The association between subclinical hypothyroidism and metabolic syndrome: an update meta-analysis of observational studies

Abstract

The association between subclinical hypothyroidism (SCH) and metabolic syndrome (MetS) has been widely discussed. This study aimed to conduct an update and comprehensive meta-analysis to reveal the risk of MetS and its components in SCH. PubMed, Embase and ISI Web of Knowledge were searched to identify relevant studies through February 20^th, 2020. Review Manager 5.3 and Stata 14.0 were used to conduct the meta-analysis. Both fixed-effects and random-effects models were used. In total, 18 articles (19 studies) incorporating 79,727 participants were included. The pooled OR for MetS comparing subjects with SCH with euthyroid subjects was 1.28 (95% CI: 1.19 to 1.39, p = 0.04, I² = 40%). Subgroup analysis results showed significant associations of SCH and MetS in the adult subgroup (OR = 1.28, 95% CI: 1.18–1.40), Asian population subgroup (OR = 1.30, 95% CI: 1.19–1.42) and cross-sectional study design subgroup (OR = 1.31, 95% CI: 1.16–1.47). Significant associations of SCH and MetS also existed in all MetS definition criteria subgroups except the Chinese Diabetes Society (CDS) subgroup. SCH was correlated with MetS and was not affected by the subgroup analysis stratified by the proportion of females in the total population, the TSH cutoff value in SCH diagnostic criteria, or the adjustment for confounding factors. SCH was identified to be associated with an increased risk of obesity, hypertension, high triglyceride (TG) levels and low high-density lipoprotein cholesterol (HDL-C) levels. In conclusion, SCH is significantly associated with an increased risk of MetS and four out of five components of MetS.

SUBCLINICAL HYPOTHYROIDISM (SCH) is defined as an increased serum thyroid-stimulating hormone (TSH) level with normal free thyroxine concentrations (FT4). This condition affects up to 10% of the adult population worldwide [1]. The debate about the relationship between SCH and cardiovascular disease (CVD) is ongoing. A number of evidence-based studies have shown that SCH is associated with an increased risk of CVD [2, 3]. Furthermore, a meta-analysis of prospective cohort studies revealed that SCH is associated with an increased risk of CVD and all-cause mortality [4]. These observational studies suggest that SCH may be a predictive biomarker for the risk of CVD and its metabolic risk factors.

On the other hand, metabolic syndrome (MetS), characterized by a cluster of risk factors for CVD, including central obesity, hypertension, high triglyceride (TG) levels, low high-density lipoprotein cholesterol (HDL-C) levels, and hyperglycemia, is a common disease in the general population [5]. MetS is associated with a 2-fold increased risk of CVD and a 5-fold increased risk of developing type 2 diabetes mellitus [5]. Until now, the association between SCH and MetS has been widely explored with controversial results for more than a decade [6-12]. Most of the studies reported that SCH leads to an increased risk of MetS, as well as its components [6-9], while others suggested opposite views [10-12].

To date, three previous meta-analyses investigating the relationship between SCH and MetS have been published [13-15]. However, the first was conducted in 2013 [13], and the other two were conducted in 2016 [14, 15]. The latest dataset collected data up to September 2015 [15]. Since then, five more observational studies examining the association between SCH and MetS and its components have emerged [6, 7, 9, 10, 16], and three were relatively large-scale and well-designed studies [6, 7, 9].

Thus, we performed an updated systematic review and meta-analysis of all available data to investigate the association between SCH and MetS and its five components.

Materials and Methods

This meta-analysis was conducted and reported according to the Meta-Analysis of

Observational Studies in Epidemiology guidelines.

Data sources and search strategy

Two authors (Ding and Zhao) independently searched relevant articles in three databases, PubMed, Embase and ISI Web of Knowledge, from inception up to February 20^th, 2020. The search strategy comprised the following terms: “subclinical hypothyroidism” or “subclinical thyroid disease” or “thyroid dysfunction” or “thyroid hormones” or “thyrotropin” and “metabolic syndrome”. The search had no population restriction or language limits. The primary retrieval resulted in 902 articles from PubMed, 869 from EMBASE, and 450 from ISI Web of Science. References of included articles and relevant reviews were also manually reviewed for eligible studies.

Inclusion criteria

Studies were identified as eligible if they met the following inclusion criteria: (1) a published observational study having clear definitions of SCH, euthyroidism and MetS; (2) individuals in the study population did not have diseases other than SCH and MetS; (3) the incidence or prevalence of MetS in groups of SCH and euthyroid subjects was clearly presented or could be calculated or converted; and (4) the odds ratio (OR), relative risk (RR) or hazard ratio (HR) and the corresponding 95% confidence interval (CI) were reported or could be calculated or converted (some studies may provide only case numbers of MetS in groups of SCH and euthyroid patients). Studies that used animal models or cell cultures, were written as reviews, case reports, or conference abstracts, and were published in languages other than English or Chinese were excluded. There were no cases of overlapping studies.

