The metabolic hormone adiponectin affects the correlation between nutritional status and pneumococcal vaccine response in vulnerable indigenous children.

BACKGROUND: Almost 200 million children worldwide are either undernourished or overweight. Only a few studies have addressed the effect of variation in nutritional status on vaccine response. We previously demonstrated an association between stunting and an increased post-vaccination 13-valent pneumococcal conjugate vaccine (PCV13) response. In this prospective study, we assessed to what extent metabolic hormones may be a modifier in the association between nutritional status and PCV13 response.
METHODS: Venezuelan children aged 6 weeks to 59 months were vaccinated with a primary series of PCV13. Nutritional status and serum levels of leptin, adiponectin and ghrelin were measured upon vaccination and their combined effect on serum post-vaccination antibody concentrations was assessed by generalized estimating equations multivariable regression analysis.
RESULTS: A total of 210 children were included, of whom 80 were stunted, 81 had a normal weight and 49 were overweight. Overweight children had lower post-vaccination antibody concentrations than normal weight children (regression coefficient -1.15, 95% CI -2.22 --0.072). Additionally, there was a significant adiponectin-nutritional status interaction. In stunted children, higher adiponectin serum concentrations were associated with lower post-PCV13 antibody concentrations (regression coefficient -0.19, 95% CI -0.24 --0.14) while the opposite was seen in overweight children (regression coefficient 0.14, 95% CI 0.049-0.22).
CONCLUSION: Metabolic hormones, in particular adiponectin, may modify the effect of nutritional status on pneumococcal vaccine response. These findings emphasize the importance of further research to better understand the immunometabolic pathways underlying vaccine response and enable a future of optimal personalized vaccination schedules.

Introduction

Vaccines are among the most powerful public health achievements in history, preventing debilitating illness and disability, and saving over 2.5 million lives each year [1]. Worldwide, they have lowered both morbidity and mortality associated with infectious diseases. However, there is substantial variation between individuals in the immune response to vaccination. For example, the antibody responses to 7- and 13-valent conjugate pneumococcal (PCV7 and PCV13) and Haemophilus influenzae type b (Hib) vaccination vary up to 40-fold in children [2]. This variation has significant consequences for both protective efficacy and duration of protection. Inadequate vaccine responses may leave children at risk of life-threatening infections. An estimated 4 to 19 million children born each year remain unprotected against vaccine-preventable childhood infections, including pertussis, invasive pneumococcal disease and measles, despite receiving routine vaccinations [3]. Several factors, including age, sex, genetics and comorbidities, are known to influence the immune response to vaccination [2]. However, national vaccination schedules and doses are generally the same for every child regardless of these variables. To replace this ‘one-size-fits-all’ model by more personalized vaccination programmes, insight into the underlying mechanisms and determinants of vaccine responses is needed.

Over the past years, nutritional status has been proposed as an extra factor responsible for the inter-individual variability in protective immunity induced by vaccines. In 2019, 144 million children under the age of five years were affected by chronic undernutrition (stunting, low height-for-age) while 38.3 million children were overweight or obese [4]. In low- and middle-income countries poor nutrition continues to cause nearly half of deaths in children under five, while simultaneously childhood overweight and obesity rates are rising at a rate 30% faster than in higher-income countries [5]. The co-existence of undernutrition alongside overweight and obesity within individuals, households and populations is a double burden of malnutrition [4, 5].

Studies in adults show that an increase in body mass index (BMI) is inversely correlated with antibody responses to Hepatitis A and B vaccines [6-10]. Similarly, a study of trivalent inactivated influenza vaccine immune responses showed a negative correlation between BMI and antibody titres after 12 months [9]. Few studies have been performed in children who are overweight. For hepatitis B and tetanus vaccines, reduced post-vaccination antibody levels were observed in overweight or obese children [10]. Varying results for vaccination with inactivated influenza vaccines have been reported, with post-vaccination antibody levels in overweight or obese children similar to or slightly higher than in normal weight children of similar age [11, 12]. Undernutrition in relationship to vaccine response has been described mainly in childhood cohorts. Studies assessing post-vaccination antibody levels in children with acute malnutrition (wasting) and/or stunting found mixed results. Severely wasted children are reported to have lower antibody responses to measles and hepatitis B vaccination [13]. For stunting, both negative and positive correlations with antibody levels after PCV vaccination have been described [14, 15].

