Association between previous consumption of sugar-sweetened beverages and diabetes remission in patients with newly diagnosed type 2 diabetic ketoacidosis

Abstract

This study examined the potential correlation between the immoderate intake of sugar-sweetened beverages (SSBs) and the subsequent rate of diabetes remission (DR). 206 individuals who met the eligibility criteria between January 2019 and June 2022 were recruited. Inquiries were conducted to gather information on the participants’ beverage consumption before the onset. Subsequently, the participants were separated into the diabetes remission group (DR group) and nondiabetes remission group (NDR group) depending on whether they met the diagnostic criteria for diabetes remission. Baseline clinical elements within the two groups were juxtaposed, and factors influencing diabetes remission were identified through logistic regression analyses. The cutoff values of each critical factor were determined based on the receiver operating characteristic curve. One hundred and nine patients reported a history of SSB consumption, while the remaining 58 reported no such history. After 1 year, 40 patients achieved remission from diabetes. Compared with the NDR group, a higher SSBs ratio, body mass index (BMI), and blood creatinine (BCr) was observed in the DR group after adjusting for confounders, SSBs (odds ratio [OR] = 3.503; 95% confidence interval [CI] = 1.334–9.202; p = 0.011) and BCr (OR = 1.038; 95% CI = 1.003–1.079; p = 0.042) emerged as independent predictors of DR. The composite index of SSBs and BCr efficaciously predicted DR (area under the ROC curve [AUC] = 0.810, p < 0.001). SSBs and BCr were independent risk factors for DR. The amalgamation of these markers could more accurately predict DR.

1.  Introduction

The global surge in the excessive intake of sugar-sweetened beverages (SSBs) is a public health issue, with usage rates exceeding the daily recommendations in numerous high-income nations and increasing in lower-to-middle-income countries, particularly among youths [1, 2]. Several studies have identified chronic, excessive SSB consumption as a significant risk factor, with a considerable body of evidence linking SSBs to weight gain [3, 4], metabolic syndrome risk [3, 5, 6], cardiovascular disease [7], cognitive disorders [8], and certain forms of cancer [9, 10]. Furthermore, numerous investigations have identified sustained SSB consumption as an independent risk factor for type 2 diabetes mellitus (T2DM), markedly elevating both the incidence and overall morbidity of the condition [11]. This has been attributed to the potential influence of a high dietary glycemic load leading to inflammation [12], hyperinsulinemia [13], insulin resistance [14, 15], ectopic lipid deposition [16], and impaired β-cell function [17, 18]. However, to our knowledge, only a few studies have examined the association between SSB consumption and acute diabetes complications.

Diabetic ketoacidosis (DKA) is the most common acute hyperglycemic emergency in patients with diabetes mellitus [19]. Predominantly occurring in individuals with uncontrolled type 1 diabetes mellitus (T1DM), DKA has also been observed in approximately 34% of individuals with poorly managed T2DM under stress-inducing conditions (such as medical or surgical illnesses) and newly diagnosed T2DM in adolescents, also known as ketosis-prone T2DM [20]. DKA typically results from a combination of predisposing factors like infections, treatment interruption, and iatrogenic factors. It is characterized by hyperglycemia, metabolic acidosis, and ketosis. DKA continues to present significant morbidity and mortality risks if optimally treated inadequately or late (particularly in developing nations), with child mortality rates ranging from 3.4% to 13.4% [21]. This considerable incidence of hospitalization due to DKA significantly impacts patients’ quality of life and socioeconomic landscapes [22]. Japanese researchers have extensively documented the association between ketoacidosis-onset T2DM and the ubiquitous presence of soft drinks and vending machines [23]. However, amidst the proliferation of sugary beverages, the landscape of soft drink-induced ketosis ketoacidosis has transitioned from an infrequent occurrence to a prevalent manifestation of DKA [24], eliciting considerable societal scrutiny.

