Among commercially insured patients with varying pharmacy benefit designs, are changes in prices for brand-name prescription drugs correlated with changes in patient out-of-pocket costs?In this cohort study of 79 brand-name drugs, there was no correlation between changes in drug prices and out-of-pocket spending. However, among the 57.3% of patients whose insurance required coinsurance or deductibles, changes in out-of-pocket spending were correlated with changes in list prices but not postrebate net prices.Some commercially insured patients are insulated from paying more when drug list prices increase, but patients with deductibles and coinsurance pay more when manufacturers raise a drug’s list price and do not directly benefit from confidential rebates paid by manufacturers to insurers. List prices set by manufacturers for brand-name prescription drugs in the US have been increasing faster than inflation, although confidential manufacturer rebates offset some of these increases. Most commercially insured patients pay at least some out-of-pocket costs for prescription drugs, and higher patient spending is associated with lower adherence and worse health outcomes.To examine whether price changes for brand-name drugs are correlated with changes in patient out-of-pocket spending and whether this association varies by insurance benefit design.A cohort study of 79 brand-name drugs with available pricing data from January 2015 to December 2017 was conducted, with data obtained from a national commercial insurance claims database.Change in the list prices set by manufacturers and estimated net prices after rebates among non-Medicaid payers.Change in median out-of-pocket spending among all patients and stratified by insurance pharmacy benefit design, including high-deductible insurance plans and plans with any amount of deductibles or coinsurance.r = 0.34; P = .002). Overall, changes in out-of-pocket spending were not correlated with changes in list prices  or net prices . Among 53.7% of patients who paid any drug deductible or coinsurance, median out-of-pocket spending increased by 15.0%, and changes were moderately correlated with changes in list prices  but not net prices .Among 79 drugs, median increases were 16.7%  for list prices, 5.4%  for net prices, and 3.5%  for out-of-pocket spending from 2015 to 2017. Changes in list prices were correlated with changes in net prices (Some commercially insured patients who pay only prescription drug copayments appear to be insulated from increases in drug prices. However, more than half of patients pay deductibles or coinsurance and may experience substantial increases in out-of-pocket spending when drug prices increase. Among these patients, there was no evidence that manufacturer rebates to insurers are associated with patients’ out-of-pocket spending. Policies to rein in unregulated annual increases in list prices for brand-name drugs may have important consequences for patient out-of-pocket spending. This cohort study examines the association between price changes for brand-name drugs and out-of-pocket spending by patients with different insurance plans. In 2018, the US spent $476 billion on prescription drugs, with 80% of spending on brand-name products.2 For patients in Medicare Part D plans, cost-sharing is typically higher and is often determined as a fixed percentage of a drug’s list price (25% of drug costs until patients reach the catastrophic coverage phase).6 As a result, Part D beneficiaries may be adversely affected by increasing list prices and may not benefit directly from rebates.7However, rebates negotiated by insurers are not necessarily reflected in the out-of-pocket prescription drug costs borne by patients. Patient spending at the pharmacy counter—including copayments, coinsurance, and deductibles—accounted for 14% of all US prescription drug spending in 2018.8 which traditionally charge patients flat copayments for prescription drugs but have increasingly shifted costs to patients by adding deductibles and/or coinsurance.9 Yet few studies have examined the association between increasing list prices and patient out-of-pocket costs for commercially insured patients. One recent study noted that list prices for 14 top-selling drugs doubled from 2010 to 2016, while median out-of-pocket costs increased by 53%.10 Another study reported that, although insulin list prices more than doubled from 2006 to 2017, patient out-of-pocket spending was relatively stable over the same period.11 The latter study stratified patients based on whether they were in a high-deductible insurance plan, but to our knowledge, no study has systematically examined the relationship between drug prices and patient out-of-pocket costs stratified by commercial pharmacy benefit design.Half of individuals in the US are insured by employer-based commercial insurance plans,Given the limited understanding of how prices affect patient out-of-pocket costs for commercially insured individuals, we examined the correlation between changes in list prices, net prices, and out-of-pocket spending among a cohort of brand-name drugs used by commercially insured patients from January 2015 to December 2017, overall and among patients with different insurance benefit designs.12 We measured median patient out-of-pocket spending using claims data from a national sample of approximately 30 million patients with employer-sponsored commercial insurance at any given time .In this cohort study, we identified the drug cohort and obtained list and net price estimates from SSR Health, which estimates prices and rebates for more than 1000 brand-name drugs manufactured by publicly traded companies.STROBE) reporting guideline for cohort studies.We obtained approval from the Mass General Brigham Institutional Review Board to use deidentified claims data for this study. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology  because pharmacy claims may not accurately capture out-of-pocket spending for these products. We also excluded brand-name drugs with generic competition before January 2018 because insurers may increase patient cost-sharing for brand-name products after generic versions are released. Generic entry date was obtained from SSR Health, supplemented by a review of when the first generic National Drug Codes were listed in a compendium of all drug products published by First Databank.Pricing estimates in SSR Health are given for an entire drug product, representing a weighted average of unit prices across all formulations and dosage forms. A given product may have variable definitions of units  and different prices per unit. Consequently, variation in the relative use of different dosage forms may result in changes in the product’s unit price over time that do not reflect variations in the price of the drug. To narrowly focus on the association between a drug’s price and out-of-pocket costs, we included only drug products that had the same unit price across all dosage forms. In addition, we included only drugs for which we could identify a standard number of units per 30-day supply based on the dosing guidelines on the drug’s US Food and Drug Administration–approved labeling. For drugs with prices that vary based on the dose prescribed, out-of-pocket spending may be influenced both by price and dose, and our aim was to focus solely on the association between price and out-of-pocket spending.For each drug included in the cohort, we used the US Food and Drug Administration–approved labeling to determine the drug’s route of administration and therapeutic category. We also estimated annual US revenue in 2015 and 2017 as a product of net price multiplied by estimated use among all patients in the US from SSR Health.12 These estimates approximate the average US price per unit after manufacturer rebates and other price concessions. We relied on SSR Health’s non-Medicaid rebate estimates because Medicaid rebates are statutory and usually higher than those for Medicare or commercial plans.16We obtained quarterly wholesale acquisition cost  and net prices from SSR Health, which estimates net prices by comparing publicly reported manufacturer revenue with estimates of total number of units sold in the US.17 We also converted unit prices to the price for a typical 30-day supply of the drug based on the dosing instructions in the US Food and Drug Administration–approved labeling information. For drugs typically dispensed for 1-time use , we converted to the price for a single use.Because drug sales volume to wholesalers can vary over a calendar year , we calculated annual averages for list and non-Medicaid net prices in 2015 and 2017, weighting each quarter equally. All annual average prices were converted to 2017 US dollars using the Consumer Price Index for all urban consumers.3 When net price exceeded list price in a given quarter, we presumed rebates of 0, so that the net price was equal to the list price for that quarter. Owing to unequal distribution of drugs throughout the year, a negative rebate in one quarter might offset a high rebate in a surrounding quarter. Therefore, assuming 0 rebates in these cases may lead to underestimates of annual average net prices. We also performed a sensitivity analysis in which we excluded drugs with a negative rebate estimate in any quarter.In some cases, SSR Health’s quarterly net price estimates exceeded list prices, usually because the number of units sold may be underreported.For each brand-name drug, we identified separate cohorts of patients in 2015 and 2017 with at least 1 claim for the drug. We excluded patients with claims paid under capitated payment models because cost data for these claims are not reliably recorded.We calculated the annual sum of out-of-pocket costs  paid across all filled prescriptions for the drug of interest in each calendar year. We also summed the annual quantity of drug prescribed (the number of units), which we converted to 30-day supplies to calculate each patient’s average annual out-of-pocket spending per 30-day supply (or 1-time use). We then calculated the median out-of-pocket spending among all users of each drug in each year, adjusted to 2017 US dollars. To ensure that unit prices were measured similarly in SSR Health and claims data, we calculated median total drug cost per unit in the claims data (including patient and payer components) and excluded drugs if this cost differed from the drug’s list price by more than 10%.r ≠ 0) at a 2-sided significance level of P < .05.For each drug, we calculated the relative change in inflation-adjusted list price, non-Medicaid net price, and median out-of-pocket spending from 2015 to 2017 . We calculated Pearson correlation coefficients between relative changes in drug prices and out-of-pocket spending to identify significant associations .Of 1117 brand-name products with any SSR Health net price estimates from 2007 to 2019, 541 drugs had continuous net price estimates in each quarter from 2015 to 2017. Ultimately, 79 self-administered brand-name drugs without generic competition met the entry criteria for the cohort  were administered orally, 8 (10%) by injection, 3 (4%) by inhalation, and the remaining 4 (5%) via topical, rectal, or ophthalmic routes. The most common therapeutic categories were oncologic (11 [14%]), antimicrobial (9 [11%]), endocrine/diabetes (9 [11%]), neurologic (9 [11%]), cardiovascular (8 [10%]), and gastrointestinal (8 [10%]).Median US annual revenue per drug was $330 million  in 2015 and $401 million  in 2017. Total revenue for all 79 drugs was $63 billion in 2015 and $67 billion in 2017. In 2017, 18 drugs (23%) had annual revenue exceeding $1 billion.Median list prices per 30-day supply increased from $333  in 2015 to $386  in 2017, with a median change in list price of 16.7%  . The unwr = 0.34; P = .002)  analyses.The median net price per 30-day drug supply was $173  in 2015 and $166  in 2017. The median change in net price was 5.4% . Unweighted results revealed a