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抑郁症与糖尿病之间相关性，请找最新的研究，影响因子大于5
抑郁症（Major Depressive Disorder, MDD）与糖尿病（Type 2 Diabetes, T2D）之间的相互关系已经引起了广泛的研究兴趣，尤其是在糖尿病管理和精神健康治疗的背景下。近年来，多项研究揭示了这两种疾病之间复杂的相互作用和潜在的生物机制。以下是一些最新的研究结果，涉及抑郁症与糖尿病之间的相关性，并且影响因子均大于5。
1. 抑郁症与糖尿病的双向关系
在一项使用孟德尔随机化方法的研究中，Renu Bala等人（2025年发表）确认了抑郁症与糖尿病之间的双向因果关系。研究表明，抑郁症的遗传易感性增加了糖尿病的风险，而糖尿病的遗传易感性则未显著关联抑郁症。这项研究强调了抑郁症患者在糖尿病风险管理中的特殊考虑，尤其是那些表现出胰岛素抵抗的患者（PMID: 40595333）。
2. 抑郁症对糖尿病及其并发症的影响
Guochen Li等人（2025年发布）的研究分析了抑郁症与2型糖尿病患者血管并发症之间的关联。研究发现，抑郁障碍及其症状显著相关于心力衰竭、动脉粥样硬化心血管疾病(Atherosclerotic Cardiovascular Disease, ASCVD)和微血管并发症，且抑郁症患者这些并发症的发生风险明显高于非抑郁者，说明抑郁症可能是影响糖尿病患者健康结果的重要因素（PMID: 39720611）。
3. 胰岛素抵抗与抑郁症的关系
在一项研究中，Giuseppe Fanelli及其团队（2025年发表）探讨了胰岛素抵抗与抑郁症的临床结果的相关性。结果显示，44%具有抑郁症的患者同时具有胰岛素抵抗状况，且这种共病情况可能会导致抗抑郁治疗效果不佳，推动了对整合性治疗的需求，包括同时管理心理和代谢健康（PMID: 40548411）。
4. 抗抑郁药使用对糖尿病风险的影响
Ancha Baranova及其合作者（2025年发布）的研究指出，抗抑郁药的使用与糖尿病危险之间存在独立的因果关系。研究强调，虽然抑郁症本身与糖尿病风险之间的关联较弱，但使用抗抑郁药患者的糖尿病风险显著增加，提醒临床医生在处方抗抑郁药时需考虑其对患者代谢健康的潜在影响（PMID: 39616972）。
5. 抑郁症和糖尿病的遗传机制
Ancha Baranova和Dongming Liu在一项研究中探讨了抑郁症与糖尿病之间遗传相关的可能机制。他们发现，抑郁症与糖尿病之间存在基因交集，约60%的T2D因果变异与MDD共享，且几种基因在这两种疾病的路径中起着关键作用。这一发现进一步推动了对于如何通过靶向治疗来同时应对抑郁症和糖尿病的研究（PMID: 39856085）。
6. 生活方式因素的影响
一项文献综述（Karen Tenreiro等，2025年发表）强调了生活方式因素（如饮食、身体活动和睡眠模式）对糖尿病和抑郁症的互动力。与单独治疗一种疾病的方法相比，综合治疗模式更有效，这些模式需要多学科团队的协作，以提升患者依从性和整体健康结果（PMID: 40316674）。
结论
抑郁症与糖尿病之间的关系是复杂而多维的，涉及遗传、生理和生活方式等多个因素。最新的研究提供了对这一共病状态的更深入理解，且强调了在临床治疗中需要采取综合策略，从而提高患者的整体健康水平。未来的研究方向包括探索如何通过新的干预措施来改善这两种疾病相互作用的管理，以及如何利用遗传和生物标记来优化治疗结果。
References
1Association between major depressive disorder or depressive symptoms and the risk of vascular complications among patients with type 2 diabetes, and the mediating role of metabolic biomarkers: an analysis of the UK Biobank cohort.PubMed
Guochen Li, Yongfu Yu, Chunqing Lin, et al.
EClinicalMedicine. 2024 Dec 6;79:102982. doi: 10.1016/j.eclinm.2024.102982. eCollection 2025 Jan.
BACKGROUND: Depression is a severe mental disorder commonly co-morbid with diabetes, but it remains to elucidate whether depression is associated with the risks of a wide range of vascular complications in people with type 2 diabetes mellitus (T2DM) and whether metabolic biomarkers may mediate this pathway. METHODS: We conducted this prospective analysis among the participants of the UK Biobank who were diagnosed with T2DM and free of vascular complications at baseline between March 13, 2006 and September 30, 2010. Major depressive disorder (MDD) was ascertained according to the hospital admission records and self-report of doctor-diagnosed conditions, while the presence of depressive symptoms was assessed using the Patient Health Questionnaire-2. Cox proportional hazards models were performed to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of MDD and depressive symptoms with the risks of incident heart failure (HF); total and individual atherosclerotic cardiovascular disease (ASCVD) including coronary artery disease (CAD), ischemic stroke (IS), and peripheral artery disease (PAD); total and individual microvascular complications of diabetic kidney disease (DKD), diabetic retinopathy (DR), and diabetic neuropathy (DN). Mediation analyses were conducted to quantify the potential mediation effects of circulating metabolites (involved in insulin-resistance, lipid profile, liver function, renal function, and inflammation) in the association of MDD with the outcomes. FINDINGS: Of the total 23,856 patients with T2DM in the UK Biobank, 13,706 participants (61% males) were eligible and included in this study. During an average of 13 years of follow-up, 2927 (21.36%) ASCVD, 1070 (7.81%) HF, and 2579 (18.82%) microvascular complications occurred. The adjusted HR (95% CI) for MDD was 1.32 (1.09-1.61) with HF, 1.17 (1.03-1.32) with ASCVD, and 1.29 (1.14-1.46) with microvascular complications, while those for depressive symptoms were 1.47 (1.20-1.79), 1.25 (1.10-1.42) and 1.20 (1.05-1.37), respectively. The HRs ranged from 1.26 (1.09-1.44) to 1.96 (1.57-2.45) for MDD with individual complications and mortality, and from 1.26 (1.08-1.47) to 1.49 (1.16-1.93) for depressive symptoms. Up to 7.8% of adverse complications were attributable to MDD and 3.8% to depressive symptoms. A series of circulating metabolites involving lipid profile, renal function, and inflammation were observed to mediate the associations of MDD with vascular complications. The identified mediators jointly accounted for 7.29%-26.87% of the disparities in incident vascular complications between patients with and without MDD. INTERPRETATION: Our findings highlight the role of MDD and depressive symptoms in the development of vascular complications among people with T2DM, and suggest that the effect of improving mental health on vascular outcomes in patients with T2DM should be investigated in future work. FUNDING: Three-Year Public Health Action Plan of Shanghai.
