Atrial fibrillation (AF) leads to increased risk for stroke. Human immunodeficiency virus (HIV) is associated with cardiovascular disease (CVD), although it is unclear if HIV is associated with AF. The purpose of this study was to evaluate the association between HIV serostatus and the prevalence of AF in the Multicenter AIDS Cohort Study.A cross sectional study was conducted among 1674 HIV-infected (HIV+) and uninfected (HIV–) men who completed resting 12-lead electrocardiograms, and/or ambulatory electrocardiogram monitoring. Multivariable logistic regression was used to evaluate the association between AF, defined as the presence of either AF or atrial flutter, and HIV+ serostatus. Associations were adjusted for demographic variables, and then also for CVD risk factors.P < .001) and were more frequently African-American . Most HIV+ men (81%) had undetectable viral load. The age and race adjusted prevalence of AF was 3.0% in HIV+ and 3.3% in HIV– men. There was only 1 case of AF among African-American men. There were no associations between AF and HIV serostatus after adjusting for demographic factors  or after further adjustment for CVD risk factors .HIV+ men were younger than HIV– men (median 55.5 vs 61.7 years, We found no association between HIV and AF in this cohort in which viral replication among the HIV+ men is generally suppressed. The overall prevalence of AF was low and was rare in African-American men. CVD events, such as myocardial infarction, heart failure, sudden cardiac death, and stroke are more common in HIV+ compared with HIV– individuals.–9The use of combination antiretroviral therapy (cART) has extended the life expectancy of people living with human immunodeficiency virus (HIV) infection (HIV+), A study from the Veterans Affairs (VA) HIV Clinical Case Registry found that low CD4+ T cell count and elevated HIV ribonucleic acid viral load were associated with incident AF. A more recent matched case-control study in the HIV Electronic Comprehensive Cohort of CVD Complications found an elevated unadjusted odds of AF among HIV+ compared with HIV– individuals, which was not significant after adjustment for demographics. Data from the Healthcare Cost and Utilization project in California found that HIV was associated with incident AF. Further understanding of the relationship between HIV and AF is important from a public health perspective.The relationship between HIV infection and atrial fibrillation (AF) is unclear.The aim of our study was to examine the frequency of AF using resting 12-lead and ambulatory electrocardiographic (ECG) monitoring among a cohort of men with predominantly suppressed viral replication and ascertain whether the frequency differs by HIV serostatus.2 The study is conducted in 4 communities in the United States: Baltimore, MD and Washington, D.C.; Los Angeles, CA; Pittsburgh, PA; and Chicago, IL. Biological and physiological data are collected every 6 months from approximately 3000 active participants.This cross-sectional study was nested within the Multicenter AIDS Cohort Study (MACS), an ongoing prospective study of the natural and treated histories of HIV-1 infection among men who have sex with men. Continuous ambulatory ECG monitoring was performed with a non-invasive adhesive wireless single-lead device  placed on the participant's chest by trained research personnel. The device was worn for up to 14 days and then mailed to iRhythm in a prepaid envelope. iRhythm provided detailed individual-level data, including the presence of AF or atrial flutter, as well as a preliminary clinical report for each participant with representative ECG tracings interpreted and verified by the MACS ZioXT reading center at Johns Hopkins University. AF burden was defined as the percentage of the analyzable time in AF, ranging from 0% to 100%. Participants with <24 hours of analyzable time and/or <25% analyzable time (relative to wear time) were excluded from the analysis. The resting 12-lead ECG measurements were recorded digitally at 10 mm/mV calibration at a speed of 25 mm/s for 10 seconds using GEMSIT MAC 1600 ECG machines  by trained research staff. ECG recordings were transmitted to the Epicare ECG Reading Center at the Epidemiological Cardiology Research Center of the Wake Forest School of Medicine, for centralized reading. Readers were masked to the HIV serostatus of participants.All active MACS participants were offered resting 12-lead ECG and ambulatory ECG examinations at their routine study visits from October 2016 to September 2017.Data were also obtained from routine semi-annual study visit records of demographics, clinical parameters, and cardiovascular risk factors. This study was approved by the Johns Hopkins institutional review board through a reliance agreement with each study site (IRB00219740). Written informed consent was obtained from the participants. The prevalence of AF was adjusted for age (5-year increments except for youngest [18–30 years] and oldest [76–91 years] age groups) and race (Caucasian vs non-Caucasian) using direct standardization and the HIV-sample as the reference population.The main exposure was HIV serostatus. Serostatus was measured prospectively by an enzyme-linked immunosorbent assay; incident seroconversion was confirmed by Western blot. We compared the distribution of characteristics between groups using the analysis of variance for normally distributed variables, the Wilcoxon rank-sum test for skewed data and the Pearson chi-squared test for categorical variables. The primary outcome of interest was the presence of AF, defined as the presence of either AF or atrial flutter on the ambulatory ECG monitor and/or the resting 12 lead ECG.P-value < .05. Analysis was performed using Stata version 15.1 .We used a logistic regression model to ascertain the odds ratio of AF by HIV serostatus and other risk factors. The uneven distribution of outcomes among the Caucasian, African American and Hispanic or other racial groups, necessitated stratification into Caucasian and non-Caucasian groups to enable adjustment for race. We analyzed the following models; model 1: unadjusted; model 2: adjusted for age and race; model 3: additionally, adjusted for body mass index, cumulative pack year of smoking, use of medications to treat hypertension or diabetes, heavy alcohol use (>13 drinks/wk), fasting blood glucose, and systolic blood pressure. The 2-sided test of statistical significance was defined as a 3The analytical sample included 1674 individuals, with 780 (47%) HIV– and 894 (53%) HIV+ participants. Baseline characteristics by HIV serostatus are shown in Table The majority of the sample, 1240 men (74%), completed both ambulatory and resting ECG monitoring; 393 men (23%) had only resting ECG, and 41 men (2%) had only ambulatory ECG monitoring. Participants wore the Zio XT ambulatory ECG for a median of 13.0 days . Among the participants who had ambulatory ECG recordings, 54 participants had <25% analyzable time (relative to wear time) and/or <24 hours of analyzable time on ambulatory ECG; therefore, their ambulatory ECG results were excluded from the analyses. These 54 participants all had technically adequate resting 12 lead ECG data. For this analysis, 1186 (71%) participants contributed both ambulatory ECG and resting ECG results, 447 (27%) participants contributed only resting ECG and 41 (2%) participants contributed only ambulatory ECG results. The overall prevalence of AF was 2.3% (38 cases). There were 36 cases of AF, 1 case of atrial flutter, and 1 case of both AF and atrial flutter. Among the 33 men with AF on ambulatory ECG monitoring, 55% (n = 18) had a low burden of AF of <5% time in AF, while 18% (n = 6) had 5% to 80% time in AF, and 27% (n = 9) were in persistent AF (100% time in AF) for the duration of the Zio XT recording.P < .001). However, after adjusting for age and race, we found no difference in the odds of AF by HIV serostatus . After further adjustment for cardiovascular risk factors, there was no difference between the odds of AF comparing HIV+ to HIV– men . The odds of AF were 8% higher for each yearly increase in age  and the odds of AF comparing non-Caucasians to Caucasians was 0.06 . There was an increase in the odds of AF with higher fasting blood glucose  and among participants who were on blood pressure medication , but no other significant associations with cardiovascular risk factors were noted. Among HIV+ men, the median CD4+ T cell counts were similar among men with AF compared with those without AF . Among the men living with HIV 92% (11/12) of those with AF were on cART and all these men had undetectable viral load. Similar results were seen when analyses only included ambulatory ECG results (data not shown).The prevalence of AF in white men was 3.6% (37/1031). There was only one case of AF among black men 1/412 = 0.24%) and no cases among the 231 Hispanic men and men of other races. There were 26 cases of AF among the 780 HIV– men and 12 cases among the 894 HIV+ men. The age and race standardized prevalence of AF was 3.3% (95% CI: 2.1–4.6) among HIV– men and 3.0% (95% CI: 1.1–5.0) among HIV+ men. Associations between HIV serostatus, cardiovascular risk factors, and the odds of AF are shown in Table  = 0.24% 4We found no difference in the frequency of AF between HIV+ and HIV– men, after adjusting for demographics or additionally adjusting for cardiovascular risk factors. As expected, AF was strongly associated with aging. There was generally a low prevalence of AF in this cohort and a remarkably low prevalence of AF in non-Caucasian men. Among HIV+ participants, there was no association between AF and CD4+ T cell counts. Our unadjusted results suggested that HIV+ men had a lower prevalence of AF than HIV– men, contrary to the hypothesis. These results appear to be secondary to the differences in age and race/ethnicity between the groups, and were further attenuated in the fully adjusted model. Previous studies have also demonstrated a lower incidence of AF in African-American compared with Caucasian individuals, however, the differences in our study population are more marked than reported previously.–19 Sardana et al demonstrated a higher risk of AF among HIV+ individuals, however, the disparity in our findings may be due to key differences in the study population, mode of outcome ascertainment, and study design.Our findings are similar to a matched case-control hospital-based study which found higher odds of AF in HIV+ participants compared with HIV– controls, however, after adjusting for confounders, the findings in that study also became null. We previously demonstrated that HIV+ men in MACS had a longer QT interval on resting ECG than HIV– men, which was associated with elevated inflammatory marker levels. Studies also suggest an increased risk for sudden cardiac death in HIV+ individuals. Although people living with HIV have a greater risk for CVD, the results of our study and others suggest that HIV is not associated with AF, a potent risk factor for stroke.People living with HIV are at increased risk for CVD, including coronary artery disease and heart failure, especially among those with prior exposure to uncontrolled viremia with resultant inflammation and immunodeficiency.4.1There are some limitations to our study, which included only men. Conclusions cannot be drawn about women, who were not represented. As with any observational study, we cannot rule out the possibility of residual confounding. The prevalence of AF among HIV+ men was low, which limited the power to assess multivariable adjusted associations between AF with HIV specific factors, such as CD4+ T cell counts, ART, and HIV viral load.There are many strengths, including the relatively large sample size, inclusion of an HIV uninfected comparison group, detailed information on CVD risk factors, and similarities in behaviors of HIV+ and at-risk HIV-uninfected participants. Most importantly, compared to prior studies, the ascertainment of AF using resting ECG and ambulatory ECG monitoring at a research study visit allows for a diagnostic assessment not affected by biases related to the frequency of medical visits, as might be seen in HIV+ individuals.4.1.1Our study included ambulatory ECG monitoring as a research examination. Future studies are needed to determine clinical indications for ambulatory ECG monitoring in individuals at risk for AF, to identify those who might benefit from anticoagulation to reduce risk for thromboembolic events.5We found no association between HIV and AF in this cohort in which viral replication among the HIV+ men is generally suppressed. The overall prevalence of AF was low and was rare in African-American men.Data in this manuscript were collected by the Multicenter AIDS Cohort Study (MACS), now the MACS/WIHS Combined Cohort Study (MWCCS). The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). MWCCS : Baltimore CRS (Todd Brown and Joseph Margolick), U01-HL146201; Data Analysis and Coordination Center , U01-HL146193; Chicago-Northwestern CRS (Steven Wolinsky), U01-HL146240; Los Angeles CRS (Roger Detels), U01-HL146333; Pittsburgh CRS , U01-HL146208. The MWCCS is funded primarily by the National Heart, Lung, and Blood Institute (NHLBI), with additional co-funding from the Eunice Kennedy Shriver National Institute Of Child Health & Human Development (NICHD), National Institute On Aging (NIA), National Institute Of Dental & Craniofacial Research (NIDCR), National Institute Of Allergy And Infectious Diseases (NIAID), National Institute Of Neurological Disorders And Stroke (NINDS), National Institute Of Mental Health (NIMH), National Institute On Drug Abuse (NIDA), National Institute Of Nursing Research (NINR), National Cancer Institute (NCI), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Deafness and Other Communication Disorders (NIDCD), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute on Minority Health and Health Disparities (NIMHD), and in coordination and alignment with the research priorities of the National Institutes of Health, Office of AIDS Research (OAR).Conceptualization: Sabina A. Haberlen, Todd T. Brown, Matthew J. Feinstein, Mallory D. Witt, Jared W. Magnani, Elsayed Z. Soliman, Katherine C. Wu, Wendy S. Post.Formal analysis: Ngozi C. Osuji, Sabina A. Haberlen.Methodology: Ngozi C. Osuji, Wendy S. Post.Project administration: Wendy S. Post.Software: Ngozi C. Osuji, Sabina A. Haberlen.Supervision: Wendy S. Post.Validation: Wendy S. Post.Writing – original draft: Ngozi C. Osuji, Sabina A. Haberlen, Wendy S. Post.Writing – review & editing: Ngozi C. Osuji, Sabina A. Haberlen, Hiroshi Ashikaga, Todd T. Brown, Matthew J. Feinstein, Mallory D. Witt, Jared W. Magnani, Elsayed Z. Soliman, Katherine C. Wu, Wendy S. Post.