Patients with a shorter duration of T2DM and better preserved islet function are generally believed to have higher chances of achieving DR [25]. However, most DR studies have primarily delved into obese diabetic patients with optimal glycemic control, scant attention being paid to patients presenting with DKA at the onset. To fill this knowledge gap and better comprehend the influence of a history of SSB consumption on diabetes status, we conducted a retrospective cohort study to evaluate the effects of prior SSB consumption on DR rates in patients with T2DM presenting with DKA. We hypothesized that a history of SSB consumption (a detrimental dietary habit) could act as a DKA predisposing factor, and due to its role in augmenting islet load and accelerating short-term islet function deterioration, it could enhance islet function recovery upon habit cessation.

2.  Research Design and Methods

2.1  Study design

In this study, we included patients newly diagnosed with type 2 diabetic ketoacidosis admitted to the First Affiliated Hospital of Nanchang University’s Department of Endocrinology and Metabolic Diseases between January 2019 and June 2022. Utilizing defined inclusion and exclusion criteria, 206 patients were initially enrolled in the study. Data on pre-onset beverage consumption, medication during follow-up, and patient examination outcomes at the 1-year mark postdiagnosis were collected via both telephonic and in-person interviews. Patients unable or unwilling to cooperate and those with unavailable follow-up data were excluded, resulting in a final study cohort of 167 patients. The study protocol was endorsed by the Ethics Committee of the First Affiliated Hospital of Nanchang University (Ethics reference: 2023-130) and was conducted as per the principles of the Declaration of Helsinki.

Inclusion criteria

1. Hyperglycemia (random blood glucose) >13.9 mmol/L;

2. Arterial blood pH < 7.30 or bicarbonate <18 mmol/L;

3. Presence of ketonuria.

Exclusion criteria

1. Confirmed type 1 diabetes;

2. Prior diabetes diagnosis;

3. Diagnosed pancreatic disease;

4. History of immunosuppressant usage;

5. Acute myocardial infarction, cerebrovascular accident, or severe heart failure;

6. Severe liver or kidney dysfunction;

7. Recent severe trauma or history of major surgery.

2.2  Definition of variables

2.2.1  Sugar-sweetened beverages

The global beverage market segmentations include hot beverages, milk-based drinks, soft drinks, and alcoholic drinks [26]. There is currently no universal categorization of SSBs. The most extensively accepted research definition considers a beverage as an SSB if it contains caloric sweeteners (e.g., sucrose, high fructose corn syrup, or fruit juice concentrates) added by manufacturers, establishments, or consumers [3]. However, some authorities have adopted more precise definitions based on sugar content per volume for regulatory initiatives. Hence, we utilized the criteria established by the New York City Board of Health, which defines SSBs as beverages containing ≥25 calories or 6.25 g of added sugar per 8 fl oz (–237 mL), for this study [3].

2.2.2  Pre-Onset SSBs Intake History

To underscore the significance of beverage consumption patterns preceding the onset of DKA, we introduced the concept of “Pre-Onset SSBs Intake History.” By considering local beverage packaging capacity, unit usage habits, and employing more precise standardization and quantification of temporal dimensions, we defined a history of sugar-sweetened beverage consumption as an average daily intake of ≥500 mL of SSBs during the two months preceding the onset of diabetes. The absence of such history was otherwise recorded.

2.2.3  Diabetes remission

As per the recommendations of the American Diabetes Association consensus group, we defined DR as HbA1c <6.5% (48 mmol/mol) (OR: fasting plasma glucose <7 mmol/L when HbA1c was unavailable), in the absence of glucose-lowering pharmacotherapy for ≥3 months [27]. In the present investigation, the period of observation for achieving DR as the primary outcome measure was set at 1 year. Remission was defined as the absence of antidiabetic medication usage for ≥3 months, coupled with a HbA1c level of <6.5% at the 1-year mark from disease onset.

2.3  Statistical analysis

SPSS (version 26.0) was utilized for all data analyses, with the threshold for statistical significance set at p < 0.05. The Shapiro-Wilk test was used to evaluate the normality of data distribution. Normally distributed quantitative variables were presented as the mean ± SD, while variables not normally distributed were expressed as the median and interquartile range. For the comparison of continuous variable distributions between two independent groups, the t-test (for normally distributed data) or Mann-Whitney U test (for skewed data distributions) was deployed. Categorical data were compared between the two groups using the chi-square test. To assess and identify the factors affecting DR, univariate and multivariate logistic regression analyses were conducted. The receiver operating characteristic (ROC) curve was leveraged to analyze the impact and delineate the cutoff values of SSBs and BCr in predicting DR, further calculating their sensitivity, specificity, positive predictive value, and negative predictive value.