Data extraction and quality assessment

The following data were extracted: the first author’s name, year of publication, region, study design, sample size of the study, mean age of the study population, characteristics of the study population, female proportion of the study population, diagnostic criteria of SCH, diagnostic criteria of MetS, and adjustment for study covariates. Two reviewers (Ding and Zhao) independently conducted study selection and data extraction. Disagreements about assessments between reviewers were resolved by discussion.

The Agency for Healthcare Research and Quality (AHRQ) test was used for the quality assessment of cross-sectional studies, while the Newcastle-Ottawa Scale (NOS) was used to assess the quality of case-control studies and cohort studies.

Statistical analysis

The meta-analysis was performed with Review Manager 5.3 (Nordic Cochran Centre, Copenhagen, Denmark) and Stata 14.0 (Stata Corp. LP, College Station, TX). The study had two outcomes: the first outcome was the risk of MetS in SCH, and the second outcome was the risk of each component of MetS in SCH. ORs were considered the most common measure of the association between SCH and MetS, and RRs and HRs in studies exploring associations between SCH and MetS were assumed to have approximately the same effect estimate as ORs. To combine the ORs and 95% CIs of all included studies into mean ORs and 95% CIs, the present study converted ORs to log ORs and then chose log ORs and standard errors (SEs) as final effect estimates. The log OR and SE of every study were calculated by RevMan’s own calculator and were pooled to aggregate a mean log OR and 95% CI. Statistical heterogeneity across studies was assessed through the Q and I² statistics. An I² statistic ≤50% was proposed to indicate low heterogeneity and recommend the use of a fixed-effects model, while an I² statistic >50% was considered to indicate high heterogeneity and recommend the use of a random-effects model. This study assessed the combined risk estimates through either fixed-effects models or random-effects models with the presence of heterogeneity. Publication bias was evaluated through Egger’s test and Begg’s test. A two-tailed p-value less than 0.05 was considered indicative of statistical significance.

Results

Literature search and study selection

The flowchart of the literature search and study selection progress is displayed in Fig. 1. The database search initially yielded 2,221 articles: 902 articles from PubMed, 869 articles from Embase and 450 articles from ISI Web of Knowledge. After the preliminary screening, 143 articles remained. After reading the titles and/or the abstracts, 116 articles were excluded. A total of 27 articles underwent full-text review. Nine articles were excluded for the reasons listed in Fig. 1. Finally, 18 articles were included in this meta-analysis [6-12, 16-26].

Fig. 1

Flowchart of the selection of studies included in this meta-analysis

Study characteristics and quality assessment

The characteristics of the 18 included articles are shown in Table 1. Eighteen articles (19 observational studies: 15 cross-sectional studies, 3 case-control studies and 1 cohort study; the article by Waring et al. included both a cross-sectional study and a cohort study) involving 79,727 participants were included. The SCH group comprised 6,149 individuals, and the euthyroid group consisted of 73,578 subjects. The study sample sizes ranged from 92 (Luboshitzky et al.) [25] to 19,006 (Rui-cen Li et al.) [9].