similar median increase in net price of 7.5% . Net prices decreased for 21 drugs (27%) and increased for 58 drugs (73%) . Changes = .002)  and unweThe median number of commercially insured patients in our data set who used each drug was 4925  in 2015 and 4078  in 2017. The sum of patients included in the median out-of-pocket spending estimates was 1.4 million in each year, although some patients may have used more than 1 drug in the cohort in a given year.Median out-of-pocket spending for a 30-day drug supply was $29  in 2015 and $30  in 2017, with a median change from 2015 to 2017 of 3.5% . Results were similar in unweighted analyses.r = 0.14; P = .22) or net price   .r = 0.43; P < .001) and those with any deductible or coinsurance   or net prices .Among patients in insurance plans that included deductibles or coinsurance, there was a moderate correlation between changes in out-of-pocket spending and list prices but no correlation with net prices . The str = .001) . Among pr = 0.30; P = .008) and those with any deductible or coinsurance   and 41% on tier 4 drugs , with coinsurance rates ranging from 24% to 34% of a drug’s list price.18 In our study, nearly half of patients paid prescription drug coinsurance.In a national survey, 84% of patients with private health insurance had a pharmacy benefit that included 3 or more drug tiers, with more expensive drugs in higher tiers requiring higher out-of-pocket costs.3 List prices increased for nearly all drugs in our cohort, with a median inflation-adjusted increase of 16.7% over 2 years. Some of these list price increases have been offset by increasing rebates provided to insurers and pharmacy benefit managers, such that 1 in 4 drugs in our cohort had a decrease in net price from 2015 to 2017. Although increasing rebates may limit the effect of list price hikes on total drug spending, we did not find any correlation between changes in a drug’s net price and out-of-pocket spending, suggesting that rebates are not being reflected in lower point-of-care prices for patients.7We found that, for patients whose prescription drug plans included deductibles or coinsurance, increases in a drug’s list price were correlated with higher out-of-pocket costs. This design is problematic because list prices for brand-name drugs, which are unregulated in the US, have long been increasing much faster than inflation.21 However, the parallel unfettered increases in list prices for brand-name drugs and greater reliance by insurers on deductibles and coinsurance have placed important financial burdens on patients using high-cost brand-name medications. Higher out-of-pocket costs have been associated with decreased medication adherence and, in some cases, worse clinical outcomes in a wide range of clinical conditions, including cancer,23 diabetes,25 and cardiovascular disease.28 Policy interventions that lower out-of-pocket spending can increase medication adherence, improve clinical outcomes, and reduce health disparities.30Tying patient out-of-pocket costs directly to the price of a drug can encourage patients to use less costly medications, and completely detaching patient expenses from drug prices could ultimately lead to higher premiums.32 However, there are concerns that caps or penalties on price increases could encourage manufacturers to set higher prices for drugs at launch,32 and if such rules were imposed only on Medicare Part D, there may be no substantial benefit for the half of Americans who are covered by private employer–based health insurance plans.8As a result, policy makers should consider how to protect patients from large increases in out-of-pocket spending resulting from year-over-year increases in list prices for prescription drugs. For example, Congress could cap annual increases on list prices or penalize manufacturers for increases above inflation through mandatory rebates in Medicare Part D, similar to the model already used in Medicaid.34 Lawmakers have proposed more general bans on rebates to public and private payers unless the rebates are directly passed on to patients at the point of sale.35 Some private insurers have already begun implementing programs to pass rebates on directly to patients.36 However, limiting confidential rebates or requiring increased transparency could undermine the bargaining power of large purchasers, leading to an overall increase in drug prices and spending.39 Thus, a ban on rebates might reduce out-of-pocket spending by patients who use certain high-cost medications but lead to higher premiums among all patients.Policy makers could also address the increasing gap between list and net prices by eliminating confidential manufacturer rebates to insurers and pharmacy benefit managers or mandating that such rebates be passed directly to patients at the point of sale. A rule finalized by the Trump Administration in November 2020 would have eliminated drug rebates in Medicare Part D, although the rule faces substantial legal hurdles and its implementation has been delayed.40 Drugs with multiple formulations or for which monthly price varies by dose are likely to have similar changes over time, although prices and subsequent out-of-pocket spending per patient may be more variable. Because we measured out-of-pocket spending from pharmacy claims, we were unable to include clinician-administered drugs, which account for a growing proportion of prescription drug spending.41This study has limitations. We examined only 79 drugs owing to strict inclusion criteria to ensure accurate measurement of prices, so our findings may not be representative of all brand-name drugs. However, the cohort includes many top-selling drugs, with the entire cohort accounting for $67 billion in spending in 2017, which represents nearly 15% of total US prescription drug spending.8; the association between increasing list prices and increasing out-of-pocket spending may be more pronounced among patients covered by Medicare, as the standard Part D benefit includes coinsurance .7 Commercially insured patients may use manufacturer coupons to offset some out-of-pocket spending, and coupon use is not captured in claims data. Coupon use has increased over the past several years,43 so not being able to account for coupons may lead to an overestimate of out-of-pocket spending for some drugs.Another limitation is that our findings are applicable only to commercially insured patients, which include approximately half of Americans45 SSR Health may underestimate Medicaid rebates, leading to a corresponding overestimate in estimated non-Medicaid rebates,46 although such an overestimate would be unlikely to substantially affect change in net prices over time.In addition, individual rebates to payers are confidential. The estimates of net prices from SSR Health are based on manufacturer revenue and may underestimate postrebate prescription drug spending by excluding fees and profits among other elements of the pharmaceutical supply chain, including wholesalers, pharmacies, and pharmacy benefit managers.Although some commercially insured patients who pay only prescription drug copayments are insulated from increases in prescription drug prices, more than half of patients pay deductibles or coinsurance and may experience increases in out-of-pocket spending when manufacturers increase list prices. Among these patients, we found no evidence that increasing manufacturer rebates directly offset increases in out-of-pocket expenses. Policy makers seeking to limit price increases among brand-name drugs should consider how any proposed policies affect patient out-of-pocket spending.


We aimed to assess whether dynamic arterial spin labeling (dASL), a novel quantitative MRI technique with minimal contamination of subject motion and physiological noises, could detect the longitudinal effect of focused attention meditation (FAM) on resting-state functional connectivity (rsFC). A total of 10 novice meditators who recorded their FAM practice time were scanned at baseline and at the 2-month follow-up. Two-month meditation practice caused significantly increased rsFC between the left medial temporal (LMT) seed and precuneus area and between the right frontal eye (RFE) seed and medial prefrontal cortex. Meditation practice time was found to be positively associated with longitudinal changes of rsFC between the default mode network (DMN) and dorsal attention network (DAN), between DMN and insula, and between DAN and the frontoparietal control network (FPN) but negatively associated with changes of rsFC between DMN and FPN, and between DAN and visual regions. These findings demonstrate the capability of dASL in identifying the FAM-induced rsFC changes and suggest that the practice of FAM can strengthen the efficient control of FPN on fast switching between DMN and DAN and enhance the utilization of attentional resources with reduced focus on visual processing. Meditation provides evidence of a diverse set of benefits, including emotional regulation , awareneMeditation has been associated with trait changes of the brain structure using cross-sectional structural magnetic resonance imaging (sMRI) studies. Increased gray matter (GM) density ,11,12 anThe trait-related brain changes have been studied for the neural basis underlying the experience and training predominantly using blood-oxygen-level-dependent (BOLD) resting-state functional MRI (rsfMRI) . Recent Pseudo-continuous arterial spin labeling (PCASL)  providesBased on the increased PCC rsFC  after weA total of 11 healthy undergraduates at the Binghamton University  participated in the study. All participants were recruited from a university-offered meditation course. Nine participants had no prior meditation experience before, while two participants had a brief meditation period without any specific guidance in their prior yoga classes.The meditation course introduced several different types of meditation techniques and reviewed the benefit of meditation on behaviors from literature. The instructor guided students for 15 min meditation once or twice per week during class in order to have students familiar with different meditation styles. Participants were instructed during class to sit comfortably, relax shoulders, close/open eyes, concentrate on a selected point focus , and repeatedly bring the attention back to the selected focus if they noticed their attention had drifted. Participants could choose their own point of focus during the meditation practice, such as their breath, a spot on the wall, a phrase, or anything else they felt appropriate. Homework required practice FAM for at least 10 min per session and no less than 5 times per week and writing a weekly journal by describing their practice experiences.All 11 participants underwent the baseline MRI scans before any homework assignments. One subject was not able to attend the follow-up scan after 2 months. Participants reported their total practice time according to their daily logs between the baseline and follow-up scans. The total practice time was calculated as the sum of all meditation practice time, including those from class and homework practice.We scanned meditation participants, including 11 at baseline and 10 at the 2-month follow-up), on a 3 Tesla GE MR750 scanner  using a 32-channel receive-only phased-array head coil at Cornell University MRI Facility. The Institutional Review Board of Cornell University approved the study, and all participants fully understood and signed written informed consent. All methods described in this manuscript were performed in strict accordance with the approved guidelines. The scans included 3D whole-brain sagittal T1-weighted magnetization prepared rapid gradient echo (MPRAGE) images for image registration, and the resting-state 3D dynamic arterial spin labeling (dASL), specifically, MPRAGE images were acquired in 5 min 30 s with the following parameters: field of view (FOV): 25 cm, 176 slices with matrix size: 256 × 256; slice thickness: 1.0 