Latent Class Growth Analysis of Gout Flare Trajectories: A Three‐Year Prospective Cohort Study in Primary Care

Moderate drinking benefits cognitive health in middle-aged and older Chinese: A latent class growth model analysis based on CHARLS

While alcohol use among older population is linked to various health risks, recent studies indicate potential benefits from moderate consumption, highlighting a contentious debate regarding its impact on health in this demographic. This study aims to identify distinct trajectories of alcohol use among middle-aged and older adults in China and examine their associations with multidimensional health outcomes, including cardiovascular, cognitive, and psychological health. This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), tracking a sample of 10,927 participants (54.33 % female; mean age = 57.61, SD = 8.95) through five waves of data collection. Latent Class Growth Modeling (LCGM) was employed to analyze alcohol use trajectories. Health outcomes were assessed through self-reported health measures and clinical data including lipoprotein levels. Four distinct alcohol use trajectories were identified: Moderate (6.3 %), Decreasing (11.0 %), Increasing (6.9 %), and Constantly Low (75.8 %). The Moderate trajectory was associated with significant higher cognitive scores compared to Increasing (p = .027) and Constantly Low group (p = .012). Moreover, higher levels of high density lipoprotein were linked with the Increasing and Decreasing trajectories, suggesting a protective cardiovascular effect. The findings highlight the complexity of alcohol use behaviors among older adults in China and underscore the need for targeted health interventions. Understanding the nuanced impacts of different drinking patterns on multidimensional health outcomes can aid in developing more effective public health strategies and clinical practices tailored to the needs of aging populations. Further research is recommended to explore the causal relationships and long-term health impacts of these trajectories.

Metabolomic profiles underlying gout flares: a prospective study of people with gout

OBJECTIVES

To identify specific metabolomic profiles associated with gout flares in people with gout.

METHODS

Participants with gout were sequentially enrolled. In cross-sectional analysis, data were analysed according to the presence of gout flare (acute group) or absence of gout flare (intercritical group) at the time of enrolment. Participants in the intercritical group were prospectively followed and analysed according to the development of gout flares (recurrent flare group) or no gout flare (no flare group) over 1 year. Relative abundances of metabolites in serum obtained at the baseline visit were measured by untargeted liquid chromatography-mass spectrometry. Risk of incident flare was analysed using least absolute shrinkage and selection operator (LASSO)-Cox regression and time-receiver operating characteristic (ROC). Machine learning models were performed to identify biomarkers in cross-sectional and longitudinal analysis, which was further optimised using quantitative targeted metabolomics in an independent validation cohort.

RESULTS

Participants in the acute and intercritical groups showed distinct metabolic profiles, including carbohydrate, lipid and nucleotide metabolism. Many metabolites were associated with recurrent gout flare in the prospective analysis. The metabolic risk score with six LASSO-derived metabolites, including 5-methoxytryptamine, differentiated well for gout flare risk, yielding an area under the ROC curve (AUC) of 0.82 (95% CI 0.74 to 0.90). Machine learning models achieved an AUC of 0.828 for comparison between the acute and intercritical groups. For the prediction of recurrent flare, AUC reached 0.807-0.867 with combined metabolites and clinical measurements.

CONCLUSIONS

Metabolic reprogramming differentiates between the acute and intercritical stages of gout, and implicated metabolites may serve as biomarkers for future gout flares.

Associations of maternal sleep trajectories during pregnancy and adverse perinatal outcomes: a prospective cohort study

OBJECTIVE

Sleep problems are common in pregnant women and sleep is altered during pregnancy. However, the associations between sleep trajectory patterns and adverse maternal and neonatal outcomes are unclear. The current study aims to identify sleep trajectory patterns and explore their associations with adverse perinatal outcomes in a prospective cohort study.

METHODS

Pregnant women (N = 232) completed the Pittsburgh Sleep Quality Index each trimester during pregnancy in Tianjin, China. Perinatal outcomes were extracted from the hospital delivery records. Latent class growth analysis (LCGA) described the trajectories of sleep timing, duration, and efficiency. Multivariable linear regression and multivariable logistic regression were employed to evaluate associations between sleep trajectory patterns and perinatal outcomes.

