The prevalence of obesity has dramatically increased in all age groups in recent years, but obesity rates among older adults are even higher. Increasing age has been shown to be associated with lower prevalence of MHO (10). Recently, it has been reported that obesity-related comorbidities and conditions mirror those of aging and age-related diseases (24). Obesity and aging can lead to chronic low-grade inflammation and an increased incidence of chronic inflammatory diseases due to dysregulated immune responses (25–29). Macrophages seem to be primarily involved in obesity-associated inflammation, changing their phenotype from “alternatively activated” to “inflammatory” macrophages (30, 31). Moreover, other components of the innate immune system, mast cells, neutrophils, and dendritic cells, have been shown to exacerbate insulin resistance (32–34), whereas eosinophils and type 2 innate lymphoid cells can protect against adipose tissue and islet inflammation (33). More recent work focused on adaptive immune responses in obesity-induced systemic and adipose tissue inflammation. T cells, including CD8+ T cells, Th1, Th17, as well as B cells, can exacerbate inflammation, whereas Treg cells and Th2 cells can dampen inflammation and protect against insulin resistance (35–37). Several human studies investigated the association of inflammatory parameters with impaired insulin sensitivity. In particular, it has been found that the helper T cell composition in peripheral blood correlates significantly with the HOMA-IR (38) and other measures of adiposity, inflammation and glucose intolerance, whereas circulating Treg cells were reduced in obese subjects, and might identify individuals at increased risk for cardiovascular comorbidities (39, 40). Moreover, a distinct phenotype of Treg cells has been characterized in human obese omental adipose tissue (41). Another recent study revealed an impaired NK cell phenotype and NK cell subset alterations in obese individuals (42). Reduced circulating Treg cell numbers were detected in obese compared with non-obese study participants (39), and another study identified a significant inverse correlation of Th2 cells in peripheral blood with systemic insulin resistance (12). Additionally, our group found recently that insulin resistance correlates significantly with a shift in the ratio of naïve and differentiated memory CD4+ and CD8+ T cells in abdominal subcutaneous adipose tissue in female obese subjects (18). Furthermore, it has been found that peripheral frequencies of T-helper (Th)22 cells and IL-22 levels were increased in obese subjects with or without type 2 diabetes compared with lean subjects, and that Th22 cell frequencies correlated positively with HOMA-IR (38). These findings were confirmed in another study, showing that Th22 and Th17 cells were elevated in abdominal subcutaneous adipose tissue from metabolically abnormal IR obese compared with metabolically normal IS obese subjects (43). Although the association of obesity with systemic low-grade inflammation is well established, studies on the characteristics of metabolically healthy versus metabolically unhealthy obese individuals are limited.
Here, we have investigated the association of insulin resistance and immunological parameters in a sample of 437 older participants of the BASE II study. We analyzed peripheral blood immune cell subsets and cytokine levels with multiparameter flow cytometry analysis. We found that frequencies of naïve CD4+ and CD8+ T cells correlated positively with ISIOGTT, but negatively with BMI and HOMA-IR, whereas frequencies of central memory CD4+ T cells correlated negatively with ISIOGTT, but positively with BMI and HOMA-IR. Additionally, the percentage of highly differentiated effector memory CD8+ T cells was positively associated with HOMA-IR, and the expression of CD57, a surface marker putatively associated with impaired proliferation capacity and cell senescence (21), correlated positively with BMI and HOMA-IR in older participants of this study. However, the expression of PD-1, a characteristic marker for T cell exhaustion, on CD8+ T cells was not associated with metabolic measures (Supplementary Table 1). In line with previous reports (20, 44), we also identified a positive association of systemic IL-6 levels with insulin resistance, whereas other cytokines (IL-1ß, TNFα, IL-10) did not correlate significantly (Fig. 1). To address the association of MHO with insulin resistance, we divided the study participants in obese and non-obese IR and IS subgroups, based on ISIOGTT. The increased frequencies of peripheral blood CD4+ T cells in IS obese and non-obese individuals were accompanied by a selective increase of naïve CD4+ T cells. Similarly, in the CD8+ T cell compartment, the frequencies of naïve T cells were higher in IS obese and non-obese subgroups, whereas effector memory T cells were significantly lower. Altogether, the CD4+ and CD8+ T cell compartment was skewed towards a memory T cell phenotype in IR subjects (Fig. 2, Supplementary Table 2–3). Additionally, the frequencies of naïve CD4+ and CD8+ T cells were predictive for ISIOGTT, and the relationship of ISIOGTT with naïve CD4+ T cells remained significant after adjustment for sex, BMI, clinical conditions (morbidity index) and CMV-serostatus (Table 2).
