[X-force Helius PFA 2010 Activation
Hanne Rylaarsdam
The occurrence of metabolic syndrome (MetS) and the gut microbiota composition are known to differ across ethnicities yet how these three factors are interwoven is unknown. Also, it is unknown what the relative contribution of the gut microbiota composition is to each MetS component and whether this differs between ethnicities. We therefore determined the occurrence of MetS and its components in the multi-ethnic HELIUS cohort and tested the overall and ethnic-specific associations with the gut microbiota composition.
A differential, often sex-dependent, prevalence of MetS components and their combinations were observed across ethnicities. Increased blood pressure was commonly seen especially in Ghanaians, while South-Asian Surinamese and Turkish had higher MetS rates in general and were characterized by worse lipid-related measures. Regarding the gut microbiota, when ethnic-independent associations were assumed, a higher α-diversity, higher abundance of several ASVs (mostly for waist and triglyceride-related outcomes) and a trophic network of ASVs of Ruminococcaceae, Christensenellaceae, and Methanobrevibacter (RCM) bacteria were associated with better MetS outcomes. Statistically significant ethnic-specific associations were however noticed for α-diversity and the RCM trophic network. Associations were significant in the Dutch but not always in all other ethnicities. In Ghanaians, a higher α-diversity and RCM network abundance showed an aberrant positive association with high blood pressure measures compared to the other ethnicities. Even though adjustment for socioeconomic status-, lifestyle-, and diet-related variables often attenuated the effect size and/or the statistical significance of the ethnic-specific associations, an overall similar pattern across outcomes and ethnicities remained.
The occurrence of MetS characteristics among ethnicities is heterogeneous. Both ethnic-independent and ethnic-specific associations were identified between the gut microbiota and MetS outcomes. Across multiple ethnicities, a one-size-fits-all approach may thus be reconsidered in regard to both the definition and/or treatment of MetS and its relation to the gut microbiota.
Metabolic syndrome (MetS) is a risk factor for type 2 diabetes (T2D) and cardiovascular disease (CVD), which are increasingly among the main causes of morbidity and mortality worldwide. MetS represents the clustering of individual risk factors, including hypertension, central obesity, dysglycemia, and dislipidaemia [1, 2]. The exact pathogenic mechanism is not exactly known, yet insulin resistance is proposed as the underlying factor [2]. Which exact diagnostic criteria should be used is still under debate, as is the question whether MetS can be considered a single syndrome or represents multiple syndromes with different cardiovascular risk profiles [2,3,4].
Differences across ethnicities exist in the prevalence of MetS itself as well as in the prevalence of the individual components that are included in the MetS definition. For example, African American people have a higher prevalence of hypertension [5], while they suffer less often from dyslipidaemia [6] compared to their Caucasian counterparts. Lower cut-offs for central obesity are already used for males from South-Asian descent [2]. Furthermore, triglyceride levels were not considered to be associated with insulin resistance in African Americans, and Gurka et al. (2014) mentioned different correlations for the individual components with the underlying MetS construct across ethnicities [7, 8]. Next to genetic or biological aspects, (self-reported) ethnicity also entails societal, behavioral, and environmental factors [9,10,11]. As the prevalence of MetS is often influenced by such factors, including socioeconomic status, diet, physical activity, and educational level [1], this often complicates the interpretation of health disparities across ethnic groups.
Another environmental factor that is linked to MetS and which exhibits a different composition across ethnicities is the gut microbiome [12]. The gut microbiome, composed of trillions of bacteria, fungi, viruses, and their corresponding genes, has previously been proposed to be associated with insulin resistance [13]. Several studies have already identified associations between the gut microbiome and MetS and/or its components, which are proposed to be established mainly via inflammation and metabolism modulation [14, 15]. In addition, a fecal microbiota transplantation (FMT) derived from lean donors given to obese Dutch males with MetS showed a temporarily improvement in insulin sensitivity after 6 weeks compared to males receiving their own fecal microbiota, highlighting the potential therapeutic effect of the gut microbiota in MetS [13].