mm, repetition time (TR): 7 ms, echo time (TE): 3.42 ms, inversion time (TI): 425 ms, flip angle: 70, receiver bandwidth (rBW): 25 kHz. The pseudo-continuous arterial spin labeling (PCASL) sequence  was usedReconstruction of the ASL label-control difference image time series was obtained by our custom reconstruction algorithm ,40. The For each subject, the motion-corrected ASL image time series were transformed to the standard MNI brain space. Specifically, GM, white matter, and cerebrospinal fluid probability maps were generated by segmenting high-resolution MPRAGE images. The GM probability maps were co-registered to the average of motion-corrected ASL images. The motion-corrected ASL time series and the average CBF map for each subject were transformed into the standard space by applying the same transformation that was obtained by the normalization from the co-registered GM probability map to the prior GM template in standard brain space. To correct for global CBF differences in the longitudinal setting, the relative CBF map for each subject was calculated by dividing the global mean CBF signal. The relative CBF was used because it was reported to be more sensitive in statistical analyses for group comparisons .3. The centers of the seed ROIs were selected from a previous study [Due to the minimal contamination from motion artifacts and physiological noises, the dASL image time series were not processed for removing the noises. Global CBF values, averaged over the whole brain, were regressed out from the dASL image time series. Eight seeds were chosen from the default mode network (DMN) and dorsal attention network (DAN). Two seeds were from DMN: the posterior cingulate cortex (PCC) and the ventromedial prefrontal cortex (vmPFC). Six seeds were from DAN: the left middle temporal area (LMT), the right middle temporal area (RMT), the left frontal eye field (LFE), the right frontal eye field (RFE), the left superior parietal lobule (LSPL), and the right superior parietal lobule (RSPL). The seeds were chosen to cover different anatomical extents: frontal cortex, parietal cortex, and temporal cortex. All seed regions of interest (ROIs) were defined as a sphere with a volume of ~2 cmus study  . In the GLM, the z score maps from each seed (and the relative CBF maps) were used as the dependent variables. The status index (baseline or follow-up), subject index (whether two scans are from the same subject or not), and gender were used as the independent variables. Age was not used as a covariate because the participants had a maximum of a 1-year age difference. The voxel-level significance threshold was set to p < 0.005. A cluster-level p-value of 0.05 was used to correct for multiple comparisons that were controlled by family-wise error (FWE). A more liberal voxel-level p-value threshold of 0.01 and FWE-corrected cluster-level p-value threshold of 0.05 were also used to view the trend if the above stringent p values failed to detect the longitudinal effects of meditation. The automated anatomical labeling (AAL) atlas binary mask was used to analyze only the GM regions.The relative CBF maps and z-score maps from all seed ROIs in the standard space were smoothed with a 6 × 6 × 6 mmp-value and FWE-corrected cluster-level p-value thresholds were set as 0.005  and 0.05.To investigate the association of the longitudinal changes in CBF measures with meditation practice time, the difference of the z-score maps and relative CBF maps between the baseline and follow-up was modeled using SPM12 via GLM. In GLM, the difference maps from each contrast were used as the dependent variables. Gender and practice time were used as independent variables. The voxel-level t-test with gender as a covariate and multiple linear regression analysis with gender and meditation practice time as covariates, respectively.Post hoc regional analyses were performed to visualize the longitudinal changes and the relationship between the longitudinal changes and meditation practice time in those CBF measures. The significant clusters obtained from either voxel-based analysis were separated into different ROIs. The regional value for each ROI was calculated as the mean signal value over all the voxels within the region. Post hoc regional analyses were performed using paired dASL image time series was of good quality. Representative dASL images from four random time points and their temporal signal-to-noise ratio (SNR) map across all time points are shown in p = 0.52) were found after correcting for gender effects from the baseline to follow-up. No significant relative CBF changes were observed from the baseline to follow-up using a voxel-level p < 0.005. With a liberal voxel-level p < 0.01, we observed a relative CBF increase in the junction of occipital, temporal and parietal regions (corrected cluster-level p = 0.027) (p = 0.002)   and a re= 0.002)  at follop < 0.005. With a liberal voxel-level p < 0.01, we observed that more practice time was associated with less longitudinal CBF changes in the junction of occipital, temporal, and cerebellum regions  (p < 0.001) (p = 0.012)  a and bet< 0.001) b and dec= 0.012) c at foll= 0.012) .p < 0.005. After adjusting for the gender effect, the practice time was positively associated with the longitudinal changes in rsFC between the PCC seed and the insular and prefrontal areas  increased PCC-rsFC and vmPFC-rsFC with the mPFC/ACC area and (2) increased PCC-rsFC with the insula area. These results extend the prior cross-sectional study that showed greater DMN-rsFC with the mPFC region  and PCC-Meditation practice time was associated with increased dorsal lateral prefrontal cortex (DLPFC) rsFC with almost all the DAN seeds, including RMT, LSPL/RSPL, LFE/RFE. The DLPFC region, a key region in the frontoparietal control network (FPN), serves to initiate and regulate attention and cognitive control in response to salient stimuli ,32. Our Meditation practice time was associated with decreased vmPFC-rsFC with DLPFC. These findings are consistent with reduced rsFC between DMN and FPN/CEN for meditators ,54. ConvMeditation practice time was associated with decreased RMT-rsFC or LSPL/RSPL-rsFC with visual/occipital regions. These results are in line with lower meditative-state connectivity between attention and visual networks in more experienced meditators . These rWe found a significant negative correlation between PCC-precuneus/inferior parietal connectivity and meditation practice time. These results were a little surprising to us because we observed a positive correlation between PCC-mPFC and mPFC-mPFC connectivity and meditation practice time. However, these results are consistent with the anticorrelation in DMN  . These fWe observed that the individual differences in meditation practice time were associated with rsFC in markedly more pairs of brain regions using the dASL technique compared with the multi-echo BOLD fMRI technique (results shown as a separate publication ) in the This study has some limitations. First, the study has a small sample size. However, the study was intended to serve as a preliminary study to test the sensitivity of dASL in meditation effects. The observed significant rsFC changes after a 2-month meditation practice with this small sample size support the large effect size from the dASL method. Second, a control group was not included in the study. However, the longitudinal changes of rsFC were related to meditation practice time, suggesting that the rsFC changes emerge from meditation practice itself, not from expectancy bias. Third, a short follow-up period and only one follow-up time were used in the study. Therefore, the results may not be generalized to the effect of long-term meditation. Further investigation with a larger sample size, matched control group and longer follow-up is warranted to validate the effect of meditation practice time on brain rsFC changes.Our significant findings using dASL support further investigation into the power of the technique in meditation studies. Two-month meditation is associated with the changes of rsFC and the changes of rsFC are associated with practice time. These findings suggest that the practice of meditation can strengthen the efficient control of FPN on fast switching between DMN and DAN and improve the utilization of attentional resources with reduced focus on visual processing.


Periodontitis is among the most common chronic diseases worldwide, and it is one of the main reasons for tooth loss. Comprehensive profiling of the metabolite content of the saliva can enable the identification of novel pathways associated with periodontitis and highlight non-invasive markers to facilitate time and cost-effective screening efforts for the presence of periodontitis and the prediction of tooth loss.n = 938) of the Study of Health in Pomerania (SHIP-2) using linear regression models adjusting for common confounders. We took forward any candidate metabolite associated with at least two oral variables, to test for an association with a 5-year tooth loss over and above baseline oral health status using negative binomial regression models.We first investigated cross-sectional associations of 13 oral health variables with saliva levels of 562 metabolites, measured by untargeted mass spectrometry among a sub-sample (We identified 84 saliva metabolites that were associated with at least one oral variable cross-sectionally, for a subset of which we observed robust replication in an independent study. Out of 34 metabolites associated with more than two oral variables, baseline saliva levels of nine metabolites were positively associated with a 5-year tooth loss. Across all analyses, the metabolites 2-pyrrolidineacetic acid and butyrylputrescine were the most consistent candidate metabolites, likely reflecting oral dysbiosis. Other candidate metabolites likely reflected tissue destruction and cell proliferation.Untargeted metabolic profiling of saliva replicated metabolic signatures of periodontal status and revealed novel metabolites associated with periodontitis and future tooth loss.The online version contains supplementary material available at 10.1186/s12916-021-02035-z. Periodontitis is the sixth-most prevalent health condition, affecting 743 million people worldwide , and is Periodontitis is a multifactorial inflammatory disease driven by a dysbiotic microbiome, eliciting host defense mechanisms through interactions of pathogen-associated molecular patterns. The oral cavity can be colonized by over 700 different microbial species  and at lUp to now, reliable biomarkers for periodontitis do not exist; therefore, developing a robust, consistent, and distinct screening tool to detect periodontitis or to improve the management of periodontitis is of high priority. The saliva is an easily sampled body fluid that has been used to map metabolic events and to survey changes in biological processes in periodontitis . MetabolIn the middle-aged and older persons, periodontitis increases the risk for tooth loss , 22. PerHere, we systematically characterize the molecular fingerprint of current and future oral health status among 938 participants of the population-based Study of Health in Pomerania (SHIP-2) using non-targeted metabolomic analysis covering more than 500 metabolites. We show numerous associations between oral phenotypes and saliva metabolites and replicate previous findings –20. We cThe Study of Health in Pomerania (SHIP) is a population-based cohort study in the north-eastern part of Germany . Using aFor the present study, we used data from SHIP-2 and SHIP-3. In total, 2333 subjects aged 30 to 93 years participated in SHIP-2. Among these subjects, we selected 1000 participants, who had dental follow-up data in SHIP-3, for metabolic profiling of saliva samples using the Metabolon HD4 platform . Out of those, we excluded 22 participants whose saliva measurements did not pass the quality control and another 40 participants due to missing values in variables