Circulating propionate and butyrate are associated with metabolic improvements following probiotic and dietary fiber supplementation in patients on antipsychotics: a post-hoc analysis of a randomized controlled trial

Abstract

Background

Second-generation antipsychotics, a cornerstone of psychiatric disorder management, render treated patients highly prone to metabolic abnormalities. To address this unmet clinical need, this post-hoc analysis drew on data from a previous trial to examine the correlations between plasma short-chain fatty acid (SCFA) level alterations and metabolic changes in the context of probiotic-fiber intervention.

Methods

In this trial, individuals diagnosed with schizophrenia or bipolar disorder, who were undergoing stable atypical antipsychotic therapy, were recruited for this study. They were subsequently randomized in a 1:1:1:1 ratio to four treatment groups: combined probiotics (1680 mg/d) and dietary fiber (60 g/d); probiotics (1680 mg/d) with dietary fiber placebo; dietary fiber (60 g/d) with probiotics placebo; and double placebo (probiotics placebo plus dietary fiber placebo). Assessments were conducted at screening/baseline, week 4, and week 12, and the measurement of circulating SCFAs was performed via liquid chromatography-mass spectrometry. The analysis, employing the last-observation-carried-forward method, encompassed 79 participants who provided at least one follow-up plasma sample for the quantification of SCFAs.

Results

The 12-week combined administration of probiotics and dietary fiber was associated with changes in circulating levels of SCFAs and improvements in metabolic indices. More importantly, the higher levels of propionate were associated with decreased weight (adjusted odds ratio [OR]: 0.61 per quartile increase, 95% confidence interval [CI]: 0.38–0.96) and homeostatic model assessment of insulin resistance (HOMA-IR) (adjusted OR:0.58, 95% CI: 0.36–0.94). Also, the higher levels of butyrate were associated with a 42% lower odds (adjusted OR: 0.58, 95%CI:0.36–0.93) of elevated body mass index (BMI) and a 49% lower odds (adjusted OR: 0.51, 95%CI:0.31–0.86) of elevated insulin levels.

Conclusions

The findings of this study suggested that elevated circulating levels of butyrate and propionate might be associated with reduced weight gain and improved insulin resistance in individuals receiving antipsychotic medications.

Trial registration

ClinicalTrials.gov NCT03379597, trial registration date: 11/29/2017. Overall Recruitment Status: completed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-026-08272-x.

Introduction

The introduction of chlorpromazine into clinical practice half a century ago sparked a revolution in the treatment of schizophrenia. Antipsychotic medications (APs) rapidly became the cornerstone of pharmacological treatment for psychiatric disorders, which have been the first-line treatment for both schizophrenia and bipolar disorder [1]. Due to the superior therapeutic efficacy and fewer motor side effects, prescribing of second-generation antipsychotics (SGAs) has shown a consistent upward trend over the years [1–6]. However, SGAs have a significant impact on glucose and lipid regulation by primarily affecting the hypothalamus, liver, pancreatic β-cells, adipose tissue, and skeletal muscle, leading to elevated body weight, dyslipidemia, and insulin resistance [7]. The high prevalence of metabolic syndrome (MetS) among individuals with psychiatric disorders is associated with a mortality rate that is two to three times higher than that of the general population, along with a reduction in life expectancy of 10–20 years [8–14]. Hence, identifying and implementing evidence-based therapeutic interventions to halt early indicators of metabolic pathologies from escalating to severe complications is of paramount importance.

Short-chain fatty acids (SCFAs), a significant class of bio-products produced by the gut microbiota through the fermentation of non-digestible carbohydrates [15], predicate host health across cellular, tissue, and organ functions, influencing gut barrier integrity, glucose regulation, immune response modulation, and obesity [16–18]. Numerous studies have investigated the direct and indirect effects of SCFAs on metabolic conditions. Oral administration of acetate to obese and diabetic rats decreased weight gain and enhanced glucose tolerance [19]. The separate administration of propionate and butyrate could improve glucose homeostasis in rodent models [20, 21]. In humans, prolonged administration of inulin-propionate ester led to a notable decrease in weight gain [22]. In two separate randomized controlled trials, adherence to a high-fiber dietary intervention increased circulating SCFA concentrations, which correlated with significant weight reduction and favorable modifications to metabolic risk markers [23, 24]. Another randomized clinical trial utilizing specially formulated isoenergetic diets, in conjunction with fecal shotgun metagenomics analysis, demonstrated that enhanced diversity and abundance of fiber-promoted bacterial strains producing SCFAs were positively correlated with greater improvements in hemoglobin A1c levels among participants, the deliberate restoration of SCFA producers could offer a novel ecological strategy for the management of type 2 diabetes mellitus (T2DM) [25].