Table 1 Characteristics of included studies

Author/Publication year	Region/study desigh	Sample size/SCH/euthyroid	Mean age	Characteristics of population/Women (%)	SCH diagnosis criteria	Severity classification of SCH	MetS diagnosis criteria	ORs/HRs/RRs; 95% CI	Adjustment for Covariates	Iodine intake	Autoantibodies
Li. RC (10) 2020	China/CS	19,006 2,811/16,195	42.9 ± 9.08	Adults/43.2%	An elevated TSH level with normal FT4	/	NCEP ATP III	1.216 (1.056, 1.401)	Not mentioned	/	/
Lee. MK (11) 2019	Korea/CS	1,006 143/863	14.2 ± 2.5	Adolescents/47%	TSH >4.5 mIU/mL with normal FT4	/	IDF	1.50 (0.54, 4.11)	Age, gender, and BMI	/	/
Liu. FH (8) 2018	Taiwan/CS	16,146 203/15,943	SCH: 53.8 ± 14.4 EU: 48.1 ± 12.1	Adults/39.8% (in Total) 50.2% (in SCH) 39.7%/ (in EU)	Elevated TSH levels (5.5–10 mIU/L)	/	NCEP ATP III	1.738 (1.298, 2.327)	Age and BMI	/	/
Bermúdez. V (17) 2018	Venezuela/CS	391 41/350	/	Adults/52.9%	TSH ≥4.12 mIU/L with normal FT4	/	JIS	2.06 (1.72, 3.959)	None	Iodine sufficient area	/
Mehran.L (7) 2017	Iran/CS	5,042 294/4,748	40.3 ± 14.4	Adults/57.2%	TSH >5.06 mIU/L and 0.91≤FT4≤1.55	/	JIS	1.76 (1.04, 2.97)	Age, gender, smoking and BMI	Iodine sufficient area	TPOAb was measured
Zhang. J (18) 2014	China/CS	895 29/866	SCH 14 ± 2 EU 14 ± 1	Adolescents/46.6%	TSH >5.73 mIU/L with normal FT4	/	IDF	1.908 (0.645, 5.644)	Age, gender, HOMA-IR, and BMI	/	TPOAb was measured
Posadas-Romero (19) 2014	Mexico/CS	753 133/620	SCH 56.1 ± 8.4 EU 52 ± 9.6	Adults/54.1%	High serum TSH with normal FT4	/	AHA/NHLBI	0.89 (0.53, 1.53)	Age, gender, BMI, visceral adipose tissue, high sensitivity C-reactive protein, free thyroxine, and LDL-C	/	/
Pesic. M (20) 2014	Serbia/CC	120 60/60	SCH 52.0 ± 8.79 EU 51.07 ± 10.50	Adults/71.7%	TSH >4.0 mIU/L (repeated in a three-month period) with normal FT4	Moderately elevated TSH levels TSH: 4.0–10.0 mIU/L Signifificantly elevated TSH >10.0 mIU/L	NCEP ATP III	1.75 (0.836, 3.662)	None	/	TPOAb and TgAb were measured
Sun. Y (21) 2014 (Chinese paper)	China/CS	1,846 146/1,700	62.3 ± 9.1	Adults/62.9%	TSH >4.8 mIU/L with normal FT4	/	CDS	1.211 (0.855, 1.716)	None	Iodine sufficient area	/
Gan. X (22) 2014 (Chinese Paper)	China/CS	2,160 110/2,050	47.2 ± 13.3	Adults/52.9%	TSH >4.78 mIU/L with normal FT4	/	CDS	1.708 (1.106, 2.638)	None	Iodine sufficient area	/
Nakajima. Y (9) 2013	Japan/CS	10,350 454/9,896	48 ± 9	Adults/38.4%	TSH >4.0 mIU/L with normal FT4	/	NCEP ATP III	2.70 (1.10, 5.60)	Age	Iodine sufficient area	/
Waring. A (23) 2012	US/CS	2,047 268/1,779	73.6 ± 2.9	Older adults/53%	TSH 4.5–20 mIU/L with normal FT4	Mild SCH (TSH 4.5–9.9 mIU/L, normal FT4)	NCEP ATP III	1.00 (0.70, 1.50)	Age, gender, race, smoking status, BMI, and HOMA-IR	/	/
						Marked SCH (TSH 10–20 mIU/L, normal FT4)		2.30 (1.00, 5.00)
	US/Cohort	2,047 268/1,779	73.6 ± 2.9	Older adults/53%	TSH 4.5–20 mIU/L with normal FT4	Mild SCH (TSH 4.5–9.9 mIU/L, normal FT4)	NCEP ATP III	1.20 (0.70, 1.90)	Age, gender, race, smoking status, BMI, and HOMA-IR	/	/
						Marked SCH (TSH 10–20 mIU/L, normal FT4)		2.20 (0.60, 7.50)
Wang. CY (13) 2012	Taiwan/CS	8,816 412/8,404	SCH 53.01 ± 11.55 EU 51.62 ± 11.67	Adults/42.7%	TSH 4–10 mIU/L	/	NCEP ATP III	1.047 (0.760, 1.443)	Age and gender	/	/
Liu. C (12) 2011	China/CS	6,339 538/5,801	48.9 ± 13.7	Adults/62.1%	TSH >4.5 mIU/L with normal FT4 and FT3	/	IDF	1.274 (1.040, 1.562)	None	/	/
Lai. Y (24) 2011	China/CS	1,385 102/1,283	SCH 46 ± 12 EU 45 ± 14	Adults/60.3%	TSH >4.8 mIU/L with normal FT4 and FT3	/	CDS	1.426 (0.889, 2.287)	Age, gender, HOMA-IR and BMI.	/	/
Erdogan. M (25) 2010	Turkey/CC	300 100/200	SCH 42.36 ± 14.06 EU 42.47 ± 13.05	Adults/89%	High serum TSH with normal FT4 and FT3	/	NCEP ATP III	1.093 (0.659, 1.813)	None	/	TPOAb and TgAb were measured
Luboshitzky. R (26) 2010	Israel/CC	92 43/49	SCH 55.9 ± 7.4 EU 52.0 ± 8.1	Adults/100%	TSH >4.5 mIU/L with normal FT4 (8.7–22.6 nmol/L)	/	AHA/NHLBI	5.160 (1.811, 14.704)	Age, BMI and waist size	/	TPOAb and TgAb were measured
Garduno-Garcia (27) 2010	Mexico/CS	3,033 262/2,771	42.3 ± 10	Adults/51.2%	4.5 ≤TSH <10 mIU/L with normal FT4 (10–25 pmol/L)	Mild SCH (TSH 4.5–9.9 mIU/L, normal FT4)	NCEP ATP III	1.021 (0.778, 1.339)	None	/	/

Abbreviations: CS, Cross-sectional study; CC, Case-control study; SCH, Subclinical hypothyroidism; EU, Euthyroidism; FL, Fatty liver; MetS, Metabolic syndrome; TSH, Thyroid stimulating hormone; FT4, Free thyroxine; FT3, Free Triiodothyronine; NCEP-ATP III, National Cholesterol Education Program-Adult Treatment Panel III; IDF, International Diabetes Federation; CDS, Chinese Diabetes Society; AHA/NHLBI, American Heart Association/National Heart, Lung, and Blood Institute; JIS, IDF/AHA/NHLBI/WHF/IAS/IASO-2009 criteria; BMI, Body mass index; HOMA-IR, Homoeostasis model of insulin resistance. TPOAb, thyroid peroxidase antibody; TgAb:thyroglobulin antibody.

