We aimed to investigate the association between a new definition of metabolic health (MH) and subclinical atherosclerosis in a cohort of patients without previous cardiovascular disease (CVD). In total, 7824 community-dwelling adults were categorized as normal weight, overweight, or obese. Metabolically healthy obesity (MHO) was defined as obesity accompanied by all of the following criteria: systolic blood pressure (BP) < 130 mmHg, no use of BP-lowering medication, waist-hip ratio <0.832 (women) and <0.887 (men), and no prevalent diabetes. Carotid atherosclerosis was defined as carotid plaque or mean carotid intima-media thickness ≥ 1.1 mm. The prevalence of carotid atherosclerosis was 8.3% and 1113 (14.2%) patients were classified as having MHO. All individuals classified as metabolically unhealthy were at an increased risk of carotid atherosclerosis independent of body mass index categories. Conversely, the risk of carotid atherosclerosis in individuals with MHO was not significantly increased compared to that in metabolically healthy normal weight participants . This new definition of MH was able to identify people with MHO without an increased risk of CVD in an Asian community cohort. The prevalence of obesity continues to rise exponentially worldwide and is extensively associated with multiple comorbidities, such as cardiovascular diseases (CVD), diabetes, hypertension, and several cancers ,2,3. ObeStudies on MHO and CVD have shown inconsistent results, as the diagnostic criteria varied among the studies ,10,11,12The Cardiovascular and Metabolic Diseases Etiology Research Center (CMERC) cohort consisted of community-dwelling Korean adults aged 30–64 years who were free from myocardial infarction, heart failure, or stroke. All participants completed health questionnaires and examinations. The sampling and measurement procedures in the CMERC cohort have been described in detail previously ,18. BrieEvery participant’s height and body weight were measured while they wore light clothing and without shoes and were measured to the nearest 0.1 cm or 0.1 kg. BMI was calculated as body weight in kilograms divided by the square of standing height in meters. Waist circumference (WC) was measured at the midpoint between the lower point of the rib cage and the upper point of the iliac crest. Hip circumference was measured in the widest region of the hip.Blood samples were collected from the antecubital vein after overnight fasting. All analyses were performed at a single laboratory center . Fasting blood glucose and creatinine levels were determined using a colorimetry method . Hemoglobin A1c (HbA1c) levels were determined using high-performance liquid chromatography with a Variant II Turbo . Fasting insulin levels were determined using a radioimmunoassay with an SR-300 apparatus . Total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and high-sensitivity C-reactive protein (hs-CRP) levels were assayed by enzymatic methods . To evaluate insulin resistance, the homeostasis model assessment of insulin resistance (HOMA-IR) was calculated using the following formula: [fasting glucose (mg/dL) × fasting insulin (μIU/mL)]/405 . The ChrParticipants were classified as “metabolically healthy” or “metabolically unhealthy” according to the recently proposed definition of MH by Zembic et al., which consists of three cardiometabolic components , with mo2), overweight (OW) (23 to <25 kg/m2), OB class 1 (25 to <30 kg/m2) or OB class 2 (≥30 kg/m2) [BMI was classified as normal weight (NW) (<23 kg/m) 23 to < kg/m2, OCarotid artery ultrasonography was performed using high-resolution ultrasonographic systems . All operators were trained according to a predefined protocol. Carotid intima-media thickness (CIMT) was scanned at the 1-cm segment of the common carotid arteries proximal to the bulb region. The mean and maximum CIMT values were measured on both the right and left sides. A carotid artery plaque was considered present if the thickness was greater by 50% or more than the surrounding intima-media thickness (IMT), or if its size was ≥1.5 mm . CarotidThe hepatic steatosis index (HSI) was used to define NAFLD, and those with HSI > 36 were considered to have NAFLD: HSI = 8 × (ALT/AST ratio) + BMI  .p-value less than 0.05.Data are presented as mean ± standard deviation or median (interquartile range) for continuous variables and as numbers (percentages) for categorical variables. Comparisons among the three groups according to obesity status  were performed by using one-way analysis of variance with Bonferroni-corrected post hoc comparisons  or Kruskal-Wallis tests with post hoc pairwise comparisons . Due to the right-skewed distribution, HOMA-IR, TG, and hs-CRP levels were entered into the models as log-transformed units. Multivariate logistic regression analyses were performed to examine the independent association of obesity phenotypes with carotid atherosclerosis or NAFLD after adjustment for age, sex, TG, HDL-C, LDL-C, hsCRP, HbA1c, HOMA-IR, eGFR, smoking status, alcohol consumption, physical activity, and the other definition of MH according to the NCEP-ATP III criteria. We repeated the same analysis using MH definition by NCEP-ATP III. All statistical analyses were performed using SPSS software . Statistical significance was defined as a two-sided There were, in total, 7824 participants with a mean age of 51.5 years and mean BMI of 24.2, and 65.1% were female . The prep for trend = 0.003) even after adjustment for age, sex, TG, HDL-C, LDL-C, hsCRP, HbA1c, eGFR, HOMA-IR, smoking status, alcohol consumption, physical activity, and the NCEP definition. When MH was defined by the NCEP-ATP III criteria, similar associations were observed only in OW and OB subjects. Moreover, none of the groups displayed increased risks when adjusted for the new definition  who had adiponectin levels measured at baseline. Consistent with these animal studies, MH individuals who are OW or with obesity had higher levels of adiponectin than metabolically unhealthy individuals who are OW or with obesity . Although it is beyond the scope of this study, it is plausible that metabolically healthy groups of individuals may have a favorable body fat distribution with higher circulating levels of adiponectin, thus protecting them from CVD. Interestingly, this protection was limited to subclinical atherosclerosis but not fatty liver, which is consistent with a previous study [The underlying mechanism involved in the MHO phenotype is not fully understood; however, accumulating evidence suggests that less visceral adiposity and less insulin resistance are involved in the MHO phenotype . Adiponeus study .There was a marked difference in the prevalence of NAFLD between the groups. The prevalence of NAFLD increased substantially with increasing BMI, regardless of MH status. The prevalence of NAFLD in NW, OW and obese participants were 1.3%, 9.0% and 55.0% respectively. These findings are further supported by previous large-scale cohort studies, which found that obesity was strongly associated with incident NAFLD and worsening of liver fibrosis in NAFLD independent of MH ,40,41. AWe found that the CVD risk among those with the MHO phenotype did not increase compared to that of the MHNW. To date, recommendations for obesity treatment do not consider differences between healthy and unhealthy OW or OB phenotypes. However, the stratification of OB individuals based on their cardiometabolic phenotype may be important for identifying those who are to be prioritized for early pharmacological treatment in addition to lifestyle intervention. Future longitudinal studies are needed to evaluate the impact of lifestyle and treatment interventions on changes in body phenotypes and their effect on the delay or acceleration of subclinical atherosclerosis.However, this study has several limitations. First, as in all cross-sectional studies, we cannot conclude that there is a causal effect of body phenotype and subclinical atherosclerosis. Although we tried to adjust for known risk factors using multivariate modeling, there may have been residual confounding by unmeasured and measured variables. However, similar results were observed when the same definition of MH was applied to an independent prospective cohort . SecondlThis study had several strengths. This study included a large, community-based cohort study of middle-aged Asian participants with a broad spectrum of clinical variables as clinical risk factors. To our knowledge, this is the first study to validate the utility of this new, revised definition of MH by evaluating associations with carotid atherosclerosis, in comparison with other definitions in an Asian population without clinical CVD.In conclusion, with a new and simple definition of MH, we were able to classify people with MHO who are not at an increased risk for subclinical atherosclerosis. This new definition was also helpful in detecting lean people at risk. Routine screening of CVD risk among the metabolically unhealthy, regardless of BMI category, may provide an opportunity to institute intensive pharmacological therapy to prevent subsequent CVD.