Compelling evidence suggests that hypoxia exposure alters metabolism, at least partly through effects on AT. However, it remains to be elucidated whether mild intermittent hypoxia (MIH) exposure impacts the AT proteome. We performed a randomized, single-blind, and cross-over study to investigate the effects of seven consecutive days of MIH  compared to normoxia (FiO2 21%) exposure on the AT proteome in overweight/obese men. In vivo AT insulin sensitivity was determined by the gold standard hyperinsulinemic-euglycemic clamp, and abdominal subcutaneous AT biopsies were collected under normoxic fasting conditions following both exposure regimens (day 8). AT proteins were isolated and quantified using liquid chromatography-mass spectrometry. After correction for blood contamination, 1,022 AT protein IDs were identified, of which 123 were differentially expressed following MIH (p < 0.05). We demonstrate for the first time that MIH exposure, which markedly reduces in vivo AT oxygen tension, impacts the human AT proteome. Although we cannot exclude that a single differentially expressed protein might be a false positive finding, several functional pathways were altered by MIH exposure, also after adjustment for multiple testing. Specifically, differentially expressed proteins were involved in redox systems, cell-adhesion, actin cytoskeleton organization, extracellular matrix composition, and energy metabolism. The MIH-induced change in AT TMOD3 expression was strongly related to altered in vivo AT insulin sensitivity, thus linking MIH-induced effects on the AT proteome to metabolic changes in overweight/obese humans.Adipose tissue (AT) oxygen tension (pO The prevalence of obesity has increased drastically over the last decades, with nearly a third of the world’s population living with overweight or obesity . Obesity2) has been implicated in AT dysfunction in both rodents and humans, as reviewed recently , affects various pathways involved in energy metabolism, suggestive of increased glycolytic metabolism and triacylglycerol synthesis (2 mainly increased expression of proteins related to the extracellular matrix (ECM) in human adipose stem cells  exposure on the AT proteome are lacking.However, to investigate and better understand the effects of hypoxia exposure on a more functional level, proteomics analysis seems highly valuable. Indeed, previous studies have indicated that the proteomic profile of ynthesis . In addiem cells . These iin vivo AT insulin sensitivity.In the present study, we investigated the impact of MIH compared to normoxia exposure on the abdominal subcutaneous AT proteome in overweight and obese men, using untargeted liquid chromatography-mass spectrometry, to elucidate the physiological and functional adaptations in human AT evoked by MIH. Secondly, we explored the associations between MIH-induced alterations in AT protein expression and 2) male subjects were recruited to participate in the present study. Subjects also needed to be aged between 30 and 65 years and insulin resistant, defined as HOMA-IR index ≥2.2. Exclusion criteria were smoking, cardiovascular disease, type 2 diabetes mellitus, liver or kidney malfunction, use of medication known to affect body weight and glucose metabolism, and marked alcohol consumption . Furthermore, subjects had to be weight stable (weight change <3.0 kg) for at least three months prior to the start of the study. Participants were asked to refrain from drinking alcohol and perform no exercise 24 h prior to the start and during exposure regimen. The study, registered at Netherlands Trial Register (NL7120/NTR7325), was performed according to the Declaration of Helsinki and was approved by the Medical-Ethical Committee of Maastricht University. All subjects gave their written informed consent before participation in the study.Twelve overweight and obese (BMI ≥28 kg/m2) and normobaric normoxia (21% FiO2) for 7 consecutive days (3 cycles of 2 h/d with 1 h of normoxia exposure between hypoxic cycles), separated by a 3–6 weeks. wash-out period. As described previously, hypoxia exposure was performed in an in-house manufactured airtight clinical room with the capability to accurately adjust oxygen availability at the Metabolic Research Unit Maastricht . The oxygen level was set and maintained at 15.0 ± 0.2% for the hypoxia exposure regimen. Participants were blinded for the exposure regimen  exposure. At day 8, following the 7-day MIH/normoxia exposure regimens, fasting abdominal subcutaneous adipose tissue biopsies were collected, and a two-step hyperinsulinemic-euglycemic clamp was performed to determine AT, hepatic, and peripheral insulin sensitivity under normoxic conditions, as described previously  using needle aspiration under local anesthesia (2% lidocaine), 6–8 cm lateral from the umbilicus. After thorough rinsing with sterile saline, visible blood vessels were removed using sterile tweezers. Subsequently, specimens were snap-frozen using liquid nitrogen and stored at −80°C for further analysis. Due to sampling issues, we could not collect enough material following hypoxia exposure in one participant. Therefore, paired adipose tissue biopsies (following both MIH and normoxia exposure) were available for analysis in Frozen AT (~100 mg) was ground in a mortar with liquid nitrogen. Per microgram of grounded powder, 2 μl of 50  mm ammonium bicarbonate with 5 M urea, was added to dissolve the powder. The solution was freeze-thawed in liquid nitrogen 3 times after which it was vortexed for 5 min. The homogenate was centrifuged at 20,000 g for 30 min at 10°C. The supernatant was carefully collected and protein concentrations were determined with a Bradford-based protein assay .A total of 100 μg protein in 50 μl 50 mm ammonium bicarbonate (ABC) with 5 M urea was used. 5 μl of DTT solution  was added and incubated at room temperature for 45 min. The proteins were alkylated by adding 6 μl of IAA solution . The reaction was taken place at room temperature for 45 min in the darkness. The alkylation was stopped by adding 10 μl of DTT solution (to consume any unreacted IAA) and incubated at room temperature for 45 min. For the digestion, 2 μg trypsin/lysC was added to the protein and incubated at 37°C for 2 h. 200 μl of 50 mm ABC was added to dilute the urea concentration and further incubate at 37°C for 18 h. The digestion mixture was centrifuged at 2.500 g for 5 min and the supernatant, which contained the peptide mixture, collected for the use of LCMS analysis.A nanoflow HPLC instrument  was coupled online to a Q Exactive (Thermo Scientific) with a nano-electrospray Flex ion source (Proxeon). Each sample was run separately for label free quantification. 5 μl of the peptide mixture was loaded into a C18-reversed phase column . The peptides were separated with a 240 min linear gradient of 4–50% in buffer A (100% water with 0.1% TFA) with buffer B (80% acetonitrile and 0.08% formic acid) at a flow rate of 300 nl/min. MS data were acquired using a data-dependent top-10 method, dynamically choosing the most abundant precursor ions from the survey scan  in positive mode. Survey scans were acquired at a resolution of 70,000 and a maximum injection time of 120 ms. Dynamic exclusion duration was 30 s. Isolation of precursors was performed with a 1.8 m/z window and a maximum injection time of 200 ms. Resolution for HCD spectra was set to 17,500 and the Normalized Collision Energy was 30 eV. The under-fill ratio was defined as 1.0%. The instrument was run with peptide recognition mode enabled, but exclusion of singly charged and charge states of more than five.The MS data were searched using Proteome Discoverer 2.2 Sequest HT search engine (Thermo Scientific), against the UniProt human database. The false discovery rate (FDR) was set to 0.01 for proteins and peptides, which had to have a minimum length of 6 amino acids. The precursor mass tolerance was set at 10 ppm and the fragment tolerance at 0.02 Da. One miss-cleavage was tolerated, oxidation of methionine was set as a dynamic modification. Carbamidomethylation of cysteines was set as fixed modifications. Label free quantitation was conducted using the Minora Feature Detector node in the processing step and the Feature Mapper node combined with the Precursor Ions Quantifier node in the consensus step with default settings within Proteome Discoverer 2.2 .The inter-run variation was normalized using ppm fractional normalization. To reduce the influence of the blood protein contamination on the AT proteome, we retrieved information from the UniProt database and GeneCards to set up a blood protein exclusion list with known proteins exclusively expressed in blood, including all immunoglobulins . The amovalue of p < 0.05 (MIH versus normoxia exposure) were selected for further biological annotation and analysis. Subsequently, these differentially expressed proteins (p < 0.05) were imported into Cytoscape plug-in ClueGO v2.5.7 for functional analysis  of FFA plasma concentration upon 10 mU∙m−2 insulin infusion], with subsequent multiple testing correction (Benjamini-Hochberg Procedure). All statistical analyses were performed in R environment, version 3.5, with various packages  and SPSS.Only valid human AT proteins were taken into statistical analyses. First, missing values  were imputed using random forest algorithm with R missForest package v1.4. Thereafter, data were log2 transformed. The effects of MIH compared to normoxia exposure on abdominal subcutaneous AT protein expression were assessed using two-sided paired Student’s t-tests. The fold change was calculated using the back-transformed means for both conditions. The Benjamini-Hochberg procedure was applied to control for multiple testing, with false discovery rate set at 0.25, and q-values were calculated. Proteins with a analysis , based o−2) and demonstrated a HOMA-IR index ≥2.2.Characteristics of the study participants are shown in 2 (measured at day 6), using a highly accurate, microdialysis-based optochemical measurement system to continuously monitor AT pO2in vivo in humans (p < 0.001) and decreased AT pO2 .To provide the proof-of-concept in humans that MIH exposure reduces oxygen availability in abdominal subcutaneous AT, we determined both systemic oxygen saturation  and AT pOn humans . As prevn humans , MIH expp < 0.05; value of ps) proteins per individual is visualized in We quantified 1,091 accession IDs of 1,074 proteins in the AT samples with HT score > 2.5, which appeared in more than half of the samples in at least one of the conditions (MIH and/or normoxia). Several of these proteins were considered blood-specific , explained by AT specimen contamination with some blood during sample collection, despite thorough cleaning using sterile saline, as described previously . Indeed,All 123 differentially expressed proteins were functionally annotated, since MIH-induced changes in specific functional pathways provide more robust information related to functional changes than alterations in single proteins. Thus, 11 functional groups that were enriched in these differentially expressed proteins were identified . These fin vivo AT insulin sensitivity. Although AT insulin sensitivity did not significantly change following MIH exposure (data not shown), the increase in tropomodulin-3 (TMOD3) protein expression evoked by MIH exposure was positively associated with the change in AT insulin sensitivity .Since MIH exposure had a significant impact on several ECM- and cytoskeleton-related proteins, as well as proteins involved in energy metabolism, we next determined the associations between differentially expressed AT proteins and In the present randomized, single-blind, and cross-over study, we examined for the first time the effects of 7 consecutive days of MIH compared to normoxia exposure on the abdominal subcutaneous AT proteome in overweight and obese men. From the 1,011 AT-specific proteins identified, 123 proteins were differentially expressed following MIH compared to normoxia exposure. Specifically, MIH-induced marked alterations of functional groups, mainly related to oxidoreductase systems, cell-adhesion, actin cytoskeleton and ECM organization, and energy metabolism. Moreover, the MIH-induced increase in TMOD3 expression was significantly related to improved AT insulin sensitivity, suggesting a link between MIH-induced effects on the AT proteome and metabolic changes in human AT. Although we cannot exclude that a single differentially expressed protein might be a false positive finding, the fact that these functional pathways (consisting of many proteins) were significantly upregulated/downregulated after multiple testing correction and with high concordance between its protein members provides strong evidence that MIH exposure alters specific protein pathways within the adipose tissue proteome.