Both obesity and undernutrition are associated with changes in immune cell number and function that may explain the potential consequences for vaccine response induction. A systematic review on immune alterations in young undernourished children aged <5 years showed that lymphatic tissue, particularly the thymus, undergoes atrophy and cytokine patterns are skewed towards a T-helper 2 (Th2) cell response [13]. Overnutrition resulting in being overweight is thought to cause a chronic state of inflammation with systemic implications for immunity [16]. The adipose tissue is a highly active endocrine organ; responsible for the synthesis and secretion of metabolic hormones [17]. These metabolic hormones and the immune system are known to affect each other [18]. Leptin is a hormone secreted by adipocytes in proportion to adipocyte mass, regulating energy balance and fat stores. Furthermore, leptin also acts as a pro-inflammatory cytokine, linking nutritional status with neuroendocrine and immune functions. The secretion of acute-phase reactants such as interleukin-1 (IL-1) and tumor necrosis factor (TNF) are affected by leptin levels [19]. In addition to leptin, the hormone adiponectin is also secreted by adipocytes. However, in contrast to leptin, the adiponectin production is downregulated in obesity. Adiponectin regulates the expression of several pro- and anti-inflammatory cytokines [20, 21]. Another mediator of fat metabolism is ghrelin. This hormone is produced by the stomach in states of fasting. Immunologically, ghrelin has potent anti-inflammatory activity, inhibiting acute phase protein and pro-inflammatory cytokine expression [22].

Despite the growing body of evidence for the influence of nutritional status on vaccine response, there has been little attention for the physiological explanation of this association. We previously reported that indigenous Venezuelan children suffering from undernutrition (stunting) had higher antibody concentrations following PCV13 vaccination than non-stunted children [14]. Indigenous Venezuelan children are a vulnerable group of children. Although invasive pneumococcal disease has not been studied specifically in this population, a high prevalence rate of acute respiratory tract infections has been described in these children [23]. This has been associated with an increased carriage of Streptococcus pneumoniae [24]. Higher pneumococcal carriage rates in indigenous children have also been observed in other populations (e.g. Navajo and White Mountain Apache, Australian Aboriginal and Alaska Native children) and carriage peaks generally co-occur with peaks of disease [25-28]. In unvaccinated indigenous Venezuelan children, vaccine serotypes were predominant [24, 29], underlying the importance of adequate protection by a robust immune response to pneumococcal vaccination.

Here, we present the results of a follow-up study that demonstrates to what extent metabolic hormones may be a modifier in the association between nutritional status and PCV response. To determine the association between pre-vaccination metabolic hormones and post-vaccination antibody levels across the full range of variation in nutritional status, we investigated this relationship in both stunted and overweight children compared with normal weight children.

Methods

Study population and setting

The Warao Amerindians live in Antonio Díaz, a municipality located in the Orinoco River Delta in Venezuela that can only be reached by boat. This study included Warao children aged 6 weeks to 59 months from the following nine indigenous communities in Antonio Díaz: Araguabisi, Araguaimujo, Arature, Bonoina, Guayaboroina, Ibaruma, Jobure de Curiapo, Merejina, and Winikina. Door-to-door visits were made to inform all parents of age-eligible children present in these communities during study visits. Children were included between May and November 2012. The original study was registered in a primary registry of the World Health Organization (ICTRP / RPCEC) with identifier number RPCEC00000158. This study included a subset of the original cohort as described in Verhagen et al. 2016 [14], i.e., all children who were stunted or overweight with a serum sample available and an equally large control group of normal weight children. Children who met multiple definitions, i.e. being overweight and stunted, were not included in the subset. Other exclusion criteria were known immunosuppression/deficiency, previous vaccination with any pneumococcal vaccine and major congenital malformations.

Vaccination schedule

Children aged 6 weeks to 6 months, 7 to 23 months and 24 to 59 months received a primary series of PCV13 including respectively 3, 2 and 1 dose(s) following CDC guidelines [30]. PCV13 contains capsular polysaccharides of 13 serotypes of Streptococcus pneumoniae (1, 3, 4, 5, 6A, 6B, 7F, 9V, 14, 18C, 19A, 19F and 23F) conjugated to CRM-197, a non-toxic variant of diphtheria toxin, as carrier protein (Fig 1A).

Fig 1

PCV13 vaccination schedule and sampling strategy.

A. PCV13 vaccination schedule based on age. B. Overview of sampling strategy. Blood samples were taken during both the 1st and the 2nd sampling moment. In the pre-vaccination serum samples (1st sampling) PCV13 IgG levels and metabolic hormones were determined. In the post-vaccination serum samples (2nd sampling) only PCV13 IgG levels were determined. * Median 6.7 weeks (IQR 6.4–6.9 weeks).

Data collection

Patient characteristics

Physical examination was performed in all included children, including anthropometric measurements. Pre-vaccination anthropometric measurements were transformed into weight-for-age Z-scores (WAZ), height-for-age Z-scores (HAZ) and Body Mass Index (BMI) Z-scores based on WHO standards [31]. Stunting was defined as HAZ <-2 standard deviation (SD). Overweight was defined as a BMI above +1SD. A normal nutritional status was defined as a BMI ≤ +1SD and a HAZ ≥ -2SD.