Among the 19 studies, 13 studies were conducted in Asian countries, and 6 were conducted in non-Asian countries. Regarding the age of the population, two studies (Min-Kyung Lee et al. and Jingfan Zhang et al.) [10, 17] focused on adolescents, the studies by Waring et al. [22] were limited to older adults (70–79 years old), and the rest were about the general adult population. For the definition of MetS, 9 studies chose the National Cholesterol Education Program’s Adult Treatment Panel III (NCEP-ATP III) criteria, three chose the International Diabetes Federation (IDF) criteria, three selected the China Diabetes Society (CDS) criteria, two selected the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) statements, and two used the Joint Interim Statement (JIS) criteria. Regarding the female proportion of the study population, six studies had populations that were fewer than 50% female, seven studies had populations that were between 50% and 60% female, and the other six studies included populations that were more than 60% female.

Most studies defined SCH as an elevated TSH level with a normal FT4 concentration. Four studies identified the TSH level to be elevated above the cutoff value of 4.0–4.5 mU/L with a normal FT4 concentration, nine studies had a TSH cutoff value of 4.5–5.0 mU/L, three studies had a TSH cutoff value above 5.0 mU/L, and three studies did not have a defined cutoff value of TSH.

Out of the 19 studies, one did not mention whether the odds ratios were adjusted or not, 7 provided the specific number of people who had MetS in both the SCH and euthyroid groups, 8 provided the adjusted odds ratios adjusted for factors including “gender”, and 3 provided the adjusted odds ratios adjusted for factors not including “gender”.

The AHRQ test was used for the quality assessment of cross-sectional studies, while case-control studies and cohort studies were assessed by the NOS. Every item question of the quality assessment form was listed in turn, and a “Yes” was a confirmation of the question. The results in Table 2 indicated that the quality of the 19 included studies was relatively high.

Table 2 Quality assessment of included studies

A. Quality assessment of cross-sectional studies (AHRQ)
	1	2	3	4	5	6	7	8	9	10	11
Li RC et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No
Li MK et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes
Liu FH et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes	No
Bermúdez et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes	No
Mehran et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes
Zhang JF et al.	Yes	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No
Waring et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Liu C et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes	No
Posadas-Romero et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Lain YX et al.	Yes	Yes	No	Yes	Yes	Yes	Yes	Yes	No	Yes	No
Nakajima et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Wang CY et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No
Sun Y et al.	Yes	Yes	No	Yes	Yes	Yes	Yes	Yes	No	Yes	No
Gan XL et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes	No
Garduno-Garcia et al.	Yes	Yes	No	Yes	Yes	Yes	Yes	Yes	No	Yes	No
B. Quality assessment of case-control studies (NOS)
	1	2	3	4	5A	5B	6	7	8
Erdogan et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Luboshitzky et al.	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Pesic et al.	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes	Yes
C. Quality assessment of case-control studies (NOS)
	1	2	3	4	5A	5B	6	7	8
Waring et al. (cohort)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

A: Quality assessment of cross-sectional studies by the Agency for Healthcare Research and Quality (AHRQ)

1, define the source of information; 2, list inclusion and exclusion criteria for exposed and unexposed subjects or refer to previous publications; 3, indicate time period used for identifying patients; 4, indicate whether or not subjects were consecutive if not population-based; 5, indicate if evaluators of subjective components of study were masked to other aspects of the status of the participants; 6, describe any assessments undertaken for quality assurance purpose; 7, explain any patient exclusion from analysis; 8, describe how confounding was assessed and/or controlled; 9, if applicable, explain how missing data were handled in the analysis; 10, summarize patient response rate and completeness of data collection; 11, clarify what follow-up, if any, was expected and the percentage patients for which incomplete data or follow-up was obtained.

B: Quality assessment of case-control studies by the Newcastle-Ottawa Scale (NOS)

1, the case definition is adequate; 2, representativeness of cases; 3, controls drawn from community; 4, no history of disease in controls; 5A, study controlled for age; 5B, study controlled for other factor(s); 6, secure record of exposure; 7, same method of ascertainment for cases and controls; 8, same non-response rate for both groups.

C: Quality assessment of cohort study by the Newcastle-Ottawa Scale (NOS)

1, truly representative of the average older white and black population in the community; 2, drawn from the same community as the exposed cohort; 3, secure record of exposed cohort; 4, outcome of interest was not present at start of study; 5A, study controlled for age; 5B, study controlled for other factor(s); 6, independent blind assessment of outcome; 7, follow-up was long enough for outcomes to occur; 8, subjects lost to follow up unlikely to introduce bias.