Objective The present study aims to compare the maternal and fetal outcomes of parturients with and without a gestational diabetes diagnosis.Methods A case-control study including parturients with (cases) and without (control) a gestational diabetes diagnosis, who delivered at a teaching hospital in Southern Brazil, between May and August 2018. Primary and secondary data were used. Bivariate analysis and a backward conditional multivariate logistic regression were used to make comparisons between cases and controls, which were expressed by odds ratio (OR), with a 95% confidence interval (95%CI) and a statistical significance level of 5%.Results The cases (n = 47) were more likely to be 35 years old or older compared with the controls (n = 93) (p < 0.001). The cases had 2.56 times greater chance of being overweight (p = 0.014), and a 2.57 times greater chance of having a positive family history of diabetes mellitus (p = 0.01). There was no significant difference regarding weight gain, presence of a previous history of gestational diabetes, height, or delivery route. The mean weight at birth was significantly higher in the infants of mothers diagnosed with diabetes (p = 0.01). There was a 4.7 times greater chance of macrosomia (p < 0.001) and a 5.4 times greater chance of neonatal hypoglycemia (p = 0.01) in the infants of mothers with gestational diabetes.Conclusion Therefore, maternal age, family history of type 2 diabetes, obesity and pregestational overweightness are important associated factors for a higher chance of developing gestational diabetes. Diabetes mellitus (DM) is a group of metabolic diseases characterized by hyperglycemia resulting from insulin deficiency. Its incidence has been increasing over the years. The number of adults with DM worldwide increased from 30 million in 1985 to 135 million in 1995, and to 173 million in 2002, and currently this number is ∼ 415 million. In women, when detected for the first time during pregnancy, this condition is classified as gestational diabetes (GD), which is considered an important risk factor for the future development of type 2 DM (T2DM), and has a prevalence of 1–37.7%, with a worldwide average of 18%.The risk factors for the development of GD should be evaluated in each pregnant woman so that the diagnosis can be established prematurely, enabling an adequate and early treatment. Some of the factors associated with the development of GD are overweightness, obesity or excessive weight gain during pregnancy, family history of T2DM in a first-degree relative, previous history of GD, hypertension, or preeclampsia in the current pregnancy.During pregnancy, insulin resistance increases due to the release of diabetogenic placental hormones. Pregnant women with GD have higher insulin resistance than pregnant women without GD; therefore, the postprandial glycemic values in these pregnant women are even higher. The complications of GD include fetal macrosomia, birth injury, increased rates of cesarean sections, neonatal hypoglycemia, neonatal respiratory distress syndrome (RDS), prematurity, and fetal death. Pregnant women with GD have an increased chance of developing T2DM after delivery. They also have an increased risk of developing hypertensive disorders during pregnancy, characterizing the pregnancy as high-risk, a fact that demands greater care and follow-up to prevent possible complications and death.Considering the increasing presence of the main risk factors for the development of GD in women of childbearing age and the fact that pregnancies associated with GD are characterized as high-risk, therefore requiring more caution and attention, the present research aimed at comparing the maternal and fetal outcomes of parturients with and without a diagnosis of GD.This was a case-control study including parturients with and without a GD diagnosis, who delivered between May and August 2018 at Hospital Nossa Senhora da Conceição (HNSC), located in the city of Tubarão, in the state of Santa Catarina, Southern Brazil.The HNSC has an obstetric center that is a reference for high-risk management in the South of Santa Catarina, and ∼ 200 deliveries are performed there monthly. Using the Statcalc function of the Epi Info 3.5.4  software, and with the purpose of conducting a case-control study in the ratio of cases: 1:2 controls, we assumed a prevalence of ∼ 19% of obesity as a risk factor for the development of GD in the general population, considering that the incidence of gestational diabetes mellitus (GDM) in obese pregnant women is three times higher than in the general population. The sample size was calculated as 140 parturients (47 cases and 93 controls).The present study included parturients with and without a GD diagnosis, who delivered at the HNSC between May and August of 2018; after being informed, they accepted to participate in the study and signed an Informed Consent Form (ICF). Parturients with type 1 DM (DM1) and T2DM were excluded. The diagnostic criteria of GD followed the Consensus on Gestational Diabetes: 2017 Update.Data collection was performed from May to August 2018. The data source used was primary, in which the researchers contacted the parturients admitted to the HNSC, who, through the ICF, agreed to participate; and secondary, in which the researchers sought information related to the newborn (NB) in electronic medical records. The controls were selected among the puerperae without GD in the same period.The researchers visited the hospital daily and contacted all of the parturients admitted to explain the research and to apply the questionnaire to those who accepted to participate.2); method of diagnosis; GA at diagnosis (in weeks); family history of DM; previous history of GD; presence of gestational hypertension; delivery route ; GA at delivery (weeks); fetal weight at birth (kg); Apgar score at 1 and 5 minutes; hypoglycemia at birth; RDS; fetal death; and the NB gender . The pregestational and current (time of delivery) weight and height values were mentioned by the participants, considering the impossibility of the researchers to be present full time to carry out the measurements. The BMI was calculated by the researchers (weight/height2), overweightness (BMI 25–29.99 kg/m2), and obesity (BMI ≥ 30 kg/m2).The instrument used was a self-administered questionnaire and a protocol developed by the researchers containing the variables of interest, such as: age (years); weight (kg); weight gain ; height (cm); previous BMI kg/m; method In the present study, excess weight was characterized as overweightness and obesity, and gestational hypertension was characterized by decompensated chronic hypertension during pregnancy or hypertension developed during pregnancy. The GA at delivery was defined as premature when the delivery occurred before 37 weeks. Macrosomia was defined as birth weight ≥ 4 kg in term deliveries or > the 90th percentile for the GA. Hypoglycemia at birth was defined by the pediatrician. The researchers had no access to the absolute blood glucose levels in cases of NBs with neonatal hypoglycemia.t test for quantitative variables and the Chi-squared or Fisher exact tests for categorical variables, according to data suitability. Comparisons between cases and controls were expressed using odds ratio (OR), with a 95% confidence interval (95%CI) and a statistical significance level of 5%. The variables that presented p-values < 0.20 in the bivariate analysis were submitted to backward conditional multivariate logistic regression to evaluate the independent association with the outcome.The collected data were inserted and stored in a database created with the help of the software Epi Info, version 3.5.4. The data analysis was performed using this software and complemented with the Statistical Package for the Social Sciences  software, version 20.0. Descriptive statistics were used as absolute numbers and percentages, measures of central tendency and dispersion. The bivariate analysis was performed using the Student The present research followed Resolution 466/2012 of the Brazilian National Health Council, and was approved by the Ethics in Research Committee of Universidade do Sul de Santa Catarina (UNISUL) on March 28, 2018, under opinion number 2.569.614.In the 140 women included in the present study (47 cases and 93 controls), the minimum age was 18 years old, and the maximum age was 44 years old. The mean age of the cases was 32.8 ± 6.4 years, which was statistically higher than that of the controls: 27.2 ± 6.9 years. The cases were more likely to be 35 years old or older (42.6%) compared with the controls (14%). Regarding the previous BMI, the cases presented more pregestational overweightness (72.3%) than the controls (50.5%). There was also a greater chance of obesity in the cases than in the controls .There was no significant difference in mean weight gain, previous history of GD, and height between cases and controls. The cases had a greater chance of having a positive family history of DM than the controls. The risk factors associated with the development of GD analyzed in the present study are described in p = 0.032). Furthermore, women older than 35 years of age were more obese . Considering the previous BMI and age as continuous variables, both were associated with the occurrence of GD; but, when combined in the model, age prevailed as an associated factor. However, the adequacy measure of the model (R2 ≤ 0.21) indicated little explanation for the relationship between the surveyed variables and GD, suggesting that there were other factors influencing the outcome that were not evaluated in the present study.In the conditional logistic regression, age emerged as an independent variable associated with the occurrence of GD in all models . We veriThe mean GA at diagnosis was of 25.2 ± 8.51 weeks, with a minimum GA of 7 weeks, and a maximum GA of 38 weeks. The most prevalent method of diagnosis of GD was the