considered as confounders in the statistical analysis. The final data set for statistical analysis included up to 938 individuals .Examinations were conducted in a dental chair with light and without saliva ejector or air jet. Using the half-mouth method , periodontal probing depth (PPD) and clinical attachment level (CAL) were assessed on four sites  of all teeth excluding the third molars using a periodontal probe . The presence of supragingival plaque and supragingival calculus was recorded on the same four sites at the first incisor, canine, and first molar. If a tooth was missing, the next distally located tooth was considered. The number of teeth was registered excluding wisdom teeth. A dental interview completed the measures.Coronal cavities were diagnosed visually or using a periodontal probe  . CariousFive calibrated and licensed dentists performed the dental examinations. Calibration exercises were conducted every 6–12 months. In SHIP-2, intra-class correlations of 0.76–0.88 per examiner and an inter-class correlation of 0.74 for CAL were achieved. For PPD, the intra-class correlation was 0.70–0.78 per examiner and the inter-class correlation was 0.70. In SHIP-3, intra-class correlations of 0.92–0.97 per examiner and an inter-class correlation of 0.98 for CAL were achieved. For PPD, the intra-class correlation was 0.69–0.92 per examiner and the inter-class correlation was 0.91. For caries assessments in SHIP-2, intra-rater kappas were 0.83–1.00, while pairwise inter-rater kappas were 0.72–1.00.For cross-sectional analyses, the following oral examination variables were derived: mean PPD, cumulative PPD from pockets with PPD ≥4 mm (CumPPD4+), percentage of sites with PPD ≥3 (PPD 3+mm%) or 4 mm (PPD 4+mm%), mean CAL, percentage of sites with CAL ≥3 (CAL 3+mm%) or 4 mm (CAL 4+mm%), number of missing teeth (MT count), percentage of sites with plaque, percentage of sites with calculus, a variable combining information on removable prostheses and MT count (Prosthesis/MT), DF-S, and DF-S%.Probably due to methodological problems associated with the periodontal probe , distrib2 (kg/m2). Diabetes mellitus was defined as having known diabetes in SHIP-2 or glycated hemoglobin level of (HbA1c) ≥ 6.5% or non-fasting serum glucose ≥11.1 mmol/l. Known diabetes mellitus in SHIP-2 was defined as physician’s diagnosis or antidiabetic medication intake  either in SHIP-0, SHIP-1, or SHIP-2. The follow-up time of the examinations was indicated in years.Sociodemographic and behavioral parameters have been assessed by computer-assisted personal interviews. The smoking status was classified as never, former, and current smoking. Standardized measurements of the body height and weight were performed with calibrated scales, and the body mass index (BMI) was calculated as weight divided by heightFor the collection of saliva samples, a hygienic extraction system  was used. The subjects were instructed to chew on a plain cotton roll to stimulate salivation. After exactly 1 min, the rolls containing the absorbed saliva were placed into the Salivette® and centrifuged immediately at 1000 g for 20 min at 4°C. The ejection of food remnants, cell debris, and insoluble materials led to a supernatant, which was stored at −80°C until further analysis.Metabolic profiling of saliva samples was conducted by Metabolon Inc.  using the Metabolon Discovery HD4 platform. For each sample, four different ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods were applied to allow for maximum coverage of the small molecule content. Metabolon uses an internal reference library with more than 3000 pure chemical compounds to identify metabolites based on retention time/index, mass to charge ratio, and chromatographic data. All named compounds fulfill tier 1 or tier 2 (indicated by a star) criteria according to the metabolomics reporting standards . We usedAfter processing, 562 saliva metabolites remained for statistical analyses. Out of these, 64 metabolites could not be unambiguously assigned to a chemical identity and are designated to hereafter with an X, combined with a unique number.Firstly, to replicate previous findings , we crosTo test candidate metabolites for associations with a 5-year tooth loss, we used negative binomial regression models adjusted for the above-named confounders as well as respective baseline levels of the outcome and the follow-up time (as an offset variable). Only metabolites with ≥2 significant cross-sectional associations with PPD-, CAL-related variables, DFS, DFS%, or MT count were selected as exposure variables in longitudinal analyses. We derived incidence rate ratios (IRRs) and 95% confidence intervals.p values of multiple unrelated statistical tests on the same cohort follows a uniform distribution if the null hypothesis is true. In other words, the p value can take each value between zero and one with an equal chance and an accumulation of small p values indicates the presence of true positive effects. The 5% threshold reflects this acceptance rate. Adjusted p values were referred to as q values.We accounted for chance findings due to multiple testing by controlling the false discovery rate (FDR) at 5%, that is, approximately 5% of the metabolites presented as significant associations might be false-positive ones. This type of error control makes use of the fact that the distribution of We used Gaussian graphical modeling to derive a data-driven metabolic network based on 260 metabolites with less than 25% missing values and imputed remaining missing values using a Random Forest approach as implemented in the R package “missForest.”For metabolites with significant associations in the longitudinal study part, we performed phenome-wide association (PheWAS) and genome-wide association studies (GWAS). The method is described in detail in the Supplementary Methods  and Stata/SE 14.2 .All statistical analyses were done in R version 3.6.1  of oral variables.Each of the oral variables was significantly associated with saliva levels of at least one out of 84 metabolites Fig. . Out of We observed high correlation coefficients between this study and our previous work when comparing effect estimates . This indicated an additional effect of metabolite levels over and above what might have already been present at baseline.A total of 34 metabolites were associated with two or more oral variables, which we used as candidates for longitudinal analyses to minimize the testing burden. Of the 34 candidate metabolites, nine were significantly associated with a 5-year tooth loss, even after adjustment for the baseline oral health status, with IRRs ranging between 1.19 and 1.28  in a standard GWAS analysis  of the nine identified metabolites was also associated with oral health status in the cross-sectional analysis, clearly emphasizing the gain using the state-of-the-art metabolomics techniques for biomarker discovery. In accordance with previous studies, the identified molecular signatures for tooth loss point to surrogates of bacterial metabolism, cell proliferation, and host defense mechanisms  gingivalis. P. gingivalis is considered a central periodontal pathogen, which shifts a healthy into a dysbiotic subgingival microbiome, initiating inflammatory breakdown [P. gingivalis, cleave protein substrates after arginine and lysine residues in oligopeptides [Various oral bacteria rely on the breakdown of host dipeptides as an energy resource and the documented association of dimethylarginine and N6,N6,N6-trimethyllysine with a 5-year tooth loss might well fit into this pattern. Briefly, periodontal tissue breakdown is carried out by activated proteases of host and bacteria, resulting in higher levels of dipeptides , 56, frereakdown . Argininpeptides , 60.We replicated our previous observation of a strong and consistent association between saliva levels of phenylacetate and oral variables and further showed that saliva levels were associated with a 5-year tooth loss. While we speculated previously that 3-phenylacetate, similar to 3-phenylpropionate and 3-(4-hydroxyphenyl)propionate, likely originates from microbial metabolism, the current study allowed us to expand on this. The derived metabolic network puts those metabolites in close proximity to 2-pyrr, which might point towards a common community of bacteria contributing to the saliva pool of all of these metabolites. Further, our PheWAS approach identified waist circumference (which is a host factor) positively associated with phenylacetate, which led us to hypothesize on a systemic origin of phenylacetate. This metabolite has been measured in body fluids other than saliva, including blood and urine –63, and The association between saliva levels of N-acetyltaurine and a 5-year tooth loss is puzzling, since little is known about this compound in general. It may relate to the antioxidative properties of its closely related compound taurine , 67, whiMajor strengths of our analyses are the longitudinal design, large sample size, replication of cross-sectional findings , and a dPeriodontitis is often overlooked  and undePreventive dental care in which consumers can evaluate their periodontal health and communicate with the dental team on an ongoing or as-needed basis may reduce health care cost and offer new possibilities to deliver care. A point-of-care (POC) device, based on non-invasive biomarkers, such as identified in the present study, may open a new avenue for such periodontal home care. If patients were able to screen themselves for periodontitis, they could initiate self-treatment, because incipient and moderate periodontitis can be halted or at least retarded by improved oral home care measures .To improve prediction, many attempts were made and some commercial kits based MMP-8 as the product of the host response or on putative periodontal pathogens were launched, but neither of these biomarkers was sensitive and specific enough and no breakthrough was achieved for monitoring periodontal stability . FurtherIf a combination of biomarkers was predictive for future tooth loss, the periodontal community would have a tool to deliver a tailored periodontal therapy. A large multicenter study showed that hitherto used clinical variables were insufficient to predict tooth loss . The 201We identified a distinct profile of saliva metabolites associated with tooth loss. Specifically, 2-pyrr and butyrylputrescine were strongly associated with a 5-year tooth loss over and above current oral status and might hence have potential as biomarkers for screening purposes to identify periodontally diseased subjects (diagnostic), but also to identify subjects at risk for more tooth loss (prediction). The metabolic profile seemed to be enriched for markers of a dysbiotic oral microbiome and the respective host response. The ability of identified candidate metabolites to enable effective screening or prediction has to be established in appropriate settings. However, it should be noticed that tooth loss is not an outcome that is specific to periodontitis. Thus, the saliva metabolites associated with tooth loss may also be linked with other non-periodontal mechanisms. We further note that replication of the results in cohorts of different age and ethnic distribution, and health conditions is warranted, as is the integration of oral microbiome data to improve our understanding of how host and microbial metabolism is interlinked and possibly drives periodontitis progression.Additional file 1. Title of data: Detailed information about baseline characteristics of study participants, the in-vitro test, the phenome-wide association study, and the genome-wide association study. Description of data: We provide detailed information about baseline characteristics of study participants, the phenome-wide association study ), and the genome-wide association study (for 2-pyrr and butyrylputrescine). Information is split into the Supplementary Methods, the Supplementary Results, the Tables S1-S12, and the Figures S1-S4.