RESULTS

Trajectories were identified for bedtime (early, 49.1%; delaying, 50.9%), wake-up time (early, 82.8% of the sample; late, 17.2%), duration (short, 5.2%; adequate 78.0%; excessive, 16.8%), and efficiency (high, 88.4%; decreasing, 11.6%). Compared with women in more optimal sleep groups, those in the late wake-up, excessive duration, and decreasing efficiency groups had babies with shorter birth lengths (β range, -0.50 to -0.28, p < 0.05). Moreover, women in the decreasing efficiency group had babies with lower birth weight (β, -0.44; p < 0.05). Women in the delaying bedtime group had greater odds of preterm delivery (OR, 4.57; p < 0.05), while those in the decreasing efficiency group had greater odds of cesarean section (OR, 3.12; p < 0.05).

CONCLUSIONS

Less optimal sleep trajectory patterns during pregnancy are associated with perinatal outcomes. Therefore, early assessment of maternal sleep during pregnancy is significant for identifying at-risk women and initiating interventions to reduce perinatal outcomes.

Effect and mechanism of aqueous extract of Chinese herbal prescription (TFK) in treating gout arthritis

ETHNOPHARMACOLOGICAL RELEVANCE

With the rapid development of China's economic level, great changes have taken place in people's diet structure, gout has become a common disease that puzzles people's health, seriously affects the realization of China's "Healthy China" strategic goal. Gouty arthritis (GA) is a common joint disease caused by chronic purine metabolism disorder. Currently, drugs used to treat GA are allopurinol and colchicine. However, these drugs can only temporarily relieve the clinical symptoms of GA with significant side effects. More and more basic and clinical studies have confirmed that Traditional Chinese medicine has definite curative effect on GA.

AIM OF THE STUDY

To elucidate the potential molecular mechanism of Tongfengkang (TFK) in the treatment of GA, and to provide experimental basis for the search and development of efficient and low-toxicity Chinese medicine for GA treatment.

MATERIALS AND METHODS

Aqueous extract of TFK (AETFK) were determined by liquid phase high resolution mass spectrometry and the possible effective constituents were screened out. Acute GA model rats were established to detect the anti-inflammatory and detumification effects of AETFK on GA and explore the potential mechanism. The effect of AETFK on serum uric acid and urinary uric acid levels in acute GA rats was determined by automatic biochemical analyzer, and the effect of AETFK on the expression of acute GA-related immunoinflammatory factors were determined by protein thermal fluorescence chip. The effect of AETFK on the concentration of neutrophils in the joint fluid of acute GA rats were determined by Reichs-Giemsa staining. The effect of AETFK on macrophage activation was detected by ELISA. In order to further investigate the mechanism of AETFK in the treatment of GA, a rat model of hyperuricemia was established to detect the effect of AETFK on the level of uric acid in hyperuricemia model rats. Biochemical indexes of liver and kidney and hematoxylin-eosin staining (HE) were used to evaluate the effects of AETFK on the organs, and to preliminatively evaluate the safety of ventilation confufang.

RESULTS

Compared with the model group, the joint swelling degree of GA rats in AETFK treatment group were significantly reduced, and the levels of blood uric acid and urine uric acid were also significantly decreased. Protein thermal fluorescence microarray results showed that the levels of gout - related inflammatory factors in GA rats in AETFK treatment group were significantly lower than those in control group. Reichsen-giemsa staining and ELISA showed that AETFK could reduce the activation of macrophages and the accumulation of neutrophils in the joint fluid. The results of liver and kidney biochemical indexes and HE staining showed that no obvious tissue damage was observed in the organs of rats treated with AETFK.

CONCLUSIONS

AETFK not only has significant anti-inflammatory effects on GA, but also can significantly reduce the level of blood uric acid in GA rats, without obvious toxic and side effects. These effects may be related to AETFK's inhibition of neutrophil enrichment and macrophage activation during early inflammation.

COVID-19 Vaccination and Gout Flare Risk in Patients With Infrequent or Frequent Flares: A Prospective Cohort Study

OBJECTIVE

To assess post-COVID-19 vaccination gout flare risk with differing baseline flare burden.

METHODS

We prospectively studied gout patients with infrequent or frequent flares, defined as ≤1 flare/year or ≥2 flares/year, respectively. COVID-19 vaccine-naive patients managed with urate-lowering therapy between February and June 2021 were included and voluntarily decided on vaccination. Participants were followed for 12 weeks after enrollment or first vaccine dose. Gout flares and risk factors were compared between groups.