Building on our previous finding that a probiotic-fiber combination alleviates AP-related metabolic abnormalities by enhancing gut microbiota abundance [26], we conducted this post-hoc analysis. Leveraging data and samples from the original trial, we aimed to investigate whether the intervention modulated plasma SCFA levels and to evaluate potential correlations between these alterations and metabolic improvements.

Materials and methods

Participants and procedure

The design and results (effectiveness and safety) of this trial have been described elsewhere [26, 27]. Briefly, this was a randomized, placebo-controlled, double-blind clinical trial, which has been conducted in the Department of Psychiatry, the Second Xiangya Hospital from August 2019 to June 2021.

Eligible participants included individuals aged 18–45 years diagnosed with schizophrenia or bipolar disorder according to the DSM-5 criteria, all of whom were receiving stable atypical antipsychotic therapy. After a baseline screening, 136 participants were randomly assigned (1:1:1:1) to one of four 12-week intervention arms using a 2 × 2 factorial design: (1) probiotics (1680 mg/d) plus dietary fiber (60 g/d); (2) probiotics plus fiber placebo; (3) dietary fiber plus probiotic placebo; or (4) double placebo. Randomization was achieved via a computer-generated block sequence (block size of eight). To ensure trial integrity, an independent research assistant not involved in the study performed the randomization and blinding. The full study methodology has been described in the published protocol [27].

Measures

Clinical assessments were conducted by trained researchers at baseline, week 4, and week 12. Baseline evaluations comprised demographic characteristics, medical history, anthropometric measurements (weight and height), and biochemical parameters (including fasting glucose, fasting insulin, and lipid profiles). Psychiatric symptoms were evaluated using standardized clinical scales. All baseline measures were consistently reassessed during subsequent follow-up visits. Additionally, a self-reported appetite questionnaire was used to track longitudinal appetite changes throughout the study. Patients self-assessed appetite at baseline and follow-up, using their pre-medication status as the reference (100%); values > 100% reflected increased appetite, while values < 100% reflected decreased appetite.

Measurement of circulating SCFAs

While circulating SCFAs - integrated outcomes of synthesis, absorption and tissue utilization - are not the gold standard for clinical research, they correlate more strongly with metabolic parameters than fecal SCFAs [28]. Given the pre-existing data constraints of this post-hoc analysis, circulating SCFAs represent the best feasible option among accessible indicators. The measurement of circulating SCFAs, including formate, acetate, propionate, butyrate, isobutyrate, and valerate, was performed via liquid chromatography-mass spectrometry (LC-MS) (AB Sciex LC-MS system QTRAP^® 6500). The methodological details have been provided in the supplementary materials.

Statistical analysis

The analyses were performed using the Statistical Package for Social Sciences, version 27.0 (SPSS Inc., Chicago, Illinois). For participants with at least one follow-up visit and stored plasma sample, the last-observation carried-forward (LOCF) method was employed for efficacy analysis based on the intention-to-treat principle.

Differences among the groups were determined using one-way ANOVA, with corresponding baseline values, sex, age, and disease course as covariates, and then multiple comparisons between groups were performed using the least significant difference (LSD) t-test. Two-tailed p-values < 0.05 were considered statistically significant. Results were presented as means with 95% confidence intervals (CIs). Indices demonstrating statistically significant group differences were chosen for further analysis of their correlations. Initially, Spearman correlation analysis was conducted to explore potential associations between SCFA levels and metabolic indices. Subsequently, a forward stepwise logistic regression analysis was performed to identify which SCFAs were most strongly associated with metabolic indices. The alterations in metabolic indices following treatment were identified as the binary dependent variable, while the candidate SCFAs—namely acetate, propionate, butyrate, and valerate—were stratified into quartiles as independent variables. Results were presented as odds ratios (ORs) with 95% CIs. Given that the measurement of SCFAs was not pre-specified in the study protocol, this analysis was exploratory in nature.

Results

Participant demographic information

Seventy-nine participants had at least one follow-up plasma sample collected for quantification of SCFAs. The demographic and clinical characteristics, as well as baseline measurements, are presented in Table 1 and Supplement Table 1.