Here, we defined parameters for insulin sensitivity to define MHO, but more recently, the following criteria have been proposed in addition to the diagnosis of obesity (BMI > 30 kg/m2): fasted serum triglycerides ≤ 1.7 mmol/l (≤ 150 mg/dl); HDL cholesterol serum concentrations > 1.0 (> 40 mg/dl) (in men) or > 1.3 mmol/l (> 50 mg/dl) (in women); systolic blood pressure (SBP) ≤ 130 mmHg; diastolic blood pressure ≤ 85 mmHg; fasting blood glucose ≤ 6.1 mmol/l (≤ 100 mg/dl); no drug treatment for dyslipidemia, diabetes, or hypertension; and no cardiovascular disease manifestation (7). Interestingly, frequencies of systemic naïve CD4+ and CD8+ T cells of older participants of the BASE-II study, were positively associated with HDL cholesterol serum concentrations, whereas associations with fasting blood glucose and fasted serum triglycerides were negative (data not shown). However, we did not assess these criteria to define MHO in the present study, as most of these criteria are affected by age and can also influence each other.
Our study has several important limitations. First, we could only include a small subgroup of BASE-II study participants based on the availability of flow cytometric data and cytokine level measurements of blood samples. Although the distribution of the analyzed subgroup is similar to the whole cohort (e.g. equal gender distribution), the sample size is rather small after further subdivision into obese and non-obese, IS and IR groups, and the different numbers of IS (n = 243) versus IR (n = 84) non-obese participants could limit the power to detect significant associations. However, the sample size of obese IS (n = 28) and IR (n = 32) participants is very similar, and with regard to the multiparameter flow cytometry analysis we conducted, the sample size is still reasonable. Moreover, the relative homogeneity of the participants with regard to age (65–80 years) could strengthen our study; age-related changes in immune function and T cell alterations have been described previously (23, 26).
Second, we measured insulin sensitivity using HOMA-IR and ISIOGTT obtained from OGTT, whereas the hyperinsulinemic-euglycemic clamp technique is considered the most reliable method available for estimating insulin resistance and is used as reference standard. In the present study, only half of the obese subgroup was considered IR with ISIOGTT < 4 and 32% of the obese subgroup was considered IR with HOMA-IR < 2.9. On the other hand, 19% of the non-obese subgroup was considered IR with ISIOGTT < 4, and 4.6% with HOMA-IR > 2.9. Due to significant inter laboratory variations in insulin assays, the normal range of these parameters needs to be established for each laboratory, and could also explain differences in the percentages of participants considered IR, here. However, the measurements of HOMA-IR and ISIOGTT are minimally invasive, and, thus, still suitable for clinical uses. Moreover, these surrogate parameters of insulin resistance are widely used in observational studies which allows for comparison between different studies.
Third, we assessed peripheral blood immune cell frequencies in this study, which often correlate with immune cell profiles in adipose tissues (12), but further investigation on immune cell parameters in abdominal subcutaneous and visceral adipose tissue and adipose tissue dysfunction (45) is needed to elucidate biological mechanisms linking obesity to insulin resistance.