The prognostic values of inflammation-based markers in well-differentiated pancreatic neuroendocrine neoplasms, diagnosed according to the new 2017 World Health Organization classification, have remained unclear. Therefore, we assessed the ability to predict the recurrence of such markers after curative resection in patients with these neoplasms.Circulating/systemic neutrophil–lymphocyte, monocyte–lymphocyte, platelet–lymphocyte, and platelet–white cell ratios were evaluated in 120 patients who underwent curative resection for well-differentiated pancreatic neuroendocrine neoplasms without synchronous distant metastasis between 2001 and 2018. Recurrence-free-survival and overall survival were compared using Kaplan–Meier analysis and log-rank tests. Univariate or multivariate analyses, using a Cox proportional hazards model, were used to calculate hazard ratios with 95% confidence intervals.Univariate analysis demonstrated that preoperative neutrophil–lymphocyte ratio, tumor size, European Neuroendocrine Tumor Society TMN classification, 2017 World Health Organization classification, and venous invasion were associated with recurrence. The optimal preoperative neutrophil–lymphocyte ratio cut-off value was 2.62, based on receiver operating characteristic curve analysis. In multivariate analysis, a higher preoperative neutrophil–lymphocyte ratio  and 2017 World Health Organization classification  were independent recurrence predictors.The circulating/systemic neutrophil–lymphocyte ratio is a useful and convenient preoperative prognostic marker of recurrence in patients with well-differentiated pancreatic neuroendocrine neoplasm based on the 2017 World Health Organization classification.The online version contains supplementary material available at 10.1186/s12893-021-01178-3. Pancreatic neuroendocrine neoplasm (PanNEN) is a biologically heterogeneous and relatively rare malignancy, with an incidence rate of approximately 5 cases per 1 million person-years, which accounts for 1–2% of primary pancreatic neoplasms . AlthougSeveral studies, however, have demonstrated that tumorigenesis and clinical manifestations of well-differentiated PanNEN are distinctively different from poorly differentiated PanNEN , and thus, the determinants of treatment should be considered separately , 9. The Recently, systemic immune-inflammatory markers have been reported as factors that influence the outcomes of treatments, such as surgery, and the efficacy of chemotherapy in patients with various types of malignancies –18. HoweIn this study, we sought to evaluate whether systemic immune-inflammatory markers can be preoperative prognostic factors for predicting recurrence and overall survival after curative resection in patients with well-differentiated PanNEN based on the new 2017 WHO classification.We analyzed 132 consecutive cases who underwent surgery for primary, histologically confirmed PanNEN at the Department of Surgery, Tohoku University Hospital, between 2001 and 2018. Eight patients with synchronous hepatic metastasis during the surgery, two patients with NEC, one patient not suitable for curative resection, and one patient with an active infection at blood sampling were excluded from the study. Finally, 120 patients with well-differentiated PanNEN were enrolled in this study. Patient characteristics , perioperative factors , pathological findings , and prognosis were investigated retrospectively. Histopathological findings were assessed by experienced pathologists . For all the patients, visual assessment ‘‘eyeballing calculation’’ was performed to assess Ki-67 index. TNM staging was adopted according to the European Neuroendocrine Tumor Society (ENETS) classification , and thePostoperative follow-up evaluation included physical examinations, laboratory tests, and enhanced computed tomography , once every 6 months. There were no patients who received neoadjuvant or adjuvant therapies in this cohort. Treatment after recurrence was determined by the available evidence at the time of surgery and based on the patient’s condition.Recurrence-free-survival (RFS) and overall survival (OS) were calculated from the date of surgery to the date of recurrence, the date of death from any cause, or the date of last follow-up. To determine the appropriate cut-off values, we used receiver operating characteristic (ROC) curves and determined the area under the curve (AUC). Differences between groups were determined using t-tests in the case of normally distributed variables or by the Wilcoxon rank-sum test in the case of abnormally distributed variables for examining differences in continuous variable distributions, and Pearson’s chi-square tests for categorical variables. RFS probabilities were compared for various categories of interest using the Kaplan–Meier method with the log-rank test.Prognostic factors were assessed with univariate and multivariate analyses, using Cox’s proportional hazards model. Hazard ratios (HR) with 95% confidence intervals (CIs) were calculated. P < 0.05 was considered to indicate statistical significance.All statistical analyses were performed using the JMP Pro 14.2.0 statistical software  and GraphPad Prism Version 8.4.2 .This study was approved by the ethics committee of the Tohoku University Hospital . It was performed in adherence to the tenets of the Declaration of Helsinki and its later amendments. The need to obtain written informed consent was waived due to the retrospective nature of the study.The demographic and clinicopathological features of the 120 patients who underwent curative resection of PanNEN are shown in Table Postoperative recurrences were observed in 12 cases (10%). The sites of recurrence were in the liver in 10 patients, the para-aortic lymph node in 1, and the lung in 1. The 5- and 10-year RFS rates for the entire cohort were 92.0% and 78.7%, respectively. Three patients died due to PanNEN, 10 patients died due to other diseases, and the remaining 107 patients were alive at the end of the surveillance period. Thus, the 5- and 10-year disease-specific survival rates were 100% and 92.5%, respectively. The NLR was significantly higher in patients with recurrence than in those without recurrence , while the MLR, PLR, and PWR were not statistically significantly different between those with and those without recurrence   and tumor size has an AUC of 0.801 (95% CI 0.675–0.945). Arrows indicate optimal cut-off values. Fig. S2. Recurrence-free-survival and overall survival for PanNENs stratified by the 2017 WHO classification. Recurrence-free-survival and overall survival of neuroendocrine carcinoma (NEC) were significantly shorter than those of well-differentiated PanNENs (NET G1-3). Fig. S3. Distribution of NLR in PanNENs stratified by the 2017 WHO classification. The value of NLR in patients with NEC was significantly higher than that in patients with well-differentiated PanNEN.


Octogenarians and beyond have often been neglected in the populational study of disease despite being at the highest point of non-modifiable disease risk burden and the fastest-growing age group for the past decade. This study examined the characteristics and in-hospital management of octogenarian patients with acute coronary syndrome (ACS) in a multi-ethnic, middle-income country in South East Asia.n = 3162) were identified. Demographics, in-hospital intervention, and evidence-based pharmacotherapies over the 10-years were examined and compared across groups of interests using the Chi-square test. Multivariate logistic regression was used to calculate the adjusted odds ratio of receiving individual therapies according to patients’ characteristics.This retrospective study utilised the Malaysian National Cardiovascular Disease- ACS (NCVD-ACS) registry. Consecutive patient data of those ≥80 years old admitted with ACS at 24 participating hospitals from 2008 to 2017 (p < 0.001). Within the 10-year, there were positive increments in cardiovascular intervention and pharmacotherapies. Only 10% of octogenarians with ACS underwent percutaneous coronary intervention (PCI), the majority being STEMI patients . More than 80% were prescribed aspirin (91.3%) either alone or combined, dual antiplatelet therapy (DAPT) (83.3%), anticoagulants (89.7%) and statins (89.6%), while less being prescribed angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (47.6%) and beta-blockers (43.0%). Men were more likely to receive PCI than women (adjusted Odds Ratio (aOR): 0.698; 95% CI: 0.490–0.993). NSTEMI  and unstable angina (UA)  were less likely to receive PCI but more likely given anticoagulants  than STEMI. The presence of cardiovascular risk factors and comorbidities influences management.Octogenarians made up 3.8% of patients with ACS in the NCVD-ACS registry  from 2008 until 2017. The largest ethnic group was Chinese (44%). Most octogenarians (95%) have multiple cardiovascular risk factors, with hypertension (82%) being the main. Non-ST-elevation myocardial infarction (NSTEMI) predominated (38%, Octogenarians with ACS in this country were mainly treated with cardiovascular pharmacotherapies. As the number of octogenarians with ACS will continue to increase, the country needs to embrace the increasing use of PCI in this group of patients.The online version contains supplementary material available at 10.1186/s12877-021-02724-7. The ageing population are increasing globally, a phenomenon known as the ‘silver tsunami’. An estimated 125 million individuals reaching 80 years and older were considered octogenarians in 2018 . The preMalaysia is a multi-ethnic, upper-middle-income country in South-East Asia with a 32 million population . MalaysiThough healthcare services are available for all, demographic variation may influence patient management. Variations in intervention and pharmacotherapies for ACS patients in multi-ethnic Malaysia have previously been described , 16. LesThis cross-sectional descriptive study is a part of research approved by the Medical Review and Ethics Committee (MREC), Malaysian Ministry of Health (MOH), with approval code of NMRR-19-4066-52,389 (IIR). MREC waived informed consent for NCVD.Anonymised patient data were obtained from the NCVD-ACS registry, which forms part of the NCVD registry. This registry was endorsed by the Ministry of Health, Malaysia, and co-sponsored by the National Heart Association of Malaysia (NHAM) . The NCVThere was no patient or public participation in the development of this study’s research question and outcome. All consecutive patient data from the year 2008 to 2017 were obtained retrospectively from the Malaysian NCVD-ACS registry.n = 3162). Patient information obtained were demographics, risk factors, and co-morbidities, summarised in Table Malaysian citizens aged 80 or older (known as octogenarian) diagnosed with ACS  based on the American College of Cardiology/American Heart Association (ACC/AHA) definitions  were ideIn-hospital interventions included in this study were coronary artery bypass graft (CABG) and percutaneous coronary intervention (PCI), both emergency and non-emergency. Further details of the PCI intervention are available in the Malaysian NCVD-PCI registry . The phap) value of less than 0.05 is considered statistically significant. Statistical analysis was performed using Statistical Package for Social Science (SPSS) software .Continuous variables (age) are presented as mean ± standard deviation (SD), while categorical variables are presented as frequencies and percentages. Patient characteristics were compared across gender using Student’s t-test and chi-square test as appropriate. Choice of in-hospital intervention and evidence-based pharmacotherapies were analysed as cumulative, comparing the different ACS stratum using the chi-square test. The trends over the years were analysed using the linear trend test. Variations of in-hospital management for different groups of interest according to demographics, risk factors and co-morbidities were analysed using multivariate logistic regression, presented as adjusted odds ratio (aOR) with a 95% confidence interval (CI). The choice of covariates was based on factors influencing ACS management in the literature and included gender, ethnicity, types of ACS, risk factors and co-morbidities . A probap < 0.001) .A total of 3162 octogenarians  was admitted with ACS from 2008 to 2017. Octogenarians made up 3.8% of the total ACS patients cumulatively during the study period and have increased significantly over the years from 3.7% in 2008 to 17.2% in 2017 (Most patients (95%) already had at least one CV risk factor at presentation. Hypertension was the highest recorded risk factor at 82%, followed by DM at 47%. There were significant differences in the CV risk factors profile between gender, with women having higher rates of hypertension, DM, and dyslipidaemia Table . In compp < 0.001).Cumulatively, aspirin (93.5%), DAPT (85.1%), anticoagulants (81.7%) and statins (89.6%) were prescribed at near maximal levels, whilst about half were prescribed ACEIs/ARBs (51.5%) and beta-blockers (54.9%) in the 10-years as presented in Fig. PCI intervention was performed at greater frequencies than CABG in all ACS stratum, Table . Less thThis study found an increasing trend of octogenarians with ACS in this multi-ethnic country, with NSTEMI presentation predominated. The Swedish National registry reported that the cumulative incidence of MI for those aged 80 years old and above are the highest amongst other age groups . Thus, oIn contrast to the ACS trends in the younger age group , 25, theThere is a high prevalence of octogenarians with multiple CV risk factors, affecting the presentation, severity, and complexity of managing ACS. Hypertension remains the major risk factor. Thus, advocating evidence-based management to treat hypertension may result in a more favourable outcome . More thThere was an increasing trend of PCI in octogenarians with STEMI, despite the rates being relatively low compared to what is being practised in other countries . The advThe prescribing of pharmacotherapies in the octogenarians with ACS were in accordance with international standard . Recent We need to bridge the knowledge gap on the effectiveness and risks of current management by performing clinical studies in the local setting. Physicians need to be aware of age-related physiological changes that may affect the pharmacokinetics and pharmacodynamics of these drugs in octogenarians while considering the immediate and long-term benefits. An example that could be modelled is Finland’s follow-up accumulation of risk factor study  and the This study uses data from a nationwide cardiology registry obtained from 24 voluntary hospitals to represent an unselected group of octogenarian patients with ACS in a real-world setting in this country. The database is well maintained, and training is provided regularly for those involved to ensure data quality . A one-tThis registry only captures data of those reported to the participating hospitals; thus there may be a selection bias. Information that measures socioeconomic status that may influence demographics presentation, such as occupation and educational levels, were also unavailable. Elderly patients have varied tolerability towards intervention and pharmacotherapies . The speThe variations in PCI and evidence-based pharmacotherapies may impact short- and long-term outcomes in this group of patients , 47. ThiAs octogenarians with ACS continue to increase, the country needs to plan for disease management and prevention. Despite being the most vulnerable age group, octogenarians were not receiving aggressive treatment as younger patients. The use of evidence-based pharmacotherapies was maximised instead. It is expected that ACS management will continue to improve with an increasing number of octogenarians, better technologies, enhanced cardiologist skills and knowledge of disease in this age group.Additional file 1.


