Skip to main content
Download PDF

Preserved seasonal variation in glycemic control in patients with type 2 diabetes mellitus during COVID-19: a 3-year-long retrospective cohort study in older adults in Japan

BMC Endocrine Disorders volume 24, Article number: 70 (2024) Cite this article

Abstract

Background

The coronavirus disease 2019 (COVID-19) pandemic has changed our lifestyle by imposing restrictions, such as physical distancing. The effect of COVID-19 prevalence on seasonal variations in glycemic control in patients with diabetes mellitus (DM) remains unknown.

Methods

This single-center retrospective cohort study evaluated glycemic control in patients with type 2 DM who visited Sugi Cardiovascular Hospital in December 2021. We evaluated the clinical findings of all patients treated regularly between March 1, 2019, and December 31, 2021, including the periods both before and after the COVID-19 pandemic. All the standard treatments were approved. Furthermore, seasonal changes in hemoglobin A1c (HbA1c) levels were evaluated using stratified analyses based on age.

Results

This study analyzed 86 patients (mean age, 69.6 ± 9.2 years; men, 57). Median HbA1c (National Glycohemoglobin Standardization Program [Union of Clinical Chemistry]) levels in spring (March) were 7.70% (interquartile range (IQR):7.23%–8.30%) [60.6 mmol/mol (IQR:55.4–67.2 mmol/mol)], 7.35% (IQR:6.90%–7.90%) [56.8 mmol/mol (IQR:51.9–62.8 mmol/mol)], and 7.50% (IQR:7.10%–8.00%) [58.5 mmol/mol (IQR:54.1–63.9 mmol/mol)] in 2019, 2020, and 2021, respectively. During these periods, HbA1c levels and body mass index (BMI) revealed significant seasonal variations “high in spring” and “low in autumn.” Median HbA1c levels in spring (March) and autumn (September) were 7.86% [61.2 mmol/mol] and 7.48% [57.4 mmol/mol] in 2019 (P < 0.001), 7.50% [57.7 mmol/mol] and 7.17% [54.2 mmol/mol] in 2020 (P < 0.001), and 7.61% [58.3 mmol/mol] and 7.19% [53.8 mmol/mol] in 2021 (P < 0.001). Seasonal variations in HbA1c levels and BMI were maintained over the past 3 years, including the pandemic period. None of the patients in this study developed COVID-19 during the study period.

Conclusions

Seasonal variations in glycemic control in patients with DM were not influenced by lifestyle modifications associated with COVID-19. Maintenance of physical activity is necessary to prevent the development of sarcopenia. Moreover, seasonal variations in glycemic metabolism should be considered an independent factor for DM management. Additional extensive multifacility investigations are necessary to corroborate our findings.

Peer Review reports

Introduction

Seasonal variations in glycemic metabolism are observed in patients with diabetes mellitus (DM) [1, 2]. The hemoglobin A1c (HbA1c) level is high in winter and low in summer (i.e., highest in March and lowest in September) in patients with type 2 DM in Japan [3, 4]. Decreased physical activity and increased caloric intake in winter contribute to seasonal variations in HbA1c levels [5] and insulin sensitivity [6]. Lifestyle: Food intake (quantity, quality, and frequency), exercise, and sleep have been investigated for their possible association with glycemic control [7, 8], including seasonal variation in glycemic control [9]. Seasonal events and celebrations have the potential to lead to cyclical variations in HbA1c levels [10]. Furthermore, lifestyle indirectly affects various complications of DM by controlling blood pressure, lipid metabolism, and body weight [11]. Thus, lifestyle modifications may affect glycemic control [12] and the management of patients with DM, either negatively by exacerbating the disease or positively by improving it.

The 2016 Kumamoto earthquake in Japan worsened glycemic control in patients with DM, probably through lifestyle modifications [13]. Coronavirus disease 2019 (COVID-19) spread worldwide in 2019; thus, many people were forced to change their lifestyles, particularly because of the lockdowns observed worldwide. The estimated number of COVID-19 patients in Japan was 390,190 on January 31, 2021; however, no lockdown was imposed [14]. However, people were encouraged to avoid closed spaces, crowded places, and close-contact settings (i.e., the 3Cs), maintain physical distancing [15], and limit movement under the terms of the COVID-19-related state of emergency declared between April 7, 2020, and May 25, 2020; January 8, 2021, and March 21, 2021; April 25, 2021, and June 20, 2021; and July 12, 2021, and September 30, 2021, in Japan. Generally, people in Japan had modified their lifestyles in strict accordance with these recommendations.

