of saline (10 mg/mL) or ketamine (30 mg/kg) and their grooming behavior was recorded untethered 1 h post-injection. Twenty-four hours post-injection grooming behavior was recorded across a 5-min baseline period followed by 5 min of laser on and 5 min of laser off post-stimulation. This experiment was performed 11 weeks post-surgery.2+ transient peaks throughout the signal using custom peak detection code using a running average method to calculate the peak to trough value. We used a 10 s trough window (window during convolution for finding running average trough) and a 1 s temporal window (minimum amount of time between peaks). Once peaks were detected, we then calculated the average frequency and amplitude of these peaks. We restricted the analysis of grooming to epochs longer than 3 s. Prior to analysis of the photometry signals post-injection, we determined which KO mice were responders versus non-responders to ketamine by calculating a grooming index to assess change in grooming behavior induced by ketamine /. We then generated the average and standard deviation for WT animals. KO animals had to have a reduction in grooming behavior post-ketamine was at least 1 standard deviation different from the change in grooming behavior of WT animals to be included in the fiber photometry analysis. Only 2 out of 15 KO mice did not meet this criterion. We analyzed the number of calcium transients and the amplitude of these transients that occurred during grooming. We also generated peri-event time histograms (PETH) of the neural activity aligned to the beginning and end of grooming bouts and generated normalized area under the curve analysis (AUC) for each grooming epoch (for the entirety of the grooming epoch) and for the 2-s window immediately after grooming ended. In order for grooming bouts to be included in the PETH analysis, we implemented a 5 s threshold in which the start of the grooming epoch had to be at least 5 s after the end of the prior grooming epoch. This allowed us a 5-s window prior to groom start that we knew did not have any signal interference from other grooming epochs that we used to generate the necessary baseline mean signal used to convert the photometry signals into z-score.Prior to surgery we quantified grooming behavior over 10 min for each animal to confirm the increased grooming phenotype in the SAPAP3 KO mice. Mice were allowed to recover from surgery for 4–5 weeks before fiber photometry recordings. For dmPFC-DMS fiber photometry recordings, SAPAP3 KOs and WT littermates underwent 2 days of recording baseline grooming behavior. Each recording session was 20 min. Following the end of recording on the second day of baseline, mice received an injection of saline solution  and were returned to their home cage. Behavioral measurements and fiber photometry recordings then occurred 24 h later. After 7 days post-saline injection mice were then administered a dose of ketamine  and returned to their home cage. Behavioral measurements and fiber photometry recordings were taken 24 h after ketamine injection. Fiber photometry signals were demodulated and analyzed using custom MATLAB code. Briefly, the 405 nm signal was regressed against the 470 nm signal and the polyfit MATLAB function (least-squares method) was used to determine the coefficients to calculate a fitted 405 nm signal. The DF/F was then calculated /). For peak amplitude and frequency calculations, we first detected all Ca57. This setup was controlled by an RZ5P fiber photometry processor (TDT) and Synapse software (TDT). The RZ5P/Synapse software controlled a 4-channel LED Driver , which in turn controlled two fiber-coupled LEDs: 470 nm for GCaMP stimulation and 405 nm to control for artifactual fluorescence . These LEDs were sinusoidally modulated at 210 Hz (470 nm) and 320 Hz (405 nm) and connected to a Fluorescence Mini Cube with 4 ports (Doric Lenses), and the combined LEF output was connected through a fiber optic patch cord  to the cannula via a ceramic sleeve (Thorlabs). The emitted light was focused onto a Visible Femtowatt Photoreceiver Module  and sampled at 60 Hz. Noldus behavioral analysis was synchronized to the photometry setup using TTL pulses generated every 10 s following the start of the Noldus trial.In vivo calcium imaging data were acquired using a custom-built rig based on a previously described setup from the Deisseroth labFollowing the conclusion of our behavioral experiments, animals were anesthetized using 5% isoflurane and given a lethal dose (1.0 ml) cocktail of ketamine/xylazine . They were then transcardially perfused with 10 mL of 1X PBS followed by 10 mL 4% paraformaldehyde (PFA). Brains were left in 4% PFA overnight and then transferred to a 30% sucrose solution until slicing. The brains were frozen and