Subclinical hypothyroidism and the risk of metabolic syndrome

The pooled ORs for the association between SCH and MetS showed that the risk of MetS was significantly increased in SCH subjects (OR = 1.28, 95% CI: 1.19 to 1.39, p < 0.00001, P_{heterogeneity} = 0.04, I² = 40%). (Fig. 2) Because the heterogeneity was relatively low (I² = 40% <50%), we chose the fixed-effects model.

Fig. 2

Forest plot showing the association between subclinical hypothyroidism and metabolic syndrome using a fixed-effects model

The subgroup analysis was performed based on seven factors: age, geographical region, study design, the definition criteria for MetS, the proportion of females in the total population, the TSH cutoff value in the SCH diagnostic criteria, and the adjusted confounding factors. The results of the associations between SCH and the risk of MetS according to the subgroup analysis are shown in Table 3.

Table 3 Subgroup analysis of the association between subclinical hypothyroidism and the risk of metabolic syndrome

Factor	No. of Studies	Odds ratio	95% CIs	p for Difference	Model	Subgroup Difference
Stratified by age
Adults	15	1.28	1.18, 1.40	<0.00001	FEM	I2 = 33.7% p = 0.22
Adolescents	2	1.16	0.86, 1.56	0.34
Older adults	2	2.27	1.13, 4.58	0.02
Stratified by geographical region
Non-Asian	6	1.20	0.97, 1.47	0.09	FEM	I2 = 0% p = 0.47
Asian	13	1.30	1.19, 1.42	<0.00001
Stratified by study design
Cross-sectional study	15	1.31	1.16, 1.47	<0.00001	REM	I2 = 0% p = 0.48
Case-control study	3	1.92	0.87, 4.28	0.11
Cohort study	1	2.20	0.60, 8.07	0.23
Stratified by the definition criteria of MetS
NCEP-ATP III	9	1.32	1.10, 1.58	0.003	REM	I2 = 0% p = 0.43
IDF	3	1.24	1.04, 1.46	0.01
CDS	2	2.02	0.36, 11.23	0.42
AHA/NHLBI	3	1.40	1.10, 1.77	0.006
JIS	2	1.87	1.24, 2.82	0.003
Stratified by the proportion of female in the total population
<50%	6	1.31	1.08, 1.58	0.006	REM	I2 = 0% p = 0.84
50%–60%	7	1.45	1.08, 1.94	0.01
≥60%	6	1.37	1.09, 1.72	0.008
Stratified by the TSH cutoff value
4.0 ≤TSH <4.5	4	1.61	1.02, 2.56	0.08	REM	I2 = 23.0% p = 0.27
4.5 ≤TSH <5.0	9	1.40	1.14, 1.72	0.08
TSH ≥ 5.0	3	1.48	1.08, 2.02	0.11
No definite cutoff value	3	1.18	1.04, 1.35	0.50
Stratified by the factors adjusted for
None or not mentioned	8	1.24	1.12, 1.38	0.37	REM	I2 = 57.6% p = 0.09
Factors including “gender”	8	1.24	1.03, 1.50	0.33
Factors not including “gender”	3	2.47	1.34, 4.56	0.11

NCEP-ATP III, National Cholesterol Education Program-Adult Treatment Panel III; IDF, International Diabetes Federation; CDS, Chinese Diabetes Society; AHA/NHLBI, American Heart Association/National Heart, Lung, and Blood Institute; JIS, IDF/AHA/NHLBI/WHF/IAS/IASO-2009 criteria; TSH, Thyroid stimulating hormone; FEM, Fixed effects model; REM, Random effects model.

When stratified by the age of the study population, the results revealed a significant association between SCH and MetS in both adults (OR = 1.28, 95% CI: 1.18 to 1.40, p < 0.00001, P_{heterogeneity} = 0.02, I² = 48%) and older adults (OR = 2.27, 95% CI: 1.13 to 4.58, p = 0.02, P_{heterogeneity} = 0.96, I² = 0%), with the exception of adolescents (OR = 1.16, 95% CI: 0.86 to 1.56, p = 0.34, P_{heterogeneity} = 0.60, I² = 0%) (Supplementary Fig. 1).

The results of the subgroup analysis based on geographical region showed that SCH was significantly associated with an increased risk of MetS in the Asian population (OR = 1.30, 95% CI: 1.19 to 1.42, p < 0.00001, P_{heterogeneity} = 0.07, I² = 40%) but not in the non-Asian population (OR = 1.20, 95% CI: 0.97 to 1.47, p = 0.09, P_{heterogeneity} = 0.09, I² = 47%) (Supplementary Fig. 2).

With regard to the study design, the results of the subgroup analysis indicated that SCH was significantly correlated with MetS only in the cross-sectional study subgroup (OR = 1.31, 95% CI: 1.16 to 1.47, p < 0.00001, P_{heterogeneity} = 0.09, I² = 35%). The pooled ORs showed no significant association between SCH and MetS in the case-control study subgroup (OR = 1.92, 95% CI: 0.87 to 4.28, p = 0.11, P_{heterogeneity} = 0.03, I² = 71%) or cohort subgroup (OR = 2.20, 95% CI: 0.60 to 8.07, p = 0.23, not applicable for heterogeneity) (Supplementary Fig. 3).