Table 1.

Demographic and clinical characteristics of participants across treatment groups at baseline

	Probiotics + dietary fiber (N = 21)		Probiotics (N = 19)		Dietary fiber (N = 20)		Placebo (N = 19)
	N		N		N		N
Sex, female	21		15		19		18
Diagnosis, schizophrenia	7		12		9		8
	Mean	SD	Mean	SD	Mean	SD	Mean	SD	Normal range
Age, year	25.86	7.47	23.89	6.44	27.00	6.10	27.42	7.38
Duration, mo	63.86	83.42	44.42	66.41	65.70	47.77	54.26	63.22
Dose, mg	350.48	175.53	447.11	247.78	365.88	186.53	359.17	200.55
Appetite, %	144.29	22.49	140.53	23.39	143.50	33.13	146.67	28.08
Weight, kg	68.03	11.28	71.37	15.07	70.84	13.80	67.35	9.03
BMI, kg/m2	26.35	3.99	27.10	3.44	27.79	4.51	27.02	3.87	18.50–23.90 kg/m2
Fasting glucose, mmol/L	4.80	0.74	4.60	0.57	4.33	0.68	4.52	1.42	3.90–6.10 mmol/L
Fasting insulin, uIU/mL	16.81	9.49	18.05	7.46	11.73	5.60	12.28	5.05	6.40–15.00 uIU/mL
HOMA-IR	3.64	2.21	3.64	1.52	2.33	1.25	2.66	1.88	< 2.67
Triglyceride, mmol/L	1.57	1.03	1.55	0.79	1.77	0.85	1.95	1.12	< 1.71 mmol/L
Total cholesterol, mmol/L	4.44	0.70	4.45	0.72	4.53	0.77	4.59	0.54	2.90–5.20 mmol/L
HDL-C, mmol/L	1.23	0.23	1.13	0.24	1.20	0.34	1.35	0.26	> 1.04 mmol/L
LDL-C, mmol/L	2.70	0.64	2.88	0.61	2.85	0.55	2.84	0.49	< 3.12 mmol/L

Dose was calculated as chlorpromazine equivalent dosage; BMI was calculated as weight in kilograms divided by height in meters squared; HOMA-IR was calculated as insulin level (mIU/L) × fasting glucose(mmol/L)/22.5. Data were presented with mean and SD. BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance; LDL-C, low-density lipoprotein cholesterol; SD, standard deviations

Effects of probiotics, dietary fiber, or their combination on metabolic indices

Following a 12-week intervention, the co-administration of probiotics and dietary fiber resulted in a significant improvement in metabolic indicators, including weight (-2.37, adjusted 95% CI: -4.13 to -0.60 kg), body mass index (BMI) (-0.95, adjusted 95% CI: -1.64 to -0.26 kg/m²), insulin levels (-2.07, adjusted 95% CI: -5.09 to 0.95μIU/mL), homeostatic model assessment of insulin resistance (HOMA-IR) (-0.54, adjusted 95% CI: -1.21 to 0.14), and total cholesterol levels (-0.40, adjusted 95% CI: -0.73 to -0.07mmol/L), compared to the placebo group: weight (2.89, adjusted 95% CI: 0.99 to 4.79 kg, p < 0.001), BMI (1.19, adjusted 95% CI: 0.45 to 1.93 kg/m², p < 0.001), insulin levels (4.03, adjusted 95% CI: 1.05 to 7.01μIU/mL, p = 0.006), HOMA-IR (1.08, adjusted 95% CI: 0.42 to 1.74, p = 0.001), and total cholesterol levels (0.29, adjusted 95% CI: -0.04 to 0.62mmol/L, p = 0.004). The administration of either probiotics (-0.22, adjusted 95% CI: -0.89 to 0.45, p = 0.008) or dietary fiber (0.06, adjusted 95% CI: -0.54 to 0.66, p = 0.02) as a single intervention was shown to improve HOMA-IR levels when compared to placebo. Regarding glucose, triglyceride, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels, while there were no statistically significant improvements between the intervention and placebo groups, we observed a trend towards preventing deterioration (Fig. 1, Supplement Table 2).

Fig. 1.