RESULTS

Of 530 participants, 308 (58.1%) had infrequent flares and 222 (41.9%) had frequent flares at baseline, with 248 (142 infrequent and 106 frequent) receiving two-dose COVID-19 vaccination. Vaccination increased cumulative flare incidence at 12 weeks in the infrequent but not the frequent flare group (26.1% vs 10.8%, P = 0.001, compared with 60.4% vs 65.5%, P = 0.428). Flare incidence in the final 4 weeks of observation decreased significantly only in the vaccinated infrequent flare group (4.3% vs 12.0%, P = 0.017). Multivariable analyses showed that vaccination (odds ratio [OR] 2.82, 95% confidence interval [95% CI] 1.50-5.30, P = 0.001), flare in the preceding year (OR 1.95, 95% CI 1.03-3.71, P = 0.04), and body mass index (OR 1.09, 95% CI 1.01-1.19, P = 0.03) were independently associated with increased flare risk in the infrequent flare group. Baseline serum urate (mg/dl) was an independent risk factor in the frequent flare group (OR 1.23, 95% CI 1.05-1.45, P = 0.012).

CONCLUSION

COVID-19 vaccination was associated with increased early gout flares only in patients with previously infrequent flares.

Cohort study investigating gout flares and management in UK general practice

BACKGROUND

Gout is the most common inflammatory arthritis and is almost exclusively managed in primary care, however the course and severity of the condition is variable and poorly characterised. This research aims improve understanding about the frequency of, and factors associated with, gout flares in the UK and characterise the factors associated with the initiation of ULT.

METHODS

Using the Clinical Practice Research Database, patients with a coded incident gout diagnosis without a prior prescription for urate-lowering therapy (ULT) were identified. Gout flares post diagnosis and ULT initiation were identified through prescribing and coded data. Patient characteristics, co-morbidities and co-prescribing were co-variants. Factors associated with gout flares and ULT initiation were analysed using cox-proportional hazard model and logistic regression.

RESULTS

Fifty-one thousand seven hundred eighty-four patients were identified: 18,605 (35.9%, 95%CI 35.5-36.3%) had experienced ≥ 1 recurrent flare, 17.4% (95%CI 17.1-17.8%) within 12 months of diagnosis. Male sex, black ethnicity, higher BMI, heart failure, CKD, CVD and diuretic use were associated with flares, with the highest HR seen with high serum urate levels (≥ 540 µmol/L HR 4.63, 95%CI 4.03-5.31). ULT initiation was associated with similar variables, although higher alcohol intake and older age were associated with lower odds of ULT initiation but were not associated with flares. ULT was initiated in 27.7% (95%CI 27.3-28.0%): 5.7% (95%CI 5.5-5.9%) within 12 months of diagnosis. ULT initiation rates were higher in patients with recurrent flares.

CONCLUSION

Approximately one in six people with incident gout had a second flare within 12 months. Factors associated with flare recurrence and ULT initiation were similar, but ULT initiation occurred later after diagnosis than previously thought.

Exploration of model misspecification in latent class methods for longitudinal data: Correlation structure matters

Modeling longitudinal trajectories and identifying latent classes of trajectories is of great interest in biomedical research, and software to identify latent classes of such is readily available for latent class trajectory analysis (LCTA), growth mixture modeling (GMM) and covariance pattern mixture models (CPMM). In biomedical applications, the level of within-person correlation is often non-negligible, which can impact the model choice and interpretation. LCTA does not incorporate this correlation. GMM does so through random effects, while CPMM specifies a model for within-class marginal covariance matrix. Previous work has investigated the impact of constraining covariance structures, both within and across classes, in GMMs-an approach often used to solve convergence problems. Using simulation, we focused specifically on how misspecification of the temporal correlation structure and strength, but correct variances, impacts class enumeration and parameter estimation under LCTA and CPMM. We found (1) even in the presence of weak correlation, LCTA often does not reproduce original classes, (2) CPMM performs well in class enumeration when the correct correlation structure is selected, and (3) regardless of misspecification of the correlation structure, both LCTA and CPMM give unbiased estimates of the class trajectory parameters when the within-individual correlation is weak and the number of classes is correctly specified. However, the bias increases markedly when the correlation is moderate for LCTA and when the incorrect correlation structure is used for CPMM. This work highlights the importance of correlation alone in obtaining appropriate model interpretations and provides insight into model choice.

Four trajectories of 24-hour urine protein levels in real-world lupus nephritis cohorts

OBJECTIVES

A 24-hour urine protein (24hUP) is a key measurement in the management of lupus nephritis (LN); however, trajectories of 24hUP in LN is poorly defined.