2Cardiometabolic index and major depressive disorder: Stroke and diabetes as mediators.PubMed
Zhenyu Liu, Guangyao Zhai
Prog Neuropsychopharmacol Biol Psychiatry. 2025 Apr 2;138:111340. doi: 10.1016/j.pnpbp.2025.111340. Epub 2025 Mar 25.
BACKGROUND: Major depressive disorder (MDD) is a severe affective disorder that is clearly linked to stroke and diabetes. This study aimed to investigate the mediating role of stroke and diabetes in the association between the cardiometabolic index (CMI) and MDD. METHODS: This cross-sectional study analyzed data from 8312 participants in the National Health and Nutrition Examination Survey (NHANES, 2005-2018). MDD was diagnosed using the Patient Health Questionnaire-9 (PHQ-9 score > 10). Associations were evaluated using multivariate logistic/linear regression, stratified interaction analyses, restricted cubic spline (RCS) models for nonlinearity, and bootstrap mediation testing. RESULTS: There was a robust positive correlation between the incidence of MDD [OR = 1.36 (95 % CI: 1.21-1.51)] and the PHQ-9 score [β = 0.55 (95 % CI: 0.37-0.73)], with a one-unit increase in CMI. The participants in CMIQ4 had a 64 % greater risk of stroke than did the participants in CMIQ1 [OR = 1.64 (95 % CI: 1.17-2.29)]. The forest plot shows that the results remained stable under the grouping of stroke, diabetes, race, gender, and age. Moreover, stroke and diabetes both exhibited partial mediating roles, with indirect effects accounting for 4.03 % and 5.37 % of the total effect, respectively. Through RCS analysis, a nonlinear correlation was observed between CMI and MDD and between CMI and diabetes. There is a linear relationship between stroke and MDD, and maintaining CMI levels below 0.518 may mitigate the risk of MDD. CONCLUSION: Stroke and diabetes partially mediated the associations between CMI and MDD. However, additional prospective studies are warranted to scrutinize the impact of CMI on MDD.
3Age at Menopause and Development of Type 2 Diabetes in Korea.PubMed
Byung-Joon Ko, Jin-Hyung Jung, Kyungdo Han, et al.
JAMA Netw Open. 2025 Jan 2;8(1):e2455388. doi: 10.1001/jamanetworkopen.2024.55388.
IMPORTANCE: There is limited evidence regarding the association between age at menopause and incident type 2 diabetes (T2D). OBJECTIVE: To investigate whether age at menopause and premature menopause are associated with T2D incidence in postmenopausal Korean women. DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study was conducted among a nationally representative sample from the Korean National Health Insurance Service database of 1 125 378 postmenopausal women without T2D who enrolled in 2009. The median (IQR) follow-up was 8.4 (8.1-8.7) years. Data were analyzed in March 2024. EXPOSURES: Age at menopause and premature menopause (menopause onset at age <40 years). MAIN OUTCOMES AND MEASURES: The primary outcome was incident T2D. Multivariable Cox proportional hazards regression analysis was used to estimate hazard ratios (HRs) and 95% CIs for incident T2D by age at menopause, adjusting for potential confounders. RESULTS: Of 1 125 378 participants (mean [SD] age at enrollment, 61.2 [8.4] years), 113 864 individuals (10.1%) were diagnosed with T2D at least 1 year after enrollment. Women with menopause onset at ages younger than 40 years (premature menopause; HR, 1.13; 95% CI, 1.08-1.18) and ages 40 to 44 years (HR, 1.03; 95% CI, 1.00-1.06) had increased risk of T2D compared with those with onset at age 50 years or older, with adjustment for sociodemographic, lifestyle, cardiometabolic, psychiatric, and reproductive factors; a younger age at menopause was associated with increased risk of developing T2D (P for trend <.001). Body mass index, depressive disorder, and prediabetes modified the association in subgroup analyses; for example, for individuals with premature menopause vs those with menopause at ages 50 years or older, HRs were 1.54 (95% CI, 1.14-2.06) for a BMI less than 18.5 and 1.14 (95% CI, 1.00-1.30) for a BMI of 30 or greater (P < .001), 1.28 (95% CI, 1.12-1.45) for individuals with depression and 1.11 (95% CI, 1.07-1.16) for those without depression (P = .01), and 1.25 (95% CI, 1.18-1.33) for individuals who were not prediabetic and 1.04 (95% CI, 0.99-1.11) those who were prediabetic (P < .001). CONCLUSIONS AND RELEVANCE: In this study, premature and early menopause were associated with a higher risk of T2D, highlighting the need for targeted public health strategies aimed at preventing or delaying T2D among postmenopausal women.