sliced on a sliding microtome (Leica Biosystems) and placed in cryoprotectant in a well-plate. Slices were then washed in 1X PBS, mounted on slides (Fisherbrand Superfrost Plus), and air dried (covered). ProLong Gold antifade reagent  was pipetted on top of the slices, a cover slip  was placed on top, and the slides were left to dry overnight (covered). Viral injection, fiber photometry cannula implant, and optogenetic cannula implant placements were histologically verified on a fluorescence microscope . Neural and behavioral data of mice with incorrectly targeted fiber photometry implants were removed from analysis.P < 0.05 for all experimental results. The type of statistical analysis used was determined independently for each experiment and is listed in the relevant results section along with sample size, exact p values, degrees of freedom (df) and F values. Figure captions also provide statistical information including information on post hoc multiple comparisons tests where appropriate. Bar and line graphs represent data-set means and error bars represent standard error of the mean (s.e.m.).Statistical analyses were performed using GraphPad Prism 8 software package. Statistical significance was set at Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Although various dietary patterns have been indicated to be associated with the resting metabolic rate [RMR], limited data are available in this field. This study was therefore focused on the association between dietary patterns and resting metabolic rate among participants with overweight and obesity.2), aged 18–50. Anthropometric assessments, physical activity and biochemical measurements were assessed. RMR was also measured by means of indirect calorimetry. Dietary intake of participants was evaluated by 147-item semi-quantitative food frequency questionnaire [FFQ].This cross-sectional study was conducted on 304 women with overweight or obesity (BMI ≥ 25 kg/mP = 0.05), intakes of protein (P = 0.003), minerals (P = 0.001) as well as fat free mass [FFM] (P = 0.002), bone mineral content (P = 0.001), skeletal muscle mass (P = 0.001), soft lean mass (P = 0.002) and visceral fat area (P = 0.05). Also, there was a considerable association between higher adherence to the unhealthy dietary pattern [UHDP] and fasting blood sugar [FBS] (P = 0.05). Using multinomial logistic regression has been shown that the medium adherence to the HDP was marginally significant with decreased resting metabolic rate [Dec. RMR] group in crude model . After controlling for various confounders such as age, FFM, physical activity, and energy intake, the association between Dec. RMR group and the lowest quartile of the HDP  became significant as well as the association between Dec. RMR group and medium adherence to the HDP . The medium adherence to the UHDP in crude model was also significant with increased resting metabolic rate [Inc. RMR] group .There was a significant association between higher adherence to the healthy dietary pattern [HDP] and RMR (Our study showed that there are significant associations between dietary patterns and RMR status.The online version contains supplementary material available at 10.1186/s12902-022-00958-z. The prevalence of obesity, which has been shown to be relevant in the context of the development of various chronic diseases such as type 2 diabetes, cardiovascular diseases [CVDs] and various cancers, has been raised at an alarming rate , 2; so tIt is now well accepted that different transitions in dietary intakes as well as eating habits play prominent roles in increased rate of obesity, as a multi-factorial disorder –8. AlthoThe basal metabolic rate [BMR], as a complementary indicator of our body’s metabolism and compositions, has been shown to be the main component of every individual’s energy expenditure and could represent 60 to 75% of every participant’s energy intake. Hence, an expression of resting metabolism could be exactly related to higher risks of obesity and more importantly, could be used as a practical tool for the prevention of overweight and obesity –12. PrioDespite huge evidence in terms of the associations between dietary factors and RMR among western populations, we are aware of no Iranian study examining these relationships in this regard. Examining the roles of dietary patterns is particularly relevant for RMR since various mechanisms have been suggested that higher energy intake could be resulted in augmented body fat accumulation through reducing RMR. We hypothesized that evaluating the dietary pattern in relation to RMR is specifically more relevant for Iranian women since obesity is more prevalent among them which in return will clarify this issue more perfectly. Therefore, the purpose of the current study was to examine the