Sampling and laboratory methods

Blood samples were taken just before the first vaccination and again at 1.5 months (median 6.7 weeks (IQR 6.4–6.9 weeks)) after completion of the primary PCV13 vaccination series (Fig 1B). For a detailed description of the sampling and storage procedures we refer to Verhagen et al. 2016 [14]. Determination of pneumococcal serotype-specific serum immunoglobulin G (IgG) concentrations was performed at the National Institute for Public Health and the Environment in Bilthoven, The Netherlands, using a fluorescent bead-based multiplex immunoassay [32].

Metabolic hormones were determined in pre-vaccination serum samples. Serum ghrelin and leptin levels were analysed (in 100 μL) by radioimmunoassay (GHRT-89HK and HL-81K respectively, EMD Millipore Corp.; Missouri USA) as specified by the manufacturer’s instructions. Serum adiponectin concentration (High Molecular Weight (HMW) Adiponectin) was analysed (in 10 μL) by chemiluminescence enzyme immunoassay on a Lumipulse analyser G600II (234778, FuijRebio, Gent; Belgium) as specified by the manufacturer’s instructions.

Ethical considerations

Approval by the ethical committee of the Instituto de Biomedicina, Caracas, Venezuela, was granted. In addition, written permission to carry out the study was obtained from the Delta Amacuro Indigenous Health Office and from community leaders of each included community. Children were included upon written informed consent of parents or primary caregivers.

Statistical analyses

Categorical variables were analysed using Chi-square or Fisher’s exact test, as appropriate. For continuous variables, the unpaired Student’s t test, nonparametric Mann-Whitney U test or Kruskal Wallis test was used depending on whether the variables were normally distributed (Kolmogorov-Smirnov’s test, p >0.05). We used the mean of serotype-specific log-transformed pneumococcal antibody levels as a read-out for pneumococcal vaccine response. Linear regression was performed using the log-transformed antibody concentrations. In our multivariable models we included the following covariates of interest and potential confounders: age (continuous), time from primary series completion until post-vaccination blood sampling (continuous), leptin (continuous), adiponectin (continuous) and ghrelin (continuous, per 20 pg/mL) concentration, community (categorical), mean pre-vaccination antibody response (continuous), BMI (categorical), and HAZ (categorical). Generalized estimating equations (GEE) were used to fit a multivariable linear regression model aimed at identifying possible associations between nutritional status and metabolic hormones and their interactions (independent variables) and post-vaccination log-transformed antibody concentrations (dependant variable), while adjusting for the potential confounders mentioned above. GEEs account for correlation and lack of independence of responses for individuals in clusters within communities using an independence working covariance structure and robust variance estimators. Models were run separately for both stunted vs. non-stunted (i.e. including normal weight and overweight children) and overweight vs. non-overweight children (i.e. including normal weight and stunted children). For all statistical analyses SPSS software version 25 was used. Statistical significance was set to p-value <0.05.

Results

The study included 210 children: 80 stunted, 81 normal weight and 49 overweight children. From the 210 samples, insufficient material led to the inability of analysing 16 leptin levels and 7 ghrelin levels. These data points were considered as missing. In samples with sufficient volume, values of leptin, adiponectin and ghrelin were all above measurement detection limits (respectively 0.96 μg/L, 0.09 mg/L and 93 ng/L).

Baseline characteristics of all included children are presented in Table 1. The mean age of included children was 35 months (SD: 16 months). Interestingly, the HAZ score of overweight children was significantly lower than the HAZ score of children with a normal weight (median -1.26 SD vs. -0.45 SD, p <0.01).

Table 1

Baseline characteristics of the study population.

	All (n = 210)	Stunting (n = 80)	Normal weight (n = 81)	Overweight (n = 49)	p-value
Sex, n (%)					0.45
Male	106 (50.5)	44 (55)	38 (47)	22 (45)
Female	104 (49.5)	36 (45)	43 (53)	27 (55)
Age in months (mean, SD)	35 (16)	36 (15)	36 (15)	31 (17)	0.27
Anthropometric measurements, median
Weight-for-age Z-scores	-0.57	-1.81a	-0.24b	0.46c	<0.01
Height-for-age Z-scores	-1.24	-2.77a	-0.45b	-1.26c	<0.01
BMI-for-age Z-scores	0.38	0.16a	0.02a	1.54b	<0.01
Metabolic hormones, median (IQR)
Leptin (ng/mL)	7.77 (2.43)	7.62 (2.25)a	7.54 (2.51)a	8.88 (3.32)b	<0.01
Adiponectin (μg/mL)	6.92 (3.51)	7.11 (3.67)	6.37 (3.78)	6.92 (3.13)	0.69
Ghrelin (pg/mL)	684 (307)	708 (345)	686 (287)	630 (328)	0.17

Baseline characteristics are shown for the total study population and groups of stunted, normal weight and overweight children (see methods for the specific definition of the nutritional status groups). Different superscript letters in the same row indicate a significant difference (p <0.05) between the medians. Identical superscript letters in the same row indicate no significant difference between the median values for those categories. IQR = interquartile range.