A recent analysis based on mathematical modeling in India predicted that lockdown measures would cause a substantial increase in HbA1c levels and DM-related complications [16]. Conversely, other studies have reported that lockdown measures do not exacerbate glycemic control in patients with type 1 DM and young adults with type 2 DM [17, 18]. However, the effect of COVID-19 on seasonal variations in glycemic control in patients with type 2 DM remains unclear.

This single-center retrospective cohort study sought to investigate the changes in glycemic control of patients with type 2 DM due to lifestyle modifications caused by the pandemic. In this study, we analyzed the changes in glycemic control of patients with type 2 DM during the pre-pandemic and pandemic periods for 3 years. Clarifying lifestyle modifications based on seasonal glycemic variations in patients with type 2 DM may improve the management of these patients.

Materials and methods

This study enrolled first a total of 98 patients with type 2 DM who visited the Department of Endocrinology and Metabolism of Sugi Cardiovascular Hospital between March 1, 2019, and December 31, 2021. We excluded five and seven patients from the yearly cohorts of 2019–2020 and 2020–2021, respectively (Fig. 1). Thus, data from 86 patients were analyzed (Fig. 1 and Table 1). Physical and laboratory data were collected every 3 months during the hospital visits between March 1, 2019, and December 31, 2021, for each patient. All procedures complied with the ethical standards of the Institutional Review Board of Sugi Cardiovascular Hospital and the 2013 Declaration of Helsinki. This study was approved by the Ethics Committee of Sugi Cardiovascular Hospital (20,201,207), and the patients provided written informed consent.

Fig. 1
figure 1

Flow chart of the present study. This study included patients who visited our hospital regularly for three years (between 2019 and 2021)

Full size image
Table 1 Participant characteristics
Full size table

Physical and laboratory data of the participants, including complications, treatment, body weight, body mass index (BMI), and blood pressure, were evaluated before and after the spread of COVID-19. Hypertension was defined as the requirement for antihypertensive therapy, systolic blood pressure ≥ 140 mm Hg, or diastolic blood pressure ≥ 90 mm Hg. Dyslipidemia was defined as dyslipidemia therapy, low-density lipoprotein cholesterol level of ≥ 140 mg/dL, or serum triglyceride level of ≥ 150 mg/dL. Coronary artery disease was defined as a history of angina or myocardial infarction. The results of laboratory tests, including HbA1c levels, as per the National Glycohemoglobin Standardization Program values (%) with accompanying International Federation of Clinical Chemistry values (mmol/mol) in brackets, and the indicators of DM profile, liver function, renal function, and lipid profile were also evaluated. HbA1c was measured by high-performance liquid chromatography (HPLC) using HLC-723GR01® (Tosoh Bioscience, Yamaguchi, Japan) with a coefficient of variation of less than 2%.

Patients’ diet and exercise changes were surveyed during the COVID-19 period using a written questionnaire in December 2021. Data were compared every 3 months to assess seasonal variation, starting in March, just before the first state of emergency was declared. March, June, September, and December represent spring, summer (hot season), autumn, and winter (cold season), respectively [19]. Additionally, seasonal glycemic variations were analyzed by comparing the median HbA1c levels determined at each visit and differences across the study period. Moreover, we conducted stratified analyses of seasonal variations in glycemic control by categorizing the patients into two age groups: older (≥ 70 years) and younger (< 70 years), and two lifestyle groups: with or without lifestyle changes (food intake or physical activity).

Statistical analysis

Quantitative data are presented as mean ± standard deviation for normally distributed variables and as median plus interquartile range (IQR) and range for non-normally distributed variables. Normally distributed variables compared with variables showing a non-normal distribution are presented as median plus IQR and range. The normal distribution of each variable was confirmed using the Shapiro–Wilk test (Supplementary Table S1). Comparisons of categorical variables, such as sex, complications, and drugs in each group, were performed using Fisher’s exact test. Continuous variables were analyzed using the Wilcoxon signed-rank test and Kruskal–Wallis test for 2 and ≥ 3 paired samples, respectively, and analysis of variance (ANOVA). Statistical significance was set at P < 0.05. All analyses were performed using JMP Pro 14 software (SAS Institute Inc., Cary, NC, USA).