There were significant gains in clinicians’ receipt of training in 9/9 tobacco education areas (ps ≤ 0.0118). From pre- to post-implementation, there were mean increases in the use of the 5A’s  and other evidence-based interventions for tobacco cessation, with statistically significant gains seen in NRT provision/referral (p < 0.0001). Several program goals were achieved or exceeded; however, 100% center participation in specialized clinical trainings was among notable exceptions. One program withdrew due to competing pandemic concerns; all others implemented comprehensive TFW policies. Overall, TTTF may have improved participating opioid treatment centers’ capacity to address tobacco use, although study limitations, including lower post-implementation evaluation response rates, suggest that results require replication in other opioid addiction treatment settings. Tobacco use is exceedingly high among individuals receiving care for opioid addiction, but not commonly addressed by clinicians in treatment settings. Taking Texas Tobacco Free (TTTF) is a comprehensive tobacco-free workplace (TFW) program that builds treatment centers’ capacity to address tobacco use with evidence-based tobacco cessation policies and practices. Here, we examine the process and outcomes of TTTF’s implementation within 7 opioid addiction centers. Program goals were structured according to the RE-AIM framework. Pre- and post-implementation data were collected from client facing and non-client facing employees to assess changes in education, training receipt, knowledge, and intervention behaviors, relative to program goals. Centers reported tobacco screenings conducted and nicotine replacement therapy (NRT) delivered through 6 months post-implementation. Overall, 64.56% of employees participated in TTTF-delivered tobacco education, with a 54.9% gain in tobacco control and treatment knowledge ( The opioid epidemic has garnered national attention in recent years. However, its toll is modest compared to tobacco; for example, in 2018, there were an estimated 46,802 opioid-related deaths, as opposed to the 480,000 tobacco-related deaths occurring annually in the United States (U.S.) ,4. DespiThe lack of tobacco control efforts among smokers with OUD may reflect multi-faceted challenges to smoking cessation . For exaThe implementation of a comprehensive tobacco-free workplace program in OUD treatment settings is under-studied; such programs simultaneously address multiple-levels of influence on tobacco use with evidence-based tobacco control policies and practices . The incTaking Texas Tobacco Free (TTTF) is a comprehensive, evidence-based tobacco-free workplace program seeking to increase the capacity of partnering agencies to address tobacco use. TTTF’s multicomponent approach to implementation includes: (1) providing education for all employees regarding the risks of tobacco use and benefits of quitting, (2) training clinicians on screening and treating tobacco dependence; (3) implementing an enforceable tobacco-free workplace policy; and (4) providing nicotine replacement therapy (NRT) and other resources  to facilitate tobacco cessation ,25,26. FThere is limited to no literature to inform effective implementation of a tobacco-free workplace program in OUD treatment settings. Despite positive outcomes from prior TTTF implementations ,28, the Our funding permitted program implementation in >18 enrolled Texas substance use treatment centers. Any substance use treatment centers that was willing to fully participate in the program was eligible for inclusion; recruitment was not restricted to opioid treatment programs. Recruitment was accomplished through multiple means, including via professional organization listservs, postal mail solicitation, email solicitation by the team and prior TTTF partners, word of mouth, our website, etc. Centers were accepted into the program on a rolling, first-come, first-served basis between 2017 and 2020.n = 3) and Dallas (n = 4) metropolitan areas and served clients across 5 counties in Texas  and are the subject of this report. Of Texas’s 254 counties, Harris, Dallas, and Tarrant are among the largest, with approximately 9.5 million residents between them; Nueces is the 16th largest with a population of 362,294; and Grayson is the 34th largest with 136,212 residents [Overall, we enrolled 19 substance use treatment centers into the program; 7 of these were stand-alone OUD treatment centers. These OUD treatment centers spanned the Houston  roles and titles varied, as some provided direct services  whereas others provided case management or counseling. Non-client facing staff included leadership, human resources professionals, etc. The participating treatment centers had an estimated 116,087 annual client contacts .Study procedures were approved by the University of Houston’s IRB. Enrollment began when leadership signed memorandums of understanding, which detailed program requirements and participant responsibilities. Within ~2–3 weeks following enrollment, pre-implementation data were gathered online from leadership and employees, including information on existing tobacco-related policies, number and type of employees at these centers, their smoking status, receipt of tobacco control/treatment education, and clinician behaviors in addressing tobacco use with clients. The next steps entailed the provision of 1.5-to-2 h training sessions on tobacco control and treatment, tailored for tobacco use among individuals with OUD, to all employees. While these trainings were highly recommended to all employees, they were not mandated in consideration of feasibility and logistics issues inherent to these frequently understaffed and under-resourced centers ,36. ThesFollowing TTTF implementation, online post-implementation surveys were distributed to employees and clinicians-only to measure the effects that trainings and other TTTF procedures had on center employees and their work with clients. Active program implementation within participating centers, from enrollment through post-implementation data collection, was ~7.3 months long on average (range = 6–8 months). Data on conducted tobacco use assessments and distributed NRT were collected quarterly through 6 months post-implementation to measure sustainment. Programs goals were aligned to the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, which provides a useful model for translating research into practice . ThreshoReach  was conceptualized as: (1) educating >60% of employees at each center with our 1.5–2 h trainings; (2) achieving statistically significant increases in employees’ exposure to educational concepts from pre- to post-implementation; and embedding specialized knowledge into each center by: (3a) having at least 1 clinician attend the Certified tobacco Treatment Specialist training, and (3b) by having at least 60% of clinicians attend the Motivational Interviewing training. Effectiveness  was conceptualized as: (1) achieving a 50% employee knowledge gain from the provided TTTF education; and (2) a > 2% reduction in employee tobacco use. This project was focused on clinician and employee behavior change and did not include direct data collection with clients about their tobacco quit attempts or abstinence outcomes. This was because clinicians’ routine provision of empirically supported interventions for tobacco cessation are known to subsequently lead to increased client quit rate . Thus, eAdoption  was envisioned as: (1) >80% of enrolled centers completing the TTTF program; (2) 100% of program completers having implemented an enforceable tobacco-free workplace policy with prominent signage that disallowed tobacco use anywhere on the workplace, including on its grounds. Implementation  was envisioned as achieving >65% of responding clinicians reporting the regular provision of tobacco use screening and empirically based interventions to clients by post-implementation. This included: (1) the 5A’s of tobacco screening (Asking about tobacco use) and intervention ; and (2) behavioral counseling, NRT or referral for NRT, and non-nicotine-based medications  or referral for medication.Maintenance  was conceptualized as >80% of completing centers being compliant with: (1) the provision of reports of tobacco use assessments administered, and (2) NRT dispensed within the 6 months following program implementation. Tobacco education reach was collected with pre- and post-implementation surveys administered to all employees and to clinicians-only, respectively. These surveys were distributed within each center by a center-designated program champion. Survey items were face-valid, investigator-generated queries. The employee survey items relevant to this goal consisted of 2 items: “In the last 12 months, have you received any education regarding the hazards of smoking?” and “In the last 12 months, have you received any training regarding the hazards of smoking and benefits of quitting that are specific to individuals with substance use disorders?” (options = yes or no). The clinician survey consisted of 9 additional items that queried receipt of specialized training and use of evidence-based smoking cessation interventions. Data were collected anonymously to promote honesty and to minimize any perception of risk to employment status based on responses. Program records were used to evaluate the number of clinicians who attended Certified Tobacco Treatment Specialist and Motivational Interviewing trainings. A 10-item test was administered before and after each education session to measure knowledge gained from the training. Items included: “Which of these tobacco treatment medications requires a prescription?” , and “Smoking cessation interventions were associated with ____ increased likelihood of long-term alcohol and drug abstinence following substance abuse treatment” . The test items directly reflected content of the educational session. Data were collected anonymously to promote honesty and to minimize any perception of risk to employment status based on responses. Employee tobacco use was measured on pre-implementation and post-implementation employee surveys with the following items: “Currently, how often do you smoke cigarettes?”  