Further, significantly higher leptin levels were observed in overweight children compared with stunted and normal weight children (p <0.01). No significant differences in adiponectin or ghrelin levels were observed between children of varying nutritional status.

Only 2.4% of the children had a mean IgG antibody concentration <0.35 μg/ml. For serotype specific IgG concentrations, the number of children with an IgG level <0.35 μg/ml ranged from 0.5–47.1% (S1 Table).

When assessing the relationship between metabolic hormones and vaccine response univariately, an overall positive correlation between both adiponectin and leptin and vaccine response was observed. However, stratification by nutritional status showed that this was only significant in overweight children (Fig 2, Table 2).

Fig 2

A: Correlation between adiponectin and post vaccination pneumococcal IgG antibody concentrations. Correlation, shown by a linear regression line, between pre-vaccination adiponectin levels and post-vaccination log-transformed pneumococcal IgG antibody concentrations for stunted, normal weight and overweight children. r = Pearson correlation coefficient. *p = <0.05. $ P-value value of interaction term in multivariable regression model (Table 3). B: Correlation between ghrelin and post vaccination pneumococcal IgG antibody concentrations. Correlation, shown by a linear regression line, between pre-vaccination ghrelin levels and post-vaccination log-transformed pneumococcal IgG antibody concentrations for stunted, normal weight and overweight children. r = Pearson correlation coefficient. $ P-value of interaction term in multivariable regression model (Table 3). C: Correlation between leptin and post vaccination pneumococcal IgG antibody concentrations. Correlation, shown by a linear regression line, between pre-vaccination leptin levels and post-vaccination log-transformed pneumococcal IgG antibody concentrations for stunted, normal weight and overweight children. r = Pearson correlation coefficient. *p = <0.05. $ P-value of interaction term in multivariable regression model (Table 3).

Table 2

Stratified univariate analysis of the effect of metabolic hormones on vaccine response in children with varying nutritional status.

	Serum log mean IgG post-vaccination
	Unadjusted regression coefficient*	95% CI	p-value
All children
Leptin (ng/mL)	0.10	0.034–0.17	<0.01
Adiponectin (μg/mL)	0.062	0.012–0.11	0.016
Ghrelin (per 20 pg/mL)	0.008	-0.004–0.020	0.19
Stunted children
Leptin (ng/mL)	0.071	-0.059–0.20	0.28
Adiponectin (μg/mL)	-0.059	-0.14–0.023	0.16
Ghrelin (per 20 pg/mL)	0.012	-0.006–0.029	0.20
Normal weight children
Leptin (ng/mL)	0.055	-0.037–0.15	0.24
Adiponectin (μg/mL)	0.062	-0.009–0.13	0.087
Ghrelin (per 20 pg/mL)	0.006	-0.010–0.023	0.44
Overweight children
Leptin (ng/mL)	0.18	0.028–0.32	0.020
Adiponectin (μg/mL)	0.22	0.12–0.33	<0.01
Ghrelin (per 20 pg/mL)	0.006	-0.027–0.039	0.72

A p-value <0.05 is considered statistically significant (in bold).

* Linear regression coefficient.

Table 3

Results from multivariable linear GEE models for post-PCV13 serum antibody levels in stunted and overweight children.

	Serum log mean IgG post-vaccination
	Adjusted regression coefficient *	95% CI	p-value
Undernutrition
Nutritional status (stunted vs. non-stunted)	0.92	-0.015–1.85	0.054
Metabolic hormones
Leptin (ng/mL)	0.003	-0.039–0.045	0.88
Adiponectin (μg/mL)	0.076	0.018–0.13	0.010
Ghrelin (per 20 pg/mL)	0.007	-0.013–0.027	0.50
Interaction between nutritional status and metabolic hormones
Leptin (ng/mL) * stunting	0.036	-0.10–0.18	0.62
Adiponectin (μg/mL) * stunting	-0.19	-0.24 –-0.14	<0.01
Ghrelin (per 20 pg/mL) * stunting	0.005	-0.010–0.020	0.53
Overnutrition
Nutritional status (overweight vs. non-overweight)	-1.15	-2.22 –-0.072	0.036
Metabolic hormones
Leptin (ng/mL)	0.001	-0.062–0.064	0.97
Adiponectin (μg/mL)	-0.034	-0.076–0.008	0.11
Ghrelin (per 20 pg/mL)	0.009	0.001–0.016	0.02
Interaction between nutritional status and metabolic hormones
Leptin (ng/mL) * overweight	0.045	-0.098–0.19	0.54
Adiponectin (μg/mL) * overweight	0.14	0.049–0.22	<0.01
Ghrelin (per 20 pg/mL) * overweight	-0.004	-0.037–0.029	0.80

Linear regression coefficients were adjusted for the covariates that were included in the regression model in addition to stunting or overweight: age in months, time from primary series completion until post-vaccination blood sampling, leptin, adiponectin, ghrelin, community, mean pre-vaccination antibody response. A p-value <0.05 is considered statistically significant (in bold).