To explore the effect of the definition criteria of MetS on the association, the studies were categorized into NCEP-ATP III, IDF, CDS, AHA/NHLBI and JIS subgroups according to the definition criteria. The results of the subgroup analysis showed a significantly positive association between SCH and the risk of MetS in the NCEP-ATP III subgroup (OR = 1.32, 95% CI: 1.10 to 1.58, p = 0.003, P_{heterogeneity} = 0.06, I² = 47%), IDF subgroup (OR = 1.24, 95% CI: 1.04 to 1.46, p = 0.01, P_{heterogeneity} = 0.76, I² = 0%), CDS subgroup (OR = 1.40, 95% CI: 1.10 to 1.77, p = 0.006, P_{heterogeneity} = 0.48, I² = 0%) and JIS subgroup (OR = 1.87, 95% CI: 1.24 to 2.82, p = 0.003, P_{heterogeneity} = 0.71, I² = 0%). However, there was no association observed in the AHA/NHLBI subgroup (OR = 2.02, 95% CI: 0.36 to 11.23, p = 0.42, P_{heterogeneity} = 0.003, I² = 88%) (Supplementary Fig. 4).

Regarding the proportion of females in the total population, the results showed that SCH was significantly associated with an increased risk of MetS in all three subgroups: the <50% subgroup (OR = 1.31, 95% CI: 1.08 to 1.58, p = 0.006, P_{heterogeneity} = 0.09, I² = 48%), 50%–60% subgroup (OR = 1.45, 95% CI: 1.08 to 1.94, p = 0.01, P_{heterogeneity} = 0.06, I² = 51%) and >60% subgroup (OR = 1.37, 95% CI: 1.09 to 1.72, p = 0.008, P_{heterogeneity} = 0.15, I² = 38%) (Supplementary Fig. 5).

The results of the subgroup analysis based on the TSH cutoff value in the SCH diagnostic criteria revealed a positive relationship between SCH and an increased risk of MetS in all four subgroups: the 4.0≤TSH<4.5 subgroup (OR = 1.61, 95% CI: 1.02 to 2.56, p = 0.04, P_{heterogeneity} = 0.08, I² = 56%), 4.5≤TSH<5.0 subgroup (OR = 1.40, 95% CI: 1.14 to 1.72, p = 0.001, P_{heterogeneity} = 0.08, I² = 43%), TSH≥5.0 subgroup (OR = 1.48, 95% CI: 1.08 to 2.02, p = 0.01, P_{heterogeneity} = 0.11, I² = 55%) and no definite TSH cutoff value subgroup (OR = 1.18, 95% CI: 1.04 to 1.35, p = 0.01, P_{heterogeneity} = 0.50, I² = 0%) (Supplementary Fig. 6).

According to the factors studies were adjusted for, the studies were categorized into the “None or no mentioned” subgroup, factors including the “gender” subgroup and factors not including the “gender” subgroup. The results indicated that SCH was significantly correlated with MetS in all three subgroups: the “None or no mentioned” subgroup (OR = 1.24, 95% CI: 1.12 to 1.38, p < 0.0001, P_{heterogeneity} = 0.37, I² = 8%), factors including “gender” subgroup (OR = 1.24, 95% CI: 1.03 to 1.50, p = 0.03, P_{heterogeneity} = 0.33, I² = 12%) and factors not including “gender” subgroup (OR = 2.47, 95% CI: 1.34 to 4.56, p = 0.004, P_{heterogeneity} = 0.11, I² = 55%) (Supplementary Fig. 7).

Subclinical hypothyroidism and the risk of each metabolic syndrome-specific component

After summarizing the results from the included studies with available data on the risk of each specific MetS component, SCH was observed to be significantly associated with four out of five components of MetS: central obesity, hypertension, high TG level and low HDL-C level. (Table 4) The pooled OR was 1.21 (95% CI: 1.07 to 1.37, p = 0.003, P_{heterogeneity} = 0.18, I² = 28%) for central obesity (Supplementary Fig. 8), 1.76 (95% CI: 1.13 to 2.74, p = 0.01, P_{heterogeneity} <0.00001, I² = 94%) for hypertension (Supplementary Fig. 9), 1.36 (95% CI: 1.13 to 1.65, p = 0.001, P_{heterogeneity} = 0.002, I² = 63%) for high triglyceride level (Supplementary Fig. 10) and 1.16 (95% CI: 1.03 to 1.31, p = 0.01, P_{heterogeneity} = 0.06, I² = 45%) for low HDL-C level (Supplementary Fig. 11) and 1.12 (95% CI: 0.99 to 1.28, p = 0.08, P_{heterogeneity} = 0.26, I² = 18%) for hyperglycemia (Supplementary Fig. 12).