The difference between baseline and week 12 (Δchange) of metabolic indexes. BMI was calculated as weight in kilograms divided by height in meters squared; HOMA-IR was calculated as insulin level (mIU/L) × fasting glucose(mmol/L)/22.5. Data were presented with mean and 95% confidence intervals (CIs). P-value for the overall differences among four groups was tested initially by the omnibus analysis and based primarily on analysis of covariance (ANCOVA) with baseline levels of the variables as covariates. Follow-up pairwise comparisons were performed when the overall omnibus analysis p-value was significant. BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance; LDL-C, low-density lipoprotein cholesterol. *: p<0.05, **: p<0.01, ***: p<0.001

Moreover, the study revealed that the appetite of participants in the three intervention groups decreased to varying extents. The co-administration group exhibited the most substantial reduction of -50.38% (adjusted 95% CI: -73.22 to -27.53, p = 0.01) compared to the placebo group (-2.17%, adjusted 95% CI: -26.78 to 22.43). While not statistically significant, the probiotics-only group exhibited a decrease of -28.98% (adjusted 95% CI: -53.58 to -4.38) and the dietary fiber only group experienced a reduction of -19.31% (adjusted 95% CI: -42.48 to 3.86) compared to the placebo group (Fig. 1, Supplement Table2).

Effects of probiotics, dietary fiber, or their combination on SCFAs

The acetate was remarkably increased in the co-administration group (24449.52, adjusted 95% CI: 9072.69 to 39826.34 nmol/L) after 12-week treatment, compared with varying degrees of reduction in probiotics only (-101.96, adjusted 95% CI: -16666.65 to 16462.73nmol/L, p = 0.02), dietary fiber only (-1899.30, adjusted 95% CI: -17857.32 to14058.73nmol/L, p = 0.04) and placebo (-4058.95, adjusted 95% CI: -20152.32 to 12034.43nmol/L, p = 0.01) groups. Propionate was increased in three intervention groups, significant increases occurred in co-administration (1618.38, adjusted 95% CI: 773.46 to 2463.29nmol/L, p = 0.004) and probiotics-only group (1067.76, adjusted 95% CI: 153.41 to 1982.11nmol/L, p = 0.047) compared with placebo (-234.97, adjusted 95% CI: -1118.40 to 648.45nmol/L). Similarly, butyrate was remarkably increased in co-administration group (5078.09, adjusted 95% CI: 1918.63 to 8237.54nmol/L, p = 0.004), probiotics only (4039.36, adjusted 95% CI: 651.86 to 7426.87nmol/L, p = 0.02), and dietary fiber only (1078.74, adjusted 95% CI: -2123.28 to 4280.76nmol/L, p = 0.01) compared with placebo (-1686.30, adjusted 95% CI: -4975.50 to 1602.90nmol/L). As for valerate, significant increases occurred in co-administration (74.17, adjusted 95% CI: 4.34 to 144.00nmol/L, p = 0.02) and probiotics only group (83.27, adjusted 95% CI: 10.31 to 156.23nmol/L, p = 0.01) compared with placebo (-51.26, adjusted 95% CI: -124.87 to 22.35nmol/L), and there was no significance between dietary fiber only (-32.09, adjusted 95% CI: -102.66 to 38.49nmol/L) and placebo group (Fig. 2, Supplement Table3).

Fig. 2.

The difference between baseline and week 12 (Δchange) of all short-chain fatty acids. Data were presented with mean and 95% CIs. P-value for the overall differences among four groups was tested initially by the omnibus analysis and based primarily on ANCOVA with baseline levels of the variables as covariates. Follow-up pairwise comparisons were performed when the overall omnibus analysis p-value was significant. *: p<0.05, **: p<0.01

The correlation between metabolic indices and SCFAs

The results of the Spearman correlations revealed statistically significant negative associations between acetate levels and appetite (r = -0.274, p = 0.015), as well as between body weight and acetate (r = -0.227, p = 0.045) and propionate (r = -0.279, p = 0.013) levels. Furthermore, BMI showed negative correlations with acetate (r = -0.230, p = 0.043, propionate (r = -0.285, p = 0.011), and butyrate (r = -0.224, p = 0.048) levels. Additionally, insulin levels were negatively correlated with propionate (r = -0.275, p = 0.022), butyrate (r = -0.365, p = 0.002), and valerate (r = -0.273, p = 0.025) levels. HOMA-IR values were predominantly associated with propionate (r = -0.259, p = 0.031) and butyrate (r = -0.324, p = 0.007) levels (Fig. 3).