association of dietary pattern with the deviation of normal RMR [DNR] among a group of Iranian adult women.Three hundred and sixty women with body mass index [BMI] ≥ 25 were recruited from health centers of all the regions of West and Central Tehran, using community-based sampling based on-comfort sampling. We investigated obese individuals aged 18 to 50 who were generally in good health. Participants were excluded if they were taking medications or had history of type one diabetes mellitus. Smoking, alcohol consumption, pregnancy, lactation, and having vigorous exercise were considered as additional exclusion criteria. In addition, those participants who had been following an arbitrary special dietary pattern as well as those with chronic disease affecting their diet were excluded. Also, Women were excluded who did not respond to more than 70 food items in the food frequency questionnaire [FFQ], or who reported a total daily energy intake [EI] outside the range of 800–4200 kcal . After an overnight (12 h) fast, volunteers attended to the clinical health centers in the Biochemical Laboratory of the School of Nutritional and Dietetics at Tehran University of Medical Sciences and standard anthropometric parameters were recorded. All participants provided signed informed written consent forms. The whole project was ethically approved by the Bioethics Committee of Tehran University of Medical Sciences, Tehran, Iran IR.TUMS.VCR.REC.1395.1593.2 and BMI ≥ 30 kg/m2, respectively.We measured height, weight, as well as waist, and hip circumferences for each participant. All these measurements were performed by researchers who have extensive experience in compliance with WHO recommendations using a solar digital scale and a free-standing portable height meter. Participants were weighed without shoes and heavy outdoor clothing and weight recorded to the last 0.1 kg. Standing height was measured with a precision of 0.5 cm without shoes, by trained observers. BMI was also calculated by weight (kg)/ height (m) squared. Waist-to-hip ratio [WHR] was also calculated based on the dimensionless ratio of the circumference of the waist to that of the hips. WHR determines how much fat is stored on your waist, hips, and buttocks. We used BMI and WHR to classify overweight and obesity among participants. Based on WHO, a waist circumference of 102 cm (40 in.) or more in men, or 88 cm (35 in.) or more in women, is associated with obesity. Overweight and obesity were also defined as 25 ≤ BMI ≤ 29.9 kg/mBC-418MA - Tanita (United Kingdom) [The body composition of all subjects was assessed with the use of Body Composition Analyzer Kingdom) . The dev2 consumed and CO2 produced. The RMR was evaluated in the morning after a comfortable night’s sleep and following an overnight fasting. They asked participants to avoid severe exercise and alcohol or caffeine consumption for a day before RMR assessment. RMR was measured for 30 min in a quiet room and in steady position. The respiratory exchange ratio and oxygen uptake [VO2] were investigated within the average 20 min of the resting period predictive. RMR was determined using the Harris-Benedict equation, which considers age, weight and height for individuals.RMR was measured by indirect calorimetry . Based on the manufacturer’s instructions, prior to each test, gas exchange and ventilation was calibrated. The spiroergometric system with a high-resolution were conveyed by an amperometric solid electrolyte sensor for O2 assessment and an infrared sensor for CO2 evaluation, which recorded continuously through breath-by-breath gas analysis. A little division of breathing air was conducted through the volume flow sensor utilizing an ergonomically mask. The RMR was assessed by measuring the amount of On = 37) , normal RMR (n = 87) , and Dec. RMR (n = 172) .In order to examine the association of body composition analysis, biochemical characters and RMR components with RMR status, the individuals were categorized to three groups: increased RMR [Inc. RMR], normal RMR and decreased RMR [Dec. RMR]. These groups were categorized based on the deviation of normal Resting Metabolic Rate [DNR]. In order to determine the DNR, we used indirect calorimetry (METALYZER 3B-R3). Moreover, we used fine standard deviation of variance for RMR to describe the DNR in this study. Then, we categorized the participants according to scoring of DNR. Such that, following definitions were considered for each of the mentioned groups: Inc. RMR , which is based on United States Department of Agriculture [USDA] food composition table and modified for Iranian foods, to compute energy and nutrient content of foods. Validity and reliability of the FFQ have been confirmed previously .The short-form International Physical Activity Questionnaire [IPAQ] was used to assess the physical activity. The validity and reliability of the IPAQ have been checked . This quBlood samples were accumulated between 8:00 and 10:00 in the morning, following 10 to 12 h of overnight fasting during the previous night. The serum after centrifuging and the liquated was stored at a temperature of − 80 °C. All measurements were taken at the biochemical laboratory of the school of nutritional science and dietetics. GOD/PAP [glucose oxidase phenol 4-Aminoantipyrine Peroxidase] method was used for the measured of fasting glucose and triglyceride levels as well as cholesterol levels were evaluated by the CHOD-PAP method. Moreover, total cholesterol [TC] levels and direct high-density lipoprotein [HDL] were measured by the Immunoinhibition assay. Aspartate aminotransferase [AST] and Serum alanine aminotransferase [ALT] were specified by athe International Federation of Clinical Chemistry and Laboratory Medicine method. Serum high-sensitivity C-reactive protein [hs-Crp] was determined using a high-sensitivity immunoturbidimetric assay . Pars Azmoon kit was used for all of the assessments .P values were considered significant at < 0.05.In the current study, we first assessed the normal distribution of data using Kolmogorov-Smirnov test. Then, we obtained two major dietary patterns by the use of the factor analysis method (29). In this method, varimax rotation was constructed, according to the 22 food groups. Dietary patterns were identified based on the eigenvalues (> 1.5), KMO = 0.706, factor interpretability, scree plot, and variance explained. Then two dietary patterns were considered as Healthy and Unhealthy Dietary patterns. Then, we categorized participants into tertiles of adherence to the healthy and unhealthy dietary patterns divided into three groups  and all statistical analyses were separately done for adherences to these dietary patterns. To describe our study population, we used mean ± standard deviation values. We applied one-way analysis of variance [ANOVA] to examine significant differences in continuous variables across these tertiles. To compare these differences again as well as adjusting the confounders’ effect at the same time, ANCOVA was applied. To determine the association of these adherences to dietary patterns with DNR, multinomial logistic regression was used in two models (crude and adjusted models). In the adjusted model, we adjusted for age (continuous) and energy intake as well as FFM and physical activity (METs/d). All confounding variables were established risk factors for DNR based on literature. The higher adherence to the both dietary patterns was considered as the reference category in all analyses. Results were demonstrated as odds ratios [ORs] and 95% confidence intervals [CIs] compared with the DNR groups. All statistical analyses were conducted using SPSS software . The mean age, weight, height, BMI, waist circumference and fat free mass [FFM] of the participants in this cross-sectional study were 36.49 years (SD = 8.38), 80.89 kg SD = 12.45), 161.38 cm (SD = 5.90), 31.04 kg/m2 (SD = 4.31), 99.01 cm (SD = 10.05) and 46.80 kg (SD = 5.64), respectively , protein (P = 0.003), minerals (P = 0.001), FFM (P = 0.002), bone mineral content (P = 0.001), skeletal muscle mass (P = 0.001), soft lean mass (p = 0.002), HDL-c (P = 0.83) and body fat mass (P = 0.32), and lower waist-hip ratio (P = 0.37), visceral fat area (P = 0.05), body fat percentage (P = 0.39), LDL (P = 0.58), hs- CRP (P = 0.67) and TC (P = 0.60) compared to the lower HDP group. But some findings were not statistically significant. There were no significant differences in terms of RMR/kg, RQ, visceral fat, body fat percentage and biochemical characteristics between the three groups (P > 0.05) , Visceral fat (P = 0.69), FBS (P = 0.05), GPT (P = 0.84) and hs-CRP (P = 0.59) compare to low unhealthy pattern group. However, there were no significant association in these items (P > 0.05). After adjustment for the confounders including age, FFM, physical activity and energy intake was observed the significant difference in FBS (P = 0.01)  Table .P < 0.006), RMR (P < 0.0001), RMR/kg (p < 0.0001), VO2 (P < 0.0001), RMR/BSA (P < 0.0001) as well as higher dietary intake of minerals (P < 0.0001), protein (P < 0.0001) and higher bone mineral content (P < 0.0001), soft lean mass (P < 0.0001), skeletal muscle mass (P < 0.0001), body fat percentage (P = 0.001) compared to Dec. RMR group. All significant outcomes remained robust after adjusting for the confounders including age, energy intake, physical activity, and FFM. Furthermore, body fat mass (P = 0.01) has become also significant.Individuals were categorized into three groups; Inc. RMR, normal RMR and Dec. RMR according to DNR scores. The differences