Table 4 The association between subclinical hypothyroidism and each specific component of metabolic syndrome (MetS)

MetS component	No. of Studies	Odds ratio	95% CIs	p for Difference	Pheterogeneity, I2	Model
Central obesity	11	1.21	1.07, 1.37	0.003	0.18, 28%	FEM
Hypertension	12	1.76	1.13, 2.74	0.01	<0.00001, 94%	REM
High triglyceride	12	1.36	1.13, 1.65	0.001	0.002, 63%	REM
Low HDL-C	10	1.16	1.03, 1.31	0.01	0.06, 45%	FEM
Hyperglycemia	12	1.12	0.99, 1.28	0.08	0.26, 18%	FEM

CI, confidence interval; HDL-C, high-density lipoprotein cholesterol; FEM, Fixed effects model; REM, Random effects model.

Publication bias

To investigate potential publication bias, we examined Begg’s and Egger’s tests, which showed publication bias (Begg’s test: p = 0.025; Egger’s test: p = 0.016). (Fig. 3) Then, the trim-and-fill method was used to collect the pooled ORs. After 6 studies were added (a total of 25 studies), publication bias still existed (p = 0.042) (Supplementary Fig. 13).

Fig. 3

Egger’s publication bias plot

Discussion

The unique strengths and novelty of the study

Our study is an updated and comprehensive meta-analysis based on 19 observational studies and performed to investigate the association between SCH and risk of MetS. Although three meta-analyses on the same topic had been published before (one in 2013 and the other two in 2016), our study still has novelty and three unique strengths:

First, our study is an updated meta-analysis. The original literature searches of the previous three meta-analyses ended in 2016, while five studies among the 19 we included were performed after 2016. These five studies incorporated 41,591 participants (SCH: 3,492; euthyroid: 38,099), which was nearly 52% of the total 79,727 patients in our meta-analysis. Moreover, the number of included studies in our meta-analysis was greater than that in any of the three previous meta-analyses (there were 6, 9 and 8 studies included in these three studies). Second, to extract the most complete and detailed data from the included studies, we did not make the inclusion criteria too restrictive. Unlike the first meta-analysis by Ye et al., which was confined only to cross-sectional studies, or the study by Eftekharzadeh et al., which applied a uniform MetS definition (NCEP-ATP III) to their data synthesis, studies eligible for our meta-analysis were not limited to a particular type of study design or specific MetS criteria. Although setting strict inclusion criteria for the literature may have the benefit of results that were not affected by potential flaws, we still believe that relatively broad but reasonable inclusion criteria combined with multiple stratification to perform subgroup analyses should be an optimal solution. With this in mind, we performed comprehensive subgroup analyses based on seven potential factors (age, geographical region, study design, the definition criteria for MetS, the proportion of females in the total population, the TSH cutoff value in the SCH diagnostic criteria, and the adjusted confounding factors), which was more than any of the three previous meta-analyses and is the third strength of our study.

The novel finding of our study is that SCH was associated with a 1.28-fold significantly increased risk of MetS compared with euthyroidism in our population, and for the first time, we found that the risk of MetS in SCH subjects seemed to differ according to age, ethnicity, study design and the definition criteria for MetS.

The association between SCH and MetS

The association between SCH and MetS has been extensively discussed because thyroid hormones have multiple effects on energy homeostasis and metabolism [27]. Our results indicated a significantly increased risk of MetS in SCH subjects, and the risk of MetS in SCH seemed to differ with variations in age, ethnicity, study design and the definition criteria for MetS.

Regarding age, the current results revealed that adults with SCH were more likely to develop MetS than adolescents. In fact, studies investigating the effect of SCH on body metabolism in adolescents are limited and remain controversial. The prevalence of SCH was estimated to be between 1.7% and 9.5% in children and adolescents [28]. The majority of the studies considered SCH to be a benign and remitting condition with a low risk of progression to overt thyroid dysfunction in children [29], while others suggested that SCH in obese children or adolescents should be carefully monitored to avoid increasing metabolic risk factors [30].

Regarding differences in ethnicity, our meta-analysis showed that Asian-SCH subjects had a higher predisposition to MetS than non-Asian-SCH participants, which is consistent with a previous study by Yang et al. [14]. Ethnicity may lead to differences in the association between SCH and MetS due to biological and sociocultural effects. TSH reference ranges were shown to vary across races [31], which results in significant differences in the prevalence of SCH among ethnic groups [32]. Some characteristics of the Asian population, such as an adequate seafood diet rich in iodine, a longer life expectancy and notable population aging progress, may also contribute to ethnic differences between Asian and non-Asian regions. However, the possibility that random error may account for this difference could not be excluded. Actually, we included only 6 studies in the non-Asian subgroup, and there were fewer participants (total: 6,344, SCH: 764, EU: 5,580) in the non-Asian subgroup than in the Asian subgroup (total: 73,380, SCH: 5,385, EU: 67,995). Thus, we expect more studies to explore the underlying reason for the racial differences occurring between Asian and non-Asian regions from biological and sociocultural aspects in the future.