OGTT, which established the diagnosis in 76.6% (36) of the cases, while 23.4% (11) were diagnosed by fasting glucose (FG).The mean birth weight was significantly higher in the infants of mothers diagnosed with GD. Regarding the delivery route, no statistically significant difference was found between mothers with and without GD, and those who had GD also had no greater chance of having gestational hypertension than the controls.There were no significant differences in the mean GA at delivery, and 22 out of the 140 (15.7%) parturients had preterm births. The mean Apgar score at 1 minute ranged from 1 to 10, while at 5 minutes it ranged from 7 to 10. One infant had RDS, and no deaths were observed in the overall cohort. The outcomes related to the development of GDs are described in Out of the 47 women diagnosed with GD, 87.2% (41) underrwent some type of treatment, including diet alone (27/57.4%), insulin alone (04/8.5%), diet plus oral medication (08/17%), and diet plus insulin (02/4.3%). A total of 12.8% (06) of the cases underwent no treatment despite medical advice to do so. None of the participants used only oral medication. There were no significant differences regarding submission or not to treatment and the occurrence of maternal and fetal outcomes. A comparison between submission or not to treatment in relation to the outcomes is shown in Observing the type of treatment to which the women with GD were submitted and the related factors, we found no significant differences regarding the use of insulin in relation to maternal age, previous BMI, and overweightness. Regarding pregnancy outcomes, the use of insulin was not associated with the occurrence of preterm delivery, and although cesarean sections were performed more frequently in women taking insulin, this finding was not significant. The evaluation of the other types of treatment in regard to the related factors was limited by the small sample of women with GD who underwent treatment. The relationship between the use of insulin for treatment in women with GD, the related factors, and pregnancy outcomes is shown in p = 0.0003). Regardless of the presence of GD, macrosomia was a more common event in males (19/72 [26.4%]) than in females (8/68[11.8%]); p = 0.028. In the same way, hypoglycemia was an outcome more frequent among infants of mothers with GD (5/47 [10.6%]) than among those of mothers without the disease (2/93 = 2.2%); p = 0.042.Out of the NBs with macrosomia (27), 17 (63%) were born to mothers with GD, and 10 (10.8%) were infants of women without GD (n = 430 women) of the complications found. In a case-control studyn = 206 cases and n = 286 controls) in China, older maternal age was also associated with risk of developing GD. Findings in Southern Brazil of two cohortsn = 375) and 30 ± 6 years (n = 216). This relationship between age and GD was evident in the present study, in which the mean age of the cases was higher than that of the controls.The literature shows that age > 35 years is a risk factor for the development of GD. Alves et al2 . Miao et al,n = 832), showed that, in general, 21.4% (n = 178) of the women were obese or overweight, and 35.2% (n = 298) presented excessive weight gain during pregnancy. In the present study, there was no significant difference in the mean weight gain between cases and controls. This is probably due to the fact that, in the present study, weight values were referred by the parturients, and were not measured.The cases had a greater chance of being overweight and obese than the controls. Feleken = 996) and without GD (n = 996) showed that a positive family history of DM was significantly more frequent in the cases than in the controls (15.6% versus 2.4% respectively). In relation to the presence of previous history of GD, differently from what was found in the present study, Bhat et aln = 300) were 5.3 times more likely to have a previous history of GD than the controls (n = 300). This difference is, perhaps, due to the fact that in their Study, the number of cases was at least one-sixth times lower.Corroborating the data obtained in the present research, a retrospective study in ChinaIn patients with GD, the mean height of the cases was above the one considered as a predisposing factor for GD (150 cm). However, in a cohort studyn = 4,053) of the sample had a GD diagnosis, with a mean GA at diagnosis of 21 weeks, varying from 15 to 27 weeks. Furthermore, in a prospective studyn = 35; controls: n = 465), 11.4% (n = 4) of the sample had their diagnosis in the initial 16 weeks, and 88.6% (n = 31), in 24–28 weeks.The mean GA at diagnosis is within the period of highest physiological insulin resistance in pregnancy, and the one recommended for GD screening (24–28 weeks). The most prevalent method of diagnosis of GD was the OGTT, probably due to the fact that most diagnoses were established in the period of higher hyperglycemia risk, in which the investigation is performed through the OGTT and not the FG. In a studyn = 201; controls: n = 201), found hypoglycemia at birth to be the most frequent complication of GD, and identified hypoglycemia and RDS as important causes of neonatal morbidity; in relation to the birth weight of the NBs, there were no statistical differences. In the present study, the mean birth weight and rate of neonatal hypoglycemia were significantly higher in infants of mothers with GD, and, when considering RDS, the same did not occur in the study by Miranda et al.Miranda et al,n = 703) in a hospital in the city of Joinville, Southern Brazil: 52.2% (n = 367) had cesarean sections as the delivery route.The proportion of cesarean sections in the present study was similar to that found in a retrospective cohort studyn = 86) conducted in the city of Canoas, Southern Brazil, suggested a greater frequency of prematurity in women with GD; 3 deaths were recorded in the study. Likewise, in a retrospective cohortn = 255; controls: n = 267), women with GD showed twice the risk of delivering preterm infants. In addition, the study showed that the association between GD and a low Apgar score at 1 and 5 minutes was not significant. In the present study, there were no significant differences regarding the Apgar score and the mean GA at birth in infants of mothers with and without GD. The absence of neonatal deaths in our study is probably related to the current greater care dedicated to pregnant women with GD and also to therapy individualization, which reduces unwanted GD outcomes.Zanrosso et al,n = 159 pregnant women) conducted in the city of Maceió, Northeastern Brazil, in which the outcomes of interest were GD and gestational hypertensive syndrome (GHS), observed no association between GD and gestational hypertension, since none of the diseases occurred concomitantly.As in the present study, a transversal studyn = 799; controls: n = 2,843) in 3 maternity hospitals in Chennai, India, showed similar results: most women underwent a diet as the only treatment 42.2% (n = 338) or diet associated with insulin 57.1% (n = 457), while only 0.5% (n = 4) used oral medication.According to the Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) study,n = 705 women with GD) performed in the city of Joinville, Southern Brazil, in which all women underwent some kind of treatment, 10.22% (n = 72) of the treated pregnant women developed hypertensive pregnancy disease, and 4.8% (n = 34) of the infants were premature.Regarding the relationship between outcomes and treatment in GD patients, lack of treatment is known to increase the risk of unwanted outcomes. In the present study, undergoing treatment did not interfere in the occurrence of macrosomia, hypertension, or prematurity. This differs from the observation of a systematic review,n = 50), and the type of treatment performed did not interfere with the delivery route, reflecting the same finding observed in the present study, except that the study in question found a higher rate of cesarean sections among parturients taking insulin. Watanabe et aln = 4) of them were overweight, and 10% (n = 1) had a BMI 30 kg/m2 before pregnancy, which was not significant.Also in the aforementioned study,n = 612 GD cases) showed that the mean age of the women who used insulin was 31.4 years versus 30.9 years for those who did not use it. Furthermore, women who took insulin had a higher BMI than those who did not require it (28.3 ± 7.00 kg/m2). In the present study, overweight parturients had more chance of using insulin than those who were not overweight, a finding with no statistical significance.Regarding the maternal age and insulin use, no significant differences were found either. Differently, a retrospective studyn = 149; controls: n = 711), the prevalence of NBs with macrosomia ranged from 5 to 20%. In addition, the study showed that male NBs had 3.33 times more chance of being macrosomic when compared with female NBs. This finding was also observed in the present study. The predominance of macrosomia in males may be associated with the fact that, during the third trimester, male fetuses tend to gain more weight than female ones.According to Ribeiro et alA limitation of the present study was the reduced number of cases, which hinders the generalization of the findings to other populations. Furthermore, specific information, like treatment adherence and body weight, could not be measured, and the self–reported data are subject to errors.The present study found maternal age, family history of DM2, obesity and pregestational overweightness as important factors related to a higher chance of developing GD. Regarding the neonatal outcomes, we found that the children of women diagnosed with GD had higher prevalence of fetal macrosomia and hypoglycemia at birth than the children of women who did not have GD. There was no significant difference between cases and controls regarding gestational hypertension, prematurity, cesarean section, neonatal RDS and neonatal death.