+ content and substantially increases the NADH/NAD+ ratio - and NAD(P)H-dependent dehydrogenase activity-related pathways contributed to the changes in this functional group. Out of the 31 proteins associated with oxidoreductase activity, aldehyde dehydrogenase 2 (ALDH2), glycerol-3-phosphate dehydrogenase 1 (GPD1), and 3-hydroxyacyl-CoA dehydrogenase (HADH) were most significantly downregulated. This is in agreement with the MIH-induced reduction in AT gene expression of aldo-keto reductase family 7, member A2 . The expD+ ratio . Most ofrocesses , coverinrocesses . Taken tin vitro, in particular an increase in actin-stress fibers in various cell types  and the adipocytes regulates cytoskeletal changes as well as adipocyte differentiation . In addill types .In line with the above, MIH exposure induced the expression of processes related to actin cytoskeleton organization. Indeed, it has been suggested that actin remodeling is essential during adipocyte maturation, demonstrated by major changes in actin dynamics during adipocyte expansion . FurtherInterestingly, we found that collagen fibril organization was reduced by MIH exposure. It has previously been suggested that hypoxia induces AT fibrosis, by increasing collagen type I deposition, resulting in increased stiffness of the ECM . The pre2) reduced ABDH5 and tended to reduce PLIN1 expression in 3 T3-L1 adipocytes compared to standard laboratory conditions  may reduce lipid storage and mobilization in human (pre)adipocytes , fatty acid binding protein 4 (FABP4), hormone-sensitive lipase (LIPE) and perilipin-1 (PLIN1), and PLIN4 were found. ABDH5 and PLIN1, which appeared to be most significantly downregulated, exert an important role in lipid catabolism, acting as activator for triacylglycerol hydrolases and coating protein surrounding lipid droplets in adipocytes, respectively . It has  21% O2; . In agre 21% O2; . In addiipocytes . Thus, tIndeed, MIH decreased the expression of several proteins associated with biosynthesis of polysaccharides, and therefore suggests that functional alterations in glycogen metabolism are associated with cellular adaptations to hypoxia, as previously described . HoweverNext, we found that MIH induced several AT proteins associated with iron ion homeostasis. In line with the present results, (hypobaric) hypoxia exposure has previously been implicated in iron homeostasis, affecting the regulation of several enzymes involved in iron absorption, such as repression of hepcidin . In addi2 were characterized by altered phenotype and enhanced activity (in vivo (8% O2) did not show any differences in platelet activity, and hence blood coagulation , and the altered AT expression of these proteins may be due to blood cells. We found that MIH exposure increased the functional groups “platelet degranulation” and “myeloid cell development,” reflecting functional alterations in blood cells. In line, it has been shown that human platelets exposed to 5% Oactivity . Howevergulation . Therefoin vivo MIH exposure for seven consecutive days affects the abdominal subcutaneous AT proteome in overweight and obese men. Moreover, we found that the increased expression of TMOD3 was associated with improved AT insulin sensitivity following MIH exposure, thereby linking MIH-induced adaptations in the AT proteome to metabolic changes in human AT. Although we cannot exclude that altered expression of certain proteins might be due to false positive findings, the fact that changes in several functional pathways were found after multiple testing correction provides further evidence that MIH exposure impacts the human adipose tissue proteome. Further studies are needed to confirm the present findings, and to elucidate whether adipocytes and/or other cell types present in adipose tissue are responsible for the MIH-induced changes in the human AT proteome that we found in the present study.In conclusion, the present study demonstrates for the first time that human http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD029213.The original contributions presented in the study are publicly available. This data can be found at: The studies involving human participants were reviewed and approved by Medical-Ethical Committee of Maastricht University. The patients/participants provided their written informed consent to participate in this study.RVM, EB, and GG designed the research. RVM and GG performed the sample collection. FB contributed to the sample preparation, data acquisition and the LCMS analysis. FB performed the LCMS analysis. PW and RVM performed the data analysis. RVM wrote the manuscript. PW, FB, EB, EM, and GG revised the manuscript. All authors contributed to data interpretation and approved the final version of the manuscript.This study was funded by a senior fellowship awarded to GG by the Dutch Diabetes Research Foundation (grant number: 2015.82.1818).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


RPS10, NUDT3, NCF2, SMG7, and ARPC5, were differently expressed in skeletal muscle tissue, while GPD1L was not. Furthermore, the ‘mRNA destabilisation’ biological process was enriched for sarcopenia. Our study identified RPS10, NUDT3, and GPD1L as significant genetic biomarkers for sarcopenia. These genetic loci were related to lipid and energy metabolism, suggesting that genes involved in metabolic dysregulation may lead to the pathogenesis of age-related sarcopenia.Sarcopenia is an age-related disorder characterised by a progressive decrease in skeletal muscle mass. As the genetic biomarkers for sarcopenia are not yet well characterised, this study aimed to investigate the genetic variations related to sarcopenia in a relatively aged cohort, using genome-wide association study (GWAS) meta-analyses of lean body mass (LBM) in 6961 subjects. Two Korean cohorts were analysed, and subgroup GWAS was conducted for appendicular skeletal muscle mass (ASM) and skeletal muscle index. The effects of significant single nucleotide polymorphisms (SNPs) on gene expression were also investigated using multiple expression quantitative trait loci datasets, differentially expressed gene analysis, and gene ontology analyses. Novel genetic biomarkers were identified for LBM  and ASM (rs6772958). Their related genes, including  Considering the difficulties posed by frailty, and the healthcare costs associated with age-related conditions, such as sarcopenia5, it is necessary to identify meaningful disease phenotypes and biomarkers. Several studies have suggested various criteria for defining sarcopenia10. Muscle mass is thought to be an important factor for the diagnosis of sarcopenia; of the parameters related to muscle mass, lean body mass (LBM) is frequently used to predict sarcopenia. In addition, the Asian working group for sarcopenia 2019 (AWGS 2019) recently reached the consensus that skeletal muscle index (SMI) and appendicular skeletal muscle (ASM) may also be reliable parameters11. In this respect, in addition to LBM, it is necessary to analyse SMI and ASM to understand the complex aetiology of sarcopenia, which can be attributed to a variety of factors, including oxidative stress, inflammation, mitochondrial dysregulation, and genetic factors13.Sarcopenia is the age-related loss of skeletal muscle mass and strength, accompanied by functional impairment. As such, it is associated with disability, poor quality of life, and increased mortality14. Studies have investigated the genetic factors of LBM using the associations of single nucleotide polymorphisms (SNPs)19. Moreover, as osteoporosis and sarcopenia share a common risk factor (ageing), several studies have conducted joint genome-wide association study (GWAS) analyses on overlapping genetic variants21. Notably, a GWAS into osteoporosis revealed 64 loci. In contrast, fewer loci were identified by a GWAS into muscle-related phenotypes, thus. providing fewer biological insights into pathways regarding sarcopenia20. To address this issue, a large GWAS meta-analysis was conducted using 20 cohorts of European ancestry, identifying a set of five loci  for total LBM, and SNPs related to IRS1, ADAMTSL3, and VCAN for appendicular LBM22. However, as this study analysed the entire cohort, irrespective of age, its findings regarding genes associated with sarcopenia as a senile disease were limited. In addition, the GWAS was based on European ancestry, and little is known regarding genetic determinants in elderly East Asians. Still further, few genetic studies have utilised a new index for sarcopenia (released by the AWGS in 2019) to investigate ASM or SMI11. Thus, a need exists for the investigation of genetic components associated with sarcopenia using multiple cohorts comprising elderly East Asians. The current study conducted a GWAS meta-analysis on sarcopenia phenotypes using Korean relatively aged cohorts, combining the Veterans Health Service Medical Center (VHSMC) and Korean Association Resource (KARE) cohorts.Muscle mass has a genetic trait phenotype, with a heritability estimate of over 50%P < 0.001, Table P = 1.000) between the two cohorts. The mean weight  and BMI  were statistically different between the cohorts. The LBM of the VHSMC cohort was lower than that of the KARE cohort , whereas the body fat mass (BFM) of the VHSMC cohort was higher than that of the KARE cohort . Descriptive statistics for subgroups according to sex are also presented in Table 2 and 17.49 ± 4.06 kg, respectively.A total of 7753 eligible subjects were included in this study (2518 subjects from the VHSMC cohort and 5235 from the KARE cohorts). However, 792 were excluded due to the exclusion criteria  near Glutamate Metabotropic Receptor 4 (GRM4) and High Mobility Group AT-Hook 1 (HMGA1), followed by rs3768582  near Neutrophil Cytosolic Factor 2 (NCF2). The remaining eight variants are presented as candidate loci in Table P-value was rs1592269  near GRM4 and HMGA1. The top ten candidate loci associated with BFM with P-values < 1.00 × GRM4 and HMGA1), linkage disequilibrium (LD) analysis was performed. A high (r2 = 0.935) LD between rs1187118 and rs1592269 was observed, indicating a relatively high dependency.A total of 2,360,975 SNPs were used for the GWAS meta-analysis of LBM and BFM. Quantile–quantile (Q-Q) and Manhattan plots for LBM are shown in Fig. P = 2.30 ×  zinc finger protein 860 (ZNF860) and Glycerol-3-Phosphate Dehydrogenase 1 Like (GPD1L). The Q-Q and Manhattan plots for SMI are shown in Supplementary Fig. P-value was rs6772958  near ZNF860 and GPD1L. The top ten candidate loci with P-values < 1.00 × A total of 2,804,834 SNPs were used for GWAS analyses of ASM and SMI, using only the VHSMC cohort. The Q-Q and Manhattan plots for ASM are shown in Fig. Ribosomal Protein S10 (RPS10) was highly expressed in skin sun-exposed lower leg tissue (P = 1.40 ×  Nudix Hydrolase 3 (NUDT3) was also found in the skeletal muscle tissue  and Actin Related Protein 2/3 Complex Subunit 5 (ARPC5) were highly expressed in the artery (P = 1.50 ×  P = 2.60 ×  P = 7.30 ×  RPS10 (P = 8.00 ×  NUDT3 (P = 1.19 ×  NCF2 (P = 1.26 ×  SMG7 (P = 1.03 ×  ARPC5 (P = 4.26 ×  For the genome-wide significant variants of each phenotype (LBM and ASM), the regional plots with the lead SNPs are displayed in Figs. GPD1L was highly expressed in thyroid tissue (P = 8.10 ×  GPD1L expression was not significant (P = 0.159) in the transcriptome study (GSE38718).The second phenotype of interest was ASM. The only genome-wide significant SNP, rs6772958 eQTL analysis, showed that mRNA destabilisation (GO: 0061157)’ (FDR-adjusted