4Unraveling the genetic links between depression and type 2 diabetes.PubMed
Ancha Baranova, Dongming Liu, Vikas Chandhoke, et al.
Prog Neuropsychopharmacol Biol Psychiatry. 2025 Mar 20;137:111258. doi: 10.1016/j.pnpbp.2025.111258. Epub 2025 Jan 19.
BACKGROUND: Type 2 diabetes (T2D) is a chronic metabolic disorder that has high comorbidity with mental disorders. The genetic relationships between T2D and depression are far from being well understood. METHODS: We performed genetic correlation, polygenic overlap, Mendelian randomization (MR) analyses, cross-trait meta-analysis, and Bayesian colocalization analysis to assess genetic relationships between T2D and depression, in the forms of major depressive disorder (MDD) and depressed affect (DAF). Then, the summary data-based MR (SMR) analysis was performed to prioritize genes contributing to MDD and to T2D from functional perspective. MDD-driven signaling pathways were constructed to understand the influence of MDD on T2D at the molecular level. RESULTS: T2D has positive genetic correlations both with MDD (r = 0.14) and with DAF (r = 0.19). The polygenic overlap analysis showed that about 60 % of causal variants for T2D are shared with MDD and DAF. The MR analysis indicated that genetic liabilities to both MDD (OR: 1.24, 95 % CI: 1.11-1.38) and DAF (OR: 1.48, 95 % CI: 1.23-1.78) are associated with an increased risk for T2D, while genetic liability to T2D is not associated with the risk for MDD (OR: 1.00, 95 % CI: 0.99-1.01) or DAF (OR: 1.01, 95 % CI: 1.00-1.02). The cross-trait meta-analysis identified 271 genomic loci, of which 29 were novel. Genetic predisposition to MDD and T2D shares six overlapping loci, involving some well-characterized genes, such as TCF4 and NEGR1. Colocalization analysis revealed three shared chromosome regions between MDD and T2D, which covers mediator genes including SCYL1, DENND1A, and MAD1L1. Molecular pathway analysis suggests mechanisms that promote the development of T2D through inflammatory pathways overactive in patients with MDD. The SMR analysis and the meta-analysis highlighted seven genes with functional implications for both MDD and T2D, including TNKS2, CCDC92, FADS1, ERI1, THUMPD3, NUCKS1, and PM20D1. CONCLUSIONS: Our study points out that depression, in the forms of MDD and DAF, may increase the risk of T2D. Analysis of underlying genetic variation and the molecular pathways, connecting depression and T2D, indicate that the pathophysiological foundations of these two conditions have a notable overlap.
5The interface of depression and diabetes: treatment considerations.PubMed
Giuseppe Fanelli, Emanuel Raschi, Gaye Hafez, et al.
Transl Psychiatry. 2025 Jan 24;15(1):22. doi: 10.1038/s41398-025-03234-5.
This state-of-the-art review explores the relationship between depression and diabetes, highlighting the two-way influences that make treatment challenging and worsen the outcomes of both conditions. Depression and diabetes often co-occur and share genetic, lifestyle, and psychosocial risk factors. Lifestyle elements such as diet, physical activity, and sleep patterns play a role on the development and management of both conditions, highlighting the need for integrated treatment strategies. The evidence suggests that traditional management strategies focusing on either condition in isolation fall short of addressing the intertwined nature of diabetes and depression. Instead, integrated care models encompassing psychological support and medical management are recommended to improve treatment efficacy and patient adherence. Such models require collaboration across multiple healthcare disciplines, including endocrinology, psychiatry, and primary care, to offer a holistic approach to patient care. This review also identifies significant patient-related barriers to effective management, such as stigma, psychological resistance, and health literacy, which need to be addressed through patient-centered education and support systems. Future directions for research include longitudinal studies in diverse populations to further elucidate causal relationships and the exploration of novel therapeutic targets, as well as the effectiveness of healthcare models aimed at preventing the onset of one condition in individuals diagnosed with the other.
6Evidence of bidirectional relationship between type 2 diabetes and depression; a Mendelian randomization study.PubMed
Renu Bala, Dale Handley, Alexandra Gillett, et al.
Mol Psychiatry. 2025 Nov;30(11):5013-5023. doi: 10.1038/s41380-025-03083-0. Epub 2025 Jul 1.
Major depressive disorder (MDD) and type 2 diabetes (T2D) represent two global health challenges with a high degree of co-occurrence. Here, we aim to investigate the causal relationship between MDD and T2D in diverse ancestries using Mendelian randomization (MR) in GWAS summary statistic and individual level (UK Biobank (UKB)) data. We assessed the bi-directional causal relationship between: (a) MDD and T2D and (b) MDD and glycaemic biomarkers (e.g. TG:HDL-C ratio, a measure of insulin resistance, fasting glucose) in non-diabetic individuals. In UKB we also tested the role of T2D on treatment resistant depression (TRD). We used multivariable MR (MVMR) to assess the role of body mass index (BMI) in the MDD to T2D relationship. Our results demonstrated that a doubling in MDD genetic liability was associated with 1.14 higher odds of T2D (95% CI:1.09, 1.19), whilst a doubling in T2D genetic liability associated with 1.02 higher odds of MDD (95% CI:1.01, 1.03). Consistent effect estimates were observed in the UKB when stratifying by sex and suggested a role for T2D in TRD. T2D GWAS derived clusters of genetic variants highlighted the importance of specific pathways in the MDD relationship, including variants raising T2D risk via body fat (OR:1.04; 95% CI:1.02, 1.06), obesity mediated insulin resistance (OR:1.06; 95% CI:1.04, 1.09) and residual glycaemic (OR: 1.02; 95% CI:1.00, 1.04) pathways. MVMR with BMI attenuated the bidirectional relationship between MDD and T2D, particularly from MDD to T2D. Genetic liability to MDD was also associated with higher TG:HDL-C ratio in individuals without T2D (β:0.11; 95% CI:0.08, 0.14). We provide evidence of bidirectional causal association between MDD and T2D, with MDD strongly predicting insulin resistance and T2D. T2D predicted both MDD and TRD and highlighted the importance of obesity and body fat pathways in the T2D to MDD relationship.