However, the effect of an elevated BMI (≥ 25 kg m−2) on humoral immunity to SARS‐CoV‐2 infection and COVID‐19 vaccination remains unclear.Class III obesity . We also collected blood samples from people approximately 5 months of post‐second dose COVID‐19 vaccination (the majority of whom did not have a prior SARS‐CoV‐2 infection). We measured their humoral responses to SARS‐CoV‐2, grouping individuals based on a BMI greater or less than 25 kg m−2), when accounting for age and sex differences, is associated with reduced antibody responses after SARS‐CoV‐2 infection. At 3 months of post‐infection, an elevated BMI was associated with reduced antibody titres. At 13 months of post‐infection, an elevated BMI was associated with reduced antibody avidity and a reduced percentage of spike‐positive B cells. In contrast, no significant association was noted between a BMI ≥ 25 kg m−2 and humoral immunity to SARS‐CoV‐2 at 5 months of post‐secondary vaccination.Here, we show that an increased BMI . In contrast, there was no association between increased BMI and the humoral immune response to SARS‐CoV‐2 vaccination. SARS‐CoV‐2 has resulted in > 765 million infections globally (May 2023).−2, the antibody response to SARS‐CoV‐2 infection partially declines within 6 months post infection,−2). In our study, we established a cohort of people from Brisbane, Australia who had recovered from a single SARS‐CoV‐2 infection in March 2020 and were not exposed to SARS‐CoV‐2 or vaccinated in the subsequent 13 months. We also recruited individuals from Melbourne, Australia who had been vaccinated against SARS‐CoV‐2. Using these cohorts, we sought to determine whether an elevated BMI (BMI ≥ 25 kg m−2) affected the humoral response to SARS‐CoV‐2 infection and vaccination.There is a very little evidence to identify the host factors that are associated with the susceptibility to SARS‐CoV‐2 reinfection and the impairment of an effective humoral response. Previous studies have suggested that host co‐morbidities,−2, in order to assess the effects of both overweight and obesity on the humoral response, as per previous studies.Participants who tested positive for SARS‐CoV‐2 in early 2020 were recruited for the study. To note, the SARS‐CoV‐2 infection cohort employed in the present study was relatively unique in so far as the early nature of their infection (March 2020) and the fact that vaccination did not begin in Australia until February 2021. In addition, the cohort represents humoral immune responses after a single infection, as Queensland, Australia did not have SARS‐CoV‐2 circulating in the community until state borders opened in December 2021. Blood samples from these recruited unvaccinated patients were taken from these participants at two different time points: approximately 3 months post‐infection (Visit 1) and 13 months post‐infection (Visit 2). Participants were subsequently grouped according to BMI from Visit 1 with the threshold set at 25.0 kg mTo assess the effects of BMI on the longevity of humoral responses after infection, the total spike‐specific binding antibodies , neutralisation capacity , cross‐strain neutralisation, avidity and ADCC were assessed between both visits using a paired analysis of matched samples Figure . A signiTogether with paired participant sampling at both time points, additional individual samples were collected at single time points Tables  & 2. To −2) and humoral responses to vaccination. Forty‐seven vaccinated participants without any clinical history of positive SARS‐CoV‐2 tests, and with a limited percentage with N antibodies, were recruited for the study  deleteriously affects the humoral response to SARS‐CoV‐2 infection.Here, we provide evidence that an elevated BMI  at the time of writing, only 20.7% of people in low‐income countries have received at least one dose of a COVID‐19 vaccine.−2 did not affect the humoral immune response to COVID‐19 vaccination at approximately 5 months post‐second dose of vaccination. At present, there are conflicting reports as to whether infection−2) and a healthy BMI 6 months after the second SARS‐CoV‐2 vaccine dose. However, in contrast to the findings of the present study, van der Klaauw and colleagues found that the function of these antibodies, as measured SARS‐CoV‐2 neutralisation, was reduced in individuals with severe obesity. This discrepancy may reflect the different BMI threshold of the present study (≥ 25 kg m−2) and that of van der Klaauw and colleagues (> 40 kg m−2).−2). This range applies to a larger number of individuals in society and therefore, we believe that any associated immune defects have far reaching implications. With the caveat that different findings may have been observed in the present study if different timepoints post‐infection/vaccination were sampled or different BMI cut‐offs were used, our data provide an insight into the comparisons between infection‐induced and vaccine‐induced SARS‐CoV‐2 humoral responses.Interestingly, BMI ≥ 25.0 kg mIn addition to BMI, age is known to affect the humoral response to SARS‐CoV‐2 vaccination.The present study has several limitations. Firstly, the number of donor samples employed herein were limited compared to other studies. This reflects the fact that there was only a limited number of individuals in Queensland, Australia who were infected with SARS‐CoV‐2 in March 2020. This population cohort was important to recruit and could not be supplemented with individuals infected later in the pandemic, as it would then not be possible to assess immunity at 13 months post‐infection in the absence of intervening reinfections or vaccinations. Indeed, several participants were lost from the Visit 2 cohort of the present study as they were amongst the earlier adopters of COVID‐19 vaccination in Australia. Accordingly, it is not possible to state whether the fewer statistically significant relationships observed at Visit 2 is indicative of a biological phenomenon or a function of the reduced sample size. Secondly, it is possible that the improved immunity associated with hybrid immunity,Recruitment of human donors was approved by the human ethics boards of Mater Research (HREC/MML/62705), the QIMR Berghofer Medical Research Institute (Ethics: P3618), the David Serisiser Research Biobank (DSRB) (HREC/14/QPAH/275) at Mater Misericordiae Ltd and La Trobe University (Ethics: HEC21097). All methods were performed in accordance with institutional guidelines and regulations. Written consent was obtained from all study participants. To study the impact of BMI on the humoral response to SARS‐CoV‐2 infection, individuals with a history of PCR‐confirmed SARS‐CoV‐2 infection were recruited to the study from Brisbane, Australia. To study the impact of BMI on the humoral response to vaccination, individuals with a history of ChAdOx1‐S or Pfizer‐BioNTech vaccination were recruited to the study 5 months after the second dose of vaccine was administered from Melbourne, Australia. At point of recruitment, patient details including BMI were collected.g for 10 min. Serum and plasma were heat treated at 56°C for 1 h, aliquoted accordingly and stored in −20°C prior to analysis, or in −80°C for long term storage.Blood samples were collected from patients recovered from PCR‐diagnosed SARS‐CoV‐2 infection at different timepoints by qualified phlebotomists. Blood was either collected in EDTA or heparin tubes  or BD SST™ Vacutainers® (BD Biosciences). All blood samples were processed within 24 h of blood collection. To isolate serum and plasma, tubes containing blood samples were spun down at 2000 A volume of 10 mL of blood was collected in BD Vacutainer® EDTA tubes (BD Biosciences). Peripheral blood mononuclear cells (PBMC) were isolated with Lymphoprep  according to manufacturer's instructions. Isolated PBMCs were subsequently frozen down in foetal calf serum  containing 10% DMSO  at −80°C until analysis. For long‐term storage, PBMCs were stored in liquid nitrogen.2 incubator in DMEM (Dulbecco's Modified Eagle Medium) media containing 10% foetal bovine serum  and 1% Penicillin–Streptomycin‐Glutamine . Purified DNA was incubated for 20 min with Polyethylenimine (PEI)  at a ratio of 1:3. Following incubation, the DNA:PEI mix was added to the cell culture flasks in DMEM containing 1% FBS, 5% cocktail of supplements referred to as Mastermix , and placed back in the CO2 incubator for 7 days at 37°C. The supernatant was harvested and purified over affinity and size exclusion column . The protein was run on an SDS‐page gel to confirm purity and then biotinylated and stored at −20°C.The design of a prefusion‐stabilised SARS‐CoV‐2 spike, called HexaPro, was performed essentially as described previously.