P = 0.090) was enriched, which is involved in skeletal muscles related genes. The term contains a pathway of alpha-ketoglutarate-dependent dioxygenase FTO -]-demethylase FTO), which is involved in the regulation of fat mass, adipogenesis, and body weight. Thus, it contributes to the regulation of body size and body fat accumulation23.In addition, gene ontology (GO) analyses of biological processes revealed that the term ‘RPS10, NUDT3, NCF2, SMG7, and ARPC5, were expressed in skeletal muscle tissue. In addition, in the biological process, the term ‘mRNA destabilisation (GO: 0061157)’ (FDR-adjusted P = 0.090) was enriched for sarcopenia. This process contains alpha-ketoglutarate-dependent dioxygenase FTO. These results suggest that the pathogenesis of sarcopenia requires further investigation using a metabolic pathway linked to mRNA.This study discovered novel genetic biomarkers of LBM (rs1187118) and ASM (rs6772958) from the VHSMC and KARE cohorts, which comprise relatively aged  Koreans. Their related genes for LBM, such as 13. A previous study on genetic markers for sarcopenia identified the loci near FTO, ESR1, NOS3, KLF5, and HLA-DQA1 to be associated with physical phenotypes, such as low handgrip strength and decreased LBM26. Nonetheless, these identified loci can only explain a small portion of phenotypic variations; thus, additional genetic loci should be identified. A recent large meta-analysis of the Cohorts for Heart and Ageing Research in Genome Epidemiology (CHARGE) Consortium and various other cohorts identified only a few loci, such as FTO and VCAN for LBM22. Therefore, assuming that identifying genetic variants for sarcopenia is challenging, we conducted GWAS analysis on a cohort comprising elderly subjects. The findings revealed that several genetic variants related to metabolism could be of importance in determining the pathogenesis of sarcopenia. Previous sarcopenia GWAS for European descendants showed association with FTO28 and several loci, including TGFA and HLA-DRB129.The aetiology of sarcopenia is complex and includes oxidative stress, inflammation, inadequate diets, a sedentary lifestyle, and genetic factorsGRM4 and HMGA1, with a high LD between rs1187118 and rs1592269. HMGA1 is overexpressed in adipose tissue, impairs adipogenesis, and prevents diet-induced obesity, and insulin resistance30. The top loci for LBM and BFM were similar, and those of ASM and SMI were similar since the parameter of LBM was calculated from body weight minus BFM, and SMI was calculated from ASM/height2. Hence, it would be useful to calculate the correlation and genetic correlation for each parameter. In VHSMC cohorts, the correlation and genetic correlation were 0.078 and 0.078 between LBM and BFM, respectively, whereas those between SMI and ASM were 0.948 and 0.948, respectively. In KARE cohorts, the correlation was − 0.02 between LBM and BFM with the genetic correlation being 0.349.Our meta-analysis for both LBM and BFM showed significant differences in the intergenic area of RPS10, NUDT3, NCF2, SMG7, and ARPC5 were differentially expressed in the muscle tissue for sarcopenia. However, this finding requires further validation. As the regional locations of HMGA1, RPS10, and SIMM29 were in the upper stream of NUDT3, and may represent a regulatory function for the association of NUDT3 with sarcopenia, further focus should be directed towards NUDT3. A previous study by Singh et al. suggested that NUDT3 was a candidate target-locus, and emphasised the need for real-world validation using transcriptome-wide association study (TWAS) approaches that combine GWAS and eQTL summary data24. In the current study, NUDT3 was found to be related to LBM in an elderly cohort. NUDT3 belongs to the MutT or Nudix protein families, which act as homeostatic checkpoints at important stages in inositol phosphate metabolic pathways. These pathways, such as phosphatidyl-1d-myo-inositol and glycerophospholipid metabolism, from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (https://www.kegg.jp/pathway/map00564)31 may, therefore, be related to LBM. For these reasons, it is necessary to understand the metabolic aspects of sarcopenia. A study into DEGs in skeletal muscle tissues from patients with cachexia32 showed that NCF2 was identified from signal pathways related to inflammation. These findings are consistent with the findings of the present study. SMG7 encodes a protein that is essential for nonsense-mediated mRNA decay, which is related to body height and BMI-adjusted waist circumference from a GWAS catalogue (https://www.ebi.ac.uk/gwas/). As SMG7 is linked with telomerase reverse transcriptase (TERT), sarcopenia may be related to muscle cell senescence via microRNA-19533. In addition, ARPC5 encodes the actin related protein2/3 complex, which exhibited a negative fold change in expression related to the cytoskeleton in muscle tissue34. As these findings may represent secondary changes, or may be postulated from bioinformatics analysis, further studies are needed.The eQTL analysis for muscle mass using GTEx datasets showed that GPD1L is a significant genetic marker for ASM and SMI in the VHSMC cohort. Although this is a novel finding for GWAS using ASM as a parameter for sarcopenia, it requires further validation by studies from several cohorts. A tissue-based study into rat muscle identified GPD1L as a candidate locus for sarcopenia35. These findings were also observed in a previous study that investigated the sarcopenic muscle tissue of elderly women36, in which GPD1L was found to be downregulated via cytoplasmic energy metabolism. In addition, a systemic genetic approach identified that GPD1L and its molecular mechanism for obesity in human adipose tissue were associated with energy metabolism37. GPD1L expression was found to be negatively correlated with microRNA-210 (miR-210) levels, and was consistently downregulated in obese subjects37. They hypothesised that the decreased miR-210 levels increased GPD1L, thus inhibiting hypoxic transcription factor-1α (HIF-1α) activity. A previous study into the circulating miRNAs in plasma revealed that miR-210 is significantly downregulated in elderly patients with sarcopenia, compared to patients without sarcopenia38. Combined with results of previous studies38, the findings presented here suggest that GPD1L could be a genetic biomarker for sarcopenia, based on both miR-210 and HIF-1α pathways. Hence, an additional biomarker for sarcopenia may be postulated from this metabolic research. Recent studies into plasma biomarkers for sarcopenia have identified higher levels of amino acids and lower levels of phosphatidylcholines (PCs) and lysophosphatidylcholine (lysoPC)40. The association between GPD1L and PCs or lysoPC and sarcopenia may involve (1) dysregulation of GPD1L related to decreased PCs and lysoPC from previous lipid biomarkers40, or (2) an increase in the glycerol-3 phosphate pathway inducing changes in glycolysis via GPD1L. However, the results of the present study can only be used to suggest a genetic hypothesis; thus, further follow-up studies are needed.Additionally, the present study found that FTO and muscle phenotypes27. In addition, alpha-ketoglutarate is a component of the tricarboxylic acid cycle, which is related to the HIF-1α pathway. This evidence suggests that a simultaneous understanding of both genes and gene-metabolic pathways is necessary to understand the pathogenesis of sarcopenia.Analysis of the enriched biological processes identified via GO analysis of the cohorts revealed that alpha-ketoglutarate-dependent dioxygenase FTO is related to sarcopenia. This finding is consistent with those of a previous study on the influences of NUDT3 and RPS10 were replicated using a real cohort, which was an approach suggested by a previous study using the TWAS of muscle tissue24. Furthermore, our study focused on East Asian subjects, which have not been fully evaluated, unlike other ethnic groups. In this regard, we conducted phenome-wide association studies (pheWAS) using the “Common metabolic disease knowledge portal” (https://hugeamp.org), indicating that SNPs such as rs1187118, rs3768582, and rs6772958 are related to metabolic conditions such as waist-hip ratio, lipid metabolism, and body fat percentage in the European population  for LBM and BFM examinations, as it is a non-invasive method for measuring body composition. However, dual-energy X-ray absorptiometry (DXA) is the standard method for muscle mass. BIA and DXA have different limitations for studies using body composition measurements. A previous study that compared these two methods found that BIA overestimated ASM compared to DXA41. In addition, BIA devices differed in the two cohorts , which may be a confounding factor. In a technical review of BIA for people with high body fat, InBody 3.0 tended to be lower, with a difference of about 2% in an extreme case (unpublished data). A previous study showed that different BIA devices were reliable by high intraclass correlation coefficients and low standard errors42. Since the focus of our study was on muscle mass rather than fat mass, and we analysed each cohort using different PCs, differences associated with the BIA device between the two cohorts would not significantly influence the LBM values and analysis results presented in this study. However, it is necessary to consider these when interpreting research results.Nevertheless, certain limitations were noted in this study. First, although novel signals for LBM and ASM were discovered with genome-wide significance, our results were based on bioinformatics analysis and, therefore, must be replicated in other Asian cohorts or multi-ethnic samples. A large number of samples for phenotypes, such as ASM and SMI, will improve the study’s validity. Hence, further studies, including replication or meta-analysis, are needed in other cohorts of the Asian population. Moreover, the number of SNPs  in the VHSMC cohort was limited as we set the imputation accuracy to 0.9. These points should be considered in the interpretation of the results. Second, the difference in ageing biology between sexes further hinders the identification of meaningful biomarkers for age-related conditions. Although GWAS analysis was conducted according to sex, the results did not show significant loci with genome-wide significance. It is expected that a metabolite-GWAS, considering sex as a factor, could help address this problem. Third, bioinformatics analysis revealed that genetic variants and metabolic pathways were related to sarcopenia, however, the causality of this hypothesis requires further investigation. Moreover, previous studies on genetic variants in sarcopenia have shown that these variants may be associated with the effects of genetic, metabolic, and environmental factorsRPS10, NCF2, SMG7, ARPC5, and NUDT3 were identified to be significant biomarkers for LBM. In addition, the loci near GPD1L were identified as significant biomarkers for ASM and SMI, which serve as novel index for sarcopenia. These genes are related to metabolism pathways, such as glycerophospholipid pathways, energy metabolic pathway, the inositol phosphate and HIF-1α pathways, and alpha-ketoglutarate-dependent dioxygenase FTO. Further studies are required to evaluate the aetiology of sarcopenia.In conclusion, sarcopenia can result in adverse outcomes, such as an increased risk of falls, a decreased quality of life, and mortality. Thus, it is necessary to identify a biomarker for this condition. Here, the loci near genes such as 11, were excluded. After exclusion, 6961 participants were enrolled across both cohorts  in the VHSMC cohort and using InBody 3.0  in the KARE cohort. Each subject stood on the footplate and held both of the hand electrodes. The screen automatically displayed measurements of LBM (kg), skeletal muscle mass (kg), BFM (kg), and body fat percentage (%). LBM and BFM data were available for both cohorts and were used as initial phenotypes for analysis. Subgroup analysis was conducted using ASM or SMI, which were derived from BIA; these data were available only for the VHSMC cohort. The parameters were defined according to the consensus