Power analysis was performed to confirm whether the number of participants was sufficient to collect the baseline data. The mean baseline and standard deviation of HbA1c (NGSP) were 7.86% and 1.08%, respectively (Table 2). Based on previous studies on the clinical significance [20] and effects of lifestyle modifications [7], an improvement of 0.5% in HbA1c level was considered clinically significant. The required number of participants when the α and β error values were set as 0.05 and 0.2, respectively, was 65. Thus, 86 participants were considered sufficient for this study.

Table 2 Seasonal variations in clinical parameters in 2019, 2020, and 2021 (n = 86)
Full size table

Patient and public involvement statement

The design, conduct, reporting, and dissemination plans of the present study were not influenced by the patients or the public.

Results

Baseline characteristics of the participants

The baseline characteristics of the 86 participants are listed in Table 1. All participants were Japanese. None of the participants had developed COVID-19. The mean age of the participants was 69.6 ± 9.2 years, and there were 57 men (66.3%). The median duration of DM was 13.5 (IQR:9–23; range:1–50) years. Current smokers and alcohol drinker (≥ 4 days/week) were 31 (36.0%) and 22 (25.6%), respectively, and none of them had a low income (< 150,000 JPY/M). Regarding complications associated with DM, neuropathy, retinopathy, and nephropathy were observed in 43 (50.0%), 22 (25.6%), and 33 (38.4%) patients, respectively. Hypertension, dyslipidemia, and cardiovascular disease were observed in 57 (66.3%), 49 (57.0%), and 24 (27.9%) patients, respectively. Among the 25 patients (29.1%) who stated that their overall lifestyle had changed during the COVID-19 pandemic, 12 (14.0%) increased their dietary intake and 20 (23.3%) decreased their physical activity level (Table 1). The universal health insurance system supports patients in accessing hospitals in Japan and covers all patients in this study.

Seasonal variations in glycemic control and other clinical parameters before and after the spread of COVID-19

The mean HbA1c levels, body weight, and BMI continuously exhibited a significant difference (low in September and high in March) during the 3-year period. However, low-density lipoprotein levels showed no seasonal variations in 2020 and 2021 but showed some changes in 2019 (Table 2 and Fig. 2A). No other parameters showed continuous seasonal variation between spring (March) and autumn (September) during the study period (Table 2).

Fig. 2
figure 2

Comparison of HbA1c levels throughout the study period (March 2019 and December 2021). (A) Changes in HbA1c levels during the study period. The difference between spring (Spr: March), summer (Sum: June), autumn (Aut: September), and winter (Win: December) in HbA1c levels are presented in (B) 2019, (C) 2020, and (D) 2021. Each value is presented in comparison with the levels in the spring (March) of each year. In 2020, median HbA1c levels significantly decreased from 7.35% [56.8 mmol/mol] in spring (March) to 7.00% [53.0 mmol/mol] in autumn (September) (P < 0.001) during the peak of COVID-19 prevalence in Japan. In 2019, the median HbA1c level significantly decreased from 7.70% [60.6 mmol/mol] in spring (March) to 7.20% [55.2 mmol/mol] in autumn (September) (P < 0.001). No significant difference in HbA1c levels was observed between 2020 and 2019. Additionally, HbA1c levels in autumn (September) for each year are presented. HbA1c levels in the spring (March) of 2019, 2020, and 2021 did not show any significant differences (P = 0.083). The horizontal lines and open squares represent the median and interquartile range, respectively, at each point. Vertical bars at each point indicate the range. The Wilcoxon signed-rank test was used to compare spring (March) and autumn (September) data for each year, and analysis of variance was used to compare spring (March) data. Statistical significance was set at P < 0.05. Abbreviations: Aut, autumn; n.s., not significant; Spr, spring; Sum, summer; Win, winter

Full size image

The median HbA1c levels in spring (March) and autumn (September) were 7.86% [61.2 mmol/mol] and 7.48% [57.4 mmol/mol] in 2019 (P < 0.001), 7.50% [57.7 mmol/mol] and 7.17% [54.2 mmol/mol] in 2020 (P < 0.001) and 7.61% [58.3 mmol/mol] and 7.19% [53.8 mmol/mol] in 2021 (P < 0.001), indicating that the seasonal variation in HbA1c levels was preserved. In this context, the variation in 2020 and 2021 (during the COVID-19 pandemic) did not vary significantly from that in 2019 (Table 2 and Fig. 2A–D). Additionally, HbA1c levels in spring (March) did not show a significant difference among the 3 years (2019, 2020, and 2021; Table 2 and Fig. 2E). Furthermore, no significant differences were observed in the HbA1c values in summer (June) and winter (December) for 3 years, although HbA1c levels in autumn (September) showed significant differences among 3 years (Fig. 2E). Age-stratified analysis of the participant data revealed similar seasonal variations between older and younger patients (Fig. 3A). Seasonal variation was revealed in both patients with and without diet and exercise lifestyle changes (Fig. 3B).