To gain more insight in the effect of ethnicity, including rarely studied ethnic minorities, on the occurrence of MetS, its individual components, and the combination of these risk factors, we used the Healthy Life in Urban Setting (HELIUS) cohort [16, 17] in Amsterdam, the Netherlands. Furthermore, we analyzed the link between the gut microbiota and MetS and its components in a subgroup of this cohort of which gut microbial sequencing data was available. Those insights could help to evaluate if a one-size-fits-all approach for MetS is still appropriate in regard to its definition, treatment, and the role of the gut microbiota across different ethnicities.
For the analysis on the gut microbiota composition, we included the subset of the participants from the total dataset in whom gut microbiota data were available after quality control of this data (see below) [18]. Participants who used antibiotics in the past 3 months or of unknown use were excluded. A number of 3443 participants were finally included in the gut microbiota dataset.
After a positive response, subjects received a confirmation letter of an appointment for a physical examination and a digital or paper version of the questionnaire (depending on the preference of the subject) to fill out at home. At the research locations, participants underwent a physical examination, during which measurements of blood pressure and anthropometric (e.g., weight, height and waist circumference) characteristics were obtained. Measures of waist circumference, systolic blood pressure, and diastolic blood pressure were performed in duplicate and then averaged. Furthermore, participants were asked to bring their prescribed medications, which were coded according to the Anatomical Therapeutic Chemical (ATC) classification. Fasting blood samples were drawn after an overnight fast and were analyzed by the main laboratory department of the Academic Medical Center in Amsterdam to determine glucose, lipid (total cholesterol, HDL-cholesterol and triglyceride levels), and HbA1c profiles. More detailed information about the measurements is described elsewhere [19].
Ethnicity of the participant was defined according to his/her country of birth as well as that of his/her parents, which is currently the most widely accepted and most valid assessment of ethnicity in the Netherlands [20]. Specifically, a participant is considered to be of non-Dutch ethnic origin if he/she fulfills either of the following criteria: (1) he or she was born in another country and has at least one parent born in another country (first generation) or (2) he or she was born in the Netherlands but both his/her parents were born in another country (second generation). Of the Surinamese immigrants in the Netherlands, approximately 80% are of either African or South-Asian origin. After data collection, Surinamese subgroups were classified according to self-reported ethnic origin. Participants were considered to be of Dutch origin if the person and both parents were born in the Netherlands.
Apart from age and sex, we considered the following covariates obtained via the questionnaire: socioeconomic status (highest obtained educational level, occupational level and employment status), lifestyle (physical activity, smoking and alcohol use), and dietary habits (sugar intake and fruit intake). In gut microbiota analyses, we also took proton pump inhibitor (PPI) use into account, as this is a known confounder of the gut microbiota.
The highest educational level obtained in the Netherlands or in the country of origin was categorized as higher (higher vocational schooling or university), intermediate (intermediate vocational schooling or intermediate/higher secondary schooling), lower (lower vocational schooling or lower secondary schooling), or elementary (never been to school or elementary schooling only). Current employment status was indicated as either working, not in work force, unemployed, or unable to work. The categories academic, higher, intermediate, lower, and elementary were used to indicate occupational status. For the lifestyle-related variables, we used a binary indicator for physical activity (i.e., 30 min of moderate/intensive exercise for at least 5 days a week, which is conform the Dutch Standard for Health exercise) and alcohol use (used alcohol in the last 12 months). Smoking was categorized into yes, former, and never. Since we did not have the same Food Frequency Questionnaire for all ethnicities, we derived composite variables as proxies for dietary habits. We used regularly fruit intake (yes/no) as a proxy for a healthy diet, which was indicated as eating at least one piece of fruit for at least 5 days/week. In regard to an unhealthy diet, we used the daily ingestion (yes/no) of sugar drinks as a proxy. This variable was considered to be present if participants responded that they had a daily consumption of either fruit juice, tea with sugar, regular soft drink, sports drink, fruit syrup, fruit drink, malt beer, or coffee with sugar or when a participant consumed 7 of those drinks 1 to 6 days a week.
For the subsequent analyses, except for analyses on combinations of components, all analyses were performed for the binarized outcomes of all MetS components and MetS itself as well as on the continuous outcomes of the components.
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