of the AWGS 201943. Genotypes were identified with a K-medoid clustering-based algorithm to minimise the batch effect44. The PLINK 45 and ONETOOL46 software packages were used for quality control procedures and association analyses. Samples matching any of the following criteria were excluded: (1) sex inconsistencies or (2) a call rate of up to 97%. SNPs were filtered if the call rate was lower than the Hardy–Weinberg equilibrium (HWE) test (P < 1 ×  https://imputationserver.sph.umich.edu). Only ‘non-European’ or ‘mixed’ populations from Haplotype Reference Consortium release v1.147 were used for reference purposes. Pre-phasing and imputation were performed using Eagle v2.448 and Minimac449, respectively. After the imputation processes, imputed SNPs were removed if the R-squared  was less than 0.9 or there were duplicated SNPs, missing genotype rates were more extensive than 0.05, P-values for HWE were less than 1 ×  http://coda.nih.go.kr) or the Genome Aggregation Database (GnomAD) with East Asian subjects (https://gnomad.broadinstitute.org/). Finally, 2422 subjects  from the VHSMC cohort and 5235 subjects  from the KARE cohort were used for analysis.Genomic DNA was separated from venous blood samples, and 100 ng DNA was genotyped using Korea Biobank Array Affymetrix Axiom 1.1 , which was designed by the Korean National Institute of Healthhttp://csg.sph.umich.edu/abecasis/meta). Cochran’s Q-test for heterogeneity was conducted; its P-value was marked with ‘HetPVal’50, where HetPVal < 0.05 indicates heterogeneity between two datasets51. The dense regional association result of each GWAS was plotted using the LocusZoom software52. The threshold for statistical significance in this model was P < 5.0 ×  Baseline characteristics of the study population are presented herein as means with standard deviation (SD) for continuous variables and numbers, and as proportions for categorical variables. Genome-wide analyses were conducted using a linear model; PLINK was used within each cohort. Age, sex, and ten principal component scores were included as covariates. Meta-analyses of the VHSMC and KARE cohorts were performed using the METAL software , which provides a variety of human tissues from donors using the densely genotyped data to assess genetic variations within their genomes. Genes related to metabolites were analysed using KEGG pathway analysis31. Associated genes were further investigated for DEGs in the skeletal muscles of subjects 19 to 28 and 65 to 76 years of age from the Gene Expression Omnibus (GEO) dataset (GSE38718)53. In addition, biological process, cellular component, and molecular function GO analyses were performed using gene set enrichment analysis. The Benjamini–Hochberg false discovery rate (FDR)-adjusted 0.1 significance level was applied for multiple hypothesis test corrections54.Expression Quantitative trait (eQTL) studies were performed using the Genotype-Tissue Expression (GTEx) dataset (VHSMC cohort and IRB No. 2021-05-005 for KARE cohort) since this study was performed in retrospective manner, and the study was conducted in compliance with the Helsinki Declaration.The institutional review boards of the Veterans Health Service Medical Center approved this study protocol and informed consent waiver (IRB No. 2020-02-015 for Informed consent waiver was approved by the institutional review boards of the Veterans Health Service Medical Center since this study was performed in a retrospective manner and anonymised and de-identified data were used for the analyses. The KARE cohort and VHSMC cohort obtained the informed consents from participants.Supplementary Information.


Background: Dual anti-retroviral therapy is the main proven valuable intervention type for treating naïve HIV/AIDS. Currently, no high-quality evidence is available regarding the best dual schemes.Objectives: The aim of this study is to evaluate the effectiveness and safety of PIs/r-based dual therapy in treatment-naïve HIV/AIDS patients by using network meta-analysis.Methods: Randomized controlled trials of PIs/r-based dual therapy in treatment-naïve HIV/AIDS were searched based on Embase, PubMed and Cochrane library database from January 2006 to June 2021. Taking viral suppression rate, CD4+T cell count changes from baseline as the primary indicator and adverse events rate as secondary indicator, the network meta-analysis was performed on Review Manager and STATA software. Heterogeneity was assessed by the Q statistic and I2. We registered our protocol in Prospero with ID CRD42021275466.Results: Among 15 randomized controlled trials  were reviewed in this study. According to the forest map, DRV/r + INSTIs was more effective compared to triple therapy (TT) in viral suppression , in CD4+T cell count changes from baseline , in adverse events . Furthermore, SUCRA ranking analysis indicated that DRV/r + INSTIs was superior to TT in viral suppression (DRV/r + INSTIs 75.5% > TT 41.2%) and in immune construction (DRV/r + INSTIs 67% > TT 42%). In addition, DRV/r + INSTIs was similar to TT in adverse events (DRV/r + INSTIs 54.9% ≈ TT 54.7%).Conclusion: DRV/r + INSTIs was obviously superior to TT in viral suppression and immune reconstruction, and was not higher than TT in adverse events.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/, identifier CRD42021275466 Filippo et al.  + one or two Nucleoside/Nucleotide Reverse Transcriptase Inhibitors (NRTIs). European AIDS Clinical Society (EACS) Guidelines  recommenNetwork meta-analysis  is an extension of traditional meta-analysis based on indirect comparison or combining results of indirect comparison and direct comparison. It combines clinical evidence of direct comparison and indirect comparison, and ranks the efficacy of different therapy schemes . TherefoTwo researchers (Liu Hui and Han Xiaoxu) searched to original reports using the Embase, PubMed and the Cochrane library from January 2006 to June 2021, using both medical subject heading (MeSH) terminology and relevant keywords to identify articles that analyzed the effectiveness and safety of dual versus triple antiretroviral therapy in HIV-naïve patients. Search terms include: “HIV” (or “acquired immunodeficiency syndrome”), “antiretroviral” (“protein inhibitors” or “PIs”), “randomized clinical trials” (or “RCTs”). We made a protocol before commencing the study and registered it on the PROSPERO International Prospective Register (CRD42021275466).+T cell count from baseline and the number of adverse events.All studies in this meta-analysis had to meet the following inclusion criteria: 1) RCTs; 2) Treatment-naïve, HIV/AIDS patients who are greater than 18 years old; 3) Intervention measures include dual simplified schemes and triple schemes based on PIs/r; 4) One or more of the following outcomes are assessed: the number of HIV suppression cases, the change value of CD4Exclusion criteria: 1) Research types: summary, abstract, letter, conference or report; 2) The researches about experienced HIV/AIDS or non-HIV/AIDS patients; 3) Comparative study without PIs/r drugs; 4) Primitive study related to the efficacy and adverse events of only one drug.+T cell count from baseline and adverse events rate.Data extraction is separately recorded by two researchers. If there is any inconsistency in the recorded information, the third researcher will be consulted to solve this difference. The basic information included in this study: the first author, the year of publication, country, the intervention measures, the cases of each intervention group, the age and the follow-up time (weeks). The outcome indicators of naïve HIV/AIDS patients include viral suppression rate, △CD4The RCT Cochrane Risk of Bias Tool (version 5.0) was used to evaluate the quality of the included studies, including: 1) allocation concealment, 2) randomization method, 3) blind method, 4) integrity of outcome data, 4) selective report results, and 5) other biases. The Review Manager 5.4 software provided by Cochrane Collaboration network was used to draw the literature quality evaluation chart, which aims to evaluate the bias of the research directly.I2 > 50%, p-value < 0.05. Subsequently, STATA (version 15) software was used to draw the network meta-analysis evidence network plot, funnel plot, consistency test plot, ranking plots of the extracted data. Interestingly, the SUCRA package provided by STATA software was not only used to obtain the ranking results of each intervention measure, but also calculate the Area Under the Curve (AUC) to draw the cumulative probability ranking diagram. Furthermore, the outcome indicators were clustered based on the SUCRA value, which can provide evidence to screen the best DT between the two clustering indicators.Before the analysis, we rechecked the extracted data. Firstly, the network meta-analysis for the extracted data was performed by using Review Manager 5.3 software. Both Odds Ratio (OR) and 95% Confidence Interval (95% CI) were used for discontinuous variable analysis, and means/SD was used for continuous variable analysis. Effect sizes were synthesized using a fixed-effect model except when there was significant heterogeneity, for which the random-effects model was used. Statistical heterogeneity was considered significant when A flow chart describing the identification and selection of the articles included is shown in +T cells is between 120 and 348 cells/mm3. Characteristics of intervention measures: two studies directly compared LPV/r + PIs and TT; two studies directly compared LPV/r + NRTIs and TT; two studies directly compared LPV/r + INSTIs and TT; two studies directly compared LPV/r + CCR5 and TT; five studies directly compared DRV/r + INSTIs and TT; two studies directly compared LPV/r + NNRTIs with TT, DRV/r + CCR5 with TT.The main characteristics of 15 studies included in the meta-analysis are summarized . ResearcThe summary of study quality evaluation is shown in A total of 11 studies have collected the indicators of viral suppression rate, and the network diagram is shown in I2 = 16%. Although it was suggested that there was no significant statistical difference in viral suppression between DT and TT , the OR was tendency to DT. According to the concrete analysis of different dual schemes, DRV/r + INSTIs , LPV/r + INSTIs  was superior to TT.As shown in Consequently, SUCRA value was used to sort different schemes in viral suppression rate . It held+T cell counts is an indicator related to immune reconstruction. A total of nine articles referred to the number of CD4+T cells change from baseline, the network diagram and comparison-correction funnel diagram was shown in I2 = 76%, p < 0.001, CI (0.71–3.11)] and the overall effect test was applied . According to the concrete analysis of different dual schemes, it was shown that LPV/r + PIs , I2 = 56%), LPV/r + NNRTIs ], DRV/r + INSTIS  were superior to TT. The following relationship was got by SUCRA value ] and the overall effect test was applied . According to the concrete analysis of different dual schemes, it was shown that there was no significant difference in the possibility of adverse events among DRV/r + INSTIS , DRV/r + CCR5  and TT. The following relationship was got by SUCRA value , but the possibility of adverse events in DRV/r + INSTIs scheme was lower than that in LPV/r + NRTIs , LPV/r + INSTIs , LPV/r + CCR5 , DRV/r + CCR5  and TT ], respectively.In +T cell changes were analyzed by pairwise clustering  The number of individual intervention measures or outcome indicators included in this study is small, and there may be some publication bias; 2) In terms of literature quality evaluation, most of the studies are open label research, and the random methods of some studies are not clearly described, which may bring distribution bias to a certain extent; 3) There are some differences in the TT schemes included in the study, which may affect the results.PIs/r-based dual simplified therapy can be used as a reliable simplified scheme for naïve AIDS patients. Among them, DRV/r + INSTIs dual simplified scheme had remarkable effectiveness in inhibiting HIV replication and immune reconstruction, and was expected to become an effective alternative to triple scheme.