* Linear regression coefficient.

In line with our previous results, multivariable analysis showed a trend towards higher antibody levels in stunted compared with non-stunted children (regression coefficient 0.92, 95% CI -0.015–1.85, Table 3). In addition, overweight children appeared to have a significantly lower PCV response compared with non-overweight children (regression coefficient -1.15, 95% CI -2.22 –-0.072). Interestingly, an opposing effect of adiponectin concentrations on post-vaccination antibody levels was observed for stunted vs. overweight children. In stunted children, the interaction of adiponectin levels with stunting was significantly negatively associated with post-vaccination antibody response (regression coefficient -0.19, 95% CI -0.24 –-0.14). In contrast, in overweight children, the interaction of adiponectin levels with overweight was significantly positively associated with post-vaccination antibody levels in multivariable analysis (regression coefficient 0.14, 95% CI 0.049–0.22). Hence, an increase in adiponectin levels in stunted children is associated with lower PCV antibody levels while an increase in adiponectin levels in overweight children is associated with higher PCV antibody levels (Fig 2A, Table 3). Further, we observed a borderline significant association between ghrelin levels and post-vaccination antibody levels in overweight children (regression coefficient 0.009, p = 0.02) (Table 3). No significant association of leptin with PCV13 response was observed in multivariable analysis.

Discussion

The importance of understanding the relationship between nutritional status and vaccine response is underlined by the fact that malnutrition is an increasing global phenomenon. Worldwide, stunting affects around 22% of children under 5 years of age and the rise in childhood obesity prevalence is alarming [33]. In addition, the co-existence of stunting and overweight, i.e. the double burden of malnutrition, is a growing problem since the prevalence of overweight and obesity in many low- and middle income countries has become greater than or equal to prevalence rates observed in high income countries [34]. Vulnerable populations such as indigenous children are even more at risk of developing dual forms of malnutrition [35]. This double burden of malnutrition was also present in our cohort of indigenous Warao children; overweight was accompanied by lower height-for-age Z scores at the individual level. We had previously observed an association between stunting and increased IgG antibody concentrations following PCV13 vaccination [14]. This follow-up study adds the observation that overweight children had a lower PCV13 antibody response. Because of the low number of children with an overall antibody response below the commonly used cut-off of 0.35 μg/ml [36], we cannot comment on the association between nutritional status and a response <0.35 μg/ml. Further, metabolic hormones, particularly adiponectin, appeared to modify the relationship between nutritional status and PCV13 vaccine response.

The relationship between overweight and vaccine response has been studied for hepatitis A, hepatitis B, tetanus and inactivated trivalent influenza vaccination [6, 8–12, 37–40]. Most studies confirm our observation that overweight individuals have lower post-vaccination antibody levels [6, 8–10, 37–40], of which some included children in their study population [10, 37, 40]. In these studies, several explanations were suggested. Chronically elevated levels of interleukin-6 (IL-6) in obese individuals might interfere with the humoral immune system leading to decreased antibody production [37, 41, 42]. Others showed impaired CD8+ T-cell responses in overweight individuals accompanied by less IFN-γ production possibly leading to a decreased vaccination response [9]. Our findings add another explanation for the apparent influence of nutritional status on vaccine response, i.e. the modifying effect of metabolic hormones, particularly adiponectin.

Adiponectin plays an important role in regulating immune responses. By binding to its receptors (AdipoR1 and AdipoR2), expressed on B-cells, it induces secretion of the B-cell derived peptide PEPITEM. This peptide inhibits migration of T cells without affecting recruitment of other leukocytes [20, 43]. Since antibodies are produced by B lymphocytes, the direct effect of adiponectin on those B cells expressing AdipoR may play a role in the mediating effect of adiponectin on the relationship between nutritional status and antibody concentrations. Inflammasomes may also contribute to the mediating effect of adiponectin. Inflammasomes are large multimolecular complexes representing a key signaling platform that triggers inflammatory and immune responses. Adiponectin has shown to inhibit inflammasome activity, thereby suppressing the secretion of pro-inflammatory cytokines. Hence, lower adiponectin levels in obese individuals can lead to increased inflammasome activity and a pro-inflammatory state. However, more research is needed to investigate the exact role of inflammasomes in stunted and overweight individuals and their involvement in vaccine response [44].

In addition, many other signalling pathways might play a role in the effect of adiponectin on vaccine-induced immune response. In vitro studies show that adiponectin exposure can promote a transient pro-inflammatory response by the upregulation of pro-inflammatory cytokines, such as TNF-α, IL-6 and IL-8 via activation of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-ƙB) transcription factor [20, 45, 46]. However, high and prolonged exposure of macrophages to adiponectin leads to an anti-inflammatory milieu by suppression of TNF-α and IL-6 synthesis and induction of IL-10 [20, 46]. In macrophages previously stimulated with lipopolysaccharide (LPS), a less profound inflammatory response is observed upon subsequent exposure, compared with the response upon first exposure, to adiponectin [46, 47]. In stunted children, high levels of LPS have been observed and evidence suggests that chronic LPS exposure may lead to an immunoparalytic state in children with chronic malnutrition [48]. We speculate that increased adiponectin levels in stunted children with an already impaired immune response may contribute to a diminished antibody response upon vaccination.