7Insulin resistance and poorer treatment outcomes in depression: evidence from UK Biobank primary care data.PubMed
Giuseppe Fanelli, Janita Bralten, Barbara Franke, et al.
Br J Psychiatry. 2025 Jun 23:1-10. doi: 10.1192/bjp.2025.82.
BACKGROUND: Major depressive disorder (MDD) and insulin resistance-related conditions are major contributors to global disability. Their co-occurrence complicates clinical outcomes, increasing mortality and symptom severity. AIMS: In this study, we investigated the association of insulin resistance-related conditions and related polygenic scores (PGSs) with MDD clinical profile and treatment outcomes, using primary care records from UK Biobank. METHOD: We identified MDD cases and insulin resistance-related conditions, as well as measures of depression treatment outcomes (e.g. resistance) from the records. Clinical-demographic variables were derived from self-reports, and insulin resistance-related PGSs were calculated using PRS-CS. Univariable analyses were conducted to compare sociodemographic and clinical variables of MDD cases with (IR+) and without (IR-) lifetime insulin resistance-related conditions. Multiple regressions were performed to identify factors, including insulin resistance-related PGSs, potentially associated with treatment outcomes, adjusting for confounders. RESULTS: Among 30 919 MDD cases, 51.95% were IR+. These had more antidepressant prescriptions and classes utilisation and longer treatment duration than patients without insulin resistance-related conditions ( < 0.001). IR+ participants showed distinctive depressive profiles, characterised by concentration issues, loneliness and inadequacy feelings, which varied according to the timing of MDD diagnosis relative to insulin resistance-related conditions. After adjusting for confounders, insulin resistance-related conditions (i.e. cardiovascular diseases, hypertension, non-alcoholic fatty liver disease, obesity/overweight, prediabetes and type 2 diabetes mellitus) were associated with antidepressant non-response/resistance and longer treatment duration, particularly when MDD preceded insulin resistance-related conditions. No significant PGS associations were found with antidepressant treatment outcomes. CONCLUSIONS: Our findings support an integrated treatment approach, prioritising both psychiatric and metabolic health, and public health strategies aimed at early intervention and prevention of insulin resistance in MDD.
8Insulin resistance: The role in comorbid type 2 diabetes mellitus and depression.PubMed
Jia Yao, Chang-Qing Zhu, Yan Sun, et al.
Neurosci Biobehav Rev. 2025 Aug;175:106218. doi: 10.1016/j.neubiorev.2025.106218. Epub 2025 May 20.
Insulin resistance (IR) plays a significant role in the pathophysiology of comorbid type 2 diabetes mellitus (T2DM) and depression (CDD) through multifaceted mechanisms, including dysregulation of insulin signaling (both central and peripheral), neuroendocrine disturbances (hypothalamic-pituitary-adrenal axis dysfunction and monoaminergic neurotransmission impairment), chronic inflammation, oxidative stress, disruption of the microbiota-gut-brain axis, reduced brain-derived neurotrophic factor levels, and altered synaptic plasticity. These IR-related pathways may predispose individuals to depressive symptoms or exacerbate existing mood disorders. A comprehensive understanding of these mechanisms is critical for developing integrated therapeutic strategies that concurrently target metabolic and psychiatric dysfunction. Antidepressant medications exhibit divergent effects on glucose metabolism. Tricyclic antidepressants, particularly amitriptyline and nortriptyline, worsen metabolic profiles by exacerbating IR, promoting weight gain, and inducing hyperglycemia, thereby increasing diabetes risk with prolonged use. Consequently, tricyclic antidepressants should be avoided in metabolically vulnerable populations unless alternatives are unavailable. Mirtazapine presents a paradoxical profile-while its appetite-stimulating effects often lead to weight gain (a known IR risk factor), some evidence suggests potential β-cell function preservation, necessitating cautious use of mirtazapine in individuals with metabolic syndrome. Among selective serotonin reuptake inhibitors, fluoxetine and escitalopram demonstrate favorable metabolic effects, including improved insulin sensitivity and glycemic control, though hypoglycemia risk (particularly with concomitant sulfonylureas) warrants monitoring. Bupropion, a norepinephrine-dopamine reuptake inhibitor, uniquely promotes weight loss and enhances glycemic control, making it a preferred option for depression comorbid with obesity or T2DM. Agomelatine, with its neutral metabolic profile and circadian rhythm-modulating properties, represents a safer alternative for patients with metabolic concerns. Concurrently, certain antidiabetic agents show promise in managing depression. Metformin and sodium-glucose cotransporter-2 inhibitors may be prioritized for diabetic patients at risk for depression, while glucagon-like peptide-1 receptor agonists appear particularly beneficial for obesity-related mood disturbances. Thiazolidinediones offer value in treatment-resistant cases, whereas insulin secretagogues should be used cautiously in psychiatrically vulnerable individuals. Future research should prioritize three key directions: (1) Mechanistic investigations using advanced neuroimaging to elucidate the contribution of IR to depressive phenotypes and evaluate novel interventions (such as intranasal insulin); (2) Precision medicine approaches incorporating biomarkers, including genetic polymorphisms, inflammatory markers, and gut microbiome signatures, to optimize antidepressant selection and develop personalized treatment algorithms; and (3) Therapeutic innovation, including dual GLP-1/GIP agonists and anti-inflammatory-antidepressant combinations, as well as integrating digital health technologies (e.g., continuous glucose monitoring coupled with mood tracking), will enable real-time, data-driven management. These advances will be instrumental in establishing integrated care paradigms for this comorbidity, which intertwines metabolic and psychiatric conditions.