3.  Results

3.1  Clinical characteristics of participants

In the present study, a total of 206 participants were initially enrolled, from which 167 (74%) had comprehensive data to evaluate diabetes status at baseline and at least one subsequent follow-up consultation. The baseline characteristics of the participants in the follow-up group (FG) and the loss-to-follow-up group (LFG) are presented in Supplementary Table 1. We found no statistically significant differences in all baseline characteristics between the two groups. Table 1 depicts the fundamental traits of all participants at the point of diagnosis of type 2 diabetic ketoacidosis and in each group categorized based on the existence or nonexistence of DR at the 1-year mark. Of these 167 participants, 65.3% (109/167) reported a history of SSBs consumption, which was in line with our preliminary clinical impressions. Among all participants, 24.0% (40/167) belonged to the diabetes remission (DR group), while 76.0% (127/167) belonged to the nondiabetes remission (NDR group). Among the baseline clinical parameters collected, only BMI, BCr, and the presence or absence of sugar-sweetened beverage consumption history (SSBs) before the illness were associated with DR at 1 year after disease onset, with corresponding p-values of 0.028, 0.048, and 0.03. Compared with the NDR group, individuals within the DR group were younger and had higher levels of glycosylated hemoglobin, fasting C-peptide, and serum uric acid. However, the differences in these parameters between the two groups were not statistically significant (p > 0.05).

Table 1

Clinical characteristics of participants in the DR and NDR groups

	Total (n = 167)	DR (n = 40)	NDR (n = 127)	p value
SSBs ratio (%)	65.3
		85	59	0.003*
Male ratio (%)	84.4
		92.5	81.9	0.107
Age (years)	32 (24, 44)
		29.5 (22.25, 38.75)	32 (25, 46)	0.139
BMI (kg/m2)	25.41 (22.21, 27.88)
		26.65 ± 3.3	25.08 ± 4.76	0.028*
HbA1c (%)	12.6 (11.38, 14.33)
		12.7 (11.38, 13.6)	12.5 (11.38, 14.6)	0.683
FBG (mmol/L)	15.17 (12.76, 18.38)
		14.74 (12.81, 17.6)	15.19 (12.68, 18.57)	0.816
FCP (ng/mL)	1.06 (0.41, 1.73)
		1.08 (0.79, 1.76)	1.055 (0.26, 1.73)	0.267
WBC (×109/L)	7.8 (5.85, 11.63)
		7.5 (5.81, 11.38)	7.975 (6.01, 11.71)	0.865
HGB (g/L)	153 (141, 167)
		154.4 ± 16.43	153.8 ± 18.56	0.524
PLT (×109/L)	242 (194.5, 291.25)
		230 (184, 269)	246 (198, 295)	0.167
ALT (U/L)	22 (14.7, 38)
		23.5 (15.05, 47.38)	21.9 (14.5, 34.9)	0.450
ALB (U/L)	74 (71, 77)
		47.5 (45.75, 48.6)	47 (44.28, 48.425)	0.460
TG (mmol/L)	1.56 (1.09, 2.72)
		1.91 (1.47, 2.51)	1.53 (1.02, 2.78)	0.237
TC (mmol/L)	4.42 (3.65, 4.84)
		4.41 (3.5, 5.06)	4.415 (3.69, 4.83)	0.992
BCr (umol/L)	70.85 (61.15, 80)
		78.1 ± 16.46	69.22 ± 13.49	0.048*
BUA (mmol/L)	387(301.08, 474.5)
		422.45 ± 101.81	382.59 ± 121.49	0.176

SSBs, sugar-sweetened beverages; BMI, body mass index; FBG, fasting plasma glucose; FCP, fasting C-peptide; WBC, white blood cell; HGB, hemoglobin; PLT, platelets; ALT, alanine amino transaminase; ALB, albumin; TG, triglyceride; TC, total cholesterol; BCr, blood creatinine; BUA, blood uric acid.