Fig. 3
figure 3

Seasonal glycemic variation of HbA1c levels; stratified analysis for age or lifestyle changes. Seasonal glycemic variation in HbA1c levels (A) in older (age: ≥ 70 years; n = 44) or younger (age: < 70 years; n = 42) patients and (B) with (n = 25) or without (n = 61) lifestyle changes in spring (Spr: March), summer (Sum: June), autumn (Aut: September), and winter (Win: December) during the 3-year study period (2019–2021). The horizontal lines in the squares represent the medians at each point. Squares and vertical bars indicate the interquartile range and range, respectively, at each point. Both older and younger patients with type 2 diabetes mellitus demonstrated similar seasonal changes in HbA1c levels. Additionally, this variation was preserved during the COVID-19 outbreak

Full size image

Changes in medical treatments during the study period

Metformin was the most frequently administered class of drugs (33 patients, 39.8%), followed by dipeptidyl peptidase-IV inhibitor (31 patients, 36.1%). Insulin injection was administered to 22 patients (25.6%), and the median total daily insulin dose was 24.5 (IQR:19.3–37.5; range 10–103) units (Supplementary Table S2). The use of metformin, dipeptidyl peptidase-IV inhibitors, sodium glucose cotransporter 2 inhibitors, and glucagon-like peptide-1 receptor agonists increased during the study period (Supplementary Table S2). Comparing the data from March and September of each year, the use of dipeptidyl peptidase-IV inhibitors increased in 2019, but there was no difference in the use of other oral hypoglycemic agents.

Discussion

COVID-19 had no effect on seasonal variations in the glycemic profiles of patients with type 2 DM in this study. Two reasons may explain this finding. First, there were few restrictions on outdoor activities as COVID-19 was not severe in this region. Second, the lifestyle of older patients after the spread of COVID-19 did not change because their lifestyle before the pandemic was already consistent with their avoidance of 3Cs. In other words, the outcomes of the present study showed that glycemic control in older patients with DM was not affected by physical distancing, although it could be affected in the long-term. Therefore, lifestyle modifications due to COVID-19 may be age-dependent because children and adolescents are forced to modify their own lifestyles [21, 22]. However, in this study, there were no differences between the older (≥ 70 years) and younger (< 70 years) groups.

After the spread of COVID-19, HbA1c levels changed to the same degree as they did in the previous year—before the pandemic. In contrast, COVID-19 has been reported to adversely affect lifestyle elements, including diet and exercise, in the general population [23]. Disturbance in eating habits, decline in physical activity level, and decrease in the frequency of blood glucose self-monitoring have been reported in patients with type 2 DM [21]. Hence, it has been suggested that COVID-19 worsens glycemic control in patients with DM. The effects of the COVID-19 pandemic on T2DM management remain controversial. Recent studies have reported COVID-19 pandemic-associated worsening of glycemic management [24, 25]. Japanese receipt data reported a 5.86% decrease in the number of hospital visits of patients with DM after the COVID-19 pandemic compared to that before the pandemic [26]. Conversely, HbA1c levels among low-income individuals decreased in the United States during the COVID-19 pandemic when access to healthcare was reduced [27].

In this study, BMI was found to have seasonal variations similar to HbA1c; however, the pandemic had no effect on such variations. This finding was similar to the fact that lockdowns in Spain due to COVID-19 led to no overall anthropometric changes [28]. Body weight reduction related to reduction in body fat accumulation leads to improved glycemic profiles through a reduction in insulin resistance in patients with type 2 DM [29]. However, skeletal muscle atrophy should also be noted as a cause of body weight reduction, which is important because older patients comprised a large percentage of the participants in this study. Following the Great East Japan Earthquake, muscle weakness was reported to be a problem in older people who were forced to live in evacuation zones [30]. Short-term disuse has been suggested as a risk factor for skeletal muscle atrophy in older patients [31]. Based on the results of previous studies [32], clinicians should be aware that sarcopenia is a risk factor for body weight reduction. Furthermore, seasonal variations overcome lifestyle-related reductions in physical activity, which may result in loss of body muscle volume.