Fig. 3.

Associations between short-chain fatty acids and metabolic indices. Spearman correlations were performed to investigate associations between short-chain fatty acids (SCFAs) and metabolic indices, which exhibited significant alterations following 12-week interventions

Further analysis was conducted using forward stepwise logistic regression to identify which SCFAs were most strongly associated with metabolic indices. We found the higher levels of propionate were associated with decreased weight (OR: 0.60 per quartile increase, 95% CI: 0.39–0.93) and HOMA-IR (OR:0.62, 95% CI: 0.40–0.97), these relationships remained significant after adjustment for age, sex and disease duration (adjusted OR: 0.61, 95% CI: 0.38–0.96; adjusted OR: 0.58, 95% CI:0.36–0.94, respectively). Also, the higher levels of butyrate were associated with a 42% lower odds(OR:0.58, 95% CI: 0.37–0.90; adjusted OR: 0.58, 95% CI:0.36–0.93) of elevated BMI and a 49% lower odds (OR:0.51, 95% CI: 0.32–0.82; adjusted OR: 0.51, 95% CI:0.31–0.86) of elevated insulin levels (Fig. 4, Supplement Table 4).

Fig. 4.

The influence of short-chain fatty acids on metabolic indices. Forward stepwise logistic regression was performed to investigate the impact of changes in SCFAs on metabolic alterations. Candidate SCFAs (acetate, propionate, butyrate, and valerate) were classified in quartiles. Odds ratio adjustment was performed for age, sex, and disease duration in the analysis

Discussion

In this study, we examined the potential metabolic benefits conferred by circulating SCFAs in individuals receiving APs, where these SCFAs may be derived from elevated microbial fermentation efficiency induced by probiotic-fiber co-administration. This post-hoc analysis suggested that 12-week co-administration of probiotics and dietary fiber might be associated with alterations in circulating SCFA concentrations, specifically acetate, propionate, butyrate, and valerate. Notably, elevated propionate and butyrate levels may be associated with improved outcomes in body weight and insulin sensitivity. In contrast, there was no significant difference in the effects observed in groups receiving either probiotics or dietary fiber alone. Conversely, the placebo group exhibited the most substantial reduction in circulating SCFAs, which coincided with the poorest metabolic profiles after 12-week intervention.

Several clinical trials have shown the beneficial effects of SCFAs on metabolic indices in not only healthy individuals but also individuals with impaired insulin sensitivity, preexisting overweight/obesity, or multiple gastrointestinal disorders. Sanna et al. [29] found that elevated butyrate levels were correlated with enhanced insulin response after an oral glucose tolerance test in a large sample involving 952 normoglycemic individuals. Conversely, disruptions in propionate production or absorption were linked to heightened susceptibility to T2DM. A systematic review and meta-analysis of 23 studies indicated that SCFAs administered directly, as sodium salts or esters, or indirectly, as pre-/probiotics or high-fiber diets could notably improve fasting insulin and HOMA-IR [30]. Vitale et al. [31] illustrated that an 8-week fiber-rich diet (8.1 ± 2.3 g/1000 kcal) led to improved postprandial glucose levels and insulin sensitivity in overweight/obese individuals at high cardiometabolic risk, closely related to plasma butyrate levels. A pilot study involving 60 adults with a BMI ≥ 25 kg/m² and either prediabetes or T2DM demonstrated that probiotics might be a complementary therapy to metformin by stimulating butyrate production, potentially enhancing glycemic control [32]. The acute consumption of amylose-rich breads improved insulin metabolism, exhibiting enduring effects beyond the immediate postprandial period and impacting the metabolic response to subsequent meals. These metabolic benefits were linked to the generation of SCFAs, specifically propionate [33]. Notably, circulating propionate and butyrate levels were correlated with changes in BMI observed 12 months after bariatric surgery (BS) in individuals with severe obesity [34]. Accumulating animal experimental evidence supported the key role of SCFAs in bridging gut ecosystem and systemic metabolism, as they could mitigate or reverse weight gain and adiposity. An animal study demonstrated that SCFAs, propionate and butyrate, produced through the fermentation of dietary fiber by the gut microbiota, contributed to metabolic advantages in terms of body weight and glucose regulation by stimulating intestinal gluconeogenesis (IGN) through distinct mechanisms [20]. Specifically, butyrate induced IGN gene expression through a cAMP-dependent pathway, whereas propionate, serving as a substrate for IGN, triggers IGN gene expression via a gut-brain neural pathway involving the fatty acid receptor FFAR3 [20]. Similarly, research demonstrated that elevated levels of butyrate and propionate obtained from gamma-aminobutyric acid-enriched rice bran could effectively mitigate weight gain and insulin resistance induced by a high-fat diet. This effect might be attributed to the stimulation of gut hormone release (such as leptin, glucagon-like peptide-1, and gastrin), leading to reduced food intake through the regulation of hypothalamus/appetite pathways, as well as the alleviation of insulin resistance by inhibiting ceramide synthesis [35]. Moreover, peripheral acetate administration could increase pro-opiomelanocortin (POMC) and reduce agouti-related peptide (AgRP) expression in the hypothalamus, which suggested that acetate had a direct effect on the hypothalamic control of appetite [36], as evidenced by the observed negative correlations between acetate levels and appetite in our findings. All three SCFAs showed protective effects against diet-induced obesity, with butyrate and propionate exhibiting greater efficacy compared to acetate [21]. In accordance with the findings of our present exploration, a negative correlation was observed between metabolic indexes and SCFAs, including acetate, propionate, and butyrate, while butyrate and propionate showed a more pronounced association with body weight loss and improved insulin resistance.