We observed significantly higher leptin levels in overweight children compared to normal weight and stunted children. This can be explained by the fact that leptin is secreted in proportion to adipocyte mass [19]. Several studies have investigated the immunomodulatory effect of leptin. In both in vitro and in murine studies, leptin appears to have a pro-inflammatory effect by inducing the proliferation of mainly naive T cells/T-helper 1 (Th1) cells, while inhibiting the proliferation of memory T cells and Th2 cells [21, 49]. Hence, high levels of leptin in overweight individuals could contribute to a more pro-inflammatory state. However, a study performed in Gambian children aged 7–9 years showed no relationship between leptin and vaccine response [50]. Similarly, our study also suggests that the net effect of leptin levels on pneumococcal conjugate vaccine response is not significant.

Reference values of adiponectin, leptin and ghrelin in healthy individuals are subject to a great variability since there are many influencing factors (among others age, sex and nutritional status). Results of a European study investigating normative values of adiponectin and leptin in children aged <9 years old concluded that the medians of total adiponectin and leptin levels were respectively 8.6–12.7 μg/ml and 1.2–2.2 ng/ml [51]. However, ranges were wide for both hormones and measurement method dependent biases are likely present. Notably, in our study HMW adiponectin was measured with the Lumipulse assay. HMW adiponectin is a fraction of total adiponectin, estimated about 30–50% of total adiponectin [52], nevertheless it has been shown that both total and HMW are interchangeable as they have similar utility when assessing adiponectin levels in blood [52]. Reference values, as supplied by Fuijrebio, of HMW adiponectin in healthy, non-obese adults below 40 years of age in this method are 0.8–9.8 μg/mL for males and 2.1–11.3 μg/mL for females. For ghrelin reference levels in children, only small studies were performed showing an inverse correlation between ghrelin levels and age [53, 54].

Serum sampling in this study took place at a random time during the day; therefore, we cannot exclude an effect of hour-to-hour variation on our study results. However, it seems unlikely that this variation should cause spurious results in a consistent pattern across groups of higher/lower vaccine responses. Since plasma levels of metabolic hormones are affected by the consumption of food during the day [55], consistent differences between stunted and overweight children, related to food consumption patterns, may have occurred. The absence of information on food consumption during the day of sampling is a limitation of this study. However, in general, most indigenous children only eat one meal per day which takes place in the evening, implicating that most children will have been in fasting state during the day.

This study included both stunted and overweight children, all belonging to the indigenous Venezuelan Warao population. Therefore, a possible confounding effect of genetic background or living environment is limited. In addition, we adjusted for possible confounding factors such as age by multivariable analysis to further strengthen the reliability of our results. However, the validity and generalizability of our findings should be substantiated by studying populations in other parts of the world. Moreover, our results are specific for PCV 13 vaccination and may be different for live attenuated or inactivated vaccines. Finally, due to the limited laboratory capacity in these isolated villages, that can only be reached by boat trips of up to >12 hours, we did not have the possibility to further investigate possible causative mechanisms underlying our observations. Therefore, additional research is needed to study the underlying cellular immune mechanisms and to determine whether our findings can be extrapolated to other vaccines.

Because our study shows that nutritional status is an important factor influencing vaccine response, we propose to take these and other factors into account when determining optimal vaccination protocols and strategies. However, current medical practice in vaccinology is to universally administer the same set of vaccines and doses to everyone in the population, in the absence of a contraindication. This approach is based on a national population-level paradigm, allowing the widespread delivery of vaccines, and as a result, the control of many infectious diseases. The major weakness of this approach is that it ignores individual variability in immunologic response and reactogenicity, as well as differences in dose amount needed to generate immunity. The concept of optimizing care by treating patients based on their individual needs and characteristics is increasingly applied in several medical disciplines. Further insights into the underlying mechanisms of the induction of vaccine responses can result in replacement of the ‘one-size-fits-all’ model by more personalized or sub-population-based vaccination programs that will hopefully yield better immunization results.

In conclusion, this is to our knowledge the first explorative study on the role of metabolic hormones and nutritional status on PCV response in a large cohort of indigenous children. Our results suggest a role for the metabolic hormone adiponectin in vaccine response in children with an altered nutritional status. In stunted children, an increase in adiponectin was associated with a decreased post-vaccination antibody concentration, while the opposite was observed for overweight children. A better understanding of the effect of metabolic hormones on vaccine response in children with an altered nutritional status will support optimal use of existing vaccines and can guide the development of new vaccines with optimal protective efficacy in children with varying nutritional status.