Seasonal variations in HbA1c levels in patients with type 2 DM may not be eliminated by COVID-19-associated lifestyle modifications. Circadian rhythms, which are synchronized with light and temperature [3, 33], are also closely associated with glucose metabolism, and their disturbances interrupt glucose metabolism [34]. Changes in both temperature and seasonal lifestyle are considered key factors contributing to seasonal glycemic variation. People living in areas with lower temperature changes show lower seasonal fluctuations in HbA1c levels [35, 36]. The present study revealed that seasonal variations in HbA1c levels were not affected by lifestyle modifications due to COVID-19. Additionally, COVID-19 infection exacerbated DM profiles based on the prohibition of regular treatments for DM and dexamethasone administration [37]; however, none of the participants had been infected with symptomatic COVID-19 during the study period. Information on seasonal variations in glycemic control independent of lifestyle modifications may improve the management of patients with type 2 DM.

This study had three limitations. First, the small number of participants in the current study warrants further large-scale studies to validate the findings. Second, this was a single-center study, and we could not conclude whether our results could be generalized to other countries. Third, we could not exclude the influence of medications in this retrospective study. Future international prospective studies may clarify the physiology of seasonal glycemic variation.

Conclusions

The COVID-19 pandemic in this area did not prevent seasonal variations in the glycemic and BMI profiles of older patients with type 2 DM, who were permitted to receive regular treatments and did not develop COVID-19 during the study period. In Japan, seasonal variations substantially affect the glycemic profile and BMI of patients with type 2 DM. Thus, these variations in glycemic profile and BMI in patients with type 2 DM should be regulated beyond lifestyle modifications due to COVID-19 in Japan. Even if the pandemic alters the level of physical activity in patients with type 2 DM, more studies on the effects of seasonal variations on human physical functions may help develop strategies for better management of patients with type 2 DM.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to some restrictions that applied by the ethical committee but are available from the corresponding author on reasonable request.

Abbreviations

ANOVA:

Analysis of variance

BMI:

Body mass index

COVID-19:

Coronavirus disease 2019

DM:

Diabetes mellitus

HbA1c:

Hemoglobin A1c

IQR:

Interquartile range

NGSP:

National Glycohemoglobin Standardization Program

References

  1. Zhang Y, Tong M, Wang B, et al. Geographic, gender, and seasonal variation of diabetes: a nationwide study with 1.4 million participants. J Clin Endocrinol Metab. 2021;106:e4981-92.

    PubMed  Google Scholar 

  2. Ryu OH, Lee S, Yoo HJ, et al. Seasonal variations in glycemic control of type 2 diabetes in Korean women. J Endocrinol Invest. 2014;37:575–81.

    Article  CAS  PubMed  Google Scholar 

  3. Sakamoto M, Matsutani D, Minato S, et al. Seasonal variations in the achievement of guideline targets for HbA1c, blood pressure, and cholesterol among patients with type 2 diabetes: a nationwide population-based study (ABC Study: JDDM49). Diabetes Care. 2019;42:816–23.

    Article  CAS  PubMed  Google Scholar 

  4. Tanaka K, Meguro S, Tanaka M, et al. Difference of seasonal variation between glycated albumin and glycated haemoglobin. Ann Clin Biochem. 2018;55:583–7.

    Article  CAS  PubMed  Google Scholar 

  5. Ishii H, Suzuki H, Baba T, Nakamura K, Watanabe T. Seasonal va Ann Clin Biochem. 2018;55:583–7riation of glycemic control in type 2 diabetic patients. Diabetes Care. 2001;24:1503.

  6. Asplund J. Seasonal variation of HbA1c in adult diabetic patients. Diabetes Care. 1997;20:234.

    Article  CAS  PubMed  Google Scholar 

  7. Johansen MY, MacDonald CS, Hansen KB, et al. Effect of an intensive lifestyle intervention on glycemic control in patients with type 2 diabetes: A randomized clinical trial. JAMA. 2017;318:637–46.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wing RR, Bolin P, Brancati FL, et al. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med. 2013;369:145–54.

    Article  CAS  PubMed  Google Scholar 

  9. Yoshimura E, Tajiri E, Hatamoto Y, et al. Changes in season affect body weight, physical activity, food intake, and sleep in female college students: A preliminary study. Int J Environ Res Public Health. 2020;17:8713.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hawkins RC. Circannual variation in glycohemoglobin in Singapore. Clin Chim Acta. 2010;411:18–21.