and “Do you regularly use any of the following products?” . Responses to these 2 items were collated to determine employee tobacco use status at pre- and at post-implementation (options = tobacco user vs. not a tobacco user). Program completion and tobacco-free workplace policy implementation was assessed using program records. Specifically, program completion was defined as accepting the following: education for staff, training for clinicians, NRT, workplace signage (if permissible based on center location) and passive dissemination materials . The study team kept a shared spreadsheet to indicate when these education and training sessions took place, as well as the NRT, signage, and passive dissemination materials ordered. The implementation of a tobacco-free workplace policy was overseen by the TTTF project manager, who confirmed an existing policy or assisted with and reviewed development of a new policy. The project manager worked from a program implementation timeline to ensure that if a new policy were developed, it was approved by the board or human resources (if applicable) and communicated to all staff via an email highlighting the center’s participation in the TTTF program. Furthermore, employees were trained during program implementation on how to have conversations with clients/guests/visitors violating the policy to ensure reasonable enforcement.Clinician tobacco intervention practices were assessed pre- and post-implementation with clinician surveys. Items assessed included the use of the 5A’s for smoking  and other empirically supported treatments for tobacco use . Items iTTTF staff collected quarterly data via an online survey regarding the number of tobacco use assessments conducted and number of NRT boxes distributed to clients and employees during the 6 months after the TTTF program was implemented in each center, respectively. Descriptive statistics regarding outcomes of interest were calculated. Pre- to post-measurements were aggregated by center, not the individual respondent level. Data missingness due to program withdrawal or failure to administer measures was assessed. Handling of this form of missingness in analyses was determined a priori, using a commonsense approach whereby all centers were included in analyses when both pre- and post-data were available. In cases where post-data was missing due to withdrawal or failure to administer measures, only centers providing complete data were included in analyses. In this study, the calculations of the survey response rate and item response rate were defined as follows: survey response rate = number of respondents who started this survey/number of all eligible sample members; item response rate = number of respondents who responded to this item/number of respondents who started this survey.Tobacco control and treatment education reach, for all employees and clinicians-only, were summarized using descriptive statistics. Achievement of statistical significance was not a part of most program goals see ; howeverThe proportion of program completers and centers adopting/refreshing a tobacco-free workplace policy were obtained from program records . Changes in clinician intervention on tobacco use with clients were examined with descriptive statistics. Statistical significance of change over time was assessed using Cochran–Mantel–Haenszel tests , for thorough reporting. Quarterly report data on tobacco use assessment and NRT administration  were summed, respectively. All statistical analyses were conducted using SAS version 9.4 .n = 2) who attended the education/training delivery did not participate in pre- or post-knowledge tests. An additional center (Center 1) did not provide post-implementation employee data, citing very high rates of employee turnover since the initial assessment that would impact comparisons with pre-implementation data. However, high turnover rates were not seen amongst clinicians; post-implementation clinician data were provided by this center.Of the 7 OUD treatment centers that enrolled in this program, one withdrew from participation due to competing priorities and staff shortages due to the pandemic (Center 7). This withdrawal occurred following the provision of the tobacco control and treatment education/training; thus, employees completed pre-implementation surveys. However, employees  participated in these trainings between the 7 treatment centers, indicating achievement of the reach part of the goal.Secondly, we strove for statistically significant increases in exposure to education concepts among employees. Due to data missingness, analyses of clinician data were conducted with 6 centers whereas analyses of non-client facing staff were conducted with 5 centers. Although increases in exposure to education concepts were realized over time for both clinicians and non-client facing staff, statistically significant changes were only observed amongst clinicians . Thus, tn = 6) for Motivational Interviewing training (28.57%). Centers that did not participate indicated their clinicians already had Motivational Interviewing training or indicated that scheduling conflicts and center capacity precluded attendance. Thus, this aspect of our reach goal—sending 1 or more clinicians to Certified Tobacco Treatment Specialist training and 60% to Motivational Interviewing trainings—was not achieved.Thirdly, the goal for 100% center participation in specialized training was only partially achieved. TTTF sponsored 4 clinicians from 3 centers (3/7 centers = 42.86%) to attend Certified Tobacco Treatment Specialist training. Unfortunately, remaining centers could not send a clinician to the training due to competing work demands. Two centers sent clinicians (p < 0.001). Thus, this aspect of the 2nd program goal was achieved.The 2nd program goal was to achieve a 50% knowledge gain from the tobacco control and treatment education sessions, as evaluated by 46 pre- and 39 post-tests from 6 centers that participated in this aspect of data collection. The average pre-test scores were 5.28 (SD = 1.83) and the average post-test scores were 8.18 (SD = 1.19) out of a possible 10. Overall, knowledge gain ranged from 19.4 to 84.2% with a 54.9% knowledge gain in all 6 centers combined (p = 0.3202) and low response rates at post-implementation call into question whether the difference in tobacco use prevalence was truly because there were fewer tobacco users at post-implementation, or simply because there were fewer respondents. Thus, the data are not sufficient to determine whether this part of Goal 2 was met. Tobacco using employees decreased by >2% from pre- to post-implementation from 5 centers providing data for both time points. Overall, 9 employees (28.13%) reported being a tobacco user at pre-implementation, compared to 3 employees (13.64%) at post-implementation. The percent change in tobacco use amongst employees over time was 51.40%. These changes, however, were not statistically significant  enrolled centers completed the TTTF program, meeting this program goal of maintaining the participation of >80% of centers in the program until its end.n = 6) implemented an enforceable and complete tobacco-free workplace policy. Permanent tobacco-free workplace signage was accepted by all the completing centers except one where this was not practicably feasible. Specifically, the single completing center that did not accept signage from the program was located on the 2nd floor of a building that housed multiple businesses; the overall building already had tobacco control signage and tenants were not authorized to install permanent signage on building grounds.At pre-implementation, only a single center (Center 2) had a pre-existing tobacco-free workplace policy that included e-cigarettes and prohibited tobacco use both indoors and on the workplace grounds. By post-implementation, 100% of centers completing the program  was to achieve ≥65% of responding clinicians reporting the use of the 5A’s and other empirically supported treatments for tobacco use by post-implementation survey. There were 36 pre- and 28 post-tests from 6 centers with which to evaluate this goal.Overall, >65% of clinicians reported using each of the 5A’s at post-implementation . HoweverThe final program goal was to have centers engage in regular tobacco use assessments and NRT delivery through 6 months post-implementation. In total, participating centers delivered 272 tobacco use assessments post-implementation. Overall, 11 shipments of NRT  were made to 6 still-enrolled centers during TTTF post-implementation. Across centers, 193 clients and 13 employees received 495 and 54 boxes of NRT, respectively. Thus, the goal of 80% of centers reporting tobacco use assessments delivery and NRT dispensation to clients through 6 months post-implementation, relevant to only 6 of the centers, was achieved . However, only 3 out of 7 centers were able to send a clinician to Certified Tobacco Treatment Specialist training and only 2 out of 7 took advantage of Motivational Interviewing training. Given provided resources were free, these were missed opportunities to embed specialized knowledge within the center that could serve as an ongoing resource to colleagues and clients. Moreover, although some centers cited prior Motivational Interviewing training as a reason to forgo attendance, studies show that expertise in its delivery is enhanced by continued training and coaching opportunities . Center ps < 0.10). As seen in other work [Results of our study indicated self-reported increases among clinicians in delivery of the 5A’s whereby 74.1% to 96.2% of clinicians reporting engaging in their use post-intervention. While these gains relative to pre-intervention practices did not reach statistical significance, rates of Asking about tobacco use, Advising clients to quit, and Assisting clients to quit were marginally significant , but instead be comfortable with a range of possibilities—particularly in provision of behavioral counseling. While it is not clear why clinicians in these settings did not widely adopt behavioral counseling or non-nicotine-based medications in their practice, it is evident that there is room for improvement. Clinician compliance in these areas may be improved in future work with qualitative data to better understand barriers to the adoption and use of these interventions. Future studies may also seek to collect client-level data to help contextualize treatment and implementation-related outcomes and potentially improve uptake of certain evidence-based practices by clinicians. Although this was not part of the current work’s methodology, other studies have been able to collect individual-level data to support their implementation. For example, a study looking at New York’s statewide organizational tobacco control programs found significant decreases in smoking among clients in methadone-providing centers, following increases in the provision of NRT and other tobacco-related services . While tFinally, although this was not an explicit program goal, all participating centers ordered passive dissemination materials encouraging quit attempts from TTTF for posting or distribution to their clientele. As prior work has suggested that clinicians view a paucity of passive dissemination materials as a barrier to screening and treatment of tobacco , the intA particular limitation of using the RE-AIM framework to evaluate TTTF’s implementation was the definition of the specific subgoals that maybe somewhat idiosyncratic to the present work. For example, a pragmatic application of RE-AIM for tobacco treatment programs was suggested for the field, but this came out following our funding and was not used to shape our methodology . Future Other limitations include an anonymous data collection procedure that prevented linking pre- and post-responses within employees, and non-response rates that may affect the generalization of results. Specifically, the survey response rate was low (50–60%) when considering the potential number of eligible employees in enrolled centers . It may be that there was non-response bias wherein participants who responded differed from those who did not. In addition, the total number of employees of the enrolled centers was reported prior to program implementation and not assessed again during the multi-month roll-out, which might have yielded imprecise post-implementation survey response rates using the number of eligible staff from the pre-implementation data as the denominator. This is especially possible given the known high staff turnover in substance use treatment settings ; thus, tThis potential for non-response bias suggests that the findings should be interpreted with some caution and within the context of the limitations of this research outlined above ,60. For Finally, there is the possibility that for the part of Goal 2 relevant to decreases in employee tobacco use that social desirability biases  could have influenced responses to suggest more favorable use rates. We mitigated this possibility by not explicitly communicating to partnering centers the goals of the program regarding reducing employee tobacco use. However, implementing a deidentified or coded data collection method in future implementations may help to further establish the reliability and validity of self-reported measures from employees, such as tobacco use, that could then subsequently stimulate tailored follow-ups for improvement in various program areas .In conclusion, TTTF’s implementation within OUD treatment settings affected both clinical and policy changes consistent with tobacco control best practices ,62. Over