Background: Our aim was to describe patterns of sitting over time and determine the sociodemographic predictors of sitting over time among women living in socioeconomically disadvantaged neighbourhoods. Methods: Women age between 18 and 45 years  reported their sitting time, sociodemographic , and health  three times over 5 years. Linear mixed modelling was used to determine the predictors of change in sitting over time, adjusting for covariates. Results: Mean baseline sitting time was 40.9 h/week, decreasing to 40.1 h/week over five years. Greater sitting time was reported in participants ≤25 years of age, living with obesity, living in urban areas, self-reported poor/fair health, working full-time, with higher education, never married and with no children. Annually, the average sitting time decreased by 0.4 h/week  in women working full-time but increased by 0.1 h/week  who were not working. Similarly, annual sitting time decreased by 0.6 h/week  in women with no children but increased by 0.4 h/week  and 0.9 h/week  among those with two and three/more children, respectively. Conclusion: Among disadvantaged women, those not working and with two or more children may be at particular risk for increased sitting time and warrant further attention. Sitting is a sedentary behaviour (SB) where energy expenditure is ≤1.5 metabolic equivalent (METs) . High leAdditionally, understanding the factors associated with sitting time may help identify population groups at increased risk of chronic disease and identify potential targets for intervention. A range of factors including age ,10,11, oDescribe patterns of time spent in sitting over time;Identify baseline sociodemographic predictors of sitting time patterns over time.Among women from socioeconomically disadvantaged neighbourhoods, this study aimed to:Data were obtained from a postal survey conducted for the Resilience for Eating and Activity Despite Inequality (READI) study, approved by the Deakin University Human Research Ethics Committee (ID-2006-091), Australia. Participants provided written consent.READI was a prospective cohort study of women aged 18–45 years living in socioeconomically disadvantaged neighbourhoods  in the state of Victoria, Australia, described in detail elsewhere . BrieflyTotal time spent sitting per week was reported via the long version of the International Physical Activity Questionnaire (IPAQ-L)  at each 2) was calculated from self-reported weight (kg) and height (m), and classified as normal (BMI 18–25 kg/m2), overweight (BMI 26–30 kg/m2) or obese (BMI > 30 kg/m2) [Participants reported age, weight, height, educational qualification , employment status , weekly income , marital status , number of children (<18 years) living at home , smoking status . Participants on self-reported health status were asked to respond the following “would you say your health is: excellent, very good, good, fair, poor”. Age was categorised  as per previous studies that have reported being young (18–25 years) was associated with higher sitting time compared to older ,11. Body0 kg/m2) . Urban-rn) and percentage (%) for categorical data and by the mean and standard deviation for continuous data. Baseline characteristics of participants lost to follow-up were compared to the participants who completed surveys at all three time points because the difference between the samples could affect the internal validity of the study results [Participant characteristics were summarised by frequency  variable. Each model included fixed effects terms for time (years since baseline) and sociodemographic and health exposure variables and an interaction term for these two factors . The interaction term estimates the additional change in sitting time per year associated with each category of the exposure variable. A random intercept was specified for each participant to account for individual differences in baseline sitting time, and time was also specified as a random effect to allow the effect of time to vary among individuals. The correlation between the repeated measurements over time was modelled using an exponential covariance structure with an unstructured covariance matrix for the random effect. All models were optimised using maximum likelihood estimation. A likelihood-ratio test was used to compare the models with and without a random effect of time. As a random effect for time significantly improved all models’ fit, it was retained throughout. Adjusted models used the criteria of a 10% change in estimates to identify confounders for each individual model .Model estimates are presented as baseline differences for categories of exposure variables compared with the reference category, overall average change per year, and additional annual change for each category of the exposure compared with the reference category. The estimated annual change in sitting time for each category of the exposure was calculated by adding the overall average change per year and the category-specific additional change per year.A number of observations missing for both exposure and outcome were described in p < 0.05 considered statistically significant.Data analysis was conducted in 2019 using STATA version 15  with The first sensitivity analysis examined the impact of missing data by conducting a complete case analysis where only data used from participants who responded to all three surveys.The second sensitivity analysis examined the potential impact of high/implausible sitting time . We hypothesised that participants with high values may have reported on total weekday and weekend estimates rather than on single-day estimates of weekday and weekend. This hypothesis was somewhat supported as a high proportion of implausible values were divisible by five (weekdays) and two (weekends). Observations that remained higher than 960 min/day after dividing by five and two were truncated to 960 min/day.The results from both sensitivity analyses were compared to the main results to determine whether there were changes in the magnitude or direction of the estimated effect or in statistical inference.Mean sitting time was 40.9 h/week (2454 min/week) at T1, decreasing to 40.3 h/week (2418 min/week) (T2) and 40.1 h/week (2406 min/week) (T3). Compared to women who participated only at T1 or only T1 and T2, women remaining in the study were older, non-smokers, had better health, higher education, higher incomes, living in rural areas, working part-time, married, and had children .Mean sitting time was typically highest among youngest participants, living with obesity, living in urban areas, with poor/fair health, higher education, working full-time, higher income, never married, and with no children .In adjusted models, women living in rural areas sat 3.5 h/week less at baseline than women living in urban areas, women working part-time and those not working sat 5.1 and 7.6 h less per week at baseline than women working full-time, women with one, two, or three or more children sat 3.7, 5.5 and 7.4 h/week less, respectively, at baseline than women with no children . Higher In regard to the annual change in sitting time in the adjusted model for employment status, annual sitting time decreased on average by 0.4 h/week (95% CI: −0.7 to −0.05), or 24 min/week. However, women who were not working at baseline increased sitting time by 0.1 h/week (95% CI: −0.2 to 0.6), or 6 min/week per year  + average change in sitting time among those not working (0.5) = overall annual increase (0.1 h)).In regard to the annual change in sitting time in the adjusted model for the number of children, annually sitting time decreased on average by 0.6 h/week (95% CI: −0.2 to 1.3), or 36 min/week. However, women with two children at baseline increased sitting time by 0.4 h/week (95% CI: −0.2 to 0.5) or 24 min/week per year  + average change in sitting time among women with two children (1.0) = overall annual increase (0.4 h/week)). Similarly, women with three or more children at baseline increased sitting time by 0.9 h/week (95% CI: 0.3 to 1.3) or 54 min/week per year  + change in sitting time among women with three or more children (1.5) = overall annual increase (0.9 h/week)).Sensitivity analyses using complete case data indicated small changes in estimates , but results remained consistent, and no changes in inferences were apparent . SensitiThis study described patterns of sitting time over time and identified sociodemographic and health factors associated with changes in sitting over time among young to middle-aged women living in socioeconomically disadvantaged neighbourhoods. Based on the finding of adjusted models, in this sample, sitting time declined over time, identical to previously reported studies on European  and AustOur study found that younger women 18–24 years) sat more than older women (35–45 years), consistent with the Australian Longitudinal Study on Women Health (ALSWH) study . In cont–24 yearsThis study describes the longitudinal patterns of total sitting time among women living in socioeconomically disadvantaged neighbourhoods, which extends previous research that has typically only assessed socioeconomic patterns of SB based on income and education ,35,39,40The current study showed that single women sat more than married women. This finding is consistent with existing hypotheses where marriage appears to be protective against unhealthy behaviours . SimilarThe strengths of this study included its prospective design with data collected at three time points over five years in a large population-based sample of both urban and rural-dwelling low SEP women. It is one of the first studies internationally to describe sitting time patterns and predictors of sitting time patterns over time and among women living in socioeconomically disadvantaged areas, a group at high risk for chronic disease. Limitations of this study include the self-reported data. IPAQ-L sitting items have good test-retest reliability over 4 weeks , but it This study identified two population subgroups (not working and with two or more children) of women from socioeconomically disadvantaged neighbourhoods at the greatest risk of increasing sitting time. Given the detrimental health effects of sitting time, these groups should be targeted in future interventions for reducing SB. Furthermore, for a better understanding of potential factors for these associations and inform the development of SB interventions for these groups, future studies need to investigate the predictors associated with a change in domain  and context-specific  sitting behaviours.