* p < 0.05

3.2  Evaluation of factors influencing diabetes remission

Numerous factors may contribute to DR. To analyze these, we initially employed univariate regression analyses to identify potential risk factors influencing DR, using a p-value of <0.1 and denoting DR and nonremission as dependent variables. The findings indicated that a history of sugar-sweetened beverage consumption (SSBs), BMI, TC, and BCr could potentially impact the achievement of DR, with corresponding p-values of 0.004, 0.074, 0.069, and 0.046, respectively (Table 2). To adjust for confounders, we conducted a multivariate logistic regression analysis on these possible influencing factors. This revealed that SSBs (odds ratio [OR] = 3.503; 95% confidence interval [CI] = 1.334–9.202; p = 0.011) and BCr (OR = 1.038; 95% CI = 1.003–1.079; p = 0.042) are independent determinants of DR (Fig. 1).

Table 2

Univariate logistic regression analysis of diabetes remission determinants

Variables	β	SE	Waldc 2	OR (95% CI)	p value
SSBs	1.368	0.478	8.189	3.929 (1.539, 10.030)	0.004*
Male	1.003	0.643	2.435	2.728 (0.773, 9.618)	0.119
Age	–0.024	0.015	2.665	0.976 (0.949, 1.005)	0.103
BMI	0.075	0.042	3.198	1.077 (0.993, 1.169)	0.074*
HbA1c	–0.067	0.087	0.599	0.935 (0.788, 1.109)	0.439
FBG	–0.023	0.033	0.484	0.978 (0.917, 1.042)	0.486
FCP	0.146	0.176	0.686	1.157 (0.819, 1.635)	0.408
WBC	0	0.032	0	1 (0.94, 1.064)	0.994
HGB	0.161	0.3	0.287	1.175 (0.652, 2.115)	0.592
PLT	–0.003	0.002	1.077	0.997 (0.993, 1.002)	0.299
ALT	0	0.013	0.001	1 (0.975, 1.025)	0.973
ALB	0.009	0.098	0.008	1.009 (0.832, 1.223)	0.929
TG	0.071	0.256	0.076	1.073 (0.65, 1.771)	0.782
TC	–0.541	0.298	3.296	0.582 (0.325, 1.044)	0.069*
BCr	0.037	0.018	3.97	1.037 (1.001, 1.075)	0.046*
BUA	0.003	0.003	0.892	1.003 (0.997, 1.009)	0.345

SSBs, sugar-sweetened beverages; BMI, body mass index; FBG, fasting plasma glucose; FCP, fasting C-peptide; WBC, white blood cell; HGB, hemoglobin; PLT, platelets; ALT, alanine amino transaminase; ALB, albumin; TG, triglyceride; TC, total cholesterol; BCr, blood creatinine; BUA, blood uric acid.

* p < 0.1

Fig. 1

Forest plot of a multivariate logistic regression analysis of diabetes remission determinants

3.3  Optimal cutoff value for each independent influencing factor

An analysis of the ROC curve for independent risk factors affecting DR (Fig. 2) demonstrated that the area under the ROC curve (AUC) for BCr was 0.606, respectively. Based on the Youden index, the critical values for BCr in predicting DR were determined at 70.15, respectively (Table 3). Furthermore, a ROC curve analysis of the combination of independent risk factors showed that the AUC reached its maximum value (AUC = 0.810) after the combination of SSBs and BCr.

Fig. 2

The ROC curve for diabetes remission prediction of SSBs and BCr

Table 3

ROC curve analysis of each independent influencing factor

	AUC	p value	95% CI	Cutoff value	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)
BCr	0.606	0.043	0.510–0.702	70.15	92.5	29.9	29.4	92.7
SSBs + BCr	0.810	<0.001	0.598–0.777	—	80	58.3	37.6	91.4

4.  Discussion

Within our cohort of newly diagnosed T2DM patients with diabetic ketoacidosis, we observed that a significant number of participants reported a pre-onset consumption history of SSBs. These participants were significantly younger and exhibited higher BMIs. Our analysis also revealed that the pre-onset consumption of SSBs and BCr were independent predictors of DR. Notably, patients with a history of SSB consumption were found to be 3.5 times more likely to achieve DR compared to those with no such history.