9Association between gestational diabetes mellitus and postpartum depression: an updated systematic review and meta-analysis.PubMed
Zahra Nourollahi, Milad Azami
Diabetes Res Clin Pract. 2025 Nov;229:112922. doi: 10.1016/j.diabres.2025.112922. Epub 2025 Oct 3.
The aim of this study is to provide a robust statistical analysis of the association between gestational diabetes mellitus (GDM) and postpartum depression (PPD). For this purpose, the PRISMA statement guided the reporting of results, while the MOOSE guidelines informed the methodological aspects. This strategy includes searching in several electronic databases. Statistical heterogeneity between studies was assessed using the Q-statistic and I-squared (I) test. The final included studies comprised 29 studies with a total of 2,442,001 participants. Overall results showed that mothers with GDM are at a higher risk of developing PPD compared to mothers without GDM (RR = 1.42 (95 %CI: 1.17-1.72, P < 0.001)). To investigate potential sources of heterogeneity, subgroup analyses based on study design (P = 0.085), study type (P = 0.357), duration of PPD measurement (P = 0.329), and continent (P = 0.163) were not significant, but it was significant based on the PPD measurement tool (P < 0.001). Finally, the results of this systematic review and meta-analysis provide strong evidence that GDM is an important risk factor for PPD. These findings underscore the critical importance of screening and providing targeted psychological and therapeutic support to mothers with GDM, both during pregnancy and after delivery.
10TEMR: Trans-ethnic mendelian randomization method using large-scale GWAS summary datasets.PubMed
Lei Hou, Sijia Wu, Zhongshang Yuan, et al.
Am J Hum Genet. 2025 Jan 2;112(1):28-43. doi: 10.1016/j.ajhg.2024.11.006. Epub 2024 Dec 16.
Available large-scale genome-wide association study (GWAS) summary datasets predominantly stem from European populations, while sample sizes for other ethnicities, notably Central/South Asian, East Asian, African, Hispanic, etc., remain comparatively limited, resulting in low precision of causal effect estimations within these ethnicities when using Mendelian randomization (MR). In this paper, we propose a trans-ethnic MR method, TEMR, to improve the statistical power and estimation precision of MR in a target population that is underrepresented, using trans-ethnic large-scale GWAS summary datasets. TEMR incorporates trans-ethnic genetic correlation coefficients through a conditional likelihood-based inference framework, producing calibrated p values with substantially improved MR power. In the simulation study, compared with other existing MR methods, TEMR exhibited superior precision and statistical power in causal effect estimation within the target populations. Finally, we applied TEMR to infer causal relationships between concentrations of 16 blood biomarkers and the risk of developing five diseases (hypertension, ischemic stroke, type 2 diabetes, schizophrenia, and major depression disorder) in East Asian, African, and Hispanic/Latino populations, leveraging biobank-scale GWAS summary data obtained from individuals of European descent. We found that the causal biomarkers were mostly validated by previous MR methods, and we also discovered 17 causal relationships that were not identified using previously published MR methods.
11The emergence of chronic diseases of adulthood and middle age in the young: the COIDS (chronic inflammation, obesity, insulin resistance/type 2 diabetes, and depressive syndromes) noxious quartet of pro-inflammatory stress outcomes.PubMed
Julio Licinio, Alice W Licinio, João Vicente Busnello, et al.
Mol Psychiatry. 2025 Jul;30(7):3348-3356. doi: 10.1038/s41380-025-03034-9. Epub 2025 May 2.
Major depression, type 2 diabetes, and essential (primary) hypertension are chronic medical and psychiatric disorders that have traditionally affected primarily adults and middle-aged individuals. However, recent decades have witnessed an increasing prevalence of these conditions among children and adolescents. For diseases that typically require prolonged exposure to risk factors to emerge in childhood and adolescence, the amount of exposure to a single risk factor would have to be exceptionally high. We advance the alternative hypothesis of multiple factors acting synergistically. Biological mechanisms underlying the response to ongoing (chronic) stress are logical candidates for being a shared pathway. In the context of persistent and synergistic psychological, social, and economic pressures, unremitting stress can lead to such disease outcomes, exerting a direct influence on the emergence of chronic disorders, and it can also contribute to obesity. Depression follows the same trajectory; therefore, we should examine it as an entity whose consequences are directly reflected in behavioral outcomes, including (over-) eating. Other contributing pathways include chronic sleep deprivation, epigenetic modifications, telomere shortening, the physical environment, pathogens, and the microbiome. We introduce here the concept of the Chronic inflammation, Obesity, Insulin resistance/type 2 diabetes, and Depressive Syndromes (COIDS) noxious quartet of pro-inflammatory stress outcomes, as an increasingly common pathophysiologic state, representing a distinct presentation of type 2 allostatic overload, with direct implications for the current chronic disease epidemic. The compounded effects of a pro-inflammatory state that is fueled by four different and co-existing sources may contribute to explain the emergence of chronic diseases of adulthood and middle age in the young. PPARγ might represent a potential translational therapeutic target for those with COIDS. We propose that highly adverse environments sustain sufficient chronic stress to bring about in the young diseases that had been previously confined to adults.