Maize is one of the most important food crops worldwide. Roots play important role in maize productivity through water and nutrient uptake from the soil. Improving maize root traits for efficient water uptake will help to optimize irrigation and contribute to sustainable maize production. Therefore, we investigated the protein profiles of maize cv. Anyu308 root system divided into Upper root zone (UR), Middle root (MR), and Lower root (LR), by label free quantitative shotgun proteomic approach (LFQ). The aim of our study was to identify proteins and mechanisms associated with enhanced water uptake in different maize root zones under automatic irrigation system.At field capacity, MR had the highest water uptake than the UR and LR. We identified a total of 489 differentially abundant proteins (DAPs) by pairwise comparison of MR, LR, and UR. Cluster analysis of DAPs revealed MR and UR had similar protein abundance patterns different from LR. More proteins were differentially abundant in MR/UR compared to LR/MR and LR/UR. Comparisons of protein profiles indicate that the DAPs in MR increased in abundance, compared to UR and LR which had more downregulated DAPs. The abundance patterns, functional category, and pathway enrichment analyses highlight chromatin structure and dynamics, ribosomal structures, polysaccharide metabolism, energy metabolism and transport, induction of water channels, inorganic ion transport, intracellular trafficking, and vesicular transport, and posttranslational modification as primary biological processes related to enhanced root water uptake in maize. Specifically, the abundance of histones, ribosomal proteins, and aquaporins, including mitochondrion electron transport proteins and the TCA cycle, underpinned MR’s enhanced water uptake. Furthermore, proteins involved in folding and vascular transport supported the radial transport of solute across cell membranes in UR and MR. Parallel reaction monitoring analysis was used to confirmed profile of the DAPs obtained by LFQ-based proteomics.The list of differentially abundant proteins identified in MR are interesting candidates for further elucidation of their role in enhanced water uptake in maize root. Overall, the current results provided an insight into the mechanisms of maize root water uptake.The online version contains supplementary material available at 10.1186/s12864-022-08394-y. Globally, agriculture is the largest freshwater user . In ChinZea mays L.) is one of the most important cereal crops in China and accounts for about 40% of the country’s cropland for cereal crops, particularly in the North and Northeast region; where groundwater is the major source of irrigation  chloroplastic (Zm00001d043845 _P031) was up-regulated in LR compared to the abundance of its isoform in UR and MR respectively. We noted that additionally, we observed the abundance of anthranilate synthase (Zm00001d028536 _P002), aspartate aminotransferase (Zm00001d016198 _P004), and ketol-acid reductoisomerase (Zm00001d044035 _P001), these proteins were up-regulated in LR and are functionally related to nitrogen metabolic precursors and nitrogen-transport process . Other aSimilar to LR, most of the amino acid biosynthesis and transport-related proteins increased specifically in abundance in the UR, such as lysine histidine trans-porter 2 (Zm00001d035157 _P002), glycine cleavage system H protein, glutamate dehydrogenase (Zm00001d025984 _P002), and phenylalanine ammonia-lyase (Zm00001d033286 _P001). Taken together, DAPs related to inorganic nitrogen , could cWater uptake from the soil and its radial transport toward the vascular tissues is achieved by membrane transport proteins, including water channels and other specialized transmembrane transporters . MembranTo our knowledge this study is the first to apply LFQ-based quantitative proteomics to characterize protein profiles in different root zones  that are related to water uptake in maize under smart irrigation. Analysis of soil water potential within each root zone indicated that MR had more water uptake compared to UR and LR. LFQ examination of protein abundance pattern revealed profile of the proteins associated with root water uptake at UR, MR, and LR respectively. GO and KEGG analysis highlighted common and significant differences in the protein function underlaying water uptake in each root zone. For example, proteins associated with carbohydrate metabolism and energy production were common to the root zones and possibly necessary for normal water uptake. However, abundance of proteins involved in chromatin structures and dynamics, ribosomal protein synthesis, electron transport chain, water channels, inorganic ion transport and transmembrane and vesicular transporters played key role in enhancing root water uptake observed in MR. Based on our results we proposed a model in Fig. Zea mays L.) was chosen for this study and was grown in the greenhouse of the Institute of Cotton Research of the Chinese Academy of Agricultural Sciences (ICR CAAS) in Anyang, Henan, China (longitude 36°06 N and latitude 114°21 E). The experimental research on the maize plants including sample collection was performed according to institutional guidelines under the local legislation. The plant growth condition was set to a photoperiod of 14 h, day/night, with a temperature of 25 °C/22 °C, relative humidity of 60%, and light intensity of 500 μmol m-2 s-1. The multi-depth soil moisture meter (Smart Moisture), an intelligent internet soil moisture sensor data logger, was used to monitor the moisture changes in re-al-time at various soil depths. The plants were grown triplicates in 10 cm diameter polyvinyl chloride (PVC) pots and filled with a mixture of silt and quartz sand, sieved to a particle diameter of < 1 mm and density of 1.5 g cm-3. The smart moisture was placed in the middle of the pots, and the soil surface surrounding the smart moisture was covered with a layer of black film to eliminate the soil evaporation. Several holes with a diameter of 1.2 mm were drilled at the bottom and sides of the pots to allow for water drainage and irrigation. Maize seeds were germinated on moist filter paper for 48 h, and three maize seedlings were planted in each container. The total number of pots was 6.Materials and the maize variety Anyu308  at field water capacity in each soil layer  using the Smart Moisture meter until irrigation compensation point was reached. Afterward, the final reading of soil water content at each soil layer  was taken. The smart moisture meter took a total of 4 reading cycles from the field holding to the irrigation compensation point. The root water uptake was indirectly determined as the absolute difference in water content of soil in each layer measured between the field holding capacity and the irrigation compensation point .Three representative plants from each replicate were sampled, the roots were washed, the length measured, then based on the soil layer divided the into three distinct roots zones, i.e., Upper root (0 cm-20 cm), Middle root (20 cm-40 cm), and Lower root (40 cm-60 cm). Each distinct zone was weighed and used for label-free quantitative proteomics (LFQ) experiment.Protein extraction was performed using three individual biological replicates for each sample. Briefly, 1 g of leaf tissue was weighed, homogenized by grinding in liquid nitrogen, lysed with lysis buffer , ultrasonicated on ice for 5 min, and centrifuged . The supernatant was transferred to a clean tube. Proteins were precipitated in pre-cooled acetone at − 20 °C for 2 h, washed 2× in 75% ethanol, and resolved in lysis buffer. Protein concentration in the lysate was estimated using bicinchoninic acid (BCA) protein assay kit . The rest of the lysate was frozen at − 80 °C until use.Filter-aided sample preparation (FASP) method was used for the on-filter digestion of proteins . Here prThe peptide samples were analyzed using the Easy-nLC nanoflow HPLC system connected to Orbitrap Fusion mass spectrometer . A total of 1 μg of each sample was loaded onto the Thermo Scientific EASY column (two columns) using an autosampler at a flow rate of 200 nL/min. The sequential separation of peptides on Thermo Scientific EASY trap column  and analytical column  was accomplished using a segmented 1 h gradient from 5 to 28% Solvent B (0.1% formic acid in 100% ACN) for 40 min, followed by 28-90% Solvent B for 2 min and then 90% Solvent B for 18 min. The column was re-equilibrated to its initial highly aqueous solvent composition before each analysis. The mass spectrometer was operated in positive ion mode, and MS spectra were acquired over a range of 375-1500 m/z. The MS scan and MS/MS scan resolving powers at 200 m/z for the Orbitrap Fusion were set as 120,000 and 50,000, respectively. Data Dependent Mode is Top Speed, Cycle Time is 3 s, and ions were fragmented through higher energy collisional dissociation. The maximum ion injection times were set at 50 ms for the survey scan and 105 ms for the MS/MS scans, and the automatic gain control target values for Master scan modes were set to 4e5, and MS/MS was 1e5. The dynamic exclusion duration was the 40s. The raw files were analyzed using the Maxquant 1.6.5.0 software (Thermo Fisher Scientific) . Search p-values < 0.05 by Student t-test and a fold-change of > 2 or < 0.5 in abundance between any two groups were considered significant. GO annotation for the differentially abundant proteins (DAPs) was derived using Blast2GO software v 4.1.9 [http://www.genome.jp/kegg/mapper.html). Fisher’s exact test was used to per-form the pathway enrichment statistics; p < 0.05 was set as the threshold used for enrichment analysis of KEGG pathways [Maxquant 1.6.5.0 Protein quantitation values were exported for further analysis in Excel. Proteins of  v 4.1.9 . Pathwaypathways . The seapathways . The grapathways .The LC-PRM/MS analysis was used to confirm the abundance of the proteins obtained by LFQ-based quantitative proteomics analysis. The peptide information suitable for PRM were selected and was imported into the Xcalibur soft-ware program for the PRM setup. Here, 2-μg peptide from each sample was taken for LC-PRM/MS analysis. After sample loading, the chromatographic separation was performed using a Thermo Scientific EASY-nLC nano-HPLC system. The following buffer was used: solution A, 0.1% formic acid aqueous solution, solution B, a mixed solution of 0.1% formic acid, acetonitrile, and water (95% 12 of acetonitrile). First, the column was equilibrated with 95% solution A. The sample was injected into a Trap column  and then subjected to gradient separation through a chromatography column  at a flow rate of 300 nL/min. Afterwards the peptides were separated and subjected to targeted PRM/MS using a Q-Exactive Plus mass spectrometer (Thermo Scientific). The obtained PRM data was analyzed using the Skyline 4.1 software program.Total RNA was extracted from each maize root sample using TRIZOL reagent . The RNA quantity and quality were determined with a NanoDrop 2000 spectrophotometer  according to the manufacturer’s instructions. The cDNA was synthesized from the RNA using the PrimeScript Reverse Transcriptase Kit  for quantitative real-time polymerase chain reaction (qPCR). The gene-specific primers for the qPCR are listed in Additional file Additional file 1: Table S1. List of proteins identified by LFQ analysis.Additional file 2: Table S2. List of peptides identified by LFQ.Additional file 3: Figure S1. Correlation plots of three biological replicates of all samples. R2 values are consistent and are noticeably higher between the replicates.Additional file 4: Table S3. List of differentially expressed proteins.Additional file 5: Table S4. List of GO terms assigned to DAPs identified in the three root zones.Additional file 6: Table S5. List of COG for differentially expressed proteins.Additional file 7: Table S6. List of KEGG terms for differentially expressed proteins.Additional file 8: Table S7. List of protein-protein interaction network.Additional file 9: Table S8. List of candidate proteins associated with enhanced root water uptake in maize.Additional file 10: Table S9. The list of gene-specific primers selected for qRT-PCR analysis.