Several factors, such as BMI, duration of diabetes, and remaining islet cell function, among others, can influence the probability of DR [27]. However, our study failed to identify any statistically significant differences between the two groups in these respects. This might be attributed to the fact that our study cohort comprised only patients with newly diagnosed diabetes, who presented almost identical baseline clinical levels for these risk factors. Our analysis underscored the study’s novel findings, highlighting a robust correlation between BCr, reflecting the pre-onset consumption history of SSBs, and the likelihood of DR.

The significance of creatinine levels as a facilitative factor for DR merits thorough examination within the realm of metabolic physiology. BCr, a metabolic byproduct stemming from muscle metabolism, is utilized in the diagnostic evaluation of renal impairment. Moreover, it stands as a dependable marker reflecting an individual’s muscle mass, nutritional profile, and physical activity levels across various clinical contexts [28]. One conceivable mechanism resides in its intimate correlation with muscle mass [29] and physical activity [30]. Elevated levels of creatinine, indicative of increased muscle mass and physical activity, often demonstrate correlations with improved insulin sensitivity. Subsequently, this facilitates enhanced glucose utilization efficiency [31], potentially fostering a favorable metabolic milieu conducive to DR.

Extant literature suggests that the health impacts of different types of beverages can vary widely based on their sugar content and other ingredients [11, 32, 33]. Numerous studies suggest that a high intake of low-energy-density beverages, such as plain water, low-fat milk, and coffee, not only proves beneficial to health but also reduces the rates of occurrence of obesity, type 2 diabetes, CVD, and overall mortality. In contrast, SSB consumption tends to exert adverse effects. Consequently, various countries and organizations have issued recommendations for the restriction of added sugar intake and reduction of sugary drink consumption [34-36].

SSBs are drinks sweetened with added sugars such as sucrose or high fructose corn syrup. These beverages are known to contribute to weight gain through several mechanisms [37, 38]. First, they introduce additional liquid calories to the diet, which do not elicit the same satiety response as solid foods [39]. This means that individuals may not counterbalance their diet to account for the additional calories consumed from SSBs, leading to a caloric surplus and potential weight gain over time. Second, the rapid glucose absorption from SSBs can induce hyperinsulinemia [40]. Especially for beverages with high sugar content, this quick glucose uptake causes blood sugar spikes, prompting the pancreas to release substantial insulin amounts. This insulin surge can enhance de novo adipogenesis [41], contributing further to weight gain. Furthermore, SSBs trigger the brain’s reward system, eliciting the release of dopamine, a neurotransmitter associated with pleasure [42, 43]. This response can stimulate cravings for more sugary items and promote calorie overconsumption. Also, it can lead to habit formation and conditioned associations of SSB intake with reward, thus fueling a cycle of craving and consumption that can foster overeating and weight gain. Consequently, the consistent theoretical and empirical evidence offers a robust explanation for why participants with a history of SSB consumption were younger and had larger BMIs relative to those with no such history.

The unique contribution of this study lies in the finding that participants with a pre-onset SSB consumption history were more likely to achieve DR after 1 year compared with those with no such history (OR: 3.503; 95% CI:1.334–9.202; p = 0.011). Our follow-up observations revealed that all subjects either significantly reduced or completely eliminated their SSB consumption after being diagnosed with diabetes. The SSB group, due to higher baseline SSB intake, reaped greater benefits from this dietary change. The hyperglycemia, hyperinsulinemia, and obesity arising from their initial unhealthy dietary choices were more likely to be rectified. Despite the lack of statistical significance, it is noteworthy that the age of onset was younger in the DR group than in the NDR group (29.5[22.25, 38.75] vs. 32[25, 46]; p = 0.139). Younger patients are generally more likely to have better preserved beta-cell function and shorter abnormal blood glucose level durations [44]. These factors collectively enhance DR achievement.