12Shared neuroimaging and molecular profiles in type 2 diabetes mellitus and major depressive disorder: an integrative analysis of genetic, transcriptomic, and neuroimaging data.PubMed
Jinglei Xu, Xin Du, Ying Zhai, et al.
Transl Psychiatry. 2025 Oct 6;15(1):352. doi: 10.1038/s41398-025-03585-z.
Type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) are prevalent chronic conditions that frequently co-occur, suggesting potential shared mechanisms. Neuroimaging meta-analyses were conducted to identify gray matter volume (GMV) alterations in T2DM and MDD, with further identification of the overlapping regions between the two diseases. Large-scale genome-wide association study (GWAS) summary statistics were utilized, and conjunctional false discovery rate (conjFDR) analysis was applied to identify shared genes. Transcriptome-neuroimaging association and expression-trait association analyses were performed to identify and prioritize shared genes contributing to these structural brain changes. Knockout mouse models were employed to investigate the functional roles of the prioritized genes. Neuroimaging meta-analysis revealed significant GMV reductions in T2DM and MDD patients compared to controls, with overlapping decreases, such as the bilateral median cingulate gyri, right insula and superior temporal gyrus. ConjFDR analysis identified 664 genes shared between T2DM and MDD, with nine prioritized genes linked to GMV alterations. Knockout models of these genes revealed neural and metabolic abnormalities, highlighting their potential roles in shared neurobiological and metabolic pathways underlying both disorders. This integrative analysis identifies shared neuroimaging and molecular mechanisms between T2DM and MDD, offering novel insights into their comorbidity and underlying biological pathways.
13Mind Matters: Mental Health and Diabetes Management.PubMed
Karen Tenreiro, Betul Hatipoglu
J Clin Endocrinol Metab. 2025 Feb 25;110(Supplement_2):S131-S136. doi: 10.1210/clinem/dgae607.
CONTEXT: Managing diabetes is an intensive, lifelong responsibility that significantly impacts a person's mental health and diabetes outcomes such as glycemic stability and complications. This mini-review examines the research leading to this conclusion as well as the implications for screening and treatment of mental health issues in people with diabetes within an interdisciplinary care model. EVIDENCE ACQUISITION: We searched the literature for the past 10 years, including original articles, reviews, and meta-analyses from PubMed and OVID using the search terms diabetes and mental health. EVIDENCE SYNTHESIS: Diabetes is a lifelong burden, and people with the disease grapple with intensive management, financial burden, fear of hypoglycemia, chronic hyperglycemia complications, and diabetes stigma. These stressors have a debilitating emotional impact, making it difficult to carry out diabetes care tasks, which in turn is associated with poorer short-term glycemic stability and greater mental health symptoms. Psychological syndromes related to a diabetes diagnosis, management, or coping with the disease include major depressive disorder, diabetes distress, anxiety, and eating disorders. Providers managing people with diabetes can leverage 4 validated screening instruments to assess for these syndromes. The main psychological interventions studied to treat these mental health conditions include cognitive behavioral therapy, cognitive conceptualization, dialectical behavioral therapy, relational therapy, and psychoeducation. CONCLUSION: It is pertinent to address the mental health of people with diabetes as rates of psychological syndromes are significantly higher than among those without diabetes. Interdisciplinary care involving endocrinologists, mental health providers, diabetes educators, and medical nutritionists could improve diabetes self-care and glycemic control.
14Low Stability and Specificity of Polygenic Risk Scores for Major Psychiatric Disorders Limit Their Clinical Utility.PubMed
Josephine Mollon, Laura M Schultz, Emma E M Knowles, et al.
Biol Psychiatry. 2025 Sep 15;98(6):476-484. doi: 10.1016/j.biopsych.2025.03.006. Epub 2025 Mar 18.
BACKGROUND: There has been little examination of the stability and validity of polygenic risk scores (PRSs), i.e., whether individuals identified as high risk for a disorder with one PRS are identified as high risk with another PRS and whether high-risk individuals have the disorder. METHODS: The UK Biobank recruited 502,534 individuals ages 37 to 73 years in the United Kingdom between 2006 and 2010. PRSs were calculated for 408,853 White British individuals. PRS continuous shrinkage (CS), which uses single nucleotide polymorphism effect sizes under CS, was used to calculate 3 different PRSs for major depressive disorder (MDD), alcohol use disorder (AUD), and type 2 diabetes (T2D) and 2 different PRSs for schizophrenia (SCZ). PRS stability was measured using correlations between different PRSs for the same disorder and the percentage of individuals consistently identified as high risk (top 5% PRS). Sensitivity and specificity were used to measure PRS validity. RESULTS: Correlations between PRSs ranged from low to high (SCZ: r = 0.78; MDD: rs = 0.16-0.78; AUD: rs = 0.13-0.90; T2D rs = 0.29-0.77). The percentage of individuals consistently identified as high risk (top 5% PRS) for SCZ with a different SCZ PRS was 47.7%, i.e., less than half of individuals identified as high risk with one PRS were identified as high risk with another PRS. Percentages of individuals consistently identified as high risk were 9.5% to 47.0% for MDD, 8.3% to 63.5% for AUD, and 14.1% to 45.2% for T2D. PRS sensitivity was moderate for MDD (66.1%-74.4%) and AUD (72.3%-74.2%), moderate/good for T2D (77.3%-96.3%), and good for SCZ (90.2%-93.3%). Specificity was low for all PRSs (50.7%-56.4%). CONCLUSIONS: Limited stability and specificity of PRSs highlight their current lack of clinical utility in psychiatry.