This ranges from direct viral cytopathic effect to triggering immunopathological mechanisms through which COVID-19 can further exert tissue damage. One of the important features is the elevated neutrophil-to-lymphocyte-count ratio, especially during moderate and severe COVID-19 and the elevated circulating neutrophil extracellular traps (NETs) [Staphylococcus aureus [As indubitably the most catastrophic pandemic of the 21s (NETs) . NETs ars aureus .Given the known pathophysiology of COVID-19 so far, we set out to investigate the prevalence of ANCAs among 100 randomly selected hospitalized patients with confirmed COVID-19 diagnoses in 2020. Blood specimens were drawn from these patients at the Cleveland Clinic hospitals and stored at the biorepository for future studies. Our study was approved by the institutional review board. There were two groups of 50 patients: moderate and severe, the latter was defined as patients with any of intensive care unit (ICU) admission, mechanical ventilation, extracorporeal membrane oxygenation (ECMO), or death, whereas the moderate cases did not meet any of the above-mentioned criteria. Samples were chosen randomly by the institutional biorepository staff based on the requested criteria and then were blinded to the investigators. Samples were collected from 9 to 28 days (median 19.5) post onset of signs and symptoms from patients with a median age of 59 years (range: 21–91). In total, 51 patients were male and the rest were female. Overall, 48% had ICU admission, 31% had mechanical ventilation, none received ECMO, and 15% died. All samples were screened for ANCAs by IFA using ethanol-fixed slides. Given the almost pervasive fluorescence activity among the screen slides, the samples were all also examined for ANCAs using formalin-fixed slides as the confirmatory test. All samples were also tested for anti-PR3 and anti-MPO antibodies using a random-access platform based on multiplex-fluorescence immunoassay (MFIA) principle. The three tests mentioned above are routinely performed in our laboratory.P = 0.13, Fisher exact). There was no association between age and sex with ANCA positivity. A subtle, but nonsignificant trend, was noticed with P-ANCA positivity with increasing age. Most interestingly, there was a strong association between confirmed C-ANCA status with ICU admission  in contrast to the confirmed P-ANCA status, whereas this association was marginal for mechanical ventilation  , suggesting some had weak reactivity below the cutoff. Of 15 patients who died, 17.5% had confirmed ANCA results compared with 11.6% of survivors with no confirmed ANCA result, though this did not reach statistical significance. Furthermore, 63.4% of severe cases had confirmed C-ANCA results compared with 34.9% of patients with no confirmed ANCAs (OR: 1.81, 95% CI: 0.86–3.75; .0, suggeOur finding of such high prevalence of ANCAs among hospitalized patients was unexpected and very exciting. To the best of our knowledge, this is the first time that such study is done in these patients, especially using IFA for ANCAs. As mentioned earlier, there are a number of neutrophil enzymes that can trigger ANCAs, but with the exception of PR3 and MPO, they are not tested for using current immunoassays such as MFIA that was used here. In fact, when NETs are produced, they are decorated with these enzymes, so under natural circumstances they can destroy microorganisms such as bacteria that are stuck in these NETs. Since this is a local reaction, the nearby endothelial cells are damaged not only directly by these detrimental enzymes but also through triggering immunothrombosis by these enzymes. The latter can be particularly problematic in tissues with large vasculature such as lungs, leading to pulmonary embolism. Through production of C5a, complement activation due to the presence of NETs attracts more neutrophils to the site, further exacerbating the situation.Since we did not have samples available before the onset of COVID-19 or after hospital discharge, we could not follow up on the ANCA status over time. It is important to highlight that both ANCA and MPO/PR3 tests detected IgG antibodies, thus, we could not rule out the presence of IgM antibodies, given the short period after COVID-19 onset, hence little time for a class-switched autoimmune response to fully develop. This means the several screen-positive-only sera would have possibly been confirmed had the study included specimens drawn several weeks or months after onset. Of note, that corticosteroids are used for managing COVID-19 patients on mechanical ventilation or in the ICU, and are also used routinely for managing patients with ANCA-associated vasculitides, points to a possible shared pathomechanism. This study calls for longitudinal follow-ups in patients with a history of confirmed COVID-19 diagnosis who have been experiencing autoimmune phenomena suggestive of vasculitis. Routinely investigating ANCA among hospitalized COVID-19 patients, especially shortly after admission, may serve as a biomarker to predict worse outcomes. This notion needs substantiating through longitudinal studies.APC Waiver


The SSA− and SSA+CIE− groups were poorly distinct. The presence of anti-SSA antibodies solely in CIE was significantly associated with the occurrence of extraglandular manifestations of pSS (HR = 4.45 (2.35–8.42)). Contrary to CIE, methods using non-native antigens to detect anti-SSA antibodies were unable to predict the occurrence of systemic expression of pSS.Electrophoresis-derived techniques for anti-SSA/Ro60 KDa (anti-SSA) antibodies detection have been progressively replaced by methods using non-native antigens. We aimed to compare the patients’ phenotypes and the occurrence of extraglandular manifestations in primary Sjögren’s syndrome according to the method used to detect anti-SSA antibodies. Sera from patients with a diagnosis of pSS according to ACR/EULAR 2016 criteria between 2008 and 2017 were tested for anti-SSA antibodies using methods with non-native antigens  and one with native antigens (counterimmunoelectrophoresis (CIE)). The population was split into three groups according to anti-SSA antibodies status: absence (SSA−), presence in any method except for CIE (SSA+CIE−), and presence in CIE (SSA+CIE+). The patients in the SSA+CIE+ group ( Anti-SSA/Ro60 (anti-SSA) antibodies represent a cornerstone in primary Sjögren’s syndrome (pSS). Even if they are not a mandatory criterion for the pSS diagnosis, they are present in 39% to 73% of patients from large cohorts depending on recruitment ,2,3,4. OFormer gold standard techniques for detecting anti-SSA antibodies were RNA precipitation, double-immunodiffusion, counterimmunoelectrophoresis (CIE), immunoprecipitation, and Western blotting . Among tTo our knowledge, most studies comparing the predictive value of anti-SSA antibodies in pSS did not differentiate between these methods in the interpretation of their results. This could be troubling due to the different analytical conditions of the immunoassays. Indeed, recent and widespread methods  use purified or recombinant proteins that are denatured through preparation process or during a coating step, whereas epitopes recognised by anti-SSA antibodies are highly conformational and the binding is often lost with denaturation ,32,33. IpSS is associated with extraglandular manifestations that reflect the systemic activity of the disease. These manifestations are of interest because they represent the severity of pSS. Identifying markers to predict the occurrence of extraglandular manifestations is thus a major concern.This study aimed to compare the onset of extraglandular involvement in pSS patients according to the absence or presence of anti-SSA antibodies using three methods of detection: CIE, LIA, and multiplex assays.n° 2018/55) and was conducted in compliance with the declaration of Helsinki. All participants gave non-opposition informed consent. This study applied the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement to observational studies.The study was approved by the Ethical Committees of Angers University Hospital  according to ACR/EULAR 2016 criteria  months. The fourth patient without extraglandular manifestation suffered from inflammatory arthralgia.With 85% females, a median age at diagnosis of 56 years, and 51% patients presenting anti-SSA antibodies, the general characteristics of our study population were consistent with the characteristics of published large cohorts ,3,4. IncAnti-SSA antibodies have been historically associated with pSS on the basis of a detection method that uses the native structure of the antigen and highlights autoantibodies able to precipitate  . Since tOver the past three decades, anti-SSA antibodies have been largely associated with extraglandular involvement in pSS ,11,12,13We demonstrated that patients with anti-SSA antibodies, which were undetectable with CIE, had a weak pSS systemic expression and were comparable to the SSA− group. This would first lead one to hypothesise the identification of a not yet pathogenic autoantibody in the case of latent autoimmune disease. However, when we detailed the composition of this group of patients, we noticed that 6/14 (43%) did not present significant sialadenitis on the MSGB, although they present sicca syndrome. This means that, without anti-SSA antibodies detection by multiplex and/or dots, those patients would not fulfil the criteria for pSS according to the ACR/EULAR consensus [On the other hand, 4/84 (4.8%) patients presented anti-SSA antibodies only detected on CIE and all had a systemic expression of pSS. This confirms that denaturing methods failed to detect anti-SSA antibodies in some patients with a significant expression of pSS .Among the four methods of detection we used, the main cleavage appeared between multiplex and CIE. The solid phase dots did not seem to define a third group compared with multiplex and CIE. Indeed, among patients with possible false positivity , dots were positive for 5 patients out of 6.One limitation of our study is the absence of ELISA, another common method to detect anti-SSA antibodies. However, some authors reported similar results to ours when using those assays, identifying a profile that did not correlate with the patient’s clinical presentation ,46 and a3 is limited. We also chose to exclude small fibre neuropathy from extraglandular manifestations because it has been associated with the profile of patients without anti-SSA antibodies [A second limitation of our study could be the definition of extraglandular manifestations, which differed slightly from ESSDAI, for example. However, we thought it important to only consider objective and significant involvements in order to represent the systemic activity of pSS as clinically relevant. For example, the clinical significance of thrombopenia between 100,000 and 150,000/mmtibodies ,37,38 anIn this study, we highlighted that the method used to detect anti-SSA antibodies is a major concern. Methods using denatured antigens were unable to predict the occurrence of systemic expression of pSS contrary to CIE. Patients with anti-SSA antibodies that were undetectable with CIE (no line of precipitation) did not significantly differ from patients without anti-SSA antibodies. Moreover, our study suggests a potential false positivity for the diagnosis of pSS in patients without significant sialadenitis and with non-precipitating anti-SSA antibodies (only identified using denaturing methods). Our results raise questions regarding the value of using only non-native methods to detect anti-SSA antibodies in pSS.