In our study, a poor association between SCH and MetS was observed in the case-control study and cohort study subgroups. The small quantity of included studies in these two subgroups might account for this result, and random error cannot be excluded. Moreover, SCH was identified to be associated with MetS in all MetS definition criteria subgroups except the CDS criteria subgroup in our meta-analysis, indicating that the diagnostic criteria selected for MetS may have an effect on the assessment of MetS risk in SCH. The reason may be explained from one point of view: the CDS criteria define central obesity as BMI≥25 kg/m², while the NCEP-ATP III, IDF, AHA/NHLBI and JIS criteria all use the waist circumference threshold for MetS evaluation. Although a high BMI value is usually used to classify obesity, it is not equivalent to central obesity, which was confirmed in a study conducted by Eftekharzadeh et al. [15]. In fact, studies have demonstrated that BMI is only an imperfect measure of abnormal or excessive body fat accumulation and that using waist circumference as a measure of body fat distribution may improve visceral adiposity prediction [33, 34].

Simultaneously, three other important factors closely related to SCH should not be ignored in assessing the relationship between SCH and MetS: the severity classification of SCH, iodine intake condition and thyroid antibodies (thyroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TgAb)) levels. SCH can be categorized as grade 1 (mild SCH) when TSH levels are between the upper limit of the reference range and 9.9 mU/L and as grade 2 (marked SCH) if TSH levels are 10 mU/L or higher, and the guidelines recommend different approaches for each grade [35]. Although 90% of SCH subjects are in grade 1 status [35], most of the 19 current studies included in our meta-analysis did not clearly mention the grade of their SCH subjects (more details can been seen in Table 1). For iodine intake, after the World Health Organization recommended the use of iodized salt to prevent iodine deficiency, the current iodine intake status has been sufficient or even excessive in many countries or regions, including China, Korea, Taiwan, Mexico, the U.S., and Europe (which are exactly the countries or regions in which the 19 studies in our meta-analysis were conducted) [36-45]. However, more than adequate iodine intake may increase the risk of SCH and autoimmune thyroiditis [36, 46] and has a 17.3% prevalence in SCH subjects [47]. Moreover, thyroid antibodies could be predictors of SCH in the risk of progression to overt hypothyroidism [48, 49], and MetS is reported to be more prevalent in children of TPO-Ab-positive mothers [50], indicating that positive thyroid antibodies may be associated with an adverse cardiovascular health profile [51]. However, due to incomplete information about the iodine intake condition and thyroid antibody measurement in the 19 included studies (more details are shown in Table 1), subgroup analysis could not be performed to study in depth the impact that these three factors may have on assessing the relationship between SCH and MetS. In summary, more well-designed studies considering more potential factors in the correlation between SCH and MetS are expected to verify our findings or hypothesis.

The association between SCH and the components of MetS

In addition, we found that SCH is associated with four out of five components of MetS: obesity, hypertension, a high TG level and a low HDL-C level. SCH has been extensively reported to be associated with obesity [52], high triglyceride levels [53, 54], low HDL-C levels [53] and increased insulin resistance [55, 56]. Among these metabolic parameters, obesity is the most indispensable component of MetS, and insulin resistance is assumed to be both the key feature and the major underlying cause of MetS. Our results revealed a positive correlation between SCH and lipid dysfunction, including high triglyceride levels and low HDL-C levels. This can be explained by the fact that a lack of thyroid hormones (THs) to some extent in SCH may decrease the transcription of the low-density lipoprotein (LDL) receptor gene and suppress essential enzyme activity involved in the metabolism of lipoproteins, ultimately leading to dyslipidemia [53, 57].

Our meta-analysis illustrated a propensity for hypertension in SCH, which is consistent with a previously established conclusion. Two large-scale studies have demonstrated the association between TSH levels above the upper normal limit and hypertension [58, 59], and a meta-analysis showed that replacement therapy with levothyroxine could reduce blood pressure in SCH patients [60]. However, no significant association between SCH and hyperglycemia was found in our study.

Limitations of the study

Two limitations should be acknowledged when interpreting the present findings. First, except for one cohort study, the majority of our included studies were cross-sectional (15/19) and case-control (3/19) designs, which might make it difficult to discern causality from the association between SCH and MetS. More longitudinal large-scale prospective cohort studies are needed to demonstrate these results. Second, publication bias was present, as evidenced by Begg’s and Egger’s tests (Begg’s test: p = 0.025; Egger’s test: p = 0.016). Publication bias may arise because studies with statistically significant results are more likely to be accepted and published. This result suggested that studies that do not support a positive correlation between SCH and MetS may be less likely to be published. The results of our trim-and-fill method confirmed this point: after adding six potentially unpublished studies/datasets on this issue, publication bias still existed. This is consistent with previous research conducted by Ye et al.

In conclusion, our meta-analysis indicated that SCH was associated with an increased risk of MetS, as well as four out of five of its components: central obesity, hypertension, high TG levels and low HDL-C levels. The risk of MetS in SCH subjects seemed to differ according to age, ethnicity, study design and the definition criteria for MetS.

Acknowledgments

This research was supported by Grant 81873638 from the National Natural Science Fund, Grant 2018JM70990 from the Natural Science Foundation of Shaanxi Province and Grant 2018YFC1311500 from the National Key R&D Program of China.

Author Disclosure Statement

No competing financial interests exist.

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