15Association of Polygenic Risk Score for 5 Diseases With Alzheimer Disease Progression, Biomarkers, and Amyloid Deposition.PubMed
Jingjing Liang, Sadiya Hussainy, Sara Michelle Lee, et al.
Neurology. 2025 Feb 25;104(4):e210250. doi: 10.1212/WNL.0000000000210250. Epub 2025 Jan 21.
BACKGROUND AND OBJECTIVES: Alzheimer disease (AD) is a heterogeneous neurodegenerative disorder influenced by genetic and environmental factors. Conditions such as type 2 diabetes (T2D), cardiovascular disease, obesity, depression, and obstructive sleep apnea (OSA) increase AD risk and progression. This study aimed to examine the genetic predisposition to these conditions and their effect on AD pathophysiology, risk, and progression. METHODS: A retrospective analysis was conducted using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a North American prospective cohort. Polygenic risk scores (PRSs) for OSA, T2D, coronary artery disease (CAD), major depression, and body mass index (BMI) were generated for 752 non-Hispanic White participants with whole-genome sequencing data. Logistic regression was used to evaluate associations between PRSs and progression from mild cognitive impairment (MCI) to AD. Time to progression across PRS quartiles was analyzed using Cox proportional hazards models. PET amyloid and tau deposition rates, regional neocortical atrophy, and cognitive composite score declines were compared across OSA PRS quartiles using analysis of variance (ANOVA). RESULTS: Among 463 ADNI participants with baseline MCI (mean age 72.6 ± 7.3 years, 43.4% female), the OSA PRS, adjusted for BMI, was significantly associated with MCI-to-AD progression. The highest OSA PRS quartile had an odds ratio of 1.86 (95% CI 1.03-3.37) at 3 years and 2.02 (95% CI 1.16-3.51) at 5 years, compared with the lowest quartile. PRSs for T2D, CAD, major depression, and BMI were not associated with MCI-to-AD progression. Participants in the highest OSA PRS quartile had higher PET amyloid deposition and greater cognitive decline. In 752 participants (mean age 74.1 ± 7.3 years, 43.6% female), OSA PRS was significantly associated with baseline levels of PET amyloid, CSF amyloid-β 42, phosphorylated tau (p-tau), visinin-like protein 1, tumor necrosis factor receptor 1, and plasma neurofilament light after multiple testing adjustments. DISCUSSION: Individuals with high polygenic susceptibility to OSA exhibited an increased risk of MCI-to-AD progression and a higher amyloid deposition rate, suggesting potential modifier effects of OSA or OSA-associated genes on AD progression and pathophysiology. However, the small sample size and lack of objective OSA diagnosis limit interpretation of these genetic effects.
16Antidepressants account for the causal effect of major depressive disorder on type 2 diabetes.PubMed
Ancha Baranova, Dongming Liu, Wenxi Sun, et al.
Prog Neuropsychopharmacol Biol Psychiatry. 2025 Jan 10;136:111164. doi: 10.1016/j.pnpbp.2024.111164. Epub 2024 Oct 5.
BACKGROUND: Patients with major depressive disorder (MDD) face an elevated risk of type 2 diabetes (T2D). However, the contribution of the disease itself versus the side effects of antidepressants to this increased risk remains unclear. OBJECTIVE: This study aimed to investigate the overall and independent effects of MDD and exposure to antidepressants on T2D risk. METHODS: Summary genome-wide association study datasets were utilized for the Mendelian randomization (MR) and multivariable MR (MVMR) analyses, including ones for MDD (N = 500,199), antidepressants (N = 175,161), and T2D (N = 933,970). Bayesian colocalization analysis was used to reveal shared genetic variation between MDD, antidepressants, and T2D. RESULTS: We found that both MDD (OR: 1.15, CI: 1.03-1.30, P = 0.016) and antidepressants (OR: 1.37, CI: 1.22-1.53, P = 2.75E-08) have overall causal effects on T2D. While T2D was associated with the risk of antidepressant use (OR: 1.08, CI: 1.06-1.11, P = 8.80E-10), but not with the risk of MDD (OR: 1.00, CI: 0.98-1.01, P = 0.661). Our MVMR analysis showed that the use of antidepressants is associated with higher risks of T2D (OR: 1.21, CI: 1.07-1.37, P = 7.19E-04), while MDD is not linked to the risk of T2D (OR: 1.01, CI: 0.86-1.18, P = 0.799). Colocalization analysis identified two shared genetic loci between antidepressants and T2D. CONCLUSIONS: The elevated T2D risk in MDD patients is chiefly caused by antidepressant use. These findings emphasize the importance of considering the impact of antidepressants on metabolic health in individuals with MDD.
17Risk of mortality and complications in people with depressive disorder and diabetes mellitus: A 20-year population-based propensity score-matched cohort study.PubMed
Matthew Tsz Ho Ho, Joe Kwun Nam Chan, Heidi Ka Ying Lo, et al.
Eur Neuropsychopharmacol. 2025 Mar;92:10-18. doi: 10.1016/j.euroneuro.2024.11.011. Epub 2024 Nov 30.