Number and percentage of children with a serotype specific post vaccination IgG (μg/ml) response below 0.35 μg/ml, stratified by nutritional status.

(DOCX)

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7 Jul 2021

PONE-D-21-12590

The metabolic hormone adiponectin modifies the association between nutritional status and pneumococcal vaccine response in vulnerable indigenous children

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2. Please provide the catalog numbers and sources of all kits referred to in lines 177-183.

3. Please provide the registration information for the original clinical study.

4. Please list the names of the nine indigenous Warao communities where participants were from.

5. Thank you for stating the following in the Financial Disclosure section:

[The original study was supported by Pfizer Venezuela and the Fundacion para la Investigación en Micobacterias, Caracas, Venezuela. Lilly M. Verhagen was supported by a Clinical Research Talent fellowship of the UMC Utrecht, The Netherlands.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.].

We note that one or more of the authors have an affiliation to the commercial funders of this research study: Pfizer Venezuela

1. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

2. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests). If this adherence statement is not accurate and  there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

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We will update your Data Availability statement on your behalf to reflect the information you provide.

7. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1) Figures 2A-2C only showed the simple regression lines of IgG levels ~ metabolic hormone levels. The data points should also be plotted with different color /shape for the three categories. The significance levels of the interaction terms in the multiple regression models should be reported here to quantitatively show whether the correlations are different between categories.

2) Table 1, it’s still unclear to me how to interpret the different superscript letters. Please be more specific.

3) Table 2, it is unclear the GMRs were between which two groups. For example, for All children category, how was GMR calculated? If it’s a ratio, what are numerator and denominator, respectively? Note that for results in Table 3, it’s clear the comparison is between under-/over-nutrition group and normal group. However, it’s unclear here.

4) Table 3, note that unadjusted GMR are the same for under- and over nutrition groups, and it’s the same as that in Table 2 (1.11, 1.06, and 1.01). Again, please clarify.

5) In the main text, GMR was interpreted as OR, which is incorrect.

**********

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Reviewer #1: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

20 Sep 2021

Dear dr. Magni,

We thank you for your constructive comments and suggestions. Your comments are highly appreciated, and we have incorporated the provided feedback accordingly. The revised manuscript has been uploaded; please find our response to each specific comment below.

Journal requirements

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affilaffilaf.pdf

We have adjusted the manuscript style according to the PLOS ONE’s style requirements.

2. Please provide the catalog numbers and sources of all kits referred to in lines 177-183.

We have added the catalog numbers and sources of all kits to the ‘sampling and laboratory methods’ section.

3. Please provide the registration information for the original clinical study.

The original study was registered in a primary registry of the World Health Organization (ICTRP / RPCEC) with identifier number RPCEC00000158. We have included this in the methods section.

4. Please list the names of the nine indigenous Warao communities where participants were from.

The nine indigenous Warao communities are named in the revised version of our manuscript.

5. Thank you for stating the following in the Financial Disclosure section:

[The original study was supported by Pfizer Venezuela and the Fundacion para la Investigación en Micobacterias, Caracas, Venezuela. Lilly M. Verhagen was supported by a Clinical Research Talent fellowship of the UMC Utrecht, The Netherlands. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.].

We note that one or more of the authors have an affiliation to the commercial funders of this research study: Pfizer Venezuela

1. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

None of the authors has or had an affiliation to Pfizer Venezuela. Jacobus de Waard has received funding from Pfizer Venezuela for the here presented study. However, the funder did not provide support in the form of salaries for authors, nor did they have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Pfizer Venezuela only provided financial support in the form of vaccine (PCV13) supply and research materials.

The specific roles of the authors are articulated in the ‘author contributions’ section.

2. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests). If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

There were no competing interests, as clarified above. We have also included this in the updated Funding Statement and Competing Interest Statement in our cover letter.

6. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

We have uploaded the data in accordance with the provided guidelines and adjusted the text accordingly. The data are now freely available via the following URL: https://datadryad.org/stash/share/LUfQ9WNDrEOw5Aq9YIpEcugbAU7FHfyE0oVmK_abeC4

However, please note that this is a temporary URL because the status of our project is currently set to ‘private for peer review’. We will replace this URL with a definite one upon acceptance of our manuscript.

7. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

We have added the captions for our supporting table at the end of our manuscript.

Reviewers’ comments

1. Figures 2A-2C only showed the simple regression lines of IgG levels ~ metabolic hormone levels. The data points should also be plotted with different color /shape for the three categories. The significance levels of the interaction terms in the multiple regression models should be reported here to quantitatively show whether the correlations are different between categories.

We have adjusted the figures 2A-2C. The figures now also include the plotted data points in addition to the regression lines. The significance levels of the interaction terms in the multiple regression models are added to the legend of the figures.