METHODS

Two LN cohorts that underwent renal biopsies at Renji Hospital were included. Patients received standard of care in a real-world setting and 24hUP data were collected over time. Trajectory patterns of 24hUP were determined using the latent class mixed modelling (LCMM). Baseline characters were compared among trajectories and multinomial logistic regression was used to determine independent risk factors. Optimal combinations of variables were identified for model construction and user-friendly nomograms were developed.

RESULTS

The derivation cohort composed of 194 patients with LN with 1479 study visits and a median follow-up of 17.5 (12.2-21.7) months. Four trajectories of 24hUP were identified, that is, Rapid Responders, Good Responders, Suboptimal Responders and Non-Responders, with the KDIGO renal complete remission rates (time to complete remission, months) of 84.2% (4.19), 79.6% (7.94), 40.4% (not applicable) and 9.8% (not applicable), respectively (p<0.001). The 'Rapid Responders' distinguish itself from other trajectories and a nomogram, composed of age, systemic lupus erythematosus duration, albumin and 24hUP yielded C-indices >0.85. Another nomogram to predict 'Good Responders' yielded C-indices of 0.73~0.78, which composed of gender, new-onset LN, glomerulosclerosis and partial remission within 6 months. When applied to the validation cohort with 117 patients and 500 study visits, nomograms effectively sorted out 'Rapid Responders' and 'Good Responders'.

CONCLUSION

Four trajectories of LN shed some light to guide the management of LN and further clinical trials design.

Dietary and Lifestyle-Centered Approach in Gout Care and Prevention

PURPOSE OF REVIEW

We aim to provide a comprehensive review of the available literature to inform dietary recommendations for patients with gout and hyperuricemia that have the potential to simultaneously lower serum urate and reduce gout morbidity while addressing gout's cardiometabolic comorbidities holistically.

RECENT FINDINGS

The global burden of gout is rising worldwide, particularly in developed nations as well as in women. Patients with gout are often recommended to follow a low-purine (i.e., low-protein) diet to avoid purine-loading. However, such an approach may lead to increased consumption of unhealthy carbohydrates and fats, which in turn contributes to metabolic syndrome and subsequently raises serum urate levels and leads to adverse cardiovascular outcomes. On the other hand, several well-established diets for cardiometabolic health, such as the Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diets, in combination with weight loss for those who are overweight or obese, also have beneficial effects on relevant gout endpoints. It is important to recognize not only the direct effect of diet on hyperuricemia and gout, but its mediated effect through obesity and insulin resistance. Thus, several preeminent healthy dietary patterns that have proven benefits in cardiometabolic health have the power to holistically address not only gout morbidity but also its associated comorbidities that lead to premature mortality among patients with gout.

Onset of comorbidities and flare patterns within pre-existing morbidity clusters in people with gout: 5-year primary care cohort study

Objective

To investigate the onset of comorbidities and pattern of flares over 5 years according to baseline comorbidity clusters in people with gout.

Methods

In a prospective primary care-based cohort study, adults aged ≥18 years with gout were identified from primary care medical records in 20 general practices across the West Midlands, UK and followed up over 5 years. Four clusters of participants have been defined previously according to baseline comorbidity status. The associations of (i) incident comorbidities and (ii) gout flares with baseline cluster membership were estimated using age and sex-adjusted Poisson regression and mixed effects ordinal logistic regression, respectively.

Results

The comorbidity with the highest incidence was coronary artery disease (39.2%), followed by hypertension (36.7%), chronic kidney disease stage ≥3 (18.1%), obesity (16.0%), hyperlipidaemia (11.7%), diabetes (8.8%) and cancer (8.4%). There were statistically significant associations observed between cluster membership and incidence of coronary artery disease, hyperlipidaemia, heart failure and hypertension. In each cluster, nearly one-third of participants reported two or more gout flares at each time-point. History of oligo/polyarticular flares (odds ratio [OR]= 2.16, 95% confidence interval [CI]: 1.73, 2.70) and obesity (1.66, 95% CI: 1.21, 2.25) were associated with increasing flares whereas current use of allopurinol was associated with lower risk (0.42, 95% CI: 0.34–0.53). Cluster membership was not associated with flares.

Conclusion

Substantial numbers of people in each cluster developed new comorbidities that varies by cluster membership. People also experienced multiple flares over time, but these did not differ between clusters. Clinicians should be vigilant for the development of new comorbidities in people with gout.