Subclinical hypothyroidism and anxiety may contribute to metabolic syndrome in Sichuan of China: a hospital-based population study

Abstract

The prevalence of Metabolic syndrome (MetS) in Sichuan of China has not yet been estimated. Meanwhile the association among anxiety, subclinical hypothyroidism (SCH) and MetS was less well-studied. The data was retrieved retrospectively from Health Promotion Center of West China Hospital database between 2014 and 2017. Internal validation by randomizing into training and testing panel by 9:1 and external validation with National Health and Nutrition Examination Survey (NHNES) were conducted. 19006 subjects were included into analysis, and 3530 (18.6%) of them were diagnosed with MetS. In training panel, age, sex (male), SCH (presence), SAS score, alcohol (Sometimes & Usual) and smoking (Active) were identified as independent risk factors for MetS, which was confirmed in testing panel internally. NHNES data validated externally the association between free thyroxine (fT4) and MetS components. The C-indices of predicting MetS nomogram were 0.705 (95% CI: 0.696-0.714) and 0.728 (95% CI: 0.701-0.754) in training and testing panel respectively. In conclusion, MetS prevalence was 18.6% in Sichuan. SCH and anxiety may be associated with MetS independently. A risk scale-based nomogram with accurate and objective prediction ability was provided for check-up practice, but more cohort validation was needed.

Figure 1

Subjects inclusion and exclusion flowchart. In all, 21477 subjects underwent complete physical examination in Health Promotion Center of West China Hospital from 2014 to 2017. 1295 of them were diagnosed with overt hypo/hyperthyroidism and were removed from final analysis. 487 and 689 were also excluded because of lacking written informed consents and incomplete information, respectively.

Figure 2

Psychological condition and sleep quality distribution diagram. The blue, red and green bars were used to represent three different levels for SDS, SAS and PSQI scores, respectively. The length of bars stood for the percentage of relevant levels in all aberrant groups, in which Normal/Excellent level was excluded for diagram beautifying. For SDS, Mild, Moderate and Severe groups accounted for 68.21%, 28.75% and 3.04%; for SAS, the proportions were 81.06%, 16.13% and 2.81%; and for PSQI, 80.33%, 18.62% and 1.05% subjects reported their sleep quality as Satisfied, Less Satisfied and Unsatisfied, respectively.

Figure 3

Pearson correlation graph between components of MetS and independent risk factors with adjusted dot plot. All independent risk factors and components of MetS were represented in transverse axis and vertical axis respectively. The presence of a color dot in the cross of vertical axes indicated an identified significant correlation between the two elements, and different size of the dot relates to corresponding range of Pearson’s correlation coefficient (r). Size 1, 2, 3 meant a r value range of 3 × 10⁻⁴ − 3 × 10⁻³, 3 × 10⁻³–3 × 10⁻² and 3 × 10⁻²–3 × 10⁻¹ respectively. Except SAS score did not correlate with triglyceride, all other components had reciprocal correlation significantly.

Figure 4

Nomogram for prediction of related risk factors and the possibility of MetS. All independent risk factors with statistical significance (sex, age, SAS score, SCH, alcohol and cigarette consumption) were included and unified into one risk factor MRRF (MetS-related risk factor). The 1st line is the standard scale compared to each downward line (2–7), by which a specific score of each line could be calculated. Then sum of 6 lines (scores) will be put on the 8^th line as the total points, and projected to the last line, which stands for the possibility of MetS. The C-indices of predicting MetS nomogram were 0.705 (95% CI: 0.696, 0.714) and 0.728 (95% CI: 0.701, 0.754) in training and testing panel respectively, indicating that the proposed nomogram of MetS prediction performed well in all MetS subjects.