Despite the significant findings, this study has several constraints. First, given that plasma SCFAs were not pre-specified in the original trial protocol and no adjustments were applied for multiple comparisons, this study should be considered inherently exploratory. Our findings are therefore hypothesis-generating rather than confirmatory, and the potential inflation of Type I error warrants due consideration. Second, with respect to missing data, the application of LOCF—which rests on stringent assumptions—may introduce bias, warranting cautious interpretation of the findings. Third, the relatively modest sample size available for SCFA quantification may have limited the statistical power to detect more subtle correlations or interactions. Fourth, the lack of longitudinal follow-up beyond 12 weeks leaves the long-term efficacy of this intervention unknown. Lastly, potential confounders, such as the specific type of atypical antipsychotic used and individual lifestyle factors (e.g., exercise and caloric intake), were not exhaustively controlled, which may introduce bias into the correlation analysis.

Conclusion

Our study demonstrated that a 12-week synbiotic-like intervention (probiotics plus dietary fiber) could effectively modulate the circulating SCFA levels in patients on antipsychotics. Specifically, the increases in propionate and butyrate might serve as potential mediators in mitigating weight gain and enhancing insulin sensitivity. Future large-scale, prospective clinical trials are essential to validate these SCFA-mediated pathways and to refine therapies for psychiatric populations at high metabolic risk.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

AgRP

Agouti-related peptide

ANCOVA

Analysis of covariance

ANOVA

Analysis of variance

APs

Antipsychotic medications

BMI

Body mass index

CI

Confidence interval

DSM-5

The diagnostic and statistical manual of mental disorders fifth edition

HDL-C

High-density lipoprotein cholesterol

HOMA-IR

Homeostatic model assessment of insulin resistance

IGN

Intestinal gluconeogenesis

LC-MS

Liquid chromatography-mass spectrometry

LDL-C

Low-density lipoprotein cholesterol

LOCF

Last-observation-carried-forward

LSD

Least significant difference

MetS

Metabolic syndrome

OR

Odds ratios

POMC

Pro-opiomelanocortin

SCFAs

Short-chain fatty acids

SGAs

Second-generation antipsychotics

T2DM

Type 2 diabetes mellitus

Author contributions

The authors’ responsibilities were as follows - PS and JH designed research; JMX, CCL, TNS, DYK, and HT conducted research; JMX and FKL performed statistical analysis; YC and ZS conducted LC-MS detection; JMX wrote the manuscript; RRW, JPZ, JH, and PS had primary responsibility for final content. All authors have read and approved the final manuscript.

Funding

The trial was supported by the Natural Science Foundation for the Distinguished Young Scholars (Category A) of Hunan Province (Grant No. 2025JJ20088), and the National Natural Science Foundation of China (Grant No. 82571770; Grant No. 82074103; Grant No. 82325020).

Data availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This trial was approved by the ethics committee of the Second Xiangya Hospital (approval No. 2017027), and all procedures were conducted in compliance with the principles outlined in the Declaration of Helsinki of 1975. Informed consent was obtained from all participants before enrolment.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.