    Article  CAS  PubMed  Google Scholar 

  11. Schellenberg ES, Dryden DM, Vandermeer B, et al. Lifestyle interventions for patients with and at risk for type 2 diabetes: a systematic review and meta-analysis. Ann Intern Med. 2013;159:543–51.

    Article  PubMed  Google Scholar 

  12. Misra A, Bloomgarden Z. Diabetes during the COVID-19 pandemic: A global call to reconnect with patients and emphasize lifestyle changes and optimize glycemic and blood pressure control. J Diabetes. 2020;12:556–7.

    Article  CAS  PubMed  Google Scholar 

  13. Kondo T, Miyakawa N, Motoshima H, et al. Impacts of the 2016 Kumamoto Earthquake on glycemic control in patients with diabetes. J Diabetes Investig. 2019;10:521–30.

    Article  CAS  PubMed  Google Scholar 

  14. Iwasaki A, Grubaugh ND. Why does Japan have so few cases of COVID-19? EMBO Mol Med. 2020;12:e12481.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lim WS, Liang CK, Assantachai P, et al. COVID-19 and older people in Asia: Asian Working Group for Sarcopenia calls to actions. Geriatr Gerontol Int. 2020;20:547–58.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ghosal S, Sinha B, Majumder M, et al. Estimation of effects of nationwide lockdown for containing coronavirus infection on worsening of glycosylated haemoglobin and increase in diabetes-related complications: A simulation model using multivariate regression analysis. Diabetes Metab Syndr. 2020;14:319–23.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Capaldo B, Annuzzi G, Creanza A, et al. Blood glucose control during lockdown for COVID-19: CGM metrics in Italian adults with type 1 diabetes. Diabetes Care. 2020;43:e88–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sankar P, Ahmed WN, Mariam Koshy VM, et al. Effects of COVID-19 lockdown on type 2 diabetes, lifestyle and psychosocial health: A hospital-based cross-sectional survey from South India. Diabetes Metab Syndr. 2020;14:1815–9.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Narita K, Kario K. Management of seasonal variation in blood pressure through the optimal adjustment of antihypertensive medications and indoor temperature. Hypertens Res. 2023;46:806–8.

    Article  PubMed  Google Scholar 

  20. Little RR, Rohlfing CL. The long and winding road to optimal HbA1c measurement. Clin Chim Acta. 2013;418:63–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ghosh A, Arora B, Gupta R, et al. Effects of nationwide lockdown during COVID-19 epidemic on lifestyle and other medical issues of patients with type 2 diabetes in North India. Diabetes Metab Syndr. 2020;14:917–20.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Pietrobelli A, Pecoraro L, Ferruzzi A, et al. Effects of COVID-19 lockdown on lifestyle behaviors in children with obesity living in Verona, Italy: A longitudinal study. Obesity (Silver Spring). 2020;28:1382–5.

    Article  CAS  PubMed  Google Scholar 

  23. Ammar A, Brach M, Trabelsi K, et al. Effects of COVID-19 home confinement on eating behaviour and physical activity: Results of the ECLB-COVID19 International Online Survey. Nutrients. 2020;12:1583.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sawada M, Ohkuma K, Aihara M, et al. Impact of the COVID-19 pandemic on the glycemic control, eating habits, and body compositions of people with diabetes mellitus: A retrospective longitudinal observational study. J Diabetes Investig. 2023;14:321–8.

    Article  PubMed  Google Scholar 

  25. Hussein YHH, Soliman AM. Dietary habits, lifestyle changes, and glycemic control in patients with type 2 diabetes mellitus during coronavirus disease 2019 (COVID-19): A cross-sectional study in Egypt. J Family Community Med. 2023;30:1–11.

    Article  PubMed  Google Scholar 

  26. Machida M, Nishioka Y, Noda T, Imamura T. Research note changes in the number of outpatient visits in Japan during the COVID-19 pandemic. BMC Res Notes. 2023;16:70.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Patel MR, Zhang G, Leung C, et al. Impacts of the COVID-19 pandemic on unmet social needs, self-care, and outcomes among people with diabetes and poor glycemic control. Prim Care Diabetes. 2022;16:57–64.