While the baseline characteristics appear ostensibly similar between the groups, except for discrepancies in SSBs, BMI, and BCr, the observed inconsistencies in attaining DR within cohorts designated as DR and NDR groups offer compelling insights into the intricate dynamics governing disease evolution and therapeutic response. This incongruity underscores the nuanced interplay among a plethora of factors dictating disease trajectory and modulating the efficacy of therapeutic interventions. Noteworthy contributors may include individual variations in adipose tissue thresholds [45], the extent of BMI reduction post-disease onset [46], adherence to dietary restrictions [47], and engagement in physical activity regimens [48]. These multifaceted determinants collectively influence the likelihood of DR achievement, elucidating the complexity of personalized diabetes management strategies.

It is imperative to underscore that while a prior history of SSBs consumption may exert an influence on the implementation of DR, its presence does not ensure the successful attainment. Furthermore, this study confines its evaluation of DR to a one-year timeframe, necessitating caution in extrapolating long-term prognostic implications. The presence or absence of such a history is merely one factor to consider when evaluating outcomes related to DR. It is important to recognize that each individual’s response to lifestyle modifications and medical interventions can vary depending on their unique circumstances. By adopting a comprehensive approach that includes dietary adjustments, regular physical activity, weight management, adherence to medication, and medical supervision, individuals with or without a history of SSB consumption can work toward improving glycemic control and potentially achieve DR, leading to better overall well-being for them.

However, for those with diabetes or at risk of developing the condition, minimizing or avoiding SSB consumption is crucial to optimize their chances of attaining successful disease management and improving their overall health. Opting for healthier alternatives such as water, unsweetened beverages, or those sweetened with artificial sweeteners can support glycemic control and improve the likelihood of achieving DR.

5.  Strengths and limitations of this study

The criterion of consuming ≥500 mL of SSBs daily, as part of the definition of “pre-onset SSBs intake history,” provides a clear and quantifiable standard, enhancing uniformity and comparability across studies. By focusing on the 2-month period preceding the onset of diabetes, it ensures temporal relevance, thus reducing potential biases associated with retrospective recall. Additionally, taking into account local beverage consumption patterns enhances the generalizability of results and facilitates comparisons between studies. Nevertheless, the application of a daily threshold of ≥500 mL may not fully capture the range of individual susceptibility to the diabetogenic effects of SSBs. It is crucial to recognize the multifaceted etiology of diabetes, necessitating a comprehensive evaluation of various dietary and lifestyle factors. Moreover, reliance on self-reported dietary data introduces the risk of recall bias, emphasizing the need for objective measures. Despite its methodological strength, this approach is limited by factors such as arbitrary threshold determination, incomplete assessment of covariates, and susceptibility to recall bias. Future research efforts should aim to address these methodological limitations to better understand the relationship between SSBs consumption and diabetes risk.

Although this study contributes novel insights into the relationship between the history of SSB consumption and newly diagnosed type 2 diabetes with diabetic ketoacidosis, there are several limitations to consider. First, a significant number of participants were lost to follow-up, potentially introducing compliance bias. Second, the sample size was relatively small, leading to potential statistical errors and difficulties in further subgroup analyses based on the type and sugar content of pre-existing beverage consumption.

6.  Conclusions

In conclusion, this study’s findings indicated a higher proportion of participants with a history of SSB consumption before the onset of diabetic ketoacidosis. Furthermore, these individuals were younger and had a higher BMI compared with those without such a history. Notably, our results demonstrate that both SSB consumption and baseline BCr independently influence DR after 1 year. The integration of these markers has the potential to enhance the accuracy of predicting DR, thereby providing valuable insights for tailoring follow-up care in patients diagnosed with T2DM.

Competing Interest

The authors have no conflicts of interest to declare.

Funding

The manuscript was supported by grants from the National Natural Science Funds of China (81760168) and the Postgraduates Innovation Special Fund Project by Education Department of Jiangxi province (YC2023-B079).

Author Contribution

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Shanshan Li, Jinying Wang, Yuanyuan Deng, Junping Zhang and Yun Zou. The first draft of the manuscript was written by Shanshan Li and Jixiong Xu. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Ethics Approval

The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Nanchang University (Date: 2023-05-11/Ethics number: 2023-130), and the study was conducted in accordance with the declaration of Helsinki.

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Acknowledgments

None stated.

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