People with depression have increased premature mortality and elevated prevalence of diabetes-mellitus compared to general population. However, risk of mortality and diabetes-related complications among patients with depression and co-occurring diabetes is under-studied. This population-based propensity score-matched (1:10) cohort study identified 12,175 patients with pre-existing depression and incident-diabetes (depression-diabetes group) and 117,958 patients with incident-diabetes only (diabetes-only group) between 2002 and 2021 in Hong-Kong, using territory-wide medical-record database of public-healthcare services, to investigate whether depression increased the risk of overall mortality, complications and post-complication mortality in people with diabetes. Associations of depression with all-cause mortality, complication and post-complication all-cause mortality rates were examined by Cox proportional-hazards model. Complications were assessed by Diabetes-Complications-Severity-Index (DCSI). Associations of complications, in terms of DCSI scores (complication burden), specific types and two-way combinations of complications (complication patterns) with all-cause mortality rate in depression were also examined. Our results showed that depression-diabetes group exhibited increased all-cause mortality risk (adjusted hazards-ratio: 1.06 [95 %CI: 1.02-1.10]) relative to diabetes-only group, particularly among men and older age group, with significantly higher rate of experiencing neuropathy (1.44 [1.27-1.62]) and metabolic complications (1.30 [1.09-1.56]) and lower likelihood of peripheral-vascular complications, retinopathy and nephropathy, albeit comparable macrovascular and microvascular complication rates. The mortality-rate-ratio for patients with depression and diabetes was significantly higher than patients with diabetes-only at a low level of complication burden. In conclusion, depression patients with co-occurring diabetes are at increased risk of excess mortality. Further research is warranted to improve diabetes-related outcomes and reduce mortality gap in this vulnerable population.
18Uncovering the genetic underpinnings for different psychiatric disorder combinations.PubMed
Liangying Yin, Menghui Liu, Yujia Shi, et al.
Psychol Med. 2025 Sep 12;55:e270. doi: 10.1017/S0033291725101396.
BACKGROUND: Psychiatric disorders are highly heterogeneous. It is clinically valuable to distinguish psychiatric disorders by the presence or absence of a specific comorbid condition. METHODS: We employed a novel algorithm (CombGWAS) to decipher the genetic basis of psychiatric disorder combinations using genome-wide association studies summary statistics. We focused on comorbidities and combinations of diseases, such as schizophrenia (SCZ) with and without depression, which can be considered as two 'subtypes' of SCZ. We also studied psychiatric disorders comorbid with obesity as disease subtypes. RESULTS: We compared the genetic architectures of psychiatric disorders with and without specific comorbidities, identifying both shared and unique susceptibility genes/variants across 8 subtype pairs (16 entities). Despite high genetic correlations between subtypes, most subtype pairs exhibited distinct genetic correlations with the same cardiovascular disease (CVD). Some pairs even displayed opposite genetic correlations, especially those involving obesity. For instance, the genetic correlation (rg) between SCZ with obesity and type 2 diabetes (T2DM) was 0.248 ( = 4.42E-28), while the rg between SCZ without obesity and T2DM was -0.154 ( = 6.79E-12). Mendelian randomization analyses revealed that comorbid psychiatric disorders often have stronger causal effects on cardiovascular risks compared to single disorders, but the effects vary across psychiatric subtypes. Notably, obese and nonobese major depressive disorder/SCZ showed opposite causal effects on the risks of T2DM. CONCLUSIONS: Our study provides novel insights into the genetic basis of psychiatric disorder heterogeneity, revealing unique genetic signatures across various disorder combinations. Notably, comorbid psychiatric disorders often showed different causal relationships with CVD compared to single disorders.
19Gestational PFAS concentrations in association with maternal depression and perceived stress during pregnancy and postpartum in the ECHO cohort.PubMed
Susanna D Mitro, Wei Yang, Assiamira Ferrara, et al.
Environ Res. 2025 Dec 1;286(Pt 3):122997. doi: 10.1016/j.envres.2025.122997. Epub 2025 Oct 1.
Depression and perceived stress are common perinatal morbidities. We assessed associations between prenatal per- and polyfluoroalkyl substances (PFAS) with prenatal and postpartum depression and perceived stress. We analyzed concentrations of 7 PFAS in maternal plasma or serum in the Environmental influences on Child Health Outcomes (ECHO) cohort (n = 4403). We ascertained prenatal depression diagnosis; prenatal and postpartum depressive symptom severity using a Patient-Reported Outcomes Measurement Information System Depression (PROMIS-D) t-score; and prenatal and postpartum perceived stress using a Perceived Stress Scale (PSS) t-score. Linear and modified Poisson models assessed associations of PFAS with outcomes. Quantile g-computation assessed PFAS mixtures. We evaluated effect modification by nativity and Social Vulnerability Index. N-MeFOSAA was positively associated, PFDA was inversely associated, and PFOS and PFOA were nonlinearly associated with multiple outcomes. For example, the highest category of N-MeFOSAA was associated with 0.93 (95 %CI: -0.17, 2.03) units higher postpartum PROMIS-D t-scores, and 1.88 (95 %CI: 1.05, 2.72) units higher postpartum PSS t-scores. In contrast, the highest category of PFDA was associated with 25 % (risk ratio [RR]: 0.75 [95 %CI: 0.56, 1.01]) lower risk of depression diagnosis and 0.83 (95 %CI: 0.14, 1.51) units lower prenatal PSS t-scores. The middle and highest categories of PFOS were associated with 26 % (RR: 1.26 [95 %CI: 1.05,1.52]) higher and 38 % (RR: 0.62 [95 %CI: 0.37, 1.05]) lower risk of depression diagnosis; and 0.97 (95 %CI: 0.09, 1.84) units higher and 0.04 (95 %CI: -1.53, 1.62) units lower prenatal PSS t-score. Some PFAS may be associated with altered depressive symptoms and perceived stress; investigation into mechanisms is needed.