−1 SARS‐CoV‐2 spike proteins were coated on ELISA plates (Thermo Fisher Scientific) overnight at 4°C. Plates were blocked and serially diluted sera or plasma samples were added to the plates and incubated at 37°C for 1 h. Spike‐specific antibodies were detected with mouse anti‐human IgG HRP  for 1 h at 37°C. Plates were visualised with 2,2′‐Azino‐bis  in the presence of hydrogen peroxide  at 405 nm on SpectraMAX 190. EC50 values were background subtracted and calculated on Graphpad Prism 9.0.2  using ‘log(agonist) versus response; variable slope (4 parameters)’ function.Anti‐spike IgG antibodies in patient samples were assessed with SARS‐CoV‐2 spike ELISAs essentially as described previously.m sodium thiocyanate (Sigma Aldrich) for 15 min at room temperature before incubating with secondary antibodies. An avidity index (%) was derived from EC50 values from treated samples over EC50 values from untreated samples.Avidity assays were conducted similarly to spike ELISAs described above, except samples were treated with 1 m sulphuric acid and the optical density at 450 nm (OD450) was read using the SpectraMAX M5. The final OD450 values were obtained after the background subtraction of mean + 3 × standard deviation OD450 of four healthy individuals who were neither SARS‐CoV‐2 infected nor vaccinated.Anti‐Nucleocapsid IgG antibody in sera was quantified using modified ELISA as previously described.2 in Dulbecco modified Eagle medium  supplemented with 10% foetal calf serum  and 1% Pen Strep Glutamine . The cells were maintained in DMEM with 2% FCS and 1% PSG during viral infection to generate stocks.Vero E6 cells and Vero E6‐hTMPRSS2 cells were maintained at 37°C and 5% COSARS‐CoV‐2 isolates hCoV‐19/Australia/QLD02/2020  and hCoV‐19/Australia/QLD1893C/2021  were initially isolated from patient nasopharyngeal aspirates and inoculated on Vero E6 cells. Subsequently, passage 2 of these samples were kindly provided by Queensland Health Forensic and Scientific Services, Queensland Department of Health and stocks were generated in Vero E6‐hTMPRSS2 cells as described previously.50 readings were calculated on Graphpad Prism 9.0.2 (Dotmatics) using the ‘log(inhibitor) versus normalised response’ function.Microneutralisation assays were performed essentially as previously described.10 dilution curve was performed to calculate the area under the curve using GraphPad Prism v9.Antibody‐dependent cellular cytotoxicity function of recovered patient samples were analysed with ADCC reporter assay  accordingly to manufacturer's instructions.The Decoy tetramer was prepared as previously described.Six higher BMI and six lower BMI samples were randomly chosen for B cell analysis. Staining for flow cytometry analysis was performed using cryo‐preserved PBMCs. PBMCs were stained with Fixable Viability Stain 780  diluted in PBS 2% FCS for 15 min at room temperature. Cells were washed with PBS 2% FCS and then stained with CD20‐AF700 , CD27‐V450 , CD38‐FITC , IgD‐BV510 , CD10‐PE‐Cy7 , CD21‐FITC , FcRL5‐BV510 , CD19‐BV650 , CD14‐PerCP‐Cy5.5  CD16‐PerCP‐Cy5.5 , HexaPro spike protein Tetramer‐PE (1:50) and HLA‐A2‐M1 Decoy Tetramer‐PE*DL650  diluted in PBS 2% FCS for 20 min on ice. Cells were washed with PBS 2% FCS and fixed for 1 h using Cytofix (BD Biosciences), then washed with PBS 2% FCS and resuspended in PBS 2% FCS. Cells were analysed using an LSRFortessa X‐20 (BD Biosciences) and flow cytometry data was analysed using FlowJo™ v10.8 software (BD Biosciences). A representative gating strategy is shown in Supplementary figure U‐test whilst the difference between categorical values was determined on Graphpad Prism 9.0.2 (Dotmatics) using a Chi‐squared test and paired analysis was performed with two‐way ANOVAs. Heteroscedasticity was diagnosed by inspection of the residual for a non‐weighted Multiple Linear Regression (MLR) model with Age, BMI and Gender as predictor variables for all categories of response variable . For non‐weighted MLR we first fitted a non‐weighted MLR to the squared‐residuals themselves to estimate the change in variance that is occurring in the response variable. That is,wi=1/ri=1/σi2 for our weighted Multiple Linear Regression (MLR) model. The outputs from our variance‐weighted multiple linear regression model were the coefficients b1 (Intercept), b2 (Age), b3 (BMI) and b4 (Gender). We performed a t‐test on each coefficient in the model with null hypothesis that the coefficient is not significantly different from zero. A P‐value of less than 0.05 (95% confidence limit) was set to determine which variables in the variance‐weighted MLR model have a coefficient that is not significantly different from zero. All analysis was performed in MATLAB.Differences between continuous variables was analysed on Graphpad v9.0.2 (Dotmatics) using a Mann–Whitney Marcus ZW Tong: Data curation; investigation; methodology; writing – original draft; writing – review and editing. Julian D. J. Sng: Data curation; methodology. Meagan Carney: Formal analysis; software; visualization. Lucy Cooper: Formal analysis; methodology. Samuel Brown: Data curation; methodology. Katie E Lineburg: Data curation; investigation; methodology. Keng Yih Chew: Data curation; funding acquisition; investigation; methodology; project administration. Neve Collins: Data curation; methodology. Kirsten Ignacio: Data curation; methodology. Megan Airey: Data curation; methodology. Lucy Burr: Data curation; investigation; project administration; resources; writing – review and editing. Briony A Joyce: Methodology. Dhilshan Jayasinghe: Data curation; methodology. Christopher LD McMillan: Methodology; resources. David A Muller: Methodology; resources. Anurag Adhikari: Investigation; methodology; resources. Linda A Gallo: Investigation; supervision. Emily S Dorey: Data curation; investigation; project administration; resources. Helen L Barrett: Project administration; investigation; conceptualization; supervision; writing – review and editting. Stephanie Gras: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; supervision; validation; writing – review and editing. Corey Smith: Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; resources; supervision; writing – review and editing. Kim Good‐Jacobson: Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; writing – review and editing. Kirsty R Short: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; supervision; writing – original draft; writing – review and editing.KRS is a consultant for Sanofi, Roche and NovoNordisk. The opinions and data presented in this manuscript are of the authors and are independent of these relationships. Other authors declare no competing interests.Supporting informationClick here for additional data file.