Obese individuals have an increased risk of hypothyroidism. This study investigated the sex-specific association between obesity phenotypes and the development of hypothyroidism.The study population was derived from a health management cohort in Shandong Provincial Hospital from 2012 to 2016. In total, 9011 baseline euthyroid adults were included and classified into four groups according to obesity phenotype: metabolically healthy nonobese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy nonobese (MUNO), and metabolically unhealthy obese (MUO). The median follow-up time was 1.92 (1.00–2.17) years. Incidence density was evaluated and a generalized estimation equation method was used to investigate the associations between obesity phenotypes and the development of hypothyroidism.The incidence densities of hypothyroidism in males with a consistent obesity phenotype were 12.19 (8.62–16.76), 15.87 (11.39–21.56), 14.52 (6.74–27.57), and 19.88 (14.06–27.34) per 1000 person-years in the MHNO, MHO, MUNO, and MUO groups, respectively. After adjusting for confounding factors, compared with the MHNO phenotype, the MHO, MUNO, and MUO phenotypes were independent risk factors for developing hypothyroidism in males. In the subgroup analysis, the MHO and MUO phenotypes were independent risk factors for developing hypothyroidism in males under 55 years, while the MUNO phenotype was an independent risk factor in males over 55 years. The MHO, MUNO, and MUO phenotypes were not independent risk factors for hypothyroidism in females.Both obesity and metabolic abnormities are associated with a higher risk of hypothyroidism in males. The underlying mechanism of the sex and age differences in this association needs further investigation. Hypothyroidism refers to the common pathological condition of thyroid hormone deficiency and includes overt hypothyroidism and subclinical hypothyroidism . In ChinObesity is also a global health problem, with growing incidence worldwide and serious adverse health effects. In recent years, the relationship between obesity and thyroid dysfunction has drawn increased attention, and it has been suggested that obesity might not be only the result but also the cause of thyroid dysfunction –7. A metAlthough the deleterious metabolic effects of obesity are well recognized, metabolic responses to obesity differ at the individual level . ApproxiOnly a few previous studies have investigated whether thyroid function could identify obesity phenotypes in euthyroid subjects –20. HoweThis was a retrospective cohort study. The study population was derived from the health management cohort composed of individuals who underwent a comprehensive health examination in Shandong Provincial Hospital. The inclusion criteria were as follows: 1) adults aged 18 or older; (2) participants who had at least two visits with thyroid function tests between 2012 and 2016; and (3) participants with normal thyroid function at baseline. Of 97,679 participants who underwent health examination in Shandong Provincial Hospital between 2012 and 2016, 11,190 individuals fulfilling the inclusion criteria were examined for eligibility. Our exclusion criteria were as follows: (1) missing vital data, such as age and data necessary for categorization of obesity phenotype; (2) self-reported history of thyroid disease, use of medication that influences thyroid function, radioactive iodine therapy, or thyroidectomy; and (3) conditions that affect thyroid status during the study period, such as pregnancy, severe hepatic or renal dysfunctions, or malignant tumor. Ultimately, 6081 males and 2930 females were included in our study . Blood pressure was measured twice after a 5–15 min rest period and the mean values were reported. Thyroid-stimulating hormone (TSH), free thyroxine (FT4), and free triiodothyronine (FT3) were measured by chemiluminescent methods . Thyroperoxidase antibodies (TPO-Ab) were measured using ADVIA Centaur XP . Laboratory tests also included serum glucose, lipid profile, hepatic function, and renal function measured by the ARCHITECT ci16200 Integrated System .The data were collected from the health management database in Shandong Provincial Hospital. Information on medical history and lifestyle was gathered by a standardized questionnaire. After an overnight fast of at least 10 h, each subject underwent a standardized medical examination, including anthropometric and laboratory tests. Height and weight were measured after participants removed their shoes and heavy clothing. Body mass index (BMI) was calculated as weight (kg)/height squared  MHO: BMI ≥ 25 kg/m2 and fewer than two metabolic syndrome components; (3) MUNO: BMI < 25 kg/m2 and at least two metabolic syndrome components; and (4) metabolically unhealthy obese (MUO): BMI ≥ 25 kg/m2 and at least two metabolic syndrome components.Obesity was defined as BMI ≥ 25 kg/mr Asians –23. Accor Asians , metabolr Asians . AccordiThe reference ranges were 0.27–4.2 mIU/L for TSH, 12–22 pmol/L for FT4, 3.1–6.8 pmol/L for FT3, and 0–60 IU/L for TPO-Ab. Euthyroidism was defined as serum TSH, FT4, and FT3 levels within the reference ranges. Hypothyroidism was defined as TSH > 4.2 mIU/L and FT4 < 12 pmol/L (overt hypothyroidism) or TSH > 4.2 mIU/L and FT4 levels within the reference range .Continuous variables with a normal distribution are presented as the mean (standard deviation), and continuous variables with a skewed distribution were presented as the median . Categorical variables are presented as numbers (percentages). Differences in continuous variables were compared by using one-way analysis of variance or Kruskal–Wallis one-way analysis of variance. Bonferroni correction was applied to all multiple comparisons. For categorical variables, the chi-square test or Fisher’s exact test was used for group comparisons. The incidence density of hypothyroidism was calculated using the number of incident cases of hypothyroidism and person-years of follow-up in each group. A mid-P exact test was applied to calculate 95% confidence intervals (CIs) and compare incidence densities between groups.p value < 0.05 was considered statistically significant. All statistical analyses were performed in RStudio (version 1.0.143) with R (version 4.0.0).The generalized estimation equation (GEE) method was used to investigate the associations between different metabolic obesity phenotypes and the development of hypothyroidism, which is an extension of the generalized linear model to allow for analysis of repeated measurements or other correlated observations . First ip < 0.001)    based on obesity phenotypes are summarized in Table p < 0.05). Individuals with the MHO and MUO phenotypes had higher BMI and alanine aminotransferase (ALT) levels than individuals with the MHNO and MUNO phenotypes . Regarding thyroid function, baseline serum TSH levels, FT4 levels, and TPO-Ab positivity were comparable between the four groups, while individuals with the MHO and MUO phenotypes had significantly higher FT3 than the MHNO participants . In addition, baseline TC, LDL-C, aspartate aminotransferase (AST), creatinine (Cr), estimated glomerular filtration rate (eGFR), and smoking were also significantly different between groups .The baseline characteristics of males N = 608 based onN = 2930) based on obesity phenotypes are summarized in Supplementary Table p < 0.05). Baseline serum TSH levels, FT4 levels, and FT3 levels were comparable between the four groups. In addition, there were significant differences in baseline TPO-Ab positivity, HDL-C, ALT, Cr, eGFR, and smoking between groups .Baseline characteristics of females (p < 0.05).As the follow-up time was different between groups, we utilized incidence density to assess the number of incident hypothyroidism per unit time in different groups. The incidence densities of hypothyroidism in males with consistent obesity phenotypes during follow-up were 12.19 (8.62–16.76), 15.87 (11.39–21.56), 14.52 (6.74–27.57), and 19.88 (14.06–27.34) per 1000 person-years in the MHNO, MHO, MUNO, and MUO groups, respectively Fig. . Considep < 0.001). Among females with consistent obesity phenotypes, the incidence densities of hypothyroidism were 35.66 (29.70–42.49), 33.52 (20.49–51.95), 63.64 (27.83–125.90), and 65.00 (36.15–108.40) per 1000 person-years in the MHNO, MHO, MUNO, and MUO groups, respectively , while participants with the MUNO and MUO phenotypes had 2.03-fold (p = 0.007) and 1.91-fold (p = 0.001) increased odds of developing hypothyroidism, respectively . Further adjusting for potential confounders did not change this association Table . In addiThe present cohort study demonstrated that the non-MHNO group had a significantly higher incidence density of hypothyroidism than the MHNO group in males, and the MHO, MUNO, and MUO phenotypes were independent risk factors for the development of hypothyroidism compared with the MHNO phenotype in males but not in females. In addition, age modifies the relationship between obesity phenotypes and hypothyroidism in males. Compared with the MHNO phenotype, the MHO and MUO phenotypes were independent risk factors for the development of hypothyroidism in younger males, while the MUNO phenotype was an independent risk factor in older males.To the best of our knowledge, this is the first cohort study investigating the association between different obesity phenotypes and the incidence of hypothyroidism focusing on sex and age differences. A few previous studies have investigated the relationship between thyroid function within the reference range and different obesity phenotypes –20. As tThe most common cause of hypothyroidism in iodine-sufficient areas is chronic autoimmune thyroiditis . HoweverIt is well known that the prevalence of hypothyroidism is significantly higher in females than in males . Our stuPrevious studies have reported increased serum TSH levels in older patients and the association between thyroid dysfunction and lipid profiles differs according to age , 28. ThuSeveral limitations should be noted when interpreting the findings from this study. First, this cohort study was an observational study, which is incapable of inferring causal relationships. The relationship between obesity and thyroid function is complex and bidirectional . The thyDespite some limitations, our study had several strengths. First, this was the first cohort study investigating the sex-specific association between different obesity phenotypes and the development of hypothyroidism. Second, our study innovatively found that not only the MUO phenotype but also the MHO and MUNO phenotypes were independent risk factors for the development of hypothyroidism in males, which provides information regarding risk factors for hypothyroidism in males. As hypothyroidism occurs more frequently in females, less attention has been given to males in the past. Our findings suggested that males with unhealthy obesity phenotypes had a higher risk of hypothyroidism, which deserves attention in clinical practice. In addition, we found that the relationship between obesity phenotypes and hypothyroidism differs in different age groups among males. Finally, we used the GEE method for the analysis of the repeated measurements, which makes full use of each observation at each time point, thus improving both the accuracy and credibility of the results.In conclusion, our results suggested that in the health management cohort, both obesity and metabolic abnormities were associated with a higher risk of hypothyroidism in males. In younger males, excessive adiposity per se was an independent risk factor for hypothyroidism, while in older males metabolic abnormalities were associated with an increased risk of developing hypothyroidism. In contrast to the findings in males, no association between obesity phenotypes and hypothyroidism was observed among females in our population. Our study highlights sex and age differences in the association between obesity phenotypes and the development of hypothyroidism in a baseline euthyroid population, and attention should be paid to males with unhealthy obesity phenotypes. Further research is needed to confirm the generalizability of our findings and elucidate the possible mechanisms underlying the sex and age differences in the association.Supplementary Information