between the normal, Dec. and Inc. RMR groups were analyzed by one-way ANOVA tests. Individuals in Inc. RMR group had significantly higher height (P = 0.07). After controlling for the confounders like age, FFM, physical activity, and energy intake, the association between Dec. RMR group and the lowest quartile of the HDP  became significant and also, the association between Dec. RMR group and medium adherence to the HDP .The association between HDP and UHDP and RMR across DNR are shown in Table P = 0.04). However, after controlling for confounders, no significant association was remained with Dec. RMR  and Inc. RMR .The medium adherence to the UHDP in crude model was significant with Inc. RMR group (Inc. RMR: OR: 2.59; 95% CI: 1.01–6.65, Several studies suggested that overweight and obesity have been rapidly increased worldwide. This cross-sectional study, in order to deal with the complications of obesity and overweight, tried to find the mediatory role of HDP and UHDP on RMR which is one of the major factors for controlling body weight. Besides, HDP and UHDP might be advantageous to decrease the development of overweight and obesity. Therefore, we investigated the effects of HDP and UHDP on the possible link between DNR and obesity.This cross-sectional study has shown that dietary patterns can be associated with DNR among women with overweight and obesity. Two dietary patterns were identified, by using the factor analysis technique; these were HDP and UHDP. It was found that higher adherence to the HDP could be associated with several factors, including FFM, protein, minerals, bone mineral content, skeletal muscle mass and soft lean mass. This result may have been attributed to the higher intake of protein, soluble and insoluble fiber, omega-3 fatty acid, calcium, magnesium, iron, potassium, and vitamin C in healthy dietary pattern group –22. This2, RMR, RMR/kg, RMR/BSA, protein, minerals, skeletal muscle mass, bone mineral content, soft lean mass, body fat percentage. These results were consistent with the former study that demonstrated fat mass was related to an increased metabolic rate among women with up to 40% body fat. Increased fat mass has a considerable influence on the metabolic function. Fat mass could influence the metabolic rate by chronic changes in hormonal concentrations. Skeletal muscle is the easily manipulated contributor to RMR and also, by altered substrate oxidation and metabolic rate. Muscle mass is the main location for substrate oxidation and is associated with improved glucose regulation and insulin, but the association between body composition and the metabolic function is still unknown. The previous study of RMR and visceral fat presented that visceral fat distribution plays an important role in determining the RMR. But in another study, this correlation was not observed.The other outcomes are that enhanced RMR is strongly related to higher height, VOWe found a significant association HDP and UHDP with DNR’s groups in the present study. The HDP, including the frequent intake of vegetables, fruits, nuts, low-fat dairy products, eggs, red and white meat, olive and legumes. We observed that after adjustment for age, physical activity, FFM, and energy intake; risk of RMR decreasing among women with the lower and medium adherence to the HDP was higher, compared to higher adherence to the HDP. Moreover, odds of RMR increasing among women with the medium adherence to the UHDP was higher, compared to greater adherence to the UHDP.The present study has several strengths. As far as we know, this is the first study which determine the possible relationship of HDP and UHDP with DNR among Iranian women with overweight and obesity. However, during the interpretation of our findings, some limitations should be also noticed. The main limitation is the cross-sectional design of our study, which prohibit us inferring causality. Therefore, further prospective studies are needed to confirm our findings. As the other limitation of this study, the little number of participants in the same-sex sample could be mentioned. In addition, measurement error is another potential limitation, as is in all dietary assessment methods. Due to using FFQ to assess usual dietary intakes, misclassification of study individuals is another concern. However, we used a validated FFQ for assessment of dietary intakes. Furthermore, we cannot exclude residual confounders despite adjusting for a wide range of potential confounders.In conclusion, our study has shown significant associations between dietary patterns and RMR status. Our findings should be confirmed by future prospective studies.Additional file 1: Supplementary Table 1. Food groups used in the factor analysis and factor loadings for each of the identified dietary patterns.