KwaZulu-Natal ranked second highest among South African provinces for the number of laboratory-confirmed cases during the second wave of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. The seroprevalence of SARS-CoV-2 among certain vulnerable groups, such as people living with HIV in KwaZulu-Natal, is unknown.The study aimed to determine the prevalence of SARS-CoV-2 immunoglobulin G (IgG) in HIV-positive versus HIV-negative patients.This was a retrospective analysis of residual clinical blood specimens unrelated to coronavirus disease 2019 (COVID-19) submitted for diagnostic testing at Inkosi Albert Luthuli Central Hospital, Durban, from 10 November 2020 to 09 February 2021. Specimens were tested for SARS-CoV-2 immunoglobulin G on the Abbott Architect analyser.p < 0.0001) and increased with increasing age, with a statistically significant difference between the farthest age groups . The seroprevalence increased from 17% on 10 November 2020 to 43% on 09 February 2021 during the second wave.A total of 1977/8829 (22.4%) specimens were positive for SARS-CoV-2 antibodies. Seroprevalence varied between health districts from 16.4% to 37.3%, and was 19% in HIV-positive and 35.3% in HIV-negative specimens. Seroprevalence was higher among female patients (23.6% vs 19.8%; Our results highlight that during the second COVID-19 wave in KwaZulu-Natal a large proportion of people living with HIV were still immunologically susceptible. The reduced seropositivity in people with virological failure further emphasises the importance of targeted vaccination and vaccine response monitoring in these individuals.This study contributes to data on SARS-CoV-2 seroprevalence before and during the second wave in KwaZulu-Natal, South Africa, which has the highest HIV prevalence globally. Reduced seropositivity was found among people living with HIV with virological failure, highlighting the importance of targeted booster vaccination and vaccine response monitoring. KwaZulu-Natal province ranked second-highest nationally for the number of laboratory-confirmed cases during the second wave of the SARS-CoV-2 epidemic in South Africa.1Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the most significant pandemics in history. In Africa, South Africa has the highest number of confirmed cases  is most accurate for the diagnosis of acute SARS-CoV-2 infection.7Even though South Africa has passed the third, fourth and fifth waves of the SARS-CoV-2 epidemic and is currently experiencing a transition phase, serological surveillance remains a vital tool to understand the true extent of SARS-CoV-2 exposure and inform public health responses.10 A population-based seroepidemiological survey in Gauteng province in South Africa, that started 8 weeks into the first wave and ended at the peak of the second wave, estimated that there were 2.89 million SARS-CoV-2 infections, which is 7–8 times higher than the reported 332 000 PCR cases.10 Local SARS-CoV-2 seroprevalence rates were over 60% in black South African blood donors11 and 30% – 40% among patients accessing care during the first wave in the Cape Town Metropolitan sub-districts.12 Similarly, seroprevalence studies in Spain, Geneva and New York reported seroprevalence higher than the number of reported cases.15Seroprevalence in 2020 was estimated to be 5–10 times higher than reported cases identified by PCR.16 However, serosurveys require extensive resources, and involve active recruitment and prospective follow-up of individuals. Using residual clinical specimens submitted for either routine screening or diagnostic testing could provide a snapshot17 and is a convenient way to conduct passive surveillance. In South Africa, seroprevalence studies using convenience specimens have been conducted by the South African Blood Bank Services (SANBS) in the Western Cape and Gauteng.18Ideally, population-based serosurveys should be used to estimate seroprevalence nationally.19 and has the second highest number of reported SARS-CoV-2 infections nationally. Second-wave infections in South Africa caused by the beta variant were associated with increased disease severity, especially in people living with HIV20 when no vaccines were available. Thus, an accurate estimate of SARS-COV-2 seroprevalence among people living with HIV was important in the early phase of the SARS-CoV-2 epidemic for outbreak surveillance, implementation of vaccination strategies, and to assess the effectiveness of non-pharmaceutical interventions in KwaZulu-Natal. We therefore determined the SARS-CoV-2 immunoglobulin G (IgG) seroprevalence among people with HIV infection in KwaZulu-Natal.KwaZulu-Natal has the world’s highest HIV prevalenceThis study was approved by University of KwaZulu-Natal Biomedical Research Ethics Committee (Ref. BCA256/010). Informed patient consent was not required as this was a retrospective study using residual diagnostic specimens. No additional specimens were collected. All residual clinical specimens were de-identified and labelled with a unique study number; patient privacy and confidentiality data were protected in accordance with the Declaration of Helsinki. Data were collected in a password-protected Excel spreadsheet  on a dedicated computer behind firewall-protected servers, which was only accessible to the primary investigator.The authors retrospectively tested residual clinical sera and plasma specimens submitted to the Department of Virology at Inkosi Albert Luthuli Central Hospital, Durban, for diagnostic testing unrelated to COVID-19 from 10 November 2020 to 09 February 2021, representing the period before and during the second wave (22 November 2020 to 27 March 2021) of the SARS-CoV-2 epidemic in KwaZulu-Natal.http://www.openepi.com/)21 based on a SARS-CoV-2 prevalence of 5% to 10% before the second wave peak with a maximum 1% margin error and using published seroprevalence data from studies done globally , the assumed point prevalence was calculated.A minimum sample size of 7294 was estimated using OpenEpi  sampsi function, the sample size was calculated to compare SARS-CoV-2 seropositivity in HIV-positive and HIV-negative individuals with a power of 80%, two-sided alpha of 0.05 and equal size in each group. A sample size of 3682 would be sufficient to detect a 1% difference if the prevalence is low (1%) and a 3% difference if the prevalence is high (13%).22Using the STATASera and plasma specimens with sufficient volumes from the viral serology  were selected for SARS-CoV-2 IgG testing. Specimens with inadequate volumes (< 100 µL) were excluded. The selected specimen results were linked to demographic information extracted from the laboratory information system. Specimens from the same patient were tested once only.23 Sera and plasma specimens were stored at 2 °C – 8 °C for not more than 7 days and −20 °C for long-term (> 7 days) storage. The assay is an automated two-step chemiluminescent microparticle immunoassay for qualitative detection of SARS-CoV-2 IgG antibodies against the SARS-CoV-2 nucleocapsid protein. The assay was performed and interpreted according to the manufacturer’s instructions.24Testing for SARS-CoV-2 IgG was performed on the Abbott Architect using the Abbott Architect SARS-CoV-2 IgG assay .