    Article  PubMed  Google Scholar 

  28. López-Moreno M, López MTI, Miguel M, et al. Physical and psychological effects related to food habits and lifestyle changes derived from Covid-19 home confinement in the Spanish population. Nutrients. 2020;12:3445.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Campos RMDS, Masquio DCL, Corgosinho FC, et al. Effects of magnitude of visceral adipose tissue reduction: impact on insulin resistance, hyperleptinemia and cardiometabolic risk in adolescents with obesity after long-term weight-loss therapy. Diab Vasc Dis Res. 2019;16:196–206.

    Article  CAS  PubMed  Google Scholar 

  30. Greiner C, Ono K, Otoguro C, et al. Intervention for the maintenance and improvement of physical function and quality of life among elderly disaster victims of the Great East Japan Earthquake and Tsunami. Appl Nurs Res. 2016;31:154–9.

    Article  PubMed  Google Scholar 

  31. Wall BT, Dirks ML, van Loon LJC. Skeletal muscle atrophy during short-term disuse: implications for age-related sarcopenia. Ageing Res Rev. 2013;12:898–906.

    Article  CAS  PubMed  Google Scholar 

  32. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing. 2019;48:16–31.

    Article  PubMed  Google Scholar 

  33. Caldart CS, Carpaneto A, Golombek DA. Synchronization of circadian locomotor activity behavior in Caernorhabditis elegans: Interactions between light and temperature. J Photochem Photobiol B. 2020;211: 112000.

    Article  CAS  PubMed  Google Scholar 

  34. Koopman ADM, Rauh SP, van ’t Riet E, et al. The association between social jetlag, the metabolic syndrome, and type 2 diabetes mellitus in the general population: A new Hoorn study. J Biol Rhythms. 2017;32:359–68.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Tseng CL, Brimacombe M, Xie M, et al. Seasonal patterns in monthly hemoglobin A1c values. Am J Epidemiol. 2005;161:565–74.

    Article  PubMed  Google Scholar 

  36. Higgins T, Saw S, Sikaris K, et al. Seasonal variation in hemoglobin A1c: is it the same in both hemispheres? Diabetes Sci Technol. 2009;3:668–71.

    Article  Google Scholar 

  37. Villar J, Ferrando C, Martínez D, et al. Dexamethasone treatment for the acute respiratory distress syndrome: a multicentre, randomised controlled trial. Lancet Respir Med. 2020;8:267–76.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the medical staff of Sugi Cardiovascular Hospital for their support in clinical management. The authors would like to thank Enago (www.enago.jp) for the English language review.

Funding

This study did not receive any specific grants from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Division of Endocrinology and Metabolism, Department of Internal Medicine, Kurume University School of Medicine, 67 Asahi-Machi, Kurume, Fukuoka, 830-0011, Japan

    Shimpei Iwata, Kenji Ashida, Mutsuyuki Demiya, Ayako Nagayama, Nao Hasuzawa, Satoko Yoshinobu, Aya Sonezaki, Junichi Yasuda, Seiichi Motomura & Masatoshi Nomura

  2. Sugi Cardiovascular Hospital, 950-1 Taguma, Ohmuta, Fukuoka, 837-0916, Japan

    Shimpei Iwata, Yoshio Katsuki & Kenzo Sugi

Authors
  1. Shimpei Iwata
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kenji Ashida
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mutsuyuki Demiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ayako Nagayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nao Hasuzawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Satoko Yoshinobu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Aya Sonezaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Junichi Yasuda
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Seiichi Motomura
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yoshio Katsuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Kenzo Sugi
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Masatoshi Nomura
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SI and KA: Conceptualization and design, data collection and interpretation, and manuscript preparation. Contributed equally to this study. MN: Interpretation of data and critical revision of the manuscript. YK and KS: Data collection and critical revision of the manuscript. MD, AN, NH, SY, AS, JY, and SM: Critical revision of the manuscript. All authors have approved the final version of the manuscript.

Corresponding author

Correspondence to Kenji Ashida.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Sugi Cardiovascular Hospital (20201207), and the patients provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iwata, S., Ashida, K., Demiya, M. et al. Preserved seasonal variation in glycemic control in patients with type 2 diabetes mellitus during COVID-19: a 3-year-long retrospective cohort study in older adults in Japan. BMC Endocr Disord 24, 70 (2024). https://doi.org/10.1186/s12902-024-01602-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12902-024-01602-8

Keywords

BMC Endocrine Disorders

ISSN: 1472-6823

Contact us