2. Table 1, it’s still unclear to me how to interpret the different superscript letters. Please be more specific.

Significantly different median values in the same row are indicated with different superscript letters, while median values that are not significantly different are indicated with identical superscript letters. We have further clarified the interpretation of the superscript letters in the footnote.

3. Table 2, it is unclear the GMRs were between which two groups. For example, for All children category, how was GMR calculated? If it’s a ratio, what are numerator and denominator, respectively? Note that for results in Table 3, it’s clear the comparison is between under-/over-nutrition group and normal group. However, it’s unclear here.

We indeed noted that our presentation of the results was unclear using a GMR. Therefore, we have now used linear regression coefficients for the presentation of the results in tables 2 and 3.

We have also clarified the comparator groups in the methods section. To avoid over-adjusting, we did not include all malnutrition measures in the same regression model, but instead created two different regression models for undernutrition and overnutrition. We clarified how we analysed malnutrition indicators in the Methods section of the revised manuscript.

4. Table 3, note that unadjusted GMR are the same for under- and over nutrition groups, and it’s the same as that in Table 2 (1.11, 1.06, and 1.01). Again, please clarify.

We thank the reviewer for pointing this out. The unadjusted GMR were the same for all variables, except for nutritional status, because these represented the univariate correlations between e.g. metabolic hormones and antibody concentrations. To be comprehensive, we had initially added these univariate analysis to both tables. However, we acknowledge the unclarity this may have caused and we have now removed the GMR and added linear regression coefficient, in response to the previous comment.

Hence, we have changed tables 2 and 3 so that the stratified univariate analysis is shown in table 2 while table 3 now contains the multivariable linear GEE models.

5. In the main text, GMR was interpreted as OR, which is incorrect.

We thank the reviewer for this observation. Since we now use linear regression coefficients in response to the comments above, we adjusted the text accordingly in the results section.

We hope that our modifications render our revised manuscript suitable for publication in PLOS ONE.

Sincerely,

On behalf of all authors,

Lilly M. Verhagen

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

1 May 2022

13 Jun 2022

Dear dr. Neyrolles,

It is a great pleasure to submit to PLOS ONE our revised manuscript entitled ‘The metabolic hormone adiponectin affects the correlation between nutritional status and pneumococcal vaccine response in vulnerable indigenous children’.

We again thank the Editor and Reviewer for their constructive comments and suggestions. All extra suggestions made by the Reviewer as well as the editorial comments have been implemented in the revised manuscript. The explanatory details are listed below.

Editor comments

1. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

We have meticulously gone through the reference list but could not find any retracted paper.

We have made one small adjustment to the reference list; in the methods section, we previously referred to the article of Verhagen et al. considering the determination of antibodies using fluorescent bead-based multiplex immunoassay, however, to fully capture the technical details of the assay, we now refer to the article of Stoof et al.

Following a previous comment of the editor, we have adjusted the title of our manuscript slightly to remove any implication of causality, into 'The metabolic hormone adiponectin affects the correlation between nutritional status and pneumococcal vaccine response in vulnerable indigenous children'.

Reviewers’ comments

1. Are there any data on invasive pneumococcal disease in the population studied or other indigenous populations? This would add to the justification for the study & would be useful to know

Although there are no specific data on invasive pneumococcal disease in our study population, high prevalence rates of acute respiratory tract infections and pneumococcal carriage rates have been observed in this population. These high rates are also seen in other indigenous populations. We have added this as extra information in the introduction section of our revised manuscript.

2. Minor editorial advices:

We have changed the grammatical adjustments accordingly. Also, we have moved lines 193-7 to the result section. Regarding the question why HMW adiponectin is measured rather than total levels; the Lumipulse assay used for HMW measurements is a very reliable method and it has previously been shown in a comparative analysis that total and HMW adiponectin have similar utility when assessing adiponectin levels in blood (van Andel M, Drent ML, van Herwaarden AE, Ackermans MT, Heijboer AC. A method comparison of total and HMW adiponectin: HMW/total adiponectin ratio varies versus total adiponectin, independent of clinical condition. Clin Chim Acta. 2017;465:30–3).

We are confident that the current manuscript meets the quality criteria of PLOS ONE, and that the subject will be appreciated by the Journal’s readership.

Sincerely,

On behalf of all authors,

Lilly M. Verhagen

Submitted filename: Response to reviewers.docx

Click here for additional data file.

17 Jun 2022

The metabolic hormone adiponectin affects the correlation between nutritional status and pneumococcal vaccine response in vulnerable indigenous children

PONE-D-21-12590R2

Dear Dr. Verhagen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Again, we apologize for the delay in processing your manuscript.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Olivier Neyrolles

Section Editor

PLOS ONE

12 Jul 2022

PONE-D-21-12590R2

The metabolic hormone adiponectin affects the correlation between nutritional status and pneumococcal vaccine response in vulnerable indigenous children

Dear Dr. Verhagen:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Olivier Neyrolles

Section Editor

PLOS ONE