® version 15 . We performed a logistic regression analysis to determine whether age, gender, HIV seropositivity, and HIV viral load were associated with SARS-CoV-2 seropositivity, with significance level set to 0.05.Statistical analysis was done using STATAn = 248) or mismatched laboratory information (n = 6). A total of 8829  (p < 0.0001)  while male patients had lower odds of seropositivity ( 0.0001) . Seropos 0.0001) . Seropos 0.0001)  was high25We assessed the SARS-CoV-2 IgG seropositivity in convenience specimens submitted to the Department of Virology at Inkosi Albert Luthuli Central Hospital, Durban, before and during the peak of the second COVID-19 wave caused by the beta variant. We observed an increase in seropositivity from 17% before to 43% during the second wave in KwaZulu-Natal, reflective of the increase in the number of infected individuals during the second wave, with a mean SARS-CoV-2 seropositivity of 22.4% across the study period. There were statistically significant associations between SARS-CoV-2 seropositivity, age, gender, and HIV status. Our results indicate that the true extent of the SARS-CoV-2 epidemic in KwaZulu-Natal may have been underestimated due to reporting of PCR results alone. This is similar to other local seroprevalence studies that report higher seroprevalence compared to the number of reported PCR cases.18 The lower prevalence may be because the specimens were not collected immediately after the first wave in KwaZulu-Natal; they were collected 4 months later, thus affecting the detection of IgG, which wanes over time. The sentinel surveillance studies were started directly after the first wave in both Cape Town and Gauteng.The seropositivity in our study was lower than the seroprevalence reported by surveillance studies in Cape Town (40%) and Gauteng (27.8%).26 However, in individuals on antiretroviral therapy, immune responses to SARS-CoV-2 are reported to be similar to that in HIV-negative individuals.28 People on antiretroviral therapy may display immune reconstitution;26 however, chronic immune activation and impaired B-cell responses in individuals on antiretroviral therapy with virologic failure may result in reduced antibody responses28 and lower IgG concentrations,29 accounting for the lower seropositivity in those with HIV infection.Furthermore, the majority (63%) of the specimens in our study were from HIV-positive individuals; 10% of these specimens had an HIV viral load greater than 1000 copies/mL and reduced SARS-CoV-2 seropositivity. HIV viral load greater than 1000 copies/mL had a statistically significant association with lower odds of SARS-CoV-2 IgG positivity. HIV infection is associated with a reduced immune response to infectious pathogens.30 which may be lower than the detectable limit of the assay. Based on a local evaluation of the performance of the Abbott Architect SARS-CoV-2 IgG assay, sensitivity is highest 30–41 days after a positive PCR, following moderate to severe infection.23 However in our study reduced seropositivity was most likely due to reduced immunity in people with uncontrolled HIV and the timing of sampling in relation to the epidemic waves.23Lower SARS-CoV-2 IgG concentrations may affect the detectability of IgG,25 and internationally.15We have demonstrated that SARS-CoV-2 seropositivity increased with age, with children under 10 years having the lowest seropositivity (11.1%). Similar patterns have been described locally31 suggesting that PCR testing alone may have underestimated infection rates or that these children were asymptomatic. However, a seroprevalence in children under 9 years during the first wave in Gauteng was reported to be about 20%,18 most likely due to Gauteng having the highest infection rate nationally during the entire epidemic.1Our reported seropositivity (10%) in children under 10 years during the second wave in KwaZulu-Natal indicated that these children may have been more vulnerable to SARS-CoV-2 infection. However, the rate of infections in South Africa among these children was 1.4%,1We found no discernible pattern in the district seroprevalence during the second wave. However, the rate of infections in the Ethekwini, iLembe, Ugu, and Uthukela districts were subsequently lower in the third wave.Firstly, the specimens tested were from individuals accessing public healthcare and thus may not represent the general population. However, it potentially represents people living with HIV accessing antiretroviral therapy in our province.32 Thirdly, seropositivity may be underestimated since SARS-CoV-2 IgG detection and durability is directly proportional to disease severity.34 Although antibody positivity was still high in another study at 8 months after mild and asymptomatic infection,35 overall detectability and durability may be reduced in HIV-positive persons.36Secondly, surveillance using residual clinical specimens without exposure time data, as done in this study, may underestimate seropositivity.37Fourthly, the Abbott Architect SARS-CoV-2 IgG assay detects IgG to nucleocapsid antigens only and at the time was the only approved commercial serological assay by the South African Health Products Regulatory Authority.32 Nucleocapsid IgG declines more rapidly than spike IgG. However, nucleocapsid IgG is more sensitive within the first 14 days of SARS-CoV-2 infection as compared to spike IgG.34 Lastly, antibody cross-reactivity with other Betacoronaviruses may lead to false positives.38 However, the Abbott Architect SARS-CoV-2 IgG has a specificity of 99%.38The use of nucleocapsid IgG solely to determine seroprevalence may underestimate the true rate of past infections.40Our results highlight that after the second wave in KwaZulu-Natal, many people accessing public healthcare were immunologically susceptible. Furthermore, reduced seropositivity in HIV-positive individuals with HIV viral loads greater than 1000 copies/mL further emphasised the importance of targeted vaccination of people living with HIV and monitoring response to vaccination in these individuals.


Although similar to trunk and limb skeletal muscles, masticatory muscles are believed as unique in both developmental origins and myogenesis. Gαi2 has been demonstrated to promote muscle hypertrophy and muscle satellite cell differentiation in limb muscles. However, the effect of Gαi2 on masticatory muscles is still unexplored. This study aimed to identify the role of Gαi2 in the proliferation and differentiation of masticatory muscle satellite cells, further exploring the metabolic mechanism of masticatory muscles. The proliferation rate, myotube size, fusion index of masticatory muscle satellite cells and Pax7, Myf5, MyoD, Tcf21 and Musculin expressions were significantly decreased by Gαi2 knockdown, while in cells infected with AdV4‐Gαi2, the proliferation rate, myotube size, fusion index and Tbx1 expression were significantly increased. Masticatory muscle satellite cells also displayed phenotype transformation as Gαi2 changed. In addition, Gαi2 altered myosin heavy chain (MyHC) isoforms of myotubes with less MyHC‐2A expression in siGαi2 group and more MyHC‐slow expression in AdV4‐Gαi2 group. In conclusion, Gαi2 could positively affect the adult myogenesis of masticatory muscle satellite cells and maintain the superiority of MyHC‐slow. Masticatory muscle satellite cells may have their unique Gαi2‐regulated myogenic transcriptional networks, although they may share some common characteristics with trunk and limb muscles. The mice were all healthy and had no trauma, being provided with free access to normal chow and water. Animal studies were approved by the Ethics Committee of Wuhan University School and Hospital of stomatology. All procedures were proceeded according to the institutional guidelines on the use of animals in research.2 incubator at 37°C throughout all of the experimental procedures.Primary masticatory muscle satellite cells were isolated from masseters of C57BL10 mice as previously reported.2.2siGαi2 and siRNA negative control (siCtrl) construct were purchased from Genepharma. Masticatory muscle satellite cells cultured in GM2 were transfected with siGαi2 and siCtrl construct using Lipofectamine® 2000 Reagent (Invitrogin).Adenoviral constructs AdV4‐Gαi2 and control empty vector AdV4‐NC were generated, amplified and purified by Genepharma. Masticatory muscle satellite cells cultured in GM2 were infected for 24 h with AdV4‐Gαi2 or AdV4‐NC (multiplicity of infection = 200). After infection, the cells were cultured in GM2 or DM for another 48 h.2.3The cell proliferation after Gαi2 knockdown or overexpression was evaluated by Cell‐Light™ EdU Apollo®488 In Vitro Imaging Kit (RiboBio) according to the manufacturer's instructions. Briefly, cells were labelled with 5‐ethynyl‐2′‐deoxyuridine  at a final concentration of 50 μM for 2 h. Then, the absorbance was determined at a wavelength of 488 nm.2.42, PEG 35000  in distilled water with a pH of 7.4. Cells were then fixed with 4% paraformaldehyde (PFA) in CSB for 15 min and permeabilized with 0.2% Triton in CSB at room temperature. After the nonspecific binding was blocked with 10% goat serum (Gibco), myotubes were incubated with monoclonal antibody MF20 which recognized all MyHC isoforms  at 4°C overnight. Subsequently, cells were stained with Alexa488 and 4,6‐diami‐dino‐2‐phenylindole (DAPI) (Aspen), and mounted in Vectashield mounting medium (biosharp).To determine changes in myotube size and fusion index, myotubes were washed with cytoskeleton stabilising buffer (CSB) containing 80 mM PIPES, 5 mM EGTA, 1 mM MgClFor the measurement of myotube diameter, three pictures were taken in each well. Thirty largest myotubes from each picture were selected and measured by Image‐Pro Plus 6.0. After dividing myotubes into thirds, the myotube diameter was calculated as the mean of distances between the midpoints of each portion. To analyse fusion index, the nuclei of 60–90 myotubes in each well were counted to obtain the average number of nuclei per myotube.2.5For immunofluorescent detection of different phenotypes of muscle satellite cells under different conditions, the following antibodies were used: rabbit polyclonal anti‐MyoD  and mouse monoclonal anti‐Pax7 . After fixation with 4% PFA, cells were incubated with primary antibodies at 4°C overnight, nonspecific binding was blocked with 10% goat serum (Gibco) and the primary antibodies were visualized using appropriate species‐specific 488 and 549 fluoro‐chrome‐conjugated secondary antibodies , stained with DAPI (Aspen), and mounted in Vectashield mounting medium (biosharp).2.6−ΔΔCT method.RNA was isolated with Total RNA kit I (OMEGA) and cDNA was prepared from 200 ng RNA according to the manufacturer's instructions (Thermo Fisher Scientific). The relative level of gene expression was determined by quantitative real‐time polymerase chain reaction (qRT‐PCR) using a 7900 HT Fast Real‐Time PCR System (Applied Biosystems). Primers used for detection are listed in Table 2.7t‐test. p‐value < 0.05 was considered as statistically significant. Statistical analysis was conducted with SPSS 13.0 statistical software (IBM Corp).All the experiments in this study were independently performed three times. Data was expressed as mean ± standard deviation. Differences between two groups were evaluated using paired Student's 3Primary masticatory muscle satellite cells were successfully isolated, cultured and differentiated into long and multinuclear myotubes Figure .3.1Fluorescence‐labelled siRNA was used to suppress the expression of Gαi2 in masticatory muscle satellite cells Figure . Knockdo3.2Adenovirus was employed to overexpress Gαi2 in masticatory muscle satellite cells under growth and differentiation conditions Figure . Under g3.3Masticatory muscle satellite cells were immunostained for Pax7/MyoD after knocking down or overexpressing Gαi2 to explore the role of Gαi2 in the functional status of cells Figure . Muscle In experiments of knocking down of Gαi2, the percentage of different phenotypes of masticatory muscle satellite cells was calculated and the percentage of Pax7+/MyoD+ cells was found to be higher in the siGαi2 group (Pax7+/MyoD+ 62.37 ± 4.00%) compared with the siCtrl group (Pax7+/MyoD+ 51.78 ± 3.31%). By contrast, the percentage of both Pax7+/MyoD− and Pax7−/MyoD+ cells were obviously lower in the siGαi2 group  than the siCtrl group (Pax7+/MyoD− 16.09 ± 2.09% Pax7−/MyoD+ 32.13 ± 1.80%) Figure .In experiments of over‐expression of Gαi2, there were less cells with Pax7+/MyoD− and Pax7−/MyoD+ in AdV4‐Gαi2 group  compared with AdV4‐NC group . Additionally, a greater number of Pax7+/MyoD+ cells were found in the AdV4‐Gαi2 group (Pax7+/MyoD+ 74.30 ± 2.80%) compared with the AdV4‐NC group (Pax7+/MyoD+ 57.52 ± 2.11%) Figure .3.4The most accepted methods to classify muscle fibre types are based on specific myosin profiles, especially the MyHC isoform complement.Under differentiation condition, when Gαi2 was suppressed in masticatory muscle satellite cells, the mRNA expression of MyHC‐2A was significantly decreased but other types of MyHC had no significant changes Figure . In cont4Masticatory muscle mass and function, similar to other skeletal muscles, are maintained by signalling networks that control the proliferation and differentiation of cells as well as the synthesis and degradation of proteins. There is a strong correlation between masticatory muscles and dentofacial deformities. Masticatory muscle hyperfunction could increase sutural growth and affect the growth rotation pattern of the mandible, influencing the transversal and vertical dimensions of craniofacial bones.Gαi2, as a required component of various GPCR actions, plays an important part in regulating many biological processes including development and differentiation. For example, β2‐adrenoceptor agonists have been indicated in regulating skeletal muscle mass.In this study, we explored the possible role of Gαi2 in the proliferation and differentiation of masticatory muscle satellite cells by knocking down and overexpressing Gαi2. Our results showed that Gαi2 can positively affect the proliferation rate, myotube diameter and fusion index of masticatory muscle satellite cells. The mRNA expression level of some transcription factors, cell phenotypes and muscle fibre types also displayed alteration as Gαi2 changed. Since proteins we extracted from primary culture of masticatory muscle satellite cells were insufficient to support a reliable Western blot analysis and significant heterogeneity may arise when culturing primary cells from different mice together, we did not obtain a reliable result of Western blot analysis and the protein level of transcription factors and MyHC isoforms was not displayed in this study.First, we focused on three crucial transcription factors associated with adult myogenesis of skeletal muscle satellite cells, including Pax7, MyoD and Myf5. Pax7 regulates the expansion and differentiation of muscle satellite cells during both neonatal and adult myogenesis.Skeletal muscle satellite cells may at different functional status undergoing divergent cellular fates: Pax7+/MyoD+ cells in an activated state, Pax7+/MyoD− cells retaining self‐renewal and Pax7−/MyoD+ cells with myogenic differentiation capacity.From the perspective of physiology, skeletal muscle fibres could change their structural and functional properties in reaction to different circumstances. The expression of MyHC isoforms is regarded as the most acceptable criterion to classify skeletal muscle fibre types and the transition between different types of MyHC represents the remodelling of muscles.In summary, our study demonstrated that Gαi2 could positively affect the adult myogenesis of masticatory muscle satellite cells and maintain the superiority of MyHC‐slow. Masticatory muscle satellite cells may have their unique Gαi2‐regulated myogenic transcriptional networks, although they may share some common characteristics with trunk and limb muscles.Lin Kong: Conceptualization (supporting); data curation ; formal analysis ; investigation ; writing – original draft . Yi Fang: Conceptualization (supporting); data curation ; formal analysis ; investigation ; writing – original draft . Mingyuan Du: Funding acquisition (supporting); methodology ; resources ; writing – review and editing (supporting). Yunlong Wang: Methodology ; resources ; writing – review and editing (supporting). Hong He: Conceptualization (supporting); supervision ; writing – review and editing (supporting). Zhijian Liu: Conceptualization (lead); funding acquisition (lead); supervision ; writing – review and editing (lead).The authors declare no conflict of interest.Figure S1.Click here for additional data file.